A Hybrid Model to Assess the Impact of Climate Variability on Streamflow for an Ungauged Mountainous Basin

Clim Dyn (2018) 50:2829–2844 DOI 10.1007/s00382-017-3775-x

A hybrid model to assess the impact of climate variability on streamflow for an ungauged mountainous basin

Chong Wang1,2,3 · Jianhua Xu1,2,3 · Yaning Chen4 · Ling Bai5 · Zhongsheng Chen6

Received: 22 October 2016 / Accepted: 21 June 2017 / Published online: 28 June 2017 © Springer-Verlag GmbH Germany 2017

Abstract To quantitatively assess the impact of climate climate, the streamflow respectively increased 1.5 × 108 and variability on streamflow in an ungauged mountainous 3.3 × 108 m3 per decade in the Kumarik River and the Tosh- basin is a difficult and challenging work. In this study, a kan River; and (3) the contribution of the temperature and hybrid model combing downscaling method based on earth precipitation to the streamflow, which were 64.01 ± 7.34, data products, back propagation artificial neural networks 35.99 ± 7.34 and 47.72 ± 8.10, 52.26 ± 8.10%, respectively (BPANN) and weights connection method was developed in the Kumarik catchment and Toshkan catchment. Our to explore an approach for solving this problem. To validate study introduced a feasible hybrid model for the assessment the applicability of the hybrid model, the Kumarik River of the impact of climate variability on streamflow, which and Toshkan River, two headwaters of the Aksu River, can be used in the ungauged mountainous basin of North- were employed to assess the impact of climate variability west China. on streamflow by using this hybrid model. The conclusion is that the hybrid model presented a good performance, Keywords Hybrid model · Assessment · Climate and the quantitative assessment results for the two head- variability · Streamflow · Ungauged mountainous basin · waters are: (1) the precipitation respectively increased by Northwest China 48.5 and 41.0 mm in the Kumarik catchment and Tosh- kan catchment, and the average annual temperature both increased by 0.1 °C in the two catchments during each dec- 1 Introduction ade from 1980 to 2012; (2) with the warming and wetting Global warming is an indisputable fact (Allen et al. 2009). * Jianhua Xu As a result, glaciers shrink worldwide, Northern Hemi- [email protected] sphere spring snow cover decreases in extent, precipitation

1 in North Hemisphere mid-latitude land areas increases, Key Laboratory of Geographic Information Science and extreme weather and climate events rises (Pachauri (Ministry of Education), East China Normal University, Shanghai 200241, China et al. 2014). The headwaters of inland rivers in Northwest China are mainly recharged by glaciers, snow meltwater 2 Research Center for East‑West Cooperation in China, East China Normal University, Shanghai 200241, China and precipitation (Chen et al. 2006). So the climate variability alters hydrological process and affects the quantity 3 School of Geographic Sciences, East China Normal University, Shanghai 200241, China and quality of water resources in inland river basins (Guo and Shen 2016; Ma et al. 2008). However, most of the 4 State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy headwaters of the inland rivers are located in high-altitude of Sciences, Urumqi 830011, China ungauged mountains. It’s difficult to quantitatively assess 5 School of Economics and Management, Nanchang the climate variability and its impact on streamflow. Hence, University, Nanchang 33003, China it’s necessary to develop a hybrid model to solve this 6 College of Land and Resources, China West Normal problem. University, Nanchong 637002, China

Vol.:(0123456789)1 3 2830 C. Wang et al.

Many researches are interested in the impact of climate To quantitatively assess the impact of climate vari- variability on streamflow in inland river basins (Yang et al. ability on streamflow for an ungauged mountainous basin, 2015). Inland rivers are sensitive against climate variability We conducted a hybrid model integrating a downscaling (Xu et al. 2009) Take the Tarim Basin, for instance. The method based on earth data products, Mann–Kendall trend annual temperature increased by almost 1 °C in the basin test, back propagation artificial neural networks (BPANN) since 1955, and the annual precipitation showed a rising and weights connection method, and selected the headwa- trend at a rate of 6–8 mm per decade (Chen et al. 2006). ter basins of the Aksu River (i.e. the Kumarik River and Because of the increasing mountainous temperature and Toshkan River) as the typical ungauged basins to validate precipitation, glacier and snow meltwater increased, and the model. the streamflow in tributaries of the Tarim River augmented (Chen et al. 2013; Xu et al. 2013a, b, c, d). However, the lack of meteorological stations limited the research accu- 2 Materials and methods racy in mountainous area such as headwaters. Previous studies proposed many methods to reveal cli- 2.1 Description of study area mate characteristics in ungauged mountains (Neupane et al. 2015; Zhao et al. 2016). The reanalysis earth data provided As one of the tributaries of Tarim River in Northwest spatial distribution of climate (Getirana et al. 2011), but China, the Aksu River provides about 70–80% of water for the low resolution eliminated the climate heterogeneity at the mainstream (Chen et al. 2003; Duethmann et al. 2015). the scale of catchment. HHMM (nonhomogeneous hidden However, the water of the Aksu River is mainly from the Markov model), SDSM (statistical downscaling model) and headwaters originating from Tianshan Mountains. other downscaling models could reconstruct the regional As the main headwaters of the Aksu River, the Kumarik climate, but researches showed that the precipitation simu- River and Toshkan River are located on the southern slope lation accuracy still need to be improved (Liu et al. 2011; of the western Tianshan Mountains (Fig. 1), which are in Mahmood and Babel 2013). It was also difficult to accu- the typical ungauged basins with vast area, scarce mete- rately describe the nonlinear relationship between the cli- orological and hydrological observation stations and insuf- mate and streamflow with multivariate linear regression ficient data. So, we selected the two headwater basins as method (Zhang and Zuo 2015). Hydrological model based the study target. The latitude is from 75.5°E to 80.3°E, on physical process could simulate the climate and stream- and the longitude is from 40.2°N to 42.6°N. The altitude flow, but the operation was complex and the uncertainty of the Kumarik catchment is from 1409 to 7077 m with an was increasing with the adding data and parameters (Dueth- average of 3730 m. In the Toshkan catchment, the altitude mann et al. 2015). It’s difficult to quantitatively assess the is from 1931 to 5934 m with an average of 3730 m. The impact of climate variability on streamflow in the ungauged areas of the two catchments are respectively 12,983 and mountainous basin with vast area, scarce meteorological 18,331 km2. The areas of glaciers are 2583 and 887 km2 in and hydrological observation stations and insufficient data the two catchments. The study area has a typical temperate in Northwest China. continental climate. Based on observations from Aheqi and

