Climate variability and land use change are recognized as two important factors influencing hydrological processes. Studies showed that the increased temperature could result in corresponding changes in the timing and volume of spring flood and evapotranspiration [1
]. The variability of precipitation, especially the changes in frequency and intensity of extreme precipitation events, could lead to a variation in stream flow and peak flow. Land use changes are directly linked to changes in the hydrological processes, such as evapotranspiration, interception, and infiltration, etc., and then to the impact of groundwater recharge, baseflow, and streamflow, etc. [3
In general, the methods of assessing the impacts of climate variability/land use change on hydrological processes can be mainly divided into three groups, which are time series analysis, paired catchments experiments, and hydrological modeling [7
]. Time series analysis is easy to implement as it is a statistical method [8
]. However, it can only be applied to simple analysis of the hydrological effects of climate variability/land use change, and lacks physical mechanisms. Paired catchments experiments and hydrological modeling can be applied to research into the interaction between hydrological and climate variability/land use change [11
]. The advantage of paired catchments experiment is that it can remove climate variability/land use change through the comparison of two catchments under the similar climatic conditions/land use [12
]. Nevertheless, this approach is difficult to apply to larger basins and is very time consuming [13
]. In recent years, hydrological models have often been applied to study the relationship between climate variability/land use change and hydrological processes, for the inhomogeneity of climate and land use can be introduced to these models [3
Among the widely used hydrological models, the Soil Water Assessment Tool (SWAT) is a utility model, as it has the capability for simulating many processes such as the hydrologic cycle, sediment transportation, and soil erosion. In the SWAT model, with GIS (Geographic Information System) and other interface tools, information on topographic, land use, and soil data can be conveniently written to input parameters of the model. Because SWAT is an open-source model, researchers can conveniently improve the simulation performance in specific study areas. Meanwhile, the SWAT model can be easily calibrated and incorporate changes in land use [17
]. As a result, it is a widely used tool for the study of the hydrological effects of environmental change [18
]. Yin [21
] applied the SWAT model to quantify the impact of climate variability/land use change on the streamflow over a long historical time period. Kim [22
] used SWAT model to evaluate the separated and combined impacts of future changes and land use changes on the streamflow. Commonly, researchers analyze the trend and change points of historical hydro-meteorology data before building the SWAT model [13
]. The former can help to understand the effects of climate variability on streamflow and the latter can help to select a period with no significant human activity in order to build the SWAT model [23
Precipitation is an important input variable in the SWAT model. The accuracy of hydrological simulation could be directly affected by the quantity and quality of precipitation data. Regular and sufficient rain gauges can aid in revealing the exact spatial distribution of precipitation. However, in some areas there are fewer rain gauges and it may difficult to exactly reflect the spatial distribution of precipitation [24
]. Combining satellite data and observed data to estimate rainfall is an effective method to solve the problem. Based on this method, several products have been developed and widely applied in hydrological modeling [26
]. The China Meteorological Assimilation Driving Datasets for the SWAT model (CMADS), which was developed by Dr. Xianyong Meng from the China Institute of Water Resources and Hydropower Research (IWHR), is one such product and has been widely applied in numerous studies [28
]. For example, Liu [28
] found that CMADS had unique advantages in hydrological simulations compared with observed data and Climate Forecast System Reanalysis (CFSR). Zhao [30
], based on CMADS, applied three methods to analyze the parameter uncertainty in a mountain-loess transitional watershed. Cao [31
] applied CMADS to estimate hydrological elements in the Lijiang River basin.
The Hailiutu River basin, which belongs to the middle Yellow River basin, is a typical sandy grass shoal watershed in the Erdos plateau in northwest China. This basin has undergone climate variability and land use change in recent decades [32
]. Studies showed that in the central-south Yellow River basin the intensity of precipitation extremes increased between the years 1986 and 2011, and may have resulted in increased surface runoff [33
]. The annual average air temperature showed a significant increasing trend in recent decades which may induce the occurrence of increased evapotranspiration [13
]. Studies also showed that streamflow change is more sensitive to precipitation than temperature in the Yellow River basin, especially in the middle section [36
]. Because of the implementation of the desert greening policy, the land use of study area has changed, i.e., increasing the area of shrub and grass land and decreasing the area of sand land [32
]. The increased shrub and grass land could result in decreased baseflow and increased surface runoff and evapotranspiration [6
]. Meanwhile, the water demand has increased in recent decades, especially after the Erdos Plateau was targeted as a priority area of the western development strategy for China in the 21st century. Accordingly, groundwater extraction, construction of reservoirs, and diversion of dams have increased [38
]. The streamflow in the Hailiutu River basin is mainly recharged by groundwater, especially in the dry season. More extraction of groundwater would result in decreased streamflow [41
]. Engineering measures such as reservoir construction and diversion of dams may affect high flow by reducing overland flow and peak streamflow [43
]. The annual streamflow of Huangfuchuan basin, which is located in the same climate zone of Hailiutu River basin, has decreased significantly because the increased of construction of check dams [10
]. Therefore, it is necessary to study the impacts of climate variability/land use change and other factors such as the increase of ground water extraction, and construction of reservoirs and diversion dams on the streamflow in the Hailiutu River basin.
