J. Geogr. Sci. 2012, 22(1): 3-14 DOI: 10.1007/s11442-012-0907-4 2012 Science Press Springer-Verlag Spatial and temporal change in the potential evapotranspiration sensitivity to meteorological factors in China (1960 2007) LIU Changming 1,2, * ZHANG Dan 1,3, LIU Xiaomang 1, ZHAO Changsen 1 1. Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China; 2. College of Resources and Environment, Beijing Normal University, Beijing 100875, China; 3. Graduate University of Chinese Academy of Sciences, Beijing 100049, China Abstract: Potential evapotranspiration (E 0 ), as an estimate of the evaporative demand of the atmosphere, has been widely studied in the fields of irrigation management, crop water demand and predictions in ungauged basins (PUBs). Analysis of the sensitivity of E 0 to meteorological factors is a basic research on the impact of climate change on water resources, and also is important to the optimal allocation of agricultural water resources. This paper dealt with sensitivity of E 0 over China, which was divided into ten drainage systems, including Songhua River basin, Liaohe River basin, Haihe River basin, Yellow River basin, Yangtze River basin, Pearl River basin, Huaihe River drainage system, Southeast river drainage system, Northwest river drainage system and Southwest river drainage system. In addition, the calculation method of global radiation in Penman-Monteith formula was improved by optimization, and the sensitivities of Penman-Monteith potential evapotranspiration to the daily maximum temperature (S Tmax ), daily minimum temperature (S Tmin ), wind speed (S U2 ), global radiation (S Rs ) and vapor pressure (S VP ) were calculated and analyzed based on the long-term meteorological data from 653 meteorological stations in China during the period 1960 2007. Results show that: (1) the correlation coefficient between E 0 and pan evaporation increased from 0.61 to 0.75. E 0 had the decline trends in eight of ten drainage systems in China, which indicates that pan evaporation paradox commonly exists in China from 1960 to 2007. (2) Spatially, T max was the most sensitive factor in Haihe River basin, Yellow River basin, Huaihe River drainage system, Yangtze River basin, Pearl River basin and Southeast river drainage system, and VP was the most sensitive factor in Songhua River Basin, Liaohe River basin, Northwest river drainage system while R s was the most sensitive factor in Southwest river drainage system. For the nation-wide average, the most sensitive factor was VP, followed by T max, R s, U 2 and T min. In addition, the changes in sensitivity coefficients had a certain correlation with elevation. (3) Temporally, the maximum values of S Tmax and S Rs occurred in July, while the maximum values of S Tmin, S VP and S U2 occurred in January. Moreover, trend analysis indicates that S Tmax had decline trends, while S Tmin, S U2, S Rs and S VP had increasing Received: 2011-08-11 Accepted: 2011-09-16 Foundation: National Natural Science Foundation of China, No.40971023; National Basic Research Program of China, No.2010CB428406 Author: Liu Changming, Professor and CAS Academician, E-mail: liucm@igsnrr.ac.cn * Corresponding author: Zhang Dan, Ph.D Candidate, E-mail: nuistgiszd@163.com www.geogsci.com springerlink.com/content/1009-637x
4 Journal of Geographical Sciences trends. Keywords: Penman-Monteith potential evapotranspiration; meteorological factors; sensitivity; ten drainage systems in China 1 Introduction Potential evapotranspiration (E 0 ) represents evaporative capacity for a region and/or a basin under sufficient water supply (Allen et al., 1998). Due to lack of observed evapotranspiration data, E 0 is usually used to estimate the actual evapotranspiration. E 0 is widely discussed in water resources management (Liu et al., 2002; Guo et al., 2009), and considered as an important research field of water and energy balance (Liu et al., 1999). In recent decades, decreasing trends in calculated E 0 and observed pan evaporation have been reported in many regions of the world associated with climate change (Peterson et al., 1995; Liu et al., 2004), which is known as pan evaporation paradox (Brutsaert and Parlange, 1998). E 0 would change with the variation of meteorological factors. Therefore, quantitative estimation on sensitivity of E 0 to meteorological factors is significant in studying the impact of climate change on regional water cycle and water-energy transformation. Saxton (1975) found the most sensitivity factor of E 0 was net radiation in western Iowa; Hupet and Vanclooster (2001) indicated that maximum temperature was the most susceptive factor of E 0 in Belgium; Gong et al. (2006) found relative humidity was the most sensitivity factor in the Yangtze River basin; Liu et al. (2009) concluded water vapor was the most sensitivity factor