Our study presents a method to estimate high-resolution snow water equivalent (HR-SWE) using low-resolution climate data (LR-CD). By using a data-driven approach, we analyze historical weather patterns from LR-CD to generate HR-SWE maps. Machine learning and statistical relationships between LR-CD and HR-SWE enable estimation for dates without HR-SWE data. This method enhances water resource management and climate impact assessments, especially in data-scarce regions.

Using a newly introduced inverse hydrological modeling framework, we demonstrate that streamflow observations have the potential to improve snow mass reconstructions, but that non-uniqueness in the snow-streamflow relationship and uncertainties in the inverse modeling chain can easily stand in the way. We also show that streamflow is most helpful in estimating catchment-aggregated properties of snow mass reconstructions, in particular catchment-aggregated melt rates.

The remote-sensing InSAR technique is vital for monitoring slope instabilities but requires understanding. This paper delves into differences between satellite and GB-InSAR. It offers a tool to determine the optimal GB-InSAR installation site, considering various technical, meteorological, and topographical factors. By generating detailed maps and simulating radar image characteristics, the tool eases the setup of monitoring campaigns for effective and accurate ground movement tracking.

We aim to combine multiple global climate models (GCMs) to enhance the robustness of future projections. We introduce a novel approach, called "α pooling", aggregating the cumulative distribution functions (CDFs) of the models into a CDF more aligned with historical data. The new CDFs allow us to perform bias adjustment of all the raw climate simulations at once. Experiments with European temperature and precipitation demonstrate the superiority of this approach over conventional techniques.

This study assesses the impact of climate change on the timing, seasonality and magnitude of mean annual minimum (MAM) flows and annual maximum flows (AMF) in the Volta River basin (VRB). Several climate change projection data are use to simulate river flow under multiple greenhouse gas emission scenarios. Future projections show that AMF could increase with various magnitude but negligible shift in time across the VRB, while MAM could decrease with up to 14 days of delay in occurrence.