Modeling streamflow driven by climate change in data-scarce mountainous basins

• Published in: The Science of the total environment Vol. 790; p. 148256
• Main Authors: Fan, Mengtian, Xu, Jianhua, Chen, Yaning, Li, Weihong
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 10.10.2021
• Subjects: Climate change; Data-scarce mountainous basins; Integrated modeling; Streamflow simulation; ERAT; R2; IMF; PNAI; AAOI; NAOI; RMSE; Integrated modeling; NDVI; TMPA; MAE; ARB; Climate change; MLR; Streamflow simulation; ENSOI; CEEMDAN; ESDP; KRB; AOI; RBFANN; EMD; Data-scarce mountainous basins
• ISSN: 0048-9697; 1879-1026
• DOI: 10.1016/j.scitotenv.2021.148256

The impacts of climate change on the water environment have aroused widespread concern. With global warming, mountainous basins are facing serious water supply situations. However, there are limited meteorological stations on mountains, which thus creates a challenge in terms of accurate simulation of streamflow and water resources. To solve this problem, this study developed a method to model streamflow in data-scarce mountainous basins. Selecting the two head waters originating in the Tienshan mountains, Aksu and Kaidu Rivers, we firstly reconstructed precipitation and temperature dynamics based on Earth system data products, and then integrated the radial basis function artificial neural network and complete ensemble empirical mode decomposition with adaptive noise to model streamflow. Comparison with the observed streamflow according to hydrological stations indicated that the proposed approach was highly accurate. The modeling results showed that the El-Niño Southern Oscillation, temperature, precipitation, and the North Atlantic Oscillation are the main factors driving streamflow, and the streamflow decreased in both the Aksu River and Kaidu River between 2000 and 2017. [Display omitted] •To model the streamflow driven by climate in data-scarce mountainous basins•Precipitation and temperature were reconstructed by the Earth data products.•The runoff was simulated using machine learning based on climate reconstruction.•The simulated results were verified by observed data from hydrological stations.