Effects of climate change on annual streamflow using climate elasticity ...

Theor Appl Climatol (2013) 112:169–183 DOI 10.1007/s00704-012-0714-y

ORIGINAL PAPER

Effects of climate change on annual streamflow using climate elasticity in Poyang Lake Basin, China Shanlei Sun & Haishan Chen & Weimin Ju & Jie Song & Hao Zhang & Jie Sun & Yujie Fang

Received: 20 December 2011 / Accepted: 3 July 2012 / Published online: 24 July 2012 # Springer-Verlag 2012

Abstract Hydrological processes depend directly on climate conditions [e.g., precipitation, potential evapotranspiration (PE)] based on the water balance. This paper examines streamflow datasets at four hydrological stations and meteorological observations at 79 weather stations to reveal the streamflow changes and underlying drivers in four typical watersheds (Meigang, Saitang, Gaosha, and Xiashan) within Poyang Lake Basin from 1961 to 2000. Most of the less than S. Sun Applied Hydrometeorological Research Institute, Nanjing University of Information Science and Technology (NUIST), 210044 Nanjing, China S. Sun : H. Chen (*) Key Laboratory of Meteorological Disaster of Ministry of Education, Nanjing University of Information Science and Technology (NUIST), Ningliu Road 219, 210044 Nanjing, China e-mail: [email protected] W. Ju International Institute for Earth System Science, Nanjing University, 210093 Nanjing, China J. Song Department of Geography, Northern Illinois University, DeKalb 60115 IL, USA H. Zhang College of Hydrology and Water Resources, Hohai University, 210098 Nanjing, China J. Sun Wuhan Regional Climate Center, 430074 Wuhan, China Y. Fang Key Laboratory of Poyang Lake Environment and Resource Utilization Ministry of Education, Nanchang University, 330029 Nanchang, China

90th percentile of daily streamflow in each watershed increases significantly at different rates. As an important indicator of the seasonal changes in the streamflow, CT (the timing of the mass center of the streamflow) in each watershed shows a negligible change. The annual streamflow in each watershed increases at different rates, with a statistically significant trend (at the 5 % level) of 9.87 and 7.72 mm year−1, respectively, in Meigang and Gaosha watersheds. Given the existence of interactions between precipitation and PE, the original climate elasticity of streamflow can not reflect the relationship of streamflow with precipitation and PE effectively. We modify this method and find the modified climate elasticity to be more accurate and reasonable using the correlation analysis. The analyses from the modified climate elasticity in the four watersheds show that a 10 % increase (decrease) in precipitation will increase (decrease) the annual streamflow by 14.1–16.3 %, while a 10 % increase (decrease) in PE will decrease (increase) the annual streamflow by −10.2 to −2.1 %. In addition, the modified climate elasticity is applied to estimate the contribution of annual precipitation and PE to the increasing annual streamflow in each watershed over the past 40 years. Our result suggests that the percentage attribution of the increasing precipitation is more than 59 % and the decreasing in PE is less than 41 %, indicating that the increasing precipitation is the major driving factor for the annual streamflow increase for each watershed.

1 Introduction The global mean temperature had increased by 0.74±0.18 °C in the past 100 years and this increase may even accelerate in the future (IPCC 2007). This warming had significantly influenced the natural ecosystems and environment and will lead to an increase in both floods and droughts (IPCC 2007). The changes in hydrological cycles caused by climate change, including precipitation, evapotranspiration, soil moisture,

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river flow, and groundwater, have the potential to severely affect environmental quality, economic development, and social well-being (Nash and Gleick 1991; Liu and Fu 1993; Milly et al. 2005; Gedney et al. 2006; Oki et al. 1995; Kundzewicz et al. 2007; Zhang et al. 2007). Understanding changes in water resources and underlying driving forces has become hot issues all over the world (Lins and Slack 1999; Xu et al. 2010; Zhang et al. 2001; Wang et al. 2008; Zheng et al. 2009). Lins and Slack (1999) applied the nonparametric Mann–Kendall test method to study temporal trends of streamflow at 395 gauging stations across the U.S.A., and suggested that streamflow showed increasing trends during the period of 1944–1993 in most regions, except for the northwest and the southeast Pacific. Zhang et al. (2001) pointed out that annual and monthly streamflow at most of months decreased significantly from 1947 to 1996 in southern Canada, especially in August and September. Based on an evapotranspiration model and the statistic analytical methods (i.e., the nonparametric Mann– Kendall test and the stepwise regression), Wang et al. (2008) detected the quantitative hydrologic sensitivity to climate variability and found that precipitation was more influential than PE in affecting annual streamflow and monthly streamflow as well. Zheng et al. (2009) utilized the climate elasticity concept to assess impacts of climate on streamflow and found that streamflow was more sensitive to precipitation than to PE during the period of 1960–2000. Xu et al. (2010) analyzed the trends of the major hydroclimatic variables from 1960 to 2007 in the Tarim River basin of China and concluded that the impacts of increasing air temperature on the streamflow showed different characteristics, depending on the location and seasons. Over the last century, the average temperature across China has experienced a dramatic increase (Ding et al. 1994; Zhai et al. 2004; Zhang et al. 2005; Yang et al. 2010; Li et al. 2010) with critical water stress [The Standing Committee of the National People’s Congress (NPC) of the People’s Republic of China 1994]. As one of the most advanced economic regions in China, the Yangtze River Basin also is one of the most serious flooding regions. With increasing temperature and precipitation in this basin, the frequency and intensity of floods over the past years have increased significantly (Hu et al. 2007; IPCC 2001), resulting in serious economic losses. As the largest freshwater lake in China, Poyang Lake exchanges water with the Yangtze River and acts as the reservoir of floods in the middle and lower reaches of the Yangtze River. Its capacity of flood diversion has decreased continuously with ecological and environmental degradation (e.g., serious soil and water losses and the reduction of lake area and volume). The surface area of this lake shrunk by 25 % and its capacity decreased by 22 % from 1954 to 1998 (Jiang 2007), making the basin to be higher vulnerable to floods. Both droughts

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and floods in the basin have occurred frequently and alternatively in the recent decades. Furthermore, the floods have increased in severity since 1990. In the summers of 1998, 1996, and 1995, this basin experienced three most severe floods (in descending order) in the last 50 years (Wang and Dong 2000; Jiang and Shi 2003; Shankman et al. 2006). Therefore, it is of great importance to quantify the impact of climate change on the hydrological cycles. The responses of hydrological cycle in the Yangtze River Basin to climate change have received increased attention recently (Xiong and Guo 2004; Guo et al. 2008; Chen et al. 2007; Zhao et al. 2009). However, most studies are focused primarily on qualitatively analyzing the effects of long-term variability of climate variables (e.g., precipitation and temperature) on water resources. The changes in the monthly streamflow and the daily percentile streamflow, and the timing of the mass center of the annual streamflow (CT) are necessary and important for understanding the streamflow changes, but have received little attention. Estimating the streamflow responses to climate change quantitatively and revealing the causes of the changes in streamflow over the past years have important implications for water resources exploitation and utilization, agriculture production, and economy development. Therefore, the present study aims to: (1) detect the trends of streamflow at different temporal scales, and the CT of the annual streamflow during the period of 1961–2000; (2) refine the climate elasticity of streamflow concept and estimate the streamflow responses to changes in precipitation and PE; (3) quantify the contribution of precipitation and PE to the annual streamflow changes (1961–2000) for each watershed using the modified climate elasticity concept.

