Inappropriate use of land and poor ecosystem management have accelerated land degradation and reduced the storage capacity of reservoirs. To mitigate the effect of the increased sediment yield, it is important to identify erosion-prone areas in a 287 km2 catchment in Ethiopia. The objectives of this study were to: (1) assess the spatial variability of sediment yield; (2) quantify the amount of sediment delivered into the reservoir; and (3) prioritize sub-catchments for watershed management using the Soil and Water Assessment Tool (SWAT). The SWAT model was calibrated and validated using SUFI-2, GLUE, ParaSol, and PSO SWAT-CUP optimization algorithms. For most of the SWAT-CUP simulations, the observed and simulated river discharge were not significantly different at the 95% level of confidence (95PPU), and sources of uncertainties were captured by bracketing more than 70% of the observed data. This catchment prioritization study indicated that more than 85% of the sediment was sourced from lowland areas (slope range: 0–8%) and the variation in sediment yield was more sensitive to the land use and soil type prevailing in the area regardless of the terrain slope. Contrary to the perception of the upland as an important source of sediment, the lowland in fact was the most important source of sediment and should be the focus area for improved land management practice to reduce sediment delivery into storage reservoirs. The research also showed that lowland erosion-prone areas are typified by extensive agriculture, which causes significant modification of the landscape. Tillage practice changes the infiltration and runoff characteristics of the land surface and interaction of shallow groundwater table and saturation excess runoff, which in turn affects the delivery of water and sediment to the reservoir and catchment evapotranspiration.

Land use planners require up-to-date and spatially accurate time series land resources information and changing pattern for future management. As a result, assessing the status of land cover change due to population growth and arable expansion, land degradation and poor resource management, partial implementation of policy strategies, and poorly planned infrastructural development is essential. Thus, the objective of the study was to quantify the spatiotemporal dynamics of land use land cover change between 1995 and 2014 using 5 multi-temporal cloud-free Landsat Thematic Mapper images. The maximum likelihood (ML)-supervised classification technique was applied to create signature classes for significant land cover categories using means and variances of the training data to estimate the probability that a pixel is a member of a class. The final Bayesian ML classification resulted in 12 major land cover units, and the spatiotemporal change was quantified using post-classification and statistical change detection techniques. For a period of 20 years, there was a continuously increasing demand for arable areas, which can be represented by an exponential growth model. Excepting the year 2009, the built-up area has shown a steady increase due to population growth and its need for infrastructure development. There was nearly a constant trend for water bodies with a change in slope significantly less than +0.01%. The 2014 land cover change statistics revealed that the area was mainly covered by cultivated, wood, bush, shrub, grass, and forest land mapping units accounting nearly 63%, 12%, 8%, 6%, 4%, and 2% of the total, respectively. Land cover change with agro-climatic zones, soil types, and slope classes was common in most part of the area and the conversion of grazing land into plantation trees and closure area development were major changes in the past 20 years.

Change in land-use management practices such as cultivation of steep slopes, overgrazing, and no or limited fallow periods, and slope position affects the quality of soils. As a result, assessing soil physicochemical properties and subsequent implications on soil fertility is essential for understanding the influence of agro-ecosystem revolution on agricultural soil quality and efficiency. In this research, we assessed the effect of land-use management practices on selected soil properties under varying terrain slopes and with and without soil conservation measures in a highly disturbed landscape in the northern part of Ethiopia in 2016. Based on the result, for all slope positions considered—namely, lower (1%-15%), middle (15%-30%), and upper (30%-45% and above)—with and without soil conservation, soil moisture content, porosity, silt, and clay proportions were lower in the cultivated land compared with grazing and forestland-use units. Conversely, soil bulk density and the sand fraction were higher in the cultivated land than grazing and forestland units, relatively. Observing changes in a terrain slope position, sand content of forest, grazing, and cultivated land units increased from lower to upper slope position whereas silt and clay fraction generally showed a decreasing trend from lower to an upper slope positions. In all slope positions with and without conservation practice, cation exchange capacity, exchangeable K ⁺ , Ca ²⁺ , and Mg ²⁺ showed a significant increase from cultivated land to grazing and then forestland. The mean value of pH and electrical conductivity of cultivated lands with and without soil conservation were significantly low in all slope categories. Summarizing the analysis of variance for selected soil chemical properties with different slope positions, except available phosphorous, all chemical properties considered in this study are statistically significant ( P < .05). In summary, the result confirmed that soil properties were strongly influenced by terrain slope, land use, and changes in management practice. Consequently, to conserve soil resources, policymakers need to implement appropriate land conservation strategies based on land-use structure and slope variation.

