Applications of Satellite-Based Rainfall Estimates in Flood Inundation Modeling—A Case Study in Mundeni Aru River Basin, Sri Lanka
The performance of Satellite Rainfall Estimate (SRE) products applied to flood inundation modelling was tested for the Mundeni Aru River Basin in eastern Sri Lanka. Three SREs (PERSIANN, TRMM, and GSMaP) were tested, with the Rainfall-Runoff-Inundation (RRI) model used as the flood inundation model....
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| Format: | Article |
| Language: | English |
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MDPI AG
2017-09-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/9/10/998 |
| _version_ | 1798030963986399232 |
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| author | Shuhei Yoshimoto Giriraj Amarnath |
| author_facet | Shuhei Yoshimoto Giriraj Amarnath |
| author_sort | Shuhei Yoshimoto |
| collection | DOAJ |
| description | The performance of Satellite Rainfall Estimate (SRE) products applied to flood inundation modelling was tested for the Mundeni Aru River Basin in eastern Sri Lanka. Three SREs (PERSIANN, TRMM, and GSMaP) were tested, with the Rainfall-Runoff-Inundation (RRI) model used as the flood inundation model. All the SREs were found to be suitable for applying to the RRI model. The simulations created by applying the SREs were generally accurate, although there were some discrepancies in discharge due to differing precipitation volumes. The volumes of precipitation of the SREs tended to be smaller than those of the gauged data, but using a scale factor to correct this improved the simulations. In particular, the SRE, i.e., the GSMaP yielding the best simulation that correlated most closely with the flood inundation extent from the satellite data, was considered the most appropriate to apply to the model calculation. The application procedures and suggestions shown in this study could help authorities to make better-informed decisions when giving early flood warnings and making rapid flood forecasts, especially in areas where in-situ observations are limited. |
| first_indexed | 2024-04-11T19:48:35Z |
| format | Article |
| id | doaj.art-dee56ecc513e4ba28f3030de744c9593 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-04-11T19:48:35Z |
| publishDate | 2017-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-dee56ecc513e4ba28f3030de744c95932022-12-22T04:06:22ZengMDPI AGRemote Sensing2072-42922017-09-0191099810.3390/rs9100998rs9100998Applications of Satellite-Based Rainfall Estimates in Flood Inundation Modeling—A Case Study in Mundeni Aru River Basin, Sri LankaShuhei Yoshimoto0Giriraj Amarnath1International Water Management Institute (IWMI), Battaramulla 10120, Sri LankaInternational Water Management Institute (IWMI), Battaramulla 10120, Sri LankaThe performance of Satellite Rainfall Estimate (SRE) products applied to flood inundation modelling was tested for the Mundeni Aru River Basin in eastern Sri Lanka. Three SREs (PERSIANN, TRMM, and GSMaP) were tested, with the Rainfall-Runoff-Inundation (RRI) model used as the flood inundation model. All the SREs were found to be suitable for applying to the RRI model. The simulations created by applying the SREs were generally accurate, although there were some discrepancies in discharge due to differing precipitation volumes. The volumes of precipitation of the SREs tended to be smaller than those of the gauged data, but using a scale factor to correct this improved the simulations. In particular, the SRE, i.e., the GSMaP yielding the best simulation that correlated most closely with the flood inundation extent from the satellite data, was considered the most appropriate to apply to the model calculation. The application procedures and suggestions shown in this study could help authorities to make better-informed decisions when giving early flood warnings and making rapid flood forecasts, especially in areas where in-situ observations are limited.https://www.mdpi.com/2072-4292/9/10/998satellite rainfall observationflood monitoringflood inundation simulationrainfall runoff model |
| spellingShingle | Shuhei Yoshimoto Giriraj Amarnath Applications of Satellite-Based Rainfall Estimates in Flood Inundation Modeling—A Case Study in Mundeni Aru River Basin, Sri Lanka Remote Sensing satellite rainfall observation flood monitoring flood inundation simulation rainfall runoff model |
| title | Applications of Satellite-Based Rainfall Estimates in Flood Inundation Modeling—A Case Study in Mundeni Aru River Basin, Sri Lanka |
| title_full | Applications of Satellite-Based Rainfall Estimates in Flood Inundation Modeling—A Case Study in Mundeni Aru River Basin, Sri Lanka |
| title_fullStr | Applications of Satellite-Based Rainfall Estimates in Flood Inundation Modeling—A Case Study in Mundeni Aru River Basin, Sri Lanka |
| title_full_unstemmed | Applications of Satellite-Based Rainfall Estimates in Flood Inundation Modeling—A Case Study in Mundeni Aru River Basin, Sri Lanka |
| title_short | Applications of Satellite-Based Rainfall Estimates in Flood Inundation Modeling—A Case Study in Mundeni Aru River Basin, Sri Lanka |
| title_sort | applications of satellite based rainfall estimates in flood inundation modeling a case study in mundeni aru river basin sri lanka |
| topic | satellite rainfall observation flood monitoring flood inundation simulation rainfall runoff model |
| url | https://www.mdpi.com/2072-4292/9/10/998 |
| work_keys_str_mv | AT shuheiyoshimoto applicationsofsatellitebasedrainfallestimatesinfloodinundationmodelingacasestudyinmundeniaruriverbasinsrilanka AT girirajamarnath applicationsofsatellitebasedrainfallestimatesinfloodinundationmodelingacasestudyinmundeniaruriverbasinsrilanka |