2 Study region and data 2.1 Study region Poyang Lake Basin is located in the middle reaches and the south bank of the Yangtze River, China, covering an area of 1.6×105 km2 and accounting for 9 % of the Yangtze River Basin and even nearly 96.85 % of the land area of Jiangxi Province (Fig. 1). The size of the lake water body fluctuates greatly in season. The water body area can reach up to 4,000 km2 in the summer and shrinks to less than 3,000 km2 in the fall and winter. This lake receives water primarily from Ganjiang River, Xiushui River, Fuhe River, Raohe River, and Xinjiang River. The topography in Poyang Lake Basin is diverse, mainly including mountains, hills, and alluvial plains. Mountains spread mainly in the western and eastern parts, with a maximum elevation of 1,800 m above the sea level, while low alluvial plains are primarily in its central areas, mainly distributed in areas along Ganjiang River.

Effects of climate change on annual streamflow

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Fig. 1 The study area, Poyang Lake Basin located in southern China. Seventy-nine weather stations and four gauge stations are also shown

To estimate the impacts of climate change on the historical streamflow variations, four typical watersheds inside Poyang Lake Basin were chosen by considering: (1) the continuity and completeness of long-term hydroclimatic observations; (2) the locations of watersheds in Poyang Lake Basin; and (3) the footprint of human activities (e.g., the hydropower production and navigation). The selected four typical watersheds, Meigang, Saitang, Gaosha, and Xiashan, have less human influences and fewer missing data than other watersheds. They are located in the northeast, southeast, middle-west, and northwest parts of Poyang Lake Bain (Fig. 1), respectively. The boundaries of individual watershed were delineated using the hydrological analysis tools of ArcGIS 9.2 software package based on the 90 m STRM_Version1 (http://dds.cr.usgs.gov/srtm/) digital elevation model (DEM) data. The drainage areas are 1.53×104, 3.07×103, 5.22×103, and 1.59×104 km2 for Meigang, Saitang, Gaosha, and Xiashan, respectively. The dominant land use/cover type in the four watersheds is forest based on the statistics of the 1×1 km resolution land-use/cover map in 2000 provided by the Environmental and Ecological Science Data Center for West China, National Natural Science Foundation of China (http://westdc.westgis.ac.cn). Forests cover 67.19, 62.62, 82.36 and 73.8 % of the watersheds in Meigang, Saitang, Gaosha, and Xiashan, respectively. The percentages of other land use/cover types are listed in Table 1. The study region belongs to the subtropical monsoon climate zone and it has a temperate and humid climate with

abundant sunlight (annual sunshine duration ranged from 1,473.3 to 2,077.5 h during the period of 1961–2000). Precipitation and PE (the left panel of Fig. 2) both exhibit distinct seasonality. For each watershed, monthly precipitation increases very quickly from January to June, and then decreases sharply, while monthly PE increases from January, peaks in July and then decreases. The 40-year (1961–2000) means of precipitation and PE (the right panel of Fig. 2) are above 1,500 and 1,100 mm, respectively. 2.2 Data processing Daily meteorological data from 1961 to 2000 collected from 79 weather stations (six stations are outside Poyang Lake Basin) are used in this study (Fig. 1), including daily

Table 1 Percentages of land-use/cover in the four watersheds in 2000 Land-use/ cover

Meigang (%)

Saitang (%)

Gaosha (%)

Xiashan (%)

Forested land Arable land Settlement Water Orchard Pasture

67.19 26.11 1.08 1.46 0.39 3.79

62.62 26.73 2.13 0.89 0.53 7.09

82.36 14.59 0.38 0.77 0.17 1.72

73.8 17.35 0.69 1.01 0.44 6.71

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Fig. 2 Monthly and annual mean precipitation a and PE b averaged for the period of 1961 to 2000

precipitation (P, millimeters), sunshine percentage (percent), wind speed (U10, meters per second), maximum temperature (degrees Celsius), minimum temperature (degrees Celsius), mean temperature (degrees Celsius), actual water vapor pressure (ea, kilopascals), and relative humidity of air (percent). They were collected from the National Climatic Centre of the China Meteorological Administration. As there are only two weather stations with radiation observed in the study area, the methods proposed by Wang (2006) and Tong (1989) are used to estimate daily total incoming solar radiation (megajoule per square meter per day) and long-wave radiation (megajoule per square meter per day), respectively. The net radiation (megajoule per square meter per day) is calculated the difference between the total incoming solar radiation and the long-wave radiation. The major input climate variable for computing the total incoming solar radiation is sunshine percentage, while the long-wave radiation is estimated using the mean temperature, actual water vapor pressure and sunshine duration. Details about the equations and related parameters are documented in Wang (2006) and Tong (1989). The daily PE (millimeters) is calculated using the Penman method modified by Tong (1989). The equation can be expressed as:

PE ¼

Δ Rn G Δ þ f ðU2 Þ ðes eaÞ Δþg l Δþg

ð1Þ

where Δ (kilopascal per degree Celsius) is the slope of the saturation vapor pressure curve; γ (kilopascal per degree Celsius) is the psychometric constant; λ (megajoule per millimeter) represents the latent heat of evapotranspiration. Rn (megajoule per square meter per day) is the net radiation; G (megajoule per square meter per day) is the soil heat flux density and assumed to be zero at the annual time step. f(U2) (millimeter per kilopascal per day) is the function of wind speed (Sun et al. 2010); U2 is the wind speed at 2-m height which is converted from the wind speed at 10-m height; es (kilopascal) is the saturation vapor pressure; ea (kilopascal) is the air vapor pressure. The various items in Eq. (1) are calculated following Allen et al. (1998). Methods of inverse distance weighted (IDW), spline function, and kriging are often employed to spatially interpolate the meteorological variables. Our previous work found no evident differences in the average annual values of climate variables in the study region among the three interpolation methods. Hence, the spline function method in the ArcGIS 9.2 platform is employed to interpolate the


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annual total precipitation and PE of 79 stations into a resolution of 1×1 km. The time series of the regional means in the four watersheds are calculated for the period from 1961 to 2000 to assess the impacts of climate on streamflow variations. The hydrological data used in this study are the daily streamflow (1961–2000) measured at Meigang, Saitang, Gaosha, and Xiashan gauge stations (Fig. 1). For the convenience of comparing and interpreting the characteristics of the streamflow, precipitation, and PE in all of the watersheds, we have transformed the unit cubic meter per second of the streamflow into mm using their catchment area. There are two data quality issues that need to be mentioned here: (1) missing values and (2) inhomogenity. If the days with missing values in 1 month are less than 15, the daily missing hydroclimatic variables are interpolated using the simple linear method. For the present study, the days with missing values for climate variables are all less than 15 for each month, and the daily streamflow at the four hydrological stations has no missing values. For the long-term observational record, data inhomogeneities, if not accounted for properly, can largely influence the robustness of research results (Wijngaard et al. 2003). Because the information about station history metadata is sporadic and not publically available, we cannot adjust the data homogeneity. However, we have applied the Pettitt test, a method for testing time series homogeneity (Wijngaard et al. 2003), to our data and find that precipitation and PE at most of stations pass the significant test at the 5 % level. Additionally, the Pettitt test for the time series of the regional averaged precipitation and PE found no inhomogeneous points in each watershed during the period of 1961–2000. For the present study, we believe that the effect of the time series inhomogeneity on our results is limited as the regional averaged precipitation and PE are used to analyze the cause of streamflow changes.

