Articles | Volume 21, issue 9
https://doi.org/10.5194/nhess-21-2829-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-21-2829-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
16 Sep 2021
Research article |
| 16 Sep 2021
| 16 Sep 2021
Global flood exposure from different sized rivers
School of Civil Engineering, University of Leeds, LS2 9JT, Leeds, UK
Mark A. Trigg
School of Civil Engineering, University of Leeds, LS2 9JT, Leeds, UK
P. Andrew Sleigh
School of Civil Engineering, University of Leeds, LS2 9JT, Leeds, UK
Christopher C. Sampson
Fathom, Square Works, 17–18 Berkeley Square, BS8 1HB, Bristol, UK
Andrew M. Smith
Fathom, Square Works, 17–18 Berkeley Square, BS8 1HB, Bristol, UK
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Total article views: 2,351 (including HTML, PDF, and XML)
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Cited
15 citations as recorded by crossref.
- The Role of Global Data Sets for Riverine Flood Risk Management at National Scales M. Bernhofen et al. 10.1029/2021WR031555
- Water Resources in Africa under Global Change: Monitoring Surface Waters from Space F. Papa et al. 10.1007/s10712-022-09700-9
- Unraveling Long-Term Flood Risk Dynamics Across the Murray-Darling Basin Using a Large-Scale Hydraulic Model and Satellite Data S. Ceola et al. 10.3389/frwa.2021.797259
- Integrating social vulnerability into high-resolution global flood risk mapping S. Fox et al. 10.1038/s41467-024-47394-2
- Flood hazard potential reveals global floodplain settlement patterns L. Devitt et al. 10.1038/s41467-023-38297-9
- Comparing the suitability of global gridded population datasets for local landslide risk assessments A. Opdyke & K. Fatima 10.1007/s11069-023-06283-5
- Using global datasets to estimate flood exposure at the city scale: an evaluation in Addis Ababa A. Carr et al. 10.3389/fenvs.2024.1330295
- Climate threats to coastal infrastructure and sustainable development outcomes D. Adshead et al. 10.1038/s41558-024-01950-2
- Connecting hydrological modelling and forecasting from global to local scales: Perspectives from an international joint virtual workshop A. Dasgupta et al. 10.1111/jfr3.12880
- Can geomorphic flood descriptors coupled with machine learning models enhance in quantifying flood risks over data-scarce catchments? Development of a hybrid framework for Ganga basin (India) V. Tripathi & M. Mohanty 10.1007/s11356-024-33507-3
- Effects of cryospheric hydrological processes on future flood inundation and the subsequent socioeconomic exposures in Central Asia N. Wang et al. 10.1088/1748-9326/aca491
- Unknown risk: assessing refugee camp flood risk in Ethiopia M. Bernhofen et al. 10.1088/1748-9326/acd8d0
- On the right track of flood planning policy? Land uptake in Central-European floodplains (1990–2018) M. Dolejš et al. 10.1016/j.landurbplan.2022.104560
- Understanding flood seasonality and flood regime shift in the Congo River Basin G. Bola et al. 10.1080/02626667.2022.2083966
- Stepwise integration of analytical hierarchy process with machine learning algorithms for landslide, gully erosion and flash flood susceptibility mapping over the North-Moungo perimeter, Cameroon A. Mfondoum et al. 10.1186/s40677-023-00254-5
13 citations as recorded by crossref.
- The Role of Global Data Sets for Riverine Flood Risk Management at National Scales M. Bernhofen et al. 10.1029/2021WR031555
- Water Resources in Africa under Global Change: Monitoring Surface Waters from Space F. Papa et al. 10.1007/s10712-022-09700-9
- Unraveling Long-Term Flood Risk Dynamics Across the Murray-Darling Basin Using a Large-Scale Hydraulic Model and Satellite Data S. Ceola et al. 10.3389/frwa.2021.797259
- Integrating social vulnerability into high-resolution global flood risk mapping S. Fox et al. 10.1038/s41467-024-47394-2
- Flood hazard potential reveals global floodplain settlement patterns L. Devitt et al. 10.1038/s41467-023-38297-9
- Comparing the suitability of global gridded population datasets for local landslide risk assessments A. Opdyke & K. Fatima 10.1007/s11069-023-06283-5
- Using global datasets to estimate flood exposure at the city scale: an evaluation in Addis Ababa A. Carr et al. 10.3389/fenvs.2024.1330295
- Climate threats to coastal infrastructure and sustainable development outcomes D. Adshead et al. 10.1038/s41558-024-01950-2
- Connecting hydrological modelling and forecasting from global to local scales: Perspectives from an international joint virtual workshop A. Dasgupta et al. 10.1111/jfr3.12880
- Can geomorphic flood descriptors coupled with machine learning models enhance in quantifying flood risks over data-scarce catchments? Development of a hybrid framework for Ganga basin (India) V. Tripathi & M. Mohanty 10.1007/s11356-024-33507-3
- Effects of cryospheric hydrological processes on future flood inundation and the subsequent socioeconomic exposures in Central Asia N. Wang et al. 10.1088/1748-9326/aca491
- Unknown risk: assessing refugee camp flood risk in Ethiopia M. Bernhofen et al. 10.1088/1748-9326/acd8d0
- On the right track of flood planning policy? Land uptake in Central-European floodplains (1990–2018) M. Dolejš et al. 10.1016/j.landurbplan.2022.104560
2 citations as recorded by crossref.
- Understanding flood seasonality and flood regime shift in the Congo River Basin G. Bola et al. 10.1080/02626667.2022.2083966
- Stepwise integration of analytical hierarchy process with machine learning algorithms for landslide, gully erosion and flash flood susceptibility mapping over the North-Moungo perimeter, Cameroon A. Mfondoum et al. 10.1186/s40677-023-00254-5
Latest update: 29 Jun 2024
Short summary
The use of different global datasets to calculate flood exposure can lead to differences in global flood exposure estimates. In this study, we use three global population datasets and a simple measure of a river’s flood susceptibility (based on the terrain alone) to explore how the choice of population data and the size of river represented in global flood models affect global and national flood exposure estimates.
The use of different global datasets to calculate flood exposure can lead to differences in...
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