- Time: 13:00
- Location: MIchael Sadler SR LG.15
In a follow-up seminar to the Global Flood Partnership 2022 meeting held recently at the University of Leeds, we have three talks by regional experts exploring the realities of flood risk for: Nigeria, the Congo Basin and Bangladesh. All three speakers have travelled to Leeds to share their experience and knowledge in this in-person seminar.
After the presentations (1 – 2pm) there will be a time for discussion and networking as well as a free lunch!
All are welcome, please register here for catering purposes.
The event is supported by the GCRF Water Security and Sustainable Development Hub, School of Civil Engineering, water@leeds, University of Leeds Global Research Development and the Global Flood Partnership.
- Taiwo Ogunwumi, Geohazard Risk Mapping Initiative, Nigeria: Flood susceptibility mapping of internally displaced persons camps in Maiduguri, Borno state Nigeria
- Gode Bola Bosongo, CRREBaC, University of Kinshasa, DRC: Applications of Open-Access Data for Flood risks assessment in the Congo River Basin
- Kabir Uddin, International Centre for Integrated Mountain Development, Nepal: Potential flood hazard zonation and flood shelter suitability mapping for disaster risk mitigation in Bangladesh using geospatial technology
Geohazard Risk Mapping Initiative, Nigeria: Flood susceptibility mapping of internally displaced persons camps in Maiduguri, Borno state Nigeria
Increasing climate variability is causing an increase in flood occurrence, affecting vulnerable individuals who have been displaced by the insurgency and conflicts that occurred in the past years. The decade-long insurgency in northeast Nigeria is one of the most pressing humanitarian crises on the continent, with over 2 million people displaced from their homes by conflict and political instability. These yearly floods destroy campsites occupied by displaced families who fled from conflict zones.
This study applied the multi-criteria approach, Geographical Information System, and Remote Sensing. Analytical processes such as buffering, slope generation, interpolation, reclassification, and the weighted overlay were performed through the GIS environment to generate a flood susceptibility map indicating four zones (very high, high, low and very low-risk). A total number of 25 of 35 Internal displaced campsites are within the high susceptibility while 4 camps are prone to a very high flood risk level (Sulumburi Dogon Iche, Gra, Sulumburi Camp, and Fulatari Farin Ruwa). Based on our results, camps located close to water bodies will be submerged by flood during the coming raining seasons if actions are not taken in time.
This paper is a vital for appropriate government agencies such as town planners, emergency management agencies, international NGOs, the United Nations Office for the Coordination of Humanitarian Affairs, and other important stakeholders who can implement preparedness, evacuation planning, and early warning to the populace and internally displaced individual at various campsite and settlement within the inferred flood risk zones.
Gode Bola Bosongo
CRREBaC, University of Kinshasa, DRC: Applications of Open-Access Data for Flood risks assessment in the Congo River Basin
In developing countries, where the availability of in-situ data is a challenge, new approaches are needed to investigate flood risks. This study explores how open-access remotely sensed and other geospatial datasets can supplement ground-based data in data-sparse regions of developing countries. The Congo Basin is a data-sparse region, and it is used as a case study to evaluate the significance of open-access datasets for flood risk analysis. Multiple flood data including high-resolution NASA and DFO flood images, global flood hazard maps at a grid cell resolution of 3 arc-seconds were used through GIS. Flood exposure incorporated infrastructure and population and revealed that the most exposed territories represent 1% of total exposure which is estimated at 2.65% of the basin’s population and 3.67 % of infrastructures are located in flood prone areas. This study has shown that global data can make up for the absence of ground data for the purpose of flood risk assessment.
This study demonstrates the first and potentially most important stage in developing flood responses by determining the flood hazards areas and the population/infrastructures that would be exposed. The flood risk maps produced in this study provide information necessary to support policy decision of flood disasters prevention, including prioritisation of interventions to reduce flood risk in the CRB.
International Centre for Integrated Mountain Development, Nepal: Potential flood hazard zonation and flood shelter suitability mapping for disaster risk mitigation in Bangladesh using geospatial technology
Bangladesh is one of the most flood-affected countries in the world. In the last few decades, flood frequency, intensity, duration, and devastation have increased in Bangladesh. Identifying flood-damaged areas is highly essential for an effective flood response. This study aimed at developing an operational methodology for rapid flood inundation and potential flood damaged area mapping to support a quick and effective event response. Sentinel-1 images from March, April, June, and August 2017 were used to generate inundation extents of the corresponding months. The 2017 pre-flood land cover maps were prepared using Landsat-8 images to identify major land cover on the ground before flooding. The overall accuracy of flood inundation mapping was 96.44% and the accuracy of the land cover map was 87.51%. The total flood inundated area corresponded to 2.01%, 4.53%, and 7.01% for the months April, June, and August 2017, respectively. Based on the Landsat-8 derived land cover information, the study determined that cropland damaged by floods was 1.51% in April, 3.46% in June, 5.30% in August, located mostly in the Sylhet and Rangpur divisions. Finally, flood inundation maps were distributed to the broader user community to aid in hazard response. The data and methodology of the study can be replicated for every year to map flooding in Bangladesh.