Water Resource Engineering

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Browsing Water Resource Engineering by Author "Abiy Getachew"

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    Hydrological processes under changing climate and land use scenarios in the Baro– Akobo River Basin, Ethiopia
    (Addis Ababa University, 2023-11-04) Abiy Getachew; Tekalegn Ayele (PhD); Yihun Taddele (PhD) Co-Advisor
    Water is an essential component of agricultural productivity and is crucial for food security. It is also a vital component of the environment. Water security is becoming a global issue, but the issue is chronic in most developing countries, including sub-Saharan Africa. Therefore, sustainable water resource management is essential to achieve food and water security in Ethiopia. The Baro– Akobo River basin, which is found in the southwest part of Ethiopia, has witnessed a substantial change in population, climate, and land use during the last four decades. The climate of the region is warming at an alarming rate, and it is expected that this tendency will persist in the coming century. On the other hand, the impacts of climate and land use change on the hydrology of this basin are not well understood. Understanding the impacts of climate and land use change on basin hydrology is critical for developing effective water management practices. Therefore, this study examined the individual and combined impacts of climate and land use changes on the basin hydrology. This study combines a statistical, geospatial, and hydrological model to investigate the hydrologic impacts of climate and land use change. First, seven raw and bias-corrected RCMs (RCA4 (CNRM), RCA4 (ICHEC), RCA4 (MPI), CCLM4 (CNRM), CCLM4 (MPI), REMO (MPI)) and the ensemble mean were evaluated for model skill in reproducing the observed baseline climate for the period 1975–2005 at several weather stations in the basin. A pixel-to-point approach was used to compare RCMs against weather stations. Monthly distribution patterns and several statistical metrics were used for evaluating the performance of RCMs in capturing the historical observed climate of the basin. The Mann-Kendall test and Sen's slope estimate were used to examine the decreasing and increasing trend of the climatic variables, as well as to estimate the magnitudes of the significant decreasing and increasing trends. Furthermore, the ensemble mean and five bias corrected RCMs were used to examine climate and hydrological changes in the baseline and future 2021–2050 (2030s) and 2071-2100 (2080s) periods under the representative concentration pathway (RCP4.5 and RCP8.5) scenarios. On the other hand, the geospatial and synergistic techniques of cellular automata (CA) and artificial neural networks (ANN) were used to develop historical and future land use change scenarios. The maximum likelihood classifier (MLC) in the Earth Resource Data Analysis System (ERDAS) was used to conduct land use change classification for Landsat imagery from 1985, 2002, and 2019. Three land use maps with seven classes were identified, and then a change detection process was conducted. The classified (1985–2002) and (2002–2019) land use maps, along with a transition matrix and spatial drivers, were put into the Module for Land-use Change Evaluation (MOLUSCE) model to predict land use maps for 2019 (current) and 2040 (a business-as-usual scenario) land use scenarios, respectively, using the CA-ANN multilayer perceptron methods (MLP). Besides, this study considered a further increase in altitudinal forest expansion and watershed management practices (conservation) in a land use scenario. Then the best-performing climate model (ensemble mean) under RCP4.5 and RCP8.5 for the 2023–2040 time frame and plausible land use scenarios considering the current, business as usual, and conservation was used as inputs to the calibrated models to examine the individual and combined impacts of climate and land use changes on the hydrology. Three independent climate datasets, including observed, Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS), and Climate Forecast System Reanalysis (CFSR), were used to calibrate and validate the Soil and Water Assessment Tool (SWAT) model for the periods 1990–1998 and 1999–2002, respectively. The Sequential Uncertainty Fitting 2 (SUfI2) method in the SWAT-CUP program was used for model calibration and sensitivity. The Nash–Sutcliffe Efficiency (NSE), Coefficient of Determination (R2), and Percent Bias (PBIAS) as well as two uncertainty measurements (r-factor and p-factor), were used to assess the model's performance. Then the calibrated model using the water balance equation was used to examine the individual and combined impacts of climate and land use change scenarios on the basin hydrology. The climate and land use change impacts on hydrology were analyzed on monthly, seasonal, and annual scales with respect to the baseline period. Statistical tests such as the t-test and Levene test were also used to determine the change in mean and standard deviation between the baseline and different climate and land use scenarios. Besides, the Indicators of Hydrologic Alterations (IHA) method was used to assess the hydrologic changes between the baseline conditions and future climate change scenarios. Results from the global climate models (GCMs) downscaled through the CCLM4, RCA4, and REMO modeling schemes are characterized by several biases, such as shifting the rainy season and under- and overestimation of the observed climate. However, the skill of the models was substantially enhanced after bias correction. All models best capture the annual cycle with less bias. Therefore, it was beneficial to account for such biases using a robust statistical bias correction method before utilizing RCM simulations to generate climatic scenarios and climate impact scenarios. Results from future bias-corrected RCMs show a consistent increase in monthly Tmax and Tmin under RCP4.5 and RCP8.5 in the 2030s and 2080s relative to the baseline climate, while rainfall does not show consistency. The future climate change projections from the ensemble mean show an increase in the R20mm, CDD, R95p, RX1, and RX5 indices, but the R10 indices show a decreasing value under both RCPs. Observed climate and CHIRPS rainfall combined with the CFSR dataset yielded reasonable and comparable streamflow simulation performance in terms of statistical metrics at both Baro and Sore hydrological stations. All climate impact scenarios from the ensemble mean demonstrated a decline in surface runoff and water yield and an increase in evapotranspiration. Except for the extreme flow segment (i.e., 0–3% exceedance probability), the projection for simulation under climate change scenarios shows a decrease in flow. The increase in temperature and the decrease in rainfall is attributed to a relatively higher impact than the combined and land-use change-alone scenarios. This will have an impact on future agricultural production and water availability. Moreover, the projected increase in rainfall extremes, the expansion of agricultural land, and urbanization all lead to increased surface runoff and flooding. Therefore, to implement adaptation and mitigation strategies, the inclusion of predicted climate and land use change in hydrological impact studies is useful.