prime-HYD - High Mountain Asian HYDrological variability

• Applicant: Professor Paolo Reggiani, Ph.D.
• Subject Area: Hydrogeology, Hydrology, Limnology, Urban Water Management, Water Chemistry, Integrated Water Resources Management ; Atmospheric Science
• Term: from 2017 to 2023
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 367416348

Precipitation is one of the most important climate elements linking complex atmospheric processes with the hydrological cycle, snow cover and mass balances of glaciers and ice caps. Precipitation is also a key variable for water resource management and natural disaster prevention and mitigation such as floods and droughts. This holds particularly true for the study region of the proposed project bundle PRIME, which encompasses High Mountain Asia (HMA), i.e., the Tibetan Plateau (TP) and its surrounding high-mountain ranges. Research within PRIME aims at generating and validating improved gridded precipitation data sets relying on new remote sensing (RS) information and advanced limited-area atmospheric modeling for the HMA, known as HAR* (i). The project bundle subsequently investigates spatial and temporal patterns, region-specific large-scale drivers and meso- to local-scale processes controlling precipitation variability (ii). The improved accuracy and enhanced knowledge on precipitation type and variability enhances the understanding of spatial and temporal variations of the regional hydrological cycle, including ice mass balances, seasonal snow cover fluctuations and surface water storage in different sub-regions of High Asia (iii). The sub-project PRIME-HYD in particular focusses on the surface water cycle and how it is affected by precipitation and temperature variability. To this end two test bed basins in HMA are selected: the endorheic Pangong lake basin (1) at and the Upper Brahmaputra basin (2), both originating in close proximity on the TP. A distributed hydrological model is set up for both basins which includes an ice and snow simulation component. The model is driven by a downscaled probabilistic precipitation product that is obtained through combined Bayesian processing of RS precipitation and limited area atmospheric model output, conditioned on ground observations. RS temperature and 2m air temperature from HAR* simulations, which is necessary for ice and snowpack simulations, will be processed analogously. Once the hydrologic model has been calibrated and validated in terms of snow cover extension and flow rates over a selected time window, it is used to study the propagation of sub-decadal scale atmospheric forcing variability through the surface water cycle and its effects on river flow rates and lake level dynamics on the TP. The hydrologic signals will be analyzed in the frequency domain to detect potential variability patterns. Importantly, the study also delivers one of the first ground-validated hydrological models for to date scientifically underexplored drainage basins on the TP. The ground observations will be obtained through association of the Chinese Ministry of Water Resources with the project.

• DFG Programme: Research Grants