Search

International audience; The metamodel-based approach (also referred to as the surrogate approach) is commonly applied to overcome the computational burden of numerical models that are used to simulate the evolution of reservoir fluids and pressures in response to any production scheme. In this study, we propose an adaptation of this approach for aquifer thermal energy storage (ATES) systems. ATES systems are characterized by cyclic loading/unloading production schemes, which result in a strong similarity in the dynamics of the intercyclic evolution of variables such as the temperature at the producer well. Instead of training several metamodels, i.e., one per cycle (“independent” metamodelling approach), we take advantage of the intercyclic similarity to train a single metamodel within the setting of multifidelity cokriging (“multicyclic” metamodelling approach). To explore the predictive performance of this approach, we applied a random subsampling validation approach multiple times to 300 simulation results of a realistic ATES system in the Paris basin by considering three characteristics, i.e., the minimum and maximum temperature, and the rate of temperature decrease at each cycle. Numerical experiments with varying training dataset sizes (from 33 to 66% of the total number of results) and using 100 test samples show that (1) the predictive error of the multicyclic metamodelling reaches lower levels (by 20–50%) than that of the independent approach; (2) this higher predictive performance is achieved while saving computational time cost because the training phase only needs a few tens of “complete” simulations (run over all cycles) together with a few hundreds of “partial” simulations (stopped at the first cycle); the latter simulations are less expensive to evaluate because of shorter simulated time.

Metamodelling techniques (also referred to as surrogate modelling) have shown high performance to overcome the computational burden of numerical hydrodynamic models for fast prediction of key indicators of marine flooding (e.g. total flooded area). To predict flood maps (e.g. spatial distribution of maximum value of water depth during a flood event), a commonly-used approach is to rely on principal component analysis to reduce the high dimensionality of the flood map (related to the number of pixels typically of several 1000 s) by transforming the spatial output into a low number of latent variables (typically

Occurrences of tropical cyclones at a location are rare, and for many locations, only short periods of observations or hindcasts are available. Hence, estimation of return values (corresponding to a period considerably longer than that for which data are available) for cyclone-induced significant wave height (SWH) from small samples is challenging. The STM-E (space-time maximum and exposure) model was developed to provide reduced bias in estimates of return values compared to competitor approaches in such situations and realistic estimates of return value uncertainty. STM-E exploits data from a spatial neighbourhood satisfying certain conditions, rather than data from a single location, for return value estimation. This article provides critical assessment of the STM-E model for tropical cyclones in the Caribbean Sea near Guadeloupe for which a large database of synthetic cyclones is available, corresponding to more than 3000 years of observation. Results indicate that STM-E yields values for the 500-year return value of SWH and its variability, estimated from 200 years of cyclone data, consistent with direct empirical estimates obtained by sampling 500 years of data from the full synthetic cyclone database; similar results were found for estimation of the 100-year return value from samples corresponding to approximately 50 years of data. In general, STM-E also provides reduced bias and more realistic uncertainty estimates for return values relative to single-location analysis.

We provide a computationally-efficient scheme for the estimation of joint extremes of significant wave height (Hs) and wind speed (U) for tropical cyclones. The method incorporates the simple spatial extremes method (STM-E) of Wada et al., 2019 (for spatial extremes of each of Hs and U) and the conditional extremes model of Heffernan and Tawn, 2004 (for conditional modeling of Hs given extreme U and U given extreme Hs). We demonstrate the methodology in application to data generated from hindcast simulations and track shifting of past tropical cyclones in the neighborhood of Réunion island in the Indian Ocean. Following the STM-E approach, spatio-temporal maxima (STM) and exposures (E) of both Hs and U are extracted for each tropical cyclone event. Marginal extreme value distributions are then estimated independently for the STM samples of Hs and U, providing a means for marginal extrapolation to long return periods. Exposure data are used to estimate densities for the spatial distribution of Hs and U. The conditional extremes model is then used to characterize the joint structure of STM for Hs and U, conditional on one of those variables being extreme. The estimated joint return values for Hs and U are validated by comparison with the original data.

International audience; Expert judgment is widely used to inform forecasts (e.g. using the 5 th , 50 th and 95 th percentile of some variable of interest) for a large variety of applications related to environment systems. This task can rely on Cooke's classical model (CM) within the probabilistic framework, and consists in combining expert information after a preliminary step where experts are weighted using calibration and informativeness scores estimated using some seed questions for which the answers can be obtained. In the literature, an alternative model (PM) has been proposed using

Pre-existing faults cross-cutting caprock formations constitute potential leakage pathways during geological CO2 sequestration. When the fault filling material is calcite-rich, dissolution of the f...

In 2017, Irma and Maria highlighted the vulnerability of small islands to cyclonic events and the necessity of advancing the forecast techniques for cyclone-induced marine flooding. In this context, this paper presents a generic approach to deriving time-varying inundation forecasts from ensemble track and intensity forecasts applied to the case of Reunion Island in the Indian Ocean. The challenge for volcanic islands is to account for the full complexity of wave overtopping processes while also ensuring a robustness and timeliness that are compatible with emergency requirements. The challenge is addressed by following a hybrid approach relying on the combination of process-based models with a statistical model (herein, a random-forest classifier) trained with a precalculated database. The latter enables one to translate any time series of coastal marine conditions into the time-varying probability of inundation for different sectors. The application detailed for the case of Cyclone Dumile at Sainte-Suzanne city shows that the proposed approach enables quick discrimination, in both space and time, thereby identifying safe and exposed areas and demonstrating that probabilistic forecasting of marine flooding by overtopping is feasible. The whole method can be easily adapted to other territories and scales provided that validated process-based models are available. Beyond early warning applications, the developed database and statistical models may also be used for informing risk prevention and adaptation strategies.

