Improving spatiotemporal groundwater estimates after natural disasters using remotely sensed data: a case study of the Indian Ocean Tsunami
The Indian Ocean Tsunami of December 26, 2004 devastated coastal ecosystems across South Asia. Along the coastal regions of South India, increased groundwater levels (GWL), largely caused by saltwater intrusion, infiltration from inundated land, and disturbance of freshwater lenses, were reported. M...
| Main Authors: | , |
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| Format: | Journal Article |
| Language: | Inglés |
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Springer
2016
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| Online Access: | https://hdl.handle.net/10568/68437 |
| _version_ | 1855533125105352704 |
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| author | Chinnasamy, Pennan Sunde, M.G. |
| author_browse | Chinnasamy, Pennan Sunde, M.G. |
| author_facet | Chinnasamy, Pennan Sunde, M.G. |
| author_sort | Chinnasamy, Pennan |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | The Indian Ocean Tsunami of December 26, 2004 devastated coastal ecosystems across South Asia. Along the coastal regions of South India, increased groundwater levels (GWL), largely caused by saltwater intrusion, infiltration from inundated land, and disturbance of freshwater lenses, were reported. Many agencies allocated funding for restoration and rehabilitation projects. However, to streamline funding allocation efforts, district-level groundwater inundation/recession data would have been a useful tool for planners. Thus, to ensure better preparedness for future disaster relief operations, it is crucial to quantify pre- and post-tsunami groundwater levels across coastal districts in India. Since regional scale GWL field observations are not often available, this study instead used space gravimetry data from NASA’s Gravity Recovery and Climate Experiment (GRACE), along with soil moisture data from the Global Land Data Assimilation Systems (GLDAS), to quantify GWL fluctuations caused by the tsunami. A time-series analysis of equivalent groundwater thickness was developed for February 2004–December 2005 and the results indicated a net increase of 274 % in GWLs along coastal regions in Tamil Nadu following the tsunami. The net recharge volume of groundwater due to the tsunami was 16.8 km3, just 15 % lower than the total annual groundwater recharge (19.8 km3) for the state of Tamil Nadu. Additionally, GWLs returned to average within 3 months following the tsunami. The analysis demonstrated the utility of remotely sensed data in predicting and assessing the impacts of natural disasters. |
| format | Journal Article |
| id | CGSpace68437 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2016 |
| publishDateRange | 2016 |
| publishDateSort | 2016 |
| publisher | Springer |
| publisherStr | Springer |
| record_format | dspace |
| spelling | CGSpace684372025-06-17T08:24:00Z Improving spatiotemporal groundwater estimates after natural disasters using remotely sensed data: a case study of the Indian Ocean Tsunami Chinnasamy, Pennan Sunde, M.G. groundwater water levels water storage natural disasters disaster risk management tsunamis rain flooding salt water intrusion remote sensing coastal area soil moisture ecosystems The Indian Ocean Tsunami of December 26, 2004 devastated coastal ecosystems across South Asia. Along the coastal regions of South India, increased groundwater levels (GWL), largely caused by saltwater intrusion, infiltration from inundated land, and disturbance of freshwater lenses, were reported. Many agencies allocated funding for restoration and rehabilitation projects. However, to streamline funding allocation efforts, district-level groundwater inundation/recession data would have been a useful tool for planners. Thus, to ensure better preparedness for future disaster relief operations, it is crucial to quantify pre- and post-tsunami groundwater levels across coastal districts in India. Since regional scale GWL field observations are not often available, this study instead used space gravimetry data from NASA’s Gravity Recovery and Climate Experiment (GRACE), along with soil moisture data from the Global Land Data Assimilation Systems (GLDAS), to quantify GWL fluctuations caused by the tsunami. A time-series analysis of equivalent groundwater thickness was developed for February 2004–December 2005 and the results indicated a net increase of 274 % in GWLs along coastal regions in Tamil Nadu following the tsunami. The net recharge volume of groundwater due to the tsunami was 16.8 km3, just 15 % lower than the total annual groundwater recharge (19.8 km3) for the state of Tamil Nadu. Additionally, GWLs returned to average within 3 months following the tsunami. The analysis demonstrated the utility of remotely sensed data in predicting and assessing the impacts of natural disasters. 2016-03 2015-10-06T06:03:40Z 2015-10-06T06:03:40Z Journal Article https://hdl.handle.net/10568/68437 en Limited Access Springer Chinnasamy, Pennan; Sunde, M. G. 2015. Improving spatiotemporal groundwater estimates after natural disasters using remotely sensed data: a case study of the Indian Ocean Tsunami. Earth Science Informatics, 14p. (Online first). doi: https://doi.org/10.1007/s12145-015-0238-y |
| spellingShingle | groundwater water levels water storage natural disasters disaster risk management tsunamis rain flooding salt water intrusion remote sensing coastal area soil moisture ecosystems Chinnasamy, Pennan Sunde, M.G. Improving spatiotemporal groundwater estimates after natural disasters using remotely sensed data: a case study of the Indian Ocean Tsunami |
| title | Improving spatiotemporal groundwater estimates after natural disasters using remotely sensed data: a case study of the Indian Ocean Tsunami |
| title_full | Improving spatiotemporal groundwater estimates after natural disasters using remotely sensed data: a case study of the Indian Ocean Tsunami |
| title_fullStr | Improving spatiotemporal groundwater estimates after natural disasters using remotely sensed data: a case study of the Indian Ocean Tsunami |
| title_full_unstemmed | Improving spatiotemporal groundwater estimates after natural disasters using remotely sensed data: a case study of the Indian Ocean Tsunami |
| title_short | Improving spatiotemporal groundwater estimates after natural disasters using remotely sensed data: a case study of the Indian Ocean Tsunami |
| title_sort | improving spatiotemporal groundwater estimates after natural disasters using remotely sensed data a case study of the indian ocean tsunami |
| topic | groundwater water levels water storage natural disasters disaster risk management tsunamis rain flooding salt water intrusion remote sensing coastal area soil moisture ecosystems |
| url | https://hdl.handle.net/10568/68437 |
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