Shan ZOU defended successfully her PhD


On 29th November 2021, Shan Zou successfully defended her PhD "Research on water resources change under climate change and human activities in Syr Darya River Basin, Central Asia". She obtained the double degree of Doctor in Sciences: Geography (UGent) and Doctorate of Science: Cartography and Geographic Information System (XIEG-CAS, UCAS). Supervisors were Prof. Tim Van de Voorde and Prof. Philippe De Maeyer, both from the Department of Geography (UGent), and Prof. Jilili Abuduwaili from XIEG (Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences). With the increasing global climate change and human activities, the quantity, quality and spatial distribution of water resources in Central Asia have undergone significant changes with more extreme hydrometeorological events, which have exacerbated the uncertainty of water resources (especially for the typical transboundary rivers in Central Asia e.g. Syr Darya River). Based on the hydrological and meteorological data, water consumption and socio-economic development data, this study first addresses the spatial-temporal distribution analysis of regional climate change in Central Asia, indicating a significant upward trend in both precipitation and temperature in Central Asia . Next, the study analyzes climate change and examines the correlation between temperature, precipitation and runoff in the Syr Darya River basin. Finally, the study constructs a SWAT distributed hydrological model based on an improved glacier module and then simulates future water resources in the upper Syr River basin by using Phase 6 (CMIP6) data from the Coupled Model Intercomparison Project (CMIP6). Overall, the results can provide an in-depth understanding of climate change in Central Asia and changes in the water resources of the Syr Darya River basin, which is of great importance for the protection and improvement of water management in Central Asia. Moreover, by combining the SWAT_Glacier model and the data from different sources, the improved methods prove to be very useful for the simulation of water resources in Central Asian regions with sparse data.