Abstract

The United States has dense weather radar and rain gage networks that provide potentially useful rainfall inputs for a variety of hydrologic applications, especially in heterogeneous urban settings where the time and length scales of hydrologic processes are short and our understanding of the complex interactions of extreme rainfall and runoff is poor. 10-year (2001-2010) high-resolution (1 km2, 15-minute resolution) bias-corrected radar rainfall datasets have been developed for the Charlotte, Atlanta, and Baltimore metropolitan areas using the Hydro-NEXRAD radar rainfall processing system and dense urban rain gage networks. The bias-corrected radar rainfall fields accurately capture the spatial and temporal structure of heavy rainfall, as case studies of the catastrophic floods on July 22, 1997 in Charlotte and September 19-22, 2009 in Atlanta demonstrate. An example application of radar-based rainfall estimates for rainfall frequency analysis based on the principles of stochastic storm transposition (SST) and using a catalogue of major storm events is presented.

The technique can be readily extended to flood frequency analysis by way of a high-resolution hydrologic model. A high-resolution model of the extensively urbanized Little Sugar Creek watershed in Charlotte is being developed using the Gridded Surface Subsurface Hydrologic Analysis (GSSHA) model. This model will allow the development of SST-based assessment of flood frequency across a drainage network as well as enable the evaluation of the impacts of different land-use and stormwater management scenarios on flood frequency.

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