Variation of reference evapotranspiration and its contributing climatic factors in the Poyang Lake catchment, China

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1 HYDROLOGICAL PROCESSES Hydrol. Process. 28, (2014) Published online 2 December 2013 in Wiley Online Library (wileyonlinelibrary.com) DOI: /hyp Variation of reference evapotranspiration and its contributing climatic factors in the Poyang Lake catchment, China Xuchun Ye, 1,2 Xianghu Li, 2 Jian Liu, 3 Chong-Yu Xu 4,5 and Qi Zhang 2 * 1 School of Geographical Sciences, Southwest University, Chongqing , China 2 State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, CAS, Nanjing , China 3 Water Research Institute of Shandong Province, Jinan , China 4 Department of Geosciences, University of Oslo, Oslo, Norway 5 Department of Earth Sciences, Uppsala University, Uppsala, Sweden Abstract: By using linear regression (parametric), Mann Kendall (nonparametric) and attribution analysis methods, this study systematically analysed the changing properties of reference evapotranspiration (ETr) calculated using the Penman Monteith method over the Poyang Lake catchment during and investigated the contribution of major climatic variables to ETr changes and their temporal evolution. Generally, a significant decreasing trend of annual ETr is found in the catchment. The decrease of annual ETr in the Poyang Lake basin is mostly affected by the decline of summer ETr. Over the study period, climatic variables, i.e. sunshine duration (SD), relative humidity (RH), wind speed (WS) and vapour pressure all showed decreasing trends, whereas mean daily temperature (DT) increased significantly. Multivariate regression analysis indicated that SD is the most sensitive climatic variable to the variability of ETr on annual basis, followed by RH, WS and DT, whereas the effect of vapour pressure is obscure. Although recent warming trend and decrease of relative humidity over the catchment could have increased ETr, the combined effect of shortened SD and reduced WS negated the effect and caused significant decrease of ETr. Our investigation reveals that the relative contributions of climatic variables to ETr are temporally unstable and vary considerably with large fluctuation. In consideration of the changes of climatic variables over time, further analysis indicated that changes of mean annual ETr in were primarily affected by SD followed by WS, RH and DT with reference to 1960s. However, WS became the predominant factor during the period compared with reference period 1960s, and followed by SD. Copyright 2013 John Wiley & Sons, Ltd. KEY WORDS climate change; reference evapotranspiration; Penman Monteith method; multiple regression analysis; the Poyang Lake basin Received 14 August 2013; Accepted 11 November 2013 INTRODUCTION Global warming characterized by the increasing temperature has become a worldwide indisputable fact since the late 19th century. Rising global surface temperatures are likely to increase the water holding capacity and water vapour transport in the atmosphere, which in turn cause the changes in atmospheric circulation (Menzel and Bürger 2002; Bates et al., 2008). Evidence of the changes of precipitation, runoff and soil moisture suggests that the hydrological cycle has been accelerated in many parts of the world during the past century (e.g. Groisman et al., 1999; Alan et al., 2003; Gao et al., 2006, 2012; Huntington, 2006; Zhang et al., 2009a). Extreme climate *Correspondence to: Zhang Qi, State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, CAS, Nanjing , China. qzhang@niglas.ac.cn events, such as the frequency and severity of heat waves and very heavy precipitation, are expected to increase in recent decades and cause more and more severe floods and droughts (e.g. Bates et al., 2008; Déry et al., 2009; Thompson, 2012; Xiong et al., 2013). Actual evapotranspiration (ET), the sum of evaporation and plant transpiration, is one of the most active and complicated hydrological components that links water balance and land surface energy balance in an ecosystem (Xu and Singh, 2005, Xu et al., 2005). The process of ET governs the moisture transfer between soil and the atmosphere in a catchment and is heavily influenced by surface land-use changes and climate variations (Fisher et al., 2011). Reference evapotranspiration (ETr) is often used to estimate actual ET in water balance studies (e.g. Xu and Chen 2005). In a warming climate, ETr is expected to increase. However, contrary to the expectation, many studies have shown that observed pan evaporation and calculated ETr were declining in many Copyright 2013 John Wiley & Sons, Ltd.

