Human activities and climate simultaneously affect water cycling and provision. Here, we study the impacts of climate variability and human activities on green and blue water provision (or the flows of both green water and blue water) in the inland Heihe River Basin as simulated by the Soil and Water Assessment Tool (SWAT). The results show that total green and blue water flow varied significantly from 1980 to 2010. Direct human activities did not significantly change the sum of green and blue water flow volumes. However, land use change led to a transformation of 206 million m3/year in the entire river basin from green to blue water flow, while farmland irrigation expansion resulted in a transformation of 66 million m3 from blue to green water flow. The synchronous climate variability, with an upward precipitation trend, caused an increase of green water flow by 469 million m3/year and an increase of blue water flow by 146 million m3/year at the river basin level over the study period. The results provide a general approach to investigate the impacts of historical human activities and climate variability on water provision at the river basin level.

INTRODUCTION

The impact of climate change on water availability has caused sustainability concerns around the world (Vörösmarty et al. 2000; Piao et al. 2010). Climate change has already influenced the freshwater availability in many regions (Kundzewicz et al. 2008). In particular in arid regions, climate change has caused intense water use competition among humans, agriculture, and ecosystems (Vörösmarty et al. 2000; Cheng 2003). This influences socio-economic sustainability and ecosystem health, and may lead to ecosystem degradation (Falkenmark 2003; Cheng & Zhao 2006; Piao et al. 2010). Therefore, comprehensive studies on water provision for humans and ecosystems in the context of climate change are critical for an in-depth understanding of their variability for better water resources management.

In recent years, scholars have paid much attention to blue water provision, but they have not paid sufficient attention to green water, which is important for both food production and terrestrial ecosystem maintenance (Falkenmark 1995; Falkenmark & Rockström 2006; Cheng & Zhao 2006). Blue water is the water that is stored in rivers, lakes, aquifers, and wetlands, while green water refers to the soil water from precipitation that is evapotranspired in the processes of plant transpiration, interception, and soil evaporation (Falkenmark 1995). Rost et al. (2008), Liu & Yang (2009); Liu et al. (2009) estimated that green water accounts for more than 80% of the consumptive water use of global crop production. Furthermore, water use in other ecosystems, such as grassland and forest ecosystems, is dominated by green water (Rockstrtöm 1999 ; Rost et al. 2008).

Water provision in a catchment is influenced not only by climate variability and change, but also by more direct activities of humans (Wang et al. 2005). Human activities can change water cycle dynamics and the green and blue water proportions in a river basin (Liu et al. 2007; Sivapalan et al. 2012), but, as shown here, they may not increase or decrease total quantities of water resources (apart from possible moisture transport to regions outside the watershed area). Separating human activities’ influence from climate records remains difficult (Sivapalan et al. 2003, 2012), although this would be required for solving questions of water resources management (Sivapalan et al. 2011).

In fact, there are interactions among climate factors, human activities, and water resources. Water vapor cycling is a critical part of the climate system; climate change will also influence the spatial and temporal variability of water resources in a watershed (Chen et al. 2007; Ren 2011; Sivapalan et al. 2011). On the one hand, human activities such as land use change and irrigation will change the discharge generation mechanism and the evapotranspiration fluxes; on the other hand, human activities such as greenhouse gas emissions will trigger global or regional warming, consequently influencing water resources’ distribution (Thamapark et al. 2006; Sivapalan et al. 2012). The dual impacts of human activities and climate factors may cause a water shortage or crisis, especially in arid and semi-arid regions (Xiao & Xiao 2004). Therefore, assessments of the joint impacts of human activities and climate variability, and trends in water provision, are needed to understand green and blue water variability and the sustainability of freshwater use. Some studies on climate change and human activities' impacts focused on assessing the climate–human interactions as a whole (Zang et al. 2012; Shi et al. 2013 ), but did not quantitatively separate the influences of climate change and human activities on transformations of green and blue water flows. Meanwhile, most of the water transformation studies have focused on local precipitation exchange, precipitation and evaporation exchange, and surface water and ground water exchange (Xu et al. 2003). Therefore, green and blue water flows' transformation still needs to be further studied.

