1. Introduction
The global hydrological cycles are significantly influenced by the average global land-surface air temperature (LSAT) which has been rising since the late 19th century due to human activities [
1,
2]. The speed at which climate and land use/cover have changed has accelerated as a direct result of population growth and human-induced economic developments, thereby affecting the interception, infiltration, and evaporation processes of the hydrological cycles in wide spatial and temporal domain [
3,
4,
5,
6]. As a result, the generation and distribution of streamflow and water resources are deeply affected due to the changes of precipitation, temperature, and evapotranspiration in the hydrological processes [
7,
8]. Therefore, a better understanding of the contributions of climate change and land use/cover change (LUCC) to the streamflow contributes to the long-term planning and management of water resources.
Generation mechanisms and flow routing processes of the natural watershed runoff are related to human activities. Thus, the clarification of the driving factors and their impacts on the change of regional runoff has drawn attention from the fields of hydrology, geology, and ecology [
3,
4,
9,
10,
11,
12,
13]. Three methods are generally utilized to quantitatively analyze the effects of climate change and LUCC on the characteristics of regional hydrology, including the experimental watershed investigation approach, long time series analysis based on hydro-meteorological data, and simulation based on hydrological models [
14,
15]. With the further development of computer science and information technology, simulations based on hydrological models are becoming more and more popular due to the advantages of adaptability, convenience, efficiency, and cost saving.
Xiying River basin is one of the eight tributary basins of the Shiyang River basin in northwestern China. It is located in a typical arid inland basin. In this arid region, runoff is generated from the upstream mountainous watersheds and disappears at the plains because of irregular topography and distinctive hydrological systems. Consequently, runoff from the upper reaches directly affects the balance of the ecological system and human activities such as agriculture development in the downstream of the Xiying River basin; as such, it is important to predict streamflow accurately, especially under the influences of global climate change and human activities. However, it is a challenge to simulate streamflow in mountainous contributing areas in respect of complex topography and hydrological processes. Meanwhile, investigations on the quantitative analysis and separation of the impacts of climate change and LUCC on streamflow in this river basin were rarely reported. Li [
16] simulated the changing trends of runoff under different climate scenarios using a hydrological statistical method. The influences of precipitation and temperature were investigated by means of an incremental scenario method, showing that the annual runoff of Xida River increased with increasing annual precipitation and decreased with rising annual temperature. However, the future climate and LUCC was not taken into account. Wang
et al. [
17] analyzed the effects of climate change on streamflow of the Xiying and Zamu rivers in the Shiyang River basin by using regional climate model data from the providing regional climates for impacts studies (PRECIS) under scenarios A2 and B2 based on the SWAT model. Recently, there has been a rapidly increasing number of investigations devoted to the separation of the impacts of climate change and human activities on streamflow using the climate elasticity method based on the Budyko hypothesis [
18,
19,
20]. Mann-Kendall test and change point analysis were applied to identify trends and change points of the hydrometeorological variables, and then the impacts of climate variation and human activities on runoff were further discussed quantitatively utilizing climate elasticity method [
21,
22,
23,
24], this research strategy is also can be found in several other references [
25,
26,
27]. Errors introduced by the first-order approximation (first-order Taylor expansion) of the Mezentsev-Choudhury-Yang equation (which is an analytical solution for the coupled water-energy balance on long-term time and catchment scales based on the Budyko hypothesis) were also investigated to improve the theoretical framework [
28,
29]. However, the climate elasticity method assumes that climate variation and human activities are independent of each other [
30], which is different from the fact that human activities would be affected by climate variation in respect to land use and vegetation. Meanwhile, human activities such as extensive urbanization and expanded population may cause changes in climate, such as through increasing greenhouse gas concentration, for example. Moreover, elastic parameter n [
31,
32], representing the land surface condition, is affected by factors including vegetation coverage, soil properties, topographical features, snow index, and precipitation intensity which would change during the study period, and the value of n is usually determined subjectively based on the Budyko hypothesis. In addition, hydrometeorological variables such as potential evapotransporation (PET), runoff, and actual evapotransporation (AET) may be influenced because a limited number of hydro-meteorological stations can be used in the climate elasticity method which is based on long-term hydro-meteorological observation data.
