Estimating snow water equivalent using unmanned aerial vehicles for determining snow-melt runoff

•We propose a method for rapid, high-resolution estimation of snow water equivalent.•Our method employs unmanned aerial vehicles and spatial statistics.•The method is validated in the field in the Kwisa river basin in southwestern Poland.•Snow water equivalent is balanced with reconstructed snow-melt runoff.•Drone-based and reconstructed snow-melt runoff differ by −11.2, +1.6 and +18.9%. Although global warming decreases winter snow storage and snow-melt peak discharges in the majority of basins in warm regions, there exist specific catchments in cold regions where climate change increases the risk of snow-melt floods in winters and earlier-than-usual springs. The volume of water contained in a snowpack is quantified by snow water equivalent, the near-real-time estimation of which is essential to issue early warnings against snow-melt floods. We propose a new approach for rapid and high-resolution estimation of snow water equivalent for small mountainous basins, employing unmanned aerial vehicles known as drones. Numerical maps of snow water equivalent are automatically produced for subareas of a river basin by combining drone-based snow depth maps with snow density estimates. The reconstructions for subareas are extrapolated to the entire river basin using a zonal version of multiple linear regression. Validation is conducted on the example of the Kwisa river in southwestern Poland by balancing drone-based snow water equivalent with snow-melt runoff separated using a hydrologic model from discharges continuously measured during a thawing period. Differences between the drone-based and reconstructed snow-melt runoff are found to be of −11.2, +1.6 and +18.9%, providing evidence for the considerable worth skills of the new drone-based flood risk assessment method.