3 Methodology

T ¼r

.pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ð n 2Þ ð 1 r 2 Þ

ð3Þ

The significant level of a linear trend can be obtained by comparing the T value with Student’st test. 3.2 Timing of the mass center of streamflow The timing of the mass center of the streamflow (Stewart et al. 2005) is calculated from: CT ¼

P ðt Q Þ Pi i Qi

ð4Þ

where ti (day) is the time in days from the beginning of the water year (1 January), and Q i (millimeter) is the corresponding streamflow for water year day i. 3.3 Changes of streamflow with precipitation and PE To estimate the impacts of climate change/variability on streamflow, Risbey and Entekhabi (1996) proposed a method that the annual departures for streamflow, precipitation, and temperature are plotted on a precipitation-temperature plane so that each point in the plane represented 1 year of observed data. Contours of streamflow percentage change are interpolated with these points using the adjustable tension continuous curvature surface gridding algorithm proposed by Smith and Wessel (1990). Fu et al. (2007a, b) pointed out that the method is just one of many interpolation methods and may not be the best one for a specific watershed. The ArcGIS Geostatistical Analyst model in the ArcGIS 9.2 platform provides two groups of interpolation techniques: deterministic (including IDW, global polynomial, local polynomial, and radial basis functions) and geostatistical models [Kriging (including ordinary, simple, universal, and disjunctive)]. By comparing these interpolation methods, the best fitting contours can be obtained to analyze the relationships between precipitation, PE, and streamflow (Fu et al. 2007a, b; Liu and Cui 2011).

3.1 Temporal trends of hydroclimatic variables 3.4 Contribution of precipitation and PE to streamflow The trend of a hydroclimatic variable can be fitted using the following linear equation as: bxt ¼ a0 þ a1 t ðt ¼ 1; 2; . . . ; nÞ

ð2Þ

where bxt , a0, a1, and t represent the fitted value of the variable, intercept, temporal trend, and time, respectively; n (n040) is the sample size. A positive (negative) value of a1 indicates an increasing (decreasing) trend. A larger magnitude of a1 denotes a stronger trend. To test the probability of whether the linear trend value is statistically significantly different from zero, the statistic variable (T, Huang and Chen 2011) is given as:

For unimpaired catchments with streamflow that is not subject to regulation or diversion, the streamflow can be expressed using a function of climate variables without consideration of the catchments characteristics: Q ¼ F ðP; PE Þ

ð5Þ

where Q (millimeters) represents the streamflow. P and PE are the dominant climate factors on the hydrological cycle. Following Eq. (5), variations in the streamflow caused by climate changes can be approximated as:

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0

0

ΔQ ¼ FP ΔP þ FE ΔPE

ð6Þ

where ΔQ (millimeters), ΔP (millimeters), and ΔPE (millimeters) are changes in the streamflow, precipitation, 0 0 and PE, respectively, with FP ¼ @Q=@P and FE ¼ @Q=@PE. From the aspect of water balance, P describes the water supply while PE represents the evaporative demand/energy supply. PE can better represent the effects of climate change on water balance than temperature (Nash and Gleick, 1991; Fu et al. 2007a, 2007b) because PE integrates the effects of temperature, wind speed, solar radiation, and vapor pressure as expressed in the Penman equation. To evaluate the sensitivity of streamflow to climate changes, Schaake (1990) introduced the climate elasticity of streamflow (εX). Dooge (1992) and Dooge et al. (1999) termed it a sensitivity factor, while Kuhnel et al. (1991) termed it a magnification factor. εX is defined by the proportional change in streamflow divided by the proportional change in a climate variable (X), such as precipitation or PE. It can be expressed as: ΔQ Q "X ¼ ð7aÞ ΔX X To overcome the numerical problem that εX approaches infinity with X approaching X , Sankarasubramanian et al. (2001) have further verified the nonparametric estimator as: ΔQ X "X ¼ median ð7bÞ ΔX Q Based on the Eq. (7a), εX is a conditional climate elasticity of streamflow index accounting for the effects of other climate variables (e.g., εP for precipitation and εPE for PE). Under the assumption of no changes in catchment water table and soil moisture, Eqs. (6) and (7a) can be rewritten as: 8 ΔQP =Q 0 > > > "P ¼ ΔP=P > < ΔQPE =Q 0 "PE ¼ ΔPE PE > = > > > : ΔQ ¼ ΔQ þ ΔQ ¼ "0 ΔP þ "0 ΔPE Q P PE P PE P PE

ð7cÞ

to obtain the climate elasticity of a single climate variable; where ΔQP (millimeters) and ΔQPE (millimeters) represent the streamflow variation due to the changes in precipitation and PE, respectively. 0 0 Combining Eq. (7c) and Budyko hypothesis ["P þ "PE ¼ 1, (Budyko 1948)], we can get: 8 > ΔQ Q ΔPE PE 0 > > "P ¼ > < ΔP P ΔPE PE ð8aÞ > ΔP P ΔQ Q 0 > > > : "PE ¼ ΔPP ΔPEPE

Using Eq. (7b), the modified climate elasticity of streamflow can be expressed as: 8 ΔQ=QΔPE =PE 00 > > < "P ¼ Median ΔP=PΔPE=PE ð8bÞ ΔP=PΔQ=Q 00 > > : "PE ¼ Median ΔP PΔPE PE = = By differentiating Eq. (7c), the contribution of precipitation or PE alone to the percentage streamflow is quantified as: 8 d ðΔQÞ d ðΔQ Þ d ðΔQ Þ P PE > < dt ¼ 0 dt þ dt ΔP ΔQP ¼ "P P Q ð9Þ > :ΔQ ¼ "0 ΔPE Q PE PE PE where

d ðΔQX Þ dt

(millimeter per year) represents the temporal

Þ trend of the streamflow due to precipitation or PE only; d ðΔQ dt (millimeter per year) is the temporal trend of the streamflow in the past 40 years.