in the Haihe River basin. Different estimations of E 0 lead to different sensitivity to meteorological factors. The Penman-Monteith method recommended by Food and Agriculture Organization (FAO) is one of the most widely used formulas to calculate E 0 (Allen et al., 1998; Chen et al., 2005). However, there are some special experimental parameters which need to be regionally adjusted so as to improve the accuracy of E 0 estimation. In addition, there have been relatively few studies on the E 0 sensitivity to meteorological factors, and the sensitivity study on the basin scale was not available in the literature (Yin et al., 2010; Liu et al., 2011). In this study, E 0 was calculated with the optimized solar radiation formula by Qiu Xinfa (Zeng et al., 2008), and the sensitivities of E 0 to the five meteorological factors (S Tmax, S Tmin, S U2, S Rs and S VP ) were calculated over the 10 drainage systems in China, then the spatial and temporal changes in E 0 sensitivities were investigated. The result could benefit the studies on impacts of climate change on hydrological cycle and agriculture irrigation management. 2 Data and method 2.1 Data A dataset of 653 national meteorological observational stations with daily mean maximum and minimum air temperature at 1.5 m height, wind speed measured at 10 m height, relative humidity at 1.5 m and sunshine duration for the period 1960 2007 was used in this study. All of the data were strictly quality controlled and some missing data were interpolated by inverse distance weight method (Taiwan province was not included). In addition, pan evaporation of 20 cm diameter of 317 stations from 1961 to 2000 and solar radiation data of 116 stations were incorporated for accuracy validation of improved E 0. The data was pro-
LIU Changming et al.: Spatial and temporal change in the potential evapotranspiration sensitivity 5 vided by the National Climatic Center of the China Meteorological Administration. China was divided into ten drainage systems to better describe the spatial character of the change in sensitivity of E 0. Seven of the ten drainage systems were devided based on the Seven Main Rivers of China (the Songhua River, Liaohe River, Haihe River, Yellow River, Huaihe River,Yangtze River, Pearl River), including the neighboring rivers running to the sea. In addition, rivers in Zhejiang and Fujian provinces, southwest rivers and inland rivers were classified into other three drainage systems correspondingly. Inverse distance weight interpolation was adopted for element spatiality with a resolution of 5 km 5 km (Qian et al., 2006). Meteorological stations and ten drainage systems were shown in Figure 1. Figure 1 Location of 653 meteorological stations and 10 drainage systems 1. Songhua River basin; 2. Liaohe River basin; 3. Haihe River basin; 4.Yellow River basin; 5. Huaihe River drainage system; 6. Yangtze River basin; 7. Southeast river drainage system; 8. Pearl River basin; 9. Southwest river drainage system; 10. Northwest river drainage system. 2.2 Method 2.2.1 Estimation of potential evapotranspiration by FAO and its improvement The Penman-Monteith method by FAO has been widely accepted for calculating E 0 and therefore is also used in this study. In this method the land cover is regarded as hypothetical reference grass with an assumed height of 0.12 m, a fixed surface resistance of 70s/m and an albedo of 0.23. The FAO method can be expressed as (Allen et al., 1998): 900 0.408 Rn G U2( vps vp) Tmean 273 E0 (1) (1 0.34 U2) where E 0 is the potential evapotranspiration (mm), R n is the net radiation at reference surface (MJ/(m 2 day)), G is the soil heat flux density (MJ/(m 2 day)), T mean is the daily mean temperature ( ) which is the mean of maximum temperature (T max ) and minimum temperature (T min ), U 2 is the wind speed at 2 meters height (m/s), vp s is the saturated vapor pressure (kpa),
6 Journal of Geographical Sciences vp is the actual vapor pressure (kpa), is the slope of vapor pressure curve versus temperature (kpa/ ), γ is the psychrometric constant (kpa/ ). R n is a function of global radiation which can be written as R n =f(r s ), where R s can be estimated as: S Rs as bs Ra (2) N where S is the actual duration of sunshine (h), N is the maximum possible duration of sunshine (h), R a is the extraterrestrial radiation intensity (MJ/(m 2 day)), a s and b s are experimental coefficients with recommended values of a s =0.25 and b s =0.50, respectively. Solar radiation is the original energy source for the earth, and it is critical for improving its estimated accuracy. China has a vast territory and different kinds of climate zones, so one group of experimental coefficients used in China are unreasonable. An optimized method of solar radiation by Qiu Xinfa was incorporate to this research (Zeng et al., 