сряда, 17 февруари 2016 г.

Доклад за Благоевградска Бистрица

В изпълнение на програмата на Сдружение Балканка за спасяване на Българските реки от убийственото влияние на ВЕЦ-овете у нас, във връзка с поетия ангажимент да помагаме на структурите на МОСВ и БД, представяме доклад относно посетени и проверени съоръжения, преграждащи речни корита на територията на БЗУВЗБР.

Преди доклада, се налага да коментираме вчерашното събитие, наречено Обеществено обсъждане на ПУРБ и ПУРН на ЗБР, споделяйки нашето дълбоко разочарование от това, на което станахме свидетели.

Разбира се, че не бяхме подготвени да присъстваме там, за да изслушаме подробната лекция на тема действащо законодателство, изнесена с назидателен тон от г-жа Вангелия Иванова, още повече че познаваме законодателството до необходимата степен. Нито бяхме готови да слушаме непрекъснато, че заплатите на БДУВЗБР се плащат само от икономическите субекти в района, поради което единствените критерии за взимане на решения в ЗБР са икономическите интереси. Че икономическите интереси взимат решения в ЗБР вече сме се убедили, но се надяваме поне това със заплатите да не е вярно.

По-лошото е, че всичките ни предложения бяха отхвърлени, с мотиви и основания, които не можем да приемем. Само няколко примера:

1. За зоните за защита на водите за опазване на стопански ценни видове риби - щяло да се постигне добро състояние на водните тела и така щели да се възстановят рибните популации в критично състояние, определящи настоящото състояние като лошо? Е, как ще го постигнете това добро състояние, като нямате никакви мерки?

2. По-лошата новина - мерките, предвидени в ПУРБ на ЗБР относно Петровска река щели да бъдат изпълнени чак през 2021г.? Тези мерки, всъщност, трябва да поправят вредата от едно разрешение за водовземане/ползване на БДУВЗБР, издадено в нарушение на ЗВ. Можем да споделим какво би следвало да се направи, за да се спазят закоодателството и общественият интерес, но очевидно е безмислено. Само ще напомним, че подобни мерки са записани и в стария ПУРБ, срокът на разрешителното е трябвало да се удължи преди 18.01.2015 и поне тогава мерките е трябвало да бъдат изпълнени под ръководството на директора на БДУВЗБР, но не са.

3. Никакви мерки не са и няма да бъдат предвидени в ПУРБ за водовземанията на водните обекти на територията на НПРила, след които реките са сухи като барут.

4. Няма да бъдат предвидени смекчаващи мерки за водните тела, като - разрушаване на ненужни миграционни бариери и изграждане на рибни проходи на останалите, които нямат. Въпреки че дори БДЗБР призна за вярно нашето твърдение, че във всички чуждестранни ПУРБ такива мерки има предвидени

5. Няма да бъдат предвидени мерки при маловодие - да се спира отнемането на вода от реките. Подобни заповеди са издавани в предходни години, но при друго ръководство на БДУВЗБР, а г-жа Иванова сподели своите виждания, че такова нещо не може да бъде правено.

6. Единствено не разбрахме какво точно стана с предложението - всички водовземания да се преработят така, че водното количество да може да бъде измервано. Надяваме се да осъзнавате, че никакво опазване на водите не може да се осъществява, след като ползвателите не могат да измерват водата, която изпускат, а БДУВЗБР също не може да я измерва, за да ги контролира. Въпреки че за всички наши сигнали през миналата година г-жа Иванова бодро отговаряше, че при техни проверки екологичният минимум е бил изпускан навсякъде, а само на Петровска река имаше измервателна рейка, но не за водата, която отива в реката, а за водата в канала.

И все пак, въпреки че за първи път ставаме свидетели на обществено обсъждане, на което всички предложения се отхвърлят, ние донякъде бяхме подготвени, че това ще стане. Славата на ръководителя на БДУВЗБР беше достигнала и до нас, така да се каже.

Но трябва да споделим дълбокото си възмущение от това, което се случи с предложението на нащите приятели от Българско Каяк Общество, които не познават достатъчно подробно законодателството и г-жа Иванова.

Става дума за тяхното предложение - осем километров участък от Струма в Кресненското дефиле да се обяви като зона за защита на водите, определена за отдих и водни спортове.

От гледна точка на закона за водите, считаме за напълно правилно изявлението на г-жа Иванова, че областният управител определя такива зони. Но, доколкото тези зони се описват и в ПУРБ, считаме, че всеки нормален ръководител на БД, който трябва да се грижи и за обществения интерес, а не само на интереса на бизнеса, би могъл да не чака да го молят коленопреклонно да бъдат включени такива зони в ПУРБ, а би могъл да обсъди въпроса с областния управител и да направи необходимите предложения, за да бъдат записани зоните в ПУРБ. Излежда БДУВЗБР, концентрирана да угажда само на икономическите субекти, не знае за Кресненското дефиле - най-култовото място за водни спортове у нас.

Най-обидното е, че членовете на БКО помагат при всяко наводнение и вадят хора от най-опасните и трудни за достъп места, докато министри, областни управители и ръководители на БД дават интервюта пред телевизиите за действията, които се предприемат.

Съзнаваме много добре, че ръководството на БДУВЗБР е свикнало, всеки който иска да отправи предложение, да се моли ниско наведен, но свободните хора не се навеждат. От БКО внесоха вчера и писмено предложение в БДУВЗБР, дано някой да помогне на г-жа Иванова за взимане на правилното решение.

На фона на описаното, напълно разбираемо е, че единственото съпричастие, проявено от г-жа Иванова, бе изразено относно предложение на оператора на каскада Благоевградска Бистрица за изграждане на изравнител на реката, придружено от подробно описание на процедурата, по която това може да стане, без да се споменава, че такива неща не се отразяват в ПУРБ. Допускаме, че тя не се е сетила и за наличието на една действаща и няколко бъдещи ВЕЦ на същата река, които ще останат на сухо, затова просто го отбелязваме с цел облекчаване взаимодействието на БДУВЗБР с всички икономически субекти в района, а не само с един.

За отбелязване е и следният интересен феномен:

След като за ПУРБ бяхме обвинени, чи задваме прекалено много въпроси, въпреки че изчакахме малко, за да може някой друг да започне пръв, за ПУРН чакахме почти две минути и никой не зададе въпрос. Чак след като се престрашихме, представител на гр.Сандански несмело попита нещо след нас... Но най-лошото е, че абсолютно никой от залата не изказа похвала за разглежданите планове, за да оправдае високото самомнение на авторите им.

Що се отнася до нас, ние считаме, че ПУРБ на ЗБР е най-зле направения план, в сравнение с ПУРБ на всички останали басейнови дирекции и не бива да бъде приеман. Толкова по-зле за авторите му, че не приеха никакви предложения и още по-зле, че същата БДУВЗБР е партньор по проекта на нашите приятели от РЕЦ - ANCHOR за доказване на устойчивостта.

Въз основа на гореизложеното, доколкото директорът на БЗУВЗБР би следвало да провежда държавната политика за управление на водите на басейново ниво /чл.154, ал.3 от ЗВ/, считаме, че настоящият директор на БДУВЗБР - г-жа Вангелия Иванова, си е сбъркала местоработата. Защото въпросната държавна политика се очаква да предвижда обща и равна възможност за ползване на водите от всички български граждани и/или икономически субекти, да не говорим за насърчаване на взаимодействието с НПО, особено с тези от тях, които не се редят на държавната хранилка и смеят да имат собствено мнение. Ето защо, считаме, че г-жа Вангелия Иванова е много подходяща за най-висока длъжност в областта на енергетиката, но не и за длъжността, която заема понастоящем.

И сега - към основния доклад.

При предходни наши посещения в Благоевград, наблюдавахме част от новите съоръжения, изградени в реката. Става дума за дейностите извършени в рамките на парка Бачиново - рибни проходи и други защитни мерки в речното корито.

Обърнахме специално внимание на рибните проходи. Ето какво установихме при посещение в средата на януари 2015 при нормално ниво на реката за сезона:

https://www.youtube.com/watch?v=dEYKs0ieXFg

Надяваме се да забелязвате, че през прохода не преминава вода, следователно риби не могат да минат при нормално ниво на реката.

А ето какво установихме вчера при пълноводие, след „Общественото" обсъждане:

https://www.youtube.com/watch?v=rajBp2G81S8

https://www.youtube.com/watch?v=NIcsekQmfk0

https://www.youtube.com/watch?v=PLV8_DUifWo&feature=youtu.be

През рибния проход водата фучи и трещи, и отново никакви риби не могат да минат.

Длъжни сме също да споделим, че типа, геометрията и местоположението на прохода не са подходящо избрани.

Разбира се, вече не смеем да предложим никакви мерки, нито ще питаме защо са изхарчени безмислено обществени средства, защото е ясно какво ще ни отговорят.

Накрая - ние се справяме и ще се справим с нашите цели сами. Но искрено се надяваме някой да помогне на приятелите ни - каякарите, защото тези хора са много полезни, когато се наложи. Дано не се случва, но сме сигурни, че пак ще помагат, дори и на територията на БДУВЗБР...

Благодарим за разбирането и съдействието

събота, 13 февруари 2016 г.

Река за пет милиона лева

https://mittag.wordpress.com/2015/11/20/vlahi/

Posted on November 20, 2015by mittag

Стотици ВЕЦ нанасят тежки екологични поражения на българските реки, докато собствениците пълнят джобове с щедри субсидии за „зелена” енергия

Димитър Събев, Руслан Йорданов

Четири частни водноелектрически централи са се наредили на пълноводната река Влахинска, спускаща се в стръмно ждрело от Пиринския първенец връх Вихрен към Струма. Общата инсталирана мощност на техните турбини е близо 7 мегавата и само през 2014 година тук са произведени над 29 000 мегаватчаса електроенергия. В пари това се равнява на около 5 млн. лева: обществото плаща на собствениците на малки ВЕЦ щедри „зелени” субсидии.

Последиците за околната среда от този доходоносен бизнес са плачевни. След пускането в експлоатация на първите ВЕЦ през 2006 г. големи участъци от река Влахинска редовно пресъхват. Съхнат и крайречните гори. Популациите на балканската пъстърва, на редкия ручеен рак и на видрата са унищожени или стресирани, неблагоприятни ефекти вероятно има и за едрите бозайници. Също и напояването на околните градини става проблемно.

Поречието на река Влахинска е включено в защитената зона „Кресна – Илинденци”, в която се концентрира уникално биологично разнообразие. Авторитетни учени отдавна алармират за неблагоприятните ефекти от построените там ВЕЦ, но кой да ги чуе? Става дума за твърде много пари и връзки. Вместо да се търсят начини за намаляване на вредите от ВЕЦ, ново инвестиционно предложение за удвояване на капацитета на една от съществуващите централи си пробива път през институциите.

Малки ВЕЦ, големи проблеми
Не е лесно да се обясни как малка ВЕЦ уврежда околната среда. Първата асоциация е за заоблени перки, задвижвани от водата и оползотворяващи естествената енергия, създавана от течението: само екологични талибани биха били против. Но използваната технология е съвсем различна от горната идилична картина. За да се гарантира редовно водоподаване и да се увеличи напорът на водите, реките се улавят в тръби някъде над ВЕЦ и се запращат към турбините. Ако собственикът е нетърпелив да си избие инвестицията, цялата река се „канализира” и рибите, съвсем буквално, остават на сухо.

Под МВЕЦ ТАС 2 – Влахи. Снимка: dams.reki.bg

По принцип има изисквания колко вода е длъжна да остави всяка конкретна ВЕЦ след водохващането, но само по принцип. Българските институции не разполагат със сертифицирана апаратура, която да измерва водния отток, изтъква Димитър Куманов от Сдружение „Риболовен клуб Балканка”. И да имаха такава уреди, служителите на регионалните басейнови дирекции и екоинспекции са подложени на натиск: ако се покажат твърде стриктни към ВЕЦ, които често са собственост на важни бизнесмени или политици, те бързо ще се простят с работното си място.

„От една година нашият риболовен клуб не лови риба, а обикаля страната и заснема какво остава от реките след построяване на ВЕЦ на тях”, споделя Куманов. Изводът му е, че поне 60% от реките, на които има ВЕЦ са сухи през лятото. Той изрежда: Крива река над Сестримо с ВЕЦ „Света Петка“: суха като барут. След ВЕЦ „Бързия“ – никаква вода в реката. ВЕЦ „Давидково 2“, ВЕЦ „Сливка“, ВЕЦ „Хладилника“…

Глоби все пак се налагат. Веднъж например е констатирано, че Петровска река в продължение на 7 км. е оставена абсолютно суха и собственикът на ВЕЦ-а там е санкциониран с 5 хил. лв. За сравнение, бабичка, хваната да бракониерства с мрежа е наказана с 6 хил.лв. „Реките ги няма, няма го и животът – не само в тях, а и около тях”.

Любомир Костадинов, експерт по язовири и водни електроцентрали в екологичната организация WWF допълва: „Строежите в реките дават много бърз и всеобхватен ефект. Реката е свързана екосистема: ако построиш ВЕЦ на това място, отражението от него може се усети на 3 км. надолу по течението, на 3 км нагоре, или и в двете посоки“. Според Костадинов един от основните проблеми на ВЕЦ е прекъсването на биокоридорите, по които протича хранителната и размножителна миграция на рибите.

У нас предписанията за изграждане на т.нар. рибни проходи, по които да минава пъстървата или се пренебрегват, или се изпълняват некачествено, съгласни са Костадинов и Куманов. Издигането на бентове освен това спира преноса на седименти, заради което в доста засегнати реки се получава вдълбаване на коритото.

Димитър Манев от Българското каяк общество добавя още нюанси: „Реките ни са пълноводни едва три месеца в годината. Пиковото им ниво не е повече от месец, а през останалото време са на абсолютния минимум. ВЕЦ допълнително влошават ситуацията, като се стигне до момента, когато вече не може да се говори за река. За няколко сезона тя прораства с растителност и започва да изчезва. Около ВЕЦ-овете винаги се натъкваме на тотална разруха и унищожаване на коритото и бреговете“.

Най-интересното тепърва предстои: обществото субсидира собствениците на ВЕЦ като производители на чиста енергия, тоест награждава приноса им към опазването на климата и околната среда.

Директорите на водопада

Да се върнем към река Влахинска, нейните ресурси и фирмите, които ги оползотворяват. Най-долу по реката се намира МВЕЦ „Сокол“ на фирма „Бийстън енерджи“ АД. Централата е произвела над 8 000 мегаватчаса електроенергия през 2014 г., според базата данни на Агенцията за устойчиво енергийно развитие. Опитите да разберем колко е спечелила от това фирмата удрят на камък, тъй като „Бийстън“ не е публикувала в Търговския регистър нито един годишен финансов отчет.
Глобата за подобен пропуск е до 3000 лв. годишно, което не е чак толкова висока цена за анонимност. Все пак с повече упоритост в книгата на акционерите може да се открие, че една пета от pushkarov (2)

дяловете на „Бийстън енерджи“ притежава Иван Пушкаров – икономически министър в две правителства от началото на прехода.

Водещ съдружник е Димитър Соколов, бивш заместник – министър на енергетиката и шеф на Комитета по енергетика, по-късно генерален директор на енергийния бизнес на „Мултигруп“. Третият участник е бивш местен кадър на държавната “Енергопроект”.

Нагоре по реката е ВЕЦ „Влахи“, собственост на „Снабдяване, заготовки и монтаж“ ООД на известния благоевградски приватизатор Иван Пишиев. На върха са двете централи на „Хидроекоенерго Тас“ ЕООД с едноличен собственик на капитала Тодор Стайков. Играч в тютневия бизнес, до 2009 г. Стайков заема престижната длъжност заместник – председател на Българската търговско – промишлена палата. Любопитна подробност в биографията му е, че още през 1991 г. той специализира бизнес лобизъм в САЩ. Стайков вади над 16 000 мегаватчаса „зелена“ енергия от река Влахинска.

Както проличава, във Влахинския ВЕЦ клуб обществото е подбрано. За лош късмет на инвеститорите към реката имат интерес и няколко екологични организации: в с. Влахи има Образователен център за едрите хищници, Училище за природа и ферма „Семпервива“ с единственото у нас голямо стадо каракачански овце.

Сигналите за потенциалния отрицателен ефект от ВЕЦ върху реката не са взети под внимание преди десетина години, когато се проектират и съгласуват първите централи. Но опитът да се удвои капацитета на МВЕЦ „ТАС“ на Тодор Стайков вече идва в повече. На 1 декември 2014 г. РИОСВ – Благоевград спира инвестиционното предложение, тъй като пораженията върху защитена зона „Кресна – Илинденци“ ще нараснат неприемливо с новите съоръжения.

„Хидроекоенерго ТАС“ обжалва в Административния съд в Благоевград и привежда в своя подкрепа заключения на плеяда експерти. Оценките в полза на инвеститора са подписани от хора с научни титли, но има основания да се усъмним в обективността им. Фирмата, изготвила ОВОС за разширения МВЕЦ например е същата, която през 2010 г. прецени, че новата открита златна мина и флотационна фабрика на „Дънди прешъс металс“ край Крумовград не крие рискове за хората и природата. За протокола, това е „Данго проект консулт“ ЕООД. Независими чужди експерти са коментирали, че нивото на неговия ОВОС за „Дънди“ е безобразно.

Доц. д-р Валентин Богоев, ръководител на Катедра по Екология и опазване на природната среда на Софийския университет прави заключение, че разширяването на Влахинския ВЕЦ няма да увреди природата. В неговата оценка е допусната елементарна грешка, занижаваща (дали неволно?) с един порядък площта на защитената зона, засегната от строителството. При това доцентът се концентрира върху „точковия“ ефект на мястото, където ще се изградят новите съоръжения и не коментира кумулативния ефект от 4-5 ВЕЦ на малка планинска река. Най-същественото: Богоев е автор на няколко положителни ОВОС през 2005 и 2006 г. за малки ВЕЦ на Струма, както и за голф игрище край Разлог.

На другото блюдо на везните е авторитетът на проф. д-р Николай Спасов, директор на Националния природонаучен музей, на доц. д-р Стоян Бешков от НПМ и на доц. д-р Георги Хинков от Института за гората при БАН. Тези учени аргументират позиции, че екосистемите на река Влахинска са тежко засегнати от работата на електроцентралите и разширение на съоръженията е недопустимо.

Благоевградският административен съд не им се доверява, отменя отказа на РИОСВ и задължава регионалната екоинспекция да се произнесе отново по казуса. Темида е сляпа, но съдия Стоянка Пишиева, произнесла се по делото, е сродница на собственик на Влахинска ВЕЦ.

Поредната подмяна

В 15 часа на 30 октомври 2015 г. малките ВЕЦ произвеждат 3.3% от електроенергийния товар на България, при 4% за фотоволтаиците и 5% за вятърните централи. Есенните месеци у нас са маловодни, така че МВЕЦ се справят наистина отлично. При това недоволството срещу „скъпата зелена енергия“ ги подминава: хората имат негативи към „перките“ и „соларите“, но към малките водни турбини са сякаш неутрални.

Към днешна дата в България работят близо 250 ВЕЦ. Част от тях са подязовирни или иригационни, броят на малките ВЕЦ е около 200. Независимо дали са новопостроени или приватизирани стари, те осигуряват редовен и значителен доход на своите собственици. До тази година НЕК заплащаше енергията от различните видове МВЕЦ на цена от 156 до 197 лв. за мегаватчас без ДДС. През февруари 2015 г. Държавната комисия за енергийно регулиране понижи цената до 112.48 лв./МВтч при средногодишна продължителност на работа 2 500 часа.

Дори след това понижение, енергията от малките ВЕЦ се изкупува двойно по-скъпо от тази на големите държавни ВЕЦ. Които впрочем през повечето време не работят, за да се даде простор на частната инициатива. Може би точно това има предвид Пушкаров, когато в интервю от 2012 г. казва: „По нагласа имам доста силни социални тежнения“.

Сходни тежнения към зелени субсидии имат и банките, които са финансирали строежа на доста МВЕЦ: в нашиия конкретен случай предприятието „Хидроекоенерго“ е заложено в „Уникредит Булбанк“ срещу заем от 2 млн. евро. Новите по-ниски цени на ДКЕР не изглеждат добре на фона на лихвите по кредити и на търсените от бизнеса печалби. Натискът за разширяване на МВЕЦ тепърва ще се засилва и състоянието на екосистемите и популациите все по-често ще се третира като досадна пречка.

У нас все още няма методика за определяне на минималния воден отток. Не трябва да се допуска ВЕЦ да вземат повече от 30% от водите на една река, но на практика всички водни централи в България взимат повече, констатира Любомир Костадинов от WWF. Според него сме свидетели на поредната подмяна в България: малките ВЕЦ може действително да решат енергийните проблеми на развиващия се свят. Става дума за наистина малки съоръжения, някои на цена 1000 долара, които се монтират в руслото на реките, без да ги променят. Това, което е създадено да носи енергия до бедни хора в отдалечени райони, у нас се е превърнало в източник на лихви и печалби за рентиерите.

Перфектната кражба
Разработка на бившата държавна фирма „Енергопроект“ от 2000 г. е установила около 900 потенциални точки за малки ВЕЦ по нашите реки. Този документ, и досега пазен за вътрешно ползване, е разпалил интереса на бизнеса: почти целият хидроенергиен потенциал на България е резервиран. Освен завършените към 200 обекта, разрешения има за още 250, без да броим други поне 500 издадени разрешения за водовземане. Предстоят им дълги съгласувателни процедури, но първата крачка вече е направена.

Ако всичко това се реализира, обликът на българските планини коренно ще се промени, рибното ни богатство – доколкото то е останало, ще бъде затрито, а сметките на потребителите на ток критично ще набъбнат.

„Водата е най-ценният обществен ресурс, за вода ще се водят войни“, обобщава Димитър Куманов от Сдружение „Балканка“. „Изземвайки водата на реките, ВЕЦ извършват най-голямата и най-ловката кражба на обществени ресурси в България, перфектна престъпна схема“. Освен че окупират публичен ресурс и увреждат околната среда, обществото плаща субсидии на малките ВЕЦ пред погледа на всички държавни служби, които се хранят с данъци.

Наистина, не е ли направило никому по върховете впечатление, че на малката Влахинска река са построени 4 ВЕЦ, чиито собственици годишно си поделят кръгло пет милиона лева? Разбира се, може и много повече: във водосбора на Огоста има 30 малки ВЕЦ, а на река Благоевградска Бистрица – 12. Е, осем от тях са на напорния водопровод на града и „само“ 4 са изградени по течението на планинската река, но пък за още 4 ВЕЦ-а има издадени разрешения…

Едва ли бихме го казали по-добре от доц. Георги Хинков: „Останал е един малък незасегнат участък от река Влахинска. Ще позволите ли това чудесно местенце да бъде превърнато в смрадлив канал? Колко са останали непокътнатите такива места в България. Малко са вече.“

Публикувано в evromegdan.bg

петък, 12 февруари 2016 г.

Hydroelectric Energy Advantages and Disadvantages | Green World Investor

Hydroelectric Energy Advantages and Disadvantages | Green World Investor

Hydroelectric Energy Advantages

No Fuel Cost - Hydro Energy does not require any fuel like most other sources of energy.This is a huge advantage over other fossil fuels whose costs are increasing at a drastic rate every year.Electricity prices are increasingly rapidly in most parts of the world much faster than general inflation.Price shocks due to high fuel costs are a big risk with fossil fuel energy these days
Low Operating Costs and little Maintenance - Operating labor cost is also usually low, as plants are automated and have few personnel on site during normal operation.
Low Electricity Cost – The Electricity produced from Hydro Power is quite low making it very attractive to construct hydro plants.The payback period is estimated to be between 5-8 years for a normal hydro power plant.Hydro Plants also have long lives of between 50-100 years which means that they are extremely profitable
No Greenhouse Gas Emissions/Air Pollution – Hydroelectricity does not produce any GHG emissions or cause air pollution from the combustion of fossil fuels unlike coal,oil or gas.This makes them very attractive as a source of cheap,non carbon dioxide producing electricity.
Energy Storage – Pumped Hydro Storage is possible with most of the hydro power plants.This makes them ideal storage for wind and solar power which are intermittent in nature.Hydro Dams can be modified at low costs to allow pumped storage.
Small Size Possible - Hydroelectricity can be produced in almost any size from 1 MW to 10000 MW which makes it very versatile.Small Hydro Plants are being encouraged by government as they cause less ecological affects than large hydro plants.Even micro hydro plants are possible
Reliability - Hydro Power is much more reliable than wind and solar power though less than coal and nuclear as a baseload source of power.Hydroelectricity is more or less predictable much in advance though it can decrease in summer months when the water is low in the catchment areas.
High Load Factor - The Load Factor for Solar and Wind Energy ranges from 15-40% which is quite low compared to Fossil Fuel Energy.Hydroelectricity on the other hand has a load factor of almost 40-60% .
Long Life - Hydro Plants has a very long life of around 50- 100 years which is much longer than that of even Nuclear Power Plants.The long life implies that the lifecycle cost of a Hydel Power Plant becomes very low in the long term

Hydroelectric Energy DisAdvantages
1) Environmental, Dislocation and Tribal Rights - Large Dam construction especially in populated areas leads to massive Tribal Displacement,Loss of Livelihood and Religious Infringement as potentially sacred Land is occupied by the Government.
2) Wildlife and Fishes get Affected - The Fishes are the most affected species from Dam Construction as the normal flow of the river is completely changed form its river character to a lake one.Submergence of land also leads to ecological destruction of the habitat of land based wildlife.

3) Earthquake Vulnerability – Large Dam Construction has been linked to increased propensity of Earthquakes.Massive Earthquakes in China and Uttarakhand in India were linked to the building of Massive Dams in these countries
4) Siltation When water flows it has the ability to transport particles heavier than itself downstream. This has a negative effect on dams and subsequently their power stations, particularly those on rivers or within catchment areas with high siltation
5) Tail Risk,Dam Failure - Because large conventional dammed-hydro facilities hold back large volumes of water, a failure due to poor construction, terrorism, or other cause can be catastrophic to downriver settlements and infrastructure. Dam failures have been some of the largest man-made disasters in history.The Banqiao Dam Failure in Southern China directly resulted in the deaths of 26,000 people, and another 145,000 from epidemics.
6) Cannot be Built Anywhere - This disadvantage of Hdyro Energy is present with other forms of Energy as well.Some forms of Energy are just better suited to some places.For example you can’t build a nuclear plant on top of an earthquake prone region,you can’t build a wind farm near the Dead Sea etc.Hydro Energy can only be built in particular places though enough of those places exist globally
7) Long Gestation Time - The time to construct a large hydro power project can take between 5-10 years which leads to time and cost overruns.

Can hydropower and river ecosystems get along?

Global energy needs are rising rapidly, as are greenhouse gas emissions from economic developments. Hydropower may seem an obvious choice for the provision of clean electricity, yet the negative environmental impacts of hydropower make it necessary to find solutions that will balance energy needs with those of the river.

For most Danube countries, hydropower is important. Countries are turning to clean energy sources to meet renewable energy targets, all the while trying to achieve 'good quality' status for water bodies in the Danube River Basin. How can Danube countries bring these seemingly conflicting goals together?

All Danube countries – even non-EU states – have committed themselves to implementing the EU Renewable Energy Directive, which is part of a package of energy and climate change legislation that provides targets for greenhouse gas emission savings. The directive encourages energy efficiency and the use of renewable energy sources. Growing energy demands, increased electricity prices as well as international climate protection targets are a major driver towards the expansion of hydropower generation and the construction of new facilities in some Danube countries.
Negative effects for the environment. However, hydropower – and the hydromorphological alterations through the construction of required facilities – can have negative impacts on the environment. The Danube River Basin District Management Plan endorsed by the Danube countries in 2009 identified hydromorphological alterations as one of the four most significant
water management issues in the Danube River Basin.
Hydromorphological alterations can cause river and habitat interruptions, disconnect wetlands and floodplains, interrupt sediment transport, provoke changes in the natural structure of rivers such as river depth, width and flow regimes as well as interrupt natural fish migration routes.
In addition, these effects work against efforts to meet the requirements of the EU Water Framework Directive and its river protection goals to ensure that all waters achieve the 'good status' by 2015, a goal all Danube countries have committed to. The big question for countries is how to balance the seemingly conflicting needs of both the EU Water Framework Directive as well as the EU Renewable Energy Directive.
"The main – and extremely demanding – challenge is to find the right balance between increasing energy demand as well as energy savings and the protection of waters and ecology," says Karl Schwaiger, Head of the Austrian Delegation to the ICPDR.
Working toward guidelines. The Danube Declaration, signed by Danube countries in 2010, asked the ICPDR to work in close cooperation with the hydropower sector and all relevant stakeholders to integrate the needs of energy and the environment. Specifically, the Declaration called for "a broad discussion process with the aim of developing guiding principles on integrating environmental aspects in the use of existing hydropower plants, including a possible increase of their efficiency, as well as in the planning and construction of new hydropower plants".
Three lead countries – Austria, Romania and Slovenia – were nominated to steer the process of developing these guiding principles on hydropower in the frame of the ICPDR. These countries are supported by a Team of Experts, which includes representatives from the energy and environmental administrations of different Danube countries, as well as NGOs and other stakeholder groups. The team supports integration of the different objectives, and provides knowledge and input on the process.
For the ICPDR hydropower activity to succeed, the full participation of key players from both the environmental and energy sectors is vital. "It is necessary that this process is an inclusive one, so that all stakeholders have the possibility to contribute in this shared process to develop sustainable and viable solutions," says Schwaiger. "Thus the involvement of all stakeholders is really crucial to achieve viable and environmentally acceptable solutions."
Gathering data. The first step in the process is to develop an Assessment Report on Hydropower Generation in the Danube Basin, an advanced version of which was presented and discussed in February 2012. The report is based on answers to questionnaires sent to all Danube countries in August 2011, as well as on data from reports, documents and other databases (such as those of the ICPDR and the European Union).
The Assessment Report summarises key information on hydropower generation in the context of water management, flood protection, biodiversity and nature protection. In parallel, the collection of case studies and good practice examples on hydropower is ongoing. This input provides the basis for discussions and facilitates the development of the Guiding Principles on Hydropower.

