A global open-source database of flood-protection levees on river deltas (openDELvE)

Nienhuis, Jaap H.; Cox, Jana; O'Dell, Joey; Edmonds, Douglas A.; Scussolini, Paolo

doi:10.5194/nhess-22-4087-2022

Cited by 5 publications

(4 citation statements)

References 50 publications

(53 reference statements)

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“…https://aquaknow.jrc.ec.europa.eu/en/content/ historical-irrigation-dataset-hid. • OpenDELvE (Nienhuis et al, 2022). This contains maps, locations and metadata of known levees over global river deltas.…”

Section: Other Human Interventions On Hydrologymentioning

confidence: 99%

“…This contains information on which global areas are equipped for irrigation, in time‐series covering 1900–2005, at 5′ of resolution. https://aquaknow.jrc.ec.europa.eu/en/content/historical-irrigation-dataset-hid. OpenDELvE (Nienhuis et al, 2022). This contains maps, locations and metadata of known levees over global river deltas.…”

Section: Possible Solutions For Flood Event Attributionmentioning

confidence: 99%

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Challenges in the attribution of river flood events

Scussolini,

Luu,

Philip

et al. 2023

WIREs Climate Change

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Advances in the field of extreme event attribution allow to estimate how anthropogenic global warming affects the odds of individual climate disasters, such as river floods. Extreme event attribution typically uses precipitation as proxy for flooding. However, hydrological processes and antecedent conditions make the relation between precipitation and floods highly nonlinear. In addition, hydrology acknowledges that changes in floods can be strongly driven by changes in land‐cover and by other human interventions in the hydrological system, such as irrigation and construction of dams. These drivers can either amplify, dampen or outweigh the effect of climate change on local flood occurrence. Neglecting these processes and drivers can lead to incorrect flood attribution. Including flooding explicitly, that is, using data and models of hydrology and hydrodynamics that can represent the relevant hydrological processes, will lead to more robust event attribution, and will account for the role of other drivers beyond climate change. Existing attempts are incomplete. We argue that the existing probabilistic framework for extreme event attribution can be extended to explicitly include floods for near‐natural cases, where flood occurrence was unlikely to be influenced by land‐cover change and human hydrological interventions. However, for the many cases where this assumption is not valid, a multi‐driver framework for conditional event attribution needs to be established. Explicit flood attribution will have to grapple with uncertainties from lack of observations and compounding from the many processes involved. Further, it requires collaboration between climatologists and hydrologists, and promises to better address the needs of flood risk management.This article is categorized under: Paleoclimates and Current Trends > Modern Climate Change Paleoclimates and Current Trends > Detection and Attribution Assessing Impacts of Climate Change > Observed Impacts of Climate Change

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Section: Other Human Interventions On Hydrologymentioning

confidence: 99%

Section: Possible Solutions For Flood Event Attributionmentioning

confidence: 99%

Challenges in the attribution of river flood events

Scussolini,

Luu,

Philip

et al. 2023

WIREs Climate Change

Self Cite

View full text Add to dashboard Cite

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“…These structures can alter the propagation of streamflow and possibly slow down channel flow which could impact the shape of the hydrograph (Bates, 2021). There are ongoing efforts to create databases of infrastructure (Nienhuis et al, 2022) and corrected DEMs that can be incorporate into models (Schumann & Bates, 2018;Woodrow et al, 2016). We do not account for subsurface infrastructure (e.g., sewers) but we expect their influence on the extent of pluvial flooding to be small as they were likely completely inundated during this extreme event.…”

Section: Simulating Total Water Levels Inland and At The Coastmentioning

confidence: 99%

Determining the Relative Contributions of Runoff and Coastal Processes to Flood Exposure across the Carolinas during Hurricane Florence

Grimley,

Sebastian,

leijnse

et al. 2023

Preprint

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Estimates of flood inundation from tropical cyclones (TCs) are needed to better understand how exposure varies inland and at the coast. While reduced-complexity flood inundation models have been previously shown to efficiently simulate the drivers of TC flooding across large regions, a lack of detailed validation studies of these models, which are being applied globally, has led to uncertainty about the quality of the predictions of inundation depth and extent and how this translates to exposure. In this study, we complete a comprehensive validation of a reduced-complexity hydrodynamic model (SFINCS) for simulating pluvial, fluvial, and coastal flooding. We hindcast Hurricane Florence (2018) flooding in North and South Carolina, USA using high-resolution meteorologic data and coastal water level output from an ocean recirculation model (ADCIRC). We compare modeled water levels to traditional validation datasets (e.g., water level gages, high-water marks) as well as property-level records of insured damage to draw conclusions about the model’s performance. We demonstrate that SFINCS can accurately simulate coastal and runoff drivers of TC flooding at large scales with minimal computational requirements and limited calibration. We use the validated model to attribute flood extent and building exposure to the individual and compound flood drivers during Hurricane Florence. The results highlight the critical role runoff processes have in TC flood exposure and support the need for broader implementation of models that are capable of realistically representing the compound effects resulting from coastal and runoff processes.

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“…In addition, there are many countries where such GIS data are not available. In this context, for example, methods have been proposed to locate levees from LIDAR data using semi-automatic algorithms [ 15 , 16 ], but such data are not yet fully available [ 17 ]. Therefore, improvements in the availability and quality of levee data are needed in order to improve the accuracy of inundation simulations.…”

Section: Introductionmentioning

confidence: 99%

Extraction of Coastal Levees Using U-Net Model with Visible and Topographic Images Observed by High-Resolution Satellite Sensors

Xia,

Tonooka

2024

Sensors

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Coastal levees play a role in protecting coastal areas from storm surges and high waves, and they provide important input information for inundation damage simulations. However, coastal levee data with uniformity and sufficient accuracy for inundation simulations are not always well developed. Against this background, this study proposed a method to extract coastal levees by inputting high spatial resolution optical satellite image products (RGB images, digital surface models (DSMs), and slope images that can be generated from DSM images), which have high data availability at the locations and times required for simulation, into a deep learning model. The model is based on U-Net, and post-processing for noise removal was introduced to further improve its accuracy. We also proposed a method to calculate levee height using a local maximum filter by giving DSM values to the extracted levee pixels. The validation was conducted in the coastal area of Ibaraki Prefecture in Japan as a test area. The levee mask images for training were manually created by combining these data with satellite images and Google Street View, because the levee GIS data created by the Ibaraki Prefectural Government were incomplete in some parts. First, the deep learning models were compared and evaluated, and it was shown that U-Net was more accurate than Pix2Pix and BBS-Net in identifying levees. Next, three cases of input images were evaluated: (Case 1) RGB image only, (Case 2) RGB and DSM images, and (Case 3) RGB, DSM, and slope images. Case 3 was found to be the most accurate, with an average Matthews correlation coefficient of 0.674. The effectiveness of noise removal post-processing was also demonstrated. In addition, an example of the calculation of levee heights was presented and evaluated for validity. In conclusion, this method was shown to be effective in extracting coastal levees. The evaluation of generalizability and use in actual inundation simulations are future tasks.

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A global open-source database of flood-protection levees on river deltas (openDELvE)

Cited by 5 publications

References 50 publications

Challenges in the attribution of river flood events

Challenges in the attribution of river flood events

Determining the Relative Contributions of Runoff and Coastal Processes to Flood Exposure across the Carolinas during Hurricane Florence

Extraction of Coastal Levees Using U-Net Model with Visible and Topographic Images Observed by High-Resolution Satellite Sensors

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