FLOOD RISK ASSESSMENT AND MAPPING OF THE BENUE RIVER BASIN USING GIS”

FLOOD RISK ASSESSMENT AND MAPPING OF THE BENUE RIVER BASIN USING GIS”

ABSTRACT

Flooding is one of the most pervasive and destructive natural hazards confronting communities worldwide, and the Benue River Basin in Nigeria remains among the most chronically flood-affected river systems on the African continent. This study presents a comprehensive flood risk assessment and spatial mapping of the Benue River Basin using Geographic Information System (GIS) and Remote Sensing (RS) technologies. The research integrates multi-source geospatial datasets including Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM), Landsat satellite imagery, hydrological data, land use/land cover (LULC) maps, rainfall records, and socio-demographic information to delineate flood hazard zones and assess community vulnerability. An Analytical Hierarchy Process (AHP)-based Multi-Criteria Decision Making (MCDM) framework was adopted to derive weighted composite flood risk indices from key conditioning factors: elevation, slope, drainage density, distance from river, rainfall intensity, soil type, LULC, and topographic wetness index (TWI). Flood risk was classified into five distinct zones  very low, low, moderate, high, and very high  and mapped at sub-basin and local government area (LGA) levels. Results indicate that significant portions of communities along the main channel and floodplains of the Benue River system are situated within high to very high flood risk zones, with upstream contributions from the Lagdo Dam in Cameroon further compounding risk exposure. The findings have direct implications for disaster risk reduction planning, land-use management, early warning system design, and climate adaptation strategies in the basin. It is recommended that relevant government agencies, urban planners, and disaster management authorities adopt the generated flood risk maps as decision-support tools for proactive flood governance across the region.

Keywords: Flood Risk Assessment, GIS, Remote Sensing, Benue River Basin, AHP, Flood Mapping, Nigeria, DEM, LULC, Flood Vulnerability

