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International audience; Land reclamation in the Maldives is widespread. Current land reclamation practices, however, lack a systematic approach to anticipate sea-level rise and do not account for local flood risk differences to inform location and design choices. To address these limitations, this paper applies two decision-support tools: a hazard threshold analysis, and a cost-benefit analysis. Both tools produce site-specific estimates of land elevations or flood defence heights but do so for different goals. The hazard threshold analysis identifies hazard-based solutions that meet an acceptable flood probability for an intended lifespan without follow-up actions by reliability optimisation. The cost-benefit analysis identifies risk-based solutions using dynamic programming. We apply both tools to two land reclamation sites, a newly reclaimed airport island and a land extension of an inhabited island, in the Maldives. We find that total hazard-based heights for long-term planning horizons are highly uncertain, with local height differences of up to 1.9 m across sealevel rise scenarios by 2100. Risk-based Island elevations, in contrast, differ much less across scenarios, offering a practical advantage for decision-making. However, land reclamation choices on location, land elevation and investment in flood protection are not only driven by hazardrelated aspects, such as reef characteristics, swell exposure, and sea-level rise, but also by estimates of exposed assets, reclamation, and flood protection costs. Taken together, the two decision-support tools are helpful for improving adaptation decisions and are also applicable in other small island regions.

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.

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).

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.

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

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%).

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.

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.

Idier, D.; Aurouet, A.; Bachoc, F.; Baills, A.; Betancourt, J.; Durand, J.; Mouche, R.; Rohmer, J.; Gamboa, F.; Klein T.; Lambert, J.; Le Cozannet, G.; Le Roy, S.; Louisor, J.; Pedreros, R., and Veron, A.L., 2020. Toward a user-based, robust and fast running method for coastal flooding forecast, early warning, and risk prevention. In: Malvarez, G. and Navas, F. (eds.), Global Coastal Issues of 2020. Journal of Coastal Research, Special Issue No. 95, pp. 1111–1116. Coconut Creek (Florida), ISSN 0749-0208.Scientific progresses now allow properly modelling coastal flooding events. Such models are nevertheless very expensive in terms of computation time (>hours) which prevents any use for forecast and warning or even for estimating the coastal flood hazard return period together with uncertainties. In addition, there is a gap between model outputs and information actually needed by the decision makers. Within the RISCOPE project, we aim at developing a user-based method contributing to forecast, early-warning and prevention of coastal flooding risks. The method should be robust, fast and integrate the complexity of coastal flood processes. To build such coastal flooding models, the solution explored relies on meta-models, i.e. mathematical functions which estimate, with good precision and at a negligible computational cost (

International audience; Ensuring the validity and credibility of Bayesian Belief Network (BBN) as a modelling tool for expert systems requires appropriate methods for sensitivity analysis (SA), in order to test the robustness of the BBN diagnostic and prognostic with respect to the parameterisation of the conditional probability model (CPM). Yet, the most widely used techniques (based on sensitivity functions for discrete BBNs) only provide a local insight on the CPM influence, i.e. by varying only one CPM parameter at a time (or a few of them) while keeping the other ones unchanged. To overcome this limitation, the present study proposes an approach for global SA relying on Beta Regression using gradient boosting (potentially combined with stability selection analysis): it presents the benefit of keeping the presentation intuitive through a graph-based approach, while being applicable to a large number of CPM parameters. The implementation of this approach is investigated for three cases, which cover a large spectrum of situations: (1) a small discrete BBN, used to capture medical knowledge, demonstrates the proposed approach; (2) a linear Gaussian BBN, used to assess the damage of reinforced concrete structures, exemplifies a case where the number of parameters is too large to be easily processed and interpreted (>40 parameters); (3) a discrete BBN, used for reliability analysis of nuclear power plant, exemplifies a case where analytical solutions for sensitivity can hardly be derived. Finally, provided that the validity of the BBR model is carefully checked, we show that the proposed approach can provide richer information than traditional SA methods at different levels: (i) it selects the most influential parameters; (ii) it provides the functional relation between the CPM parameter and the result of the probabilistic query; and (iii) it identifies how the CPM parameters can lead to situations of high probability, while quantifying the confidence in the occurrence of these situations.

Sandy shorelines are constantly evolving, threatening frequently human assets such as buildings or transport infrastructure. In these environments, sea-level rise will exacerbate coastal erosion to an amount which remains uncertain. Sandy shoreline change projections inherit the uncertainties of future mean sea-level changes, of vertical ground motions, and of other natural and anthropogenic processes affecting shoreline change variability and trends. Furthermore, the erosive impact of sea-level rise itself can be quantified using two fundamentally different models. Here, we show that this latter source of uncertainty, which has been little quantified so far, can account for 20 to 40% of the variance of shoreline projections by 2100 and beyond. This is demonstrated for four contrasting sandy beaches that are relatively unaffected by human interventions in southwestern France, where a variance-based global sensitivity analysis of shoreline projection uncertainties can be performed owing to previous observations of beach profile and shoreline changes. This means that sustained coastal observations and efforts to develop sea-level rise impact models are needed to understand and eventually reduce uncertainties of shoreline change projections, in order to ultimately support coastal land-use planning and adaptation.

In the area of sea-level rise, recent research has focused on assessing either likely or high end future sea levels, but less attention has been given to &ldquo, low-end&rdquo, sea-level projections, exploring best-case potential sea-level changes and providing the basis for estimating minimum adaptation needs. Here, we provide global and regional probabilistic sea-level projections using conservative projections of glaciers and ice-sheets melting and a selection of models from the Coupled Model Intercomparison Project phase 5 (CMIP5) delivering moderate thermal expansion. Our low-end sea-level projections are higher than previously estimated because they rely on modeling outcomes only, and do not add any expert judgement, aiming essentially at delivering more realistic upper tails. While there are good reasons to believe that our projections are excessively optimistic, they can be used as low-end sea-level projections in order to inform users with low aversion to uncertainty. Our low-end sea-level projection exceeds 0.5 m along most inhabited coasts by 2100 for business as usual greenhouse gas emissions (RCP8.5), which is relevant for adaptation practitioners as long as efficient climate change mitigation policies are not implemented. This means that without efficient climate mitigation, an acceleration of sea-level rise can hardly be avoided during the 21st century.

International audience; Changes in mean sea level (MSL) are a major, but not the unique, cause of changes in high-percentile sea levels (HSL), e.g. the annual 99.9th quantile of sea level (among other factors, climate variability may also have huge influence). To unravel the respective influence of each contributor, we propose to use structural time series models considering six major climate indices (CI) (Artic Oscillation, North Atlantic Oscillation, Atlantic Multidecadal Oscillation, Southern Oscillation Index, Nino 1 + 2 and Nino 3.4) as well as a reconstruction of MSL. The method is applied to eight century-long tide gauges across the world (Brest (France), Newlyn (UK), Cuxhaven (Germany), Stockholm (Sweden), Gedser (Danemark), Halifax (Canada), San Francisco (US), and Honolulu (US)). The treatment within a Bayesian setting enables to derive an importance indicator, which measures how often the considered driver is included in the model. The application to the eight tide gauges outlines that MSL signal is a strong driver (except for Gedser), but is not unique. In particular, the influence of Artic Oscillation index at Cuxhaven, Stockholm and Halifax, and of Nino Sea Surface Temperature index 1 + 2 at San Francisco appear to be very strong as well. Asimilar analysis was conducted by restricting the time period of interest to the 1st part of the 20th century. Over this period, we show that the MSL dominance is lower, whereas an ensemble of CI contribute to a large part to HSL time evolution as well. The proposed setting is flexible and could be applied to incorporate any alternative predictive time series such as river discharge, tidal constituents or vertical ground motions where relevant.

