Complex Coastal Settings Research Articles

Valuing ecosystem services in complex coastal settings: An extended ecosystem accounting framework for improved decision-making

What gets measured gets managed is an axiom common to the business world that also applies to the management of environmental assets and processes. But what is the most adequate way to measure ecosystem value to optimise ecosystem management? In this paper, we unpack three valuation frameworks often applied in understanding ecosystem services and their benefits: 1) the Ecosystem Services framework, operationalised by the United Nations System of Environmental Economic Accounting - Ecosystem Accounting (SEEA-EA) framework; 2) value-centric approaches operationalised by the Total Economic Value framework; and 3) First Nations Peoples (FNP) frameworks, which seek to capture values from FNPs’ perspective. By assessing the strengths and weaknesses of these value frameworks for managing the World’s largest reef ecosystem—the Australian Great Barrier Reef—we construct an extended SEEA-EA valuation framework tailored to complex coastal settings. The significance of our approach is the inclusion of the whole range of benefits from all coastal and marine uses and users and therefore the integration of non-market and FNP values into the more traditional market-based valuation approach. Assessments that jointly consider multiple values originating from these three different frameworks are more likely to produce sustainable management outcomes than more restrictive approaches.

Shoreline modelling on timescales of days to decades

AbstractClimate change is resulting in global changes to sea level and wave climates, which in many locations significantly increase the probability of erosion, flooding and damage to coastal infrastructure and ecosystems. Therefore, there is a pressing societal need to be able to forecast the morphological evolution of our coastlines over a broad range of timescales, spanning days-to-decades, facilitating more focused, appropriate and cost-effective management interventions and data-informed planning to support the development of coastal environments. A wide range of modelling approaches have been used with varying degrees of success to assess both the detailed morphological evolution and/or simplified indicators of coastal erosion/accretion. This paper presents an overview of these modelling approaches, covering the full range of the complexity spectrum and summarising the advantages and disadvantages of each method. A focus is given to reduced-complexity modelling approaches, including models based on equilibrium concepts, which have emerged as a particularly promising methodology for the prediction of coastal change over multi-decadal timescales. The advantages of stable, computationally-efficient, reduced-complexity models must be balanced against the requirement for good generality and skill in diverse and complex coastal settings. Significant obstacles are also identified, limiting the generic application of models at regional and global scales. Challenges include the accurate long-term prediction of model forcing time-series in a changing climate, and accounting for processes that can largely be ignored in the shorter term but increase in importance in the long term. Further complications include coastal complexities, such as the accurate assessment of the impacts of headland bypassing. Additional complexities include complex structures and geology, mixed grain size, limited sediment supply, sources and sinks. It is concluded that with present computational resources, data availability limitations and process knowledge gaps, reduced-complexity modelling approaches currently offer the most promising solution to modelling shoreline evolution on daily-to-decadal timescales.

Effect of Fluvial Discharges and Remote Non-Tidal Residuals on Compound Flood Forecasting in San Francisco Bay

Accurate and timely flood forecasts are critical for making emergency-response decisions regarding public safety, infrastructure operations, and resource allocation. One of the main challenges for coastal flood forecasting systems is a lack of reliable forecast data of large-scale oceanic and watershed processes and the combined effects of multiple hazards, such as compound flooding at river mouths. Offshore water level anomalies, known as remote Non-Tidal Residuals (NTRs), are caused by processes such as downwelling, offshore wind setup, and also driven by ocean-basin salinity and temperature changes, common along the west coast during El Niño events. Similarly, fluvial discharges can contribute to extreme water levels in the coastal area, while they are dominated by large-scale watershed hydraulics. However, with the recent emergence of reliable large-scale forecast systems, coastal models now import the essential input data to forecast extreme water levels in the nearshore. Accordingly, we have developed Hydro-CoSMoS, a new coastal forecast model based on the USGS Coastal Storm Modeling System (CoSMoS) powered by the Delft3D San Francisco Bay and Delta community model. In this work, we studied the role of fluvial discharges and remote NTRs on extreme water levels during a February 2019 storm by using Hydro-CoSMoS in hindcast mode. We simulated the storm with and without real-time fluvial discharge data to study their effect on coastal water levels and flooding extent, and highlight the importance of watershed forecast systems such as NOAA’s National Water Model (NWM). We also studied the effect of remote NTRs on coastal water levels in San Francisco Bay during the 2019 February storm by utilizing the data from a global ocean model (HYCOM). Our results showed that accurate forecasts of remote NTRs and fluvial discharges can play a significant role in predicting extreme water levels in San Francisco Bay. This pilot application in San Francisco Bay can serve as a basis for integrated coastal flood modeling systems in complex coastal settings worldwide.

