Analysis and prediction of reference evapotranspiration with climate change in Xiangjiang River Basin,China

Xin-e To,Hu Chen,*,Chong-yu Xu,b,Yu-kun Hou,Meng-xun Jie

aState Key Laboratory of Water Resources and Hydropower Engineering Science,Wuhan University,Wuhan 430072,PR China

Analysis and prediction of reference evapotranspiration with climate change in Xiangjiang River Basin,China

Xin-e Taoa,Hua Chena,*,Chong-yu Xua,b,Yu-kun Houa,Meng-xuan Jiea

aState Key Laboratory of Water Resources and Hydropower Engineering Science,Wuhan University,Wuhan 430072,PR China

bDepartment of Geosciences,University of Oslo,Oslo N-0316,Norway

Reference evapotranspiration(ET0)is often used to estimate actual evapotranspiration in water balance studies.In this study,the present and future spatial distributions and temporal trends ofET0in the Xiangjiang River Basin(XJRB)in China were analyzed.ET0during the period from 1961 to 2010 was calculated with historical meteorological data using the FAO Penman-Monteith(FAO P-M)method,whileET0during the period from 2011 to 2100 was downscaled from the Coupled Model Intercomparison Project Phase 5(CMIP5)outputs under two emission scenarios,representative concentration pathway 4.5 and representative concentration pathway 8.5(RCP45 and RCP85),using the statistical downscaling model(SDSM).The spatial distribution and temporal trend ofET0were interpreted with the inverse distance weighted(IDW) method and Mann-Kendall test method,respectively.Results show that:(1)the mean annualET0of the XJRB is 1 006.3 mm during the period from 1961 to 2010,and the lowest and highest values are found in the northeast and northwest parts due to the high latitude and spatial distribution of climatic factors,respectively;(2)the SDSM performs well in simulating the presentET0and can be used to predict the futureET0in the XJRB;and(3)CMIP5 predicts upward trends in annualET0under the RCP45 and RCP85 scenarios during the period from 2011 to 2100. Compared with the reference period(1961-1990),ET0increases by 9.8%,12.6%,and 15.6%under the RCP45 scenario and 10.2%,19.1%,and 27.3%under the RCP85 scenario during the periods from 2011 to 2040,from 2041 to 2070,and from 2071 to 2100,respectively.The predicted increasingET0under the RCP85 scenario is greater than that under the RCP45 scenario during the period from 2011 to 2100. ©2015 Hohai University.Production and hosting by Elsevier B.V.This is an open access article under the CC BY-NC-ND license(http:// creativecommons.org/licenses/by-nc-nd/4.0/).

Reference evapotranspiration(ET0);Spatial-temporal variation;Climate change;Statistical downscaling;Xiangjiang River Basin

1.Introduction

According to theFifth Assessment Report of the Intergovernmental Panel on Climate Change(IPCC),the global warming trend,which is mainly caused by the increasing amount of greenhouse gas emissions,will continue(IPCC, 2014).China has also experienced a pronounced warming over the last few decades(Piao et al.,2010).Risingtemperature is expected to strengthen the hydrological cycle because of the ability of warm air to hold and redistribute more moisture,which causes the change in atmospheric circulation(Allen and Ingram,2002;Wentz et al.,2007;Bates et al.,2008;Durack et al.,2012;Ye et al.,2013).Despite responding to climate change in different ways,changes in precipitation,runoff,groundwater fow,evapotranspiration, and soil moisture indicate that the hydrological cycle has been intensifed along with the rising temperature around the world during the 20th century(Allen and Ingram,2002;Alan et al., 2003;Wentz et al.,2007;Gao et al.,2012;Mitchell et al., 2012;Wang et al.,2012;Allan et al.,2013).Evapotranspiration is an important component of the hydrological cycle and water balance in addition to precipitation and runoff,whichalso play key roles in the energy budget in the earthatmosphere system(Xu et al.,2006;Wang et al.,2007).As the only connection linking the water balance and land surface energy balance,evapotranspiration is considered the most signifcant indicator for climate change and the water cycle (Xu et al.,2005;Xu and Singh,2005;Wang et al.,2013).The process of evapotranspiration governs the moisture transfer between soil and atmosphere in a watershed and is heavily infuenced by climate change(Fisher et al.,2011).Evapotranspiration,together with precipitation,determinates the humidity of a region.

