Dominant climatic factor driving annual runo ff change at catchments scale over China

Dominant climatic factor driving annual runoff change at catchments scale over China Z. Huang and H. Yang State Key Laboratory of Hydro-Science and Engineering, Department of Hydraulic Engineering, Tsinghua University, Beijing, 100084, China Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China University of Chinese Academy of Sciences, Beijing, 100049, China


Introduction
Climate change has become increasingly significant, and it has important impacts on hydrology cycle and the water resource management.Changes in climatic factors and runoff have been observed in many different regions of China.The reduction of precipitation occurred in the Hai River Basin, the upper reach of the Yangtze River Basin and the Yellow River Basin, and the increase occurred in the in the western China (Yang et al., 2014).A 29 % decline of surface wind speed occurred in China during 1966 to 2011, which would have lead to a 1-6 % increase in runoff and a 1-3 % decrease in evapotranspiration at most regions in China (Liu et al., 2014).Most of the river basins in north China have exhibited obvious decline in mean annual runoff, such as the Shiyang River Basin (Ma et al., 2008), the Yellow River Basin (Yang et al., 2004;Tang et al., 2007;Cong et al., 2009), and the Hai River Basin (Ma et al., 2010).The Introduction

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Full hydrologic processes have been influenced by different climatic factors.For example, decline in land surface wind speed could lead to decrease in evapotranspiration and changes in precipitation may affect water generation and concentration.However, the dominant climatic factor driving annual runoff change is still unknown in many catchments of China.
There are several approaches to investigate the feedback of annual runoff to climate change, such as the hydrologic models (Yang et al., 1998(Yang et al., , 2000;;Arnold et al., 1998;Arnold and Fohrer, 2005), the climate elasticity method (Schaake, 1990;Sankarasubramanian et al., 2001) and the statistics method (Vogel et al., 1999).Therein, the climate elasticity method was widely used in quantifying the effects of climatic factors on runoff, such as in the Yellow River Basin (Zheng et al., 2009;Yang and Yang, 2011), the Luan River Basin (Xu et al., 2013), the Chao-Bai Rivers Basin (Ma et al., 2010), and the Hai River Basin (Ma et al., 2008;Yang and Yang, 2011).
A simple climate elasticity method was firstly defined by Schaake (1990) to estimate the impacts of precipitation (P ) on annual runoff (R): where ε P is the precipitation elasticity.To consider the effects of precipitation and air temperature on runoff, Fu et al. (2007) calculated the runoff change as: where ε a and ε b are the precipitation elasticity and air temperature elasticity, respectively.
Five categories of methods can be used to estimate climate elasticity (Sankarasubramanian et al., 2001), and the analytical derivation method has been widely used in many studies because it is not only clear in theory but also does not need a large amount of historical observed data.Arora (2002)  1 − F 0 (φ) where φ = E/P and F 0 (φ) is a Budyko formula and F 0 (φ) is the derivation to φ.The climate elasticity of runoff was evaluated in the upper reach of the Yellow River Basin by using Eq. ( 3) (Zheng et al., 2009).To evaluate the impact from other climatic factors, Yang and Yang (2011) proposed an analytical method, which was based on the Penman equation and the annual water balance equation, to quality the runoff change to changes in different climatic factors.By taking advantage of the mean annual climatic factors in the study period, the runoff elasticity to precipitation (P ), mean air temperature (T ), net radiation (R n ), relative humidity (RH), and wind speed (U 2 ) were derived, and the runoff change can be expressed as follows: where ε P , ε R n , ε T , ε U 2 , and ε RH are the runoff elasticity to precipitation (P ), net radiation (R n ), mean air temperature (T ), wind speed (U), and relative humidity (RH), respectively.However, this method was only tested in several catchments of the nonhumid Northern China.
There are large spatial variations in both geography characteristics and climate type over China, which would result in a large variation in the hydrologic response to climate change.Therefore, the current study aims to: (1) further validating the method proposed by Yang and Yang (2011) At catchment scale, there is abvious relationship between evaporation, precipitation and potential evaporation, which is referred as the Budyko hypothesis (Budyko, 1961).An analytical equation of the Budyko hypothesis was inferred by Yang et al. (2008): where the parameter n represents the characteristics of the catchment, for example land use and coverage change, vegetation, slope and climate seasonality (Yang et al., 2014).The water balance equation can be simplified as P = E + R at catchment scale for the long term, so runoff can be expressed as follows: To attribute the contribution of changes in P and E 0 to runoff, Yang and Yang (2011) derived a new equation: where ε 1 and ε 2 are the climate elasticity of runoff to P and E 0 , respectively; and they can be estimated as . The potential evaporation E 0 (mm day −1 ) can be evaluatedby the Penman equation (Penman, 1948): and the physical meaning of these symbols were shown in Table 1.Introduction

