Kassahun Birhanu Tadesse

Land degradation and sedimentation are global issues stemming from inappropriate land management practices within watersheds, primarily due to soil erosion. The primary aim of this investigation was to estimate sediment yield, pinpoint erosion-prone areas, and determine effective strategies for reducing erosion and sediment yield within the Mormora watershed utilizing the SWAT model. The performance of the SWAT model was assessed through calibration and validation procedures employing the Sequential Uncertainty Fitting 2 (SUFI-2) algorithm within the SWAT Calibration and Uncertainty Procedures (SWAT-CUP). Calibration was conducted for the period 1993 to 2006, while validation was carried out for 2007 to 2013, focusing on streamflow and sediment yields at gauging station. Various metrics including R ² , NSE, RSR, and PBIAS were used to assess model performance. All model performance metrics indicated high accuracy. The average annual sediment yield at the outlet of the watershed was 1.19 million t/year with a spatial average of 8.54 t/ha/year. About 47.33% of the watershed was critical areas demanding implementation of soil conservation strategy. The effectiveness of five watershed management scenarios was compared to existing baseline conditions for their effectiveness in sediment yield reduction. The results indicate soil erosion decreased by 28.3% to 55.9% by applying filter strips, 61.7% to 68.4% by grassed waterways, 71.38% by terracing, 62.64% by contouring, and 46.3% by applying stone/soil bunds. Ultimately, terracing emerged as the most effective strategy for mitigating soil erosion within the study area. Consequently, the research outcomes and the developed methodology serve as a valuable resource for decision-makers, experts, and researchers involved in sustainable watershed management.

Water pollution is a common problem for dams situated within an urban or agricultural catchment. This can negatively affect the hydro ecosystem, drinking, recreational and other uses of water. In this study, the drinking water quality class of the Roodeplaat Dam, South Africa which faces pollution problems was modeled using machine learning algorisms in Python Jupyter Notebook 6.0.0. Eleven monthly water quality parameters recorded at five sampling stations from January 1981 to September 2017 were used for training and testing the model. Five machine learning classifiers: Gaussian Naïve Bayes (GNB), K-nearest neighbors (KNN), Decision Tree (DT), Support Vector Machines (SVM), and Linear Regression (LR) at a test size of 20%, 25%, 30%, and 40% were used to classify water into five classes (Excellent to Very bad). It was investigated that the dam water has only three classes good, medium, and bad. The prediction accuracies of machine learning algorithms from the highest to the lowest were 96.39%, 96.17%, 92.25%, 90.20, and 54.19% for KNN, DT, SVM, GNB, and LR, respectively. Therefore, KNN at a test size of 30% was recommended to classify the water quality of Roodeplat Dam accurately. Hence, machine learning algorithms can be used to identify the class of water quality before the water is treated and distributed for drinking use.

Eutrophication is a common problem for the dams situated within an urban or agricultural catchment with a high source of untreated plant nutrients. This can negatively affect the hydro ecosystem, recreational and other uses of water. On the other hand, such eutrophic dams could also be a rich source of nutrients for agricultural use. Thinking irrigation as an alternative means of eutrophic reservoir water management, water suitability for irrigation was investigated for Roodeplaat Dam, South Africa using physicochemical parameters recorded (1981–2017) at five sampling stations. Irrigation suitability was evaluated in terms of nutrient content, salinity, soil infiltration, and toxicity problems to irrigated plants. Plant nutrients: NH4_N, NO3_NO2_N, PO4_P, are within the normal range. But K is above the normal range for irrigation use and can cause nitrogen deficiency. The salinity of 0.44 dS/m was found within the normal range with none restriction for irrigation use. Na toxicity in terms of SAR and Cl toxicity was none for both surface and sprinkler irrigated plants. The pH value of 8.4 was within the normal range (6.5–8.4). Generally, the quality of the reservoir water was categorized under C2S1 (medium salinity and low sodium hazard). Irrigation water can cause slight to moderate soil infiltration problem. Therefore, eutrophic reservoirs can be a potential source of readily available nutrients for irrigation and, hence irrigation use can be considered as one of the remedial measures to decrease nutrient accumulation in the reservoir. The existing water conveyance system can be used to transport water to agricultural fields.

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.

