Resilience to climate-induced conflict in the Horn of Africa
The Shuffled Complex Evolution—Universal Algorithm (SCE‐UA) is an automatic calibration algorithm that has shown success in finding a globally optimum objective function with more efficiency than other methods. We incorporated the SCE‐UA into our novel modeling environment, utilizing an ontology‐bas...
| Autores principales: | , , |
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| Formato: | Capítulo de libro |
| Lenguaje: | Inglés |
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International Food Policy Research Institute
2014
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| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/150107 |
| _version_ | 1855539317030518784 |
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| author | Headey, Derek D. Maystadt, Jean-François Calderone, Margherita Bernal |
| author_browse | Calderone, Margherita Bernal Headey, Derek D. Maystadt, Jean-François |
| author_facet | Headey, Derek D. Maystadt, Jean-François Calderone, Margherita Bernal |
| author_sort | Headey, Derek D. |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | The Shuffled Complex Evolution—Universal Algorithm (SCE‐UA) is an automatic calibration algorithm that has shown success in finding a globally optimum objective function with more efficiency than other methods. We incorporated the SCE‐UA into our novel modeling environment, utilizing an ontology‐based simulation (OntoSim‐Sugarcane) framework adapted to analyze groundwater table (WT) fluctuations and drainage practices on four farm basins in the Everglades Agricultural Area of south Florida.Utilizing two water years (WY96–97) of farm WT fluctuations observed at a portion (<16 ha) of each farm basin, two parameters—lateral hydraulic conductivities of soil profile and vertical hydraulic conductivity of underlying limestone—were automatically calibrated. Regardless of farms, the best parameter sets that minimize the objective function of daily root mean square error could be found after 1500 simulation runs. The quality of matching simulated to observed values of farm WT were further assessed by the Nash–Sutcliffe efficiency coefficient (NSE). The NSE ranged from 0.38 to 0.75 (calibration period, WY96–97) and 0.10 to 0.76 (validation period, WY98–99) on all four farms. These results indicate that this coupling strengthens the capability of OntoSim‐Sugarcane to model hydrology by objectively finding the best parameter sets. Copyright © 2014 John Wiley & Sons, Ltd. |
| format | Book Chapter |
| id | CGSpace150107 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2014 |
| publishDateRange | 2014 |
| publishDateSort | 2014 |
| publisher | International Food Policy Research Institute |
| publisherStr | International Food Policy Research Institute |
| record_format | dspace |
| spelling | CGSpace1501072025-11-06T04:16:11Z Resilience to climate-induced conflict in the Horn of Africa Headey, Derek D. Maystadt, Jean-François Calderone, Margherita Bernal economic shock nutrition security environmental factors shock economic development social protection pastoralism malnutrition drought food security weather conflicts food prices social safety nets resilience climate change The Shuffled Complex Evolution—Universal Algorithm (SCE‐UA) is an automatic calibration algorithm that has shown success in finding a globally optimum objective function with more efficiency than other methods. We incorporated the SCE‐UA into our novel modeling environment, utilizing an ontology‐based simulation (OntoSim‐Sugarcane) framework adapted to analyze groundwater table (WT) fluctuations and drainage practices on four farm basins in the Everglades Agricultural Area of south Florida.Utilizing two water years (WY96–97) of farm WT fluctuations observed at a portion (<16 ha) of each farm basin, two parameters—lateral hydraulic conductivities of soil profile and vertical hydraulic conductivity of underlying limestone—were automatically calibrated. Regardless of farms, the best parameter sets that minimize the objective function of daily root mean square error could be found after 1500 simulation runs. The quality of matching simulated to observed values of farm WT were further assessed by the Nash–Sutcliffe efficiency coefficient (NSE). The NSE ranged from 0.38 to 0.75 (calibration period, WY96–97) and 0.10 to 0.76 (validation period, WY98–99) on all four farms. These results indicate that this coupling strengthens the capability of OntoSim‐Sugarcane to model hydrology by objectively finding the best parameter sets. Copyright © 2014 John Wiley & Sons, Ltd. 2014 2024-08-01T02:50:43Z 2024-08-01T02:50:43Z Book Chapter https://hdl.handle.net/10568/150107 en https://doi.org/10.2499/9780896296787 Open Access application/pdf International Food Policy Research Institute Calderone, Margherita; Headey, Derek D. and Maystadt, Jean-François. 2014. Resilience to climate-induced conflict in the Horn of Africa. In Resilience for food and nutrition security. Eds. Fan, Shenggen; Pandya-Lorch, Rajul and Yosef, Sivan. Chapter 8. Pp. 65-74. Washington, DC: International Food Policy Research Institute (IFPRI). https://hdl.handle.net/10568/150107 |
| spellingShingle | economic shock nutrition security environmental factors shock economic development social protection pastoralism malnutrition drought food security weather conflicts food prices social safety nets resilience climate change Headey, Derek D. Maystadt, Jean-François Calderone, Margherita Bernal Resilience to climate-induced conflict in the Horn of Africa |
| title | Resilience to climate-induced conflict in the Horn of Africa |
| title_full | Resilience to climate-induced conflict in the Horn of Africa |
| title_fullStr | Resilience to climate-induced conflict in the Horn of Africa |
| title_full_unstemmed | Resilience to climate-induced conflict in the Horn of Africa |
| title_short | Resilience to climate-induced conflict in the Horn of Africa |
| title_sort | resilience to climate induced conflict in the horn of africa |
| topic | economic shock nutrition security environmental factors shock economic development social protection pastoralism malnutrition drought food security weather conflicts food prices social safety nets resilience climate change |
| url | https://hdl.handle.net/10568/150107 |
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