Hydraulic Engineering

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End date: 2025Subject: search.filters.subject.HEC-HMS

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    DEVELOPING OPTIMAL RESERVOIR OPERATION (CASE OF KOKA MULTIPURPOSE RESERVOIR, ETHIOPIA
    (Hawassa University, 2023-07-18) GEMECHU SHIFERAW BELACHEW
    Reservoir operation is the most challenging task in the management of water resources systems. Hence, water resources models are needed to minimize these challenges, as much as possible if they are supplemented with sound engineering judgments. In this study, the rainfall runoff simulation HEC-Hydrologic Model System (HEC-HMS) and reservoir simulation model (HEC-ResSim) were applied to the koka multipurpose reservoir to optimize the power fluctuation observed and water scarcity due to reduced water release from the reservoir. Inflow was generated by the HEC-HMS model by using twenty years of daily meteorological data collected from NMA. The generated inflow was calibrated and validated with 16 years of observed flow data from Melka Kunture, Hombole, and Mojo gauging stations. The performance of the model was evaluated by NSE, R2, RMSE, and PBIAS performance indices criteria reviewed in different literature. For example, the NSE value of Melka Kunture, Hombole, Mojo, and Koka Inlet is 0.75, 0.78, 0.63, and 0.8 for calibration, and 0.7, 0.72, 0.53, and 0.75 for validation, respectively. The value of R2 for Melka Kunture, Hombole, Mojo and Koka Inlet is also 0.74, 0.77, 0.64 and 0.77 for calibration and 0.74, 0.75, 0.52 and 0.76 for validation respectively. Although the model slightly underestimates the flow, for both calibration and validation, the model shows acceptable performance to generate an inflow of the upper awash watershed. To simulate the reservoir the inflow generated by the HEC-HMS model, reservoir physical, and operational data were collected from governmental organizations and provided to the model. Since the HEC-ResSim model cannot optimize the constraints directly, trial and error have been applied through a prioritization rule between three main demands; i.e., Hydropower, Irrigation, and Domestic, Municipal and Industrial demands. The best alternative was selected based on the power target, release target, and pool elevation target. Accordingly, from the three alternatives applied in the simulation, ALT-1 gives maximum power and maximum release that supports the downstream water needs. When power demand was given the highest rule priority, the reservoir generate an average energy of 504.76MWh per day or 55GWh per year which is greater than the power generated in ALT- 2 and ALT-3 by 32% and 64%, respectively. The reservoir reaches its minimum elevation of 103.93m in June (except in the drought year of 2003 and 2016) and its maximum elevation of 110.39m in August. Generally, the reservoir can support the downstream water needs safely if the operation will be conducted by the power demand priority rule.
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    FLOOD RISK MAPPING FOR FLOOD DISASTER REDUCTION IN THE CASE OF TEJI RIVER, AWASH BASIN, ETHIOPI
    (Hawassa University, 2024-07-10) DENEKE GOTA
    Floods pose significant threats globally, causing immense damage to lives, societies, and economies. This study aimed to assess flood hazards, evaluate vulnerabilities, and determine flood risk along the Teji River floodplain. To achieve the objectives of a study, advanced hydrological and hydraulic modeling techniques were analyzed, using data from various sources, including rainfall from the National Meteorological Agency, stream flow data from the Ministry of Water Resources, and land use/land cover data from USGS. The HEC-HMS model accurately calibrated and validated using observed stream flow data, the result of model calibration gives Nash Sutcliffe efficiency (NSE) of 0.78, Percent Bias (PBIAS) of 4.1, coefficient of determination (R2) of 0.79, and Relative Mean Square Error (RMSE) of 5.04. During the validation period, the model gives (R2) of 0.81, NSE of 0.79, PBIAS of 1.89, and RMSE of 1.29. After model calibration and validation, flood hydrographs for different return periods were generated. These hydrographs served as inputs for the HEC-RAS hydraulic model, integrated with GIS software to map flood inundation areas. The resulting flood inundation maps revealed extensive flood-prone areas along the Teji River, with maximum flood depths of 14.6 meters and maximum velocities of 6.5 m/s during a 100-year flood event. Flood hazard maps classified areas into different hazard categories from low to extreme hazard, and 43% of inundated area falling under extreme, very high, and high hazard levels, 57% of inundated area falling under medium, and low hazard levels. Vulnerability analysis considered indicators such as flood depth, velocity, duration, slope, land