2 6152 X. YE ET AL. places of the world (Peterson et al., 1995; Lawrimore and Peterson, 2000; Thomas, 2000; Roderick and Farquhar, 2002, Chen et al., 2005; Xu et al., 2006; Roderick et al., 2009; McVicar et al., 2012). This is known as evaporation paradox, a famous scientific problem that has been widely discussed. For the reasons of evaporation paradox, most studies concluded that the decreasing trend of solar radiation and wind speeds (WSs) would be the major causes, whereas temperature actually plays a lesser role (Thomas 2000; Gong et al., 2006; Xu et al., 2006; Fan and Thomas, 2013). Because of the complicated process of ET, our knowledge to the mechanism of decreasing ET is still limited. Studies from Roderick and Farquhar (2002) indicated that the decrease in pan evaporation was consistent with the observed decrease in sunshine hours, which resulted from the increasing cloudiness or aerosols concentration (global dimming). Also, Liu et al. (2004) suggested that the aerosol caused decrease in solar irradiance [sunshine duration (SD)] was most likely the driving force for the reduced pan evaporation in China. The observed decline of SD has partly reversed in the last decade (Wild et al., 2005), whereas terrestrial WS has been observed to decrease on a global scale (McVicar et al., 2012). In addition, humaninduced impacts such as influences from rapid urbanization, aerosol concentration on changes of evaporation have become more and more important. In China, several studies have reported that the trends of ETr decreased in most parts (Thomas 2000; Chen et al., 2006; Gao et al., 2006; Zhang et al., 2011a). Zhang et al. (2004) indicated that the decreasing net total radiation of major cities in the Yangtze River Delta region is mainly attributed to the increased air pollution, implying evident human influences on net solar radiation. Zhang et al. (2011a) pointed out that in the east and south China, urbanization greatly influences the ETr by directly decreasing net solar radiation. However, there is no conclusive evidence for the decreasing WS in the rapidly growing urban areas of China (Guo et al., 2011). The Yangtze River basin belongs to the subtropical and temperate climate zone, which is mainly affected by the East Asia Monsoon climate in summer. The advance and retreat of the monsoons determine to a large degree the timing of the rainy season and the amount of rainfall throughout the basin (Xu et al., 2006). Previous studies demonstrated that the trends of temperature, precipitation, storm and ET have changed obviously in the basin under the background of global warming (e.g. Jiang et al., 2005; Su et al., 2005; Xu et al., 2006). The Poyang Lake catchment, located on the south bank in the middle reaches of the Yangtze River is one of the most sensitive regions of climate change in the Yangtze River basin. In recent decades, increasing frequency and severity of droughts and floods has occurred in the catchment (Wang et al., 2008; Min et al., 2011). These extreme hydrological events have raised wide concerns for the lake ecology and water resources management in the Yangtze River basin. Studies on hydrological response have revealed that the changes of annual streamflow in the catchment were primarily caused by the changes of climate anomalies in the Yangtze River catchment (e.g. Guo et al., 2008; Zhao et al., 2010; Ye et al., 2013). Hu and Feng (2001) pointed out that the increase of warm season rainfall in the regions south of the Yangtze River and the Poyang Lake basin in 1990s could be a consequence of the southward shift of the major rain bands in eastern China and increase of precipitation intensity. Therefore, there is no doubt that the change of regional climate has exerted tremendous influences on local hydrological cycle. Variation of ETr means the changes of regional climatic variables and therefore changes in atmospheric water demand. In-depth investigation on spatio-temporal variation of ETr changes is essential for the improved understanding of the mechanism of climate change impacts on hydrological cycle. By conducting the sensitivity analysis, Xu et al. (2006) suggested that the most important predictor for the decreasing trend in the ETr and pan evaporation in the Yangtze River basin is net total radiation followed by WS. However, they did not identify the specific relationship between ETr and its driving climatic variables. Some studies investigated the relative contribution of climatic variables to ETr (e.g. Zhang et al., 2009b, 2011a,2011b) but neglected any temporal changes. Recent study from Fan and Thomas (2013) indicated that temporal evolution of contribution of climatic variables was unstable in Yunnan Province, southwest China, and they proposed to further analyse the temporal evolution of climatic contributions to ETr over a wide range of climates and locations. Because of strong spatial heterogeneity in the Yangtze River basin, influence of climatic variables on ETr may vary in different regions. There is a need to better understand and quantify the contribution of climatic variables to the spatio-temporial changes of ETr in the Poyang Lake catchment, which partly motivated this study. This study was conducted in the context of an on-going project to investigate the changing water balance across the Poyang Lake catchment and the mechanism of droughts and floods occurred in the lake area. The objectives of this study are as follows: (1) to evaluate temporal trends of ETr and major climatic variables in the Poyang Lake catchment, (2) to explore the quantitative relationship between ETr and the influencing climatic variables, and (3) to identify the contributions of major climatic variables to ETr and their temporal evolution. We believe that this study will help to determine the importance of contributing factors to the change of ETr and to achieve a better understanding of the climate change impacts on hydrological cycle under global warming.

