Abstract

Separating the impacts of climate change and human activity on actual evapotranspiration (ET) is important for reducing comprehensive risk and improving the adaptability of water resource systems. In this study, the spatiotemporal distribution of actual ET in the Aksu River Basin, Northwest China, during the period 2000–2015 was evaluated using the Vegetation Interfaces Processes model and Moderate Resolution Imaging Spectroradiometer-Normalized Difference Vegetation Index. The impact of climate change and human activity on actual ET were separated and quantified. The results demonstrated that: (1) the annual pattern of actual ET per pixel exhibited the highest values for arable land (average 362.4 mm/a/pixel), followed by forest land and grassland (average of 159.6 and 142.8 mm/a/pixel, respectively). Significant increasing linear trends (p < 0.05) of 3.2 and 1.8 mm/a were detected in the arable land and forest land time series, respectively; (2) precipitation was the most significant of the selected climate factors (precipitation, average temperature, sunshine duration, and wind speed) for all ecosystems. The second most significant was wind speed; (3) human activity caused 89%, 98%, and 80% of the changes in actual ET of forest, grass, and arable land, respectively, while climate change caused 11%, 2%, and 20% of the changes in actual ET, in the Aksu River Basin during 2000–2015.

INTRODUCTION

Evapotranspiration (ET) is a significant hydrological factor associated with energy balance and water budget that represents water consumption (Liu et al. 2016; Chen et al. 2017), but is difficult to evaluate at the catchment scale (Liu et al. 2016). The accurate assessment of ET plays an important role in effective water resource management (Yeh et al. 1998) and in the quantification of water resource carrying capacity (Loucks 2000). Generally, ET estimation at the basin scale is based on land surface hydrologic models combined with weather data (e.g., Wood et al. 1992), land-atmosphere models with reanalysis, or remote sensing (e.g., Rodell et al. 2004; Mu et al. 2007). Although commonly used and well-established models have typically been verified by flux tower ET observations (e.g., Running et al. 1999; Xu et al. 2006), results often vary across multiple spatiotemporal scales (Liu et al. 2016). In this study, the Vegetation Interfaces Processes (VIP) model established by Mo & Liu (2001) and developed by Mo et al. (2011) and Mo et al. (2015) was applied to estimate actual ET at the pixel scale of remote sensing imagery, with the incorporation of ground surface properties. The model involves multiple modules that simulate energy, water, and carbon exchanges between terrestrial ecosystems and the atmosphere (Mo et al. 2014). Furthermore, the model has been widely applied in China, including the Lushi Basin (Mo et al. 2004), Xitiao River catchment (Mo & Meng 2011), Northeast China (Mo et al. 2014), and North China (Mo et al. 2017). These studies demonstrate the broad applicability of the VIP model.

Climate factors and human activity greatly impact ET (Qiu et al. 2008; Liu et al. 2010; Zhang et al. 2011, 2013; Bai et al. 2014; Li 2014; Sun et al. 2016). Shi et al. (2013) concluded that climate change played the greatest role in changes to global ET during 1982–2008. However, Bai et al. (2014) found that the contribution of climate change to the ET trend was weak in the arid region of Northwest China, with relatively strong human activity during 1971–2006 (Bai et al. 2014). In addition, separating the contribution of climate change from that of human activity (i.e., irrigation, fertilization, grazing, management practices, and land use changes) is difficult (Lobell et al. 2011; Wang et al. 2016) yet important for understanding the impact of climate change on water consumption, carbon sequestration, and adaptation strategies (Chen et al. 2017). Multiple prior studies have explored the separation and quantification of the contributions of climate change and human activity. For example, Qiu et al. (2008) evaluated the ET of winter wheat and its response to different irrigation regimes in the North China Plain. Bai et al. (2014) determined that an increase in cropland area explained 60.5% of the increased total ET, and management practices accounted for 16.8% of the increased total ET. Chen et al. (2017) concluded that climate change and human activity in the North China Plain contributed 0.188 and 0.466 mm/a, respectively, to the actual ET trend of 0.654 mm/a.

The Aksu River is located in the arid northwest region of China, and nearly half of its water originates as glacier and snow melt from the mountains (Li et al. 2016). In recent years, hydrological processes in the basin have been severely affected by climate change. Jiang et al. (2005) reported that temperature increases had a greater effect on runoff than precipitation in the Aksu River. Xu et al. (2011) determined that a close relationship exists between variations in annual runoff and regional climate change. Li et al. (2016) reported that the contributions of climate factors and human activity to the runoff in the Aksu River Basin were 94% and 6%, respectively. However, few studies have attempted to separate and quantify the contributions of climate change and human activity to actual ET in the Aksu River Basin, while simultaneously considering land use changes.

