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Analysis and prediction of reference evapotranspiration with climate change in Xiangjiang River Basin, China
Accepted Manuscript Analysis and prediction of reference evapotranspiration with climate change in Xiangjiang River Basin, China Xin-e Tao, Hua Chen, Chong-yu Xu, Yu-kun Hou, Meng-xuan Jie PII:
S1674-2370(15)00084-8
DOI:
10.1016/j.wse.2015.11.002
Reference:
WSE 38
To appear in:
Water Science and Engineering
Please cite this article as: Tao, X.-e, Chen, H., Xu, C.-y., Hou, Y.-k., Jie, M.-x., Analysis and prediction of reference evapotranspiration with climate change in Xiangjiang River Basin, China, Water Science and Engineering (2015), doi: 10.1016/j.wse.2015.11.002. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
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Analysis and prediction of reference evapotranspiration with climate change in Xiangjiang River Basin, China1 Xin-e Taoa, Hua Chena,*, Chong-yu Xua, b, Yu-kun Houa, Meng-xuan Jiea State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan 430072, China b Department of Geosciences, University of Oslo, Oslo N-0316, Norway Received 6 April 2015; accepted 9 September 2015
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Abstract
1. Introduction
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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 of ET0 in the Xiangjiang River Basin (XJRB) in China were analyzed. ET0 during the period from 1961 to 2010 was calculated with historical meteorological data using the FAO Penman-Monteith (FAO P-M) method, while ET0 during 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 of ET0 were interpreted with the inverse distance weighted (IDW) method and Mann-Kendall test method, respectively. Results show that: (1) the mean annual ET0 of 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 present ET0 and can be used to predict the future ET0 in the XJRB; and (3) CMIP5 predicts upward trends in annual ET0 under the RCP45 and RCP85 scenarios during the period from 2011 to 2100. Compared with the reference period (1961 to 1990), ET0 increases 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 increasing ET0 under the RCP85 scenario is greater than that under the RCP45 scenario during the period from 2011 to 2100. Key words: Reference evapotranspiration ( ET0 ); Spatial-temporal variation; Climate change; Statistical downscaling; Xiangjiang River Basin
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According to the Fifth 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). Rising temperature 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 flow, evapotranspiration, and soil moisture indicate that the hydrological cycle has been intensified 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, which also play key roles in the energy budget in the earth-atmosphere 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 significant 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
This work was supported by the National Natural Science Foundation of China (Grants No. 51190094, 51339004, and 51279138). * Corresponding author Email address: [email protected] (Hua Chen)
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and atmosphere in a watershed and is heavily influenced 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 difficult 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 estimate ETa in most cases, such as scheduling irrigation, managing water resources, analyzing regional climate humidity conditions, and providing information for hydrological, agricultural, and environmental models. ET0 is defined as the rate of evapotranspiration from a hypothetical crop with an assumed crop height of 0.12 m, a fixed 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 affecting ET0 are climatic factors (Allen et al., 1998). Therefore, ET0 is 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 of ET0 , 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 of ET0 based on historical data and identifying the main climatic factors affecting ET0 (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 of ET0 . General circulation models (GCMs) are used to predict future changes in ET0 . 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 definite 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 first 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; ET0 downscaling 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 of ET0 on the Loess Plateau in China, found that ET0 significantly increased during the period from 1961 to 2009 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 after 2050. Li et al. (2012) suggested that the increasing trend in ET0 could possibly influence water resources on the Loess Plateau in the 21st century and some countermeasures should be taken to reduce the adverse impacts. Wang et al. (2013) investigated the spatial and seasonal changes in ET0 on the Tibetan Plateau and determined that, considering its spatial-temporal variation, an average ET0 series presented a zigzag pattern (increasing–decreasing–increasing) with two joint points in 1973 and 1993, and predicted a continuous increasing trend in ET0 in the 21st century. In the Yangtze River Basin, there have already been studies related to ET0 . For example,
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2. Materials and methods 2.1 Study area and data
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Gao et al. (2012) detected a decreasing trend in ET0 , caused mainly by a significant decrease of the net total radiation and secondarily by a significant decrease of the wind speed over the basin. Ye et al. (2013) analyzed the variation of ET0 and its affecting factors in the Poyang Lake Catchment. However, there has been no prediction of ET0 in 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 the Fifth Assessment Report of IPCC were used in this study for ET0 prediction, specifically 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 of ET0 in the XJRB during the period from 1961 to 2010, (2) to investigate the adaptability of SDSM for ET0 downscaling in the study area, and (3) to predict future ET0 during the period from 2011 to 2100 with the SDSM under the RCP45 and RCP85 scenarios, in order to study how ET0 will change in the XJRB in the 21st century.
