Quality assessment of shallow groundwater table is very important because it is the water that recharges deeper aquifers and constantly feeds the water levels of our surface streams and wetlands. Continuous monitoring of large number of quality parameters is essential for effective maintenance of water quality through appropriate control measures. However, it is very difficult and laborious task for regular monitoring of all the parameters even if adequate manpower and laboratory facilities are available. Therefore, this study presents the statistical analysis of physico- chemical parameters (pH, EC, TDS, Na, K, Ca, Mg, HCO3, Cl, CO3, SO4, TH, B, F) using correlation and Principal Component Analysis. The statistical analysis of the groundwater quality variables indicated that most of the variables are highly correlated. The strong correlation is an opportunity to develop a regression equation and monitor using few parameters. This provides an easy and rapid method of continuous groundwater quality monitoring. Moreover, groundwater of the area showed significant compositional variation. The compositional variability has implications for the source and origin of groundwater quality in the study area.

Rainfall forecast information is important for the planning and management of water resources and agricultural activities. Turksvygbult rainfall near the Magoebaskloof Dam (South Africa) has never been modelled and forecasted. Hence, the objective of this study was to forecast its monthly rainfall using the SARIMA model. GReTL and automatic XLSTAT software were used for forecasting. The trend of the long-term rainfall time series (TS) was tested by Mann–Kendall and its stationarity was proved by various unit root tests. The TS data from Oct 1976 to Sept 2015 were used for model training and the remaining data (Oct 2015 to Sept 2018) for validation. Then, all TS (Oct 1976 to Sept 2018) were used for out of sample forecasting. Several SARIMA models were identified using correlograms that were derived from seasonally differentiated TS. Model parameters were derived by the maximum likelihood method. Residual correlogram and Ljung–Box Q tests were used to check the forecast accuracy. Based on minimum Akaike information criteria (AI) value of 5642.69, SARIMA (2, 0, 3) (3, 1, 3) ₁₂ model was developed using GReTL as the best of all models. SARIMA (1, 0, 1) (3, 1, 3) ₁₂, with minimum AI value of 5647.79, was the second-best model among GReTl models. This second model was also the first best automatically selected model by XLSTAT. In conclusion, these two best models can be used by managers for rainfall forecasting and management of water resources and agriculture, and thereby it can contribute to economic growth in the study area. Hence, the developed SARIMA forecasting procedure can be used for forecasting of rainfall and other time series in different areas.

Groundwater is a vital resource for domestic, agricultural, and industrial activities, as well as for ecosystem services. Despite this, the resource is under significant threat, due to increasing contamination from anthropogenic activities. Therefore, to ensure its reliability for present and future use, effective management of groundwater is important not only in terms of quantity (i.e. abstraction) but also quality. This can be achieved by identifying areas that are more vulnerable to contamination and by implementing protective measures. To identify the risk and delineate areas that are more exposed to pollution, various groundwater vulnerability assessment techniques have been developed across the globe. This paper presents an overview of some of the commonly used groundwater vulnerability assessment models in terms of their unique features and their application. Special emphasis is placed on statistical methods and overlay-index techniques. The assessment of the literature shows that statistical methods are limited in application to the assessment of groundwater vulnerability to pollution because they rely heavily on the availability of sufficient and quality data. However, in areas where extensive monitoring data are available, these methods estimate groundwater vulnerability more realistically in quantitative terms. Many works of research indicate that index-overlay methods are used extensively and frequently in groundwater vulnerability assessments. Due to the qualitative nature of these models, however, they are still subject to modification. This study offers an overview of a selection of relevant groundwater vulnerability assessment techniques under a specificset of hydro-climatic and hydrogeological conditions.

This study tried to assess the impact of climate change on water resources of the upper Awash River sub- basin (Ethiopia) using a statistical downscaling model (SDSM). The future climatic parameters (rainfall, maximum and minimum temperatures) were generated by downscaling outputs of HadCM3 (Hadley Centre Coupled Model, ver-sion 3) general circulation model to watershed level for A2a (medium-high) and B2a (medium-low) emission scenarios at representative stations (Addis Ababa, Ginchi and Bishoftu). These SDSM generated climatic data were used to develop current/baseline period (1971–2010) and future climate change scenarios: 2020s (2011–2040), 2050s (2041– 2070) and 2080s (2071–2099). The projected future rainfall and mean monthly potential evapotranspiration at these stations were weighted and fed to HBV hydrological model (Hydrologiska Byråns Vattenbalansavdelning model) for future stream flow simulation. These simulated future daily flow time series were processed to monthly, seasonal and annual time scales and the values were compared with that of base period for impact assessment. The simulation result revealed the possibility for significant mean flow reductions in the future during Summer or “Kiremt” (main rainy season) and apparent increase during “Belg” or winter (dry season). Autumn flow volume showed decreasing trend (2020s), but demonstrated increasing trend at 2050s and 2080s. A mean annual flow reduction (ranging from 13.0 to 29.4%) is also expected in the future for the three studied benchmark periods under both emission scenarios. Generally, the result signals that the water resources of upper Awash River basin will be expected to be severely affected by the changing climate. Therefore, different adaptation options should be carried out in order to reduce the likely impact and ensure water security in the sub-basin.