Of all the components in the hydrological cycle,evapotranspiration is the most diffcult to estimate.There are few direct measures for actual evapotranspiration(ETa)over global land areas.Because of the lack of observation data,reference evapotranspiration(ET0)is often used to estimateETain most cases,such as scheduling irrigation,managing water resources,analyzing regional climate humidity conditions,and providing information for hydrological,agricultural,and environmental models.ET0is defned as the rate of evapotranspiration from a hypothetical crop with an assumed crop height of 0.12 m,a fxed surface resistance of 70 s/m,and an albedo of 0.23,which would closely resemble the evapotranspiration from an extensive surface of green grass of uniform height, actively growing, well-watered, and completely shading the ground(Allen et al.,1998).With abundant water available at the reference evapotranspiring surface,the main factors affectingET0are climatic factors (Allen et al.,1998).Therefore,ET0is a climatic variable and can be computed using meteorological data(Xu et al.,2006). The FAO Penman-Monteith(FAO P-M)method is recommended as the sole standard method for the calculation ofET0, and the required data are available.The FAO P-M method is relatively complete in theory,with comprehensive consideration of various climatic factors affecting evapotranspiration (Allen et al.,1998).

There have been many related studies on hydro-climatic changes at the regional scale in recent years,mainly focusing on analyzing the spatial distribution and temporal trend ofET0based on historical data and identifying the main climatic factors affectingET0(Bandyopadhyay et al.,2009; Zhang et al.,2011;Liu et al.,2012;Zuo et al.,2012;Ye et al.,2013).It is also necessary to use climate models for the reliable prediction ofET0.General circulation models (GCMs)are used to predict future changes inET0.Due to the coarse spatial resolution of GCM outputs,downscaling techniques are used to obtain the weather and climate information at a local scale from relatively coarse-resolution GCMs(Wilby and Dawson,2007).

Downscaling methods are divided into two categories,the physically based dynamic downscaling method and the empirically based statistical downscaling method.The dynamic downscaling models,in particular the regional climate model (RCM),have a clear physical meaning.Their defnite weaknesses,however,are higher computational cost and strong dependence on the boundary conditions induced by GCMs (Wang et al.,2013).The statistical downscaling method establishes a statistical relationship between observations and large-scale variables,and the relationship is then used based on the GCM data to obtain the local scale variables.The statistical downscaling method has been widely used because of its convenience in implementation and low computation requirements (Chu et al.,2010;Wang et al.,2013).As the frst tool of the downscaling method,which is freely offered to researchers on climate change impacts,the statistical downscaling model (SDSM)is widely used,due to its simplicity and superior capability(Fowler and Wilby,2007;Wilby et al.,2002).

Previous work in China has mainly focused on temperature and precipitation downscaling;ET0downscaling began to appear in recent years(Li et al.,2012;Wang et al.,2013).Li et al.(2012)examined the present and future spatiotemporal characteristics ofET0on the Loess Plateau in China,found thatET0signifcantlyincreasedduringtheperiodfrom1961to2009 due to a downward trend in relative humidity and an upward trend in temperature,and demonstrated a continuous increasing trend in the 21st century with a more pronounced upward trend after2050.Liet al.(2012)suggestedthatthe increasingtrendinET0could possibly infuence water resources on the Loess Plateauinthe21stcenturyandsomecountermeasuresshouldbe taken to reduce the adverse impacts.Wang et al.(2013)investigated the spatial and seasonal changes inET0on the Tibetan Plateau and determined that,considering its spatial-temporal variation,an averageET0series presented a zigzag pattern (increasing-decreasing-increasing)with two joint points in 1973 and 1993,and predicted a continuous increasing trend inET0in the 21st century.

In the Yangtze River Basin,there have already been studies related toET0.For example,Gao et al.(2012)detected a decreasing trend inET0,caused mainly by a signifcant decrease of the net total radiation and secondarily by a signifcant decrease of the wind speed over the basin.Ye et al. (2013)analyzed the variation ofET0and its affecting factors in the Poyang Lake Catchment.However,there has been no prediction ofET0in the Yangtze River Basin until now,despite the importance of the Yangtze River Basin in China.It is necessary to mention that human activities,such as the construction of hydraulic engineering like the Three-Gorges Dam, contribute most to the variation of evaporation in the Yangtze River Basin.In this study,the Xiangjiang River Basin(XJRB), which is a sub-basin of the Yangtze River Basin,was chosen for its natural ecological environment and the fact that it is not seriously affected by human activities.The latest outputs of the Coupled Model Intercomparison Project Phase 5(CMIP5) recommended by theFifth Assessment Report of IPCCwere used in this study forET0prediction,specifcally the new generation of two emission scenarios,representative concentration pathway 4.5 and representative concentration pathway 8.5(RCP45 and RCP85),instead of the old climate scenarios.

The primary objectives of this study were(1)to analyze the spatial distribution and temporal trend ofET0in the XJRB during the period from 1961 to 2010,(2)to investigate the adaptability of SDSM forET0downscaling in the study area, and(3)to predict futureET0during the period from 2011 to 2100 with the SDSM under the RCP45 and RCP85 scenarios,in order to study howET0will change in the XJRB in the 21st century.