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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, flows 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 to 19°C. In addition, the basin has an average annual rainfall of 1 200 to 1 700 mm, most of which falls from April to July, and a large variability in runoff, with a range of 100 to 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 analyze ET0 in 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 verified 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 meters 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 meters above sea level. Of these meteorological stations, 11 are located in Hunan Province, two in Jiangxi Province, and one in Guangdong Province. 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
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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 gradually increased radiative force, finally stabilizing at 4.5 W/m2 in the RCP45 scenario, which is as effective as a level of 850 ppm CO2-equivalent concentration by the year 2100, and 8.5 W/m2 in the RCP85 scenario, which is as effective as a level of more than 1 370 ppm CO2-equivalent concentration by the year 2100. 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 influence of economic activities on the environment in the XJRB. Thus, the prediction of future CO2 levels in the XJRB could be based on a coordinated development of the economy and the environment.
Fig. 1 Location of meteorological stations in XJRB
2.2 FAO P-M method
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The FAO P-M method is recommended as the sole standard method for ET0 calculation (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 verified 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 daily ET0 in this study, and monthly and annual ET0 were the accumulation of daily ET0 : ET0 =
0.408∆ ( Rn − G ) + γ 900 (Tmean + 273) u2 ( es − ea )
∆ + γ (1 + 0.34u2 )
(1)
where ET0 is reference evapotranspiration (mm), ∆ is the slope of the saturated vapour pressure (kPa/°C), Rn is the net radiation at the crop surface (MJ/(m2·d)), G is the soil heat flux density (MJ/(m2·d)), Tmean is the mean daily air temperature at a height of 2 m (°C), u2 is wind speed at a height of 2 m (m/s), es–ea is the saturation vapour pressure deficit (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 first helps users select the most representative large-scale
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2.4 Mann-Kendall test method
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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 of ET0 downscaling. 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 all the 14 stations in the XJRB. NCEP reanalysis data were used as the inputs to the SDSM to downscale ET0 , 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., ET0 in this study). After calibration with data from 1961 to 1990 and validation with data from 1991 to 2003, the relationships between selected predictors and ET0 at each station were built.
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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 significance of the trends in ET0 and other climatic factors. The equations of the Mann-Kendall test method are given below (Mann, 1945; Kendall, 1975) and the statistic S is calculated as n −1
S =∑
n
∑ sgn( x
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i
− xj )
1 sgn( xi − x j ) = 0 −1
(2) xi > x j xi = x j xi < x j
(3)
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where xi and xj are the sequential data values, and n is the length of the data set. The statistic S tends to be normally distributed for large n, with mathematical expectation and variance given by Hamed and Rao (1998): (4) E (S ) = 0
Var ( S ) =
q 1 n ( n − 1)( 2n + 5) − ∑t p ( t p − 1)( 2t p + 5) 18 p −1
(5)
where t p is the number of ties for the pth value, and q is the number of tried values. Then, the standard statistic Z, following the standard normal distribution, is formulated as Z =
The null hypothesis of no trend is rejected if
( S − 1) Var ( S ) 0 ( S + 1) Var ( S )
S >0 S =0 S <0
(6)
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Z > Z1−α
(7)
2
where Z is the standard normal variate, and α is the significance level for the test, taken to be 0.05 in this study. A positive value of Z indicates an increasing trend, whereas a negative value indicates a decreasing trend. Z1−α 2 is the critical value of Z from the standard normal table, and, for the significance level of 5%, the value of Z1−α 2 is 1.96.
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2.5 Performance assessment
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Three statistics, i.e., the relative error ( Re ), coefficient of determination ( R2 ), and Nash-Suttcliffe efficiency coefficient (NSE) (Nash and Sutcliffe, 1970), were used to evaluate the performance of SDSM for ET0 downscaling. R2 represents the strength of the relationship between the observed value and simulated value, and NSE reflects how well the volume and timing of the simulated value fit with the observed value. The closer the value of Re is to 0 and the values of R2 and NSE are to 1, the more successfully the model performs.
