St. Petersburg University Scientists Develop Model to Predict Water Levels in Remote Mountain Lakes
Researchers at St. Petersburg State University have developed MoLeFo, a physics-based mathematical model that predicts daily water levels in remote mountain lakes where no monitoring stations exist. Tests on Lake Tamozhennoye in Russia's Altai Mountains showed an average error of no more than 3%.
The model relies on three data sources: air temperature from the nearest government weather station, precipitation from IMERG satellite-based reanalysis, and solar radiation from climate reference datasets. Notably, the satellite-derived precipitation data proved more accurate than measurements from ground-based weather stations because the global model accounts for complex mountain terrain and integrates observations from multiple locations.
Failures of mountain lakes can trigger catastrophic debris flows and flooding. The research was supported by a grant from the Russian Science Foundation, and the results were published in the Vestnik of St. Petersburg State University.
