Cloud model combined with multiple weighting methods to evaluate hydrological alteration and its contributing factors

Abstract

Understanding the hydrological alterations caused by climate change and human activities and the underlying impact factors are essential for sustainable water resource management in river basins. Several statistical methods, particularly the Indicator of Hydrologic Alteration (IHA), have been successfully employed to assess the degree of hydrologic alteration. This study proposed a new approach, the cloud model combined with the multiple weighting method (CM-MW), for better quantification of the overall degree of hydrologic alteration. This approach adopts game theory to select reasonable weights for the IHA parameters, therefore improving the accuracy and reliability of the evaluation. By estimating the similarity between the evaluation cloud and the category cloud, this method quantifies the percent data for which the overall degree of hydrological alteration (ODHA) would fall into the respective category. Analysis based on the Budyko framework indicated that in the Illinois River Basin, the dominant causes of hydrologic alteration for watersheds with moderate ODHA and low ODHA were human activities and climate change. The proposed method had a distinct advantage over the conventional approach in classifying the overall hydrological alteration, which would provide more accurate information for comprehensive and adaptive river basin management.