3. Results and discussion
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3.1 Spatial distribution of mean annual ET0 during period from 1961 to 2010
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The spatial distribution of mean annual ET0 in 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 annual ET0 of the XJRB during the period from 1961 to 2010 is 1 060.3 mm, and the variation coefficient for the mean annual ET0 of the 14 stations is 0.09, indicating a low spatial variation of ET0 in the XJRB. The lowest mean annual ET0 appears at the Nanyue Station, located at the center of the basin, with a value of 768.1 mm, while the highest mean annual ET0 appears 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 annual ET0 in the upper XJRB was higher than it was in the lower XJRB. In the upper XJRB, ET0 increases 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 annual ET0 in XJRB during period from 1961 to 2010 (units: mm)
3.2 Temporal trend in annual ET0 during period from 1961 to 2010 The spatial distribution of the temporal trend in annual ET0 at 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
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the mean annual ET0 of the XJRB during the period from 1961 to 2010. Downward trends in annual ET0 are detected at nine of the 14 meteorological stations, of which four show significant downward trends with α < 0.05. Five stations present upward trends, and only one station shows a significant upward trend with α < 0.05. Stations with significant 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 annual ET0 change rate of –1.24 mm/year and the Chenzhou Station with an annual ET0 change rate of –1.36 mm/year, and two such stations outside the XJRB, i.e., the Yichun Station with an annual ET0 change rate of –1.55 mm/year and the Lianxian Station with an annual ET0 change rate of –0.90 mm/year. A significant upward trend in annual ET0 occurs at the Shaoyang Station, located at the boundary of the XJRB, with an annual ET0 change rate of 0.89 mm/year. As for the mean annual ET0 of the XJRB, an insignificant 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 annual ET0 at each station in XJRB during period from 1961 to 2010
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In order to explore the reasons for change in ET0 , 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, and WS. As shown in Figs. 4(b) and (c), a significant upward trend in Tmean , with a change rate of 0.018°C/year, and a significant downward trend in RH, with a change rate of –0.085%/year, are factors contributing to the upward trend in annual ET0 , while a significant downward trend in SD, with a change rate of –0.011 h/year, and a significant downward trend in WS, with a change rate of –0.015 m/(s·year), are factors contributing to the downward trend in annual ET0 .
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Fig. 4 Linear trend of mean annual ET0 of XJRB and climatic factors during period from 1961 to 2010
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As shown in Fig. 4, significant changes in the mean annual ET0 of the XJRB and RH begin in 2002. RH decreases approximately by 10% (from 81.5% to 71.3%) from 2002 to 2004 and the mean annual ET0 of 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 annual ET0 of the XJRB in the period from 2002 to 2010 is affected by the increase of Tmean and WS and the decrease of RH. Linear regression models were also established for annual ET0 and the climatic factors, and the values of R2 between annual ET0 and different climatic factors are listed in Table 1. Moreover, the significance test, the t-test, was performed on linear regression relationships between annual ET0 and the climatic factors, and the values of the significance level, α, are also listed in Table 1.
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Table 1 Correlation between annual ET0 and climatic factors during period from 1961 to 2010 Climatic factors Tmean RH SD WS
R2 0.20
α <0.50
0.51 0.75 0.06
>0.50 <0.02 <0.50
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The correlation between annual ET0 and SD is significant with α < 0.02. Meanwhile, the value of R2 between annual ET0 and SD is the highest one of the four R2 values. Though the R2 between annual ET0 and RH is the second highest, the correlation between annual ET0 and RH is insignificant, with α > 0.50. As for Tmean and WS, the significance levels of correlations between annual ET0 and Tmean and annual ET0 and WS are both less than 0.50. However, the values of R2 between annual ET0 and Tmean and between annual ET0 and WS are quite low, indicating that ET0 is more sensitive to SD in the XJRB for the period from 1961 to 2010.
