Satellite-based Flood Modeling Using TRMM-based Rainfall Products.
Amanda Harris1, Sayma Rahman2, Faisal Hossain3
1Department of Civil and Environmental Engineering, Tennessee Technological University, Cookeville, TN 38505, USA.
Sensors (Basel, Switzerland)
|September 15, 2017
Summary
Satellite rainfall data offers cost-effective flood prediction but coarse scales pose challenges. Bias adjustment improves flood prediction accuracy but increases false alarms, requiring further research for reliable institutional use.
Area of Science:
- Hydrology
- Remote Sensing
- Water Resource Management
Background:
- Satellite rainfall data is increasingly available and cost-effective for flood prediction.
- Current satellite rainfall products have coarse spatial scales (0.25°-0.50°), limiting their use in dynamic hydrologic modeling for basin-scale flood events.
Purpose of the Study:
- To assess the hydrologic implications of coarse-scale satellite rainfall data uncertainty.
- To investigate the effectiveness of bias adjustment for satellite rainfall data in flood prediction.
Main Methods:
- Utilized NASA's Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) 3B41RT product.
- Assessed the data on the Upper Cumberland river basin (970 km²) in Kentucky.
- Evaluated the impact of bias adjustment on flood prediction accuracy and false alarms.
Main Results:
- Bias adjustment of satellite rainfall data can partially improve flood prediction.
- Bias adjustment leads to an increase in false alarms for peak flow events.
- The current coarse resolution of satellite data presents limitations for detailed hydrologic modeling.
Conclusions:
- While bias adjustment shows promise, it is not a complete solution for integrating satellite rainfall data into flood modeling.
- Further research is needed to develop rational, regime-based adjustment procedures before satellite data can be widely adopted by flood modelers.
- The coarse spatial resolution of current satellite rainfall products remains a significant challenge for accurate basin-scale flood prediction.
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