Assessing and Quantifying Impacts of Land Use and Climate Changes on Hydrological Processes: Review

Land use and climate changes have become prominent drivers of changes in hydrologic responses. Understanding impacts of land use and climate changes on hydrology is vital for water resources management. Many studies have used computer models to examine the linkages and interactions between land use, climate and hydrologic responses. The objective of this paper is to summarize these studies and provide critical review of limitations of the methodologies. Approaches used to analyze the impacts of land use and climate changes on hydrologic responses can be grouped as (i) the impacts of climate changes, (ii) the impacts of land use changes, and (iii) the combined and separate impacts of land use and climate changes. Findings of this review include (i) the combined effects of land use and climate changes are different from the separate impacts, (ii) effects of land use and climate changes on hydrologic responses vary from basin to basin depending on the basin’s physical characteristics, climate condition, and the scope of climate change and land use change. These findings imply that basin specific impact analysis using integrated modeling approach that examines the combined effects of land use and climate changes is decisive for planning and management of water resources. Keywords: land use change; climate change; integrated watershed modeling; combined and separate impacts; hydrologic impacts of land use change; hydrologic impacts of climate change

Hydro-geomorphological characterization of Dhidhessa River Basin, Ethiopia

Dhidhessa River Basin is physio-graphically and hydrologically important in the Blue Nile basin, however, its morphometry and hydrology are not well known. This study aimed to characterize hydro-geomorphology of the basin via basin morphometry analysis. SRTM DEM, geological and hydrological maps of the area were used in ArcGIS 10.3 environment for this analysis. Results showed that a 33,468 km total stream length of all orders was found distributed within 28,637 km2 drainage area in a dendritic pattern. According to morphometric parameter classification, total stream length and stream order of the basin were high whereas stream length ratio, bifurcation ratio and hydrologic storage coefficient were low. Furthermore, drainage area was large, drainage frequency was coarse, basin shape was more elongated, drainage density was medium, infiltration number was low, overland flow was long and constant of channel maintenance was high. Moreover, the basin's relief, relief ratio, ruggedness number, gradient ratio and the slope was high. In general, the study asserted that the basin was underlain by uniform resistant rocks, less prone to flooding, with high water resources potential and susceptible to soil erosion. The morphometric analysis approach pursued in this study was cost- and time-effective for basin characterization. Keywords: Dhidhessa River Basin, Hydro-geomorphology, Hydrological processes, Morphometric parameters, Water resource potentia

Integrating Satellite Images and Machine Learning for Flood Prediction and Susceptibility Mapping for the Case of Amibara, Awash Basin, Ethiopia

Flood is one of the most destructive natural hazards affecting the environment and the socioeconomic system of the world. The effects are higher in the developing countries due to their higher vulnerability to disaster and limited coping capacity. The Awash basin is one of the flood-prone basins in Ethiopia where the frequency and severity of flooding has been increasing. Amibara district is one of the flood-affected areas in the Awash basin. To minimize the effects of flooding, reliable and up-to-date information on flooding is highly required. However, flood monitoring and forecasting systems are lacking in most basins of Ethiopia including the Awash basin. Therefore, this study aimed to (i) identify important flood causative factors, (ii) evaluate the performance of random forest (RF), linear regression, support vector machine (SVM), and long short-term memory (LSTM) machine learning models for flood prediction and susceptibility mapping in the Amibara area. For developing flood prediction and susceptibility modeling, nine causative factors were considered, namely elevation, slope, aspect, curvature, topographic wetness index, soil texture, rainfall, land use/land cover, and curve number. The Pearson correlation coefficient and information gain ratio (InGR) techniques were used to evaluate the relative importance of the factors. The machine learning models were trained and tested using 400 historic flood points collected from the 10 September 2020 Sentinel 2 image, during which a flood event occurred in the area. Multiple metrics, namely precession, recall, F1-score, accuracy, and receiver operating characteristics (area under curve), were used to evaluate the performance of the models. The results showed that all the factors considered in this study were important; elevation, rainfall, topographic wetness index, aspect, and slope were more important while land use/land cover, curve number, curvature, and soil texture were less important. Furthermore, the results showed that random forest outperformed in predicting and mapping flooding for the study area whereas the linear regression model showed the next best performance to RF. However, SVM performed poorly in flood prediction and susceptibility mapping. The integration of satellite and field datasets coupled with state-of-the-art-machine learning models are novel approaches and thus improved the accuracy of flood prediction and susceptibility mapping. Such methodology improves the state-of-the-art knowledge in this field and fills the gaps of traditional flood mapping techniques. Thus, the results of the study can provide crucial information for informed decision-making in the processes of designing flood control strategies and risk management