# Scenario-Based Impact Assessment of Land Use/Cover and Climate Changes on Watershed Hydrology in Heihe River Basin of Northwest China

Authors: Feng Wu; Jinyan Zhan; Hongbo Su; Haiming Yan; Enjun Ma
Journal: Advances in Meteorology
 (2015)

---

## Abstract

This study evaluated hydrological impacts of potential climate and land use changes in Heihe River Basin of Northwest China. The future climate data for the simulation with Soil and Water Assessment Tool (SWAT) were prepared using a dynamical downscaling method. The future land uses were simulated with the Dynamic Land Use System (DLS) model by establishing Multinomial Logistic Regression (MNL) model for six land use types. In 2006–2030, land uses in the basin will experience a significant change with a prominent increase in urban areas, a moderate increase in grassland, and a great decrease in unused land. Besides, the simulation results showed that in comparison to those during 1981–2005 the temperature and precipitation during 2006–2030 will change by +0.8°C and +10.8%, respectively. The land use change and climate change will jointly make the water yield change by +8.5%, while they will separately make the water yield change by −1.8% and +9.8%, respectively. The predicted large increase in future precipitation and the corresponding decrease in unused land will have substantial impacts on the watershed hydrology, especially on the surface runoff and streamflow. Therefore, to mitigate negative hydrological impacts and utilize positive impacts, both land use and climate changes should be considered in water resource planning for the Heihe River Basin.


---

## Body

## 1. Introduction

Climate and land use/land cover (LULC) changes are amongst the greatest global environmental pressures resulting from anthropogenic activities, both of which greatly impact the hydrological cycle [1–3]. Their impact on the hydrological cycle at the basin scale has become an important research issue in hydrology community owing to the increasingly serious water scarcity [4, 5]. The hydrological response of watersheds to climate and LULC changes is an important issue of water resource planning and management [6, 7], and the potential impacts of LULC change on the hydrological cycle must be considered by water resource managers [8, 9]. For example, the LULC changes due to urbanization and deforestation can alter the hydrological processes and lead to change of flood frequency and annual mean discharge by impacting the evapotranspiration, soil infiltration capacity, and surface and subsurface flow regimes [8, 10, 11], while climate change can alter the flow routing time and peak flows [10, 12]. It is crucial to the long-term water resource planning and management to better understand the potential impacts of climate and LULC changes on the runoff and streamflow in basins [9, 13]. In particular, effective water resource management under changing conditions requires reliable information about flows and modes that can be used to simulate flow regimes under different scenarios of changing land use and climate [6].Separation of impacts of climate and LULC changes on the hydrological cycle is of great importance to improving the land use planning and water resource management [10, 14], especially in arid and semiarid regions where the climate change may significantly affect the hydrological cycle [15]. The effects of LULC and climate changes on the streamflow are more evident in the arid and semiarid regions. One typical example is the Heihe River Basin in Northwest China, which is characterized by limited water resources and special hydroclimatic and physiographic conditions [16]. Understanding the hydrological responses to potential climate change is very important for developing sustainable water resource management strategies in this region. However, the impacts of urbanization and deforestation on the hydrological cycle in arid and semiarid regions have been rarely documented [17–19]. Overall, there is still very limited understanding of the separate as well as combined impacts of LULC and climate changes on regional water and energy cycles, and therefore more in-depth research is needed, especially in the arid and semiarid regions [17].There are many studies about the hydrological impacts of land use change or climate change at the basin scale, and most of them were conducted with a hydrological model based on a series of land use data extracted from satellite images [9, 20, 21]. For example, the impacts of land use change scenarios in the Wutu watershed, North Taiwan, were assessed using the conversion of a land use model (CLUE-s) and a generalized watershed loading functions model [16]. A Soil Water Assessment Tool (SWAT) and multiple General Circulation Models (GCMs) were used to investigate the relationship between climatic and hydrological changes in the Upper Mississippi River [22]. Most of these studies assume no change in LULC [4, 23], but the impacts of climate change on hydrology vary among regions and should be investigated with regional climate change scenarios [4]. Besides, the hydrological impacts of LULC change also vary with the climatic conditions [9]. For example, water balance variables might add or subtract the impacts of climate change under varying land cover conditions. In particular, the regional LULC change can offset or magnify the changes in global average temperature and can significantly alter the impacts associated with global warming [17, 24]. In addition, some studies about the combined effects of climate and LULC changes on streamflow showed that climate change was generally more significant than LULC change in determining the basin hydrological response [25–27]. For example, climate dominates the changing streamflow in the Xinjiang River Basin of Poyang Lake, China [25]. However, the hydrological cycle in a basin is a complex process influenced by climate and the physical properties of the catchment and human activities together [4, 5]. The complexity of these factors complicates the separation between effects of land use and climatic variability on streamflow [26, 28]. Therefore, it is still a challenge to distinguish the effects of LULC change from that of concurrent climate variability [14].This study aims to separate the impacts of climate and LULC changes on the hydrological cycle in the Heihe River Basin under future scenarios to provide some useful reference information that can be used to improve the water resource management and guarantee the sustainable development. The climatic data predicted by General Circulation Models (GCMs) under RCP 4.5 scenario were used to represent the climate change scenarios for 2006–2030, and the land use data simulated with the Dynamic Land Use System (DLS) model were used to represent the land use change scenarios. The future hydrological cycle was simulated with the SWAT based on the scenario data of climate change and land use change, the hydrological impacts of which were analyzed by comparing the simulation results under different scenarios. The results of this study can provide valuable information for guiding future water resource management in the Heihe River Basin as well as other arid and semiarid regions in China.