use, and population density, highlighting 8% of the flooded area as very high and high vulnerability, 27% of the flooded area as moderate vulnerability and 65% of the flooded area as low and very low vulnerability. Combining flood hazard and vulnerability information, a comprehensive flood risk map was developed, identifying 20% of the flooded area as very high and high risk, 27% of the flooded area as moderate risk and 53% of the flooded area as low and very low risk. These high-risk zones were concentrated in the towns of Asgori and Teji, emphasizing the need for mitigation measures and emergency response plans. The flood risk map provided valuable insights for decision-making processes, guiding the implementation of structural and non-structural measures, floodplain zoning, and population relocation. This study's findings contribute to effective flood management, land-use planning, and disaster risk reduction strategies along the Teji Rive
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    FLOOD RISK MAPPING USING HEC-RAS MODEL: CASE STUDY ON WAJA WATERSHED IN RIFT VALLEY BASIN CENTRAL ETHIOPIA REGION, ETHIOPIA
    (Hawassa University, 2024-04-22) ATEREFE TAMIRAT DEBOCH
    Flood is among the most devastating natural disasters worldwide, significantly affecting human lives and property. The current study conducted on the Waja River floodplain aimed to model and maps the flood inundation, flood hazard, flood vulnerability, and flood risk associated with flooding in the area. To achieve this objective, various data sources were utilized, including meteorological, hydrologic, and topographic data collected from different organizations. The study employed several tools and materials, including the HEC HMS and HEC-RAS models, GIS software, GPS devices, and metering tape. The HEC HMS model was used to analyze flood hazard and risk by developing inflow design floods for different return periods. The model was calibrated and validated using actual stream flow data. During model calibration the NSE value was 0.75, Percent Bias (PBIAS) was 2.02, coefficient of determination (R2 ) was 0.78, and Relative Mean Square Error (RMSE) was 2.03. During the validation period, the model achieved an R2 of 0.77, NSE of 0.76, PBIAS of 1.64, and RMSE of 1.3. After calibration and validation, the annual maximum precipitation from rainfall data was extracted to develop frequency storms for different return periods. These storms were then used as input for the HEC HMS model to generate flood hydrographs. The HEC-RAS model, combined with the flood hydrographs, was used to produce flood inundation maps, which were visualized in ARC-GIS software for detailed analysis. The results of the study indicated that for return periods of 10, 25, 50, and 100 years, the areas inundated by floods were 3030 ha, 3364 ha, 3520 ha, and 3683 ha, respectively. Additionally, the maximum flood depths were found to be 6.3m, 9.2m, 12.6m, and 14.45m for the respective return periods. The maximum flood velocities were 3.8 m/s, 4.7 m/s, 5.5 m/s, and 6.8 m/s for the same return periods. Flood hazard maps were derived from the depth, velocity, and duration of floodwaters, revealing that 35% of the flooded area was categorized as having very high and high hazard, while approximately 65% was classified as medium and low hazard. The flood vulnerability map classified approximately 17% of the flooded area as having high and very high vulnerability. About 18% of the flooded area fell into the moderate vulnerability class. The majority of the flooded area, approximately 65%, had low and very low vulnerability. By combining the flood hazard and vulnerability information, the study developed a flood risk map. The results showed that 24% of the area fell into the high and very high-risk categories
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    FLOOD MODELING AND RISK MAPPING: (CASE OF KULFO RIVER IN SOUTHERN ETHIOPIA REGION
    (Hawassa University, 2024-10-22) CHALACHEW SHENKUTE
    Floods pose significant threats globally, causing immense damage to lives, societies, and economies. This study aimed to assess flood hazards, evaluate vulnerabilities, and determine flood risk along the Kulifo River floodplain. To achieve the objectives of a study, advanced hydrological and hydraulic modeling techniques were analyzed, using data from various sources, including rainfall from the National Meteorological Agency, stream flow data from the Ministry of Water Resources, and land use/land cover data from USGS. The HEC-HMS model accurately calibrated and validated using observed stream flow data, the result of model calibration gives Nash Sutcliffe efficiency (NSE) of 0.81, Percent Bias (PBIAS) of 1.77, coefficient of determination (R2 ) of 0.77, and Relative Mean Square Error (RMSE) of 4.28. During the validation period, the model gives (R2 ) of 0.79, NSE of 0.78, PBIAS