Surface water and flood-based agricultural systems: Mapping and modelling long-term variability in the Senegal river floodplain
In the alluvial plains of large rivers, annual flooding is essential for numerous ecosystem services, including flood-based agriculture, biodiversity and groundwater recharge. Remote sensing provides increased opportunities to monitor surface water dynamics across large floodplains that are currentl...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-03-01
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| Series: | Agricultural Water Management |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S0378377424005900 |
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| author | Andrew Ogilvie Cheickh Sadibou Fall Ansoumana Bodian Didier Martin Laurent Bruckmann Djiby Dia Issa Leye Papa Malick Ndiaye Donissongou Dimitri Soro Jean Homian Danumah Jean-Claude Bader Jean-Christophe Poussin |
| author_facet | Andrew Ogilvie Cheickh Sadibou Fall Ansoumana Bodian Didier Martin Laurent Bruckmann Djiby Dia Issa Leye Papa Malick Ndiaye Donissongou Dimitri Soro Jean Homian Danumah Jean-Claude Bader Jean-Christophe Poussin |
| author_sort | Andrew Ogilvie |
| collection | DOAJ |
| description | In the alluvial plains of large rivers, annual flooding is essential for numerous ecosystem services, including flood-based agriculture, biodiversity and groundwater recharge. Remote sensing provides increased opportunities to monitor surface water dynamics across large floodplains that are currently poorly captured by local hydrological monitoring and modelling due to data scarcity and the flat, heterogeneous topography. Combining the advances in earth observations with hydrological modelling and extensive in situ fieldwork, this research seeks to improve our understanding of surface water dynamics and associated agricultural practices in the Senegal river floodplain. 2813 mosaics from Landsat, MODIS and Sentinel-2 earth observations are created to map and monitor surface water variations using a site specific MNDWI classification adapted to complex, wetland environments. Validated against ground truth data, the approach is upscaled using cloud computing across this 2250 km2 floodplain over 1999–2022. Statistical regression models are then developed to estimate flooded and cultivated areas based on upstream flow values since 1950 and analyse trends and exceedance probabilities over time. Results reveal extreme interannual variations in peak flooded areas, ranging from 30,000 ha and 720,000 ha between 1950 and 2022, while annual water modules fluctuate between 210 and 1460 m3/s. After 1994, flooded areas show partial recovery, with 95th percentile reaching 89,000 ha during 1994–2022 compared to 37,000 ha in 1972–1993. Flood-based agricultural practices cover between 13,000 ha and 133,000 ha over the same period, highlighting the pronounced variability faced by local rural communities. Occurrence maps and predictive models for annual flooded and cultivated areas based on upstream flows can support early warning tools, helping to prepare for extreme floods and droughts. These outputs are crucial to assess the impact of future climatic and anthropic changes in the region, including planned dams, on the amplitude of annual floods and their associated environmental benefits. |
| format | Article |
| id | doaj-art-5a36bda0ae9f49be9989c3a6aed5e7b2 |
| institution | Kabale University |
| issn | 1873-2283 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Agricultural Water Management |
| spelling | doaj-art-5a36bda0ae9f49be9989c3a6aed5e7b22025-01-25T04:10:40ZengElsevierAgricultural Water Management1873-22832025-03-01308109254Surface water and flood-based agricultural systems: Mapping and modelling long-term variability in the Senegal river floodplainAndrew Ogilvie0Cheickh Sadibou Fall1Ansoumana Bodian2Didier Martin3Laurent Bruckmann4Djiby Dia5Issa Leye6Papa Malick Ndiaye7Donissongou Dimitri Soro8Jean Homian Danumah9Jean-Claude Bader10Jean-Christophe Poussin11G-EAU, AgroParisTech, BRGM, Cirad, INRAE, Institut Agro, IRD, Univ Montpellier, Montpellier, France; ISRA, BAME, Dakar, Senegal; Corresponding author at: G-EAU, AgroParisTech, BRGM, Cirad, INRAE, Institut Agro, IRD, Univ Montpellier, Montpellier, France.ISRA, BAME, Dakar, SenegalLaboratoire Leïdi ”Dynamics of Territories and Development”, Université Gaston Berger (UGB), Saint-Louis, SenegalG-EAU, AgroParisTech, BRGM, Cirad, INRAE, Institut Agro, IRD, Univ Montpellier, Montpellier, FranceDepartment of Civil and Water Engineering, Université Laval, Quebec, Canada; CentrEau, Water Research