## 2. Methods

### 2.1. Study Area

The Heihe River Basin is the second largest inland river basin in China, which lies between 37°43′–42°41′N and 97°23′–102°72′E with a total area of 127,96 thousand km2. This basin expands across Qinghai Province, Gansu Province, and Inner Mongolia Autonomous Region in Northwest China (Figure 1). With a total length of 821 km, the Heihe River is divided into the upper, middle, and lower reaches, where the natural and socioeconomic characteristics differ significantly. For example, the average annual precipitation is between 200 and 500 mm, less than 200 mm, and less than 50 mm in these reaches, respectively, while the annual evaporation ranges from 700 mm in the upper reach to more than 3000 mm in the lower reach [29]. Besides, the annual average temperature is 9.4°C over the last 30 years, and this basin enjoys a dry continental climate. The altitude ranges from 869 to 5542 m, with an average of 1778 m. The main land cover types are desert (57.15% of total basin area), mountains (33.16%), and oasis (8.19%) [30]. The ecosystems from the upper reach to the lower reach are linked by the hydrological cycle, but the hydrological cycle has significantly changed due to the land use change and climate change in the past decades. For example, about 65% of the irrigation water in the middle reach was extracted from the river runoff, which greatly influences the hydrological cycle of the whole basin. Therefore, a detailed and integrated simulation analysis of the water resources is critical and urgent for better water resource management in the Heihe River Basin.Figure 1
The upper and middle Heihe River Basin: (a) digital elevation model, (b) soil type, (c) slope, and (d) land use in 2010.
(a)
(b)
(c)
(d)


### 2.2. Data for Model Simulation

The spatial data (i.e., topography, soil and land use), historical climate data, and hydrological data for the watershed were first prepared for the SWAT model and DLS model. The topography was represented with the 90 m resolution digital elevation model (DEM) of Shuttle Radar Topography Mission (SRTM) (http://srtm.csi.cgiar.org/) [31]. The soil data, including texture, depth, and drainage attributes, were from the Harmonized World Soil Database (HWSD) supplied by the Environmental and Ecological Science Data Center for West China (WestDC) (http://westdc.westgis.ac.cn/). The historical land use data including 25 land use types, which were derived from Landsat TM/ETM images, were provided by Data Center of Chinese Academy of Sciences (CAS) [32]. In particular, the glacier data were obtained from WestDC (http://westdc.westgis.ac.cn/), and land use properties were directly obtained from the SWAT model database (Table 1). The historical hydrological data for SWAT model calibration and validation include the river flow data from four hydrological stations. The hydrological observation data, including annual data in Heihe River Basin during 1980–2010, were obtained from the hydrological yearbook provided by WestDC. The river flow data used for the model calibration and validation were provided by the Data Center of CAS. The historical daily weather data during 1980–2010 were collected from China Meteorological Administration (CMA), including daily precipitation, maximum and minimum temperatures, solar radiation, humidity, wind speed, and wind direction from 13 weather stations in or near the Heihe River Basin.Table 1
Data used and sources.
Data type
Data sources
Scale
Description
DEM
Shuttle Radar Topography Mission (SRTM)
90 m
Elevation
Land use
Data Center of Chinese Academy of Science
1 : 100000
The classification system contains 25 categories
Soil
WestDC (http://westdc.westgis.ac.cn/)
1 : 1000000
Some parameters were calculated using a (Soil-Plant-Atmosphere-Water) Field and Pond Hydrology model
Weather
China Meteorological Administration
Daily
5 weather stations
Hydrological observation
Hydrological yearbook
Daily
4 stations
River flow
Data Center of Chinese Academy of Science
1 : 250000
River network diversion
Glacier
WestDC (http://westdc.westgis.ac.cn/)
1 : 100000
The attributes include width, length, and depth


