Impacts Of Climatic Variability To Distributed Snow Observations In A Cold Region Agricultural Basin

Seasonal dry and wet periods have been more frequently observed in the Northern Great Plains (NGP). Generally, in the NGP, short-term dry conditions are followed by long-term fluctuating wet periods. A recent NGP drought (1999‐2004) was followed in 2005 by a wet period. The impacts of dry and wet climate on snow processes and streamflow generations are poorly understood due to lack of field-based snow accumulation data. Most studies are limited to remotely sensed snow cover estimates or numerical modeling with very limited snow data. To gain an improved understanding of the hydrological processes to climatic variability in the NGP, a series of detailed snow surveys were conducted at distributed locations (ten locations) in the Mauvais Coulee Basin (MCB, a 1032 km2 headwater basin draining to Devils Lake) draining to Devils Lake during the winters of 2017-2023. A Metric Prairie Snow Sampler (designed after the Environment Canada ESC 30) was used to estimate snow water equivalent (SWE) and snow depth. Snow samples were also weighed for calculation of SWE using gravimetric approach. The wettest year was 2017 with an average SWE of 69mm resulting in an outlet peak streamflow of 73 m3 s-1 (spring). In contrast, 2021 was dry, with only traces of SWE and a streamflow of 1 m3 s-1. The influence of land management practices (via remotely sensed tillage index), topography, and climatic parameters on snow accumulation were investigated. The relationship between tillage index and SWE is stronger in the wet winters while the SWE varies with elevation (from North to South). air temperature and snowfall were most influential factors exerting controls on SWE. Finally, the SWE was found to be an influential factor for generating large streamflow volume in most of the years during the study period. Snow observation data from the current study can be used for more applied engineering practices including forecast future flooding events, climate change scenarios, or land use practices. Furthermore, as water is in high demand, snow observation data can be used to quantify snowmelt contributions to surface water tributaries and underlying aquifers. Snow observation data can be modeled to develop long term trends providing runoff data that can be used for drinking water, irrigation, stormwater management and updates, and flood control structures.

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