This study evaluated the possibility of conjunctive use (CU) of shallow groundwater (SGW) and surface water for irrigation at the Wonji Shoa Sugar Estate (WSSE) (Ethiopia). Irrigation suitability was investigated by taking 46 SGW samples from piezometers and hand‐dug wells. Many physicochemical parameters (Mg²⁺, Na⁺, Ca²⁺, K⁺, CO₃⁻, SO4²⁻, HCO₃⁻, Cl⁻, TH, EC, TDS and pH) and other indices (MAR (magnesium adsorption ratio), SSP (soluble sodium percentage), SAR (sodium absorption ratio), RSC (residual sodium carbonate), KR (Kelly's ratio) and permeability index (PI)) were analysed following standard procedures. The salinity and infiltration problems of SGW were found to be none to moderate with no chloride and boron ion toxicity. The sodium ion toxicity problems are slight to moderate. The SGW is generally categorized under C3S1 (high salinity and low sodium hazard). However, a high value of SSP and RSC indicates a high possibility of occurrence of infiltration problems when using SGW. Hence, CU of SGW and surface water must be practised to minimize the potential problems of infiltration and salinization and their associated difficulties in soil and sugar cane productivity. Therefore, during CU planning, optimum irrigation scheduling that considers the in situ use of groundwater table must be practised. © 2019 John Wiley & Sons, Ltd. © 2019 John Wiley & Sons, Ltd.

Although the spate irrigation system is an ancient practice, it is only in the past very few decades the system has undergone little modernization interventions. However, these interventions were mostly in the aspects of heavy investment in the sophisticated head works for improving flood water diversion efficiency. In many cases, the modernization interventions were not successful due to various problems such as heavy sedimentation, high flood, disturbed local water distribution rules, or the new designs were not coherent with home-grown practices. On the other hand, successful improvements incorporate less labor intensive and relatively permanent structures with the advantages of conventional systems without considerably altering the approach of the spate irrigation practice. Thus, in this chapter, the techniques of improving traditional spate irrigation systems were reviewed. Farmer-implemented improved traditional spate irrigation systems: flow diversions; canals and control structures; management of sediment, field water, and soil moisture and agronomic practices; reactive water rights and distribution rules were assessed. Therefore, this chapter helps as a reference material for teaching, training and research activities, and it plays a great role in the efforts of sustainable spate irrigation systems development, rehabilitation and management programs.

Knowledge of future river flow information is fundamental for development and management of a river system. In this study, Waterval River flow was forecasted by SARIMA model using GRETL statistical software. Mean monthly flows from 1960 to 2016 were used for modelling and forecasting. Different unit root and Mann–Kendall trend analysis proved the stationarity of the observed flow time series. Based on seasonally differenced correlogram characteristics, different SARIMA models were evaluated; their parameters were optimized, and diagnostic check up of forecasts was made using white noise and heteroscedasticity tests. Finally, based on minimum Akaike Information (AI) and Hannan–Quinn (HQ) criteria, SARIMA (3, 0, 2) x (3, 1, 3)₁₂model was selected for Waterval River flow forecasting. Comparison of forecast performance of SARIMA models with that of computational intelligent forecasting techniques was recommended for future study.

Reservoir operation policies cannot be functional in instant decision making without forecasting the future reservoir inflows. For forecasting inflows into reservoirs with only hydrological data is available like Koga irrigation dam, multivariate forecasting models cannot be used to generate accurate river flow information. As a result, an evaluation of univariate Seasonal Autoregressive Integrated Moving Average (SARIMA) models was done for forecasting monthly Koga River flow with Gnu Regression, Econometrics and Time-series Library (GRETL) software. The stationarity of historical river flow sequence was checked by Augmented Dickey-Fuller (ADF) unit root analysis. Then, seasonality was removed from the river flow time series by seasonal differencing. Using seasonally differenced correlogram characteristics various SARIMA models were identified and evaluated, their parameters were optimized and diagnostic checks of forecasts were performed using residual correlograms and Ljung-Box tests. Finally, based on minimum Akaike Information criteria, SARIMA (1, 0, 1) (3, 1, 3)12 model was selected for Koga River flow forecasting. The stationarity test of the forecasted values of this model has proved the similarity of forecast values and patterns with those of the historical ones. Thus, irrigation managers could use this model and forecast information for optimal irrigation planning and development of reservoir operation strategies in order to protect farmers and downstream environment from water shortages. Moreover, the use of stationarity test of forecast flow patterns is useful and applicable in selecting best forecast model during forecasting of any river flows.