CHAPTER ONE

INTRODUCTION

1.1 Background of the Study

Flooding represents one of the most catastrophic and recurrent natural disasters on earth, accounting for a disproportionate share of global disaster losses in terms of human lives, displacement, agricultural destruction, and infrastructure damage. According to the Intergovernmental Panel on Climate Change (IPCC), the intensification of the global hydrological cycle due to anthropogenic climate change is rendering flood events more frequent, more intense, and more geographically extensive (IPCC, 2021). The severe and complex nature of flood disasters, particularly in developing nations where institutional capacity for disaster preparedness is limited, demands robust scientific methodologies for flood risk identification, mapping, and management. Globally, floods are among the most common and destructive hydrometeorological hazards, causing substantial loss of life, property damage, economic disruption, and a major threat to public health (Rentschler et al., 2022; Aslam et al., 2024). An estimated 1.81 billion people  representing approximately 23% of the world’s population  are exposed to significant flood risks, with the majority concentrated in East and South Asia and sub-Saharan Africa (Boadi et al., 2025; Rentschler et al., 2022). The severity, duration, and frequency of disastrous flood events are escalating, driven by population and economic growth in flood-prone regions, accelerating climate change, rapid urbanization, and unchecked land-use conversion (Devitt et al., 2023; Prabandaru et al., 2021). In 2023 alone, 176 major floods were recorded worldwide (Dyvik, 2024), underscoring the urgency with which flood risk assessment tools must be developed and deployed. In sub-Saharan Africa, flood impacts are amplified by weak urban infrastructure, inadequate drainage systems, dense settlement of floodplains, and limited institutional capacity for disaster response. Nigeria, as Africa’s most populous nation with over 200 million inhabitants (National Population Commission [NPC], 2022), is particularly vulnerable to catastrophic flooding. The country experiences recurrent annual flooding that devastates communities, farmlands, and infrastructure across multiple geopolitical zones. The 2012 floods  widely described as Nigeria’s worst in over 40 years  affected more than seven million people, displaced 2.1 million, and caused massive destruction across 32 states (Agada & Nirupama, 2015; Nkeki et al., 2013). A decade later, the 2022 floods emerged as the most devastating the country had experienced since 2012, displacing over 1.4 million people, killing more than 603 persons, injuring over 2,400 others, destroying approximately 82,035 houses, and damaging 332,327 hectares of farmland across 34 of Nigeria’s 36 states (Wikipedia, 2022 Nigeria Floods; OCHA, 2022). Economic losses from the 2022 flooding alone were estimated at ₦4.2 trillion (approximately USD 6.68 billion), a staggering toll on an economy already grappling with developmental challenges (Nexus Engineering, 2024). The situation worsened further in subsequent years. Between June and September 2024, flooding across Nigeria led to 280 deaths, 2,504 injuries, destruction of 122,330 homes, damage to 17,000 acres of farmland, and displacement of approximately 641,500 people (Global Voices, 2026). The World Weather Attribution (WWA) network, an international consortium of climate scientists, confirmed in October 2024 that human-driven climate change had made seasonal downpours across the Niger and Lake Chad basins 5–20% more intense, directly contributing to the humanitarian catastrophe that killed 1,200 Nigerians and displaced 1.2 million in 2024 (Environews Nigeria, 2026). These statistics reveal a disturbing and accelerating trend that demands urgent scientific intervention in the form of precise spatial flood risk assessment. At the heart of Nigeria’s flooding crisis lies the Benue River Basin  a vast hydrological system that drains large portions of north-central and northeastern Nigeria before converging with the Niger River at Lokoja in Kogi State. The Benue River, often referred to as Nigeria’s second-longest river, originates in the Adamawa Highlands and traverses the states of Taraba, Benue, Nasarawa, and Kogi before its confluence with the Niger. Its major tributaries include the Katsina-Ala, Donga, Taraba, Gongola, and Pai rivers (Nkeki, Henah & Ojeh, 2013). The basin encompasses an extensive lowland floodplain characterized by flat terrain, high water table, and fertile alluvial soils that have historically attracted dense agricultural settlement  characteristics that simultaneously render it extraordinarily productive and extremely susceptible to flood inundation. The Benue River Basin presents a particularly complex flood risk profile driven by a convergence of natural and anthropogenic factors. Chief among these is the operation of the Lagdo Dam in Cameroon, located upstream along the Benue River. The Lagdo Dam’s annual release of excess water during the peak wet season consistently triggers devastating downstream floods in Nigerian communities along the Benue corridor (Wikipedia, 2022; Environews Nigeria, 2026). The 2012 and 2022 catastrophic flood events were both significantly exacerbated by Lagdo Dam releases, overwhelming natural channel capacity and inundating communities across Adamawa, Taraba, Benue, Nasarawa, and Kogi states. This transboundary dimension of flood risk adds a layer of governance complexity that requires robust spatial planning and early warning systems grounded in reliable flood risk maps. The scientific community has extensively documented the hazardous nature of the Benue River Basin. Mayomi (2013) employed GIS techniques to assess the flood risk and vulnerability of 120 communities in the Benue floodplains of Adamawa State, revealing that all communities within the study area were flood-vulnerable to varying degrees, with 29 communities (32.5%) classified as highly vulnerable. Nkeki, Henah, and Ojeh (2013) developed a geospatial methodology using MODIS terra satellite imagery to assess the spatial impact of the October 2012 flooding along the Niger-Benue basin, demonstrating that the overall affected area extended to 13,702 km²  a striking demonstration of the basin’s inundation potential. Akinbobola et al. (2015) applied GIS-based watershed analysis using SRTM DEM data and the Standardized Precipitation Index (SPI) over a 30-year rainfall record, revealing that approximately 45% of Nigerian towns and villages fall within flood risk zones of the Niger-Benue basin. Ozim et al. (2021) conducted a GIS-based vulnerability assessment for 256 communities in Kogi State along the Niger-Benue river system, finding that 12.89% of communities were highly vulnerable and located primarily along river banks and within 3 km of the main channel. Abdulrahman et al. (2021), in a geospatial analysis of flood risk and vulnerability along the River Benue Basin of Kogi State, employed DEM and Landsat TM images for three temporal periods (2010, 2014, and 2018) and generated classified land use/land cover and flood physical vulnerability maps that effectively illustrated progressive changes in flood exposure. Most recently, Idogho et al. (2025) applied an integrated GIS, Multi-Criteria Decision Making (MCDM), and Analytic Hierarchy Process (AHP) approach to assess flood risk in Makurdi  the capital of Benue State situated directly along the Benue River  producing a high-resolution flood risk map that identified neighborhoods such as Wurukum and Wadata as particularly vulnerable. Their study confirmed that flooding in Makurdi poses severe annual challenges including displacement, infrastructure damage, and disruption of livelihoods, and underscored the critical value of spatially explicit flood risk maps as decision-support tools for urban planners and disaster management agencies. The global body of literature further affirms the growing role of GIS and remote sensing in flood risk assessment and management. M Amen et al. (2023) applied the AHP technique to twelve flood conditioning parameters using GIS and remote sensing for flood-prone area mapping in Duhok, Iraq. Mohammed, Hussein, and Abood (2024) utilized GIS-based AHP with nine flood influencing parameters  including elevation, slope, distance from river, rainfall, drainage density, LULC, NDVI, and TWI  to generate flood hazard maps for the Diyala governorate of Iraq, finding that approximately 64% of the study area faces moderate potential flood hazard. Bekele et al. (2024) integrated RS, GIS, and AHP to evaluate flood hazard and risk areas in the Gidabo Watershed, Ethiopia, considering drainage density, soil type, elevation, rainfall, slope, and LULC as primary flood conditioning factors. At the systematic review level, Huang et al. (2025) conducted an in-depth review of RS and GIS applications in flood disaster risk management, analyzing 274 peer-reviewed articles from the Scopus database and affirming the sustained reliability and growing sophistication of these geospatial methodologies across diverse global contexts. Despite this growing body of research, significant spatial and methodological gaps persist in flood risk assessment for the Benue River Basin. Many existing studies have focused on specific states or sub-sections of the basin without providing a comprehensive basin-wide risk analysis. Furthermore, the integration of recent high-resolution DEM data, updated LULC classification, climate-adjusted rainfall analysis, and multi-criteria weighting frameworks into a unified flood risk mapping product remains underdeveloped for this critical basin. Given the escalating flood disaster trend, the complex role of upstream dam operations, the dense settlement of the floodplain, and the acute vulnerability of communities dependent on subsistence agriculture, there is an urgent and compelling need for a rigorous, spatially comprehensive flood risk assessment of the entire Benue River Basin using contemporary GIS and remote sensing methodologies. This study is a direct response to that need.