2008). The coefficients a s and b s were estimated from 116 solar radiation stations based on the characteristic distribution of solar radiation. The spatial distribution of optimized coefficients (Figure 2) had a significant regional character. a s ranged from 0.12 to 0.29 while b s from 0.45 to 0.73. The results indicated that the mean relative error of the optimized global radiation model was 6.48% less than 15.64% of the un-optimized model. Consequently, the precision of the E 0 estimation can be improved by the optimized global radiation model. Figure 2 Spatial distribution of the improved a s and b s Furthermore, a contrast of the improved and unimproved models was carried out via unbroken pan evaporation data of 317 meteorological stations from 1961 to 2000. Although there are some differences between pan evaporation and E 0, they both represent evaporative capacity of a basin under certain climatic condition and their good relationship had been manifested by previous researches (Roderick et al., 2007; Zheng et al., 2009). Table 1 shows that multiple correlation coefficient between pan evaporation and improved E 0 was higher Table 1 Comparison of Penman-Monteith formula and improved formula Category R 2 (unimproved) R 2 (improved) Number of samples Annual model 0.88 0.90 40 Single station model 0.62 0.79 317 Annual single station model 0.61 0.75 12680 Note: R 2 is multiple correlation coefficient
LIU Changming et al.: Spatial and temporal change in the potential evapotranspiration sensitivity 7 than the unimproved model, and the multiple correlation coefficient of annual single station model can be increased from 0.61 to 0.75. Therefore, the improved Penman-Monteith method would be better for the temporal and spatial analysis about sensitivity of E 0 to meteorological factors in China. 2.2.2 Sensitivity coefficient Sensitive coefficient was defined as the ratio of change rate of E 0 and change rate of meteorological factor in this study (McCuen, 1974): E0/ E0 E0 x Sx lim (3) x/ x x E0 where E 0 is the potential evapotranspiration, x is the meteorological factor, S x is the sensitivity coefficient of E 0 related to x which is a non-dimensional form. This pattern can be easily used for comparison of different meteorological factors. Essentially, a positive/negative sensitivity coefficient of a meteorological factor indicates that E 0 will increase/decrease with the meteorological factor increasing. The larger absolute value of the sensitivity coefficient is, the larger effect a given meteorological factor has on E 0. Climatic tendency (Shi et al., 1995) and Mann-Kendall statistical test (Sun et al., 2010) were used in this study as statistical methods. 3 Results 3.1 Trends of meteorological driving factors and potential evapotranspiration Using climatic tendency and Mann-Kendall test, the trends and the significance of meteorological influencing factors and E 0 at each drainage system during 1960 2007 were attained (Table 2). Meteorological factors changed obviously over the past 50 years. Maximum temperature in ten drainage systems had increased and the increasing trend in seven drainage systems satisfied the Mann-Kendall test at the 5% significance level except for Huaihe River drainage system, Yangtze River basin and Pearl River basin. The climatic tendency in the whole China also met to the 5% significance level of Mann-Kendall test with the slope of 0.205 per decade. As for minimum temperature, the increasing trend satisfied the 5% sig- nificance level of Mann-Kendall test over all drainage systems so that a rate of 0.393 per decade was found over China as a whole. Wind speed over all drainage systems performed a decreasing trend and nine of them satisfied the 5% significance level of Mann-Kendall test except for Southwest river drainage system. The weakening rate of wind speed was 0.115 m/s per decade over China from 1960 to 2007. A decreasing trend of global solar radiation was discovered over ten drainage systems with a decreasing rate of 0.114 (MJ/m 2 d) per decade over China as a whole, but only 5 drainage systems of them passed the Mann-Kendall test. The increasing trend of vapor pressure was detected over the ten drainage systems. The climatic tendency was 0.086 hpa per decade over China as a whole and it satisfied the 5% significance level of Mann-Kendall test over Southwest river drainage system and Northwest river drainage system. So far as E 0 was concerned, the vulnerably increasing trend existed with the rising rate of 3.775 mm per decade over Songhua River basin and 1.092 mm per decade over Yellow River basin, respectively. On the contrary, the decreasing trend over the other drainage systems was found which implied the widespread existence of pan evaporation paradox. The decreasing rate was 6.204 mm per decade over China as a whole.