Getting all stakeholders together. A first ICPDR Workshop on Hydropower and Water Management, organised by Romania, was held from 21 to 22 February 2012 in Timisoara, Romania. Representatives from water management and the energy sector, including representatives from the International Hydropower Association, European Small Hydropower Association and Energy Community, discussed the draft findings of the Assessment Report as well as ways to develop a common framework to implement the requirements of renewable energy and hydropower with those of water and environment protection. Participants shared practical experience in balancing these two goals, and discussed the elaboration of the Guiding Principles.
"The workshop has given the team of members more suggestions than expected to work toward the Guiding Principles," says Aleš Bizjak of the Institute for Water of the Republic of Slovenia and representing Slovenia as one of the lead countries steering the activity. "The core group countires together with the ICPDR Secretariat are looking forward to work on issues and to finish the process next year in the best possible way."
A second workshop will be organised to present the results and outcomes of the work achieved to that point, and to pave the way for broad acceptance and practical implementation of the main outcomes and findings.
Looking to models of cooperation. The Assessment Report and the work towards the Guiding Principles build on experiences in other processes throughout the region. In particular, it builds on the work under the Common Implementation Strategy (CIS) ad hoc activity 'Hydromorphology'. The CIS is an EU process supporting the implementation of the Water Framework Directive, and various activities under this process, including two EU workshops on hydropower and the Water Framework Directive.
Furthermore, the ICPDR activity on hydropower builds on the Danube River Basin District Management Plan, as well as the outcome of the activities of the Alpine Convention regarding Hydropower Generation in the Alpine Region focusing on Small Hydropower. In addition, the collaborative work on the 'Joint Statement on Guiding Principles on the Development of Inland Navigation and Environmental Protection in the Danube River Basin', published in 2008, has been a model for cooperation between sectors.
Shared solutions. Progress toward the Guiding Principles will link to the ongoing activities under the European Strategy for the Danube Region, which aims to improve coordination and cooperation between countries to address challenges in the Danube region. It focuses on eleven priority areas, which are also related to hydropower and environmental protection. Specific actions under Priority Area 2, 'Encourage More Sustainable Energy', requires comprehensive action plans for the sustainable development of the hydropower generation potential, to allocate suitable areas for new hydropower plants as well as to develop and set up a pre-planning mechanism for the allocation of suitable areas for new hydropower projects. The ICPDR process, therefore, also contributes toward the implementation of the EU Strategy for the Danube Region.
Close contact with coordinators for Priority Area 2, as well as for Priority Areas 4 and 5 ('Restore and Maintain Quality of Waters' and 'Manage Environmental Risks') will be crucial to ensure that there is no duplication or overlapping of work regarding hydropower generation in the Danube River Basin.
Meeting the challenge of satisfying global energy needs while reducing greenhouse gas emissions and protecting freshwater ecosystems requires new approaches. Decisions about hydropower plants can no longer be made in isolation as they are part of a suite of solutions for meeting energy needs. Through the collaboration of all stakeholders, Danube countries are working to find a balance between these needs.

Kirstie Shepherd is a freelance journalist living in Vienna and has
called the Danube River Basin home since 2000.

Няколко научни публикации

Understanding the Use of Ecosystem Service Knowledge in Decision Making: Lessons from International Experiences of Spatial Planning
McKenzie, E., Posner, S., Tillmann, P., Bernhardt, J. R., Howard, K., Rosenthal, A.

Citation: Environment and Planning C: Government and Policy: doi:10.1068/c12292j

Publication Year: 2014

The limited understanding of how ecosystem service knowledge (ESK) is used in decision making constrains our ability to learn from, replicate, and convey success stories. We explore use of ESK in decision making in three international cases: national coastal planning in Belize; regional marine spatial planning on Vancouver Island, Canada; and regional land-use planning on the island of Oahu, Hawaii. Decision makers, scientists, and stakeholders collaborated in each case to use a standardized ecosystem service accounting tool to inform spatial planning. We evaluate interview, survey, and observation data to assess evidence of ‘conceptual’, ‘strategic’, and ‘instrumental’ use of ESK. We find evidence of all modes: conceptual use dominates early planning, while strategic and instrumental uses occur iteratively in middle and late stages. Conceptual and strategic uses of ESK build understanding and compromise that facilitate instrumental use. We highlight attributes of ESK, characteristics of the process, and general conditions that appear to affect how knowledge is used. Meaningful participation, scenario development, and integration of local and traditional knowledge emerge as important for particular uses.
Please contact author for a copy of this article: mckemily@gmail.com

http://www.envplan.com/abstract.cgi?id=c12292j

Global water resources affected by human interventions and climate change

Author Affiliations

Edited by Katja Frieler, Potsdam Institute for Climate Impact Research, Potsdam, Germany, and accepted by the Editorial Board August 5, 2013 (received for review January 30, 2013)

Significance

Next Section

Abstract

Humans directly change the dynamics of the water cycle through dams constructed for water storage, and through water withdrawals for industrial, agricultural, or domestic purposes. Climate change is expected to additionally affect water supply and demand. Here, analyses of climate change and direct human impacts on the terrestrial water cycle are presented and compared using a multimodel approach. Seven global hydrological models have been forced with multiple climate projections, and with and without taking into account impacts of human interventions such as dams and water withdrawals on the hydrological cycle. Model results are analyzed for different levels of global warming, allowing for analyses in line with temperature targets for climate change mitigation. The results indicate that direct human impacts on the water cycle in some regions, e.g., parts of Asia and in the western United States, are of the same order of magnitude, or even exceed impacts to be expected for moderate levels of global warming (+2 K). Despite some spread in model projections, irrigation water consumption is generally projected to increase with higher global mean temperatures. Irrigation water scarcity is particularly large in parts of southern and eastern Asia, and is expected to become even larger in the future.

Terrestrial water fluxes are affected by both climate and direct human interventions, e.g., dam operations and water withdrawals. Climate change is expected to alter the water cycle and will subsequently impact water availability and demand. Several hydrologic modeling studies have focused on climate change impacts on discharge in large river basins or global terrestrial areas under naturalized conditions using a single hydrologic model forced with multiple climate projections (1, 2). Recently, hydrological projections from eight global hydrological models (GHMs) were compared (3). In many areas, there was a large spread in projected runoff changes within the climate–hydrology modeling chain. However, at high latitudes there was a clear increase in runoff, whereas some midlatitude regions showed a robust signal of reduced runoff. The study also concluded that the choice of GHM adds to the uncertainty for hydrological change caused by the choice of atmosphere–ocean general circulation models (hereafter called GCMs) (3). Expected runoff increases in the north and decreases in parts of the middle latitudes have been found also when analyzing runoff from 23 GCMs (4).

These studies focused on the naturalized hydrological cycle, i.e., the effects of direct human interventions were not taken into account. However, in many river basins humans substantially alter the hydrological cycle by constructing dams and through water withdrawals. Reservoir operations alter the timing of discharge, although mean annual discharge does not necessarily change much. A study with the water balance model (WBM) showed that the impact of human disturbances, i.e., dams and water consumption, in some river basins is equal to or greater than the impact of expected climate changes on annual runoff over the next 40 y (5). Also, rising water demands are found to outweigh global warming in defining the state of global water systems in the near future (6). Water for irrigation is the largest water use sector, currently accounting for about 70% of global water withdrawals and nearly 90% of consumptive water use (7). A recent synthesis of simulations from seven GHMs found that irrigation water consumption currently amounts to 1,250 km³⋅y⁻¹ (±25%) and that considerable differences among models appear in the spatiotemporal patterns of water consumption (8).

Direct comparisons of the climate impact and human intervention modeling studies can be difficult given that the setups are not identical, i.e., the input forcing data and climate models vary. Also, because of the uncertainty of model-specific results, a multimodel approach is preferable in impact modeling studies (3, 9). This approach is similar to assessments performed within the climate community. Here, multimodel results on current and future water availability and consumption at the global scale from the Water Model Intercomparison Project (WaterMIP) within the European Union Water and Global Change (EU WATCH) project (9, 10), and Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP) (11) are presented. (Information on how to get access to WaterMIP and ISI-MIP simulation results can be found at www.eu-watch.org and www.isi-mip.org, respectively.) Results from these two projects are synthesized to obtain a large ensemble of impact model results. The integration of results from the different projects is achieved by extracting impacts for time periods of global mean temperature (GMT) increases of 2 and 3 K from the simulations, largely following the method of Tang and Lettenmaier (4). The advantage of this approach is that it allows presenting results in a way that is in line with temperature targets used in climate mitigation discussions.

Other studies have focused on future water scarcity using results from WaterMIP and ISI-MIP, but have analyzed changes of naturalized runoff only (3, 12). We here aim to fill this knowledge gap by comparing the different impacts from climate change and direct human impacts and analyzing their interplay. The models included take into account water withdrawals and consumption in different sectors; for more information, see Models and Data and Supporting Information, SI Models and Data. The objectives of this study are to (i) assess the relative contribution of anthropogenic impacts and climate change to river basin scale water fluxes, and (ii) identify areas where climate change can be expected to cause substantial changes in water consumption and water scarcity, focusing on water for irrigation. The effects of future changes in irrigated areas or irrigation practices are not taken into account, and only dams that currently exist are included in the analyses. In this paper, simulations considering man-made reservoirs, water withdrawals, and water consumption are referred to as human impact simulations, whereas the simulations without these disturbances are referred to as naturalized simulations. The results are mainly presented in a way intended to give an overview of impacts at larger spatial scales (river basin and country levels). However, some finer-scale results are included to reveal effects that can be concealed at coarse spatial scales.

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Results

Human Impacts Versus Climate Change.

Anthropogenic water consumption results in mean annual runoff decreases of 5% or more in many river basins during the control period (1971–2000) (Fig. 1A and Supporting Information, River Basin Information and Results). The effect is especially noticeable in heavily irrigated regions at middle latitudes across Asia, and in the western part of the United States. In some river basins in the Middle East, central Asia, and the Indian subcontinent, the median ensemble runoff decrease is more than 15% as a result of water consumption within the river basin. In several other Asian river basins, and in the Colorado, Nile, Orange, Murray–Darling River basins, the ensemble median decrease in runoff resulting from anthropogenic water consumption is between 5% and 15%.

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Fig. 1.

Comparison of human impact and climate change effects on runoff at the river basin level. Basin averaged runoff values are calculated based on simulated discharge at the outlet of the river basins, and the median ensemble results are shown. (A) Control period (1971–2000) human impact simulations compared with control period naturalized simulations. (B) Basin averaged naturalized runoff for 2 K GMT increase, compared with control period naturalized simulations. (C) Basin averaged human impact runoff for 2 K GMT, compared with control period naturalized simulations.

Water consumption always results in runoff decreases, whereas the climate change signal can be in both directions. Climate change affects naturalized runoff in river basins in all parts of the world. Projected runoff decreases are especially noticeable in the Mediterranean area and in the Middle East, but also in Central and South America and parts of Australia (Fig. 1B). Runoff is projected to increase at northern latitudes, corresponding to areas with large projected increases in precipitation (13). Runoff increases are also projected in parts of the Arabian Peninsula, the Horn of Africa, and the Indian subcontinent (Fig. 1B).

The pattern of the total impacts, i.e., runoff changes caused by both 2 K GMT increase and human impacts (Fig. 1C), is dominated by the impacts of climate change alone (Fig. 1B). However, noticeable differences exist in southwestern United States and central Asia. To highlight the relationship between the human impacts and climate change effects, differences between the absolute values of the individual impacts are presented (Fig. 2). This comparison shows that, in several river basins, current water consumption affects annual averaged runoff more than climate change (2 K) is expected to impact naturalized runoff. Fig. 2A shows the river basins in which the climate signal mitigates the human impact signal to some extent or even exceeds it, e.g., in the Nile River basin. Fig. 2B shows the river basins in which the impact of climate change adds to the human impact signal. The combined effect is hence enhancement, e.g., in the Colorado and the Indus River basin.

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Fig. 2.

(A) The difference between the absolute values in Fig. 1 A and B in basins where the human impact and climate signals are opposite, i.e., naturalized runoff increases. (B) The differences between the absolute values in Fig. 1 A and B in basins where both the climate signal and human impact signal are negative, i.e., runoff decreases. The red and yellow colors indicate that the control period human impacts are larger than future climate effects on naturalized runoff.

Despite the locally significant decreases in runoff, anthropogenic water consumption amounts to only 1.3% of median global terrestrial runoff (Fig. 3A). Among the world’s large river basins, and according to the model ensemble included in this study, the Indus River basin is the most affected by human impacts at the annual level. According to the median ensemble result, as much as 47% of current runoff is consumed within the Indus River basin (Fig. 3F). Fig. 3 also shows that the results across the model ensemble for the human impact simulations are significantly different at the river basin level. The interquartile range for the Indus River basin is from 29% to 62%, and the individual model results vary between 18% and 79%. Large intermodel variations are also found in the Huang He River basin (Fig. 3G), where the simulated anthropogenic water consumption varies between 7% and 51% of current naturalized runoff. Moreover, for most of the river basins presented, the impact of a 3 K GMT increase is more pronounced than a 2 K GMT increase, both when looking at the total effect of climate change and human impacts and when looking at the decomposed effects separately (Fig. 3). In the Colorado and Mississippi River basins, and in several river basins in Asia, the human impact effect is larger than the climate effect (Figs. 2 and 3). In the Mediterranean area, both the climate and human impact signals are negative, but the climate signal dominates (Fig. 2B).

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Fig. 3.

Box plots of relative changes in runoff for (A) the world, (B) Colorado, (C) Mississippi, (D) Nile, (E) Euphrates-Tigris, (F) Indus, and (G) Huang He for the control period (C) (1971–2000), 2 and 3 K GMT increases. The boxes illustrate the 25th, 50th, and 75th percentiles of the ensemble (47 members). The whiskers represent the total sample spread, and in addition the 5th and 95th percentiles are marked. The human impact results (orange bars) are compared with the naturalized simulations during the same time period, e.g., 2 K human impacts are compared with 2 K naturalized simulations. All climate and combined effects (blue and green bars) are compared with the control period naturalized simulations.

Irrigation Water Consumption and Scarcity.

The number of water use sectors included in the results presented so far varies between the different GHMs (Models and Data). However, all GHMs include the agriculture sector, i.e., water used for irrigation, which is the largest water consumer globally (7). Here, an index called the cumulative abstraction-to-demand (CAD) ratio (14) is used as a measure of irrigation water scarcity. The higher this number is, the closer the crops are to having their water requirements fulfilled. Thus, a decrease in CAD represents an increase in water scarcity. The highest potential irrigation water consumption numbers (water consumed given water is freely available) during the control period (1971–2000) are found in the Indian subcontinent (Fig. 4A). Although the CAD ratio is low in the Indian subcontinent (Fig. 4B), actual water consumption (water consumed taking water availability into account) in the area is still considerable, which is reflected in the human impact results for the Indus River basin (Figs. 1A and 3).

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Fig. 4.

Irrigation water consumption and cumulative abstraction-to-demand (CAD) ratio at the grid cell level. (A) Ensemble median potential irrigation water consumption, control period (1971–2000). Light gray color represents areas where there is no, or very little, irrigation. (B) Ensemble median CAD, control period. (C) Differences in CAD between the control period and the 2 K GMT increase period. Negative numbers mean the CAD ratio decreases.

The CAD ratio is projected to decrease with increasing GMT in most areas where irrigation exists today (Fig. 4C), meaning an increase in irrigation water scarcity. The CAD ratio is projected to increase in only a few scattered areas, e.g., western India. This increase in the CAD ratio can be linked to increased water availability in this area (Fig. 1C). Fig. 4 reveals some areas impacted by direct human interventions that are not revealed in Fig. 1, because subbasin variations can be concealed when presenting basin averaged results. For example, in parts of the Mississippi River basin, water consumption is considerable, whereas the effect at the basin total level is small (Figs. 1–3). A decrease in the CAD ratio is projected in the United States, southwestern Europe, Pakistan, India, and China (Fig. 4C). Some statistics on the impact of 2 and 3 K of global warming on irrigation water in these areas, in addition to the global total numbers, are presented in Fig. 5. The global median potential irrigation water consumption for the entire ensemble (47 members) is 1,171 km³⋅y⁻¹ in the control period (Fig. 5A). The interquartile range for the same time period ranges from 940 to 1,284 km³⋅y⁻¹. The corresponding number for the subensemble, i.e., for those models simulating both potential and actual water consumption (29 of the 47 members; Models and Data), is 1,174 km³⋅y⁻¹ (942–1,292 km³⋅y⁻¹). These numbers are close to the 1,250 km³⋅y⁻¹ (±25%) reported previously (8), and represent about 1% of mean annual terrestrial precipitation in the forcing datasets used here, and between 1% and 2% of simulated annual terrestrial runoff.

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Fig. 5.

Ensemble statistics on irrigation water consumption for the control period (C) (1971–2000), 2 and 3 K GMT increases for (A) the world, (B) United States, (C) southwest Europe (here comprising Portugal, Spain, and France, (D) Pakistan, (E) India, and (F) China. The upper panels show annual potential and actual irrigation water consumption. The lower panels show CAD, i.e., the relationship between the actual and potential irrigation water consumption. The boxes illustrate the 25th, 50th, and 75th percentiles of the ensemble. The whiskers represent the total sample spread, and in addition the 5th and 95th percentiles are marked.

Substantial differences exist in the ensemble estimates of the amount of potential irrigation water consumed, i.e., when water demands are always met (Fig. 5). However, potential irrigation water consumption will increase with increasing GMT, both globally and regionally (Fig. 5). Irrigation water consumed when water availability is taken into account is more similar across the ensemble, despite the differences in human impact parameterizations (Models and Data). Global actual irrigation water consumption increases slightly with increasing GMT (Fig. 5A). The projected changes in actual irrigation water consumption are less apparent than the projected changes in potential irrigation water consumption (Fig. 5). The spread in irrigation water consumption numbers for a given time period reflects the spread in human impacts seen for the river basins presented in Fig. 3. More importantly, there is a general agreement that the CAD ratio will decrease in the areas in question, and more so the more GMT increases. The global CAD ratio varies from 0.4 to 0.7 across the simulations, decreasing to 0.35–0.68 at 3 K GMT increase. The corresponding median number decreases from 0.58 to 0.52. The smallest change in the CAD ratio is found in India. Here, increased water availability (Fig. 1) results in almost constant water scarcity, despite a slight increase in potential irrigation water consumption (Fig. 4). Among the areas presented in Fig. 5, the relative decrease in the CAD ratio is most pronounced in southwestern Europe. Here, the control period median CAD ratio is simulated at 0.69, whereas the median result at 3 K GMT is 0.5. Actual irrigation water consumption does not change much with increasing GMT, indicating that the decrease in the CAD ratio for the areas considered is mainly caused by an increase in water demands.

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Discussion

The climate effects on naturalized runoff presented here are broadly consistent with results presented elsewhere (3, 4, 12). In large parts of the world, the additional impact on runoff caused by anthropogenic water consumption does not contribute much to the total changes. However, this study emphasizes the importance of taking anthropogenic water consumption into account in areas where direct human interventions are large, and highlights areas where water consumption leads to substantial changes in land surface water fluxes. It has previously been indicated that it is unlikely that irrigation has a significant global-scale impact on the Earth’s climate (15), but regional predictions within global climate models can be improved by taking into account local-scale processes (15).

Surface water evaporation from man-made reservoirs and reservoir operations causing seasonal regime shifts across multiyears can cause slight changes in annual runoff numbers. However, reservoirs influence the shape of the hydrograph profoundly in many areas of the world and seasonally impact discharge much more than the reduction caused by water consumption (16, 17). Seasonal changes in discharge caused by storing and releasing of water in reservoirs are not presented in this study, which focuses on annual runoff numbers. Also, because only annual results are presented, it is not revealed whether water scarcity is constant over the time period considered, or whether interannual or intraannual variations exist. The reservoir storage capacity within a river basin indirectly impacts annual runoff numbers through its ability to accommodate seasonal variations in flow volume and hence to satisfy irrigation water requirements. This effect has not been specifically studied here, but it has previously been indicated that nearly one-half of the irrigation water extracted globally originates from reservoirs built for irrigation purposes (16).

The model ensemble indicates that irrigation water scarcity is expected to increase with increasing GMT. About 40% of total agricultural production relies on irrigation (18). In light of this, the increase in water scarcity and potential decline in food production could affect people worldwide through food price changes on the global market (19). In areas with a projected increase in irrigation water scarcity, and hence possible decreases in food productivity, adaptation measures need to be addressed. To increase food production, better water management and improved irrigation practices (reduced losses) have been suggested (8). Irrigation area expansion in regions with sufficient freshwater is also projected to increase food production (20). These issues must all be discussed in light of other water demands, including environmental flow requirements (8).

The areas for which irrigation water consumption and water scarcity are presented in Figs. 4 and 5 do not overlap directly with the river basins presented in Figs. 1–3. However, Figs. 4 and 5 still indicate that, if more water was available for use, the anthropogenic impacts on river basin runoff seen in Figs. 1–3 would have been even larger. The range in estimates in Fig. 3 is a result of both differences in the baseline runoff (naturalized simulations) and amount of water consumed. Parameterization differences among GHMs that influence naturalized simulation results (9) will subsequently influence the human impact simulations. Reservoir operations and water withdrawal parameterizations further influence the results and contribute to the rather large differences (Figs. 3 and 5). The largest relative runoff decreases for the human impact simulations in the Colorado River basin, for example, originate from the hydrologic model simulating the lowest naturalized runoff and among the highest water consumption numbers within the river basin. In other areas, e.g., in the Indus and Huang He River basins, the differences are also influenced by whether or not multicropping is taken into account in the hydrologic model.

It should be noted that none of the models considers water transportation between river basins, e.g., water transported from the Colorado River basin to California, and groundwater extractions are poorly represented in most models. Hence, the actual irrigation water consumption numbers might be somewhat underestimated. However, three of the GHMs assume that anthropogenic water demands are always met (Models and Data). Furthermore, not all models take into account water consumption in sectors other than agriculture, although the impact may be small because those sectors currently account for only a small fraction of the total. In addition, irrigation water withdrawals and consumption depend on the irrigation map used (21). These differences in human impact parameterization clearly contribute to the spread in runoff changes and water consumption numbers, in addition to naturalized simulation differences. In addition, both GCMs and GHMs contribute substantially to the spread in future projections (3, 12).

Only climate change effects on water demands and consumption are accounted for in this study, whereas other variables, such as irrigated area and irrigation efficiencies are kept constant at the year 2000 level. Also, the indirect effect of rising CO₂ concentrations on runoff and irrigation water consumption through its direct effect on evaporative demand is not considered. Increasing CO₂ can lead to lower irrigation water demands (20, 22). However, nutrient limitations may influence crop growth. The combined effect on crop growth, irrigation water demands, and resulting food production is still somewhat uncertain (22). The positive trend in potential irrigation water consumption presented here is more profound than for specialized crop models (20). Possible reasons for this lie in the different representation of agricultural land and agrohydrological processes in the models (20). These and other impacts on the hydrological cycle should be addressed in future hydrological model developments and multimodel studies. Note also that bias correction has been applied to the GCM data (23, 24). The assumptions and implications of bias correction on forcing data used in hydrological simulations are thoroughly discussed in the study by Ehret et al. (25). Bias correction can impact present-day simulated runoff numbers strongly, but the impact on projected relative water flux changes, which is the focus in this paper, are much smaller (23, 26).

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Conclusions

Based on a large ensemble of simulations using eight GCMs and seven GHMs, this study provides a comprehensive assessment of the effects of climate change and direct anthropogenic disturbances on the terrestrial water cycle. Despite considerable spread in the individual results, a number of robust conclusions can be drawn at the regional and global scale. The results indicate that the impacts of man-made reservoirs, water withdrawals, and water consumption on the long-term global terrestrial water balance are small. However, impacts of anthropogenic interventions are significant in several large river basins. In particular, in irrigation-rich areas in Asia and in the western United States, the effect of current anthropogenic interventions on mean annual runoff is stronger than the projected changes for a 2 or 3 K increase in GMT. Climate change tends to increase potential irrigation water consumption on currently irrigated lands with further detrimental effects in regions with significant irrigation. The climate change signal on runoff can be positive or negative, and hence has the potential to alleviate or aggravate irrigation water scarcity. Globally, the relationship between actual and potential irrigation water consumption is expected to decrease, indicating an increase in irrigation water scarcity.

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Models and Data

Seven GHMs are included in this study. The nature and magnitude of human disturbances at which direct anthropogenic impacts like dams, water withdrawals, and water consumption are included in the models vary (Table 1 and Supporting Information, SI Models and Data). All models were forced with climate data from a total of eight GCMs included in the Coupled Model Intercomparison Project 3 (CMIP3) and CMIP5 archives (Table 2). CMIP3 data were prepared for the hydrological model simulations within the WATCH project (3, 23), and the CMIP5 data were prepared for ISI-MIP (24). Included in the analyses presented here are results when using forcing data from the A2 emission scenario (CMIP3 models) and RCP8.5 (CMIP5 models). Thirty-year periods of GMTs at 2 and 3 K above preindustrial level are extracted from the GCMs (Table 2). The control period (1971–2000) is assumed to be 0.4 K above preindustrial level for all GCMs.

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Table 1.

Hydrologic models

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Table 2.

First year of 30-y periods for each GCM and mean GMT increases above preindustrial level

All hydrological models are run at a daily time step at a spatial resolution of 0.5° latitude by longitude, and runoff is routed through the DDM30 river network (38). Simulation results are submitted for the period 1971–2099. Not all GHMs are run using input data from all GCMs (Table 2). Simulated discharge at the basin outlets are used when calculating basin averaged, or world total, runoff numbers. In this paper, potential water consumption represents water consumed given water is freely available. All models included in the study simulate this quantity. Four of the models—H08, the Lund-Potsdam-Jena managed land dynamic global vegetation (LPJmL), the PCRaster global water balance model (PCR-GLOBWB), and the variable infiltration capacity macroscale hydrologic model (VIC)—also simulate actual water consumption, which is defined as water consumed when water availability is taken into account. The CAD ratio (14) is used as a measure of irrigation water scarcity (Supporting Information, Glossary). Both actual and potential irrigation water consumption are calculated at a daily temporal resolution, and hence subannual variations are imbedded in the final CAD numbers.

Annual runoff and water consumption numbers are calculated for each GCM–GHM combination independently, creating an ensemble of up to 47 annual time series for the period 1971–2099. Differences between simulations are thereafter calculated for each time period of interest (Table 2) for each ensemble member. Finally, median numbers and other statistic measures are calculated. All results are treated equally, and no attempt to give weights to GCMs or GHMs based on performance has been made.

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Acknowledgments

We acknowledge the World Climate Research Programme’s Working Group on Coupled Modeling, which is responsible for CMIP, and we thank the climate modeling groups (Table 2) for making available their model output. We appreciate the reviewers’ constructive and valuable comments. This study was conducted in the framework of the ISI-MIP project and the EU WATCH Integrated Project (Contract 036946), in collaboration with the Global Water System Project. The ISI-MIP Fast Track project was funded by the German Federal Ministry of Education and Research (Bundesministerium für Bildung und Forschung) with project funding reference number OlLS1201A. Y.M. and N.H. were supported by the Environment Research and Technology Development Fund (S-10) of the Ministry of the Environment, Japan.

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Footnotes

¹To whom correspondence should be addressed. E-mail: ingjerd.haddeland@nve.no.

Author contributions: I.H., J.H., F.L., and J.S. designed research; I.H., J.H., H.B., S.E., M.F., N.H., M.K., Y.M., T.S., Z.D.T., Y.W., and D.W. performed research; I.H. and J.H. analyzed data; and I.H., J.H., H.B., S.E., M.F., N.H., M.K., F.L., Y.M., J.S., T.S., Z.D.T., Y.W., and D.W. wrote the paper.
The authors declare no conflict of interest.
This article is a PNAS Direct Submission. K.F. is a guest editor invited by the Editorial Board.
This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.1073/pnas.1222475110/-/DCSupplemental.

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Constraints and potentials of future irrigation water availability on agricultural production under climate change

Author Affiliations

Edited by Hans Joachim Schellnhuber, Potsdam Institute for Climate Impact Research, Potsdam, Germany, and accepted by the Editorial Board October 4, 2013 (received for review January 31, 2013)

Significance

Freshwater availability is relevant to almost all socioeconomic and environmental impacts of climate and demographic change and their implications for sustainability. We compare ensembles of water supply and demand projections driven by ensemble output from five global climate models. Our results suggest reasons for concern. Direct climate impacts to maize, soybean, wheat, and rice involve losses of 400–2,600 Pcal (8–43% of present-day total). Freshwater limitations in some heavily irrigated regions could necessitate reversion of 20–60 Mha of cropland from irrigated to rainfed management, and a further loss of 600–2,900 Pcal. Freshwater abundance in other regions could help ameliorate these losses, but substantial investment in infrastructure would be required.

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Abstract

We compare ensembles of water supply and demand projections from 10 global hydrological models and six global gridded crop models. These are produced as part of the Inter-Sectoral Impacts Model Intercomparison Project, with coordination from the Agricultural Model Intercomparison and Improvement Project, and driven by outputs of general circulation models run under representative concentration pathway 8.5 as part of the Fifth Coupled Model Intercomparison Project. Models project that direct climate impacts to maize, soybean, wheat, and rice involve losses of 400–1,400 Pcal (8–24% of present-day total) when CO₂ fertilization effects are accounted for or 1,400–2,600 Pcal (24–43%) otherwise. Freshwater limitations in some irrigated regions (western United States; China; and West, South, and Central Asia) could necessitate the reversion of 20–60 Mha of cropland from irrigated to rainfed management by end-of-century, and a further loss of 600–2,900 Pcal of food production. In other regions (northern/eastern United States, parts of South America, much of Europe, and South East Asia) surplus water supply could in principle support a net increase in irrigation, although substantial investments in irrigation infrastructure would be required.

