Hydrology and Earth System Sciences
Hydrology and Earth System Sciences
HESS
Articles | Volume 28, issue 13
Articles | Volume 28, issue 13
https://doi.org/10.5194/hess-28-2949-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-28-2949-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 28, issue 13
Research article
 | 
05 Jul 2024
Research article |  | 05 Jul 2024

High-resolution long-term average groundwater recharge in Africa estimated using random forest regression and residual interpolation

Anna Pazola, Mohammad Shamsudduha, Jon French, Alan M. MacDonald, Tamiru Abiye, Ibrahim Baba Goni, and Richard G. Taylor

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Cited articles

Abouelmagd, A., Sultan, M., Milewski, A., Kehew, A. E., Sturchio, N. C., Soliman, F., Krishnamurthy, R., and Cutrim, E.: Toward a better understanding of palaeoclimatic regimes that recharged the fossil aquifers in North Africa: Inferences from stable isotope and remote sensing data, Palaeogeogr. Palaeocl. Palaeoecol., 329–330, 137–149, https://doi.org/10.1016/j.palaeo.2012.02.024, 2012. a
Al-Fugara, A., Pourghasemi, H. R., Al-Shabeeb, A. R., Habib, M., Al-Adamat, R., AI-Amoush, H., and Collins, A. L.: A comparison of machine learning models for the mapping of groundwater spring potential, Environ. Earth Sci., 79, 206, https://doi.org/10.1007/s12665-020-08944-1, 2020. a
Altchenko, Y. and Villholth, K. G.: Mapping irrigation potential from renewable groundwater in Africa – a quantitative hydrological approach, Hydrol. Earth Syst. Sci., 19, 1055–1067, https://doi.org/10.5194/hess-19-1055-2015, 2015. a
Berghuijs, W. R., Luijendijk, E., Moeck, C., van der Velde, Y., and Allen, S. T.: Global Recharge Data Set Indicates Strengthened Groundwater Connection to Surface Fluxes, Geophys. Res. Lett., 49, e2022GL099010, https://doi.org/10.1029/2022GL099010, 2022. a, b
Boehmke, B. and Greenwell, B.: Feature & Target Engineering, in: Chap. 3, p. 42, ISBN 9780367816377, https://doi.org/10.1201/9780367816377, 2019. a
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This study advances groundwater research using a high-resolution random forest model, revealing new recharge areas and spatial variability, mainly in humid regions. Limited data in rainy zones is a constraint for the model. Our findings underscore the promise of machine learning for large-scale groundwater modelling while further emphasizing the importance of data collection for robust results.
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