Assessment of the spatial–temporal distribution of groundwater recharge in data-scarce large-scale African river basin

The systematic assessment of spatial and temporal distribution of groundwater recharge (GWR) is crucial for the sustainable management of the water resources systems, especially in large-scale river basins. This helps in identifying critical zones in which GWR largely varies and thus leads to negati...

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Veröffentlicht in:Environmental monitoring and assessment 2022-03, Vol.194 (3), p.157-157, Article 157
Hauptverfasser: Gelebo, Ayano Hirbo, Kasiviswanathan, K. S., Khare, Deepak
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Sprache:eng
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Zusammenfassung:The systematic assessment of spatial and temporal distribution of groundwater recharge (GWR) is crucial for the sustainable management of the water resources systems, especially in large-scale river basins. This helps in identifying critical zones in which GWR largely varies and thus leads to negative consequences. However, such analyses might not be possible when the models require detailed hydro-climate and hydrogeological data in data-scarce regions. Hence, this calls for alternate suitable modeling approaches that are applicable with the limited data and, however, includes the detailed assessment of the spatial–temporal distribution of different water balance components especially the GWR component. This paper aimed at investigating the spatial and temporal distribution of the GWR at monthly, seasonal and annual scales using the WetSpass-M physically distributed hydrological model, which is not requiring the detailed catchment information. In addition, the study conducted the sensitivity analysis of model parameters to assess the significant variation of GWR. The large-scale river basins such as the Omo river basin, Ethiopia, were chosen to demonstrate the potential of the WetSpass-M model under limited data conditions. From the modeling results, it was found that the maximum average monthly GWR of 13.4 mm occurs in July. The estimated average seasonal GWR is 32.5 mm/yr and 47.6 mm/yr in the summer and winter seasons, respectively. Further, it was found that GWR is highly sensitive to the parameter such as average rainfall intensity factor.
ISSN:0167-6369
1573-2959
DOI:10.1007/s10661-022-09778-z