Fig. 1 The study area

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Chatyr Kul (1980–2012), the average temperature is 1.9 °C River are Xiehela and Shaliguilanke. The format is ASCII and the average monthly temperature is below 0 °C from with monthly temporal resolution. The data begins in 1980 November to March next year. The annual precipitation is and end in 2012. about 250 mm and the monthly precipitation is more than 30 mm from May to September. Glacier, snow meltwater 2.3 Methods and precipitation constitute the main components for the water supply to the headwaters. Grazing activities in the We conducted a hybrid model to assess the impact of cli- catchments are small and the influence of human activities mate variability on streamflow for an ungauged mountain- is negligible in local hydrological process. ous basin (Fig. 2). The model consists of three sub models. The first sub model downscales the earth data products and 2.2 Data sources verifies the accuracy. The second sub model analyzes the climate variability. The last sub model constructs a BPANN The terrain data is from the SRTM (Shuttle Radar Topog- (back propagation based artificial neural network) based raphy Mission) V4.1 preprocessed by CIAT (International on downscaled climate data and connects the weights. The Center for Tropical Agriculture) (Reuter et al. 2007). final output is the quantitatively assessment of climate vari- USGS (United States Geological Survey)/NASA (National ability impact on streamflow. Aeronautics and Space Administration) provided the data (http://srtm.csi.cgiar.org). The spatial resolution of the ras- 2.3.1 Downscaling based on earth data products ter data is 90 m × 90 m. The glacier data is from the GLIMS (Global Land Ice There is no national basic weather station within the study Measurements from Space) (Raup et al. 2007) provided by catchment. Downscaling the reanalysis data is a feasible NSIDC (National Snow and Ice Data Center) (http://glims. way to reconstruct the historical climate variability (Xu colorado.edu). The monitoring time was on June 11, 2013. et al. 2013a, b, c, d) or provide temperature and precipi- The data format is polygon vector. tation estimation based on forecast models (Georgakakos The reanalysis data was downscaled to simulate climate et al. 2014). Liston and Elder (2006) proposed an effective change in the ungauged basins. After the accuracy test on downscaling method using the empirical temperature lapse a series of reanalysis data of temperature, the MERRA-2 rates and precipitation gradients. Brown et al. (2014) used instM_2d_lfo_Nx V5.12.4 data (http://goldsmr4.ges- this downscaling method in estimating glacier and snow disc.eosdis.nasa.gov) from GMAO (Global Modeling melt driven streamflow in Himalayas. However, based on and Assimilation Office) was selected. The data format is data visualization, we found that the real temperature lapse netCDF-4 with a spatial resolution of 0.5° × 0.625° and rates in Northwest China were generally higher than the monthly temporal resolution. The data begins in January empirical coefficients, and the real precipitation gradients 1980 and end in December 2012. changed with the altitude rather than fixed values. Based on After the accuracy test on a variety of reanalysis data of previous research, we developed an improved downscaling precipitation, the China precipitation grid data V2.0 (http:// method based on earth data products in the first sub model. data.cma.cn) from NMIC (National Meteorological Infor- The first sub model contains simulating temperature lapse mation Center) was selected. The data format is ARCGIS rates and precipitation gradients, downscaling the reanaly- standard format with a spatial resolution of 0.5° × 0.5° and sis climate data to a high resolution (90 m × 90 m) and monthly temporal resolution. The data begins in January calculating the monthly temperature and precipitation in 1980 and end in December 2012. catchment (Fig. 3). In order to verify the accuracy of the downscaling To simulate the temperature lapse rates and precipitation model, the observed temperature and precipitation data gradients, the sub model modeled the relationships between from Chinese ground meteorological database (http://data. the temperature t, precipitation p and the altitude h at first. cma.cn), NMIC (National Meteorological Information Because of the monthly temporal resolution of the reanaly- Center) was used. The meteorological stations are Aksu, sis data, we chose the monthly temperature and precipita- Keping, Aheqi and Chatyr Kul. The format is ASCII with tion observed data from meteorological stations (Aksu, monthly temporal resolution. The data begins in January Keping, Aheqi and Chatyr Kul) from 1980 to 2012 to con- 1980 and end in December 2012. struct the model. The monthly data were preprocessed to For the purpose of verifying the effect of stream simu- eliminate seasonal fluctuations and improve the fitting pre- lation, the observed streamflow data from the monitor- cision as follows: ing records of hydrological stations provided by Xinjiang 12 Tarim River Basin Management Bureau was chosen. The 12, ti = tmij∕ (1) hydrological stations of the Kumarik River and Toshkan j=1 1 3 2832 C. Wang et al.

Fig. 2 The framework of the hybrid model to assess the Earth data products impact of climate variability on streamflow Reanalysis data Observed data

First sub model

Downscaling and verification

Second sub model Third sub model

Mann-Kendall trend test BPANN training +

Analysis of climate variability Weight connection

Output

Assessment of the impact of climate variability on streamflow

Reanalysis climate data 12, pi = pmij∕ (2) j=1

Temperature or precipitation where tmij is the monthly temperature in the ith year and jth data in the ith month month, pmij is the monthly precipitation in the ith year and Simulating the jth month. Calculating changing values of temperature lapse According to the scatter plots and previous studies in temperature or precipitation according rates and to the altitude differences precipitation the similar areas (Fu et al. 2013), the relationship between gradients temperature t and altitude h was linear, and the relationship i= i + 1 Downscaling the between precipitation p and altitude h was quadratic polyno- reanalysis data mial. So we established a linear function t = mh + n and a 2 quadratic polynomial function p = ah + bh + c to simulate m, n, a, b, Yes the temperature and precipitation. The coefficients i ≤ n Observed data c were fitted with observed data. The coefficient of determi- nation R2 and F test was used to verify the fitting effect. R2 No was calculated as the following Eq. (3):

k 2 Verification ̂ yi − yi R2 = 1 − i=1 , ∑k � ̄ �2 (3) i=1 yi − yi Fig. 3 Downscaling sub model based on earth data products ∑ � �

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̂ ̄ where, yi, yi, yi are respectively observed, simulated and on observed values, we drew the scatter plot, fitted the trend 2 average observed values. k is the number of the observed line and calculated the R . The downscaled values at loca- data. tion of the meteorological stations were extracted by near- dt Then the temperature lapse rate and the precipita- est neighbor method from td and pd. The evaluation indexes dt dh tion gradient dh were calculated by differential of the fitting were calculated as the following equations: functions: k ̂ 2 1 yi − yi dt NSE = 1 − i= , = m, ∑ k � ̄ �2 (10) dh (4) i=1 y − yi ∑ � � dp = 2ah + b. k dh (5) ̂ MAE = yi − yi ∕k, (11) i=1 The sub model downscaled the monthly reanalysis climate data by calculating the changing values of tempera- k ture and precipitation according to altitude differences. At 2 RMSE = y − ŷ ∕k first, the sub model calculated the altitude of the reanaly- i i (12) i=1 sis data hr according to the altitude of SRTM hs by bilinear interpolation resampling. Then the sub model resampled ̂ ̄ yi yi yi the reanalysis temperature data tr, reanalysis precipitation where, , , are respectively the observed, downscaled k data pr and hr to the grid size of hs by bilinear interpola- and average observed data and is the number of observed tion. The preprocessing facilitated the matrix operation data. among different data type. To improve resolution, we need 2.3.2 Analysis of climate variability to change each pixel value of reanalysis data from its altitude to the altitude of SRTM data. The value changed with the altitude according to the temperature lapse rate and pre- To make a statistical description of the climate variability cipitation gradient. So finally, the downscaled climate data and test the trend in each catchment, the second sub model td and pd (90 m × 90 m) at the altitude hs were calculated by calculated the monthly temperature and precipitation in Eqs. (6) and (7): catchments by arithmetic average method. Then the annual average temperature and annual total precipitation in catch- dt dt t = t + h − h ⋅ + ∕2, ments were calculated. The statistical description consisted d r s r dh dh (6) s r of Mean, SD (standard deviation), CV (the coefficient of variation) and slope (linear trend). The slope was calcu- dp dp p = p + h − h ⋅ + ∕2, lated by fitting the linear function between the climate fac- d r s r dh dh (7) s r tors and the year based on least squares method. To test the significance of the slope, the sub model h − h dt + dt ∕2 where s r is the altitude differences, dh dh is employed the Mann–Kendall trend test (Kendall 1948; s r Mann 1945). The Mann–Kendall trend test was widely the average temperature lapse rates between altitude hs and h dp + dp ∕2 applied in meteorological and hydrological researches (Bai r, dh dh is the average precipitation gradients s r et al. 2015; Xu et al. 2013a, b, c, d, 2016a, b). Besides the between altitude hs and hr. Mann–Kendall trend test, we used a variety of methods to With the Eq. (4) and Eq. (5), the final downscaling equa- verify the trends, such as the Pettitt’s test (Pettitt 1979) to tions were: detect change-point, the Sen’s slope method (Sen 1968) ⋅ , to analyze trend. Seasonal Mann–Kendall test, correlated td = tr + hs − hr m (8) Seasonal Mann–Kendall test and seasonal Sen’s slope were ⋅ . applied to the monthly series to validate the annual trend pd = pr + hs − hr a hs + hr + b (9) test (Pohlert 2016). The steps of Mann–Kendall trend test The sub model verified the downscaled data by cal- are as follows: culating the evaluation indexes including Slope, NSE For the time series Xt = x1, x2, … , xn , the statistics S MAE (Nash–Sutcliffe efficiency coefficient), (mean abso- of Mann–Kendall trend test was calculated with the follow- RMSE lute difference) and (root-mean-square error) ing Eq. (13): between the observed climate data and the downscaled data at the same location. In order to intuitively reflect the fitting n−1 n . effect of downscaled monthly temperature and precipitation S = sgn xk − xi (13) i=1 k =i+1 1 3 2834 C. Wang et al. where, n is the length of the sample data, xi is the ith data in Input layer Hidden layer Hidden layer Output layer the time series and sgn is a sign function: tm 1, 𝜃>0 sm sgn(𝜃) = 0, 𝜃 = 0 . ⎧ (14) ⎪ −1, 𝜃<0 pm ⎨ ⎪ ⎩ w If n is more equal than 8, the statistics S is approximately wij kl w normally distributed. The average of the S equals to 0 and jk the variance was: n Fig. 4 The structure of the BPANN n(n − 1)(2n + 5) − ti(i − 1)(2i + 5) Var(s) = i=1 , 18∑ (15) of the node in the next layer. The simulating process consists t where, i is the number of the data in group i. of the forward transmission of information and the back prop- Z The standardization statistics c was calculated as agation of the error. In one training, the input and target data follows: are divided into a training group (70%), a calibration group S−1 , S > 0 (15%) and a validation group (15%) randomly. When entering Var(s) the neural network, the information is transferred from the Z = √ 0, S = 0 . c ⎧ (16) input layer to the hidden layer at first and then transferred into S+1 , < 0 ⎪ S the output layer by activation function. In this paper, the acti- ⎨ Var(s) ⎪ √ vation function was the sigmoid function. When the output ⎩ didn’t match the target, the error was transferred back. The If Zc is positive, the test series have an increasing trend, error was used to modify the connection weights by gradi- otherwise the trend is down. The null hypothesis is that the ent decent algorithm from the output layer to the hidden layer time series doesn’t vary significantly. If the absolute value and input layer. The neural network used the Levenberg–Mar- of Zc is greater than 1.96 (95% confidence level), the time quardt algorithm to accelerate the convergence. After 1000 series varies significantly. trainings, the neural network with the minimum square error was chosen as the optimal result of one BPANN fitting. 2.3.3 BP artificial neural network Because of the randomized initial parameters, each fitting result was different with others. Therefore the sub model The downscaled climate data made up for the lack of repeated the fitting multiple times. Then the mean and the observed data. In order to quantitatively assess the impact standard error of the outputs were calculated as the final of climate variability on streamflow, the third sub model result. In this research, the mean of the outputs was relatively used BPANN to simulate the nonlinear relationship among stable after ten fittings after test. 2 the monthly temperature tm, monthly precipitation pm and The sub model calculated the Slope, R , MAE and RMSE monthly streamflow sm. between the target sa and the mean of the outputs to verify the BPANN can effectively simulate the complex nonlin- BPANN fitting results. ear relationship between climate and hydrology (Xu et al. 2013a, b, c, d, 2014, 2016a, b). BPANN consists of an 2.3.4 The impact of climate variability on streamflow input layer, one or more hidden layers and an output layer. In this paper, the sub model adopted a neural network of Compared with the traditional multiple regression analysis, 5 × 5 hidden layers structure by try and error (Fig. 4). In the BPANN can improve the fitting accuracy, but the ability the neural network, tm and pm were the inputs to train the to explain the relationship among variables is weak. There- network, sm was the training target, wij was the connection fore, we developed an improved weight connection method weight between the ith neuron in the input layer and the jth based on previous researches (Fischer 2015; Olden and Jack- neuron in the hidden layer, wjk was the connection weight son 2002; Olden et al. 2004) to calculate the contribution of between the jth neuron in the hidden layer 1 and the kth temperature and precipitation on streamflow. neuron in the hidden layer 2, and wk was the connection At first, the third sub model calculated the cumulative weight between the lth neuron in the hidden layer and the weight matrix from the input layer to output layer as follows: lth neuron in the output layer. w = w w = w × w × w , In the same layer, there is no connection among the neuron io ts ps h2o h1h2 ih1 (17) nodes. The output of a neuron node just connects to the input

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w where h2o is the weight matrix (1 × 5) from the hidden 3 Results and discussion w layer 2 to the output layer, h1h2 is the weight matrix (5 × 5) w from the hidden layer 1 to the hidden layer 2, ih1 is the 3.1 Downscaling accuracy test weight matrix (5 × 2) from the input layer to the hidden layer 1, wio is the cumulative weight matrix from the input The fitting results of the temperature and precipitation layer to the output layer, wts is the cumulative weight from with altitude were tested at first (Table 1). Based on anal- the input of temperature to the output of streamflow,w ps is ysis of all months, the linear relationship between tem- the cumulative weight matrix from input of precipitation to perature and altitude was strong (R2 = 0.971) and signifi- the output of streamflow. cant (F = 2133.257, a = 0.005). The temperature declined Then the contribution could be calculated as follow steadily with the rising altitude at a rate of −0.006 °C m−1 equations: according to the slope of the linear fitting function. The lapse rate was consistent with previous research results. For wts Cts = × 100%, example, based on the average monthly temperature data of w + w (18) ts ps 460 meteorological stations from the northern Italy to the southern slope of the Alps, Rolland (2003) found that the −1 temperature lapse rate was −0.0054 to −0.0058 °C m . The quadratic polynomial relationship between the precipi- wps C = × 100%, tation and altitude was strong (R2 = 0.648) and significant ps (19) w + w F a ts ps ( = 94.101, = 0.005). The precipitation increased with the rising altitude at first, then gradually became stable at the altitude of about 3000 m and declined above 3000 m. where Cts is the contribution of temperature variability on The quadratic polynomial relationship was consistent with stream variability, Cps is the contribution of precipitation numerous studies (Chen et al. 2013). For instance, the rela- variability on stream variability. tionship between precipitation and altitude was also quad- Because the randomized initial parameters of BPANN ratic polynomial in the Kaidu basin on the southern slope would lead to different fitting results, the third sub model of the eastern Tianshan Mountains and the extreme point calculated the mean and standard error of the contributions of precipitation was at 2450 m (Fu et al. 2013). We fitted after ten fittings as the final result. the monthly climate data with the same method to show the spatiotemporal pattern of monthly temperature and precipitation. According to R2 and F test, all the fittings were strong and significant (a = 0.005). The temperature lapse rate was large from March to October and relatively small from November to February next year. The precipitation

