This study estimated the water balance of Lake Titicaca using an integrated modeling framework that considers natural hydrological processes and net irrigation consumption. The proposed approach was implemented at a daily scale for a period of 35 years. This framework is able to simulate lake water levels with good accuracy over a wide range of hydroclimatic conditions. The findings demonstrate that a simple representation of hydrological processes is suitable for use in poorly gauged regions.

While global annual glacier velocities are openly accessible, sub-annual velocity time series are still lacking. This hinders our ability to understand flow processes and the integration of these observations in numerical models. We introduce an open source Python package called TICOI to fuses multi-temporal and multi-sensor image-pair velocities produced by different processing chains to produce standardized sub-annual velocity products.

Understanding snow accumulation is important for water resource management, but measurements of snow depth in mountainous regions are sparse. We introduce a novel satellite-based approach to estimate snow depth for deep snow in mountainous regions by combining two types of satellite data: radar images and laser surface height measurements. Results suggest that our method more accurately estimate the magnitude of snowfall compared to modelled data over the Southern Norwegian Mountains.

Avalanches contribute to increasing the accumulation on mountain glaciers by redistributing snow from surrounding mountains slopes. Here we quantified the contribution of avalanches to the mass balance of Argentière Glacier in the French Alps, by combining satellite and field observations to model the glacier dynamics. We show that the contribution of avalanches locally increases the accumulation by 60–70 % and that accounting for this effect results in less ice loss by the end of the century.

Inspired by a previous intercomparison framework, our study better constrains uncertainties in glacier evolution using an innovative method to validate Bayesian calibration. Upernavik Isstrøm, one of Greenland's largest glaciers, has lost significant mass since 1985. By integrating observational data, climate models, human emissions, and internal model parameters, we project its evolution until 2100. We show that future human emissions are the main source of uncertainty in 2100, making up half.