Recently, several studies have investigated the impacts on water resources of human activities, including land use change (Postel et al. 1996; Jewitt et al. 2004; Gerten et al. 2008; Liu et al. 2009), water conservancy projects (Hayashi et al. 2008; Liu et al. 2013), and irrigation (Allen et al. 1998; Wang et al. 2003). Li & Zhou (1998) found that human activities, mainly in terms of irrigation, decreased runoff in downstream locations of the Tarim River Basin. Fang & Chen (2001) found that the groundwater table declined and land subsidence had occurred in several cities in the northern part of China due to groundwater over-exploitation. Wang et al. (2003) found that cropland irrigation decreased runoff and changed overall water flows in the Hexi corridor (Heihe River Basin, China). However, how human activities and climate factors synchronously impact on both green and blue water flows and their spatial distribution remains a rarely studied issue. In addition, many studies emphasize climate change impacts on water provision in the future by establishing future scenarios (Xu et al. 2003; Wei et al. 2009), but few focus on the past.

In this study, we investigated the impacts of three main processes, namely land use change, irrigation expansion, and climate variability, on the flows of green and blue water in the Heihe River Basin over the period 1980–2010. Based on these results, we discuss implications for future research and water management. The Heihe River Basin is the second largest inland river basin in China. It is located in the northwest of China, and it has suffered from a serious water crisis in recent years (Cheng 2003; Cheng & Zhao 2006). Following socio-economic development, water use in the midstream has increased sharply (Ma et al. 2011), and recently, human activities have changed the distribution of lakes and the inter-annual allocation of water resources in the Heihe Basin (Xiao & Xiao 2004). So far, no efforts have been made to assess the green and blue water in the context of these local and global changes. In a previous study, we analyzed the spatial and temporal distribution of the green and blue water flows under natural climate variations without considering human activities (Zang et al. 2012). Here, we built scenarios to simulate green–blue water transformation by explicitly considering different human activities, such as land use change and irrigation expansion. Specifically, in the previous study by Zang et al. (2012), the Soil and Water Assessment Tool (SWAT) model was calibrated to simulate the green and blue water flows at the whole-basin level by using climate data from 1980 to 2004 and land use data for 2000 only (Zang et al. 2012). The calibration and validation were performed successfully, as shown by high values of the Nash–Sutcliffe coefficient (Ens) (>0.87) (Nash & Sutcliffe 1970) and the coefficient of determination (R2) (>0.9). Further information on the model simulation, parameters, calibration, and validation can be found in Zang et al. (2012). In the present study, we used the calibrated parameters from that study, and further investigated the impacts of human activities such as land use change, irrigation expansion, and climate variability on the green and blue water flows. The impacts are assessed by comparing results from different scenarios with those under natural conditions. We expanded the study period to cover 1980–2010, and updated land use maps for 1986 and 2005.

There are two main objectives for this study: (1) we apply the SWAT model to analyze the impacts of human activities and climate variability on green and blue water flows for an arid river basin in a spatially explicit way; (2) we choose two time periods (around 1986 and around 2005) to analyze the green and blue water flow variability. These two periods reflect the significant socio-economic changes in the past decades, but they are both normal meteorological years and not unusually wet or dry years (Zang & Liu 2013a).

METHODOLOGY

The study area

The Heihe River originates in the Qilian Mountains, and discharges into the Juyanhai Lake. The area of the basin is 0.24 million km2, with the majority located in China and a minor part in Mongolia (Figure 1). The basin has an average altitude of over 1,200 m with a length of 821 km for the major channel. Three sections are often distinguished: upstream from the Qilian Mountain to the Yingluo Canyon, midstream from the Yingluo to Zhengyi Canyon, and downstream terminating in the Juyanhai Lake (Figure 1). The average annual air temperature is 2–3 °C in the upstream, 6–8 °C in the midstream, and 8–10 °C in the downstream area. The average annual precipitation is 200–500 mm in the upstream, 120–200 mm in midstream, and <50 mm in most downstream regions (Cheng 2003). The potential evaporation ranges from 1,000 mm year−1 in the upstream to 4,000 mm year−1 in the downstream area (Li 2009). Precipitation occurs mainly in summer and autumn (>70% of annual precipitation between May and August; Ma et al. 2011), whereas spring is dry with snow and ice melting (4% of total discharge; Li 2009); there is much snow in winter (Cheng 2003). The river discharge provides about 65% of the blue irrigation water in midstream regions, while groundwater provides over 90% in the downstream regions (Xiao et al. 2011). The main land cover types of the basin include desert (prevailing downstream), forest (upstream), and oases (midstream); these three land covers together account for 98.6% of the total basin area (Cheng 2003).
Figure 1

The Heihe River Basin with rivers, lakes, hydrological and weather stations indicated. The location of the Heihe River Basin in China is shown in the inset.

Figure 1

The Heihe River Basin with rivers, lakes, hydrological and weather stations indicated. The location of the Heihe River Basin in China is shown in the inset.