A lack of available water for agricultural production, energy projects, other forms of anthropogenic water consumption, and ecological use is already a major issue in many parts of the world and is expected to grow all of the more severe with increasing population, higher food (especially meat) demand, increasing temperatures, and changing precipitation patterns. Although population growth is generally expected to slow in the coming decades, median forecasts typically assume that the world population will grow close to another 50% above the recent milestone of 7 billion people (1). Compounding population growth are major changes to diet as rapid economic growth in much of the developing world leads to increased wealth and demand for more processed food and animal proteins in consumer diets (2, 3). At the same time that demand for food and animal feed is increasing at a historic pace, countries are also increasingly turning to agricultural commodities as a solution to high fuel prices, energy security, and growing carbon dioxide (CO₂) emissions. Population growth adds further stress by taking land out of agriculture for urban development. For example, between 1982 and 2007, about 9.3 Mha of US agricultural land were converted for development (about 1 ha every 2 min) (4). As the availability of land for agricultural uses continues to stagnate or even decline, focus has shifted to increased land-use intensification and improved management to increase yields on existing lands to meet demand challenges and moderate some fraction of the negative impact of climate change (5 ⇓–7).

Irrigation is of paramount importance to increasing productivity on existing agricultural lands, and projected per-hectare irrigation consumption is thus an important output of global gridded crop models (GGCMs). Irrigation is also by far the largest component of anthropogenic demand for fresh water and as such constitutes an essential part of the global hydrological cycle and thus of global hydrological model (GHM) simulations [Haddeland et al. (8), in this issue of PNAS]. Projected potential irrigation water consumption by crops and managed grasses (henceforth “PIrrUse”) is thus a rare overlap among typical GHM and GGCM outputs. The coordinated multisector, multimodel ensembles created in the Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP) hence allow for not only comparison among distinct models within their respective sectors, but also for a direct comparison between GHMs and GGCMs. Several studies have evaluated the potential impacts of future climate change on irrigation water requirements (9, 10) and the extent to which irrigation may aid adaptation to adverse climatic change effects (5, 6). However, these studies were constrained to a single GHM or GGCM only.

The objectives of the present analysis are to (i) compare projections of PIrrUse between GHMs and GGCMs—with and without the effects on plants of increasing atmospheric CO₂ ([CO₂])—and (ii) estimate an upper bound for the future availability of renewable fresh water for irrigation using combined projections of water supply from 10 GHMs (11 ⇓⇓⇓⇓⇓⇓⇓⇓–20) and irrigation water demand (IWD) from both GHMs and 6 GGCMs (11, 21 ⇓⇓⇓⇓–26) run as part of the Agricultural Model Intercomparison and Improvement Project [AgMIP (27)] and ISI-MIP [see SI Appendix, Tables S1–S5 for a summary of participating models and institutional contacts; see also Schewe et al. (28) in this issue of PNAS for a description of the GHMs and simulations and Rosenzweig et al. (29) also in this issue of PNAS for a detailed description of the GGCMs and simulations] to (iii) evaluate the potential impacts of (limited) irrigation water availability on future crop productivity and (iv) characterize the uncertainty in projections of global potential for irrigation-based adaptation by analyzing a consistent cross-sectoral ensemble of 5 GCMs × 10 GHMs × 6 GGCMs. We identify geographic regions in which a combination of decreased water availability and/or increased demand may reduce water available for irrigation and thus further impact agricultural production beyond what is otherwise expected from climate change, as well as regions with potential for climate change adaptation via intensified irrigation.

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Results and Discussion

Irrigation Water Consumption in GHMs and GGCMs.

Global PIrrUse on cropland currently equipped for irrigation (30) is projected to evolve in the future with climate change (Fig. 1). We find notable differences between projections of PIrrUse obtained from GGCMs and GHMs that could have a material effect on our assessment of irrigation’s potential contribution to future yield growth and climate adaptation. Without the effects of increasing [CO₂], GGCMs generally estimate flat or increasing consumption for PIrrUse on present irrigated area, but the trend is far less than the strong positive trend seen in GHMs. When the effects of increasing [CO₂] are included in GGCMs, these models project a decrease in global irrigation consumption on presently irrigated area from 8% to 15% by end of century, similar to results found for an ensemble of GCMs by Konzmann et al. (10) based on a single model (LPJmL; highlighted in Fig. 1 for the present scenarios). With the exception of LPJmL (Lund-Potsdam-Jena Managed Land Dynamic Global Vegetation and Water Balance Model), which is a GHM that includes detailed dynamic representations of plant and crop processes, the hydrological models did not consider the effects of increasing [CO₂] on plants. As all models are driven by the same climate scenario data, this conflicting behavior between model types must stem from different representations of agricultural land and agrohydrological processes in GHMs and GGCMs.

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Fig. 1.

Comparison of fractional change relative to the model specific average 1980–2010 baseline of projections of total global PIrrUse in RCP 8.5 from all GCM × GHM combination and all GCM × GGCM combination with and without the effects of increasing [CO₂]. Results from LPJmL (mean over all GCMs) with (gray dots) and without (black dots) increasing [CO₂] are shown explicitly. LPJmL is unique in that it falls into both GHM and GGCM categories and is unique among the GHMs in that it provides estimates for PIrrUse both with and without the effects of increasing [CO₂].

Each GHM and GGCM uses an individual mix of explicitly represented land use types. Projections of global total PIrrUse for individual GGCMs combine results for crops not explicitly represented (SI Appendix, Fig. S1). All GGCMs considered here simulate dynamic phenology, which accelerates growing seasons in response to warmer climates if there is no adjustment in management (i.e., static sowing dates and varieties). The shortening of the period for which irrigation water is needed can decrease projected consumption. Dynamic phenology is implemented in some GHMs (e.g., LPJmL and H08) and indeed substantial differences in the representations of agricultural land and plant types explain part of the broad range of trends in GHM projections of PIrrUse (SI Appendix, Fig. S2). See Haddeland et al. (8) in this issue of PNAS for a more detailed description of GHM representations of agricultural land and irrigation.

Reduced PirrUse from shortened cropping cycles in GGCMs is compounded by the effects of increasing [CO₂] on water use efficiency. These two mechanisms partially counteract or even reverse increasing potential evapotranspiration and temporal and spatial declines in precipitation. The latter effects are the dominant drivers in irrigation water consumption projections of models with static cropping period assumptions.

GHM and GGCM projections of irrigation water consumption are both the results of simplified representations of the complexity of existent irrigation systems. GGCMs here represent only single-cycle cropping systems with simple parameterizations of irrigation events (SI Appendix, Tables S3 and S4), whereas regions with irrigation agriculture often cultivate multiple cropping cycles within a year, especially at low latitudes where no seasons are threatened by frost. Similarly, farmers are likely to adapt to the acceleration of maturation in single-cycle systems by using slower maturing varieties. This effect, along with other adaptation strategies, was excluded in the GGCM model setup here for most model runs, as it complicates the analysis and attribution of climate change impacts. GHMs on the other hand generally ignore the effects of increasing [CO₂] on crop water use efficiency, and those with static cropping seasons likely overestimate the increase in irrigation water consumption, especially in regions with strong seasonality in temperature (31).

The differences in crop-specific irrigation water consumption as simulated by the GGCMs highlight the importance of a more complex representation of agricultural dynamics and crop types. For example, in some GHMs that include representations of a limited set of crop types (e.g., LPJmL), crops not explicitly represented are assumed to behave like perennial grasses with regard to transpiration and irrigation consumption. Given extreme differences in the projected trend of PIrrUse for grasses and most annual crops (SI Appendix, Fig. S1, especially cotton and sugarcane), approximating row crops with perennial grasses can lead to substantive differences in the overall global trend of irrigation.

Water Withdrawals and Availability.

We analyze the balance of irrigation water supply and demand at the level of food production units [FPUs, composites of river basins and economic regions following Cai and Rosegrant (32) with modifications by Kummu et al. (33); SI Appendix, Fig. S3]. We estimate potential irrigation water withdrawal or demand (PIrrWW) from PIrrUse based on average current irrigation project efficiencies from Rost et al. (34) and assume that freshwater is freely distributable within FPUs without substantial transportation costs. These large-scale assumptions average significant spatial variability in infrastructure availability (35) and water policy (36) at the local level which may substantially reduce the amount of water available (especially for new irrigation projects) in practice. For these reasons we consider the resulting estimates of water availability for irrigation as upper bounds in most FPUs. We account for environmental flow requirements and the limits from seasonal distribution by assuming an upper availability of 40% of total annual blue water supply (SI Appendix, Figs. S4 and S5) and subtract water consumption for other sectors as projected by The WaterGAP model (Water – A Global Assessment and Prognosis) [SI Appendix, Fig. S5 and Flörke et al. (37)] from the available water, assuming that irrigation water always has the lowest priority of all water consumers (which is almost always the case).

Irrigation Potential and Constraints.

If PIrrWW in a currently irrigated area is projected to be greater than or equal to the projected available renewable water, the agricultural production in that FPU is irrigation constrained (denoted by red in Fig. 2). If projected PIrrWW is less than the projected available renewable water, the FPU has an irrigation adaptation potential equal to the difference (green in Fig. 2). As the major uncertainty of these FPU-balances lies in the different assessment of IWD in GHMs and GGCMs, we consider two distinct scenarios for this input: (i) the median of all GCM × GHM combinations (IWDhydro; set represented by gray bars in Fig. 1) and (ii) the median of all GCM × GGCM combinations (IWDcrop; set represented by yellow bars in Fig. 1). Fig. 2 summarizes the spatial patterns of water availability/deficiency for these two scenarios at the FPU level. In general, ensemble elements within the IWDhydro scenario show higher baseline irrigation demand in most FPUs, less water available for the expansion of irrigation, and more FPUs requiring contraction of irrigated areas with especially notable differences across the western United States, Mexico, and much of Asia. Even though estimates of total projected irrigation consumption differ substantially in an absolute sense between the crop and water models, the spatial patterns of consumption are similar (SI Appendix, Figs. S6 and S7).

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Fig. 2.

Median potential end-of-century renewable water abundance/deficiency in average cubic kilometers per year under RCP 8.5 for (Left) all GCM × GHM combinations (IWDhydro scenario) for both supply and demand and (Right) using all GCM × GGCM combinations for irrigation demands (IWDcrop scenario). Positive values indicate areas with irrigation adaptation potential and negative values indicate irrigation constrained areas. Dark green FPUs are saturated at 50 km³/y.

Agricultural Potential with Irrigation and Climate Adaptation.

We used the GGCM simulations to derive the possible future yield increase due to conversion of rainfed cropland to irrigated cropland in FPUs with irrigation adaptation potential, and, similarly, the possible future yield decrease due to conversion of irrigated cropland to rainfed in FPUs that are projected to be irrigation constrained. The magnitude of these effects is determined by the level of water limitations in rainfed agriculture (sustained only by green water, i.e., on-field precipitation and soil moisture). Consequently, semiarid regions where crops are currently cultivated under rainfed conditions typically show the greatest yield increase under irrigation (Fig. 3). It is apparent by comparison with Fig. 2 that many regions with the largest potential for yield increases from increased irrigation are also those most likely to have binding constraints on water availability. For maximum consistency with the assumptions of the GHMs and GGCMs used to construct the two scenarios of irrigation water availability/deficiency in Fig. 2, we combine irrigation scenario IWDhydro with the climate impacts and per-hectare irrigation-based yield improvements without the effects of increasing [CO₂] and scenario IWDcrop with the production factors with increasing [CO₂]. These choices also lead to scenarios that better span the space of possible future trajectories of climate impacts and irrigation-based adaptation, as the more optimistic/pessimistic water availability scenario (IWDcrop/IWDhydro) is combined with the more optimistic/pessimistic climate impact scenario (with/without the projected beneficial effects of increasing [CO₂]). Irrigation-based yield improvement factors for scenarios without the effects of increasing [CO₂] are very similar to those in Fig. 3.

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Fig. 3.

Median potential per hectare increase in maize (Upper Left), wheat (Upper Right), soybean (Lower Right), and rice (Lower Left) yields at the end-of-century from irrigation applied on what are currently rainfed areas for scenarios with the effects of increasing atmospheric CO₂ concentrations included. Maps show median values across all 30 GCM × GGCM combinations in the ensemble for RCP 8.5.

When assuming maximum conversion of rainfed cropland to irrigated cropland in FPUs with irrigation adaptation potential and reduced irrigation water use in irrigation constrained FPUs (Fig. 2), total caloric production of maize, soybean, wheat, and rice is changed regionally (Fig. 4) according to the projected yield increases under irrigation in Fig. 3. The two scenarios (IWDhydro and IWDcrop) are similar, although differences in the western breadbasket of the United States (most notably the Missouri River Basin) and throughout much of China are significant.

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Fig. 4.

Potential change in total production of maize, soybean, wheat, and rice at end-of-century given maximal use of available water for increased/decreased irrigation use on what are currently rainfed/irrigated areas in total calories. (Left) Median of 156 GCM × GHM × GGCM combinations for scenarios constructed using GHM estimates of present-day irrigation demand. (Right) Median of 202 GCM × GHM × GGCM combinations for scenarios constructed using GGCM estimates of present-day irrigation demand.

Global Adaptation Potential and Uncertainties.

Aggregated globally, expansion of irrigation agriculture has the potential to increase production on current cropland. However, model projections indicate that even under the most optimistic assumptions about freshwater distribution and transportation within FPUs, the beneficial effect would be exhausted by detrimental climate change effects on crop yields by 2070 at the latest, for irrigation scenario IWDcrop and crop yields estimated with the inclusion of the effects of increasing [CO₂] (Fig. 5). By 2090, 57% of the median 730-Pcal reduction due to climate change with effects of increasing [CO₂] could be ameliorated by the net expansion of irrigation according to the more optimistic irrigation scenario (IWDcrop). Under the more pessimistic irrigation scenario (IWDhydro), the limitations on irrigation water supply availability further constrain the potential ameliorating effect of expanded irrigation to only 12% of the 1,840-Pcal reduction in 2090 due to climate change without effects of increasing [CO₂], highlighting the need to improve agricultural productivity by other means. This general mechanism is valid for all GCM × (GGCM or GHM) combinations, although there is considerable variation among the projections of individual ensemble members (Fig. 5).

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Fig. 5.

Comparison of the total annual global calories of maize, soybean, wheat, and rice for RCP 8.5 as projected by four sets of ensemble simulations. The first two sets assume no change in irrigated areas and consist of (i) 30 GCM × GGCM combinations with CO₂ effects and (ii) 22 GCM × GGCM combinations without CO₂ effects. The second two sets consist of (iii) 202 GCM × GHM × GGCM combinations with CO₂ effects and a global net expansion in irrigated areas according to the IWDcrop scenario, and (iv) 156 GCM × GHM × GGCM combinations without CO₂ effects and a global net expansion in irrigated areas according to the IWDhydro scenario.

Our analysis is subject to considerable uncertainties which we address in part here. Agricultural PIrrUse and corresponding increases in productivity have been simulated by the GGCMs only for irrigation management with a 100% saturation threshold for applications (i.e., once an irrigation event is triggered, water is applied until soil moisture is optimal; SI Appendix, Tables S3 and S4). Because the efficiency of irrigation water use (yield per unit water) declines at higher irrigation levels (38), water sharing and deficit irrigation could have an overall beneficial effect in constrained regions. Another source of uncertainty relates to our assumptions regarding fossil groundwater availability. Our results indicate that many regions with high shares of irrigated agriculture are likely to be constrained by future freshwater availability. Because we are concerned with the long-term sustainable supply of freshwater, we assume no water supply from fossil groundwater. This is consistent in some areas with the observed depletion of (fossil) groundwater reserves (e.g., ref. 39), but disregards the time it will take to fully deplete these resources and the possibility that aquifers may expand across FPUs and thus contribute to a better distribution of irrigation water in space and time.

Our assumption of 40% freshwater availability is a valid threshold for maximum runoff extraction at global scale, but may be high or low in specific river basins, for example, where irrigation infrastructure is prohibitively expensive, those in which periods of inundation are needed for the functioning of riparian ecosystems, or those where flushing of solid waste and sediment is essential for stream flow, water quality control, or denitrification. Regions with irrigation constraints may need to explore options to increase irrigation project efficiency, which can easily double the irrigation water supply (34). This need for improved irrigation efficiencies is also generally true if irrigation is to play a role in reducing detrimental climate change impacts on agricultural productivity (Fig. 5).

The effectiveness of CO₂ fertilization is a source of major uncertainty, with respect to not only crop productivity [Deryng et al. (31) in this issue on PNAS] but also IWD (10). It may be the only mechanism that can alleviate some climate change impacts on agricultural irrigation water consumption and crop yields (Fig. 5), which otherwise decline rapidly with increasing temperatures. There are additional socioeconomic issues associated with irrigation consumption that we do not address here. Whereas it may be technically possible to increase yields by a relatively small 5–10% per year in the eastern United States and across much of Europe through irrigation, for example, it may not be economical to do so in practice due to the cost of irrigation relative to the potential increase in production. Additional socioeconomic issues such as transboundary disputes on appropriate river discharge rates will continue to be a problem in many arid regions.

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Conclusions

We demonstrate in a unique and broad model intercomparison across two different but closely interrelated impact sectors that a conversion of currently rainfed cropland to irrigated cropland (to the extent possible given actually available water resources) would be insufficient to compensate detrimental climate change impacts on current agricultural land. The main drivers of this effect are projected water limitations, mainly in regions with already large fractions of irrigated agriculture, and the detrimental effects of climate change on agricultural productivity. Both those regions that are projected to suffer water limitations and those that are projected to have potential to expand irrigation could benefit from reduced water losses in conveyance and application and also from better-tuned deficit irrigation to increase overall efficiency of irrigation water use. Depending on local conditions, increases in irrigation capacity and efficiency need to be complemented by efforts to increase water use efficiency and soil conservation in rainfed systems as well, which have a demonstrated capacity to boost crop yields without further exploiting freshwater resources in rivers and aquifers (40). Further efforts to increase productivity, including other means of intensification, water saving, and land-use/land-cover change are needed to close what is projected to be a growing gap between agricultural production on current cropland under climate change and increasing demand for agricultural commodities. Effective climate mitigation must also be among the foremost measures to maintain current productivity on rainfed and irrigated land.

Uncertainties in these projections that result from our crop and hydrology models are generally somewhat higher than those that result from the five climate models that we use to drive the impact models, but the ensemble overwhelmingly supports the general conclusions. Nevertheless, impact model differences need to be better understood especially with respect to their implications for manageability of water consumption and climate change impacts.

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Materials and Methods

Throughout this analysis we used downscaled, bias-corrected outputs of five GCMs from the Fifth Coupled Model Intercomparison Project [CMIP5 (41)] summarized in the SI Appendix, Table S2. See Hempel et al. (42) for a discussion of the bias correction approach and Hagemann et al. (43) for a discussion of the impact of using bias corrected climate model output with GHMs. For simplicity we have considered only a single representative concentration pathway [RCP 8.5 (44)] throughout this analysis.

Water Availability.

To calculate water availability (blue water potentially available for irrigation) for each of 309 FPUs, we use simulated runoff provided by each GHM at grid cell level. Thus, we only consider the renewable surface water, including subsurface runoff, assuming that no fossil groundwater is available. Note that due to lateral water transport along river networks, the blue water available within an FPU may stem from adjacent FPUs that are (partly) located in the same river basin. To take this factor into account, we distributed the overall runoff within river basins according to the average discharge rates (taken from the GHMs) and then aggregated for each FPU. In addition, we assumed that only up to 40% of the thus computed renewable water is available for human use, so as to account for environmental flow requirements in rivers and to stay below thresholds of water stress detrimental to ecosystems and human society [following Gerten et al. (45)]. We assumed that a part of the renewable water resource is consumed for nonagricultural purposes before, and irrespective of, the crop IWD. Note that instead of water withdrawal we consider water consumption, i.e., the amount of water that is actually lost from the system (whereas a part of the withdrawn water remains available for downstream users due to return flows to the rivers).

WaterGAP Estimates for Domestic and Industrial Water Use.

We estimated spatially distributed present and future total water withdrawals for the four nonagricultural water use sectors: domestic, manufacturing, thermoelectricity, and livestock (37). We calculated country-wide estimates of future water use (water withdrawals and consumption) in the manufacturing and domestic sectors based on socioeconomic projections following the Shared Socio-Economic Pathway 2 middle-of-the-road scenario [SSP Database (46)] (47). To determine the amount of cooling water withdrawn for thermal electricity production, we multiplied for each power station its annual thermal electricity production by its water use intensity. Future projections of thermal electricity production were calculated with the Integrated Model to Assess the Global Environment (IMAGE) model (48). Input data on location, type, and size of power stations were based on the World Electric Power Plants Data Set (49). The water use intensity is impacted by the cooling system and the source of fuel of the power station. We distinguished four types of fuels (biomass and waste; nuclear; natural gas; and oil, coal, and petroleum) with three types of cooling systems [tower cooling, once-through cooling, and ponds (total nonagricultural water withdrawals summarized in SI Appendix, Fig. S2)] (50).

Maximum Agricultural Potential with Irrigation.

To understand the implications of changed irrigation water use for the balance of water consumption and freshwater supply, we translate estimates of PIrrUse into the total PIrrWW based on the current irrigation project efficiencies from Rost et al. (34), in which countries are estimated to have a total irrigation efficiency (conveyance plus application) ranging from 0.294 to 0.855. We define maximum agricultural potential with irrigation in an FPU to be total production assuming that all water available for irrigation is used. For this analysis we consider 16 of the most important global crop types (including grass/pasture). Because of the extreme diversity of global agriculture, however, it is not possible to include all crops that are important for irrigation in all regions. In total, the 16 crops simulated by at least one GGCM account for 85.5% of the global irrigated areas recorded in MIRCA2000 (monthly irrigated and rainfed crop areas around the year 2000) (SI Appendix, Fig. S1 and Table S5). We consider expansion/contraction only in those agriculture lands used for the four main staple food and feed crops in the world: maize, wheat, soybean, and rice. For all other crops, we assumed that irrigated areas remain fixed at present-day levels. If an FPU is deemed irrigation constrained in a given year (for a given element in the GCM × GHM × GGCM ensemble) we assume that a fraction of the cropland that is equipped for irrigation must go without, producing yields according to the rainfed estimate for that area instead. Thus, to reduce the irrigation demand by an amount T cubic meters in an FPU where the average water demand for irrigated areas in the given year is D cubic meters per hectare, the amount of land irrigated must be reduced by (T/D) hectare. Given an average irrigated yield of Y_I tonnes per hectare in the given FPU and an average rainfed yield of Y_R tonnes per hectare, the loss of production is thus

. We do not address here the possibility of imperfect or deficit irrigation (i.e., that all areas equipped for irrigation receive something less than 100% of the demanded water, rather than some receiving zero). If more than a single crop is irrigated in a given FPU, we assume that the same economic and cultural considerations that affect the present-day distribution of irrigation will control how changes in irrigated area are distributed; i.e., the fraction of area irrigated for each crop will remain fixed at historical levels. If, on the other hand, an FPU is deemed to have some irrigation adaptation potential, then we define the agricultural potential with irrigation to be the total production in that FPU with some fraction of the rainfed areas converted for irrigation. Here again we assume that increased irrigation water is distributed evenly according to the distribution of present-day areas equipped for irrigation. We have not allowed for any land-cover change (e.g., crop switching or an increase in the total harvested area for a given crop in a region) in this analysis.

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Acknowledgments

We acknowledge the World Climate Research Programme’s Working Group on Coupled Modeling, which is responsible for CMIP, and we thank the climate modeling groups (SI Appendix, Table S2) for making their outputs available. For CMIP, the US Department of Energy’s Program for Climate Model Diagnosis and Intercomparison provides coordinating support in partnership with the Global Organization for Earth System Science Portals. The ISI-MIP Fast Track project was funded by the German Federal Ministry of Education and Research (BMBF) with Project Funding Reference 01LS1201A. This work was also supported in part by the National Science Foundation (NSF) under Grants SBE-0951576 and GEO-1215910. The research leading to these results has received funding from the European Union’s Seventh Framework Programme FP7/2007-2013 under Grant Agreement 266992. Computing was provided by a number of sources, including the University of Chicago Computing Cooperative, the University of Chicago Research Computing Center, and through the National Institutes of Health with resources provided by the Computation Institute and the Biological Sciences Division of the University of Chicago and Argonne National Laboratory, under Grant S10 RR029030-01. Part of the computing was facilitated using the Swift parallel scripting language, supported in part by NSF Grant OCI-1148443. S.N.G. was supported by a Science, Technology, and Society Priority Group grant from the University of Nottingham. Y.M. was supported by the Environment Research and Technology Development Fund (S-10) of the Ministry of the Environment, Japan. Q.T. is supported by the 973 Program of China (2012CB955403). S.O. acknowledges support by the Formas Strong Research Environment “Land Use Today and Tomorrow.” This work has been conducted under the framework of ISI-MIP and in partnership with the AgMIP community. K.F. was supported by Federal Ministry for the Environment Grant 11 II 093 Global A SIDS and LDCs.

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Footnotes

¹To whom correspondence should be addressed. E-mail: jelliott@ci.uchicago.edu.

Author contributions: J.E., K.F., D.G., C.R., and A.C.R. designed research; J.E., D.D., C.M., M.K., D.G., M.G., M.F., Y.W., N.B., S.E., B.M.F., C.F., S.N.G., I.H., N.K., F.L., Y.M., S.O., Y.S., E.S., T.S., Q.T., and D.W. performed research; J.E., D.D., C.M., D.G., M.F., Y.W., S.E., C.F., S.N.G., I.H., N.K., F.L., Y.M., S.O., Y.S., E.S., T.S., and Q.T. contributed new analytic tools; J.E., K.F., and M.K. analyzed data; and J.E., D.D., C.M., and I.F. wrote the paper.
The authors declare no conflict of interest.
This article is a PNAS Direct Submission.
Data deposition: The data reported in this paper associated with the Inter-Sectoral Impact Model Intercomparison Project is hosted at http://esg.pik-potsdam.de.
This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.1073/pnas.1222474110/-/DCSupplemental.

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First look at changes in flood hazard in the Inter-Sectoral Impact Model Intercomparison Project ensemble
1. Rutger Dankers^a,¹,
2. Nigel W. Arnell^b,
3. Douglas B. Clark^c,
4. Pete D. Falloon^a,
5. Balázs M. Fekete^d,
6. Simon N. Gosling^e,
7. Jens Heinke^f,^g,
8. Hyungjun Kim^h,
9. Yoshimitsu Masakiⁱ,
10. Yusuke Satoh^j,
11. Tobias Stacke^k,
12. Yoshihide Wada^l, and
13. Dominik Wisser^m,ⁿ
Author Affiliations
1. Edited by Hans Joachim Schellnhuber, Potsdam Institute for Climate Impact Research, Potsdam, Germany, and accepted by the Editorial Board August 31, 2013 (received for review January 31, 2013)
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Abstract

Climate change due to anthropogenic greenhouse gas emissions is expected to increase the frequency and intensity of precipitation events, which is likely to affect the probability of flooding into the future. In this paper we use river flow simulations from nine global hydrology and land surface models to explore uncertainties in the potential impacts of climate change on flood hazard at global scale. As an indicator of flood hazard we looked at changes in the 30-y return level of 5-d average peak flows under representative concentration pathway RCP8.5 at the end of this century. Not everywhere does climate change result in an increase in flood hazard: decreases in the magnitude and frequency of the 30-y return level of river flow occur at roughly one-third (20–45%) of the global land grid points, particularly in areas where the hydrograph is dominated by the snowmelt flood peak in spring. In most model experiments, however, an increase in flooding frequency was found in more than half of the grid points. The current 30-y flood peak is projected to occur in more than 1 in 5 y across 5–30% of land grid points. The large-scale patterns of change are remarkably consistent among impact models and even the driving climate models, but at local scale and in individual river basins there can be disagreement even on the sign of change, indicating large modeling uncertainty which needs to be taken into account in local adaptation studies.
Climate change due to anthropogenic greenhouse gas emissions is expected to change the distribution and variability of precipitation (1, 2). As the water holding capacity of the atmosphere is increasing with temperature, global warming is expected to increase the intensity of precipitation events (3 ⇓–5). Such an increase in precipitation intensity is likely to have an impact on the frequency of (pluvial and fluvial) flooding.

Although an increase in flooding under climate change is often speculated on, few studies have actually provided projections of fluvial flooding at a continental or global scale (for examples see refs. 6 ⇓⇓–9). Most studies that have appeared to date focus on individual river basins or countries (e.g., refs. 10 ⇓⇓–13), and the use of different models and scenarios makes it impossible to generalize the results, to compare regional sensitivities, or to make global-scale risk assessments. Assessing the impact of climate change on fluvial flooding is furthermore hampered by biases and uncertainties in both climate and impact models.

The InterSectoral Impact Model Intercomparison Project (ISI-MIP, ref. 14) provides the opportunity to explore these uncertainties in projections of changes in flood hazard at a global scale. ISI-MIP offers a framework for comparing multiple climate impact models within and across different sectors, based on consistent climate and (where appropriate) socioeconomic scenarios, providing a quantitative estimate of impacts and uncertainties. The fast-track phase of the project involves 34 global impact models across 5 sectors. The aim of this paper is to look specifically at the relative importance of climate and impact model uncertainty in projected changes of flood characteristics, as well as the consistency in these projections. We use results from nine global hydrology and land surface models (hereafter referred to as impact models, IMs) that provided simulations of daily river discharge at a global 0.5° grid (Table 1). Each IM was driven by five global climate models (GCMs) that were selected to represent the range of global mean temperature change and relative precipitation changes in the Coupled Model Intercomparison Project Phase 5 (CMIP5) simulations (see ref. 14 for further details). In this paper we only include simulations using representative concentration pathway RCP8.5, giving us an ensemble of 45 experiments, each consisting of a historical and future period.
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Table 1.

Overview of models used in this paper
As an indicator of flood hazard we estimated the 30-y return level of river flow (Q30) at each grid cell for two 30-y periods (1971–2000 and 2070–2099). The Q30 is a moderately extreme discharge level that will be exceeded only very infrequently. Whereas the probability of exceedance (Pe) in any given year is 1/30, in any given 10-y period it amounts to almost one-third (0.29). In flood risk management it is therefore common to take even higher flow levels (such as the 100-y return level) as a threshold for protection measures or planning regulations.
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Results

The large-scale pattern of regions with projected increases and decreases in flood hazard is remarkably robust across the IMs and even the GCMs. Fig. 1 (Upper) shows the number of experiments that agree on an increase or decrease in the magnitude of Q30 by more than 10%. The magnitude of change in each GCM/IM combination is shown in Figs. S1–S5). Broadly speaking, increases in flood hazard are projected consistently in central and eastern Siberia and parts of Southeast Asia, including India. Likewise, the Q30 is generally projected to decrease in northern and eastern Europe, and parts of northwestern North America. The relatively large agreement between GCMs may be the result of consistent changes in flood-generating precipitation (2), as well as similar responses to change in the IMs. It cannot be ruled out that the bias correction applied to the climate simulations also has an influence, but this needs further investigation.
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Fig. 1.

