Response of Streamflow to Climate Changes within a Mountainous Catchment

Climate changes combined with alteration in landuse, alter the hydrological processes and has increased flood risk in urban area. A better understanding of how the climate alter streamflow in the catchment of city surrounding area would help to reduce urban flood risk. In the present study, we assessed the response of streamflow to climate changes in a mountainous catchment of city surrounding area, Dashi River catchment, China. The Distributed Hydrology-Soil-Vegetation Model (DHSVM) was used to simulate the streamflow in different scenarios of climate and landuse changes. Results showed that: (i) DHSVM showed suitable performance in terms of calibration and validation periods; and (ii) Streamflow increased/decreased with increasing/decreasing precipitation, while decreased with increasing air temperature. And the increases or decreases in precipitation can directly lead to the significant corresponding directional changes in the streamflow, especially in summer season. The results would help to understand streamflow response to changes in climate and landuse, and help to reduce the flood risk in the urban regions.


Introduction
Climate combined with landuse changes has dramatically altered the hydrological processes (Chien et al., 2013;Niraula et al., 2015). Climate changes/variability resulting in temperature rises, changes in precipitation and altering the evapotranspiration, thereby affecting the volume, peak rate, and timing of streamflow (Niraula et al., 2015;Pourmokhtarian et al., 2016). Therefore, with the intensification of global warming, extreme hydrological events are generally showing an increasing trend (Goswami et al., 2006), which have led to extreme hydrological disasters occur frequently, e.g., flood, drought (Kourgialas et al., 2015;Zhang et al., 2015). Especially, in the catchments of city surrounding area where rapidly urbanization has increased the impermeable area and weakened the capability of water storage, that resulted in an increasing trend in extreme hydrological events and a more significant threaten to urban water security (Peng et al., 2015;Bellu et al., 2016). Consequently, our objectives are: (i) to explore the parameter sensitivity, and assess the performance of DHSVM in a small mountainous catchment of city surrounding area; and (ii) to estimate the streamflow response to climate and landuse changes scenarios in the Dashi River catchment, China. The results should help to understand the MATEC Web of Conferences 203, 07006 (2018) https://doi.org/10.1051/matecconf/201820307006 ICCOEE 2018 response of streamflow to climate changes in the catchment of city surrounding area and help to address the flood risk in the city.

Study site
Dashi River is a tributary of Juma River in the Haihe River Basin, which originates from Xiayunling of Fangshan District in Beijing, China. The total length of Dashi River is 129 km, and the catchment area is 1, 280 km 2 . Dashi River catchment is located in the southwest of Beijing. Dashi River Catchment belongs to semi-humid continental monsoon climate, which is hot and rainy in summer, cold and dry in winter. The mean annual temperature is 9 -11 °C, while the mean annual precipitation (1956 to 2004) is 587.6 mm and concentrated in summer (June to August). The upper catchment, the mountainous area above hydrological station, was selected as a study area ( Figure 1) which total area is 664 km 2 . Due to influenced by extreme hydrological event, Dashi River catchment have suffered an extraordinary rainstorm in July 21, 2012, which was the strongest rainstorm in northern China since detailed meteorological records began in 1951 and also caused the greatest number of casualties and economic losses (Su et al., 2015).

Methodology and data description
The Distributed-soil-vegetation model (DHSVM) is a physically based distributed hydrologic model developed for mountainous watersheds (Wigmosta et al., 1994), which has been widely used to assess the impacts of climate changes on hydrological processes (Wigmosta et al., 1994;Safeeq and Fares, 2012;Cuo et al., 2009;Dickerson-Lange and Mitchell, 2014). One-factor-at-a-time (OFAT) were used to analyze sensitive parameters in DHSVM while examined using Bias and root mean square error (RMSE); and the Nash-Sutcliffe efficiency coefficient (NSE) (Nash and Sutcliffe, 1970) and the coefficient of determination (R 2 ) were used to evaluate the model performance at simulating.
To construct the DHSVM in Dashi River catchment, a model database was compiled using meteorological data (e.g., precipitation, wind speed, maximum and minimum temperature) and topographical data (consisting of a digital elevation map, landuse and soil map).The Meteorological data, including precipitation, temperature, wind speed, used for DHSVM calibration and validation were taken from Xiayunling climate station (Figure 1).
The DHSVM requires the digital elevation model (DEM), soil and vegetation maps to simulate the hydrological cycle. The DEM was obtained from USGS/NASA Shuttle Radar Topography Mission

Parameter sensitivity and model validation
In the sensitivity analysis, 13 soil parameters and 20 vegetation parameters of the DHSVM underwent sensitivity analyses to determine the optimal model parameters. Among these parameters, the maximum infiltration, porosity, field capacity and wilting point are the most sensitive soil parameters; while leaf area index and vegetation height are the most sensitive vegetation parameters. DHSVM was manually calibrated taking into account daily and monthly streamflow by adjusting these sensitive parameters.
During both the calibration and validation periods, simulated daily and monthly streamflow agreed reasonably with the observed streamflow for both time steps (Figure 2  increased 10% and 20%, respectively; while it decreased about -28.69%, and -52.17% when precipitation decreased -10% and 20%, respectively. Similar results for the changes in streamflow also can be found in maximum changes of streamflow. For air temperature, streamflow will decrease with increase in air temperature due to its complex effects on hydrological processes, and interestingly sensitivity of streamflow reduced with the increase in air temperature, e.g., 1 o C warming will cause streamflow increase -2.79% in scenarios, while it just will cause an increase in streamflow with -2.01, -1.13 and -0.19%, between scenario 5 and 6, scenarios 6 and 7, and scenarios 7 and 8, respectively.

Discussion
Climate changes/ variability (e.g., temperature rises and changes in precipitation) have significant impacts on regional streamflow (Chen et al., 2006;Huo et al., 2008). The Haihe River Basin have been intensified by the decreasing precipitation and steadily declining streamflow due to climate change and increasing human activity during the past decades (Yang and Tian, 2009). Bao et al (2011) concluded the streamflow would increase as precipitation increased but would decrease as temperature increased, which is similar with our result. Results of sensitivity analysis showed that changes in streamflow are more sensitive to increase in precipitation in contrast with decrease in precipitation, e.g, streamflow can increase 32.73% or decrease 28.69% with 10% increase or decrease in precipitation. Streamflow response to changes in precipitation is consistent with results provided by Qi et al. (2009). Interestingly, increase rate of streamflow decreased with increase in air temperature, e.g., streamflow was reduced by 2.01% when 1 o C increased compared with baseline condition, while it was reduced by 1.13% when 2 o C increased compared with 1 o C increase. The reasons for that mainly lay that streamflow will be reduced by increase in evaporation when other meteorological variables keep in normal condition. In fact, changes of streamflow resulted from increasing air temperature reflected complex interaction between meteorological variable. Especially, explanations for "evaporation paradox" have enhanced the understanding of hydrological mechanism for increasing air temperature (Liu and McVicar, 2012;Szilagyi, et al., 2017).

Conclusion
This study investigated the response of streamflow to the changes in climate and landuse within a (ii) Changes of streamflow was more sensitive to increasing in precipitation than that resulted from the decrease in precipitation. For air temperature, streamflow decrease with increase in air temperature, and increase rate in streamflow presented decreasing trend with the increase in air temperature, streamflow was reduced by 2.01% when 1 o C increase compared with baseline condition, while it was reduced by 1.13% in 2 o C increase when compared with 1 o C increase.