Spatial extremes assessment of geophysical variables represents a complex field largely because of the great variety of existing methodologies employed at different complexity levels, generally making use of multivariate techniques. This manuscript investigates the feasibility of a different approach, namely “Regional Frequency Analysis” (RFA), consisting into dividing the area of study into homogeneous regions whose normalized data are clustered and treated with univariate techniques. RFA is employed to analyse the spatial assessment of extreme significant wave heights within the north-east Atlantic Ocean. This expanse provides the physical basis for the determination of “homogeneous” regions, identified by means of typical storm footprints which allow the clustering of similar extreme events. Furthermore, a spatio-temporal criterion for storm tracking is implemented and incorporated into the analysis, since it is assumed that extreme marine events on a basin scale may be influenced by the same low pressure systems following similar trajectories. A 23-year wave hindcast database at high spatial resolution provided by the WWIII model on unstructured mesh grid covering the Celtic Sea, the English Channel and the Bay of Biscay was employed to perform the analysis. Due to the large size of the dataset (more than 110, 000 spatial locations), a limited number of points were selected, either uniformly or guided by the bathymetry). In both situations, RFA proves effective in clustering homogeneous regions physically and statistically. Its reliability is based on the varying impact of major meteorological events on different areas at different scales. The spatial assessment of return levels of significant wave heights achieved by means of RFA are consistent with results of a pointwise model, with findings generally more smoothed in proximity to steep gradients and when ranging from offshore to coastal areas. The adoption of a “spatio-temporal declustering” allows more detailed wave patterns to be reproduced on a smaller spatial scale, especially in the vicinity of channel systems. Effects of swell are also described accurately.

International audience

Identifying the offshore forcing and breaching conditions that lead to marine inundation is of high importance for risk management. This task cannot be conducted by using a numerical hydrodynamic model due to its high computation time cost (of several minutes or even hours). In the present study, we show how the random forest (RF) classification technique can approximate the numerical model to explore these critical conditions. We focus on the Boucholeurs site, which is located on the French Atlantic coast and exposed to overflow processes. An iterative strategy is developed for selecting the numerical simulations (a total of 200) to train the RF model. The sensitivity to the input parameters is studied using permutation-based importance measures and extended versions of the partial dependence plots. The results highlight the key interplay among the high-tide level, the surge peak and the phase difference, and the complex role of the breaching location.

Idier, D.; Falqués, A.; Garcin, M., and Rohmer, J., 2018. How observed kilometric sandy shoreline undulations depend on wave climate. In: Shim, J.-S.; Chun, I., and Lim, H.S. (eds.), Proceedings from the International Coastal Symposium (ICS) 2018 (Busan, Republic of Korea). Journal of Coastal Research, Special Issue No. 85, pp. 191–195. Coconut Creek (Florida), ISSN 0749-0208. Coastlines sometimes exhibit km-scale undulations, also called shoreline sandwaves. This work focuses on self-organized undulations and investigates how frequent they are, on which parameters they depend and whether existing theories of morphodynamic instabilities can explain them. We perform a wavelet analysis on shoreline data from areas appearing free of human or geological constraints and representing totally 1268 km around the world. It is found that 61% of cases exhibit undulations with wavelengths in the range 1–10 km. The wave parameter that correlates the best with their existence is the incidence angle with respect to shore normal and, to a lesser extent, the closure depth. The shoreline undulation probability increases with the angle, from 50% for low angles to almost 100% for large angles, suggesting that the High-Angle Wave Instability mechanism (HAWI) would be their primary cause, but that Low-Angle Wave Instability (LAWI) may also play a role. These results are consistent with recent theoretical investigations.

Uncertainty related to the source parameters of earthquake can largely impact the tsunami-induced wave characteristics, especially in the case of near-field tsunami source. The combination of numerical simulations and historical eyewitness accounts can be used to better constrain those uncertainties. In the present study, we propose a Bayesian procedure to infer (i.e. learn) the probability distribution of the source parameters of the earthquake. The strategy is based on the combination of: (1) kriging-based metamodelling techniques to overcome the high computation time cost of the numerical simulator; and (2) Approximate Bayesian Computation (ABC) procedure to perform the Bayesian inference. The procedure is applied to the Ligurian (North West of Italy) 1887 tsunami case, for which tsunami-induced sea surface elevations at the coast have been reported at four locations, namely Marseille, Imperia, Diano-Marina and Genoa. The kriging metamodels are trained using only 300 long-running numerical simulations that were performed using the FUNWAVE-TVD code. Contrary to recent inversion exercises that can benefit from current modern observation networks (like tide gauges, sea bottom pressure gauges, GPS-mounted buoys), the case of historical tsunami like Liguria is complicated by the imprecision and scarcity of the observations: this is accounted for by conducting the combined ABC-kriging procedure a large number of times (i.e. 1000); each time a new set of observations being randomly generated to account for this observational error. The combined analysis of the inference results and of the observation uncertainty reveals that: (1) the coseismic slip is the most important source parameter with a very peaky density distribution around low values ranging from 0.3 to 0.6 m; (2) The fault width has a peaky density distribution around low values ranging from 10 to 12 km; (3) The rake and azimuth distribution only slightly deviate from the uniform prior, hence indicating a low influence of those parameters; (4) The bi-modal distribution of the dip is also evidenced.

Figures 3 and 4 of the article 'Bounding probabilistic sea-level projections within the framework of the possibility theory' display a minimum value for sea level rise of 15 cm by 2100 with respect to the 1986–2005 mean for the RCP 8.5. The value of 15 cm is consistent with sea level rise rates dropping back to velocities observed during the 20th century according to recent studies, but not to the current sea level rise velocity of 3.4 mm yr−1, as incorrectly stated in the article. This error has no impact on the rest of the article, including its arguments and conclusions, but it is potentially confusing for scientists willing to reproduce the left side of figures 3 and 4. We apologise for any inconvenience caused.