(Upper) Number of experiments (out of 45 in total) showing an increase (Left) or decrease (Right) in the magnitude of Q30 of more than 10% in 2070–2099 under RCP8.5, compared with 1971–2000. (Lower Left) Average change in the magnitude of Q30 across all experiments. (Lower Left) Ratio of GCM variance to IM variance. GCM variance was computed as the variance of the change in Q30 across all GCMs for each individual IM, and then averaged over the nine IMs; IM variance was computed as the variance of the change in Q30 across all IMs for each individual GCM, and then averaged over the nine GCMs. In dark green (purple) areas GCM (IM) variance predominates.
The pattern described above resembles to some extent the findings of earlier studies (6, 9) that were based on a much smaller number of models. Both these studies noted that especially at northern higher latitudes, decreases in flood hazard can occur even in areas where the intensity and/or frequency of precipitation events is projected to increase, and attributed this to a decrease in snow accumulation in winter under warmer climate conditions. Some high-latitude areas in Fig. 1, however, show a consistent increase in Q30 magnitude, tentatively reflecting an increase in snow accumulation over winter. Clearly, in rivers where the annual hydrograph is dominated by snowmelt any changes in the magnitude of the peak flows depend on a fine balance between warmer and possibly shorter winters, and increases or decreases in the total amount of precipitation during the winter season. Similar patterns were also found for northern Europe by refs. 7 and 8.

Although the global pattern of changes in flood hazard shows a relatively large degree of consistency across the experiments, at the scale of individual river basins there can be important differences, and projections can disagree even on the sign of change. This can be seen for example in the River Nile, where IM simulations driven by the Geophysical Fluid Dynamics Laboratory Earth System Model (GFDL-ESM2M) show a relatively robust increase in Q30 magnitude, whereas the Hadley Centre Global Environment Model version 2 - Earth System (HadGEM2-ES) simulations generally show a decrease. This highlights the fact that at regional and local scale the impact of climate change on fluvial flood hazards can be highly uncertain.

This uncertainty has various causes, including climate and impact modeling uncertainty. An estimate of GCM and IM uncertainty can be obtained by looking at the variance across both types of models (Fig. 1, Lower). Especially (but not exclusively) in areas with snowmelt, IM uncertainty can predominate over GCM uncertainty, and outside the tropics there are few areas where GCM uncertainty is considerably larger than the uncertainty brought about by the different IMs.

Changes in the magnitude of the Q30 translate into an increase or decrease of the probability of occurrence of the historical 30-y discharge level. Fig. 2 summarizes the extent that the return period of the historical Q30 is expected to become longer or shorter, indicating a decrease or increase in the probability of flooding, respectively. The corresponding plots for each GCM/IM combination can be found in Figs. S6–S10). The results suggest that under these climate scenarios the historical Q30 will occur more frequently in large parts of the world: whereas at roughly one-third of the land grid points of the IMs the future return period is projected to lengthen to more than 40 y, at around 40–60% it is estimated to be less than 20 y and at 20–45% it is less than 10 y, suggesting that here the historical Q30 discharge level is 3 times more likely to occur by the end of this century. At a smaller fraction of land grid points (between 5% and 30%) the future return period is even estimated to be less than 5 y, which would mean that the current Q30 flow level can be expected to be exceeded every few years. The fraction of land where this occurs depends to some extent on the GCM/IM combination that is used, but it is worth noting that at the global scale the range in impact between IMs is of similar magnitude to the range between GCMs (Fig. 2).
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Fig. 2.

Fraction of land grid points where the estimated future return period of the historical Q30 is more than 40 y (red colors), less than 20 y (light green), less than 10 y (cyan), and less than 5 y (blue), grouped by driving GCM (horizontal axis). Results from individual impact models (denoted by their first three letters) are indicated with different symbols. Note that the exact number of land points can be different for each GCM/IM combination.
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Discussion

The results presented in the previous section suggest that under conditions of climate change flood hazard is not increasing uniformly across the globe. Decreases in the magnitude of Q30 occur particularly (but not exclusively) in areas where the hydrograph is dominated by snowmelt in spring. Nevertheless, in most model experiments an increase in flood hazard was found in more than half of the global land grid points. At a much smaller fraction (between 5% and 30%) the current 30-y flood peak is expected to occur more than once every 5 y in the future. This includes areas that are currently sparsely populated (e.g., eastern Siberia), but also some densely populated regions such as Southeast Asia. It should also be emphasized that these projections are for a high-end climate change scenario (RCP8.5), which means that a comparison with any observed trends (refs. 15 and 16) is not yet feasible.

The large-scale pattern of changes in flood hazard presented above resembles, to some extent, those found in earlier studies (in particular refs. 6 and 9) that were based on a much smaller number of experiments. At the same time our analysis demonstrates that both climate and impact model uncertainty contribute to the spread in the projected changes, especially at the regional scale. A large contribution of IM uncertainty has also been found in analyses of the ISI-MIP hydrological simulations (e.g., refs. 17 and 18) Further work is necessary to better understand and explain some of these uncertainties, and tentatively relate them to model performance under present-day conditions. Note that IM uncertainty seems to be large especially in regions that are dominated by snowmelt, albeit not everywhere. Ref. 19 found that the scheme used for simulating snow is an important cause for differences in model performance between land surface and hydrological models, and potentially this also affects projections of future changes in peak flows in these areas.

Also note that not all sources of uncertainty are taken into account in our analysis; for example, we have only looked at one scenario of future greenhouse gas concentrations. The subset of five GCMs used in ISI-MIP may not sample the full uncertainty in projections of extreme precipitation in the CMIP5 ensemble. Estimates of the Pe of extreme flow levels are inherently uncertain as well, especially for return periods beyond the length of the data (here 30 y). For a more extreme discharge level, ref. 8 found that natural variability in the climate simulations can yield differences in the estimated Pe even without a change in climate. Here this effect might be smaller because we use a less extreme flow level as indicator of flood hazard, but its exact importance may only be determined by including more ensemble members of the same climate model simulations, where available. We also have not explored to what extent the bias correction method that was applied to the GCM simulations (Materials and Methods) has influenced the results, but this is likely to add further uncertainty to the projections (20, 21). Finally, no attempt has been made here to weigh the models according to their skill in simulating present-day conditions.

The analysis presented in this paper explores uncertainties in the potential impacts of climate change on flood hazard, and is not meant to provide specific guidance on adaptation to climate change in an individual river basin. The models used in this paper all have a global scope and may not provide an accurate description of the climatological and hydrological system at a given location. Vulnerability is also not taken into account in the current analysis (for examples of this see, e.g., refs. 22 ⇓–24). Actual changes in flood hazard and indeed flood risk may depend on socioeconomic factors such as land use changes, flow control structures, flood defenses, and changes in exposure, as much as climate change, and although these human factors may dominate the response in individual river basins, they could not be taken into account in a global-scale study like this. However, the results may have a clear implication for local adaptation: studies that are based on only one, or a limited set of model simulations, may underestimate the uncertainty in the climate change signal, and the corresponding change in flood hazard. It should also be noted that large uncertainty even about the direction of change does not equate to no change. It may be worth approaching this uncertainty from a risk management perspective: if all of the model simulations can be regarded as possible or even plausible scenarios of changes in flood hazard due to climate change, those model solutions that appear less likely but nevertheless carry high risk may not simply be ignored. In those regions where even the sign of changes is uncertain, adaptation plans may therefore need to be flexible to changes in both directions (25).

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Materials and Methods

The ISI-MIP Setup.

We used natural river flow simulations from nine global hydrology and land surface models (here referred to as IMs; Table 1). With the exception of PCRaster Global Water Balance Model (PCR-GLOBWB) and Water Balance Model (WBM), these models participated previously in the Water Model Intercomparison Project (WaterMIP), and more information on the model characteristics and general hydrological performance can be found in ref. 19. A more thorough evaluation of how well these IMs are able to reproduce observed flood characteristics and trends would be desirable, but is difficult to achieve due to a lack of suitable river flow data in many regions of the world, and the simple fact that many river systems are heavily influenced by human interference such as water extraction, reservoir construction, and structural engineering works. However, the models used here can be considered state-of-the-art in global hydrological simulations.

Each IM was driven by five GCMs that were selected to represent the range of global mean temperature change and relative precipitation changes in the CMIP5 simulations (26); see ref. 14 for further details. Each GCM in turn was driven by up to four scenarios of future levels of atmospheric greenhouse gas concentrations [representative concentration pathways (RCPs); ref. 27]. In this paper we only include simulations using scenario RCP8.5, assuming that higher levels of global warming will result in stronger hydrological changes (although locally and regionally this may not always be the case, e.g., ref. 7). We thus have an ensemble of 45 experiments, each consisting of a historical and future period.

The output of each GCM was bias corrected to ensure statistical agreement with the observational data set of ref. 28 over the period 1960–1999, and downscaled to the same 0.5 × 0.5° grid. The bias correction method that was used preserves the absolute trends in globally averaged temperature and relative trends in land-averaged precipitation (29), and is a modification of the transfer function approach that has been used in WaterMIP. Ref. 30 found that applying bias correction to the output from a regional climate model can improve the realism of subsequent hydrological simulations and result in a better reproduction of observed flood statistics. However, good model performance in the present day does not necessarily imply plausible projections into the future, and it has been argued that bias correction hides rather than reduces the uncertainty of the predictions (20). An implicit assumption in bias correction is that biases in the climate simulations remain the same into the future, which is probably not very realistic. Bias correction may therefore add further uncertainty to climate impact projections (e.g., ref. 21), but this is not explored further in this paper.

Analysis of Flood Hazards.

For each experiment (that is, each GCM/IM combination), the time series of simulated daily river discharge at each land grid cell was smoothed to 5-daily running averages, which were thought to be more appropriate than daily values considering the typically generalized routing parameters used in global models. For each year, the annual maximum 5-daily flow was determined for both the historical and the future period. For each grid cell we thus have two distributions of 30 annual peak flows. To estimate the Q30 a generalized extreme value distribution (GEV) (31, 32) was fitted separately to these two sets of peak flows using a maximum likelihood approach. The goodness of fit was tested by calculating the Kolmogorov–Smirnov and Anderson–Darling statistics. Grid points with annual maxima close to 0 m³⋅s⁻¹ were excluded from the analysis as these generally resulted in poor fits. This occurs mostly in desert areas, but not in all IMs. The total number of land points that was included in the analysis is therefore slightly different in each experiment. Finally, a likelihood ratio test (31) was performed to test whether the shape parameter of the GEV is significantly different from zero, or if a Gumbel distribution should be used instead. Note that the Q30 (technically the 31-y return level) can, in principle, also be estimated directly from a 30-y time series, but a GEV-based estimate is more robust as the shape of the distribution is based on all peak flows during this period.

Changes in Q30 may be expressed as a change in magnitude between the historical and future period, suggesting higher or lower flood levels. Alternatively, the future return period (based on the future GEV) of the historical Q30 can be calculated, suggesting a change in probability and thus frequency. Although estimates of return levels can be uncertain, especially beyond the period of the data, it is important to keep in mind that any changes in the Q30 are caused by changes in the underlying set of peak flows between the historical and future period that determines the shape of the GEV.

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Acknowledgments

The constructive comments of two anonymous reviewers helped to improve the manuscript. R.D. thanks K. Williams, J. Davie, and R. Kahana for technical assistance. We acknowledge the World Climate Research Programme’s Working Group on Coupled Modelling, which is responsible for CMIP, and we thank the climate modeling groups (listed in Table 1) for producing and making available their model output. For CMIP the US Department of Energy’s Program for Climate Model Diagnosis and Intercomparison provides coordinating support and led development of software infrastructure in partnership with the Global Organization for Earth System Science Portals. The ISI-MIP Fast Track project was funded with Project Grant 01LS1201A by the German Federal Ministry of Education and Research. R.D. and P.F. were supported by the Joint UK Department of Energy and Climate Change/Department for Environment, Food and Rural Affairs Met Office Hadley Centre Climate Programme (GA01101). Y.M. was supported by the Environment Research and Technology Development Fund (S-10) of the Ministry of the Environment, Japan. H.K. and Y.S. were jointly supported by the Japan Society for the Promotion of Science “KAKENHI” (23226012) and the Japanese Ministry of Education, Culture, Sports, Science and Technology “SOUSEI” Program. This work has been conducted under the framework of ISI-MIP.
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Footnotes
- ¹To whom correspondence should be addressed. E-mail: rutger.dankers@metoffice.gov.uk.
- Author contributions: R.D. designed research; R.D., D.B.C., B.M.F., S.N.G., J.H., H.K., Y.M., Y.S., T.S., Y.W., and D.W. performed research; R.D., N.W.A., and P.D.F. analyzed data; and R.D. wrote the paper.
- The authors declare no conflict of interest.
- This article is a PNAS Direct Submission.
- This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.1073/pnas.1302078110/-/DCSupplemental.
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HydrobiologiaThe International Journal of Aquatic Sciences

10.1007/s10750-013-1780-6

Primary Research Paper

River of the dammed: longitudinal changes in fish assemblages in response to dams

Jonathan A. Freedman^{1, 3}, Benjamin D. Lorson², Richard B. Taylor², Robert F. Carline¹ and Jay R. StaufferJr.²

(1)

Pennsylvania Cooperative Fish and Wildlife Research Unit, The Pennsylvania State University, University Park, PA 16802, USA

(2)

School of Forest Resources, The Pennsylvania State University, University Park, PA 16802, USA

(3)

Department of Biology, Stetson University, 421 N. Woodland Blvd., Unit 8264, DeLand, FL 32723, USA

Jonathan A. Freedman

Email: freedjon@gmail.com

Received: 20 July 2013Revised: 1 December 2013Accepted: 3 December 2013Published online: 13 December 2013

Handling editor: Katya E. Kovalenko

Abstract

Although dams are a common feature on rivers throughout the world, their effects on diversity, composition, and structure of fish assemblages are often unclear. We used electrified benthic trawls and stable isotope analysis of δ¹³C and δ¹⁵N to determine the complex relationships between taxonomic diversity and food web structure of fish assemblages among sites in the free-flowing and impounded reaches of the Allegheny River, Pennsylvania, USA. We found higher gamma and beta fish diversity in the free-flowing section, where Brillouin diversity increased in a downstream direction; however, in the impounded section, we found decreasing diversity downstream. Analysis of similarity and non-metric multi-dimensional scaling revealed longitudinal differences in Bray–Curtis similarity between assemblages from impounded and those from free-flowing sites. Finally, using stable isotope analysis, we showed that fishes in the free-flowing section derived nutrients primarily from benthic sources while fishes in the impounded section had a stronger reliance on pelagic nutrients. Our findings reveal that dams can reduce fish taxonomic diversity, driven primarily by decreases in lotic taxa, while shifting resource use from benthic toward pelagic nutrients. A multi-faceted approach to assess the cumulative effects of dams on aquatic communities is, therefore, recommended.

Keywords

Stable isotope analysis Diversity partitioning Community ecology Food web Nutrient dynamics Impoundment

Introduction

The majority of large-river systems throughout the world are affected by dams (Nilsson et al., 2005) for purposes that include flood control, hydroelectric power generation, and facilitation of navigation or recreation. Irrespective of their purpose, the presence of dams alters the natural flow of rivers (Graf, 1999, 2006; Poff & Hart, 2002). Nutrient and sediment dynamics are affected, as detritus and sediment accumulate behind dams, thus becoming unavailable downstream (Kondolf, 1997; Vorosmarty et al., 2003; Graf, 2006). By altering flow, dams decrease the natural heterogeneity of rivers (Kondolf, 1997; Poff et al., 1997, 2007), as pool and lentic habitats predominate, and the only proxy for riffle habitats is usually immediately downstream of dams where turbulence and oxygen content of the water can be relatively high (Ward & Stanford, 1983). Altered flow regimes, and the transformation from lotic riffle-pool-run sequences to lentic habitats, also leads to subsequent changes in biotic assemblages (Power et al., 1996; Poff et al., 1997; Bunn & Arthington, 2002; Miranda et al., 2008). Species adapted to fast-flowing water are especially susceptible to such changes, while a variety of trophic shifts may occur with the arrival and dominance of lentic species at multiple trophic levels (Poff et al., 1997; Bunn & Arthington, 2002; Lytle & Poff, 2004).

In impounded reaches, aquatic vegetation and periphyton can be negatively affected by higher turbidity and sedimentation rates, and the subsequent reductions in light penetration and changes in substrate composition (Rivier & Seguier, 1985; Poff et al., 1997). Fish species in lithophilic reproductive guilds, such as many darters (Percidae: Etheostomatini), require rocky and gravel habitats, in addition to well-oxygenated, flowing water, in which to spawn and care for their eggs (Page, 1983; Simon, 1998). The loss of these habitats due to increased turbidity and sedimentation can render such habitats unsuitable for reproduction even if adults are able to survive (Berkman & Rabeni, 1987). Sedimentation-induced changes to aquatic invertebrate assemblages can also affect fish foraging behavior and efficiency (Harvey, 1986; Berkman & Rabeni, 1987; Milner & Piorkowski, 2004). Assessing direct effects of these types of disturbance on fish assemblages can be quite challenging, particularly in large-river systems; determining more subtle indirect effects and ecological shifts mediated by dams present a greater challenge still.

Longitudinal patterns along stream river gradients have been described using theoretical models such as the nutrient spiraling concept (Webster & Patten, 1979), river continuum concept (Vannote et al., 1980), process domains concept (Montgomery, 1999), and flood pulse concept (Thorp & Delong, 1994); however, these models generally assume uninterrupted continua and do not account for disruptions to water and nutrient flow caused by dams. The serial discontinuity concept (Ward & Stanford, 1983) showed how dams can not only create lentic conditions above the dam, but below the dam can effectively “reset” environmental conditions to states reflecting lower order streams. Paradoxically, dams can thus provide refugia for lotic species in impounded rivers, with higher flow and oxygenated water immediately below dams (Freedman et al., 2009a; Argent & Kimmel, 2011).

Although longitudinal patterns in relative fish abundance and diversity along a river continuum can thus be affected by the presence of dams (Ward & Stanford, 1983; Miranda et al., 2008). However, most studies have focused on smaller rivers, or on large bodied fish taxa or those that can be sampled in near shore habitats. Furthermore, while there are other studies that separately examine the effects of dams on either taxonomic diversity or nutrient dynamics, the complex relationships between these factors is not well understood. Our objectives were, therefore, to use a novel sampling gear (electrified benthic trawl; Freedman et al., 2009b) in conjunction with stable isotope analysis to examine the effects of dams on benthic fish assemblages and food webs by sampling dam-impacted and free-flowing reaches of the Allegheny River, Pennsylvania. The Allegheny River is important because it is the most northeast extension of the rich Ohio River (and thus also of the Mississippi River watershed); its diverse fauna was derived from the rich Teays/Mississippi valley via the developing Ohio River and from glacial meltwaters of that formed the Great Lakes (Hocutt et al., 1986). We hypothesized that fish diversity would be lower in the impounded section, with shifts from lotic to lentic species dominating the community. As habitat would be less diverse (contiguous deep pools in the impounded section; riffle-pool-run sequences in the free-flowing section), we also expected to see higher biotic homogenization in the impounded section (Olden et al., 2004; Poff et al., 2007). Furthermore, because our impounded sites were located downstream from our free-flowing sites, we expected that differences due to river distance between sites [measured in river kilometers (rkm)] between free-flowing and impounded sites would be greater than within these categories. Regular interruptions in nutrient and water flow caused by navigation dams would also be expected to increase homogeneity and disrupt any longitudinal patterns in diversity. We used diversity partitioning to determine the relative contribution of α (within sample) and beta (among sample) diversity to the overall (gamma) diversity of the river. Finally, we examined how dams affect nutrient flow and food webs using stable isotope analysis; with greater mean depth and habitat homogenization, we expected that fishes at impounded sites would derive fewer nutrients from benthic sources relative to those at free-flowing sites.

Materials and methods

Study area and sampling

The Allegheny River has a total length of 523 km and a watershed of approximately 30,000 km², and is comprised of three main sections (Fig. 1). From its headwaters in Pennsylvania, the upper section of Allegheny River flows into New York State before reentering Pennsylvania, and is unregulated above a hydroelectric and flood-control dam that forms the Kinzua Reservoir at River Kilometer 325. Below the Kinzua Dam, the middle section of the river is free-flowing for 211 km. The lower section’s 113 km are regulated by a series of eight navigation lock-and-dam systems until its confluence with the Monongahela River in Pittsburgh forms the Ohio River. Glacial alluvial gravel and rocks comprise the dominant substrate in the Allegheny River. Commercial gravel dredging has occurred throughout most of the nine navigation pools on the Allegheny River (Freedman et al., 2013), but only at one site above the navigation pool influence. Annual mean discharge of the Allegheny River is 189 m³ s⁻¹ at our uppermost site near Kinzua Dam (USGS Gauging Station 03012550; River Kilometer 316), 623 m³ s⁻¹ at Parker, PA, located 20 km upstream of the navigation limit (USGS Gauging Station 03031500; River Kilometer 133), and 920 m³ s⁻¹ at Lock & Dam 4 at Natrona, PA (USGS Gauging Station 03049500; River Kilometer 39). While subject to some point- and non-point source stressors such as sewage discharges and agriculture, 139 km of the middle section of the Allegheny River is designated as a National Wild and Scenic River, and is yet relatively pristine.

Fig. 1

Map of Allegheny River watershed (shaded), showing the lower section impounded by multiple navigation lock-and-dams, and the free-flowing middle section. For reference, the upper free-flowing section above the Kinzua dam is also denoted

We used electrified benthic trawls (Freedman et al., 2009b) to sample benthic fish assemblages at 66 sites in the middle and lower sections of the Allegheny River (Fig. 1). We sampled 26 sites in the lower, impounded river, with three or four sites in each navigation pool from 2 to 9; these included sites located above and below each dam, with one or two sites located in the middle of the pool (Fig. 2). We sampled 40 sites in the middle, free-flowing section of the river, from below Kinzua dam to just above the upper navigation limit in navigation pool 9. Three to eight (mean ± SD; 4.44 ± 1.63) 2-min timed trawls were conducted at each site depending on the width of the river. All fishes were identified to species in the field when possible; representative samples were retained and photo vouchers were taken for laboratory verification.

Fig. 2

Fish diversity (upper panel) and mean depth (lower panel) of sites along a longitudinal gradient in the Allegheny River. Open circles and dotted lines represent observed Brillouin diversity, while solid circles and lines were calculated using a three-site moving average. The navigation limit (dashed line) forms the break between the impounded lower section and free-flowing middle section, while the Kinzua dam (dashed line) is the upstream limit of the middle section of river. Navigation lock-and-dam structures are denoted by solid triangles, and mean depths by open triangles

Stable isotope sampling and preparation

Stable isotopes can be used to provide information about both realized trophic scenopoetic (environmental conditions) and bionomic (interactions with other organisms) niche dimensions (Newsome et al., 2007). In other words, we used them not only to determine what an organism was eating, but also whether it was deriving nutrients from benthic or pelagic sources (e.g., Post, 2002b; Vander Zanden & Vadeboncoeur, 2002; Vander Zanden et al., 2005; Newsome et al., 2007). Stable carbon isotope signatures (δ¹³C) vary across both primary producers and in response to differences in environmental variables. For instance, periphyton and phytoplankton differ in δ¹³C signatures, as do producers from deep or shallow water (Vander Zanden & Rasmussen, 1999; Post, 2002b). Variation in primary producer δ¹³C in streams and rivers is largely driven by CO₂ availability: in shallow or turbulent water, the boundary layer effect increases the availability of “fresh” CO₂, while in slow or deep water CO₂ is less available and is, therefore, “recycled” by primary producers (Peterson & Fry, 1987; Finlay et al., 1999; Trudeau & Rasmussen, 2003). The heavier stable isotope of nitrogen (¹⁵N) is conserved in organic tissues, and passes to higher consumers via bioaccumulation. Thus, δ¹⁵N is enriched at a relatively constant rate (2–5‰, mean 3.4‰) across trophic levels, and, therefore, serves to estimate trophic position within a food-web (Vander Zanden & Rasmussen, 1999; Vander Zanden & Rasmussen, 2001; Post, 2002a; Vanderklift & Ponsard, 2003). While stable isotope analysis has been used to gain insights into biotic changes that occur as a response to anthropomorphic stress, such research has tended to focus on point- and non-point-source additions to aquatic environments (Costanzo et al., 2001; Vadeboncoeur et al., 2003; Gray et al., 2004; Grey et al., 2004; Anderson & Cabana, 2005; Vander Zanden et al., 2005) rather than physical alterations to the environment. The ability of stable isotope analysis to differentiate food sources and detect trophic positions thus makes it a powerful tool for examining the effects of dams on riverine fish assemblages.

Two sites were sampled above the influence of navigation dams (free-flowing sites), and three sites were sampled in the upper navigation pools of the Allegheny River (Pools 7–8) during late summer, 2007. Adult fishes were collected using a combination of Missouri- and PSU-benthic trawls (Herzog et al., 2005; Freedman et al., 2009b). Fish samples were immediately frozen until processed in the laboratory. All fishes were identified to species, with the exception of shiners (Notropis spp.) which were not identified to species prior to stable isotope sampling, and were, therefore, grouped together and analyzed as shiner spp. Several individuals of each fish taxon (range 2–48 individuals per site) to compensate for inherent inter-individual variability, and of different size-classes where relevant, were sampled for stable isotope analysis. White muscle tissue was used if sufficient material could be obtained for fish samples as previous studies have shown it to be less variable than other tissues, with a moderate stable isotope turnover rate on the order of weeks to months (Hobson, 1999); smaller fishes were eviscerated and decapitated. To compensate for inherent differences among sites, samples were pooled by taxon for both the two free-flowing and the three dam-impacted sites.

All samples were rinsed with deionized water, placed into a clean glass vial, and dried in a drying oven at 60°C for 24–48 h. Dried samples were homogenized to a fine powder using mortar-and-pestle, or using a glass stirring rod within the vial. Samples were weighed into 0.2 mg (± 10%) aliquots, placed into 5 mm × 3.5 mm tin capsules, and analyzed for δ¹³C and δ¹⁵N using either a Thermo-Finnigan Delta Plus or Delta XP isotope-ratio mass spectrometer interfaced with a Carlo Erba NC2500 Elemental Analyzer via the Conflo II or Conflo III at the Stable Isotopes in Nature Laboratory at the University of New Brunswick, Canada.

From each sample, the ratios of ¹⁴N to ¹⁵N and of ¹²C to ¹³C were determined, and used to calculate δ¹⁵N and δ¹³C using the formula:

δ X = [(R sample / R standard) - 1] \times 1, 000,

where X refers to the rare, heavy isotope, and R is the ratio of the heavy isotope (¹⁵N, ¹³C) to the light isotope (¹⁴N, ¹²C) in the sample and in a standard. The standard for nitrogen is atmospheric nitrogen (AIR), and for carbon is carbon dioxide derived from calcium carbonate in the Pee Dee Bee formation of South Carolina (PDB). As lipids are rich in carbon relative to tissues, variable tissue-lipid contents among samples can increase overall variability of samples; we, therefore, used a lipid correction factor to standardize across samples (Eq. 3, Table 1 from Post et al., 2007). For isotopic standards, standard deviations were 0.15‰ for δ¹³C and 0.24‰ for δ¹⁵N, for elemental standards standard deviations ranged from 0.13 to 0.15‰ for δ¹³C and 0.14 to 0.25‰ for δ¹⁵N, and for biologic standards, the standard deviations ranged from 0.11 to 0.14‰ for δ¹³C and from 0.12 to 0.14‰ for δ¹⁵N. Replicate fish tissue samples varied by an average of 0.22‰ (SD 0.24‰) for δ¹³C and 0.19‰ (SD 0.18‰) for δ¹⁵N.

Statistical analysis

We calculated both observed site-to-site differences, and used three-site moving averages to visualize longitudinal trends, in Brillouin diversity of fish assemblages along the river (Fig. 2); however, all analyses were conducted on the observed data. We performed both non-metric multidimensional scaling (nMDS) and analysis of similarity (ANOSIM) based on a Bray–Curtis dissimilarity matrix of fish assemblages to examine differences among sites, using Primer 5.2.2 (Primer-E Ltd., Plymouth, UK). River sections (free-flowing middle and impounded lower) were used as factors.

To quantify the effects of dams in structuring diversity, we examined the relative contributions of alpha (α, within sample) and beta (β, among sample) diversity to the gamma (γ, total) diversity of the Allegheny River (sensu Crist et al., 2003). We performed complete randomization of 10,000 iterations using additive partitioning (Partition 3.0; Veech & Crist, 2009) wherein

γ (total diversity) = α 1 (within site) + β 1 (among site) + β 2 (among section)

to test for the presence of patterns across these hierarchical levels in the Allegheny River. We tested the null hypothesis that observed fish species richness at each hierarchical level was not significantly different from a random distribution of these fish species among samples at each of these levels.

We used circular statistics (Schmidt et al., 2007) to assess differences between fish stable isotope signatures by assessing directional changes from free-flowing to impounded sites using the software package Oriana 3.0 (Kovach, 2009). In circular statistics, the stable isotope data are transformed into linear vectors for each fish species, with an origin that is standardized as 0.0. δ¹³C is plotted on the X-axis, with ¹³C-depletion (indicative of pelagic carbon sources) to the left (270°), and 13C-enrichment (benthic carbon sources) to the right (90°). δ¹⁵N is plotted on the Y-axis, with ¹⁵N-enrichment (higher trophic level) at 0° and ¹⁵N-depletion (lower trophic level) at 180°. We defined the origin as being the free-flowing site, while the other end of the vector represents the impounded sites. The length of each vector represents the magnitude of change of stable isotopic signatures for that species, while the angle of the vector represents the directionality of that change. We used Rayleigh’s Test for Circular Uniformity to test whether the distribution of vectors was random or uniform. Alpha levels of 0.05 were used to assess significance for all analyses.

