Applications of UMRB Plains Snow Data to the Snow Data Assimilation System (SNODAS)
In response to severe drought and flood events in the Upper Missouri River Basin (UMRB) between 2011 and 2019, the Infrastructure Investment and Jobs Act provided funding to improve water monitoring in the region. The UMRB Data Value Study, led by NOAA’s National Integrated Drought Information System (NIDIS), is the assessment element of this multi-component, multi-agency project.
This project, Applications of UMRB Plains Snow Data to the SNOw Data Assimilation System (SNODAS), was part of the UMRB Data Value Study. Led by RTI International and the Cooperative Institute for Research to Operations in Hydrology (CIROH), this project explored how new snowpack monitoring data from previously unmonitored locations can improve snow modeling in the UMRB.
A More Spatially Representative Picture of Snowpack
Over the past few years, the U.S. Army Corps of Engineers (USACE) has funded the buildout of new mesonet stations across the UMRB. The researchers tested incorporating snow depth observations from these new stations into the NOAA National Operational Hydrologic Remote Sensing Center’s (NOHRSC) SNOw Data Assimilation System (SNODAS). SNODAS is a key tool to estimate snow water equivalent (SWE, the water content in the snowpack) across the U.S. SWE is an important metric for water resource management, flood forecasting, and drought monitoring.
For this project, researchers assessed whether data from the newly installed UMRB snow monitoring stations enhanced SNODAS's ability to accurately represent snowpack conditions by comparing SNODAS SWE values with and without the new UMRB stations for three snow events in 2022, 2023, and 2024. The new mesonet stations do not measure SWE directly. However, observations do include snow depth, wind speed, radiation, relative humidity, and temperature—the measurements necessary to accurately estimate SWE.
For more information, please contact Elise Osenga (elise.osenga@noaa.gov).
Research Snapshot
Paul Micheletty, RTI International
Results of This Research
The research found SWE estimates from the new UMRB stations did not always correlate well with SWE from the nearest-located station in the pre-existing network. This indicates the prior, less-dense network may not have adequately represented variability in snowpack across the landscape. On-the-ground validation is still needed to confirm the accuracy of the SWE estimates from the new stations. However, the findings from this project suggest these new stations will provide more accurate information about snowpack for locations with distinct micro-climates. Denser spatial coverage can better support local decision-making around hazards and hydrologic planning.
The outcomes of this research will benefit multiple stakeholders, including NOAA’s NOHRSC team. The insights gained will help inform improvements in snow data assimilation in this region for the SNODAS model. Data from the new UMRB stations have already been integrated into SNODAS operations.
These findings underscore the value of high-density monitoring networks like the UMRB to enhance snowpack models. Future work should optimize data assimilation techniques, expand monitoring capabilities, and verify the accuracy of new station data. This study contributes to efforts to enhance water resource decision-making in the UMRB and beyond. Additionally, findings from this study may influence future investment in high-density monitoring networks and improvements to national snow modeling efforts.
Related Data & Maps
SNODAS provides estimates of snow cover, depth, snow water equivalent (SWE) and associated variables to support hydrologic modelling and analysis.
