Accurately estimating snow water equivalent (SWE) is crucial for understanding the impacts of climate change, urbanization, and population growth on water resources. High operational costs of lidar observations limit the frequency and coverage of SWE estimates at high spatial resolutions, leading to significant data gaps. We address this challenge with a transfer learning framework that leverages abundant SWE data from California to enhance predictions in Colorado, where data are scarce. From 2016 to 2019, the disparity in SWE data collection between these states was stark: 94 snowpack maps were recorded in California’s Sierra Nevada versus only 12 in Colorado’s Rocky Mountains. We hypothesized that geographic predictors (e.g., elevation and snowfall) would exhibit similar effects on SWE across these landscapes. By conducting an explanatory factor analysis, we validated this hypothesis and refined our transfer learning model, which incorporated data from California to predict SWE in Colorado. When compared with using data from Colorado alone, transfer learning improved the mean R2 value from 0.43 to 0.56, indicating a significant enhancement of over 30% in predictive accuracy. Such advancements underscore the potential of our framework to mitigate lidar data limitations, offering a valuable tool for water resource management amidst changing environmental conditions.

We numerically study how the interactions between surface, subsurface, and rainfall create complex runoff behavior in a headwater-dominated high elevation, mountainous catchment in East River Watershed, Colorado, USA. In order to understand the effect of surface--subsurface interactions on the hydrological response, we compare model variations with different soil permeability. We compute hydrographs, hydrological indices, and spatio-temporal distributions of hydrological variables. The indices and distributions are then linked to the hydrograph at the outlet of the domain. Our results show that changing surface--subsurface interactions trigger different flow regimes, connectivity dynamics, and runoff generation mechanisms inside the catchment, and hence, affect the distributed hydrological response. Increased surface--subsurface interaction leads to a higher degree of connectivity in the catchment. Temporal dynamics of both distributed and aggregated hydrological response did not change significantly, which may be because the structural connectivity of the catchment remained untouched. Our results indicate that in higher order catchments, the effect of changes in distributed hydrological response may not always be observable through aggregated hydrological signatures.