scholarly journals A Geospatial Expose of Flood-Risk and Vulnerable Areas in Nigeria

2020 ◽  
Vol 11 (3) ◽  
pp. 87-110
Author(s):  
Chukwudi Gbadebo Njoku ◽  
Joel Efiong ◽  
Nse-Abasi Ndiyo Ayara
Keyword(s):  
Flood Risk ◽  
Niger Delta ◽  
Policy Formulation ◽  
Geospatial Techniques ◽  
Cross River ◽  
Mitigation And Adaptation ◽  
Risk Zones ◽  
Risk Vulnerability ◽  
High Flood ◽  
River Niger

Flooding is recurrent in Nigeria, occurring yearly at different scales. This geared the need for a study to reveal local government areas (LGAs) that are at risk and vulnerable to flooding. The multi-criteria approach was adopted, using geospatial techniques and data. Factors considered were elevation, slope, rainfall intensity, and distance to river. The factors were classified, reclassified, rated, and weighed in a systematic process. Nineteen states and 114 LGAs face high risks, especially communities in the Niger Delta, around the lagoons of Lagos, along River Niger, Benue, and the Cross-River. Also, 125 LGAs in 18 states face medium flood-risk vulnerability. Consideration the population density of communities, Lagos State is the most vulnerable because of LGAs with high population densities within high flood-risk zones. Other states with communities exposed to high flood-risk vulnerability include Rivers, Kogi, Cross River, Akwa Ibom, Anambra, and Delta. The study provides information key to proactive policy formulation, mitigation, and adaptation to flood risk in Nigeria.

scholarly journals Application of Geographic Information Systems for Flood Risk Analysis: A Case Study from Accra Metropolitan Area

2019 ◽  
Vol 13 (1) ◽  
pp. 81-97
Author(s):  
Alex Barimah Owusu ◽  
Mathias Agbozo
Keyword(s):  
Flood Risk ◽  
Drainage Density ◽  
Low Risk ◽  
Soil Drainage ◽  
Risk Area ◽  
The Public ◽  
Risk Areas ◽  
Risk Zones ◽  
High Flood

Abstract The main objective of the study was to identify high flood risk zones in AMA. The study also used questionnaires to assess local knowledge on what accounts for the high flood risk in their community. Spatial analysis techniques were used to model flood risk based on the following contributory factors; land cover, soil, drainage density, topography and proximity to rivers. The results show that high flood risk areas covered 46.3km2(20%), moderate risk area, 72.9km2(31.6%), low risk area 41.5km2(18%) and very low risk areas, about 6.7km2(2.9%). The high flood risk zones were low-lying areas below 50 meters above sea level and closely associated with poor drainage systems. People perceived not just low-lying areas as a paramount reason accounting for flooding but also very bad waste disposal habit of the public. These offsets the efforts of waste management companies to keep drains free of refuse.

scholarly journals A reliable rainfall–runoff model for flood forecasting: review and application to a semi-urbanized watershed at high flood risk in Italy

2016 ◽  
Vol 48 (3) ◽  
pp. 726-740 ◽  
Author(s):  
Daniele Masseroni ◽  
Alessio Cislaghi ◽  
Stefania Camici ◽  
Christian Massari ◽  
Luca Brocca
Keyword(s):  
Flood Risk ◽  
Climatic Conditions ◽  
Rainfall Runoff ◽  
Number Of Factors ◽  
Wide Range ◽  
Median Volume ◽  
Flood Estimation ◽  
High Flood ◽  
Rainfall Runoff Model ◽  
Flooding Events

Many rainfall–runoff (RR) models are available in the scientific literature. Selecting the best structure and parameterization for a model is not straightforward and depends on a broad number of factors, including climatic conditions, catchment characteristics, temporal/spatial resolution and model objectives. In this study, the RR model ‘Modello Idrologico Semi-Distribuito in continuo’ (MISDc), mainly developed for flood simulation in Mediterranean basins, was tested on the Seveso basin, which is stressed several times a year by flooding events mainly caused by excessive urbanization. The work summarizes a compendium of the MISDc applications over a wide range of catchments in European countries and then it analyses the performances over the Seveso basin. The results show a good fit behaviour during both the calibration and the validation periods with a Nash–Sutcliffe coefficient index larger than 0.9. Moreover, the median volume and peak discharge errors calculated on several flood events were less than 25%. In conclusion, we can be assured that the reliability and computational speed could make the MISDc model suitable for flood estimation in many catchments of different geographical contexts and land use characteristics. Moreover, MISDc will also be useful for future support of real-time decision-making for flood risk management in the Seveso basin.

scholarly journals GIS-Based Climate Change Induced Flood Risk Mapping in Uhunmwonde Local Government Area, Edo State, Nigeria

2020 ◽  
pp. 8-23
Author(s):  
Obot Akpan Ibanga ◽  
Osaretin Friday Idehen
Keyword(s):  
Climate Change ◽  
Local Government ◽  
Flood Risk ◽  
Vegetation Index ◽  
Agricultural Land ◽  
Local Government Area ◽  
Risk Mapping ◽  
Risk Zone ◽  
Edo State ◽  
Risk Zones

Introduction: Flood is one of the climate change induced hazards occurring in most parts of the world. It exposes humanity and many socio-ecological systems to various levels of risks. In Nigeria, extreme rainfall events and poor drainage system have caused inundation of several settlements to flooding. To contain the disaster, risk mapping were among the measures recommended. Aims: The aim of this paper is to highlight flood risk zones (FRZ) in Uhunmwonde Local Government Area (LGA), Edo State, Nigeria. Methodology: Flood risk (FR) was mapped using hazards and vulnerability and implemented using geographic information system (GIS)-based multi-criteria analysis analytic hierarchy process (MCA-AHP) framework by incorporating seven environmental and two socio-economic factors. Elevation, flow accumulation, soil water index of wettest quarter, normalized difference vegetation index, rainfall of wettest quarter, runoff of wettest quarter and distance from rivers constituted the hazard component while population density and area of agricultural land use was the vulnerability layer. The climate change induced flood risk was validated using the responses of 150 residents in high, moderate and low flood risk zones. Results: The resulting flood risk map indicated that about 40.4% of Uhunmwonde LGA fell within high flood risk zone, 35.3% was categorized under moderate flood risk zone whereas low flood risk zone extended up to about 24.3% of the LGA. The high number of respondents who reported occurrence of flooding with frequency being very often and the fact that flooding was a very serious environmental threat during on-the-spot field assessment validated the generated climate change induced flood risk. Conclusion: The utilitarian capabilities of GIS-based MCA-AHP framework in integrating remotely-sensed biophysical and climate change related flood inducing indicators with socio-economic vulnerabilities to arrive at composite flood risk was demonstrated.

Sign in / Sign up

Export Citation Format

Share Document