The National Weather Service (NWS) forecasts floods at approximately 3,600 locations across the United States (U.S.). However, the river network, as defined by the 1:100,000 scale National Hydrography Dataset-Plus (NHDPlus) dataset, consists of 2.7 million river segments. Through the National Flood Interoperability Experiment, a continental scale streamflow simulation and forecast system was implemented and continuously operated through the summer of 2015. This system leveraged the WRF-Hydro framework, initialized on a 3-km grid, the Routing Application for the Parallel Computation of Discharge river routing model, operating on the NHDPlus, and real-time atmospheric forcing to continuously forecast streamflow. Although this system produced forecasts, this paper presents a study of the three-month nowcast to demonstrate the capacity to seamlessly predict reach scale streamflow at the continental scale. In addition, this paper evaluates the impact of reservoirs, through a case study in Texas. Validation of the uncalibrated model using observed hourly streamflow at 5,701 U.S. Geological Survey gages shows 26% demonstrate PBias ≤ |25%|, 11% demonstrate Nash-Sutcliffe Efficiency (NSE) ≥ 0.25, and 6% demonstrate both PBias ≤ |25%| and NSE ≥ 0.25. When evaluating the impact of reservoirs, the analysis shows when reservoirs are included, NSE ≥ 0.25 for 56% of the gages downstream while NSE ≥ 0.25 for 11% when they are not. The results presented here provide a benchmark for the evolving hydrology program within the NWS and supports their efforts to develop a reach scale flood forecasting system for the country.(KEY TERMS: continental scale river dynamics; streamflow prediction; surface water hydrology; flood forecasting.)

We build three long short‐term memory (LSTM) daily streamflow prediction models (deep learning networks) for 531 basins across the contiguous United States (CONUS), and compare their performance: (1) a LSTM post‐processor trained on the United States National Water Model (NWM) outputs (LSTM_PP), (2) a LSTM post‐processor trained on the NWM outputs and atmospheric forcings (LSTM_PPA), and (3) a LSTM model trained only on atmospheric forcing (LSTM_A). We trained the LSTMs for the period 2004–2014 and evaluated on 1994–2002, and compared several performance metrics to the NWM reanalysis. Overall performance of the three LSTMs is similar, with median NSE scores of 0.73 (LSTM_PP), 0.75 (LSTM_PPA), and 0.74 (LSTM_A), and all three LSTMs outperform the NWM validation scores of 0.62. Additionally, LSTM_A outperforms LSTM_PP and LSTM_PPA in ungauged basins. While LSTM as a post‐processor improves NWM predictions substantially, we achieved comparable performance with the LSTM trained without the NWM outputs (LSTM_A). Finally, we performed a sensitivity analysis to diagnose the land surface component of the NWM as the source of mass bias error and the channel router as a source of simulation timing error. This indicates that the NWM channel routing scheme should be considered a priority for NWM improvement.

The NOAA National Water Model (NWM) became operational in August 2016, producing the first ever real-time, distributed, continuous set of hydrologic forecasts over the continental United States (CONUS). This project uses integrated hydrometeorological assessment methods to investigate the utility of the NWM to predict catastrophic flooding associated with an extreme rainfall event that occurred in Ellicott City, Maryland, on 27–28 May 2018. Short-range forecasts (0–18-h lead time) from the NWM version 1.2 are explored, focusing on the quantitative precipitation forecast (QPF) forcing from the High-Resolution Rapid Refresh (HRRR) model and the corresponding NWM streamflow forecast. A comprehensive assessment of multiscale hydrometeorological processes are considered using a combination of object-based, grid-based, and hydrologic point-based verification. Results highlight the benefits and risks of using a distributed hydrologic modeling tool such as the NWM to connect operational CONUS-scale atmospheric forcings to local impact predictions. For the Ellicott City event, reasonably skillful QPF in several HRRR model forecast cycles produced NWM streamflow forecasts in the small Ellicott City basin that were suggestive of flash flood potential. In larger surrounding basins, the NWM streamflow response was more complex, and errors were found to be governed by both hydrologic process representation, as well as forcing errors. The integrated, hydrometeorological multiscale analysis method demonstrated here guides both research and ongoing model development efforts, along with providing user education and engagement to ultimately engender improved flash flood prediction.