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Full Similar to Eq. ( 7), the response of potential evaporation to climatic factors can be estimated as: where ε 3 , ε 4 , ε 5 , ε 6 are the elasticity of potential evaporation to net radiation, air temperature, wind speed and relative humidity, respectively.Therein, , and ∂RH were calculated by finite differential method.Substitution of Eq. ( 9) into Eq.( 7) leads to: Denoted Eq. (10) as follows: where P * , R * n , T * , U * 2 and RH * symbolize the runoff changes caused by the changing in P , R n , T , U 2 and RH, respectively.The largest one among them is considered as the dominant climatic factor driving annual runoff change.Introduction

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Full Design General Institute, 2011).In the data set, catchment boundary and runoff ratio were available.Chinese water resources zoning was divided level by level, and there are 10 first-level basins, 80 s-level river basins and 210 third-level river basins (shown in Fig. 1a).Therein, there are no observed meteorological data in Taiwan Island and no runoff in two inland catchments in Xinjiang province.Hence 207 third-level catchments were selected in this study.Meteorological data, obtained from 736 weather stations during the period 1961-2010 from the China Meteorological Administration (CMA), included precipitation, surface mean air temperature, maximum air temperature, minimum air temperature, relative humidity, sunshine hours, and wind speed.In addition, daily solar radiation during the period 1961-2010 was collected from 118 weather stations.
To get the annual climatic factors in each catchment, firstly, a 10 km grid data set, which covers the study area, was prepared for interpolation from the observed meteorological data.Secondly, according to the 10 km grid data set, the average values of cliamte factors of each catchment were calculated.The interpolation method for climatic factors were an inverse-distance weighted technique, except air temperature which must consider the influence of elevation (Yang et al., 2006).
Since only 118 weather stations directly measured solar radiation, the daily net radiation R n (MJ m −2 day −1 ) was calculated as: and the physical meaning of these symbols were shown in Table 2. R s was calculated by using the Angström formula (Angström, 1924): where R a is the extra-terrestrial radiation; and a s and b s are parameters which were calibrated using the data at the 118 stations with solar radiation observations (Yang et al., 2006).In Eq. ( 12), e s is estimated as: Wind speed at a height of 2 m (U 2 , m s −1 ) can be calculated by the observed wind speed at 10 m height (Allen et al., 1998): Based on Eq. ( 6), the runoff ratio (α) can be estimated as follows: Furthermore, the catchment characteristics parameter n was calculated according to α, E 0 and P .

Validation of the climate elasticity method
Two steps were taken for the validation of the climate elasticity method, namely validating Eq. ( 7) and validating Eq. ( 9).To validate Eq. ( 7), two catchments were chosen, namely the Luan River Basin and the upper Hanjiang River Basin (shown in Fig. 1b).The Luan River Basin, located in north China, is a part of Hai River Basin.It has a mean annual precipitation of 455 mm, 75-85 % of which concentrates from June to September.The Hanjiang River Basin, lying in the middle and lower reaches of the Yangtze River Basin, which is the largest tributary of the Yangtze River, finally flows into Danjiangkou reservoir and has a length Introduction

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Full To validate the climate elasticity method, the results given by Eq. ( 7) were compared with the results in references Xu et al. (2013) and Sun et al. (2014).Equation ( 9) is the first-order Taylor expansion of Penman equation.On one hand, we firstly evaluated the climate elasticity of potential evaporation to air temperature, net radiation, relative humidity, wind speed and the change in these climatic factors, and further estimated the change in potential evaporation according Eq. ( 9), denoted as E * 0 .On the other hand, we calculated the potential evaporation change (E * * 0 ) as: where the function f represents the Penman equation.Then, the first approximation E * 0 was compared with E * * 0 , to evaluation the error of Eq. ( 9).

Trend analysis
The Mann-Kendall (MK) nonparametric test (Kendall, 1990) is an effective statistical tool for trend detection, especially for hydrological and meteorological time series (Mainment, 1993).The MK nonparametric test is widely used for its convenient calculation processes.The sample data are not necessary to obey some specific distribution, but they must be serially indenpendent.In this study, we firstly evaluated the significance levels of the trend of the hydrological and meteorological time series which were set at 0.05 and 0.1, and then estimated the slope of the trend: Introduction

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Full for all i < j ; where β is the magnitude of trend, and β > 0 indicates an increasing trend, and β < 0 indicates a decreasing trend.

