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.

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.

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.

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.