The U.S. National Water Model (NWM) is a large scale hydrology simulator. Although NWM achieves coupling of multi-scale hydrological processes, its predictability at individual catchments can be improved. Hydrologic post-processing is an approach to reduce systematic simulation errors with statistical models, and has been shown to improve forecast accuracy of both calibrated and uncalibrated models. In this experiment we trained a Long Short-Term Memory (LSTM) network to post-process the NWM output, and tested performance at 531 basins across the continental United States. The LSTM post-processor provided a significant benefit to nearly all aspects of NWM streamflow predictions. The LSTM also benefited from NWM input - in particular, representation of hydrologic signatures improved, which indicates better representation of physical flow patterns.

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Reservoir management is a critical component of flood management, and information on reservoir inflows is particularly essential for reservoir managers to make real-time decisions given that flood conditions change rapidly. This study's objective is to build real-time data-driven services that enable managers to rapidly estimate reservoir inflows from available data and models. We have tested the services using a case study of the Texas flooding events in the Lower Colorado River Basin in November 2014 and May 2015, which involved a sudden switch from drought to flooding. We have constructed two prediction models: a statistical model for flow prediction and a hybrid statistical and physics-based model that estimates errors in the flow predictions from a physics-based model. The study demonstrates that the statistical flow prediction model can be automated and provides acceptably accurate short-term forecasts. However, for longer term prediction (2 h or more), the hybrid model fits the observations more closely than the purely statistical or physics-based prediction models alone. Both the flow and hybrid prediction models have been published as Web services through Microsoft's Azure Machine Learning (AzureML) service and are accessible through a browser-based Web application, enabling ease of use by both technical and nontechnical personnel.(KEY TERMS: flooding; data-driven model services; AzureML; reservoir inflow.)

Height Above Nearest Drainage (HAND), a drainage normalizing terrain index, is a means able of producing flood inundation maps (FIMs) from the National Water Model (NWM) at large scales and high resolutions using reach‐averaged synthetic rating curves. We highlight here that HAND is limited to producing inundation only when sourced from its nearest flowpath, thus lacks the ability to source inundation from multiple fluvial sources. A version of HAND, known as Generalized Mainstems (GMS), is proposed that discretizes a target stream network into segments of unit Horton‐Strahler stream order known as level paths (LPs). The FIMs associated with each independent LP are then mosaiced together, effectively turning the stream network into discrete groups of homogeneous unit stream order by removing the influence of neighboring tributaries. Improvement in mapping skill is observed by significantly reducing false negatives at river junctions when the inundation extents are compared to FIMs from that of benchmarks. A more marginal reduction in the false alarm rate is also observed due to a shift introduced in the stage‐discharge relationship by increasing the size of the catchments. We observe that the improvement of this method applied at 4%–5% of the entire stream network to 100% of the network is about the same magnitude improvement as going from no drainage order reduction to 4%–5% of the network. This novel contribution is framed in a new open‐source implementation that utilizes the latest combination of hydro‐conditioning techniques to enforce drainage and counter limitations in the input data.

Flooding is one of the most significant natural disasters in the United States (US) affecting both the loss of life and property. In 2017 and 2019, river and flash flooding combined represented the leading cause of death and the second leading cause in 2018 among all natural disasters in the US (National Weather Service, 2018Service, 2020b). More than an average of 104 deaths per year are attributed to flood events from the 10 year period ending in 2019 (Service, 2020a). With respect to property damages, river and flash flooding have contributed to 60.7, 1.6, and 3.7 billion non-inflation adjusted US dollars in the annual periods of 2017-2019, respectively