International audience; In the present study, we assess the potential for initiating ductile failure in a fractured caprock due to the chemical alteration of its mechanical properties under pressure increase induced by CO2 leakage and fixed in situ boundary conditions. In this view, 2D numerically coupled reactive-transport simulations were set up by using the Opalinus Clay formation as an analogue for a caprock layer. The fractured system was viewed as a compartmentalised system that consists of a main highly permeable pathway, a moderately permeable damage zone and the intact rock. The outputs of the numerical simulations (mineral fraction, porosity changes, gas saturation, pore-fluid pressure) were converted into parameter changes of the yield surface by viewing the rock material of the three compartments (fault, damage zone and intact rock) as a composite system that consists of a clayey solid material, pores and mineral inclusions (such as carbonate and quartz). Three alteration processes were considered: (1) the effect of the mineral fraction and porosity evolution on the yield surface, (2) changes in the resulting poro-elastic properties and (3) the suction effect, i.e. the bounding effect induced by the presence of two phases, water and CO2. Our numerical investigations showed that the decrease in the friction coefficient remained negligible during leakage, while the pre-consolidation stress mainly decreased. Consequently, the damage zone of the fractured system became more collapsible over time, which was driven by low-to-moderate pressure build-up of the fluid penetrating the fault (1 MPa in our case). For the considered case, the initiation of ductile failure is likely under conditions of fixed vertical stress and zero lateral strain. This process could potentially limit the spatial spreading of CO2-induced alteration, although this remains very site specific. We recommend that characterisation efforts be intensified to obtain better insight into the properties of fracture systems in caprock-like formations (with special attention to their initial over consolidation ratio).

International audience; Despite progresses in climate change science, projections of future sea-level rise remain highly uncertain, especially due to large unknowns in the melting processes affecting the ice-sheets in Greenland and Antarctica. Based on climate-models outcomes and the expertise of scientists concerned with these issues, the IPCC provided constraints to the quantiles of sea-level projections. Moreover, additional physical limits to future sea-level rise have been established, although approximately. However, many probability functions can comply with this imprecise knowledge. In this contribution, we provide a framework based on extra-probabilistic theories (namely the possibility theory) to model the uncertainties in sea-level rise projections by 2100 under the RCP 8.5 scenario. The results provide a concise representation of uncertainties in future sea-level rise and of their intrinsically imprecise nature, including a maximum bound of the total uncertainty. Today, coastal impact studies are increasingly moving away from deterministic sea-level projections, which underestimate the expectancy of damages and adaptation needs compared to probabilistic laws. However, we show that the probability functions used so-far have only explored a rather conservative subset of sea-level projections compliant with the IPCC. As a consequence, coastal impact studies relying on these probabilistic sea-level projections are expected to underestimate the possibility of large damages and adaptation needs.

International audience; Distinguishing between two facets of uncertainty has become a standard practice in risk analysis, namely aleatory (representing variability) and epistemic (due to lack of knowledge). For representing aleatory uncertainty, there is a large consensus in the community about the use of probabilities under the frequentist perspective. However, for representing epistemic uncertainty, no unique straightforward answer exists. The different belief systems about the appropriate setting for uncertainty representation induce ontological-type uncertainty. Depending on how this representation is conducted might impact the decision-making phase especially when the chosen approach fails to provide a clear and transparent picture of the background knowledge underlying the risk analysis (Aven 2016).For representing epistemic uncertainty, several authors have advocated the use of subjective probabilities interpreted as beliefs (perceptions) about the probabilistic model within the Bayesian setting. Yet, when the “background knowledge is weak”, i.e. in situations where the available data are imprecise, diffuse, fluctuating, incomplete, fragmentary, vague, ambiguous, etc. (e.g., Dubois 2010), expressing uncertainties in the form of subjective probabilities may often be too “rich”: the identification of the probability distribution requires more information than what an expert may be able to supply, which is often restricted to the 0.50 and 0.95 fractiles or a prescribed mode and a support (Dubois and Prade 1994). Alternative uncertainty theories termed as “extra-probabilistic” (possibility theory, belief theory, imprecise probability, etc.) have been developped over the past years, which go beyond the systematic use of a single probabilistic law. The pillar of these theories is that many of such probabilistic laws may exist given the pieces of available information: these advocate bounding instead of providing a single uncertainty (probabilistic) model.In the present contribution, we analyse the pros and cons of this bounding strategy with a special focus on possibility theory. The analysis is peformed by comparison with conservative approaches using safety margins and traditional pure probabilistic approach by means of applications in the field of CO2 geological storage (Loschetter et al. 2015) and in natural hazard assessments (Rohmer and Baudrit 2011). We show that: 1. the possibility theory is a flexible setting for handling a broad range of different situations of epistemic uncertainty (e.g., representing and reasoning with vague concepts; handling imprecision; dealing with a probabilistic model whose parameters are ill-known); 2. the bounding strategy enables to convey an intuitive, understandable, transparent and communication-facilitating message on the level of epismetic uncertainty, i.e. on what is unknown.The other side of the coin is the level of sophistication added by the bounding strategy. Picturing the “flaws in the assessment process” using bounds may help the decision-making process by clearly displaying the limitations of the predictions instead of giving outcomes with an artificially high level of confidence. However, if the message on bounds is not transferred cautiously from scientists to end-users, this might undermine the confidence in the risk analysis, potentially leading to a loss of credibility in the results. A possible line of improvement is discussed through sensitivity and robustness characterisation.ReferencesAven, T. (2016). Risk assessment and risk management: Review of recent advances on their foundation. European Journal of Operational Research, 253(1), 1-13.Dubois, D. (2010). Representation, propagation, and decision issues in risk analysis under incomplete probabilistic information. Risk analysis, 30(3), 361-368.Dubois, D., Prade, H. (1994). Possibility theory and data fusion in poorly informed environments. Control Eng. Pract. 2, 811–823.Loschetter, A., Rohmer, J., de Lary, L., and Manceau, J. (2015). Dealing with uncertainty in risk assessments in early stages of a CO2 geological storage project: comparison of pure-probabilistic and fuzzy-probabilistic frameworks. Stochastic Environmental Research and Risk Assessment, 1–17.Rohmer, J. and Baudrit, C. (2011). The use of the possibility theory to investigate the epistemic uncertainties within scenario-based earthquake risk assessments. Natural hazards, 56(3), 613–632.

International audience; Predicting the fate of the injected CO2 is crucial for the safety of carbon storage operations in deep saline aquifers: especially the evolution of the position, the spreading and the quantity of the mobile CO2 plume during and after the injection has to be understood to prevent any loss of containment. Fluid flow modelling is challenging not only given the uncertainties on subsurface formation intrinsic properties (parameter uncertainty) but also on the modelling choices/assumptions for representing and numerically implementing the processes occurring when CO2 displaces the native brine (model uncertainty). Sensitivity analysis is needed to identify the group of factors which contributes the most to the uncertainties in the predictions. In this paper, we present an approach for assessing the importance of model and parameter uncertainties regarding post-injection trapping of mobile CO2. This approach includes the representation of input parameters, the choice of relevant simulation outputs, the assessment of the mobile plume evolution with a flow simulator and the importance ranking for input parameters. A variance-based sensitivity analysis is proposed, associated with the ACOSSO-like meta-modelling technique to tackle the issues linked with the computational burden posed by the use of long-running simulations and with the different types of uncertainties to be accounted for (model and parameter). The approach is tested on a potential site for CO2 storage in the Paris basin (France) representative of a project in preliminary stage of development. The approach provides physically sound outcomes despite the challenging context of the case study. In addition, these outcomes appear very helpful for prioritizing the future characterisation efforts and monitoring requirements, and for simplifying the modelling exercise.

