Modeling Shoreline Change and Resulting Wetland Response Due to Erosion and Sea-Level Rise: A Case Study in Dorchester County, Maryland

The present study was focused on developing a shoreline change forecast and wetland response model for Dorchester County, MD, to evaluate the vulnerability of wetlands to shoreline erosion and inundation due to relative sea level rise. The model considers the following forces involved in wetland stability and sustainability: inundation (as a function of topography and sea-level rise), shoreline erosion, vertical accretion and horizontal migration. To predict the long-term risk to nearshore wetlands and the potential habitat zone for wetlands in the next 50 years, shoreline change due to inundation and erosion/accretion was assessed within the frameworks of two-dimensional and three-dimensional analyses. To that end, three different scenarios were taken into account in the shoreline change forecast. The first (conservative) scenario estimated the future shoreline positions based on historic sea-level rates of change and historic erosion/accretion rates. The other two scenarios employed accelerated rates of sea-level rise and accelerated rates of shoreline erosion/accretion in the shoreline forecast. Two different approaches were employed to spatially analyze and combine the outputs of the projections based on inundation and erosion. A Maximum Change approach and a Characterization of the Inundation Forecast were carried out in each scenario. The future location of the shoreline was defined as the wetland-water boundary. The wetland-upland boundary was defined based on current topography (elevations at 2 times the tidal range above mean low water), and the potential wetland habitat was restricted to areas that are not presently developed and/or not behind a shoreline defense structure. The outputs of this model allow identification of potential future wetland habitats where wetland protection and restoration strategies can be directed. This model approach can serve as a prototype for expanded investigations in other coastal habitats

Coastal Disasters and Remote Sensing Monitoring Methods

Coastal disaster is abnormal changes caused by climate change, human activities, geological movement or natural environment changes. According to formation cause, marine disasters as storm surges, waves, Tsunami coastal erosion, sea-level rise, red tide, seawater intrusion, marine oil spill and soil salinization. Remote sensing technology has real-time and large-area advantages in promoting the monitoring and forecast ability of coastal disaster. Relative to natural disasters, ones caused by human factors are more likely to be monitored and prevented. In this paper, we use several remote sensing methods to monitor or forecast three kinds of coastal disaster cause by human factors including red tide, sea-level rise and oil spilling, and make proposals for infrastructure based on the research results. The chosen method of monitoring red tide by inversing chlorophyll-a concentration is improved OC3M Model, which is more suitable for the coastal zone and higher spatial resolution than the MODIS chlorophyll-a production. We monitor the sea-level rise in coastal zone through coastline changes without artificial modifications. The improved Lagrangian model can simulate the trajectory of oil slick efficiently. Making the infrastructure planning according the coastal disasters and features of coastline contributes to prevent coastal disaster and coastal ecosystem protection. Multi-source remote sensing data can effectively monitor and prevent coastal disaster, and provide planning advices for coastal infrastructure construction

A model integrating longshore and cross-shore processes for predicting long-term shoreline responses to climate change

We present a shoreline change model for coastal hazard assessment and management planning. The model, CoSMoS-COAST (Coastal One-line Assimilated Simulation Tool), is a transect-based, one-line model that predicts short-term and long-term shoreline response to climate change in the 21st century. The proposed model represents a novel, modular synthesis of process-based models of coastline evolution due to longshore and cross-shore transport by waves and sea level rise. Additionally, the model uses an extended Kalman filter for data assimilation of historical shoreline positions to improve estimates of model parameters and thereby improve confidence in long-term predictions. We apply CoSMoS-COAST to simulate sandy shoreline evolution along 500 km of coastline in Southern California, which hosts complex mixtures of beach settings variably backed by dunes, bluffs, cliffs, estuaries, river mouths, and urban infrastructure, providing applicability of the model to virtually any coastal setting. Aided by data assimilation, the model is able to reproduce the observed signal of seasonal shoreline change for the hindcast period of 1995–2010, showing excellent agreement between modeled and observed beach states. The skill of the model during the hindcast period improves confidence in the model’s predictive capability when applied to the forecast period (2010–2100) driven by GCM-projected wave and sea level conditions. Predictions of shoreline change with limited human intervention indicate that 31% to 67% of Southern California beaches may become completely eroded by 2100 under sea level rise scenarios of 0.93 to 2.0 m

The Influence of Sea-Level Rise on Salinity in the Lower St. Johns River and the Associated Physics

