Geospatial and AHP Approach Rainwater Harvesting Site Identification in Drought-Prone Areas, South Gonder Zone, Northwest Ethiopia

Drought is one of the most pervasive natural disasters because it depletes natural resource, environment disaster and an ecosystem devastation that support all forms of life. Remote sensing and geospatial modeling can be used in investigation of water harvesting through a scientific approach, hence...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Journal of the Indian Society of Remote Sensing 2022-07, Vol.50 (7), p.1321-1331
Hauptverfasser: Yegizaw, Endalkachew Sisay, Ejegu, Mulualem Asfaw, Tolossa, Asirat Teshome, Teka, Afera Halefom, Andualem, Tesfa Gebrie, Tegegne, Melak Abebe, Walle, Workie Mesfin, Shibeshie, Solomon Enyew, Dirar, Tiku Melak
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:Drought is one of the most pervasive natural disasters because it depletes natural resource, environment disaster and an ecosystem devastation that support all forms of life. Remote sensing and geospatial modeling can be used in investigation of water harvesting through a scientific approach, hence making decision easier and precise. So, this research uses remotely sensed data and geospatial modeling with analytical hierarchical process (AHP) to identify locations for rainwater harvesting structure in the drought-prone area of south Gondar zone. Different thematic layers have been incorporated into the analysis including soil texture, soil depth, slope, rainfall, land use, distance from road and settlement, and lineament density. The aforementioned thematic layers were assigned respective weights of their importance by AHP and combined in a GIS environment to identify the potential site. This study is important to improve agriculture productivity, animal husbandry, water management and sustainable environment. The results of this study are to prepare map of potential rainwater harvesting sites. This will allow to build dams to store water, which will be especially beneficial in drought-prone areas. The suitability map is classified into four potential classes, from highly suitable to not suitable. The results of the suitability analysis show that 20.25% of the study area is highly suitable, 66.66% is moderately suitable, 4.70% is marginally suitable, and 8.39% is not suitable. The result of a suitable map is very convenient for decision-makers and planners to quickly select the sites for rainwater harvesting structure.
ISSN:0255-660X
0974-3006
DOI:10.1007/s12524-022-01528-5