The integration of multi-source remote sensing data for the modelling of shoreline change rates in a mediterranean coastal sector

ABSTRACTA novel methodological approach is presented to estimate the shoreline change rate in complex coastal settings by using multi-source/multi-temporal shoreline data extracted from both orthoimages and digital elevation models. Several stages are covered to integrate the information available for the coastal area under study by means of a well-balanced and robust data fusion approach: i) determining the most accurate approach for extracting each shoreline from the different available sources, also estimating their corresponding accuracy, ii) finding out the best set of multi-temporal shorelines, within the available ones, to help avoid temporal oversampling and remove local severe shoreline change rate oscillations and, finally, iii) testing linear regression methods to include shoreline accuracy and automatic outlier removal in order to model the underlying general trend of shoreline evolution. A reweighted-weighted linear regression together with an evenly distributed shorelines dataset were chosen as the best approach to robustly integrate all the multisource remote sensing data available and determine which shorelines did not follow the general linear trend.

Controls of Multimodal Wave Conditions in a Complex Coastal Setting

AbstractCoastal hazards emerge from the combined effect of wave conditions and sea level anomalies associated with storms or low‐frequency atmosphere‐ocean oscillations. Rigorous characterization of wave climate is limited by the availability of spectral wave observations, the computational cost of dynamical simulations, and the ability to link wave‐generating atmospheric patterns with coastal conditions. We present a hybrid statistical‐dynamical approach to simulating nearshore wave climate in complex coastal settings, demonstrated in the Southern California Bight, where waves arriving from distant, disparate locations are refracted over complex bathymetry and shadowed by offshore islands. Contributions of wave families and large‐scale atmospheric drivers to nearshore wave energy flux are analyzed. Results highlight the variability of influences controlling wave conditions along neighboring coastlines. The universal method demonstrated here can be applied to complex coastal settings worldwide, facilitating analysis of the effects of climate change on nearshore wave climate.

Advances in management tools for modeling artificial nourishments in mixed beaches

Abstract This paper addresses the evolution of a mixed beach (Playa Granada, southern Spain) forced by an artificial nourishment project through the analysis of field observations and the modeling of hydro- and morphodynamics. The beach profile and coastline were periodically surveyed over a six-month period; a wave propagation model was calibrated by means of hydrodynamic measurements; four longshore sediment transport (LST) equations were tested through comparisons with bathymetric data; and the one-line model was applied between topographic surveys. The results indicate that severe coastline retreat (dry beach area loss > 208 m 2 /day) occurred during the 45 days following the intervention. This is mainly attributable to the morphology of the nourished coastline, the different characteristics of the sediment used for replenishment compared to natural sizes, and the occurrence of an intense westerly storm. The dry beach extension increased afterwards influenced by the westward LST due to the dominance of easterly waves. The Van Rijn formulation was found to provide the best fits to the observed volumetric changes, obtaining modeled/measured ratios of 93.1% and 77.4% for the two study beach profiles. The outputs of the one-line model based on the Van Rijn approach were also the best, with root-mean-square errors decreasing during the study period and lower than 4.6 m over the last 3 months. These results show that the joint application of a calibrated wave propagation model, the LST equation proposed by Van Rijn and the one-line model constitutes a management tool for predicting the evolution of these complex coastal settings.

Development of the Coastal Storm Modeling System (CoSMoS) for predicting the impact of storms on high-energy, active-margin coasts

The Coastal Storm Modeling System (CoSMoS) applies a predominantly deterministic framework to make detailed predictions (meter scale) of storm-induced coastal flooding, erosion, and cliff failures over large geographic scales (100s of kilometers). CoSMoS was developed for hindcast studies, operational applications (i.e., nowcasts and multiday forecasts), and future climate scenarios (i.e., sea-level rise + storms) to provide emergency responders and coastal planners with critical storm hazards information that may be used to increase public safety, mitigate physical damages, and more effectively manage and allocate resources within complex coastal settings. The prototype system, developed for the California coast, uses the global WAVEWATCH III wave model, the TOPEX/Poseidon satellite altimetry-based global tide model, and atmospheric-forcing data from either the US National Weather Service (operational mode) or Global Climate Models (future climate mode), to determine regional wave and water-level boundary conditions. These physical processes are dynamically downscaled using a series of nested Delft3D-WAVE (SWAN) and Delft3D-FLOW (FLOW) models and linked at the coast to tightly spaced XBeach (eXtreme Beach) cross-shore profile models and a Bayesian probabilistic cliff failure model. Hindcast testing demonstrates that, despite uncertainties in preexisting beach morphology over the ~500 km alongshore extent of the pilot study area, CoSMoS effectively identifies discrete sections of the coast (100s of meters) that are vulnerable to coastal hazards under a range of current and future oceanographic forcing conditions, and is therefore an effective tool for operational and future climate scenario planning.