### 2.3. Simulation of Land Use Changes with the DLS Model

The DLS model is a set of applications used to simulate the changing process of land use system and is an effective tool for simulation of spatial-temporal land use changes to assist land management [33]. The DLS model consists of three modules: a spatial regression module that identifies the relationships between land uses and the influencing factors; a scenario analysis module of land use changes that determines the land demands at the regional level; a spatial disaggregation module that allocates land use changes from a regional level to the disaggregated grid cells [34, 35].The DLS model simulates the spatial-temporal land use changes with three processes: scenario analysis of land use change, spatial regression analysis, and spatial allocation of land use changes. The first process was carried out with the scenario analysis module, which provides the data of total land demands at the annual scale during a given period. By including the scenario analyses of land use changes, a set of spatially explicit simulation results of land use change can be exported by the DLS model [36]. The total land use demands can be set by several approaches, such as trend analysis methods (e.g., linear interpolations or more sophisticated econometric models) and economic models. In this study, the total land demands during the simulation period were first determined using trend analysis methods and then were used to establish a scenario of land use change during 2006–2030.In the spatial regression module, the relationships between land uses and influencing factors were analyzed via stepwise logistic regression analysis of past land use changes and their drivers [37]. For each grid cell, the total probability for each land use type is calculated on basis of the multinomial logistic regression at the pixel scale as follows:(1)
P
i
,
k
=
1
1
+
∑
k
≠
i
e
x
k
β
k
i
=
k
e
x
k
β
k
1
+
∑
k
≠
i
e
x
k
β
k
i
≠
k
,where P
i
,
k is the probability of conversion from land type i to k in the cells under given driving factors; X
1
,
X
2
,
…
,
X
n represent the driving factors of climate, landform, location, population, economic growth, policy, and other categories; β
1
,
β
2
⋯
β
n are the regression analysis coefficients of driving factors for further estimation.In this study, all the data of land use and the influencing factors were prepared at the annual scale. The land use data in 2000 and 2005 were used in the logistic regression. The assumed driving factors were categorized into five groups: climate, geophysics, transportation, location, and socioeconomics. The data of these factors in the corresponding years were also prepared (Table2).Table 2
Results of logistical regression for the six land use types with 13 driving factors.
Cultivated land
Forest land
Grassland
Water
Built-up area
Unused land
Slope
−0.000049
0.000121
-
7.42
E
-
05
-
7.76
E
-
06
-
1.1
E
-
06
0.0000105
(
-
27.52
)
∗
∗
∗
(
-
63.65
)
∗
∗
∗
(
-
18.89
)
∗
∗
∗
(
-
7.24
)
∗
∗
∗
(
-
2.07
)
∗
(
-
2.64
)
∗
∗
Elevation
−0.000136
−3.02E − 05
0.000116
−0.0000149
−5.9E − 06
0.0000711
(−53.82)***
(−11.13)***
(−20.8)***
(−9.76)***
(−7.85)***
(
-
12.56
)
∗
∗
∗
Rain
−0.000045
0.0000228
0.0000419
0.0000136
1.4E − 06
−0.0000347
(
-
16.09
)
∗
∗
∗
(
-
7.61
)
∗
∗
∗
(
-
6.79
)
∗
∗
∗
(
-
8.05
)
∗
∗
∗
−1.7
(
-
5.54
)
∗
∗
∗
Radiation
−0.000407
0.000243
−0.000923
-
8.64
E
-
06
1.16
E
-
05
0.00108
(
-
24.27
)
∗
∗
∗
(
-
13.55
)
∗
∗
∗
(
-
24.98
)
∗
∗
∗
(−0.86)
(
-
2.34
)
∗
(
-
28.91
)
∗
∗
∗
>0°C accumulated temperature
−0.0000106
0.0000127
0.000026
−3.88E − 06
−7.2E − 07
−0.0000234
(
-
24.03
)
∗
∗
∗
(
-
26.78
)
∗
∗
∗
(
-
26.59
)
∗
∗
∗
(
-
14.55
)
∗
∗
∗
(
-
5.52
)
∗
∗
∗
(
-
23.67
)
∗
∗
∗
Soil organic matter
0.0685
0.232
−0.186
0.0152
−0.00655
−0.123
(
-
9.38
)
∗
∗
∗
(
-
29.64
)
∗
∗
∗
(
-
11.58
)
∗
∗
∗
(
-
3.46
)
∗
∗
∗
(
-
3.03
)
∗
∗
(
-
7.52
)
∗
∗
∗
pH value
0.00769
−0.0131
−0.00541
0.00171
0.000719
0.00834
(
-
7.15
)
∗
∗
∗
(
-
11.32
)
∗
∗
∗
(
-
2.28
)
∗
(
-
2.64
)
∗
∗
(
-
2.26
)
∗
(
-
3.47
)
∗
∗
∗
Population density
0.000247
-
7.82
E
-
05
-
3.16
E
-
05
0.0000206
-
3.2
E
-
06
−0.000154
(
-
32.18
)
∗
∗
∗
(
-
9.52
)
∗
∗
∗
(−1.87)
(
-
4.46
)
∗
∗
∗
(−1.40)
(
-
9.00
)
∗
∗
∗
Per capita GDP
0.000548
-
3.08
E
-
05
−0.000185
−0.00002
0.000411
−0.000723
(
-
76.02
)
∗
∗
∗
(
-
3.99
)
∗
∗
∗
(
-
11.62
)
∗
∗
∗
(
-
4.61
)
∗
∗
∗
(
-
192.54
)
∗
∗
∗
(
-
44.87
)
∗
∗
∗
Distance to railway
0.000193
0.000229
0.000979
0.00000653
1.72E − 05
−0.00142
(
-
13.55
)
∗
∗
∗
(
-
14.96
)
∗
∗
∗
(
-
31.16
)
∗
∗
∗
−0.76
(
-
4.07
)
∗
∗
∗
(
-
44.71
)
∗
∗
∗
Distance to road
−0.000221
−0.000335
0.00111
−0.000066
-
1.4
E
-
05
−0.000477
(
-
18.45
)
∗
∗
∗
(
-
26.06
)
∗
∗
∗
(
-
42.08
)
∗
∗
∗
(
-
9.13
)
∗
∗
∗
(
-
3.79
)
∗
∗
∗
(
-
17.79
)
∗
∗
∗
Distance to water source
−0.000338
0.0000239
0.000926
−0.000346
-
5.2
E
-
06
−0.000261
(
-
14.46
)
∗
∗
∗
−0.95
(
-
17.96
)
∗
∗
∗
(
-
24.55
)
∗
∗
∗
(−0.75)
(
-
4.99
)
∗
∗
∗
Distance to city
−0.000125
−0.000108
−0.000567
0.0000996
-
2
E
-
05
0.00072
(
-
8.11
)
∗
∗
∗
(
-
6.58
)
∗
∗
∗
(
-
16.76
)
∗
∗
∗
(
-
10.78
)
∗
∗
∗
(
-
4.31
)
∗
∗
∗
(
-
20.98
)
∗
∗
∗
Note:t statistics in parentheses; ∗significant at 10%; ∗
∗significant at 5%; ∗
∗
∗significant at 1%.The spatial disaggregation module is used to spatially and explicitly convert the land demands into land use changes at various locations of the study area. The spatial disaggregation is carried out in an iterative procedure based on the probability maps, conversion rules, historical land use maps, and land demands under the scenarios. The probability maps of each land use type were prepared with the logistic regression results. Besides, the rules of land use conversion were set for each land use type, whose value ranges from 0 to 1. A smaller value means one land use type is more likely to be converted to another type, and vice versa. The development-restricted areas in the study area were also specified.


### 2.4. Downscaling of GCM Climate Data

GCMs are arguably the best available tools for modeling future climate. Yet GCMs provide information at a resolution that is too coarse to be directly used in hydrological modeling [38]. Therefore downscaling is required to transform the low resolution GCM outputs to the high resolution climate features needed for hydrological simulation. The downscaling procedure is as follows. First, the average annual precipitation and temperature of 30 years (1980–2010) were calculated, which were adopted as a baseline for selecting the GCMs. We considered future climate change scenarios for the basin (Figure 2) by using the spatially distributed outputs from 10 GCMs under RCP 4.5 scenario. The climate projections of Max Planck Institute (MPI) were downscaled to the 3 km × 3 km grid in the study area, and bias was corrected and climate change scenarios were developed by MPI for Meteorology. The annual mean values of 10 GCM spatial data from 2006 to 2030 were calculated according to the basin perimeter, and one out of ten GCMs was also selected based on the historical trends and annual averages of temperature and precipitation. The MPI model was finally chosen through comparison. The result of MPI model originates in MPI, and the spatial resolution is 1.865° (LAT) × 1.875° (LON). Then the parameters in MPI GCM were transformed into the forcing data of a regional climate model in weather research and forecasting (WRF) simulation, and thereafter dynamical downscaling simulation was performed at the spatial resolution of 3 km for the period of 2006–2030. We considered the impacts of land use changes on regional climate, and the land cover data before WRF simulation were dynamically replaced with land use change data based on the simulation with the DLS model. Finally, the data simulated by the regional climate model were matched with meteorological sites, and the meteorological site data were prepared for the simulation with SWAT.Figure 2
The trends in (a) observed (grey line; running average in black) and projected (10AR5 GCMs; colored dash lines) temperature, (b) observed and projected precipitation.
(a)
(b)