Table 1 The fitting functions of the temperature and precipitation with altitude Time scales N Temperature Precipitation Fitting functions R2 F Fitting functions R2 F

All months 1584 t = −0.006 × h + 18.200 0.971 2144.357* p = −4.205E−6 × h2 + 0.025 × h − 15.405 0.648 94.101* January 132 t = −0.003 × h − 3.723 0.668 104.752* p = −1.413E−7 × h2 + 0.001 × h − 0.296 0.526 24.672* February 132 t = −0.005 × h + 4.577 0.882 455.385* p = −2.545E−6 × h2 + 0.013 × h − 9.891 0.549 27.828* March 132 t = −0.006 × h + 14.780 0.931 845.690* p = −4.888E−6 × h2 + 0.028 × h − 22.920 0.606 37.434* April 132 t = −0.007 × h + 24.170 0.947 1129.800* p = −6.488E−6 × h2 + 0.036 × h − 31.140 0.623 40.914* May 132 t = −0.008 × h + 29.174 0.959 1488.546* p = −9.985E−6 × h2 + 0.057 × h − 44.569 0.670 52.539* June 132 t = −0.007 × h + 32.180 0.961 1569.813* p = −4.147E−6 × h2 + 0.030 × h −18.004 0.625 41.346* July 132 t = −0.007 × h + 33.075 0.963 1659.869* p = −1.462E−5 × h2 + 0.077 × h − 53.764 0.600 36.281* August 132 t = −0.007 × h + 31.849 0.950 1203.333* p = −1.705E−5 × h2 + 0.087 × h − 63.439 0.588 34.085* September 132 t = −0.006 × h + 26.837 0.953 1286.261* p = −1.775E−5 × h2 + 0.088 × h − 70.730 0.595 35.349* October 132 t = −0.006 × h + 18.648 0.931 845.690* p = −1.355E−6 × h2 + 0.011 × h − 9.597 0.621 40.487* November 132 t = −0.004 × h + 8.097 0.884 464.846* p = −5.612E−7 × h2 + 0.005 × h − 4.312 0.564 30.088* December 132 =−0.0003 × h − 1.263** 0.761 178.877* p = −8.567E−8 × h2 + 0.001 × h − 0.415 0.526 24.672*

F test indicate the significance of the fitting functions. *indicates the significance of a = 0.005

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Table 2 Accuracy test of Factors Stations N Slope NSE MAE RMSE downscaling based on earth data products Temperature (°C) Aksu 396 1.01 0.99 1.58 1.90 Keping 396 0.92 0.99 1.28 1.75 Aheqi 396 0.85 0.98 2.72 3.06 Chatyr Kul 396 0.96 0.99 0.95 1.19 Precipitation (mm) Aksu 396 0.79 0.72 2.74 5.40 Keping 396 0.84 0.67 4.20 8.79 Aheqi 396 1.33 0.80 12.88 22.10 Chatyr Kul 396 0.93 0.95 2.93 5.00

The unit of MAE and RMSE is same as the original data

(a) (b) (a) (b)

scatter plot Fig. 5 The scatter plot and trend line of the downscaled and Fig. 6 The and trend line of the downscaled and scat- observed monthly temperature in verification stations.a –d are scatter observed monthly precipitation in verification stations.a –d are plots of the downscaled and observed monthly temperature in Aksu, ter plots of the downscaled and observed monthly precipitation in Keping, Aheqi and Chatyr Kul, respectively Aksu, Keping, Aheqi and Chatyr Kul, respectively increased first and then decreased with the increasing ele- validate the effect for downscaling. The all values of R2 of vation. The fitting functions of temperature and precipita- the observed and downscaled data for monthly temperature tion with altitude can be applied in simulating the tempera- and precipitation were close to 1. In summary, the downs- ture lapse rates and precipitation gradients. caling method can be applied on the research of basin cli- The downscaling method based on earth data products mate variability. can simulate the mountainous climate well (Table 2). The stations for verification near the catchments were sufficient. 3.2 The temporal and spatial climate variability The altitudes of these meteorological stations (Aksu, Kep- ing, Aheqi and Chatyr Kul) varied from 1000 to 3500 m. Based on the descriptive statistics and Mann–Kendall trend The stations located at different terrains such as alluvial fan, test, the climate characteristics were different in the two hills and mountains. The verification was also guaranteed catchments (Table 3). Although the average and the highest as the large number of observed monthly temperature and altitude of the Kumarik catchment were both higher than precipitation data. The slope was close to 1 and it meant the Toshkan catchment, the average annual temperature in the variety of the downscaled data was consistent with the the Kumarik catchment from 1980 to 2012 was −0.81 °C, observed data. The NSE was close to 1, which meant the still higher than the −2.37 °C in the Toshkan catchment. simulation effect of the downscaling method based on earth The reason might be that the median of the altitude was data products was well. MAE and RMSE were both rela- respectively 3737 and 3768 m in the two catchments, and tively small compared with the observed data, which meant the lowest altitude of the Kumarik catchment was lower the downscaled data were close to the observed data. The than the Toshkan catchment. The average annual pre- Figs. 5 and 6 showed that the effect of the downscaled data cipitation in the Kumarik catchment during 32 years was for temperature and precipitation was good. The observed 503.70 mm, which was higher than the 319.02 mm in the and downscaled data in scatter plots concentrated near the Toshkan catchment. The precipitation downscaling results trend line with a slope of 1. We also calculated the R2 to were close to the model simulation results by Duethmann

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Table 3 Descriptive statistics and trend test of the annual temperature and precipitation from 1980 to 2012 Factors Catchments Descriptive statistics Mann–Kendall Pettitt’s test Sen’s slope trend test N Mean SD CV slope Z p Change-point

Temperature (°C) Kumarik 33 −0.81 0.46 −0.57 0.01 1.1 0.18 No 0.01 Toshkan 33 −2.37 0.46 −0.19 0.01 0.6 0.35 No 0.01 Precipitation (mm) Kumarik 33 503.70 136.01 0.27 4.85 1.8* 0.34 No 4.17 Toshkan 33 319.02 95.43 0.30 4.10 2.4** 0.11 No 3.60