The objectives of this study were to (1) evaluate actual ET in the Aksu River Basin via the VIP model and the Moderate Resolution Imaging Spectroradiometer-Normalized Difference Vegetation Index (MODIS-NDVI); (2) estimate the accuracy of actual ET in the basin based on the VIP model; and (3) quantify the contributions of climate change and human activity to actual ET in the Aksu River Basin.

STUDY AREA AND MATERIALS

Study area

The Aksu River Basin is in the northern Tarim River Basin, Northwest China (Li et al. 2016) (Figure 1). The Aksu River is one of the largest tributaries of the Tarim River by runoff volume and covers an area of 5.14 × 104 km2 (Wang et al. 2010). The basin experiences a typical temperate continental climate due to the blocking effect of the terrain and a large distance to the ocean. Thus, a low average annual precipitation (64 mm), high average annual potential ET (1,890 mm), and average annual temperature range (9.2–11.5 °C) are observed in the basin (Xu et al. 2011). In addition, basin runoff is primarily derived from the Tienshan Mountains and is influenced by the complex climatic conditions and hydrological environment (Xu et al. 2011).

Figure 1

The study area. (The main image shows the study area, and the inset image shows the location of the study area.).

Figure 1

The study area. (The main image shows the study area, and the inset image shows the location of the study area.).

Agricultural development in the Aksu River Basin is dominated by oasis irrigation agriculture, for which the irrigation water originates from the river or reservoirs (Huang et al. 2015). Tarim irrigation districts are primarily located in the downstream reaches of the Aksu River Basin, where the highest water consumption occurs (Han et al. 2015). Moreover, polluted water in the basin is re-routed for irrigation and vegetable production, causing vegetation degradation and salinization of cultivated land (Ji et al. 2000; Guo et al. 2003). The difference between the supply and demand for water in the Aksu River Basin is becoming more intense with continued economic development.

Materials

Meteorological data, land use cover change (LUCC) data, digital elevation model data, and MODIS-NDVI were used to drive the VIP model in this study. Meanwhile, hydrometeorological data were applied to construct the water balance (WB) model. Then, a WB-ET model was obtained to verify the results of the VIP model simulation.

Weather and hydrological data

Daily weather data (precipitation (P), minimum temperature (Tmn), maximum temperature (Tmx), relative humidity (RH), sunshine duration (SD), wind speed (WS), and atmospheric pressure (AP)) from 2000–2015 at three weather stations were used to drive the VIP model. These data were obtained from the China Meteorological Administration (CMA) (http://data.cma.cn/), as shown in Table 1. The station data were interpolated to the entire basin based on the gradient plus inverse distance squared method proposed by Nalder & Wein (1998). Additionally, runoff data from 2000–2011 were collected from the Annals of Hydrological Statistics, compiled by the Ministry of Water Resources of the People's Republic of China. The monthly averaged runoff was used as hydrological data in this study. The hydrological stations are listed in Table 1.

Table 1

The summaries of weather and hydrological stations in the Aksu River Basin

Hydrological Stations Long.(E) Lat.(N) Weather Station Long.(E) Lat.(N) 
Xidaqiao 80.20 41.17 Keping 79.05 40.50 
Xiehela 79.61 41.56 Aheqi 78.45 40.93 
Shaliguilanke 78.61 40.95 Aksu 80.23 41.17 
Hydrological Stations Long.(E) Lat.(N) Weather Station Long.(E) Lat.(N) 
Xidaqiao 80.20 41.17 Keping 79.05 40.50 
Xiehela 79.61 41.56 Aheqi 78.45 40.93 
Shaliguilanke 78.61 40.95 Aksu 80.23 41.17 

MODIS-NDVI data

NDVI data for the period of 2000–2015, with a 16-day temporal resolution and 1-km2 spatial resolution, was downloaded from the MODIS product website (https://lpdaac.usgs.gov/). Missing data at the beginning of 2000 was substituted with that of 2001. NDVI data quality was then improved by application of the Savitzky–Golay (SG) filter (Savitzky & Golay 1964). A smoothing effect was achieved by fitting successive subsets of adjacent data points with a low-order polynomial. The filter can reduce or eliminate the impact of clouds on the quality of remote sensing images. Finally, the 16-day NDVI data was interpolated to daily data using the Lagrange polynomial method to match the temporal resolution of the VIP model.

LUCC data

(LUCC data for 2000, 2005, and 2010 were provided by the Data Center for Resources and Environmental Sciences, Chinese Academy of Sciences (RESDC) (http://www.resdc.cn). The dataset is primarily based on Landsat Thematic Mapper/Enhanced Thematic Mapper remote sensing images and an artificial visual interpretation method (Liu et al. 2014). In this study, 1-km2 resolution LUCC data were used to drive the VIP model to estimate actual ET during 2000–2002, 2003–2007, and 2008–2015.