The lower St Johns River is a low-gradient coastal river with tidal hydrodynamics that remain active from the Atlantic Ocean through to the upstream end of Lake George (river km 200). Salinity in the lower St Johns River is spatially and temporally variable, whereby the salinity distribution is driven primarily by the combination of ocean processes of tides and storm surges and hydrological processes of watershed runoff. This study examines the probability distributions and modes of behavior of salinity for present-day conditions using data, numerical modeling and eigen-analysis. The hypothesis is that long-term changes (decadal scale) in the ocean processes will cause the probability distributions of salinity to adjust, and therefore there is a quantifiable non-stationarity of salinity in the lower St Johns River (shifts in the probability distribution of salinity, as representative of salinity increase) due to sea-level rise. The numerical modeling is validated against data, then the model is applied to generate synthetic salinity records for the main river stem and tributaries of the lower St. Johns based on present-day conditions. The synthetic salinity records are transformed into probability distribution functions (PDFs) and eigen-functions. The same analysis is performed on synthetic salinity records generated by the model when applied in forecast mode (i.e., sea-level rise). Comparisons of the forecasted PDFs and eigen-functions with those for present-day conditions quantify the non-stationarity (shifts in probability distributions and changes in eigen-structure) of the salinity in the lower St Johns River. The underlying physics of the cause (sea-level rise)-effect (non-stationarity of salinity) relationship are assessed in terms of coastal/river hydrodynamics

Sea-level rise will likely accelerate rock coast cliff retreat rates

Coastal response to anthropogenic climate change is of central importance to the infrastructure and inhabitants in these areas. Despite being globally ubiquitous, the stability of rock coasts has been largely neglected, and the expected acceleration of cliff erosion following sea-level rise has not been tested with empirical data, until now. We have optimised a coastal evolution model to topographic and cosmogenic radionuclide data to quantify cliff retreat rates for the past 8000 years and forecast rates for the next century. Here we show that rates of cliff retreat will increase by up to an order of magnitude by 2100 according to current predictions of sea-level rise: an increase much greater than previously predicted. This study challenges conventional coastal management practices by revealing that even historically stable rock coasts are highly sensitive to sea-level rise and should be included in future planning for global climate change response

Validating an Operational Flood Forecast Model Using Citizen Science in Hampton Roads, VA, USA

Changes in the eustatic sea level have enhanced the impact of inundation events in the coastal zone, ranging in significance from tropical storm surges to pervasive nuisance flooding events. The increased frequency of these inundation events has stimulated the production of interactive web-map tracking tools to cope with changes in our changing coastal environment. Tidewatch Maps, developed by the Virginia Institute of Marine Science (VIMS), is an effective example of an emerging street-level inundation mapping tool. Leveraging the Semi-implicit Cross-scale Hydro-science Integrated System Model (SCHISM) as the engine, Tidewatch operationally disseminates 36-h inundation forecast maps with a 12-h update frequency. SCHISM’s storm tide forecasts provide surge guidance for the legacy VIMS Tidewatch Charts sensor-based tidal prediction platform, while simultaneously providing an interactive and operationally functional forecast mapping tool with hourly temporal resolution and a 5 m spatial resolution throughout the coastal plain of Virginia, USA. This manuscript delves into the hydrodynamic modeling and geospatial methods used at VIMS to automate the 36-h street-level flood forecasts currently available via Tidewatch Maps, and the paradigm-altering efforts involved in validating the spatial, vertical, and temporal accuracy of the model. Supplementary material: Catch the King Tide GPS data points were collected by volunteers to effectively breadcrumb their path tracing the tidal high water contour lines by pressing the \u27Save Data\u27 button in the free Sea Level Rise Mobile App every few steps along the water\u27s edge during the high tide on the morning of November 5th, 2017. https://doi.org/10.25773/276h-2b4

Modeling wind-induced waves in the Salish Sea

There have been on-going efforts for increasing coastal resilience to the risk of coastal inundation as a result of sea-level rise in Washington. Accurate coastal risk projection depends on detailed and accurate information of sea level rise, including waves and storm surge induced by windstorms. This paper presents a modeling study simulating wind-induced waves in the Salish Sea. A nested-grid modeling approach was used to provide accurate and robust model simulations at various scales. The NOAA NCEP’s WaveWatch III (WW3) model is configured at global and regional scales with wind forcing obtained from the Climate Forecast System Reanalysis (CFSR). For the Salish Sea and Washington outer coast, a high-resolution wave model is implemented with the Unstructured Simulating WAve Nearshore (UnSWAN) model. The Salish Sea wave model is driven by spectral open boundary conditions from the nested regional WW3 models. To further improve the model accuracy inside the Salish Sea, sea surface winds were obtained from a Weather Research and Forecasting (WRF) historical model simulation covering the entire west coast at a resolution of 6-km resolution. These were used to drive the Salish Sea UnSWAN model. Comparisons of model results with observed wave data at available buoy stations indicated that the model successfully reproduced the wave climates in the Salish Sea. Wave characteristics and exposure areas of large waves in the Salish Sea were analyzed based on model results simulated from 2011 to 2015