3.3 Prediction of ET0 during 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 first 30 years (1961 to 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 to 2010), which was determined to be the validation period, were used to validate the regression models. The performance of SDSM for ET0 downscaling could be evaluated through comparison with the ET0 series 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
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(Li et al., 2012; Wang et al., 2013). Taking the Yueyang Station as an example, the correlations between ET0 and the six station-scale NCEP predictors are presented in Table 2. It can be seen that the absolute value of correlation coefficients ranges from 0.56 to 0.75, indicating strong correlation between the six NCEP predictors and ET0 . Thus, it is feasible to downscale ET0 with these predictors. Selected predictor
Abbreviation
1
850 hPa specific humidity Mean sea level pressure 500 hPa zonal velocity 500 hPa geopotential height Air temperature at 850 hPa Air temperature at 2 m height
nceph_8s
2 3 4 5 6
ncepmslp ncepp5_u ncepp500 ncept850 nceptemp
Correlation coefficient 0.56 –0.68 –0.70 0.61 0.75
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Table 2 Correlations between predictors and ET0 taking Yueyang Station as an example
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Using predictors listed in Table 2, daily ET0 during the period from 1961 to 2010 was downscaled. The performance of SDSM in daily ET0 downscaling was evaluated, and it is presented in Table 3. The comparison of monthly ET0 series 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, Re values in both calibration and validation periods were within the acceptable range, and R2 and NSE are all above 0.84, demonstrating the strong applicability of the SDSM in ET0 downscaling in the study area. Thus, the established SDSM can be used to predict future ET0 in the XJRB. Table 3 Performance of SDSM in daily ET0 downscaling Period
Re (%) 0.10 7.16
R2 0.87 0.86
NSE
0.87 0.84
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Calibration Validation
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Fig. 5 Comparison of ET0 -PM with ET0 -SDSM in validation period
3.3.2 Prediction of ET0 during period from 2011 to 2100 Based on the calibration and validation of the SDSM model described above, ET0 during the period from 2011 to 2100 in the XJRB was predicted. As shown in Fig. 6, significant upward trends in the mean annual ET0 of the XJRB are detected under both the RCP45 and the RCP85 scenarios.
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Fig. 6 Prediction of mean annual ET0 of XJRB under RCP45 and RCP85 scenarios during period from 2011 to 2100
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Taking the calibration period (1961 to 1990) of SDSM model as a reference period, changes in the mean annual ET0 in three future periods, 2011 to 2040, 2041 to 2070, and 2071 to 2100, under the RCP45 and RCP85 scenarios are shown in Table 4. The mean annual ET0 of the XJRB in the reference period is 1 040.5 mm, and ET0 will 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 of 2011 to 2040, 2041 to 2070, and 2071 to 2100, respectively. For the mean annual ET0 of the XJRB, the increasing of predicted value under the RCP85 scenario is greater than that under the RCP45 scenario.
Table 4. Change rates of future mean annual ET0 of XJRB under RCP45 and RCP85 scenarios compared with reference period Period 2011–2040 2041–2070 2071–2100
Change rate (%) RCP45 scenario 9.8 12.6 15.6
RCP85 scenario 10.2 19.1 27.3
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Furthermore, the predicted variation ranges of the mean annual ET0 of the XJRB in three future periods are presented in Fig. 7, where the dotted line in Fig. 7 refers to the mean annual ET0 of the XJRB in the reference period. In the period from 2011 to 2040, the mean annual ET0 of 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 mean annual ET0 of the XJRB under the RCP85 scenario, with a change rate of 4.9% to 37.6% as compared with the reference period, is clearly higher than that under the RCP45 scenario, with a change rate of 1.8% to 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.
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Fig. 7 Variation ranges of predicted mean annual ET0 for three periods under RCP45 and RCP85 scenarios
4. Conclusions
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In this study, the spatial distribution and temporal trend of ET0 during the period from 1961 to 2010 were examined, and future ET0 during 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 annual ET0 of the XJRB is 1 060.3 mm during the period from 1961 to 2010, the lowest mean annual ET0 (768.1 mm) is found near the center of the basin, and the highest mean annual ET0 (1 087.1 mm) appears in the southwest part of the basin. Of the 14 meteorological stations selected in this study, downward trends in annual ET0 are detected at nine of them, four stations of which present significant downward trends. Five stations present upward trends, but only one of them shows a significant upward trend. The spatial variation of ET0 is caused by the combined effect of the related climatic factors. As for the mean annual ET0 of the XJRB, an insignificant downward trend with a change rate of –0.33 mm/year was detected during the period from 1961 to 2010. The temporal trend in annual ET0 is mainly caused by the significant downward trend in SD during the period from 1991 to 2010. (2) In both the calibration and validation periods, the SDSM performs well in ET0 downscaling in the study area. The comparison of monthly ET0 series downscaled by the SDSM with that calculated by the FAO P-M method shows Re , R2 , and NSE values 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 future ET0 in the XJRB. (3) Using CMIP5 outputs, upward trends in annual ET0 were 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 to 1990), ET0 will 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.
