Exposure of developing countries to sea-level rise and storm surges

Dasgupta, Susmita; Laplante, Benoı̂t; Murray, Siobhan; Wheeler, David

doi:10.1007/s10584-010-9959-6

Cited by 127 publications

(95 citation statements)

References 17 publications

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“…Note that we did explore the use of the Dynamic Interactive Vulnerability Assessment (DIVA) model, but were unable to obtain the data. We examined data from Dasgupta et al [34], which incorporate the DIVA results, but because they rely heavily on SRTM data we found that they under estimate the areas at low elevations.…”

Section: Coastal West Africa Data Integrationmentioning

confidence: 99%

Data Integration for Climate Vulnerability Mapping in West Africa

Sherbinin

Chai-Onn

Jaiteh

et al. 2015

IJGI

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Vulnerability mapping reveals areas that are likely to be at greater risk of climate-related disasters in the future. Through integration of climate, biophysical, and socioeconomic data in an overall vulnerability framework, so-called "hotspots" of vulnerability can be identified. These maps can be used as an aid to targeting adaptation and disaster risk management interventions. This paper reviews vulnerability mapping efforts in West Africa conducted under the USAID-funded African and Latin American Resilience to Climate Change (ARCC) project. The focus is on the integration of remotely sensed and socioeconomic data. Data inputs included a range of sensor data (e.g., MODIS NDVI, Landsat, SRTM elevation, DMSP-OLS night-time lights) as well as high-resolution poverty, conflict, and infrastructure data. Two basic methods were used, one in which each layer was transformed into standardized indicators in an additive approach, and another in which remote sensing data were used to contextualize the results of composite indicators. We assess the benefits and challenges of data integration, and the lessons learned from these mapping exercises.

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Section: Coastal West Africa Data Integrationmentioning

confidence: 99%

Data Integration for Climate Vulnerability Mapping in West Africa

Sherbinin

Chai-Onn

Jaiteh

et al. 2015

IJGI

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“…Some studies combine the effects of climate change, through increased eustatic sea level, and storm surge to model potential future inundation scenarios (Dasgupta et al, 2011;Kleinosky et al, 2007). The concept of minimum inundation increment is applicable in both cases, to the sea-level rise increments and to the storm surge heights used in the study.…”

Section: Minimum Increment Of Inundationmentioning

confidence: 99%

“…affected by a given water level increase, oftentimes due to sea-level rise and/or storm surges (Dasgupta et al, 2009;Dasgupta et al, 2011;Demirkesen et al, 2007;Ericson et al, 2006;Kleinosky et al, 2007;Li et al, 2009;McGranahan et al, 2007;Najjar et al, 2000;Neumann et al, 2010;Rowley et al, 2007;Schneider and Chen, 1980;Small and Nicholls, 2003;Titus and Richman, 2001;Titus et al, 1991;Weiss et al, 2011;Wu et al, 2002;Wu et al, 2009). …”

mentioning

confidence: 99%

Elevation Uncertainty in Coastal Inundation Hazard Assessments

Gesch¹

2012

Natural Disasters

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“…For economic exposure, the input is often expressed in terms of gross domestic product (GDP), interpreted as a proxy of wealth for risk assessment. (2,(16)(17)(18)(19)(20)(21)(22)(23)(24) However, census data are based on administrative units; this prevents a direct integration of wealth maps with other physical and environmental data (e.g., flood depth and inundation area, land cover/land use, elevation, climate, soil, vegetation data), which are usually only available at a spatially explicit grid-cell level, and with spatial distribution patterns that do not conform to administrative boundaries. (25) There is, however, a strong demand for economic data that are independent from enumeration and administrative areas.…”

Section: Introductionmentioning

confidence: 99%

Development of an Asset Value Map for Disaster Risk Assessment in China by Spatial Disaggregation Using Ancillary Remote Sensing Data

Liu

et al. 2017

Risk Analysis

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The extent of economic losses due to a natural hazard and disaster depends largely on the spatial distribution of asset values in relation to the hazard intensity distribution within the affected area. Given that statistical data on asset value are collected by administrative units in China, generating spatially explicit asset exposure maps remains a key challenge for rapid postdisaster economic loss assessment. The goal of this study is to introduce a top-down (or downscaling) approach to disaggregate administrative-unit level asset value to grid-cell level. To do so, finding the highly correlated "surrogate" indicators is the key. A combination of three data sets-nighttime light grid, LandScan population grid, and road density grid, is used as ancillary asset density distribution information for spatializing the asset value. As a result, a high spatial resolution asset value map of China for 2015 is generated. The spatial data set contains aggregated economic value at risk at 30 arc-second spatial resolution. Accuracy of the spatial disaggregation reflects redistribution errors introduced by the disaggregation process as well as errors from the original ancillary data sets. The overall accuracy of the results proves to be promising. The example of using the developed disaggregated asset value map in exposure assessment of watersheds demonstrates that the data set offers immense analytical flexibility for overlay analysis according to the hazard extent. This product will help current efforts to analyze spatial characteristics of exposure and to uncover the contributions of both physical and social drivers of natural hazard and disaster across space and time.

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Exposure of developing countries to sea-level rise and storm surges

Cited by 127 publications

References 17 publications

Data Integration for Climate Vulnerability Mapping in West Africa

Data Integration for Climate Vulnerability Mapping in West Africa

Elevation Uncertainty in Coastal Inundation Hazard Assessments

Development of an Asset Value Map for Disaster Risk Assessment in China by Spatial Disaggregation Using Ancillary Remote Sensing Data

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