Satellite-based Flood Modeling Using TRMM-based Rainfall Products
Increasingly available and a virtually uninterrupted supply of satellite-estimatedrainfall data is gradually becoming a cost-effective source of input for flood predictionunder a variety of circumstances. However, most real-time and quasi-global satelliterainfall products are currently available at...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2007-12-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | http://www.mdpi.com/1424-8220/7/12/3416/ |
| _version_ | 1798035297848524800 |
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| author | Greg Easson Amvrossios C. Bagtzoglou Lance Yarborough Faisal Hossain Sayma Rahman Amanda Harris |
| author_facet | Greg Easson Amvrossios C. Bagtzoglou Lance Yarborough Faisal Hossain Sayma Rahman Amanda Harris |
| author_sort | Greg Easson |
| collection | DOAJ |
| description | Increasingly available and a virtually uninterrupted supply of satellite-estimatedrainfall data is gradually becoming a cost-effective source of input for flood predictionunder a variety of circumstances. However, most real-time and quasi-global satelliterainfall products are currently available at spatial scales ranging from 0.25o to 0.50o andhence, are considered somewhat coarse for dynamic hydrologic modeling of basin-scaleflood events. This study assesses the question: what are the hydrologic implications ofuncertainty of satellite rainfall data at the coarse scale? We investigated this question onthe 970 km2 Upper Cumberland river basin of Kentucky. The satellite rainfall productassessed was NASA’s Tropical Rainfall Measuring Mission (TRMM) Multi-satellitePrecipitation Analysis (TMPA) product called 3B41RT that is available in pseudo real timewith a latency of 6-10 hours. We observed that bias adjustment of satellite rainfall data canimprove application in flood prediction to some extent with the trade-off of more falsealarms in peak flow. However, a more rational and regime-based adjustment procedureneeds to be identified before the use of satellite data can be institutionalized among floodmodelers. |
| first_indexed | 2024-04-11T20:56:12Z |
| format | Article |
| id | doaj.art-1875261c4708465982fb26a7c0916bd2 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T20:56:12Z |
| publishDate | 2007-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-1875261c4708465982fb26a7c0916bd22022-12-22T04:03:40ZengMDPI AGSensors1424-82202007-12-017123416342710.3390/s7123416Satellite-based Flood Modeling Using TRMM-based Rainfall ProductsGreg EassonAmvrossios C. BagtzoglouLance YarboroughFaisal HossainSayma RahmanAmanda HarrisIncreasingly available and a virtually uninterrupted supply of satellite-estimatedrainfall data is gradually becoming a cost-effective source of input for flood predictionunder a variety of circumstances. However, most real-time and quasi-global satelliterainfall products are currently available at spatial scales ranging from 0.25o to 0.50o andhence, are considered somewhat coarse for dynamic hydrologic modeling of basin-scaleflood events. This study assesses the question: what are the hydrologic implications ofuncertainty of satellite rainfall data at the coarse scale? We investigated this question onthe 970 km2 Upper Cumberland river basin of Kentucky. The satellite rainfall productassessed was NASA’s Tropical Rainfall Measuring Mission (TRMM) Multi-satellitePrecipitation Analysis (TMPA) product called 3B41RT that is available in pseudo real timewith a latency of 6-10 hours. We observed that bias adjustment of satellite rainfall data canimprove application in flood prediction to some extent with the trade-off of more falsealarms in peak flow. However, a more rational and regime-based adjustment procedureneeds to be identified before the use of satellite data can be institutionalized among floodmodelers.http://www.mdpi.com/1424-8220/7/12/3416/Satellite rainfallstatistical downscalingfloodsuncertainty. |
| spellingShingle | Greg Easson Amvrossios C. Bagtzoglou Lance Yarborough Faisal Hossain Sayma Rahman Amanda Harris Satellite-based Flood Modeling Using TRMM-based Rainfall Products Sensors Satellite rainfall statistical downscaling floods uncertainty. |
| 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 | Satellite rainfall statistical downscaling floods uncertainty. |
| url | http://www.mdpi.com/1424-8220/7/12/3416/ |
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