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DOI:
10.1016/j.wse.2015.11.002
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Water Science and Engineering
Please cite this article as: Tao, X.-e, Chen, H., Xu, C.-y., Hou, Y.-k., Jie, M.-x., Analysis and prediction of reference evapotranspiration with climate change in Xiangjiang River Basin, China, Water Science and Engineering (2015), doi: 10.1016/j.wse.2015.11.002. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
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Analysis and prediction of reference evapotranspiration with climate change in Xiangjiang River Basin, China1 Xin-e Taoa, Hua Chena,*, Chong-yu Xua, b, Yu-kun Houa, Meng-xuan Jiea State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan 430072, China b Department of Geosciences, University of Oslo, Oslo N-0316, Norway Received 6 April 2015; accepted 9 September 2015
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Abstract
1. Introduction
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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 of ET0 in the Xiangjiang River Basin (XJRB) in China were analyzed. ET0 during the period from 1961 to 2010 was calculated with historical meteorological data using the FAO Penman-Monteith (FAO P-M) method, while ET0 during 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 of ET0 were interpreted with the inverse distance weighted (IDW) method and Mann-Kendall test method, respectively. Results show that: (1) the mean annual ET0 of 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 present ET0 and can be used to predict the future ET0 in the XJRB; and (3) CMIP5 predicts upward trends in annual ET0 under the RCP45 and RCP85 scenarios during the period from 2011 to 2100. Compared with the reference period (1961 to 1990), ET0 increases 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 increasing ET0 under the RCP85 scenario is greater than that under the RCP45 scenario during the period from 2011 to 2100. Key words: Reference evapotranspiration ( ET0 ); Spatial-temporal variation; Climate change; Statistical downscaling; Xiangjiang River Basin
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According to the Fifth 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). Rising temperature 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 flow, evapotranspiration, and soil moisture indicate that the hydrological cycle has been intensified 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, which also play key roles in the energy budget in the earth-atmosphere 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 significant 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
This work was supported by the National Natural Science Foundation of China (Grants No. 51190094, 51339004, and 51279138). * Corresponding author Email address: [email protected] (Hua Chen)
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and atmosphere in a watershed and is heavily influenced 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 difficult 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 estimate ETa in most cases, such as scheduling irrigation, managing water resources, analyzing regional climate humidity conditions, and providing information for hydrological, agricultural, and environmental models. ET0 is defined as the rate of evapotranspiration from a hypothetical crop with an assumed crop height of 0.12 m, a fixed 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 affecting ET0 are climatic factors (Allen et al., 1998). Therefore, ET0 is 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 of ET0 , 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 of ET0 based on historical data and identifying the main climatic factors affecting ET0 (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 of ET0 . General circulation models (GCMs) are used to predict future changes in ET0 . 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 definite 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 first 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; ET0 downscaling 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 of ET0 on the Loess Plateau in China, found that ET0 significantly increased during the period from 1961 to 2009 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 after 2050. Li et al. (2012) suggested that the increasing trend in ET0 could possibly influence water resources on the Loess Plateau in the 21st century and some countermeasures should be taken to reduce the adverse impacts. Wang et al. (2013) investigated the spatial and seasonal changes in ET0 on the Tibetan Plateau and determined that, considering its spatial-temporal variation, an average ET0 series presented a zigzag pattern (increasing–decreasing–increasing) with two joint points in 1973 and 1993, and predicted a continuous increasing trend in ET0 in the 21st century. In the Yangtze River Basin, there have already been studies related to ET0 . For example,
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2. Materials and methods 2.1 Study area and data
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Gao et al. (2012) detected a decreasing trend in ET0 , caused mainly by a significant decrease of the net total radiation and secondarily by a significant decrease of the wind speed over the basin. Ye et al. (2013) analyzed the variation of ET0 and its affecting factors in the Poyang Lake Catchment. However, there has been no prediction of ET0 in 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 the Fifth Assessment Report of IPCC were used in this study for ET0 prediction, specifically 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 of ET0 in the XJRB during the period from 1961 to 2010, (2) to investigate the adaptability of SDSM for ET0 downscaling in the study area, and (3) to predict future ET0 during the period from 2011 to 2100 with the SDSM under the RCP45 and RCP85 scenarios, in order to study how ET0 will change in the XJRB in the 21st century.
