Ensemble Streamflow Forecasting Using an Energy Balance Snowmelt Model Coupled to a Distributed Hydrologic Model with Assimilation of Snow and Streamflow Observations. Issue 12 (17th December 2019)

• Record Type: Journal Article
• Authors: Gichamo, Tseganeh Z.; Tarboton, David G.
• Is Part Of: Water resources research. Volume 55:Issue 12(2019)
• Page Start: 10813
• Page End: 10838
• Publication Date: 2019-12-17
• Subjects: ensemble streamflow forecast -- Utah Energy Balance (UEB) snowmelt model -- Research Distributed Hydrologic Model (RDHM) -- data assimilation -- ensemble Kalman filter (EnKF) -- particle filter (PF); Hydrology -- Periodicals; 333.91
• DOI: 10.1029/2019WR025472
• Languages: English
• ISSNs: 0043-1397

Abstract

Abstract: In many river basins across the world, snowmelt is an important source of streamflow. However, detailed snowmelt modeling is hampered by limited input data and uncertainty arising from inadequate model structure and parametrization. Data assimilation that updates model states based on observations, reduces uncertainty and improves streamflow forecasts. In this study, we evaluated the Utah Energy Balance (UEB) snowmelt model coupled to the Sacramento Soil Moisture Accounting (SAC‐SMA) and rutpix7 stream routing models, integrated within the Research Distributed Hydrologic Model (RDHM) framework for streamflow forecasting. We implemented an ensemble Kalman filter for assimilation of snow water equivalent (SWE) observations in UEB and a particle filter for assimilation of streamflow to update the SAC‐SMA and rutpix7 states. Using leave one out validation, it was shown that the modeled SWE at a location where observations were excluded from data assimilation was improved through assimilation of data from other stations, suggesting that assimilation of sparse observations of SWE has the potential to improve the distributed modeling of SWE over watershed grid cells. In addition, the spatially distributed snow data assimilation improved streamflow forecasts and the forecast volume error was reduced. On the other hand, the assimilation of streamflow observations did not provide additional forecast improvement over that achieved by the SWE assimilation for seasonal forecast volume likely due to there being little information content in streamflow at the forecast date prior to its rising during the melt period and this application of particle filter being better suited for shorter timescales. Key Points: A spatially distributed snow data assimilation approach for an energy balance snowmelt model coupled to distributed runoff model Assimilation of sparse SNOTEL observations improved snow water equivalent at points where observations were excluded Seasonal streamflow forecast volumes predicted on 1 April were improved by snow data assimilation