2.Materials and methods

2.1.Study area and data

As an important tributary of the Yangtze River,the Xiangjiang River is the largest river in Hunan Province,in southern China.The Xiangjiang River originates in the Haiyang Mountains of Guangxi Province,fows through Yongzhou, Hengyang,Zhuzhou,Xiangtan,Loudi,Changsha,and Yueyang cities,and discharges into Dongting Lake,with a length of 856 km and a total basin area of nearly 94 660 km2.In Hunan Province,the river has a length of 670 km and a basin area of nearly 85 383 km2.The XJRB covers 40%of the total area,and 60%of the population of Hunan Province,and is responsible for 63.5%of the gross domestic product(Zhang et al.,2014).

Ranging from 110°E to 115°E and 24°N to 29°N,the XJRB has a subtropical humid monsoon climate with four distinct seasons,hot and humid summers,cold and dry winters,and a mean annual temperature of 16-19°C.In addition,the basin has an average annual rainfall of 1 200-1 700 mm,most of which falls from April to July,and a large variability in runoff, with a range of 100-20 800 m3/s in the main channel.

Three sets of data,historical meteorological data,National Centers for Environmental Prediction(NCEP)/National Center for Atmospheric Research(NCAR)reanalysis data,and future GCM grid outputs,were used in this study to calculate and analyzeET0in the XJRB for the periods from 1961 to 2010 and from 2011 to 2100,respectively.

Historical meteorological data from 14 national meteorological stations were collected from the China Meteorological Data Sharing Service System,including daily observations of air temperature(T),relative humidity(RH),sunshine duration (SD),and wind speed(WS),for the period from 1960 to 2010. Daily records of the all climate variables were verifed by the China Meteorological Administration before delivery.Of the 14 meteorological stations,seven are inside the basin and seven are close to its boundary.The location and distribution of these meteorological stations are shown in Fig.1.All of these meteorological stations are less than 300 m above sea level,except for the Nanyue Station near the central XJRB,a station located near the famous and scenic Hengshan Mountain,which has an elevation of 1 265.9 m above sea level.Of these meteorological stations,11 are located in Hunan Province,two in Jiangxi Province,and one in Guangdong Province.

Fig.1.Location of meteorological stations in XJRB.

The gridded NCEP/NCAR reanalysis data from 1961 to 2010 were interpolated into station-scale predictors with the inverse distance weighted(IDW)method.Meanwhile,the corresponding GCMs were the second generation of the Canadian Earth System Model(CanESM2)from the CMIP5.Two emission scenarios were considered in this study:the RCP45 and RCP85 scenarios.The CMIP5 models are forced by graduallyincreasedradiativeforce,fnallystabilizingat4.5W/m2in the RCP45 scenario,which is as effective as a level of 850 ppm CO2-equivalent concentration by the year 2100,and 8.5 W/m2in the RCP85 scenario,which is as effective as a level of more than1 370ppmCO2-equivalentconcentrationbytheyear2100. The RCP45 scenario represents medium-low RCP,while the RCP85 scenario represents high RCP.The XJRB has played an important role in the economic development of Hunan Province,and its environmental condition is severely concerning.In the past few years,Hunan Province committed itself to creating more balanced economic growth and reducing the infuence of economic activities on the environment in the XJRB.Thus,the prediction offuture CO2levels in the XJRB could be based on a coordinated development of the economy and the environment.

2.2.FAO P-M method

The FAO P-M method is recommended as the sole standard method forET0calculation(Allen et al.,1998).The method is physically based and explicitly incorporates physiological and aerodynamic parameters(Xu et al.,2006).Its accuracy and reliability have been widely verifed under different climatologic conditions in numerous regions around the world, including China.Following the procedures in Allen et al. (1998),the FAO P-M method was used to calculate dailyET0in this study,and monthly and annualET0were the accumulation of dailyET0:

whereET0is reference evapotranspiration(mm),Δ is the slope of the saturated vapor pressure(kPa/°C),Rnis the net radiation at the crop surface(MJ/(m2·d)),Gis the soil heat fux density (MJ/(m2·d)),Tmeanis the mean daily air temperature at a height of 2 m(°C),u2is wind speed at a height of 2 m(m/s),es-eais the saturation vapor pressure defcit(kPa),and γ is the psychrometric constant(kPa/°C).

2.3.Statistical downscaling model

The SDSM is a coupling of the stochastic weather generator and regression algorithm promoted by Wilby et al.(2002). It frst helps users select the most representative large-scale climatic variables(the predictors)by testing the variability in the climate(the predictand)at a particular site using statistical models.Then,the climate variables are utilized to generate statistical relationships with daily observed data.This relationship can be applied to obtaining daily weather data for a future time period with the GCM-derived predictors.