3 VARIATION OF ETR AND ITS CONTRIBUTING CLIMATIC FACTORS STUDY REGION The Poyang Lake catchment is located in the middle reaches of Yangtze River, covering an area of km2. The catchment is surrounded by a series of low mountains and hills in east, south and west. The largest fresh water lake in China Poyang Lake in the catchment is fed by the five primary rivers: Ganjiang River, Xiushui River, Fuhe River, Raohe River and Xinjiang River, and discharges into the Yangtze River from a narrow outlet in the north (Figure 1). Topography of the Poyang Lake catchment varies from mountainous regions (maximum elevation of about 2200 m a.s.l.) to alluvial plains in the lower reaches of the primary watercourses. The catchment belongs to a subtropical weather predominated by East Asia Monsoon. Monthly precipitation in the catchment shows a wet and a dry season in a year with short transition period in between (Figure 1). Forty-five percent of annual precipitation is concentrated in the wet season from April to June, but rainfall decreases sharply from July to September. After September, the dry season sets in and lasts through December. Seasonal pattern of ETr is different from that of precipitation with a relative symmetric distribution around July. ETr increases slowly from January to July and decreases faster after that. Obviously, ETr is larger than precipitation during July to October, usually causing severe agricultural droughts in these months, especially in September and October DATA AND METHODOLOGY Meteorological data In this study, meteorological data from 15 weather stations inside the catchment were obtained from National Climate Centre of China Meteorological Administration, all of which are standard national weather stations. Altitudes for these stations are mainly below 300 m a.s.l. except for Lushan in the north, a station located at the famous scenic mountain has an altitude of 1164 m a.s.l. Specific location and their landscape are shown in Figure 1. These stations provide daily observations of maximum and minimum temperature and daily mean air temperature at 2 m height above the ground, relative humidity (RH), SD, WS and vapour pressure (VP) covering the period of Daily records of the entire climate variables had been quality controlled by China Meteorological Administration before delivery, and there is no missing data on the variables. Penman Monteith method The Penman Monteith method has been regarded as a global standard method for computation of ETr by Food and Agriculture Organization of the United Nations (FAO) (Allen et al. 1998). The method was applied in this study because it is physically based and explicitly incorporates both physiological and aerodynamic parameters. In this method, the calculation of ETr is given as ETr ¼ 0:409ΔðRn GÞ þ γ T a900 þ273 u2 ðes ea Þ Δ þ γð1 þ 0:34u2 Þ (1) where ETr is reference evapotranspiration (mm day 1), Rn is net radiation at the crop surface (MJm 2 day 1), G is the soil heat flux density (MJm 2 day 1), Ta is mean daily air temperature at 2 m height ( C), u2 is WS at 2 m height (ms 1), es is saturation VP (kpa), ea is actual VP (kpa), es ea the saturation VP deficit (kpa), Δ is slope of the VP (kpa C 1) and γ is psychrometric constant (kpa C 1). The Penman Monteith method has been widely introduced with good details in previous studies in different regions (e.g. Gong et al., 2006; Xu et al., 2006; Hosseinzadeh Talaee et al., 2013; Ngongondo et al., 2013). The computation procedure can be found in Chapter 3 of the FAO paper 56 (Allen et al. 1998). Trend analysis Figure 1. Sketch map of the Poyang Lake catchment with distribution of 15 meteorological stations Copyright 2013 John Wiley & Sons, Ltd. An ordinary linear regression model in the form of ŷ = αt + β is used to estimate the rate of change α, with t as the time (year), ŷ being the annual or seasonal ETr and other climatic variables. Significance of the trends of the climatic series is evaluated by the Mann Kendall trend test technique (MK test). The MK test is a rank-based nonparametric method, Hydrol. Process. 28, (2014)

