scholarly journals Probabilistic coastal vulnerability assessment to storms at regional scale – application to Catalan beaches (NW Mediterranean)

2011 ◽  
Vol 11 (2) ◽  
pp. 475-484 ◽  
Author(s):  
E. Bosom ◽  
J. A. Jiménez
Keyword(s):  
Probability Distributions ◽  
Regional Scale ◽  
Probabilistic Approach ◽  
Wave Climate ◽  
Coastal Vulnerability ◽  
Large Variability ◽  
Probability Of Occurrence ◽  
Nw Mediterranean ◽  
Offshore Wave ◽  
Extreme Probability

Abstract. A methodology to assess storm-induced coastal vulnerability taking into account the different induced processes separately (inundation and erosion) is presented. It is based on a probabilistic approach where hazards time series are built from existing storm data and later used to fit an extreme probability function. This is done for different sectors along the coast defined in terms of the wave climate and for representative beach types of the area to be analyzed. Once probability distributions are available, coastal managers must decide the probability of occurrence to be accepted as well as the period of concern of the analysis in function of the importance of the hinterland. These two variables will determine the return period to be considered in the assessment. The comparison of hazards and vulnerabilities associated with the selected probability of occurrence permit to identify the most hazardous areas along the coast in a robust manner by including the spatial variability in forcing (storm climate) and receptor (beaches). The methodology has been applied to a 50 km long coastal stretch of the Catalonia (NW Mediterranean) where offshore wave conditions can be assumed to be homogeneous. In spite of this spatially constant wave field, obtained results indicate a large variability in hazards intensity and vulnerability along the coast.

scholarly journals Storm-induced coastal hazard assessment at regional scale: application to Catalonia (NW Mediterranean)

2010 ◽  
Vol 26 ◽  
pp. 83-87 ◽  
Author(s):  
E. Bosom ◽  
J. A. Jiménez
Keyword(s):  
Time Series ◽  
Hazard Assessment ◽  
Probability Distributions ◽  
Regional Scale ◽  
Wave Climate ◽  
Coastal Hazard ◽  
Sediment Grain Size ◽  
Hazardous Area ◽  
Nw Mediterranean ◽  
Local Wave

Abstract. A methodology for coastal hazard assessment at regional scale is presented and applied to the Catalan coast (NW Mediterranean). The method separately evaluates erosion and inundation hazards by using wave time series and beach characteristics (slope and sediment grain size). Obtained hazard time series are fitted to extreme probability distributions for different coastal sectors which are defined in function of local wave climate. This approach allows to compare the spatial variation of hazard intensities for a given probability of occurrence and, thus, to objectively identify the most hazardous areas along the coast in terms of erosion and inundation. Obtained results indicate that the coast north of Barcelona is more hazardous than the southern coast regarding inundation for any given probability. With respect to storm-induced erosion, the central coast of Catalonia is the less hazardous area, although spatial variations in erosion along the coast are smaller than the observed for inundation.

scholarly journals Quantifying Uncertainty in Projections of Regional Climate Change: A Bayesian Approach to the Analysis of Multimodel Ensembles

2005 ◽  
Vol 18 (10) ◽  
pp. 1524-1540 ◽  
Author(s):  
Claudia Tebaldi ◽  
Richard L. Smith ◽  
Doug Nychka ◽  
Linda O. Mearns
Keyword(s):  
Statistical Model ◽  
Temperature Change ◽  
Bayesian Model ◽  
General Circulation ◽  
Probability Distributions ◽  
Regional Scale ◽  
Probabilistic Approach ◽  
General Circulation Models ◽  
Boreal Winter ◽  
Ensemble Averaging

Abstract A Bayesian statistical model is proposed that combines information from a multimodel ensemble of atmosphere–ocean general circulation models (AOGCMs) and observations to determine probability distributions of future temperature change on a regional scale. The posterior distributions derived from the statistical assumptions incorporate the criteria of bias and convergence in the relative weights implicitly assigned to the ensemble members. This approach can be considered an extension and elaboration of the reliability ensemble averaging method. For illustration, the authors consider the output of mean surface temperature from nine AOGCMs, run under the A2 emission scenario from the Synthesis Report on Emission Scenarios (SRES), for boreal winter and summer, aggregated over 22 land regions and into two 30-yr averages representative of current and future climate conditions. The shapes of the final probability density functions of temperature change vary widely, from unimodal curves for regions where model results agree (or outlying projections are discounted) to multimodal curves where models that cannot be discounted on the basis of bias give diverging projections. Besides the basic statistical model, the authors consider including correlation between present and future temperature responses, and test alternative forms of probability distributions for the model error terms. It is suggested that a probabilistic approach, particularly in the form of a Bayesian model, is a useful platform from which to synthesize the information from an ensemble of simulations. The probability distributions of temperature change reveal features such as multimodality and long tails that could not otherwise be easily discerned. Furthermore, the Bayesian model can serve as an interdisciplinary tool through which climate modelers, climatologists, and statisticians can work more closely. For example, climate modelers, through their expert judgment, could contribute to the formulations of prior distributions in the statistical model.