Wilby and Dawson(2007)suggested that identifying empirical relationships between predictors,such as mean sea level pressure,and single site predictands,such as air temperature at stations,is central to all statistical downscaling methods and is often the most time-consuming step in the process(Wang et al.,2013).In this study,the screen variable function in SDSM 4.2 was used when appropriate predictors were being chosen for model calibration ofET0downscaling. As the atmospheric circulation features were not remarkably different in the XJRB,the predictors were chosen according to integrated consideration of correlation analysis results of three stations randomly picked from the 14 stations in the XJRB.

NCEP reanalysis data were used as the inputs to the SDSM to downscaleET0,the predictand in this study,in the calibration period from 1961 to 1990 to develop the regression model,and in the validation period from 1991 to 2003 to validate the model.Multiple regression models for each station were derived from the selected station-scale predictors and station-scale predictands(i.e.,ET0in this study).After calibration with data from 1961 to 1990 and validation with data from 1991 to 2003,the relationships between selected predictors andET0at each station were built.

2.4.Mann-Kendall test method

The rank-based nonparametric Mann-Kendall test method (Mann,1945;Kendall,1975;Hamed and Rao,1998),which has been widely used for trend detecting in climatic and hydrologic time series because it does not need to assume any distribution form for the data and has a similar effect to the parametric method(Burn and Elnur,2002;Bandyopadhyay et al.,2009;Gao et al.,2007;Ye et al.,2013),was applied to evaluate the signifcance of the trends inET0and other climatic factors.The equations of the Mann-Kendall test method are given below(Mann,1945;Kendall,1975)and the statisticSis calculated as wherexiandxjare the sequential data values,andnis the length of the data set.The statisticStends to be normally distributed for largen,with mathematical expectation and variance given by Hamed and Rao(1998):

wheretpis the number of ties for thepth value,andqis the number of tried values.Then,the standard statisticZ, following the standard normal distribution,is formulated as

The null hypothesis of no trend is rejected if

whereZis the standard normal variate,and α is the signifcance level for the test,taken to be 0.05 in this study.A positive value ofZindicates an increasing trend,whereas a negative value indicates a decreasing trend.Z1-α/2is the critical value ofZfrom the standard normal table,and,for the signifcance level of 5%,the value ofZ1-α/2is 1.96.

2.5.Performance assessment

Three statistics,i.e.,the relative error(Re),coeffcient of determination(R2),and Nash-Sutcliffe effciency coeffcient (NSE)(Nash and Sutcliffe,1970),were used to evaluate the performance of SDSM forET0downscaling.R2represents the strength of the relationship between the observed value and simulated value,andNSErefects how well the volume and timing of the simulated value ft with the observed value.The closer the value ofReis to 0 and the values ofR2andNSEare to 1,the more successfully the model performs.

3.Results and discussion

3.1.Spatial distribution of mean annual ET0during

period from 1961 to 2010

The spatial distribution of mean annualET0in the XJRB during the period from 1961 to 2010 is presented in Fig.2, interpolated by the IDW method using data from all 14 meteorological stations.The mean annualET0of the XJRB during the period from 1961 to 2010 is 1 060.3 mm,and the variation coeffcient for the mean annualET0of the 14 stations is 0.09,indicating a low spatial variation ofET0in the XJRB. The lowest mean annualET0appears at the Nanyue Station, located at the center of the basin,with a value of 768.1 mm,while the highest mean annualET0appears at the Lingling Station,located in the southwest part of the basin,with a value of 1 087.1 mm.From Fig.2,it can also be seen that the mean annualET0in the upper XJRB was higher than it was in the lower XJRB.In the upper XJRB,ET0increases from the southwest part of the basin(1 050 mm)to the southern part of the basin(1 080 mm)and then decreases from the southern part of the basin(1 080 mm)to the southeast part of the basin (1 020 mm).

Fig.2.Spatial distribution of mean annualET0in XJRB during period from 1961 to 2010(units:mm).

3.2.Temporal trend in annual ET0during period from

1961 to 2010

The spatial distribution of the temporal trend in annualET0at each station in the XJRB during the period from 1961 to 2010 is shown in Fig.3,while Fig.4(a)presents the linear trend of the mean annualET0of the XJRB during the period from 1961 to 2010.Downward trends in annualET0are detected at nine of the 14 meteorological stations,of which four show signifcant downward trends with α＜0.05.Five stations present upward trends,andonlyonestationshowsasignifcantupwardtrendwith α＜0.05.Stations with signifcant downward trends are mainly situated in the middle and eastern parts of the basin,with two such stations inside the XJRB,i.e.,the Shuangfeng Station with an annualET0change rate of-1.24 mm/year and the Chenzhou Station with an annualET0change rate of-1.36 mm/year,and two such stations outside the XJRB,i.e.,the Yichun Station with an annualET0change rate of-1.55 mm/year and the Lianxian Station with an annualET0change rate of-0.90 mm/year.A signifcant upward trend in annualET0occurs at the Shaoyang Station,locatedattheboundaryoftheXJRB,withanannualET0change rate of 0.89 mm/year.As for the mean annualET0of the XJRB,an insignifcant downward trend with a change rate of-0.33 mm/year is detected during the period from 1961 to 2010.