### 2.5. Simulation of Hydrological Cycle with the SWAT Model

The study area was first divided into subwatersheds, which were subdivided into hydrological response units (HRUs). Besides, for each subwatershed, the climate data used are taken from the GCM grid point that is the closest to its centroid. To improve performance, the SWAT model was calibrated and validated by adjusting several parameters and comparing the simulated streamflow with observed values. The most sensitive parameters were identified with the built-in sensitivity analysis tool in SWAT [39]. The daily streamflow observation data from Yingluoxia Hydrological Station in 2004 were used for calibration, and the observation data in 2005 were used for validation. It should be noted that the first three years were used as a warm-up period to mitigate the effects of unknown initial conditions, which were then excluded from the subsequent analysis. The ability of the SWAT model to replicate the temporal trends in the historical hydrological observations was assessed using the coefficient of determination (R
2), the Nash and Sutcliffe (1970) model efficiency (NSE), and the root mean square error (RMSE).


## 2.1. Study Area

The Heihe River Basin is the second largest inland river basin in China, which lies between 37°43′–42°41′N and 97°23′–102°72′E with a total area of 127,96 thousand km2. This basin expands across Qinghai Province, Gansu Province, and Inner Mongolia Autonomous Region in Northwest China (Figure 1). With a total length of 821 km, the Heihe River is divided into the upper, middle, and lower reaches, where the natural and socioeconomic characteristics differ significantly. For example, the average annual precipitation is between 200 and 500 mm, less than 200 mm, and less than 50 mm in these reaches, respectively, while the annual evaporation ranges from 700 mm in the upper reach to more than 3000 mm in the lower reach [29]. Besides, the annual average temperature is 9.4°C over the last 30 years, and this basin enjoys a dry continental climate. The altitude ranges from 869 to 5542 m, with an average of 1778 m. The main land cover types are desert (57.15% of total basin area), mountains (33.16%), and oasis (8.19%) [30]. The ecosystems from the upper reach to the lower reach are linked by the hydrological cycle, but the hydrological cycle has significantly changed due to the land use change and climate change in the past decades. For example, about 65% of the irrigation water in the middle reach was extracted from the river runoff, which greatly influences the hydrological cycle of the whole basin. Therefore, a detailed and integrated simulation analysis of the water resources is critical and urgent for better water resource management in the Heihe River Basin.Figure 1
The upper and middle Heihe River Basin: (a) digital elevation model, (b) soil type, (c) slope, and (d) land use in 2010.
(a)
(b)
(c)
(d)


## 2.2. Data for Model Simulation

The spatial data (i.e., topography, soil and land use), historical climate data, and hydrological data for the watershed were first prepared for the SWAT model and DLS model. The topography was represented with the 90 m resolution digital elevation model (DEM) of Shuttle Radar Topography Mission (SRTM) (http://srtm.csi.cgiar.org/) [31]. The soil data, including texture, depth, and drainage attributes, were from the Harmonized World Soil Database (HWSD) supplied by the Environmental and Ecological Science Data Center for West China (WestDC) (http://westdc.westgis.ac.cn/). The historical land use data including 25 land use types, which were derived from Landsat TM/ETM images, were provided by Data Center of Chinese Academy of Sciences (CAS) [32]. In particular, the glacier data were obtained from WestDC (http://westdc.westgis.ac.cn/), and land use properties were directly obtained from the SWAT model database (Table 1). The historical hydrological data for SWAT model calibration and validation include the river flow data from four hydrological stations. The hydrological observation data, including annual data in Heihe River Basin during 1980–2010, were obtained from the hydrological yearbook provided by WestDC. The river flow data used for the model calibration and validation were provided by the Data Center of CAS. The historical daily weather data during 1980–2010 were collected from China Meteorological Administration (CMA), including daily precipitation, maximum and minimum temperatures, solar radiation, humidity, wind speed, and wind direction from 13 weather stations in or near the Heihe River Basin.Table 1
Data used and sources.
Data type
Data sources
Scale
Description
DEM
Shuttle Radar Topography Mission (SRTM)
90 m
Elevation
Land use
Data Center of Chinese Academy of Science
1 : 100000
The classification system contains 25 categories
Soil
WestDC (http://westdc.westgis.ac.cn/)
1 : 1000000
Some parameters were calculated using a (Soil-Plant-Atmosphere-Water) Field and Pond Hydrology model
Weather
China Meteorological Administration
Daily
5 weather stations
Hydrological observation
Hydrological yearbook
Daily
4 stations
River flow
Data Center of Chinese Academy of Science
1 : 250000
River network diversion
Glacier
WestDC (http://westdc.westgis.ac.cn/)
1 : 100000
The attributes include width, length, and depth


