Best Practices for Elevation-Based Assessments of Sea-Level Rise and Coastal Flooding Exposure

Gesch, Dean B.

doi:10.3389/feart.2018.00230

Cited by 104 publications

(128 citation statements)

References 162 publications

(226 reference statements)

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“…Figure 2 shows an example for New York City, NY, and Newark, NJ. Comprehensive, distributed data on elevations are crucial to accurate assessments of inundation probabilities associated with rising sea levels as well as event-scale storm surges and compound flooding (Gesch, 2018). It must be pointed out that the most vulnerable communities are not in affluent Western nations but in developing countries that have limited resources to protect or recover from disasters.…”

Section: Predicting Socio-economic Vulnerabilities and Inundation Thrmentioning

confidence: 99%

Collaborative Science to Enhance Coastal Resilience and Adaptation

Nichols¹,

Wright

Bainbridge

et al. 2019

Front. Mar. Sci.

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Impacts from natural and anthropogenic coastal hazards are substantial and increasing significantly with climate change. Coasts and coastal communities are increasingly at risk. In addition to short-term events, long-term changes, including rising sea levels, increasing storm intensity, and consequent severe compound flooding events are degrading coastal ecosystems and threatening coastal dwellers. Consequently, people living near the coast require environmental intelligence in the form of reliable shortterm and long-term predictions in order to anticipate, prepare for, adapt to, resist, and recover from hazards. Risk-informed decision making is crucial, but for the resulting information to be actionable, it must be effectively and promptly communicated to planners, decision makers and emergency managers in readily understood terms and formats. The information, critical to forecasts of extreme weather and flooding, as well as long-term projections of future risks, must involve synergistic interplay between observations and models. In addition to serving data for assimilation into models, the observations are also essential for objective validation of models via hind casts. Linked observing and modeling programs that involve stakeholder input and integrate engineering, environmental, and community vulnerability are needed to evaluate conditions prior to and following severe storm events, to update baselines, and to plan for future changes over the long term. In contrast to most deep-sea phenomena, coastal vulnerabilities are locally and regionally specific and prioritization of the most important observational data and model predictions must rely heavily on input from local and regional communities and decision makers. Innovative technologies and nature-based solutions are already helping to reduce vulnerability from coastal hazards in some localities but more focus on local circumstances, as opposed to global solutions, is needed. Agile and spatially distributed response capabilities will assist operational organizations in predicting, preparing for and mitigating potential community-wide disasters. This white paper outlines the rationale, synthesizes recent literature and summarizes some data-driven approaches to coastal resilience.

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Section: Predicting Socio-economic Vulnerabilities and Inundation Thrmentioning

confidence: 99%

Collaborative Science to Enhance Coastal Resilience and Adaptation

Nichols¹,

Wright

Bainbridge

et al. 2019

Front. Mar. Sci.

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“…Line 334: But the uncertainties could be accounted for. See: Gesch, 2013;Gesch 2018; Author comment: This is a preliminary assessment of the uncertainties -the purpose of which is to indicate that the uncertainties are generally smaller than the projected sea-level rise. Also, to give the reader an idea of the different uncertainties involved in the mapping method.…”

Section: Nhessdmentioning

confidence: 99%

“…However, we believe that the sum of these mapping errors are generally smaller than the projected sealevel rise, which gives us confidence in our results. Future work should look at how these uncertainties can be incorporated into our mapping and web tool (Gesch, 2013(Gesch, , 2018. "…”

Section: Nhessdmentioning

confidence: 99%

“…The results could be improved by attaching confidence levels to the estimates of impacted area and objects. This would entail not just describing the accuracy of the DEM (and the associated datum conversions), but applying that cumulative vertical uncertainty to characterize the confidence of the results (see Gesch, 2013 andGesch, 2018 for details on how this is done). All the needed information is already available with the comprehensive DEM accuracy assessment that has been done and all the other uncertainty information that is listed in Table 7.…”

mentioning

confidence: 99%

“…Added to discussion (Section 4.1): "Future work should look at how these uncertainties can be incorporated into our mapping and web tool (Gesch, 2013(Gesch, , 2018." (See also reply to comment to line 334) Comment on lines 207-216 (discussion of Smola) in section 3.1, and Figures 13 and 14 that it refers to: The area affected is important, but without knowing the total area of each of the ten municipalities (assuming there's variability in the areas) it's hard to see which ones are affected the most.…”

mentioning

confidence: 99%

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Reply to reviewer 2

Breili¹

2019

Preprint

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This paper is an effective study of sea-level rise and coastal flooding exposure in Norway based on a high-quality DEM. It exhibits a sound, straightforward approach that uses many of the best practices that have been established for these types of coastal assessments. The paper documents well the data, methods used, and results, and the tables and figures effectively support the material in the text. Overall, the Discussion section is very good, especially the factors affecting uncertainty and the accuracy of the DEM.

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Performance assessment of global open‐access digital elevation models in China mainland coastal region

Ding

Liu

et al. 2023

Earth Surf Processes Landf

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Digital elevation models (DEMs) are the fundamental datasets for coastal ecosystem monitoring, and several global open‐accessed DEMs have recently been reported. In coastal regions, a comprehensive vertical accuracy assessment of these DEMs has not yet been carried out. In this study, eight open‐access DEM datasets, including SRTM‐3, SRTM‐1, TanDEM‐X, ASTER GDEM v3, MERIT DEM, AW3D30, NASADEM, and CoastalDEM, were investigated across the coastal region of the Chinese mainland using high accuracy ICESat‐2 data as a reference. Statistical tools including mean absolute error (MAE) and root mean square error (RMSE) were selected to describe the data error/uncertainty and spatial distribution. Moreover, the effects of elevation ranging, slope degree, geomorphogenesis and landuse on vertical accuracy were further analyzed to assess their applicability. The assessment results revealed that the CoastalDEM and NASADEM datasets had the highest accuracy, with MAE values of 1.68 and 1.88 m and RMSE values of 2.55 and 2.61 m, for 3‐arc second and 1‐arc second resolution DEMs, respectively. Other DEMs with close accuracies include AW3D30, SRTM‐1, MERIT, and SRTM‐3 DEM. The results proved that the CoastalDEM outperformed other datasets, indicating its applicability in coastal regions.

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Best Practices for Elevation-Based Assessments of Sea-Level Rise and Coastal Flooding Exposure

Cited by 104 publications

References 162 publications

Collaborative Science to Enhance Coastal Resilience and Adaptation

Collaborative Science to Enhance Coastal Resilience and Adaptation

Reply to reviewer 2

Performance assessment of global open‐access digital elevation models in China mainland coastal region

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