Validation of the climate elasticity method
Table 3 showed the comparison of climate contribution to runoff which were estimated by the climate elasticity method and the hydrological models.The climate contribution to runoff is −14 and −21.4 % in the upper Luan River Basin, 12.4 and 9.1 % in the lower Luan River Basin and −19.6 and −19.0 % in the upper Hanjiang River Basin, which were estimated by the climate elasticity method and the hydrological models respectively.The result provided a strong evidence for using the climate elasticity method to evaluate the climate elasticity and the response of runoff to climate change both in humid and arid catchments.Figure 2a showed the relationship between the potential evaporation change evaluated by Eq. ( 9) and that evaluated by Eq. ( 17), and most of the point were around the line y = x.The relative errors (shown in Fig. 2b) mostly ranged from −3-1 %.High correlativity of them and the small relative errors showed the accuracy of Eq. ( 9), which maked it possible to express potential evaporation change as a function of cliamtic factors variation.

The mean annual climatic factors
The mean annual precipitation, net radiation, air temperature, wind speed, and relative humidity for each catchment during 1961-2010 were shown in Fig. 3.The mean annual Introduction

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Full in the southeast coastal area, and it had a typical spatial variation of decreasing from the southeast to the northwest.The net radiation differed from 3-10 (MJ m −2 day −1 ) in China, of which the largest value occurred in the Qinghai-Tibet Plateau and the lowest value occurred in Sichuan Basin.The mean annual air temperature in China had a range of −3.3-23.8 • C, with a typical spatial variation of decreasing from the south to the north.The wind speed in 2 m height in China ranged from 1 to 4 m s −1 , and the high value occurred in the north and the coastland and the lowest value occurred in Sichuan Basin.The relative humidity, which ranged from 35 % in the northwest to 82 % in the southeast, had a positive correlation with precipitation.According to Eq. ( 6), we can evaluate the mean annual runoff.The annual mean runoff had a range of 0 to 1176 mm a −1 which had a similar spatial variation with that of pricipitation.and had no rules, and the reason will be discussed in discussion part.The value of ε U 2 ranged from −0.01 to −0.94 (−0.22 on average).The high value of −0.95 < ε U 2 < −0.5 mostly occurred in the north China.The value of ε RH ranged from 0.05 to 3 (0.74 on average), and the distributions of them agreed with that of precipitation.

Changes in the climatic factors
The changes in climatic factors were shown in Fig. 5.There is a large spatial variation in precipitation change which increased in the northwest China (ranging from 5 to 11 % decade −1 , p < 0.05) and decreased in Yellow River Basin, Hai River Basin and the upper reach of Yangtze River Basin (ranging from −5 to −2.5 % decade −1 , p < 0.05), but there were no significant change trend shown in 63 % of these 207 catchments.
Net radiation showed a decrease in most catchments.Large decrease (ranging from −6 to −3 % decade −1 ) occurred in the Hai River Basin, the Huai River Basin and the lower reach of Yangtze River Basin (p < 0.05), while small decrease (ranging from −3 to −0 % decade −1 ) occurred in the majority of the northern China.No significant change trend was shown in the Qinghai-Tibet Plateau.
Air temperature increased all over the China.Large increase (ranging from 0.4 to 0.8 • C decade −1 ) mainly occurred in the northern China (p < 0.05), while small decrease (ranging from 0 to 0.4 • C decade −1 ) occurred in the majority of the southeast.
Wind speed decreased in most catchments, ranging from −11 % decade Positive contribution and negative contribution of air temperature to runoff change were both small when compared with other climatic factors.
Positive contribution of wind speed to runoff change occurred in most catchments except for part of the upper reach of Yangtze River Basin.In the Hai River Basin and the Liao River Basin, the positive contribution reached the most, ranging from 2 to 6 % decade −1 , while in other catchments the wind speed effected the runoff small.
Negative contribution of relative humidity to runoff change occurred in most catchments except for part of the northwest where the positive contribution of relative humidity to the change of runoff ranges 0-2 % decade −1 .and had a positive contribution in the northwest, part of the northeast and the southeast China.Therein, the largest positive contribution from climate change to runoff occurred in the northwest, ranging from 10 to 30 % decade −1 , while the largest negative contribution occurs in the middle reach of the Yellow River Basin and the Hai River Basin, ranging from −13 to −8 % decade −1 .