## 2.3. Simulation of Land Use Changes with the DLS Model

The DLS model is a set of applications used to simulate the changing process of land use system and is an effective tool for simulation of spatial-temporal land use changes to assist land management [33]. The DLS model consists of three modules: a spatial regression module that identifies the relationships between land uses and the influencing factors; a scenario analysis module of land use changes that determines the land demands at the regional level; a spatial disaggregation module that allocates land use changes from a regional level to the disaggregated grid cells [34, 35].The DLS model simulates the spatial-temporal land use changes with three processes: scenario analysis of land use change, spatial regression analysis, and spatial allocation of land use changes. The first process was carried out with the scenario analysis module, which provides the data of total land demands at the annual scale during a given period. By including the scenario analyses of land use changes, a set of spatially explicit simulation results of land use change can be exported by the DLS model [36]. The total land use demands can be set by several approaches, such as trend analysis methods (e.g., linear interpolations or more sophisticated econometric models) and economic models. In this study, the total land demands during the simulation period were first determined using trend analysis methods and then were used to establish a scenario of land use change during 2006–2030.In the spatial regression module, the relationships between land uses and influencing factors were analyzed via stepwise logistic regression analysis of past land use changes and their drivers [37]. For each grid cell, the total probability for each land use type is calculated on basis of the multinomial logistic regression at the pixel scale as follows:(1)
P
i
,
k
=
1
1
+
∑
k
≠
i
e
x
k
β
k
i
=
k
e
x
k
β
k
1
+
∑
k
≠
i
e
x
k
β
k
i
≠
k
,where P
i
,
k is the probability of conversion from land type i to k in the cells under given driving factors; X
1
,
X
2
,
…
,
X
n represent the driving factors of climate, landform, location, population, economic growth, policy, and other categories; β
1
,
β
2
⋯
β
n are the regression analysis coefficients of driving factors for further estimation.In this study, all the data of land use and the influencing factors were prepared at the annual scale. The land use data in 2000 and 2005 were used in the logistic regression. The assumed driving factors were categorized into five groups: climate, geophysics, transportation, location, and socioeconomics. The data of these factors in the corresponding years were also prepared (Table2).Table 2
Results of logistical regression for the six land use types with 13 driving factors.
Cultivated land
Forest land
Grassland
Water
Built-up area
Unused land
Slope
−0.000049
0.000121
-
7.42
E
-
05
-
7.76
E
-
06
-
1.1
E
-
06
0.0000105
(
-
27.52
)
∗
∗
∗
(
-
63.65
)
∗
∗
∗
(
-
18.89
)
∗
∗
∗
(
-
7.24
)
∗
∗
∗
(
-
2.07
)
∗
(
-
2.64
)
∗
∗
Elevation
−0.000136
−3.02E − 05
0.000116
−0.0000149
−5.9E − 06
0.0000711
(−53.82)***
(−11.13)***
(−20.8)***
(−9.76)***
(−7.85)***
(
-
12.56
)
∗
∗
∗
Rain
−0.000045
0.0000228
0.0000419
0.0000136
1.4E − 06
−0.0000347
(
-
16.09
)
∗
∗
∗
(
-
7.61
)
∗
∗
∗
(
-
6.79
)
∗
∗
∗
(
-
8.05
)
∗
∗
∗
−1.7
(
-
5.54
)
∗
∗
∗
Radiation
−0.000407
0.000243
−0.000923
-
8.64
E
-
06
1.16
E
-
05
0.00108
(
-
24.27
)
∗
∗
∗
(
-
13.55
)
∗
∗
∗
(
-
24.98
)
∗
∗
∗
(−0.86)
(
-
2.34
)
∗
(
-
28.91
)
∗
∗
∗
>0°C accumulated temperature
−0.0000106
0.0000127
0.000026
−3.88E − 06
−7.2E − 07
−0.0000234
(
-
24.03
)
∗
∗
∗
(
-
26.78
)
∗
∗
∗
(
-
26.59
)
∗
∗
∗
(
-
14.55
)
∗
∗
∗
(
-
5.52
)
∗
∗
∗
(
-
23.67
)
∗
∗
∗
Soil organic matter
0.0685
0.232
−0.186
0.0152
−0.00655
−0.123
(
-
9.38
)
∗
∗
∗
(
-
29.64
)
∗
∗
∗
(
-
11.58
)
∗
∗
∗
(
-
3.46
)
∗
∗
∗
(
-
3.03
)
∗
∗
(
-
7.52
)
∗
∗
∗
pH value
0.00769
−0.0131
−0.00541
0.00171
0.000719
0.00834
(
-
7.15
)
∗
∗
∗
(
-
11.32
)
∗
∗
∗
(
-
2.28
)
∗
(
-
2.64
)
∗
∗
(
-
2.26
)
∗
(
-
3.47
)
∗
∗
∗
Population density
0.000247
-
7.82
E
-
05
-
3.16
E
-
05
0.0000206
-
3.2
E
-
06
−0.000154
(
-
32.18
)
∗
∗
∗
(
-
9.52
)
∗
∗
∗
(−1.87)
(
-
4.46
)
∗
∗
∗
(−1.40)
(
-
9.00
)
∗
∗
∗
Per capita GDP
0.000548
-
3.08
E
-
05
−0.000185
−0.00002
0.000411
−0.000723
(
-
76.02
)
∗
∗
∗
(
-
3.99
)
∗
∗
∗
(
-
11.62
)
∗
∗
∗
(
-
4.61
)
∗
∗
∗
(
-
192.54
)
∗
∗
∗
(
-
44.87
)
∗
∗
∗
Distance to railway
0.000193
0.000229
0.000979
0.00000653
1.72E − 05
−0.00142
(
-
13.55
)
∗
∗
∗
(
-
14.96
)
∗
∗
∗
(
-
31.16
)
∗
∗
∗
−0.76
(
-
4.07
)
∗
∗
∗
(
-
44.71
)
∗
∗
∗
Distance to road
−0.000221
−0.000335
0.00111
−0.000066
-
1.4
E
-
05
−0.000477
(
-
18.45
)
∗
∗
∗
(
-
26.06
)
∗
∗
∗
(
-
42.08
)
∗
∗
∗
(
-
9.13
)
∗
∗
∗
(
-
3.79
)
∗
∗
∗
(
-
17.79
)
∗
∗
∗
Distance to water source
−0.000338
0.0000239
0.000926
−0.000346
-
5.2
E
-
06
−0.000261
(
-
14.46
)
∗
∗
∗
−0.95
(
-
17.96
)
∗
∗
∗
(
-
24.55
)
∗
∗
∗
(−0.75)
(
-
4.99
)
∗
∗
∗
Distance to city
−0.000125
−0.000108
−0.000567
0.0000996
-
2
E
-
05
0.00072
(
-
8.11
)
∗
∗
∗
(
-
6.58
)
∗
∗
∗
(
-
16.76
)
∗
∗
∗
(
-
10.78
)
∗
∗
∗
(
-
4.31
)
∗
∗
∗
(
-
20.98
)
∗
∗
∗
Note:t statistics in parentheses; ∗significant at 10%; ∗
∗significant at 5%; ∗
∗
∗significant at 1%.The spatial disaggregation module is used to spatially and explicitly convert the land demands into land use changes at various locations of the study area. The spatial disaggregation is carried out in an iterative procedure based on the probability maps, conversion rules, historical land use maps, and land demands under the scenarios. The probability maps of each land use type were prepared with the logistic regression results. Besides, the rules of land use conversion were set for each land use type, whose value ranges from 0 to 1. A smaller value means one land use type is more likely to be converted to another type, and vice versa. The development-restricted areas in the study area were also specified.