Fig.3.Temporal trend in annualET0at each station in XJRB during period from 1961 to 2010.

In order to explore the reasons for change inET0,the linear regression method was used to analyze the temporal trends in the related climatic factors(Figs.4(b)through(e)),including mean air temperature(Tmean),RH,SD,andWS.As shown in Figs.4(b)and(c),a signifcant upward trend inTmean,with a change rate of 0.018°C/year,and a signifcant downward trend inRH,with a change rate of-0.085%/year,are factors contributing to the upward trend in annualET0,while a signifcant downward trend inSD,with a change rate of -0.011 h/year,and a signifcant downward trend inWS,with a change rate of-0.015 m/(s·year),are factors contributing to the downward trend in annualET0.

As shown in Fig.4,signifcant changes in the mean annualET0of the XJRB andRHbegin in 2002.RHdecreases approximately by 10%(from 81.5%to 71.3%)from 2002 to 2004 and the mean annualET0of the XJRB increases by approximately 120 mm(from 946.6 mm to 1 065.4 mm)from 2002 to 2004.The leap of the mean annualET0of the XJRB in the period from 2002 to 2010 is affected by the increase ofTmeanandWSand the decrease ofRH.

Linear regression models were also established for annualET0and the climatic factors,and the values ofR2between annualET0and different climatic factors are listed in Table 1. Moreover,the signifcance test,thet-test,was performed on linear regression relationships between annualET0and the climatic factors,and the values of the signifcance level,α,are also listed in Table 1.

The correlation between annualET0andSDis signifcant with α＜0.02.Meanwhile,the value ofR2between annualET0andSDis the highest one of the fourR2values.Though theR2between annualET0andRHis the second highest,the correlation between annualET0andRHis insignifcant,with α＞0.50.As forTmeanandWS,the signifcance levels of correlations between annualET0andTmeanand annualET0andWSare both less than 0.50.However,the values ofR2between annualET0andTmeanand between annualET0andWSare quite low,indicating thatET0is more sensitive toSDin the XJRB for the period from 1961 to 2010.

Fig.4.Linear trend of mean annualET0of XJRB and climatic factors during period from 1961 to 2010.

3.3.Prediction of ET0during period from 2011 to 2100

3.3.1.Calibration and validation of SDSM for ET0

downscaling

In this study,the NCEP reanalysis data for the period from 1961 to 2010 were divided into two periods,and the data of the frst 30 years(1961-1990),which was determined to be the calibration period,were used to construct downscaling models.Based on the multiple regression equations between the selected predictors and predictand(ET0)for each station, the data of the later 20 years(1991-2010),which was determined to be the validation period,were used to validate the regression models.The performance of SDSM forET0downscaling could be evaluated through comparison with theET0series calculated by the FAO P-M method.

Six of the 26 predictors of the NCEP reanalysis data were selected as the inputs for the SDSM model;they are listed in Table 2 and are similar to the predictors used in previous studies(Li et al.,2012;Wang et al.,2013).Taking the Yueyang Station as an example,the correlations betweenET0and the six station-scale NCEP predictors are presented in Table 2.It can be seen that the absolute value of correlation coeffcients ranges from 0.56 to 0.75,indicating strongcorrelation between the six NCEP predictors andET0.Thus,it is feasible to downscaleET0with these predictors.

Table 1 Correlation between annualET0and climatic factors during period from 1961 to 2010.

Using predictors listed in Table 2,dailyET0during the period from 1961 to 2010 was downscaled.The performance of SDSM in dailyET0downscaling was evaluated,and it is presented in Table 3.The comparison of monthlyET0series calculated by the FAO P-M method(ET0-PM)and downscaled with the SDSM model(ET0-SDSM)in the validation period is shown in Fig.5.According to Table 3,Revalues in both calibration and validation periods were within the acceptable range,andR2andNSEare all above 0.84,demonstrating the strong applicability of the SDSM inET0downscaling in the study area.Thus,the established SDSM can be used to predict futureET0in the XJRB.

Table 2 Correlations between predictors andET0taking Yueyang Station as an example.

Table 3 Performance of SDSM in dailyET0downscaling.