Simulation experiments

To study climatic and anthropogenic impacts on green and blue water resources in the Heihe Basin, we have set up four simulation experiments as follows: scenario A fixes land use and climate conditions around 1986 (land use in 1986 and weather/climate for 1984–1986); scenario B uses land use in 1986 and meteorological conditions for 2004–2006; scenario C uses land use in 2005 and meteorological conditions for 2004–2006; scenario D assumes all crops are irrigated in addition to scenario C (Figure 2). This is a realistic assumption in the studied basin due to the low precipitation there. Based on these scenarios, we have analyzed the impacts on green and blue water flows of climate variability (the difference between B and A), land use change (the difference between C and B), irrigation effect (the difference between D and C), and all factors (the difference between D and A), respectively. Three-year averages are selected to demonstrate our results. This is mainly due to the consideration that a longer period average, for instance, the 10 years between 1980 and 1989, may hide the actual climate variations, while one single year can lead to sharp variations, for instance, in anomalously wet or dry years. The two periods were used for comparison (1984–1986 and 2004–2006). We selected these two periods for comparison because: (1) in this paper, we used land use data for 1986 and 2005, and the simulation results are expected to be more accurate in the years closer to these 2 years; (2) these two periods were not representative of any typical dry or wet years (Zang & Liu 2013a); (3) there were significant land use changes between the two periods, as shown in the land use maps. Nevertheless, the climate conditions of the two periods differ. The latter period has higher precipitation and higher temperature. Annual precipitation was on average 180 mm in 1984–1986 and 196 mm in 2004–2006; average annual temperature was 4.9 °C in 1984–1986 and 6.3 °C in 2004–2006.
Figure 2

The scenarios' setup and research framework. 1986 is the average of 1984–1986; 2005 is the average of 2004–2006.

Figure 2

The scenarios' setup and research framework. 1986 is the average of 1984–1986; 2005 is the average of 2004–2006.

The SWAT model

In this study, we used the SWAT model to simulate green and blue water flow for the Heihe River Basin. SWAT is a semi-distributed water assessment model (Neitsch et al. 2004), which has been applied widely in different regions across the world (Schuol et al. 2008; Faramarzi et al. 2009). We selected the SWAT model for this study mainly due to two reasons. First, it has been successfully used for assessments of water cycling processes under different environmental conditions (Gassman et al. 2007; Schuol et al. 2008; Faramarzi et al. 2009); and second, it has been successfully tested to simulate hydrological processes in the Heihe River Basin (Huang & Zhang 2004; Li et al. 2009), including the green and blue water flows (Zang et al. 2012).

We used the version of SWAT-2005 that works in Arcview-3.3. The study area was separated into 32 sub-basins (Appendix 2, Figure A2, available in the online version of this paper) with information on topography, land use type, soil attributes, and management, and 308 and 311 hydrological response units (HRUs) in 1986 and 2006, respectively (different land use inputs generate different numbers of HRUs). Evapotranspiration was estimated by the Hargreaves method (Hargreaves et al. 1985), surface runoff was calculated with an SCS curve number (CN) method (Neitsch et al. 2004), while snowmelt was computed by an energy balance approach (Neitsch et al. 2004). The actual evapotranspiration includes plant transpiration and soil evaporation. The SWAT model first calculates rainfall interception by plant canopy, then the maximum plant transpiration and soil evaporation using an approach similar to Ritchie (1972). The actual plant transpiration and soil evaporation are then calculated based on the soil moisture balance following Neitsch et al. (2004). The land use types influence the surface runoff generation rate and evapotranspiration. In SWAT, different land cover types (urban, crop, and forest) correspond to different parameters, for instance, the CN (Neitsch et al. 2004). Irrigation influences the discharge in water channels and transpiration of irrigated crops. We only worked on the part of the river basin located within China and did not include the Mongolian part due to the lack of data. We have set the groundwater parameters in the SWAT model, such as the initial depth of water in the shallow aquifer, the initial depth of water in the deep aquifer, the recession constant of the slow shallow aquifer, the coefficient that separates the seepage from the slow shallow aquifer into the seepage to the deep aquifer, and the recharge rate of the slow shallow aquifer. The parameter values were obtained from Zhang et al. (2004) and Zhou et al. (2009). Other parameters of groundwater have been calibrated in previous research (Zang et al. 2012). The groundwater parameter values were all defined at a sub-basin level because of the lack of more detailed information. We used the SUFI-2 approach from the SWAT-CUP interface (Abbaspour 2007) to optimize parameters. The Ens and R2 were used to evaluate the goodness of the calibration and validation process. In addition to previous calibration and validation in Zang et al. (2012), we validated the results by comparing the simulated results with observed data at the midstream Zhengyi Canyon, at which the river discharge is seriously affected by human activities. The validation period spans from 1981 to 1987 and from 2000 to 2006, and these two periods contain our study periods.