1.2 Statement of the Problem

Despite the chronic and escalating nature of flood disasters in the Benue River Basin, systematic and basin-wide flood risk assessment using integrated GIS and remote sensing approaches remains limited and fragmented. Existing studies are often confined to individual states, specific flood events, or limited spatial extents, leaving policymakers and disaster management authorities without comprehensive, reliable flood risk maps that cover the full extent of the basin. The absence of up-to-date, spatially explicit flood risk information hampers effective land-use planning, emergency response, infrastructure siting, and community-level flood preparedness across the region. Moreover, the increasing severity of flooding attributable to climate change and the recurring impact of the Lagdo Dam releases underscore the inadequacy of reactive disaster management approaches and highlight the critical need for proactive, evidence-based spatial planning grounded in rigorous flood risk mapping. This study addresses these knowledge and policy gaps by conducting a comprehensive GIS-based flood risk assessment and mapping of the entire Benue River Basin.

1.3 Aim and Objectives of the Study

The aim of this study is to assess and map flood risk across the Benue River Basin using Geographic Information System (GIS) and Remote Sensing techniques.

The specific objectives are to:

i. Delineate the hydrological boundaries and sub-basin characteristics of the Benue River Basin using SRTM DEM data;

ii. Identify and analyze the key flood conditioning factors including elevation, slope, rainfall intensity, drainage density, soil type, LULC, distance from river, and TWI within the basin;

iii. Develop flood hazard, vulnerability, and risk maps for the Benue River Basin using an AHP-based multi-criteria evaluation approach;

iv. Assess the exposure of communities, agricultural land, and critical infrastructure to flood risk at the LGA level; and

v. Provide evidence-based recommendations for flood risk management, early warning systems, and land-use planning in the Benue River Basin.

1.4 Research Questions

The study is guided by the following research questions:

i. What are the spatial characteristics of flood conditioning factors within the Benue River Basin?

ii. Which communities, agricultural zones, and infrastructure corridors within the basin are exposed to high and very high flood risk?

iii. How do natural factors (topography, rainfall, drainage) and anthropogenic factors (LULC change, dam operations) interact to determine flood risk in the basin?

iv. What role can GIS-derived flood risk maps play in informing disaster risk reduction and land-use planning strategies for the Benue River Basin?