Fig.5.Comparison ofET0-PM withET0-SDSM in validation period.

Fig.6.Prediction of mean annualET0of XJRB under RCP45 and RCP85 scenarios during period from 2011 to 2100.

3.3.2.Prediction of ET0during period from 2011 to 2100

Based on the calibration and validation of the SDSM model described above,ET0during the period from 2011 to 2100 in the XJRB was predicted.As shown in Fig.6,signifcant upward trends in the mean annualET0of the XJRB are detected under both the RCP45 and RCP85 scenarios.

Taking the calibration period(1961-1990)of SDSM model as a reference period,changes in the mean annualET0in three future periods,2011-2040,2041-2070,and 2071-2100,under the RCP45 and RCP85 scenarios are shown in Table 4.The mean annualET0of the XJRB in the reference period is 1 040.5 mm,andET0will increase by 9.8%,12.6%, and 15.6%under the RCP45 scenario,and 10.2%,19.1%,and 27.3% underthe RCP85 scenario in the periodsof 2011-2040,2041-2070,and 2071-2100,respectively.For the mean annualET0of the XJRB,the increasing of predicted value under the RCP85 scenario is greater than that under the RCP45 scenario.

Furthermore,the predicted variation ranges of the mean annualET0of the XJRB in three future periods are presented in Fig.7,where the dotted line in Fig.7 refers to the mean annualET0of the XJRB in the reference period.In the period from 2011 to 2040,the mean annualET0of the XJRB under the RCP45 scenario is similar to that under the RCP85 scenario.A difference occurs in the period from 2041 to 2070,when the meanannualET0oftheXJRBundertheRCP85scenario,witha change rate of 4.9%-37.6%as compared with the reference period,is clearly higher than that under the RCP45 scenario, with a change rate of 1.8%-22.6%as compared with the reference period.In the period from 2071 to 2100,the gap is further widened,because the RCP45 scenario represents medium-low RCP,and the RCP85 scenario represents high RCP.

Table 4 Change rates of future mean annualET0of XJRB under RCP45 and RCP85 scenarios compared with reference period.

Fig.7.Variation ranges of predicted mean annualET0for three periods under RCP45 and RCP85 scenarios.

4.Conclusions

In this study,the spatial distribution and temporal trend ofET0during the period from 1961 to 2010 were examined,and futureET0during the period from 2011 to 2100 was downscaled from CMIP5 outputs under two emission scenarios(the RCP45 and RCP85 scenarios)using the SDSM.The major conclusions are as follows:

(1)The mean annualET0of the XJRB is 1 060.3 mm during the period from 1961 to 2010,the lowest mean annualET0(768.1mm)isfoundnearthecenterofthebasin,andthehighest mean annualET0(1 087.1 mm)appears in the southwest part of the basin.Of the 14 meteorological stations selected in this study,downward trends in annualET0are detected at nine of them,four stations of which present signifcant downward trends.Fivestationspresentupwardtrends,butonlyoneofthem shows a signifcantupwardtrend.The spatialvariation ofET0is causedbythecombinedeffectoftherelatedclimaticfactors.As for the mean annualET0of the XJRB,an insignifcant downward trend with a change rate of-0.33 mm/year was detected during the period from 1961 to 2010.The temporal trend in annualET0is mainly caused by the signifcant downward trend inSDduring the period from 1991 to 2010.

(2)In both the calibration and validation periods,the SDSM performs well inET0downscaling in the study area. The comparison of monthlyET0series downscaled by the SDSM with that calculated by the FAO P-M method showsRe,R2,andNSEvalues of 0.10%,0.87,and 0.87 in the calibration period and 7.16%,0.86,and 0.84 in the validation period, respectively.In general,the SDSM can be used to predict futureET0in the XJRB.

(3)Using CMIP5 outputs,upward trends in annualET0were predicted under the RCP45 and RCP85 scenarios during the period from 2011 to 2100.The increase under the RCP85 scenario will be greater than that under the RCP45 scenario. Compared with the reference period(1961-1990),ET0will increase by 9.8%,12.6%,and 15.6%under the RCP45 scenario and 10.2%,19.1%,and 27.3%under the RCP85 scenario in the periods from 2011 to 2040,2041 to 2070,and 2071 to 2100,respectively.

Alan,D.Z.,Justin,S.,Edwin,P.M.,Bart,N.,Eric,F.W.,Dennis,P.L.,2003. Detection of intensifcation in global-and continental-scale hydrological cycles:Temporal scale of evaluation.J.Clim.16(3),535-547.http:// dx.doi.org/10.1175/1520-0442(2003)016＜0535:DOIIGA＞2.0.CO;2.