The unit of MEAN and SD is same as the original data. *Indicates the significance ofa = 0.05. **Indicates the significance of a = 0.01 et al. (2015). He found the average annual precipitation in obvious change-points of the time series data of tem- the Kumarik and Toshkan catchment from 1957 to 2004 perature and precipitation. For the purpose of studying was respectively 474 (450–526) mm and 386 (372–399) the inter-decadal relationship between the climate fac- mm. tors and streamflow (Bai et al. 2015), we divided the data The annual differences of temperature in the Kumarik series into four periods (i.e. 1980–1989, 1990–1999, and catchment were greater than the Toshkan catchment. It 2000–2012), and analyzed the nonlinear variation of cli- might be because the percent of the area above 4000 m matic-hydrological processes in different periods. was respectively 35 and 19% in the Kumarik and Toshkan The average value and the slope (rate of change) of catchment, and the higher the altitude, the higher tempera- downscaled (90 m × 90 m) grid annual temperature in ture variability among years (Ohmura 2012). However, the different periods showed the spatial and temporal tem- annual differences of precipitation were similar in both perature variability (Figs. 7, 8). The temperature spatial catchments. It was possible related to the decreasing pre- distribution was similar in different periods. The temper- cipitation above 3500 m. ature was generally above 0 °C in valleys and alluvial fan The Mann–Kendall trend test results showed that the and below 0 °C in mountains. However, the temperature average annual temperature in the Kumarik and Toshkan trend was different in each period. From 1980 to 1989, catchment from 1980 to 2012 both appeared an increas- the increasing trend of temperature in the two catch- ing trend (Z = 1.07; Z = 0.6) at the rate of 0.1 °C per dec- ments was relatively slow. The rising trend was close to ade. The annual precipitation in the Kumarik and Toshkan 0 °C per decade in most regions except the eastern moun- catchment showed a significant increasing trend (Z = 1.81, tains of the Kumarik catchment. From 1990 to 2000, the a = 0.05; Z = 2.40, a = 0.01) at the rate of 48.5 mm per dec- temperature was increasing in both catchments. The ris- ade and 41.0 mm per decade. ing trend was higher in alluvial fan than the mountains. Sen’s slope test indicated the increasing trend of annual The northeast mountains of the Toshkan catchment had temperature and precipitation in the two catchments. The the slowest rising trend. The rising trend in the Kumarik seasonal tests (Table 4) also showed the increasing trends catchment was generally higher than the Toshkan catch- of the monthly temperature and precipitation (Z > 0; ment. From 2000 to 2012, the temperature was declining slope > 0). The climate became warming and wetting in the in both of the catchments. The decreasing trend was slow headwaters of the Aksu River in recent 30 years, which was in the eastern mountains of the Kumarik catchment than consistent with Xu’s (2006) research result. other regions. From 1980 to 2012, the temperature in We used the Pettitt’s test to detect change-point at a both catchments was increasing slowly. The rising trend significant level of 0.05 (a = 0.05), and did not find in eastern Toshkan catchment was the slowest. The rising

Table 4 The seasonal test of Factors Catchments N Seasonal Mann– Seasonal Mann–Kendall Seasonal the monthly temperature and Kendall test test with correlations Sen’s precipitation from 1980 to 2012 Z Z slope

Temperature (°C) Kumarik 396 1.3* 1.1 0.01 Toshkan 396 0.8 0.7 0.01 Precipitation (mm) Kumarik 396 0.8 0.8 0.02 Toshkan 396 1.5* 1.3* 0.04

*Indicates the significance of a = 0.1

1 3 2838 C. Wang et al.

Fig. 7 The average of downscaled annual temperature in different annual temperature from 2000 to 2012; d is the average annual tem- periods. a Is the average annual temperature from 1980 to 1989; b is perature from 1980 to 2012 the average annual temperature from 1990 to 1999; c is the average

Fig. 8 The slope (rate of change) of downscaled annual temperature the slope of annual temperature from 2000 to 2012; d is the slope of in different periods.a Is the slope of annual temperature from 1980 annual temperature from 1980 to 2012 to 1989; b is the slope of annual temperature from 1990 to 1999; c is trend in the Kumarik catchment was generally higher The increasing trend was slow in mountains, and it might than the Toshkan catchment. be related to the buffering effect from the widespread gla- The temperature variability in the inland river basins of cier and snow, diverse vegetation and the stability of moun- Northwest China was related to the Siberian high inten- tain ecosystems against the global climate variability (Li sity and the carbon dioxide concentration (Chen et al. et al. 2013). The temperature trend in the headwaters of the 2015). Because of the weakening of the Siberian high in Aksu River was consistent with the regular in Northwest recent 30 years, the frequency of the southward cold air China (Chen et al. 2015). The temperature in Northwest was reducing and the strength was decreasing (Xu et al. China was slowly increasing in the 1980s, then experienced 2006). So the temperature increasing trend decreased from an abrupt changing in 1987 and quickly increasing in the the northern to the southern China (Yatagai and Yasunari following 1990s. The temperature variability was related to 1994), and the increasing trend in the northern Kumarik the rapid increasing of population, industrialization, urban- catchment was generally higher than the southern Toshkan ization and greenhouse gas emissions (Li et al. 2013). catchment (Xu et al. 2006). The increasing trend was fast in The average value and the slope (rate of change) of the alluvial fan because of the lower altitude and latitude. downscaled (90 m × 90 m) grid annual precipitation in

1 3 A hybrid model to assess the impact of climate variability on streamflow for an ungauged… 2839 different periods showed the spatial and temporal precipita- precipitation in the two catchments still kept rising. From tion variability (Figs. 9, 10). The precipitation spatial dis- 1980 to 2012, the precipitation was increasing in both of tribution was similar in different periods. The precipitation the catchments and the rising trend in the Kumarik catch- decreased from the northeast to southwest. As a result, the ment was greater than the Toshkan catchment. precipitation was the most abundant in the mountains of the The precipitation in the Kumarik catchment was greater Kumarik catchment and the least in the west and southern than the Toshkan catchment. It was because the glacier mountains of the Toshkan catchment. The precipitation and snow were widely distributed in the Kumarik catch- trend was different in each period. From 1980 to 1989, the ment, and the agricultural activity and evaporation were precipitation increasing trend decreased from west to east much in the city of Aksu near the outlet of the Kumarik and became decreasing in the Kumarik catchment. From catchment (Xu et al. 2006). So the Kumarik catchment 1990 to 1999, the precipitation in the two catchments was was wetter than the Toshkan catchment. That the increas- increasing. Only in the alluvial fan of the Kumarik catch- ing trend in mountains was higher than the alluvial fan and ment and low altitude valleys of the Toshkan catchment valleys was consistent with the Fu et al.’s (2013) research. the rising trend was slightly slow. From 2000 to 2012, the It was because the mountainous terrain was advantageous

Fig. 9 The average of downscaled annual precipitation in different annual precipitation from 2000 to 2012; d is the average annual pre- periods. a Is the average annual precipitation from 1980 to 1989; b is cipitation from 1980 to 2012 the average annual precipitation from 1990 to 1999; c is the average

Fig. 10 The slope (rate of change) of downscaled annual precipita- 1999; c is the slope of annual precipitation from 2000 to 2012; d is tion in different periods.a Is the slope of annual precipitation from the slope of annual precipitation from 1980 to 2012 1980 to 1989; b is the slope of annual precipitation from 1990 to

1 3 2840 C. Wang et al.

