【精品硕博论文-水利水电工程】气候变化条件下流域径流响应分析与水电站适应性调度研究(英语)

学位论文气候变化条件下流域径流响应分析与水电站适应性调度研究RESEARCHONTHEHYDROLOGICALRESPONSESANDADAPTIVEOPERATIONOFHYDROPOWERSTATIONSUNDERCLIMATECHANGEDEDICATIONTHISDISSERTATIONISSOLELYDEDICATEDTOMYBELOVEDFATHERMRMUNAWARHUSSAIN,MYMOTHERANDMYBROTHERSMRSAMEERMUNAWARANDUSAMAMUNAWAR,MYSISTERS/NEPHEWS/NIECESANDRESTOFMYFAMILYMEMBERSWHOSEFAITHFULNESS,ENCOURAGEMENTANDBESTWISHESFORTHEFULFILLMENTOFTHISDOCTORALDEGREEPREFACECLIMATECHANGEISAMAJORISSUENOWADAYTHEGLOBALTEMPERATUREISINCREASINGANDAIRTEMPERATUREHASRISENUPTO085OCBETWEEN18802012WITHTHELAST30YEARSBEINGTHEWARMESTYEARSGLOBALWARMINGISPROGRESSIVELYBRINGINGCONSIDERATIONSTOWARDCLIMATECHANGESCLIMATECHANGEINFLUENCESONWATERSHEDINFLOWSUPPLYINNUMEROUSWAYSITMAYMODIFYSEASONALPRECIPITATIONANDTEMPERATURE,CHANGETHETIMINGOFSTREAMFLOW,ANDDECREASETHEABILITYOFPREVAILINGSUPPLIESTOMEETWATERREQUIREMENTSCLIMATECHANGEAFFECTSTHESPATIALANDTEMPORALCHANGESINWATERRESOURCESBESIDES,THEINCREASEINTEMPERATUREOFTHEGLOBE,THEREISASIGNIFICANTCHANGEINPRECIPITATIONHASBEENNOTICEDTHUS,CLIMATECHANGEANDITSULTIMATEEFFECTSONWATERRESOURCESHOLDGREATIMPORTANCETOSCIENTISTANDENGINEERSTHENORTHERNPARTOFCHINAISBECOMINGWARMERMORERAPIDLYTHANTHESOUTHERNPARTINTHISPHDDEGREEDISSERTATIONMYFINDINGSABOUTCLIMATECHANGEIMPACTATWATERRESOURCESAREPRESENTEDTHEAIMOFTHEPRESENTEDRESEARCHWASTOFINDTHECLIMATECHANGEIMPACTATWATERRESOURCESANDITSULTIMATEIMPACTATADAPTIVEOPERATIONOFHYDROPOWERGENERATIONOFXINANJINAGWATERSHEDTHISSTUDYFOCUSTODETERMINEIFCLIMATECHANGEIS/HASOCCURREDINTHESTUDYAREAANALYSISWASCARRIEDOUTTOEXAMINETHEIMPACTOFCLIMATECHANGEONTHEPRECIPITATIONANDTEMPERATUREOFXINANJIANGWATERSHEDPREDICTIONSOFFUTURETRENDOFRIVERFLOWSWEREALSOCARRIEDOUTTHUSSTUDYALSODEVELOPEDWATERAVAILABILITYSCENARIOSFORTHEXINANJIANGWATERSHED,BASEDONTHEENHANCEDDATABASEANDUSINGSTATEOFTHEARTMODELSANDTECHNIQUESPARTICLESWARMOPTIMIZATIONTECHNIQUEUSEDTODETERMINETHEEXTENTOFELECTRICITYTHATCANBEGENERATEDINTHEFUTURETRENDCHANGEOFPRECIPITATIONANDTEMPERATUREFORPASTANDFUTUREHAVEBEENFIGUREOUTINTHISSTUDYANDPRESENTEDINTOCHAPTERFOURASSESSMENTOFWATERRESOURCESOFTHEAREAANDFUTUREPREDICTIONOFWATERHAVECARRIEDOUTANDPRESENTEDINTOCHAPTERFIVEOFTHISDISSERTATIONCHAPTERSIXEXPLAINEDTHEOPTIMALELECTRICITYAMOUNTTHATCANBEGENERATEDFROMFUTURERUNOFFSTHEDECADESOF2020S,2050SAND2080STHEMANNKENDALL,SPEARMANSRHO,THEILSENSSTATISTICALAPPROACHESALONGWITHSWATHYDROLOGICALMODELCHANGEFACTORDOWNSCALINGTECHNIQUEANDPARTICLESWARMOPTIMIZATIONTECHNIQUESWEREAPPLIEDINTHISSTUDYTHEFINDINGSOFTHISSTUDYWILLBEHELPFULFORFUTUREWORKINTHISREGARDSTHISSTUDYWILLALSOHELPFULFORDESIGNER,CITYPLANNERSANDENGINEERSFORTHEPLANNINGOFFUTUREWATERRESOURCESALLOCATIONFINDINGSWILLALSOHELPFULFORTHEOPTIMALELECTRICITYGENERATIONANDITSUTILIZATIONTHEINNOVATIONPOINTSOFTHISSTUDYAREPRESENTEDASBELOWINTEGRATEDASSESSMENTMODELHASBEENDEVELOPEDFORTHISSTUDYAREATOSTUDYTHECLIMATECHANGEIMPACTATWATERRESOURCES,STATISTICALANALYSIS,DOWNSCALINGOFFUTURESCENARIOSANDFORHYDROLOGICALSIMULATIONCHANGEFACTORSTATISTICALDOWNSCALINGANDPREWHITENINGTECHNIQUESHAVEBEENUSEDTODOWNSCALETHEDATAANDTOREMOVETHESERIALEFFECTSFROMTHEDATARESPECTIVELYMANNKENDALL,SPEARMANSRHO,THEILSENSANDSEQUENTIALMANNKENDALLNONPARAMETRICTECHNIQUESHAVEBEENINTEGRATEDALONGWITHARCSWATHYDROLOGICALMODELANDSWATCUPINTERFACETOASSESSTHECLIMATECHANGEIMPACTATTHEHYDROLOGYOFTHEWATERSHEDPARTICLESWARMOPTIMIZATIONTECHNIQUEALONGWITHTHEBESTPARAMETERS,THATCANGIVEBESTOPTIMALRESULTS,HASBEENUSEDINORDERTOGETOPTIMALELECTRICITYGENERATIONPLANAMATHEMATICALMODELFORTHEFUTURESTREAMFLOWALONGWITHCONSTRAINTSANDOBJECTIVEFUNCTIONSHASALSOBEENDEVELOPEDFORTHISSTUDYAREAHADGMES2ANDCCSM4MODELSUNDERRCP45ANDRCP85SCENARIOSOFCMIP5DATAHASBEENUSEDTODOWNSCALEMETEOROLOGICALDATATOLOCALSCALEUNTIL2100FORTHISSTUDYAREACHANGEINMONTHLY,SEASONALLYANDANNUALLYPRECIPITATION,TEMPERATUREANDSTREAMFLOWTRENDSOFXINANJIANGWATERSHEDHAVEBEENDETERMINEDUSINGMANNKENDALLANDSPEARMANSRHONONPARAMETRICTECHNIQUESMAGNITUDEANDABRUPTCHANGEINTHESETRENDSFORXINANJIANGWATERSHED,UNDERCLIMATECHANGEUSINGCMIP5DATA,HAVEBEENDETERMINEDBYUSINGTHEILSENSANDSEQUENTIALMANNKENDALLNONPARAMETRICTECHNIQUESRESPECTIVELYCHANGEYEARFORPRECIPITATIONANDTEMPERATURETRENDSINTHEFUTUREHASBEENALSOPREDICATEDFORXINANJIANGWATERSHEDINORDERTODETERMINETHEAMOUNTOFRUNOFFANDITSDIFFERENCEWITHPASTYEARSRUNOFFS,ARCSWATHYDROLOGICALMODELALONGWITHSWATCUPHASBEENUSEDUNDERCLIMATECHANGESCENARIOSUSINGCMIP5DATAFORXINANJIANGWATERSHEDPARTICLESWARMOPTIMIZATIONTECHNIQUEHASBEENAPPLIEDATTHESEFUTURESTREAMFLOWSTODETERMINETHEELECTRICITYAMOUNTTHATCANBEGENERATEDFROMTHEXINANJIANGHYDROPOWERSTATION序言气候变化是当前广泛关注的热点问题。