Multi‐Scale Hydrologic Evaluation of the National Water Model Streamflow Data Assimilation

Abstract

Based on the multi-scale evaluation at 70 Iowa locations, the National Water Model streamflow data assimilation leads to improved downstream predictions, compared to open-loop and persistence methods.ABSTRACT: Streamflow predictions derived from a hydrologic model are subjected to many sources of errors, including uncertainties in meteorological inputs, representation of physical processes, and model parameters. To reduce the effects of these uncertainties and thus improve the accuracy of model prediction, the United States (U.S.) National Water Model (NWM) incorporates streamflow observations in the modeling framework and updates model-simulated values using the observed ones. This updating procedure is called streamflow data assimilation (DA). This study evaluates the prediction performance of streamflow DA realized in the NWM. We implemented the model using WRF-Hydro ® with the NWM modeling elements and assimilated 15-min streamflow data into the model, observed during 2016-2018 at 140 U.S. Geological Survey stream gauge stations in Iowa. In its current DA scheme, known as "nudging," the assimilation effect is propagated downstream only, which allows us to assess the performance of streamflow predictions generated at 70 downstream stations in the study domain. These 70 locations cover basins of a range of scales, thus enabling a multi-scale hydrologic evaluation by inspecting annual total volume, peak discharge magnitude and timing, and an overall performance indicator represented by the Kling-Gupta efficiency. The evaluation results show that DA improves the prediction skill significantly, compared to open-loop simulation, and the improvements increase with areal coverage of upstream assimilation points.

Land Data Assimilation: Harmonizing Theory and Data in Land Surface Process Studies

“…However, there have been very few attempts to directly assimilate in situ streamflow observations. For example, streamflow observations were assimilated to update upstream river water storage and improve downstream streamflow forecasting (S. F. Liu et al., 2012; Seo et al., 2021; Y. Zhang et al., 2017). In these applications, the densities of observations upstream dramatically affect the DA performance.…”

Recent Advances and New Frontiers in Riverine and Coastal Flood Modeling

“…To account for the uncertainties involved in the hydrologic model and improve NWM flood forecasting skills, streamflow observations are operationally assimilated into the WRF-Hydro hydrological model. Streamflow DA in the NWM improves model simulation and forecasting of initial conditions by correcting modeled streamflow using observations at gauging stations (B.-C. Seo et al, 2021). While past efforts have proven that streamflow DA and multivariate assimilation of streamflow and other hydrological fluxes are significantly useful, operational flood forecasting systems do not often use DA (R. E. Emerton et al, 2016) due to their sophistication and implementation process.…”

Comparison of Evapotranspiration from the National Water Model Retrospective Analysis with Remotely Sensed Estimates from OpenET

“…Multiple studies have evaluated the NWM in a wide range of research and applications. These include streamflow (Seo et al 2021;Hansen et al 2019), water management operations (Viterbo et al 2020) and snowpack simulation (Garousi-Nejad and Tarboton 2022). In a recent study conducted by Abdelkader et al (2023) to assess the NWM's streamflow retrospective version 2.1 dataset for the entire CONUS, favorable agreement was found between the NWM and observed streamflow in catchments with natural flow.…”