Green and blue water flow and data

The actual evapotranspiration is green water flow, whereas the sum of surface runoff, lateral flows, and groundwater recharge is treated as blue water flow (Schuol et al. 2008). To account for the relative importance of the two flows, we defined the green water coefficient (GWC) as the ratio of green water flow to the total flow (green and blue water flows) (Liu & Yang, 2009). The relative change rate (RCR = [(Vi-V0)/V0] × 100%) was applied to indicate the relative change of a variable between the scenarios. V refers to a variable such as green water flow, 0 indicates the initial time period, and i the ending time period.

The data on daily climate, digital elevation model (DEM), and land use in 1986 were obtained from the Heihe Data Research Group (HDRG) (http://westdc.geodata.cn). We used climate data between 1980 and 2010 from 19 weather stations for our simulation: seven upstream, seven midstream, and five downstream stations. The irrigation area, irrigation depth, and irrigation parameters that are needed as input to the SWAT model were obtained from published literature (Ge & Li 2011; Wang et al. 2012) and the Ministry of Water Resources irrigation test web site (http://www.syzz.org.cn/about.asp?id=23) (see Appendix 1, Table A1, available in the online version of this paper). In the irrigation scenario (D), we assumed all cropland was irrigated. The irrigation source of cropland in upstream and midstream regions was assumed to be from river reaches, and that in downstream regions from the shallow aquifer. The irrigation districts' data were obtained from the HDRG (http://westdc.geodata.cn); the total irrigation area in upstream and midstream areas is 1.88 million ha. The 1 km2 land use data for 1986 and 2005 were obtained from the Institute of Geographic Science and Nature Resources Research, Chinese Academy of Sciences (http://www.geodata.cn/Portal/aboutWebsite/aboutus.jsp?isCookieChecked=true). The spatial resolution of land use data of 1 km2 may lead to the neglect of several small villages, towns, and mining areas. To make the simulation more accurate, we consider towns and villages in the SWAT model at the HRU level, for instance, there are three settings for urban and resident areas; high, middle, and low intensity. The residents’ intensity can be seen from Figure 4. The soil data were obtained from the Harmonized World Soil Database (HWSD) (http://www.iiasa.ac.at) with a spatial resolution of about 1 km. This data set includes 63 soil types for the Heihe River Basin, and for each soil type parameters for two layers are available, i.e., for 0–30 cm and 30–100 cm.
Figure 3

The validation of discharge simulated by the SWAT model for the Zhengyi Canyon (in the Heihe Basin's midstream area), for two periods.

Figure 3

The validation of discharge simulated by the SWAT model for the Zhengyi Canyon (in the Heihe Basin's midstream area), for two periods.

RESULTS AND DISCUSSION

Validation results

According to our validation for the midstream Zhengyi Canyon, at which the river discharge is seriously affected by human activities, the Ens and the R2 in both periods were as high as 0.69 and 0.77, respectively (Figure 3). This implies that the SWAT model setup used here can satisfactorily simulate the hydrological processes even with intensive human activities. From Figure 3 we can see that the observed data show obvious peaks in March and troughs in May in every year. The crests can be explained by the melt water entering the river reaches in March and the troughs by irrigation starting in May (Xiao et al. 2011).
Figure 4

The distribution of irrigation districts and residential population density in the Heihe River Basin. One resident point represents 100 person/km2, the irrigation districts mainly contain the cropland irrigated mostly by reach water in the midstream area. Cropland in the downstream area irrigated by groundwater was not included.

Figure 4

The distribution of irrigation districts and residential population density in the Heihe River Basin. One resident point represents 100 person/km2, the irrigation districts mainly contain the cropland irrigated mostly by reach water in the midstream area. Cropland in the downstream area irrigated by groundwater was not included.