The dominant climatic factors driving runoff change
Figure 8 showed the dominant climatic factors driving runoff in the 207 catchments.
In most catchments, the runoff change was dominated by precipitation.In addition, the runoff change was mainly determined by net radiation in the lower reach of the Yangtze River Basin, the Pearl River Basin, the Huai River Basin and the southeast area, and by wind speed in part of the northeast, part of the Inner Mongolia and part of the northeast Area.

Climate elasticity
The climate elasticity method is widely used to evaluate hydrologic cycle in many catchments in China.Yang et al. (2014) calibrated precipitation elasticity to be 1.1 to 4.8 in China, which is the same with our result.What's more, in previous study, the precipitation elasticity were evaluated as 2.6 in the Luan River Basin (Xu et al., 2013), as 2.4 in the Chao-Bai Rivers Basin (Ma et al., 2010), as 1.4 to 1.7 in the Poyang Lake (Sun et al., 2013), as 1.4 for the Beijiang River catchment of the Pearl River Basin (Wang et al., 2013), as 1.0-2.0 in the Dongjiang River catchment of the Pearl River Basin (Jiang et al., 2007).Those results were also in good agreement with our results for ε P in the same regions.
Wind speed elasticity, which stands for the sensitivity of annual runoff change to wind speed change, was negative across China with small sensitivity in the southern Introduction

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Full The net radiation elasticity and the relative humidity elasticity agreed with the result evaluated by Yang and Yang (2011) in Futuo River catchment of the Hai River Basin and 89 catchments of the Hai River and the Yellow River Basins of China and are also similar to the result calculated by Tang et al. (2013) in the Yellow River Basin.
The air temperature elasticity ranges from −0.1 to 0.1, which were similar to other studies in the same regions (Yang and Yang, 2011;Tang et al., 2013;Yang et al., 2014).However, the air temperature elasticity is obvious small when compared to other climatic elasticities.Next, we will discuss the cause why air temperature elasticity is small.Air temperature elasticity is calculated by the following equation: where ε 2 is the runoff elasticity to potential evaporation, ranging from −3 to 0 in China.So the value of ε T is mainly determined by ∂E 0 ∂T .Figure 9 showed the relationship between T and where and 10 showed the trend of and e s as the change of temperature according to the connection between ∆ and T and between e s and T .∂E 0 ∂∆ ranged from −5.5 to 9.3 (0.22 on average), while ∂E 0 ∂e s ranged from 0.3 to 1.9 (0.85 on average).From the results, it could be found that the absolute value of ∂∆ .Furthermore, the derivatives and e s with respect to temperature is small, which leads to the small value of Changing in air temperature would affect the atmosphere, which results in potential evaporation change, further affecting runoff.What's more changeing air temperature would also affect atmospheric movement, resulting in precipitation change (Gardner, 2009).In fact, changes in air temperature have great effects on runoff.The climate elasticity method only analyzes the direct impact of air temperature on runoff but ignores the indirect impact.Chiew et al. (2009) evaluated that the indirect impact of air temperature on runoff would be important, and a degree global warming will result in −10-3 % changes in runoff.

Effect of climate change to runoff
Recently, many studies have been carried out to assess the effects of climate change on runoff.Xu et al. (2013) reported that the runoff increase caused by cliamte change were 8.8 and 9.2 mm simulated by GBHM and the climate elasticity model in Luan River Basin.Tang et al. (2013) analyzed response of natural runoff to climate change in the Yellow River Basin by using the climate elasticity method and SWAT model, and the two methods also gave similar conclusion.Their results agreed with that revealed in this study.Introduction

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Full Previous studies reported that precipitation decrease was the dominant factor of declining runoff in Futuo River catchment (Yang and Yang, 2011) and the Yellow River Basin (Tang et al., 2013), which agreed with our result.
Remarkably, in some catchments of the northeast, the Inner Mongolia and the northwest Area, declining wind speed has the greatest contribution to runoff change.McVicar et al. (2012) stressed that the impact of wind speed change on actual evapotranspiration and runoff was situation dependent.Wind speed decline tended to result in the decline of actual evapotranspiration and complementary increase of streamflow in wet river basins but has little impacts in dry basins (Liu et al., 2014), which was similar to our results.In previous studies, when assessing the impacts of changes in meteorological factors on runoff in China, wind speed declines were often identified as being important (Tang et al., 2011;Liu et al., 2014).And in the part of the northeast, part of the Inner Mongolia and part of the northwest area, due to the small hydrology changes and the stable precipitation, wind speed decline became the main contribution factor to runoff change.
The dominant climatic factor to runoff change was determined by the geographic conditions and climate.In this study, we analyzed the contribution of climatic factors to runoff change by the climate elasticity method which only stresses on the direct impact of climate change on runoff but ignores the relation between climatic factors.And the relationship needs further study.