4 6154 X. YE ET AL. which has been widely applied for trend detecting in hydro-climatic time series because of its robustness against the influence of abnormal data and especially its reliability for biased variables (e.g. Burn and Hag Elnur 2002; Chen et al. 2007; Zhang et al. 2009a,2009c; Li et al. 2013a,2013b). The adoption of MK trend test is started from the calculation of the statistic: where S ¼ Xn 1 X n i¼1 j¼iþ1 sgn x j x i 8 >< þ1 x j > x i sgn x j x i ¼ 0 x j ¼ x i >: 1 x j < x i where x i and x j are the sequential data values and n is the length of the data set. The statistics S is approximately normally distributed when n 8, with the mean and the variance as follows: (2) (3) ES ðþ¼0 (4) " # VS ðþ¼ nn ð 1Þð2n þ 5Þ Xn t i ii ð 1Þð2i þ 5Þ =18 where t is the extent of any given time. The standardized statistics (Z) for one-tailed test is formulated as 8 p >< ðs 1Þ= ffiffiffiffiffiffiffiffiffiffiffiffiffi varðþ S ðs > 0Þ Z ¼ 0 ðs ¼ 0Þ ðs þ 1Þ= ffiffiffiffiffiffiffiffiffiffiffiffiffi (6) >: p varðþ S ðs < 0Þ The null hypothesis of no trend is rejected if Z > 1.96 at the 0.05 significance level and rejected if Z > 2.32 at the 0.01 significance level. A positive value of Z denotes an increasing trend, and a negative value corresponds to a decreasing trend. Because the presence of serial and cross correlations can influence the identification of trends (Yue et al., 2003; Khaliq et al., 2009), autocorrelation (serial correlation) was examined through the autocorrelation and partial autocorrelation function for all the meteorological data before trend analysis. Attribution analysis The general purpose of multiple regression is to learn more about the relationship between several independent or predictor variables and a dependent or criterion variable (Haan, 1977), and it can be used to determine the relative i¼1 (5) importance of different independent variables in explaining the dependent variable (e.g. Clow, 2010; Myoung et al., 2011; Tang et al., 2011; Zhang et al., 2011b). In this study, the multiple regression analysis was used to evaluate the effects of climatic variables on ETr. Before application of this method, normalization of original data of ETr and climatic variables is performed using Equation (7): X is ¼ x i x min (7) x max x min where X is is normalized variable, x i is the value of the sequential data and x min and x max are the minimum and maximum values of the sequential data. The relationship between normalized ETr and normalized climatic variables was then analysed by stepwise multiple regression, with normalized ETr as dependent variable and normalized climatic variables as the predictors (independent variables). The general formula of multiple regression is shown as follows: Y s ¼ ax 1s þ bx 2s þ cx 3s þ (8) where Y s is normalized dependent variable of ETr; X 1s, X 2s, X 3s, are normalized predictors of climatic variables; and a, b and c are regression coefficients. In this study, the candidate variables were selected into the model based on 0.05 significance level, and the significance level for a predictor to be removed from the model was 0.1. F-test is used to test the overall significance of a multiple regression model as reflected by the ratio of R-square values (coefficient of determination) (Haan, 1977). On the basis of the regression coefficients, relative contribution rate of each predictor X is in explaining Y s can be estimated as jj a η 1 ¼ (9) jjþb a jjþc jjþ Actual contribution rate is given as η 2 ¼ a ΔX 1s (10) ΔY s where ΔX 1s is the change of X 1s and ΔY s is the change of Y s. RESULTS Trends of reference evapotranspiration Mean annual ETr of the Poyang Lake catchment during the period is 1053 mm with a range from 940 to 1209 mm. As shown in Figure 2a, variation of annual ETr shows a long-term decreasing trend with change rate

5 VARIATION OF ETR AND ITS CONTRIBUTING CLIMATIC FACTORS 6155 Figure 2. Linear trends and area-averaged curve of annual and seasonal reference evapotranspiration over the Poyang Lake catchment during of mm per decade. The decreasing trend of ETr was almost monotonic before 2002 but increased obviously after that. Seasonal ETr of the Poyang Lake catchment declined except in the spring season (Figures 2b e). ETr in spring shows a long-term increasing trend with change rate of 1.52 mm per decade. The annual variation of ETr in spring shows relative larger fluctuation before 1975 than after that. Seasonal ETr in summer, autumn and winter all decreased during the study period, with the largest change rate in summer ( 9.64 mm per decade) and the least in winter ( 1.71 mm per decade). Among the three seasons, only the decreasing trend in summer was statistically significant (p < 0.01) (Table I). Particularly obvious was a distinct minimum of ETr in summer around 1997, whereas the decreasing trends of ETr in autumn and winter were almost monotonic during the study period. In consideration of change rate, it can be concluded that the decrease of annual ETr in the Poyang Lake catchment is mostly affected by the decline of ETr in summer. Spatially, Ganzhou station has the highest annual ETr rate of ~1144 mm followed by Nanchang station. Lushan in the north, the famous scenic mountain in China shows the lowest annual ETr of ~890 mm in the catchment. Generally, the discrepancy of ETr across the catchment is not big except for Lushan station; the difference of trends of annual