Results

Fish distribution, abundance, and diversity

Diversity in the free-flowing middle section generally increased from the Kinzua dam until the dam influence near 116 rkm and was variable in the dam-impacted lower section, but generally declined downstream (Fig. 2). We caught more fishes in the free-flowing section (46.6 fish per trawl) than in the impounded section (18.8 fish per trawl), as well as higher taxonomic richness with 44 taxa in the free-flowing section compared to 34 taxa in the impounded section (Table 1). Mean diversity was lower in the impounded section (mean Brillouin diversity 1.04 ± 0.34 SD) than in the free-flowing section (1.44 ± 0.35; t test, df = 64, t-stat = −4.59, P < 0.00002125; Fig. 2). Mean depth of sites in the impounded section was 4.7 m (range 0.3–14.9 m) and in the free-flowing section was 2.2 m (range 0.3–10.0 m; Fig. 2).

Table 1

Fish species captured and relative abundance (number per trawl sample) in the impounded lower section and free-flowing middle section of the Allegheny River

Scientific name	Common name	Catch-per-sample
Scientific name	Common name	Impounded	Free-flowing
Petromyzontidae
Ichthyomyzon bdellium	Ohio Lamprey	0	0.02
Petromyzontid sp.	Lamprey Larvae	0	0.01
Cyprinidae
Campostoma anomalum	Central Stoneroller	0	0.01
Cyprinus carpio	Common Carp	0.01	0
Erimystax dissimilis	Streamline Chub	0.57	3.11
Exoglossum laurae	Tonguetied Minnow	0	0.02
Hybopsis amblops	Bigeye Chub	0	0.15
Luxilus chrysocephalus	Striped Shiner	0	0.02
Nocomis micropogon	River Chub	0	0.07
Notropis atherinoides	Emerald Shiner	0	0.03
Notropis photogenis	Silver Shiner	0	0.16
Notropis volucellus	Mimic Shiner	0.13	7.50
Notropis spp.	Shiner Species	0.03	0
Pimephales notatus	Bluntnose Minnow	0	0.38
Catosomidae
Catostomus commersonii	White Sucker	0	0.01
Hypentelium nigricans	Northern Hogsucker	0	0.03
Moxostoma anisurum	Silver Redhorse	0.02	0
Moxostoma duquesnei	Black Redhorse	0	0.01
Moxostoma erythrurum	Golden Redhorse	0.01	0
Moxostoma spp.	Redhorse Species	0.02	0
Ictaluridae
Ictalurus punctatus	Channel Catfish	0.28	0.02
Noturus eleutherus	Mountain Madtom	0	0.05
Noturus flavus	Stonecat	0.03	0.01
Noturus stigmosus	Northern Madtom	0.01	0.01
Pylodictis olivaris	Flathead Catfish	0.03	0.02
Percopsidae
Percopsis omiscomaycus	Trout-Perch	0.06	0.11
Atherinopsidae
Labidesthes sicculus	Brook Silverside	0	0.01
Centrarchidae
Ambloplites rupestris	Rock Bass	0.01	0.07
Lepomis cyanellus	Green Sunfish	0	0.01
Lepomis macrochirus	Bluegill	0	0.05
Micropterus dolomieu	Smallmouth Bass	0.37	1.63
Micropterus punctulatus	Spotted Bass	0.01	0
Micropterus salmoides	Largemouth Bass	0.02	0
Micropterus sp.	Black Bass Species	0.02	0
Percidae
Etheostoma blennioides	Greenside Darter	0.61	4.65
Etheostoma caeruleum	Rainbow Darter	0.68	6.87
Etheostoma camurum	Bluebreast Darter	1.01	1.17
Etheostoma flabellare	Fantail Darter	0.09	0.34
Etheostoma maculatum	Spotted Darter	0.01	0.21
Etheostoma nigrum	Johnny Darter	3.36	0.17
Etheostoma tippecanoe	Tippecanoe Darter	0.80	0.39
Etheostoma variatum	Variegate Darter	0.02	1.17
Etheostoma zonale	Banded Darter	0.42	5.19
Percina caprodes	Logperch	2.44	1.32
Percina copelandi	Channel Darter	6.41	1.52
Percina evides	Gilt Darter	0.72	3.05
Percina macrocephala	Longhead Darter	0.30	1.44
Percina maculata	Blackside Darter	0	1.51
Percina (hybrid)	Darter hybrid	0	0.01
Perca flavescens	Yellow Perch	0.02	0
Sander vitreus	Walleye	0.04	0.02
Sander sp.	Walleye or Sauger	0.07	0.04
Sciaenidae
Aplodinotus grunniens	Freshwater Drum	0.19	0
Cottidae
Cottus bairdi	Mottled Sculpin	0	4.01
Mean Number of Fish per Trawl		18.8	46.6

We caught a total of 10, 038 fishes comprising 54 taxa: 53 species and 1 hybrid (Table 1). Fishes from the family Percidae (primarily darters) comprised 90.5% of the total catch in the impounded section, while catch from the free-flowing section comprised 62.4% percids and 24.6% cyprinids (minnow family). However, percids were more abundant in the free-flowing section, with a catch rate of percids (29.1 per trawl) almost double that in the impounded section (17.0 per trawl; Table 1). The most prevalent percids in the impounded section were tolerant species such as Channel Darter (34.2% of total catch), Johnny Darter (17.9%), and Logperch (13.0%). In the free-flowing section, the most prevalent percids were species with more lotic requirements such as Rainbow Darter (14.7% of total catch), Banded Darter (11.1%), and Greenside Darter (10.0%; Table 1). Overall, the most prevalent species in the free-flowing section was Mimic Shiner (16.1% of total catch; Table 1), while Mottled Sculpin (8.6%) were also prevalent, particularly in the upper reaches of the section. The free-flowing site with the lowest diversity (0.33) was located at river km 296.1. This site was the deepest in the free-flowing section (9.0 m deep) and we captured just 22 fishes: 19 Trout-Perch and three Mottled Sculpin. This site was also characterized by sandy substratum, which was also noted at the other three sites in the impounded section where Trout-Perch were collected.

Twenty taxa (19 species and one hybrid) were found only in the free-flowing section, while 10 taxa were found only in the impounded section (Table 1). These contributed to a section beta diversity (β ₂) of 14.5 which represented 26.9% of gamma species richness but was not significantly different than expected using diversity partitioning (P > 0.05; 10,000 iterations; Table 2; Fig. 3). The mean numbers of species that were not shared among sites (β ₁) were higher than expected from the 10,000 randomizations (72.3% of gamma diversity versus 56.7%; P < 0.001). The mean numbers of species shared among sites (α ₁) were lower than expected (27.7% versus 43.3% expected; P < 0.001; Table 2; Fig. 3).

Table 2

Additive partitioning results for fish communities among sites in impounded and free-flowing sections of the Allegheny River

Spatial scale	Diversity component	Observed mean diversity	Expected mean diversity	Contribution to gamma diversity (%)
River	γ	54
Section	α ₂	39.5	39.5	73.1
Section	β ₂	14.5	14.5	26.9
Site	α ₁	10.94	17.11	27.7
Site	β ₁	28.56	22.39	72.3

Fig. 3

Diversity partitioning results (10,000 iterations) for the Allegheny River showing species richness between the free-flowing middle and impounded lower sections (β ₂), among sites (β ₁), and within sites (α ₁). The symbol plus indicates that observed diversity was greater than expected, while minus indicates that observed diversity was lower than expected

There was differentiation between fish assemblages from the free-flowing and impounded sections of the Allegheny River along Axis 1 of the nMDS, with all fish assemblages from impounded sites having values of <0, while only one free-flowing site value of <0 along this axis (nMDS, Stress 0.17; Fig. 4). ANOSIM also revealed significant differences in site similarity between free-flowing and impounded sites (Global R: 0.62, significance level 0.1). Fish assemblages in the impounded section were equally dissimilar between 0 and 59 rkm (navigation pools 2–5) and 59–115 rkm (navigation pools 6–9) subsections. Fish assemblages from the free-flowing section were progressively more dissimilar from impounded section assemblages with the increasing distance upstream (Fig. 4). Fish assemblages from sites located below dams in both the 0–59 and 59–116 rkm subsections were not more similar to free-flowing sites than that of sites located mid-pool or above dams.

Fig. 4

nMDS plot of Bray–Curtis similarity among sites in the Allegheny River. Downward triangles represent impounded lower section sites, while upward triangles represent free-flowing middle section sites. Symbol shading represents subsections defined by rkm

Food webs and nutrient dynamics

Small fishes from free-flowing sites were less depleted in ¹³C, consistent with reliance on benthic-derived nutrients (Fig. 5). Fishes from impounded sites had ¹³C depleted δ¹³C signatures, consistent with increased reliance on pelagic-derived nutrients rather than benthic-derived nutrients. Only Mottled Sculpin from free-flowing sites had δ¹³C signatures more negative than −24.00‰.

Fig. 5

Bivariate plots of δ¹³C and δ¹⁵N for fish assemblages in the free-flowing middle section (upper plot) and impounded lower section (lower plot) of the Allegheny River. Symbols indicate mean stable isotope values (±s.e.) of individual species while light gray crosses represent stable isotope values for individual fish

There was a directional shift in δ¹³C from free-flowing to impounded sites. Circular statistics revealed that fishes from the lower section (Rayleigh’s Test, Z = 11.437, P = 0.00000143; Fig. 6) shifted to increased reliance on pelagic-derived nutrients at impounded sites. There was no significant effect of dam influence on the trophic position of fishes. Fantail Darter (from mean δ¹⁵N 13.30 ± 0.38 SD at free-flowing sites to 14.61 ± 1.41 at undredged sites) and Johnny Darter (from δ¹⁵N 10.99 ± 0.25 to 12.93 ± 1.00) appeared to be exceptions as they both increased mean trophic position from free-flowing to undredged sites.

Fig. 6

Circular plots of δ¹³C (horizontal axis) and δ¹⁵N (vertical axis). Enriched δ¹³C indicative of benthic-derived nutrients is to the right and depleted δ¹³C representing pelagic-derived nutrients is to the left. Higher and lower δ¹⁵N values are indicative of higher and lower trophic positions and are oriented to the top and bottom of the plot, respectively. Individual arrows represent mean δ¹³C and δ¹⁵N values of individual species: vector direction indicates shifts in δ¹³C and δ¹⁵N between sites in the free-flowing middle section and impounded lower section, while the length of the vector indicates the magnitude of the difference. The solid line is the overall mean, and the line at the circumference is the 95% confidence interval

Discussion

Effects of dams on fish distribution, abundance, and diversity

We detected significant differences between fish communities in the impounded lower section and free-flowing middle sections of the Allegheny River. These were largely influenced by higher fish abundance and taxonomic diversity at sites in the free-flowing section. Mottled Sculpins were captured at 23 of the 24 uppermost sites; they were among the most abundant taxa in the free-flowing section, but were not captured below river km 210. This may be due to thermal limitations and habitat preference, as this species is generally associated with cool, clear, flowing water (Scott & Crossman, 1973; Trautman, 1981). Lotic-adapted darters such as Greenside, Rainbow, Banded, and Gilt darters were more abundant in the free-flowing section than in the impounded section. Darter species that were more abundant in the lower section included Johnny Darter, Channel Darter, and Logperch, all of which are better adapted to slower-moving and lentic conditions than most darters (Page, 1983). Streamline Chub and Mimic Shiners were also more prevalent in the free-flowing section although they have different habitat preferences. Both species are found in streams and rivers, but while Mimic Shiners are more tolerant of both silt and lotic conditions than Streamline Chub, neither species thrives in high-silt environments that characterizes much of the impounded section (Trautman, 1981).

Fish assemblages from below dams were dissimilar to above-dam sites. Contrary to our expectations, however, below-dam sites were not more similar to free-flowing sites. Species that are adapted to lotic conditions may find refugia below dams within rivers where turbulence is greatest (Freedman et al., 2009a; Argent & Kimmel, 2011). For instance, lotic fish species listed as threatened by the state of Pennsylvania were found at higher abundances in dam tailwaters on the Ohio River (Freedman et al., 2009a), and a similar trend was noted for dams in the Allegheny River (Argent & Kimmel, 2011). Despite providing refugia for lotic species, fish assemblages from habitats immediately below dams were equally dissimilar to free-flowing sites as were other dam-impacted sites. This indicates a fundamental impact of dams on these fish assemblages.

This may be driven, in part, by the lower diversity in the impounded section than the free-flowing section. Additive partitioning revealed higher heterogeneity in fish assemblages between impounded and free-flowing sites than expected. There was no evidence of increased homogeneity per se among the impounded sites relative to free-flowing sites. At the same time, despite generally lower diversity at lower river km in the impounded section, there was no pattern of longitudinal changes in this section apparent in the MDS analysis. The free-flowing section, however, showed a downstream pattern in increased diversity that was also apparent in MDS analysis. Fish assemblages in the free-flowing section are thus generally consistent with the river continuum concept (Vannote et al., 1980) in that there were increases in taxonomic diversity and mixed assemblages of lotic and lentic species at downstream sites. The disruption of this pattern, with generally lower taxonomic diversity and a sharp drop in lotic species, and a lack of longitudinal changes in the dam-impacted section are consistent with the serial discontinuity concept (Ward & Stanford, 1983).

While the locks in navigation lock-and-dam systems provide access between pools, the dams inhibit fish movement. For instance, river darter, Percina shumardi, has been captured to the base of the second lock-and-dam of the Ohio River (DaShields lock-and-dam in the Montgomery Pool; Freedman et al., 2009a). Extensive sampling has not collected this species upstream of this dam in the Ohio River or in the Allegheny or Monongahela rivers (Freedman et al., 2009a, b; Stauffer et al., 2010; Argent & Kimmel, 2011), suggesting that it is recolonizing the Pennsylvania section of the Ohio River from downstream refugia rather than simply having been missed in prior surveys. The range of this species may expand upstream into the Allegheny and Monongahela rivers, but this dispersal will likely be slowed by the presence of navigation dams. The extirpation from the Ohio River of anadromous species such as Lake Sturgeon, Acipenser fulvescens, can be at least partially explained by the presence of dams (Pearson & Pearson, 1989). The presence of locks may help to mediate this issue, as juvenile paddlefish, Polyodon spathula, stocked in the Ohio River were confirmed to have passed through locks in both upstream and downstream directions (Barry et al., 2007). The use of navigation lock chambers by fishes can also be confirmed by lock chamber rotenone surveys on The Ohio River in which almost 3 × 10⁶ fishes comprising 116 fish taxa were collected in 377 sampling events: an average of almost 8,000 fishes per collection (Thomas et al., 2005).

Effects of dams on food webs and nutrient dynamics

Stable isotope analysis revealed a shift toward increased reliance on pelagic-derived nutrients by fishes at impounded sites relative to the free-flowing sites above the navigation dam influence. This shift is consistent with shifts from allochthonous to autochthonous and benthic to pelagic nutrients from low-order streams to high-order rivers predicted and observed in other studies (e.g., Vannote et al., 1980; Finlay, 2001), and also with patterns of increased depth caused by the downstream presence of dams at these sites. These results are also consistent with shifts from benthic-driven primary production (e.g., periphyton) to pelagic production (e.g., phytoplankton) as a result of cultural eutrophication (Vadeboncoeur et al., 2003; Chandra et al., 2005; Vander Zanden et al., 2005). Such shifts are generally considered to be the result of eutrophication increasing concentration and productivity of pelagic primary producers, thus starving benthic producers of both nutrients for growth and sunlight for photosynthesis (Vadeboncoeur et al., 2003; Chandra et al., 2005). Anthropogenically increased depth and turbidity may have similar effects (Freedman et al., 2013). As average depth increases from headwater streams to high-order rivers, relatively less light reaches the river floor, from near 100% of non-refracted light in small clear streams to zero in turbid and deep water, thus decreasing benthic production. Dams increased the mean depth from <3 m in pools in the free-flowing Allegheny River to a constant minimum of 4–5 m (or more) in dam-impacted reaches, and can, therefore, decrease benthic production without any influences from eutrophication or other increases in relative turbidity. Since the free-flowing reach of the river includes runs and riffles as well as pools, while the homogenous impounded reaches effectively consist only of long pools possibly with minimal lotic habitat immediately downstream of dams, a loss of some benthic nutrient pathways becomes even more likely.

Reliance on benthic-derived nutrients at free-flowing sites appears to be high while at dam-impacted sites δ¹³C signatures suggest a trend toward increasing reliance on pelagic-derived nutrients. While a full range of benthic and pelagic nutrients seem to be available, fishes are relying more on pelagic nutrients; this is consistent with the theory that reliance on pelagic nutrients (and decreased reliance on benthic nutrients) would increase with the increased depth due to the navigation dams. Pelagic production is also lower in lower order rivers, with zooplankton diversity and biomass consequently increasing downstream (e.g., Vannote et al., 1980; Ward & Stanford, 1983). The relative reliance on pelagic nutrient sources in the free-flowing section may, therefore, be a combination of higher availability of benthic nutrients and lower availability of pelagic nutrients.

Rafinesque (1820) referred to the Allegheny River as being “almost perfectly clear,” while our Secchi depths ranged from 142 to 145 cm downstream of an active dredging operation and 157–198 cm in other impounded areas of the river (JAF, unpublished data), thus offering supporting evidence that this is no longer the case. We found Secchi depths in the range of 330 cm, however, in a pool above the dam influence, so there does appear to be a negative effect of dams on water clarity. Although other studies have found that dams can decrease turbidity through retention of fine sediments (e.g., Kondolf, 1997), the Allegheny River system may differ due to the size of the dams (relatively small compared to dams constructed for hydroelectric power generation, flood-prevention, and similar purposes) and locks, both of which may allow for the passage and resuspension of fine sediments. Land use does not differ greatly between the upper navigation pools and lower free-flowing section, consisting primarily of forested land with some residential properties. The approximately 214 km of the Allegheny River between the Kinzua Dam and the end of the navigation dam influence near East Brady PA, likely experience full light penetration except during times of high discharge due to shallow depths and lower turbidity. Despite the upstream presence of the Kinzua dam, free-flowing sites likely represent similar reference states to the historical condition, and with similar fish assemblages and food-web structure.

According to the river continuum concept, nutrient sources shift from allochthonous inputs to autochthonous primary production along the longitudinal river gradient (Vannote et al., 1980). These gradients can be reset by dams, creating higher-flow downstream conditions that mimic higher order streams. Conversely, deeper and slower flowing conditions above dams can be more similar to lower order rivers. Plankton communities are sparse in higher order streams and rivers relative to lower order rivers and impoundments. In the Allegheny River, therefore, nutrient inputs in the free-flowing section would comprise primarily benthic sources, possibly with increased allochthonous inputs. In the dam-impacted section, however, increased phytoplankton production due to environmental conditions combined with lower benthic production would lead to increased reliance on such pelagic producers. The Allegheny River also has a long history of extractive gravel dredging. Dredged portions can exceed 20 m depth, with no light penetration deeper than approximately 10 m. We focused our sampling on undredged areas <7 m deep; however, in another study, we found that dredged areas accumulate terrestrial detritus, and to alter nutrient and sediment flow (Freedman et al., 2013).

Conclusions

We found significant differences among fish community compositions at sites in impounded and free-flowing sections of the Allegheny River. Furthermore, the shift from communities characterized by lotic-adapted species and those intolerant of silt, to those dominated by generalist and tolerant large-river species was very abrupt. In particular, the longitudinal gradient in fish community similarity and downstream trend toward increasing taxonomic diversity was disrupted in the impounded section, where we found decreasing downstream diversity but no concurrent trend in similarity. These findings were consistent with the stable isotope results, which showed shifts away from the benthic production that characterized the free-flowing section toward increasing reliance on pelagic-derived nutrients in the impounded section. These shifts were likely due to a decrease in benthic production due to increased depth, turbidity, and siltation (Freedman et al., 2013), and would also be consistent with a decline of lotic and intolerant species. By using an electrified benthic trawl, we were able to sample small benthic fishes that are difficult to sample using traditional methods. However, although we did also capture some non-benthic taxa, our sampling method was biased toward the capture of benthic rather than pelagic or littoral fishes. Since benthic fishes may be particularly impacted by habitat alterations due to increased depth (Freedman et al., 2013), our findings cannot necessarily be extrapolated to the entire fish community.

While dams can provide economic benefits, it is necessary to understand the effects that they can have on individual fish species, populations, and communities. While dam removal can restore habitats, and subsequently invertebrate and fish populations (Maloney et al., 2008), many factors need to be considered prior to restoration (Poff & Hart, 2002). Stable isotope analysis of δ¹³C and δ¹⁵N is an appropriate tool for assessing differences in fish assemblages between sites with varying degrees of influence from dams, and should be considered for before-after-control-impact (BACI) study designs. It is, therefore, important for managers and policy makers to consider not only the direct effects of habitat alterations on taxonomic diversity, but also indirect effects on ecosystem functioning. Furthermore, alterations in water flow, prey availability, and migration due to dams can even effect changes in fish ecomorphology and functional morphology in certain species (Curry et al., 2004; Palkovacs et al., 2007; Langerhans, 2008; Freedman, 2010; Haas et al., 2010), further confounding these issues. Dams influence riverine fish in many ways; a complete understanding of ecological processes is, therefore, necessary for informed conservation and management decisions.

Acknowledgments

We thank A. Anderson, V. Cavener, D. Cooper, H. Goldstein, A. Henning, R. Lorson, R. Lorson, T. Stecko, K. Taylor, T. Vasilopoulos, and R. Yoder for their invaluable field and laboratory assistance. The Stable Isotopes in Nature Laboratory at the University of New Brunswick performed the stable isotope analysis. An earlier draft of this manuscript was improved by addressing comments and suggestions from two anonymous reviewers. This research received funding and support from State Wildlife Grant T-42 administered by the Pennsylvania Fish and Boat Commission; from the Pennsylvania Department of Conservation and Natural Resources through Wild Resources Conservation Program Grants WRCP-06171 and WRCP-07269; and from the United States Geological Survey, Pennsylvania Cooperative Fish and Wildlife Research Unit.

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Environmental Management

10.1007/s00267-013-0212-8

The Role of Published Information in Reviewing Conservation Objectives for Natura 2000 Protected Areas in the European Union

Otars Opermanis¹, Brian MacSharry¹, Jerome Bailly-Maitre¹, Douglas Evans¹ and Zelmira Sipkova¹

(1)

European Topic Centre on Biological Diversity, Museum National d’Histoire Naturelle, 57 Rue Cuvier, 75231 Paris, France

Otars Opermanis

Email: otars.opermanis@mnhn.fr

Received: 8 February 2013Accepted: 25 November 2013Published online: 7 December 2013

Abstract

Protected areas are designated to protect species and other features known to be present at the time of designation, but over time the information about the presence of protected species may change and this should call for a continued review of conservation objectives. Published scientific literature is one of the possible information sources that would trigger a review of conservation objectives. We studied how published data on new discoveries of protected animal species were taken into account by the nature conservation authorities in updating species lists of Natura 2000 sites in the European Union, which are the basis for conservation planning at the site-level. Over the period studied (2000–2011) only 40 % of published new protected species records were recognized by the authorities. The two main reasons for this seem to be a reliance on other sources of information by authorities and the difficulty in finding relevant information in scientific papers. The latter is because published faunistic information is very fragmented among different journals, and often insufficient in details. We recommend better cooperation between authors, publishers, and nature conservation authorities in terms of information presentation, publishing policy, and a regular review of published information.

Keywords

Protected species New records Scientific knowledge Management objectives Published information Natura 2000

Introduction

One of the key steps in systematic conservation planning is the identification of conservation objectives for protected areas (Margules and Pressey 2000; Louette et al. 2011). For each protected area, conservation objectives are defined to conserve targeted species, habitats, and other features, often linked to legislation or other obligations such as the European Union (EU) Habitats Directive or the Ramsar Convention on wetlands. However, over time some species may be lost from a site, others may be discovered by surveys or monitoring activities, either newly arrived or just not previously recorded (Gaston et al. 2008). Therefore management plans of protected areas in most countries include monitoring and periodic review of conservation objectives, usually at a frequency of at least once every 10 years (Kruk et al. 2010).

The 1992 EU Habitats Directive (a legal instrument applying to all Member States) established the Natura 2000 network of protected areas targeted at habitats and species listed in annexes to the directive together with sites designated under an earlier directive on wild birds. The network now includes more than 26,000 sites and is one of the largest networks in the World (Sundseth and Creed 2008; Evans 2012). It is regarded as largely complete for terrestrial and freshwater habitats and species but incomplete for marine features. Natura 2000 network plays a key role in addressing the 2020 target of halting biodiversity loss in the European Union (European Commission 2011a).

The Habitats Directive includes a system for periodic review of the conservation objectives of each site (European Environment Agency 2012a). Each site in the network is described using an agreed format known as a Standard Data Form (European Commission 2011b). Together with the details such as name, area, and coordinates, the form notes each of the habitat types and species listed on Annexes I and II of the Habitats Directive and Annex I of the Birds Directive that occur in the Natura 2000. The form is electronic and the forms for the >26,000 sites are stored as a database managed by the European Environment Agency. Site management plans must take into account the ecological requirements of the species listed on the Standard Data Forms (European Commission 2012a) and the European Commission demands that the forms are revised regularly to provide up to date information on the sites. An annual update has been suggested although to date the frequency has varied greatly between Member States (European Environment Agency 2012a). The date that a targeted species or habitat is first noted on the form for a given site can be considered as the legal starting point for planning and implementing conservation actions under the EU directives to maintain or restore the feature at favorable conservation status.

Information on changes in species and habitats present on a site can be collected and made available to the national or, in some countries, regional authorities who are responsible for revising the Standard Data Forms in several ways, and Table 1 summarizes this process in three consecutive steps: data collection, data communication, and data recognition. It is most likely that the largest proportion of new information arrives from national Natura 2000 monitoring schemes designed and funded by national or regional nature conservation authorities (e.g., Goverse et al. 2006; Mehtala and Vuorisalo 2007; Henry et al. 2008). In this case, there should be an automatic flow of information from field to Standard Data Forms. Data on species, and to lesser extent habitats, are also available from other sources including scientific projects, activities of non-governmental organizations (NGO), and citizen science programs (Schmeller et al. 2009). It is not clear how such information is communicated to the authorities or how such information is dealt with by the authorities.

Table 1

Logical framework of projected data-flow on how information on new discoveries of species in existing Natura 2000 sites reach and are dealt with by the national nature conservation authorities

Data-flow stages	Our assumptions
How collected?
Authority funded study, monitoring of protected areas	Clear target is to observe changes in species composition and trends. The results would most likely be reflected in Standard Data Forms through technical reporting
Ecological studies where species inventories are not a primary target	Even if inventories of flora and fauna are not a subject of the study, still, the fact that a species of interest was studied in Natura 2000 site could attract attention by the authorities. Result reflection in Standard Data Forms is uncertain
Miscellaneous scientific projects and activities by individual scientists	Some scientists are targeted on faunistics or floristics (particularly of less studied taxa) and systematics. Result reflection in Standard Data Forms is uncertain
NGO activity and citizen science	Potentially a great source of information, especially in countries where this is developed. Result reflection in Standard Data Forms is uncertain
How communicated to national authorities?
Oral or written: informal	Probably a rare occasion as authorities usually need written record, but this cannot be excluded. This happens in the Member States where administration traditions are not very formal and there is a good communication between authorities and scientists
Written: technical report	Mostly brings information from scientific inventory activities that were initiated with the aim to monitor changes in Natura 2000 sites
*Written: published	Theoretically any study can be reflected there and information given may overlap with all other ways of communication
Online: database	Although in an increasing number of European countries online databases report new species findings, it is uncertain how and whether this information reaches responsible authorities
How treated by the authorities?
*Recognized and accepted	Best outcome, but still it is not possible to track which communication type has contributed to this
*Recognized but disregarded	High uncertainty! Not possible to detect the fact whether it was recognized but disregarded for a variety of reasons or simply not accepted, again due to a number of possible reasons
*Not recognized	High uncertainty! Probably authorities are not paying enough attention about data that are available apart studies organized by themselves

Asterisks mark the limits of this study but all other possibilities are also discussed

In this paper we focus on data published in scientific journals, the media that we assume forms the majority of non-official data available to the public, including responsible authorities. The advantages of this form of communication are that (1) information becomes public, (2) it is usually peer-reviewed; giving the results increased credibility, and (3) it is likely to provide sufficient details (e.g., when, where, how many individuals, etc.) allowing a critical examination before inclusion into the relevant Standard Data Form(s). Published information is appreciated by nature conservation authorities while this is not often the case with informal ways of communication (e.g., Postiglione 2006).

Although the designation of protected areas for biodiversity has a long history (European Environment Agency 2012b), research is still ongoing to help select the most appropriate protected areas for species and habitats in terms of quantity, quality, and connectivity (e.g., Parrish et al. 2003; Schabetsberger et al. 2004; Ioja et al. 2010; Jantke et al. 2011). Only relatively recently has the need for setting conservation objectives and implementing management actions in designated protected areas been emphasized or re-emphasized, at least in connection with Natura 2000 (e.g., Ostermann 1998; Louette et al. 2011). The conservation objectives of protected areas are dependent on the species and habitats present, so any changes in protected species composition should be taken into account. Therefore, in this study we firstly aimed to assess the number and availability of new species records from existing Natura 2000 sites that are published in scientific journals. Secondly, we checked whether such information is taken into account by nature conservation authorities and recorded in the Standard Data Forms and what potential factors may affect recognition of a new species record. Finally, we investigated the relationships between the events of protected area designation, new protected species record, publishing of this record, and the start of conservation action for the species in a particular protected area. Analyses of these important links are virtually absent from the scientific literature, among related subjects we found only studies examining factors that affected the timing of species listings under the USA Endangered Species Act (Tear et al. 1995; Ando 1999), but this is of greater relevance to any future review of annexes of European Union nature directives (e.g., Cardoso 2012; Maes et al. 2013).

Methods

General Assumptions

We searched for information on four important events in the process of updating lists of protected species in existing Natura 2000 sites: date of site designation, date of discovery of a previously not listed protected species, date of publication of this record, and date of entry of a new record in the Standard Data Form of the site (Fig. 1). We modeled possible flows of these events that would enable us to assess how important was the role of publications in including newly recorded species in Standard Data Forms of existing Natura 2000 sites. Further interpretation of sequences of events and the time periods between the events is given in Table 2.