## 2.4. Downscaling of GCM Climate Data

GCMs are arguably the best available tools for modeling future climate. Yet GCMs provide information at a resolution that is too coarse to be directly used in hydrological modeling [38]. Therefore downscaling is required to transform the low resolution GCM outputs to the high resolution climate features needed for hydrological simulation. The downscaling procedure is as follows. First, the average annual precipitation and temperature of 30 years (1980–2010) were calculated, which were adopted as a baseline for selecting the GCMs. We considered future climate change scenarios for the basin (Figure 2) by using the spatially distributed outputs from 10 GCMs under RCP 4.5 scenario. The climate projections of Max Planck Institute (MPI) were downscaled to the 3 km × 3 km grid in the study area, and bias was corrected and climate change scenarios were developed by MPI for Meteorology. The annual mean values of 10 GCM spatial data from 2006 to 2030 were calculated according to the basin perimeter, and one out of ten GCMs was also selected based on the historical trends and annual averages of temperature and precipitation. The MPI model was finally chosen through comparison. The result of MPI model originates in MPI, and the spatial resolution is 1.865° (LAT) × 1.875° (LON). Then the parameters in MPI GCM were transformed into the forcing data of a regional climate model in weather research and forecasting (WRF) simulation, and thereafter dynamical downscaling simulation was performed at the spatial resolution of 3 km for the period of 2006–2030. We considered the impacts of land use changes on regional climate, and the land cover data before WRF simulation were dynamically replaced with land use change data based on the simulation with the DLS model. Finally, the data simulated by the regional climate model were matched with meteorological sites, and the meteorological site data were prepared for the simulation with SWAT.Figure 2
The trends in (a) observed (grey line; running average in black) and projected (10AR5 GCMs; colored dash lines) temperature, (b) observed and projected precipitation.
(a)
(b)


## 2.5. Simulation of Hydrological Cycle with the SWAT Model

The study area was first divided into subwatersheds, which were subdivided into hydrological response units (HRUs). Besides, for each subwatershed, the climate data used are taken from the GCM grid point that is the closest to its centroid. To improve performance, the SWAT model was calibrated and validated by adjusting several parameters and comparing the simulated streamflow with observed values. The most sensitive parameters were identified with the built-in sensitivity analysis tool in SWAT [39]. The daily streamflow observation data from Yingluoxia Hydrological Station in 2004 were used for calibration, and the observation data in 2005 were used for validation. It should be noted that the first three years were used as a warm-up period to mitigate the effects of unknown initial conditions, which were then excluded from the subsequent analysis. The ability of the SWAT model to replicate the temporal trends in the historical hydrological observations was assessed using the coefficient of determination (R
2), the Nash and Sutcliffe (1970) model efficiency (NSE), and the root mean square error (RMSE).


## 3. Result and Discussion

### 3.1. Calibration and Validation

The SWAT model was calibrated for 2004 and validated for 2005 using the daily streamflow observation data from four gauging stations within the study area. Finally, fifteen parameters were selected for the calibration (Table2), which are associated with snow (SFTMP, SMTMP, SMFMX, SMFMN, and TIMP), runoff (CN2), groundwater (ALPHA_BF and GW_DELAY), soil (SOL_AWC), channel (CH_N and CH_K2), and evaporation (ESCO) processes. After the sensitivity analysis, 9 relatively more sensitive parameters were identified for the calibration. Most of the parameters were adjusted based on multiple trials, and the SWAT model was calibrated using an automatic calibration technique with the program Sequential Uncertainty Fitting Version (SUFI-2). With SUFI-2, sensitive initial and default parameters related to hydrology varied simultaneously until an optimal solution was achieved. The most sensitive parameters with their best ranges and best-fitted values are shown in Table 3. Finally, these best-fitted values were used to adjust the initial model inputs for the simulation during 2006–2030. The model was validated using daily streamflow observation data from the Yingluoxia Hydrological Station in 2005. The validation results show that the NSE is 0.78 and R
2 of the observed and simulated data is 0.81 (Figure 3), demonstrating the high behavioral performance of the SWAT model.Table 3
List of calibration parameters and the optimized values.
Parameter
Description
Range
Optimized value
TLAPS
Temperature lapse rate[°C/km]
0, −10
−3.8
PLAPS
Precipitation lapse rate[mm H2O/km]
0, 100
5.8
SFTMP
Snowfall temperature[°C]
−2, +2
0.9
SMTMP
Snow melt base temperature[°C]
−5, +5
2.1
SNOEB
Initial snow water content in elevation band[mm]
50, 230
100
TIMP
Snowpack temperature lag factor
0.38–0.62
0.49
SMFMN
Melt factor for snow on December 21[mm H2O/°C-day]
3.05–3.51
3.25
SMFMX
Melt factor for snow on June 21[mm H2O/°C-day]
5.85–6.27
6.02
SURLAG
Surface runoff lag time[days]
4.18–5.19
4.68Figure 3
The calibration and validation on streamflow of the SWAT.
(a)
(b)


