Spatial analysis of groundwater potential using remote sensing and GIS-based multi-criteria evaluation in Raya Valley, northern Ethiopia

Fenta, Ayele Almaw; Kifle, Addis; Gebreyohannes, Tesfamichael; Hailu, Gebrerufael

Spatial analysis of groundwater potential using remote sensing and GIS-based multi-criteria evaluation in Raya Valley, northern Ethiopia | Analyse spatiale du potentiel d’eau souterraine à l’aide d’images satellites et d’évaluation multicritères à partir d’un SIG dans la vallée Raya, Ethiopie du Nord Análisis espacial del potencial del agua subterránea usando sensores remotos y múltiples criterios de evaluación basados en GIS en el Raya Valley, norte de Etiopía 利用基于遥感及GIS的多标准评估方法对埃塞俄比亚北部Raya山谷进行地下水潜力空间分析 Análise espacial do potencial de água subterrânea através do uso de deteção remota e de avaliação multicritério com base em SIG no Vale de Raya, norte da Etiópia

2015

Fenta, Ayele Almaw | Kifle, Addis | Gebreyohannes, Tesfamichael | Hailu, Gebrerufael

Sustainable development and management of groundwater resources require application of scientific principles and modern techniques. An integrated approach is implemented using remote sensing and geographic information system (GIS)-based multi-criteria evaluation to identify promising areas for groundwater exploration in Raya Valley, northern Ethiopia. The thematic layers considered are lithology, lineament density, geomorphology, slope, drainage density, rainfall and land use/cover. The corresponding normalized rates for the classes in a layer and weights for thematic layers are computed using Saaty’s analytical hierarchy process. Based on the computed rates and weights, aggregating the thematic maps is done using a weighted linear combination method to obtain a groundwater potential (GP) map. The GP map is verified by overlay analysis with observed borehole yield data. Map-removal and single-parameter sensitivity analyses are used to examine the effects of removing any of the thematic layers on the GP map and to compute effective weights, respectively. About 770 km²(28 % of the study area) is designated as ‘very good’ GP. ‘Good’, ‘moderate’ and ‘poor’ GP areas cover 630 km²(23 %), 600 km²(22 %) and 690 km²(25 %), respectively; the area with ‘very poor’ GP covers 55 km²(2 %). Verification of the GP map against observed borehole yield data shows 74 % agreement, which is fairly satisfactory. The sensitivity analyses reveal the GP map is most sensitive to lithology with a mean variation index of 6.5 %, and lithology is the most effective thematic layer in GP mapping with mean effective weight of 52 %.

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