Identification of hotspots and cold-spots of groundwater potential using spatial statistics
Study region: The Guna-Tana landscape is located in Ethiopia. This landscape is seriously facing water scarcity problems, that’s why we studied this landscape and provided the hotspots of groundwater potential areas in this region. Study focus: In this study the hotspots and cold-spots of groundwate...
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| Format: | Article |
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Elsevier
2024-12-01
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| Series: | Journal of Hydrology: Regional Studies |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2214581824003264 |
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| author | Tao Liu Imran Ahmad Mithas Ahmad Dar Martina Zelenakova Lema Misgan Gebrie Teshome Kifle Gashaw Sintayehu Angualie |
| author_facet | Tao Liu Imran Ahmad Mithas Ahmad Dar Martina Zelenakova Lema Misgan Gebrie Teshome Kifle Gashaw Sintayehu Angualie |
| author_sort | Tao Liu |
| collection | DOAJ |
| description | Study region: The Guna-Tana landscape is located in Ethiopia. This landscape is seriously facing water scarcity problems, that’s why we studied this landscape and provided the hotspots of groundwater potential areas in this region. Study focus: In this study the hotspots and cold-spots of groundwater potential at, 99, 95, and 90 % confidence levels has been deciphered. Using Gi-Bin values, four classes has been identified viz., 2–3 (highly favorable), 0–1 (fairly favorable), −2 to −1 (poorly favorable) and −3 (very poorly favorable). The hotspots was subjected to ordinary least squared (OLS) regression to understand the impact of chosen parameters (viz., geology, land-use, soil, rainfall, slope, and distance to rivers) towards groundwater potential. New hydrological insights for the region: The absence of redundancy among the selected parameters was indicated by the VIF values of the parameters, which were determined to be less than 7.5. It was discovered that the Robust Probability (Robust_Pr) was statistically significant (p < 0.01). The OLS model appears to have captured the variability of exploratory variables, as evidenced by the decreased values of Akaike's Information Criterion (AICc). The Adjusted R-squared value of 0.9119 indicates that exploratory variables has successfully explained 91.19 % of the variance of the model. |
| format | Article |
| id | doaj-art-e36b279f093a439e9b2fcc22ae0d0a3b |
| institution | OA Journals |
| issn | 2214-5818 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Hydrology: Regional Studies |
| spelling | doaj-art-e36b279f093a439e9b2fcc22ae0d0a3b2025-08-20T02:21:07ZengElsevierJournal of Hydrology: Regional Studies2214-58182024-12-015610197710.1016/j.ejrh.2024.101977Identification of hotspots and cold-spots of groundwater potential using spatial statisticsTao Liu0Imran Ahmad1Mithas Ahmad Dar2Martina Zelenakova3Lema Misgan Gebrie4Teshome Kifle5Gashaw Sintayehu Angualie6Tianjin Sino-German University of Applied Sciences, Tianjin, 300350, ChinaDepartment of Water Resource and Irrigation Engineering, Woldia University, Wodia Town, Ethiopia; Corresponding author.Integrated Watershed Management Programme, Department of Rural Development and Panchayati Raj, Government of Jammu and Kashmir, IndiaDepartment of Environmental Engineering, Faculty of Civil Engineering, Technical University of Kosice, Kosice 042 00, SlovakiaGovernment Technology Specialist, Ministry of Innovation and Technology, Addis Ababa, EthiopiaDepartment of Water Resource and Irrigation Engineering, Woldia University, Wodia Town, EthiopiaDepartment of Water Resource and Irrigation Engineering, Woldia University, Wodia Town, EthiopiaStudy region: The Guna-Tana landscape is located in Ethiopia. This landscape is seriously facing water scarcity problems, that’s why we studied this landscape and provided the hotspots of groundwater potential areas in this region. Study focus: In this study the hotspots and cold-spots of groundwater potential at, 99, 95, and 90 % confidence levels has been deciphered. Using Gi-Bin values, four classes has been identified viz., 2–3 (highly favorable), 0–1 (fairly favorable), −2 to −1 (poorly favorable) and −3 (very poorly favorable). The hotspots was subjected to ordinary least squared (OLS) regression to understand the impact of chosen parameters (viz., geology, land-use, soil, rainfall, slope, and distance to rivers) towards groundwater potential. New hydrological insights for the region: The absence of redundancy among the selected parameters was indicated by the VIF values of the parameters, which were determined to be less than 7.5. It was discovered that the Robust Probability (Robust_Pr) was statistically significant (p < 0.01). The OLS model appears to have captured the variability of exploratory variables, as evidenced by the decreased values of Akaike's Information Criterion (AICc). The Adjusted R-squared value of 0.9119 indicates that exploratory variables has successfully explained 91.19 % of the variance of the model.http://www.sciencedirect.com/science/article/pii/S2214581824003264GWRHotspot of groundwater potentialMoran`s I OLS |
| spellingShingle | Tao Liu Imran Ahmad Mithas Ahmad Dar Martina Zelenakova Lema Misgan Gebrie Teshome Kifle Gashaw Sintayehu Angualie Identification of hotspots and cold-spots of groundwater potential using spatial statistics Journal of Hydrology: Regional Studies GWR Hotspot of groundwater potential Moran`s I OLS |
| title | Identification of hotspots and cold-spots of groundwater potential using spatial statistics |
| title_full | Identification of hotspots and cold-spots of groundwater potential using spatial statistics |
| title_fullStr | Identification of hotspots and cold-spots of groundwater potential using spatial statistics |
| title_full_unstemmed | Identification of hotspots and cold-spots of groundwater potential using spatial statistics |
| title_short | Identification of hotspots and cold-spots of groundwater potential using spatial statistics |
| title_sort | identification of hotspots and cold spots of groundwater potential using spatial statistics |
| topic | GWR Hotspot of groundwater potential Moran`s I OLS |
| url | http://www.sciencedirect.com/science/article/pii/S2214581824003264 |
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