Regression Downscaling of Coarse Resolution Globsnow Snow Water Equivalent Estimates in the Red River Basin

The spatial and temporal heterogeneity of seasonal snow and its impact on socio-economic and environmental functionality make accurate, real-time estimates of snow water equivalent (SWE) important for hydrological and climatological predictions. Remote sensing techniques facilitate a cost effective, temporally and spatiallyconsistent approach to SWE monitoring in areas where insitu measurements are notsufficient. Passive microwave remote sensing has been used to successfully estimate SWE globally by measuring the microwave attenuation from the Earth’s surface as a function of SWE. However, passive microwave derived SWE estimates at local scales are subject to large errors given the coarse spatial resolution of observations (~625 km²).Regression downscaling techniques can be implemented to increase the spatial resolution of gridded datasets with the use of related auxiliary datasets at a finer spatial resolution. These techniques have been successfully implemented to remote sensing datasets such as soil moisture estimates, however, limited work has applied such techniques to snow-related datasets.This study focuses on assessing the feasibility of using regression downscaling to increase the spatial resolution of the European Space Agency’s (ESA) Globsnow SWE product in the Red River basin, an agriculturally important region of the northern United States.

Prior to downscaling Globsnow SWE, three regression downscaling techniques (Multiple Linear Regression, Random Forest Regression and Geographically Weighted Regression) were assessed in an internal experiment using 1 km grid scale Snow Data Assimilation System (SNODAS) SWE estimates, developed by the National Weather Service’s National Operational Hydrological Remote Sensing Center (NOHRSC). SNODAS SWE estimates for 5-year period between 2013-2018 were linearly aggregated to a 25 km grid scale to match the Globsnow spatial resolution. Three regression downscaling techniques were implemented along with correlative datasets available at the 1 km grid scale to downscale the aggregated SNODAS data back to the original 1 km grid scale spatial resolution. When compared with the original SNODAS SWE estimates, the downscaled SWE estimates from the Random Forest Regression performed the best. Random Forest Regression Downscaling was then implemented on the original Globsnow SWE data for the same time period, as well as a corrected Globsnow SWE dataset. The downscaled SWE results from both the corrected and uncorrected Globsnow data were validated using the original SNODAS SWE estimates as well as in situ SWE measurements from a set of 40-45 (depending on the season) weather stations within the study region. Spatial and temporal error distributions were assessed through both validation datasets. The downscaled results from the corrected Globsnow dataset showed similar overall statistics to the original SNODAS SWE estimates, performing better than the downscaled results from the uncorrected Globsnow SWE dataset. The overall aim of this study is to assess the applicability of regression downscaling as a reliable and reproducible method for local scale SWE estimation in areas where finer resolution data such as SNODAS does not exist. Therefore, the goal is to reproduce the optimal regression downscaling procedure in an area other snow dominated regions across the globe using in situ snow transect data for validation.