Fig. 1

Possible sequences of the important events for updating species lists for individual Natura 2000 sites: four modeled scenarios. Event legends: A site designation, B discovery of new species in the site, C date of publication of this new record, D entering the record in the SDF

Table 2

Possible interpretation of event-flows with the reference to Fig. 1

Value	Name	Assumptions
… → B	Species already in SDF before reported discovered by the study	Another, older dataset probably used by authorities, or the ‘new’ discovery of species comes from a new locality within existing protected area^a
B → C	Time between discovery and publication	Less time indicates author’s responsibility, quality of manuscript (as most journals claim to be peer-reviewed), and the journal’s ability to fit an important message as early as possible in its publication schedule
C → D	Time between publication and entering the species record in SDF	Less time indicates a good ability of nature conservation authorities to review scientific news unless the fact of species discovery is not reported in parallel from another source, which is also a good outcome
B → D	Time between discovery and entering the species record in SDF	Pure conservation value, sooner it is, the better
B → …	Species not reported in SDF	If species record does not reach SDF, this is a useful reference for the European Commission to ask reasons why this has not yet happened

^aBut even in this case a confirmation of the presence of species is a valuable contribution for site managers and authorities

This study was based on a comprehensive literature review and by analyzing historical editions of Natura 2000 databases submitted by European Union Member States to the European Commission. The following steps were implemented and described in following chapters: (1) finding new species records for existing Natura 2000 sites in scientific literature, (2) linking these records to an existing Natura 2000 site, (3) examining if and when this record was entered in the Standard Data Forms.

New Species Records from Existing Natura 2000 Sites

Literature review was performed through a combination of key-word searches using Google Scholar and Scopus search engines and a systematic review of the contents of the journals which publish relevant faunistic reviews, as we restricted this study to animals (Appendix). The majority of these journals were published by a national academic or non-governmental organization, but exceptionally some interesting records were also found in international journals. Our primary aim was to detect any potentially first (new) records of Habitats Directive Annex II animal species in European Union countries. In some cases, however, it was not possible to determine whether the species record is actually ‘new’ or ‘first’, as the aim of the paper was to describe the current fauna of the site without direct reference to the previous inventories. Such publications were at first included in this study, but rejected at later stages of data filtering when, for example, it proved that Standard Data Forms contained a record of a species earlier than it was reported found by the respective publication (last option; Fig. 1).

Useful information for our purpose was typically found in original papers of the following categories: (1) papers reporting newsworthy findings of a species in new geographical locations, (2) inventory results of a geographical unit, (3) ecological research of particular species or a group of species, and (4) literature reviews. The last category, however, was used only in relatively few cases when reviews provided recent information collected over a short period of time.

European and national Atlases on the distribution of species of different taxa were not considered as in most cases we could not obtain precise locations of observations or dates of records. In addition, the majority of published European and national Red Lists do not show the exact locations of threatened species, as well as lack exact dating, and therefore could not be used. We did not consider available online databases whose information to some degree overlaps with that of paper publications. Some European Union countries have well-functioning online reporting systems for species recording, including those of Annex II of the Habitats Directive (for example, in Sweden and the United Kingdom), which are largely based on citizen science. We acknowledge this data source as very important (Table 1), particularly in a view to the future, possibly even taking over the role of the published information (see “Discussion” section).

We focused on species discovery reports that were published in a period that would correspond to the post-designation period of most Natura 2000 sites in different groups of European Member States. For the 15 ‘old’ Member States we examined a publication period between 2000 and 2011, for the 12 ‘new’ Member States who joined the European Union in or after 2004 we focused on period between 2004 and 2011, with the exception of Bulgaria and Romania, who joined only in 2007 (2007–2011). We systematically searched 60 journals (Appendix). A few references came from other journals whose contents we could not access in full and from conference proceedings. Our work cannot be considered as a complete review of the publications reporting new species in existing Natura 2000 sites in a given period, but we believe it is sufficiently large to show trends in information use.

The date of the new record in most cases was attributed to a month/year precision. Exceptionally in some studies authors provided only the dates of the study period, but not the exact date for a record of a particular species of interest. In such cases we used the completion date. As regards the date of publishing, in most cases it was also possible to record exact month/year based on information provided in journals’ web-pages.

The Standard Data Forms have a field ‘Documentation’ where all site-specific references can be recorded (European Commission 2011b). However, this data field is an ‘optional’ category, and such information has not systematically recorded by all Member States. A quick examination showed that 9 (of 27) Member States had no or very few records in this field. Another eight states had records completed for approximately 50 % of sites and the remaining ten had records for all or nearly all sites. But even where records were present, they were mostly internal references (legal acts) related to site establishment or only references to a few species suggesting that these entries were filled at the time of site designation and have never been updated.

During our search for new records in existing Natura 2000 sites we came across situations when papers reported the complete opposite—the absence of species from the Natura 2000 site where it was previously reported (e.g., de Bruyne 2002; Romano et al. 2007; Strugariu et al. 2008). We did not analyze such records and possible nature conservation authority responses any further as we believe that applying the precautionary principle, there should be more research than one negative observation before removing the species from the Standard Data Form as species may be re-discovered after some time (e.g., van Dijk 2006). Instead, the appropriateness of current management should be assessed to avoid local species extinctions, as, for example, is described by Konvicka et al. (2008).

Linking Records of Species to an Existing Natura 2000 Sites

The information in publication on the new species record was used to link with an existing Natura 2000 site. We used the following cues: Natura 2000 site code or name, a name of a national protected area (often matching with the name of Natura 2000 designation), geographical coordinates (enabling us to use ESRI ArcGIS 10.0 to establish a link with a Natura 2000 site), a detailed map of a study area (enabling us to judge with a high confidence whether the location falls into a Natura 2000 site), or kilometers and direction from a local village/town, or a point on a small scale map that fell within borders of existing Natura 2000 site. In total, our web-based search yielded 182 relevant publications reporting at least one potentially new record of an Annex II species in a Natura 2000 site, and 414 such records in total.

Information from the Standard Data Form

Analysis of historical Natura 2000 databases of the European Environment Agency archive provided us with the date for when the site was designated and the date when the species was first entered in the Standard Data Form of a particular site. This analysis also indicated which published records were not recognized by the authorities and were absent from Standard Data Forms by December 2011. Dates were obtained with a minimum precision of month-year.

Analysis of Natura 2000 databases, however, reduced the sample size as 71 species records appeared to occur prior to site designation, suggesting that Standard Data Forms were not complete at the time of designation. Other 113 species records, as reported by publications, appeared after the appearance of species record in the Standard Data Forms. Thus the final sample size comprised 190 genuinely new records for the existing Natura 2000 sites from 101 scientific papers covering 19 (out of 27) European Union Member States and 61 species.

Given that publications were the starting point of this study, we do not know the number of new species records in Natura 2000 sites that were not published and thus not detected by our literature search (i.e., many might pass event C, as in Fig. 1). In order to acquire at least some data about this, we examined how many species have been added to existing Natura 2000 sites in the period 2009–2011 and cross-checked which additions might have been a result of relevant publications detected in this study.

Statistical Methods

All statistics were performed using R software (R Development Core Team 2012). We used survival analysis to study the ‘lifetimes’ and distributions of published new records of protected species in Natura 2000 sites until they are taken into account by nature conservation authorities. This was the reverse of the usual application of this method, for example in medical sciences, where a terminating event (hazard) of observation means ‘death’ while in our case it is positive, i.e., recognition of a new species record. Our dataset was right-censored as many of the published papers may be still recognized (i.e., entered in the Standard Data Forms) after the closing date in this study, i.e., December 2011. We used the non-parametric Kaplan–Meier estimator of survival probability to observe patterns of new species record acceptance probability by authorities over time since species discovery and publication (Fig. 3). Chi square tests of the R function ‘survdiff’ were used to analyze differences in lifetime distributions by different factors: publication type, language of publication and whether a particular records belongs to an ‘old’ or ‘new’ (joined in or after 2004) European Union Member State.

Results

Only 40 % of a total of 190 published records of protected species in existing Natura 2000 sites (our sample size) had been entered in Standard Data Forms by December 2011. Reported new species were discovered from a few months up to 16 years after designation of Natura 2000 site (Fig. 2, events A–B). On average, new species records were published within less than 2 years after recording (Fig. 2, events B–C). Forty-three percent of new records were actually first entered in Standard Data Form and only later published (on average 16.2 months later; Fig. 2, events C–D negative values). The remaining 57 % records were first published and then entered Standard Data Forms on average 21.7 months later (Fig. 2, events C–D positive values).

Fig. 2

Distribution of time intervals between events with mean values and Standard Errors. Legend A–B Time from site designation to discovery of new species. B–C Time from discovery of new species to date of publication. B–D Time from discovery of new species to entry in the SDF. C–D Time from date of publication to entry in the SDF. Negative values for C–D indicate cases when event D happened before C

Of 41 publications reporting more than 1 new species record, 39 % had partial recognition of new records, i.e., some species from the same publication were entered in Standard Data Forms, some not. This indicates that authorities were somehow selective in using published information or, alternatively, different source of information were used that reported only the part of the records.

The probability of the new species record being entered in Standard Data Forms showed a steady increase over time since species discovery and since publication of the new record (Fig. 3). The most prominent increase of probability happened soon after discovery, but in most cases before publication (Figs. 2, 3a); this suggests the presence of informal communication between researchers and authorities. Figure 3b shows that after approximately 80 months (or 6.6 years) after publication the probability of recognition (i.e., entering in SDF) does not increase. This means that approximately 80 % of the so far non-recognized records still have a chance of being entered in SDFs, as the margin of 80 months since publication of the record has not yet passed. Probabilities of entering a record in a SDF were marginally higher in new European Union Member States than ‘old’ Member States (χ ² = 3.8; d.f. = 1; P = 0.05), but did not differ between papers published in English or other language (χ ² = 1.2; d.f. = 1; P = 0.27) and between types of published papers (χ ² = 1.5; d.f. = 3; P = 0.68).

Fig. 3

Cumulative probability (Kaplan–Meier) and 95 % confidence intervals of entering the newly discovered species into Standard Data Forms since time of discovery (a) and since publishing the record of discovery (b)

In the 3-year period (2009–2011), 355 new species records were added to the Standard Data Forms of existing Natura 2000 sites in 16 Member States of the European Union. Only 14 of those records were possibly triggered by publications found in this study.

Discussion

While the availability of biodiversity data is considered to be a major issue in conservation (Bisby 2000; Yesson et al. 2007), our results showed that the current impact of available scientific literature on updating management objectives for Natura 2000 sites is low. As often indicated by authors, one of the most important motivations for publishing faunistic news is to improve conservation of species. Then a situation where 60 % of published records are not recognized by the authorities is not satisfactory. At the same time we see that the process of updating of the Standard Data Forms is still ongoing as most Member States are apparently aware of the need for such updates, but they are clearly not initiated by scientific papers whose contribution accounts for less than 4 % of all new entries in Standard Data Forms. Therefore it is essential to understand the reasons for this situation, firstly by identifying the data sources that ‘compete’ with published papers in bringing information to the attention of nature conservation authorities and secondly by analyzing what does not work with existing scientific media as there is such a low rate of using published information in practical conservation.

It seems that the main contributors of new faunistic data to Standard Data Forms are the Natura 2000 monitoring programmes which are run in most countries. Although there is a variation among Member States, data collected are reported directly to nature conservation authorities without ‘formalizing’ this information in the form of scientific publications and thus without making this information widely accessible. Even if such reports are available on the web, or upon request from agencies, the search for them could be cumbersome requiring personal contacts and possibly knowledge of the local language (Amano and Sutherland 2013). A portion of the new data may be later published by individual authors participating in such monitoring programmes (some authors indicated the source of funding in their publications), and this could be an explanation why so often (43 %) new species records were first entered in Standard Data Forms and only later published.

Likewise, much faunistic data have been gathered by numerous projects funded by the European Union LIFE programme (http://ec.europa.eu/environment/life/project/Projects/index.cfm). Even though species inventories were not a major component of many projects, an element of species inventory was probably present in many of them and brought many new observations as LIFE projects focused mainly on existing Natura 2000 sites. But even if each project has its own web-page where the main achievements are described, as well as the European Commission’s online database of all past and present LIFE projects is freely accessible, the available reports rarely provide necessary details of new species records and their locations.

The other potential holders of information about the distribution of protected species are web-sites with online databases which are usually run by national nature conservation agencies often with contributions by citizen scientists. Participation of the public ensures large numbers of incoming records, in some cases with million records per year, and scientific papers will never be able to compete with them in quantity of observations (Schmeller et al. 2009). Other advantages are that an online database reflects the findings almost immediately (while publishing a new species record takes time: on average almost 2 years as shown in this study) and also that writing up the paper could be a challenge for some potential data providers, while inserting a new observation in a database only takes a few mouse clicks.

Such web-sites and public databases of species records are well-developed, for example, in Sweden (Swedish Species Gateway; http://www.slu.se/en/collaborative-centres-and-projects/artdatabanken/) and United Kingdom (National Biodiversity Network; http://www.nbn.org.uk/). In these countries, in the field of biological recording, citizen scientists have largely replaced professional scientists (Silvertown 2009). This may be the reason why we could not find any relevant papers for this study from Sweden and the United Kingdom. However, the rate at which national nature conservation authorities are using new information from public databases remains unknown. However, in many other countries, for example in Eastern Europe, nature conservation is a relatively new addition to the political agenda and public involvement in data collection is still not actively practiced by the various levels of government or such involvement is not well-developed (e.g., Eben 2006; Schmeller et al. 2009). Here published information should still have a role in decision-making but this could change in future.

Our study showed that there is still a significant proportion of new species records that were missed or possibly not recognized by authorities, suggesting that scientific publications do contain some information that authorities cannot obtain except by checking journals. Even though 190 relevant records found by our study compares poorly with more than 300,000 species records in the European Natura 2000 database of December 2011, in theory, each single new record should be validated by the relevant authorities.

If we assume that authorities are aware of the publication about the discovery of new protected species in existing Natura 2000 sites, we could think of two reasons why this information is not taken into account in updating Standard Data Forms and site conservation objectives. First, a minority of European Union Member States are not adding any new features on Standard Data Forms in principle as they consider the set of species at the site designation as final conservation objective and new species is seen as an additional management burden at the sites. In our opinion, this approach cannot be justified, as most often species are missing from the ‘initial lists’ due to lack of information at the time of site designation (see also Lozano et al. 2013), but not due to temporal changes in species presence at the site. Secondly, the absence of recognition could be explained by the fact that many ‘first records’ are often observations of species in extreme locations with respect to their distribution range, often accidental (e.g., in mammals), and one such observation does not immediately oblige authorities to recognize it as a subject to define conservation objectives for a site.

But apparently the main reason of not-using published information is because it was not searched for or a search did not yield the expected results. To search biological information in the web, in spite of today’s powerful search engines, including Google Scholar available to practically everyone, requires some time and experience and such persons may not be available. It is unlikely that administrative staff in ministries would search for literature themselves; this is rather a job for agencies or contracted scientists. It is also known that the recent economic crisis forced environment ministries and agencies to significantly reduce staff that could otherwise be directed to better monitoring of published information.

Even if someone is willing to search for newly published information, the following difficulties are likely to be encountered. Using different keyword combinations (e.g., involving species and country names, etc.) may produce only partial lists of potentially relevant papers. But from these papers, it is possible to identify journals which could be searched systematically as it is likely that they were publishing other similar papers. Still, even a list of journals produced in such a way may not be complete. It may prove useful to also search for European scientific societies (for example, entomological societies) and then look for the ‘publications’ section of their web-pages. Almost in each country it is not sufficient to look only in their home journals because many, if not most, researchers also publish their results in journals abroad.

During our search for information for this paper we observed that, except for some sensational new records, very few international peer-reviewed journals publish descriptive faunistic reviews but focus exclusively on analytical and experimental studies. Journals with low Impact Factors, or with no Impact Factor reported, and most often published by national or regional scientific and conservation societies and natural history museums (Appendix), proved most important. But many such journals mostly publish faunistic observations from less studied exotic regions of the world than the territory of the European Union. The ratio of home-related articles is often at best 1:10. This is particularly true for North-West Europe which is well surveyed and it is less likely that new species will be found in new locations. Meanwhile, there are a number of journals with relevant content in Eastern and Mediterranean Europe. Even the relatively few papers from the North-West Europe did not much contribute to knowledge about protected species in Natura 2000 sites. For example, it was hard to find any published observations on protected bat species from most of Finland, however, extreme observations above the Arctic Circle were still published (Siivonen and Wermundsen 2008). Also, searches should not be restricted to faunistic papers as we came across a number of analytical ecology studies (e.g., habitat selection, food analysis, behavior) on Habitats Directive Annex II species in Natura 2000 sites (suggesting that there are abundant populations making such study possible) but 70 % of such populations were not reported in Standard Data Forms (e.g., reported by Scalici and Gilbertini 2007; Tartally and Varga 2008; Zografou et al. 2009). In conclusion, the potentially useful information is much dispersed and a search might take a considerable amount of time, particularly when undertaken for the first time.

Further difficulties can be encountered after an interesting title and journal has been identified. Very often abstracts, usually available in all cases, are not enough to validate a new species record and to check if it is linked to an existing Natura 2000 site. Most journals where we found relevant papers followed a policy of free-access, and full-length papers can be accessed if the respective web-page is functioning correctly. However, many interesting journals with higher Impact Factors require payment to access or an institutional subscription, often not available to staff in conservation agencies and ministries. Some journals of scientific societies first require a membership to the society before one can access journal contents. Some journals have not yet made their older volumes available electronically. Eventually, if a reader has successfully acquired a promising full-length paper, s/he may soon be disappointed by its quality. Sometimes authors are not very cooperative in presenting data in a complete and easily understandable way or simply do not understand how to allow the best use of their data for practical conservation. In our study in some papers we struggled to find even such basic elements as the date (period) and place of observation; this problem has been previously noted by authors of review papers (e.g., Boldogh 2006). However, keeping high publishing standards is also a responsibility of editors and reviewers as almost all of journals we studied claim to be peer-reviewed.

On a more positive note, our results showed that when published records of new species were recognized by nature conservation authorities and entered in the Standard Data Form, the mean time interval between the species discovery and recognition (entry in Standard Data Form) was only around 2 years (Fig. 2 B–D). An even more remarkable finding was that very often the entry in Standard Data Forms occurred before publication, which indicates the informal communication between researchers and authorities. This seems to be a case particularly in relatively small European Union countries, for example in Hungary and Latvia, where ‘everyone knows everyone’ and new information quickly finds its way from the field to responsible authorities (Zita Zsembery, Ilona Mendzina, pers. comm.). Also, many extreme cases of delayed recognition probably have explanations, for example, the snail Vertigo moulinsiana was collected in a Latvian site in 1997 and incorrectly identified as a very similar species Vertigo geyeri. After a number of years, the collection was revisited and the identification error was corrected and the record was published 7 years after discovery in 2004 (Pilate 2004).

Even if the Natura 2000 network is now considered to be close to completion (European Commission 2012 b), there will always be a need to update site records that are essential in reviewing conservation objectives at the site-level. It seems that the currently insufficient use of published papers in updating Natura 2000 Standard Data Forms is due to the difficulty of finding relevant information rather than due to ignorance from the nature conservation authorities. It is also clear that published papers are currently not the most important source in updating information about protected areas. Scientific journals may lose this role completely in the near future unless they become better organized in terms of providing freely available, exact, and concise information about new records of protected species in the Natura 2000 network. We call for better cooperation between field scientists/authors, publishers, and nature conservation authorities as any new information on presence of species at the site-level can be very useful for conservation even in such a relatively well-studied region as Europe. To authors reporting new species records we recommend to indicate clearly (1) precise coordinates and (2) Natura 2000 site details (code and official name can be found in: http://natura2000.eea.europa.eu/#), date(s), numbers, as well as any other information, e.g., habitat requirements, that could be useful for future management planning to fulfil conservation objectives.

It could be worthwhile to examine experience at a global scale in communicating new faunistic information. For example an existing journal ‘Check List: Journal of Species Lists and Distribution’ (http://www.checklist.org.br/) is a bimonthly peer-review online journal, devoted to publishing lists of species and notes on the geographic distribution of any taxon as such reports have traditionally been neglected in other journals. As this journal focuses mainly, but not exclusively, on the Americas, a similar European journal (or electronic platform) would be most welcome. This could help to prevent the current fragmentation of information and feed not only the Natura 2000 process with new data but also support red-listing and reviewing the lists of protected species.

We also recommend that national authorities responsible for nature conservation should regularly check the recent scientific literature for data completeness, even if they have other sources of information available. Published literature is probably the most cost-effective mean of obtaining information on new species records.

Acknowledgments

Data collection and analysis was supported within a framework of the European Topic Centre on Biological Diversity of the European Environment Agency. We thank Mora Aronsson, Marita Arvela, Michael Hosek, Ilona Mendzina, Dominique Richard, Carlos Romao, Frank Vassen, and Zita Zsembery for useful comments on earlier drafts of this paper. Bruno Opermanis helped with R. We thank the three referees for valuable suggestions on manuscript improvement. The views expressed are those of the authors and should not be taken as views of the European Topic Centre on Biological Diversity.

Appendix

List of journals which were systematically searched for this study (publishing authority in brackets)

Acta Chiropterologica (Museum and Institute of Zoology, Polish Academy of Sciences)

Acta Entomologica Slovenica (Slovenian Entomological Society)

Acta Entomologica Musei Nationalis Pragae (National Museum Prague)

Acta Herpetologica (Firenze University)

Acta Ichthyologica et Pisatoria (West Pomeranean University of Technology)

Acta Societatis Zoologicae Bohemicae (Czech Zoological Society)

Acta Theriologica (Polish Academy of Sciences)

Acta Universitatis Latviensis, Biology (University of Latvia)

Acta Zoologica Bulgarica (Institute of Biodiversity and Ecosystem Research)

Acta Zoologica Academiae Scientarum Hungaricae (Hungarian Natural History Museum/Biological section of the Hungarian Academy of Sciences)

Acta Zoologica Lituanica (Institute of Ecology/Nature Research Centre)

Annales Zoologica Cracoviensia (Institute of Systematics and Evolution of Animals, Polish Academy of Sciences)

Annales Zoologici (Museum and Institute of Zoology, Polish Academy of Sciences)

Beitrage zur Entomofaunistik (Austrian Socitety for Entomofaunistics)

Biharean Biologist (University of Oradea)

Biologia Bratislava (Slovak Academy of Sciences)

Bonn Zoological Bulletin (Zoological Research Museum Alexander Koenig)

Bulletin de la Société Herpétologique de France (Herpetological Society of France)

Bulletin of the Irish Biogeographical Society (Irish Biogeographical Society)

Cynthia (Catalan Butterfly Monitoring Scheme)

Entomologia Hellenica (Hellenic Entomological Society)

Entomologica Fennica (Entomological Society of Finland)

Entomologische Berichten (Netherlands Entomological Society)

Entomologisk Tidskrift (Swedish Entomological Society)

European Journal of Entomology (Czech Entomological Society)

Faunistische Abhandlungen (Museum of Zoology, Dresden)

Fragmenta Faunistica (Museum and Institute of Zoology, Polish Academy of Sciences)

Folia Malacologica (Association of Polish Malacologists)

Folia Zoologica (Institute of Vertebrate Biology (Brno))

Graelsia (National Museum of Natural Sciences, Madrid)

Gredleriana (South Tyrol Nature Museum)

Herpetozoa (Austrian Herpetological Society)

Hystrix (Italian Theriological Association)

Insect Conservation and Diversity (Royal Entomological Society)

Italian Journal of Zoology (Italian Society of Zoologists)

Journal of Insect Conservation (Springer)

Journal of Conchology (The Conchological Society of Great Britain and Ireland)

Latvijas entomologs (Latvian Entomological Society)

Lynx (Natural History Museum, Prague)

Mammalia (De Gruyter)

Malacologica Bohemoslovaca (Institute of Zoology, Slovak Academy of Sciences)

Mitteilungen der Deutschen Malakozoologischen Gesellschaft (German Malacozoological Society)

Mollusca (Museum of Zoology, Dresden)

Natura Sloveniae (Ljubljana Biotechnical Faculty/National Institute of Biology)

Natura Somogyensis (Somogy County Museums)

Nederlandse Faunistische Mededelingen (Naturalis Biodiversity Center)

New and Rare for Lithuania Insect Species Records and Descriptions (Lithuanian Entomological Society)

North-Western Journal of Zoology (University of Oradea)

Oryx (Flora and Fauna International)

Phegea (Flemish Entomological Society)

Salamandra (German Society for Herpetology)

Silva Gambreta (Sumava National Park)

Small Carnivore Conservation Newsletter (IUCN, Species Survival Commission)

Tentacle (IUCN/SSC Specialist Group)

Tiscia (Tisza Research Committee, University of Szeged)

Travaux du Museum National d’Histoire Naturelle (‘Grigore Antipa’ National Museum of Natural History, Bucharest)

Vertebrate Zoology/Zoologische Abhandlungen (Senckenberg Nature Research Society)

Vespertilio (Czech Society for Bat Conservation)

Wissenschaftliche Mitteilungen aus dem Niederosterreichischen Landesmuseum (State Museum of Lower Austria)

Zoologische Mededelingen (Netherlands Centre for Biodiversity)

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Environmental Management

10.1007/s00267-013-0222-6

Riverine Threat Indices to Assess Watershed Condition and Identify Primary Management Capacity of Agriculture Natural Resource Management Agencies

Jeffrey D. Fore^{1, 5}, Scott P. Sowa², David L. Galat¹, Gust M. Annis³, David D. Diamond³ and Charles Rewa⁴

(1)

Department of Fisheries and Wildlife Sciences, University of Missouri, Columbia, MO, USA

(2)

The Nature Conservancy, Michigan Field Office, Lansing, MI 48906, USA

(3)

Missouri Resource Assessment Partnership, School of Natural Resources, University of Missouri, Columbia, MO, USA

(4)

NRCS Resource Assessment Division, Beltsville, MD, USA

(5)

Present address: The Nature Conservancy, West Tennessee Program Office, Jackson, TN, USA

Jeffrey D. Fore

Email: jfore@tnc.org

Received: 1 September 2012Accepted: 13 December 2013Published online: 4 January 2014

Abstract

Managers can improve conservation of lotic systems over large geographies if they have tools to assess total watershed conditions for individual stream segments and can identify segments where conservation practices are most likely to be successful (i.e., primary management capacity). The goal of this research was to develop a suite of threat indices to help agriculture resource management agencies select and prioritize watersheds across Missouri River basin in which to implement agriculture conservation practices. We quantified watershed percentages or densities of 17 threat metrics that represent major sources of ecological stress to stream communities into five threat indices: agriculture, urban, point-source pollution, infrastructure, and all non-agriculture threats. We identified stream segments where agriculture management agencies had primary management capacity. Agriculture watershed condition differed by ecoregion and considerable local variation was observed among stream segments in ecoregions of high agriculture threats. Stream segments with high non-agriculture threats were most concentrated near urban areas, but showed high local variability. 60 % of stream segments in the basin were classified as under U.S. Department of Agriculture’s Natural Resources Conservation Service (NRCS) primary management capacity and most segments were in regions of high agricultural threats. NRCS primary management capacity was locally variable which highlights the importance of assessing total watershed condition for multiple threats. Our threat indices can be used by agriculture resource management agencies to prioritize conservation actions and investments based on: (a) relative severity of all threats, (b) relative severity of agricultural threats, and (c) and degree of primary management capacity.

Electronic supplementary material

The online version of this article (doi:10.1007/s00267-013-0222-6) contains supplementary material, which is available to authorized users.

Keywords

Agricultural conservation Threat index Management capacity Watershed condition Threat assessment Missouri River basin

Introduction

Restoring natural resources is a process of implementing conservation practices at the correct places to achieve a desired set of conditions (Palmer et al. 2005). Decisions on where to focus conservation practices are complicated in stream ecosystems because sources of environmental stress (hereafter, threats) can be distributed anywhere within a watershed and may be far removed from the site of interest, thus highlighting the importance of considering total watershed condition (Wang et al. 1997). Managers are increasingly faced with conservation planning over large spatial extents (e.g., states or large river basins) and need tools to help prioritize and select streams on which to focus conservation efforts and resources. Biological assessments of ecological condition are one such tool, but are incomplete over large spatial extents (often <1 % of stream miles in a basin is represented; Sowa et al. 2007). Thus, management agencies have difficulty in selecting and prioritizing watersheds since ecological condition of unsampled streams is largely unknown. Managers can instead use existing geospatial datasets to conduct watershed scale threat assessments to identify overall watershed condition, identify potential sources of stress in known ecologically degraded streams, and determine if an agency’s conservation practices are suitable to address the threats in a watershed (i.e., an agency has primary management capacity).

Threat assessments are typically conducted by developing a multi-metric threat index that uses geospatial data to specify the location and quantify the extent and magnitude of human threats in a watershed by summarizing watershed condition with a single score (Danz et al. 2007; Mattson and Angermeier 2007). Threat indices are advantageous over landuse and individual threat metric maps (e.g., locations of point-source discharges) because indices represent overall watershed condition and relativize watershed condition estimates to the most threatened watershed. Threat indices can be quantified and mapped at a stream segment (length of stream between two confluences) resolution over large spatial extents to include sites lacking a direct assessment of ecological condition. Since most threat indices are made up of multiple threat metrics that represent an array of human disturbances, they can sometimes be used to infer the likely source of environmental stress.

When dealing with watersheds where ecological degradation is known, managers should use information from threat assessments to guide conservation practice implementation. Following the conceptual example in Fig. 1, low Index of Biotic Integrity (IBI; Karr 1981) scores can identify ecologically degraded streams and individual IBI metrics (e.g., proportion of lithophilous spawning fishes) that represent functional community traits and may identify the stressor (Leonard and Orth 1986). Stressors can be identified using functional traits of fish communities (e.g., lithophilous spawners) because they can be linked to specific physical drivers or processes that are altered by human threats (Poff 1997; Sutherland et al. 2002). Conservation planning is then improved because managers have identified the likely threats causing degradation and are able to make informed decisions regarding the appropriate conservation strategies to address the appropriate threats (Fig. 1). Identifying the likely cause of impairment is particularly important from a logistical standpoint because most resource management agencies have the ability and capacity to address only a select suite of threats.