ETr at all the metero-stations is also not evident. As shown in Figure 3, the annual ETr at all metero-stations Table I. The Z-value of Mann Kendall trend test for the five climatic variables and calculated reference evapotranspiration on seasonal and annual basis DT WS SD RH VP ETr Annual 3.55** 7.97** 4.44** ** Spring 2.65** 7.70** ** 2.56** 1.03 Summer ** 4.37** ** Autumn ** Winter 2.42** 6.88** 2.72** , delineates negative trends based on the MK test; DT, daily temperature ( C); WS, wind speed (m s 1 ); SD, sunshine duration (h); RH, relative humidity (%); VP, vapour pressure (kpa); ETr, reference evapotranspiration (mm y 1 ). *Delineate significance at **Delineate significance at shows decreasing trends, among which nine stations with significant decreasing trends (p < 0.05, i.e. Z < 1.96 in the Figure 3) are mainly located in the central, southwest and northeast parts of the Poyang Lake basin, and the other six stations with decreasing but not significant trends are located in northwest of the basin and some mountains regions in the east and southwest. Trends of climatic variables Area-averaged time series of the five climatic variables were analysed to investigate the variation of

6 6156 X. YE ET AL. Figure 3. Spatial distribution of annual trends of reference evapotranspiration during regional climate in the Poyang Lake catchment, which include mean daily temperature (DT), WS, SD, RH and VP. Linear trends and area-averaged curve of these climatic variables during the study period are shown in Figure 4. Over the past 49 years ( ), the variation of DT experienced an obvious decrease before 1984 and rapid increase after then, especially from the end of 1990s (Figure 4a). This may be related to the intensified global warming and rapid development of local industry. The increasing rate of DT is about 0.16 C per decade. The decrease of WS was almost monotonic during the study period with a change rate of 0.19 m s 1 per decade (Figure 4b). The decline rate of WS is particularly obvious in 1990s. SD shows a stable decrease of 0.2 h per decade (Figure 4c). Generally, the decrease of RH is about 0.31% per decade, but the annual variation of RH keeps stable until 2002 and then a step change occurred in recent years (Figure 4d). A slight decrease of 0.01 kp per decade for VP is found during the study period (Figure 4e). In order to evaluate the significance of variation of the climatic series, annual and seasonal trends were further Figure 4. Linear trends and area-averaged curve of meteorological variables during

7 VARIATION OF ETR AND ITS CONTRIBUTING CLIMATIC FACTORS 6157 analysed by the MK test. Results of the trend test for the five variables are displayed in Table I. It is seen that on annual basis, with the exception of DT, which shows an increasing trend, averaged WS, SD, RH and VP all show negative trends. Among which, DT, WS and SD have undergone significant trends at 0.01 significance level. On seasonal basis, statistically significant (0.01) positive trends of DT were detected for spring and winter; however, the positive trends in summer and autumn are not significant. Among the five climatic variables, WS is the only one which decreased significantly in all seasons. In summer and winter, SD decreased significantly during , however, the negative trends in spring and autumn are not significant. Both RH and VP decreased significantly in spring, but the trends in other seasons are not significant. Impact of climatic variables on reference evapotranspiration Reference evapotranspiration is a kind of measure of the evaporative demand of the atmosphere and is affected mainly by climatic factors (Zhang et al., 2011a; Ngongondo et al., 2013). This section presents the results of stepwise multiple regression analysis with ETr as dependent variable and other climatic variables as independent variables. Annual and seasonal regression coefficients of the five climatic variables are shown in Table II. It can be easily seen from the table that among the five climatic variables, VP is the only variable that was not entered into the regression model. This result indicates that the four variables DT, WS, SD and RH are the general factors influencing the variation of ETr at annual and seasonal scale in the catchment, whereas VP is less important. Very high R 2 value indicates good performance of these regression models. Among the four climatic variables that entered into the models, with the exception of RH, regression coefficients of the other three variables are greater than 0, which means, as expected, that RH is the only variable that shows negative correlation with ETr. In consideration of Table II. Standardized stepwise regression coefficients for climatic variables at catchment scale