### 3.2. Future Climate under the RCP 4.5 Scenario

Based on the downscaled GCM climate data, we calculated the mean temperature and precipitation of the 9 lattice points around the grid that included Qilian meteorological station. The results were compared with the mean monthly temperatures and precipitation of the meteorological station during 1981–2005. The monthly mean temperatures of the 25 years ranged from −3 to 3°C and increased by around 0.8°C. The mean monthly precipitation ranges from −0.3% to 10% and increased by around 7.8% (Figure4). The increase range of mean monthly precipitation is large, while the range of reduction is smaller.Figure 4
The difference of average monthly temperature and precipitation between 1981–2005 and 2006–2030 in Qilian Weather observed station.
(a)
(b)


### 3.3. Future Land Use Change Simulated with DLS

The results suggested the change in one land use type was influenced by multiple factors, and the 13 driving factors can reasonably explain the spatial patterns of all land use types. For example, the existence of forest land was significantly influenced by all the 13 driving factors, while the existence of cultivated land and grassland was affected by the altitude, distance, and soil factors. The future land uses for 2006–2030 were simulated with the DLS model by combining the probability maps prepared with logistic regression analysis, the land demands under different scenarios, and the map of development-restricted areas. The simulation results indicated that the most dramatic land use changes during 2006–2030 will mainly occur in the upper reach and some parts of the middle reach of Heihe River Basin. Compared to 2005, the areas of forest land and unused land in 2030 will decrease by 6.2% and 1.6%, respectively, while the areas of built-up land, cultivated land, and grassland will increase by 1.7%, 1.3%, and 4.8%, respectively (Figure5). The significant increase of grassland area may mainly result from the steady pasture construction, and this uptrend may continue in the future owing to the increasing demand for pasture products.Figure 5
Comparison of the land uses interpreted in 2000, 2005 and simulated in 2010, 2030.
(a)
(b)
(c)
(d)


### 3.4. Impacts of Climate and Land Use Changes on Watershed Hydrology

Four simulation experiments were designed based on the land use data and climate data. In the baseline experiment for the period during 1981–2005, the water yield was simulated with the land use data in 2000, 2005 and the weather station observations during 1981–2005 (Figure6(b)). Then three scenarios for the period during 2006–2030 were designed based on the land use and climate change (Figure 6(a)), the results from which were compared with that in the baseline experiment. In the first scenario during 2006–2030, the water yield was simulation with the land use data in 2010 and 2030, temperature data during 2006–2030, and the precipitation data during 1981–2005. The simulation result shows that the impacts of future land use change on the water yield vary with seasons, and the land use change will have negative overall influence on the water yield, with an influence degree of −1.8% according to the annual mean water yield.Figure 6
Comparison of water yields under three scenarios.
(a)
(b)The second scenario during 2006–2030 was based on scenarios of temperature and land use changes. The second experiment used the land use data in 2010 and 2030, scenario data of temperature during 2006–2030, and precipitation data during 1981–2005. The analysis of climate change scenarios shows that the average temperature will rise by 0.8°C between 1981–2005 and 2006–2030. The simulation result in the second experiment shows that the land use and temperature changes will make the water yield change by 0.6%–1.1%, the change range of which is relatively smaller compared to the simulation results under the scenario with only land use change. The reasons may be that the temperature rise and melting of a small amount of snow slightly offset the adverse effects of land use change. At the same time, the higher temperatures will result in more winter precipitation in the form of rain rather than snow, leading to the hydrologic consequences including increased winter discharge, a shift in the spring snowmelt peak to earlier in the season, and decreased summer discharge.The third scenario during 2006–2030 involves scenarios of changes in all of land use, temperature, and precipitation. The land use data in 2010 and 2030 and temperature and precipitation data during 2006–2030 were used under the third scenario. The simulation result shows that these three factors jointly have positive impacts on the water yield, making the basin water yield increase by about 9.8%. The increase of the basin water yield is mainly caused by the change in precipitation, which will increase by around 10.8% during 2006–2030 in comparison to that during 1981–2005. Overall, the simulation results suggest that the basin water yield will increase in the future under different scenarios of climate and land use changes.


## 3.1. Calibration and Validation

The SWAT model was calibrated for 2004 and validated for 2005 using the daily streamflow observation data from four gauging stations within the study area. Finally, fifteen parameters were selected for the calibration (Table2), which are associated with snow (SFTMP, SMTMP, SMFMX, SMFMN, and TIMP), runoff (CN2), groundwater (ALPHA_BF and GW_DELAY), soil (SOL_AWC), channel (CH_N and CH_K2), and evaporation (ESCO) processes. After the sensitivity analysis, 9 relatively more sensitive parameters were identified for the calibration. Most of the parameters were adjusted based on multiple trials, and the SWAT model was calibrated using an automatic calibration technique with the program Sequential Uncertainty Fitting Version (SUFI-2). With SUFI-2, sensitive initial and default parameters related to hydrology varied simultaneously until an optimal solution was achieved. The most sensitive parameters with their best ranges and best-fitted values are shown in Table 3. Finally, these best-fitted values were used to adjust the initial model inputs for the simulation during 2006–2030. The model was validated using daily streamflow observation data from the Yingluoxia Hydrological Station in 2005. The validation results show that the NSE is 0.78 and R
2 of the observed and simulated data is 0.81 (Figure 3), demonstrating the high behavioral performance of the SWAT model.Table 3
List of calibration parameters and the optimized values.
Parameter
Description
Range
Optimized value
TLAPS
Temperature lapse rate[°C/km]
0, −10
−3.8
PLAPS
Precipitation lapse rate[mm H2O/km]
0, 100
5.8
SFTMP
Snowfall temperature[°C]
−2, +2
0.9
SMTMP
Snow melt base temperature[°C]
−5, +5
2.1
SNOEB
Initial snow water content in elevation band[mm]
50, 230
100
TIMP
Snowpack temperature lag factor
0.38–0.62
0.49
SMFMN
Melt factor for snow on December 21[mm H2O/°C-day]
3.05–3.51
3.25
SMFMX
Melt factor for snow on June 21[mm H2O/°C-day]
5.85–6.27
6.02
SURLAG
Surface runoff lag time[days]
4.18–5.19
4.68Figure 3
The calibration and validation on streamflow of the SWAT.
(a)
(b)


