Abstract
In this study, we assess the validity of an African-scale groundwater pollution model for nitrates. In a previous study, we identified a statistical continental-scale groundwater pollution model for nitrate. The model was identified using a pan-African meta-analysis of available nitrate groundwater pollution studies. The model was implemented in both Random Forest (RF) and multiple regression formats. For both approaches, we collected as predictors a comprehensive GIS database of 13 spatial attributes, related to land use, soil type, hydrogeology, topography, climatology, region typology, nitrogen fertiliser application rate, and population density. In this paper, we validate the continental-scale model of groundwater contamination by using a nitrate measurement dataset from three African countries. We discuss the issue of data availability, and quality and scale issues, as challenges in validation. Notwithstanding that the modelling procedure exhibited very good success using a continental-scale dataset (e.g. R2 = 0.97 in the RF format using a cross-validation approach), the continental-scale model could not be used without recalibration to predict nitrate pollution at the country scale using regional data. In addition, when recalibrating the model using country-scale datasets, the order of model exploratory factors changes. This suggests that the structure and the parameters of a statistical spatially distributed groundwater degradation model for the African continent are strongly scale dependent.
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This study was carried out within the framework of a doctoral research programme, and has been supported by the Islamic Development Bank (IDB) under the Merit Scholarship Programme (MSP) for theses and the ‘Fonds Spécial de Recherche’ (FSR) of the Université Catholique de Louvain. Several people from across the world helped with data acquisition, namely T. Gleeson (McGill University), N. Moosdorf (Hamburg University), and M. Cissé (DGPRE/Senegal).
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Ouedraogo, I., Defourny, P. & Vanclooster, M. Validating a continental-scale groundwater diffuse pollution model using regional datasets. Environ Sci Pollut Res 26, 2105–2119 (2019). https://doi.org/10.1007/s11356-017-0899-9
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DOI: https://doi.org/10.1007/s11356-017-0899-9