Introduction
Urban flooding poses a significant challenge to urban areas in developing countries, where rapid urbanisation, inadequate infrastructure, and climate change exacerbate the risks. Nakuru Town, located in Kenya’s Rift Valley region, is no exception. As one of Kenya’s fastest-growing urban centres, Nakuru Town faces recurrent flooding events that disrupt livelihoods, damage infrastructure, and threaten public safety. The integration of Geographic Information Systems (GIS) and Remote Sensing technologies offers a promising approach to understanding and mitigating these challenges through spatial analysis. This proposal paper aims to explore the spatial dimensions of urban flooding in Nakuru Town, focusing on identifying vulnerable areas, assessing contributing factors, and proposing actionable solutions. The paper outlines the problem statement, main and specific objectives, justification, detailed methodology, and expected results. By leveraging GIS and Remote Sensing, this study seeks to contribute to sustainable urban planning and disaster risk reduction in Nakuru Town.
Statement of the Problem
Nakuru Town frequently experiences urban flooding, particularly during the rainy seasons, due to a combination of topographic factors, poor drainage systems, and unplanned urban expansion. These floods result in significant economic losses, displacement of residents, and health hazards such as waterborne diseases. According to Ouma and Tateishi (2014), flooding in urban areas like Nakuru is often exacerbated by the lack of integrated planning and insufficient data on flood-prone zones. Despite the recurrent nature of these events, there is a limited understanding of the spatial distribution and specific causes of flooding in Nakuru Town. Traditional methods of flood assessment often fail to capture the dynamic spatial patterns and underlying risk factors. Consequently, local authorities struggle to implement effective mitigation strategies. This study seeks to address this gap by employing GIS and Remote Sensing tools to map flood vulnerabilities and inform policy.
Main Objectives
The primary objective of this research is to conduct a spatial analysis of urban flooding in Nakuru Town using GIS and Remote Sensing techniques. This overarching goal aims to provide a comprehensive understanding of flood patterns, risk areas, and contributing factors to support urban planning and disaster management. By creating detailed flood risk maps and identifying key drivers of flooding, the study intends to contribute to the development of targeted interventions that reduce the impact of floods on Nakuru Town’s population and infrastructure.
Specific Objectives
To achieve the main objective, the study will focus on the following specific objectives:
1. To map flood-prone areas in Nakuru Town using GIS and Remote Sensing data.
2. To identify the spatial and environmental factors contributing to urban flooding in the study area.
3. To assess the socio-economic impacts of flooding on vulnerable communities in Nakuru Town.
4. To propose mitigation strategies based on the spatial analysis of flood risks.
Justification
The need for a spatial analysis of urban flooding in Nakuru Town is evident given the increasing frequency and severity of flood events in the region. Flooding poses a direct threat to the town’s population, particularly low-income communities residing in informal settlements that are often located in flood-prone areas. Moreover, Nakuru Town serves as a critical economic hub in Kenya, and recurrent flooding undermines its potential for sustainable growth. The application of GIS and Remote Sensing in this context is justified as these technologies provide accurate, cost-effective, and scalable tools for mapping and analysing flood risks (Sanders, 2007). By generating detailed spatial data, this study will support local authorities and planners in prioritising resources and implementing evidence-based interventions. Furthermore, the research aligns with global agendas such as the United Nations’ Sustainable Development Goals (SDGs), particularly Goal 11 on sustainable cities and communities, by addressing urban resilience to natural hazards.
Detailed Methodology
The methodology for this study is structured to address each specific objective systematically, employing GIS and Remote Sensing as core tools. The research will adopt a mixed-methods approach, combining quantitative spatial analysis with qualitative socio-economic assessments.
Objective 1: Mapping Flood-Prone Areas
To map flood-prone areas in Nakuru Town, historical flood data will be collected from local government reports and community surveys. Satellite imagery, such as data from Landsat or Sentinel-2, will be accessed through platforms like the United States Geological Survey (USGS) Earth Explorer to identify past flood extents. Digital Elevation Models (DEMs) will be used to analyse topographic features influencing water flow and accumulation. Using GIS software like ArcGIS, flood hazard zones will be delineated based on elevation, slope, and proximity to water bodies such as the Njoro River, which often overflows during heavy rains. Overlay analysis will be conducted to integrate multiple layers, including land use and rainfall data, to produce comprehensive flood risk maps.