3.5 Coefficients of correlation and partial correlation among precipitation, PE and streamflow To compare the accuracy and effectiveness of the climate elasticity of streamflow from Eqs. (7b) and (8b), the coefficients of correlation and partial correlation (Wei 2003) among precipitation, PE and streamflow are calculated. The correlation coefficients can be written as: n P

ρx;y

½ðxi xÞ ðyi yÞ ¼ sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi i n h P ð xi xÞ 2 ð yi yÞ 2 i¼1

ð10Þ

i¼1

where ρx,y represents the correlation coefficients among precipitation, PE and streamflow. ρP,PE, ρP,Q and ρPE,Q are the correlation coefficients of P and PE, P and Q, and PE and Q, respectively. Thus, the partial correlation coefficient of Q and P can be expressed with: ρQ;P ρQ;PE ρP;PE ffi ρP;QPE ¼ sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 2 h

2 i 1 ρQ;PE 1 ρP;PE

ð11Þ

and the partial correlation coefficient of Q and PE is calculated using: ρQ;PE ρQ;P ρP;PE ffi ρPE;QP ¼ sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 2 h

2 i 1 ρQ;P 1 ρP;PE

ð12Þ

The significance level of correlation and partial correlation coefficients is evaluated with Pearson’s test.


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Fig. 3 Monthly and annual streamflow averaged for the period of 1961 to 2000

4 Results and analyses 4.1 Changes of climate and streamflow 4.1.1 Climatology of monthly and annual streamflow Figure 3 shows the measured monthly and annual streamflow averaged over the period from 1961 to 2000 for the four watersheds. Overall, the monthly streamflow increases from January, peaks in June and thus decreases sharply from July, following the seasonal patterns of precipitation (Fig. 2a). The 40-year means of annual streamflow are

Fig. 4 Trends of monthly streamflow from 1961–2000

1,198.22, 868.38, 963.78, and 877.37 mm for Meigang, Saitang, Gaosha, and Xiashan watersheds, respectively. Obviously, the streamflow magnitude differs among the watersheds, possibly due to differences in the watersheds scales, land-use/cover, soil types, terrain and geological conditions. 4.1.2 Trends of streamflow Figure 4 depicts the trends of monthly streamflow from 1961 to 2000 in the four watersheds. For all the watersheds, the monthly streamflow decreases in the late spring or the

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Table 2 Decadal variations of streamflow in the four watersheds Watersheds

1961–1970 1960s (mm)

1971–1980 1970s (mm)

1981–1990 1980s (mm)

1991–2000 1990s (mm)

Meigang Saitang Gaosha Xiashan

1,117.43 860.98 888.47 806.80

1,138.01 754.49 919.64 884.96

1,108.13 871.57 900.61 877.14

1,429.30 1,429.30 986.48 1,146.40

earlier summer, with a trend of −2.51 mm year−1 in June in Xiashan passing the significance level of 5 %. In the other months, the monthly streamflow shows increasing trends, of which Meigang in March, August, and September; Saitang in January, March, and October; Gaosha in January, August, October and December; and Xiashan in August are statistically significant at the 5 % level. Larger differences in the magnitude of the monthly trends for the same basin are mainly due to the different magnitudes of the monthly climatological streamflow. Streamflow shows distinct inter-annual and decadal variations in each watershed. The decadal means of streamflow

Fig. 5 Time series of annual streamflow from 1961–2000

are higher in 1990s than in the other periods for all watersheds (Table 2). Meigang and Saitang watersheds have the lowest streamflow in 1980s (1,108.13 mm) and 1970s (754.49 mm), respectively, while the lowest streamflow appears in 1960s in Gaosha and Xiashan. For each watershed, the annual streamflow (Fig. 5) shows overall increasing trends during the study period. The streamflow increases at the 5 % significance level in Meigang and Gaosha, with a trend of 9.87 and 7.72 mm year−1, respectively. It also increases in Saitang and Xiashan watersheds, but with a small magnitude. To further explore the regional variations in the streamflow trends, the trends of daily streamflow quantile (Fig. 6) and the timing of the mass center of annual streamflow (CT, not shown) are examined, respectively. For all the watersheds, the less than 90th percentile of daily streamflow all increases at different rates, most of which pass the significance level of 5 %. Interestingly, the trends of more than 90th percentile of daily streamflow, however, fall into two groups: a sharp increase in Meigang and Gaosha watersheds and a sharp decrease in Saitang and Xiashan. Analyzing the trends of daily precipitation and PE percentiles during the same period (figures are not shown for brevity) indicates that the increases in the lower daily streamflow percentiles is due to the decreasing daily PE, while the increases in

Fig. 6 Trends of percentile of daily streamflow during the period 1961–2000


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Table 3 Temporal trends of precipitation and PE in the four watersheds

Precipitation (mm year−1) PE (mm year-1) a

Meigang

Saitang

Gaosha

Xiashan

9.05

2.68

8.72

2.44a

−5.50a

−6.23a

−3.69a

−5.27a

The trend is statistically signifcant at the 5 % levels

Meigang and Gaosha watersheds and the decreases in Saitang and Xiashan watersheds for the higher (more than 90th) daily streamflow percentiles may be caused by the changes in daily precipitation and the location of each watershed. Except for the decreased CT in Xiashan, CTs in the other three watersheds display increasing trends. However, the trends are very small and do not pass the significance test in each watershed.

methods for each basin and ΔQ Q are given in Table 4 and Fig. 7, respectively. Although the magnitudes and patterns of the streamflow responses to changes in precipitation and PE differ somewhat among the four watersheds, generally the streamflow is related positively to precipitation but negatively to PE, with a greater sensitivity to precipitation than to PE. In dry years (i.e., 1 year with a negative precipitation anomaly), the streamflow in Meigang and Saitang watersheds shows a nonlinear to weak linear relationship with increasing precipitation, while a linear response is found in wet years (i.e., 1 year with a positive precipitation anomaly). For Gaosha and Xiashan, the weak linear to nonlinear relationship between precipitation and streamflow in dry years is found with increasing precipitation, but a nonlinear response is found with the increasing precipitation in wet years. 4.3 Comparisons of climate elasticity of streamflow from two different methods

4.1.3 Temporal trends of annual precipitation and PE Temporal trends of the annual precipitation and PE from 1961 to 2000 are listed in Table 3. For precipitation, all the watersheds show a positive trend between 2.44 and 9.05 mm year−1, of which Xiashan watershed passes the significance level of 5 %, while the slopes of PE show statistically significant negative trends ranging from −3.69 to −6.23 mm year−1. 4.2 Relationship among precipitation, PE and streamflow

The annual streamflow percentage change ΔQ Q , as a function of the percentage change in annual precipitation

ΔP P and PE ΔPE PE , is interpolated with the ArcGIS Geostatistical Analyst. Johnston et al. (2001) recommended that the best geostatistical model for spatial interpolation is the one that produces the standardized mean (dimensionless) closest to zero, the smallest root-mean-square prediction error (streamflow change in percentage), the average standard error (streamflow change in percentage) closest to the root-mean square prediction error, and the standardized root-mean-square prediction error (dimensionless) closest to one. Accordingly, the prediction errors of the best interpolation

Table 4 Prediction errors of the best interpolation methods in ArcGIS Geostatistical Analyst

Based on the simple watershed balance model and Budyko hypothesis, most of the researchers (Kuhnel et al., 1991; Dooge, 1992; Sankarasubramanian et al., 2001; Sankarasubramanian and Vogel, 2002; Fu et al., 2007a, b) have applied the concept of climate elasticity to investigate the streamflow responses to climate change (e.g., precipitation, PE, temperature and the aridity index). However, it is known that there are interactions among individual climate variables, and such interactions may lead the calculation of streamflow response to a single variable to be inaccurate and even unreasonable. To solve the problem of the interactions between precipitation and PE, we have derived the modified version [Eq. (8a)] of climate elasticity of streamflow based on the original one [Eq. (7a)] and Budyko hypothesis. Using the two versions of climate elasticity, the elasticity of streamflow to precipitation and PE are calculated and listed

00 in Table 5. Evidently, for each watershed "P "P ranges from

00 1.44 to 1.94 (from 1.41 to 1.67) while "PE "PE is between −2.83 and −1.67 (between −0.67 and −0.41). The signs of

00

00 "P "P and "PE "PE indicates that the streamflow is expected to have a positive trend with the increasing precipitation but a negative trend with the increases in PE. The streamflow responses to PE in Meigang, Saitang, and Gaosha

Basin

Methods

Mean

Standardized Root-mean- Average Root-mean-square mean square standard error standardized


Ordinary kriging Disjunctive kriging Disjunctive kriging Universal kriging with first order of trend

−0.013 0.0085 −0.01 0.0015

−0.043 0.042 0.13 0.13

0.20 0.15 0.17 0.15

0.17 0.15 0.13 0.13

1.08 1.03 1.09 1.08

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Fig. 7 Contour plot of percentage streamflow change as a function of percentage precipitation change and PE change

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Table 5 Elasticity of precipitation and PE to streamflow Watersheds


Equation (6b)

4.4 Quantifying the streamflow response to precipitation or PE alone

Equation (7a) 00

00

"P

"PE

"P

"PE

1.61 1.44 1.74 1.94

−2.40 −1.67 −2.83 −1.93

1.57 1.41 1.67 1.61

−0.57 −0.41 −0.67 −0.61

(excluding Xiashan) estimated using the original method are much more sensitive to precipitation than those estimated using the modified method, indicating that the interaction between precipitation and PE may have misestimated the streamflow response to PE significantly. Next, we use the correlation analysis to further investigate the differences of the two methods. The positive correlation between precipitation and Q (ρP,Q) and the negative correlation between PE and Q (ρPE,Q) are statistically significant at the 1 % level in each watershed (Table 6), suggesting that the streamflow will increase (decrease) with the increasing (decreasing) precipitation, but decrease (increase) with the increasing (decreasing) PE. Moreover, ρP,Q greater than ρPE,Q for each watershed reveals that the impact of precipitation on the streamflow is more important than that of PE. All the correlation coefficients between precipitation and PE (ρP,PE) are statistically significant at the 1 % level, indicating that the interactions between precipitation and PE are stronger. Eliminating the effects of PE’s (precipitation’s) effects on the streamflow, the partial correlation coefficients ρP,Q-PE (ρPE,Q-P) between precipitation and Q (between PE and Q) are listed in Table 6. The smaller differences between ρP,Q and ρP,Q-PE indicate that the effects of precipitation on the streamflow are related independently to the smaller effects of PE, while the greater differences between ρPE,Q and ρPE,Q-P suggest that the effects of PE on the streamflow are related dependently to the bigger effects of precipitation.

Table 6 Coefficients of correlation and partial correlation among annual precipitation, PE, and streamflow Watersheds

ρP,PE

ρP,Q

ρPE,Q

ρP,Q-PE

ρPE,Q-P


−0.65a −0.57a −0.52a −0.55a

0.97a 0.90a 0.94a 0.94a

−0.58a −0.58a −0.47a −0.52a

0.96a 0.85a 0.92a 0.91a

0.32 −0.19 0.09 −0.03

ρP,PE correlation coefficients of P and PE, ρP,Q correlation coefficients of P and Q, ρPE,Q correlation coefficients of PE and Q, ρP,Q-PE represents the partial correlation coefficient of Q and P, ρPE,Q-P the partial correlation coefficient of Q and PE a

Correlation coefficient is statistically significant at the 1 % level with Pearson’s test

The analyses in “Section 4.3” show that the modified climate elasticity of streamflow is more reasonable than the original one, and the response of streamflow in each watershed is more sensitive to precipitation than that to PE. Using the modified climate elasticity, we can estimate the annual streamflow responses to changes in precipitation or PE only. The analyses indicate that a 10 % increase (decrease) in precipitation will result in the annual streamflow increase (decrease) by 15.7, 14.1, 16.7, and 16.1 % in Meigang, Saitang, Gaosha and Xiashan, respectively. Whereas a 10 % increase (decrease) in PE will decrease (increase) the annual streamflow by −5.7, −4.1, −6.7, and −6.1 % in Meigang, Saitang, Gaosha, and Xiashan, respectively. 4.5 Contribution of climate change to the past streamflow variations Considering the interannual variations of climate elasticity, the contribution of precipitation or PE only to the streamflow changes are calculated with the equation of d ðΔQX Þ=dt (Table 7). In the past 40 years, the increases in precipitation and the decreases in PE cause the streamflow to increase, with the former as the dominant contributor. The percentage contributions of precipitation in Meigang and Xiashan watershed are both more than 80 %, while the percentage contribution of PE is less than 20 %. In summary, the above analyses indicate that the major reasons for the increasing streamflow trends in all of the four watershed basins are the increases in precipitation. This conclusion is consistent with the previous one by Zhao et al. (2009) and Sun et al. (2012) that precipitation is the major determinant of streamflow in Poyang Lake Basin. On the other hand, Sun et al. (2012) also stated that the percentage variations of the streamflow during 1961–2000 caused by the increases in precipitation ranged from 30–77 %, while the streamflow percentage changes due to the decreasing PE were between 14 and 45 %. The differences in methodologies and variables considered [such as the river level used in the study of Sun et al. (2012)] are possibly the main reasons for the small differences between our results and others.

5 Discussion Climate change can influence the streamflow through complicated interactions among climate, vegetation, soil and hydrological processes at the basin scale, and such interactions depend on many factors (Dooge et al. 1999; Vogel et al. 1999; Sankarasubramanian et al. 2001). Therefore,

180 Table 7 Contribution of precipitation and PE to streamflow change

S. Sun et al.

Watersheds

Streamflow Change d ðΔQÞ dt

−1

(mm year )

Attribution of precipitation . d ðΔQP Þ dt

(%)

d ðΔQÞ dt

d ðΔQP Þ dt

Attribution of PE .

