Abstract
This study proposes a framework that (i) uses data assimilation as a post processing technique to increase the accuracy of water depth prediction, (ii) updates streamflow generated by the National Water Model (NWM), and (iii) proposes a scope for updating the initial condition of continental-scale hydrologic models. Predicted flows by the NWM for each stream were converted to the water depth using the Height Above Nearest Drainage (HAND) method. The water level measurements from the Iowa Flood Inundation System (a test bed sensor network in this study) were converted to water depths and then assimilated into the HAND model using the ensemble Kalman filter (EnKF). The results showed that after assimilating the water depth using the EnKF, for a flood event during 2015, the normalized root mean square error was reduced by 0.50 m (51%) for training tributaries. Comparison of the updated modeled water stage values with observations at testing locations showed that the proposed methodology was also effective on the tributaries with no observations. The overall error reduced from 0.89 m to 0.44 m for testing tributaries. The updated depths were then converted to streamflow using rating curves generated by the HAND model. The error between updated flows and observations at United States Geological Survey (USGS) station at Squaw Creek decreased by 35%. For future work, updated streamflows could also be used to dynamically update initial conditions in the continental-scale National Water Model.
Highlights
Flooding is among the most destructive natural disasters globally
The assimilation of water depth measurements, as a post-processing technique, has the potential to reduce the error between model predictions and observations in order to generate more reliable flood inundation maps [9]
The National Water Model (NWM) was released by the National Weather Service (NWS) Office of Water Prediction (OWP) in collaboration with the National Center for Atmospheric
Summary
Flooding is among the most destructive natural disasters globally. In the United States, based on a U.S National Weather Service (NWS) report, the average annual property/human losses are estimated to be more than $8 billion [1,2,3] (Federal Emergency Management Agency (FEMA), 2013). The assimilation of water depth measurements, as a post-processing technique, has the potential to reduce the error between model predictions and observations in order to generate more reliable flood inundation maps [9]. The Kalman filter [13] is a commonly used data assimilation technique that was initially developed to update the state variables of linear systems [14]. This method has been used for nonlinear problems as well [15]. Proposing a scope to update continental-scale hydrologic models (e.g., NWM)
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