8 Journal of Geographical Sciences Table 2 Climate trends of meteorological factors and E 0 in ten drainage systems (per decade) Drainage system T max ( ) T min ( ) U 2 (m/s) R s (MJ/m 2 d) VP (hpa) E 0 (mm) Songhua River basin 0.327 * 0.558 * 0.180 * 0.080 0.066 3.775 Liaohe River basin 0.274 * 0.463 * 0.191 * 0.120 * 0.089 4.299 Haihe River basin 0.241 * 0.490 * 0.166 * 0.307 * 0.084 10.154 Yellow River basin 0.273 * 0.385 * 0.083 * 0.097 0.068 1.092 Huaihe River drainage system 0.112 0.321 * 0.127 * 0.331 * 0.079 13.512 Yangtze River basin 0.132 0.232 * 0.090 * 0.185 * 0.058 7.137 Southeast river drainage system 0.296 * 0.344 * 0.140 * 0.323 * 0.124 4.037 Pearl River basin 0.106 0.206 * 0.061 * 0.215 * 0.013 6.592 Southwest river drainage system 0.125 * 0.337 * 0.057 0.036 0.074 * 3.037 Northwest river drainage system 0.228 * 0.473 * 0.134 * 0.040 0.125 * 9.671 China average 0.205 * 0.393 * 0.115 * 0.114 0.086 6.204 Note: * means a trend passing the 5% significance level of Mann-Kendall test. 3.2 Spatial distribution of the sensitivity coefficients Spatial distributions of annual sensitivity coefficients (Figure 3) were obtained by inverse distance weight interpolating in ArcGIS 9.3. Sensitivity for maximum temperature (Figure 3a) ranged from 1.16 to 1.98. The positive value was found over Pearl River basin, Southeast river drainage system, Huaihe River drainage system and middle and lower reaches of Yangtze River while negative value over Songhua River basin and Northwest river drainage system. Spatial distribution of S Tmin (Figure 3b) was similar to S Tmax where E 0 increased with rising temperature in eastern China while decreased over Songhua River basin, upper reaches of Yangtze River and Yellow River. Spatial pattern for S U2 displayed in Figure 3b had a rising trend from southwest to northeast. S U2 was higher over Northwest river drainage system, Songhua River basin, Liaohe River basin, Haihe River basin and middle reaches of Yellow River while lower over Yangtze River, Pearl River, Southwest river drainage system, Southeast river drainage system and upper reaches of Yellow River. Compared with wind speed, S Rs (Figure 3d) was higher in Yangtze River basin, Pearl River basin, Southwest river drainage system and Southeast river drainage system while lower over Songhua River basin and northern Southwest river drainage system. As for vapor pressure, we can see a longitude-trend of S VP from west to east, and the positive value in west while negative value in east. Combined with Table 3, we found that the most sensitive meteorological factor was maximum temperature in Haihe River basin, Yellow River basin, Huaihe River drainage system, Yangtze River basin, Pearl River basin and Southeast river drainage system, and the most sensitive meteorological factor was vapor pressure in Songhua River Basin, Liaohe River basin, Northwest river drainage system while global radiation in Southwest river drainage system. According to the above discussion, we concluded that the most sensitive factor was vapor pressure, followed by maximum temperature, global radiation, wind speed and minimum temperature over China. 3.3 Correlation between sensitivity coefficients and altitude According to the altitude data of stations, relationship between sensitivity coefficients and
LIU Changming et al.: Spatial and temporal change in the potential evapotranspiration sensitivity 9 Figure 3 Spatial distributions of the mean annual sensitivity coefficient in China Table 3 Statistics of the sensitivity coefficients of ten drainage systems Drainage system S Tmax S Tmin S U2 S Rs S VP Songhua River basin 0.02 0.35 0.27 0.18 1.03 Liaohe River basin 0.53 0.03 0.27 0.29 0.67 Haihe River basin 0.63 0.06 0.24 0.34 0.60 Yellow River basin 0.50 0.01 0.19 0.40 0.48 Huaihe River drainage system 0.98 0.28 0.19 0.41 0.91 Yangtze River basin 0.75 0.23 0.12 0.50 0.68 Southeast river drainage system 1.07 0.44 0.10 0.54 0.97 Pearl River basin 1.02 0.46 0.11 0.57 0.77 Southwest river drainage system 0.46 0.03 0.11 0.54 0.31 Northwest river drainage system 0.37 0.10 0.25 0.31 0.39 China average 0.52 0.02 0.20 0.39 0.58
