Abstract ID: 3.13146 | Not reviewed | Requested as: Talk | TBA | TBA

Research on water resources in high mountain regions often faces the challenge of integrating scarce and diverse data sets. For my PhD, I combine field observations with global and regional model-based and remote sensing datasets to simulate glacio-hydrological processes in Central Asia from the 1980s to 2100. My glacio-hydrological model is forced with reanalysis data and downscaled GCM projections, while calibration relies on a variety of glaciological and hydrological datasets, including integrated point measurements, UAV- and satellite-based geodetic mass balances, and discharge estimates from different methods.
The available data come from a variety of sources, including Soviet-era records, fragmented post-independence datasets, and measurements from my own field campaigns in Kyrgyzstan, Uzbekistan, and Mongolia. These datasets vary widely in temporal coverage, measurement techniques, and technical standards, leading to significant uncertainties.
This talk will highlight the importance of field observations for reducing uncertainties in large-scale model inputs, while also discussing how meso- and macro-scale models are sensitive to uncertainties in observational data. Examples from several study sites in the Tian Shan will illustrate these challenges and provide a basis for discussion on how to effectively integrate field data in modeling high mountain hydrology.