Objective 2: Identifying Spatial and Environmental Factors
This objective focuses on determining the factors contributing to urban flooding in Nakuru Town. Spatial datasets, including land cover, soil type, and drainage network maps, will be acquired from reputable sources such as the Kenya National Bureau of Statistics (KNBS) and the Regional Centre for Mapping of Resources for Development (RCMRD). Remote Sensing will be used to assess changes in land use over time, particularly the conversion of natural landscapes to impervious urban surfaces, which increases surface runoff. Rainfall data from the Kenya Meteorological Department will be spatially interpolated using GIS to understand precipitation patterns. Correlation analysis will be performed to identify relationships between environmental variables and flood occurrences.
Objective 3: Assessing Socio-Economic Impacts
To evaluate the socio-economic impacts of flooding, primary data will be collected through structured questionnaires and interviews with residents in flood-prone areas of Nakuru Town. A purposive sampling technique will target communities in low-lying areas and informal settlements such as Kaptembwa and Rhonda. Secondary data on property damage, displacement, and health impacts will be sourced from local government and NGO reports. GIS will be used to spatially map socio-economic vulnerabilities by overlaying flood risk zones with population density and income distribution layers. This approach will highlight communities most at risk and guide resource allocation for disaster response.
Objective 4: Proposing Mitigation Strategies
Based on the findings from the spatial analysis, mitigation strategies will be formulated. These may include recommendations for improving drainage systems, enforcing zoning regulations to prevent construction in flood-prone areas, and promoting community-based early warning systems. GIS will be used to simulate the potential impact of proposed interventions, such as the construction of retention ponds or flood barriers, on reducing flood risks. Stakeholder engagement with local authorities and community leaders will ensure that proposed solutions are contextually relevant and feasible for implementation in Nakuru Town.
Expected Results
The study anticipates several key outcomes. First, detailed flood risk maps of Nakuru Town will be produced, clearly identifying high-risk zones and enabling targeted interventions. Areas near the Njoro River and low-lying informal settlements are expected to emerge as the most vulnerable. Second, the research will reveal critical spatial and environmental factors contributing to flooding, such as poor land use practices and inadequate drainage infrastructure. Third, the socio-economic analysis is likely to highlight the disproportionate burden of flooding on low-income communities, with significant impacts on housing, health, and livelihoods. Finally, the proposed mitigation strategies are expected to provide a practical framework for reducing flood risks, potentially including infrastructural improvements and policy recommendations. These results will contribute to a deeper understanding of urban flooding in Nakuru Town and support evidence-based decision-making for disaster risk reduction.
Conclusion
Urban flooding remains a pressing issue for Nakuru Town, driven by rapid urbanisation, environmental degradation, and inadequate planning. This proposal has outlined a comprehensive approach to addressing this challenge through the spatial analysis of flood risks using GIS and Remote Sensing. By mapping flood-prone areas, identifying contributing factors, assessing socio-economic impacts, and proposing mitigation strategies, the study aims to generate actionable insights for urban planners and policymakers. The expected results, including detailed risk maps and practical recommendations, have the potential to enhance Nakuru Town’s resilience to flooding and contribute to sustainable urban development. Furthermore, this research underscores the value of geospatial technologies in addressing complex environmental challenges, offering a replicable model for other flood-prone urban areas in Kenya and beyond. Ultimately, the findings will inform local strategies for disaster risk reduction, aligning with broader goals of urban sustainability and community well-being.
References
- Ouma, Y. O. and Tateishi, R. (2014) Urban flood vulnerability and risk mapping using integrated multi-parametric AHP and GIS: Methodological overview and case study assessment. Water, 6(6), pp. 1515-1545.
- Sanders, B. F. (2007) Evaluation of on-line DEMs for flood inundation modeling. Advances in Water Resources, 30(8), pp. 1831-1843.
(Note: Due to limitations in access to specific URLs or more recent local data on Nakuru Town at the time of writing, only general academic sources have been cited. Additional references, such as government reports or specific datasets from Kenyan institutions like KNBS or RCMRD, would ideally be included in a full study but are not accessible here for verified citation or URLs. The word count has been met and slightly exceeded to ensure compliance with the requirement.)
Word Count: 1520 (including references)
