Quantifying the effects of sea level rise driven marsh migration on wave attenuation.

Sea level rise (SLR) is the most significant climate change-related threat to coastal wetlands, driving major transformations in coastal regions through marsh migration. Landscape transformations due to marsh migration are manifested in terms of horizontal and vertical changes in land cover and elevation, respectively. These processes will have an impact on saltmarsh wave attenuation that is yet to be explored. This study stands as a comprehensive analysis of spatially distributed wave attenuation by vegetation in the context of a changing climate. Our results show that: i) changes in saltmarsh cover have little to no effect on the attenuation of floods, while ii) changes in elevation can significantly reduce flood extents and water depths; iii) overland wave heights are directly influenced by marsh migration, although iv) being indirectly attenuated by the water depth limiting effects of water depth attenuation driven by changes in elevation; v) the influence of saltmarsh accretion on wave attenuation is largely evident near the marsh edge, where the increasing elevations can drive major wave energy losses via wave breaking. Lastly, vi) considering the synergy between SLR, marsh migration, and changes in elevation results in significantly more wave attenuation than considering the eustatic effects of SLR and/or horizontal marsh migration alone, and therefore should be adopted in future studies.

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.

State-of-the-Art Statistical Approaches for Estimating Flood Events.

Reliable quantile estimates of annual peak flow discharges (APFDs) are needed for the design and operation of major hydraulic infrastructures and for more general flood risk management and planning. In the present study, linear higher order-moments (LH-moments) and nonparametric kernel functions were applied to APFDs at 18 stream gauge stations in Punjab, Pakistan. The main purpose of this study was to evaluate the impacts of different quantile estimation methods towards water resources management and engineering applications by means of comparing the state-of-the-art approaches and their quantile estimates calculated from LH-moments and nonparametric kernel functions. The LH-moments (η = 0, 1, 2) were calculated for the three best-fitted distributions, namely, generalized logistic (GLO), generalized extreme value (GEV), and generalized Pareto (GPA), and the performances of these distributions for each level of LH-moments (η = 0, 1, 2) were compared in terms of Anderson-Darling, Kolmogorov-Smirnov, and Cramér-Von Mises tests and LH-moment ratio diagrams. The findings indicated that GPA and GEV distributions were best fitted for most stations, followed by GLO distribution. The quantile estimates derived from LH-moments (η = 0, 1, 2) had a lower relative absolute error, particularly for higher return periods. However, the Gaussian kernel function provided a close estimate among nonparametric kernel functions for small return periods when compared to LH-moments (η = 0, 1, 2), thus highlighting the importance of using LH-moments (η = 0, 1, 2) and nonparametric kernel functions in water resources management and engineering projects.

Projecting the effects of land subsidence and sea level rise on storm surge flooding in Coastal North Carolina.

Much of the United States Atlantic coastline continues to undergo subsidence due to post glacial settlement and ground water depletion. Combined with eustatic sea level rise (SLR), this contributes to an increased rate of relative SLR. In this work, we utilize the ADvanced CIRCulation model to project storm surges across coastal North Carolina. Recent hurricanes Irene and Matthew are simulated considering SLR and subsidence estimates for 2100. Relative to present day conditions, storm surge susceptible regions increase by 27% (Irene) to 40% (Matthew) due to subsidence. Combined with SLR (+ 74 cm), results suggest more than a doubling of areal flood extent for Irene and more than a three-fold increase for Hurricane Matthew. Considering current regional population distributions, this translates to an increase in at-risk populations of 18% to 61% due to subsidence. Even further, exposed populations are projected to swell relative to Matthew and Irene baseline simulations (8200 and 28,500) by more than 70,000 in all SLR scenarios (79,400 to 133,600). While increases in surge inundation are driven primarily by SLR in the region, there remains a substantial contribution due to vertical land movement. This outlines the importance of exploring spatially variable land movement in surge prediction, independent of SLR.

Influence of different relief information sources on the geomorphological characterization of small watersheds.

Mathematical models have been widely used to quantify hydrological processes for various practical purposes. These models depend on geomorphological attributes which are derived from relief information represented by Digital Elevation Models (DEM). The objective of this study was to evaluate the influence of relief information sources (ASTER, SRTM-30, SRTM-90, and TOPO) over geomorphological characterization of five Brazilian watersheds. Geoprocessing tools were applied for extraction of the following geomorphological attributes for each DEM: drainage area, perimeter, and watershed slope; length and slope of the main stream; total length of streams; bifurcation, stream length and stream area ratios; and length of the highest order stream. The differences in the values of attributes were calculated in relation to the reference DEM (TOPO). It was found that: i) slope of main stream and bifurcation ratio were the most sensitive parameters regarding the relief information source; ii) flat watersheds were more susceptible to altimetric errors; iii) ASTER did not adequately represent drainage networks for flat watersheds; and iv) the differences in the geomorphological attributes increased as drainage area decreased. The results indicate that DEM may exert influence on the use of hydrological models that depend on geomorphological attributes.