International audience; Over recent years, numbers of tools for seismic risk analysis have been developed to evaluate casualties and losses induced by earthquakes. A recent overview of available models can be found in (Molina et al. 2010). These predictions software require a large number of quantitative parameters (parametric uncertainty) but also model structures (model uncertainty). The choice of the appropriate model and the determination of exact values of these parameters remain very difficult. Therefore, sensitivity analysis and uncertainty quantification have to be performed for these kinds of studies. In this context, the aim of this article is to present a methodology for getting better insight in the role played by the different uncertainty sources (parametric and model) based on a variance-based global sensitivity analysis. Contrary to Rohmer et al. (2014), we used a less greedy estimation algorithm (Benaïchouche & Rohmer 2016), which both allows providing global sensitivity measures, but also information on local sensitivity. The application case is the risk analysis performed for Aquila (Italy, 2009) earthquake.

To prevent the release of large quantities of CO2 into the atmosphere, carbon capture and storage (CCS) represents a potential means of mitigating the contribution of fossil fuel emissions to global warming and ocean acidification. Fluvial saline aquifers are favourite targeted reservoirs for CO2 storage. These reservoirs are very heterogeneous but their heterogeneities were rarely integrated into CO2 reservoir models. Moreover, contrary to petroleum reservoirs, the available dataset is very limited and not supposed to be enriched. This leads to wide uncertainties on reservoir characteristics required for CSS management (injection location, CO2 plume migration, etc.). Stochastic simulations are classical strategies in such under-constrained context. They aim at generating a wide number of models that all fit the available dataset. The generated models serve as support for computing the required reservoir characteristics and their uncertainties. A challenge is to optimize the uncertainty computations by selecting stochastic models that should have a priori very different flow behaviours. Fluid flows depend on the connectivity of reservoir rocks (channel deposits). In this paper, it is proposed to study the variability of the Betti numbers in function of different fluvial architectures. The aim is to quantify the impact of fluvial heterogeneities and their spatial distribution on reservoir rock topology and then on CO2 storage capacities. Representative models of different scenarios of channel stacking and their internal heterogeneities are generated using geostatistical simulation approaches. The Betti numbers are computed on each generated models and statistically analysed to exhibit if fluvial architecture controls reservoir topology.

International audience; Due to the corrosive nature of dissolved CO2, the potential short or long term alteration of rock properties, represents a major issue in several sites where natural CO2 circulation is observed, as well as in reservoirs targeted for storage of anthropogenic CO2. To date, this has been primarily studied from a transport-chemical perspective, with laboratory evidence of microstructural modifications together with the consequences for flow properties. Compared to the transport-chemical aspects, the mechanical-chemical aspects have been less investigated, though it is to be expected that mechanical properties (e.g. elastic properties, failure parameters, and time-dependent mechanical behaviour) could potentially be affected in a similar manner to hydraulic parameters. Yet, since CO2 is a weak acid, the pH drop is expected to be moderate with a likely lower limit close to 4.0. The buffering of pH by calcite minerals present in most reservoirs targeted for storage may further limit the pH drop, as well as confining it to a localized rock volume around the injection well. This leads to the question of the magnitude and time/spatial scales of chemically-mediated mechanical processes during CO2 sequestration. The authors propose to address this issue by reviewing recent laboratory-based studies restricted to sedimentary rocks, namely: reservoir rocks (carbonate or sandstone), intact or fractured caprocks and fault rocks. Key findings include the following: 1. the short-term impact on the elastic and inelastic behaviour of intact caprocks remains limited; 2. shear strength weakening is likely to be respectively low and low-to-moderate for shale/clay-rich and anhydrite-rich faults, but without modifying slip stability in either case; 3. the largest impact is located within carbonate reservoirs, but with a broad range of reported responses depending on hydrodynamic conditions (closed or open) and on dissolution regime (uniform or channelling); and 4. creep experiments confirm that CO2-induced dissolution may enhance long-term compaction of carbonate reservoirs, but the magnitude of acceleration (varying from non-significant to 50 times) depends to a large extent on site-specific conditions (grain size, pH, temperature, effective stress state, etc.), which renders any direct extrapolation from laboratory to reservoir scale difficult. Finally, some directions for future research studies are discusse

International audience; Estimating the probability of eruption by jointly accounting for different sources of monitoring parameters overtime is a key component for volcano risk management. In the present study, we are interested in the transition froma state of low-to-moderate probability value and to the one of high probability value: the latter value generallysupports the call for evacuation. By using the data of MESIMEX exercise at the Vesuvius volcano, we investigatedthe potential for time-varying indicators related to the correlation structure or to the variability of the probabilitytime series for detecting in advance this critical transition. We found that changes in the power spectra and inthe standard deviation estimated over a rolling time window both present an abrupt increase, which marks theapproaching shift. Our numerical experiments revealed that the transition from an eruption probability of 10-15%to >70% could be identified up 3 hours in advance. This additional lead time could be useful to place different keyservices (e.g., emergency services for vulnerable groups, commandeering additional transportation means, etc.) ona higher level of alert before the actual call for evacuation.

International audience; Several studies have focused on the role of damage zone (DZ) on the hydromechanical behaviour of faults by assuming a fractured DZ (i.e. low stiffness/high permeability). Yet, this vision may not be valid in all geological settings, in particular, in high-porosity reservoirs as targeted by several underground exploitations. We investigate the impact of a high-stiff/low-permeable DZ on the shear reactivation of a blind, undetectable normal fault (1 km long, ≤10 m offset), with a 0.5 m thick low-porosity/permeability fault core during fluid injection into a high-porosity reservoir. The spatial distribution of effective properties (elastic moduli, Biot's coefficients and permeability) of DZ including deformation bands (DB; elliptic inclusions) and intact rock were derived using upscaling analytical expressions. The influence of DZ on the hydromechanical behaviour of the fault zone was numerically explored using 2-D plane-strain finite-element simulations within the framework of fully saturated isothermal porous media by accounting for an orthotropic elastic rheology. The numerical results showed that the presence of DB plays a protective role by reducing the potential for shear reactivation inside the fault core. On the other hand, they favour shear failure in the vicinity of the fault core (off-fault damage) by accelerating the decrease of the minimum principal effective stress while limiting the decrease of the maximum one. This behaviour is strongly enhanced by the fault-parallel DZ effective stiffness, but limited by the combined effect of fault-normal DZ effective permeability and of the Biot's coefficients. This can have implications for the location and size of aftershocks during fault reactivation.

International audience; Reactive flow at depth (either related to underground activities, like enhancement of hydrocarbon recovery and CO2 storage, or to natural flow like in hydrothermal zones) can alter fractures’ topography, which might in turn change their seismic responses. Depending on the flow and reaction rates, instability of the dissolution front can lead to a wormhole-like pronounced erosion pattern. In a fractal structure of rupture process, we question how the perturbation related to well-spaced long channels alters rupture propagation initiated on a weak plane and eventually the statistical feature of rupture appearance in frequency–magnitude distribution (FMD). Contrary to intuition, a spatially uniform dissolution is not the most remarkable case, since it affects all the events proportionally to their sizes leading to a downward translation of FMD: the slope of FMD (b-value) remains unchanged. The parameter–space study shows that the increase of b-value (of 0.08) is statistically significant for optimum characteristics of the erosion pattern with spacing to length ratio of the order of ∼1/40: large-magnitude events are more significantly affected leading to an imbalanced distribution in the magnitude bins of the FMD. The larger the spacing, the lower the channel's influence. Besides, a spatial analysis shows that the local seismicity anomaly concentrates in a limited zone around the channels: this opens perspective for detecting these eroded regions through high-resolution imaging surveys.

