Dam failures have severe consequences on human life and property. In the case of an earth filled Kibimba Dam located in Eastern Uganda, the occurrence of a flood equal to or larger than the probable maximum food (PMF) could result in catastrophic economic losses including loss of human life. This study utilized the USACE Hydrologic Engineering Center's River Analysis System (HEC-RAS) and Hydrologic Engineering Center’s Geographic River Analysis System (HEC-GeoRAS) to analyze the potential dam break of Kibimba Dam, considering overtopping and piping failure scenarios. The results of the analysis revealed that the spillway of Kibimba Dam possesses sufficient capacity to safely discharge a flood resulting from a probable maximum flood peak of 400 m³/s. Therefore, the dam is not susceptible to breach under the overtopping failure mode. However, the dam failed under the piping failure mode. To assess the downstream impact of the dam break, the breach hydrographs resulting from piping failure were examined. Consequently, the study investigated the effects of flood propagation downstream of the dam. This resulted in varying inundation depths of up to 6 m and velocities ranging from 1.2 to 10 m/s. These findings highlight the devastating consequences of Kibimba Dam's failure, particularly affecting rice field plantations, infrastructure, and other economic activities in the downstream area. Therefore, the outcomes of this study are crucial for the development of Emergency Action Plans that incorporate dam breach and flood routing analyses specific to the affected downstream regions.

Inland water pollution poses significant risks to aquatic environments, affecting ecological and human health while increasing drinking-water treatment costs. Continuous monitoring using reliable techniques such as satellite remote sensing is essential. This study investigates the spatial and temporal water quality dynamics of Lake Okeechobee by integrating satellite imagery with in situ water samples. The objectives are (1) to analyze water quality trends and (2) to develop linear regression models for predicting Chlorophyll-a (Chla), turbidity, and Trophic State Index (TSI) for eutrophication monitoring. Landsat 8 and 9 imagery, coupled with water quality data from monitoring stations in the South Florida Water Management District’s DBHydro database, provided measurements of Chla, total nitrogen (TN), total phosphorus (TP), and turbidity. Statistical analyses determined optimal regression models with adjusted R² values of 0.69 and 0.93 for individual band Chla and turbidity models and 0.65, 0.82, and 0.66 for spectral band ratio models of Chla, turbidity, and TSI, respectively. Northwestern and southwestern peripheries of Lake Okeechobee exhibited Chla concentrations exceeding the 20 µg/L threshold, while turbidity peaked near the lake’s center. From 2013 to 2019, TN increased significantly, followed by a slight decline from 2019 to 2023, whereas TP exhibited no clear trend. The TSI ranged from mesotrophic to hypereutrophic states. This study demonstrates satellite remote sensing as an efficient tool for monitoring water quality changes, guiding restoration measures, and identifying highly contaminated zones through threshold-based segmentation of satellite-derived water quality maps, rather than assessing the lake as a uniform whole.

Satellite remote sensing has emerged as a reliable and cost-effective approach for monitoring inland water quality, offering spatial and temporal advantages over traditional in situ methods. Lake Victoria, the largest tropical lake and a critical freshwater resource for East Africa, faces increasing eutrophication driven by nutrient inflows from agriculture, urbanization, and industrial activities. This study assessed the spatiotemporal dynamics of water quality along Uganda’s Lake Victoria coast by integrating field measurements (2014–2024) with Landsat 8/9 imagery. Chlorophyll-a, a proxy for algal blooms, and Secchi disk depth, an indicator of water clarity, were selected as key parameters. Cloud-free satellite images were processed using the Dark Object Subtraction method, and spectral reflectance values were correlated with field data. Linear regression models from single bands and band ratios showed strong performance, with adjusted R² values of up to 0.88. When tested on unseen data, the models achieved R² values above 0.70, confirming robust predictive ability. Results revealed high algal concentrations for nearshore and clearer offshore waters. These models provide an efficient framework for monitoring eutrophication, guiding restoration priorities, and supporting sustainable water management in Lake Victoria.