UMRB Data Regression Analysis to Improve Hydrologic Model Performance
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 was led by the U.S. Army Corps of Engineers (USACE) and funded by NIDIS as part of the UMRB Data Value Study. The project explored the potential usefulness of in situ soil moisture data for setting parameters for and training a model used to forecast streamflow and runoff.
The USACE Missouri River Water Management Division owns and operates multiple reservoirs within the UMRB. To operate these reservoirs, USACE relies on hydrological modeling software to generate runoff and streamflow forecasts from rainfall events. The USACE’s rainfall runoff generation forecasts are based on the Hydrologic Engineering Center–Hydrologic Modeling System (HEC-HMS).
This project explored using UMRB in situ soil moisture data to set parameters for and train modeled soil conditions in HEC-HMS. To do this, the researchers used data from soil moisture sensors recently added to the UMRB region as part of the USACE-led mesonet station build-out across Montana, Nebraska, North Dakota, South Dakota, and Wyoming.
For 10 instrumented sub-basins in the UMRB, the researchers investigated the relationship between soil moisture from UMRB stations and modeled soil conditions from HEC-HMS. Even with only a small set of new soil moisture sensors with limited data records available, the researchers found a promising correlation. Currently, USACE hydrologic models do not allow for direct inputs of in situ soil moisture data, but hydrologists can use soil moisture observations to calibrate the models before they run. The correlation demonstrated by this analysis indicates that in situ data could potentially be used to automate future adjustments and calibrations to modeled soil conditions. Switching from a manual to a more automated process to set model parameters would speed up generation of real-time runoff forecasts and improve forecasting capabilities.
For more information, please contact Elise Osenga (elise.osenga@noaa.gov).
Research Snapshot
Matthew Nelson, U.S. Army Corps of Engineers Omaha District Hydrology Section
Zachary Haddix, Rachel Schulz, and Sadie Reinig, U.S. Army Corps of Engineers Omaha District Hydrology Section
Results of This Research
- This analysis provides evidence that in situ soil moisture data and HEC-HMS modeled soil moisture show sufficient correlation to use for calibration and adjustment of modeled soil parameters in the UMRB.
- Using soil moisture to set model parameters (initialize) and train HEC-HMS rainfall runoff forecasts reduced the time necessary to calibrate the model, allowing for a more rapid production of these forecasts.