METHOD

VIP model

The VIP model simulates canopy transpiration and evaporation from the canopy intercept and soil surface based on the Penman–Monteith equation (Mo et al. 2017). Transpiration and photosynthesis processes are coupled by the Ball–Berry relationship between leaf stomatal conductance and net assimilation rate (Mo et al. 2017). In terms of the radiation budget, shortwave radiation transfer in the canopy discriminates the near-infrared radiation and leaf spectral properties in the visible range, as well as the proportion of diffusive irradiance and direct beam radiation (Mo et al. 2011). The curve-number equation is applied on a daily scale to calculate runoff generation over the land surface using daily net precipitation (Mo et al. 2017). The evaporation calculations for soil and vegetation can be expressed as follows:  
formula
(1)
 
formula
(2)
where , and are the slope of the saturation vapor pressure temperature relationship (kPa/°C), the temperature stress factor, and the water stress factor, respectively. Meanwhile,,, , D, , and represent net radiation (MJ/m2/day) absorbed by the canopy, vegetation coverage, specific heat of the air (kJ/kg/°C), saturation vapor pressure (hPa), aerodynamic resistance (kg/m/s2), and a psychrometric constant considering the least resistance of the leaf (kPa/°C), respectively. Irrigation water is also considered in the VIP model, in which it is assumed that irrigation occurs during some months for some crops, but without considering the specific amount of irrigation. The primary basis is that the soil moisture in arable land after irrigation is regarded as achieving the potential evaporation level. If the potential evaporation is very large and the soil moisture cannot reach the potential evaporation level, the soil water loss is regarded as the actual evaporation. Soil evaporation is limited by the potential evaporation of the surface and the exudation rate of soil moisture. The smaller value is selected as the soil evaporation, as in Equation (3).  
formula
(3)
 
formula
(4)
 
formula
(5)
where Esp, Eex, Rns, and G are the potential evaporation of the surface (mm), the leakage rate of soil water (mm), the net radiation absorbed by the soil, and the soil heat flux, respectively. In addition, S is the desorptivity (mm/day0.5), generally affected by soil texture, porosity, and other factors; it ranges from 3 mm/day0.5 to 5 mm/day0.5 (Choudhury & Digirolamo 1998). This study selected an S value of 3 mm/day0.5. Meanwhile, fc is the coverage of surface vegetation, which can be obtained from Equation (6):  
formula
(6)
where and are the maximum NDVI and minimum NDVI values, respectively. In this formula, a is the empirical value, ranging from 0.6 to 1.25, which was defined as 0.6 in this study.

Water balance model

Similar to the WB model at the catchment scale, the equivalent actual ET in the Aksu River Basin is established according to:  
formula
(7)
where ET is actual ET (mm), and P (mm), Rin (mm), Rout (mm), and (mm) represent precipitation, inflow, outflow, and changes in the terrestrial water storage of the basin, respectively. Because the multiple annually averaged changes in terrestrial water storage can be regarded as zero, Equation (8) can be simplified as:  
formula
(8)

For this study in the Aksu River Basin, two hydrological stations (Shaliguilanke and Xiehela) in the mountainous region were defined as inflow stations, and one station (Xidaqiao) was defined as the outflow station.

Separating the impacts of climate change and human activity

To separate the impacts of climate change and human activity, possible changes in actual ET derived from land use changes were included as impacts of human activity. The difference between the two actual ET trends derived from the two VIP model simulations was attributed to changes in land use, as follows:  
formula
(9)
where QLUCC is the change in the trend of actual ET caused by land use change; T1 is the change in the trend of actual ET derived from the VIP model when considering land use changes (i.e., LUCC data from 2000, 2005, and 2010 were used to drive the VIP model during 2000–2015); and T2 is the change in the trend of actual ET derived from the VIP model without considering land use changes (i.e., LUCC data from 2000 were applied to drive the VIP model during 2000–2015).

Subsequently, changes in the actual ET caused by other human activities (i.e., irrigation, fertilization, grazing, and management practices, among others) and by climate change are processed based on the methods of Nicholls (1997) and Chen et al. (2017). Due to their non-climatic influences on ET (Nicholls 1997; Lobell & Field 2007; Tao et al. 2008; Veron et al. 2015), these factors can be removed by applying a first-difference detrending method (i.e., the difference of the value between one year and the previous year) (Chen et al. 2017). The relationships between the detrended climate factors and the detrended ET are evaluated by partial correlation analysis, which can explore the relationship of two variables independent of the influences of other factors (Nicholls 1997; Xiao et al. 2015; Dass et al. 2016). The highest partial correlation coefficient indicates the most dominant climate factor (Chen et al. 2017).