Catchment average Coefficient DT WS SD RH VP Intercept R 2 Annual Spring Summer Autumn Winter DT, daily temperature; WS, wind speed; SD, sunshine duration; RH, relative humidity; VP, vapour pressure. trend test of the climatic variables (Table II), the significant declines of WS and SD are responsible for the decrease of annual ETr, but the increase of DT and decrease of RH play a complementary role of enhancing ETr. Only in spring season, the increase of ETr is mainly attributed to the effect of increase of DT and decrease of RH. Values of the regression coefficients further indicate that SD is the most sensitive climatic variable in influencing ETr on annual basis, followed by RH, WS and DT. Little changes of the relative importance of these climatic variables were found on seasonal basis. In order to investigate the spatial difference of the impacts of climatic variables on ETr, the same method of stepwise multiple regression analysis for each climatic station across the catchment was used. As shown in Table III, SD and RH at all the stations were entered into the regression models, but WS at Lushan, DT and WS at Nancheng were not included at 0.01 significant level. VP did not enter into the regression model at all the stations, and the result is not shown in the table. This result indicates that among the four climatic variables, only the changes of DT and WS are not important to ETr at certain areas of the catchment. Contribution of climatic variables to reference evapotranspiration According to the standardized stepwise regression coefficients for climatic variables, relative contribution of these climatic variables on the change of ETr can be calculated by using Equation (9). Statistical result indicates that the relative contributions of DT, WS, SD and RH on the change of annual ETr are 18.3%, 19.6%, 33.4% and Station Table III. Standardized stepwise regression coefficient for climatic variables of each station across the catchment Coefficient DT WS SD RH Intercept R 2 Boyang Ganzhou Guangchang Guixi Ji an Jingdezhen Lushan Nanchang Nancheng Suichuan Xiushui Xunwu Yichuan Yushan DT, daily temperature; WS, wind speed; SD, sunshine duration; RH, relative humidity.

8 6158 X. YE ET AL. Figure 5. Annual and seasonal relative contribution of the climatic variables to reference evapotranspiration 28.7%, respectively. As shown in Figure 5, when individual seasons were analysed, it is clear that SD shows the highest contribution during spring and summer seasons, especially in summer season its contribution rate is over 52%. Although RH becomes the highest contribution variable in autumn and winter seasons, all the variables show a relatively similar level during autumn season. The relative contribution of DT increased greatly in the winter season. WS always shows the least contribution in all seasons. A visual inspection of the spatial distribution in Figure 6 shows that the relative contribution of SD is particularly high in northwest and southeast parts of the catchment. Beside this, no conclusive patterns can be found in the figure. In order to analyse the temporal evolution of relative importance of climatic variables contributing to ETr, we further performed multiple regression for the moving windows of 10 years width ( , , ,, ) by using the four climatic variables as independent variables, which produced a time series of relative contribution for the whole catchment (Figure 7). It is obvious that the relative contribution of the driving climatic variables to ETr changed over time. Relative contributions of SD and RH show almost opposite fluctuation at decadal scale during the study period with large range of % and %, respectively. Both the contribution of DT and WS are very low before 1985; however, their contributions increase substantially during 1990s. Although evaluation of relative contribution of climatic variables is important in revealing the sensitivity of driving factors on the change of ETr, actual contributions are sometimes expected according to the changes of climatic variables. By using the regional climate condition of 1960s as the reference baseline period, actual contributions of climatic variables in other decades were analysed on the basis of the actual changes of climatic variables and ETr, and the results are shown in Table IV. It is clear that ETr in all the other decades decreased compared with 1960s, especially in 1990s. Changes of mean annual ETr in were primarily affected by SD followed by WS, RH and DT with reference to 1960s. The actual contributions of climatic variables varied during different decades. During the periods of 1970s and 1980s, actual contributions of the four climatic variables are all positive, with SD being the largest. A