Hybrid Approach for Estimating Snow Water Equivalent in Siberian Basins Using GRACE and Climate Data

Snow Water Equivalent (SWE) is a vital measure for understanding the hydrology and climate of snow-covered regions, particularly in Siberia. Siberian river basins play an important role in managing freshwater fluxes to the Arctic Ocean, which influences global climate systems and regional hydrological extremes. However, accurate SWE prediction in Siberia is hindered by sparse observational networks, limitations in standard hydrological models, and errors in remote sensing data. To overcome these issues, this study proposes a novel two-part model that incorporates GRACE satellite observations and meteorological data to estimate SWE. The study concentrates on the Siberian river basins of the Yenisei, Ob, Kolyma, Amur, and Lena. The model's first component employs GRACE mascon data to calculate snow mass changes, providing an independent, observation-based method. The second component estimates snow mass based on precipitation and temperature datasets. A Kalman filter structure then incorporates these two data sources, further improving temporal resolution and mitigating uncertainty. Validation against numerous datasets, including in-situ data and hydrological models (GLDAS NOAH, VIC, WGHM, and CLSM), as well as GlobSnow, validates the proposed methodology's resilience. The study used 382 in-situ stations throughout the Siberian region. The results demonstrate significant agreement with all models; NSE values for all models exceed 0.78, with the exception of the VIC model, which has a higher amplitude than the other models. The in-situ data mean for the DJF and MAM seasons is highly consistent with the hybrid new model, with positive values in Kolyma. The total trend in the Yenisei basin is - 0.46±0.35 mm/year and - 0.40±0.31 mm/year for the in-situ and hybrid models, respectively. The Amor basin has the least amount of SWE compared to the other basins because its average temperature is greater. This hybrid technique improves SWE estimation while also providing insights into the region's hydrological dynamics and climatic feedbacks.

Keywords: GRACE, Snow, Hydrology, Climate change, Hydrological models, In-situ.