International audience; CO2 capture and storage (CCS) is recognized as a promising solution to tackle greenhouse gas emissions. Key issues associated with CCS relate to the integrity of the reservoir and the containment effectiveness. Some risk events (e.g. regional over-pressurization, leakage through a fault or an abandoned well) identified in the risk analysis may be linked with surface deformations anomalies, which can be detected and followed using surface deformation measurements.At In-Salah (Algeria), Interferometric Synthetic Aperture Radar (InSAR) data are available for all points due to ideal surface conditions. If a similar injection occurred in constrained conditions (large cover of vegetated areas for instance), a geodetic network (set of corner reflectors that constitute artificial measurements points) could be used to compensate the scarcity of existing persistent scatterers. The present study aims at exploring the feasibility of using such a geodetic network as part of the monitoring plan in constrained conditions. The sizing of such a network is discussed regarding three monitoring objectives: regional-scale surveillance, local anomaly with known and unknown spatial locations. In the context of In-Salah, a very limited number of measurement points (∼20) enables capturing the regional deformation pattern. The addition of a series of less than 5 supplementary points brings useful information to detect local anomalies of small-to-moderate (e.g. subsidence, with 1 km radius and 2 mm/y maximal amplitude) size for a known position. For detecting an unpredicted anomaly, the measurements network density needs to be largely increased, making the method more expensive. Though the results are not directly transposable in other settings, this experience feedback brings useful orders of magnitude and an original risk-oriented approach.

Assessing the impact of multiple sources of uncertainty in flow models requires simulating the response associated with a large set of stochastic realizations ( $$>\!\!1,\!000$$ ) of both the spatial variable (e.g. permeability field) and of the large number of model parameters (e.g., of the relative permeability curve). Yet, this Monte Carlo approach may be hindered by the high computation time cost of long-running flow simulators (CPU time $$> $$ several hours). A possible strategy can then rely on the combination of meta-modelling techniques and of basis set expansion like kernel principal component analysis to reduce the high-dimensionality ( $$>\!\!10,\!000$$ grid cells) of the spatial variable. Using a synthetic heterogeneous channelized reservoir, it is shown that $$\sim $$ 50 principal components PCs are still necessary to retain $$\sim $$ 80 % of the total variability: this might pose difficulties for the construction of the meta-model by imposing a large set of training samples. This computational limitation can be overcome by improving the meta-modelling phase through boosting techniques, which allow selecting during the fitting process the input variables (PCs or model parameters), which are the most informative regarding the prediction accuracy, and allow in this manner reducing the number of necessary model runs. The procedure is applied to a synthetic flow model of a CO $$_{2}$$ geological storage with low CPU time. By comparing the P10, P50 and P90 quantiles of the pressure build-up estimated using the true simulator (1,500 simulations) with the ones estimated using the meta-model. The joint impact of spatial variability and of 12 uncertain parameters is shown to be accurately captured using a number of training samples of the order of the number of PCs ( $$\sim $$ a few tens). The level of performance proved to be as high as the one of the distance-based alternative proposed in the literature to solve the problem of joint propagation.

Over the last decade, many cliff erosion studies have focused on frequency-size statistics using inventories of sea cliff retreat sizes. By comparison, only a few paid attention to quantifying the spatial and temporal organisation of erosion scars over a cliff face. Yet, this spatial organisation carries essential information about the external processes and the environmental conditions that promote or initiate sea-cliff instabilities. In this article, we use summary statistics of spatial point process theory as a tool to examine the spatial and temporal pattern of a rockfall inventory recorded with repeated terrestrial laser scanning surveys at the chalk coastal cliff site of Mesnil-Val (Normandy, France). Results show that: (1) the spatial density of erosion scars is specifically conditioned alongshore by the distance to an engineered concrete groyne, with an exponential-like decreasing trend, and vertically focused both at wave breaker height and on strong lithological contrasts; (2) small erosion scars (10−3 to 10−2 m3) aggregate in clusters within a radius of 5 to 10 m, which suggests some sort of attraction or focused causative process, and disperse above this critical distance; (3) on the contrary, larger erosion scars (10−2 to 101 m3) tend to disperse above a radius of 1 to 5 m, possibly due to the spreading of successive failures across the cliff face; (4) large scars significantly occur albeit moderately, where previous large rockfalls have occurred during preceding winter; (5) this temporal trend is not apparent for small events. In conclusion, this study shows, with a worked example, how spatial point process summary statistics are a tool to test and quantify the significance of geomorphological observation organisation.