distinct decrease of ETr is found in 1990s, in addition to the primary contribution of decreased SD, the effect of WS became obvious. However, the actual contribution of DT is negative during 1990s due to increased temperature reference to 1960s. The actual contribution of WS became the largest during , which is up to 99.0%, and followed by SD (71.1%). The actual contributions of DT and RH are negative compared with the decrease of ETr during this period, that is because obvious increase of DT and decrease of RH. Generally, our analysis of actual contribution indicates that the most important climatic variables on the change of ETr are SD and WS, and then RH and DT, which is a little different from their relative contribution. Figure 6. Spatial patterns of relative contribution of climatic variables to reference evapotranspiration Figure 7. Temporal evolution of relative contribution of climatic variables to reference evapotranspiration

9 VARIATION OF ETR AND ITS CONTRIBUTING CLIMATIC FACTORS 6159 Table IV. Changes and actual contribution of climatic variables to reference evapotranspiration in reference to Period Δ ETr (change) Change (Δ)/actual contribution [η 2 (%)] DT WS SD RH / / / / / / / / / / / / / / / / / / / /7.6 DT, daily temperature; WS, wind speed; SD, sunshine duration; RH, relative humidity; ETr, reference evapotranspiration. DISCUSSIONS Like most parts of China (Thomas 2000; Chen et al., 2006; Zhang et al., 2011a), Poyang Lake catchment shows significant decreasing trend of ETr over the past decades. Our analysis suggests that the reduced SD (radiation) was the leading factor for ETr decrease at most stations in the catchment. This is consistent with the findings of general decrease in pan evaporation in Northern Hemisphere associated with observed widespread decreases in sunlight due to increasing cloud coverage and aerosol concentration during the past 50 years (Roderick and Farquhar, 2002; Yin et al., 2010). Du et al. (2007) indicated that the decrease of annual and summer SD in Tibet during was mainly related to the increase of atmospheric water VP and precipitation. However, the major influencing factors to changes of ETr are different in those Western Plateaus and northwest of China. Zhang et al. (2009b) concluded that WS predominated the changes of ETr in the north of the Qinghai-Tibetan Plateau, whereas radiation was the leading factor in the southeast. A comparative study conducted in the Aksu River basin in Xinjiang Province, northwest China indicated that RH is the primary driver of ETr in high altitude area, but WSs predominated in other areas (Zhang et al., 2011b). Zhang et al. (2011a) found that in the regions east of 100 E net total solar radiation is the main cause of decreasing ETr rates, whereas RH is recognized as the most important variable for ETr in northwest China. For the whole Poyang Lake catchment, the decreasing trends of ETr are somewhat different at the 15 stations. Major reasons for this lie in the differences in regional climate due to different geographical location and topography and the different intensities of human activities, such as land-use/land-cover change and urbanization in such a large scale catchment. Changes of ETr are the integrated consequences of more than one influencing factors. On annual basis, the integrated negative effect from the decreasing SD and WS is much larger than the positive effect from the increasing DT and decreasing RH, which leads to the significant decrease of ETr. However, contrary to other seasons, significant decrease of RH plays a more important role for the increasing trend of ETr in spring. Although temperature is often seen as the primary driver of evapotranspiration changes (IPCC 2007), it is not a decisive factor for the ETr change in the Poyang Lake catchment, even though DT has increased significantly over the past 50 years. Generally, relative contribution analysis indicates that SD among the climatic variables is the most sensitive climatic variable in influencing ETr on annual basis, followed by RH, WS and DT. The so called evaporation paradox just considers the effect of DT but neglects the sensitivity and changes of other relevant climatic variables that accompanied with the increasing temperature. Spatial discrepancy of relative contribution of climatic variables to ETr across the catchment is mainly caused by the differences in regional climate and human activities. Relative contributions of climatic variables to ETr are temporally unstable and vary considerably. Our investigation indicates that relative contributions of SD and RH in the Poyang Lake catchment show large fluctuation but almost opposite direction during the study period. The reason for this is that rainfall-runoff