FLOOD HAZARD MAPPING OF THE LOWER BENUE RIVER BASIN

FLOOD HAZARD MAPPING OF THE LOWER BENUE RIVER BASIN | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article FLOOD HAZARD MAPPING OF THE LOWER BENUE RIVER BASIN Olusegun Adeaga, Tamarabrakemi Akoso, Temitope Idowu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6999964/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Flooding is a recurrent and intensifying hazard in West Africa, exacerbated by climate variability, land use change, and limited flood management infrastructure. This study assesses flood hazard dynamics in the Lower Benue River Basin (LBRB), Nigeria, through the integration of Sentinel-1 Synthetic Aperture Radar (SAR) imagery and land use/land cover (LULC) data between June and November 2022. Using Google Earth Engine (GEE), multi-temporal SAR data were pre-processed and classified employing Random Forest, K-Nearest Neighbours, and Maximum Likelihood algorithms to detect and map flood extents. October 2022 was identified as the peak flood month, with an inundated area of approximately 145 km². Spatial analysis revealed that croplands (75.5% inundated), wetlands (100%), and built-up areas (67.7%) were most exposed. Site-specific assessments in Makurdi, Gbajimba, Loko, and Oguma highlighted varying flood impacts driven by topography, land cover patterns, and settlement expansion. Results underscore the urgency of floodplain zoning, nature-based mitigation strategies, and early warning systems. This study demonstrates the applicability of SAR-based flood monitoring in data-scarce regions and provides a replicable framework for hazard assessment and disaster preparedness in tropical basins. Flood hazard Floodplain Lower Benue River Remote sensing Risk management Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction Flooding is among the most devastating and frequent natural hazards globally, with far-reaching implications for human lives, infrastructure, livelihoods, and ecosystems. In recent decades, floods have increased in frequency and intensity, driven by complex interactions between climate change, rapid urbanization, land use transformations, and declining ecosystem buffers. The impact of floods has intensified globally due to climate change, leading to more frequent and unpredictable events in recent years (Pralle, 2019 ). Thus, global population being affected by floods is expected to double by 2030, reaching 147 million people annually, up from the current 72 million (WRI, 2020; Hofste et al., 2019 ). The annual cost of flood damage worldwide from 2000 to 2019 is estimated at $ 651 billion (Tellman et al., 2021 ) and anticipated to rise in the coming years, with increase in the number of individuals being affected. Nowhere is this more evident than in Sub-Saharan Africa, where structural vulnerability, limited disaster preparedness, and poverty compound the destructive impacts of floods (Desai, 2021 ; UNEP, 2020; Adelekan et al., 2016). Nigeria, the most populous country in Africa, has consistently ranked among the worst-hit nations by flood disasters, and within Nigeria, the Benue River Basin is one of the most affected regions. The Lower Benue River Basin (LBRB), encompassing parts of Benue, Nasarawa, Taraba, and Kogi states, is hydrologically complex and socio-economically significant. It supports over 10 million people who rely on the river and its floodplains for agriculture, fishing, and transport. The basin’s land cover includes fertile croplands, wetlands, savanna vegetation, and growing urban settlements. However, its low-lying topography, combined with seasonal rainfall peaks and dam discharges, renders it prone to recurrent and severe flooding. These floods often result in massive displacement, food insecurity, and infrastructural collapse. For example, the October 2022 floods displaced over 1.4 million people nationally, with the LBRB among the worst-affected zones (NEMA, 2022). The risk of flooding in the LBRB is not solely a hydrological phenomenon but anthropogenic drivers including land cover modification, uncontrolled settlement expansion, and vegetation loss have significantly altered natural flood attenuation processes. Conversion of wetlands and floodplains into cropland and settlements has increased runoff generation while reducing infiltration and storage capacity. Concurrently, upstream water releases from hydroelectric dams and climate variability-induced extreme rainfall events contribute to peak discharges exceeding channel capacity. Over the years, satellite observations have shown an increase in impervious surfaces and cropland encroachment into areas formerly classified as wetlands or gallery forests (Ayanlade et al., 2021 ). A major limitation in existing flood risk management approaches in Nigeria is the lack of dynamic, high-resolution spatial data that can identify vulnerable land cover classes and the temporal progression of flood exposure. Traditional flood assessments have relied on post-disaster reports, manual ground surveys, and low-resolution imagery, which fail to provide timely or spatially explicit insights needed for mitigation planning. There is an urgent need for integrated tools that combine flood mapping with land use/land cover (LULC) analysis, especially during flood peaks when cloud cover limits optical satellite visibility. Remote sensing technologies have revolutionized the monitoring of hydrological extremes, offering scalable, repeatable, and synoptic data that can detect, measure, and analyze flood extents across large areas. Among these technologies, Synthetic Aperture Radar (SAR) systems such as those aboard the Sentinel-1 satellites are particularly useful for flood mapping due to their all-weather, day-and-night acquisition capabilities. SAR sensors transmit microwave signals that penetrate cloud cover and measure surface backscatter, making them ideal for tropical flood monitoring during the rainy season when optical sensors are often ineffective. Combining SAR-based flood detection with LULC datasets yields actionable insights into vulnerability, exposure, and land use conflict. In addition, understanding the spatial patterns of inundation across land cover types helps assess the effectiveness of natural buffers (e.g., wetlands, forests) and the impact of anthropogenic alterations. Several studies have utilized this approach in different contexts. For instance, Schumann and Moller ( 2015 ) employed SAR imagery and land cover data to evaluate floodplain dynamics in Bangladesh, while Dieng et al. (2021) analyzed flood-prone areas in the Senegal River Basin using Sentinel-1 and land classification models. However, such integrated methods remain underutilized in the Nigerian context, especially in the Benue Basin. This knowledge gap constrains disaster preparedness, spatial planning, and climate adaptation efforts. The motivation for this study stems from the need to generate high-resolution, spatio-temporal insights into flood dynamics in the LBRB, particularly during peak inundation periods. The devastating 2022 floods offered an opportunity to analyze flood-land cover interactions using state-of-the-art satellite technology. October was selected for analysis because it coincides with the climax of the rainy season and flood discharge from upstream tributaries and dams. The increasing frequency and intensity of flooding in the Lower Benue River, particularly along the banks of the River Benue, extends beyond immediate property damage, with profound consequences on human life, agriculture, and infrastructure. The 2022 flood incident raises questions about the impact of urban development on the region's vulnerability to floods, going by the increasingly impermeable surfaces disruption to the natural flow of excess precipitation in the lower River Benue catchment area with enormous economic losses. This research aims to provide a high-resolution, validated flood extent data for the LBRB and filled the spatial planning gap on estimating the land cover types (e.g., croplands, wetlands, settlements) most exposed to seasonal flooding and provide detailed empirical evidence to inform land use planning, floodplain zoning, and ecological restoration in flood-prone landscapes. The study therefore specifically focuses on mapping the extent of flooding in the LBRB during October 2022 using Sentinel-1 SAR imagery, quantifying inundation proportions per land cover type and identify spatial patterns of flood exposure and assess temporal changes compared to previous flood years with better discussion on the implications of floodable land cover trends for disaster risk reduction, agricultural planning, and wetland conservation. Thus, this study provides a scientific basis for understanding flood dynamics at the land cover interface in one of Nigeria’s most vulnerable basins. By leveraging open-access Sentinel-1 SAR data and LULC information, it demonstrates a replicable and cost-effective framework for operational flood monitoring. The study offers an option to enhances understanding of spatial-temporal flood dynamics in relation to anthropogenic land use patterns in a tropical African river basin and provides empirical evidence that can support national and state-level disaster management, zoning regulations, and flood risk mapping. The outputs can inform early warning systems, urban planning, ecosystem restoration, and agriculture insurance schemes in the Benue Basin and similar flood-prone regions in Sub-Saharan Africa. 2. Study Area and Physical Setting The Lower Benue River Basin (LBRB) is a significant hydrological sub-basin within the larger Niger River drainage system, covering an estimated area of 140,000 km². The basin stretches predominantly across the central eastern region of Nigeria, encompassing substantial portions of Benue, Taraba, Nasarawa, and Kogi States, and ultimately draining into the confluence of the Niger and Benue Rivers at Lokoja. The Lower Benue represents the final segment of the 1,400 km-long Benue River, which originates from the Adamawa Plateau in Cameroon and travels westward through northeastern Nigeria. The LBRB serves as a vital ecological and socio-economic corridor, supporting large populations through agriculture, fisheries, transportation, and informal trade. It also forms a transitional ecological zone between the Sudan Savannah in the north and the Guinean Forest-savanna mosaic in the south. The river and its floodplains play a crucial role in sediment and nutrient deposition, making the region highly fertile and attractive for wet-season and dry-season farming. The basin is characterized by a predominantly low-lying alluvial terrain, with altitudes generally ranging between 100 m and 300 m above sea level, except for some isolated inselbergs and escarpments in Nasarawa and Taraba States. The Benue River flows through a broad valley interspersed with seasonal tributaries and distributaries such as the Katsina-Ala, Donga, and Taraba rivers. The floodplain width varies considerably, from as narrow as 1–2 km to over 15 km in places such as Makurdi and Agatu (Fig. 1 ). The relative flatness of the terrain contributes significantly to the propensity for prolonged overbank flooding, especially during the wet season. Soils in the basin are predominantly fluvisols, alluvial loams, and hydromorphic clays, which exhibit high water-holding capacities but poor drainage in saturated conditions. This further accentuates inundation risk during peak rainfall months. The Lower Benue Basin lies within the tropical wet-and-dry climate zone (Aw), according to Köppen’s classification, with pronounced seasonality in rainfall and temperature patterns. Rainfall is uni-modal, beginning in April and ending around October, with peaks typically observed in August–September. Annual precipitation varies from 1,200 mm in the northern edges to over 1,800 mm in the southern portions near the Niger confluence. Rainfall is influenced by the West African Monsoon system and occasionally enhanced by local convective storms. The Benue River’s discharge pattern reflects this seasonality, with low flows between December and April and peak discharges between August and October, coinciding with the rainy season climax and dam releases upstream. The average discharge of the Benue River at Makurdi is about 3,500–4,500 m ³/s during peak flows, but extreme events (such as in 2012 and 2022) have recorded levels above 7,000 m ³/s , causing extensive inundation in the floodplains. Peak discharge occurs during the rainy season (June–October), with flow rates sometimes exceeding 5,500 m³/s. In the dry season (November–April), the discharge can drop significantly, often below 1,000 m³/s. Major floods occur approximately once every 5–7 years, with minor flooding during rainy seasons. The Lower Benue Basin is projected to experience a 5–15% reduction in average annual flow by 2050 due to climate change. The Lower Benue carries an annual sediment load estimated at 20–30 million tonnes, influenced by upstream erosion and tributary contributions. Complicating the hydrology of the basin are upstream dams, notably the Lagdo Dam in Cameroon and several hydropower reservoirs on Nigerian tributaries. These artificial interventions alter the timing and magnitude of downstream flows, occasionally resulting in compounded flood events when heavy rainfall and dam releases coincide. The LBRB features a mosaic of land cover types that reflect both natural ecosystems and human land use systems. Recent LULC classifications using Landsat and Sentinel-2 data suggest that between 2000 and 2020, croplands expanded by more than 30%, often at the expense of natural wetlands and forests (Nonki et al., 2019; Adebola et al., 2018 ).). This land use transformation has diminished the basin’s flood absorption capacity and increased surface runoff. The LBRB is home to several million people, many of whom engage in subsistence agriculture, fishing, and trading. Livelihoods are closely tied to seasonal cycles, with dependence on rainfall and river levels for both rainfed and flood-recession farming. Settlements along the Benue River and its tributaries especially Makurdi, Gboko, Logo, Agatu, Wadata, and Lokoja have been historically prone to flooding. Thus, the confluence of natural susceptibility, anthropogenic pressure, and institutional weakness makes the LBRB a hotspot of climate-induced hydrological risk in West Africa. The Lower Benue Basin offers a unique landscape for flood remote sensing due to a wide Floodplains that is large enough to be detected by medium-to-high resolution satellite sensors such as Sentinel-1. The land cover heterogeneity of mix cropland, wetlands, forests, and settlements provides diverse land surface signatures and flood susceptibility patterns. The region is also consistently highlighted in national disaster risk reduction strategies and is part of regional frameworks such as the Benue River Basin Development Authority (BRBDA) plans. This combination of environmental significance, vulnerability, and policy relevance makes the Lower Benue Basin an appropriate and urgent case for spatio-temporal floodable land cover analysis using Earth Observation tools. 3. Materials and Methods Methodology 3.1 Data Characteristics The dataset in this research is retrieved from the Google Earth Engine Data Catalog. Different types of datasets are used in this study include the study area shapefile, the SAR (Sentinel 1) images, topography data, etc. Sentinel-1 radar dataset 10m resolution. The key pre-processing stages have already been completed for Sentinel’s ground range detected (GRD) products provided in the GEE platform ( https://developers.google.com/earth-engine/datasets/catalog/sentinel ). Data from both cross-polarized (VH), and like-polarized (VV) channels were acquired as Level-1 GRD products. Both polarization available for this GRD product and their combinations have been utilized for flood mapping in this research (Table 1 ). Table 1 Satellite image characteristics Acquisition Date Image I.D Satellite Imagery Spectral Bands Spatial resolution June 1 st, 2022 June_A Sentinel-1A VV, VH 20*5m June 17th 2022 June_B Sentinel-1A VV, VH 20*5m July 2nd 2022 July_A Sentinel-1A VV, VH 20*5m July 16, 2022 July_B Sentinel-1A VV, VH 20*5m August 1, 2022 August_A Sentinel-1A VV, VH 20*5m August 16, 2022 August_B Sentinel-1A VV, VH 20*5m September 1, 2022 September_A Sentinel-1A VV, VH 20*5m September 16, 2022 September_B Sentinel-1A VV, VH 20*5m October 1, 2022 October_A Sentinel-1A VV, VH 20*5m November 1, 2022 November_A Sentinel-1A VV, VH 20*5m November 18th 2022 November_B Sentinel-1A VV, VH 20*5m January 15th 2022 Sentinel-2A R, G, B, NIR, SWIR, Red-Edge 10m Based on the data acquired the focus of the flood mapping is based on the rainy season of 2022 with emphases on October flooding event. The 2022 flood in the Lower Benue River underscored the region’s vulnerability to extreme hydrological events. Figure 2 details a series of steps, grouped under SAR Pre-processing, Image Processing, and Mapping, to identify and extract flooded areas in this study. Each stage of this methodology is integral to ensuring the accuracy and reliability of the flood maps. The methodology involves the acquisition of post-event (DS-1) and pre-event (DS-2) SAR images. These datasets represent imagery captured after and before the flood event, respectively, providing a comparative basis for detecting changes induced by flooding. The first step, Radiometric Calibration, ensures that the SAR data's backscatter values are physically meaningful, allowing for consistent comparison across different images. This step is critical as it removes any distortions or inconsistencies in the raw SAR data. The speckle filtering is applied using a Lee Sigma filter with a kernel size of 5×5 to remove the speckle noise, inherent in SAR imagery that can obscure meaningful features and hinder accurate flood detection. The filtering process reduces this noise while preserving significant edges and features, improving the interpretability of the imagery. The final step in pre-processing entails the Geometric Correction, this involves aligning the SAR data to a consistent spatial reference system. This ensures that the imagery accurately represents real-world locations, a crucial prerequisite for spatial analysis and integration with other geospatial datasets. The image processing phase entails the building up of the corrected Images to identify the threshold that distinguish water bodies from non-water surfaces image. Thresholding is a crucial step that sets a specific backscatter value to identify and separate inundated areas, leveraging on the distinct backscatter characteristics of water in SAR imagery. Building upon the thresholding results, the methodology employs Supervised Classification techniques to refine the identification of flooded areas. Three classification algorithms are highlighted namely