The behaviour of the injected CO2 on the long term is of primary importance and numerous authors have pointed out the essential role of trapping mechanisms in the evolution of the storage. In terms of safety and more specifically of CO2 containment, two of the greatest issues concerns the location of the plume - in comparison with the potential leakage pathways - and the quantity of mobile CO2 in case of unwanted upward migration. The migration of the injected plume has therefore been the subject of numerous studies. All these studies highlight the site- and condition-specificity of the evolution of the trapped plume fraction. Particularly, the structural characteristics of the geological formations, the initial hydrogeological conditions and absolute flow properties of the reservoir, as well as the multiphase flow parameters (relative permeability and capillary pressure) relatively to the CO2/brine system have been shown to influence the pressure evolution and the behaviour of the mobile gaseous plume after the injection. Knowledge of these parameters is often limited especially in the early stages of a storage project (before or in the first years of injection); the assessment of the fate of the injected CO2 is therefore associated to several sources of uncertainties. The improvement of the knowledge on a specific site, before and during the life-time of the storage operations is essential as it may allow a decrease of the uncertainty on the outputs. However, additional data acquisition is likely to be expensive and time consuming, and the parameters to be characterized might be numerous. Thus, under time and budget constraints, ranking the parameters to be estimated according to their impact for the long term assessment of the CO2 fate seems necessary. Such an importance ranking (aka sensitivity analysis) has been advocated by regulators in the European directive on CO2 storage for instance. However, performing such a sensitivity analysis might be challenging in the context of CO2 storage. First, assumptions in the modelling procedure can be strong, because data can be scarce and might not be well-defined on a specific site. This is particularly the case for multiphase flow parameters (relative permeability and capillary pressure including hysteresis effects) curves specific to the CO2/brine system and to the storage conditions; most of the time they do not benefit from previous studies of the site, even in sedimentary basin that are historically well-characterized with regards to intrinsic hydraulic properties. Secondly, a robust sensitivity analysis requires a large amount of simulations, which might not be feasible when using computationally intensive simulations of the CO2 fate on long term at reservoir-scale. Meta-modelling techniques (aka response surface or reduced order models) can be developed to circumvent such a difficulty. In this work, we propose an approach to assess the importance ranking of uncertainty sources, with regards to the long term fate of the mobile CO2 on a specific case, considering notably solution and residual trapping. We consider the case of a large-scale storage site in the Paris basin (France) at the post-screening stage, which is characterized by a relatively high but typical level of uncertainty. In a first step, the data at disposal have been collected and the outcome variables of interest according to our problem selected (in our case, the dissolved CO2, the residual CO2, and the distance of the mobile plume from the injection point over time). A 3D dynamic model has been developed from a geological model of the targeted formation. In a second step, choices in terms of input parameters and associated uncertainty representation have been made. In total, we investigate the sensitivity to seven different input parameters: porosity, permeability, permeability anisotropy, regional hydraulic gradient, relative permeability, capillary pressure and mesh size. According to the different levels of knowledge, the uncertainty on the input parameters have been represented either by a continuous probability distribution (when enough information is available) or by a set of possible scenarios (when little information is available), i.e. by a categorical variables taking on discrete values, each of them assigned to a different scenario. Considering each of the variables of interest, the approach for importance ranking is divided into two steps: 1. qualitative analysis: some reference simulations are precisely studied in the purpose of producing tornado diagrams for a better understanding of input parameters variations qualitative effects; 2. Quantitative analysis: a robust global variance-based sensitivity analysis VBSA relying on the Sobol' indices is chosen and performed. Yet, the implementation of VBSA in our case is hindered by two major limitations. First, VBSA is computationally intensive and requires a large number of model runs, of the order of 10,000, which is impractical using a large-scale flow model with CPU time typically reaching a few hours. The second problem is related to the nature of the model input parameter: several of them are represented by a set of possible scenarios to which a discrete categorical indicator is assigned. To circumvent both limitations, VBSA is combined with ACOSSO-like meta-modelling technique, which can jointly account for continuous variables and categorical variables. This strategy is applied by using 300 large-scale complex dynamic 3D model runs. This procedure gives the temporal evolution of the sensitivity indices assigned to each input parameter considering each variable of interest. Interestingly, porosity, relative permeability and capillary pressure have the main influence but with different magnitude over time and depending on the variable of interest. In summary, the approach, generic and applicable to different uncertain contexts, allows to answer two main issues: 1) the identification of the inputs parameter ranges or scenarios that contribute to a higher safety or contrarily to unwanted situations; 2) the identification of the main sources of variability as well as the parameters with negligible influence and therefore the prioritization of the effort to be made to improve the predictability of the numerical simulations.

International audience; Stability analysis generally relies on the estimate of failure probability P. When information is scarce, incomplete, imprecise or vague, this estimate is imprecise. To represent epistemic uncertainty, possibility distributions have shown to be a more flexible tool than probability distributions. The joint propagation of possibilistic and probabilistic information can rely on more advanced techniques such as the classical random sampling of the cumulative probability distribution F and of the intervals from the possibility distributions π. The imprecise probability P is then associated with a random interval, which can be summarized by a pair of indicators bounding it. In the present paper, we propose a graphical tool to explore the sensitivity on these indicators. This is conducted by means of the contribution to sample probability of failure plot based on the ordering of the randomly generated levels of confidence associated with the quantiles of F and to the α-cuts of π. This presents several advantages: (1) the contribution of both types of uncertainty, aleatoric and epistemic, can be compared in a unique setting; (2) the analysis is conducted in a post-processing step, i.e. at no extra computational cost; (3) it allows highlighting the regions of the quantiles and of the nested intervals which contribute the most to the bounds of P. The method is applied on two case studies (a mine pillar and a steep slope stability analysis) to investigate the necessity for extra data acquisition on parameters whose imprecision can hardly be modelled by probabilities due to the scarcity of the available information (respectively the extraction ratio and the cliff geometry).

International audience; Epistemic uncertainties can be classified into two major categories: parameter and model. While the first one stems from the difficulties in estimating the values of input model parameters, the second comes from the difficulties in selecting the appropriate type of model. Investigating their combined effects and ranking each of them in terms of their influence on the predicted losses can be useful in guiding future investigations. In this context, we propose a strategy relying on variance-based global sensitivity analysis, which is demonstrated using an earthquake loss assessment for Pointe-à-Pitre (Guadeloupe, France). For the considered assumptions, we show: that uncertainty of losses would be greatly reduced if all the models could be unambiguously selected; and that the most influential source of uncertainty (whether of parameter or model type) corresponds to the seismic activity group. Finally, a sampling strategy was proposed to test the influence of the experts' weights on models and on the assumed coefficients of variation of parameter uncertainty. The former influenced the sensitivity measures of the model uncertainties, whereas the latter could completely change the importance rank of the uncertainties associated to the vulnerability assessment step.

International audience; Variance-based global sensitivity analysis (GSA) is a powerful procedure for importance ranking of the uncertain input parameters of a given flow model. The application of GSA is made possible for long-running flow simulators (computation (CPU) time more than several hours) by relying on meta-modeling techniques. However, such flow models can involve one or several spatial inputs, for instance, the permeability field of a reservoir and of a caprock formation in the context of CO2 geological storage. Studying the sensitivity to each of these spatial inputs motivated the present work. In this view, we propose a strategy which combines (1) a categorical indicator (i.e., a pointer variable taking discrete values) assigned to the set of stochastic realizations associated with each spatial input (spatial maps) and (2) meta-modeling techniques, which jointly handle continuous and categorical inputs. In a first application case, a costless-to-evaluate numerical multiphase flow model was used to estimate the sensitivity indices. Comparisons with results obtained using the meta-model showed good agreement using a two-to-three ratio of the number of learning samples to the number of spatial maps. On this basis, the strategy can be recommended for cases where the number of maps remains tractable (i.e., a few hundred), for example, for moderately complex geological settings, or where a set of such maps can be selected in a preliminary stage using ranking procedures. Finally, the strategy was applied to a more complex multiphase flow model (CPU time of a few hours) to analyze the sensitivity of CO2 saturation and injection-induced pressure build-up to seven homogeneous rock properties and two spatial inputs.

available at: http://link.springer.com/article/10.1007%2Fs11069-012-0536-3#; International audience; Predicting the temporal evolution of landslides is typically supported by numerical modelling. Dynamic sensitivity analysis aims at assessing the influence of the landslide properties on the time-dependent predictions (e.g. time series of landslide displacements). Yet, two major difficulties arise: (1) Global sensitivity analysis require running the landslide model a high number of times (>1,000), which may become impracticable when the landslide model has a high computation time cost (>several hours); (2) Landslide model outputs are not scalar, but function of time, that is, they are n-dimensional vectors with n usually ranging from 100 to 1,000. In this article, I explore the use of a basis set expansion, such as principal component analysis, to reduce the output dimensionality to a few components, each of them being interpreted as a dominant mode of variation in the overall structure of the temporal evolution. The computationally intensive calculation of the Sobol' indices for each of these components are then achieved through meta-modelling, that is, by replacing the landslide model by a "costless-to-evaluate" approximation (e.g. a projection pursuit regression model). The methodology combining "basis set expansion--meta-model--Sobol' indices" is then applied to the Swiss La Frasse landslide to investigate the dynamic sensitivity analysis of the surface horizontal displacements to the slip surface properties during the pore pressure changes. I show how to extract information on the sensitivity of each main modes of temporal behaviour using a limited number (a few tens) of long-running simulations.