全球温度呈现逐渐升高的趋势，其中18802012年升幅达到085，最近的30年是全球最热的年份。全球变暖逐步引发人们对气候变化的思考，气候变化可以通过多种方式影响着流域的径流供给，改变降雨量的季节分配和温度的四季变化，从而改变径流量的年内变化，降低区域水资源的供给能力。气候变化还可能影响水资源在空间和时间上的分布特点。此外，研究发现随着全球气温的升高，降水上有明显的变化。因此，气候变化以及气候变化对水资源的最终影响对科研人员而言显得非常重要。中国北方变暖的速度要更快于南方。在这篇博士论文中，作者给出了气候变化对水资源影响的相关研究结果。该研究的目的在于探究气候变化对新安江流域水资源的影响以及新安江水电站最优水力发电方案的终极响应。论文重点在于确定研究区域中的气候是否正在或者已经发生了变化。通过进行数据分析来验证气候变化对新安江流域降水和温度的影响，同时对河流流量的未来趋势进行了预测。此外，该研究基于增强型数据库并使用最先进的模型技术改进了新安江流域的水资源供应方案，并通过粒子群优化技术来确定未来可以产生的发电量。同时，在论文第四章中详细分析了过去和未来的降雨量和气温的变化趋势。论文第五章则主要评价了研究区域内水资源量并对未来水资源量进行预测。论文第六章阐述了未来2020年代、2050年代和2080年代的最优发电量。论文采用了MANNKENDALL、SPEARMANSRHO以及THEILSENS统计方法，ARCSWAT水文模型、降尺度技术和粒子群优化方法也得到了应用。这项研究的结果将有助于未来在这方面的相关研究工作，对设计师、城市规划师和工程师进行未来水资源的配置规划也有很好的启发，同时将有助于该地区充分利用水资源进行发电和相关利用。ABSTRACTRAPIDECONOMICDEVELOPMENTS,CLIMATECHANGE,ANDAFASTGROWTHINTHEHUMANINHABITANTSARECONSIDEREDASTHEMAJORCAUSESOFGROWINGWATERRELATEDCOMPLICATIONSWORLDWIDETHEFLUCTUATINGHYDROLOGICALCONDITIONSANDWATERNECESSITIESSTANCEACHALLENGETOTHEMANAGINGOFWATERASSETSSYSTEMSASTHESEAREPLANNEDTOPRESERVEAFRAGILEEQUILIBRIUMBETWEENWATERSUPPLYANDCLAIMWITHTHEPREDICTABLECHANGES,THISEQUILIBRIUMISLIKELYTOBEINTERRUPTED,EVENTUALLYNECESSITATINGEDITIONOFTHECURRENTINFRASTRUCTURERESERVOIRSARETHECRUCIALELEMENTINORDINARILYWATERRESOURCESSYSTEMSTOGUARANTEEASTEADYWATERSUPPLYYET,SINCERESERVOIRSCOULDBECATEGORIZEDASSOMEWHATOBSTINATETYPESOFINFRASTRUCTURE,ONEOFTHEFEWCHOICESFOREDITIONISTODETERMINETHEFUTUREWATERRESOURCESANDADJUSTTHEIROPERATIONTHISSTUDYEMPHASIZEDTHEHYDROLOGICALRESPONSESANDADAPTIVEOPERATIONOFHYDROPOWERSTATIONSUNDERCLIMATECHANGETHEPREDICATIONOFFUTUREPRECIPITATIONANDTEMPERATURETRENDATANDITSCOMPARISONWITHPASTYEARSTRENDWERESTUDIEDPOSSIBLEBEGINNINGTOCHANGEINPRECIPITATIONANDTEMPERATURETRENDHASALSOBEENSTUDIEDINORDERTOGETTHEIDEAABOUTFUTUREWATERRESOURCESCLIMATECHANGEIMPACTATSTREAMFLOWANDOPTIMALELECTRICITYGENERATIONUSINGFUTURESTREAMFLOWSWEREALSOSTUDIEDTHEPAPERCOVERAGEANDACHIEVEMENTAREASFOLLOW1TWOGCMSCCSM4ANDHADGEM2ESOFCMIP5HAVEBEENDOWNSCALEDACCORDINGTOLOCALCONDITIONOFWATERSHEDUSINGCHANGEFACTORSTATISTICALDOWNSCALINGMETHODINORDERTOREMOVETHESERIALEFFECTOFDATA,NONPARAMETRICPREWHITENINGTECHNIQUEWASUSEDNONPARAMETRICMANNKENDALLMK,SPEARMANSRHOSP/SR,THEILSENSTSAANDSEQUENTIALMANNKENDALLSQMKTECHNIQUESWEREAPPLIEDATDIFFERENTMONTHLY,SEASONALANDANNUALPRECIPITATION/TEMPERATUREDATASERIESMANNKENDALLANDSPEARMANSRHOHAVEAPPLIEDTODETECTTHESIGNIFICANTTRENDINTOADATASERIES,WHEREASTOGETTHEMAGNITUDEOFTRENDTSAHAVEUSEDTHESQMKNONPARAMETRICTECHNIQUEHASAPPLIEDINORDERTOGETTHEIDEAABOUTTHEBEGINNINGTOCHANGEINPRECIPITATIONTREND2THESOILANDWATERASSESSMENTTOOLSWATHYDROLOGICALMODELWASAPPLIEDTODETECTTHESTREAMFLOWTHEHYDROLOGYOFTHEXINANJIANGWATERSHEDWASSTUDIEDBYAPPLYINGTHESWATINTERFACEUSINGOBSERVEDANDCMIP5DOWNSCALEDMETROLOGICALDATATHEMODELWASSUCCESSFULLYCALIBRATEDANDVALIDATEDUSINGTHESWATCUPINTERFACETOOLELECTRICITYGENERATIONALSOEFFECTEDBYSTREAMFLOW3PRECIPITATIONANDTEMPERATUREDATASERIESHAVEBEENDIVIDEDINTOPAST19602010ANDFUTURE20112060DATASERIESANDSTATISTICALTECHNIQUESHAVEAPPLIEDATTHESEDATASERIESINORDERTOGETANIDEAABOUTTHEOVERALLCHANGEINTHETRENDSOFPRECIPITATIONANDTEMPERATUREINTHESTUDYAREATHEPREDICATIONOFFUTUREPRECIPITATIONANDTEMPERATURETRENDANDITSCOMPARISONWITHPASTYEARSTRENDWEREALSOCARRIEDOUTUSINGSTATISTICALTECHNIQUESPOSSIBLEBEGINNINGTOCHANGEINPRECIPITATIONANDTEMPERATURETRENDHASALSOBEENSTUDIEDINORDERTOGETTHEIDEAABOUTFUTUREWATERRESOURCESRESULTSREVEALEDTHATTHEREISNOSIGNIFICANCECHANGEINANNUALPRECIPITATIONTRENDFORPASTANDFUTUREDATASERIES,WHEREASWINTERANDAUTUMNHAVEINCREASINGANDSUMMERANDSPRINGDECREASINGPRECIPITATIONTRENDCHANGEINPRECIPITATIONTRENDHASOBSERVEDDURING20202030DECADEINTHESTUDYAREAFORFUTUREDATASERIES4GCMSDATAHAVEBEENDOWNSCALEDUSINGCHANGEFACTORSTATISTICALDOWNSCALINGMETHODANDCATEGORIZEDAS201120402050S,204120702050SAND207120992080SRESULTSREVEALEDANINCREASEINTHEMEANMAXIMUMANDMINIMUMTEMPERATURETHEMAXIMUMINCREASEINTEMPERATUREISUPTO440CFORHADGEM2ESDURING2080SFORTHEMEANMAXIMUMTEMPERATURESERIES,WHEREASTHEREISANINCREASEOFUPTO330CDURINGTHESAMEPERIODFORTHEMEANMONTHLYMINIMUMTEMPERATUREDATASERIESRESULTSREVEALEDTHATTHEMEANMONTHLYPRECIPITATIONTRENDATMOSTNORTHSIDEOFTHESTUDYAREAISTOTALLYOPPOSITETHANOTHERPARTOFTHESTUDYREGIONMEANMONTHLYPRECIPITATIONDATASERIESRESULTSOFPASTANDFUTUREDATASERIESREVEALEDTHATOCTOBER,NOVEMBER,DECEMBERANDJANUARYHAVEPOSITIVEINCREASINGTRENDWHEREASINSIGNIFICANTNEGATIVETRENDHAVEFOUNDINMARCH,APRIL,MAYDATASERIESOFMOSTLYSTATIONSINTHESTUDYAREA5THECALIBRATIONANDVALIDATIONOFSWATPRODUCEDAGOODSIMULATIONTHENASHSUTCLIFFEEFFICIENCYNSHEANDCOEFFICIENTOFDETERMINATIONR2WEREUSEDTOTESTTHEEFFICIENCYOFTHEMODELTHENSHEANDR2VALUESFORCALIBRATIONWERE84AND86,RESPECTIVELY,AND80AND81,RESPECTIVELY,FORTHEVALIDATIONPERIODSRESULTSREVEALEDTHATTHEINCREASEINMONTHLYSTREAMFLOWISMOREIN2020SAND2080STHAN2050SMAXIMUMINCREASEINMONTHLYSTREAMFLOWWASOBSERVEDDURINGJULY,AUGUSTANDSEPTEMBERFORBOTHGCMSANDRCPSUNDER2050S2020SAND2080SPRESENTEDMOREINCREASEINSTREAMFLOWTHAN2050SFORALLGCMSANDRCPSINSEASONALANDANNUALDATASERIESHADGEM2ESEXHIBITSLARGERINCREASEINMONTHLYSTREAMFLOWTHANCCSM4ANNUALSTREAMFLOWDATASERIESPRESENTSMAXIMUMINCREASEINSTREAMFLOWUNDER2020SAND2080S6AMATHEMATICALMODELWASDEVELOPEDTOGETOPTIMALELECTRICITYGENERATIONUSINGPARTICLESWARMOPTIMIZATIONTECHNIQUEFORADAPTIVEOPERATIONOFHYDROPOWERSTATIONASITWASOBSERVED,THESCENARIOSWITHLARGEAMOUNTOFSTREAMFLOWGIVEMAXIMUMELECTRICITYGENERATIONRESULTSREVEALEDTHATONLY1123108KWHELECTRICITYCOULDBEGENERATEDUSINGTHEBASEPERIODFLOW1980S,WHEREASUPTO233108KWHCANBEGENERATEDUSINGTHE2080SSTREAMFLOWFORRAINYYEARKEYWORDSWATERRESOURCES,CLIMATECHANGE,CMIP5,DOWNSCALING,ADAPTIVEHYDROPOWEROPERATION摘要世界范围内水资源相关难题增多的原因主要归结于经济的快速增长，气候变化和人口数量增加。波动的水文环境和必需用水对于水资产管理系统构成了挑战，而这系统正计划在水供应与索取之间保持一个脆弱的平衡点。根据可预见的变化显示，这种平衡可能会被打破，并最终成为当前的基础设施的必需版。水库通常是水资源系统中保证稳定供水的重要元素。然而，由于水库被归类于难以控制的基础设施，成为决定未来水资源及其调整操作的少有选择之一。论文第一部分着重于对新安江流域未来降雨和气候趋势的预测以及和过去几年该趋势的比较。为了得到未来水资源的新思路，关于降雨和气候可能开始改变的趋势也会被文章涉及研究。CCSM4数据模型中的RCP45情景根据当地流域条件利用变化因素的降尺度方法已按比例缩小。为了消除数据的序列效应，使用非参数预白化技术。非参数的MANNKENDALL（MK,SPEARMANSRHO（SP/SR,THEILSENSTSA方法和连续MANNKENDALLSQMK技术适用于2个不同的降水/气温数据系列19602010和20112060。MANNKENDALL和SPEARMANSRHO适用于检测数据序列的显著趋势，然而，获取趋势的幅度需使用TSA。SQMK非参数化技术的使用是为了得到降水趋势开始变化的构想。结果显示在过去和未来的数据序列中年降水量趋势都没有太大的变化，只是降水趋势在冬天和秋天有所增加，在夏天和春天减少。在20202030十年间，观察到研究区域中降水量的变化。结果显示气温升高。论文的第二部分着重于气候变化对新安江流域水资源的影响及基于未来河流流量的最优发电（在2020,2050和2080的几十年）。在RCP45和RCP85C情境下，CMIP5数据中的2个大气环流模型CCSM4和HADGEM2ES被用来评估研究区域中未来的气候，降水量，河流流量和发电量。ARCSWAT水文模型和降尺度的偏差系数及粒子群优化技术都被用来检测水流流量，并分别用来减少未来气候变量和计算最优发电量。