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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, flows 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 to 19°C. In addition, the basin has an average annual rainfall of 1 200 to 1 700 mm, most of which falls from April to July, and a large variability in runoff, with a range of 100 to 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 analyze ET0 in 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 verified 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 meters 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 meters above sea level. Of these meteorological stations, 11 are located in Hunan Province, two in Jiangxi Province, and one in Guangdong Province. 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
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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 gradually increased radiative force, finally stabilizing at 4.5 W/m2 in the RCP45 scenario, which is as effective as a level of 850 ppm CO2-equivalent concentration by the year 2100, and 8.5 W/m2 in the RCP85 scenario, which is as effective as a level of more than 1 370 ppm CO2-equivalent concentration by the year 2100. 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 influence of economic activities on the environment in the XJRB. Thus, the prediction of future CO2 levels in the XJRB could be based on a coordinated development of the economy and the environment.
Fig. 1 Location of meteorological stations in XJRB
2.2 FAO P-M method
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The FAO P-M method is recommended as the sole standard method for ET0 calculation (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 verified 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 daily ET0 in this study, and monthly and annual ET0 were the accumulation of daily ET0 : ET0 =
0.408∆ ( Rn − G ) + γ 900 (Tmean + 273) u2 ( es − ea )
∆ + γ (1 + 0.34u2 )
(1)
where ET0 is reference evapotranspiration (mm), ∆ is the slope of the saturated vapour pressure (kPa/°C), Rn is the net radiation at the crop surface (MJ/(m2·d)), G is the soil heat flux density (MJ/(m2·d)), Tmean is the mean daily air temperature at a height of 2 m (°C), u2 is wind speed at a height of 2 m (m/s), es–ea is the saturation vapour pressure deficit (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 first helps users select the most representative large-scale
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2.4 Mann-Kendall test method
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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 of ET0 downscaling. 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 all the 14 stations in the XJRB. NCEP reanalysis data were used as the inputs to the SDSM to downscale ET0 , 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., ET0 in this study). After calibration with data from 1961 to 1990 and validation with data from 1991 to 2003, the relationships between selected predictors and ET0 at each station were built.
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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 significance of the trends in ET0 and other climatic factors. The equations of the Mann-Kendall test method are given below (Mann, 1945; Kendall, 1975) and the statistic S is calculated as n −1
S =∑
n
∑ sgn( x
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(2) xi > x j xi = x j xi < x j
(3)
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where xi and xj are the sequential data values, and n is the length of the data set. The statistic S tends to be normally distributed for large n, with mathematical expectation and variance given by Hamed and Rao (1998): (4) E (S ) = 0
Var ( S ) =
q 1 n ( n − 1)( 2n + 5) − ∑t p ( t p − 1)( 2t p + 5) 18 p −1
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where t p is the number of ties for the pth value, and q is the number of tried values. Then, the standard statistic Z, following the standard normal distribution, is formulated as Z =
The null hypothesis of no trend is rejected if
( S − 1) Var ( S ) 0 ( S + 1) Var ( S )
S >0 S =0 S <0
(6)
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Z > Z1−α
(7)
2
where Z is the standard normal variate, and α is the significance level for the test, taken to be 0.05 in this study. A positive value of Z indicates an increasing trend, whereas a negative value indicates a decreasing trend. Z1−α 2 is the critical value of Z from the standard normal table, and, for the significance level of 5%, the value of Z1−α 2 is 1.96.
