prime-HYD - High Mountain Asian HYDrological variability
Atmospheric Science
Final Report Abstract
The prime-HYD ("High Asian HYDrological Variability") project was sponsored as an additional component of two already ongoing research initiatives on High Mountain Asia (HMA): prime-RS and prime-SG. The research is oriented towards the analysis of precipitation, and its application to the estimation of surface runoff and river flows. Precipitation is one of the most important climatic variables, linking complex atmospheric processes with the water, snow and glacier ice balance. Precipitation is also a key parameter for the management of water resources and the mitigation of natural disasters such as floods and drought. This is also true for the chosen study region of the prime collaborative project with its geographical focus on HMA and the Tibetan Plateau (TP) in particular. The prime research project aims to produce and validate improved gridded precipitation datasets based on new remote sensing data, and advanced atmospheric models with a regional focus. The joint project also investigates spatial and temporal pattern, region-specific large-scale drivers, and meso-to local-scale processes that control precipitation variability. The improved accuracy in precipitation estimation and the increased knowledge of precipitation events and their variability should improve the understanding of spatial and temporal changes of the hydrological cycle, including ice mass balances, seasonal variations in snow cover and surface water storage. The prime-HYD subproject specifically addresses the hydrological impacts of precipitation and runoff simulation in data-poor regions and produces a retrospective analysis of runoff for the upper Brahmaputra basin using a hydrological model, which is driven by atmospheric reanalysis, satellite measurements of precipitation and a limited number of ground measurement records, that are respectively weighted and merged through a Bayesian processor of uncertainty. For the selected HMA study site, discharges simulated from reanalysis and climate data records score lowest against observations at selected river gauging stations, whereas high-resolution satellite estimates perform better, but are still outperformed by precipitation fields obtained from analyzed precipitation and by merged products corrected on ground observations. Optimal results are obtained through Bayesian combination of a whole ensemble of precipitation estimation products.
Publications
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Intercomparison of Gridded Precipitation Datasets over a Sub-Region of the Central Himalaya and the Southwestern Tibetan Plateau. Water, 12(11), 3271.
Hamm, Alexandra; Arndt, Anselm; Kolbe, Christine; Wang, Xun; Thies, Boris; Boyko, Oleksiy; Reggiani, Paolo; Scherer, Dieter; Bendix, Jörg & Schneider, Christoph
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Post-processing climate projections of precipitation for the Po river basin: will Italy's North become water-constrained?. Hydrology Research, 53(11), 1414-1427.
Boyko, Oleksiy; Reggiani, Paolo & Todini, Ezio
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Towards Informed Water Resources Planning and Management. Hydrology, 9(8), 136.
Reggiani, Paolo; Talbi, Amal & Todini, Ezio