10 Journal of Geographical Sciences altitude was studied. Due to the various climatic regions and rugged terrain, we took Yellow River basin as an example which had 77 meteorological stations. Sensitivity coefficients of maximum temperature and wind speed decreased with the increasing altitude. Their multiple correlation coefficients were 0.66 and 0.53, respectively. However, there was a weak relationship between sensitivity coefficient and global radiation and altitude. So did the vapor pressure. These correlations were shown in Figure 4. We argue that the different manifestation was a complex feedback mechanism among terrain, vegetation, soil and atmosphere. Figure 4 Correlations between the sensitivities and altitude 3.4 Monthly variation of the sensitivity coefficients For further analysis, variation of the monthly mean sensitivity coefficients (Figure 5) was investigated. Both sensitivity coefficients of wind speed and global radiation were positive which meant that E 0 would increase with the rising meteorological factors. S U2 ranged from 0.11 to 0.31 which was the maximum in August while minimum is January. As for S Rs, the sensitivity ranged from 0.01 to 0.64 which was the maximum in July while minimum in December. However, S VP was negative with the maximum of 0.93 in May while minimum of 0.40 in January. Finally, the maximum value of S Tmax turned up in July while that of S Tmin appeared in January. 3.5 Trends of sensitivity coefficients Over the past 50 years from 1960 to 2007, climatic tendencies of sensitivity coefficients were shown in Table 4. S Tmax was decreasing except for Songhua River basin and Yellow
LIU Changming et al.: Spatial and temporal change in the potential evapotranspiration sensitivity 11 Figure 5 Variation of monthly mean sensitivity coefficient in China Table 4 Climate trends in sensitivity coefficients of ten drainage systems (per decade) Drainage system S Tmax S Tmin S U2 S Rs S VP Songhua River basin 0.0126 0.0208 * 0.0102 * 0.0120 0.0292 * Liaohe River basin 0.0041 0.0104 * 0.0039 0.0017 0.0248 * Haihe River basin 0.0044 0.0125 * 0.0048 0.0007 0.0216 * Yellow River basin 0.0003 0.0079 * 0.0026 0.0007 0.0150 Huaihe River drainage system 0.0181 0.0064 0.0035 0.0001 0.0244 Yangtze River basin 0.0154 0.0001 0.0018 0.0005 0.0247 Southeast river drainage system 0.0180 0.0012 0.0081 * 0.0033 0.0368 Pearl River basin 0.0204 0.0035 0.0043 0.0028 0.0219 Southwest river drainage system 0.0051 0.0037 * 0.0036 0.0055 0.0024 Northwest river drainage system 0.0041 0.0095 * 0.0042 0.0065 * 0.0155 China average 0.0060 0.0107 * 0.0075 0.0111 0.0259 Note: * means trend passing the Mann-Kendall test at %5 significance level. River basin and the slope was 0.0060 per decade over China average. Unlike maximum temperature, S Tmin was increasing except for Southeast river drainage system. Moreover, the trend satisfied the Mann-Kendall test at the 5% significance level over Songhua River basin, Liaohe River basin, Yellow River basin, Haihe River basin, Southwest river drainage system and Northwest river drainage system. The increasing rate was 0.0107 per decade over China and also met with the Mann-Kendall test at the 5% significance level. With respect to wind speed, there was an increasing trend of S U2 except for Southwest river drainage system and Northwest river drainage system; moreover, the tendency was significant at the 5% significance level over Songhua River basin and Southeast river drainage system. The rising rate was 0.0075 per decade in the whole nation. In terms of S Rs, a non-significant increasing rate was detected over China but tendency was significant in Northwest river drainage system. Lastly, S VP was increasing at a rate of 0.0259 per decade. The rate satisfied the Mann-Kendall test at 5% significance level over Songhua River basin, Liaohe River basin and Haihe River basin. 4 Discussion Pan evaporation paradox was a hot issue in the research of evapotranspiration/evaporation. But some researches indicated that increasing trend of E 0 appeared over China (Cong et al.,
12 Journal of Geographical Sciences 2010) and Australia (Gifford et al., 2005) in recent years. Did it mean the common degree of pan evaporation paradox was disappearing? There was an increasing trend of E 0 after 1993 in China during the period from 1960 to 2007 (Figure 6). Results in Section 3.1 indicated there was a slender increasing tendency in E 0 over Yellow River basin during the past 48 years. However, Liu and Zeng (2004) argued there was a decreasing tendency of pan evaporation from 1961 to 2000 over Yellow River basin. Whether or not the dropping phenomenon of pan evaporation paradox is credible necessitates to be further analyzed by using much longer series of observation data. Figure 6 Trend of potential evapotranspiration during 1960 2007 In this study, we also found that both S U2 and S Rs had good relationships with E 0. Figure 