Process‐Based Atmosphere‐Hydrology‐Malaria Modeling: Performance for Spatio‐Temporal Malaria Transmission Dynamics in Sub‐Saharan Africa
Abstract With the goal of eradication by 2030, Malaria poses a significant health threat, profoundly influenced by meteorological and hydrological conditions. In support of malaria vector control efforts, we present a high‐resolution, coupled physically‐based modeling approach integrating WRF‐Hydro...
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Wiley
2024-06-01
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| Series: | Water Resources Research |
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| Online Access: | https://doi.org/10.1029/2023WR034975 |
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| author | Mame Diarra Bousso Dieng Adrian M. Tompkins Joël Arnault Ali Sié Benjamin Fersch Patrick Laux Maximilian Schwarz Pascal Zabré Stephen Munga Sammy Khagayi Ibrahima Diouf Harald Kunstmann |
| author_facet | Mame Diarra Bousso Dieng Adrian M. Tompkins Joël Arnault Ali Sié Benjamin Fersch Patrick Laux Maximilian Schwarz Pascal Zabré Stephen Munga Sammy Khagayi Ibrahima Diouf Harald Kunstmann |
| author_sort | Mame Diarra Bousso Dieng |
| collection | DOAJ |
| description | Abstract With the goal of eradication by 2030, Malaria poses a significant health threat, profoundly influenced by meteorological and hydrological conditions. In support of malaria vector control efforts, we present a high‐resolution, coupled physically‐based modeling approach integrating WRF‐Hydro and VECTRI. This model approach accurately captures topographic details at the scale of larvae habitats in the Nouna Health and Demographic Surveillance Systems in Sub‐Saharan Africa. Our study demonstrates the proficiency of the high‐resolution hydrometeorological model, WRF‐Hydro, in replicating observed climate characteristics. Comparisons with in‐situ local weather data reveal root mean square errors between 0.6 and 0.87 mm/day for rainfall and correlations ranging from 0.79 to 0.87 for temperatures. Additionally, WRF‐Hydro's surface hydrology reproduces the seasonal and intraseasonal variability of the ponded water fraction with 96% accuracy, validated against Sentinel‐1 data at a 100‐m resolution. The VECTRI model demonstrates sensitivity to surface hydrology representation, particularly when comparing conceptual and detailed physical process models, for variables such as larvae density, mosquito abundance, and EIR. The model's ability to replicate the seasonality of malaria transmission aligns well with available cohort malaria data suggesting its potential for predicting the impacts of climate change on mosquito abundance and transmission intensity in endemic tropical and subtropical zones. This integrated approach opens avenues for enhanced understanding and proactive management of malaria. |
| format | Article |
| id | doaj-art-cbb5c348a85144ea8412d51f689a4d12 |
| institution | Kabale University |
| issn | 0043-1397 1944-7973 |
| language | English |
| publishDate | 2024-06-01 |
| publisher | Wiley |
| record_format | Article |
| series | Water Resources Research |
| spelling | doaj-art-cbb5c348a85144ea8412d51f689a4d122025-08-20T03:30:57ZengWileyWater Resources Research0043-13971944-79732024-06-01606n/an/a10.1029/2023WR034975Process‐Based Atmosphere‐Hydrology‐Malaria Modeling: Performance for Spatio‐Temporal Malaria Transmission Dynamics in Sub‐Saharan AfricaMame Diarra Bousso Dieng0Adrian M. Tompkins1Joël Arnault2Ali Sié3Benjamin Fersch4Patrick Laux5Maximilian Schwarz6Pascal Zabré7Stephen Munga8Sammy Khagayi9Ibrahima Diouf10Harald Kunstmann11Institute of Meteorology and Climate Research (IMK‐IFU) Campus Alpin Karlsruhe Institute of Technology (KIT) Garmisch‐Partenkirchen GermanyInternational Centre for Theoretical Physics (ICTP) Trieste ItalyInstitute of Meteorology and Climate Research (IMK‐IFU) Campus Alpin Karlsruhe Institute of Technology (KIT) Garmisch‐Partenkirchen GermanyCentre de Recherche en Santé de Nouna (CRSN) Nouna