International audience; Shoreline change is driven by various complex processes interacting at a large range of temporal and spatial scales, making shoreline reconstructions and predictions challenging and uncertain. Despite recent progress in addressing uncertainties related to the physics of sea-level rise, very little effort is made towards understanding and reducing the uncertainties related to wave-driven shoreline response. To fill this gap, 2 the uncertainties associated with the long-term modelling of shoreline change are analysed at a high-energy cross-shore transport dominated site. Using the state-of-the-art LX-Shore shoreline change model, we produce a probabilistic shoreline reconstruction, based on 3000 simulations over the past 20 years at Truc Vert beach, southwest France, whereby sea-level rise rate, depth of closure and three model free parameters are considered uncertain variables. We further address the relative impact of each source of uncertainty on the model results performing a Global Sensitivity Analysis. This analysis shows that the shoreline changes are mainly sensitive to the three parameters of the wave-driven model, but also that the sensitivity to each of these parameters is strongly modulated seasonally and interannually, in relation with wave energy variability, and depends on the time scale of interest. These results have strong implications on the model skill sensitivity to the calibration period as well as for the predictive skill of the model in a context of future climate change affecting wave climate and extremes.

International audience; Model uncertainties are generally integrated in environmental long-running numerical simulators via a categorical variable. By focusing on Gaussian process (GP) models, we show how different categorical kernel models (exchangeable, ordinal, group, etc.) can bring valuable insights into the correlation of the simulator output values computed for different levels of the categorical variable, i.e., the interlevel dependence structure. Supported by two real case applications (cyclone-induced waves and reservoir modeling), we have proposed a cross-validation approach to select the most appropriate kernel by finding a trade-off between predictability, explainability, and stability of the covariance coefficients. This approach can be used effectively to support some physical assumptions regarding the categorical variable. Through comparison to tree-based techniques, we show that GP models can be considered a satisfactory compromise when only a few model runs (∼100) are available by presenting a high predictability and a concise and graphical way to map the interlevel dependence structure.

While the understanding and modelling of RSLC due to ocean density and mass changes have greatly improved over the past few decades, RSLC contributions due to vertical ground motions (VGMs) remain a major source of uncertainty, in particular for local scales. Here, VGMs relate to ground motions that have imprints of a few kilometres, as opposed to broad scale land motion such as Glacial Isostatic Adjustment (GIA). VGMs can be caused by processes such as groundwater extraction or the load of infrastructure on recent sediment deposits, all of which vary in space and time, and are difficult to project in the future.A common technique to account for the VGM contribution in RSLC projections is to use GNSS stations to identify present days rates, identify causes and extrapolate rates where appropriate. However, previous works that followed this approach generally considered a unique GNSS solution (e.g., only NGL) together with a unique trend estimate algorithm, while often several GNSS solution based on different trend estimate techniques are available (i.e., ULR, NGL, JPL and GFZ). Ignoring these alternative estimates and their potential discrepancies may therefore ignore further sources of uncertainty.Here, we analyse how VGMs derived from 4 GNSS records (ULR, NGL, JPL and GFZ) and trend estimates affect uncertainty in future RSLC at tide gauges. Residual local VGM trend distributions are obtained from a Monte Carlo procedure by removing the effect of GIA and solid earth deformation induced by contemporary mass redistribution. This procedure is repeated using three different trend estimate techniques, all based on the MIDAS robust trend estimator, where GNSS timeseries offsets are (i) automatically detected, (ii) initially declared or (iii) manually corrected. We find that the choices of GNSS solution or the trend estimate technique can both substantially affect estimates of the VGMs leading to differences of about 1mm/year, which is approximately one third of sea-level rise rates observed today. Accounting for the uncertainty and discrepancy in GNSS records can change AR6 RSLC median projections by more than -/+ 15% in 2100. This is larger than the differences between SROCC and AR6. Likely ranges and skewness of projected RSLC distributions are also affected. We suggest that sea-level scenarios, whether median, likely, probabilistic or high-end, can benefit substantially from advances in observing and projecting local vertical ground motions.

International audience; In this communication, we aim at getting a deep insight into the role of coastal flooding drivers is of high interest for the planning of adaptation strategies for future climate conditions. Based on the methods described in Rohmer et al. (202), NHESS, we measure the contributions of the offshore forcing conditions (wave/wind characteristics, still water level and sea level rise (SLR) projected up to 2200) to the occurrence of a flooding event at Gâvres town on the French Atlantic coast in a macrotidal environment. This procedure faces, however, two major difficulties, namely (1) the high computational time costs of the hydrodynamic numerical simulations; (2) the statistical dependence between the forcing conditions. By applying a Monte-Carlo-based approach combined with multivariate extreme value analysis, our study proposes a procedure to overcome both difficulties by calculating sensitivity measures dedicated to dependent input variables (named Shapley effects) using Gaussian process (GP) metamodels. On this basis, our results show the increasing influence of SLR over time, and a small-to-moderate contribution of wave/wind characteristics, or even negligible importance in the very long term (beyond 2100). These results were discussed in relation to our modelling choices, in particular the climate change scenario, as well as the uncertainties of the estimation procedure (Monte Carlo sampling and GP error).

Sea-level projections are usually calculated from numerical simulations using complex long-term numerical models (or a chain of models) as part of multi-model ensemble studies. Because of their importance in supporting the decision-making process for coastal risk assessment and adaptation, improving the interpretability of these projections is of great interest. Specifically, it is assumed that clear and transparent explanations of projected sea-level changes can increase the trust of the end-users, and ultimately favor their engagement in coastal adaptation. To this end, we adopt the local attribution approach developed in the machine learning community, and we combine the game-theoretic approach known as ‘SHAP’ (SHapley Additive exPlanation, Lundberg & Lee, 2017) with tree-based regression models. We applied our methodology to sea-level projections for the Greenland ice sheet computed by the ISMIP6 initiative (Goelzer et al., 2020) with a particular attention paid to the validation of the procedure. This allows us to quantify the influence of particular modelling decisions and to express the influence directly in terms of sea level change contribution. For Greenland, we show that the largest predicted sea level change, 19cm in 2100, is primarily attributable to >4.5cm (i.e. nearly 25%) to the choice of the model parameter that controls the retreat of marine-terminating outlet glaciers, i.e. to the modelling of the retreat rate of tidewater glaciers; other modelling decisions (choice of global climate model, formulation of the ice sheet model ISM, model grid size, etc.) have only a low-to-moderate influence for this case (with contribution of 1-2cm). This type of diagnosis can be performed on any member of the ensemble, and we show how the aggregation of all local attribution analyses can help guide future model development as well as scientific interpretation, particularly with regard to model spatial resolution or the selection of a specific model formulation.This study was supported by the PROTECT project, which received funding from the European Union's Horizon 2020 research and innovation program under grant agreement No 869304.ReferencesGoelzer, H., et al. (2020). The future sea-level contribution of the Greenland ice sheet: a multi-model ensemble study of ISMIP6. The Cryosphere 14, 3071-3096.Lundberg, S.M., & Lee, S.I. (2017). A unified approach to interpreting model predictions. In Proceedings of the 31st international conference on neural information processing systems (pp. 4768-4777).