the Random Forest, K-Nearest Neighbours (KNN), and Maximum Likelihood Classification (MLC). These machine learning algorithms analyze the spectral and spatial characteristics of the imagery to categorize pixels into water and non-water classes. Supervised classification is a common approach for information extraction from images and consists of two main stages: training and classification. During the training stage, a set of representative samples are selected for each class and in the classification stage, a classifier is used for the assessment of the probability of every image pixel to belong to the classes. Based on the highest probability, each pixel is classified as a particular feature in the given classification stage. The histograms of pre-processed images have been used to determine the training sets (Fig. 3 ) based on three classes (i.e. water bodies, flooded areas and others). The water class show the unchanged (permanent) water areas in both datasets using the three classification methods. The RF classifier samples the data iteratively and randomly and produces a large group of classification and regression trees. The results represent the statistical mode of many decision trees achieving a more robust model than a single classification tree (Lee & Pottier, 2017 ). RF generally supplies better classification results than the ML method (Van Beijma et al. 2014 ; Balzter et al. 2015 ). The KNN classifies the closest training examples in the feature space and it is considered to be a simple machine learning algorithm. In this method, a pixel is assigned to the class among its k nearest neighbours (Lee & Pottier, 2017 ; Schumann & Moller, 2015 ). In the ML classification, a pixel with the maximum likelihood is assigned to the corresponding class. In this method, the threshold values for each class are obtained from the training data sets. Depending on the threshold value, the probability of a given pixel for a given class is calculated and all pixels are classified into a specific class (Garga, 2015 ). The final phase is the mapping of the extracted flooded areas through the integration of the classification step, the methodology produces detailed flood maps that delineate the spatial extent of inundation. These maps serve as a valuable resource for assessing flood impacts, planning relief efforts, and informing long-term mitigation strategies. Overall, this methodological flowchart demonstrates a structured approach to flood mapping using Sentinel-1 SAR data. By integrating robust pre-processing, advanced classification techniques, and precise mapping, the methodology ensures accurate and actionable insights into flood events. Each step is interconnected, with the reliability of later phases depending on the quality and precision of earlier stages, underscoring the importance of a comprehensive and systematic workflow. This study also integrates Sentinel-1 Synthetic Aperture Radar (SAR) imagery and multi-temporal Land Use Land Cover (LULC) data to detect and map floodable land cover in the Lower Benue River Basin (LBRB). The methodology follows a structured geospatial workflow involving data acquisition, preprocessing, flood extent detection, LULC classification, overlay analysis, and statistical interpretation. Processing was implemented primarily using Google Earth Engine (GEE) and QGIS, leveraging cloud computing for handling large datasets and temporal stacks. 4. Results and Discussion This section presents the spatial and statistical outcomes of flood mapping, land cover intersections, and temporal trends derived from Sentinel-1 SAR and LULC data analyses in the Lower Benue River Basin (LBRB). The multi-temporal analysis of flood dynamics along the Lower Benue River based on Sentinel-1 satellite imagery for the 2022 rainy season depicting the flood time-series, flood maps, areal statistics, and a composite inundation map that evaluate spatial and temporal patterns of flood occurrence between June and November 2022 (Fig. 4 ). The sentinel imagery floodable area of the part of Lower Benue River provides a comprehensive spatial and temporal analysis of flood dynamics in the Lower Benue River using Sentinel satellite imagery and are sequence from June to November 2022 corresponding to different acquisition dates, marked as June_A, June_B, July_A, etc., and reflecting a progressive inundation and recession pattern. This sequencing also allows for a detailed examination of flood extents over time, highlighting critical changes in flood behaviour, and identifying the most affected areas within the Lower Benue River basin. By analysing the floodable areas captured in the Sentinel imagery, better understand of the underlying factors contributing to flood risks and inform future management strategies. 4.1 Spatio-temporal Distribution of Flood-Prone Areas in Lower Benue Basin Flood areal extent varied significantly throughout the study period. As shown in the line graph (top right), early-season flood extents remained relatively low from June to mid-August, with average inundated areas between 60 and 75 km². The bi-monthly analysis of floodable area during the months of June-November (rainy season) for the study period 2022 depicts an average of 73.54 km² (± 28.3 km²) floodable area (Fig. 5 ). During the period June 1st to June 17th (June_A to June_B), there is a reduction in the area covered by water from 58.96 km² to 50.32 km², while between July 2nd and July 16th (July_A to July_B), the floodable area extent increases slightly from 48.5 km² to 51.4 km², indicating a potential rise in flood extent. The period from August 1st to August 16th (August_A to August_B) also experiences an increase in floodable area from 54.6 km² to 56.3 km². September 1st to September 16th (September_A to September_B) experiences a quantum increase in the flooded area, from 76.4 km² to 83.6 km² before attaining the peak on 1st October (October_A) at a flooded area of approximately 145km² before receding to 94.1 km² on November 1st (November_A to November_B) and remaining relatively stable by November 18th (93.9 km²). This display that a progressive increase began in late August, peaking dramatically on October, 2022, with an inundated area of approximately 145 km², the dashed line represents the average flood extent, providing a baseline for comparison. Flooded area ranged from a minimum of 58 km² in June to a maximum of 144 km² in October 2022. The flood extent was relatively stable during the early rainy season but increased sharply in late September and peaked in October, reflecting extreme hydrological conditions This surge likely results from the combination of peak seasonal rainfall and late rainy season runoff, reflecting a lag between precipitation inputs and hydrological response in the floodplain as well as probably upstream discharge from major reservoirs. Following the October peak, a sharp decline in inundation was observed in November, indicating the commencement of the floodwater recession phase, possibly due to better drainage or reduced inflow. The sequential Sentinel-1 imagery reveals a consistent flood expanse along the river corridor from August onwards while the composite map aggregates all identified inundation zones during the study period and delineates the spatial patterns of recurrent flooding activities. Notably, floodwaters extended considerably in areas along the low-lying alluvial plain and are also prone to both seasonal overbank flooding and backwater effects in places around Makurdi, Agatu, Gwer West, Omala, and parts of Dekina and Tarka LGAs. Thus, the months of September and October seem to be critical months with substantial floodable areas, while November shows a slight reduction in the flooded area compared to October but remains relatively high. This dynamic nature of flood extent is valuable for understanding the temporal evolution of flood inundation and provides a valuable tool for disaster management, flood response planning, and effective flood mitigation plans. The fluctuation in flood extent over the months highlights the importance of temporal resolution in flood monitoring and early warning systems, especially in dynamic floodplains like the Lower Benue. This provides a valuable tool for monitoring flood progression and its spatial extent, identifying flood-prone settlements along the Lower Benue and provision of planning interventions, such as early warning systems, flood defenses, or resettlement The composite inundation footprint illustrates the shift in flood-prone zones over time and accumulate risk across seasons based on topographic heterogeneity, river morphology, and land use practices, particularly in agricultural floodplains and peri-urban settlements. The cumulative inundation zones include important settlements like Makurdi, Agatu, Omala, and Gwer, all situated near frequently inundated zones. this zones are also highlighted as regions more vulnerable to seasonal flooding while flood recurrence zones are evident from overlapping flood extents across multiple months (color overlaps). In addition, the spatial pattern suggests floodplain expansion eastward over time, with repeated inundation in certain pockets indicating chronic flood risk. Thus, based on the potential impact on human habitation and infrastructure, the urban planning and settlement regulation near flood zones must be enforced in areas like Agatu and Makurdi while appropriate disaster preparedness strategies should focus on areas that exhibit persistent or overlapping inundation across multiple months. 4.2 Landuse / Land cover distribution in Part of Lower Benue River Basin (2022) The baseline estimated land cover distribution for the Lower Benue River Basin in 2022 indicates that the cropland dominates the landscape, reflecting the basin's agrarian character, while the forest and vegetation occupy mostly the less developed upland areas (Fig. 6 ). Built-up areas are concentrated around towns like Makurdi and Gbajimba, while the wetlands, shrubs, and water bodies cover the remaining portions, performing flood buffering and ecological balance functions. The spatial arrangement of these classes influences flood exposure, with croplands and wetlands situated primarily within the basin's floodplains, while urban and semi-urban areas extend toward river corridors. Thus, accurate land cover classification is crucial for quantifying land use vulnerability and estimating the socio-economic and ecological impacts of flooding (Foody, 2002 ; Giri et al., 2005 ). Furthermore, the urban and peri-urban development increasingly encroaches upon historical river channels and flood zones, and the seasonality of flood impact aligns with peak rainfall and river discharge in the basin, suggesting a strong hydrological driver of flood expansion during the wet season. The baseline land cover distribution of the pre-and during the October 2022 flood in part of the Lower Benue River Basin (Table 2 ) depicts that the dominant land cover types include the cropland which covers 1,320 km² (40.0%) of the total area pre-flood, with 997 km² (75.53%) areal flooded. Forest and vegetation occupy 880 km² (26.7%) and 353 km² (40.11%) being inundated. Built-up areas account for 520 km² (15.8%) and had 352 km² (67.69%) affected, leading to flooding. Wetlands cover about 300 km², representing 9.1% of the total area covered, are fully saturated. Shrubs pre-flood areal cover 110 km², with 95 km² (86.36%) of the area covered experiencing flooding. Water bodies remained unchanged at 170 km², inundating other land covers during flooding. Overall, out of the pre-total land cover of 3,300 km² about 2,097 km² were flooded, constituting 63.55% of the total pre-flood area. Table 2 Land cover distribution in the Lower Benue River Basin, 2022 Land Cover Type Pre Flood (km²) Flood (km²) % flooded % of Non- flood Cropland 1320 997 75.5 24.5 Forest/Vegetation 880 353 40.1 59.9 Built-up Areas 520 352 67.7 32.3 Wetlands 300 300 100 0 Shrubs 110 95 86. 4 13.6 Water Bodies 170 170 100 0 The monthly land types flooded from June to October 2022. In June, 156 km² were flooded, with 90 km² of cropland and 22 km² of built-up areas. July's total increased to 227 km², with 106 km² of cropland. August saw further flooding, reaching 378 km², including 170 km² of cropland. In September, flooding rose to 500 km², significantly affecting cropland and built-up areas. In October, flooding peaked at 836 km², with 404 km² of cropland being the most impacted. In total, about 2097 km² of areal covered was flooded over these months, as detailed in the changes in land use affected by floods (Fig. 6 &Table 3 ). Table 3 Monthly Flood Dynamics (June–October 2022) in Lower Benue Basin Month Cropland Built-up Wetlands Forest Shrub Total Flooded (km²) June 90 22 16 22 6 156 July 106 43 38 29 11 227 August 170 87 66 40 15 378 September 227 96 76 80 21 500 October 404 104 104 182 42 836 Total 997 352 300 353 95 2097 The integration of monthly flood distribution with baseline land cover data enables the need for more understanding of land use vulnerability in the Lower Benue Basin. The exposure of critical resources such as croplands and urban zones, necessitates the adoption of climate-resilient spatial planning frameworks and re-evaluation of land use zoning in floodplains (IPCC, 2022). This is necessary for to establishment of protective greenbelts around wetlands and riverbanks, the adoption of seasonal flood early warning systems integrated with agricultural calendars and strategic relocation of high-risk informal settlements and infrastructure. Such measures not only enhance the resilience of communities in the Lower Benue Basin but also ensure the sustainable management of natural resources. By fostering collaboration between local authorities, stakeholders, and residents, it becomes possible to create a more robust framework for adapting to the challenges posed by climate change. 4.3 Selected location in Part of the Lower Benue River Basin (June to October 2022) Lower Benue River Basin is among Nigeria’s most flood-prone regions due to its extensive floodplains, seasonal rainfall intensity, and growing pressure from agricultural and urban expansion. For a better understanding of the spatial heterogeneity of flood impacts on land cover transformations, four representative settlements were chosen based on their peculiarity land use/ land cover and location along the River Benue and its tributaries namely Gbajimba, Loko, Makurdi, and Oguma before and during the peak flood month of October 2022. Specifically, in Gbajimba, farmland and lowlands flooding are evident, causing agricultural loss and displacement. Loko experienced likely floodplain overflow, affecting forests and cropland, which resulted in forest flooding and ecological disturbance. In Makurdi, urban flooding poses risks to infrastructure and sanitation in built-up areas and low-lying zones. The Oguma region's flooding impacted, affecting cropland and forests, resulting in biodiversity and soil loss (Table 4 ). Thus, the croplands were consistently the most impacted land use type, followed by natural vegetation and, in Makurdi, built-up areas. This spatial disparity impact assessment demonstrates the need for location-specific flood mitigation strategies that consider land use configuration, topography, and socioeconomic vulnerability. Table 4 Cross-Location of Flood Impacts Location Observations Affected Land Cover Key Impacts Entire Basin Overall flood extent Cropland, wetlands, lowland settlements Lives & properties loss, Displacement Gbajimba Farmland & lowlands flooding Cropland, riparian vegetation, Lowland Settlements Agricultural loss, displacement Loko Likely floodplain overflow Forest margins, floodplains Cropland, forest Forest flooding, ecological disturbance Makurdi Urban flooding & infrastructure risk Built-up areas, roads, cropland, urban edges, low-lying zones Infrastructure risk, sanitation crisis Oguma Ecological and rural impact Cropland, Forests, farmlands Biodiversity and soil loss The flood Severity across the four settlements based on inundation in October 2022 shows that the cropland was consistently the most impacted land use, especially in Gbajimba and Loko while direct flooding impact on Built-up areas is evident in Makurdi, due to urban flood exposure while the wetlands served as both flood absorbers and conduits, experiencing significant water expansion (Fig. 7 ). There is therefore the need to targeted relocation or protection of critical croplands. Urban flood zoning and drainage investment in Makurdi is also necessary while engaging in the ecosystem restoration of wetlands to increase flood absorption capacity. Increasing flood absorption capacity will not only mitigate the impacts of urban flooding but also enhance biodiversity and improve water quality in the region. Furthermore, collaborative efforts involving the local communities, government agencies, and environmental organisations in the implementation of these strategies should be fully engaged. Section 4.3.1 to 4.3.4 provides more clarity on the selected settlement based on Figs. 5 and 7 . 4.3.1 Gbajimba and Environs Gbajimba and Environs is a low-lying agrarian settlement in the Lower Benue River Basin, situated near the river channel, the area is highly susceptible to seasonal inundation, especially during the peak of the rainy season. The localized flood patterns depict that flooding could be associated with topographic depressions (lowland) or human-modified landscapes. Before inundation, the landscape was dominated by cropland and riparian vegetation and patches of built-up zones but the October 2022 flood event depicts a substantial floodwater spread, inundating large portions of farmland and disrupting settlement peripheries depending on proximity to water channels while land cover classes like vegetation or shrubs are temporarily inundated, becoming an extension of open water or wetland. Prior to the flood event, the total area analyzed was approximately 164.4 km² (Table 5 ). The dominant land cover was cropland (47.8%), followed by vegetation/forest (28.1%), and built-up areas (9.2%). The land cover types before and after a flood show that out of the pre-flood 78. 6 km² of Cropland coverage about 51 km² (64. 9%) are flooded. Vegetation/forest showed that out of the 46. 2 km² pre-flood, only 21. 2 km² (45. 9%) were inundated. Built-up areas had 12. 4 km² before flooding, with 8. 4 km² (67. 7%) affected. Wetlands experienced complete flooding, with 100% of the 8. 4 km² affected. Shrubs totaled 5. 6 km² pre-flood, with 4. 3 km² (76. 8%) flooded. The overall flooding affected 93.3km² in total, resulting in 56. 