## 3.2. Future Climate under the RCP 4.5 Scenario

Based on the downscaled GCM climate data, we calculated the mean temperature and precipitation of the 9 lattice points around the grid that included Qilian meteorological station. The results were compared with the mean monthly temperatures and precipitation of the meteorological station during 1981–2005. The monthly mean temperatures of the 25 years ranged from −3 to 3°C and increased by around 0.8°C. The mean monthly precipitation ranges from −0.3% to 10% and increased by around 7.8% (Figure4). The increase range of mean monthly precipitation is large, while the range of reduction is smaller.Figure 4
The difference of average monthly temperature and precipitation between 1981–2005 and 2006–2030 in Qilian Weather observed station.
(a)
(b)


## 3.3. Future Land Use Change Simulated with DLS

The results suggested the change in one land use type was influenced by multiple factors, and the 13 driving factors can reasonably explain the spatial patterns of all land use types. For example, the existence of forest land was significantly influenced by all the 13 driving factors, while the existence of cultivated land and grassland was affected by the altitude, distance, and soil factors. The future land uses for 2006–2030 were simulated with the DLS model by combining the probability maps prepared with logistic regression analysis, the land demands under different scenarios, and the map of development-restricted areas. The simulation results indicated that the most dramatic land use changes during 2006–2030 will mainly occur in the upper reach and some parts of the middle reach of Heihe River Basin. Compared to 2005, the areas of forest land and unused land in 2030 will decrease by 6.2% and 1.6%, respectively, while the areas of built-up land, cultivated land, and grassland will increase by 1.7%, 1.3%, and 4.8%, respectively (Figure5). The significant increase of grassland area may mainly result from the steady pasture construction, and this uptrend may continue in the future owing to the increasing demand for pasture products.Figure 5
Comparison of the land uses interpreted in 2000, 2005 and simulated in 2010, 2030.
(a)
(b)
(c)
(d)


## 3.4. Impacts of Climate and Land Use Changes on Watershed Hydrology

Four simulation experiments were designed based on the land use data and climate data. In the baseline experiment for the period during 1981–2005, the water yield was simulated with the land use data in 2000, 2005 and the weather station observations during 1981–2005 (Figure6(b)). Then three scenarios for the period during 2006–2030 were designed based on the land use and climate change (Figure 6(a)), the results from which were compared with that in the baseline experiment. In the first scenario during 2006–2030, the water yield was simulation with the land use data in 2010 and 2030, temperature data during 2006–2030, and the precipitation data during 1981–2005. The simulation result shows that the impacts of future land use change on the water yield vary with seasons, and the land use change will have negative overall influence on the water yield, with an influence degree of −1.8% according to the annual mean water yield.Figure 6
Comparison of water yields under three scenarios.
(a)
(b)The second scenario during 2006–2030 was based on scenarios of temperature and land use changes. The second experiment used the land use data in 2010 and 2030, scenario data of temperature during 2006–2030, and precipitation data during 1981–2005. The analysis of climate change scenarios shows that the average temperature will rise by 0.8°C between 1981–2005 and 2006–2030. The simulation result in the second experiment shows that the land use and temperature changes will make the water yield change by 0.6%–1.1%, the change range of which is relatively smaller compared to the simulation results under the scenario with only land use change. The reasons may be that the temperature rise and melting of a small amount of snow slightly offset the adverse effects of land use change. At the same time, the higher temperatures will result in more winter precipitation in the form of rain rather than snow, leading to the hydrologic consequences including increased winter discharge, a shift in the spring snowmelt peak to earlier in the season, and decreased summer discharge.The third scenario during 2006–2030 involves scenarios of changes in all of land use, temperature, and precipitation. The land use data in 2010 and 2030 and temperature and precipitation data during 2006–2030 were used under the third scenario. The simulation result shows that these three factors jointly have positive impacts on the water yield, making the basin water yield increase by about 9.8%. The increase of the basin water yield is mainly caused by the change in precipitation, which will increase by around 10.8% during 2006–2030 in comparison to that during 1981–2005. Overall, the simulation results suggest that the basin water yield will increase in the future under different scenarios of climate and land use changes.


## 4. Discussion and Conclusions

In the Heihe River Basin, the upper reaches are featured with the generation and use of blue water, while the lower reach and surrounding areas are characterized by natural ecosystems and a low population density. LULC is defined as syndromes of human activities such as agriculture, forestry, and building construction, and most of previous studies only focused on the hydrological influence of LULC change in the upper reach. The separation between hydrological impacts of land use and climate changes has never been studied in the upper and middle reaches of the Heihe River Basin. However, we argue that studying the hydrological processes in the upper and middle reaches is essential since water supply to the lower reach is impacted by both the climate change and human activities in the upper and middle reaches.In this study, we analyzed the impacts of potential climate and land use changes on the water yield in the upper and middle reaches of Heihe River Basin based on the simulation with the SWAT model. The results show that the water yield was more affected by climate change than by land use change. This indicates that the predicted increase in precipitation will exert more significant impacts on the watershed hydrology than the predicted land use changes will. However, the analysis of the projected streamflow changes shows that there are higher uncertainties in the dry season compared with the wet season in the simulation with the hydrological model and GCMs climate data. It is difficult to accurately project the hydrological changes since there are various uncertainties associated with the future Green House Gas (GHG) emission scenarios, GCM structure, downscaling method, LULC, and hydrological models. In particular, water resource managers are generally confronted with complex problems in sustainable management and conservation of water resources due to the uncertainties in the future hydrological projection under climate and land use changes. It is therefore crucial to consider both land use and climate changes in water resource planning for the Heihe River Basin so as to mitigate their negative hydrological impacts, and more valuable information may be provided to the water resource managers if these uncertainties in the future hydrological projection can be effectively reduced through advanced modeling and research.



---

*Source: 410198-2015-10-18.xml*