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

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...

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Autores principales:	Juseth E. Chancay, Edgar Fabian Espitia-Sarmiento
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	IMERG PERSIANN GSMAP SMAP GR4H model complex topography areas Science Q
Acceso en línea:	https://doaj.org/article/11fab83c49de43be9debe5c34e8c9300
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spelling	oai:doaj.org-article:11fab83c49de43be9debe5c34e8c93002021-11-11T18:57:04ZImproving Hourly Precipitation Estimates for Flash Flood Modeling in Data-Scarce Andean-Amazon Basins: An Integrative Framework Based on Machine Learning and Multiple Remotely Sensed Data10.3390/rs132144462072-4292https://doaj.org/article/11fab83c49de43be9debe5c34e8c93002021-11-01T00:00:00Zhttps://www.mdpi.com/2072-4292/13/21/4446https://doaj.org/toc/2072-4292Accurate 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.Juseth E. ChancayEdgar Fabian Espitia-SarmientoMDPI AGarticleIMERGPERSIANNGSMAPSMAPGR4H modelcomplex topography areasScienceQENRemote Sensing, Vol 13, Iss 4446, p 4446 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	IMERG PERSIANN GSMAP SMAP GR4H model complex topography areas Science Q
spellingShingle	IMERG PERSIANN GSMAP SMAP GR4H model complex topography areas Science Q Juseth E. Chancay Edgar Fabian Espitia-Sarmiento 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
description	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.
format	article
author	Juseth E. Chancay Edgar Fabian Espitia-Sarmiento
author_facet	Juseth E. Chancay Edgar Fabian Espitia-Sarmiento
author_sort	Juseth E. Chancay
title	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
title_short	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
title_full	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
title_fullStr	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
title_full_unstemmed	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
title_sort	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
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/11fab83c49de43be9debe5c34e8c9300
work_keys_str_mv	AT jusethechancay improvinghourlyprecipitationestimatesforflashfloodmodelingindatascarceandeanamazonbasinsanintegrativeframeworkbasedonmachinelearningandmultipleremotelysenseddata AT edgarfabianespitiasarmiento improvinghourlyprecipitationestimatesforflashfloodmodelingindatascarceandeanamazonbasinsanintegrativeframeworkbasedonmachinelearningandmultipleremotelysenseddata
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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

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