Getting a deep insight into the role of coastal flooding drivers is of high interest for the planning of adaptation strategies for future climate conditions. Using global sensitivity analysis, we aim to measure the contributions of the offshore forcing conditions (wave/wind characteristics, still water level and sea level rise (SLR) projected up to 2200) to the occurrence of the flooding event (defined when the inland water volume exceeds a given threshold YC) at Gâvres town on the French Atlantic coast in a macrotidal environment. This procedure faces, however, two major difficulties, namely (1) the high computational time costs of the hydrodynamic numerical simulations; (2) the statistical dependence between the forcing conditions. By applying a Monte-Carlo-based approach combined with multivariate extreme value analysis, our study proposes a procedure to overcome both difficulties through the computation of sensitivity measures dedicated to dependent input variables (named Shapley effects) with the help of Gaussian process (GP) metamodels. On this basis, our results outline the key influence of SLR over time. Its contribution rapidly increases over time until 2100 where it almost exceeds the contributions of all other uncertainties (with Shapley effect > 40 % considering the representative concentration pathway RCP4.5 scenario). After 2100, it continues to linearly increase up to > 50 %. The SLR influence depends however on our modelling assumptions. Before 2100, it is strongly influenced by the digital elevation Model (DEM); with a DEM with lower topographic elevation (before the raise of dykes in some sectors), the SLR effect is smaller by ~40 %. This influence reduction goes in parallel with an increase in the importance of wave/wind characteristics, hence indicating how the relative effect of the flooding drivers strongly change when protective measures are adopted. By 2100, the joint role of RCP and of YC impacts the SLR influence, which is reduced by 20–30 % when the mode of the SLR probability distribution is high (for RCP8.5 in particular) and when YC is low (of 50 m3). Finally, by showing that these results are robust to the main uncertainties in the estimation procedure (Monte-Carlo sampling and GP error), the combined GP-Shapley effect approach proves to be a valuable tool to explore and characterize uncertainties related to compound coastal flooding under SLR.

International audience; Given recent scientific advances, coastal flooding events can be properly modelled. Nevertheless, such models are computationally expensive (requiring many hours), which prevents their use for forecasting and warning. In addition, there is a gap between the model outputs and information actually needed by decision makers. The present work aims to develop and test a method capable of forecasting coastal flood information adapted to users’ needs. The method must be robust and fast and must integrate the complexity of coastal flood processes. The explored solution relies on metamodels, i.e., mathematical functions that precisely and efficiently (within minutes) estimate the results that would provide the numerical model. While the principle of relying on metamodel solutions is not new, the originality of the present work is to tackle and validate the entire process from the identification of user needs to the establishment and validation of the rapid forecast and early warning system (FEWS) while relying on numerical modelling, metamodelling, the development of indicators, and information technologies. The development and validation are performed at the study site of Gâvres (France). This site is subject to wave overtopping, so the numerical phase-resolving SWASH model is used to build the learning dataset required for the metamodel setup. Gaussian process- and random forest classifier-based metamodels are used and post-processed to estimate 14 indicators of interest for FEWS users. These metamodelling and post-processing schemes are implemented in an FEWS prototype, which is employed by local users and exhibits good warning skills during the validation period. Based on this experience, we provide recommendations for the improvement and/or application of this methodology and individual steps to other sites.

Non-stationary extreme value analysis is a powerful framework to address the problem of time evolution of extremes and its link to climate variability as measured by different climate indices CI (like North Atlantic Oscillation NAO index). To model extreme sea levels (ESLs), a widely-used tool is the non-stationary Generalized Extreme Value distribution (GEV) where the parameters (location, scale and shape) are allowed to vary as a function of some covariates like the month-of-year or some CIs. A commonly used assumption is that only a few CIs impact the GEV parameters by using a linear model, and most of the time by focusing on two GEV parameters (location or/and the scale parameter). In the present study, these assumptions are revisited by relying on a data-driven spline-based GEV fitting approach combined with a penalization procedure. This allows identifying the type (non- or linear) of the CI influence for any of the three GEV parameters directly from the data, and evaluating the significance of this relation, i.e. without making any a priori assumptions as it is traditionally done. This approach is applied to the monthly maxima of sea levels derived from eight of the longest (quasi century-long) tide gauge dataset (Brest, France; Cuxhaven, Germany; Gedser, Denmark; Halifax, Canada; Honolulu, US; Newlyn, UK; San Francisco, US; Stockholm, Sweden) and by accounting for four major CIs (the North Atlantic Oscillation, the Atlantic Multidecadal Oscillation, the Nino 1 + 2 and the Southern Oscillation indices). From this analysis, we show that: (1) the links between CIs and different parameters of a GEV distribution fitted to ESL data are most of the time linear, but some of them present significant non-linear shapes; (2) multiple CIs should be considered to predict ESLs, and (3) the CI influence of the GEV distribution is not limited to the location parameter. These results are useful to understand current modes of variability of ESLs, and ultimately to improve coastal resilience through more precise extreme water level assessments.