Multivariate linear regression can quantify the contributions of climate factors (Zhang et al. 2010); the major climate factors with detrended processing are the predictor variables for the detrended ET:  
formula
(10)
where ai, Xids, and Yds are the regression coefficients of climate factors, the normalized detrended climate factors, and the normalized detrended actual ET, respectively.
Nicholls (1997) and Lobell & Field (2007) assumed that climate trends and detrended climate factors have similar roles to dependent variables; the contribution of climate factors to the changes in actual ET can be obtained from the regression coefficients and trends of climate factors, as follows:  
formula
(11)
 
formula
(12)
where Xis_trend, Qc, Ys_trend, Ytrend, and Qac are the trend of normalized climate factors, the climatic contribution to the trend of normalized actual ET, the trend of normalized actual ET, the trend of actual ET, and the actual climatic contribution to the trend of actual ET, respectively.
The remaining trend of actual ET after removing the climatic contribution can be attributed to human activity (i.e., irrigation, fertilization, grazing, and management practices, among others), and can be quantified as follows (Lobell & Asner 2003):  
formula
(13)
 
formula
(14)
where Qh and Qah are the contribution of human activities to the trend of normalized ET and the actual contribution of human activities to the trend of ET, respectively.
In addition, the relative contributions of climate change and human activity are calculated as:  
formula
(15)
 
formula
(16)
where RCc and RCh are the relative contributions of climate change and human activity, respectively.

RESULTS AND DISCUSSION

LUCC in the Aksu River Basin

Land use changes in the Aksu River Basin were investigated using the LUCC dataset for 2000, 2005, and 2010, as shown in Figure 2. While grassland and glaciers converge in the north of the basin, unused and desert land characterize the southwest of the basin. Grassland covers the largest area of the Aksu River Basin. Arable, residential, and industrial land are mostly located near the middle and lower reaches of the river. Arable land increased from 4,672 km2 in 2000 to 5,420 km2 in 2005, and 5,598 km2 in 2010. In contrast, forest land decreased from 1,291 km2 in 2000 to 1,213 km2 in 2005, and 1,203 km2 in 2010. Similarly, grassland decreased from 19,996 km2 in 2000, to 19,524 km2 in 2005, and 19,467 km2 in 2010. Residential and industrial land collectively increased by 5.6% from 2000 to 2005. Chen et al. (2016) observed similar LUCC patterns and reported a decreasing trend in natural land cover and an increase in artificial land cover in the Tarim River Basin.

Figure 2

The land use in the Aksu River Basin during 2000, 2005, and 2010.

Figure 2

The land use in the Aksu River Basin during 2000, 2005, and 2010.

Because MODIS-NDVI was a significant driving factor of the VIP model, it was also analyzed. The monthly spatiotemporal distribution of NDVI in the Aksu River Basin during 2000–2015 is shown in Figure 3. Figure 3(a) and 3(b) are the original NDVI, while Figure 3(c) and 3(d) are the SG-filtered NDVI. The highest NDVI values were concentrated on the river bank and arable land, and the lowest NDVI was located in the north of the basin in a glaciated area. The NDVI time series exhibited an insignificant increasing trend during 2000–2015, with maximum and minimum NDVI recorded in July 2012 and January 2008, respectively. This increase can be attributed to an increase in arable land during this period (Huang et al. 2015). A significant smoothing effect can be seen in Figure 3(d), justifying the use of the filter to eliminate the effect of clouds on the remote sensing images.

Figure 3

The spatiotemporal distribution of NDVI in the Aksu River Basin.

Figure 3

The spatiotemporal distribution of NDVI in the Aksu River Basin.

Assessment of actual ET using the VIP model

To assess the accuracy of the actual ET derived from the VIP model, the driving factors of the WB model and WB-ET model were analyzed (Figure 4). Precipitation decreased from 2000–2007 but increased from 2008–2011. Meanwhile, the maximum retained runoff (i.e., RinRout) occurred in 2005 and significant seasonal fluctuations are evident in both time series. While the VIP-ET series exhibited significant seasonal fluctuations, VIP-ET displayed a significant correlation with WB-ET. For instance, apart from the period 2003–2005, the amplitudes and phases of the actual ET time series were consistent, with a coefficient of determination of 0.79 at a 95% significance level and a root mean square error (RMSE) of 13.7 mm. From this result, we conclude that VIP-ET can be applied to determine the actual ET on the ground, e.g., for vegetation and arable land.

Figure 4

Comparison of the actual ET for water balance model and VIP model.

Figure 4

Comparison of the actual ET for water balance model and VIP model.

Spatiotemporal distribution of actual ET from the VIP model in the Aksu River Basin

Figure 5 shows the annual spatiotemporal distribution of actual ET derived from the VIP model, including consideration of land use changes. The pixels of actual ET were divided by ecosystem (i.e., forest land, grassland, and arable land) based on LUCC data. The annual mode of actual ET per pixel displays the highest values for arable land (average 362.4 mm/pixel), followed by forest land and grassland (average of 159.6 and 142.8 mm/pixel). In addition, in the annual time series of actual ET (Figure 5(b), 5(d), and 5(f)), the lowest value occurred in 2000 (108 mm for forest, 98 mm for grassland, and 315 mm for arable land), followed by 2009. The highest value occurred in 2013, followed by 2010 and 2003. Significant increasing linear trends (p < 0.05) of 3.17 and 1.8 mm/a were observed in arable and forest time series, respectively. As shown in Figure 2, the largest amount of water consumption in grassland can be attributed to this land use type having the largest area. In addition, the increase of actual ET in arable regions was due to the increased arable land area.

