Abstract. The salt mining industrial exploitation located in Vauvert (France) has been injecting water at high pressure into wells to dissolve salt layers at depth. The extracted brine has been used in the chemical industry for more than 30 years, inducing a subsidence of the surface. Yearly leveling surveys have monitored the deformation since 1996. This dataset is supplemented by synthetic aperture radar (SAR) images, and since 2015, global navigation satellite system (GNSS) data have also continuously measured the deformation. New wells are regularly drilled to carry on with the exploitation of the salt layer, maintaining the subsidence. We make use of this careful monitoring by inverting the geodetic data to constrain a model of deformation. As InSAR and leveling are characterized by different strengths (spatial and temporal coverage for InSAR, accuracy for leveling) and weaknesses (various biases for InSAR, notably atmospheric, very limited spatial and temporal coverage for leveling), we choose to combine SAR images with leveling data, to produce a 3-D velocity field of the deformation. To do so, we develop a two-step methodology which consists first of estimating the 3-D velocity from images in ascending and descending acquisition of Sentinel 1 between 2015 and 2017 and second of applying a weighted regression kriging to improve the vertical component of the velocity in the areas where leveling data are available. GNSS data are used to control the resulting velocity field. We design four analytical models of increasing complexity. We invert the combined geodetic dataset to estimate the parameters of each model. The optimal model is made of 21 planes of dislocation with fixed position and geometry. The results of the inversion highlight two behaviors of the salt layer: a major collapse of the salt layer beneath the extracting wells and a salt flow from the deepest and most external zones towards the center of the exploitation.

Abstract. The salt mining industrial exploitation located in Vauvert (France) has been injecting water at high pressure in wells to dissolve salt layers at depth. The extracted brine is used in chemical industry for more than thirty years, inducing a subsidence of the surface. Yearly levelling surveys monitor the deformation since the 1996. This dataset is supplemented by synthetic aperture radar images, and since 2015, GNSS data are also continuously measuring the deformation. New wells are regularly drilled to carry on with the exploitation of the salt layer, maintaining the subsidence. We make use of this careful monitoring by inverting the geodetic data to constrain a model of deformation. As InSAR and levelling are characterized by different strengths (spatial and temporal coverage for InSAR, accuracy for levelling) and weaknesses (various biases for InSAR, notably atmospheric, very limited spatial and temporal coverage for levelling), we choose to combine SAR images with levelling data, to produce a 3-D velocity field of the deformation. To do so, we develop a two-step methodology which consists first by estimating the 3-D velocity from images in ascending and descending acquisition of Sentinel 1 between 2015 and 2017, and second by applying a weighted regression kriging to improve the vertical component of the velocity in the areas where levelling data are available. GNSS data are used to control the resulting velocity field. We design an analytical model based on the geological and geophysical data. The model is made of 21 planes of dislocation with fixed position and geometry. We invert the combined geodetic dataset to estimate the slip and tensile motions of each plane. The results of the inversion highlight two behaviours of the salt layer: a major collapse of the salt layer beneath the extracting wells and a salt flow from the deepest and most external zones towards the centre of the exploitation.

The performance of blue-green infrastructure (BGI) has been well documented in temperate and subtropical climates, but evidence supporting its application in cold climates, especially during snowmelt, is still scarce. To address this gap, the present study proposes a modeling method for simulating the performance of bioretention cells during snowmelt according to different spatial implementation scenarios. We used the Storm Water Management Model (SWMM) of a catchment in a medium-sized city in Quebec, Canada as a case study. Pollutants commonly found in the snow (TSS, Cr, Pb, Zn, Cl–) were included in the model using event mean concentrations (EMCs) documented in the literature. Bioretention cells performed best on industrial road sites for the entire snowmelt period. Bioretention cell performance was affected by snow management procedures applied to the roads in residential areas. Not modeling the snow cover build-up and meltdown in the simulation led to higher runoff and bioretention cell performance. Modeling results facilitated the identification of bioretention cell sites that efficiently controlled runoff during snowmelt. Such information is needed to support decision planning for BGI in cities with cold climate.