characteristics in the catchment present periodic variation features at decade scale, which was reported in previous studies (e.g. Liu et al., 2009; Ye et al., 2012). Because air humidity is closely related to the amount of precipitation, while SD will be shortened due to the increase of cloudiness when more precipitation happened. The relative contribution of DT and WS increases substantially during 1990s should be attributed to the quick change of these variables in the period. In consideration of the changes of climatic variables, actual contribution rates of these variables may vary in different periods. In reference to 1960s, changes of mean annual ETr of the catchment in were primarily affected by SD and followed by WS, RH and DT. Although SD still ranked as the most important variable, actual contribution of WS is bigger than RH and DT compared with their relative contributions. Consistent with the global decrease of terrestrial WS (McVicar et al., 2012),

10 6160 X. YE ET AL. significant decrease of WS was found in the Poyang Lake catchment during the study period. Wang et al. (2004) attributed the significant decline of surface WS in China during the past 50 years to a weakening of winter and summer monsoon, and this attribution in the Poyang Lake catchment needs to be further studied and confirmed, whereas Xu et al. (2006) found that local land-cover change was the primary cause for decreasing WSs in China. From the perspective of this, the rapid increase of forest coverage in the Poyang Lake catchment (Ye et al., 2013), especially since 1990s would be responsible for the significant decrease of WS after that; however, more evidence needs to be identified. As an important water resource and iconic ecosystem in a region, changes of ETr in the Poyang Lake catchment would have special impact on local actual ET processes, which determine the water use efficiency (Tomer and Schilling, 2009). Because the Poyang Lake catchment belongs to a wet climate, annual precipitation is much larger than annual ET. Therefore, variability of annual ET is mainly controlled by the changes of ETr, a typical phenomenon of energy limited region. Preliminary results from previous studies (e.g. Liu et al., 2010; Wang et al., 2010; Ye et al., 2013) revealed that annual actual ET in the Poyang Lake catchment shows a long-term decreasing trend during the past decades, which is consistent with the changing trend of ETr. Issues on how the changes of annual and seasonal ETr would relate to actual ET and consequently affect the water balance of the catchment were not made in this paper and left to future study. CONCLUSIONS In this study, the spatio-temporal changes of ETr calculated using the Penman Monteith method was examined over the Poyang Lake catchment during by using linear regression (parametric), Mann Kendall (nonparametric) and attribution methods. Impact of climatic variables, i.e. DT, WS, SD, RH and VP on the change of ETr was thoroughly investigated. Some interesting conclusions are obtained as follows: 1. Significant decreasing trend of annual ETr was found in the Poyang Lake catchment over the period The decrease of annual ETr in the Poyang Lake basin is mostly affected by the decline of summer ETr. 2. Climatic variables DT, WS, SD and RH are the general factors in influencing the variation of ETr at annual and seasonal scale of the catchment, whereas the effect of VP is obscure. Analysis on relative contribution of climatic variables to the change of ETr suggested that SD is the most sensitive factor, followed by RH, WS and DT. 3. The study revealed that relative contributions of climatic variables to ETr are temporally unstable and vary considerably, which can be linked to their observed inter-annual variations. The quick change of DT and WS during 1990s should be responsible to the substantially increase of their relative contributions in this period. 4. In consideration of the changes of climatic variables in the catchment, actual contributions to the changes of ETr show little difference from their relative effects. This study indicates that changes of mean annual ETr in were primarily affected by SD followed by WS, RH and DT with reference to 1960s. However, WS became the predominant factor during the period compared with reference period 1960s and followed by SD. 5. The results confirmed the evidence from a large number of studies that the current warming of atmosphere will not automatically lead to increased ETr as claimed by the IPCC (2007). In the Poyang Lake catchment, the combined effect of shortened SD and reduced WS negated the effect of warming temperature and decreasing RH and consequently caused significant decrease of ETr. 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