Groundwater lowering can produce dramatic changes in the physiological performance and survival of plant species. The impact of decreasing water availability due to climate change and anthropogenic groundwater extraction on coastal dune ecosystems has become of increasing concern, with uncertainties about how vegetation will respond in both the short and long terms. We aimed to evaluate the water‐use responses of different plant functional types to increasing groundwater table depth and how this would affect their physiology in Mediterranean coastal dune systems differing in aridity. We modelled water‐table depth, quantified the contribution of different soil layers to plant water through Bayesian isotope mixing models and used a combination of spectral and isotope data to characterize plant ecophysiology. We found that increasing depth to groundwater triggered water uptake adjustments towards deeper soil layers only in the dry season. These adjustments in water source use were made by conifer trees (Pinus pinea, P. pinaster) and hygrophytic shrubs (Erica scoparia, Salix repens) but not by the xerophytic shrub Corema album. Moreover, we observed a greater use of groundwater under semi‐arid conditions. Accompanying the greater use of water from deep soil layers as a response to increasing groundwater depth, the semi‐arid dimorphic‐rooted conifer tree P. pinea and hygrophytic shrub E. scoparia declined their water content (WI), without implications on photosynthetic parameters, such as chlorophyll content (CHL), photochemical index (PRI) and δ13C. Unexpectedly, under semi‐arid conditions, the shallow‐rooted xerophytic shrub C. album, associated with an absence of water source use adjustments, showed a decline in WI, CHL and PRI with groundwater table lowering. We provide insight into how different species, belonging to different functional types, are acclimating to groundwater changes in a region experiencing climatic drought and a scarcity in groundwater due to anthropogenic exploitation. Greater depth to groundwater combined with limited precipitation can have a significant effect on plants’ water source use and ecophysiology in semi‐arid coastal dune ecosystems. A http://onlinelibrary.wiley.com/doi/10.1111/1365-2435.13110/suppinfo is available for this article.

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Numerical 3D high-resolution models of subsurface petroelastic properties are key tools for exploration and production stages. Stochastic seismic inversion techniques are often used to infer the spatial distribution of the properties of interest by integrating simultaneously seismic reflection and well-log data also allowing accessing the spatial uncertainty of the retrieved models. In frontier exploration areas, the available data set is often composed exclusively of seismic reflection data due to the lack of drilled wells and are therefore of high uncertainty. In these cases, subsurface models are usually retrieved by deterministic seismic inversion methodologies based exclusively on the existing seismic reflection data and an a priori elastic model. The resulting models are smooth representations of the real complex geology and do not allow assessing the uncertainty. To overcome these limitations, we have developed a geostatistical framework that allows inverting seismic reflection data without the need of experimental data (i.e., well-log data) within the inversion area. This iterative geostatistical seismic inversion methodology simultaneously integrates the available seismic reflection data and information from geologic analogs (nearby wells and/or analog fields) allowing retrieving acoustic impedance models. The model parameter space is perturbed by a stochastic sequential simulation methodology that handles the nonstationary probability distribution function. Convergence from iteration to iteration is ensured by a genetic algorithm driven by the trace-by-trace mismatch between real and synthetic seismic reflection data. The method was successfully applied to a frontier basin offshore southwest Europe, where no well has been drilled yet. Geologic information about the expected impedance distribution was retrieved from nearby wells and integrated within the inversion procedure. The resulting acoustic impedance models are geologically consistent with the available information and data, and the match between the inverted and the real seismic data ranges from 85% to 90% in some regions.

Iterative geostatistical seismic inversion methods are widely used to predict petro-elastic rock properties from seismic reflection data. When the model perturbation technique uses two-point geostatistics, these methods struggle to reproduce complex and nonstationary geological environments such as faults, folds and highly variable depositional systems. These limitations are often due to the use of a global variogram model to express the expected spatial continuity pattern of the property of interest. In complex geological environments a global variogram model might be unable to detect local heterogeneities and rapid variations of lithology, and result in nonrealistic geological models. Local heterogeneities might be predicted from the data using seismic attribute analysis, which can be imposed during geostatistical seismic inversion as local anisotropy models. In these approaches, the information about the local spatial continuity patterns is fixed and will guide and condition the entire inversion procedure, which can lead to errors and uncertainty in areas where this approach is not appropriate due to high local uncertainty of geological features, given the poor signal-to-noise ratio of the data or the presence of important geological features below the seismic resolution. This work proposes an iterative geostatistical seismic inversion method which iteratively updates the local spatial continuity models based on the trace-by-trace misfit between observed and predicted seismic data. The update of the local spatial continuity models aims at surpassing the limitations of the seismic inversion methods that use a fixed a priori variogram model. The method is successfully illustrated in a challenging two-dimensional synthetic data set and in a real case application. The results demonstrate the benefit of updating iteratively the imposed local spatial continuity patterns based on the data misfit. The inverted models are capable of better predicting the location of faults and reproducing the continuity of sinuous channels.

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