3 machine learning methods (OLS, DT and DNN) were applied to generate a new ensemble dataset. The new precipitation (temperature) ensemble dataset is more accurate with the R=0.81 (0.99). The analyses indicate that Europe and North America contribute more to global warming than Oceania, Africa and South America. The global continent break through 1.5 °C, 2 °C and 3 °C warming target in 2024, 2031 and 2048, meanwhile the precipitation will be accelerated polarization under SSP5-8.5.
To bridge the gap between the global climate model (GCM) and local climate variables, a spatiotemporally distributed downscaling model was developed. The method was applied to the Poyang Lake watershed to analyze the precipitation changes in the 21th century. The results showed increasing heterogeneities in temporal and spatial distribution under future climate warming. Analyses with temperature increases showed that precipitation changes appeared significantly correlated to climate warming.
To solve the problem of estimating and verifying stream flow without direct observation data, we estimated stream flow in ungauged zones by coupling a hydrological model with a hydrodynamic model, using the Poyang Lake Basin as a test case. To simulate the stream flow of the ungauged zone, we built a SWAT model for the ungauged zone and verified the ungauged stream flow by comparing the two lake model scenarios with and without considering the ungauged stream flow.
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Leibniz University Hannover
Institute of Photogrammetry and GeoInformation
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