Fig. 1

Conceptual diagram illustrating potential decision pathways and outcomes of conducting ecological condition and threat assessments. Solid arrows represent the alternative decision paths which resource managers could follow when conducting each assessment independent of the other. Dotted arrows and borders represent decision pathways and potential assessment outcomes when ecological condition and threat assessments are coupled. Italic font represents intermediate outcomes of the decision path (e.g., lithophils were identified as limiting biotic integrity in the ecological condition assessment)

Although currently developed threat indices are useful for conservation planning because ecological condition can be inferred from threat information, they are not designed to identify primary management capacity. Agencies who use estimates of ecological degradation to focus their conservation programs may be less successful in improving ecological condition if they work in watersheds where they do not have primary management capacity (because the agency’s conservation practices are not designed to remediate the threats causing degradation). The primary management capacity of the US Department of Agriculture’s Natural Resource Conservation Service (NRCS) lies in working with producers on mostly privately owned agriculture lands. The agency’s conservation practices principally address environmental stresses caused by agriculture activities, not urban or industrial activities. In the absence of interagency coordination, conservation practice effectiveness would be greatest in watersheds where the most prevalent threats are within the agency’s primary management capacity and threats outside its capacity were minimal. Coordination among multiple resource management agencies can be facilitated by knowing the prevalence of threats both within and outside each agency’s primary management capacity, thus increasing conservation practice effectiveness. We argue that resource management agencies, like NRCS, would benefit from having stream segment-scale threat indices to assess the relative watershed contribution of various threats (e.g., agriculture vs. urban) for use in strategically allocating resources and more effectively coordinating with other management agencies across a large geography and multiple spatial scales.

To that end, our goal was to develop a suite of threat indices and provide a framework for NRCS and other resource management agencies to select and prioritize watersheds to implement agricultural conservation practices for each of the 450,000+ stream segments in the Missouri River basin (MORB). Our objectives were to: (1) quantify the watershed percentages or densities of 17 threat metrics that represent major sources of ecological stress to stream communities across the basin, (2) conduct a threat assessment to assess total watershed condition for each stream segment using five threat indices developed from the threat metrics: agricultural, urban, point-source pollution, infrastructure, and all non-agricultural threats, and (3) identify stream segments where NRCS has primary management capacity (i.e., the threats in a watershed can best be addressed through agricultural conservation practices applied with NRCS assistance). A conceptual example is provided to demonstrate how decisions regarding conservation can be influenced using threat and ecological condition assessments. In addition, although management capacity was not identified for agencies that address non-agricultural threats, the threat indices or scoring criteria used to delineate management capacity herein could be formulated to represent specific agencies as needed.

Methods

Study Area

The MORB is well suited for developing threat indices because of its large geographic area, considerable variation in watershed and stream conditions, and extensive landscape modification (Galat et al. 2005; Revenga et al. 1998). The MORB drains about 1,371,017 km² of the United States and 25,100 km² of Canada (Fig. 2) (Galat et al. 2005). Restoring conditions of MORB altered riverine habitats presents significant challenges to resource managers due to, among other things, the size of the basin and the diversity and spatial distribution of existing threats. Dominant land use and land cover within the basin includes 25 % cropland, 48 % grassland/pasture, 10 % forest, 11 % shrub, 3 % urban, 2 % wetland, and 1 % open water (Homer et al. 2004). Agriculture threats (row-crop and grazing) are most prevalent across MORB but considerable spatial heterogeneity exists among agriculture and non-agriculture threats (e.g., point-source pollution, urbanization, and mining activities) making the prioritization of agriculture lands to be enrolled in conservation practices a significant challenge.

Fig. 2

Map of the MORB and Bailey’s (1983) division classifications (Color figure online)

Geographic Framework

The base stream layer was acquired from the work done for the Missouri River Basin Aquatic Gap Project (Annis et al. 2009). These stream networks represent a modified version of the 1:100,000 National Hydrography Dataset (NHD; http://nhd.usgs.gov). The primary modification of the NHD was the repair of gross underrepresentation of stream density in portions of the basin corresponding to select 1:100,000 scale topographic maps. The resulting stream networks were also processed to remove loops and braids within the network that caused problems with geoprocessing tasks of quantifying threat prevalence throughout the MORB. We used 30-m digital elevation models from the NHDPlus (http://nhd.usgs.gov) and ArcHydro Tools (ArcGIS 9.3, ESRI, Redlands, CA, USA) to create corresponding local catchment polygons (i.e., the land immediately draining a stream segment) for each of the 464,118 individual stream segments in the resulting MORB stream network. The resulting stream segments and catchment polygons were used as the spatial framework for quantifying and mapping the individual threat metrics and multimetric threat indices for this project.

Rationale and General Approach to Threat Index Development

Seventeen threats metrics were used to develop five multimetric indices representing major categories of sources of ecological stress and to identify NRCS management capacity: agricultural, urban, point-source pollution, infrastructural (those occurring directly in stream channels), and all non-agriculture threats (Table 1). The threat metrics used in the indices were chosen because data were publicly available, reasonably consistent in coverage across the MORB, and represent the major threats to aquatic systems. The agricultural threat index represents the major agricultural threat metrics to aquatic systems (Table 1). Row-crop agriculture and grazing affect sedimentation regimes (Waters 1995) while channelization directly modifies channel structure, physical habitat (Frothingham et al. 2001), and hydrology (Rhoads et al. 2003). The threat metrics in the urban index (Table 1) represent hydrologic alterations from impervious surfaces (Roy et al. 2005), pollution from densely populated areas (Hatt et al. 2004; Young and Thackston 1999), and potential increases in sedimentation due to construction from increasing population density (Wolman and Schick 1967). The point-source pollution index represents pollution sources that have potential direct effects on aquatic biota (Table 1). The infrastructure index represents threat metrics occurring in a stream channel that can be readily mapped in a GIS (Table 1). Road and rail stream crossings affect physical stream habitats (Bouska et al. 2010) and dams cause alterations in physical habitat (Kondolf 1997; Ligon et al. 1995) and hydrologic regime (Poff et al. 2007). The non-agriculture index collectively represents all threats from the urban, point-source pollution, and infrastructure indices (Table 1).

Table 1

Threat metrics and their data sources used to calculate five threat indices within the MORB

Threat dataset (measurement unit)	Modified	Threat index					Data sources	Source date
Threat dataset (measurement unit)	Modified	AG	UR	PSP	IN	NAG	Data sources	Source date
Row-crop agriculture (% of watershed)	No	X					U.S.G.S.—2001 NLCD	2006
Row-crop agriculture (% of watershed)	No	X					Canada National Land and Water Information Service	2007
Estimated grazing (% of watershed)	Yes	X					U.S. Department of Agriculture—2006 Agriculture Census	2006
Estimated grazing (% of watershed)	Yes	X					U.S.G.S.—2001 NLCD	2006
Channelized streams (km/km²)	Yes	X					U.S.G.S.—24 k NHD	Varies
							U.S.G.S. and EPA—100 k NHD	2006
							U.S.G.S. Wetland Mapper Team—National Wetlands Inventory	2006
Impervious surface ( % of watershed)	Yes		X			X	U.S.G.S.—2001 NLCD	2006
Impervious surface ( % of watershed)	Yes		X			X	Canada National Land and Water Information Service	2007
Population density 2000 (#/km²)	Yes		X			X	U.S. Census Bureau—2000 Block Data	2000
Population density 2000 (#/km²)	Yes		X			X	Statistics Canada	2007
Population change 1990–2000 (#/km²)	Yes		X			X	U.S. Census Bureau—1990 Block Data	1990
							U.S. Census Bureau—2000 Block Data	2000
							Statistics Canada	2007
Coal mines (#/km²)	Yes			X		X	EPA—Better Assessment Science Integrating Point and Non-point Sources	2001
							Canada National Pollutant Release Inventory Data	2008
							University of Nebraska—Lincoln	1996
							Iowa Department of Natural Resources	2003
Lead mines (#/km²)	No			X		X	EPA—Better Assessment Science Integrating Point and Non-point Sources	2001
Other mines (#/km²)	Yes			X		X	U.S.G.S.	2005
Other mines (#/km²)	Yes			X		X	Canada National Pollutant Release Inventory Data	2008
CERCLIS sites (#/km²)	Yes			X		X	EPA—Envirofacts	2007
Toxic release inventory sites (#/km²)	Yes			X		X	EPA—Envirofacts	2007
RCRA sites (#/km²)	Yes			X		X	EPA—Envirofacts	2007
NPDES sites (#/km²)	Yes			X			EPA—Envirofacts	2006, 2008
Landfills (#/km²)	Yes			X		X	EPA—Better Assessment Science Integrating Point & Non-point Sources	2001
							Missouri Department of Natural Resources	2006
							Canada National Pollutant Release Inventory Data	2008
Dams (#/km²)	No				X	X	National Inventory of Dams. U.S. Army Corps of Engineers	1996
Dams (#/km²)	No				X	X	Canadian National Topographic Database	Unknown
Road stream crossing (#/km²)	Yes				X	X	Census Bureau—TIGER	1999
							Statistics Canada	2008
							Missouri Resource Assessment Partnership—Streams	2009
Rail stream crossing (#/km²)	Yes				X	X	Census Bureau—TIGER Database	1999
							Statistics Canada	2008
							Missouri Resource Assessment Partnership—Streams	2009

AR Agricultural, UR urbanization, PSP point-source pollution, IN infrastructure, NAG non-agricultural, U.S.G.S United States Geological Survey, NLCD National Land Cover Database, EPA United States Environmental Protection Agency, NHD National Hydrography Database, TIGER Topologically Integrated Geographic Encoding and Referencing database, CERCLIS Comprehensive Environmental Response, Compensation, and Liability Information System, RCRA Resource Conservation & Recovery Act, NPDES National Pollutant Discharge Elimination System

Each threat metric and index represents the potential “risk” of environmental stress as a function of its prevalence. Several authors (Table 2) have used weighting schemes that relate threat metrics to expected or empirically derived biological responses so that ecological degradation can be inferred from threat metric or index scores. For example, Mattson and Angermeier (2007) used professional judgment to weight threat severity by the degree to which each threat was perceived to affect the five components of biological integrity (Karr et al. 1986). Esselman et al. (2010) used multivariate correlations with threat metrics to biological endpoints to weight threat severity and indices across the conterminous U.S. However, we assumed risk to be equal across all threats (i.e., all threat metrics were assumed to have the same potential risk) and positively associated with threat prevalence because our intent was to represent management capacity and not ecological condition. In a perfect world, weighting threat metrics could be done objectively because a dose/response relationship would exist for every (or most) threat metrics as they relate to multiple biological endpoints. We acknowledge that a vast literature exists on species/habitat relationships but there is generally incomplete or no knowledge regarding thresholds of biological change or degradation related to prevalence of threat metrics such as those used in this study. There are, however, two notable exceptions that when agricultural land use generally exceeds 50 % watershed area (Wang et al. 1997) and urban land use exceeds 10 % (Snyder et al. 2003; Wang et al. 2001; Wang et al. 2000) watershed area, there are often significant biological changes. Our threat metrics or indices were not weighted because we believe that weighting should be done objectively for all metrics and not a select few. Of the 17 threat metrics we used, there was evidence to suggest that only two metrics (row-crop agriculture and urban lands) could be objectively weighted to biological degradation. Lastly, research has demonstrated that subjectively weighted threats indices do not provide additional information over unweighted indices (Paukert et al. 2011) and we believe use of a subjective weighting system would decrease the ability of our indices to identify management capacity since weighting would be inconsistent and inaccurate across all threat metrics.

Table 2

Comparison of published threat indices, their components, and weighting procedures

Index component	Mattson and Angermeier (2007)	Danz et al. (2007)	Sowa et al. (2007)	Annis et al. (2009)	Esselman et al. (2010)	Fore et al. (this study)
Spatial extent for development	Upper Tennessee River basin (55,400 km²)	U.S. Great Lakes basin (765,000 km²)	Missouri, USA (180,533 km²)	EPA Region 7 (IA, KS, MO, NE; 739,769 km²)	Conterminous USA (8.08 × 10⁶ km²)	MORB (1.39 × 10⁶ km²)
Spatial grain	Subwatersheds of 8-digit HUC	0.3–17,000 km²	Aquatic ecological system types ~250–1,500 km²	Stream segment 1:100,000 NHD	Stream segment 1:100,000 NHD	Stream segment 1:100,000 NHD
Feature representation	Polygon	Polygon	Polygon	Polygon and line	Polygon and line	Polygon and line
Specificity to ecosystem	Relative to Upper Tennessee River basin	Relative to US Great Lakes basin	Relative to state of Missouri	Relative to EPA Region 7	Relative to conterminous USA	Relative to Bailey’s (1983) divisions
Assessment units fixed or continuous	Fixed	Fixed	Fixed	Continuous—upstream and local watersheds	Continuous—upstream and local watersheds	Continuous—upstream and local watersheds
No. of threats	12	86	11	36	17	17
Spatial scale of threat quantification	Total contributing area	Total contributing area	Total contributing area	Total and local contributing area	Total and local contributing area	Total and local contributing area
“Dose” quantification	Dose represented for individual threats as product of rank-based frequency and severity scores; ranks based on thresholds in literature and quartile scores Cumulative threats represent as sum of individual threats	Multivariate representation of threat categories via principal component axes Cumulative threats represented by summing axis scores for each threat category	Dose for individual threats represented as discrete rank scores; based on thresholds in literature and quartile scores Cumulative threats represented as numeric combination of individual threats	Dose is standardized and non-discrete for individual and cumulative threats; also standardized to stream size and across measurement units	Multivariate representation of threats via principal component axes; standardized to stream size	Dose is standardized and non-discrete for individual and cumulative threats; also standardized by ecoregion and across measurement units
Threat density weighting	Used literature values to identify thresholds and quartile scores	Weighting not used	Used literature values to identify thresholds and quartile scores	Used non-discrete ranking procedure	Weighting not used	Used non-discrete ranking procedure
Threat severity weighting	Used expert judgment, as perceived to affect to biological integrity	Weighting not used	Weighting not used	Used distance weighting for some threats; weights subjectively assigned	Used biological weighting via multivariate correlation to threats	Weighting not used
Empirical construction or validation	Not empirically constructed. Validated by Paukert et al. (2011); found density and severity weighting produced similar results to no weighting.	Indices related to fish and bird ecological condition metrics	Not empirically constructed or validated	Showed positive relationship with macroinvertebrate index of biological integrity—an overall decline in ecological condition	Empirically constructed using biological samples to weight threat severity	Not empirically constructed or validated

We relativized our threat indices to by five ecoregions using Bailey’s (1983) division-level classification to reflect potential differences in threat prevalence and magnitude that may affect ecological response (Frimpong and Angermeier 2010).

Modified Threat Metric Data

Four threat metrics required creation or modification from their original form. Grazing and stream channelization threats were not appropriately represented in existing data sources and were modified. Impervious surfaces were overestimated in the NLCD and population change information needed to be quantified. Refer to online supplemental information for details of data modification.

Quantifying Threat Prevalence

The number of threats evaluated in an index affects its comprehensiveness and ability to identify all potential sources of stress. Threats were quantified to assess their prevalence and were recorded as unit density, usually as proportion of watershed (e.g., proportion of row-crop) or number of units per watershed area (e.g., number of discharges per square kilometer), for an assessment region. The most precise prevalence estimates are those represented by the actual threat density or proportion of watershed value (e.g., 25 % of watershed area) and least precise are estimates that categorize prevalence (e.g., 0–25 % = 1 etc.). The spatial grain at which threat prevalence is quantified affects the ability of threat indices to inform decisions regarding placement of conservation practices. As spatial grain increases (e.g., from local contributing areas to 8-digit Hydrologic Unit Codes), the representation of threat prevalence becomes more generalized and the ability to identify fine-scale spatial patterns is reduced or eliminated.

Individual threat metric prevalence was quantified within each of the local catchment polygons so that the threat assessment can be summarized regionally while retaining the resolution to inform localized planning. Then we used customized Arc Macro Language (ESRI, Redlands, CA, USA) programs to sum all these values for each individual stream segment’s entire watershed (i.e., the local catchment and all upstream catchments that a segment drains). We then divided these summed values by the overall watershed area to quantify the prevalence, per unit area or as proportion of watershed, of each threat metric within the watershed of each segment.

Threat Metric and Index Calculations

We normalized and calculated scores for each threat metric so that comparisons could be made among threats recorded in different measurement units (e.g., to compare proportion of watershed vs. point densities) . Each threat index was normalized to a common scale so that direct comparisons could be made among indices and ecoregions.

Threat metrics were removed from our dataset, to reduce redundancy in our representation of threats (Stoddard et al. 2008), if they were significantly correlated with a threat metric that could appropriately represent the removed metric (e.g., road density can be represented as an impervious surface, but not by cropland). Metrics were removed if they were correlated with at least one variable and their Pearson correlation coefficient was >0.55 and P < 0.05 (corrected for multiple comparisons using Bonferroni adjustments).

Threat metric scores (

Ts) were calculated as

Ts i, j, k = [T r i , j , k max ( T r i , j , k )] \times 100,

where Tr_i,j,k is the ranked value of threat prevalence (as total contributing area) for every i stream segment (i.e., the stream segment with the lowest threat prevalence received a rank of one and the stream segment with the highest density received the highest rank) in the j ecoregion for the k threat metric. Ties in threat prevalence were given the same rank. Since nearly all the metrics were point densities (e.g., #/km²) they were often very skewed, especially in large watersheds. Ranking improved their normalization and each segment’s ranked threat prevalence score (Tr, numerator in the above equation) was divided by maximum ranked value for its corresponding ecoregion [max(Tr_i,j), denominator in the above equation] and multiplied by 100 (range 0–100). Ranking was used because metrics represented as unit densities (e.g., #/unit area) were generally skewed and this method of transformation improved normalization. Ranking had little effect on the distribution of metrics recorded as proportion of watershed. Ts values of 100 represent the highest threat prevalence.

Threat index scores (TI) were calculated and normalized by summing the corresponding threat metric scores (Ts) for each index (Table 1)

TI i, j = [\sum n k = 1 Ts i , j , k max ( \sum n k = 1 Ts i , j , k )] \times 100,

where Ts_i,j.k is the threat metric score for every i stream segment in j ecoregion for threat metrics scores k through n. The Ts scores were summed for each index (numerator in above equation) and divided by the maximum summed value of Ts within each ecoregion. Index scores of 100 represent stream segments with highest potential stress within each ecoregion. Final threat index scores were then incorporated into a seamless stream layer database and mapped in ArcGIS (ESRI, Redlands, CA, USA).

Agriculture Conservation Program Primary Management Capacity Matrix

A matrix was developed to determine the degree of NRCS management capacity for stream segments based on watershed condition. Since conservation programs target a limited suite of threats, implementing agricultural conservation practices in watersheds where NRCS has primary management capacity should increase conservation practice effectiveness. We did not distinguish between private and public lands, and it should be noted that public lands are generally not within NRCS management capacity; management capacity of public lands is under the agency responsible for managing those lands. The matrix was used to determine the relative degree of NRCS management capacity by assessing the potential stress from agricultural threats, relative to potential non-agricultural stress for each segment. For each stream segment, its agriculture and non-agriculture threat index scores were given a quartile score (i.e., index score 0–25 = 1, 25–50 = 2, etc.; Table 3). (Different scoring criteria would be acceptable to formulate this matrix if quartile scores were deemed too coarse a resolution; this is intended to be an illustrative example.) The upper half of the matrix was populated by dividing the agriculture (y) and non-agriculture (z) quartile scores for each X _yz. Matrix scores were then transposed to the corresponding X _yz on the lower half of the matrix and given negative values. Positive scores indicate stream segments where NRCS is most likely to have primary management capacity and the more positive the score, the more likely NRCS is to have greater management capacity (Table 3). Matrix scores ≥2 were considered to represent primary NRCS management capacity. (The threshold of ≥2 is presented here as an illustration and could be altered to suit an agency’s needs.) We used a scoring matrix because the interpretation of agricultural/non-agriculture ratios was unintuitive when non-agricultural threats were greater than agricultural threats. For example, a 75:25 agriculture/non-agriculture ratio equals 3, but a 25:75 ratio equals 0.33 where scoring matrix values would be 3 and −3, respectively.

Table 3

Scoring matrix used to identify stream segments where NRCS had primary management capacity (see text for explanation) in the MORB

Non-agriculture quartile scores	Agriculture threat quartile scores
Non-agriculture quartile scores	1 (0–25)	2 (25–50)	3 (50–75)	4 (75–100)
1 (0–25)	1	2	3	4
2 (25–50)	−2	1	1.5	2
3 (50–75)	−3	−1.5	1	1.3
4 (75–100)	−4	−2	−1.3	1

Stream segments that had scores ≥2 were considered to be under the primary management capacity of NRCS. Values in parentheses represent threat index scores

Illustrative Example of Coupling Threat and Ecological Condition Assessments

We obtained fish IBI scores from sites in MORB (Fig. 3) to identify ecologically degraded streams and to illustrate how the conceptual process outlined in Fig. 1 could be utilized by resource managers. This comparison was not intended to validate the threat indices nor was it an attempt to establish empirical relationships with threat index scores and IBI scores. This illustration is meant to demonstrate how information from threat indices can be used by managers to infer likely causes of ecological degradation when adequate ecological condition assessments have been performed.

Fig. 3

Map depicting four stream sites within the MORB where Fish IBI scores were computed. Numbers on map depict site numbers that are referenced in text. Refer to Table 4 for IBI scores and Table 5 for individual IBI metric scores (Color figure online)

We haphazardly selected four streams that spanned the overall range of IBI scores. The IBI scores were computed for the US Environmental Protection Agency’s (EPA) Regional Environmental Monitoring and Assessment Program in EPA Region 7 (M. Combes, unpublished data). The IBI was applicable to streams across the entire EPA Region 7 and contained the metrics (all metrics were evaluated as “number of”): native species, native families, native individuals, sensitive individuals, tolerant individuals, benthic species, native sunfish species, minnow species, long-lived species, introduced species, trophic strategies, native carnivore species, native omnivore and herbivore species, and reproductive strategies. The individual metrics for the IBI were evaluated to identify potential stressors causing ecological degradation. Threat index scores for each site were then compared relative to the overall IBI score and the individual IBI metrics to illustrate how information from threat indices and ecological condition assessments can inform conservation decisions.

Results

Percent impervious surface was significantly and highly correlated with developed open (r = 0.59), low (r = 0.96), medium (r = 0.91), and high (r = 0.66) urban land-use variables from the NLCD as well as road density (r = 0.85); therefore, these metrics were not included in the threat indices. As a result, percent impervious surface in a contributing area was used to represent correlated urban land use and road density threats.

Patterns of potential stress were evident at regional scales, but we identified localized patterns of potential stress that showed considerable spatial heterogeneity. Agriculture threats are most prominent across the MORB and on average stream segments have higher potential agriculture stress (Table 4). Mean scores for all threat indices, thus potential stress, significantly varied among the five ecoregions of the MORB (Table 4). Visual examination of mapped output for the agriculture threat index (Fig. 4) illustrates that regional patterns in threat stress exist, e.g., high agriculture threats in the east-central portion of the basin. Although potential agricultural stress appears consistent within ecoregions, there was considerable spatial heterogeneity in potential agriculture stress at localized scales (i.e., within regions of high agricultural stress, there are many watersheds with low to moderate agricultural stress; see inset in Fig. 4). Similar patterns exist when examining non-agriculture threat stress across the basin (Fig. 5). Non-agriculture threats primarily occur in or near urban areas, but were sometimes common and high outside of urban areas (e.g., within areas of high agricultural stress). These patterns were not evident on landcover maps because they do not account for watershed condition (see inset in Fig. 5).

Table 4

Mean and standard error of threat index values for the MORB calculated within Bailey’s (1983) divisions

Threat index	MORB	Division
Threat index	MORB	Hot Continental	Prairie	Temperate Desert	Temperate Steppe	Temperate Steppe Regime Mountains
Agriculture	43.24 (0.03)	32.30^a (0.15)	48.81^b (0.05)	33.29^c (0.14)	46.76^d (0.03)	26.75^e (0.08)
Urbanization	28.57 (0.02)	35.47^f (0.15)	34.07^g (0.06)	27.04^h (0.11)	26.57^h (0.03)	25.65ⁱ (0.07)
Infrastructure	16.55 (0.02)	18.90^k (0.11)	22.27^l (0.05)	18.56^k (0.12)	14.73^m (0.03)	13.10ⁿ (0.06)
Point-source pollution	17.26 (0.01)	23.52^p (0.08)	17.52^q (0.02)	26.58^r (0.05)	16.00^s (0.01)	17.23^t (0.02)
Non-agriculture	22.94 (0.01)	28.07^u (0.09)	25.83^v (0.03)	31.16^w (0.08)	20.94^x (0.01)	22.20^y (0.04)

One-way analysis of variance was conducted to determine if means significantly differed by division. Superscripts of different letters indicate a significant difference in mean threat index scores among the ecoregions (row comparisons only). Refer to Fig. 2 for map of divisions

Fig. 4

Map of the agriculture threat index scores (target threats) for every stream segment within the US portion of the MORB. Threat index scores were calculated using threat prevalence information quantified for every stream segment’s upstream watershed area. Threat index scores were calculated separately for each division classification (see Fig. 2). Maximum threat scores are relative to the most threatened stream segment in each division (Color figure online)

Fig. 5

Map of the non-agriculture threat index scores (non-target threats) for every stream segment within the US portion of the MORB. Threat index scores were calculated using threat prevalence information quantified for every stream segment’s upstream watershed area. Threat index scores were calculated separately for each division classification (see Fig. 2). Maximum threat scores are relative to the most threatened stream segment in each division (Color figure online)

Matrix scores ≥2 were considered representative of segments where NRCS had primary management capacity. Based on this criterion, NRCS had primary management capacity for 60 % of stream segments in MORB. NRCS had primary management capacity in 55 % of Prairie Division, 76 % of Temperate Steppe Division, 24 % of Hot Continental Division, 14 % of Temperate Desert Division, and 29 % of Temperate Steppe Regime Mountain stream segments. Regional patterns in NRCS primary management capacity were evident across MORB and generally followed the patterns of potential agriculture stress (Fig. 6).

Fig. 6

Map of NRCS primary management capacity for every stream segment within the US portion of the MORB. Streams with management capacity scores ≥2 (see text and Table 2) were considered to be under NRCS management capacity (Color figure online)

The four sites evaluated for fish biotic integrity ranged from poor (site 1 = 14) to excellent (site 4 = 96) ecological condition and two sites were intermediate (Table 5). Sites 1, 2, and 3 had low benthic metric scores (Table 6) (fishes that feed and reproduce in the benthos and are sensitive to sedimentation; Barbour et al. 1999). Sites 1 and 3 had the highest agricultural threat index scores (Table 5). Site 2 had a relatively high urban threat index score and moderate point-source pollution and infrastructure threat index scores (Table 5). All threat indices in site 4 had low scores (Table 5).

Table 5

Fish IBI and threat index scores from four stream sites in MORB

Site	IBI score	Index
Site	IBI score	Agriculture	Point-source pollution	Urbanization	Infrastructure	Non-agriculture
1	14	78.76	24.66	42.97	27.47	33.96
2	37	14.04	46.72	93.19	55.14	67.23
3	63	41.15	15.22	19.09	7.75	16.46
4	96	6.33	18.44	13.10	25.93	20.90

Higher IBI scores indicate higher biotic integrity. Higher threat index scores indicate higher threat prevalence

Table 6

Fish IBI and metric scores for four streams in the MORB

Site	IBI score	Number of IBI metric scores
Site	IBI score	NAT	NAF	IND	SENS	TOL	BNTH	SUN	MIN	LOL	INT	TRO	NAC	NOH	REP
1	14	0.94	2.41	1.52	0	0	0	0	1.9	0	10	0	0	0	2.91
2	37	2.05	4.65	3.75	0	0.32	0	4.55	1.19	3.47	10	2.07	10	10	0
3	63	7.06	5.75	6.73	0	4.26	1.51	8.32	8.74	7.85	10	7.65	10	4.46	6
4	96	10	10	10	10	7.53	10	10	10	10	10	10	10	10	10

Higher IBI and metric scores indicate higher biotic integrity

NAT Native species, NAF native families, IND native individuals, SENS sensitive species, TOL tolerant species, BNTH benthic species, SUN native sunfish species, MIN minnow species, LOL long-lived species, INT introduced species, TRO trophic strategies, NAC native carnivore species, NOH native omnivore and herbivore species, REP reproductive strategies

Discussion

Threat indices were used to identify regional and local patterns of multiple agriculture and non-agriculture threats for every stream segment in MORB. The threat patterns were similar to those of landcover maps; however, unlike landcover maps our threat indices represent watershed condition for multiple threats. Within highly impacted agriculture regions, there was considerable variation in watershed condition among stream segments. This highlights the importance of cautiously using landcover map information to make resource conservation decisions.

Coupling ecological condition and threat assessments allows resource managers to identify ecologically degraded sites, the threats most likely causing degradation, and helps provide information needed to select appropriate conservation practices. The spatial resolution (stream segments) of our threat indices allows them to be coupled with field-based ecological data and potential sources of stress can be evaluated for essentially any biological stream sample. However, users should recognize that the indices cannot inform farm-scale planning efforts because our maps and resulting threat index scores represent total watershed condition.

The IBI scores and their underlying metrics provide an informative example of how threat index information can be used by managers to infer likely stresses and their sources in ecologically degraded streams. This example serves as an illustration of how managers can use threat indices and biological information to quickly and efficiently reduce uncertainty regarding the threats most likely causing ecological degradation. Our focus was to develop threat indices that management agencies can use as a coarse filter to guide their management actions based on watershed condition and not ecological degradation. Furthermore, the IBI example does not need to be comprehensive (i.e., include a larger sample of sites) because we were not attempting to validate the usefulness of our indices at identifying ecological degradation. Using Fig. 1 and our IBI data, sites 1, 2, and 3 had overall IBI scores that indicated ecological degradation and the benthic IBI metric was low in each stream (relative to the least disturbed site 4). This suggested that sedimentation was an ecological stress (Barbour et al. 1999). The high agricultural threat index scores from sites 1 and 3 suggested that potential sedimentation stresses most likely originated from agriculture threats and possibly from urbanization threats in site 1. This indicates that agricultural conservation practices administered by agencies like NRCS would be the most appropriate for stream restoration. Threat index scores for site 2 suggested that sedimentation stress originated from urban threats and that point-source pollution and infrastructure threats may contribute additional stresses (Table 5). In this instance, conservation practices or policies administered by state or federal water quality authorities (e.g., USEPA and local municipalities) would be most appropriate. Finally, site 4 had the highest ecological condition and correspondingly low threat index scores, suggesting a need for proactive (i.e., preventing further degradation) rather than restorative conservation practices.