scholarly journals Coastal vulnerability assessment: through regional to local downscaling of wave characteristics along the Bay of Lalzit (Albania)

2019 ◽  
Vol 19 (1) ◽  
pp. 287-298 ◽  
Author(s):  
Francesco De Leo ◽  
Giovanni Besio ◽  
Guido Zolezzi ◽  
Marco Bezzi
Keyword(s):  
Field Measurements ◽  
Computational Procedure ◽  
Wave Climate ◽  
Data Availability ◽  
Limited Data ◽  
Coastal Vulnerability ◽  
Wave Characteristics ◽  
Local Wave ◽  
Run Up ◽  
Offshore Wave

Abstract. Coastal vulnerability is evaluated against inundation risk triggered by wave run-up through the evaluation of vulnerability levels (referred to as VLs) introduced by Bosom and Jiménez (2011). VLs are assessed through different wave climate characterizations, referring to regional (offshore wave climate) or local (nearshore wave climate) scales. The study is set along the Bay of Lalzit, a coastal area near Durrës (Albania). The analysis reveals that the results vary due to uncertainties inherent in the run-up estimation, showing that the computational procedure should be developed by taking into account detailed information about the local wave climate. Different approaches in choosing wave characteristics for run-up estimation significantly affect the estimate of shoreline vulnerability. The analysis also shows the feasibility and challenges of applying VL estimates in contexts characterized by limited data availability through targeted field measurements of the coast geomorphology and an overall understanding of the recent coastal dynamics and related controlling factors.

scholarly journals Coastal vulnerability assessment: through regional to local downscaling of wave characteristics along the Bay of Lalzit (Albania)

2018 ◽  
Author(s):  
Francesco De Leo ◽  
Giovanni Besio ◽  
Guido Zolezzi ◽  
Marco Bezzi
Keyword(s):  
Field Measurements ◽  
Computational Procedure ◽  
Wave Climate ◽  
Data Availability ◽  
Coastal Vulnerability ◽  
Wave Characteristics ◽  
Local Wave ◽  
Climate Scale ◽  
Run Up ◽  
Offshore Wave

Abstract. Coastal vulnerability is evaluated against inundation risk triggered by waves run-up, through the employment of coastal vulnerability indexes (referred to as “CVI”) introduced by Bosom García and Jiménez Quintana (2011). CVI are assessed through different wave climate characterizations, referring to regional (offshore wave climate) or local (near-shore wave climate) scale. The study is set along the Lalzit bay, a coastal area nearby Durres (Albania). The analysis reveals that the results vary due to uncertainties inherent in the run-up estimation, showing that the computational procedure should be developed by taking into account detailed information about local wave climate, especially concerning seasonal behaviour and fluctuations. Different approaches in choosing wave characteristics for run-up estimation affect significantly the estimate of shoreline vulnerability. The analysis also shows the feasibility and challenges of applying CVI estimates in contexts characterized by limited data availability, through targeted field measurements of the coast geomorphology and an overall understanding of the recent coastal dynamics and related controlling factors.

Differential Credibility of Climate Modes in CMIP6

2021 ◽  
pp. 1
Author(s):  
Jacob Coburn ◽  
S.C. Pryor
Keyword(s):  
Southern Oscillation ◽  
Probability Distributions ◽  
Critical Role ◽  
Regional Scale ◽  
Southern Annular Mode ◽  
Climate Projections ◽  
Annular Mode ◽  
Climate Modes ◽  
First Order ◽  
Skill Scores

AbstractThis work quantitatively evaluates the fidelity with which the Northern Annular Mode (NAM), Southern Annular Mode (SAM), Pacific-North American pattern (PNA), El Niño-Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO) and Atlantic Multidecadal Oscillation (AMO) and the first-order mode interactions are represented in Earth System Model (ESM) output from the CMIP6 archive. Several skill metrics are used as part of a differential credibility assessment (DCA) of both spatial and temporal characteristics of the modes across ESMs, ESM families and specific ESM realizations relative to ERA5. The spatial patterns and probability distributions are generally well represented but skill scores that measure the degree to which the frequencies of maximum variance are captured are consistently lower for most ESMs and climate modes. Substantial variability in skill scores manifests across realizations from individual ESMs for the PNA and oceanic modes. Further, the ESMs consistently overestimate the strength of the NAM-PNA first-order interaction and underestimate the NAM-AMO connection. These results suggest that the choice of ESM and ESM realizations will continue to play a critical role in determining climate projections at the global and regional scale at least in the near-term.

