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Título : Improving Hourly Precipitation Estimates for Flash Flood Modeling in Data-Scarce Andean-Amazon Basins: An Integrative Framework Based on Machine Learning and Multiple Remotely Sensed Data
Autor : Espitia Sarmiento, Edgar F
Chancay, Juseth E.
Palabras clave : IMERG
PERSIANN
GSMAP
SMAP
GR4H model
Complex topography areas
Upper Napo River Basin
Fecha de publicación : 2021
Editorial : Scopus
Citación : Chancay, Juseth E., and Edgar F. Espitia-Sarmiento. 2021. "Improving Hourly Precipitation Estimates for Flash Flood Modeling in Data-Scarce Andean-Amazon Basins: An Integrative Framework Based on Machine Learning and Multiple Remotely Sensed Data" Remote Sensing 13, no. 21: 4446. https://doi.org/10.3390/rs13214446
Citación : PRODUCCIÓN CIENTÍFICA - ARTÍCULO CIENTÍFICO;A-IKIAM-000330
Resumen : Accurate estimation of spatiotemporal precipitation dynamics is crucial for flash flood forecasting; however, it is still a challenge in Andean-Amazon sub-basins due to the lack of suitable rain gauge networks. This study proposes a framework to improve hourly precipitation estimates by integrating multiple satellite-based precipitation and soil-moisture products using random forest modeling and bias correction techniques. The proposed framework is also used to force the GR4H model in three Andean-Amazon sub-basins that suffer frequent flash flood events: upper Napo River Basin (NRB), Jatunyacu River Basin (JRB), and Tena River Basin (TRB). Overall, precipitation estimates derived from the framework (BC-RFP) showed a high ability to reproduce the intensity, distribution, and occurrence of hourly events. In fact, the BC-RFP model improved the detection ability between 43% and 88%, reducing the estimation error between 72% and 93%, compared to the original satellite-based precipitation products (i.e., IMERG-E/L, GSMAP, and PERSIANN). Likewise, simulations of flash flood events by coupling the GR4H model with BC-RFP presented satisfactory performances (KGE* between 0.56 and 0.94). The BC-RFP model not only contributes to the implementation of future flood forecast systems but also provides relevant insights to several water-related research fields and hence to integrated water resources management of the Andean-Amazon region.
URI : http://repositorio.ikiam.edu.ec:8443/jspui/handle/RD_IKIAM/479
ISSN : https://doi.org/10.3390/rs13214446
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