Table 5 Descriptive statistics Rivers N Descriptive statistics Mann–Kend- Pettitt’s test Sen’s slope and trend test of the annual all trend test streamflow from 1980 to 2012 Mean (m3 s−1) SD CV slope Z p Change-point

Kumarik 33 51.42 7.14 0.14 0.15 1.47* 0.004 1994 0.16 Toshkan 33 29.87 5.76 0.19 0.33 3.27** 0.002 1994 0.35

*Indicates the significance of a = 0.1. **Indicates the significance of a = 0.01

Table 6 The seasonal test of the monthly streamflow from 1980 to glaciers, the streamflow in the Kumarik catchment was 2012 greater than the Toshkan catchment. From the SD and Rivers N Seasonal Seasonal Mann–Ken- Seasonal CV, the annual difference of streamflow in the Kumarik Mann–Kend- dall test with correla- Sen’s catchment was greater than the Toshkan River. It might all test tions slope be related to the annual melting difference of glacier and Z Z snow caused by temperature variability. The Mann–Kan- Kumarik 396 6.8* 3.9* 0.28 dall test shows that the streamflow in the Kumarik and Toshkan 396 10.1* 4.6* 0.54 Toshkan catchment were both increasing significantly (Z = 1.47, a = 0.1; Z = 3.27, a = 0.01) at a rate of 1.5 × 108 * Indicates the significance of a = 0.01 and 3.3 × 108 m3 per decade respectively. Sen’s slope test indicated the increasing trend of annual streamflow in the to formatting precipitation, together with the increasing two catchments. The seasonal tests (Table 6) also showed water vapor caused by warming climate and melting gla- the increasing trends of the monthly streamflow (Z > 0; cier and snow (Yang et al. 2009). The decreasing precipi- slope > 0) and the trends were significant (a = 0.01). The tation in 1980s verified the previous research (Chen et al. Pettitt’s test detected the change-points in year 1994. 2013; Li et al. 2013). The rapid increasing of precipitation Table 7 shows the change trends of climate on the in 1990s and recent years was related to the climate warm- streamflow by comparing the slope of temperature, pre- ing and the acceleration of water vapor cycle (Chen et al. cipitation and streamflow in catchment based on down- 2013). The trend of temperature and precipitation in differ- scaled data. In the 1980s, the streamflow and the precipi- ent periods might be related to the climate fluctuations and tation both decreased while the temperature was relatively ENSO events (Infanti and Kirtman 2016). Tree-ring studies stable in the Kuamrik catchment. In the same period, the showed that Northwest China experienced cyclical change streamflow and temperature kept relatively stable while between warm and cold together with wet and dry in the the precipitation was slightly decreasing in the Toshkan past 200–300 years (Chen et al. 2012; Yang et al. 2012). catchment. In the 1990s, the streamflow of the Kuamrik and Toshkan river both increased with the rising tem- 3.3 The impact of temperature and precipitation perature and precipitation. It was consistent with Chen on streamflow et al.’s (2013) research that the Tarim River basin was warmest and wettest and the streamflow was increasing The annual streamflow increased in both of the Kumarik fastest in the 1990s. Because of the faster rising rates of catchment and Toshkan catchment from 1980 to 2012 temperature and precipitation, the streamflow increas- (Table 5). The increasing trend was related to the rising ing trend in the Kumarik catchment was also faster than temperature and precipitation in the two catchments and the Toshkan catchment. At the beginning of twenty-first consistent with Xu et al.’s (2006) research. The aver- century, although the precipitation was increasing, the age annual streamflow of the Kumarik River and Tosh- streamflow in both catchments decreased with the declin- kan River in 33 years was respectively 51.42 × 108 and ing temperature. The decreasing trend of the streamflow 29.87 × 108 m3. Because of the more precipitation and in the Kumarik catchment was faster than the Toshkan

Table 7 The slope of annual Factors 1980–1989 1990–1999 2000–2012 temperature, precipitation and streamflow in different periods Kumarik Toshkan Kumarik Toshkan Kumarik Toshkan

Temperature −0.01 −0.01 0.04 0.03 −0.03 −0.04 Precipitation −17.92 −5.02 12.51 9.30 11.23 10.03 Streamflow −0.49 0.04 2.07 1.00 −0.86 −0.42

1 3 A hybrid model to assess the impact of climate variability on streamflow for an ungauged… 2841

(a) (a)

(b) (b)

Fig. 11 The monthly streamflow observed and simulated with Fig. 12 The annual streamflow observed and simulated with BPANN. a Is the monthly streamflow observed and simulated in the BPANN. a Is the annual streamflow observed and simulated in the Kumarik river; b is the monthly streamflow observed and simulated Kumarik river; b is the streamflow observed and simulated in the in the Toshkan river Toshkan river catchment. It might be because the Kumarik catchment the simulated data was consistent with the observed data. was more sensitive to the temperature variability. The NSE was close to 1 which illustrated the BPANN Based on the idea and framework of the hybrid model with a hidden layer of 5 × 5 could simulate the streamflow mentioned previously in the methodology of this study, well after ten fittings. The MAE and the RMSE are both we computed the simulation values of streamflow by the small which represented the high precision of the simula- BPANN based on downscaled temperature and precipitation results (Table 9). We calculated the simulated annual tion at the monthly scale (Fig. 11). streamflow by add up monthly values. The simulated In order to compare and validate the simulated results annual streamflow was consistent with the observed series from the hybrid model, we also simulated the monthly (Fig. 12). streamflow with the monthly temperature and precipita- In summary, the BPANN could simulate the nonlinear tion by the classical approach, i.e. the multiple regression. relationship among the monthly and annual temperature, Tables 8 and 9 reveal the compared results. It evident that precipitation and streamflow. The fitting results could the simulated precise of streamflow by the BPANN based guarantee the high accuracy of quantitatively assessing the on downscaled climate data is higher than the simulated impact of the climate variability on streamflow. results by the multiple regression. Based on the monthly BPANN and weight connec- Figure 11 showed that the BPANN well simulated the tion method, the impact of the temperature and pre- monthly streamflow with the monthly temperature and cipitation on streamflow could be assessed quantita- precipitation. The slope was close to 1 and indicated that tively (Table 9). The contribution of the temperature

Table 8 The multiple Rivers Fitting functions Accuracy test regression fitting functions and accuracy test of the monthly P00 P10 P01 P20 P11 P02 N Slope NSE MAE RMSE temperature, precipitation and streamflow Kumarik 76.36 21.53 −0.59 1.70 0.08 0.002421 396 0.88 0.88 48.20 65.34 Toshkan 70.02 9.36 0.61 0.41 0.02 0.000745 396 0.76 0.76 26.59 43.79

The fitting function iss = p00 + p10 × t + p01 × p + p20 × t^2 + p11 × t × p + p02 × p^2. Variable s is the monthly streamflow, t is the monthly temperature and p is the monthly precipitation

Table 9 The accuracy test of Rivers Accuracy test Contribution (%) BPANN and the contribution assessing N Slope NSE MAE RMSE Temperature Precipitation

Kumarik 396 0.94 0.95 25.24 42.80 64.01 ± 7.34 35.99 ± 7.34 Toshkan 396 0.81 0.83 22.39 37.15 47.74 ± 8.10 52.26 ± 8.10

The input data of the BPANN are monthly temperature and precipitation. The target data is the monthly streamflow. The unit of MAE and RMSE is same as the original data (m 3 s−1)