7 shows the complementary relationship between S U2 and S Rs in the Yellow River Basin. The correlation coefficient between E 0 and S U2 was 0.88, while it was 0.70 between E 0 and S Rs. It indicates that S Rs was increasing with a decreasing E 0, while S U2 is contrary. This is because both global radiation and wind speed were important drivers of the latent and sensible heat flux. The latent heat flux increases when the sensible heat flux decreases according to the ground energy balance if the soil heat flux is ignored, and vice versa. Further scientific methods and theories should be developed on the complex feedback mechanisms amongst wind speed, global radiation and potential evapotranspiration. Figure 7 Correlations between sensitivities and potential evapotranspiration
LIU Changming et al.: Spatial and temporal change in the potential evapotranspiration sensitivity 13 5 Conclusions Based on the observation data of 653 meteorological stations, sensitivities of E 0 to meteorological factors were calculated over ten drainage systems in conjunction with the improved Penman-Monteith method. Some conclusions can be obtained as follows. (1) The relative error of optimized global radiation was reduced by 50% and the correlation coefficient was raised from 0.61 to 0.75. (2) There was an increasing trend of maximum temperature, minimum temperature and vapor pressure with both temperatures having passed the Mann-Kendall test at a 5% significance level. Wind speed and global radiation was decreasing over the past 48 years. Wind speed change has passed the Mann-Kendall test at a 5% significance level. There was a declining tendency in E 0 across all basins except for Songhua River basin and Yellow River basin, which implies pan evaporation paradox in the river basin scale is universal. (3) Spatially, the most sensitive meteorological factor was the maximum temperature over Haihe River basin, Yellow River basin, Huaihe River drainage system, Yangtze River basin, Pearl River basin and Southeast river drainage system; the most sensitive meteorological factor was vapor pressure over Songhua River basin, Liaohe River basin and Northwest river drainage system while global radiation over Southwest river drainage system. There were noticeable negative correlations between altitude and S Tmax /S U2. (4) Temporally, S Tmax was decreasing while S Tmin, S U2, S Rs and S VP were increasing during the period from 1960 to 2007. The results would be significant for researches in agricultural water management and climate change. The feedback mechanism among potential evapotranspiration, terrain, vegetation, soil, atmosphere and actual evaporation will be quantified in our future study. References Allen R G., Pereira L S, Raes D et al., 1998. Crop evapotranspiration: Guidelines for computing crop water requirements. FAO Irrigation and Drainage Paper 56. Food and Agriculture of the United Nations, Rome. Brutsaert W, Parlange M B, 1998. Hydrologic cycle explains the evaporation paradox. Nature, 396: 30. Chen D, Gao G., Xu C Y et al., 2005. Comparison of Thornthwaite method and Pan data with the standard Penman-Monteith estimates of potential evapotranspiration for China. Climate Research, 28: 123 132. Cong Zhentao, Zhao Jingjing, Yang Dawen et al., 2010. Understanding the hydrological trends of river basins in China. Journal of Hydrology, 388: 350 356. Gifford R M, Farquhar G D, Nicholls N et al., 2005. Workshop summary on pan evaporation: An example of the detection and attribution of climate change variables. Australia Academy of Sciences, 22 23. Gong L B, Xu C Y, Chen D L et al., 2006. Sensitivity of the Penman-Monteith reference evapotranspiration to key climatic variables in Changjiang (Yangtze River) Basin. Journal of Hydrology, 329: 620 629. Guo S L, Guo J, Zhang J et al., 2009. VIC distributed hydrological model to predict climate change impact in the Hanjiang Basin. Science in China: Series E, 52(11): 3234 3239. Hupet F, Vanclooster M, 2001. Effect of the sampling frequency of meteorological variables on the estimation of the reference evapotranspiration. Journal of Hydrology, 243: 192 204. Liu C M, Zhang X Y, Zhang Y Q, 2002. Determination of daily evaporation and evapotranspiration of winter wheat and maize by large-scale weighing lysimeter and micro-lysimeter. Agricultural and Forest Meteorology, 111: 109 120. Liu Changming, Sun Rui, 1999. Ecological aspects of water cycle: Advances in soil vegetation atmosphere of
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