Towards Real‐Time Continental Scale Streamflow Simulation in Continuous and Discrete Space

Abstract

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.)

Challenges and Opportunities for Creating Intelligent Hazard Alerts: The “FloodHippo” Prototype

“…Ongoing collaboration between National Oceanic and Atmospheric Association's (NOAA) Office of Water Prediction, the National Center for Atmospheric Research (NCAR), and the academic community have created the National Water Model (NWM), which generates real‐time streamflow forecasts for each of the 2.67 million reaches in the U.S. National Hydrography Dataset (NHD) for a range of time scales (Maidment ; Cosgrove and Klymmer ; Salas et al. ). Combined with active research into the development of synthetic rating curves, the potential for near‐real‐time flood mapping throughout the U.S. exists (Wang ; Liu et al.…”

“…With regard to flooding in particular, efforts to couple weather forecasting tools and hydrologic models have resulted in operational streamflow forecasting, mirroring existing capabilities for hurricane and fire prediction (Bauer et al 2015;NOAA 2016b;Ogden et al 2015). Ongoing collaboration between National Oceanic and Atmospheric Association's (NOAA) Office of Water Prediction, the National Center for Atmospheric Research (NCAR), and the academic community have created the National Water Model (NWM), which generates real-time streamflow forecasts for each of the 2.67 million reaches in the U.S. National Hydrography Dataset (NHD) for a range of time scales (Maidment 2015;Cosgrove and Klymmer 2016;Salas et al 2018). Combined with active research into the development of synthetic rating curves, the potential for near-real-time flood mapping throughout the U.S. exists (Wang 2013;Liu et al 2016;Johnson et al 2017;Afshari et al 2018).…”

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

“…Accordingly, NWM retrospective analysis output for the Neuse River at Goldsboro, provided to us by NWC, were used as input for our models. National-scale validation of this retrospective analysis by Salas et al (2018) found that 26% of gages had bias of less than 25% and 11% had Nash-Sutcliffe efficiency > 0.25.…”

Seasonal hydroclimatic ensemble forecasts anticipate nutrient and suspended sediment loads using a dynamical-statistical approach

“…We show here for the first time the ability of S2S climate predictions to result in both skillful water quantity and quality forecasts. This has important implications for US national-level, and possibly other regions, operational hydrological forecasting strategies (Demargne et al 2014, Gochis et al 2015, Salas et al 2018. We demonstrate that it is possible to equip current operational forecasting systems, in a cost and time efficient manner, with new prediction capabilities for water quality, which are currently lacking in the US.…”