scholarly journals Automated classification of the atmospheric circulation patterns that drive regional wave climates

2014 ◽  
Vol 14 (8) ◽  
pp. 2145-2155 ◽  
Author(s):  
J. Pringle ◽  
D. D. Stretch ◽  
A. Bárdossy
Keyword(s):  
Atmospheric Circulation ◽  
Low Pressure ◽  
Wave Climate ◽  
Automated Classification ◽  
Coastal Vulnerability ◽  
Extreme Wave ◽  
Atmospheric Circulation Patterns ◽  
Circulation Patterns ◽  
Spatially Distributed ◽  
Wave Heights

Abstract. Wave climates are fundamental drivers of coastal vulnerability; changing trends in wave heights, periods and directions can severely impact a coastline. In a diverse storm environment, the changes in these parameters are difficult to detect and quantify. Since wave climates are linked to atmospheric circulation patterns, an automated and objective classification scheme was developed to explore links between synoptic-scale circulation patterns and wave climate variables, specifically wave heights. The algorithm uses a set of objective functions based on wave heights to guide the classification and find atmospheric classes with strong links to wave behaviour. Spatially distributed fuzzy numbers define the classes and are used to detect locally high- and low-pressure anomalies. Classes are derived through a process of simulated annealing. The optimized classification focuses on extreme wave events. The east coast of South Africa was used as a case study. The results show that three dominant patterns drive extreme wave events. The circulation patterns exhibit some seasonality with one pattern present throughout the year. Some 50–80% of the extreme wave events are explained by these three patterns. It is evident that strong low-pressure anomalies east of the country drive a wind towards the KwaZulu-Natal coastline which results in extreme wave conditions. We conclude that the methodology can be used to link circulation patterns to wave heights within a diverse storm environment. The circulation patterns agree with qualitative observations of wave climate drivers. There are applications to the assessment of coastal vulnerability and the management of coastlines worldwide.

scholarly journals Complex coastlines responding to climate change: do shoreline shapes reflect present forcing or "remember" the distant past?

2016 ◽  
Author(s):  
Christopher W. Thomas ◽  
A. Brad Murray ◽  
Andrew D. Ashton ◽  
Martin D. Hurst ◽  
Andrew K. A. P. Barkwith ◽  
...  
Keyword(s):  
Climate Change ◽  
Wave Climate ◽  
Quasi Equilibrium ◽  
Morphological Response ◽  
Antecedent Conditions ◽  
Coastline Evolution ◽  
Aspect Ratios ◽  
Order Of Magnitude ◽  
Offshore Wave ◽  
Hysteresis Behaviour

Abstract. A range of planform morphologies emerge along sandy coastlines as a function of offshore wave climate. It has been implicitly assumed that the morphological response time is rapid compared to the time scales of wave-climate change, meaning that coastal morphologies simply reflect the extant wave climate. This assumption has been explored by focussing on the response of two distinctive morphological coastlines – flying spits and cuspate cusps – to changing wave climates, using a coastline evolution model. Results indicate that antecedent conditions are important in determining the evolution of morphologies, and that sandy coastlines can demonstrate hysteresis behaviour. In particular, antecedent morphology is particularly important in the evolution of flying spits, with characteristic timescales of morphological adjustment on the order of centuries for large spits. Characteristic timescales vary with the square of aspect ratios of capes and spits; for spits, these timescales are an order of magnitude longer than for capes (centuries vs. decades). When wave climates change more slowly than the relevant characteristic timescales, coastlines are able to adjust in a quasi-equilibrium manner. Our results have important implications for the management of sandy coastlines where decisions may be implicitly and incorrectly based on the assumption that present-day coastlines are in equilibrium with current conditions.

scholarly journals Automated classification of the atmospheric circulation patterns that drive regional wave climates

2014 ◽  
Vol 2 (2) ◽  
pp. 1127-1151
Author(s):  
J. Pringle ◽  
D. D. Stretch ◽  
A. Bárdossy
Keyword(s):  
Atmospheric Circulation ◽  
Low Pressure ◽  
Wave Climate ◽  
Automated Classification ◽  
Coastal Vulnerability ◽  
Extreme Wave ◽  
Atmospheric Circulation Patterns ◽  
Circulation Patterns ◽  
Wave Heights ◽  
Objective Classification

