The Ensemble Framework For Flash Flood Forecasting (EF5) v1.2: description and case study

Abstract

Abstract. The Ensemble Framework For Flash Flood Forecasting (EF5) was developed specifically for improving hydrologic predictions to aid in the issuance of flash flood warnings by the US National Weather Service. EF5 features multiple water balance models and two routing schemes which can be used to generate ensemble forecasts of streamflow, streamflow normalized by upstream basin area (i.e., unit streamflow), and soil saturation. EF5 is designed to utilize high-resolution precipitation forcing datasets now available in real time. A study on flash-flood-scale basins was conducted over the conterminous United States using gauged basins with catchment areas less than 1000 km2. The results of the study show that the three uncalibrated water balance models linked to kinematic wave routing are skillful in simulating streamflow.

Can the re-infiltration process be ignored for flood inundation mapping and prediction during extreme storms?

“…Combined with our previous study that underlies the importance of infiltration and initial soil moisture for flood inundation modeling, we highly recommend taking into consideration the initial soil moisture state, as it has not been well-recognized in the hydraulic model community. This can be achieved via three ways: 1) warm up the model for a relatively long period prior to the simulation period (Chen et al, 2020); 2) parameterize the initial soil moisture and calibrate it, similar to the way we treat initial in-channel water depth (Xue et al, 2013); 3) approximate it using observations or other model simulations, like what has been done in the real case study in Section 3.2 (Flamig, Vergara, & Gourley, 2020). The first approach is ideal because it eliminates uncertainties in parameterization (such as equifinality) or error propagation from observations/simulations to models; it is, however, the most computationally expensive approach for hydraulic modeling compared to the other two.…”

“…Hydrologic modeling is so far a common approach to deliver timely flood information for the sake of scalability and efficiency (Gourley et al, 2017). Yet, conventional hydrologic models bear large uncertainties in such developed regions, which is mainly due to 1) simplified representation of terrain (Dullo et al, 2021) and 2) one-dimensional routing that raises issues in flat regions (Flamig, Vergara, & Gourley, 2020;Getirana & Paiva, 2013;Li et al, 2021b). On the other hand, hydraulic models do not excel in representing hydrologic processes.…”

“…CREST-iMAP inherits the previous version of the CREST model, which simulates saturation excess runoff as the primary runoff generation process (Wang et al, 2011;Xue et al, 2013;Flamig, Vergara, & Gourley, 2020). The schematic model structure is depicted in Figure 2.…”

“…These soil-related a-priori parameters can be approximated from a look-up table at an individual grid cell basis (Chow, Maidment, & Mays, 1988). There are also CONUS-wide optimized parameter sets that are configured for operational flood monitoring systems (Flamig, Vergara, & Gourley, 2020). The impervious area ratio, IM from 0% to 100%, is obtained directly from the NLCD dataset; the manning's n coefficient is derived from the LULC via a look-up table.…”

CREST-VEC: A framework towards more accurate and realistic flood simulation across scales

“…It is a distributed hydrologic model whose primary purposes are (1) flood simulation and forecasting, (2) evaluating the hydrologic utility of satellite precipitation datasets, and (3) water resources management (Xue et al, 2013;Tang et al, 2016;Gourley et al, 2017;Chen et al, 2020;Li et al, 2021b). Owing to its relatively simple structure and computationally efficient simulation, the CREST model has been promoted by the NOAA NSSL for real-time flash flood forecasting over the continental U.S. and its territories (Gourley et al, 2017;Flamig et al, 2020). As shown in Fig.…”