ARCSWAT模型被证明是水资源管理的有效工具。通过使用已观测和预测的计量数据的ARCSWAT界面对新安江流域水文进行研究。使用ARCSWATCUP接口工具成功的校准和验证了此模型。ARCSWAT的校准和验证产生了一个良好的模拟形式。NASHSUTCLIFFE效率系数NSHE和决定系数R2被用于检测模型的效率。NASHSUTCLIFFE效率系数NSHE和决定系数R2的校准值分别为84和86，验证值分别为80和81。变化系数法被用于缩小大气环流模型规模。结果显示到2080年代，每月平均最高气温增量可达440C，最低温度增量可达330C。大多情况下，研究区域中的降水和未来水流流量也有所增加。发电也会受到水流量的影响。通过量子群优化技术改进的数学模型来得到最优发电量。正如研究所示，大流量的水流将带来最大的最优发电量。结果显示，在1980年代，使用基础周期流量产生的电量只有1123108KWH。然而，使用2080年代多雨年份河流流量所产生的电量可达到233108KWH。大气环流和控制原型仿真情况的不同造成未来降水和流量的不确定性。这篇文章的创新点如下针对研究区采用综合评价模型来研究气候变化对水资源的影响,统计分析及未来情景下的降尺度方法应用于水文模拟为新安江水电站制定未来来水情况下的优化发电方案和数学模型在RCP45和RCP85情景下基于HADGMES2和CCSM4模型数据对新安江流域到2100年的降水、气温和径流量变化进行分析对新安江流域未来降水和气温趋势发生变化的年份进行了预测TABLEOFCONTENTSDEDICATIONVPREFACEI序言IIIABSTRACTIV摘要VIICHAPTER1INTRODUCTION111STUDYBACKGROUND1111CLIMATECHANGEINCHINA2112WATERRESOURCESOFCHINA4113PROBLEMSTATEMENT612LITERATUREREVIEW7121CLIMATECHANGEIMPACTASSESSMENT7122STATISTICALTECHNIQUESUSEDFORCLIMATECHANGEANALYSIS11123DOWNSCALINGOFGCMS12124HYDROLOGICALMODELING14125OPTIMIZATIONTECHNIQUESUSEDFORADAPTIVEOPERATIONOFHYDROPOWERSTATIONS1613RESEARCHCONTENTS17131INTEGRATEDASSESSMENTMODELFORCLIMATECHANGEIMPACTSONWATERRESOURCES18132ADAPTIVEOPERATIONOFHYDROPOWERSTATIONSUNDERCLIMATECHANGE18133CLIMATECHANGEANALYSISOFXINANJIANGWATERSHED18134ASSESSMENTOFCLIMATECHANGEIMPACTONWATERRESOURCESFORXINANJIANGWATERSHED18135ADAPTIVEOPERATIONOFXINANJIANGHYDROPOWERSTATION19CHAPTER2INTEGRATEDASSESSMENTMODELFORCLIMATECHANGEIMPACTSONWATERRESOURCES2121STRUCTUREOFINTEGRATEDMODEL2122CHANGEFACTORCFDOWNSCALINGMETHOD2223STATISTICALANALYSISOFCLIMATECHANGEFACTORS22231SERIALCORRELATIONEFFECT22232MANNKENDALLMKTRENDTEST23233SPEARMANSRHOTEST24234THEILSENAPPROACHTSA25235IDENTIFICATIONOFPRECIPITATIONSHIFTS2524HYDROLOGICALSIMULATION26241SWATHYDROLOGICALMODEL26242MODELINPUT29243SWATCUP30244MODELEFFICIENCY3025SUMMARY31CHAPTER3ADAPTIVEOPERATIONOFHYDROPOWERSTATIONSUNDERCLIMATECHANGE3231MATHEMATICALMODELFORADAPTIVEHYDROPOWEROPERATION3232OPTIMIZATIONTECHNIQUES33321BRIEFHISTORYOFOPTIMIZATIONTECHNIQUES33322PARTICLESWARMOPTIMIZATION33323PARTICLESWARMOPTIMIZATIONPSOALGORITHM34324PARAMETERSSELECTIONFORPSO3533SUMMARY39CHAPTER4CLIMATECHANGEANALYSISOFXINANJIANGWATERSHED4041STUDYAREA40