Figure 5

The spatiotemporal distribution of actual ET in the Aksu River Basin with considering the changes of land use.

Figure 5

The spatiotemporal distribution of actual ET in the Aksu River Basin with considering the changes of land use.

To separate the contribution of LUCC to actual ET, the VIP model was run again with climate factors and parameters identical to those used in the first process. However, the input land use was fixed at conditions recorded in 2000. The annual spatial distribution of actual ET from forest, grassland, and arable land is shown in Figure 6(a), 6(c), and 6(e). Arable land displayed the highest per pixel actual ET (367 mm/a/pixel), followed by forest and grassland (162 and 146 mm/a/pixel). In addition, the time series of actual ET (Figure 6(b), 6(d), and 6(f)) displayed significant fluctuations. The grassland and forest land time series exhibited relatively lower actual ET during 2000, 2009, and 2014–2015, while the arable time series was more stable. Meanwhile, significant increasing trends (p < 0.05) were observed in the time series of actual ET from forest and arable land, which were 2.36 and 3.17 mm/a/a, respectively. Thus, irrespective of land use changes, there was a strong similarity in the spatiotemporal distribution of actual ET derived from the VIP model. The difference in the two slopes for actual ET from the two VIP model runs was regarded as the LUCC contribution. It is notable that there was a difference of 0.5, 0.3, and −0.002 mm/a/a in the results of the VIP model for forest, grass, and arable land cover. This means that LUCC contributed 17.6%, 43.3%, and 0.08% to actual ET for forest, grass, and arable land, respectively.

Figure 6

The spatiotemporal distribution of actual ET in the Aksu River Basin without considering the changes of land use.

Figure 6

The spatiotemporal distribution of actual ET in the Aksu River Basin without considering the changes of land use.

Separating the impact of climate change and human activity on water consumption in the Aksu River Basin

The VIP model was driven by multiple factors: P, Ta, Tmx, Tmn, AP, RH, WS, and SD. Thus, the analysis of their impacts on ET is important to quantify the contribution of climate change to actual ET. However, Chen et al. (2017) concluded that there were significant auto-correlations among these climate factors. Thus, it was necessary to check the correlation among climate factors prior to multiple regression. The simple correlation coefficients and their significance levels for every two original climate factors and detrended climate factors for forest land, grassland, and arable land are displayed in Figure 7. Ta, Tmn, and Tmx agree well with each other at the 95% significance level, as does RH with WS and Ta with AP. Ukkola & Prentice (2013) and Cao et al. (2014) reported that SD, Ta, P, RH, and WS were the key climate factors influencing ET; thus, Tmn, Tmx, AP, and RH were not considered in the study. Subsequently, the relationships between ET and the above climate factors (i.e., P, Ta, SD, and WS) were analyzed for the Aksu River Basin.

Figure 7

The correlation coefficients and their significance level for the climate factors driving the VIP model.

Figure 7

The correlation coefficients and their significance level for the climate factors driving the VIP model.

To obtain the multiple regression models, both the climate factors and actual ET were detrended. Figure 8 depicts the percentile distribution of the original annual actual ET and detrended actual ET for different ecosystems in the Aksu River Basin during 2000–2015. Figure 8(a) shows that the actual ET of arable land is the highest, while Figure 8(b) shows that the detrended actual ET of arable land exhibits the narrowest range. This result means that the impact from climate change on actual ET was evident for arable land, but unclear for grassland. The normalized regression coefficients for the multiple linear regression model are shown in Table 2. P was most significant among the selected climate factors (P, Ta, SD, and WS) for all ecosystems, and WS was the second most important. Ta and SD displayed varying significance for different ecosystems.

Table 2

Normalized regression coefficients for the three ecosystems

Categories a1 a2 a3 a4 
Forest 0.911 0.001 0.068 −0.236 
Grass 0.881 0.074 0.148 −0.275 
Arable 0.878 −0.19 0.06 −0.222 
Categories a1 a2 a3 a4 
Forest 0.911 0.001 0.068 −0.236 
Grass 0.881 0.074 0.148 −0.275 
Arable 0.878 −0.19 0.06 −0.222 
Figure 8

The ranges of actual ET for the multi-ecosystems in the Aksu River Basin. (The red line represents the median percentile, and the red stars represent the extremes. The whiskers are 5% and 95%). Please refer to the online version of this paper to see this figure in colour: http://dx.doi.org/10.2166/nh.2018.136.