The salt problem in Ethiopia has been further exacerbated by a number of factors, including poor water quality, ineffective on-farm water management techniques, and a lack of appropriate and technically sound drainage infrastructure at irrigation sites. Despite its importance, no systematic review or documentation of the extent and consequences of the problem has been made so far. This scientific review primarily focuses on original studies published in the country, notably in arid and semi-arid regions where salinity issues have a significant influence. The data indicated that soil and irrigation water salinity have a substantial link with crops and agricultural communities in Ethiopia. Salinity has a significant impact on soil and water fertility, resulting in poorer agricultural production, food insecurity, and poverty. Salinity has a significant impact on crops in the country, from the germination stages to the harvesting stages during the growing season. If the current state of soil and water management continues, the severity of both soil and irrigation water salinity will reach an irreversible level that significantly impedes the country’s agricultural production capacity. As a result, cultured irrigation water treatment, crop selection based on salinity and sodicity levels, irrigation water quality, leaching, and fertilizer use in combination with organic manures are scientifically proven actions to address the salinity problem. Furthermore, to adequately reclaim and manage salinity in Ethiopia’s dryland saline zone, multi-stakeholder participation is required.

Soil erosion is exacerbated by unsustainable land-use activities and poor management practices, undermining reservoir storage capacity. To this effect, appropriate estimation of sediment would help to adopt sustainable land-use activities and best management practices that lead to efficient reservoir operations. This paper aims to investigate the spatial variability of sediment yield, amount of sediment delivery into the reservoir, and reservoir sedimentation in the Koga Reservoir using the Soil and Water Assessment Tool (SWAT). Sediment yield and the amount entered into the reservoir were also estimated using a rating curve, providing an alternative approach to spatially referenced SWAT generated suspended sediment load. SWAT was calibrated from 1991 to 2000 and validated from 2002 to 2007 using monthly observations. Model performance indicators showed acceptable values using Nash-Sutcliffe efficiency (NSE) correlation coefficient (R²), and percent bias (PBIAS) for flow (NSE = 0.75, R² = 0.78, and PBIAS = 11.83%). There was also good agreement between measured and simulated sediment yields, with NSE, R², and PBIAS validation values of 0.80, 0.79, and 6.4%, respectively. The measured rating curve and SWAT predictions showed comparable mean annual sediment values of 62,610.08 ton/yr and 58,012.87 ton/yr, respectively. This study provides an implication for the extent of management interventions required to meet sediment load targets to a receiving reservoir, providing a better understanding of catchment processes and responses to anthropogenic and natural stressors in mixed land use temperate climate catchments. Findings would benefit policymakers towards land and water management decisions and serve as a prototype for other catchments where management interventions may be implemented. Specifically, validating SWAT for the Koga Reservoir is a first step to support policymakers, who are faced with implementing land and water management decisions.

This research optimized the adsorption performance of rice husk char (RHC4) for copper (Cu(II)) from an aqueous solution. Various physicochemical analyses such as Fourier transform infrared spectroscopy (FTIR), field-emission scanning electron microscopy (FESEM), carbon, hydrogen, nitrogen, and sulfur (CHNS) analysis, Brunauer–Emmett–Teller (BET) surface area analysis, bulk density (g/mL), ash content (%), pH, and pH_ZPC were performed to determine the characteristics of RHC4. The effects of operating variables such as the influences of aqueous pH, contact time, Cu(II) concentration, and doses of RHC4 on adsorption were studied. The maximum adsorption was achieved at 120 min of contact time, pH 6, and at 8 g/L of RHC4 dose. The prediction of percentage Cu(II) adsorption was investigated via an artificial neural network (ANN). The Fletcher–Reeves conjugate gradient backpropagation (BP) algorithm was the best fit among all of the tested algorithms (mean squared error (MSE) of 3.84 and R² of 0.989). The pseudo-second-order kinetic model fitted well with the experimental data, thus indicating chemical adsorption. The intraparticle analysis showed that the adsorption process proceeded by boundary layer adsorption initially and by intraparticle diffusion at the later stage. The Langmuir and Freundlich isotherm models interpreted well the adsorption capacity and intensity. The thermodynamic parameters indicated that the adsorption of Cu(II) by RHC4 was spontaneous. The RHC4 adsorption capacity is comparable to other agricultural material-based adsorbents, making RHC4 competent for Cu(II) removal from wastewater.