Impact of land use change

The impacts of land use change are assessed by comparing the differences in green and blue water flows given land use in 1986 and 2006, respectively (holding all other factors unchanged). According to our model simulation results (Table 1), at the river basin level, land use change has resulted in an increase of blue water flow by 206 million m3 (equivalent to a 7% increase) and a concurrent decrease in green water flow by the same amount. These changes are simulated in particular for the midstream and a part of the downstream region (Figure 5). The relative change rate in the sub-basins there was more than 50% (Figure 5). Green and blue water flows also have spatial differences (Table 2), especially for midstream sub-basins, where the storage change was between −30 and 41 mm. Irrigation water use and land use change are the two main factors that caused the hydrological variability according to the SWAT model. In the east of the midstream area, the main reason for the increased blue water flow was that urbanization expanded rapidly in these sub-basins (Figure 4; Table 3), especially in the regions where the biggest city (Zhangye City) is located. From 1986 to 2005, the town area and mining land uses have increased by 47% and 21%, respectively (Table 3). These types of land cover are characterized by hardened ground and roads, which generally decrease infiltration and actual evapotranspiration, and increase surface runoff. However, in the central midstream area, the blue water flows have increased by more than 50% in some sub-basins (Figure 5). The main reason is that the bare rock and bare land increased there (Table 3; Appendix 2, Figure A1, available in the online version of this paper; Nian et al. 2011); these land covers have similar hydrological characters to urban areas, as they can decrease infiltration and increase runoff generation (Li et al. 2009). In two sub-basins close to the upstream, blue water flow has decreased (Figure 5), probably because of the increasing coverage of forests from 1986 to 2005 there. Forests can reduce runoff generation, but increase evapotranspiration (Wouter et al. 2006). From 1986 to 2005, the irrigation area in the Heihe River Basin has increased by 27% (Table 3). In an earlier study, irrigation expansion was shown to require a large amount of water from rivers and groundwater (Wang et al. 2003). This will decrease discharge of rivers and aquifers, and thus the blue water flow. This can also explain the general increasing trend of green water flows in midstream with irrigation expansion (Figure 6). In our simulations, the total green and blue water flow in the basin did not change in response to the land use changes (Figure 7), but the GWC was found to have decreased from 81–90 to 71–75%, in particular in the middle part of the midstream (Figure 8). The impact of land use change on the variability of blue water flow was found to be even larger than the impact of climate variability.
Table 1

The increase or decrease in blue water flow, green water flow and total water flows due to land use change, irrigation expansion, climate variability and all the above factors combined in the Heihe River Basin (units: million m3)

VariablesImpact of land use changeImpact of irrigation expansionImpact of climate variabilityImpact of all factors
Blue water flow 206 −66 146 286 
Green water flow −206 66 469 329 
Total water flows 615 615 
VariablesImpact of land use changeImpact of irrigation expansionImpact of climate variabilityImpact of all factors
Blue water flow 206 −66 146 286 
Green water flow −206 66 469 329 
Total water flows 615 615 
Table 2

Variability of green/blue water flows among different scenarios (units: mm)

Sub-basin numberGB (SB-SA)GB (SC-SB)GB (SD-SC)GB (SD-SA)B (SB-SA)B (SC-SB)B (SD-SC)B (SD-SA)G (SB-SA)G (SC-SB)G (SD-SC)G (SD-SA)
−1 −2 
−1 −2 
−3 −2 −2 10 
−1 
−1 −1 −1 
−1 −1 −1 
−3 −2 −2 10 
−1 
10 
11 −10 −9 −5 −3 −6 −6 
12 −10 −10 −4 −4 −7 −7 −7 
13 −10 −8 −7 −5 −2 −1 −3 
14 −10 −9 −8 −1 −4 −2 −4 
15 −7 −7 −7 −8 −14 
16 −7 −7 −8 −8 
17 −2 
18 −1 −9 −3 
19 −10 −9 −8 −5 −11 −2 −2 10 
20 −1 −9 −4 
21 −2 −1 
22 −1 −1 −2 −2 
23 −1 −1 −4 −3 −2 
24 −16 −16 −19 −5 −6 −30 15 
25 −10 −9 −4 −2 −6 −1 −7 
26 −1 −2 −3 −9 −9 −4 −22 25 
27 20 21 11 13 18 −10 
28 −3 −3 −3 10 −2 −10 −6 
29 −11 −11 −2 −9 −5 −9 −6 −8 
30 41 41 14 20 34 27 −21 
31 
32 51 51 29 −7 −13 21 38 
Sub-basin numberGB (SB-SA)GB (SC-SB)GB (SD-SC)GB (SD-SA)B (SB-SA)B (SC-SB)B (SD-SC)B (SD-SA)G (SB-SA)G (SC-SB)G (SD-SC)G (SD-SA)
−1 −2 
−1 −2 
−3 −2 −2 10 
−1 
−1 −1 −1 
−1 −1 −1 
−3 −2 −2 10 
−1 
10 
11 −10 −9 −5 −3 −6 −6 
12 −10 −10 −4 −4 −7 −7 −7 
13 −10 −8 −7 −5 −2 −1 −3 
14 −10 −9 −8 −1 −4 −2 −4 
15 −7 −7 −7 −8 −14 
16 −7 −7 −8 −8 
17 −2 
18 −1 −9 −3 
19 −10 −9 −8 −5 −11 −2 −2 10 
20 −1 −9 −4 
21 −2 −1 
22 −1 −1 −2 −2 
23 −1 −1 −4 −3 −2 
24 −16 −16 −19 −5 −6 −30 15 
25 −10 −9 −4 −2 −6 −1 −7 
26 −1 −2 −3 −9 −9 −4 −22 25 
27 20 21 11 13 18 −10 
28 −3 −3 −3 10 −2 −10 −6 
29 −11 −11 −2 −9 −5 −9 −6 −8 
30 41 41 14 20 34 27 −21 
31 
32 51 51 29 −7 −13 21 38 

