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Satellite-based Flood Modeling Using TRMM-based Rainfall Products
Increasingly available and a virtually uninterrupted supply of satellite-estimated rainfall data is gradually becoming a cost-effective source of input for flood prediction under a variety of circumstances. However, most real-time and quasi-global satellite rainfall products are currently available...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Molecular Diversity Preservation International (MDPI)
2007
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3841903/ https://www.ncbi.nlm.nih.gov/pubmed/28903302 |
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author | Harris, Amanda Rahman, Sayma Hossain, Faisal Yarborough, Lance Bagtzoglou, Amvrossios C. Easson, Greg |
author_facet | Harris, Amanda Rahman, Sayma Hossain, Faisal Yarborough, Lance Bagtzoglou, Amvrossios C. Easson, Greg |
author_sort | Harris, Amanda |
collection | PubMed |
description | Increasingly available and a virtually uninterrupted supply of satellite-estimated rainfall data is gradually becoming a cost-effective source of input for flood prediction under a variety of circumstances. However, most real-time and quasi-global satellite rainfall products are currently available at spatial scales ranging from 0.25° to 0.50° and hence, are considered somewhat coarse for dynamic hydrologic modeling of basin-scale flood events. This study assesses the question: what are the hydrologic implications of uncertainty of satellite rainfall data at the coarse scale? We investigated this question on the 970 km(2) Upper Cumberland river basin of Kentucky. The satellite rainfall product assessed was NASA's Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) product called 3B41RT that is available in pseudo real time with a latency of 6-10 hours. We observed that bias adjustment of satellite rainfall data can improve application in flood prediction to some extent with the trade-off of more false alarms in peak flow. However, a more rational and regime-based adjustment procedure needs to be identified before the use of satellite data can be institutionalized among flood modelers. |
format | Online Article Text |
id | pubmed-3841903 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2007 |
publisher | Molecular Diversity Preservation International (MDPI) |
record_format | MEDLINE/PubMed |
spelling | pubmed-38419032013-11-29 Satellite-based Flood Modeling Using TRMM-based Rainfall Products Harris, Amanda Rahman, Sayma Hossain, Faisal Yarborough, Lance Bagtzoglou, Amvrossios C. Easson, Greg Sensors (Basel) Full Research Paper Increasingly available and a virtually uninterrupted supply of satellite-estimated rainfall data is gradually becoming a cost-effective source of input for flood prediction under a variety of circumstances. However, most real-time and quasi-global satellite rainfall products are currently available at spatial scales ranging from 0.25° to 0.50° and hence, are considered somewhat coarse for dynamic hydrologic modeling of basin-scale flood events. This study assesses the question: what are the hydrologic implications of uncertainty of satellite rainfall data at the coarse scale? We investigated this question on the 970 km(2) Upper Cumberland river basin of Kentucky. The satellite rainfall product assessed was NASA's Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) product called 3B41RT that is available in pseudo real time with a latency of 6-10 hours. We observed that bias adjustment of satellite rainfall data can improve application in flood prediction to some extent with the trade-off of more false alarms in peak flow. However, a more rational and regime-based adjustment procedure needs to be identified before the use of satellite data can be institutionalized among flood modelers. Molecular Diversity Preservation International (MDPI) 2007-12-20 /pmc/articles/PMC3841903/ /pubmed/28903302 Text en © 2007 by MDPI (http://www.mdpi.org). Reproduction is permitted for noncommercial purposes. |
spellingShingle | Full Research Paper Harris, Amanda Rahman, Sayma Hossain, Faisal Yarborough, Lance Bagtzoglou, Amvrossios C. Easson, Greg Satellite-based Flood Modeling Using TRMM-based Rainfall Products |
title | Satellite-based Flood Modeling Using TRMM-based Rainfall Products |
title_full | Satellite-based Flood Modeling Using TRMM-based Rainfall Products |
title_fullStr | Satellite-based Flood Modeling Using TRMM-based Rainfall Products |
title_full_unstemmed | Satellite-based Flood Modeling Using TRMM-based Rainfall Products |
title_short | Satellite-based Flood Modeling Using TRMM-based Rainfall Products |
title_sort | satellite-based flood modeling using trmm-based rainfall products |
topic | Full Research Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3841903/ https://www.ncbi.nlm.nih.gov/pubmed/28903302 |
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