International audience; A prerequisite to the large scale industrial development of CO2 Capture and geological Storage is the demonstration that the storage is both efficient and safe. In this context, precise uplift/subsidence monitoring techniques constitute a key component of any CO2 storage risk management. Space-borne Differential SAR (Synthetic Aperture Radar) interferometry is a promising monitoring technique. It can provide valuable information on vertical positions of a set of scatterer undergoing surface deformation induced by volumetric changes through time and space caused by CO2 injection in deep aquifers. To what extent ? To date, InSAR techniques have been successfully used in a variety of case-studies involving the measure of surface deformation caused by subsurface fluid withdrawal / injection. For instance, groundwater flow characterization in complex aquifers systems, oil / gas field characterization, verification of enhanced oil recovery efficiency, monitoring of seasonal gas storage. The successful use of InSAR is strictly related to the favourable scattering conditions in terms of spatial distribution of targets and their temporal stability. In arid regions, natural radar scatterers density can be very high, exceeding 1,000 per km². But future onshore industrial-scale CO2 storage sites are planned in more complex land-covers such as agricultural or vegetated terrains. Those terrains are characterized by poor to moderate radar scatterers density, which decrease the detection limits of the space-borne interferometric technique. The present study discusses the limits and constraints of advanced InSAR techniques applied to deformation measurements associated with CO2 injection/storage into deep aquifers in the presence of agricultural and vegetated land-covers. We explore different options to enhance the measurement performances of InSAR techniques. As a first option, we propose to optimize the deployment of a network of "artificial" scatterers, i.e. corner reflectors (artificial devices installed on ground to provide high backscatter to the radar signal) to complement the existing "natural" network. The methodology is iterative and adaptive to the spatial and temporal extent of the detectable deforming region. We take into account the need of a change in sensors characteristics (for a very long term monitoring 10-50 years) that could result in a need of re-organisation of the network. Our discussion is supported by the estimates of the expected spatio-temporal evolution of surface vertical displacements caused by CO2 injection at depth by combining the approximate analytical solutions for pressure build-up during CO2 injection in deep aquifers and the poro-elastic behaviour of the reservoir under injection. As second option, we then review different advanced InSAR algorithms that could improve the displacement measurements using natural scatterers over vegetated areas.

In : International Coastal Symposium (ICS) : 2013 Proceedings (Plymouth, Uniteg Kingdom); International audience; Predicting very large sea cliff instabilities (extremes) are of primary importance for coastal risk management. Numerous works have addressed this problem based on the same paradigm: if events exhibit the same statistical properties across a broad range of sizes, the probability of very large extreme events can be evaluated by extrapolating the frequency-size distribution (traditionally represented by a power law model). We address this issue with an inventory of >8,500 sea cliff failures collated from repeated terrestrial laser scanning (TLS) surveys along a coastal chalk cliff in Normandy (France). The largest recorded event, a fault-bounded compartment of >70,000 m3, stands out as an extreme event. We use our inventory to revisit the techniques used to fit power-law distribution with a statistically robust weighted maximum likelihood estimator, which is much less sensitive to outliers. The estimator assigns a weight between 0 and 1 to observations according to their representativeness and rank order. We show that the low weight assigned to the largest sea-cliff instability is statistically significant (with p-value

International audience; Persistent scatterers synthetic aperture radar interferometry (PSI) analysis is viewed as a promising monitoring technique for multiyear detection of ground surface displacements during long term CO2 injection into deep aquifers (of the order of 25 to 50 years). This technique was successfully utilized at the CO2 storage site of In-Salah (Algeria). Nevertheless, this success may not be generalized to future onshore industrial-scale CO2 storage sites, which are planned in far more complex contexts mostly presenting natural terrains, either agricultural or vegetated areas, i.e. characterized by poor PS density, which might locally drop to zero, hence hampering the deployment of the PSI technique. The objective of the present paper is to discuss the constraints on the PSI application regarding the specificities (magnitude, spatial or temporal scales and evolution) of the deformation phenomenon associated with the CO2 injection into storage aquifers. This discussion is supported by the estimates of the expected spatio-temporal evolution of surface vertical displacements caused by CO2 injection in deep aquifers by combining the approximate solutions for pressure build-up and for the associated vertical displacements in an elastic half-space. A possible option to enhance the performance of the PSI technique is explored, namely the deployment of a network of corner reflectors CR corresponding to artificial devices installed on ground to complement the existing "natural" PS network. A methodology is then proposed to optimize the CR network (number and position) in an iterative strategy (e.g., every 10 years) relying on the prediction of surface deformation. The methodology is illustrated by a CO2 injection scenario in the Paris basin context.

Available (free) at: http://www.nat-hazards-earth-syst-sci.net/12/2943/2012/nhess-12-2943-2012.html; International audience; High water level at the coast may be the result of different combinations of offshore hydrodynamic conditions (e.g. wave characteristics, offshore water level, etc.). Providing the contour of the "critical" set of offshore conditions leading to high water level is of primary importance either to constrain early warning networks based on hydro-meteorological forecast or observations, or for the assessment of the coastal flood hazard return period. The challenge arises from the use of computationally intensive simulators, which prevent the application of a grid approach consisting in extracting the contour through the systematic evaluation of the simulator on a fine design grid defined in the offshore conditions domain. To overcome such a computational difficulty, we propose a strategy based on the kriging meta-modelling technique combined with an adaptive sampling procedure aiming at improving the local accuracy in the regions of the offshore conditions that contribute the most to the estimate of the targeted contour. This methodology is applied to two cases using an idealized site on the Mediterranean coast (southern France): (1) a two-dimensional case to ease the visual analysis and aiming at identifying the combination of offshore water level and of significant wave height; (2) a more complex case aiming at identifying four offshore conditions (offshore water level and offshore wave characteristics: height, direction and period). By using a simulator of moderate computation time cost (a few tens of minutes), the targeted contour can be estimated using a cluster composed of a moderate number of computer units. This reference contour is then compared with the results of the meta-model-based strategy. In both cases, we show that the critical offshore conditions can be estimated with a good level of accuracy using a very limited number (of a few tens) of computationally intensive hydrodynamic simulations.

