http://hdl.handle.net/20.500.12283/345 2026-04-04T00:30:23Z http://hdl.handle.net/20.500.12283/4664 Hydro-informatics for flood risk modelling and zonation Omoding, Abel he researchers used hydro-informatics methods to model and map flood risks in the Kafu River catchment area. To improve flood preparedness, mitigation planning, and sustainable watershed management, the study focused on identifying and analysing the main causes of flooding, developing a comprehensive flood risk model, and ensuring its accuracy and reliability. A multidisciplinary approach was adopted to accomplish these goals. Historical river flow and rainfall data spanning 30 years (1993–2023) were collected and analysed to assess rainfall trends and flood recurrence intervals. The research integrated modelling techniques, geographic information systems, community surveys, and field-based data collection in a mixed-method approach. Hydrological modelling was performed using HECHMS software, with rainfall-runoff relationships developed through the SCS Unit Hydrograph method for direct runoff transformation and the SCS Curve Number method for loss estimation. Peak flows and related flood depths for five return periods were simulated by calibrating both the hydraulic model (HEC-RAS) and the hydrological model (HEC-HMS). Estimated peak discharges for the 10-, 50-, 100-, 200-, and 500-year return periods were 203.26 m³/s, 281.02 m³/s, 313.90 m³/s, 346.66 m³/s, and 389.87 m³/s, respectively, based on the Gumbel distribution. The maximum flood depths at the Kimengo cross-section near Kafu Bridge were 5.13 m, 6.34 m, 6.67 m, 7.13 m, and 7.46 m when these discharges were input into the HECRAS model. Statistical analyses, including the Chi-square test of independence and flood depth comparisons, confirmed the significant relationship between flood frequency and proximity to river channels. The model's ability to predict flood depths was validated by a strong correlation (R² > 0.90) between field data and simulated results. The study identified key factors contributing to flooding as land use changes—particularly deforestation and wetland encroachment—as well as heavy rainfall events and insufficient drainage infrastructure. Flood risk maps highlighted the areas surrounding the main Kafu River channel and its primary tributaries as most vulnerable to flooding. The findings demonstrated that combining GIS tools with hydrological and hydraulic models offers a strong framework for flood risk assessment and zoning. Overall, the developed model proved to be a valuable and reliable tool for flood risk management within the Kafu catchment area. To reduce flood impacts, it was recommended that local authorities and water resource managers prioritise flood-prone zones in future land use and infrastructure planning, employ GIS-based flood risk frameworks, and enhance community awareness. Dissertation 2025-01-01T00:00:00Z http://hdl.handle.net/20.500.12283/4649 Development of a vegetable irrigation scheduling tool using GIS, remote sensing and soil moisture machine learning predictions Kusemererwa, Joseph Water scarcity and poor irrigation timing continue to constrain vegetable yields in smallholder farming systems. This study developed a Python based irrigation scheduling tool for six vegetables at the Busitema Habuleke Irrigation Scheme that integrates GIS, remote sensing data, and machine learning (ML) soil moisture predictions. The study addressed the challenge of impractical and costly field-based monitoring for irrigation scheduling and the lack of packaged, data driven tools for smallholder farmers. The specific objectives were to (1) analyse spatial and temporal factors influencing root zone volumetric water content (VWC), (2) develop and compare ML models for VWC prediction, and (3) implement and validate an irrigation scheduling tool that generates trigger-based irrigation recommendations. For Objective 1, spatial and statistical analyses revealed that soil moisture distribution closely followed rainfall, evapotranspiration, and soil texture patterns. Higher VWC values were recorded in loam soils, while areas with sandy textures exhibited lower moisture levels. The mean VWC was 19.34% (SD = 5.86%), and rainfall, field capacity, bulk density, and available water content (AWC) were all recognised as significant contributors. Multiple regression explained 88% of the variance in VWC (R² = 0.88), with rainfall (β ≈ 0.305) and AWC (β ≈ 0.295) being the best predictors (p < 0.001). Sensitivity analysis showed that a 10% change in rainfall and AWC caused a 3% and 2.9% change in VWC, respectively. Under Objective 2, three ML models, Random Forest (RF), Long Short Training Memory (LSTM), and Extreme Gradient Boosting (xGBoost), were developed and compared using soil, climatic, and topographic data from 2010 to April 2025. XGBoost exhibited the best performance (NSE = 0.985, RMSE = 0.022, MAE = 0.017), followed by Random Forest (NSE = 0.954) and LSTM (NSE = 0.873). The superior accuracy of XGBoost was attributed to its strong ability to capture non linear relationships between soil moisture and environmental variables. For Objective 3, the validated irrigation scheduling tool produced irrigation recommendations. Seasonal gross requirements for tomatoes under drip ≈ 420 mm season total; green pepper sprinkler ≈ 540– 560 mm and event-level recommendations of carrots: start irrigation at VWC ≈ 0.22–0.25 m³/m³ with 1.5–4 mm pulses were obtained. The tool schedules exhibited lower variability and greater alignment with reference evapotranspiration-based methods compared to farmer practice, leading to measurable improvements in yield consistency. Overall, the integration of in situ calibration, remote sensing, and ML models provides a practical, site specific tool for smallholder irrigation management. Future work should focus on adding offline functionality, expanding farmer trials, and performing uncertainty analysis to enhance operational reliability. Key words: Vegetable irrigation, Irrigation scheduling, GIS, Remote sensing, Soil moisture prediction, Machine learning, Smallholder farming. Dissertation 2025-01-01T00:00:00Z http://hdl.handle.net/20.500.12283/4635 Development of a decision support system for supplementary drip irrigation using soil moisture sensor method Epolait, Anthony The global population, currently at 8.1 billion, is projected to reach 9.7 billion by 2050, intensifying the global demand for food. Africa, particularly Uganda, is experiencing rapid population growth, with Uganda’s population reaching 45.9 million in 2024 and agriculture remaining the cornerstone of its economy. Despite 80% of Uganda's land being arable, only 35% is under cultivation. Agriculture contributes significantly to GDP and employment but remains heavily reliant on rain-fed systems, which are increasingly threatened by climate-induced droughts and floods. Irrigation has proven essential in boosting agricultural productivity, with irrigated lands contributing up to 40% of global food output. However, poor irrigation practices in Uganda have led to waterlogging, inefficient water use, and declining crop yields. Although the government has promoted various irrigation systems through the National Irrigation Policy and Vision 2040, improper application continues to undermine food security and sustainable resource management. This study proposes the development of a precision decision support irrigation system that utilizes real-time data from soil moisture, temperature, and humidity sensors to guide irrigation intervals, depth, and water quantity. Such a system promises improved crop productivity, water-use efficiency, and alignment with Sustainable Development Goals (SDG 2 and SDG 12), thereby supporting Uganda’s long-term agricultural resilience and food security. Dissertation 2025-01-01T00:00:00Z http://hdl.handle.net/20.500.12283/4621 Geospatial evaluation of land suitability of agricultural mechanization and irrigation in Agago district. Baseke, Bridget Prossy Agricultural productivity in Uganda remains constrained by limited adoption of mechanization and irrigation, particularly in rural districts such as Agago. This study aimed to evaluate the land suitability for agricultural mechanization and irrigation in Agago District using geospatial analysis and multicriteria decision-making techniques. High-resolution spatial data on topography, soil properties, land cover, water availability, and infrastructure were collected from authoritative sources, including the USGS, HWSD, MODIS, and UBOS. The study employed the Analytic Hierarchy Process (AHP) to assign weights to key factors influencing mechanization and irrigation potential. Using ArcGIS 10.8.2, individual suitability maps for mechanization and irrigation were developed and then integrated to create a combined land suitability model. The results revealed that 13.31% of the district is highly suitable for both mechanization and irrigation, 76.26% is moderately suitable, 10.26% is marginally suitable, and only 0.17% is unsuitable. Validation of the model through laboratory analysis of soil pH and texture at selected ground-truth locations showed a high level of accuracy, with an R² of 0.92 and an 88% agreement rate between predicted and observed values. These findings underscore the reliability of geospatial modeling in guiding agricultural land-use planning and investment prioritization. The study concludes that Agago District holds significant potential for sustainable agricultural transformation through targeted mechanization and irrigation interventions. The generated suitability maps offer a critical decision-support tool for policymakers, extension officers, and development partners working toward climate-resilient agriculture in Northern Uganda. Dissertation 2025-01-01T00:00:00Z