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2.5 Performance assessment
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3. Results and discussion
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3.1 Spatial distribution of mean annual ET0 during period from 1961 to 2010
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The spatial distribution of mean annual ET0 in 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 annual ET0 of the XJRB during the period from 1961 to 2010 is 1 060.3 mm, and the variation coefficient for the mean annual ET0 of the 14 stations is 0.09, indicating a low spatial variation of ET0 in the XJRB. The lowest mean annual ET0 appears at the Nanyue Station, located at the center of the basin, with a value of 768.1 mm, while the highest mean annual ET0 appears 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 annual ET0 in the upper XJRB was higher than it was in the lower XJRB. In the upper XJRB, ET0 increases 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 annual ET0 in XJRB during period from 1961 to 2010 (units: mm)
3.2 Temporal trend in annual ET0 during period from 1961 to 2010 The spatial distribution of the temporal trend in annual ET0 at 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
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the mean annual ET0 of the XJRB during the period from 1961 to 2010. Downward trends in annual ET0 are detected at nine of the 14 meteorological stations, of which four show significant downward trends with α < 0.05. Five stations present upward trends, and only one station shows a significant upward trend with α < 0.05. Stations with significant 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 annual ET0 change rate of –1.24 mm/year and the Chenzhou Station with an annual ET0 change rate of –1.36 mm/year, and two such stations outside the XJRB, i.e., the Yichun Station with an annual ET0 change rate of –1.55 mm/year and the Lianxian Station with an annual ET0 change rate of –0.90 mm/year. A significant upward trend in annual ET0 occurs at the Shaoyang Station, located at the boundary of the XJRB, with an annual ET0 change rate of 0.89 mm/year. As for the mean annual ET0 of the XJRB, an insignificant 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 annual ET0 at each station in XJRB during period from 1961 to 2010
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In order to explore the reasons for change in ET0 , 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, and WS. As shown in Figs. 4(b) and (c), a significant upward trend in Tmean , with a change rate of 0.018°C/year, and a significant downward trend in RH, with a change rate of –0.085%/year, are factors contributing to the upward trend in annual ET0 , while a significant downward trend in SD, with a change rate of –0.011 h/year, and a significant downward trend in WS, with a change rate of –0.015 m/(s·year), are factors contributing to the downward trend in annual ET0 .
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Fig. 4 Linear trend of mean annual ET0 of XJRB and climatic factors during period from 1961 to 2010
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As shown in Fig. 4, significant changes in the mean annual ET0 of the XJRB and RH begin in 2002. RH decreases approximately by 10% (from 81.5% to 71.3%) from 2002 to 2004 and the mean annual ET0 of 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 annual ET0 of the XJRB in the period from 2002 to 2010 is affected by the increase of Tmean and WS and the decrease of RH. Linear regression models were also established for annual ET0 and the climatic factors, and the values of R2 between annual ET0 and different climatic factors are listed in Table 1. Moreover, the significance test, the t-test, was performed on linear regression relationships between annual ET0 and the climatic factors, and the values of the significance level, α, are also listed in Table 1.
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Table 1 Correlation between annual ET0 and climatic factors during period from 1961 to 2010 Climatic factors Tmean RH SD WS
R2 0.20
α <0.50
0.51 0.75 0.06
>0.50 <0.02 <0.50
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The correlation between annual ET0 and SD is significant with α < 0.02. Meanwhile, the value of R2 between annual ET0 and SD is the highest one of the four R2 values. Though the R2 between annual ET0 and RH is the second highest, the correlation between annual ET0 and RH is insignificant, with α > 0.50. As for Tmean and WS, the significance levels of correlations between annual ET0 and Tmean and annual ET0 and WS are both less than 0.50. However, the values of R2 between annual ET0 and Tmean and between annual ET0 and WS are quite low, indicating that ET0 is more sensitive to SD in the XJRB for the period from 1961 to 2010.