411XINANJIANGWATERSHED40412HYDROPOWERPROJECT4142DATACOLLECTIONANDDATANEEDED4143PRELIMINARYSTATISTICALRESULTS4344COMPARISONSOFFUTUREANDPASTTRENDREGARDINGTOMKABOUTHALFOFTHESTUDIESWEREDONEFOREUROPESTUDIESINTHEUNITEDSTATESHAVEBEENCOMPLETEDATNATIONALSCALEANDFORDEFINITEWATERSHEDSMANYOFWHICHHAVEBEENCOMPLETEDFORWATERSHEDSINTHEWESTERNPARTOFTHEUSCOUNTINGALLORSOMEPORTIONSOFTHECOLUMBIARIVERBASIN49,52,5660COLORADORIVERBASIN49,52,6164,ANDMISSOURIRIVERBASIN48,49,52,57,6569SHRESTHAETAL1999EXAMINEDMAXIMUMTEMPERATUREDATAFORTHEPERIODOF197194INNEPALRESULTSREVEALEDANINCREASINGTRENDAFTER1977WITH0068TO01200CINCREASEPERYEARINTHEHIMALAYANREGIONSANDMIDDLEMOUNTAIN,WHILETHESOUTHERNPLAINSSHOWLESSTHAN00300CPERYEARINCREASEINTEMPERATURETHEDATAOF14STATIONSSPREADINGBACKTOTHEEARLY1960SPROPOSESTHATTHECURRENTWARMINGTRENDSWEREHEADEDBYSIMILAREXTENSIVECOOLINGTRENDSDISSEMINATIONSOFANNUALANDSEASONALTEMPERATURETRENDSDISPLAYASIGNIFICANTINCREASINGTRENDINTHEMIDDLEMOUNTAINSANDHIMALAYA,WHILEEVENCOOLINGTRENDSORLOWWARMINGWEREEXHIBITEDINTHESOUTHERNAREASTHETEMPORALANDSPATIALDISTRIBUTIONOFTHETEMPERATURETRENDSHIGHLIGHTSTHEIMPACTOFMONSOONCIRCULATION70HINGANEETAL1985EXAMINEDTHESURFACEAIRTEMPERATURESININDIAANDINDICATEDANINCREASEOF0480CINTHEMEANANNUALTEMPERATUREOVERTHEPASTCENTURY71LIANDTANGSTUDIEDTHEVARIATIONSINTHEAIRTEMPERATUREOFTIBETANPLATEAUANDFOUNDADECREASINGTRENDDURING1950TO1970ANDANINCREASEAFTER197072SINGHANDKUMAR1997ANDBENGTSSON2005SUGGESTEDTHATEVAPORATIVELOSSINCREASEASCONSEQUENCESOFINCREASEDTEMPERATUREANDHENCESNOWCOVERVOLUMEREDUCED,WITHANEXPECTEDREDUCTIONOF9FOR1CINCREASEINTEMPERATUREINTHEWESTERNHIMALAYASFORTHESATLUJRIVERBASIN73ARCHER2003APPLIEDLINEARREGRESSIONATSHYOKANDHUNZARIVERBASINSANDFOUND17INCREASEINSUMMERRUNOFFINTHESHYOKRIVERANDA16INCREASEINTHEHUNZARIVERWITHARISEOF1CINTHEMEANSUMMERTEMPERATURE74ARCHERANDFOWLER2004USEDTHEPRECIPITATIONDATAFROM1961TO1999FORTHEDETERMINATIONOFRAINFALLTRENDSANDFOUNDANINCREASEOF120,103AND22MM/DECADEATDIR,SHAHPURANDSKARDUCLIMATESTATIONOFUPPERINDUSBASIN,RESPECTIVELY1KHAN2001ANALYZEDTHERIVERFLOWOFTHEUPPERINDUSBASINANDDETERMINEDTHATTHEANALYSISOFTHERIVERFLOWSTIMESERIESRELATEDWITHCLIMATICDAT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