Figure 8

The ranges of actual ET for the multi-ecosystems in the Aksu River Basin. (The red line represents the median percentile, and the red stars represent the extremes. The whiskers are 5% and 95%). Please refer to the online version of this paper to see this figure in colour: http://dx.doi.org/10.2166/nh.2018.136.

Based on the method of Nicholls (1997) and Chen et al. (2017), the contribution of LUCC to actual ET was incorporated into the evaluation. The contributions of climate change and human activity to actual ET in the Aksu River Basin are shown in Table 3. The results indicate that human activity dominated actual ET changes in the Aksu River Basin, while climate change caused less significant changes. Human activity contributed 89%, 98%, and 80% to actual ET changes in forest, grass, and arable land, respectively, and climate change caused actual ET changes of 11%, 2%, and 20%, respectively, during 2000–2015. The relatively well-developed irrigation agriculture and widespread animal husbandry in the basin could result in a more significant impact of human activity on arable land and grassland, but not forest land. Bai et al. (2014) determined that this was the case in the Sangonghe Catchment, Northwest China, where human activity explained 77.3% of the changes in actual ET, whereas climate factors accounted for only 22.7%. We conclude that human activity dominates changes in actual ET in the Aksu River Basin.

Table 3

Contributions of human activities and climate change to actual ET

Categories Forest Grass Arable 
Human activities 0.89 0.98 0.80 
Climate change 0.11 0.02 0.20 
Categories Forest Grass Arable 
Human activities 0.89 0.98 0.80 
Climate change 0.11 0.02 0.20 

CONCLUSION

The spatiotemporal distribution of actual ET in the Aksu River Basin during 2000–2015 was evaluated using the VIP model and 1-km2 MODIS-NDVI data. The actual ET derived from the VIP model was accessed by a WB model, and the contributions of climate change and human activity on the trends of actual ET were separated and quantified. The major conclusions are as follows.

  1. Based on LUCC data, a decreasing trend was observed in forest land and grassland, but an increasing trend was observed in arable land in the Aksu River Basin. Arable land increased from 4,672 km2 in 2000 to 5,420 km2 in 2005 and 5,598 km2 in 2010. In contrast, forest land decreased from 1,291 km2 in 2000 to 1,213 km2 in 2005 and 1,203 km2 in 2010. Grassland decreased from 19,996 km2 in 2000 to 19,524 km2 in 2005 and 19,467 km2 in 2010.

  2. The annual pattern of actual ET per pixel displayed the highest values for arable land (average 362.4 mm/a/pixel), followed by forest land and grassland (average of 159.6 and 142.8 mm/a/pixel, respectively). Significant increasing linear trends (p < 0.05) of 3.2 and 1.8 mm/a were detected in the arable land and forest land time series, respectively. In terms of actual ET derived from the VIP model with constant LUCC, arable land displayed the highest per pixel actual ET (367.2 mm/a/pixel), followed by forest and grassland (162.1 and 145.7 mm/a/pixel, respectively). Meanwhile, significant increasing trends (p < 0.05) were observed in the time series of actual ET from forest and arable land, which were 2.4 and 3.2 mm/a, respectively.

  3. Ta, Tmn, and Tmx agreed well with each other at a 95% significance level, as did RH with WS, and Ta with atmospheric pressure. Precipitation was the most important factor affecting the selected climate factors (P, Ta, SD, WS) for all ecosystems, and the second most important factor was wind speed.

  4. Human activity contributed 89%, 98%, and 80% to actual ET changes in forest land, grassland, and arable land, respectively, while climate change contributed to actual ET changes of 11%, 2%, and 20%, respectively, in the Aksu River Basin during 2000–2015.

ACKNOWLEDGEMENTS

This research is supported by the National Basic Research Program of China (973 Program, No. 2015CB452701) and the National Natural Science Foundation of China (No. 41571019). The first author thanks the National Aeronautics and Space Administration (NASA) for providing the MODIS data used in this study.

