Water scarcity and soil erosion are the main constraints small holder farmers are facing in Tigray, the northern most part of Ethiopia. Both very high and very low precipitation can cause a damage to agriculture which is the case in semi-arid regions like Tigray. While too little rainfall cannot support the growth of crops resulting in crop failure, the short but intense rainfall also causes a runoff thereby washing away essential soil nutrients. Installation of different micro/macro-catchment rainwater harvesting can address both water scarcity and soil erosion if they are properly designed prior to construction. This research was intended to develop a methodology for identifying suitable rainwater harvesting (rwh) sites by using weighted overlay analysis. It also utilizes Ahp (analytical hierarchy process) as effective multi-criterion decision-making tool in eastern Tigray at Kilte Awlaelo district on an area of 1001 km2. This method was chosen because it is simple to use, cost effective, flexible and widely adopted. Physical, hydrological, climate and socio-economic aspects were taken into account during criteria selection. The result indicated four suitability classes with 8.74% highly suitable areas (85.25 km2), 56% suitable areas (550.75 km2), 30.8% moderately suitable areas (303.2 km2) and 4.46% less suitable areas (43.87 km2). The produced rwh suitability map was also validated by both ground truth on google earth pro and a field trip to the study site. In situ and ex situ rwh including bench terraces, wells, and exclosure areas were identified during the field visit that verified the suitability model. Finally, depending on weight and scale of criteria and sub-criteria that matched to each identified suitable areas, different micro-catchment and macro-catchment techniques of water harvesting are recommended. This methodology can be utilized as decision-making tool for rwh practitioners, local and foreign organizations working on soil water conservation programmes and policy-makers during their early planning stages.
In this study first spatial pattern of the level of human modification of terrestrial lands and second its relation with population density was studied at Taiba level in the Tigray regional state of Ethiopia. For the level of human modification of terrestrial lands global Human Modification dataset (gHM) was used and for population density Gridded Population of the World, Version 4 (GPWv4.11) dataset was used. Both the data set were preprocessed before geostatistical analysis. To measure the distribution pattern Global Moran's I statistics, Cluster and Outlier Analysis (Anselin Local Morans I) statistics was used. To measure the relation between population density and human modification of terrestrial lands geographically weighted regression was used. In the case of first objective the resulting z-score of 50.50, confirm the tabias with high Human Modification of terrestrial lands are highly clustered. In case of second objective the results shows 214 Tabias containing high value and are surrounded by Tabias with high values (HH), 10 Tabias containing high value and is surrounded by Tabias with low values (HL). The relation between population density and human modification of terrestrial lands was found positive with R2= 0.506. This research will help the government and planners for proactive spatial planning to maintain biodiversity and ecosystem function before important environmental values are lost in tabias containing high value and is surrounded by Tabias with high values.
Groundwater is the most valuable treasury commodity in the world, yet it is depleted on a daily basis. Hand arrangement is crucial in assembly for delineating a potential groundwater zones. Geographic Information System (GIS) and Remote Sensing (RS) data with Analytical Hierarchy Process (AHP) approach have proven critical for micro level analysis of groundwater potentials. This exploration was authorized in order to locate a prospective groundwater area in the Virutachalam Taluk of Southern India. The Inverse Distance Weightage (IDW) technique was used to determine the groundwater potential precinct by thematic layers of drainage, drainage density, geology, lineament, lineament density, geomorphology, soil, and slopes. Overall, the prospective groundwater zone in the study area was classified as excellent (20.66 %), good (60.29 %), moderate (16.38 %) and poor (2.73 %). This optional analysis offers an excellent possible groundwater zone for patches in the northern and central sections of Kotteri and Kammapuram in Virudhachalam Taluk. The survey revealed that the approach of inverse distance weighting provides an operating mechanism for suggesting groundwater potential zones for clear expansion and groundwater control in not the same hydro-geological settings.
The main objective was to explore the connection between flood and drought hazards and their impact on crop land and human migration. The Flood and Drought effect on Cropland Index (FDCI), hot spot analysis and the Global Regression Analysis method was applied for the identification of the relationship between human migration and flood and drought hazards. The spatial pattern and hot and cold spots of FDCI, spatial autocorrelation and Getis-OrdGi* statistic techniques were used respectively. The FDCI was taken as an explanatory variable and human migration was taken as a dependent variable in the environment of the geographically weighted regression (GWR) model which was applied to measure the impact of flood and drought hazards on human migration. FDCI suggests a z-score of 4.9, which shows that the impact of flood and drought frequency on crop land is highly clustered. In the case of the hot spots analysis, out of seventy districts in Uttar Pradesh twenty-one were classified as hot spot and eight were classified as cold spots with a confidence level of 90 to 99%. Hot spot indicate maximum and cold spots show minimum impact of flood and drought hazards on crop land. The impact of flood and drought hazards on human migration show that there are fourteen districts where migration out is far more than predicted while there are ten districts where migration out is far lower.
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