Assessing the effects of using high-quality data and high-resolution models in valuing flood protection services of mangroves

Numerical modeling of wave attenuation: implications of representing vegetation found in coastal saltmarshes in the Chesapeake Bay

Coastal communities are vulnerable to wave and storm surges during extreme events, highlighting the need to increase community resilience. The effectiveness of natural wetlands in attenuating waves is vital to designing strategies for protecting public safety. This study aimed to understand how vegetation attenuates waves and determine the best method for modeling vegetation's impact on wave dynamics. The researchers compared two different vegetation representations in numerical models, implicit and explicit, using SWAN and XBeach at varying spatial resolutions. The study focused on two marshes in the Chesapeake Bay, using field measurements to investigate the accuracy of each method in representing wave attenuation by vegetation and the implications of explicitly representing average characteristics of one vegetation species on a regional level. Results showed that explicit modeling using average vegetation characteristics provided more accurate results than the implicit model, which only showed wave attenuation due to topography. The finer scale resolution and site-specific vegetation characteristics further improved the accuracy of wave attenuation observed. Understanding the trade-offs between different vegetation representations in numerical models is essential to accurately represent wave attenuation and design effective protection strategies for coastal communities.

Characterizing global satellite-based indicators for coastal vulnerability to erosion management as exemplified by a regional level analysis from Northeast Brazil

The detection of coastal vulnerability to erosion is crucial for decision-making regarding the economy, ecology, health, security, among other issues. Most of the studies gather a large data set about physical and anthropogenic interference's on the vulnerability of coastal erosion regions around the world. However, for developing nations like Brazil, with extensive shoreline, it is challenging to develop and maintain an in situ infrastructure to offer a systematical scientific data set. In this context, several methods like Coastal Vulnerability Index (CVI) for monitoring the dynamic behavior of coastal systems require in situ collected data. Therefore, this contribution explores the use of global open source satellite-based indicators to assess coastal vulnerability to erosion at a regional level adopting an uncorrelated orthogonal basis set of Principal Component Analysis (PCA). For this, the data set covers many spheres of the environment like biophysical and social factors, adopting the Pernambuco State's coast, Brazil, as a case study. The results showed the direct relationship between a high level of urbanization and low vegetation with the high coastal vulnerability to erosion. PC1 revealed built-up and surface temperature vary inversely to the soil organic carbon and vegetation cover along about 20 km (≈10% of the shoreline extension). The hotspots were in the urban cluster (Paulista, Olinda, Recife, and Jaboatao dos Guararapes), combined with high shoreline change around -2 m/yr. PC2 showed the natural action of wind on wave heights combined with sediment removal and the backshore settlement along 10 km of extension (≈5.5% of the shoreline), with the highly vulnerable sites concentrated in Itamaraca Island and C. S. Agostinho. This approach benefits from the multi-satellite and multi-resolution data sets integration to unravel the statistical influence of each variable able to guide stakeholders.

How far spatial resolution affects the ensemble machine learning based flood susceptibility prediction in data sparse region

