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Recharge Estimation Approach in a Data-Scarce Semi-Arid Region, Northern Ethiopian Rift Valley

Author

Listed:
  • Sisay S. Mekonen

    (Chair of Hydrology and River Basin Management, School of Engineering and Design, Technical University of Munich, 80333 Munich, Germany
    Faculty of Water Resources and Irrigation Engineering, Arba Minch University, Arba Minch P.O. Box 21, Ethiopia)

  • Scott E. Boyce

    (Chair of Hydrology and River Basin Management, School of Engineering and Design, Technical University of Munich, 80333 Munich, Germany
    U.S. Geological Survey, California Water Science Center, 4165 Spruance Rd., Suite 200, San Diego, CA 92101-0812, USA)

  • Abdella K. Mohammed

    (Faculty of Hydraulic and Water Resources Engineering, Arba Minch University, Arba Minch P.O. Box 21, Ethiopia)

  • Lorraine Flint

    (Earth Knowledge Inc., Tucson, AZ 85751-0743, USA)

  • Alan Flint

    (Earth Knowledge Inc., Tucson, AZ 85751-0743, USA)

  • Markus Disse

    (Chair of Hydrology and River Basin Management, School of Engineering and Design, Technical University of Munich, 80333 Munich, Germany)

Abstract

Sustainable management of groundwater resources highly relies on the accurate estimation of recharge. However, accurate recharge estimation is a challenge, especially in data-scarce regions, as the existing models are data-intensive and require extensive parameterization. This study developed a process-based hydrologic model combining local and remotely sensed data for characterizing recharge in data-limited regions using a Basin Characterization Model (BCM). This study was conducted in Raya and Kobo Valleys, a semi-arid region in Northern Ethiopia, considering both the structural basin and the surrounding mountainous recharge areas. Climatic Research Unit monthly datasets for 1991 to 2020 and WaPOR actual evapotranspiration data were used. The model results show that the average annual recharge and surface runoff from 1991 to 2020 were 73 mm and 167 mm, respectively, with a substantial portion contributed along the front of the mountainous parts of the study area. The mountainous recharge occurred along and above the valleys as mountain-block and mountain-front recharge. The long-term estimates of the monthly recharge time series indicated that the water balance components follow the temporal pattern of rainfall amount. However, the relation of recharge to precipitation was nonlinearly related, showing the episodic nature of recharge in semi-arid regions. This study informed the spatial and temporal distribution of recharge and runoff hydrologic variables at fine spatial scales for each grid cell, allowing results to be summarized for various planning units, including farmlands. One third of the precipitation in the drainage basin becomes recharge and runoff, while the remaining is lost through evapotranspiration. The current study’s findings are vital for developing plans for sustainable management of water resources in semi-arid regions. Also, monthly groundwater withdrawals for agriculture should be regulated in relation to spatial and temporal recharge patterns. We conclude that combining scarce local data with global datasets and tools is a useful approach for estimating recharge to manage groundwater resources in data-scarce regions.

Suggested Citation

  • Sisay S. Mekonen & Scott E. Boyce & Abdella K. Mohammed & Lorraine Flint & Alan Flint & Markus Disse, 2023. "Recharge Estimation Approach in a Data-Scarce Semi-Arid Region, Northern Ethiopian Rift Valley," Sustainability, MDPI, vol. 15(22), pages 1, November.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:22:p:15887-:d:1279142
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    References listed on IDEAS

    as
    1. Delphine Renard & David Tilman, 2019. "National food production stabilized by crop diversity," Nature, Nature, vol. 571(7764), pages 257-260, July.
    2. Belay Z. Abate & Tewodros T. Assefa & Tibebe B. Tigabu & Wubneh B. Abebe & Li He, 2023. "Hydrological Modeling of the Kobo-Golina River in the Data-Scarce Upper Danakil Basin, Ethiopia," Sustainability, MDPI, vol. 15(4), pages 1-23, February.
    3. Ahmed Khaled Abdella Ahmed & Mustafa El-Rawy & Amira Mofreh Ibraheem & Nassir Al-Arifi & Mahmoud Khaled Abd-Ellah, 2023. "Forecasting of Groundwater Quality by Using Deep Learning Time Series Techniques in an Arid Region," Sustainability, MDPI, vol. 15(8), pages 1-16, April.
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