Decision-making in the area of coastal adaptation is facing major challenges due to ambiguity (i.e., deep uncertainty) pertaining to the selection of a probability model for sea level rise (SLR) projections. Possibility distributions are mathematical tools that address this type of uncertainty since they bound all the plausible probability models that are consistent with the available data. In the present study, SLR uncertainties are represented by a possibility distribution constrained by likely ranges provided in the IPCC Fifth Assessment Report and by a review of high-end scenarios. On this basis, we propose a framework combining probabilities and possibilities to evaluate how SLR uncertainties accumulate with other sources of uncertainties, such as future greenhouse gas emissions, upper bounds of future sea level changes, the regional variability of sea level changes, the vertical ground motion, and the contributions of extremes and wave effects. We apply the framework to evaluate the probability of coastal flooding by the year 2100 at a local, low-lying coastal French urban area on the Mediterranean coast. We show that when adaptation is limited to maintaining current defenses, the level of ambiguity is too large to precisely assign a probability model to future flooding. Raising the coastal walls by 85 cm creates a safety margin that may not be considered sufficient by local stakeholders. A sensitivity analysis highlights the key role of deep uncertainties pertaining to global SLR and of the statistical uncertainty related to extremes. The ranking of uncertainties strongly depends on the decision-maker’s attitude to risk (e.g., neutral, averse), which highlights the need for research combining advanced mathematical theories of uncertainties with decision analytics and social science.

Motivated by risk assessment of coastal flooding, we consider time-consuming simulators with a spatial output. The aim is to perform sensitivity analysis (SA), quantifying the influence of input parameters on the output. There are three main issues. First, due to computational time, standard SA techniques cannot be directly applied on the simulator. Second, the output is infinite dimensional, or at least high dimensional if the output is discretized. Third, the spatial output is non-stationary and exhibits strong local variations. We show that all these issues can be addressed all together by using functional PCA (FPCA). We first specify a functional basis, such as wavelets or B-splines, designed to handle local variations. Secondly, we select the most influential basis terms, either with an energy criterion after basis orthonormalization, or directly on the original basis with a penalized regression approach. Then FPCA further reduces dimension by doing PCA on the most influential basis coefficients, with an ad-hoc metric. Finally, fast-to-evaluate metamodels are built on the few selected principal components. They provide a proxy on which SA can be done. As a by-product, we obtain analytical formulas for variance-based sensitivity indices, generalizing known formula assuming orthonormality of basis functions., Comment: 26 pages, 12 figures, 1 algoritm

The treatment of uncertainty using extra-probabilistic approaches, like intervals or p-boxes, allows for a clear separation between epistemic uncertainty and randomness in the results of risk assessments. This can take the form of an interval of failure probabilities; the interval width W being an indicator of “what is unknown.” In some situations, W is too large to be informative. To overcome this problem, we propose to reverse the usual chain of treatment by starting with the targeted value of W that is acceptable to support the decision-making, and to quantify the necessary reduction in the input p-boxes that allows achieving it. In this view, we assess the feasibility of this procedure using two case studies (risk of dike failure, and risk of rupture of a frame structure subjected to lateral loads). By making the link with the estimation of excursion sets (i.e., the set of points where a function takes values below some prescribed threshold), we propose to alleviate the computational burden of the procedure by relying on the combination of Gaussian process (GP) metamodels and sequential design of computer experiments. The considered test cases show that the estimates can be achieved with only a few tens of calls to the computationally intensive algorithm for mixed aleatory/epistemic uncertainty propagation.

Landslide are ubiquitous phenomena affecting many countries around the world. In recent years, in the context of landslide risk reduction and management an increasing number of landslide susceptibility or hazard maps were carried out at the national scale. These analyses are generally based on: (1) an existing inventory (national database or compilation of work carried out individually); (2) empirical indirect or data-driven methods. However, it appears that few studies, at this scale of work, take into account the temporality of events and/or the triggering factors to tend towards hazard assessment. This statement is often due to a lack of information, especially for emerging countries, where a lack of spatial and temporal information on events and on triggering factors subsist. Thus, if landslide inventories provide the first information to assess the susceptibility, at this scale of work, it is also necessary to identify and analyze the components inducing hazard in order to assesses properly the associated risks (i.e. the annual frequency of events). This identification can be carried out by several ways with: (1) direct approaches based on the analysis of temporal data of past landslides (e.g. computations of the exceedance probability of landslide occurrence estimated by Poisson or binomial distributions); or (2) by indirect approaches based on the analysis of triggering factors (e.g. rainfalls volume, intensity and duration). This contribution focus on the methodology adopted during the GEMMAP[i] project to assess landslide hazard at national scale (i.e. 1:250,000) for Malawi, a landlocked country in southeastern Africa. This country is characterized by its topography composed of mountains crossed by the Great Rift Valley and the Malawi Lake. It is experiencing many slope instabilities principally due to intense rainfalls from tropical cyclones to depressions. The methodology is based on an approach quantifying the different failure probabilities at the spatial and temporal levels following the JTC-1 guidelines. Thus, after having improved the landslide inventory by visual remote sensing and field surveys, integrated information on their type, activity, and triggering periods; susceptibility analyses to different types of landslides were carried out by a data driven method. Then temporal analyses of the events were performed, taking into account: (1) the recurrence time for different phenomena (i.e. debris-flows, debris-slides and slides for the period 1946–2019) and (2) the rainfall periods induced by several and different tropical meteorological events (World Meteorological Organization). This analysis has led to compute the exceedance probability (i.e. based on Poisson distribution) of landslide reactivation for six return periods from 1 to 100 years following different typical meteorological events. The computations were performed for each susceptibility class associated to each type of landslide. Finally, the methodology allows elaborating different landslide hazard scenarios at national scale for the near or more distant future. [i] The "Geological Mapping and Mineral Assessment of Malawi" project is led by the BRGM, with international partners: GTK (Geological Survey of Finland), and CGS (Geological Survey of South Africa) for the Government of Malawi through the Geological Survey Department (GSD).