REFERENCES

REFERENCES
Cao
,
G. L.
,
Han
,
D. M.
&
Song
,
X. F.
2014
Evaluating actual evapotranspiration and impacts of groundwater storage change in the North China Plain
.
Hydrological Processes
28
(
4
),
1797
1808
.
Chen
,
Y. N.
,
Li
,
Z.
,
Li
,
W. H.
,
Deng
,
H. J.
&
Shen
,
Y. J.
2016
Water and ecological security: dealing with hydroclimatic challenges at the heart of China's Silk Road
.
Environment Earth Science
75
(
10
),
881
.
Chen
,
X. J.
,
Mo
,
X. G.
,
Hu
,
S.
&
Liu
,
S. X.
2017
Contributions of climate change and human activities to ET and GPP trends over North China Plain from 2000 to 2014
.
Journal of Geographical Sciences
27
(
6
),
661
680
.
Dass
,
P.
,
Rawlins
,
M. A.
,
Kimball
,
J. S.
&
Kim
,
Y.
2016
Environmental controls on the increasing GPP of terrestrial vegetation across northern Eurasia
.
Biogeosciences
13
(
1
),
45
62
.
Guo
,
Y.
,
Xu
,
H.
,
Li
,
W.
&
He
,
Y.
2003
The investigation on the sewage outlets and the analysis on the water pollution along the mainstream of Tarim River
.
Arid Zone Research
20
,
35
38
.
Han
,
S. M.
,
Hu
,
Q. L.
,
Yang
,
Y. H.
,
Wang
,
J. S.
,
Wang
,
P.
&
Wang
,
Q.
2015
Characteristics and driving factors of drainage water in irrigation districts in arid areas
.
Water Resource Management
29
(
14
),
5323
5337
.
Ji
,
F.
,
Ma
,
Y.
&
Fan
,
Z.
2000
Salt pollution cycle on cultivated land between drainage and irrigation in the main stream of Tarim River
.
Agro-environment Protection
19
,
133
136
.
Jiang
,
Y.
,
Zhou
,
C. H.
&
Cheng
,
W. M.
2005
Analysis on runoff supply and variation characteristics of Aksu drainage basin
.
Journal of Natural Resources
20
(
1
),
27
34
.
Li
,
B. F.
,
Chen
,
Y. N.
&
Xiong
,
H. G.
2016
Quantitatively evaluating the effects of climate factors on runoff change for Aksu River in northwestern China
.
Theoretical and Applied Climatology
123
(
1
),
97
105
.
Liu
,
Y. A.
,
Wang
,
E. L.
,
Yang
,
X. G.
&
Wang
,
J.
2010
Contributions of climatic and crop varietal changes to crop production in the North China Plain, since 1980s
.
Global Change Biology
16
(
8
),
2287
2299
.
Liu
,
J. Y.
,
Kuang
,
W. H.
,
Zhang
,
Z. X.
,
Xu
,
X. L.
,
Qin
,
Y. W.
,
Ning
,
J.
,
Zhou
,
W. C.
,
Zhang
,
S. W.
,
Li
,
R. D.
,
Yan
,
C. Z.
,
Wu
,
S. X.
,
Shi
,
X. Z.
,
Jiang
,
N.
,
Yu
,
D. S.
,
Pan
,
X. Z.
&
Chi
,
W. F.
2014
Spatio-temporal characteristics, patterns and causes of land-use changes in China since the late 1980s
.
Journal of Geographical Sciences
24
(
2
),
195
210
.
Liu
,
W. B.
,
Wang
,
L.
,
Zhou
,
J.
,
Li
,
Y. Z.
,
Sun
,
F. B.
,
Fu
,
G. B.
,
Li
,
X. P.
&
Sang
,
Y. F.
2016
A worldwide evaluation of basin-scale evapotranspiration estimates against the water balance method
.
Journal of Hydrology
538
,
82
95
.
Lobell
,
D. B.
&
Field
,
C. B.
2007
Global scale climate-crop yield relationships and the impacts of recent warming
.
Environmental Research Letters
2
(
1
),
014002
.
Lobell
,
D. B.
,
Schlenker
,
W.
&
Costa-Roberts
,
J.
2011
Climate trends and global crop production since 1980
.
Science
333
(
6042
),
616
620
.
Loucks
,
D. P.
2000
Sustainable water resources management
.
Water International
25
,
3
10
.
Mo
,
X. G.
&
Liu
,
S. X.
2001
Simulating evapotranspiration and photosynthesis of winter wheat over the growing season
.
Agric. For. Meteorol.
109
,
203
222
.
Mo
,
X. G.
&
Meng
,
D. J.
2011
Simulation of the impacts of climate change on the water budget of the Xitiao River catchment, China
.
Hyroclimatology: Variability and Change
(
Franks
,
S. W.
, ed.)
51
(
1
),
45
65
.
Mo
,
X. G.
,
Liu
,
S. X.
,
Lin
,
Z. H.
&
Zhao
,
W.
2004
Simulating temporal and spatial variation of evapotranspiration over the Lushi basin
.
J. Hydrol.
285
(
1
),
125
142
.
Mo
,
X. G.
,
Liu
,
S. X.
,
Lin
,
Z. H.
&
Qiu
,
J. X.
2011
Patterns of evapotranspiration and GPP and their responses to climate variations over the North China Plain
.
Acta Geographical Sinca
66
(
5
),
589
598