GB is total green and blue water flows; B is blue water flow; G is green water flow; SA is scenario A; SB is scenario B; SC is scenario C; SD is scenario D. SB-SA is the difference between B and A; the same as previously.

Table 3

The land use area and relative change rate of the Heihe River Basin from 1986 to 2005

Land typeLand area in 1986 (km2)Land area in 2005 (km2)Relative change rate from 1986 to 2005 (%)
Forest high 1,609 1,848 15 
Forest middle 3,738 4,187 12 
Forest low 671 683 
Forest mixed 53 24 −55 
Grass high 4,586 4,813 
Grass middle 7,329 7,402 
Grass low 27,173 23,336 −14 
River 171 136 −20 
Lake 352 316 −10 
Reservoir 87 67 −23 
Glacier 184 181 −2 
River shoal 785 422 −46 
Town 75 110 47 
Village 324 328 
Mining 99 120 21 
Gobi and desert 133,540 138,948 
Saline 6,385 5,916 −7 
Swamp 680 589 −13 
Bare land 3,829 4,094 
Bare rock 32,378 35,611 
Others 4,408 4,811 
Dry land 161 147 −15 
Irrigated land 5,280 6,787 28 
Land typeLand area in 1986 (km2)Land area in 2005 (km2)Relative change rate from 1986 to 2005 (%)
Forest high 1,609 1,848 15 
Forest middle 3,738 4,187 12 
Forest low 671 683 
Forest mixed 53 24 −55 
Grass high 4,586 4,813 
Grass middle 7,329 7,402 
Grass low 27,173 23,336 −14 
River 171 136 −20 
Lake 352 316 −10 
Reservoir 87 67 −23 
Glacier 184 181 −2 
River shoal 785 422 −46 
Town 75 110 47 
Village 324 328 
Mining 99 120 21 
Gobi and desert 133,540 138,948 
Saline 6,385 5,916 −7 
Swamp 680 589 −13 
Bare land 3,829 4,094 
Bare rock 32,378 35,611 
Others 4,408 4,811 
Dry land 161 147 −15 
Irrigated land 5,280 6,787 28 
Figure 5

The blue water flow in the Heihe River Basin in different scenarios. The impacts are assessed with relative change rate. The impacts of climate variability, land use change, and irrigation expansion are assessed by comparing results between scenarios B and A, scenarios C and B, and scenarios D and C, respectively. The impacts of all factors together are assessed by comparing results between scenarios D and A.

Figure 5

The blue water flow in the Heihe River Basin in different scenarios. The impacts are assessed with relative change rate. The impacts of climate variability, land use change, and irrigation expansion are assessed by comparing results between scenarios B and A, scenarios C and B, and scenarios D and C, respectively. The impacts of all factors together are assessed by comparing results between scenarios D and A.

Figure 6

The green water flow in the Heihe River Basin in the different scenarios; details as in Figure 5.

Figure 6

The green water flow in the Heihe River Basin in the different scenarios; details as in Figure 5.

Figure 7

Impacts of all factors (human activities and climate variability) on the total water flows in the Heihe River Basin.

Figure 7

Impacts of all factors (human activities and climate variability) on the total water flows in the Heihe River Basin.

Figure 8

The GWC in the Heihe River Basin in different scenarios.

Figure 8

The GWC in the Heihe River Basin in different scenarios.