To estimate return levels of wave heights (Hs) induced by tropical cyclones at the coast, a commonly-used approach is to (1) randomly generate a large number of synthetic cyclone events (typically >1,000); (2) numerically simulate the corresponding Hs over the whole domain of interest; (3) extract the Hs values at the desired location at the coast and (4) perform the local extreme value analysis (EVA) to derive the corresponding return level. Step 2 is however very constraining because it often involves a numerical hydrodynamic simulator that can be prohibitive to run: this might limit the number of results to perform the local EVA (typically to several hundreds). In this communication, we propose a spatial stochastic simulation procedure to increase the database size of numerical results with synthetic maps of Hs that are stochastically generated. To do so, we propose to rely on a data-driven dimensionality-reduction method, either unsupervised (Principal Component Analysis) or supervised (Partial Least Squares Regression), that is trained with a limited number of pre-existing numerically simulated Hs maps. The procedure is applied to the Guadeloupe island and results are compared to the commonly-used approach applied to a large database of Hs values computed for nearly 2,000 synthetic cyclones (representative of 3,200 years – Krien et al., NHESS, 2015). When using only a hundred of cyclones, we show that the estimates of the 100-year return levels can be achieved with a mean absolute percentage error (derived from a bootstrap-based procedure) ranging between 5 and 15% around the coasts while keeping the width of the 95% confidence interval of the same order of magnitude than the one using the full database. Without synthetic Hs maps augmentation, the error and confidence interval width are both increased by nearly 100%. A careful attention is paid to the tuning of the approach by testing the sensitivity to the spatial domain size, the information loss due to data compression, and the number of cyclones. This study has been carried within the Carib-Coast INTERREG project (https://www.interreg-caraibes.fr/carib-coast).

Most sandy coasts worldwide are under chronic erosion, which increasingly put at risk coastal communities. In the context of adaptation to climate change and sea-level rise (SLR), predictions of shoreline evolution patterns are critical for decision-making. Sandy shorelines are highly dynamic environments, which respond to multiple complex processes interacting at different spatial and temporal scales, making shoreline predictions challenging, especially on long time scales (decades and centuries). However, modelling shoreline predictions inherit uncertainties in the primary driver boundary conditions (e.g. sea-level rise and wave forcing) as well as uncertainties related to model assumptions and/or misspecifications of the physics. In this work, we analyze the uncertainties associated with shoreline evolution by 2100 of the high-energy, cross-shore transport dominated, sandy beach of Truc Vert (France). Using two equilibrium shoreline models based on different disequilibrium principles, and the Bruun Rule, we explicitly resolved wave-driven shoreline change produced continuous probabilistic predictions of the Truc Vert shoreline evolution to 2100 for two carbon emission scenarios (RCP 8.5 and 4.5), incorporating uncertainties related to SLR, depth of closure, and model free parameters. The shoreline models were forced with continuous wave projection time series, issued by the National Oceanography Center (UK) for the RCP 4.5 and 8.5 scenarios, based on a single global climate model. We assigned a probability distribution to each uncertain input variable. For both shoreline models, an optimization algorithm was used to identify all the realistic combinations of model free parameters leading to a skillful hindcast against 8 years of in situ shoreline data. A Gaussian distribution was assigned to the yearly probabilistic SLR estimates based on SROCC to 2100, and depth of closure. We further addressed the relative impact of each source of uncertainty on the model results performing a Global Sensitivity Analysis (GSA). The results show that, for both RCP scenarios, shoreline response position during the first half of the century is mainly sensitive to the equilibrium model parameters, with the influence of SLR emerging in the second half of the century. The results also reflect the strong relation between the model parameters uncertainties and the interdecadal variability of wave conditions. Using a single wave time series, such variability and related chronology has a much stronger impact on shoreline change with the Splinter model than the Yates model, highlighting that the choice of the modelling approach is critical to future shoreline change estimates in changing wave climates. We also emphasize the need for more continuous wave projections in order to generate ensemble wave time series and include uncertainty in future wave conditions.

Coastal impacts of climate change and the related mitigation and adaptation needs requires assessments of future sea-level changes. Following a common practice in coastal engineering, probabilistic sea-level projections have been proposed for at least 20 years. This requires a probability model to represent the uncertainties of future sea-level rise, which is not achievable because potential ice sheets mass losses remain poorly understood given the knowledge available today. Here, we apply the principles of extra-probabilistic theories of uncertainties to generate global and regional sea-level projections based on uncertain components. This approach assigns an imprecision to a probabilistic measure, in order to quantify lack of knowledge pertaining to probabilistic projections. This can serve to understand, analyze and communicate uncertainties due to the coexistence of different processes contributing to future sea-level rise, including ice-sheets. We show that the knowledge gained since the 5th Assessment report of the IPCC allows better quantification of how global and regional sea-level rise uncertainties can be reduced with lower greenhouse gas emissions. Furthermore, Europe and Northern America are among those profiting most from a policy limiting climate change to RCP 2.6 versus RCP 4.5 in terms of reducing uncertainties of sea-level rise.

Global mean sea level rise and its acceleration are projected to aggravate coastal erosion over the 21st century, which constitutes a major challenge for coastal adaptation. Projections of shoreline retreat are highly uncertain, however, namely due to deeply uncertain mean sea level projections and the absence of consensus on a coastal impact model. An improved understanding and a better quantification of these sources of deep uncertainty are hence required to improve coastal risk management and inform adaptation decisions. In this work we present and apply a new extraprobabilistic framework to develop shoreline change projections of sandy coasts that allows consideration of intrinsic (or aleatory) and knowledge-based (or epistemic) uncertainties exhaustively and transparently. This framework builds upon an empirical shoreline change model to which we ascribe possibility functions to represent deeply uncertain variables. The model is applied to two local sites in Aquitaine (France) and Castellón (Spain). First, we validate the framework against historical shoreline observations and then develop shoreline change projections that account for possible (although unlikely) low-end and high-end mean sea level scenarios. Our high-end projections show for instance that shoreline retreats of up to 200m in Aquitaine and 130m in Castellón are plausible by 2100, while low-end projections revealed that 58 and 37m modest shoreline retreats, respectively, are also plausible. Such extended intervals of possible future shoreline changes reflect an ambiguity in the probabilistic description of shoreline change projections, which could be substantially reduced by better constraining sea level rise (SLR) projections and improving coastal impact models. We found for instance that if mean sea level by 2100 does not exceed 1m, the ambiguity can be reduced by more than 50%. This could be achieved through an ambitious climate mitigation policy and improved knowledge on ice sheets. This research has been supported by the BRGM, IHCantabria and the ERA4CS-ECLISEA project (grant no. 690462).