Error analysis
In Eq. ( 10), the net radiation R n and the air temperature T were considered as two independent elements.But in fact, according to Eqs. ( 12) and ( 13) the net radiation R n is associated to the air temperature T .To verify the impact of the relationship between net radiation and air temperature on Eq. ( 12), the effect of change in air temperature to change in net radiation R n must be evaluated as follows: Introduction

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Full If the effect of T on R n was ignored, the relative error was less than 1 %, which was evaluated by Yang and Yang (2011) in the Futuo River Basin.
In addition, Eq. ( 10) is a first-order approximation, which probably results in errors in the estimating of climate elasticity.Yang et al. (2014) evaluated that when the changes in potential evapotranspiration (∆E 0 ) and precipitation (∆P ) are not large, the error of ε P caused by first-order approximation can be discounted, but the error will increase with changes increasing with a 0.5-5 % relative error in ε P .When ∆P = 10 mm and a 5-50 % relative error in ε P When ∆P = 100 mm.Bao et al. (2012) estimated that a 100 mm increase in precipitation causes 20 % increase in ε P by adopting the Variable Infiltration Capacity (VIC) model.

Conclusions
In this study, we used the climate elasticity method to reveal the dominant climatic factor driving annual runoff change over China.We first validated the climate elasticity method which was firstly derived by Yang and Yang (2011).On account of China being a vast country with remarkable spatial differences in climate and geography characteristics, we divided China into 207 catchments, and then evaluated the climate elasticity of runoff to precipitation, net radiation, air temperature, wind speed and relative humidity, and estimated the contribution of climate factors to runoff change for each catchment.
In the 207 catchments, precipitation elasticity, which was low in in southern China  , 14, 403-416, 2000. Yang, D., Li, C., Hu, H., Lei, Z., Yang, S., Kusuda, T., Koike, T., and Musiake, K.: Analysis of water resources variability in the Yellow River of China during the last half century using historical data, Water Resour.Res., 40, 308-322, 2004. Yang, D., Sun, F., Liu, Z., Cong, Z., and  Full  Full Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | projected a equation to calcuated the response of runoff to precipitation and potential evaporation change: , (2) evaluating the climate elasticity of climatic factors to runoff at catchments scale over China, and (3) estimating the impact of climate variation on runoff and then detecting the dominant climatic factor driving annual runoff change.
Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper |of about 925 km and an elevation of 3500-88 m.In the two catchments, runoff has a remarkable change, and the causes for runoff change were analyzed by hydrological models.Xu et al. (2013) assessed the response of annual runoff to anthropogenic activities and climate change in the Luan River Basin by using the GBHM model.Sun et al. (2014) explored the contributions from climate change and catchment properties variation to runoff change in Danjiangkou Basin by using three different methods including climate elasticity and decomposition methods, and the dynamic hydrological modeling method.
Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | precipitation in China, ranged from 30 mm a −1 in the northwest inland to 1883 mm a −1

Figure 4
Figure4showed the climate elasticity of runoff to the climatic factors for each catchment.In the 207 catchments, precipitation elasticity ε P ranged from 1.1 to 4.75 (2.0 on average), indicating that 1 % change in precipitation leads to 1.1-4.75% change in runoff.The lowest value of ε P , ranged from 1.1 to 1.5, occurred in southern China.The highest value of ε P mostly occurred in the Huai River Basin, the Liao River Basin, and the Hai River Basin, and the lower reach of Yellow River Basin.A 1 % R n change caused −0.1 to −2 % (−0.5 on average) runoff change.The high value of −0.5 < ε R n < −2.0 mostly occurred in the Huai River Basin, the Liao River Basin, and the Hai River Basin, and the downstream of Yellow River Basin, and the relatively small value of −0.1 < ε R n < −0.5 mostly occurred in southern and northwest China.The air temperature elasticity, ranging from −0.1 to 0.1, indicted that a 1 centigrade degree increase in air temperature will result in −10-10 % increase in runoff.The sensibility of runoff to the air temperature change varied from geographic position