International audience; A proper risk management scheme for CO2 storage should include an adequate monitoring plan completed with a site-specific intervention plan in order to demonstrate that any undesired consequence can be prevented, if not corrected. In the case of CO2 escape from the storage reservoir to an overlying aquifer through a vertical conduit (representing the degraded cement of a well or a permeable fault), directly modifying the leak hydraulic properties (e.g. permeability) may be unfeasible. An appealing option is to counter the driving forces of the migration (natural CO2 buoyancy and injection-induced over-pressure) by increasing the pressure over the leak through brine or water injection within the overlying aquifer, i.e. by creating a "hydraulic barrier". The present article presents and discusses the operational and strategic issues associated with this corrective technique and proposes a methodology in order to set the main design parameters (injection flow rate and duration) depending on the site specificities. The methodology is tested on a leakage scenario and three implementation cases of hydraulic barriers (brine injection 10 m away from the leak with or without delay, or 1 km away without delay) are simulated using the 3D multiphase flow transport code TOUGH2/ECO2N. We assess their effectiveness for stopping the leakage and for trapping (residual and dissolution) the CO2 accumulated in the overlying aquifer. This example shows that the hydraulic barrier can be suited to low transmissivity overlying aquifers, and that its effectiveness will primarily depend on the distance from the leak to the brine injection well. When possible, a brine injection within the overlying aquifer formation in the vicinity of the leak ensures a rapid stop of the leakage and an effective trapping of the CO2.

International audience; Stabilization of CO 2 atmospheric concentrations requires practical strategies to address the challenges posed by the continued use of coal for baseload-electricity production. Over the next two decades, CO 2 capture and sequestration (CCS) demonstration projects would need to increase several orders of magnitude across the globe in both size and scale. This task has several potential barriers which will have to be accounted for. These barriers include those that have been known for a number of years including safety of subsurface sequestration, pore-space competition with emerging activities like shale gas production, legal and regulatory frameworks, and public acceptance and technical communication. In addition water management is a new challenge that should be actively and carefully considered across all CCS operations. A review of the new insights gained on these previously and newly identified challenges, since the IPCC special report on CCS, is presented in this paper. While somewhat daunting in scope, some of these challenges can be addressed more easily by recognizing the potential advantageous synergies that can be exploited when these challenges are dealt with in combination. For example, active management of water resources, including brine in deep subsurface formations, can provide the additional cooling-water required by the CO 2 capture retrofitting process while simultaneously reducing sequestration leakage risk and furthering efforts toward public acceptance. This comprehensive assessment indicates that water, sequestration, legal, and public acceptance challenges ought to be researched individually, but must also be examined collectively to exploit the promising synergies identified herein. Exploitation of these synergies provides the best possibilities for successful large-scale implementation of CCS.

CO2 capture and storage is recognized as a promising solution to tackle greenhouse gas emissions. Containment effectiveness of the reservoir is required to ensure harmlessness to humans and to the environment. However, leakage may occur via existing faults, an abandoned well or a sealing deficiency of the cap rock, and impact surface elements. This paper focuses on the assessment of these impacts on surface targets, especially on humans. Our objective is to provide an efficient tool for decision support based on simple modeling, which enables to assess the most critical and plausible scenarios concerning human exposure to leaking CO2. We choose a reference scenario corresponding to a leakage through a fault or a well just a few meters below an occupied building. This scenario can be divided in different steps that correspond to different spatial zones. Since each of these steps has its own physical properties and consequently its own models, we offer an adequate and relevant modular approach to constitute a global tool: * Module 1: Leakage from the reservoir and migration in the saturated zone through a fault or a well. This module delivers the leakage rate at the bottom of the vadose zone with an analytical model. * Module 2: Migration in the vadose zone and the basements of the building. Through a literature review and sensitivity analyses on numerical simulations, we demonstrate that the vadose zone plays a substantial role of attenuation on the intrusion rate in the building and we highlight the most influential parameters. This module delivers the leakage rate in the building. * Module 3: Accumulation in the building. The leakage rate is translated into a CO2 concentration exposure through an analytical model of mass balance in the building. The concentration exposure is directly comparable to maximum legal concentration. * Module 4: Exposure to impurities, based on a basic model of dilution rate. We propose a first version of a global, efficient, quick and easy-to-use tool for decision support, which enables to assess the impact of CO2 leakage on human health. Additional improvements are planned for a comprehensive and robust evaluation of impacts when assessing risk.

in press; International audience; Large-scale landslide prediction is typically based on numerical modelling, with computer codes generally involving a large number of input parameters. Addressing the influence of each of them on the final result and providing a ranking procedure may be useful for risk management purposes. This can be performed by a variance-based global sensitivity analysis. Nevertheless, such an analysis requires a large number of computer code simulations, which appears impracticable for computationally-demanding simulations, with computation times ranging from several hours to several days. To overcome this difficulty, we propose a "meta-model"-based strategy consisting in replacing the complex simulator by a "statistical approximation" provided by a Gaussian-Process (GP) model. This allows computation of sensitivity measures from a limited number of simulations. For illustrative purposes, the proposed methodology is used to rank in terms of importance the properties of the elastoplastic model describing the complex behaviour of the slip surface in the La Frasse landslide (Switzerland). One limitation of the GP-based methodology is that the computation of sensitivity measures is associated with uncertainty as the simulator is approximated using a training sample of small size, i.e. a limited knowledge on the "true" simulator. This source of uncertainty can be taken into account by treating the GP model from a Bayesian perspective. This provides the full posterior probability distribution associated with the sensitivity measures, which can be summarized by a confidence interval to outline the regions where the GP model is "unsure". We show that this methodology is able to provide useful guidelines for the practical decision-making process and suggest further site investigations.

International audience; A prerequisite to the wide deployment at an industrial scale of CO2 geological storage is demonstrating that potential risks can be efficiently managed. Corrective measures in case of significant irregularities, such as CO2 leakage, are hence required as advocated by the recent European directive on Carbon Capture and Storage operations. In this regard, the objective of the present paper is to investigate four different corrective measures aiming at controlling the overpressure induced by the injection operations in the reservoir: stopping the CO2 injection and relying on the natural pressure recovery in the reservoir; extracting the stored CO2 at the injection well; extracting brine at a distant well while stopping the CO2 injection, and extracting at a distant well without stopping the CO2 injection. The efficiency of the measures is assessed using multiphase fluid flow numerical simulations. The application case is the deep carbonate aquifer of the Dogger geological unit in the Paris Basin. A comparative study between the four corrective measures is then carried using a cost-benefit approach. Results show that an efficient overpressure reduction can be achieved by simply shutting-in the well. The overpressure reduction can be significantly accelerated by means of fluid extraction but the adverse consequences are the associated higher costs of the intervention operations.

International audience; A prerequisite to the wide deployment of CO2 geological storage at an industrial scale is demonstrating that potential risks can be efficiently managed, which includes deploying an adequate monitoring during the injection phase and having intervention plans ready in case of major irregularity. This paper considers the injection of CO2 into a saline formation linked to a shallower aquifer through a leaky pathway. Brine, possibly followed by CO2, may start migrating up through the leak if sufficient pressure builds-up in the storage reservoir. For some man-made leakages (e.g. abandoned well), and more importantly for most of the natural ones (e.g. faults, fractured zone), acting on the transfer itself (i.e. on the leaky pathway) is hardly feasible. Consequently, the corrective measure hereby investigated aims at countering the main driving force of the CO2 upwards migration which is the pressure build-up under the leak by injecting brine into the shallower aquifer, thus creating a hydraulic barrier. Results show that this can be an efficient way to stop a leakage in less than a year instead of letting it continue for hundreds of years, even with a low and decreasing flow rate. It may also be implemented as a preventive measure, while continuing storing CO2.