1 3 2842 C. Wang et al. and precipitation on the streamflow was respectively 1. The precipitation respectively increased by 48.5 and 64.01 ± 7.34 and 35.99 ± 7.34% in the Kumarik catchment, 41.0 mm per decade in the Kumarik catchment and and the contribution was 47.74 ± 8.10 and 52.26 ± 8.10% Toshkan catchment during the period from 1980 in the Toshkan catchment. The Kumarik catchment was to 2012, and the average annual temperature both mainly impacted by temperature while the Toshkan catch- increased by 0.1 °C per decade in the two catchments. ment was mainly impacted by precipitation. The results 2. With the warming and wetting climate, the streamflow were consistent with the analysis based on the Table 7 respectively increased 1.5 × 108 and 3.3 × 108 m3 per and Duethmann et al.’s (2015) research that the Kumarik decade in the Kumarik River and the Toshkan River. River was mainly supplied with glacier and snow melting 3. The contribution of the temperature and precipitation water and the Toshkan River was mainly supplied with the on the streamflow was respectively 64.01 ± 7.34 and precipitation. 35.99 ± 7.34% in the Kumarik catchment, and that was Climate variability leads to the change of the regional 47.74 ± 8.10 and 52.26 ± 8.10% in the Toshkan catch- water resources (Liu et al. 2016). Especially in arid and ment. semi-arid areas, the slight change of the temperature and precipitation would result in a great effect on the stream- Our study introduced a feasible hybrid model for the flow (Gan 2000). Xu et al.’s (2006) research shows that in assessment of the impact of climate variability on stream- the Aksu River, the glacier and snow melting water and flow, which can be used in the ungauged mountainous the direct precipitation were the main water supply. So the basin of Northwest China. streamflow increased with the rising temperature and precipitation from 1980 to 2012. Further more, the impact Acknowledgements This work was supported by the Open Foun- dation of the State Key Laboratory of Desert and Oasis Ecology, of climate on streamflow was affected by the underlying Xinjiang Institute of Ecology and Geography, Chinese Academy of of the catchments (Zhang and Zuo 2015). The Kumarik Sciences (No. G2014-02-07); and the National Natural Science Foun- catchment was covered with more glacier and snow than dation of China (41630859). the Toshkan catchment, so the contribution of temperature would be greater in the Kuamrik catchment. Based on the fitting functions of the temperature and References precipitation with altitude, we downscaled the earth data products and simulated the mountainous climate changes. Allen MR, Frame DJ, Huntingford C, Jones CD, Lowe JA (2009) The climate became warming and wetting in the head- Warming caused by cumulative carbon emissions towards the waters of the Aksu River in recent 30 years. The annual trillionth tonne. Nature 458:1163–1166 streamflow increased in both of the Kumarik catchment Bai L, Chen Z, Xu J, Li W (2015) Multi-scale response of runoff to climate fluctuation in the headwater region of Kaidu and Toshkan catchment. The BPANN can simulate the River in Xinjiang of China. Theor Appl Climatol 2016:1–10. annual streamflow with the annual temperature and precipi- doi:10.1007/s00704-015-1539-2 tation well. Based on the BPANN and weight connection Brown ME, Racoviteanu AE, Tarboton DG et al (2014) An integrated method, the impact of the temperature and precipitation modeling system for estimating glacier and snow melt driven streamflow from remote sensing and earth system data prod- on streamflow could be assessed quantitatively. The hybrid ucts in the Himalayas. J Hydrol 519:1859–1869. doi:10.1016/j. model revealed regional climate change characteristics and jhydrol.2014.09.050 achieved the research objectives. Chen Y, Cui W, Li W, Zhang Y (2003) Utilization of water resources and ecological protection in the Tarim River. Acta Geographica Sinica 58:215–222. doi:10.11821/xb200302008 Chen Y, Takeuchi K, Xu C, Chen Y, Xu Z (2006) Regional climate change and its effects on river runoff in the Tarim Basin, China. 4 Conclusions Hydrol Process 20:2207–2216. doi:10.1002/hyp.6200 Chen F, Yuan Y, Wen W et al (2012) Tree-ring-based reconstruc- tion of precipitation in the Changling Mountains, China, To quantitatively assess the impact of climate variabil- since A.D.1691. Int J Biometeorol 56:765–774. doi:10.1007/ ity on streamflow for an ungauged mountainous basin, we s00484-011-0431-8 conducted a hybrid model by integrating a downscaling Chen Y, Xu C, Chen Y, Liu Y, Li W (2013) Progress, challenges and method based on earth data products, Mann–Kendall trend prospects of eco-hydrological studies in the Tarim River Basin of Xinjiang, China. Environ Manage 51:138–153. doi:10.1007/ test, back propagation artificial neural networks (BPANN) s00267-012-9823-8 and weights connection method, and validated the model Chen Y, Li Z, Fan Y, Wang H, Deng H (2015) Progress and pros- by the mountainous basin of the Aksu River in Northwest pects of climate change impacts on hydrology in the arid region China. The conclusion is that the hybrid model presented a of northwest China. Environ Res 139:11–19. doi:10.1016/j. envres.2014.12.029 good performance, and the quantitative assessment results Duethmann D, Bolch T, Farinotti D et al (2015) Attribution for the mountainous basin of the Aksu River are as follows: of streamflow trends in snow and glacier melt-dominated

1 3 A hybrid model to assess the impact of climate variability on streamflow for an ungauged… 2843