8% of the area flooded. The water bodies channel is also over-saturated, inundating peri-urban margins and other land cover/ land uses as well as submerging formerly productive agricultural and vegetative land. Table 5 Land cover distribution in Gbajimba and Environs Land Cover Type Pre Flood (km²) Flood (km²) % flooded % of Non- flood Cropland 78.6 51 64.9 35.1 Vegetation/Forest 46.2 21.2 45.9 54.1 Built-up Areas 12.4 8.4 67.7 32.3 Wetlands 8.4 8.4 100.0 0.0 Shrubs 5.6 4.3 76.8 23.2 Water/Flooded Area 13.2 13.2 100 0.0 The inundated land use/ land cover from June to October 2022 (Table 6 ) shows that the Cropland inundation increases from 3. 4 km² in June to 17 km² in October, totaling 51 km² over the period. Vegetation/forest also rises from 1. 5 km² to 7. 6 km², with a total of 21. 2 km². Built-up areas grow from 0. 6 km² to 2. 8 km², totaling 8. 4 km². The wetlands also account for 8. 4 km² saturated, and shrubs are at 4. 3 km². In total, the combined area is 93. 3 km² across all categories. This diverse landscape reflects the dynamic nature of land use and vegetation changes over the specified period. Such variations highlight the importance of monitoring environmental shifts to inform conservation and development strategies. Table 6 Monthly Flood Dynamics (June–October 2022) in Gbajimba and Environs Land cover June July August September October Total (km²) Cropland 3.4 5.5 9.8 15.3 17 51 Vegetation/Forest 1.5 2.4 3.9 5.8 7.6 21.2 Built-up 0.6 1 1.4 2.6 2.8 8.4 Wetlands 0.8 1.2 1.4 1.8 3.2 8.4 Shrubs 0.2 0.5 0.8 1.2 1.6 4.3 Total (km²) 6.5 10.6 17.3 26.7 32.2 93.3 The surroundings are characterised by vast croplands intermingled with riparian vegetation, and floodable water inundates the residential and agricultural zones. With approximately two-thirds of all croplands inundated, crop harvest yields are devastated, threatening food security in the region, while the expansion of wetlands and open water signals a redistribution of surface hydrology due to river system overflow and poor drainage capacity, resulting in the inundation of built-up areas, particularly low-income housing on marginal lands, confirming the vulnerability of unprotected floodplain zones. Gbajimba's crops and rural infrastructure are vulnerable to seasonal flooding, necessitating the adoption of flood-resistant agricultural practices and enhanced land use zoning plans. Community-based flood preparedness, seasonal early warning dissemination, and ecological buffer protection, particularly along riparian zones, continue to be excellent hydrological mitigation solutions for achieving a flood-resilient environment. A flood-resistant environment not only protects vulnerable communities, but it also increases agricultural productivity and preserves local ecosystems. These measures necessitate, among other things, combined efforts by government agencies, local groups, and residents. Loko and Environs Loko and Environs is a low-lying settlement on the Lower Benue River Basin's floodplain, with a diversified wetlands landscape of agricultural fields, gallery trees, and riverside linear hamlet communities. Because of its proximity to Benue River tributaries, the area's hydrological profile renders it very vulnerable to seasonal floods, with flood concentrations occurring along riverbanks and floodplains used for crop production. The floodplain is also highly prone to flooding and human settlement encroachment. Depending on the storm's intensity and length, significant crop losses are expected in natural marshes or swamps. Floodwaters being induced by local rainfall and river flow surges commonly impact natural and human land use patterns in Loko and Environs. In October 2022, flooding covered 50.4% (77 km²) of the pre-flood area of 152.7 km², the cropland had 64.1 km² pre-flood, but 39.9 km² was inundated, leading to 62.2% inundation. The vegetation/forest area is about 34.3 km², out of which 19 km² (55.4%) is flooded while the Built-up areas occupied 9.7 km² of pre-flood areas had 5.9 km² (60.8%) flooded. The 8.9 km² of wetlands remain inundated and saturated during flooding. Shrubs pre-flood areal extent covered 3.6 km², with 91.7 percent of it flooded while the water bodies areal extent of 32.1 km² remains saturated, and overflowed to other land use/land cover (Table 7 ). Table 7 Land cover distribution in Loko and Environs Land Cover Type Pre Flood (km²) Flood (km²) % flooded % of Non-flood Cropland 64.1 39.9 62.2 37.8 Vegetation/Forest 34.3 19 55.4 44.6 Built-up Areas 9.7 5.9 60.8 39.2 Wetlands 8.9 8.9 100.0 0.0 Shrubs 3.6 3.3 91.7 8.3 Water/Flooded Area 32.1 32.1 100.0 0.0 The monthly inundation land cover from June to October 2022 (Table 8 Table 8 Monthly Flood Dynamics (June–October 2022) in Loko and Environs Land Cover Type June July August September October Total (km²) Cropland 2.6 4.3 7.5 11.3 14.2 39.9 Vegetation/Forest 1.2 2 3.6 5.3 6.9 19 Built-up Areas 0.4 0.6 1.1 1.7 2.1 5.9 Wetlands 0.9 1.3 1.7 2.2 2.8 8.9 Scrubs 0.2 0.3 0.4 0.8 1.6 3.3 Total (km²) 5.3 8.5 14.3 21.3 27.6 77 )shows that cropland covers rose from 2.6 km² in June and 14.2 km² in October, totaling 39.9 km². In June, vegetation/forest covered 1.2 km² of area, increasing to 6.9 km² in October for a total of 19 km². During the defined period, built-up areas increase from 0.4 km² to 2.1 km², reaching 5.9 km². Wetlands inundation ranges from 0.9 km² to 2.8 km², totaling 8.9 km². Scrubs expand from 0.2 km² to 1.6 km², and totalling 3.3 km². The overall land cover grows from 5.3 km² to 27.6 km², reaching 77 km². Cropland suffered the most, with more than one-third of the flooded area previously used for agriculture, while forest portions were submerged in low-lying areas and wetlands grew, demonstrating their flood-buffering role. Unfortunately, flooding's advance into built-up regions poses immediate hazards to homes and public facilities. Flood adaptation in the Loko area should include the promotion of climate-smart agriculture in flood-prone zones, with a focus on the importance of floodplain-sensitive agricultural planning, such as adaptive crop calendars. Rehabilitation of degraded riparian buffers and wetlands for natural flood mitigation should also be encouraged. Furthermore, a zoning regulation to restrict construction in flood corridors should be implemented to limit further settlement encroachment into high-risk areas, as will the installation of community-based flood early warning systems, particularly in river-adjacent towns. Makurdi and Environs Makurdi, the capital of Benue State and a significant urban center on the banks of the Benue River presents a unique state of urban flood exposure since the city is one of the most flood-prone metropolitan areas in central Nigeria. Flooding in Makurdi has major socioeconomic repercussions, including infrastructure, displacement, and sanitation. Over time, the expansion of urban infrastructure into historical floodplains has increased disaster risk, with consequences for housing, transportation, and public health. The discovered patterns call for urban planning reforms and investments in flood-resistant infrastructure. Cropland covered 84.2 km² prior to the October 2022 flood (Table 9 ) but was swamped to 45.9 km² (54.5%) during the flooding, and affected 20.9 km² (52.5%) of the 39.8 km² of built-up areal extent. The flooding also inundated 18.7 km² (43.9%) of the 42.6 km² of vegetation/forest area while the wetlands were overwhelmed at total flooding of 16.4 km², whereas shrubs had a 7.8 km² (90.7%) flooding rate. Water bodies remain saturated during the peak flooding, inundating other land covers. Overall, the floodable total land covers 109.7 km² out of the 214.6 km² total areal covered, suggesting that 51.1% of the area was inundated, leaving 48.9% non-flooded. Table 9 Land cover distribution in Makurdi and Environs Land Cover Type Pre Flood (km²) Flood (km²) % flooded % of Non- flood Cropland 84.2 45.9 54.5 45.5 Built-up Areas 39.8 20.9 52.5 47.5 Vegetation/Forest 42.6 18.7 43.9 56.1 Wetlands 16.4 16.4 100.0 0.0 Shrubs 8.6 7.8 90.7 9.3 Water/Flooded Area 23 23 0.0 0.0 The monthly land cover flooding (June to October 2022) depicts that the Cropland floods the most, with a total of 45. 9 km². Built-up areas have 20. 9 km², while vegetation/forest covers 18. 7 km², wetlands 16. 4 km², and shrubs 7. 8 km². In total, the flooded area reached 109. 7 km² by October (Table 10 ). Table 10 Monthly Flood Dynamics (June–October 2022) in Makurdi and Environs Land Cover June July August September October Total (km²) Cropland 1.7 3.4 6.1 11.8 22.9 45.9 Built-up Areas 0.8 1.6 2.7 5.2 10.6 20.9 Vegetation/Forest 0.7 1.4 2.5 4.8 9.3 18.7 Wetlands 0.8 1.7 2.6 4.8 6.5 16.4 Shrubs 0.2 0.7 1.2 2.3 3.4 7.8 Flooded (km²) 4.2 8.8 15.1 28.9 52.7 109.7 Table 11 Land cover distribution in Oguma and Environs Land Cover Pre Flood (km²) Flood (km²) % flooded % of Non-flood Cropland 75.4 43.2 57.3 42.7 Forest/Vegetation 52.6 29.4 55.9 44.1 Built-up Areas 8.7 5.4 62.1 37.9 Wetlands 15 15 100 0 Shrubs 7.2 5.9 81.9 18.1 Water/Flooded Area 39.2 0 0 Total 198.1 98.9 49.9 50.1 Cropland flooding adds to the socioeconomic pressure on food systems and livelihoods, whereas built-up area flooding shows concern about the increasing risk of urban floods. Inundation of built-up regions also suggests a direct risk to population and infrastructure considering that the Benue River is likely to overflow into neighborhoods or commercial sectors, as well as social ramifications such as displacement, infrastructure destruction, and waterborne disease epidemics. Furthermore, the ongoing use of floodplains for informal housing and infrastructure increases exposure and vulnerability to extreme events (Merz et al., 2010 ; IPCC, 2022). Thus, floodplain zoning rules and building limitations must be implemented while the urban drainage and wetland retention systems need upgrade, seasonal mapping and flood early warnings disseminated, and urban agriculture will further strengthen the system's flood resilience. Oguma and Environs – Before and After Flood Oguma is a rural agroecological zone with mixed land use that includes croplands, natural forests, extensive riparian wetlands, and scattered settlements. It is located within the floodplain corridor of the Lower Benue River Basin, adjacent to the Benue River system, and is heavily reliant on ecosystem services for agriculture and fisheries, making it vulnerable to hydrological disturbances. The landscape's low relief and proximity to meandering river channels make it susceptible to seasonal flooding. Although the natural buffer zones of forests and wetlands may provide some resilience to flood impact, the exposure of the agricultural landscape, particularly rain-fed farms and irrigation schemes, due to floodwaters spreading into vegetative zones may result in varied sediment deposition, vegetation loss, or altered aquatic habitats and rural livelihoods because the landscape is predominantly forested and agricultural. The region has an areal extent of 198.1 km² and is dominated by croplands (38.1%), forest/vegetation (26.6%), and wetlands (7.6%) while Built-up areas accounted for only 4.4%, indicating the rural nature of the landscape. Comparative pre- and during the October 2022 flood indicates that cropland areal extent of 75.4 km² before the flood had 43.2 km² (57.3%) submerged during the flood. Forests and vegetation covered 52.6 km², with 29.4 km² (55.9%) submerged. The built-up areas were 8.7 km² pre-flood and 5.4 km² (62.1%) submerged. During peak floods, 15 km² of wetland was completely submerged during flooding while Shrubs had 7.2 km² pre-flood areal extent with 5.9 km² (81.9%) submerged during flooding. The water bodies or flooded areas stood at 39.2 km²pre and during the flood, flooding to another land cover. The pre-flood land area was 198.1 km², however, only 98.9 km² was inundated, affecting almost 49.9% of the entire area (11). The monthly (June to October 2022) inundation (12) depicts that as the seasonal rainfall intensifies between July and October, floodwaters progressively inundate the terrain, affecting both ecological systems and human activities. Cropland shows the highest total inundated area of 43. 2 km², increasing from 2. 8 km² in June to 14. 7 km² in October. Forest or vegetation covers a total of 29. 4 km², also rising from 1. 9 km² in June to 10. 3 km² in October. Built-up areas account for 5. 4 km², while wetlands cover 15 km², with a gradual increase over the months. Shrubs have a total area of 5. 9 km². The total flooded area across the months reaches 98. 9 km², starting at 6. 3 km² in June and going up to 34. 2 km² in October. October flood coverage had increased substantially signal of erosion and land degradation in the affected zones with concerns about biodiversity loss, soil fertility, and the sustainability of rainfed agriculture in the area. Flooding therefore affects natural vegetation and farming zones thereby contributing to sediment or nutrient transport downstream. This transport can lead to the alteration of aquatic ecosystems, impacting fish populations and other wildlife reliant on these habitats. Moreover, the ongoing changes in land use and water management practices must be addressed to mitigate the adverse effects of flooding and promote resilience in both natural and agricultural systems. Flood expansion closely followed rainfall accumulation patterns, with minimal coverage in June and July, rapid rise through August and September, and a peak in October. These dynamics reflect the cumulative saturation of the watershed and increased river discharge during the core wet season. Although the area’s built-up exposure is relatively limited, it remains critical due to the disruption of transportation, access to services, and the risk to vulnerable populations. Wetlands, while increasingly inundated, likely serve as temporary flood retention zones, buffering downstream flow and mitigating the extent of flood damage as a critical flood absorber, but gradually increasing in saturation raises concern. There is a need to prioritize the protection of wetlands and natural flood buffers, adaptive cropping systems and seasonal land use adjustments is also needful. Community-based flood early warning systems and education and technological-aided monitoring using remote sensing and field validation will strengthen the resilience network. 5. Conclusion and Recommendation This study demonstrates the effectiveness of integrating Sentinel-1 imagery with LULC classification for flood monitoring in the Lower Benue Basin. October 2022 represented the flood peak, affecting over 140 km² of land, predominantly grassland and croplands. The study emphasizes the link between flooding, human well-being, and sustainable development, noting frequent floods can undermine social and economic stability, deepen poverty, and hinder progress toward sustainability goals. The findings are also key tools to guide and inform decision-makers on options for flood risk mitigation, land use planning, and disaster preparedness strategies in Nigeria’s riverine regions. In addressing these issues, the report recommends an integrated flood management approach that combines technical solutions with social and environmental considerations. This includes investing in infrastructure, fostering collaboration, and empowering communities. Suggested actions include establishing advanced flood monitoring systems, community-based early warning systems, disaster management plans, resilient infrastructure development, and public awareness campaigns. Strengthening cooperation with Cameroon on the Lagdo Dam's operation and adopting a holistic management approach for the Benue River Basin is also essential. Overall, effective flood management will require substantial financial investment, community engagement, and a focus on sustainable practices, with a phased plan for implementation. Continued cooperation and adequate funding will be crucial for successful outcomes. These patterns suggest a significant overlap between productive land (agriculture) and flood-prone zones, raising concerns about the vulnerability of livelihoods and food security in the basin. Furthermore, inundation of settlement areas may result in disproportionate impacts due to population concentration and infrastructure exposure. Thus, the implications of wetland inundation include the disruption of local ecosystems and biodiversity within the floodplain wetlands. Economic losses of the inundated extensive farmland along the floodplain and adjacent land include damage to crops, particularly rice, maize, and yam plantations, which are staple crops in the region, with an estimated financial loss of ~ USD 2.6 million based on inundated farmland of about 27.2 Km2. The consequences of vegetation inundation, the most dominant land cover of open areas with grasses and short trees, might result in erosional or deposition activities, depending on the soil and terrain. However, the inundation of the built-up area (settlement) of the urban areas of Makurdi and environs at approximately 1.8 Km2, implies increasing urbanization in floodplains and active deforestation activities in the Upper Benue Basin thereby reducing the natural flood mitigation capacities. Poor drainage systems, siltation in river channels, and insufficient levees in vulnerable areas amplified the flood vulnerability and exposure with massive loss of lives and properties and displacement. The dozens of deaths reported due to drowning, building collapses, and other flood-related hazards underscore the need for early warning systems and sustainable floodplain management. The observed flood patterns further demonstrate the potential of Sentinel-1 imagery for reliable flood detection in data-scarce areas with the combination of SAR-based flood mapping and LULC data gives strong insight into land cover vulnerability. As a result, integrating satellite-based flood assessments with hydrological models and ground-based monitoring will improve Lower Benue's readiness and adaptive capabilities. The observed flood patterns highlight Sentinel-1 imagery's potential for rapid, reliable flood detection in data-scarce areas providing insight into land cover vulnerability. Integrating satellite-based flood assessments with hydrological models and ground-based monitoring will improve Lower Benue's readiness and adaptive capabilities. Declarations Funding The authors received no financial support for the research, authorship, or publication of this article. Conflicts of Interest / Competing Interests The authors declare that there are no conflicts of interest or competing interests. Ethics Approval Not applicable. Consent to Participate Not applicable. Consent for Publication All authors consent to the publication of this manuscript. Availability of Data and Materials The datasets generated or analyzed during the current study are available from the corresponding author on reasonable request. Code Availability Not applicable. Data processing was conducted using standard geospatial analysis tools in Google Earth Engine and QGIS. Authors’ Contributions Olusegun Adeaga : Conceptualization, supervision, manuscript review. Tamarabrakemi Akoso : Data processing, analysis, interpretation, manuscript drafting. Temitope Idowu : Land cover classification, cartography, data interpretation. All authors read and approved the final manuscript. References Adebola AO, Ibrahim AH, Wigeti WH, Yaro NA (2018) Drainage basin morphology and terrain analysis of the lower Benue River Basin, Nigeria. Sci World J 13(1):18–22 Adelekan IO (2016) Flood risk management in the coastal city of Lagos, Nigeria. J Flood Risk Manag 9(3):255–264 Ayanlade A, Aigbiremolen MI, Oladosu OR (2021) Variations in urban land surface temperature intensity over four cities in different ecological zones. Sci Rep 11(1):20537 Balzter H, Cole B, Thiel C, Schmullius C (2015) Mapping CORINE land cover from Sentinel-1A SAR and SRTM digital elevation model data using random forests. Remote Sens 7(11):14876–14898 Desai BH (2021) 14. United Nations Environment Programme (UNEP). Yearbook Int Environ Law 32(1):293–298 Dieng D, Cannon AJ, Laux P, Hald C, Adeyeri O, Rahimi J, Kunstmann H (2022) Multivariate bias-correction of high‐resolution regional climate change simulations for West Africa: performance and climate change implications. J Geophys Research: Atmos, 127 (5), e2021JD034836. Foody GM (2002) Status of land cover classification accuracy assessment. Remote Sens Environ 80(1):185–201 Garga E (2015) The effects of insecurity and poverty on human development at the municipal level in the Northern Nigeria. J Emerg Trends Econ Manage Sci 6(7):268–276 Giri C, Zhu Z, Reed B (2005) A comparative analysis of the Global Land Cover 2000 and MODIS land cover data sets. Remote Sens Environ 94(1):123–132 Hofste RW, Kuzma S, Walker S, Sutanudjaja EH, Bierkens MF, Kuijper M, Reig P (2019) Aqueduct 3.0: Updated decision-relevant global water risk indicators. World Resources Institute , 784 Hossain MK, Meng Q (2020) A fine-scale spatial analytics of the assessment and mapping of buildings and population at different risk levels of urban flood. Land use policy 99:104829 IPCC: Climate Change (2022) : Impacts, Adaptation, and Vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, edited by: Pörtner, H.