Ensemble-based simulations of future shoreline evolution to 2100, including sea-level rise driven erosion, are performed and analysed  Future shoreline projections uncertainties are initially controlled by modelling assumptions and after 2060 by sea-level rise uncertainties  The choice of wave-driven equilibrium modelling approach and incident wave chronology are critical to future shoreline projections 1 Abstract Most sandy coasts worldwide are under chronic erosion, which increasingly put at risk coastal communities. Sandy shorelines are highly dynamic and respond to a myriad of processes interacting at different spatial and temporal scales, making shoreline predictions challenging, especially on long time scales (i.e. decades and centuries). Shoreline modelling inherits uncertainties from the primary driver boundary conditions (e.g. sea-level rise and wave forcing) as well as uncertainties related to model assumptions and/or misspecifications of the physics. This study presents an analysis of the uncertainties associated with future shoreline evolution at the high-energy, cross-shore transport dominated, sandy beach of Truc Vert (France) over the 21 st century. We explicitly resolve wave-driven shoreline change using two different equilibrium modelling approaches to provide new insight into the contributions of sea-level rise, and free model parameters uncertainties on future shoreline change in the frame of climate change. Based on a Global Sensitivity Analysis, shoreline response during the first half of the century is found to be mainly sensitive to the equilibrium model parameters, with the influence of sea-level rise emerging in the second half of the century (~2050 or later), in both Representative Concentration Pathways 4.5 and 8.5 scenarios. The results reveal that the seasonal and interannual variability of the predicted shoreline position is sensitive to the choice of the wave-driven equilibrium based model. Finally, we discuss the importance of the chronology of wave events in future shoreline change, calling for more continuous wave projection time series to further address uncertainties in future wave conditions.

Increasing our capacity to predict extreme storm surges is one of the key issues in terms of coastal flood risk prevention and adaptation. Dynamically forecasting storm surges is computationally expensive. Here, we focus on an alternative data-driven approach and set up a weather-type statistical downscaling for daily maximum storm surge (SS) prediction, using atmospheric hindcasts (CFSR and CFSv2) and 15 years of tidal gauge station measurements. We focus on predicting the storm surge at La Rochelle&ndash, La Pallice tidal gauge station. First, based on a sensitivity analysis to the various parameters of the weather-type approach, we find that the model configuration providing the best performance in SS prediction relies on a fully supervised classification using minimum daily sea level pressure (SLP) and maximum SLP gradient, with 1&deg, resolution in the northeast Atlantic domain as the predictor. Second, we compare the resulting optimal model with the inverse barometer approach and other statistical models (multi-linear regression, semi-supervised and unsupervised weather-types based approaches). The optimal configuration provides more accurate predictions for extreme storm surges, but also the capacity to identify unusual atmospheric storm patterns that can lead to extreme storm surges, as the Xynthia storm for instance (a decrease in the maximum absolute error of 50%).

Coastal floods are driven by many hydro-meteorological forcing factors, among which are mean sea levels, tides, atmospheric storm surges, and waves. Depending on these conditions, wave overtopping may occur and, in some cases, lead to a significant flood. In the present study, we investigate the effect of the stochastic character of waves on the flood itself using a phase-resolving wave model (SWASH). We focus on the macro-tidal site of G&acirc, vres (France), consider two past flood events (both resulting from wave overtopping), and investigate how the effect of randomness of waves on the flood is changing with the forcing conditions and the time span (minutes to hours). We clearly show that the effect of waves&rsquo, stochasticity on the flood itself is far from being negligible and, especially on a short time scale (~15 min), generates an uncertainty comparable to that induced by the sea-level rise scenarios, as long as the still water level remains smaller than the critical level above which overflow occurs. This implies that lower confidence should be assigned to flood projection on sites where wave overtopping is the main process leading to flood.

A key point of landslide hazard assessment is the estimation of their runout. Empirical relations linking angle of reach to volume can be used relatively easily, but they are generally associated with large uncertainties as they do not consider the topographic specificity of a given study site. On the contrary, numerical simulations provide more detailed results on the deposits morphology, but their rheological parameters can be difficult to constrain. Simulating all possible values can be time consuming and incompatible with operational requirements of rapid estimations. We propose and compare three operational methods to derive scaling power laws relating the landslide travel distance to the destabilized volume. The first one relies only on empirical relations, the second one on numerical simulations with back-analysis, and the third one combines both approaches. Their efficiency is tested on three case studies: the Samperre cliff collapses in Martinique, Lesser Antilles (0.5 to 4&times, 106 m3), the Frank Slide rock avalanche (36&times, 106 m3) and the Samperre cliff collapses in Martinique, Lesser Antilles (0.5 to 4&times, 106 m3) the Fei Tsui debris slide in Hong Kong (0.014&times, 106 m3). Purely numerical estimations yield the smallest uncertainty, but the uncertainty on rheological parameters is difficult to quantify. Combining numerical and empirical approaches allows to reduce the uncertainty of estimation by up to 50%, in comparison to purely empirical estimations. However, it may also induces a bias in the estimation, though observations always lie in the 95% prediction intervals. We also show that empirical estimations fail to model properly the dependence between volume and travel distance, particularly for small landslides (&lt, 20,000 &lt, 0.02&times, 106 m3).

International audience; Surrogate models are often used to replace costly-to-evaluate complex coastal codes to achieve substantial computational savings. In many of those models, the hydrometeorological forcing conditions (inputs) or flood events (outputs) are conveniently parameterized by scalar representations, neglecting that the inputs are actually time series and that floods propagate spatially inland. Both facts are crucial in flood prediction for complex coastal systems. Our aim is to establish a surrogate model that accounts for time-varying inputs and provides information on spatially varying inland flooding. We introduce a multioutput Gaussian process model based on a separable kernel that correlates both functional inputs and spatial locations. Efficient implementations consider tensor-structured computations or sparse-variational approximations. In several experiments, we demonstrate the versatility of the model for both learning maps and inferring unobserved maps, numerically showing the convergence of predictions as the number of learning maps increases. We assess our framework in a coastal flood prediction application. Predictions are obtained with small error values within computation time highly compatible with short-term forecast requirements (on the order of minutes compared to the days required by hydrodynamic simulators). We conclude that our framework is a promising approach for forecast and early-warning systems.

