What rainfall does not tell us—Enhancing financial instruments with satellite-derived soil moisture and evaporative stress
Advanced parametric financial instruments, like weather index insurance (WII) and risk contingency credit (RCC), support disaster-risk management and reduction in the world’s most disaster-prone regions. Simultaneously, satellite data that are capable of cross-checking rainfall estimates, the “stand...
| Main Authors: | , , , , , , , |
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| Format: | Journal Article |
| Language: | Inglés |
| Published: |
MDPI
2018
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10568/146096 |
| _version_ | 1855520539677818880 |
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| author | Enenkel, Markus Farah, Carlos Hain, Christopher White, Andrew Anderson, Martha You, Liangzhi Wagner, Wolfgang Osgood, Daniel |
| author_browse | Anderson, Martha Enenkel, Markus Farah, Carlos Hain, Christopher Osgood, Daniel Wagner, Wolfgang White, Andrew You, Liangzhi |
| author_facet | Enenkel, Markus Farah, Carlos Hain, Christopher White, Andrew Anderson, Martha You, Liangzhi Wagner, Wolfgang Osgood, Daniel |
| author_sort | Enenkel, Markus |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Advanced parametric financial instruments, like weather index insurance (WII) and risk contingency credit (RCC), support disaster-risk management and reduction in the world’s most disaster-prone regions. Simultaneously, satellite data that are capable of cross-checking rainfall estimates, the “standard dataset” to develop such financial safety nets, are gaining importance as complementary sources of information. This study concentrates on the analysis of satellite-derived multi-sensor soil moisture (ESA CCI, Version v04.2), the evapotranspiration-based Evaporative Stress Index (ESI), and CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data) rainfall estimates in nine East African countries. Based on spatial correlation analysis, we found matching spatial/temporal patterns between all three datasets, with the highest correlation coefficient occurring between October and March. In large parts of Kenya, Ethiopia, and Somalia, we observed a lower (partly negative) correlation coefficient between June and August, which was likely caused by issues related to cloud cover and the volume scattering of microwaves in sandy, hot soils. Based on simple linear and logit regression analysis with annual, national maize yield estimates as the dependent variable, we found that, depending on the chosen period (averages per year, growing or harvesting months), there was added value (higher R-squared) if two or all three variables were combined. The ESI and soil moisture have the potential to close sensitive knowledge gaps between atmospheric moisture supply and the response of the land surface in operational parametric insurance projects. For the development and calibration of WII and RCC, this means that better proxies for historical and potential future drought impact can strengthen “drought narratives”, resulting in a better match between calculated payouts/credit repayment levels and the actual needs of smallholder farmers. |
| format | Journal Article |
| id | CGSpace146096 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2018 |
| publishDateRange | 2018 |
| publishDateSort | 2018 |
| publisher | MDPI |
| publisherStr | MDPI |
| record_format | dspace |
| spelling | CGSpace1460962025-12-08T10:29:22Z What rainfall does not tell us—Enhancing financial instruments with satellite-derived soil moisture and evaporative stress Enenkel, Markus Farah, Carlos Hain, Christopher White, Andrew Anderson, Martha You, Liangzhi Wagner, Wolfgang Osgood, Daniel spatial data rain risk management remote sensing evapotranspiration weather index insurance risk prevention disaster risk management drought soil cracking soil water content risk credit Advanced parametric financial instruments, like weather index insurance (WII) and risk contingency credit (RCC), support disaster-risk management and reduction in the world’s most disaster-prone regions. Simultaneously, satellite data that are capable of cross-checking rainfall estimates, the “standard dataset” to develop such financial safety nets, are gaining importance as complementary sources of information. This study concentrates on the analysis of satellite-derived multi-sensor soil moisture (ESA CCI, Version v04.2), the evapotranspiration-based Evaporative Stress Index (ESI), and CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data) rainfall estimates in nine East African countries. Based on spatial correlation analysis, we found matching spatial/temporal patterns between all three datasets, with the highest correlation coefficient occurring between October and March. In large parts of Kenya, Ethiopia, and Somalia, we observed a lower (partly negative) correlation coefficient between June and August, which was likely caused by issues related to cloud cover and the volume scattering of microwaves in sandy, hot soils. Based on simple linear and logit regression analysis with annual, national maize yield estimates as the dependent variable, we found that, depending on the chosen period (averages per year, growing or harvesting months), there was added value (higher R-squared) if two or all three variables were combined. The ESI and soil moisture have the potential to close sensitive knowledge gaps between atmospheric moisture supply and the response of the land surface in operational parametric insurance projects. For the development and calibration of WII and RCC, this means that better proxies for historical and potential future drought impact can strengthen “drought narratives”, resulting in a better match between calculated payouts/credit repayment levels and the actual needs of smallholder farmers. 2018-11-28 2024-06-21T09:05:49Z 2024-06-21T09:05:49Z Journal Article https://hdl.handle.net/10568/146096 en Open Access MDPI Enenkel, Markus; Farah, Carlos; Hain, Christopher; White, Andrew; Anderson, Martha; You, Liangzhi; Wagner, Wolfgang; and Osgood, Daniel. 2018. What rainfall does not tell us—Enhancing financial instruments with satellite-derived soil moisture and evaporative stress. Remote Sensing 10(11), 1819. https://doi.org/10.3390/rs10111819 |
| spellingShingle | spatial data rain risk management remote sensing evapotranspiration weather index insurance risk prevention disaster risk management drought soil cracking soil water content risk credit Enenkel, Markus Farah, Carlos Hain, Christopher White, Andrew Anderson, Martha You, Liangzhi Wagner, Wolfgang Osgood, Daniel What rainfall does not tell us—Enhancing financial instruments with satellite-derived soil moisture and evaporative stress |
| title | What rainfall does not tell us—Enhancing financial instruments with satellite-derived soil moisture and evaporative stress |
| title_full | What rainfall does not tell us—Enhancing financial instruments with satellite-derived soil moisture and evaporative stress |
| title_fullStr | What rainfall does not tell us—Enhancing financial instruments with satellite-derived soil moisture and evaporative stress |
| title_full_unstemmed | What rainfall does not tell us—Enhancing financial instruments with satellite-derived soil moisture and evaporative stress |
| title_short | What rainfall does not tell us—Enhancing financial instruments with satellite-derived soil moisture and evaporative stress |
| title_sort | what rainfall does not tell us enhancing financial instruments with satellite derived soil moisture and evaporative stress |
| topic | spatial data rain risk management remote sensing evapotranspiration weather index insurance risk prevention disaster risk management drought soil cracking soil water content risk credit |
| url | https://hdl.handle.net/10568/146096 |
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