-O., Roberts, D. C., Tignor, M., Poloczanska, E. S., Mintenbeck, K., Alegría, A., Craig, M., Langsdorf, S., Löschke, S., Möller, V., Okem, A., and Rama, B., Cambridge University Press. 10.1017/9781009325844 , 2022b Lee JS, Pottier E (2017) Polarimetric radar imaging: from basics to applications. CRC Merz B, Kreibich H, Schwarze R, Thieken A (2010) Review article Assessment of economic flood damage. Nat Hazards Earth Syst Sci 10(8):1697–1724 National Emergency Management Agency (2022) Annual report on flood disaster in Nigeria . Abuja, Nigeria: NEMA Pralle S (2019) Drawing lines: FEMA and the politics of mapping flood zones. Clim Change 152(2):227–237 Schumann GJP, Moller DK (2015) Microwave remote sensing of flood inundation. Physics and Chemistry of the Earth, Parts a/b/c , 83 , 84–95 Tellman B, Sullivan JA, Kuhn C, Kettner AJ, Doyle CS, Brakenridge GR, Slayback DA (2021) Satellite imaging reveals increased proportion of population exposed to floods. Nature 596(7870):80–86 United Nations Environment Programme (2020) Emissions Gap Report 2020. UNEP, Nairobi Van Beijma S, Comber A, Lamb A (2014) Random forest classification of salt marsh vegetation habitats using quad-polarimetric airborne SAR, elevation and optical RS data. Remote Sens Environ 149:118–129 World Resources Institute (WRI (2020) Country Ranking. https://www.wri.org/applications/aqueduct/country-rankings/ Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6999964","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":506255619,"identity":"69ba1cd6-dee8-4e69-85c3-acae89bc43f5","order_by":0,"name":"Olusegun 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Basin (LBRB)\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-6999964/v1/a20b50304a728b5d0ee75e55.png"},{"id":90492948,"identity":"bd6caadc-a923-4581-ba1e-aeff66d0c7fe","added_by":"auto","created_at":"2025-09-03 10:02:06","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":52130,"visible":true,"origin":"","legend":"\u003cp\u003eMethodological flow chart\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-6999964/v1/f0355b551d899015944e505f.png"},{"id":90492541,"identity":"c25d820b-38ec-4007-b905-2f699df48d0b","added_by":"auto","created_at":"2025-09-03 09:54:06","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":34639,"visible":true,"origin":"","legend":"\u003cp\u003eHistograms of pre-processed images\u003c/p\u003e","description":"","filename":"image3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6999964/v1/5249e621ac3da529a9e14804.jpg"},{"id":90492532,"identity":"9f24596c-0e6f-4559-9779-9b1611e4051f","added_by":"auto","created_at":"2025-09-03 09:54:05","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":152749,"visible":true,"origin":"","legend":"\u003cp\u003eMonthly flooded area \u003cstrong\u003e(June–November 2022) in Lower Benue Basin\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6999964/v1/6d8f5e5d7a92fe7739272571.jpg"},{"id":90492531,"identity":"ebe86202-b380-4c99-a24e-ea89b7f86344","added_by":"auto","created_at":"2025-09-03 09:54:05","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":204643,"visible":true,"origin":"","legend":"\u003cp\u003eComposite Monthly flooded area (June–November 2022) in Lower Benue Basin\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-6999964/v1/c2a78ab6b861b6a643b9347a.png"},{"id":90492523,"identity":"d194a0ed-84a2-499a-8b4e-449db4605962","added_by":"auto","created_at":"2025-09-03 09:54:05","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":184068,"visible":true,"origin":"","legend":"\u003cp\u003eLand cover and monthly flooded areas distribution (2022) in Lower Benue Basin\u003c/p\u003e","description":"","filename":"image6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6999964/v1/c32492094ad94423a89a47c2.jpg"},{"id":90492524,"identity":"719258e6-22c8-4f0f-b60c-00ee5b5ca6f9","added_by":"auto","created_at":"2025-09-03 09:54:05","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":251864,"visible":true,"origin":"","legend":"\u003cp\u003eLand cover and peak flood distribution (2022) in Part of Lower Benue Basin\u003c/p\u003e","description":"","filename":"image7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6999964/v1/a5f8c0e3a1d1896fe88a2159.jpg"},{"id":96711001,"identity":"19468b01-56a8-4463-9ff6-7379e9fd6457","added_by":"auto","created_at":"2025-11-25 10:11:29","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2294894,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6999964/v1/8afe4df1-c172-4842-9d7a-3a515fb7655d.pdf"}],"financialInterests":"","formattedTitle":"FLOOD HAZARD MAPPING OF THE LOWER BENUE RIVER BASIN","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eFlooding is among the most devastating and frequent natural hazards globally, with far-reaching implications for human lives, infrastructure, livelihoods, and ecosystems. In recent decades, floods have increased in frequency and intensity, driven by complex interactions between climate change, rapid urbanization, land use transformations, and declining ecosystem buffers. The impact of floods has intensified globally due to climate change, leading to more frequent and unpredictable events in recent years (Pralle, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Thus, global population being affected by floods is expected to double by 2030, reaching 147\u0026nbsp;million people annually, up from the current 72\u0026nbsp;million (WRI, 2020; Hofste et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The annual cost of flood damage worldwide from 2000 to 2019 is estimated at \u003cspan\u003e$\u003c/span\u003e651\u0026nbsp;billion (Tellman et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and anticipated to rise in the coming years, with increase in the number of individuals being affected. Nowhere is this more evident than in Sub-Saharan Africa, where structural vulnerability, limited disaster preparedness, and poverty compound the destructive impacts of floods (Desai, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; UNEP, 2020; Adelekan et al., 2016). Nigeria, the most populous country in Africa, has consistently ranked among the worst-hit nations by flood disasters, and within Nigeria, the Benue River Basin is one of the most affected regions.\u003c/p\u003e\u003cp\u003eThe Lower Benue River Basin (LBRB), encompassing parts of Benue, Nasarawa, Taraba, and Kogi states, is hydrologically complex and socio-economically significant. It supports over 10\u0026nbsp;million people who rely on the river and its floodplains for agriculture, fishing, and transport. The basin\u0026rsquo;s land cover includes fertile croplands, wetlands, savanna vegetation, and growing urban settlements. However, its low-lying topography, combined with seasonal rainfall peaks and dam discharges, renders it prone to recurrent and severe flooding. These floods often result in massive displacement, food insecurity, and infrastructural collapse. For example, the October 2022 floods displaced over 1.4\u0026nbsp;million people nationally, with the LBRB among the worst-affected zones (NEMA, 2022).\u003c/p\u003e\u003cp\u003eThe risk of flooding in the LBRB is not solely a hydrological phenomenon but anthropogenic drivers including land cover modification, uncontrolled settlement expansion, and vegetation loss have significantly altered natural flood attenuation processes. Conversion of wetlands and floodplains into cropland and settlements has increased runoff generation while reducing infiltration and storage capacity. Concurrently, upstream water releases from hydroelectric dams and climate variability-induced extreme rainfall events contribute to peak discharges exceeding channel capacity. Over the years, satellite observations have shown an increase in impervious surfaces and cropland encroachment into areas formerly classified as wetlands or gallery forests (Ayanlade et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eA major limitation in existing flood risk management approaches in Nigeria is the lack of dynamic, high-resolution spatial data that can identify vulnerable land cover classes and the temporal progression of flood exposure. Traditional flood assessments have relied on post-disaster reports, manual ground surveys, and low-resolution imagery, which fail to provide timely or spatially explicit insights needed for mitigation planning. There is an urgent need for integrated tools that combine flood mapping with land use/land cover (LULC) analysis, especially during flood peaks when cloud cover limits optical satellite visibility.\u003c/p\u003e\u003cp\u003eRemote sensing technologies have revolutionized the monitoring of hydrological extremes, offering scalable, repeatable, and synoptic data that can detect, measure, and analyze flood extents across large areas. Among these technologies, Synthetic Aperture Radar (SAR) systems such as those aboard the Sentinel-1 satellites are particularly useful for flood mapping due to their all-weather, day-and-night acquisition capabilities. SAR sensors transmit microwave signals that penetrate cloud cover and measure surface backscatter, making them ideal for tropical flood monitoring during the rainy season when optical sensors are often ineffective. Combining SAR-based flood detection with LULC datasets yields actionable insights into vulnerability, exposure, and land use conflict. In addition, understanding the spatial patterns of inundation across land cover types helps assess the effectiveness of natural buffers (e.g., wetlands, forests) and the impact of anthropogenic alterations.\u003c/p\u003e\u003cp\u003eSeveral studies have utilized this approach in different contexts. For instance, Schumann and Moller (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) employed SAR imagery and land cover data to evaluate floodplain dynamics in Bangladesh, while Dieng et al. (2021) analyzed flood-prone areas in the Senegal River Basin using Sentinel-1 and land classification models. However, such integrated methods remain underutilized in the Nigerian context, especially in the Benue Basin. This knowledge gap constrains disaster preparedness, spatial planning, and climate adaptation efforts.\u003c/p\u003e\u003cp\u003eThe motivation for this study stems from the need to generate high-resolution, spatio-temporal insights into flood dynamics in the LBRB, particularly during peak inundation periods. The devastating 2022 floods offered an opportunity to analyze flood-land cover interactions using state-of-the-art satellite technology. October was selected for analysis because it coincides with the climax of the rainy season and flood discharge from upstream tributaries and dams. The increasing frequency and intensity of flooding in the Lower Benue River, particularly along the banks of the River Benue, extends beyond immediate property damage, with profound consequences on human life, agriculture, and infrastructure. The 2022 flood incident raises questions about the impact of urban development on the region's vulnerability to floods, going by the increasingly impermeable surfaces disruption to the natural flow of excess precipitation in the lower River Benue catchment area with enormous economic losses.\u003c/p\u003e\u003cp\u003eThis research aims to provide a high-resolution, validated flood extent data for the LBRB and filled the spatial planning gap on estimating the land cover types (e.g., croplands, wetlands, settlements) most exposed to seasonal flooding and provide detailed empirical evidence to inform land use planning, floodplain zoning, and ecological restoration in flood-prone landscapes. The study therefore specifically focuses on mapping the extent of flooding in the LBRB during October 2022 using Sentinel-1 SAR imagery, quantifying inundation proportions per land cover type and identify spatial patterns of flood exposure and assess temporal changes compared to previous flood years with better discussion on the implications of floodable land cover trends for disaster risk reduction, agricultural planning, and wetland conservation.\u003c/p\u003e\u003cp\u003eThus, this study provides a scientific basis for understanding flood dynamics at the land cover interface in one of Nigeria\u0026rsquo;s most vulnerable basins. By leveraging open-access Sentinel-1 SAR data and LULC information, it demonstrates a replicable and cost-effective framework for operational flood monitoring. The study offers an option to enhances understanding of spatial-temporal flood dynamics in relation to anthropogenic land use patterns in a tropical African river basin and provides empirical evidence that can support national and state-level disaster management, zoning regulations, and flood risk mapping. The outputs can inform early warning systems, urban planning, ecosystem restoration, and agriculture insurance schemes in the Benue Basin and similar flood-prone regions in Sub-Saharan Africa.\u003c/p\u003e"},{"header":"2. Study Area and Physical Setting","content":"\u003cp\u003eThe Lower Benue River Basin (LBRB) is a significant hydrological sub-basin within the larger Niger River drainage system, covering an estimated area of 140,000 km\u0026sup2;. The basin stretches predominantly across the central eastern region of Nigeria, encompassing substantial portions of Benue, Taraba, Nasarawa, and Kogi States, and ultimately draining into the confluence of the Niger and Benue Rivers at Lokoja. The Lower Benue represents the final segment of the 1,400 km-long Benue River, which originates from the Adamawa Plateau in Cameroon and travels westward through northeastern Nigeria. The LBRB serves as a vital ecological and socio-economic corridor, supporting large populations through agriculture, fisheries, transportation, and informal trade. It also forms a transitional ecological zone between the Sudan Savannah in the north and the Guinean Forest-savanna mosaic in the south. The river and its floodplains play a crucial role in sediment and nutrient deposition, making the region highly fertile and attractive for wet-season and dry-season farming.\u003c/p\u003e\u003cp\u003eThe basin is characterized by a predominantly low-lying alluvial terrain, with altitudes generally ranging between 100 m and 300 m above sea level, except for some isolated inselbergs and escarpments in Nasarawa and Taraba States. The Benue River flows through a broad valley interspersed with seasonal tributaries and distributaries such as the Katsina-Ala, Donga, and Taraba rivers. The floodplain width varies considerably, from as narrow as 1\u0026ndash;2 km to over 15 km in places such as Makurdi and Agatu (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The relative flatness of the terrain contributes significantly to the propensity for prolonged overbank flooding, especially during the wet season. Soils in the basin are predominantly fluvisols, alluvial loams, and hydromorphic clays, which exhibit high water-holding capacities but poor drainage in saturated conditions. This further accentuates inundation risk during peak rainfall months.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe Lower Benue Basin lies within the tropical wet-and-dry climate zone (Aw), according to K\u0026ouml;ppen\u0026rsquo;s classification, with pronounced seasonality in rainfall and temperature patterns. Rainfall is uni-modal, beginning in April and ending around October, with peaks typically observed in August\u0026ndash;September. Annual precipitation varies from 1,200 mm in the northern edges to over 1,800 mm in the southern portions near the Niger confluence. Rainfall is influenced by the West African Monsoon system and occasionally enhanced by local convective storms. The Benue River\u0026rsquo;s discharge pattern reflects this seasonality, with low flows between December and April and peak discharges between August and October, coinciding with the rainy season climax and dam releases upstream. The average discharge of the Benue River at Makurdi is about 3,500\u0026ndash;4,500 m\u003cb\u003e\u0026sup3;/s\u003c/b\u003e during peak flows, but extreme events (such as in 2012 and 2022) have recorded levels above 7,000 m\u003cb\u003e\u0026sup3;/s\u003c/b\u003e, causing extensive inundation in the floodplains.\u003c/p\u003e\u003cp\u003ePeak discharge occurs during the rainy season (June\u0026ndash;October), with flow rates sometimes exceeding 5,500 m\u0026sup3;/s. In the dry season (November\u0026ndash;April), the discharge can drop significantly, often below 1,000 m\u0026sup3;/s. Major floods occur approximately once every 5\u0026ndash;7 years, with minor flooding during rainy seasons. The Lower Benue Basin is projected to experience a 5\u0026ndash;15% reduction in average annual flow by 2050 due to climate change. The Lower Benue carries an annual sediment load estimated at 20\u0026ndash;30\u0026nbsp;million tonnes, influenced by upstream erosion and tributary contributions. Complicating the hydrology of the basin are upstream dams, notably the Lagdo Dam in Cameroon and several hydropower reservoirs on Nigerian tributaries. These artificial interventions alter the timing and magnitude of downstream flows, occasionally resulting in compounded flood events when heavy rainfall and dam releases coincide.