.
(in Chinese).
Mo
,
X. G.
,
Liu
,
S. X.
,
Lin
,
Z. H.
,
Wang
,
S.
&
Hu
,
S.
2015
Trends in land surface evapotranspiration across China with remotely sensed NDVI and climatological data for 1981–2010
.
Hydrological Sciences Journal-Journal des Sciences Hydrologiques
60
(
12
),
2163
2177
.
Mu
,
Q.
,
Heinsch
,
F. A.
,
Zhao
,
M.
&
Running
,
S. W.
2007
Development of a global evapotranspiration algorithm based on MODIS and global meteorology data
.
Remote Sensing of Environment
111
(
4
),
519
536
.
Nalder
,
I. A.
&
Wein
,
R. W.
1998
Spatial interpolation of climatic normals: test of a new method in the Canadian boreal forest
.
Agricultural & Forest Meteorology
92
(
4
),
211
225
.
Qiu
,
G. Y.
,
Wang
,
L. M.
,
He
,
X. H.
,
Zhang
,
X. Y.
,
Chen
,
S. Y.
,
Chen
,
J.
&
Yang
,
Y. H.
2008
Water use efficiency and evapotranspiration of winter wheat and its response to irrigation regime in the North China Plain
.
Agricultural and Forest Meteorology
148
(
11
),
1848
1859
.
Rodell
,
M.
,
Famiglietti
,
J.
,
Chen
,
J.
,
Seneviratne
,
S.
,
Viterbo
,
P.
,
Holl
,
S.
&
Wilson
,
C.
2004
Basin scale estimates of evapotranspiration using GRACE and other observations
.
Geophysical Research Letters
31
(
20
),
183
213
.
Running
,
S. W.
,
Baldocchi
,
D. D.
,
Turner
,
D. P.
,
Gower
,
S. T.
,
Bakwin
,
P. S.
&
Hibbard
,
K. A.
1999
A global terrestrial monitoring network integrating tower fluxes, flask sampling, ecosystem modeling and EOS satellite data
.
Remote Sensing of Environment
70
,
108
127
.
Savitzky
,
A.
&
Golay
,
M. J. E.
1964
Smoothing and differentiation of data by simplified least squares procedures
.
Analytical Chemistry
36
,
1627
1639
.
Sun
,
H. Y.
,
Zhang
,
X. Y.
,
Wang
,
E. L.
,
Chen
,
S. Y.
,
Shao
,
L. W.
&
Qin
,
W. L.
2016
Assessing the contribution of weather and management to the annual yield variation of summer maize using APSIM in the North China Plain
.
Field Crops Research
194
,
94
102
.
Tao
,
F. L.
,
Yokozawa
,
M.
,
Liu
,
J. Y.
&
Zhang
,
Z.
2008
Climate-crop yield relationships at provincial scales in China and the impacts of recent climate trends
.
Climate Research
38
(
1
),
83
94
.
Ukkola
,
A. M.
&
Prentice
,
I. C.
2013
A worldwide analysis of trends in water-balance evapotranspiration
.
Hydrology and Earth System Sciences
17
(
10
),
4177
4187
.
Veron
,
S. R.
,
de, Abelleyra
,
D.
&
Lobell
,
D. B.
2015
Impacts of precipitation and temperature on crop yields in the pampas
.
Climatic Change
130
(
2
),
235
245
.
Wang
,
G.
,
Shen
,
Y.
,
Zhang
,
J.
,
Wang
,
S.
&
Mao
,
W.
2010
The effects of human activities on oasis climate and hydrologic environment in the Aksu river basin, Xinjiang, China
.
Environment Earth Science
59
,
1759
1769
.
Wang
,
Z.
,
Ye
,
T.
,
Wang
,
J.
,
Cheng
,
Z.
&
Shi
,
P. J.
2016
Contribution of climatic and technological factors to crop yield: empirical evidence from late paddy rice in Hunan Province, China
.
Stochastic Environmental Research and Risk Assessment
30
(
7
),
2019
2030
.
Wood
,
E. F.
,
Lettenmaier
,
D. P.
&
Zartarian
,
V. G.
1992
A land-surface hydrology parameterization with subgrid variability for general circulation models
.
Journal of Geophysical Research
97
,
2717
.
Xu
,
J. H.
,
Chen
,
Y. N.
,
Lu
,
F.
,
Li
,
W. H.
,
Zhang
,
L. J.
&
Hong
,
Y. L.
2011
The nonlinear trend of runoff and its response to climate change in the Aksu River, western China
.
International Journal of Climatology
31
(
5
),
687
695
.
Zhang
,
S. H.
,
Liu
,
S. X.
,
Mo
,
X. G.
,
Shu
,
C.
,
Sun
,
Y.
&
Zhang
,
C.
2010
Assessing the impact of climate change on reference evapotranspiration in Aksu River Basin
.
Acta Geographical Sinca
65
(
11
),
1363
1370
.
(in Chinese)
Zhang
,
X. Y.
,
Chen
,
S. Y.
,
Sun
,
H. Y.
,
Shao
,
L. W.
&
Wang
,
Y. Z.
2011
Changes in evapotranspiration over irrigated winter wheat and maize in North China Plain over three decades
.
Agricultural Water Management
98
(
6
),
1097
1104
.