A macro-scale and semi-distributed monthly water balance model to predict climate change impacts in China

Abstract Climatic change has great implications for hydrological cycle and water resources planning. In order to assess this impact, a macro-scale and semi-distributed monthly water balance model was proposed and developed to simulate and predict the hydrological processes. GIS techniques were used as a tool to analyze topography, river networks, land-use, human activities, vegetation and soil characteristics. The model parameters were linked to these basin characteristics by regression and optimization methods. A parameterization scheme was developed and the model parameters were estimated for each grid element. Based on the different GCM and RCM outputs, the sensitivities of hydrology and water resources for China to global warming were studied. The proposed models are capable of producing both the magnitude and timing of runoff and water resources conditions. The semi-dry regions, such as Liaohe, Haihe, Ruanhe and Huaihe River basins in north China, The runoffs of these basins are small or even zero during dry season (from Oct. to May) and are very sensitive to temperature increase and rainfall decrease. While in the basins of the humid south China like Yangtze River basin, the runoffs are perennial and the base flow normally occupies a large portion of the total runoff volume. These humid basins are less vulnerable to climate change. Results of the study also indicated that runoff is more sensitive to variation in precipitation than to increase in temperature. Climate change challenges existing water resources management practices by additional uncertainty. Integrated water resources management will enhance the potential for adaptation to change.

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