\u003c/p\u003e\u003cp\u003eThe LBRB features a mosaic of land cover types that reflect both natural ecosystems and human land use systems. Recent LULC classifications using Landsat and Sentinel-2 data suggest that between 2000 and 2020, croplands expanded by more than 30%, often at the expense of natural wetlands and forests (Nonki et al., 2019; Adebola et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).). This land use transformation has diminished the basin\u0026rsquo;s flood absorption capacity and increased surface runoff. The LBRB is home to several million people, many of whom engage in subsistence agriculture, fishing, and trading. Livelihoods are closely tied to seasonal cycles, with dependence on rainfall and river levels for both rainfed and flood-recession farming. Settlements along the Benue River and its tributaries especially Makurdi, Gboko, Logo, Agatu, Wadata, and Lokoja have been historically prone to flooding. Thus, the confluence of natural susceptibility, anthropogenic pressure, and institutional weakness makes the LBRB a hotspot of climate-induced hydrological risk in West Africa.\u003c/p\u003e\u003cp\u003eThe Lower Benue Basin offers a unique landscape for flood remote sensing due to a wide Floodplains that is large enough to be detected by medium-to-high resolution satellite sensors such as Sentinel-1. The land cover heterogeneity of mix cropland, wetlands, forests, and settlements provides diverse land surface signatures and flood susceptibility patterns. The region is also consistently highlighted in national disaster risk reduction strategies and is part of regional frameworks such as the Benue River Basin Development Authority (BRBDA) plans. This combination of environmental significance, vulnerability, and policy relevance makes the Lower Benue Basin an appropriate and urgent case for spatio-temporal floodable land cover analysis using Earth Observation tools.\u003c/p\u003e"},{"header":"3. Materials and Methods","content":"\u003cp\u003e\u003cb\u003eMethodology\u003c/b\u003e\u003c/p\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Data Characteristics\u003c/h2\u003e\u003cp\u003eThe dataset in this research is retrieved from the Google Earth Engine Data Catalog. Different types of datasets are used in this study include the study area shapefile, the SAR (Sentinel 1) images, topography data, etc. Sentinel-1 radar dataset 10m resolution. The key pre-processing stages have already been completed for Sentinel\u0026rsquo;s ground range detected (GRD) products provided in the GEE platform (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://developers.google.com/earth-engine/datasets/catalog/sentinel\u003c/span\u003e\u003cspan address=\"https://developers.google.com/earth-engine/datasets/catalog/sentinel\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Data from both cross-polarized (VH), and like-polarized (VV) channels were acquired as Level-1 GRD products. Both polarization available for this GRD product and their combinations have been utilized for flood mapping in this research (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSatellite image characteristics\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAcquisition Date\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eImage I.D\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSatellite Imagery\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSpectral Bands\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSpatial resolution\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eJune 1\u003csup\u003est,\u003c/sup\u003e 2022\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eJune_A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSentinel-1A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eVV, VH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e20*5m\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eJune 17th 2022\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eJune_B\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSentinel-1A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eVV, VH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e20*5m\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eJuly 2nd 2022\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eJuly_A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSentinel-1A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eVV, VH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e20*5m\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eJuly 16, 2022\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eJuly_B\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSentinel-1A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eVV, VH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e20*5m\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAugust 1, 2022\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAugust_A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSentinel-1A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eVV, VH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e20*5m\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAugust 16, 2022\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAugust_B\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSentinel-1A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eVV, VH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e20*5m\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSeptember 1, 2022\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSeptember_A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSentinel-1A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eVV, VH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e20*5m\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSeptember 16, 2022\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSeptember_B\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSentinel-1A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eVV, VH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e20*5m\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOctober 1, 2022\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOctober_A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSentinel-1A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eVV, VH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e20*5m\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNovember 1, 2022\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNovember_A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSentinel-1A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eVV, VH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e20*5m\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNovember 18th 2022\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNovember_B\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSentinel-1A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eVV, VH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e20*5m\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eJanuary 15th 2022\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSentinel-2A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eR, G, B, NIR, SWIR, Red-Edge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e10m\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eBased on the data acquired the focus of the flood mapping is based on the rainy season of 2022 with emphases on October flooding event. The 2022 flood in the Lower Benue River underscored the region\u0026rsquo;s vulnerability to extreme hydrological events.\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e details a series of steps, grouped under SAR Pre-processing, Image Processing, and Mapping, to identify and extract flooded areas in this study. Each stage of this methodology is integral to ensuring the accuracy and reliability of the flood maps. The methodology involves the acquisition of post-event (DS-1) and pre-event (DS-2) SAR images. These datasets represent imagery captured after and before the flood event, respectively, providing a comparative basis for detecting changes induced by flooding. The first step, Radiometric Calibration, ensures that the SAR data's backscatter values are physically meaningful, allowing for consistent comparison across different images. This step is critical as it removes any distortions or inconsistencies in the raw SAR data.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe speckle filtering is applied using a Lee Sigma filter with a kernel size of 5\u0026times;5 to remove the speckle noise, inherent in SAR imagery that can obscure meaningful features and hinder accurate flood detection. The filtering process reduces this noise while preserving significant edges and features, improving the interpretability of the imagery. The final step in pre-processing entails the Geometric Correction, this involves aligning the SAR data to a consistent spatial reference system. This ensures that the imagery accurately represents real-world locations, a crucial prerequisite for spatial analysis and integration with other geospatial datasets.\u003c/p\u003e\u003cp\u003eThe image processing phase entails the building up of the corrected Images to identify the threshold that distinguish water bodies from non-water surfaces image. Thresholding is a crucial step that sets a specific backscatter value to identify and separate inundated areas, leveraging on the distinct backscatter characteristics of water in SAR imagery.\u003c/p\u003e\u003cp\u003eBuilding upon the thresholding results, the methodology employs Supervised Classification techniques to refine the identification of flooded areas. Three classification algorithms are highlighted namely the Random Forest, K-Nearest Neighbours (KNN), and Maximum Likelihood Classification (MLC). These machine learning algorithms analyze the spectral and spatial characteristics of the imagery to categorize pixels into water and non-water classes. Supervised classification is a common approach for information extraction from images and consists of two main stages: training and classification. During the training stage, a set of representative samples are selected for each class and in the classification stage, a classifier is used for the assessment of the probability of every image pixel to belong to the classes. Based on the highest probability, each pixel is classified as a particular feature in the given classification stage.\u003c/p\u003e\u003cp\u003eThe histograms of pre-processed images have been used to determine the training sets (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) based on three classes (i.e. water bodies, flooded areas and others). The water class show the unchanged (permanent) water areas in both datasets using the three classification methods.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe RF classifier samples the data iteratively and randomly and produces a large group of classification and regression trees. The results represent the statistical mode of many decision trees achieving a more robust model than a single classification tree (Lee \u0026amp; Pottier, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). RF generally supplies better classification results than the ML method (Van Beijma et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Balzter et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe KNN classifies the closest training examples in the feature space and it is considered to be a simple machine learning algorithm. In this method, a pixel is assigned to the class among its k nearest neighbours (Lee \u0026amp; Pottier, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Schumann \u0026amp; Moller, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). In the ML classification, a pixel with the maximum likelihood is assigned to the corresponding class. In this method, the threshold values for each class are obtained from the training data sets. Depending on the threshold value, the probability of a given pixel for a given class is calculated and all pixels are classified into a specific class (Garga, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe final phase is the mapping of the extracted flooded areas through the integration of the classification step, the methodology produces detailed flood maps that delineate the spatial extent of inundation. These maps serve as a valuable resource for assessing flood impacts, planning relief efforts, and informing long-term mitigation strategies.\u003c/p\u003e\u003cp\u003eOverall, this methodological flowchart demonstrates a structured approach to flood mapping using Sentinel-1 SAR data. By integrating robust pre-processing, advanced classification techniques, and precise mapping, the methodology ensures accurate and actionable insights into flood events. Each step is interconnected, with the reliability of later phases depending on the quality and precision of earlier stages, underscoring the importance of a comprehensive and systematic workflow.\u003c/p\u003e\u003cp\u003eThis study also integrates Sentinel-1 Synthetic Aperture Radar (SAR) imagery and multi-temporal Land Use Land Cover (LULC) data to detect and map floodable land cover in the Lower Benue River Basin (LBRB). The methodology follows a structured geospatial workflow involving data acquisition, preprocessing, flood extent detection, LULC classification, overlay analysis, and statistical interpretation. Processing was implemented primarily using Google Earth Engine (GEE) and QGIS, leveraging cloud computing for handling large datasets and temporal stacks.\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Results and Discussion","content":"\u003cp\u003eThis section presents the spatial and statistical outcomes of flood mapping, land cover intersections, and temporal trends derived from Sentinel-1 SAR and LULC data analyses in the Lower Benue River Basin (LBRB). The multi-temporal analysis of flood dynamics along the Lower Benue River based on Sentinel-1 satellite imagery for the 2022 rainy season depicting the flood time-series, flood maps, areal statistics, and a composite inundation map that evaluate spatial and temporal patterns of flood occurrence between June and November 2022 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The sentinel imagery floodable area of the part of Lower Benue River provides a comprehensive spatial and temporal analysis of flood dynamics in the Lower Benue River using Sentinel satellite imagery and are sequence from June to November 2022 corresponding to different acquisition dates, marked as June_A, June_B, July_A, etc., and reflecting a progressive inundation and recession pattern. This sequencing also allows for a detailed examination of flood extents over time, highlighting critical changes in flood behaviour, and identifying the most affected areas within the Lower Benue River basin. By analysing the floodable areas captured in the Sentinel imagery, better understand of the underlying factors contributing to flood risks and inform future management strategies.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e4.1 Spatio-temporal Distribution of Flood-Prone Areas in Lower Benue Basin\u003c/h2\u003e\u003cp\u003eFlood areal extent varied significantly throughout the study period. As shown in the line graph (top right), early-season flood extents remained relatively low from June to mid-August, with average inundated areas between 60 and 75 km\u0026sup2;. The bi-monthly analysis of floodable area during the months of June-November (rainy season) for the study period 2022 depicts an average of 73.54 km\u0026sup2; (\u0026plusmn;\u0026thinsp;28.3 km\u0026sup2;) floodable area (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). During the period June 1st to June 17th (June_A to June_B), there is a reduction in the area covered by water from 58.96 km\u0026sup2; to 50.32 km\u0026sup2;, while between July 2nd and July 16th (July_A to July_B), the floodable area extent increases slightly from 48.5 km\u0026sup2; to 51.4 km\u0026sup2;, indicating a potential rise in flood extent. The period from August 1st to August 16th (August_A to August_B) also experiences an increase in floodable area from 54.6 km\u0026sup2; to 56.3 km\u0026sup2;. September 1st to September 16th (September_A to September_B) experiences a quantum increase in the flooded area, from 76.4 km\u0026sup2; to 83.6 km\u0026sup2; before attaining the peak on 1st October (October_A) at a flooded area of approximately 145km\u0026sup2; before receding to 94.1 km\u0026sup2; on November 1st (November_A to November_B) and remaining relatively stable by November 18th (93.9 km\u0026sup2;). This display that a progressive increase began in late August, peaking dramatically on October, 2022, with an inundated area of approximately 145 km\u0026sup2;, the dashed line represents the average flood extent, providing a baseline for comparison. Flooded area ranged from a minimum of 58 km\u0026sup2; in June to a maximum of 144 km\u0026sup2; in October 2022. The flood extent was relatively stable during the early rainy season but increased sharply in late September and peaked in October, reflecting extreme hydrological conditions This surge likely results from the combination of peak seasonal rainfall and late rainy season runoff, reflecting a lag between precipitation inputs and hydrological response in the floodplain as well as probably upstream discharge from major reservoirs. Following the October peak, a sharp decline in inundation was observed in November, indicating the commencement of the floodwater recession phase, possibly due to better drainage or reduced inflow.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe sequential Sentinel-1 imagery reveals a consistent flood expanse along the river corridor from August onwards while the composite map aggregates all identified inundation zones during the study period and delineates the spatial patterns of recurrent flooding activities. Notably, floodwaters extended considerably in areas along the low-lying alluvial plain and are also prone to both seasonal overbank flooding and backwater effects in places around Makurdi, Agatu, Gwer West, Omala, and parts of Dekina and Tarka LGAs.