Evaluating the Effects of Forecast Lead Time on Streamflow and Inundation Predictions in Brays Bayou, Houston, Texas through Coupled Hydrologic-Hydraulics Models
Abstract
Intensified precipitation and accelerated urbanization have increased the frequency and severity of urban floods. Accurate precipitation, streamflow, and floodplain inundation forecasts are necessary to minimize the damage from these events via reservoir operations planning, resident evacuation, and relief effort mobilization. During extreme events, typically the frequency of forecasts increases to support mitigation efforts. In this study, we evaluate the value of those more frequent forecasts in terms of timing and accuracy. Quantitative Precipitation Forecasts (QPFs) developed by the National Weather Service were analyzed for their precipitation prediction skill in Brays Bayou in Houston, Texas. QPF datasets were used to drive the Distributed Hydrological Soil and Vegetation Model (DHSVM), a physics-based, distributed hydrological model, and the resulting streamflows were assessed for accuracy. Then, a 2-dimensional hydraulic model, Flood2D-GPU, was employed to produce forecasted floodplains. This study focuses on three recent floods with an emphasis on Hurricane Harvey. Results were evaluated on three aspects: 1) identifying changes in forecast accuracy with increased lead time; 2) quantifying skill scores of the forecasts through the flood forecasting system; and 3) comparing DHSVM forecasts with those used by the West Gulf River Forecasting Center to identify most accurate forecasting lead time during extreme flood events.
- Publication:
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AGU Fall Meeting Abstracts
- Pub Date:
- December 2019
- Bibcode:
- 2019AGUFM.H51V1826G
- Keywords:
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- 3354 Precipitation;
- ATMOSPHERIC PROCESSES;
- 1807 Climate impacts;
- HYDROLOGY;
- 1840 Hydrometeorology;
- HYDROLOGY;
- 1854 Precipitation;
- HYDROLOGY