Suspended sediment modeling is an important subject for decision-makers at the catchment level. Accurate and reliable modeling of suspended sediment load (SSL) is important for planning, managing, and designing of water resource structures and river systems. The objective of this study was to develop artificial intelligence- (AI-) based ensemble methods for modeling SSL in Katar catchment, Ethiopia. In this paper, three single AI-based models, that is, support vector machine (SVM), adaptive neurofuzzy inference system (ANFIS), feed-forward neural network (FFNN), and one conventional multilinear regression (MLR) modes, were used for SSL modeling. Besides, four different ensemble methods, neural network ensemble (NNE), ANFIS ensemble (AE), weighted average ensemble (WAE), and simple average ensemble (SAE), were developed by combining the outputs of the four single models to improve their predictive performance. The study used two-year (2016-2017) discharge and SSL data for training and verification of the applied models. Determination coefficient (DC) and root mean square error (RMSE) were used to evaluate the performances of the developed models. Based on the performance measure results, the ANFIS model provides higher efficiency than the other developed single models. Out of all developed ensemble models, the nonlinear ANFIS model combination method was found to be the most accurate method and could increase the efficiency of SVM, MLR, ANFIS, and FFNN models by 19.02%, 37%, 9.73%, and 16.3%, respectively, at the verification stage. Overall, the proposed ensemble models in general and the AI-based ensemble in particular provide excellent performance in SSL estimation.

Climate change alters the spacial and temporal availability of water resources by affecting the hydrologic cycle. The main objective of this paper is to review the climate change effect on the water resources of the Blue Nile River, Ethiopia. The impact of climate change on water resources is highly significant as all natural ecosystems and humans are heavily dependent on water. It alters precipitation, temperature, and streamflow of the Blue Nile river basin which is threatening the lives and livelihoods of people and life-supporting systems. Rainfall within the Blue Nile river basin is highly erratic and seasonal due to it being located in the inter-tropical convergent zone. The temperature and sediment load are shown to increase in the future while the rainfall and streamflow are decreasing. The Blue Nile basin is characterized by highly erosive rainfall, erodible soil, and shrinking forest cover. Therefore, mitigation and adaptation measures should be applied by considering these characteristics of the basin. Watershed management methods like afforestation and water conservation are recommended to reduce the impact on the Blue Nile basin.

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This study aims to calculate the water balance, interaction with irrigation water inflow, and hydrodynamics of the Lake Beseka by a spreadsheet-based model using climatic, hydrological, multi-temporal satellite images, groundwater, and lake chemistry data. The rainfall for the catchment was estimated as 558.4 mm/year, whereas the mean evaporation was computed as 2,214 mm/year by the Penman method. The annual direct rainfall contribution to the lake was obtained to be 25.84 MCM (million cubic meters) and a runoff inflow in the catchment area of 37.2 MCM. This balance pointed to the mean evaporation of 108.2 MCM/year in the lake indicating that the water inflow was greater than the outflow. A major cause for the rise of the lake level was the drainage of excess irrigation water toward the lake, mainly from the Fentale Irrigation Farm. The average increment of the groundwater level in the area was 12 cm/year from 2010 to 2014. From 1998 to 2014, the electrical conductivity was reduced by 25.6%, and calcium was increased by 96%. The study outlined that appropriate irrigation drainage should be implemented in the catchment to ensure the balance between the rainfall, infiltration, and surface runoff to optimize economic and social welfare in the area.