Burkina FasoInstitute of Meteorology and Climate Research (IMK‐IFU) Campus Alpin Karlsruhe Institute of Technology (KIT) Garmisch‐Partenkirchen GermanyInstitute of Meteorology and Climate Research (IMK‐IFU) Campus Alpin Karlsruhe Institute of Technology (KIT) Garmisch‐Partenkirchen GermanyRemote Sensing Solutions GmbH (RSS) Munich GermanyCentre de Recherche en Santé de Nouna (CRSN) Nouna Burkina FasoKenya Medical Research Institute (KEMRI) Kisumu KenyaKenya Medical Research Institute (KEMRI) Kisumu KenyaLaboratoire de Physique de l'atmosphère et de l'océan Siméon Fongang (LPAOSF‐ESP) Dakar SenegalInstitute of Meteorology and Climate Research (IMK‐IFU) Campus Alpin Karlsruhe Institute of Technology (KIT) Garmisch‐Partenkirchen GermanyAbstract With the goal of eradication by 2030, Malaria poses a significant health threat, profoundly influenced by meteorological and hydrological conditions. In support of malaria vector control efforts, we present a high‐resolution, coupled physically‐based modeling approach integrating WRF‐Hydro and VECTRI. This model approach accurately captures topographic details at the scale of larvae habitats in the Nouna Health and Demographic Surveillance Systems in Sub‐Saharan Africa. Our study demonstrates the proficiency of the high‐resolution hydrometeorological model, WRF‐Hydro, in replicating observed climate characteristics. Comparisons with in‐situ local weather data reveal root mean square errors between 0.6 and 0.87 mm/day for rainfall and correlations ranging from 0.79 to 0.87 for temperatures. Additionally, WRF‐Hydro's surface hydrology reproduces the seasonal and intraseasonal variability of the ponded water fraction with 96% accuracy, validated against Sentinel‐1 data at a 100‐m resolution. The VECTRI model demonstrates sensitivity to surface hydrology representation, particularly when comparing conceptual and detailed physical process models, for variables such as larvae density, mosquito abundance, and EIR. The model's ability to replicate the seasonality of malaria transmission aligns well with available cohort malaria data suggesting its potential for predicting the impacts of climate change on mosquito abundance and transmission intensity in endemic tropical and subtropical zones. This integrated approach opens avenues for enhanced understanding and proactive management of malaria.https://doi.org/10.1029/2023WR034975hydrometeorologymalaria transmissionpondshigh‐resolutionWRF‐HydroVECTRI |
| spellingShingle | Mame Diarra Bousso Dieng Adrian M. Tompkins Joël Arnault Ali Sié Benjamin Fersch Patrick Laux Maximilian Schwarz Pascal Zabré Stephen Munga Sammy Khagayi Ibrahima Diouf Harald Kunstmann Process‐Based Atmosphere‐Hydrology‐Malaria Modeling: Performance for Spatio‐Temporal Malaria Transmission Dynamics in Sub‐Saharan Africa Water Resources Research hydrometeorology malaria transmission ponds high‐resolution WRF‐Hydro VECTRI |
| title | Process‐Based Atmosphere‐Hydrology‐Malaria Modeling: Performance for Spatio‐Temporal Malaria Transmission Dynamics in Sub‐Saharan Africa |
| title_full | Process‐Based Atmosphere‐Hydrology‐Malaria Modeling: Performance for Spatio‐Temporal Malaria Transmission Dynamics in Sub‐Saharan Africa |
| title_fullStr | Process‐Based Atmosphere‐Hydrology‐Malaria Modeling: Performance for Spatio‐Temporal Malaria Transmission Dynamics in Sub‐Saharan Africa |
| title_full_unstemmed | Process‐Based Atmosphere‐Hydrology‐Malaria Modeling: Performance for Spatio‐Temporal Malaria Transmission Dynamics in Sub‐Saharan Africa |
| title_short | Process‐Based Atmosphere‐Hydrology‐Malaria Modeling: Performance for Spatio‐Temporal Malaria Transmission Dynamics in Sub‐Saharan Africa |
| title_sort | process based atmosphere hydrology malaria modeling performance for spatio temporal malaria transmission dynamics in sub saharan africa |
| topic | hydrometeorology malaria transmission ponds high‐resolution WRF‐Hydro VECTRI |
| url | https://doi.org/10.1029/2023WR034975 |
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