−1

(mm year )

d ðΔQPE Þ dt

(%)

d ðΔQÞ dt

d ðΔQPE Þ dt

(mm year−1)

Meigang

9.87

84

8.30

16

1.57

Saitang Gaosha Xiashan

4.35 7.72 2.99

62 59 88

2.70 4.52 2.63

38 41 12

1.65 3.20 0.36

ignoring these interactions to estimate the impacts of climate change on the annual streamflow from the elasticity may contain some uncertainties (Fu et al. 2007b). 5.1 Potential effects of the seasonal changes in precipitation and PE on streamflow From the perspective of the water balance, the annual streamflow depends on the differences between the annual amount of precipitation and PE in any watershed. It is known that the changes in seasonal precipitation and PE can also influence the annual streamflow generation. In the present study, we find that the annual CTs of precipitation and PE in each watershed during the period of 1961–2000 vary at different rates, respectively, indicating that their seasonal distributions have changed. Hence, the streamflow generation in the first half year increases because of more precipitation and less PE, whereas less precipitation and more PE in the second half year decrease the streamflow generation. However, whether or how the changes in the seasonal precipitation and PE influence the annual streamflow depends on the differences between the streamflow increases in the first half year and the streamflow decreases in the second half year. 5.2 Potential effects of land-use/cover on streamflow As land-use/cover transforms, the characteristics of the hydrological cycle (e.g., the interception of vegetation, infiltration, evapotranspiration, and runoff generation processes) will change and thus impact the relationships among precipitation, PE, and streamflow (Boyer and Pasquarell 1999; Rodda et al. 2001; Kirkby et al. 2002). Recently, the impacts of land-use/cover change (LUCC) on streamflow have been a hot spot issue. For example, Schilling et al. (2008) applied the Soil and Water Assessment Tools (SWAT) model to assess the impacts of LUCC on the annual and seasonal streamflow of the Raccoon River watershed in West Central Iowa, and pointed out that future LUCC change will affect the water balance of the watershed, with consequences largely dependent on the future LUCC trajectory. Using the SWAT model, Guo et al. (2008) analyzed the responses of

streamflow to LUCC in Xinjiang River Basin of China, and found that the variation of annual streamflow was mainly influenced by climate change rather by LUCC; He et al. (2008) stated that LUCC could influence the processes of storm-flood, evapotranspiration, streamflow, and runoff generation significantly in Hei River Basin, China. Therefore, ignoring of the LUCC’s effects on streamflow may introduce some uncertainties in our estimates presented here. On the other hand, some investigations (Liu et al. 2003; Nearing et al. 2005; Guo et al. 2008; Oudin et al. 2008; Qi et al. 2009) found that the change of annual streamflow were mainly caused by climate change while the effects of LUCC are limited. As this analysis is focused on the annual streamflow, how and to what extent LUCC impacts on annual streamflow may be small but should be further investigated using other methods [e.g., SWAT or Variable Infiltration Capacity model]. 5.3 Potential effects of vegetation on streamflow Vegetation as an important component of earth–atmosphere system, always controls transpiration, and even influences the water cycle. However, plant physiology (e.g., stomatal) and structures (e.g., leaf area index, LAI) will change obviously with increases of CO2 concentrations and a warming climate (Wilson et al. 1999; Medlyn et al. 2001; Wullschleger et al. 2002; Pritchard et al. 1999; Cowling and Field 2003), flowing by the enhancement or weakness of plant transpiration (Gedney et al. 2006; Piao et al. 2007; Field et al. 1995; Cowling and Field 2003). Shao et al. (2010) and Gu et al. (2009) pointed out that Normalized Difference Vegetation Index in each watershed increased during 1982–1999, indicating that the enhancement of vegetation activities may increase plant transpiration and even decrease the streamflow. However, the increases (decreases) in stomatal or LAI will generally accelerate (weak) plant transpiration, and the water cycle changes depend mainly on their combined impacts. 5.4 Potential effects of human activities on streamflow Except for the potential effects of LUCC and vegetation, human activities can also significantly influence the streamflow by river regulation, hydropower plants, and other


activities. Since the late 1960s, Poyang Lake Basin had been used for high head hydropower production and navigation. By the end of 2005, there were 315 hydropower stations operating in Jiangxi Province, which produced above 1,000 kW of electricity (Zhao et al. 2009). The establishment and management of these hydropower plants and their storage reservoirs may cause a considerable decrease in the streamflow due to the water releases and storages (Ye et al. 2009). Although we tried our best to select the watersheds with the less anthropogenic impacts, the human activities may also play a role in changing the annual streamflow.

6 Conclusions As an important part of the hydrological cycle, the change of streamflow can significantly affect water resources, society safety and ecosystem health. It can be used as an indicator of climate change owing to the intimate linkage between the hydrology and climate. In the present study, four typical watersheds in Poyang Lake Basin are selected to quantify the changes in the monthly streamflow, the daily streamflow percentile, the annual streamflow CT and the annual streamflow, using the historical streamflow data during the period of 1961–2000 from Meigang, Saitang, Gaosha and Xiashan watersheds. Our results show that: (1) the monthly streamflow in each watershed generally increases, except for the decrease in the months near to later spring or earlier summer; (2) The less than 90th percentile of daily streamflow in each watershed increases sharply, whereas the more than 90th percentile daily streamflow increases in Meigang and Gaosha watersheds, and decreases in Saitang and Xishan watersheds; (3) the CTs in all the watersheds exhibit no apparent changes; (4) The annual streamflow increases at different rates ranging from 2.99 to 9.87 mm year−1, particularly in Meigang and Gaosha watersheds where the increase is statistically significant at the 5 % level. The hydrological processes are influenced by many aspects, such as climate, soil, vegetation, human activities, and their interactions. Climate change (e.g., precipitation and PE) is associated closely with the variations in the hydrological processes. The interactions among climate variables will complicate the accurateness of results applying the original climate elasticity by Schaake (1990). Therefore, we develop a modified climate elasticity of streamflow method based on the original one and Budyko hypothesis (Budyko 1948). By combining with the correlation analysis among precipitation, PE, and streamflow, the modified climate elasticity is shown to be more reasonable and accurate than the original one. The analyses based on the modified climate elasticity in the four watersheds indicates that a 10 % increase (decrease) in precipitation will increase (decrease) the annual streamflow by 14.1–16.3 %, while a

181

10 % increase (decrease) in PE will decrease (increase) the annual streamflow by −10.2 to −2.1 %. Under the assumption that the influences of other factors on the annual streamflow can be ignorable, the increasing trend of the annual streamflow over the past 40 years in each watershed is primarily due to the increase in precipitation, with the percentage attribution more than 59 %. Acknowledgments This work was jointly supported by the National Basic Research Program of China (grant no. 2011CB952000) and the National Natural Science Foundation of China (grant no. 40775061 and grant no. 41075082). The authors would like to thank all the data providing agencies and people who supported the research. Thanks also go to Dr. Liming Zhou (SUNY, Albany) and Dr. Yixing Yin (NUIST, Nanjing) for their careful review of the language and kind help in improving the English translation.