\u003c/p\u003e\u003cp\u003eThus, the months of September and October seem to be critical months with substantial floodable areas, while November shows a slight reduction in the flooded area compared to October but remains relatively high. This dynamic nature of flood extent is valuable for understanding the temporal evolution of flood inundation and provides a valuable tool for disaster management, flood response planning, and effective flood mitigation plans. The fluctuation in flood extent over the months highlights the importance of temporal resolution in flood monitoring and early warning systems, especially in dynamic floodplains like the Lower Benue. This provides a valuable tool for monitoring flood progression and its spatial extent, identifying flood-prone settlements along the Lower Benue and provision of planning interventions, such as early warning systems, flood defenses, or resettlement\u003c/p\u003e\u003cp\u003eThe composite inundation footprint illustrates the shift in flood-prone zones over time and accumulate risk across seasons based on topographic heterogeneity, river morphology, and land use practices, particularly in agricultural floodplains and peri-urban settlements. The cumulative inundation zones include important settlements like Makurdi, Agatu, Omala, and Gwer, all situated near frequently inundated zones. this zones are also highlighted as regions more vulnerable to seasonal flooding while flood recurrence zones are evident from overlapping flood extents across multiple months (color overlaps). In addition, the spatial pattern suggests floodplain expansion eastward over time, with repeated inundation in certain pockets indicating chronic flood risk. Thus, based on the potential impact on human habitation and infrastructure, the urban planning and settlement regulation near flood zones must be enforced in areas like Agatu and Makurdi while appropriate disaster preparedness strategies should focus on areas that exhibit persistent or overlapping inundation across multiple months.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e4.2 \u003cb\u003eLanduse / Land cover distribution\u003c/b\u003e in Part of \u003cb\u003eLower Benue River Basin (2022)\u003c/b\u003e\u003c/h2\u003e\u003cp\u003eThe baseline estimated land cover distribution for the Lower Benue River Basin in 2022 indicates that the cropland dominates the landscape, reflecting the basin's agrarian character, while the forest and vegetation occupy mostly the less developed upland areas (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Built-up areas are concentrated around towns like Makurdi and Gbajimba, while the wetlands, shrubs, and water bodies cover the remaining portions, performing flood buffering and ecological balance functions. The spatial arrangement of these classes influences flood exposure, with croplands and wetlands situated primarily within the basin's floodplains, while urban and semi-urban areas extend toward river corridors. Thus, accurate land cover classification is crucial for quantifying land use vulnerability and estimating the socio-economic and ecological impacts of flooding (Foody, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Giri et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Furthermore, the urban and peri-urban development increasingly encroaches upon historical river channels and flood zones, and the seasonality of flood impact aligns with peak rainfall and river discharge in the basin, suggesting a strong hydrological driver of flood expansion during the wet season.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe baseline land cover distribution of the pre-and during the October 2022 flood in part of the Lower Benue River Basin (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) depicts that the dominant land cover types include the cropland which covers 1,320 km\u0026sup2; (40.0%) of the total area pre-flood, with 997 km\u0026sup2; (75.53%) areal flooded. Forest and vegetation occupy 880 km\u0026sup2; (26.7%) and 353 km\u0026sup2; (40.11%) being inundated. Built-up areas account for 520 km\u0026sup2; (15.8%) and had 352 km\u0026sup2; (67.69%) affected, leading to flooding. Wetlands cover about 300 km\u0026sup2;, representing 9.1% of the total area covered, are fully saturated. Shrubs pre-flood areal cover 110 km\u0026sup2;, with 95 km\u0026sup2; (86.36%) of the area covered experiencing flooding. Water bodies remained unchanged at 170 km\u0026sup2;, inundating other land covers during flooding. Overall, out of the pre-total land cover of 3,300 km\u0026sup2; about 2,097 km\u0026sup2; were flooded, constituting 63.55% of the total pre-flood area.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eLand cover distribution in the Lower Benue River Basin, 2022\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLand Cover Type\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePre Flood (km\u0026sup2;)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFlood (km\u0026sup2;)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e% flooded\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e% of Non- flood\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCropland\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1320\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e997\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e75.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e24.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eForest/Vegetation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e880\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e353\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e40.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e59.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBuilt-up Areas\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e520\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e352\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e67.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e32.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWetlands\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e300\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e300\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eShrubs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e110\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e86. 4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e13.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWater Bodies\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e170\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e170\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe monthly land types flooded from June to October 2022. In June, 156 km\u0026sup2; were flooded, with 90 km\u0026sup2; of cropland and 22 km\u0026sup2; of built-up areas. July's total increased to 227 km\u0026sup2;, with 106 km\u0026sup2; of cropland. August saw further flooding, reaching 378 km\u0026sup2;, including 170 km\u0026sup2; of cropland. In September, flooding rose to 500 km\u0026sup2;, significantly affecting cropland and built-up areas. In October, flooding peaked at 836 km\u0026sup2;, with 404 km\u0026sup2; of cropland being the most impacted. In total, about 2097 km\u0026sup2; of areal covered was flooded over these months, as detailed in the changes in land use affected by floods (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e \u0026amp;Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMonthly Flood Dynamics (June\u0026ndash;October 2022) in Lower Benue Basin\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMonth\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCropland\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eBuilt-up\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eWetlands\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eForest\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eShrub\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eTotal Flooded (km\u0026sup2;)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eJune\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e156\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eJuly\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e106\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e227\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAugust\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e170\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e378\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSeptember\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e227\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e500\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOctober\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e404\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e104\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e104\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e182\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e836\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e997\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e352\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e300\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e353\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2097\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe integration of monthly flood distribution with baseline land cover data enables the need for more understanding of land use vulnerability in the Lower Benue Basin. The exposure of critical resources such as croplands and urban zones, necessitates the adoption of climate-resilient spatial planning frameworks and re-evaluation of land use zoning in floodplains (IPCC, 2022). This is necessary for to establishment of protective greenbelts around wetlands and riverbanks, the adoption of seasonal flood early warning systems integrated with agricultural calendars and strategic relocation of high-risk informal settlements and infrastructure.\u003c/p\u003e\u003cp\u003eSuch measures not only enhance the resilience of communities in the Lower Benue Basin but also ensure the sustainable management of natural resources. By fostering collaboration between local authorities, stakeholders, and residents, it becomes possible to create a more robust framework for adapting to the challenges posed by climate change.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e4.3 Selected location in Part of the Lower Benue River Basin (June to October 2022)\u003c/h2\u003e\u003cp\u003eLower Benue River Basin is among Nigeria\u0026rsquo;s most flood-prone regions due to its extensive floodplains, seasonal rainfall intensity, and growing pressure from agricultural and urban expansion. For a better understanding of the spatial heterogeneity of flood impacts on land cover transformations, four representative settlements were chosen based on their peculiarity land use/ land cover and location along the River Benue and its tributaries namely Gbajimba, Loko, Makurdi, and Oguma before and during the peak flood month of October 2022.\u003c/p\u003e\u003cp\u003eSpecifically, in Gbajimba, farmland and lowlands flooding are evident, causing agricultural loss and displacement. Loko experienced likely floodplain overflow, affecting forests and cropland, which resulted in forest flooding and ecological disturbance. In Makurdi, urban flooding poses risks to infrastructure and sanitation in built-up areas and low-lying zones. The Oguma region's flooding impacted, affecting cropland and forests, resulting in biodiversity and soil loss (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Thus, the croplands were consistently the most impacted land use type, followed by natural vegetation and, in Makurdi, built-up areas. This spatial disparity impact assessment demonstrates the need for location-specific flood mitigation strategies that consider land use configuration, topography, and socioeconomic vulnerability.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCross-Location of Flood Impacts\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLocation\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eObservations\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAffected Land Cover\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eKey Impacts\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEntire Basin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOverall flood extent\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCropland, wetlands, lowland settlements\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLives \u0026amp; properties loss, Displacement\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGbajimba\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFarmland \u0026amp; lowlands flooding\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCropland, riparian vegetation, Lowland Settlements\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAgricultural loss, displacement\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLoko\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLikely floodplain overflow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eForest margins, floodplains Cropland, forest\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eForest flooding, ecological disturbance\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMakurdi\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUrban flooding \u0026amp; infrastructure risk\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eBuilt-up areas, roads, cropland, urban edges, low-lying zones\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eInfrastructure risk, sanitation crisis\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOguma\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEcological and rural impact\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCropland, Forests, farmlands\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eBiodiversity and soil loss\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe flood Severity across the four settlements based on inundation in October 2022 shows that the cropland was consistently the most impacted land use, especially in Gbajimba and Loko while direct flooding impact on Built-up areas is evident in Makurdi, due to urban flood exposure while the wetlands served as both flood absorbers and conduits, experiencing significant water expansion (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). There is therefore the need to targeted relocation or protection of critical croplands. Urban flood zoning and drainage investment in Makurdi is also necessary while engaging in the ecosystem restoration of wetlands to increase flood absorption capacity. Increasing flood absorption capacity will not only mitigate the impacts of urban flooding but also enhance biodiversity and improve water quality in the region. Furthermore, collaborative efforts involving the local communities, government agencies, and environmental organisations in the implementation of these strategies should be fully engaged. Section \u003cspan refid=\"Sec9\" class=\"InternalRef\"\u003e4.3.1\u003c/span\u003e to 4.3.4 provides more clarity on the selected settlement based on Figs.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e and \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv id=\"Sec9\" class=\"Section3\"\u003e\u003ch2\u003e4.3.1 Gbajimba and Environs\u003c/h2\u003e\u003cp\u003eGbajimba and Environs is a low-lying agrarian settlement in the Lower Benue River Basin, situated near the river channel, the area is highly susceptible to seasonal inundation, especially during the peak of the rainy season. The localized flood patterns depict that flooding could be associated with topographic depressions (lowland) or human-modified landscapes. Before inundation, the landscape was dominated by cropland and riparian vegetation and patches of built-up zones but the October 2022 flood event depicts a substantial floodwater spread, inundating large portions of farmland and disrupting settlement peripheries depending on proximity to water channels while land cover classes like vegetation or shrubs are temporarily inundated, becoming an extension of open water or wetland.\u003c/p\u003e\u003cp\u003ePrior to the flood event, the total area analyzed was approximately 164.4 km\u0026sup2; (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The dominant land cover was cropland (47.8%), followed by vegetation/forest (28.1%), and built-up areas (9.2%). The land cover types before and after a flood show that out of the pre-flood 78. 6 km\u0026sup2; of Cropland coverage about 51 km\u0026sup2; (64. 9%) are flooded. Vegetation/forest showed that out of the 46. 2 km\u0026sup2; pre-flood, only 21. 2 km\u0026sup2; (45. 9%) were inundated. Built-up areas had 12. 4 km\u0026sup2; before flooding, with 8. 4 km\u0026sup2; (67. 7%) affected. Wetlands experienced complete flooding, with 100% of the 8. 4 km\u0026sup2; affected. Shrubs totaled 5. 6 km\u0026sup2; pre-flood, with 4. 3 km\u0026sup2; (76. 8%) flooded. The overall flooding affected 93.3km\u0026sup2; in total, resulting in 56. 8% of the area flooded. The water bodies channel is also over-saturated, inundating peri-urban margins and other land cover/ land uses as well as submerging formerly productive agricultural and vegetative land.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eLand cover distribution in Gbajimba and Environs\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLand Cover Type\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePre Flood (km\u0026sup2;)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFlood (km\u0026sup2;)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e% flooded\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e% of Non- flood\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCropland\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e78.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e64.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e35.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVegetation/Forest\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e46.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e21.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e45.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e54.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBuilt-up Areas\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e12.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e67.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e32.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWetlands\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e8.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e100.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eShrubs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e76.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e23.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWater/Flooded Area\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e13.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe inundated land use/ land cover from June to October 2022 (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e) shows that the Cropland inundation increases from 3. 4 km\u0026sup2; in June to 17 km\u0026sup2; in October, totaling 51 km\u0026sup2; over the period. Vegetation/forest also rises from 1. 