Irrigation development necessitates suitable lands for higher yield production and the development of long-term irrigation systems. The purpose of this research was to identify appropriate irrigation lands for irrigation in the Minch Yekest watershed in West Amhara, Ethiopia. Geospatial and multi-criteria decision-making techniques were used in this study. For land suitability analysis for surface irrigation, slope, land use, altitude, distance from the water source, soil characteristics, and available water storage capacity parameters were used. To find the best location for surface irrigation, the values were weighted and combined using the weighted overlay tool. The irrigation land suitability of each physical land parameter was classified into four suitability classes (S1, S2, S3, and N) based on the Food and Agricultural Organization guideline. According to the findings, 63% of the watershed area is highly suitable, 6.25% is moderately suitable, 28.69% is marginally suitable, and 2.06% is not suitable for the aforementioned purposes. The methodological approach and study findings could help policymakers make better decisions when developing irrigation projects in Ethiopia.
Floods have destroyed people’s lives as well as social and environmental assets. Flooding is becoming more severe and frequent as a result of climate change and an increase in human-induced land-use changes, which puts pressure on river channels and causes changes in river morphology. The study was aimed to assess flood danger and map inundation areas in Ethiopia’s Teji watershed, which is prone to flooding. The basic flood-producing factors in this study were derived from soil, slope, elevation, drainage-density and land use land cover data. The opinions of public institutions and expert decisions were gathered to determine the weight of the factors in the analytic hierarchy process. The collected data were processed using the ArcGIS environment and the analytic hierarchy method to produce a flood danger map. According to the findings of this study, approximately 43.28 and 13.09% of the area were vulnerable to high and very high flood risk zones, respectively. As a result, flood prediction, early warning and management practices could be implemented on a regular and sustainable basis.
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