Using Steady‐State Backwater Analysis to Predict Inundated Area from National Water Model Streamflow Simulations

Abstract

National Water Model (NWM) simulates the hydrologic cycle and produces streamflow forecasts for 2.7 million reaches in the National Hydrography Dataset for continental United States (U.S.). NWM uses Muskingum–Cunge channel routing, which is based on the continuity equation. However, the momentum equation also needs to be considered to obtain more accurate estimates of streamflow and stage in rivers, especially for applications such as flood‐inundation mapping. Here, we used a steady‐state backwater version of Simulation Program for River NeTworks (SPRNT) model. We evaluated SPRNT’s and NWM’s abilities to predict inundated area for the record flood of Hurricane Matthew in October 2016. The Neuse River experienced record‐breaking floods and was well‐documented by U.S. Geological Survey. Streamflow simulations from NWM retrospective analysis were used as input for the SPRNT simulation. Retrospective NWM discharge predictions were converted to stage. The stages (from both SPRNT and NWM) were utilized to produce flood‐inundation maps using the Height Above Nearest Drainage method which uses the local relative heights to find out the local draining potentials and provide spatial representation of inundated area. The inundated‐area accuracies for NWM and SPRNT (based on comparison to a remotely sensed dataset) were 65.1% and 67.6%, respectively. These results show using steady‐state SPRNT results in a modest improvement of inundation‐forecast accuracy compared to NWM.

Effects of Groundwater‐Surface Water Exchange Mechanism in the National Water Model over the Northern High Plains Aquifer, USA

“…Previous studies have evaluated the NWM prediction performance and found that the NWM overpredicts streamflow in many cases, but the causes of this overprediction are not fully understood (Johnson et al 2017; Hansen et al 2019). Many of the studies that have evaluated the NWM’s performance have primarily focused on flood inundation mapping which requires accurate streamflow predictions, but these studies do not directly evaluate the streamflow prediction ability of the model (Afshari et al 2018; Zheng et al 2018; Johnson et al 2019; Shastry et al 2019) In this study we aim to directly evaluate the NWM’s streamflow prediction ability. We do this by examining whether the NWM performance impairment under hydrologic extremes can be correlated with the effects of losing stream regimes due to the absence of a two‐way groundwater‐surface water interaction in the NWM.…”

Extending Height Above Nearest Drainage to Model Multiple Fluvial Sources in Flood Inundation Mapping Applications for the U.S. National Water Model

“…Nobre et al (2016) showed evidence for utilizing the drainage normalizing HAND data set as a proxy for flood potential to make static flood inundation maps from known stages. The terrain index also provides additional utility in the observation of riverine flood inundation mapping from remote sensing especially in areas of high electromagnetic interference such as vegetated and anthropogenic areas (Aristizabal et al, 2020;Aristizabal & Judge, 2021;Huang et al, 2017;Shastry et al, 2019;Twele et al, 2016). developed a methodology for determining stage-discharge relationships known as SRCs by sampling reach-averaged parameters from HAND data sets and inputting into the Manning's equation (Gauckler, 1867;Manning et al, 1890).…”

Extending Height Above Nearest Drainage to Model Multiple Fluvial Sources in Flood Inundation Mapping

“…A. D. Nobre et al (2016) showed evidence for utilizing the drainage normalizing HAND dataset as a proxy for flood potential to make static flood inundation maps from known stages. The terrain index also provides additional utility in the observation of riverine flood inundation mapping from remote sensing especially in areas of high electromagnetic interference such as vegetated and anthropogenic areas (Aristizabal et al, 2020;Shastry et al, 2019;Huang et al, 2017;Twele et al, 2016;Aristizabal & Judge, 2021). developed a methodology for determining stage-discharge relationships known as SRCs by sampling reach-averaged parameters from HAND datasets and inputting into the Manning's equation (Gauckler, 1867;Manning et al, 1890).…”