Understanding rainfall distribution in space and time is crucial for sustainable water resource management and agricultural productivity. This study investigated the spatial distribution and temporal trends of rainfall in Amhara region using time series rainfall data of Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) for the period 1981–2017. Coefficient of variation, standardized anomaly index (SAI), precipitation concentration index (PCI) and seasonality index (SI) were used to evaluate rainfall variability and seasonality. Mann–Kendall's test was also employed for rainfall trend analysis. Results showed that the region has been experiencing variable rainfall events that cause droughts and floods over different years. SAI also witnessed the presence of inter-annual variability of rainfall with negative and positive anomalies in 59.46% and 40.54% of the analyzed years, respectively. PCI and SI results implied that the area had irregular and strong irregular rainfall distribution. Trend analysis results showed an overall increase in the annual and seasonal rainfall (except winter) during the study period. The information obtained from this study could serve as a proxy for rainfall variability and trend in the study area which might be used as input for decision-makers to take appropriate adaptive measures in various agricultural and water resources sectors.

This study aimed to evaluate the overall groundwater hydrogeochemical evolution and suitability for drinking in Andasa watershed (Northwestern Ethiopia). To achieve this, analyses of hydrogeochemical, multivariate (PCA, HCA), correlation matrix, and Water Quality Index (WQI) methodologies were employed. A total of 64 groundwater samples which had been collected during winter season (February-April, 2018) were assessed for ionic and composite variables (major physicochemical and composite parameters). The samples have been gathered from deep wells, shallow wells, hand dug wells and springs which are spatially distributed throughout the watershed. The study results revealed that major ions dominating are Ca>Mg>Na>K and HCO 3 > Cl>SO 4 > NO 3 > PO 4 > F for cations and anions, respectively. Getnet Taye Bawoke ABOUT THE AUTHORS Getnet Taye Bawoke obtained his BSc. (in Applied Geology) as well as MSc. (in Hydrogeology) degree from Mekelle University, Ethiopia. Now, he is working as an assistant professor in Bahir Dar University, Ethiopia. His research works include hydrogeochemistry, isotope hydrology, rainfall variability, groundwater recharge, modeling, managed aquifer recharge and redox conditions for petroleum exploration. His research interest focuses mainly on topics of groundwater including: Hydrogeochemistry, Isotope, Water Quality, Recharge, Geochemistry, Management, Modeling and Managed Aquifer Recharge. Zelalem Leyew Anteneh obtained his BSc.

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Ever increase in population growth and drastic climatic changes augment the demand and exploration of groundwater from time to time. An integrated approach of remote sensing (RS), geographic information system (GIS) and multicriteria decision analysis (MCDA) of analytical hierarchical process (AHP) were applied to delineate groundwater potential (GWP) zones in Andasa-Tul watershed, Upper Blue Nile Basin, Ethiopia. For this purpose, nine GWP influencing thematic layers comprising lithology, lineament density, geomorphology, slope, soil, drainage density, land use/land cover, rainfall and depth to groundwater level were used. The thematic layers and classes within them were given scale values based on literature and experts' decision and calculated using Satty's AHP. The thematic layers have been integrated via their weights/rates using weighted overlay spatial function tool of ArcGIS to provide GWP map. The result shows that GWP map comprises very good (13.4%), good (7%), moderate (23.6 %), poor (35.4%) and very poor (20.5%) zones. Validation of the GWP map with existing water point yields shows 84.21 % agreement indicating good accuracy of the method. The map removal sensitivity analysis result reveals that GWP is more sensitive to lithology (mean variation index, 1.92 %) and less sensitive to geomorphology (mean variation index, 0.59 %). Similarly, from the single layer sensitivity analysis, lithology and slope are found to be more effective parameters, whereas rainfall and depth to groundwater level are less effective variables.