3.3 Prediction of ET0 during 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 first 30 years (1961 to 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 to 2010), which was determined to be the validation period, were used to validate the regression models. The performance of SDSM for ET0 downscaling could be evaluated through comparison with the ET0 series 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
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(Li et al., 2012; Wang et al., 2013). Taking the Yueyang Station as an example, the correlations between ET0 and the six station-scale NCEP predictors are presented in Table 2. It can be seen that the absolute value of correlation coefficients ranges from 0.56 to 0.75, indicating strong correlation between the six NCEP predictors and ET0 . Thus, it is feasible to downscale ET0 with these predictors. Selected predictor
Abbreviation
1
850 hPa specific humidity Mean sea level pressure 500 hPa zonal velocity 500 hPa geopotential height Air temperature at 850 hPa Air temperature at 2 m height
nceph_8s
2 3 4 5 6
ncepmslp ncepp5_u ncepp500 ncept850 nceptemp
Correlation coefficient 0.56 –0.68 –0.70 0.61 0.75
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Table 2 Correlations between predictors and ET0 taking Yueyang Station as an example
0.74
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Using predictors listed in Table 2, daily ET0 during the period from 1961 to 2010 was downscaled. The performance of SDSM in daily ET0 downscaling was evaluated, and it is presented in Table 3. The comparison of monthly ET0 series 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, Re values in both calibration and validation periods were within the acceptable range, and R2 and NSE are all above 0.84, demonstrating the strong applicability of the SDSM in ET0 downscaling in the study area. Thus, the established SDSM can be used to predict future ET0 in the XJRB. Table 3 Performance of SDSM in daily ET0 downscaling Period
Re (%) 0.10 7.16
R2 0.87 0.86
NSE
0.87 0.84
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Calibration Validation
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Fig. 5 Comparison of ET0 -PM with ET0 -SDSM in validation period
3.3.2 Prediction of ET0 during period from 2011 to 2100 Based on the calibration and validation of the SDSM model described above, ET0 during the period from 2011 to 2100 in the XJRB was predicted. As shown in Fig. 6, significant upward trends in the mean annual ET0 of the XJRB are detected under both the RCP45 and the RCP85 scenarios.
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Fig. 6 Prediction of mean annual ET0 of XJRB under RCP45 and RCP85 scenarios during period from 2011 to 2100
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Taking the calibration period (1961 to 1990) of SDSM model as a reference period, changes in the mean annual ET0 in three future periods, 2011 to 2040, 2041 to 2070, and 2071 to 2100, under the RCP45 and RCP85 scenarios are shown in Table 4. The mean annual ET0 of the XJRB in the reference period is 1 040.5 mm, and ET0 will 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 of 2011 to 2040, 2041 to 2070, and 2071 to 2100, respectively. For the mean annual ET0 of the XJRB, the increasing of predicted value under the RCP85 scenario is greater than that under the RCP45 scenario.
Table 4. Change rates of future mean annual ET0 of XJRB under RCP45 and RCP85 scenarios compared with reference period Period 2011–2040 2041–2070 2071–2100
Change rate (%) RCP45 scenario 9.8 12.6 15.6
RCP85 scenario 10.2 19.1 27.3
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Furthermore, the predicted variation ranges of the mean annual ET0 of the XJRB in three future periods are presented in Fig. 7, where the dotted line in Fig. 7 refers to the mean annual ET0 of the XJRB in the reference period. In the period from 2011 to 2040, the mean annual ET0 of 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 mean annual ET0 of the XJRB under the RCP85 scenario, with a change rate of 4.9% to 37.6% as compared with the reference period, is clearly higher than that under the RCP45 scenario, with a change rate of 1.8% to 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.
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Fig. 7 Variation ranges of predicted mean annual ET0 for three periods under RCP45 and RCP85 scenarios
4. Conclusions
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In this study, the spatial distribution and temporal trend of ET0 during the period from 1961 to 2010 were examined, and future ET0 during 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 annual ET0 of the XJRB is 1 060.3 mm during the period from 1961 to 2010, the lowest mean annual ET0 (768.1 mm) is found near the center of the basin, and the highest mean annual ET0 (1 087.1 mm) appears in the southwest part of the basin. Of the 14 meteorological stations selected in this study, downward trends in annual ET0 are detected at nine of them, four stations of which present significant downward trends. Five stations present upward trends, but only one of them shows a significant upward trend. The spatial variation of ET0 is caused by the combined effect of the related climatic factors. As for the mean annual ET0 of the XJRB, an insignificant downward trend with a change rate of –0.33 mm/year was detected during the period from 1961 to 2010. The temporal trend in annual ET0 is mainly caused by the significant downward trend in SD during the period from 1991 to 2010. (2) In both the calibration and validation periods, the SDSM performs well in ET0 downscaling in the study area. The comparison of monthly ET0 series downscaled by the SDSM with that calculated by the FAO P-M method shows Re , R2 , and NSE values 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 future ET0 in the XJRB. (3) Using CMIP5 outputs, upward trends in annual ET0 were 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 to 1990), ET0 will 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.
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