Abstract. Wave climates are fundamental drivers of coastal vulnerability and changing trends in wave height, period and direction can severely impact coastlines. In a diverse storm environment, the changes in these parameters are difficult to detect and quantify. Since wave climates are linked to atmospheric circulation patterns an automated and objective classification scheme was developed to explore links between synoptic scale circulation patterns and wave climate variables, specifically wave heights. The algorithm uses a set of objective functions based on wave heights to guide the classification. Fuzzy rules define classification types that are used to detect locally high and low pressure anomalies through a process of simulated annealing. The optimized classification focuses on extreme wave events. The east coast of South Africa was used as a case study. The results show that three dominant patterns drive extreme wave events. The circulation patterns exhibit some seasonality with one pattern present throughout the year. Some 50–80% of the extreme wave events are explained by these three patterns. It is evident that strong low pressure anomalies east of the country drive a wind towards the KwaZulu-Natal coastline which results in extreme wave conditions. We conclude that the methodology can be used to link circulation patterns to wave heights within a diverse storm environment. The circulation patterns agree with qualitative observations of wave climate drivers. There are applications to the assessment of coastal vulnerability and the management of coastlines worldwide.

scholarly journals Development of a Bayesian network for probabilistic risk assessment of pesticides

2021 ◽  
Author(s):  
Sophie Mentzel ◽  
Merete Grung ◽  
Knut Erik Tollefsen ◽  
Marianne Stenrod ◽  
Karina Petersen ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Bayesian Network ◽  
Environmental Risk ◽  
Environmental Risk Assessment ◽  
Probability Distributions ◽  
Probabilistic Approach ◽  
Probabilistic Risk Assessment ◽  
Risk Calculation ◽  
Link Information ◽  
Probabilistic Risk

Conventional environmental risk assessment of chemicals is based on a calculated risk quotient, representing the ratio of exposure to effects of the chemical, in combination with assessment factors to account for uncertainty. Probabilistic risk assessment approaches can offer more transparency, by using probability distributions for exposure and/or effects to account for variability and uncertainty. In this study, a probabilistic approach using Bayesian network (BN) modelling is explored as an alternative to traditional risk calculation. BNs can serve as meta-models that link information from several sources and offer a transparent way of incorporating the required characterization of uncertainty for environmental risk assessment. To this end, a BN has been developed and parameterised for the pesticides azoxystrobin, metribuzin, and imidacloprid. We illustrate the development from deterministic (traditional) risk calculation, via intermediate versions, to fully probabilistic risk characterisation using azoxystrobin as an example. We also demonstrate seasonal risk calculation for the three pesticides.

Investigation of long-term changes of coastal wave directionality patterns and their connections with NAO climatic index: UK case study

2021 ◽  
Author(s):  
Antonia Chatzirodou
Keyword(s):  
Climate Change ◽  
Flood Risk ◽  
Wave Climate ◽  
Wave Direction ◽  
Climatic Index ◽  
Coastal Flood ◽  
Time Period ◽  
Wave Directionality ◽  
Coastal Flood Risk ◽  
Offshore Wave

<p>The effects of climate change are at the spotlight of scientific research. In coastal science the effects of sea-level rise (SLR) on coastal areas, mainly as a result of melting of ice sheets and thermal volume expansion consist an intensive area of research. As well the changing ocean wave field due to greenhouse effect and interactions of atmospheric processes is under investigation. Researchers have placed focus on significant wave height changes and their associated impacts on the coastal environment, with evidence suggesting that the number, intensity and location of storms will change. It is suggested that equal attention should be placed on the mean wave direction changes and the effects that these changes may have on the coastlines and surrounding coastal infrastructure. Following that, this study investigated the changes in wave direction data since 1979 to 2019 covering 40 years’ time period at 11 offshore UK coastal locations. The selected locations lie close to WaveNet, Cefas’ strategic wave monitoring network points for the UK. Stakeholders use the data to provide advice and guidance to all involved parties including responders and communities about coastal flood risk. On a longer timescale the data provide evidence to coastal engineers and scientists of the wave climate change patterns and the implications this may have on coastal structures and flood defences design. Based on this initiative, this study investigated UK offshore wave climate changes by performing a longer timescale analysis of changes of wave direction patterns. The wave direction data were taken from ECMWF ERA5 6-hour hind cast data catalogue which covers 40 years’ time period from 1797-2019 (Copernicus Climate Change Service (C3S), 2017). MATLAB software coding was primarily utilized for data processing and analyses. Following that, inferential statistics were applied to map inter-decadal statistical changes in wave direction patterns, suggesting that wave directionality patterns have presented changes at 11 offshore locations tested.  The connections of wave directions with North Atlantic Oscillation (NAO) Climatic Index are currently investigated through use of machine learning approaches. The results of this study can be confidently used in wave transformation computational models coupled with hydro-morphodynamic models to downscale offshore wave direction changes to UK coastal areas. This can help identify susceptible coasts to offshore wave climate change. Susceptibility is regarded in form of coastal erosion and accretion rates changes as a result of altered offshore wave conditions, which might affect coastal flood risk with potential impacts on critical infrastructure.  </p>

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