Wonji Shoa Sugar Estate (WSSE), located in the flood plain of the Awash River (Ethiopia), has been under long-term (>60 years) irrigation, industrial activities and agro-chemical usage. In this study, the hydrochemical properties of ground-water bodies available at WSSE have been characterized for quality compositions. Water samples were collected from groundwater monitoring piezometers distributed in the sugarcane plantation and then analysed for physico-chemical quality parameters (pH, EC,major cations and anions) following standard procedures. Other chemical indices (e.g., total dissolved solids (TDS),total hardness(TH),magnesium absorption ratio(MAR), base exchange (r1), meteoric genesis(r2)) were de-rived from the measured water quality parameters. The compositional variability and groundwater classification has been presented using the Box and Piper plots. The potential sources of minerals were suggested for each of the considered water sources based on their quality characteristics. Both trilinear Piper plot and meteoric genesis index revealed that groundwaterof the area is shallow meteoric water percolation type with a changing of hydrochemical facies and mixing trend. Ground-water of the area, is group 1 (Ca-Mg-HCO3) type, with no dominant cations and HCO3 are the dominant anions. Overall, the study result elucidates that the chemical composition of GW of the area showed spatial variability depending upon the variations in hydrochemical inputs from natural processes and/or anthropogenic activities within the region. The local an-thropogenic processes could be discharges from sugar factory, domestic sewage and agricultural activities.

This study presents the spatial variability and dynamics of soil organic carbon (SOC), soil organic matter (SOM) and soil pH contents at the Wonji Shoa Sugar Estate (WSSE), Ethiopia. Soil samples were collected immediately after the sug-arcane was harvested and then analysed for SOC, SOM and pH content using standard procedures. The analysis resultsshowed that the pH value varied between 6.7–8.4 (neutral to moderately alkaline) and 7.3–8.5 (neutral to strongly alkaline) for the top and bottom soil profiles, respectively. The SOM content is in the range of 1.1–6.7% and 0.74–3.3% for the upper and lower soil layers, respectively. Nearly 45% of the samples demonstrated a SOM content below the desirable threshold (<2.1%) in the bottom layer and, hence, inadequate. Moreover, most of the topsoil layer (95%) has an SOM content exceed-ing the desirable limit and hence is categorized within the normal range. Interestingly, the SOC content showed a spatial variability in both the surface and sub-surface soil layers. A lower SOC and SOM content was found for the sub-soil in the south and southwestern part of the plantation. A further decline in the SOC and SOM content may face the estate if the cur-rent waterlogging condition continues in the future for a long period. Overall, the study result emphasizes the need to min-imize the pre-harvest burning of sugarcane and action is needed to change the irrigation method to green harvesting to fa-cilitate the SOC retention in the soil and minimize the greenhouse emission effect on the environment, hence improving soil quality in the long-term.

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.

Water is becoming a scarce resource due to the immense intensification of agricultural activity, climate change, and demographic pressure. Hence, information on water use/management and their associated management practices is essential for selecting, planning, implementing, and monitoring schemes that optimise water use to meet the increasing demand related to basic human needs and welfare. This study presents the farmers’ perception of climate change from a gender sensitive perspective to promote adaptation and optimise irrigation/agricultural productivity in a compound surface-ground water system within the Anger sub-basin (Ethiopia). The study results showed that climate change affects water demand and supply routes in which more than 65% of the decrease in lake water level is due to climate change and overuse of surface water. The research findings show that women’s recognition and apprehension of climate change is much greater than men’s. Thus, women’s role in farming is important for ensuring food security at the household level. Gender sensitivity and can play a role in preventing the change in climate through optimising irrigation efficiency and suggesting the need for further research on its application to science. The study demonstrates that women’s participation in agricultural tasks, crisis management, and informal institutions is more vigorous than men’s.
On the other hand, the understanding and communication of farmers is based on experience and concerns about the climate impact. Moreover, studies showed that climate change has a potential impact on the access to water supply for agriculture, urbanisation, and the environment. Therefore, there is a need to assess the dynamics of surface- groundwater interaction as affected by climate change and gender inequality to optimise the irrigation system.