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 multitemporal 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.

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.

Understanding topography effects on soil properties is vital to modelling landscape hydrology and establishing sustainable on-field management practices. This research focuses on an arable area (117 km2) in Southwestern Ethiopia where agricultural fields and bush cover are the dominant land uses. We postulate that adapting either of the soil data resources, coarse resolution FAO-UNESCO (Food and Agriculture Organization of the United Nations Educational, Scientific and Cultural Organization) soil data or pedo-transfer functions (PTFs) is not reliable to indicate future watershed management directions. The FAO-UNESCO data does not account for scale issues and assigns the same soil property at different landscape gradients. The PTFs, on the other hand, do not account for environmental effects and fail to provide all the required data. In this regard, mapping soil property spatial dynamics can help understand landscape physicochemical processes and corresponding land use changes. For this purpose, soil samples were collected across the watershed following a gridded sampling scheme. In areas with heterogeneous topography, soil is spatially variable as influenced by land use and slope. To understand the spatial variation, this research develops indicators, such as topographic index, soil topographic wetness index, elevation, aspect, and slope. Pearson correlation (r), among others, was used to investigate terrain effects on selected soil properties: organic matter (OM), available water content (AWC), sand content (%), clay content (%), silt content (%), electrical conductivity (EC), moist bulk density (MBD), and saturated hydraulic conductivity (Ksat). The results show that there were statistically significant correlations between elevation-based variables and soil physical properties. Among the variables considered, the ‘r’ value between topographic index and soil attributes (i.e., OM, EC, AWC, sand, clay, silt, and Ksat) were 0.66, 0.5, 0.7, 0.55, 0.62, 0.4, and 0.66, respectively. In conclusion, while understanding topography effects on soil properties is vital, implementing either FAO-UNESCO or PTFs soil data do not provide appropriate information pertaining to scale issues.

Understanding the spatiotemporal trend of land cover (LC) change and its impact on humans and the environment is essential for decision making and ecosystem conservation. Land degradation generally accelerates overland flow, reducing soil moisture and base flow recharge, and increasing sediment erosion and transport, thereby affecting the entire basin hydrology. In this study, we analyzed watershed-scale processes in the study area, where agriculture and natural shrub land are the dominant LCs. The objective of this study was to assess the time series and spatial patterns of LCC using remotely-sensed data from 1973 to 2018, for which we used six snapshots of satellite images. The LC distribution in relation to watershed characteristics such as topography and soils was also evaluated. For LCC detection analysis, we used Landsat datasets accessed from the United States Geological Survey (USGS) archive, which were processed using remote sensing and Geographic Information System (GIS) techniques. Using these data, four major LC types were identified. The findings of an LC with an overall accuracy above 90% indicates that the area experienced an increase in agricultural LC at the expense of other LC types such as bushland, grazing land, and mixed forest, which attests to the semi-continuous nature of deforestation between 1973 and 2018. In 1973, agricultural land covered only 10% of the watershed, which later expanded to 48.4% in 2018. Bush, forest, and grazing land types, which accounted for 59.7%, 16.7%, and 13.5% of the watershed in 1973, were reduced to 45.2%, 2.3%, and 4.1%, respectively in 2018. As a result, portions of land areas, which had once been covered by pasture, bush, and forest in 1973, were identified as mixed agricultural systems in 2018. Moreover, spatial variability and distribution in LCC is significantly affected by soil type, fertility, and slope. The findings showed the need to reconsider land-use decision tradeoffs between social, economic, and environmental demands.