catchments of the Tarim River, Central Asia. Water Resour Olden JD, Joy MK, Death RG (2004) An accurate comparison of Res 51:4727–4750. doi:10.1002/2014WR016716 methods for quantifying variable importance in artificial neural Fischer A (2015) How to determine the unique contributions of networks using simulated data. Ecol Modell 178:389–397 input-variables to the nonlinear regression function of a multi- Pachauri RK, Allen MR, Barros VR et al (2014) Climate change layer perceptron. Ecol Modell 2015:60–63 2014: synthesis report. Contribution of Working Groups I, II and Fu AH, Chen YN, Li WH, Li BF, Yang YH, Zhang SH (2013) Spa- III to the fifth assessment report of the Intergovernmental Panel tial and temporal patterns of climate variations in the Kaidu on Climate Change. IPCC, Switzerland River Basin of Xinjiang, Northwest China. Quat Int 311:117– Pettitt AN (1979) A non-parametric approach to the change-point 122. doi:10.1016/j.quaint.2013.08.041 problem. J R Stat Soc 28:126–135 Gan TY (2000) Reducing vulnerability of water resources of Cana- Pohlert T (2016) Non-Parametric Trend Tests and Change-Point dian prairies to potential droughts and possible climatic warm- Detection. CC BY-ND 4.0. http://creativecommons.org/licenses/ ing. Water Resour Manag 14:111–135 by-nd/4.0/. Accessed 28 May 2017 Georgakakos KP, Graham NE, Modrick TM, Murphy MJ, Shamir Raup B, Racoviteanu A, Khalsa SJS, Helm C, Armstrong R, Arnaud E, Spencer CR, Sperfslage JA (2014) Evaluation of real- Y (2007) The GLIMS geospatial glacier database: a new tool time hydrometeorological ensemble prediction on hydro- for studying glacier change. Glob Planet Change 56:101–110. logic scales in Northern California. J Hydrol 519:2978–3000. doi:10.1016/j.gloplacha.2006.07.018 doi:10.1016/j.jhydrol.2014.05.032 Reuter HI, Nelson A, Jarvis A (2007) An evaluation of void-filling Getirana ACV, Espinoza JCV, Ronchail J, Rotunno Filho OC interpolation methods for SRTM data. Int J Geogr Inf Sci (2011) Assessment of different precipitation datasets and 21:983–1008 their impacts on the water balance of the Negro River basin. J Rolland C (2003) Spatial and seasonal variations of air temperature Hydrol 404:304–322. doi:10.1016/j.jhydrol.2011.04.037 lapse rates in alpine regions. J Clim 16:1032–1046 Guo Y, Shen Y (2016) Agricultural water supply/demand changes Sen PK (1968) Estimates of the regression coefficient based on Kend- under projected future climate change in the arid region of all’s Tau. J Am Stat Assoc 63:1379–1389 northwestern China. J Hydrol 540:257–273. doi:10.1016/j. Xu C, Chen Y, Li W, Chen Y (2006) Climate change and hydrologic jhydrol.2016.06.033 process response in the Tarim River Basin over the past 50 years. Infanti JM, Kirtman BP (2016) North American rainfall and tem- Chin Sci Bull 51:25–36. doi:10.1007/s11434-006-8204-1 perature prediction response to the diversity of ENSO. Clim Xu C, Chen Y, Hamid Y, Tashpolat T, Chen Y, Ge H, Li W (2009) Dyn 46:3007–3023. doi:10.1007/s00382-015-2749-0 Long-term change of seasonal snow cover and its effects on river Kendall MG (1948) Rank correlation methods. Oxford Univ Pr, runoff in the Tarim River basin, northwestern China. Hydrol England Process 23:2045–2055. doi:10.1002/hyp.7334 Li B, Chen Y, Shi X, Chen Z, Li W (2013) Temperature and pre- Xu J, Chen Y, Li W et al (2013a) Combining BPANN and wavelet cipitation changes in different environments in the arid region analysis to simulate hydro-climatic processes—a case study of of northwest China. Theor Appl Climatol 112:589–596. the Kaidu River, North-west China. Front Earth Sci 7:227–237. doi:10.1007/s00704-012-0753-4 doi:10.1007/s11707-013-0354-2 Liston GE, Elder K (2006) A meteorological distribution system for Xu J, Chen Y, Li W, Nie Q, Hong Y, Yang Y (2013b) The nonlin- high-resolution terrestrial modeling (MicroMet). J Hydromete- ear hydro-climatic process in the Yarkand River, northwestern orol 7:217–234. doi:10.1175/JHM486.1 China. Stoch Environ Res Risk Assess 27:389–399. doi:10.1007/ Liu Z, Xu Z, Charles SP, Fu G, Liu L (2011) Evaluation of two s00477-012-0606-9 statistical downscaling models for daily precipitation over an Xu Z, Liu P, Liu W (2013c) Automated statistical downscaling in sev- arid basin in China. Int J Climatol 31:2006–2020. doi:10.1002/ eral river basins of the Eastern Monsoon region, China. IAHS joc.2211 AISH Publ 2013:81–85 Liu Y, Yang W, Qin C, Zhu A (2016) A review and discussion on Xu C, Chen Y, Chen Y, Zhao R, Ding H (2013d) Responses of surface modeling and assessing agricultural best management prac- runoff to climate change and human activities in the arid region tices under global climate change. J Sustain Dev 9:245 of Central Asia: a case study in the Tarim River Basin, China. Ma Z, Kang S, Zhang L, Tong L, Su X (2008) Analysis of impacts Environ Manage 51:926–938. doi:10.1007/s00267-013-0018-8 of climate variability and human activity on streamflow Xu J, Chen Y, Li W, Nie Q, Song C, Wei C (2014) Integrating wave- for a river basin in arid region of northwest China. J Hydrol let analysis and BPANN to simulate the annual runoff with 352:239–249. doi:10.1016/j.jhydrol.2007.12.022 regional climate change: a case study of Yarkand River, North- Mahmood R, Babel MS (2013) Evaluation of SDSM developed west China. Water Resour Manag 28:2523–2537. doi:10.1007/ by annual and monthly sub-models for downscaling tem- s11269-014-0625-z perature and precipitation in the Jhelum basin, Pakistan Xu J, Chen Y, Bai L, Xu Y (2016a) A hybrid model to simulate the and India. Theor Appl Climatol 113:27–44. doi:10.1007/ annual runoff of the Kaidu River in northwest China. Hydrol s00704-012-0765-0 Earth Syst Sci 20:1447–1457. doi:10.5194/hess-20-1447-2016 Mann HB (1945) Nonparametric tests against trend. Econometrica J Xu J, Chen Y, Li W, Liu Z, Tang J, Wei C (2016b) Understand- Econom Soc 1945:245–259 ing temporal and spatial complexity of precipitation distribu- Neupane RP, White JD, Alexander SE (2015) Projected hydrologic tion in Xinjiang, China. Theor Appl Climatol 123:321–333. changes in monsoon-dominated Himalaya Mountain basins doi:10.1007/s00704-014-1364-z with changing climate and deforestation. J Hydrol 525:216–230. Yang YH, Li WH, Wei WS, Hao XM, WAN M, LI H (2009) Dis- doi:10.1016/j.jhydrol.2015.03.048 crepancy analysis of the climate changes among mountain, plain, Ohmura A (2012) Enhanced temperature variability in high-altitude oasis and desert in an inland river basin in the northern slopes climate change. Theor Appl Climatol 110:499–508. doi:10.1007/ of the Tianshan Mountains—a case study in the Sangong river s00704-012-0687-x basin. J Glaciol Geocryol 31:1094–1100 Olden JD, Jackson DA (2002) Illuminating the “black box”: a ran- Yang Y, Chen Y, Li W, Yu S, Wang M (2012) Climatic change of domization approach for understanding variable contribu- inland river basin in an arid area: a case study in northern Xin- tions in artificial neural networks. Ecol Modell 154:135–150. jiang, China. Theor Appl Climatol 107:143–154. doi:10.1007/ doi:10.1016/S0304-3800(02)00064-9 s00704-011-0467-z

1 3 2844 C. Wang et al.

Yang D, Gao B, Jiao Y, Lei H, Zhang Y, Yang H, Cong Z (2015) Zhang X, Zuo Q (2015) Analysis of water resource situation of the A distributed scheme developed for eco-hydrological mod- Tarim River basin and the system evolution under the changing eling in the upper Heihe River. Sci China Earth Sci 58:36–45. environment. J Coastal Res 73:9–16. doi:10.2112/SI73-003.1 doi:10.1007/s11430-014-5029-7 Zhao J, Xu Z, Singh VP (2016) Estimation of root zone storage capac- Yatagai A, Yasunari T (1994) Trends and decadal-scale fluctuations ity at the catchment scale using improved Mass Curve Tech- of surface air temperature and precipitation over China and Mon- nique. J Hydrol 540:959–972. doi:10.1016/j.jhydrol.2016.07.013 golia during the recent 40 year period (1951–1990). J Meteorol Soc Jpn 72:937–957

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