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
College of Dryland Agriculture and Natural Resources, Dept. of Land Resources Management and Environmental Protection, Mekelle University, P.O. Box 231, Mekelle, Tigray, Ethiopia; Institute of Geo-information and Earth Observation Sciences, Mekelle University, P.O. Box 231, Mekelle, Tigray, Ethiopia; College of Natural and Computational Sciences, Dept. of Earth Sciences, Mekelle University, P.O. Box 231, Mekelle, Tigray, Ethiopia
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 %. © 2014, Springer-Verlag Berlin Heidelberg.