Integrating full-process high resolution hydrodynamic simulations within early warning system (EWS) for marine flooding is hindered by the large computation time cost of such numerical models. This problem can be alleviated through the statistical analysis of pre-calculated simulation results to build a fast (low computation time cost) statistical predictive model (named metamodel). Despite the success of this approach, a direct application of such techniques for EWS is not straightforward in all settings, more particularly in environments where the stochastic character of waves has a significant effect on the induced flood, i.e., where overtopping is on a duration smaller than 500 times the offshore wave period. In such environments, the numerical simulator is not deterministic and provides statistical quantities of the flooding indicators. By focusing on the estimates of quantiles, the objective of the present study is to explore the applicability of random forest (RF) models for marine flooding prediction by providing two levels of information: (1) the quantile of interest via a quantile random forest regression model (qRF); (2) the flooding probability via a classification random forest (cRF). We use the macrotidal site of Gâvres (French Atlantic coast) as an application case for which ~ 2000 numerical simulations were performed (i.e. stochastic simulations given 100 different extreme-but-realistic offshore meteo-oceanic input conditions were repeated 20 times) to compute local and global flooding indicators (respectively the maximum water depth at the coast and the total volume of water entering the territory). Through an extensive repeated cross-validation procedure, we tune the qRF parameters leading to high coefficient of determination of ~ 90% for the quantiles at 25–50–75%, and we show that the qRF models outperform the commonly used Tobit regression model. The comparison with the numerical results on historical events shows very satisfactory prediction for events both leading to major flooding and to absence of impact. For low quantile level and minor-to-moderate flooding events, the second level provided by the cRF-derived flooding probability shows its added value by enabling the EWS user to nuance the qRF prediction and to tag some situations where the prediction remains unsure.

Global scale assessment of coastal flood damage and adaptation costs under 21st century sea-level rise are associated with a wide range of uncertainties including those in future projections of socioeconomic development (SSP scenarios), of greenhouse gas emissions (RCP scenarios), and of sea-level rise (SLR). These uncertainties also include structural uncertainties related to the modeling of extreme sea levels, vulnerability functions, and the translation of flooding-induced damage to costs. This raises the following questions: what is the relative importance of each source of uncertainty in the final global-scale results? Which sources of uncertainty need to be considered? What uncertainties are of negligible influence? Hence, getting better insights in the role played by these uncertainties allows to ease their communication and to structure the message on future coastal impacts and induced losses. Using the integrated DIVA Model (see e.g. Hinkel et al., 2014, PNAS), we extensively explore the impact of these uncertainties in a global manner, i.e. by considering a large number (~3,000) of scenario combinations and by analyzing the associated results using a regression-based machine learning technique (i.e. regression decision trees). On this basis, we show the decreasing roles, over time, of the uncertainties in the extremes’ modeling together with the increasing roles of SSP and of RCP after 2030 and 2080 for the damage and adaptation costs respectively. This means that mitigation of climate change helps to reduce uncertainty of adaptation costs, and choosing a particular SSP reduces the uncertainty on the expected damages. In addition, the tree structure of the machine learning technique allows an in-depth analysis of the interactions of the different uncertain factors. These results are discussed depending on the SLR data selected for the analysis, i.e. before and after the recently released IPCC SROCC report on September 2019.

The characterisation of past coastal flood events is crucial for risk prevention. However, it is limited by the partial character of historical information on flood events and the lack or limited quality of past hydro-meteorological data. In addition coastal flood processes are complex, driven by many hydro-meteorological processes, making mechanisms and probability analysis challenging. These issues are tackled by joining historical, statistical and modelling approaches. We focus on a macrotidal site (Gâvres, France) subject to overtopping and investigate the 1900-2010 period. A continuous hydro-meteorological database is built and a damage event database is set up based on archives, newspapers, maps and aerial photographies. Using together historic information, hindcasts and hydrodynamic models, we identified 9 flood events, among which 5 significant flood events (4 with high confidence: 1924, 1978, 2001, 2008; 1 with a lower confidence: 1904). These flood events are driven by the combination of sea-level rise, tide, atmospheric surge, offshore wave conditions and local wind. The critical conditions leading to flood are further analysed, including the effect of coastal defences, showing that the present coastal defences would not have allowed to face the hydro-meteorological conditions of 09/02/1924 for instance, whose bi-variate return periods of exceedance Tr (still water level relative to the mean sea level and significant wave height) is larger than 1000 y. In addition, Tr is expected to significantly decrease with the sea-level rise, reaching values smaller than 1 y, for 8 of the 9 historical events, for a sea-level rise of 0.63 m, which is equal to the median amount of sea-level rise projected by the 5th Assessment Report of the IPCC in this region for RCP8.5 in 2100.

The Maldives, located in the Indian Ocean, are the paradigm of low-lying coral-reef islands where adaptation to climate change is essential. Besides the mean sea level rise in this region, that is expected to be around 1 m by the end of the century according to the last IPCC report, these islands are exposed to one of the largest swells in terms of significant wave height and peak period. In this study we characterize, using the output of global wind-wave models forced by wind fields from the CMIP5 ensemble delivered by CSIRO, the present conditions and future projections of the waves around the Maldivian archipelago, as well as the return periods for the predominant swell directions. We then propagate extreme waves inside the domain using WaveWatch III model run onto a high-resolution grid (down to 500 m at the coast). Finally, we evaluate the coastal impacts of extreme swell waves in two strategic case study islands with different exposures and where land reclamation and different adaptation solutions have been/are being done. To do so, we propagate waves using SWASH model with rising mean sea level towards the shoreline and assess the flooding extend under different conditions. This kind of studies are essential to help the policy makers in defining the most accurate and appropriate adaptation strategies.