Allan,C.,Xia,J.,Pahl-Wostl,C.,2013.Climate change and water security: Challenges for adaptive water management.Curr.Opin.Environ.Sustain. 5(6),625-632.http://dx.doi.org/10.1016/j.cosust.2013.09.004.

Allen,M.R.,Ingram,W.J.,2002.Constraints on future changes in climate and the hydrologic cycle.Nature 419(6903),224-232.http://dx.doi.org/ 10.1038/nature01092.

Allen,R.G.,Pereira,L.S.,Raes,D.,1998.Crop evapotranspiration:Guidelines for computing crop water requirements.In:FAO Irrigation and Drainage Paper No.56.FAO,Rome.

Bandyopadhyay,A.,Bhadra,A.,Raghuwanshi,N.S.,Singh,R.,2009.Temporal trends in estimates of reference evapotranspiration over India.J. Hydrol. Eng. 14(5), 508-515. http://dx.doi.org/10.1061/(ASCE) HE.1943-5584.0000006.

Bates,B.,Kundzewicz,Z.W.,Wu,S.,Palutikof,J.,2008.Climate Change and Water.Intergovernmental Panel on Climate Change(IPCC),Geneva.

Burn,D.H.,Elnur,M.A.H.,2002.Detection of hydrologic trends and variability.J.Hydrol.255(1),107-122.http://dx.doi.org/10.1016/S0022-1694(01)00514-5.

Chu,J.T.,Xia,J.,Xu,C.Y.,Singh,V.P.,2010.Statistical downscaling of daily mean temperature,pan evaporation and precipitation for climate change scenarios in Haihe River,China.Theor.Appl.Climatol.99(1-2), 149-161.http://dx.doi.org/10.1007/s00704-009-0129-6.

Durack,P.J.,Wijffels,S.E.,Matear,R.J.,2012.Ocean salinities reveal strong global water cycle intensifcation during 1950 to 2000.Science 336(6080), 455-458.

Fisher,J.B.,Whittaker,R.J.,Malhi,Y.,2011.ET come home:Potential evapotranspiration in geographical ecology.Glob.Ecol.Biogeogr.20(1), 1-18.http://dx.doi.org/10.1111/j.1466-8238.2010.00578.x.

Fowler,H.J.,Wilby,R.L.,2007.Beyond the downscaling comparison study.Int.J.Climatol.27(12),1543-1545.http://dx.doi.org/10.1002/ joc.1616.

Gao,G.,Chen,D.,Xu,C.Y.,Simelton,E.,2007.Trend of estimated actual evapotranspiration over China during 1960-2002.J.Geophys.Res. 112(D11).http://dx.doi.org/10.1029/2006JD008010.

Gao,G.,Xu,C.Y.,Chen,D.L.,Singh,V.P.,2012.Spatial and temporal characteristics of actual evapotranspiration over Haihe River basin in China estimated by the complementary relationship and the Thornthwaite water balance model.Stoch.Environ.Res.Risk Assess.26(5),655-669.http:// dx.doi.org/10.1007/s00477-011-0525-1.

Hamed,K.H.,Rao,A.R.,1998.A modifed Mann-Kendall trend test for autocorrelated data.J.Hydrol.204(1-4),182-196.http://dx.doi.org/ 10.1016/S0022-1694(97)00125-X.

Intergovermental Panel on Climate Change(IPCC),2014.Climate Change 2014:Impacts,Adaptation,and Vulnerability.Cambridge University Press, New York.

Kendall,M.G.,1975.Rank Correlation Methods.Charles Griffn,London.

Li,Z.,Zheng,F.L.,Liu,W.Z.,2012.Spatiotemporal characteristics of reference evapotranspiration during 1961-2009 and its projected changes during 2011-2099 on the Loess Plateau of China.Agric.Forest Meteorol. 154-155(6),147-155.http://dx.doi.org/10.1016/j.agrformet.2011.10.019.

Liu,W.,Peng,S.,Wang,W.,2012.Reference evapotranspiration changes in the Haihe River basin during past 50 years.Procedia Eng.28(5),258-263. http://dx.doi.org/10.1016/j.proeng.2012.01.716.

Mann,H.B.,1945.Non-parametric tests against trend.Econometrica 13, 245-259.

Mitchell,M.,Curtis,A.,Sharp,E.,Mendham,E.,2012.Directions for social research to underpin improved groundwater management.J.Hydrol. 448(15),223-231.http://dx.doi.org/10.1016/j.jhydrol.2012.04.056.

Nash,J.,Sutcliffe,J.V.,1970.River fow forecasting through conceptual models,part I:A discussion of principles.J.Hydrol.10(3),282-290. http://dx.doi.org/10.1016/0022-1694(70)90255-6.