Although the effect of digital elevation model (DEM) and its spatial resolution on flood simulation modeling has been well studied, the effect of coarse and finer resolution image and DEM data on machine learning ensemble flood susceptibility prediction has not been investigated, particularly in data sparse conditions. The present work was, therefore, to investigate the performance of the resolution effects, such as coarse (Landsat and SRTM) and high (Sentinel-2 and ALOS PALSAR) resolution data on the flood susceptible models. Another motive of this study was to construct very high precision and robust flood susceptible models using standalone and ensemble machine learning algorithms. In the present study, fifteen flood conditioning parameters were generated from both coarse and high resolution datasets. Then, the ANN-multilayer perceptron (MLP), random forest (RF), bagging (B)-MLP, B-gaussian processes (B-GP) and B-SMOreg algorithms were used to integrate the flood conditioning parameters for generating the flood susceptible models. Furthermore, the influence of flood conditioning parameters on the modelling of flood susceptibility was investigated by proposing an ROC based sensitivity analysis. The validation of flood susceptibility models is also another challenge. In the present study, we proposed an index of flood vulnerability model to validate flood susceptibility models along with conventional statistical techniques, such as the ROC curve. Results showed that the coarse resolution based flood susceptibility MLP model has appeared as the best model (area under curve: 0.94) and it has predicted 11.65 % of the area as very high flood susceptible zones (FSz), followed by RF, B-MLP, B-GP, and B-SMOreg. Similarly, the high resolution based flood susceptibility model using MLP has predicted 19.34 % of areas as very high flood susceptible zones, followed by RF (14.32 %),B-MLP (14.88 %), B-GP, and B-SMOreg. On the other hand, ROC based sensitivity analysis showed that elevation influences flood susceptibility largely for coarse and high resolution based models, followed by drainage densityand flow accumulation. In addition, the accuracy assessment using the IFV model revealed that the MLP model outperformed all other models in the case of a high resolution imageThe coarser resolution image's performance level is acceptable but quite low. So, the study recommended the use of high resolution images for developing a machine learning algorithm based flood susceptibility model. As the study has clearly identified the areas of higher flood susceptibility and the dominant influencing factors for flooding, this could be used as a good database for flood management.

Shoreline Solutions: Guiding Efficient Data Selection for Coastal Risk Modeling and the Design of Adaptation Interventions

The Caribbean is affected by climate change due to an increase in the variability, frequency, and intensity of extreme weather events. When coupled with sea level rise (SLR), poor urban development design, and loss of habitats, severe flooding often impacts the coastal zone. In order to protect citizens and adapt to a changing climate, national and local governments need to investigate their coastal vulnerability and climate change risks. To assess flood and inundation risk, some of the critical data are topography, bathymetry, and socio-economic. We review the datasets available for these parameters in Jamaica (and specifically Old Harbour Bay) and assess their pros and cons in terms of resolution and costs. We then examine how their use can affect the evaluation of the number of people and the value of infrastructure flooded in a typical sea level rise/flooding assessment. We find that there can be more than a three-fold difference in the estimate of people and property flooded under 3m SLR. We present an inventory of available environmental and economic datasets for modeling storm surge/SLR impacts and ecosystem-based coastal protection benefits at varying scales. We emphasize the importance of the careful selection of the appropriately scaled data for use in models that will inform climate adaptation planning, especially when considering sea level rise, in the coastal zone. Without a proper understanding of data needs and limitations, project developers and decision-makers overvalue investments in adaptation science which do not necessarily translate into effective adaptation implementation. Applying these datasets to estimate sea level rise and storm surge in an adaptation project in Jamaica, we found that less costly and lower resolution data and models provide up to three times lower coastal risk estimates than more expensive data and models, indicating that investments in better resolution digital elevation mapping (DEM) data are needed for targeted local-level decisions. However, we also identify that, with this general rule of thumb in mind, cost-effective, national data can be used by planners in the absence of high-resolution data to support adaptation action planning, possibly saving critical climate adaptation budgets for project implementation.

The Global Flood Protection Benefits of Mangroves

Coastal flood risks are rising rapidly. We provide high resolution estimates of the economic value of mangroves forests for flood risk reduction every 20 km worldwide. We develop a probabilistic, process-based valuation of the effects of mangroves on averting damages to people and property. We couple spatially-explicit 2-D hydrodynamic analyses with economic models, and find that mangroves provide flood protection benefits exceeding $US 65 billion per year. If mangroves were lost, 15 million more people would be flooded annually across the world. Some of the nations that receive the greatest economic benefits include the USA, China, India and Mexico. Vietnam, India and Bangladesh receive the greatest benefits in terms of people protected. Many (>45) 20-km coastal stretches particularly those near cities receive more than $US 250 million annually in flood protection benefits from mangroves. These results demonstrate the value of mangroves as natural coastal defenses at global, national and local scales, which can inform incentives for mangrove conservation and restoration in development, climate adaptation, disaster risk reduction and insurance.