Influence of snow spatial variability on cosmic ray neutron snow water equivalent (SWE): case study in a northern prairie

Abstract. Monitoring prairie snow is difficult due to its extreme spatial variability from windy conditions, gentle topography, and low tree cover. Previous work has shown that a non-invasive, aboveground cosmic ray neutron sensor (CRNS) placed at the Central Agricultural Research Center (CARC; 47.07° N, 109.95° W), an agricultural research site within a semi-arid prairie environment managed by Montana State University, was sensitive to both the low snow amounts and spatial variability of prairie snow. In this study, we build upon previous work to understand how different snow distributions would have influenced CRNS measurements at the CARC. Specifically, we compared the changes in neutron counts and snow water equivalent (SWE) after relocating our CRNS probe at the CARC using the Ultra Rapid Neutron-Only Simulation (URANOS) and comparing them to uniform snow distributions. Neutron counts from simulations with a shallow, heterogeneous snowpack were higher compared to neutron counts from simulations with a uniform snowpack. While areas of higher snow accumulation reduced neutron counts, the low SWE made it difficult to discern a consistent relationship between SWE and neutron counts. Despite this, our analysis indicates that a naive CRNS placement was 2 to 5 times more likely to yield representative SWE estimates compared to a similarly placed snow scale. CRNS showed better agreement with lidar-derived SWE at our prairie site compared to several gridded snow products. We show CRNS can provide valuable information about shallow, heterogeneous snowpacks in prairie and other environments and can benefit future missions from UAV and satellite platforms.