International audience; As low-lying coastal areas can be impacted by flooding caused by dynamic components that are dependent on each other (wind, waves, water levels—tide, atmospheric surge, currents), the analysis of the return period of a single component is not representative of the return period of the total water level at the coast. It is important to assess a joint return period of all the components. Based on a semiparametric multivariate extreme value analysis, we determined the joint probabilities that significant wave heights (Hs), wind intensity at 10 m above the ground (U), and still water level (SWL) exceeded jointly imposed thresholds all along the Corsica Island coasts (Mediterranean Sea). We also considered the covariate peak direction (Dp), the peak period (Tp), and the wind direction (Du). Here, we focus on providing extreme scenarios to populate coastal hydrodynamic models, SWAN and SWASH-2DH, in order to compute the 100-year total water level (100y-TWL) all along the coasts. We show how the proposed multivariate extreme value analysis can help to more accurately define low-lying zones potentially exposed to coastal flooding, especially in Corsica where a unique value of 2 m was taken into account in previous studies. The computed 100y-TWL values are between 1 m along the eastern coasts and a maximum of 1.8 m on the western coast. The calculated values are also below the 2.4 m threshold recommended when considering the sea level rise (SLR). This highlights the added value of performing a full integration of extreme offshore conditions, together with their dependence on hydrodynamic simulations for screening out the coastal areas potentially exposed to flooding.

The instability mechanisms for self-organized kilometer-scale shoreline sand waves have been extensively explored by modeling. However, while the assumed bathymetric perturbation associated with the sand wave controls the feedback between morphology and waves, its effect on the instability onset has not been explored. In addition, no systematic investigation of the effect of the physical parameters has been done yet. Using a linear stability model, we investigate the effect of wave conditions, cross-shore profile, closure depth, and two perturbation shapes (P1: cross-shore bathymetric profile shift, and P2: bed level perturbation linearly decreasing offshore). For a P1 perturbation, no instability occurs below an absolute critical angle ?c0˜ 40-50°. For a P2 perturbation, there is no absolute critical angle: sand waves can develop also for low-angle waves. In fact, the bathymetric perturbation shape plays a key role in low-angle wave instability: such instability only develops if the curvature of the depth contours offshore the breaking zone is larger than the shoreline one. This can occur for the P2 perturbation but not for P1. The analysis of bathymetric data suggests that both curvature configurations could exist in nature. For both perturbation types, large wave angle, small wave period, and large closure depth strongly favor instability. The cross-shore profile has almost no effect with a P1 perturbation, whereas large surf zone slope and gently sloping shoreface strongly enhance instability under low-angle waves for a P2 perturbation. Finally, predictive statistical models are set up to identify sites prone to exhibit either a critical angle close to ?c0 or low-angle wave instability.

Muller, H.; van Rooijen, A.; Idier, D.; Pedreros, R., and Rohmer, J., 2017. Assessing storm impact on a French coastal dune system using morphodynamic modeling. Natural coastal dune systems provide protection against flooding from the ocean for many coastal communities around the world. An in-depth knowledge of their erosion mechanisms under oceanic stress can help to identify potential weak spots along the coastline and eventually prevent damage to the hinterland. The objective of this study was to assess the effects of hydro-morphodynamic phenomena on the coastal dune morphology during storm events and to evaluate their level of protection. This paper focuses on the physical processes that cause erosion and breaching of sandy dune systems, which are typically a function of the coastal morphology in combination with the hydraulic loading. An XBeach model was set up for the coast of Les Boucholeurs (France), which suffered from severe erosion, dune breaching (at six locations), and inundation of ...

The characterisation of past coastal flood events is crucial for risk prevention. However, it is limited by the partial nature of historical information on flood events and the lack or limited quality of past hydro-meteorological data. In addition, coastal flood processes are complex, driven by many hydro-meteorological processes, making mechanisms and probability analysis challenging. Here, we tackle these issues by joining historical, statistical and modelling approaches. We focus on a macrotidal site (Gâvres, France) subject to overtopping and investigate the 1900–2010 period. We build a continuous hydro-meteorological database and a damage event database using archives, newspapers, maps and aerial photographs. Using together these historical information, hindcasts and hydrodynamic models, we identify nine flood events, among which five are significant flood events (four with high confidence: 1924, 1978, 2001, 2008; one with a lower confidence: 1904). These flood events are driven by the combination of sea-level rise, tide, atmospheric surge, offshore wave conditions and local wind. We further analyse the critical conditions leading to flood, including the effect of coastal defences, showing, for instance, that the present coastal defences would not have allowed to face the hydro-meteorological conditions of 09/02/1924, whose bi-variate return periods of exceedance $$T_\mathrm{R}$$ (still water level relative to the mean sea level and significant wave height) are larger than 1000 year. In the coming decades, $$T_\mathrm{R}$$ is expected to significantly decrease with sea-level rise, reaching values smaller than 1 year, for eight of the nine historical events, for a sea-level rise of 0.63 m, which is equal to the median sea-level rise projected by the 5th Assessment Report of the IPCC in this region for RCP8.5 in 2100.

Rohmer, J.; Louisor, J.; Le Cozannet, G.; Naveau, P.; Thao, S., and Bertin, X., 2020. Attribution of extreme wave height records along the North Atlantic coasts using hindcast data: Feasibility and limitations. In: Malvarez, G. and Navas, F. (eds.), Global Coastal Issues of 2020. Journal of Coastal Research, Special Issue No. 95, pp. 1268-1272. Coconut Creek (Florida), ISSN 0749-0208.Extreme Event Attribution (EEA) aims at answering questions about how much climate change influenced the probability or intensity of a specific type of extreme meteo-oceanic event. Here, we focus on wave record breaking i.e. the occurrence of an extreme significant wave height (hs) value that exceeds all past observations. The objective is to evaluate the probability changes related to climate change by estimating the fraction of attributable risk far=1–p0/p1, where p0 and p1 are probabilities of the wave record event in two different worlds: a counterfactual world without anthropogenic forcings i.e. a “world that might have been”, and the factual world, i.e. “world that is.” To define these different worlds, we rely on a wave hindcast database, which provides very long time series (1900-2008) of hs over the whole North Atlantic Ocean Basin (NAOB). We assume that the counterfactual world corresponds to the series of annual hs maxima over the period 1900-1930, and that the factual world corresponds to the annual hs maxima over the period 1978-2008. The extreme event attribution approach dedicated to record breakings was applied over NAOB, and we show large far values (>0.5) along the northern Canadian, Scottish and southwestern Norwegian coasts (over the latitudes 50°-65°N). These results are, however, carefully discussed with respect to different uncertainty sources, namely the validity of the assumptions underlying the EEA, the statistical uncertainties, the use of hindcast data instead of global climate model's results, and the limitations in the hindcast database setup.