Therefore, the objective of this study is to quantitatively evaluate the impacts of climate change and LUCC on the streamflow in Xiying River basin using SWAT and a hydrological statistical method. Impact factors of n, including the vegetation coverage, soil properties, and topographical features conditions, were taken into consideration in the streamflow simulation by SWAT, and the hydrological statistical method was used to assess the effects of climate change and land use change on streamflow quantitatively. Thus, more accurate results can be obtained by using this research strategy than only using one hydrological statistical method, such as the climate elasticity method which can be easily influenced by the change of land surface condition and other factors. Moreover, three modeling scenarios were considered in detail, including (a) incremental change in precipitation and temperature, (b) four situations consisting of hydro-meteorological data from the periods between 1990–1999 and 2000–2008, and land use data from 2000 and 2008, and (c) projected climate data for 2010–2069 under A2 (medium-high emissions) and B2 (medium-low emissions) scenarios produced by the Hadley Centre coupled model (HadCM3). Subsequently, the individual contribution of climate change and LUCC to the streamflow in the Xiying River basin was analyzed based on the simulation results, and the future dependence of streamflow on the climate change in the Xiying River basin was further obtained and discussed.
4. Conclusions
SWAT is a useful tool for evaluating the effects of climate change and land use change on the streamflow in an arid region (i.e., the Xiying River basin) when combined with a hydrological statistical method. Although land use changes in the Xiying River basin were minor during past years due to high altitudes, the effects of climate change and LUCC on streamflow are quantitatively analyzed. The findings from this work can be applied to distinguish the effects of climate change and land use change on streamflow in other high elevation mountainous areas with similar hydro-physiographic constraints, providing a feasible method for studies considering global climate change or extreme events which could happen in the future. As described in the paper, streamflow will decrease in the future because of an increase in precipitation and a decrease in temperature, but evapotranspiration is influenced not only by abiotic factors (i.e., soil and climate) but also by the water-use strategy of established vegetation under drought conditions; as a result, the decrease of streamflow in the future would be influenced by the change of evapotranspiration which is affected by plant water-use efficiencies if there is a large change of land use. Meanwhile, the spatial distribution of elements such as evapotranspiration and soil water could be analyzed when necessary. Therefore, it would be important to understand how factors in water circulation system impact an ecosystem. Nevertheless, this study is still useful as it gives an indication of streamflow sensitivity to climate changes in the Xiying River basin.
However, uncertainties still exist when just using HadCM3 to analyze the effects of climate change on streamflow. In a future study, the use of multiple climate model projections should be strengthened for uncertainty analysis. Additionally, it also needs to be noticed that input data can introduce uncertainties into SWAT simulations, especially the inadequate information of climate caused by uneven distribution of meteorological stations in the studied area which possesses a special topography. Meanwhile, there are ten layers of soil in the soil module of SWAT, which was designed for North America, but the attributes of soil data for China are not sufficiently accurate. Insufficient input data in the studied area should be further rectified. A better understanding of uncertainty analysis will be helpful for efficient use of hydrological models to thoroughly understand the problem of streamflow under climate change or other extreme events. Finally, conclusions are drawn as follows:
The five most sensitive parameters were obtained as ALPHA_BF, GW_DELAY, GWQMN, CN2, and CH_K2 according to the sensitivity analysis, and values of the five parameters were determined for the following SWAT simulations.
Changes in precipitation and temperature strongly impact the streamflow in the Xiying River basin. In the period from 1990–2008, the streamflow was dominated by climate change which led to a 102.8% increase, whereas LUCC produced a decrease of 2.8%.
In the future period of 2010–2039, the mean annual streamflow will decrease by 5.4% and 4.5% compared with the data from 1961–1990 under scenarios A2 and B2, respectively, whereas it will decrease by 21.2% and 16.9% in the period between 2040–2069, respectively.