5 km\u0026sup2; to 7. 6 km\u0026sup2;, with a total of 21. 2 km\u0026sup2;. Built-up areas grow from 0. 6 km\u0026sup2; to 2. 8 km\u0026sup2;, totaling 8. 4 km\u0026sup2;. The wetlands also account for 8. 4 km\u0026sup2; saturated, and shrubs are at 4. 3 km\u0026sup2;. In total, the combined area is 93. 3 km\u0026sup2; across all categories. This diverse landscape reflects the dynamic nature of land use and vegetation changes over the specified period. Such variations highlight the importance of monitoring environmental shifts to inform conservation and development strategies.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMonthly Flood Dynamics (June\u0026ndash;October 2022) in Gbajimba and Environs\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLand cover\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eJune\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eJuly\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAugust\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSeptember\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eOctober\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eTotal (km\u0026sup2;)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCropland\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e9.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e15.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e51\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVegetation/Forest\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e5.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e21.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBuilt-up\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e8.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWetlands\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e8.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eShrubs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e4.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal (km\u0026sup2;)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e6.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e17.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e26.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e32.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e93.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe surroundings are characterised by vast croplands intermingled with riparian vegetation, and floodable water inundates the residential and agricultural zones. With approximately two-thirds of all croplands inundated, crop harvest yields are devastated, threatening food security in the region, while the expansion of wetlands and open water signals a redistribution of surface hydrology due to river system overflow and poor drainage capacity, resulting in the inundation of built-up areas, particularly low-income housing on marginal lands, confirming the vulnerability of unprotected floodplain zones.\u003c/p\u003e\u003cp\u003eGbajimba's crops and rural infrastructure are vulnerable to seasonal flooding, necessitating the adoption of flood-resistant agricultural practices and enhanced land use zoning plans. Community-based flood preparedness, seasonal early warning dissemination, and ecological buffer protection, particularly along riparian zones, continue to be excellent hydrological mitigation solutions for achieving a flood-resilient environment. A flood-resistant environment not only protects vulnerable communities, but it also increases agricultural productivity and preserves local ecosystems. These measures necessitate, among other things, combined efforts by government agencies, local groups, and residents.\u003c/p\u003e\u003cp\u003e\u003cb\u003eLoko and Environs\u003c/b\u003e\u003c/p\u003e\u003cp\u003eLoko and Environs is a low-lying settlement on the Lower Benue River Basin's floodplain, with a diversified wetlands landscape of agricultural fields, gallery trees, and riverside linear hamlet communities. Because of its proximity to Benue River tributaries, the area's hydrological profile renders it very vulnerable to seasonal floods, with flood concentrations occurring along riverbanks and floodplains used for crop production. The floodplain is also highly prone to flooding and human settlement encroachment. Depending on the storm's intensity and length, significant crop losses are expected in natural marshes or swamps. Floodwaters being induced by local rainfall and river flow surges commonly impact natural and human land use patterns in Loko and Environs.\u003c/p\u003e\u003cp\u003eIn October 2022, flooding covered 50.4% (77 km\u0026sup2;) of the pre-flood area of 152.7 km\u0026sup2;, the cropland had 64.1 km\u0026sup2; pre-flood, but 39.9 km\u0026sup2; was inundated, leading to 62.2% inundation. The vegetation/forest area is about 34.3 km\u0026sup2;, out of which 19 km\u0026sup2; (55.4%) is flooded while the Built-up areas occupied 9.7 km\u0026sup2; of pre-flood areas had 5.9 km\u0026sup2; (60.8%) flooded. The 8.9 km\u0026sup2; of wetlands remain inundated and saturated during flooding. Shrubs pre-flood areal extent covered 3.6 km\u0026sup2;, with 91.7 percent of it flooded while the water bodies areal extent of 32.1 km\u0026sup2; remains saturated, and overflowed to other land use/land cover (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eLand cover distribution in \u003cb\u003eLoko and Environs\u003c/b\u003e\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLand Cover Type\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePre Flood (km\u0026sup2;)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFlood (km\u0026sup2;)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e% flooded\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e% of Non-flood\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCropland\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e64.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e39.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e62.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e37.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVegetation/Forest\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e34.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e55.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e44.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBuilt-up Areas\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e9.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e60.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e39.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWetlands\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e8.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e100.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eShrubs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e91.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e8.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWater/Flooded Area\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e32.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e32.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e100.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe monthly inundation land cover from June to October 2022 (Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMonthly Flood Dynamics (June\u0026ndash;October 2022) in \u003cb\u003eLoko and Environs\u003c/b\u003e\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLand Cover Type\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eJune\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eJuly\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAugust\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSeptember\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eOctober\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eTotal (km\u0026sup2;)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCropland\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e11.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e14.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e39.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVegetation/Forest\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e5.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e6.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e19\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBuilt-up Areas\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e5.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWetlands\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e8.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eScrubs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal (km\u0026sup2;)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e14.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e21.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e27.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e77\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e)shows that cropland covers rose from 2.6 km\u0026sup2; in June and 14.2 km\u0026sup2; in October, totaling 39.9 km\u0026sup2;. In June, vegetation/forest covered 1.2 km\u0026sup2; of area, increasing to 6.9 km\u0026sup2; in October for a total of 19 km\u0026sup2;. During the defined period, built-up areas increase from 0.4 km\u0026sup2; to 2.1 km\u0026sup2;, reaching 5.9 km\u0026sup2;. Wetlands inundation ranges from 0.9 km\u0026sup2; to 2.8 km\u0026sup2;, totaling 8.9 km\u0026sup2;. Scrubs expand from 0.2 km\u0026sup2; to 1.6 km\u0026sup2;, and totalling 3.3 km\u0026sup2;. The overall land cover grows from 5.3 km\u0026sup2; to 27.6 km\u0026sup2;, reaching 77 km\u0026sup2;.\u003c/p\u003e\u003cp\u003eCropland suffered the most, with more than one-third of the flooded area previously used for agriculture, while forest portions were submerged in low-lying areas and wetlands grew, demonstrating their flood-buffering role. Unfortunately, flooding's advance into built-up regions poses immediate hazards to homes and public facilities.\u003c/p\u003e\u003cp\u003eFlood adaptation in the Loko area should include the promotion of climate-smart agriculture in flood-prone zones, with a focus on the importance of floodplain-sensitive agricultural planning, such as adaptive crop calendars. Rehabilitation of degraded riparian buffers and wetlands for natural flood mitigation should also be encouraged. Furthermore, a zoning regulation to restrict construction in flood corridors should be implemented to limit further settlement encroachment into high-risk areas, as will the installation of community-based flood early warning systems, particularly in river-adjacent towns.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMakurdi and Environs\u003c/b\u003e\u003c/p\u003e\u003cp\u003eMakurdi, the capital of Benue State and a significant urban center on the banks of the Benue River presents a unique state of urban flood exposure since the city is one of the most flood-prone metropolitan areas in central Nigeria. Flooding in Makurdi has major socioeconomic repercussions, including infrastructure, displacement, and sanitation. Over time, the expansion of urban infrastructure into historical floodplains has increased disaster risk, with consequences for housing, transportation, and public health. The discovered patterns call for urban planning reforms and investments in flood-resistant infrastructure.\u003c/p\u003e\u003cp\u003eCropland covered 84.2 km\u0026sup2; prior to the October 2022 flood (Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e) but was swamped to 45.9 km\u0026sup2; (54.5%) during the flooding, and affected 20.9 km\u0026sup2; (52.5%) of the 39.8 km\u0026sup2; of built-up areal extent. The flooding also inundated 18.7 km\u0026sup2; (43.9%) of the 42.6 km\u0026sup2; of vegetation/forest area while the wetlands were overwhelmed at total flooding of 16.4 km\u0026sup2;, whereas shrubs had a 7.8 km\u0026sup2; (90.7%) flooding rate. Water bodies remain saturated during the peak flooding, inundating other land covers. Overall, the floodable total land covers 109.7 km\u0026sup2; out of the 214.6 km\u0026sup2; total areal covered, suggesting that 51.1% of the area was inundated, leaving 48.9% non-flooded.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab9\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eLand cover distribution in \u003cb\u003eMakurdi and Environs\u003c/b\u003e\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLand Cover Type\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePre Flood (km\u0026sup2;)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFlood (km\u0026sup2;)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e% flooded\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e% of Non- flood\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCropland\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e84.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e45.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e54.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e45.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBuilt-up Areas\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e39.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e20.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e52.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e47.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVegetation/Forest\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e42.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e43.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e56.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWetlands\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e16.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e100.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eShrubs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e90.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e9.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWater/Flooded Area\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe monthly land cover flooding (June to October 2022) depicts that the Cropland floods the most, with a total of 45. 9 km\u0026sup2;. Built-up areas have 20. 9 km\u0026sup2;, while vegetation/forest covers 18. 7 km\u0026sup2;, wetlands 16. 4 km\u0026sup2;, and shrubs 7. 8 km\u0026sup2;. In total, the flooded area reached 109. 7 km\u0026sup2; by October (Table\u0026nbsp;\u003cspan refid=\"Tab10\" class=\"InternalRef\"\u003e10\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab10\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 10\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMonthly Flood Dynamics (June\u0026ndash;October 2022) in \u003cb\u003eMakurdi and Environs\u003c/b\u003e\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLand Cover\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eJune\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eJuly\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAugust\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSeptember\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eOctober\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eTotal (km\u0026sup2;)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCropland\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e11.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e22.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e45.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBuilt-up Areas\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e5.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e10.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e20.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVegetation/Forest\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e9.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e18.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWetlands\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e6.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e16.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eShrubs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e7.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFlooded (km\u0026sup2;)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e4.2\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e8.8\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e15.1\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e28.9\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e52.7\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e109.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab11\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 11\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eLand cover distribution in \u003cb\u003eOguma and Environs\u003c/b\u003e\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLand Cover\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePre Flood (km\u0026sup2;)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFlood (km\u0026sup2;)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e% flooded\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e% of Non-flood\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCropland\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e75.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e43.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e57.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e42.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eForest/Vegetation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e52.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e29.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e55.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e44.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBuilt-up Areas\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e62.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e37.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWetlands\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eShrubs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e81.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e18.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWater/Flooded Area\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e39.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e198.1\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e98.9\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e49.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e50.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eCropland flooding adds to the socioeconomic pressure on food systems and livelihoods, whereas built-up area flooding shows concern about the increasing risk of urban floods. Inundation of built-up regions also suggests a direct risk to population and infrastructure considering that the Benue River is likely to overflow into neighborhoods or commercial sectors, as well as social ramifications such as displacement, infrastructure destruction, and waterborne disease epidemics.