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Ben A. Moser, William A. Gallus Jr., and Ricardo Mantilla

Abstract

The effect of introducing radar data assimilation into the WRF Model to improve high-resolution rainfall forecasts that are used for flash flood forecasting is analyzed. The authors selected 12 heavy rainfall events and performed two WRF 24-h simulations that produced quantitative precipitation forecasts (QPFs) for each, one using the standard configuration in forecast mode (QPF-Cold) and one using radar data assimilated at initialization (QPF-Hot). Simulation outputs are compared with NWS stage IV QPEs for storm placement, area over threshold coverage, and equitable threat scores. The two QPF products and stage IV data are used to force the distributed hydrological model CUENCAS for the same 800 km × 800 km domain centered over Iowa (and to calculate peak flows across the river network). The hydrological model responses to the three products are compared in terms of spatial location and flood intensity. In general, QPF-Hot outperformed QPF-Cold in replicating stage IV QPE statistics. However, QPF-Hot was too wet in the first 2 h of the event, and storms created by the radar-assimilation techniques dissipated quickly, with rainfall forecasts resembling QPF-Cold after 12 h. Flash flooding predicted by CUENCAS using QPF-Hot was more consistent with stage IV in terms of placement and intensity; however, results were not consistent for all events evaluated. The most encouraging result is that expected flash flooding was indeed predicted in all 12 cases using QPF-Hot and not QPF-Cold even though placement and intensity were not a perfect match. The initial results of this study indicate that radar assimilation improves WRF’s ability to capture the character of storms, promising more accurate guidance for flash flood warnings.

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Felipe Quintero, Witold F. Krajewski, Ricardo Mantilla, Scott Small, and Bong-Chul Seo

Abstract

Rainfall maps that are derived from satellite observations provide hydrologists with an unprecedented opportunity to forecast floods globally. However, the limitations of using these precipitation estimates with respect to producing reliable flood forecasts at multiple scales are not well understood. To address the scientific and practical question of applicability of space-based rainfall products for global flood forecasting, a data evaluation framework is developed that allows tracking the rainfall effects in space and time across scales in the river network. This provides insights on the effects of rainfall product resolution and uncertainty. Obtaining such insights is not possible when the hydrologic evaluation is based on discharge observations from single gauges. The proposed framework also explores the ability of hydrologic model structure to answer questions pertaining to the utility of space-based rainfall observations for flood forecasting. To illustrate the framework, hydrometeorological data collected during the Iowa Flood Studies (IFloodS) campaign in Iowa are used to perform a hydrologic simulation using two different rainfall–runoff model structures and three rainfall products, two of which are radar based [stage IV and Iowa Flood Center (IFC)] and one satellite based [TMPA–Research Version (RV)]. This allows for exploring the differences in rainfall estimates at several spatial and temporal scales and provides improved understanding of how these differences affect flood predictions at multiple basin scales. The framework allows for exploring the differences in peak flow estimation due to nonlinearities in the hydrologic model structure and determining how these differences behave with an increase in the upstream area through the drainage network. The framework provides an alternative evaluation of precipitation estimates, based on the diagnostics of hydrological model results.

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Witold F. Krajewski, Daniel Ceynar, Ibrahim Demir, Radoslaw Goska, Anton Kruger, Carmen Langel, Ricardo Mantilla, James Niemeier, Felipe Quintero, Bong-Chul Seo, Scott J. Small, Larry J. Weber, and Nathan C. Young

Abstract

The Iowa Flood Center (IFC), established following the 2008 record floods, has developed a real-time flood forecasting and information dissemination system for use by all Iowans. The system complements the operational forecasting issued by the National Weather Service, is based on sound scientific principles of flood genesis and spatial organization, and includes many technological advances. At its core is a continuous rainfall–runoff model based on landscape decomposition into hillslopes and channel links. Rainfall conversion to runoff is modeled through soil moisture accounting at hillslopes. Channel routing is based on a nonlinear representation of water velocity that considers the discharge amount as well as the upstream drainage area. Mathematically, the model represents a large system of ordinary differential equations organized to follow river network topology. The IFC also developed an efficient numerical solver suitable for high-performance computing architecture. The solver allows the IFC to update forecasts every 15 min for over 1,000 Iowa communities. The input to the system comes from a radar-rainfall algorithm, developed in-house, that maps rainfall every 5 min with high spatial resolution. The algorithm uses Level II radar reflectivity and other polarimetric data from the Weather Surveillance Radar-1988 Dual-Polarimetric (WSR-88DP) radar network. A large library of flood inundation maps and real-time river stage data from over 200 IFC “stream-stage sensors” complement the IFC information system. The system communicates all this information to the general public through a comprehensive browser-based and interactive platform. Streamflow forecasts and observations from Iowa can provide support for a similar system being developed at the National Water Center through model intercomparisons, diagnostic analyses, and product evaluations.

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