For threat indices to be widely used by multiple resource agencies, indices need to be applicable to multiple ecological indicators. Most published threat indices (Table 2) are limited in their use outside of the taxa or ecological indicators they were developed for because the indices account for threat severity by weighting the relative influence of threat metrics to an ecological indicator (e.g., Esselman et al. 2010; Annis et al. 2009). Unfortunately, different taxa have been shown to respond differentially to the same source of stress (Berkman et al. 1986), therefore severity weights for threat metrics are likely applicable only to the ecological indicator being evaluated. We were unable to objectively weight our threat indices to ecological indicators because dose/response relationships are not quantified between many of the threat metrics we used (e.g., number of mines) and potential ecological indicators. Therefore, we decided that if threat metric weighting could not be done objectively for all threats that it was inappropriate to weight a limited suite of threat metrics (e.g., agriculture landcover and impervious surfaces) and misrepresent the true thresholds of severity. In a comparative analysis, Paukert et al. (2011) found that weighting threat indices produced nearly identical scores relative to scores from an unweighted index. This suggests that weighting threat indices is unlikely to increase biological realism, especially when weighting schemes involve subjectivity. Instead of accounting for severity, we argue that resource managers would be better off by establishing empirical relationships between threat indices and an ecological indicator to account for threat severity. Our contention is that because relationships between a threat index and ecological indicator are likely to vary by region and taxon (Frimpong and Angermeier 2010) that threat index scores should not be altered depending on the ecological indicator being evaluated. Instead, managers can alter their interpretation of threat index scores by establishing empirical relationships between threat indices and ecological indicators (e.g., a threat index score of 50 in one region and a score of 65 in another region may represent the equivalent degree of degradation). Doing so allows threat indices to be easily computed, avoids making assumptions about threat impacts to ecological indicators across different regions, and increases an index’s applicability to resource managers. Improving the consistency of how relationships between threat metrics and ecological indicators are defined, better mapping and reporting of threat metric data are significant challenges to future development of threat indices that appropriately account for severity.

In most watersheds, multiple threats affect ecological condition (Diana et al. 2006; Zorn and Wiley 2006), and it is likely that addressing conservation concerns for an area will involve multiple agencies who have distinct management authorities. We estimated that agriculture conservation programs such as those administered by NRCS would have primary management capacity for a majority (60 %) of the stream segments in MORB. However, some lands within the MORB are under public ownership and management (e.g., national forests and grazing lands therein are managed by the US Forest Service), and may contain agricultural threats. Public lands are areas where NRCS would not have primary management capacity, as threats on those lands would be addressed by the managing agency. Given the existing regional patterns, there is considerable heterogeneity in NRCS primary management capacity across the landscape as non-agriculture threats are often prevalent enough outside of urban areas. Three of the five MORB ecoregions had NRCS primary management capacity in only 14–29 % of stream segments, indicating that relative to agriculture, non-agriculture threats are the predominate threat. Effective conservation in those ecoregions may require collaboration among multiple resource agencies. Although management capacity was not identified for non-agriculture conservation programs, the threat indices or scoring matrix could be reformulated to meet desired needs. For example, our point-source pollution index could be viewed as best addressed by the U.S. Environmental Protection Agency because they permit and regulate point-sources of pollution (US Environmental Protection Agency 2001). Developing comprehensive management programs (among multiple management agencies) requires agencies to examine the relative contribution of stress from the threats within their management capacity to those of another agency so collaboration can be successful.

Conducting stream conservation efforts at large geographic areas involves identifying and prioritizing conservation areas (Groves et al. 2002). Threat indices can be used as tools to explicitly identify potential sources of stress on the landscape, identify agencies that have management capacity over assessment units, and establish relationships with ecological indicators to determine potentially degraded systems. The threat indices and primary management capacity scoring systems we developed could be used in a winnowing process to identify and select subsets of stream segments for stream conservation. The regional patterns we observed in potential stresses could be used as a “first-cut” in a winnowing process to select broad geographic areas to focus conservation program implementation. In the MORB example, the Prairie and Temperate Steppe Divisions tend to be the most agriculturally threatened and could be the focus of NRCS conservation programs. Next, threat indices can be used to identify individual stream segments within regions where NRCS has primary management capacity, and presumably a greater chance of achieving conservation success. Where ecological condition is known, resource managers should use threat indices to establish relationships among ecological indicators because ecological indicators respond differently to threats (Danz et al. 2007; Frimpong and Angermeier 2010). Once relationships among indicators are successfully established, resource managers can then interpret their threat index scores in an ecologically meaningful manner and select the appropriate conservation programs to implement.

Acknowledgments

We thank the United States Department of Agriculture’s Natural Resource Conservation Service under the Conservation Effects Assessment Project for funding this project. Michael Morey provided technical assistance and Aaron Garringer conducted much of the GIS work. Matt Combes of Missouri Department of Conservation provided fish IBI data. This manuscript was improved by helpful reviews from Charles Rabeni and two anonymous reviewers.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (DOCX 17 kb)

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Environment Systems and DecisionsFormerly The Environmentalist

10.1007/s10669-013-9438-5

Cost-benefit and systems analysis of passively ventilated solar greenhouses for food production in arid and semi-arid regions

N. L. Panwar^{1, 2}, Surendra Kothari² and S. C. Kaushik¹

(1)

Centre for Energy Studies, Indian Institute of Technology, Delhi, 110016, Hauz Khas, India

(2)

Department of Renewable Energy Engineering, Maharana Pratap University of Agriculture and Technology, Udaipur, 313001, Rajasthan, India

N. L. Panwar

Email: nlpanwar@rediffmail.com

Published online: 2 March 2013

Abstract

In this paper, economic feasibility of two vegetable crops (i.e., cucumber and tomato) cultivated in a naturally ventilated greenhouse, and the net present worth, cost-benefit ratio, payback period, and internal rate of return for these crops on year-round cultivation are presented. The cost-benefit ratio demonstrated that growing cucumbers and tomatoes can be economically viable in this climatic region. The present experimental study was conducted in the composite climatic condition of Udaipur (24°35′N, 73°42′E), India. The study area is defined as arid and semi-arid region of Indian climatic conditions. Droughts are a recurring phenomenon in arid and semi-arid regions creating a situation that affects not only agricultural productivity but also people’s health. In particular, the western part of the state is a desert, and its socioeconomic status influences nutrient purchasing power. A poor diet can lead to a vitamin and mineral deficiency. The state of Rajasthan has good agricultural potential; interventions using protected cultivation practices can increase the production and productivity of vegetable crops. However, the current adoption rate of such practices in the state remains very slow, even after a promotional scheme offered by the state government. The government and policymakers should consider offering demonstrations of practices at a larger level. Farmers of the state are marginal and economically poor, requiring more financial assistance. Low cost technologies would be suitable for these farmers.

Keywords

Off-season cultivation Greenhouse Economic feasibility Crop cultivation

1 Introduction

Open-field farming is not a new approach. It began with the appearance of civilization. This farming approach allows use of both good and poorer fertile soil, and it encourages cooperation among the farmers. On the other hand, it is difficult to handle pest, and animals and weeds that spread in such open-field conditions. Ultimately, the approach reduces net productivity. The open farming succeeds newer and modern agricultural technology by successfully meeting a growing demand for food by the world’s population. Drastic increases in the yield of primary crops such as rice and wheat in Indian climatic conditions have been reported. This increase in food production is mainly due to advances in scientific input, including the development of new crop varieties, the use of pesticides and fertilizers, and the construction of large irrigation systems. In the present context, consumers are very much aware of their nutritive diets and prefer organic farming produces. Organic farming is simply with the process of growing of fruits and vegetables without using any type of pesticides and chemical fertilizer.

In the present context, high pressure on current agricultural production systems at global level and facing huge challenges and changes with ongoing farming practices, declining soil fertility and crop yields, poor market access, constrained to assess land, and high inflation are constraints in the industry (Nelson et al. 2010; Yamano et al. 2011). With these, both poverty level and household food insecurity are rising across developing and underdeveloped countries (Charles et al. 2010; Kristjanson et al. 2010). Some of the raw vegetables are used as salad in many countries. Less than 200 g of vegetables per person per day is common, and this low amount, often in conjunction with poverty and poor medical services, is associated with unacceptable levels of mortality and malnutrition in preschool children and other vulnerable groups. An increase in the availability, affordability, and consumption of nutrient-dense vegetables and fruits may be a way to reduce malnutrition substantially (Keatinge et al. 2011).

The inhabitant of many developing countries’ is considered to be malnourished due to low agricultural production. There is universal agreement that in order to increase productivity, the best course is to reduce postharvest losses and create better market linkage by boosting small and marginal farmers. Off-season cultivation in partially controlled or naturally ventilated greenhouses offers one to agricultural production possibility and enables farmers to cultivate vegetables and high nutrition crops year-round. Protected cultivation minimizes seasonal fluctuations in crop yields.

Modern and easily adaptable good protected cultivation practices minimize contamination from pesticide residue, bacteria, viruses, or helminths which are hostile to humans. In the case of open-field farming pests, control is very difficult and over-spraying may poison the farmer and his family unless appropriate safety measures are taken. India accounts for one-third of all the world’s pesticide poisoning cases (Indira Devi 2007). There is no such problem with greenhouse crop production, and nutritional crops can easily grow without any dangerous chemicals. Offering highly nutrient-dense vegetables to school children is one way to overcome malnutrition (Yang et al. 2007). The application of plastic in the agricultural sector is growing and has helped farmers to increase crop production, improve food quality, and reduce the ecological footprint of their activity. Not only do plastics allow for vegetables and fruits to be grown in every season, but these products are usually of better quality than those grown in an open field (Martin 2012).

Singh et al. (2006) conducted an exhaustive study on the nutritional status of rural population in desert area of Rajasthan state, and it was found that diet of people resides in the state was grossly deficient in green leafy vegetables, fats, pulses, and legumes and other vegetables. The frequent occurrence of drought is accountable for it and affects the agricultural productivity. This might be responsible for higher under nutrition not only in children but in adults also.

Agriculture is not a profession that is voluntarily chosen by many due to the drudgery one encounters while working in the field. Consequently, migration from rural to urban areas has become a common feature in our society. It is true that in the foreseeable future, agriculture will continue to be a major occupation among people. This implies that agriculture should become more profitable, and the associated drudgery should be minimized (Kothari and Panwar 2004; Panwar et al. 2009; Black et al. 2011). The world population continues to rise. While the demand for growing more food persists, nutritional security must also be ensured for a healthy nation. Land and other resources being inelastic, farmers are required to produce more nutritious food from the available resources (Mukhopadhyay et al. 2011). The need to ensure that people have nutritious diets to the masses suggests that the production of vegetables and fruits should be adequately increased. Considering the currently low productivity levels of different crops, the targets of vegetable and fruit production appear to be difficultly until better technologies are utilized for food security (Kothari et al. 2006). Tiwari and Joshi (2012) explain the meaning of food security according to the Food and Agricultural Organization (1996) as “a situation that exists when all people, at all times, have physical, social and economic access to sufficient, safe and nutritious food that meets their dietary needs and food preferences for an active and healthy life.”

The greenhouse is a closed and isolated structure in which agricultural operations such as sowing, weeding, and irrigation, etc., can be performed. Such structures eliminate the extensive migration of pests into greenhouses and create more favorable environments that are essential for plant growth and productivity (Hanafi and Papasolomontos 1999; Al-Helal and Alhamdan 2009). Solar radiation is a source of energy for photosynthesis; therefore, a greenhouse is covered with transparent material that it allows for transmitting visible light (Lozano et al. 1996). Most greenhouses are constructed with polyethylene covers because they are easy to handle. However, a general problem with polyethylene is its short shelf life, especially in harsh weather conditions such as high temperatures, high solar intensity, and dust (Alhamdan and Al-Helal 2009). The estimated area under greenhouse crop cultivation across the world is presented in Table 1.

Table 1

The estimated greenhouse areas in the world (Giacomelli et al. 2008)

	Plastic film greenhouses and large plastic film tunnels (ha)	Glasshouses (ha)
Western Europe	140,000	29,000
Eastern Europe	25,000	1,800
Africa	27,000	600
Middle East	28,000	13,000
North America	9,800	1,350
Central/South America	12,500	0
Asia/Oceania	450,000	2,500

Considerable amount of diesel fuel consumption and fertilizer usage, mainly nitrogen, is important in energy management; much of this can be saved by using greenhouse grower. Using direct and local market improves profitability for growers while reducing the amount of energy used during transportation of products (Mohammadi and Omid 2010). Canakci and Akinci (2006) analyzed energy use pattern in greenhouse for vegetable. They found that the operational energy and energy source requirements in greenhouse vegetable production were varying from 23,883.5 to 28,034.7 and 45,763.3 to 49,978.8 MJ/1,000 m², respectively. The energy ratio of four major greenhouse vegetable crops—such as tomato, pepper, cucumber, and eggplant—was found about 0.32, 0.19, 0.31, 0.23, respectively. The net return was 595.6–2,775.3 $/1,000 m². The researcher concluded that crop yields increased with increasing total energy input.

As far as the state of Rajasthan is concerned, unavailability of regular electrical power, a low water table and small arable land holdings make the greenhouse seems a viable option to achieve better agricultural productivity. The state also frequently faces drought (Soni et al. 1980). Small greenhouse used for off-season cultivation can also improve the net profitability of marginal farmer. Therefore, a techno-economic feasibility study of naturally ventilated greenhouse has been carried out to articulate the basic production information and cost of production information under arid and semi-arid climatic conditions for establishing a greenhouse vegetable enterprise.

2 Materials and methods

Bansal and Minke (1995) divided India into six climatic zones; hot and dry, warm and moderate, cold and cloudy, cold and sunny, and composite as illustrated in Fig. 1. Udaipur (24°35′N, 73°42′E) has been chosen for this study primarily because of its composite climate, with temperatures ranging from above 40 °C in summer to below 4 °C in winter. This type of climate is predominant in the central part of India (Bansal and Bhattacharya 2009).

Fig. 1

Climatic zone of India (Bansal and Minke 1995)

The naturally ventilated greenhouse is comprised of a galvanized tubular structure in an aerodynamic shape. The dimensions of the constructed greenhouse are shown in Fig. 2. Low-density ultra violet stabilized polythene of 200 micron thickness was used for the surface of the greenhouse. A misting system is provided with 80 misting nozzle connected to 16 LDPE pipes and monoblock pump (Agritech Equipment & Services Private Limited, New Delhi, India). This system was used during extensively hot summer conditions, which generally occur in the month of May. A gravity-fed drip irrigation system was provided to fulfil the water requirement inside the greenhouse. The 15 sowing beds prepared for growing crops have widths of 75 cm, and the distance between each beds was kept at 30 cm. Seeds were sown on both sides of each bed; about 1,500 seeds were sown. The greenhouse cost [as per the rate of 16.34 US

(INR52.00 US⁻¹ as on Oct. 4, 2012)] was per square meter. Hence, the total cost of construction is 9,153.84 US$.

Fig. 2

Front view of naturally ventilated greenhouse

2.1 Crop production

Two high-yield crops, that is, tomato and cucumber were selected to grow inside the greenhouse. Recent research has revealed that eating more tomatoes and tomato products can make people healthier and decrease their risk of conditions such as cancer, osteoporosis, and cardiovascular disease (Burton-Freeman and Reimers 2011). Cucumber is in common use throughout the world (Bao-Zhong et al. 2006), and like watermelons it has 95 % water; cucumbers keep the body hydrated and help regulate the body’s temperature. They also help the body flush out toxins (Zimmer et al. 2012). Both tomatoes and cucumbers are used in salad, and many times are eaten raw. The nutritive values of both tomato and cucumber are presented in Table 2. Variety of greenhouse crops and its selling price are presented in Table 3.

Table 2

Nutritive value of tomato and cucumber (100 g)

Principle	Nutrient value
Principle	Tomato	Cucumber
Energy (kcal)	18	15
Carbohydrates (g)	3.9	3.63
Protein (g)	0.9	0.65
Total fat (g)	0.2	0.11
Cholesterol (mg)	0	0
Dietary fiber (g)	1.2	0.5
Vitamins
Folates (μg)	15	7
Niacin (mg)	0.594	0.098
Pyridoxine (mg)	0.080	0.040
Thiamin (mg)	0.037	0.027
Vitamin A (IU)	833	105
Vitamin C (mg)	13	2.8
Vitamin E (mg)	0.54	0.03
Vitamin K (μg)	7.9	16.4
Electrolytes
Sodium (mg)	5	2
Potassium (mg)	237	147
Minerals
Calcium (mg)	10	16
Iron (mg)	0.3	0.28
Magnesium (mg)	11	13
Manganese (mg)	0.15	0.079
Phosphorus (mg)	24	24
Zinc (mg)	0.17	0.20

Source: USDA national nutrient data base

Table 3

Crop and variety grown in greenhouse conditions

Crop	Variety	Selling price US$ per kg
Cucumber	Hilton	00.57
Tomato	NUN-7712	00.38

2.2 Economic indicators

To assess the economic viability of greenhouse, four different economic indicators namely net present worth (NPW), internal rate of return (IRR), benefit cost ratio (B/C ratio), and payback period have been used (Kothari et al. 2001).

2.2.1 Net present worth

The present values of the future returns can be calculated through the use of discounting. Discounting is essentially a technique by which future benefits and cost streams can be reduced to their present worth. The most straightforward discounted cash flow measure of project worth is the net present worth (NPW). The net present worth may be computed by subtracting the total discounted present worth of the cost stream from that of the benefit stream. To obtain the incremental net benefit, gross cost is subtracted from gross benefit or the investment cost from the net benefit.

The net present worth can be computed as follows:

NPW = \sum t = 1 t = n B t - C t ( 1 + i ) t

(1)

where B _t = benefit in each year (US$); C _t = cost in each year (US$); t = 1,2,…, n; i = discount rate (%).

2.2.2 Cost-benefit ratio

This is the ratio obtained when the present worth of the benefit stream is divided by the present worth of the cost stream. The cost-benefit ratio is a formal selection criterion of acceptability of project, and it should be one or greater (Kandpal and Grag 2003). The ratio is computed by taking the present worth of the gross benefit less associated cost and then comparing it with the present worth of the project cost. The corresponding cost is the value of goods and services over, and above those included in project costs needed to make the immediate products or services of the project available for use or sale. Project economic cost is the sum of installation costs, operation and maintenance cost, and replacement costs.

Mathematically cost-benefit ratio can be computed as follow:

Cost - benefit ratio = \sum t = n t = 1 B t ( 1 + i ) t \sum t = n t = 1 C t ( 1 + i ) t .

(2)

2.2.3 Internal rate of return

The internal rate of return is a very useful measure of greenhouse project worth. It is the rate of return on capital outstanding per period while it is invested in the project. It is the maximum interest that a greenhouse project could pay for the resources used if the project is to recover its investment and operating costs and still breakeven. The internal rate of return can be found out by systematic procedure of trial and error to find that discount rate which will make the net present worth of the incremental net benefit stream equal to zero.

Internal rate of return is the discount rate, i such that

\sum t = 1 t = n B t - C t ( 1 + i ) t = 0.

(3)

2.2.4 Payback period

The payback period is the length of time from the installation of the greenhouse until the net value of the incremental production stream reaches the total amount of the capital investment. It shows the length of time between cumulative net cash outflow recovered in the form of yearly net cash inflows.

The following assumptions were made to assess the economic feasibility of naturally ventilated greenhouse:

The life of greenhouse structure is 20 years.

The life of greenhouse cover is 5 years.

Discount rate is 10 %.

Two crops of cucumber and two crops of tomato can be grown in a year greenhouse.

3 Results and discussion

The freshly harvested greenhouse vegetable product illustrated in Fig. 3 was launched in local market. As market price of a product does not remain constant, the average price was selected to assess its economic feasibility. The income and expenditure of each crop grown inside the greenhouse are presented in Table 4. The survival rate of cucumber and tomato was found about 95 and 96 %, respectively. The annual income from cucumber and tomato is about 6,125.86 and 4,320.00 US

,respectively.Thesurchargeforthesecropswas1,175.00 US, which included labor, NPK, and CaNO₃. The economic indicator used to assess the economic feasibility of the greenhouse is presented in Table 5. The net present worth for cucumber and tomato crop was found to be about 28,314.59 and 15,993.92 US$, respectively (Tables 6, 7). The cost-benefit ratio of cucumber (2.17) was higher compared to that of tomato (1.77). As the cost-benefit ratio is greater than one for these crops, hence such crops seem to be economically viable. As far as the payback period is concerned, it was about 5 years and 3 months for cucumber and about 6 year and 11 months for tomato as presented in bold text (Tables 8, 9). Therefore, despite high production, tomato’s payback period was higher than that of cucumber. The internal rate of return for cucumber and tomato crop is about 35 and 20 %, respectively.

Fig. 3

Vegetable grown inside the greenhouse. a Tomato (NUN-7712). b Cucumber (Hilton)

Table 4

Total income and expenditure for different crops under greenhouse

Particular	Crops
Particular	Cucumber	Tomato
Number of sown survival plants	1,500	1,500
Number of survival plants	1,425 (95 %)	1,440 (96 %)
Yield per plant (kg)	2.5	2.6
Annual production (kg)	10,687.5	11,232
Annual income (US$)	6,165.86	4,320.00
Surcharges (US$); labor 865.38 + NPK (150) + CaNO₃ (159.62)	1,175.00	1,175.00
Seed cost per year (US$)	492.30	86.53
Cost of cultivation (US$) = surcharges + cost of seeds	1,667.30	1,261.53
Initial investment (US$)	9,153.84	9,153.84
Cost of plastic cover to be replace every 5 years (US$	865.38	865.38

Table 5

Economic indicator of selected crops

Crop	NPW (US$.)	B–C ratio	Payback period	IRR (%)
Cucumber	28,341.60	2.17	5 years 3 months	35
Tomato	15,993.94	1.77	6 years 11 months	20

Table 6

Cash flow of cucumber crop (US$)

Years	Cash outflow	Present worth of cash outflow	Cash inflow	Present worth of cash inflow	Net Present Worth (NPW)
0	9,153.85	9,153.85	0.00	0.00	−9,153.85
1	1,667.31	1,515.73	6,165.87	5,605.33	4,089.60
2	1,667.31	1,377.94	6,165.87	5,095.76	3,717.82
3	1,667.31	1,252.67	6,165.87	4,632.51	3,379.83
4	1,667.31	1,138.79	6,165.87	4,211.37	3,072.58
5	2,532.69	1,572.60	6,165.87	3,828.52	2,255.91
6	1,667.31	941.15	6,165.87	3,480.47	2,539.32
7	1,667.31	855.59	6,165.87	3,164.06	2,308.47
8	1,667.31	777.81	6,165.87	2,876.42	2,098.61
9	1,667.31	707.10	6,165.87	2,614.93	1,907.83
10	2,532.69	976.46	6,165.87	2,377.21	1,400.75
11	1,667.31	584.38	6,165.87	2,161.10	1,576.72
12	1,667.31	531.26	6,165.87	1,964.63	1,433.38
13	1,667.31	482.96	6,165.87	1,786.03	1,303.07
14	1,667.31	439.05	6,165.87	1,623.67	1,184.61
15	2,532.69	606.31	6,165.87	1,476.06	869.75
16	1,667.31	362.85	6,165.87	1,341.87	979.02
17	1,667.31	329.87	6,165.87	1,219.88	890.02
18	1,667.31	299.88	6,165.87	1,108.99	809.11
19	1,667.31	272.62	6,165.87	1,008.17	735.55
20	0.00	0.00	6,165.87	916.52	916.52
Total		24,178.89		52,493.49	28,314.60

Table 7

Cash flow of tomato crop (US$)

Years	Cash outflow	Present worth of cash outflow	Cash inflow	Present worth of cash inflow	Net present worth (NPW)
0.00	9,153.85	9,153.85	0.00	0.00	−9,153.85
1.00	1,261.54	1,146.85	4,320.00	3,927.27	2,780.42
2.00	1,261.54	1,042.59	4,320.00	3,570.25	2,527.65
3.00	1,261.54	947.81	4,320.00	3,245.68	2,297.87
4.00	1,261.54	861.65	4,320.00	2,950.62	2,088.97
5.00	2,126.92	1,320.65	4,320.00	2,682.38	1,361.73
6.00	1,261.54	712.11	4,320.00	2,438.53	1,726.42
7.00	1,261.54	647.37	4,320.00	2,216.84	1,569.47
8.00	1,261.54	588.52	4,320.00	2,015.31	1,426.79
9.00	1,261.54	535.02	4,320.00	1,832.10	1,297.09
10.00	2,126.92	820.02	4,320.00	1,665.55	845.53
11.00	1,261.54	442.16	4,320.00	1,514.13	1,071.97
12.00	1,261.54	401.97	4,320.00	1,376.49	974.52
13.00	1,261.54	365.42	4,320.00	1,251.35	885.93
14.00	1,261.54	332.20	4,320.00	1,137.59	805.39
15.00	2,126.92	509.17	4,320.00	1,034.17	525.01
16.00	1,261.54	274.55	4,320.00	940.16	665.61
17.00	1,261.54	249.59	4,320.00	854.69	605.10
18.00	1,261.54	226.90	4,320.00	776.99	550.09
19.00	1,261.54	206.27	4,320.00	706.35	500.08
20.00			4,320.00	642.14	642.14
Total		20,784.66		36,778.60	15,993.94

Table 8

Payback period of cucumber crop (US$)

Years	Present worth of cash out flow in 20 years	Cash inflow	Present worth of cash inflow	Cumulative cash inflow
1	24,178.89
2		6,165.87	5,605.33	5,605.33
3		6,165.87	5,095.76	10,701.09
4		6,165.87	4,632.51	15,333.59
5		6,165.87	4,211.37	19,544.96
6		6,165.87	3,828.52	23,373.48
7		6,165.87	3,480.47	26,853.95
8		6,165.87	3,164.06	30,018.02

Table 9

Payback period of tomato crop (US$)

Years	Present worth of cash out flow in 20 years	Cash inflow	Present worth of cash inflow	Cumulative cash inflow
	20,784.65
1		4,320.00	3,927.27	3,927.27
2		4,320.00	3,570.25	7,497.52
3		4,320.00	3,245.68	10,743.20
4		4,320.00	2,950.62	13,693.82
5		4,320.00	2,682.38	16,376.20
6		4,320.00	2,438.53	18,814.73
7		4,320.00	2,216.84	21,031.57
8		4,320.00	2,015.31	23,046.88
9		4,320.00	1,832.10	24,878.98

4 Conclusion

A number of motives exist for promoting greenhouse cultivation in the state, including the fact that it provides a solution for food security, reduces the growing period, produces high yields of highly nutritive vegetables, and allows for year-round cultivation. Given the high water table and limited canal irrigation facilities, farmers mainly grow rain-fed crops. Off-season cultivation of vegetable in controlled environments increases farmer’s income by approximately 25–40 % through vegetable sold even in the local market. The income is generated during the off-season period, when other such opportunities are not available. Women in the state tend to be responsible for marketing vegetables, managing income, which is mostly utilized for children’s; in this way, they also develop social relations and gain self-confidence.

However, promoting greenhouse cultivation faces many challenges in from a socio-economic point of view. Not many people are aware about protected cultivation, and experience in greenhouse management is limited. Hence, more technical and practical training interventions are required to popularize greenhouse use. In addition, marketing fresh product grown in a greenhouse could be challenging; if farmers sell such product at a local market, their net profit will be reduced marginally. Hence, the state government should take care to develop a market for such high-value product.

The following conclusions were drawn from the present study:

The survival rate of cucumber and tomato crop was shown to be found reasonably successful.
Yields per plant in cucumber and tomato crops of approximately 2.5 and 2.6 kg have been recorded.
This technology is suitable for cultivating cucumber and tomato crops in Rajasthan’s climatic conditions.
Economic results indicate that greenhouse cultivation increases the income of marginal farmers, and it is suitable even where irregular electrical supply is a major problem.
Cash crops such as medicinal crops can also be cultivated in greenhouses.

Acknowledgments

The author (N. L. Panwar) gratefully acknowledges Maharana Pratap University of Agriculture and Technology, Udaipur (Rajasthan), India, and Indian Institute of Technology, Delhi, for sponsorship under the quality improvement program of the Government of India. The financial support extended by Indian Council of Agricultural Research, Govt. of India is gratefully acknowledged.

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Humans alter the water cycle by constructing dams and through water withdrawals. Climate change is expected to additionally affect water supply and demand. Here, model analyses of climate change and direct human impacts on the terrestrial water cycle are presented. The results indicate that the impact of man-made reservoirs and water withdrawals on the long-term global terrestrial water balance is small. However, in some river basins, impacts of human interventions are significant. In parts of Asia and the United States, the effects of human interventions exceed the impacts expected for moderate levels of global warming. This study also identifies areas where irrigation water is currently scarce, and where increases in irrigation water scarcity are projected.
Humans directly change the dynamics of the water cycle through dams constructed for water storage, and through water withdrawals for industrial, agricultural, or domestic purposes. Climate change is expected to additionally affect water supply and demand. Here, analyses of climate change and direct human impacts on the terrestrial water cycle are presented and compared using a multimodel approach. Seven global hydrological models have been forced with multiple climate projections, and with and without taking into account impacts of human interventions such as dams and water withdrawals on the hydrological cycle. Model results are analyzed for different levels of global warming, allowing for analyses in line with temperature targets for climate change mitigation. The results indicate that direct human impacts on the water cycle in some regions, e.g., parts of Asia and in the western United States, are of the same order of magnitude, or even exceed impacts to be expected for moderate levels of global warming (+2 K). Despite some spread in model projections, irrigation water consumption is generally projected to increase with higher global mean temperatures. Irrigation water scarcity is particularly large in parts of southern and eastern Asia, and is expected to become even larger in the future.
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