of 1.09, and RMSE of 2.13. After model calibration and validation, flood hydrographs for different return periods were generated. These hydrographs served as inputs for the HEC-RAS hydraulic model, integrated with GIS software to map flood inundation areas. The resulting flood inundation maps revealed extensive flood-prone areas along the Kulifo River, with maximum flood depths of 15.2 meters and maximum velocities of 6.9 m/s during a 100-year flood event. Flood hazard maps classified areas into different hazard categories from low to extreme hazard, and 59% of inundated area falling under extreme, very high, and high hazard levels, 41% of inundated area falling under medium, and low hazard levels. Vulnerability analysis considered indicators such as flood depth, velocity, duration, slope, land use, and population density, highlighting 25% of the flooded area as very high and high vulnerability, 20% of the flooded area as moderate vulnerability and 55% of the flooded area as low and very low vulnerability. Combining flood hazard and vulnerability information, a comprehensive flood risk map was developed, identifying 32% of the flooded area as very high and high risk, 15% of the flooded area as moderate risk and 55% of the flooded area as low and very low risk. These high risk zones were concentrated in the Limat area of Arba Minch city, emphasizing the need for mitigation measures and emergency response plans. The flood risk map provided valuable insights for decision-making processes, guiding the implementation of structural and non-structural measures, floodplain zoning, and population relocation. This study's findings contribute to effective flood management, land-use planning, and disaster risk reduction strategies along the Kulifo River
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    FLOOD HAZARD MODELING AND RISK MAPPING: CASE STUDY ON LOWER WEITO RIVER RIFT VALLEY BASIN SNNPR, ETHIOPIA
    (Hawassa University, 2025-08-12) GELMA BORU
    This study focuses on the modeling and mapping of flood inundation and associated risks in the Lower Weito River, a tributary of Lake Abaya by means of coupled hydrological and hydraulic models with different return periods. Meteorological, hydrologic, and topographic data were collected from various sources. Rainfall data from 1990 to 2015 were collected from the National Meteorological Agency and the stream flow data from 1990 to 2007 were collected from the Ministry of Water and Energy. DEM 12 * 12m resolution was downloaded from Alaska satellite facility, soil data was taken from FAO and LULC data were collected from the Ministry of Water and Energy. These data were integrated using modeling tools such as HEC-HMS and HEC-RAS, along with GIS software. To examine the accuracy of the HEC-HMS model, calibration and validation is performed using observed stream flow data. The results showed a strong relationship between simulated and observed data, with R2 and NSE values of 0.82 and 0.77, for calibration periods and 0.78 and 0.75 for validation period respectively which indicating a very good agreement between observed and simulated flow . The calibrated and validated model was then used to develop flood hydrographs for different return periods based on frequency storm analysis. The result of flood frequency analysis showed minimum peak flow of 77.9m 3 /s for a 2-year return period with 24-hour storm duration and, the maximum peak flow 606.2 m3 /s occurs with a 100-year frequency storm for the same duration. The HEC-RAS model was used to generate flood inundation maps, which revealed the extent of flooded areas and the maximum flood depths and velocities for various return periods. The results indicated that the areas inundated by floods ranged from 1711.2 hectares for a 10-year return period to 2763.3 hectares for a 100-year return period. The maximum flood depths varied from 5.2 meters for a 10-year return period to 7.5 meters for a 100-year return period. The maximum flood velocities ranged from 3.15 meters per second for a 10-year return period to 7.01 meters per second for a 100-year return period. Flood hazard maps were derived by considering the depth, velocity, and duration of floodwaters. The results showed that about 0.01% of the total flooded area was under extreme hazard, 14% under very high hazard, 29% under high hazard, 35% under medium hazard, and 21% under low hazard. The flood vulnerability map classified the flooded areas into five vulnerability classes. Approximately 44% of the flooded area was classified as high and very high vulnerability, 19% as moderate vulnerability, and 37% as low or very low vulnerability. The flood risk map was developed by combining the flood hazard and vulnerability information. The results showed that 16% of the area was classified as very high to high risk, 46% as medium risk, and 38% from low to very low risk.