1.5 Significance of the Study

This study makes several important contributions to knowledge and practice. From a scientific standpoint, it provides a methodologically rigorous and spatially comprehensive flood risk map of the Benue River Basin  one that integrates updated datasets and a validated multi-criteria weighting framework, thereby advancing the state of flood hazard science in Nigeria. From a practical standpoint, the flood risk maps produced serve as critical decision-support tools for the Benue State, Kogi State, Nasarawa State, Adamawa State, and Taraba State governments, the National Emergency Management Agency (NEMA), the Nigeria Hydrological Services Agency (NIHSA), urban and regional planners, civil society organizations, and international humanitarian actors operating in the region. The study also contributes to Nigeria’s commitments under the Sendai Framework for Disaster Risk Reduction 2015–2030, the Paris Agreement on Climate Change, and the United Nations Sustainable Development Goals (SDGs), particularly SDG 11 (Sustainable Cities and Communities), SDG 13 (Climate Action), and SDG 15 (Life on Land). By identifying vulnerable communities and high-risk zones with spatial precision, the study enables targeted investment in flood-resilient infrastructure, improved early warning systems, community relocation planning, and climate adaptation measures that protect lives and livelihoods in one of Nigeria’s most flood-prone regions.

1.6 Scope of the Study

This study focuses on the Benue River Basin as defined by its watershed boundaries derived from SRTM DEM analysis. The basin encompasses major riparian communities and LGAs across Adamawa, Taraba, Benue, Nasarawa, and Kogi states. The spatial analysis is based on multi-temporal satellite imagery, DEM data, hydrological records, and climatic data covering a minimum period of thirty years to ensure statistical robustness in rainfall analysis. The study employs GIS, remote sensing, and AHP-MCDM methodologies and does not involve primary hydraulic modeling (e.g., HEC-RAS simulations), though outputs are compatible with such models for future integration.

1.7 Study Area

The Benue River Basin is located in north-central and northeastern Nigeria, roughly between latitudes 6°N and 12°N and longitudes 7°E and 15°E. The Benue River originates in the Adamawa Highlands near the border with Cameroon and flows generally westward for approximately 1,400 km before its confluence with the Niger River at Lokoja in Kogi State. The river bisects several major urban centers including Yola, Numan, Jalingo, Makurdi, and Lokoja. Makurdi, the capital of Benue State and one of the most flood-prone urban centers in Nigeria, lies between latitudes 7°38’N and 7°50’N and longitudes 8°24’E and 8°38’E and is directly bisected by the Benue River (Idogho et al., 2025). The basin is characterized by a guinea savanna vegetation zone, with tropical wet/dry climate featuring pronounced wet seasons (April–October) and dry seasons (November–March). Annual rainfall varies from approximately 900 mm in the drier northern fringes to over 1,800 mm in the wetter southern portions. The terrain is predominantly flat to gently undulating lowland floodplain, with mean elevations ranging from 50 m to 300 m above sea level. Soil types consist largely of ferralitic, alluvial, and hydromorphic soils with varying degrees of permeability and moisture retention. The basin supports a predominantly agrarian population engaged in rain-fed and floodplain cultivation of rice, yam, cassava, sorghum, and maize  a livelihood base rendered acutely vulnerable by recurring seasonal and extreme flood events.

1.8 Definition of Key Terms

Flood Risk: The potential for adverse consequences arising from flooding, expressed as the product of flood hazard (probability and magnitude of flooding), exposure (elements at risk), and vulnerability (susceptibility to harm).

Flood Hazard: The probability of occurrence of a potentially damaging flood phenomenon within a specific period of time and a given area. Flood Vulnerability: The characteristics and circumstances of a community, system, or asset that make it susceptible to the damaging effects of flooding. Geographic Information System (GIS): An integrated computer-based system for the input, storage, manipulation, analysis, and output of spatially referenced (geographic) data. Remote Sensing: The science of acquiring information about the earth’s surface without physical contact, primarily through satellite and airborne sensor data. Digital Elevation Model (DEM): A digital representation of ground surface topography or terrain, used in this study to derive slope, drainage networks, watershed boundaries, and flow accumulation patterns. Analytic Hierarchy Process (AHP): A structured multi-criteria decision-making technique that organizes and analyzes complex decisions based on the relative importance (weights) of multiple contributing factors through pairwise comparisons. Land Use/Land Cover (LULC): A classification of the earth’s surface based on observed physical characteristics (land cover) and human activity (land use), derived from satellite imagery classification. Topographic Wetness Index (TWI): A compound topographic index that quantifies the tendency of a grid cell to accumulate water based on upslope contributing area and local slope gradient.

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