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Remote Sens. 2019, 11(3), 252; https://doi.org/10.3390/rs11030252

Near-Real-Time Flood Forecasting Based on Satellite Precipitation Products

1
School of Instrumentation Science and Opto-Electronics Engineering, Beihang University, Beijing 100191, China
2
Algerian Space Agency, Algiers 16342, Algeria
3
Research Institute for Geo-Hydrological Protection, National Research Council, 06128 Perugia, Italy
4
USDA-Agricultural Research Service, Crop Production Systems Research Unit, 141 Experiment Station Road, Stoneville, MS 38776, USA
5
School of Transportation Science and Engineering, Beihang University, Beijing 100191, China
*
Authors to whom correspondence should be addressed.
Received: 25 December 2018 / Revised: 12 January 2019 / Accepted: 18 January 2019 / Published: 27 January 2019
(This article belongs to the Special Issue Selected Papers from Agro-Geoinformatics 2018)
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Abstract

Floods, storms and hurricanes are devastating for human life and agricultural cropland. Near-real-time (NRT) discharge estimation is crucial to avoid the damages from flood disasters. The key input for the discharge estimation is precipitation. Directly using the ground stations to measure precipitation is not efficient, especially during a severe rainstorm, because precipitation varies even in the same region. This uncertainty might result in much less robust flood discharge estimation and forecasting models. The use of satellite precipitation products (SPPs) provides a larger area of coverage of rainstorms and a higher frequency of precipitation data compared to using the ground stations. In this paper, based on SPPs, a new NRT flood forecasting approach is proposed to reduce the time of the emergency response to flood disasters to minimize disaster damage. The proposed method allows us to forecast floods using a discharge hydrograph and to use the results to map flood extent by introducing SPPs into the rainfall–runoff model. In this study, we first evaluated the capacity of SPPs to estimate flood discharge and their accuracy in flood extent mapping. Two high temporal resolution SPPs were compared, integrated multi-satellite retrievals for global precipitation measurement (IMERG) and tropical rainfall measurement mission multi-satellite precipitation analysis (TMPA). The two products are evaluated over the Ottawa watershed in Canada during the period from 10 April 2017 to 10 May 2017. With TMPA, the results showed that the difference between the observed and modeled discharges was significant with a Nash–Sutcliffe efficiency (NSE) of −0.9241 and an adapted NSE (ANSE) of −1.0048 under high flow conditions. The TMPA-based model did not reproduce the shape of the observed hydrographs. However, with IMERG, the difference between the observed and modeled discharges was improved with an NSE equal to 0.80387 and an ANSE of 0.82874. Also, the IMERG-based model could reproduce the shape of the observed hydrographs, mainly under high flow conditions. Since IMERG products provide better accuracy, they were used for flood extent mapping in this study. Flood mapping results showed that the error was mostly within one pixel compared with the observed flood benchmark data of the Ottawa River acquired by RadarSat-2 during the flood event. The newly developed flood forecasting approach based on SPPs offers a solution for flood disaster management for poorly or totally ungauged watersheds regarding precipitation measurement. These findings could be referred to by others for NRT flood forecasting research and applications. View Full-Text
Keywords: satellite precipitation products (SSPs); flood forecasting; discharge estimation; MILc model; integrated multi-satellite retrievals for global precipitation measurement (IMERG); tropical rainfall measurement mission multi-satellite precipitation analysis (TMPA); flood extent mapping satellite precipitation products (SSPs); flood forecasting; discharge estimation; MILc model; integrated multi-satellite retrievals for global precipitation measurement (IMERG); tropical rainfall measurement mission multi-satellite precipitation analysis (TMPA); flood extent mapping
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Belabid, N.; Zhao, F.; Brocca, L.; Huang, Y.; Tan, Y. Near-Real-Time Flood Forecasting Based on Satellite Precipitation Products. Remote Sens. 2019, 11, 252.

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