Center, Quebec, CanadaISRA, BAME, Dakar, SenegalG-EAU, AgroParisTech, BRGM, Cirad, INRAE, Institut Agro, IRD, Univ Montpellier, Montpellier, FranceG-EAU, AgroParisTech, BRGM, Cirad, INRAE, Institut Agro, IRD, Univ Montpellier, Montpellier, France; Laboratoire Leïdi ”Dynamics of Territories and Development”, Université Gaston Berger (UGB), Saint-Louis, SenegalUFR Sciences de la Terre et des Ressources Minières (STRM), Université Félix Houphouët Boigny, Abidjan, Côte d’IvoireUFR Sciences de la Terre et des Ressources Minières (STRM), Université Félix Houphouët Boigny, Abidjan, Côte d’Ivoire; CURAT, Université Félix Houphouët Boigny, Abidjan, Côte d’IvoireG-EAU, AgroParisTech, BRGM, Cirad, INRAE, Institut Agro, IRD, Univ Montpellier, Montpellier, FranceG-EAU, AgroParisTech, BRGM, Cirad, INRAE, Institut Agro, IRD, Univ Montpellier, Montpellier, FranceIn the alluvial plains of large rivers, annual flooding is essential for numerous ecosystem services, including flood-based agriculture, biodiversity and groundwater recharge. Remote sensing provides increased opportunities to monitor surface water dynamics across large floodplains that are currently poorly captured by local hydrological monitoring and modelling due to data scarcity and the flat, heterogeneous topography. Combining the advances in earth observations with hydrological modelling and extensive in situ fieldwork, this research seeks to improve our understanding of surface water dynamics and associated agricultural practices in the Senegal river floodplain. 2813 mosaics from Landsat, MODIS and Sentinel-2 earth observations are created to map and monitor surface water variations using a site specific MNDWI classification adapted to complex, wetland environments. Validated against ground truth data, the approach is upscaled using cloud computing across this 2250 km2 floodplain over 1999–2022. Statistical regression models are then developed to estimate flooded and cultivated areas based on upstream flow values since 1950 and analyse trends and exceedance probabilities over time. Results reveal extreme interannual variations in peak flooded areas, ranging from 30,000 ha and 720,000 ha between 1950 and 2022, while annual water modules fluctuate between 210 and 1460 m3/s. After 1994, flooded areas show partial recovery, with 95th percentile reaching 89,000 ha during 1994–2022 compared to 37,000 ha in 1972–1993. Flood-based agricultural practices cover between 13,000 ha and 133,000 ha over the same period, highlighting the pronounced variability faced by local rural communities. Occurrence maps and predictive models for annual flooded and cultivated areas based on upstream flows can support early warning tools, helping to prepare for extreme floods and droughts. These outputs are crucial to assess the impact of future climatic and anthropic changes in the region, including planned dams, on the amplitude of annual floods and their associated environmental benefits.http://www.sciencedirect.com/science/article/pii/S0378377424005900Surface waterRemote sensingWest AfricaLong-term monitoringFlood-based agricultural systems |
| spellingShingle | Andrew Ogilvie Cheickh Sadibou Fall Ansoumana Bodian Didier Martin Laurent Bruckmann Djiby Dia Issa Leye Papa Malick Ndiaye Donissongou Dimitri Soro Jean Homian Danumah Jean-Claude Bader Jean-Christophe Poussin Surface water and flood-based agricultural systems: Mapping and modelling long-term variability in the Senegal river floodplain Agricultural Water Management Surface water Remote sensing West Africa Long-term monitoring Flood-based agricultural systems |
| title | Surface water and flood-based agricultural systems: Mapping and modelling long-term variability in the Senegal river floodplain |
| title_full | Surface water and flood-based agricultural systems: Mapping and modelling long-term variability in the Senegal river floodplain |
| title_fullStr | Surface water and flood-based agricultural systems: Mapping and modelling long-term variability in the Senegal river floodplain |
| title_full_unstemmed | Surface water and flood-based agricultural systems: Mapping and modelling long-term variability in the Senegal river floodplain |
| title_short | Surface water and flood-based agricultural systems: Mapping and modelling long-term variability in the Senegal river floodplain |
| title_sort | surface water and flood based agricultural systems mapping and modelling long term variability in the senegal river floodplain |
| topic | Surface water Remote sensing West Africa Long-term monitoring Flood-based agricultural systems |
| url | http://www.sciencedirect.com/science/article/pii/S0378377424005900 |
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