References Allen RG, Pereira LS, Raes D, Smith M (1998) Crop evapotranspirationguidelines for computing crop water requirements—FAO Irrigation and Drainage Paper 56. FAO, 1998. ISBN 92-5-104219-5 Budyko MI (1948) Evaporation under natural conditions. Jerusalem: English translation by Isr Program for Sci Transl. Office of Technical Services, U.S. Dept. of Commerce, Washington. Boyer DG, Pasquarell GC (1999) Agricultural land use impacts on bacterial water quality in a karst ground-water aquifer. J Am Water Resour As 35(2):291–300 Chen H, Guo S, Xu C, Singh VP (2007) Historical temporal trends of hydro-climatic variables and runoff response to climate variability and their relevance in water resource management in the Hanjiang Basin. J Hydrol 344(3–4):171–184 Cowling SA, Field CB (2003) Environmental control of leaf area production: implications for vegetation and land-surface modeling. Global Biogeochem Cycles 17:1007. doi:10.1029/ 2002GB001915 Ding Y, Dai X (1994) Temperature variation in China during the last 100 years. Meteorol Month 20:19–26 Dooge JCI (1992) Sensitivity of runoff to climate change: a Hortonian approach. Bull Am Meteorol Soc 73:2013–2024 Dooge JCI, Bruen M, Parmentier B (1999) A simple model for estimating the sensitivity of runoff to long-term changes in precipitation without a change in vegetation. Adv Water Resour 23:153– 163 Field CB, Jackson RB, Mooney HA (1995) Stomatal responses to increased CO2: implications from the plant to the global scale. Plant Cell Environ 18(10):1214–1225 Fu GB, Barber ME, Chen S (2007a) The impacts of climate change on regional hydrological regimes in the Spokane River watershed. J Hydrol Eng 12:452–461 Fu GB, Charles SP, Chiew FHS (2007b) A two-parameter climate elasticity of streamflow index to assess climate change effects on annual streamflow. Water Resour Res 43:W11419. doi:10.1029/2007wr005890 Gedney N, Cox PM, Betts RA, Huntingford C, Stott PA (2006) Detection of a direct carbon dioxide effect in continental river runoff records. Nature 439(7078):835–838 Gu TT, Zhou SQ, Shao BF, Li Q (2009) Interactions between vegetation cover change and precipitation in Poyang Lake region. Chin J Ecol 28(6):1060–1066 (in Chinese with English Abstract)

182 Guo H, Qi H, Jiang T (2008) Annual and seasonal streamflow responses to climate and land-cover changes in the Poyang Lake basin, China. J Hydrol 355(1–4):106–122 He H, Zhou J, Zhang W (2008) Modelling the impacts of environmental changes on hydrological regimes in the Hei River Watershed, China. Glob Planet Chang 61:175–193 Hu Q, Feng S, Guo H, Chen G, Jiang T (2007) Interactions of the Yangtze river flow and hydrologic processes of the Poyang Lake, China. J Hydrol 347(1–2):90–100 Huang ZP, Chen YF (2011) Hydrostatistics. China Water Power Press, Beijing, p 209 (in Chinese) IPCC (2001) Climate change 2001: the scientific basis. Summary for policymakers and technical summary of Working Group I report. Cambridge University Press, Cambridge IPCC (2007) Climate Change 2007: the physical science basis. In: Solomon S, Qin D, Manning M, Chen Z, Marquis M, Averyt KB, Tignor M, Miller HL (eds) Contribution of Working Group I to the fourth assessment report of the intergovernmental panel on climate change. University Press, Cambridge Johnston K, Ver Hoef JM, Krivoruchko K, Lucas N (2001) Using ArcGIS geostatistical analyst. ESRI Press, Redlands Jiang T, Shi Y (2003) Global warming and its consequences in Yangtze River floods and damages. Adv Earth Sci 18:277–284 (in Chinese with English Abstract) Jiang Z (2007) Analysis and research about loading ability of the water environment of Poyang Lake. Nanchang University, Nanchang, China, p 13 (in Chinese with English abstract) Kirkby M, Bracken L, Reaney S (2002) The influence of land use, soils and topography on the delivery of hillslope runoff to channels in SE Spain. Earth Surf Process Landforms 27(13):1459–1473 Kuhnel V, Dooge JCI, O’Kane JPJ, Romanowicz RJ (1991) Partial analysis applied to scale problems in surface moisture fluxes. Surv Geophys 12:221–247 Kundzewicz ZW, Mata LJ, Arnell NW, Döll P, Kabat P, Jiménez B, Miller KA, Oki T, Sen Z, Shiklomanov IA (2007) Freshwater resources and their management. In: Parry ML, Canziani OF, Palutikof JP, van der Linden PJ, Hanson CE (eds) Climate Change 2007: impacts, adaptation and vulnerability. Cambridge University Press, Cambridge, U.K, Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, pp 73–210 Li Q, Li W, Si P, Gao X, Dong W, Jones P, Huang J, Cao L (2010) Assessment of surface air warming in northeast China, with emphasis on the impacts of urbanization. Theor Appl Climatol 99(3–4):469–478 Lins HF, Slack JR (1999) Streamflow trends in the United States. Geophys Res Lett 26(2):227–230 Liu C, Fu G (1993) Analysises of hydrological response to climatic changes in China. In: Zhang P, Zhang Y (eds) Climate change and influence. China Meteorological Press, Beijing, China, pp 205– 213 (in Chinese with English Abstract) Liu C, Li D, Tian Y, Hao F, Yang G (2003) An application study of DEM based distributed hydrological model on macroscale watershed. Prog Geogr 22(5):437–445 (in Chinese with English Abstract) Liu Q, Cui B (2011) Impacts of climate change/variability on the streamflow in the Yellow River Basin, China. Ecol Model 222:268–274 Medlyn BE, Barton CVM, Broadmeadow MSJ, Ceulemans R, De Angelis P, Forstreute M, Freeman M, Jackson SB, Kellomäki S, Laitat E, Rey A, Roberntz P, Sigurdsson BD, Strassemeyer J, Wang K, Curtis PS, Jarvis PG (2001) Stomatal conductance of forest species after long-term exposure to elevated CO2 concentrations: a synthesis. New Phytol 149:247–264 Milly PCD, Dunne KA, Vecchia AV (2005) Global pattern of trends in streamflow and water availability in a changing climate. Nature 438:347–350

