Abstract
Abstract Climate change and human activities have an important impact on the changing environment, leading to significant changes in the basin water cycle process. The Jialing River Basin, the largest tributary of the upper Yangtze River, is selected as the study area. Three different rainfall datasets, the China Meteorological Assimilation Driving (CMAD) dataset, the Tropical Rainfall Measuring Mission data, and gauged observation data, were used as inputs for the MIKE System Hydrological European (MIKE SHE) model. By comparing the simulation results driven by various meteorological data, the applicability of the MIKE SHE model at four stations is evaluated, and the sensitivity and uncertainty of model parameters are analyzed. Meanwhile, the impact of large hydropower stations on the runoff of the Jialing River Basin is assessed, and the influence of human activities on the runoff change is determined. The future climate change of the watershed was analyzed by using the typical representative concentration pathway (RCP) 4.5 and RCP8.5 climate scenarios. Based on the MIKE SHE model, the runoff of the Jialing River Basin in the future climate scenario is predicted, and the corresponding response of the Jialing River Basin is analyzed quantitatively. The results show that the CMAD data-driven model has better Nash–Sutcliffe efficiency and correlation coefficient for each period. By analyzing the influence of the hydropower station on the runoff process at the outlet of the basin, it is found that the hydropower station has a certain regulating effect on the runoff process at the outlet of the basin. In addition, the RCP4.5 scenario is more consistent with the future scenario, indicating that the Jialing River Basin will become colder and drier.
Highlights
Climate change triggers global temperature rises, changes in rainfall patterns, and affects the regional water cycle, as well as the hydrological situations of basins (Labat )
The objectives of this study are (1) to compare the simulation results of the MIKE SHE model driven by three precipitation products (CMADs, Tropical Rainfall Measuring Mission (TRMM), and gauged meteorological station data), evaluate the model’s applicability in the Jialing River Basin, and analyze the sensitivity and uncertainty of the model parameters
Analysis of simulation results driven by different rainfall inputs
Summary
Climate change triggers global temperature rises, changes in rainfall patterns, and affects the regional water cycle, as well as the hydrological situations of basins (Labat ). The evaluation methods of hydrological model uncertainty mainly include the Generalized Likelihood Uncertainty Estimation (GLUE) method (Beven & Binley ), the Bayesian recursive estimation method (Thieman et al ), and the Monte Carlo method (Vrugt et al ). It is always a challenge for hydrological researchers to estimate the uncertainty of distributed models effectively (Rogers et al ; Burkhart et al )
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