Figure 6
Figure6showed the contributions of climatic factors to the runoff change.The contribution of precipitation to the change of runoff had a distinctspatial variation.Positive contribution occurred in the western China and the southeast of China, especially in the northwest where the contribution of precipitation to runoff change ranges from 12 to 25 % decade −1 .While negative contribution mainly occurred in the central and northeast China.In the middle reach of the Yellow River Basin and the Hai River Basin, the negative contribution reaches the most, ranging from −18 to −10 % decade −1 .Positive contribution of net radiation to runoff change occurred in most catchments except for the Qinghai-Tibet Plateau.In the Hai River Basin, the positive contribution reached the most, ranging from 3 to 9 % decade −1 , while in other catchments the net radiation effected the runoff small.Positive contribution of air temperature to runoff change occurred in the Qinghai-Tibet Plateau and the northern part of the northeast China, while negative contribution mainly occurred in northwest and the eastern China except for the northeast China.

Figure 7
Figure 7 showed the contribution of climate factors to runoff change, which was defined as the sum of the contribution of climatic factors.Generally speaking, climate change had a negative contribution on runoff in Hai River Basin, part of the Liao River Basin, the middle and lower reaches of Yellow River Basin and the southeast China, Discussion Paper | Discussion Paper | Discussion Paper |China and high sensitivity in the Northern China.Yang and Yang (2011)  calculated wind speed elasticity ε U = −0.3 for the Futuo River catchment of the Hai River Basin by using the climate elasticity method, which was same with our result for the same catchment.Tang et al. (2013) estimated ε U = −0.59for the entire Yellow River Basin;Yang and Yang (2011)  estimated ε U ranging of −0.8 to −0.1 in the 89 catchments of the Hai River and the Yellow River Basins of China.Those results were similar to our result in the same regions.

∂E 0 ∂T
in 207 basins of China.

∂E 0 ∂T
varied in different basins, but it had increase trend as T increasing.What's more, when T < 10 • C, ∂E 0 ∂T was negative mostly, while when T > 10 • C, ∂E 0 ∂T was positive mostly.Next, we will analyze the value of ∂E 0 ∂T by the differential method.Denoting Eq. (8) as E 0 = f 1 (∆, e s ), and we can express (kPa • C −1 ) and e s (kPa) as ∆ = f 2 (T ) and e s = f 3 (T ), respectively.Due to their substitution, ∂E 0 ∂T can be expressed as: Discussion Paper | Discussion Paper | Discussion Paper | Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | or small part of the northwest and high in the Liao River Basin, the Hai River Basin, the Huai River Basin, ranged from 1.1 to 4.75 (2.0 on average).The air temperature elasticity, ranging from −0.1 to 0.1.Net radiation elasticity which ranges from −0.1 to −2 (−0.5 on average), wind speed elasticity which ranged from −0.01 to 0.94 (−0.22 on Discussion Paper | Discussion Paper | Discussion Paper | average) and relative humidity elasticity which ranged from 0.05 to 3 (0.74 on average) had similar distributions with precipitation elasticity.There was a large spatial variation in climatic factors change.Precipitation increased in the northwest China and decreased in Yellow River Basin, Hai River Basin and the upper reach of Yangtze River Basin.Net radiation showed a decrease in most catchments.Air temperature increased all over the China.Wind speed decreased in most catchments and the change of relative humidity agrees with the change of precipitation.Climate change had a negative contribution on runoff in part of the Liao River Basin, the Hai River Basin, the middle and lower reaches of Yellow River Basin and the southeast China, and had a positive contribution in the northwest, part of the northeast and the southeast China.what's more, the largest positive contribution from climate change to runoff ranged from 10 to 30 % decade −1 in the northwest China, while the largest negative contribution ranged from −13 to −8 % decade −1 in the middle reach of the Yellow River Basin and the Hai River Basin.Regarding the dominant climatic variable driving runoff change, it was precipitation in most of the 207 catchments, net radiation in the lower reach of Yangtze River Basin and the southeast, and wind speed in part of the northeast.Discussion Paper | Discussion Paper | Discussion Paper | Yang, D., Herath, S., and Musiake, K.: Comparison of different distributed hydrological models for characterization of catchment spatial variability, Hydrol.Process.
Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Figure 1.(a) Spatial distribution of the third-level river basins in China and (b) two basins for validation.

Table 3 .
Comparison between the climate contribution to runoff by using the climate elasticity method and by using the hydrological models.
P is the mean annual precipitation (mm); E 0 is mean annual potential evaporation (mm); ∆P/P is the percentage of precipitation change (%);∆E 0 /E 0 is the percentage of