S. Sun et al. Nash LL, Gleick PH (1991) Sensitivity of streamflow in the Colorado Basin to climate change. J Hydrol 125(3–4):221–241 Nearing MA, Jetten V, Baffaut C, Cerdan O, Couturier A, Hernandez M, Bissonnais YL, Nichols MH, Nunes JP, Renschler CS, Souchère V, van Oost K (2005) Modeling response of soil erosion and runoff to changes in precipitation and cover. Catena 61:131– 154 Oki T, Musiake K, Matsuyama H, Masuda K (1995) Global atmospheric water balance and runoff from large river basins. Hydrol Process 9:655–678 Oudin L, Andréassian V, Lerat J et al (2008) Has land cover a significant impact on mean annual streamflow? An international assessment using 1508 catchments. J Hydrol 357:303– 316 Piao S, Friedlingstein P, Ciais P, de Noblet-Ducoudré N, Labat D, Zaehle S (2007) Changes in climate and land use have a larger direct impact than rising CO2 on global river runoff trends. P Natl Acda Sci 104(39):15242–15247 Pritchard S, Roges HH, Prior SA, Peterson CTM (1999) Elevated CO2 and plant structure: a review. Glob Chang Biol 5:807–837 Qi S, Sun G, Wang Y, McNulty SG, Moore MJA (2009) Streamflow response to climate and landuse changes in a coastal watershed in North Carolina. Trans ASABE 52(3):739–749 Risbey JS, Entekhabi D (1996) Observed Sacramento basin streamflow response to precipitation and temperature changes and its relevance to climate impact studies. J Hydrol 184:209– 223 Rodda HJE, Stroud MJ, Shankar U, Thorrold BS (2001) A GIS based approach to modelling the effects of land-use change on soil erosion in New Zealand. Soil Use and Management 17(1):30–40 Sankarasubramanian A, Vogel RM (2002) Annual hydroclimatology of the United States. Water Resour Res 38(6), doi:10.1029/ 2001WR000619 Sankarasubramanian A, Vogel RM, Limbrunner JF (2001) Climate elasticity of streamflow in the United States. Water Resour Res 37(6):1771–1781, doi:10.1029/2000WR900330 Schaake JC (1990) From climate to flow. In: Waggoner PE (ed) Climate Change and US Water Resources. John Wiley, New York USA, pp 177–206 Schilling KE, Jha MK, Zhang YK, Gassman PW, Wolter CF (2008) Impact of land use and land cover change on the water balance of a large agricultural watershed: historical effects and future directions. Water Resour Res, 44(W00A09), doi:10.1029/ 2007WR006644 Shao BF, Zhou SQ, Gu TT, Li Q (2010) Spatio-temporal changes of NDVI and their relationships with runoff in the Poyang Lake Basin. Chin J Agrometeorol 31(2):288–294 (in Chinese with English Abstract) Shankman D, Keim BD, Song J (2006) Flood frequency in China’s Poyang Lake Region: trends and teleconnections. Int J Climatol 26(9):1255–1266 Smith WHF, Wessel P (1990) Gridding with continuous curvature splines in tensions. Geophysics 55:293–305 Sun SL, Chen HS, Ju WM, Song J, Li JJ, Ren YJ, Sun J (2012) Past and future changes of streamflow in Poyang Lake Basin, South eastern China. Hydrol Earth Syst Sci 16, 2005-2020 Sun SL, Zhou SQ, Song J, Shi JH, Gu RY, Ma FM (2010) Change in pan evaporation and its driving factors in Jiangxi Province. Trans Chin Soc Agr Eng 26(9):59–65 (in Chinese with English Abstract) Stewart IT, Cayan DR, Dettinger MD (2005) Changes toward earlier streamflow timing across Western North America. J Clim 18:1136–1155 The Standing Committee of the National People’s Congress (NPC) of the People’s Republic of China (1994) The 21st agenda of China—population, environment and development white paper. Beijing, China (in Chinese)

Effects of climate change on annual streamflow Tong H (1989) A climatic calculation method for the evaporation power in China. Journal of Nanjing Institute of Meteorology 12 (1):20–33 (in Chinese with English Abstract) Vogel RM, Wilson I, Daly C (1999) Regional regression models of annual streamflow for the United States. J Irrig Drain Eng 125:148–157 Wang J (2006) Study on the empirical formula of solar radiation in Jiangxi Province. Jiangxi Energy (in Chinese with English Abstract) 3:53–56 Wang S, Dong D (2000) Enhancement of the warming trend in China. Geophys Res Lett 27(16):2581–2584 Wang S, Zhang Z, Sun G, McNulty SG, Zhang H, Li J, Zhang M (2008) Long-term streamflow response to climatic variability in the Loess Plateau, China. J Am Water Resour As 44(5):1098–1107 Wei FY (2003) The climatological statistical diagnosis and the prediction technologies. China Meteorological Press, Beijing Wijngaard JB, Tank AMGK, Konnen GP (2003) Homogeneity of 20th century European daily temperature and precipitation series. Int J Climatol 23:679–692. doi:10.1002/joc.906 Wilson KB, Carlson TN, Bunce JA (1999) Feedback significantly influences the simulated effect of CO2 on seasonal evapotranspiration from two agricultural species. Glob Chang Biol 5:903–917 Wullschleger SD, Gunderson CA, Hanson PJ, Wilson K, Norby R (2002) Sensitivity of stomatal and canopy conductance to elevated CO2 concentrationinteracting variables and perspectives of scale. New Phytol 153:319–331 Xiong L, Guo S (2004) Trend test and change-point detection for the annual discharge series of the Yangtze River at the Yichang hydrological station. Hydrol Sci J 49(1):99–112

183 Xu Z, Liu Z, Fu G, Chen Y (2010) Trends of major hydroclimatic variables in the Tarim River Basin during the past 50 years. J Arid Environ 74(2):256–267 Yang X, Zhang Y, Ding M, Liu L, Wang Z, Gao D (2010) Observation evidence of the impact of vegetation cover on surface air temperature change in China. Chin J Geophs 53 (4):833–841 (in Chinese with English Abstract) Ye X, Zhang Q, Liu J, Li LJ, Guo H (2009) Impacts of Climate Change and Human Activities on Runoff of Poyang Lake Catchment. Chin J Glaciol Geocryol 31(5):835–842 (in Chinese with English Abstract) Zhai P, Chao Q, Zou X (2004) Progress in China’s climate change study in the 20th century. J Geogr Sci 14(1):3–11 Zhao G, Hörmann G, Fohrer N, Zhai J (2009) Streamflow trends and climate variability impacts in Poyang Lake Basin, China. Water Resour Manag 24(4):689–706 Zhang J, Dong W, Wu L, Wei J, Chen P, Lee DK (2005) Impact of land use changes on surface warming in China. Adv Atmos Sci 22 (3):343–348 Zhang W, Xiao Z, Zheng J, Ren J (2007) Long-term variation features of streamflow and its response to climate change in Nujiang Basin. Chin Sci Bull 52(Supplement II):135–141 (in Chinese with English Abstract) Zhang X, Harvey KD, Hogg WD, Yuzyk TR (2001) Trends in Canadian streamflow. Water Resour Res 37(4):987–998 Zheng H, Zhang L, Zhu R, Liu C, Sato Y, and Fukushima Y (2009) Responses of streamflow to climate and land surface change in the headwaters of the Yellow River Basin. Water Resour Res 45: W00A19. doi:10.1029/2007WR006665