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

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

doi:https://doi.org/10.5194/hess-28-2949-2024

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

Supplement

https://doi.org/10.5194/hess-28-2949-2024-supplement

Data sets

High-resolution long-term average groundwater recharge in Africa estimated using random forest regression and residual interpolation Anna Pazola https://doi.org/10.6084/m9.figshare.22591375.v1

[Input data] High-resolution long-term average groundwater recharge in Africa estimated using random forest regression and residual interpolation Anna Pazola https://doi.org/10.6084/m9.figshare.22591438.v1

Model code and software

Application of random forest regression in modelling long-term average groundwater recharge in Africa Anna Pazola https://doi.org/10.5281/zenodo.12579444

Short summary

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.