This study analyses snow cover evolution in mountainous forested terrain based on 2 m resolution simulations from a process-based model. We show that snow accumulation patterns are controlled by canopy structure, but topographic shading modulates the timing of melt onset, and variability in weather can cause snow accumulation and melt patterns to vary between years. These findings advance our ability to predict how snow regimes will react to rising temperatures and forest disturbances.

Spatiotemporally consistent high-resolution precipitation data on climate are needed for climate change impact assessments, but obtaining these data is challenging for areas with complex topography. We present a model that generates synthetic gridded daily precipitation data at a 1 km spatial resolution using observed meteorological station data as input, thereby providing data where historical observations are unavailable. We evaluate this model for a mountainous region in the European Alps.

We developed predictive semi-distributed rainfall–runoff models for nested sub-catchments in the upper Ping basin, which yielded better or similar performance compared to calibrated lumped models. The normalised difference infrared index proves to be an effective proxy for distributed root zone moisture capacity over sub-catchments and is well correlated with the percentage of evergreen forest. In validation, soil moisture simulations appeared to be highly correlated with the soil wetness index.

The Arctic is warming quickly and influencing lake water balances. We used water isotope concentrations taken from samples of 25 lakes in the Canadian Arctic and estimated the average ratio of evaporation to inflow (E / I) for each lake. The ratio of watershed area (the area that flows into the lake) to lake area (WA / LA) strongly predicted E / I, as lakes with relatively smaller watersheds received less inflow. The WA / LA could be used to predict the vulnerability of Arctic lakes to future change.

This study surveys small lakes and reservoirs, which are vital resources in the Sahel, through a multi-sensor satellite approach. Water height changes compared to evaporation losses in dry seasons highlight anthropogenic withdrawals and water supplies due to river and groundwater connections. Some reservoirs display weak withdrawals, suggesting low usage may be due to security issues. The satellite-derived water balance thus proved effective in estimating water resources in semi-arid areas.

We applied participatory methods to create a hybrid equation-based Bayesian network (BN) model to increase stakeholder understanding of catchment-scale resilience to the impacts of both climatic and socio-economic stressors to a 2050 time horizon. Our holistic systems-thinking approach enabled stakeholders to gain new perspectives on how future scenarios may influence their specific sectors and how their sector impacted other sectors and environmental conditions within the catchment system.

Cave monitoring studies including cave drip water are unique opportunities to sample water which has percolated through the soil and rock. The change in drip water chemistry is resolved over the course of 16 months, inferring seasonal and hydrological variations in soil and karst processes at the water–air and water–rock interface. Such data sets improve the understanding of hydrological and hydrochemical processes and ultimately advance the interpretation of geochemical stalagmite records.

Accurate elevation data is essential for flood risk assessment. We assess land elevation to local mean sea level of the Ayeyarwady Delta with a new, local DEM based on geodetic data and evaluate the performance of 10 global DEMs in an SLR impact assessment. Our study reveals major differences in performance between global DEMs and consequentially introduced uncertainty in SLR impact assessments, indicating potential similar uncertainties for other data-poor coastal lowlands around the world.