{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,14]],"date-time":"2026-01-14T18:17:59Z","timestamp":1768414679973,"version":"3.49.0"},"reference-count":95,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2021,11,5]],"date-time":"2021-11-05T00:00:00Z","timestamp":1636070400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Universidad Regional Amaz\u00f3nica IKIAM","award":["530207"],"award-info":[{"award-number":["530207"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/rs13214446","type":"journal-article","created":{"date-parts":[[2021,11,7]],"date-time":"2021-11-07T20:42:54Z","timestamp":1636317774000},"page":"4446","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"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"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3060-2542","authenticated-orcid":false,"given":"Juseth E.","family":"Chancay","sequence":"first","affiliation":[{"name":"Facultad de Ciencias de la Tierra y Agua, Universidad Regional Amaz\u00f3nica Ikiam, Tena 150101, Ecuador"},{"name":"C\u00e1tedra UNESCO en Manejo de Aguas Dulces Tropicales, Universidad Regional Amaz\u00f3nica Ikiam, Tena 150101, Ecuador"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5811-9379","authenticated-orcid":false,"given":"Edgar Fabian","family":"Espitia-Sarmiento","sequence":"additional","affiliation":[{"name":"Facultad de Ciencias de la Tierra y Agua, Universidad Regional Amaz\u00f3nica Ikiam, Tena 150101, Ecuador"},{"name":"Grupo de Investigaci\u00f3n de Recursos H\u00eddricos y Acu\u00e1ticos, Universidad Regional Amaz\u00f3nica Ikiam, Tena 150101, Ecuador"}]}],"member":"1968","published-online":{"date-parts":[[2021,11,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1002\/2017RG000574","article-title":"A Review of Global Precipitation Data Sets: Data Sources, Estimation, and Intercomparisons","volume":"56","author":"Sun","year":"2018","journal-title":"Rev. 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