Bridging groundwater models and decision support with a Bayesian network

Author Posting. © American Geophysical Union, 2013. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Water Resources Research 49 (2013): 6459–6473, doi:10.1002/wrcr.20496.Resource managers need to make decisions to plan for future environmental conditions, particularly sea level rise, in the face of substantial uncertainty. Many interacting processes factor in to the decisions they face. Advances in process models and the quantification of uncertainty have made models a valuable tool for this purpose. Long-simulation runtimes and, often, numerical instability make linking process models impractical in many cases. A method for emulating the important connections between model input and forecasts, while propagating uncertainty, has the potential to provide a bridge between complicated numerical process models and the efficiency and stability needed for decision making. We explore this using a Bayesian network (BN) to emulate a groundwater flow model. We expand on previous approaches to validating a BN by calculating forecasting skill using cross validation of a groundwater model of Assateague Island in Virginia and Maryland, USA. This BN emulation was shown to capture the important groundwater-flow characteristics and uncertainty of the groundwater system because of its connection to island morphology and sea level. Forecast power metrics associated with the validation of multiple alternative BN designs guided the selection of an optimal level of BN complexity. Assateague island is an ideal test case for exploring a forecasting tool based on current conditions because the unique hydrogeomorphological variability of the island includes a range of settings indicative of past, current, and future conditions. The resulting BN is a valuable tool for exploring the response of groundwater conditions to sea level rise in decision support.This work was funded by the USGS Climate and Land Use Mission Area, Research and Development Program and the USGS Natural Hazards Mission Area, Coastal and Marine Geology Program

Skill assessment of a set of retrospective decadal climate predictions with EC-Earth

Màster de Meteorologia, Facultat de Física, Universitat de Barcelona, Curs: 2020-2021, Tutors: Froila Palmeiro Nuñez, Roberto Bilbao, Pablo OrtegaThe climate system is changing with unprecedented consequences for the environment and many socioeconomic sectors. Hence the importance of predicting these changes. This study aims to produce an evaluation of the predictive skill in a decadal prediction system performed with EC-Earth. It specifically targets three variables of high relevance for human activities, such as sea surface temperature, the sea surface height anomaly (which quantifies sea level rise) and the total cloud cover (which is critical for storm development). The evaluation has mostly focused on two major ocean basins (Pacific and Atlantic), where important modes of variability like the El Ni˜no-Southern Oscillation and the Atlantic Multidecadal Variability take place, and also on the Equatorial stratosphere, where the Quasi-Biennial Oscillation, a highly predictable mode, occurs. Concerning the results, we have shown high prediction skill for all variables in the first forecast year. In the following years, we note a general reduction of the predictive skill, particularly in the southeastern Tropical Pacific, which might point to deficiencies in the model to simulate ENSO periodicity and/or regionality. Furthermore, a general lack of skill in the North Atlantic, may imply that the Atlantic Multidecadal Variability, at least in EC-Earth, is not a source of sea level predictability. Regarding the QBO, results have shown a high prediction skill, especially in the first 29 months. However, the QBO cycle periodicity is not well represented by EC-Earth, which degrades the credibility of the predictions in the subsequent forecast year

Modelling Assessment of Sandy Beaches Erosion in Thailand

This paper focuses on the spatial and temporal aspects of rising sea levels and sandy beach erosion in Thailand. The major scientific challenge tackled in this paper was to distinguish the relevance and contribution of sea level rise (including storms) to beach erosion. The Simulator of Climate Change Risks and Adaptation Initiatives (SimCLIM) and its’ impact model (CoastCLIM) with two representative concentration pathway (RCP) scenarios (RCP2.6 and RCP8.5) was utilized to forecast changes in sea level and shoreline between the years 1940-2100. Input parameters underlying the modified Brunn Rule were applied (e.g., coastal and storm characteristics). Moreover, sand loss and forced people migration were estimated using fundamental equations. The sea level is predicted to rise by 147.90 cm and the coastline will be eroded around 517.09 m by 2100, compared to levels in 1995. This level of erosion could lead to a decrease of the coastal sandy area by about 2.69 km2 and a population of 873 people, over the same period. In scientific terms, this paper quantifies the contribution and relevance of sea-level rise (SLR) to sandy beach erosion compared to other factors, including ad-hoc short-term impacts from stochastic storminess. The results also showed that 8.02 and 23.26 percent of erosion was attributed to storms and sea-level rise, respectively. Nevertheless, limited multi-century data of residual movement in Thailand could create uncertainties in distinguishing relative contributions. These results could be beneficial to national-scale data and the adaptation planning processes in Thailand