\u003c/p\u003e\u003cp\u003eFurthermore, the ongoing use of floodplains for informal housing and infrastructure increases exposure and vulnerability to extreme events (Merz et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; IPCC, 2022). Thus, floodplain zoning rules and building limitations must be implemented while the urban drainage and wetland retention systems need upgrade, seasonal mapping and flood early warnings disseminated, and urban agriculture will further strengthen the system's flood resilience.\u003c/p\u003e\u003cp\u003e\u003cb\u003eOguma and Environs \u0026ndash; Before and After Flood\u003c/b\u003e\u003c/p\u003e\u003cp\u003eOguma is a rural agroecological zone with mixed land use that includes croplands, natural forests, extensive riparian wetlands, and scattered settlements. It is located within the floodplain corridor of the Lower Benue River Basin, adjacent to the Benue River system, and is heavily reliant on ecosystem services for agriculture and fisheries, making it vulnerable to hydrological disturbances. The landscape's low relief and proximity to meandering river channels make it susceptible to seasonal flooding.\u003c/p\u003e\u003cp\u003eAlthough the natural buffer zones of forests and wetlands may provide some resilience to flood impact, the exposure of the agricultural landscape, particularly rain-fed farms and irrigation schemes, due to floodwaters spreading into vegetative zones may result in varied sediment deposition, vegetation loss, or altered aquatic habitats and rural livelihoods because the landscape is predominantly forested and agricultural.\u003c/p\u003e\u003cp\u003eThe region has an areal extent of 198.1 km\u0026sup2; and is dominated by croplands (38.1%), forest/vegetation (26.6%), and wetlands (7.6%) while Built-up areas accounted for only 4.4%, indicating the rural nature of the landscape. Comparative pre- and during the October 2022 flood indicates that cropland areal extent of 75.4 km\u0026sup2; before the flood had 43.2 km\u0026sup2; (57.3%) submerged during the flood. Forests and vegetation covered 52.6 km\u0026sup2;, with 29.4 km\u0026sup2; (55.9%) submerged. The built-up areas were 8.7 km\u0026sup2; pre-flood and 5.4 km\u0026sup2; (62.1%) submerged. During peak floods, 15 km\u0026sup2; of wetland was completely submerged during flooding while Shrubs had 7.2 km\u0026sup2; pre-flood areal extent with 5.9 km\u0026sup2; (81.9%) submerged during flooding. The water bodies or flooded areas stood at 39.2 km\u0026sup2;pre and during the flood, flooding to another land cover. The pre-flood land area was 198.1 km\u0026sup2;, however, only 98.9 km\u0026sup2; was inundated, affecting almost 49.9% of the entire area (11).\u003c/p\u003e\u003cp\u003eThe monthly (June to October 2022) inundation (12) depicts that as the seasonal rainfall intensifies between July and October, floodwaters progressively inundate the terrain, affecting both ecological systems and human activities. Cropland shows the highest total inundated area of 43. 2 km\u0026sup2;, increasing from 2. 8 km\u0026sup2; in June to 14. 7 km\u0026sup2; in October. Forest or vegetation covers a total of 29. 4 km\u0026sup2;, also rising from 1. 9 km\u0026sup2; in June to 10. 3 km\u0026sup2; in October. Built-up areas account for 5. 4 km\u0026sup2;, while wetlands cover 15 km\u0026sup2;, with a gradual increase over the months. Shrubs have a total area of 5. 9 km\u0026sup2;. The total flooded area across the months reaches 98. 9 km\u0026sup2;, starting at 6. 3 km\u0026sup2; in June and going up to 34. 2 km\u0026sup2; in October. October flood coverage had increased substantially signal of erosion and land degradation in the affected zones with concerns about biodiversity loss, soil fertility, and the sustainability of rainfed agriculture in the area. Flooding therefore affects natural vegetation and farming zones thereby contributing to sediment or nutrient transport downstream.\u003c/p\u003e\u003cp\u003eThis transport can lead to the alteration of aquatic ecosystems, impacting fish populations and other wildlife reliant on these habitats. Moreover, the ongoing changes in land use and water management practices must be addressed to mitigate the adverse effects of flooding and promote resilience in both natural and agricultural systems.\u003c/p\u003e\u003cp\u003eFlood expansion closely followed rainfall accumulation patterns, with minimal coverage in June and July, rapid rise through August and September, and a peak in October. These dynamics reflect the cumulative saturation of the watershed and increased river discharge during the core wet season. Although the area\u0026rsquo;s built-up exposure is relatively limited, it remains critical due to the disruption of transportation, access to services, and the risk to vulnerable populations. Wetlands, while increasingly inundated, likely serve as temporary flood retention zones, buffering downstream flow and mitigating the extent of flood damage as a critical flood absorber, but gradually increasing in saturation raises concern.\u003c/p\u003e\u003cp\u003eThere is a need to prioritize the protection of wetlands and natural flood buffers, adaptive cropping systems and seasonal land use adjustments is also needful. Community-based flood early warning systems and education and technological-aided monitoring using remote sensing and field validation will strengthen the resilience network.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"5. Conclusion and Recommendation","content":"\u003cp\u003eThis study demonstrates the effectiveness of integrating Sentinel-1 imagery with LULC classification for flood monitoring in the Lower Benue Basin. October 2022 represented the flood peak, affecting over 140 km\u0026sup2; of land, predominantly grassland and croplands. The study emphasizes the link between flooding, human well-being, and sustainable development, noting frequent floods can undermine social and economic stability, deepen poverty, and hinder progress toward sustainability goals. The findings are also key tools to guide and inform decision-makers on options for flood risk mitigation, land use planning, and disaster preparedness strategies in Nigeria\u0026rsquo;s riverine regions.\u003c/p\u003e\u003cp\u003eIn addressing these issues, the report recommends an integrated flood management approach that combines technical solutions with social and environmental considerations. This includes investing in infrastructure, fostering collaboration, and empowering communities. Suggested actions include establishing advanced flood monitoring systems, community-based early warning systems, disaster management plans, resilient infrastructure development, and public awareness campaigns.\u003c/p\u003e\u003cp\u003eStrengthening cooperation with Cameroon on the Lagdo Dam's operation and adopting a holistic management approach for the Benue River Basin is also essential. Overall, effective flood management will require substantial financial investment, community engagement, and a focus on sustainable practices, with a phased plan for implementation. Continued cooperation and adequate funding will be crucial for successful outcomes.\u003c/p\u003e\u003cp\u003eThese patterns suggest a significant overlap between productive land (agriculture) and flood-prone zones, raising concerns about the vulnerability of livelihoods and food security in the basin. Furthermore, inundation of settlement areas may result in disproportionate impacts due to population concentration and infrastructure exposure.\u003c/p\u003e\u003cp\u003eThus, the implications of wetland inundation include the disruption of local ecosystems and biodiversity within the floodplain wetlands. Economic losses of the inundated extensive farmland along the floodplain and adjacent land include damage to crops, particularly rice, maize, and yam plantations, which are staple crops in the region, with an estimated financial loss of ~\u0026thinsp;USD 2.6\u0026nbsp;million based on inundated farmland of about 27.2 Km2. The consequences of vegetation inundation, the most dominant land cover of open areas with grasses and short trees, might result in erosional or deposition activities, depending on the soil and terrain.\u003c/p\u003e\u003cp\u003eHowever, the inundation of the built-up area (settlement) of the urban areas of Makurdi and environs at approximately 1.8 Km2, implies increasing urbanization in floodplains and active deforestation activities in the Upper Benue Basin thereby reducing the natural flood mitigation capacities. Poor drainage systems, siltation in river channels, and insufficient levees in vulnerable areas amplified the flood vulnerability and exposure with massive loss of lives and properties and displacement. The dozens of deaths reported due to drowning, building collapses, and other flood-related hazards underscore the need for early warning systems and sustainable floodplain management.\u003c/p\u003e\u003cp\u003eThe observed flood patterns further demonstrate the potential of Sentinel-1 imagery for reliable flood detection in data-scarce areas with the combination of SAR-based flood mapping and LULC data gives strong insight into land cover vulnerability. As a result, integrating satellite-based flood assessments with hydrological models and ground-based monitoring will improve Lower Benue's readiness and adaptive capabilities.\u003c/p\u003e\u003cp\u003eThe observed flood patterns highlight Sentinel-1 imagery's potential for rapid, reliable flood detection in data-scarce areas providing insight into land cover vulnerability. Integrating satellite-based flood assessments with hydrological models and ground-based monitoring will improve Lower Benue's readiness and adaptive capabilities.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch3\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eThe authors received no financial support for the research, authorship, or publication of this article.\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003eConflicts of Interest / Competing Interests\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eThe authors declare that there are no conflicts of interest or competing interests.\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003eEthics Approval\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003eConsent to Participate\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003eConsent for Publication\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eAll authors consent to the publication of this manuscript.\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003eAvailability of Data and Materials\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eThe datasets generated or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003eCode Availability\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eNot applicable. Data processing was conducted using standard geospatial analysis tools in Google Earth Engine and QGIS.\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003eAuthors’ Contributions\u003c/strong\u003e\u003c/h3\u003e\n\u003cul\u003e\n \u003cli\u003e\u003cstrong\u003eOlusegun Adeaga\u003c/strong\u003e: Conceptualization, supervision, manuscript review.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eTamarabrakemi Akoso\u003c/strong\u003e: Data processing, analysis, interpretation, manuscript drafting.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eTemitope Idowu\u003c/strong\u003e: Land cover classification, cartography, data interpretation.\u003cbr\u003e\u0026nbsp;All authors read and approved the final manuscript.\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAdebola AO, Ibrahim AH, Wigeti WH, Yaro NA (2018) Drainage basin morphology and terrain analysis of the lower Benue River Basin, Nigeria. Sci World J 13(1):18\u0026ndash;22\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAdelekan IO (2016) Flood risk management in the coastal city of Lagos, Nigeria. J Flood Risk Manag 9(3):255\u0026ndash;264\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAyanlade A, Aigbiremolen MI, Oladosu OR (2021) Variations in urban land surface temperature intensity over four cities in different ecological zones. Sci Rep 11(1):20537\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBalzter H, Cole B, Thiel C, Schmullius C (2015) Mapping CORINE land cover from Sentinel-1A SAR and SRTM digital elevation model data using random forests. Remote Sens 7(11):14876\u0026ndash;14898\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDesai BH (2021) 14. United Nations Environment Programme (UNEP). Yearbook Int Environ Law 32(1):293\u0026ndash;298\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDieng D, Cannon AJ, Laux P, Hald C, Adeyeri O, Rahimi J, Kunstmann H (2022) Multivariate bias-correction of high‐resolution regional climate change simulations for West Africa: performance and climate change implications. J Geophys Research: Atmos, \u003cem\u003e127\u003c/em\u003e(5), e2021JD034836.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFoody GM (2002) Status of land cover classification accuracy assessment. Remote Sens Environ 80(1):185\u0026ndash;201\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGarga E (2015) The effects of insecurity and poverty on human development at the municipal level in the Northern Nigeria. J Emerg Trends Econ Manage Sci 6(7):268\u0026ndash;276\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGiri C, Zhu Z, Reed B (2005) A comparative analysis of the Global Land Cover 2000 and MODIS land cover data sets. Remote Sens Environ 94(1):123\u0026ndash;132\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHofste RW, Kuzma S, Walker S, Sutanudjaja EH, Bierkens MF, Kuijper M, Reig P (2019) Aqueduct 3.0: Updated decision-relevant global water risk indicators. \u003cem\u003eWorld Resources Institute\u003c/em\u003e, \u003cem\u003e784\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHossain MK, Meng Q (2020) A fine-scale spatial analytics of the assessment and mapping of buildings and population at different risk levels of urban flood. Land use policy 99:104829\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eIPCC: Climate Change (2022) : Impacts, Adaptation, and Vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, edited by: P\u0026ouml;rtner, H.-O., Roberts, D. C., Tignor, M., Poloczanska, E. S., Mintenbeck, K., Alegr\u0026iacute;a, A., Craig, M., Langsdorf, S., L\u0026ouml;schke, S., M\u0026ouml;ller, V., Okem, A., and Rama, B., Cambridge University Press. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1017/9781009325844\u003c/span\u003e\u003cspan address=\"10.1017/9781009325844\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, 2022b\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLee JS, Pottier E (2017) Polarimetric radar imaging: from basics to applications. CRC\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMerz B, Kreibich H, Schwarze R, Thieken A (2010) Review article Assessment of economic flood damage. Nat Hazards Earth Syst Sci 10(8):1697\u0026ndash;1724\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNational Emergency Management Agency (2022) \u003cem\u003eAnnual report on flood disaster in Nigeria\u003c/em\u003e. Abuja, Nigeria: NEMA\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePralle S (2019) Drawing lines: FEMA and the politics of mapping flood zones. Clim Change 152(2):227\u0026ndash;237\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSchumann GJP, Moller DK (2015) Microwave remote sensing of flood inundation. \u003cem\u003ePhysics and Chemistry of the Earth, Parts a/b/c\u003c/em\u003e, \u003cem\u003e83\u003c/em\u003e, 84\u0026ndash;95\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTellman B, Sullivan JA, Kuhn C, Kettner AJ, Doyle CS, Brakenridge GR, Slayback DA (2021) Satellite imaging reveals increased proportion of population exposed to floods. Nature 596(7870):80\u0026ndash;86\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eUnited Nations Environment Programme (2020) Emissions Gap Report 2020. UNEP, Nairobi\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVan Beijma S, Comber A, Lamb A (2014) Random forest classification of salt marsh vegetation habitats using quad-polarimetric airborne SAR, elevation and optical RS data. Remote Sens Environ 149:118\u0026ndash;129\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWorld Resources Institute (WRI (2020) Country Ranking. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.wri.org/applications/aqueduct/country-rankings/\u003c/span\u003e\u003cspan address=\"https://www.wri.org/applications/aqueduct/country-rankings/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Flood hazard, Floodplain, Lower Benue River, Remote sensing, Risk management","lastPublishedDoi":"10.21203/rs.3.rs-6999964/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6999964/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eFlooding is a recurrent and intensifying hazard in West Africa, exacerbated by climate variability, land use change, and limited flood management infrastructure. This study assesses flood hazard dynamics in the Lower Benue River Basin (LBRB), Nigeria, through the integration of Sentinel-1 Synthetic Aperture Radar (SAR) imagery and land use/land cover (LULC) data between June and November 2022. Using Google Earth Engine (GEE), multi-temporal SAR data were pre-processed and classified employing Random Forest, K-Nearest Neighbours, and Maximum Likelihood algorithms to detect and map flood extents. October 2022 was identified as the peak flood month, with an inundated area of approximately 145 km\u0026sup2;. Spatial analysis revealed that croplands (75.5% inundated), wetlands (100%), and built-up areas (67.7%) were most exposed. Site-specific assessments in Makurdi, Gbajimba, Loko, and Oguma highlighted varying flood impacts driven by topography, land cover patterns, and settlement expansion. Results underscore the urgency of floodplain zoning, nature-based mitigation strategies, and early warning systems. This study demonstrates the applicability of SAR-based flood monitoring in data-scarce regions and provides a replicable framework for hazard assessment and disaster preparedness in tropical basins.\u003c/p\u003e","manuscriptTitle":"FLOOD HAZARD MAPPING OF THE LOWER BENUE RIVER BASIN","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-03 09:53:29","doi":"10.21203/rs.3.rs-6999964/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8b9516aa-15da-48bf-b48b-231f95105e58","owner":[],"postedDate":"September 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-11-25T08:58:42+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-03 09:53:29","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6999964","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6999964","identity":"rs-6999964","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}