Between 1986 and 2005, besides urban area and irrigated land, forest, grassland, bare land, bare rock, and water bodies also have obviously changed (Table 3). These land use changes are a reflection of human activities. Serious human activities such as over-grazing, over-reclamation, over-exploitation of groundwater, and accelerating urbanization have caused serious ecological degradation. In the midstream and downstream parts, a large area has been changed from grassland to bare land (Zheng et al. 2005); and more seriously, bare land is easily transformed to bare rock due to the effect of sandstorms. In the past 30 years, sandstorms have blown away a huge quantity of surface sand (in many regions more than 100 mm) (Zhang et al. 2007), increasing bare rock land in the middle and downstream (Zheng et al. 2005). Furthermore, the main soil types of bare rock land are clay loam and alkalized chestnut soil; they have good water retention properties and low infiltration, but poor permeability (FAO 2009). The bare rock land can generate high runoff and decrease infiltration rates, and as a result evapotranspiration will be reduced. This is one main reason why blue water has increased in the middle of midstream and west of downstream (Figure 5).

However, land use changes – especially the bare rock land and urbanization in the basin's midstream area – also have demonstrably led to increased blue water flow (Appendix 2, Figure A1). Urban construction hardens the ground, decreases soil infiltration, and accelerates surface runoff generation (Ren et al. 2002; Hao et al. 2008); hence, it increases blue water flow. Concurrently, the increasing runoff and the decreasing infiltration rate would reduce the soil moisture for evapotranspiration; hence, the green water flow was reduced (Wouter et al. 2006; Ma et al. 2008). This is one plausible mechanism for explaining why the blue water flow increased and the green water flow decreased in the east of the midstream.

Impact of irrigation expansion

At the river basin level, the irrigation area had increased 28% from 1986 to 2005. Irrigation expansion resulted in a decrease of blue water flow by 66 million m3, according to our model (Table 1). The blue water flow decreased in particular in the midstream regions (Figure 5), where a large area of agriculture with many irrigated farmlands exists (Figure 4). The green water flow has increased by the same amount due to farmland irrigation (Figure 6) because, compared to rainfed agriculture, irrigated agriculture consumed more water and thus increased actual evapotranspiration. As in the case of the isolated land use change effect, the total green and blue water flow did not change at the river basin level due to irrigation expansion (Figure 7), and the GWC increased from 71–75% to 81–90% in particular in the eastern part of the midstream area (Figure 8).

At the same time, cropland irrigation has caused the green water flow to increase by 66 million m3, and the blue water flow to decrease by the same amount (Table 1). On the one hand, irrigation from watercourses would decrease the river discharge; on the other hand, farmland irrigation would increase the water available for crop evapotranspiration (Allen et al. 1998; Andrew & Shashi 2009).

Impact of climate variability

Climate variability, assessed as the difference between the mid-1980s and the mid-2000s, has increased both blue and green water flows by 146 million m3 and 469 million m3, respectively (Table 1), with little change in the GWC (87–88%). Spatially, although climate variability has led to an increase in blue and green water flows in most sub-basins, we can also find a clear decreasing trend for both flows in the western part of the midstream, where precipitation has decreased significantly at the p < 0.10 level from 1980 to 2010 (Figure 9). In contrast, precipitation has increased in most downstream areas; hence, blue and green water flows have increased there (Figure 9).
Figure 9

The variability of precipitation and temperature in the Heihe River Basin from 1980 to 2010. ↑ indicates increasing trend; ↓ decreasing trend, * significant at p < 0.01; ** significant at p < 0.10; NS, not significant.

Figure 9

The variability of precipitation and temperature in the Heihe River Basin from 1980 to 2010. ↑ indicates increasing trend; ↓ decreasing trend, * significant at p < 0.01; ** significant at p < 0.10; NS, not significant.

The total water flows have slightly increased by 2–3% (total green/blue water is 22.05–25.51 billion) in the past 20 years in the Heihe River Basin (Table 1), mainly as a result of increasing precipitation (Wu et al. 2010). These trends were most obvious in the upstream and midstream sub-basins, where precipitation increased significantly from 1960 to 2010 (Zang & Liu 2013b). In our results, precipitation has increased by 16 mm from 1984–1986 to 2004–2006. The change patterns were found to be largely influenced by climate variability; for example, in the western part of the midstream areas, precipitation showed a decreasing trend (Figure 9); hence, both blue and green water flows decreased, although also influenced by other factors. The average precipitation was 180 mm in 1984–1986 and 196 mm in 2004–2006 in the entire river basin. It was 281, 176, and 40 mm in up-, mid-, and downstream in 1984–1986; and 307, 192, and 45 mm in up-, mid-, and downstream in 2004–2006. Therefore, the precipitation increase was the main reason for the increase in total water flow volumes. However, different regions in the Heihe River Basin exhibited different trends – especially note the decrease in the western part of the midstream area. This probably arose from differences in the sources of precipitation among regions. Precipitation in the upstream and midstream basins was mostly from oceanic evaporation, whereas that in the downstream basin was mostly from terrestrial evapotranspiration. The different sources for precipitation could lead to differences among the basins in the magnitudes and trends of hydrological variables (Zang & Liu 2013b). However, due to the lack of observed climate data in these sub-basins (only with two stations) (Figure 9), we cannot gain more in-depth information. Further investigation of the influences of different atmospheric circulation patterns on the blue and green water flow changes is needed.