Piao,S.,Ciais,P.,Huang,Y.,Shen,Z.,Peng,S.,Li,J.,Fang,J.,2010.The impacts of climate change on water resources and agriculture in China. Nature 467(7311),43-51.http://dx.doi.org/10.1038/nature09364.

Wang,W.G.,Shao,Q.X.,Peng,S.Z.,Xing,W.Q.,Yang,T.,Luo,Y.F., Yong,B.,Xu,J.Z.,2012.Reference evapotranspiration change and thecauses across the Yellow River Basin during 1957-2008 and their spatial and seasonal differences.Water Resour.Res.48(5),W05530.http:// dx.doi.org/10.1029/2011WR010724.

Wang,W.G.,Xing,W.Q.,Shao,Q.X.,Yu,Z.B.,Peng,S.Z.,Yang,T.,Yong,B., Taylor,J.,Singh,V.P.,2013.Changes in reference evapotranspiration across the Tibetan Plateau:Observations and future projections based on statistical downscaling.J.Geophys.Res.Atmos.118(10),4049-4068. http://dx.doi.org/10.1002/jgrd.50393.

Wang,Y.,Jiang,T.,Bothe,O.,Fraedrich,K.,2007.Changes of pan evaporation and reference evapotranspiration in the Yangtze River basin.Theor.Appl. Climatol.90(1),13-23.http://dx.doi.org/10.1007/s00704-006-0276-y.

Wentz,F.J.,Ricciardulli,L.,Hilburn,K.,Mears,C.,2007.How much more rain will global warming bring?Science 317(5835),233-235.

Wilby,R.L.,Dawson,C.W.,Barrow,E.M.,2002.SDSM:A decision support tool for the assessment of regional climate change impacts.Environ. Model.Softw.17(2),145-157.http://dx.doi.org/10.1016/S13 64-8152(01) 00060-3.

Wilby,R.L.,Dawson,C.W.,2007.SDSM 4.2:A Decision Support Tool for the Assessment of Regional Climate Change Impacts,User Manual.Department of Geography,Lancaster University,Lancaster.

Xu,C.Y.,Singh,V.P.,2005.Evaluation of three complementary relationship evapotranspiration models by water balance approach to estimate actual regional evapotranspiration in different climatic regions.J.Hydrol.308, 105-121.http://dx.doi.org/10.1016/j.jhydrol.2004.10.024.

Xu,C.Y.,Widˊen,E.,Halldin,S.,2005.Modelling hydrological consequences of climate change-progress and challenges.Adv.Atmos.Sci.22(6), 789-797.http://dx.doi.org/10.1007/BF02918679.

Xu,C.Y.,Gong,L.B.,Jiang,T.,Chen,D.L.,Singh,V.P.,2006.Analysis of spatial distribution and temporal trend of reference evapotranspiration and pan evaporation in Changjiang(Yangtze River)catchment.J.Hydrol. 327(1-2),81-93.http://dx.doi.org/10.1016/j.jhydrol.2005.11.029.

Ye,X.C.,Li,X.H.,Liu,J.,Xu,C.Y.,Zhang,Q.,2013.Variation of reference evapotranspiration and its contributing climatic factors in the Poyang Lake catchment,China.Hydrol.Process.28(25),6151-6162.http://dx.doi.org/ 10.1002/hyp.10117.

Zhang,Q.,Xu,C.Y.,Chen,X.H.,2011.Reference evapotranspiration changes in China:Natural processes or human infuences?Theor.Appl.Climatol. 103(3-4),479-488.http://dx.doi.org/10.1007/s00704-010-0315-6.

Zhang,Z.,Chen,Y.,Wang,P.,Shuai,J.B.,Tao,F.L.,Shi,P.J.,2014.River discharge,land use change,and surface water quality in the Xiangjiang River,China.Hydrol.Process.28(13),4130-4140.http://dx.doi.org/ 10.1002/hyp.9938.

Zuo,D.P.,Xu,Z.X.,Yang,H.,Liu,X.C.,2012.Spatiotemporal variations and abrupt changes of potential evapotranspiration and its sensitivity to key meteorological variables in the Wei River basin,China.Hydrol.Process. 26(8),1149-1160.http://dx.doi.org/10.1002/hyp.8206.

Received 6 April 2015;accepted 9 September 2015

Available online 7 November 2015

This work was supported by the National Natural Science Foundation of China(Grants No.51339004 and 51279138).

*Corresponding author.

E-mail address:Chua@whu.edu.cn(Hua Chen).

Peer review under responsibility of Hohai University.

http://dx.doi.org/10.1016/j.wse.2015.11.002

1674-2370/©2015 Hohai University.Production and hosting by Elsevier B.V.This is an open access article under the CC BY-NC-ND license(http:// creativecommons.org/licenses/by-nc-nd/4.0/).