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
- 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-mWith 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.…