Joint impact of all factors

In response to climate, land use, and irrigation changes, the blue water flow has increased by 286 million m3 in the entire river basin (Table 1). The spatial distribution of the changes in blue water flow varies largely, with decreases in western sub-basins but increases in eastern sub-basins in the midstream areas. The relative change rate of several midstream sub-basins exceeded 30% (Figure 5). As shown above, the land use changes, such as the increase in bare rock and bare land and the accelerated urbanization and farmland irrigation are the other main reasons that caused blue water flow variability.

Green water flow showed an increasing trend with a 329 million m3 higher flow over the entire river basin, mainly due to climate variability but also due to irrigation expansion (Table 1). Decreases (>30%) are found to prevail in most midstream sub-basins, while increases were simulated for most of the sub-basins downstream (Figure 6). The total water flows increased by 615 million m3, caused almost exclusively by climate variability. Both land use change and irrigation expansion did not alter the amount of total water flows; instead, they influenced the allocation of water into green or blue flows.

CONCLUSIONS

Climate variability has led to an increase in blue and green water flows in most sub-basins due to precipitation increasing in most sub-basins. Land use change and irrigation were used as indicators for human activities; and the land use change was found to be a main factor that influences water resources variability. The results imply that human activities have led to a major shift from green to blue water flow in the study area. The transformation processes from blue to green water or green to blue should be paid more attention in future studies. Future studies will have to investigate this in an intercomparative mode for a selection of river basins where transformations of both blue and green water flows are likely to have taken place.

The results imply that land use change has led to a major shift from green to blue water flows in the study area, while irrigation expansion resulted in a shift from blue to green water flow. We explain the transformation processes from blue to green water as follows: blue water is brought through irrigation to crops or plants in different ecosystems and eventually evapotranspired as green water in terms of surface water evaporation and plant transpiration. The transformation processes from green to blue water are as follows: the infiltration rate becomes smaller due to land use change, such as more urban areas; hence, less water flow can get to unsaturated soils but terminates in water bodies in terms of surface runoff. In fact, as the hydrological processes are very complex, we cannot define the accurate transformation processes between green and blue water flow by one model (SWAT). The green and blue water transformation is a multi-directional loop system, but the model represents one-direction flow processes only, with no feedback to the atmosphere (Neitsch et al. 2004). Further research is needed to study the mechanism of green and blue water mutual transformation, especially to strengthen the research into local water vapor cycle contributions to green and blue water transformation. Moreover, the limited number and uneven distribution of weather stations represents an important source of uncertainty that influences the accuracy of results. In this study, we only used the best-fitting parameters to simulate green and blue water flows without considering the uncertainties caused by the model inputs and parameters. The implementation of water redistribution policy in the Heihe River Basin is very important for water resource utilization and ecosystem sustainability, but it is difficult to simulate such policies and their effects in hydrological models such as SWAT.

Our results provide insights into the impacts of climatic and human factors on green and blue water variability throughout the Heihe River Basin, and may help policymakers to better manage the water resources in the context of global and regional climate change. The decreasing trend due to the expansion of bare rock land and urbanization, and the increasing trend due to irrigation, are likely to apply in other river basins as well, although the impact magnitudes will certainly differ among regions. The present study demonstrates a general scenario analysis approach to studying the impacts of historical human activities and climate variability on green and blue water flows at the river basin level, which we hope can serve as a guideline for such follow-up studies for other river basins.

ACKNOWLEDGEMENTS

This study was supported by the International Science and Technology Cooperation Program of the Ministry of Science and Technology of China (2012DFA91530), the Excellent Young Scientist Foundation of the Inner Mongolia Agricultural University of China (2014XYQ-5), the National Natural Science Foundation of China (41161140353; 91325302; 91425303), the Key Project of Chinese National Programs for Fundamental Research and Development (2013CB429905-01), the 1st Youth Excellent Talents Program of the Organization Department of the Central Committee of the CPC, and the Fundamental Research Funds for the Central Universities (TD-JC-2013-2). We thank BMBF's German Fellowship Programme for S&T to support Prof. Junguo Liu's visit to the Potsdam Institute for Climate Impact Research (PIK) in Germany. We also thank the Senior Cheney Fellowship for supporting Prof Junguo Liu's visit to the University of Leeds.

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Supplementary data