The overall objective of this study is to assess the impacts of climate variability and land use change on hydrological components in the Hailiutu River basin in the Erdos plateau. The specific objectives are: (1) to analyze the temporal variation of hydrometeor-ology with trend analysis and change points testing; (2) to evaluate the performance of SWAT model for simulation of streamflow in the Hailiutu River basin; (3) to compare the impact of climate variability on hydrological components with land use change; and (4) to evaluate the effects of climate variability/land use change and other factors, such as the increase of ground water extraction and construction of reservoirs and diversion dams, on streamflow. The results obtained in this study could provide guidance for water resource management and planning in the Erdos plateau.
In this paper, the statistics analysis methods and hydrological modeling were applied to assess the impacts of climate variability and land use change on the hydrological components in the Hailiutu River basin of the Chinese Erdos Plateau, and the combined impacts of climate and land use change on streamflow reduction were also analyzed. The results are listed as follows:
The CMADS was introduced to improve the spatial expressiveness of precipitation data in the study area over the period 2008–2014. There is a good correlation between CMADS and the observed data during this period. The SWAT model was calibrated by observed data during the period 2008–2011. Compared with only using observed precipitation data, the NSE value increased from 0.80 to 0.83 when using combined data during 2008–2011 and increased from 0.45 to 0.55 when using combined data during 2012–2014. The R2 value increased from 0.63 to 0.65 when using combined data during 2012–2014. This proves that the performance of SWAT model when using combined observed and CMADS precipitation values was significantly improved. It suggests that combining the CMADS with traditional hydrological measurements might be very helpful for improving hydrological modeling.
The Mann–Kendall test of annual hydrometeorology series indicated that streamflow and wind speed showed a significantly downward trend during the period 1970–2014. A slight upward trend was detected for precipitation while a significantly upward trend was detected for temperature. From the analysis of the STARS test, the years 1986 and 2001 were detected as change points for annual streamflow. The years 1979 and 1998 were detected as change points for annual wind speed. The year 1999 was detected as a change point for all temperature series, and 2005 as the change point for annual maximum temperature series. Based on the change points of annual streamflow, the whole study period was divided into three sub-periods, i.e., 1970–1985, 1986–2000, and 2001–2014.
Compared with 1970–1985, the mean monthly streamflow during the period 1986–2000 decreased, especially in September and October. The streamflow increased during the period 2001–2014, with almost no variation compared with the period 1986–2000 from May to July. The precipitation during the period 1986–2000 substantially decreased as compared with the period 1970–1985. During the period 2001–2014, the precipitation rose to similar values as the period 1970–1985. From the comparison among monthly temperature in three periods, the annual maximum temperature showed an upward trend during January–June, and the annual minimum and average temperature presented upward trends in all the months. In the study area, the main variations of land use were increased area of shrub and grass land (from 51.10% of 1986 to 72.68% of 2010) and decreased area of sandy land (from 43.31% of 1986 to 19.48% of 2010). These variations can be explained by the implementation of the project of “Three North Forest Shelterbelts” and policy of “Closing Sandy Land and Forbidding Herding”.
As compared to the period 1970–1985, the climate variability led to a significant decrease in streamflow during the period 1986–2000, and induced a moderate increase in streamflow during the period 2000–2014. The land use changes caused increases in surface runoff, evapotranspiration, and decreases in baseflow, lateral flow, and streamflow. These changes are mainly attributed to the increase of shrub and grass land area and a decrease of sandy land area. In general, the impacts of climate variability on the value of hydrological components were more profound than land use change in the Hailiutu River basin. Therefore, the importance of increasing adaptation to climate variability should be considered when planning and managing water resources.
Compared to the period 1970–1985, the observed mean annual streamflow decreased during the periods 1986–2000 and 2001–2014. During the period 1986–2000, the climate variability and land use change resulted in a decrease of mean annual streamflow by 6.13% and 0.66%, respectively. The combined climate variability and land use change induced a decrease in mean annual streamflow by 7.07%, and was responsible for 27.87% of the decrease in observed mean annual streamflow. For the period 2001–2014, land use change induced a 1.94% decrease in mean annual streamflow. There was a positive impact of climate variability on the streamflow, with an increment of 4.82%. Under the impact of combined climate variability and land use change, it was observed that mean annual streamflow increased by 2.39%. The discrepancy between observed and simulated streamflow during the period 2001–2014 implies that the impact of other factors such as local water supply and exploitation of groundwater on the streamflow in Hailiutu River basin may not be ignored in hydrological modeling.