Dataset Information

Subsidence-Derived Volumetric Strain Models for Mapping Extensional Fissures and Constraining Rock Mechanical Properties in the San Joaquin Valley, California.

ABSTRACT: Large-scale subsidence due to aquifer-overdraft is an ongoing hazard in the San Joaquin Valley. Subsidence continues to cause damage to infrastructure and increases the risk of extensional fissures.Here, we use InSAR-derived vertical land motion (VLM) to model the volumetric strain rate due to groundwater storage change during the 2007-2010 drought in the San Joaquin Valley, Central Valley, California. We then use this volumetric strain rate model to calculate surface tensile stress in order to predict regions that are at the highest risk for hazardous tensile surface fissures. We find a maximum volumetric strain rate of -232 microstrain/yr at a depth of 0 to 200 m in Tulare and Kings County, California. The highest risk of tensile fissure development occurs at the periphery of the largest subsiding zones, particularly in Tulare County and Merced County. Finally, we assume that subsidence is likely due to aquifer pressure change, which is calculated using groundwater level changes observed at 300 wells during this drought. We combine pressure data from selected wells with our volumetric strain maps to estimate the quasi-static bulk modulus, K, a poroelastic parameter applicable when pressure change within the aquifer is inducing volumetric strain. This parameter is reflective of a slow deformation process and is one to two orders of magnitude lower than typical values for the bulk modulus found using seismic velocity data. The results of this study highlight the importance of large-scale, high-resolution VLM measurements in evaluating aquifer system dynamics, hazards associated with overdraft, and in estimating important poroelastic parameters.

SUBMITTER: Carlson G

PROVIDER: S-EPMC7539920 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Subsidence-Derived Volumetric Strain Models for Mapping Extensional Fissures and Constraining Rock Mechanical Properties in the San Joaquin Valley, California.

Carlson Grace G Shirzaei Manoochehr M Ojha Chandrakanta C Werth Susanna S

Journal of geophysical research. Solid earth 20200911 9

Large-scale subsidence due to aquifer-overdraft is an ongoing hazard in the San Joaquin Valley. Subsidence continues to cause damage to infrastructure and increases the risk of extensional fissures.Here, we use InSAR-derived vertical land motion (VLM) to model the volumetric strain rate due to groundwater storage change during the 2007-2010 drought in the San Joaquin Valley, Central Valley, California. We then use this volumetric strain rate model to calculate surface tensile stress in order to ...[more]

PMID: 33042724

Similar Datasets

Project description:Advances in satellite retrieval of aerosol type can improve the accuracy of near-surface air quality characterization by providing broad regional context and decreasing metric uncertainties and errors. The frequent, spatially extensive and radiometrically consistent instantaneous constraints can be especially useful in areas away from ground monitors and progressively downwind of emission sources. We present a physical approach to constraining regional-scale estimates of PM2.5, its major chemical component species estimates, and related uncertainty estimates of chemical transport model (CTM; e.g., the Community Multi-scale Air Quality Model) outputs. This approach uses ground-based monitors where available, combined with aerosol optical depth and qualitative constraints on aerosol size, shape, and light-absorption properties from the Multi-angle Imaging SpectroRadiometer (MISR) on the NASA Earth Observing System's Terra satellite. The CTM complements these data by providing complete spatial and temporal coverage. Unlike widely used approaches that train statistical regression models, the technique developed here leverages CTM physical constraints such as the conservation of aerosol mass and meteorological consistency, independent of observations. The CTM also aids in identifying relationships between observed species concentrations and emission sources. Aerosol air mass types over populated regions of central California are characterized using satellite data acquired during the 2013 San Joaquin field deployment of the NASA Deriving Information on Surface Conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) project. We investigate the optimal application of incorporating 275 m horizontal-resolution aerosol air-mass-type maps and total-column aerosol optical depth from the MISR Research Aerosol retrieval algorithm (RA) into regional-scale CTM output. The impact on surface PM2.5 fields progressively downwind of large single sources is evaluated using contemporaneous surface observations. Spatiotemporal R 2 and RMSE values for the model, constrained by both satellite and surface monitor measurements based on 10-fold cross-validation, are 0.79 and 0.33 for PM2.5, 0.88 and 0.65 for NO3 -, 0.78 and 0.23 for SO4 2-, and 1.01 for NH+, 0.73 and 0.23 for OC, and 0.31 and 0.65 for EC, respectively. Regional cross-validation temporal and spatiotemporal R2 results for the satellite-based PM2.5 improve by 30 % and 13 %, respectively, in comparison to unconstrained CTM simulations and provide finer spatial resolution. SO4 2- cross-validation values showed the largest spatial and spatiotemporal R2 improvement, with a 43 % increase. Assessing this physical technique in a well- instrumented region opens the possibility of applying it globally, especially over areas where surface air quality measurements are scarce or entirely absent.

Dataset Information

Subsidence-Derived Volumetric Strain Models for Mapping Extensional Fissures and Constraining Rock Mechanical Properties in the San Joaquin Valley, California.

Publications

Subsidence-Derived Volumetric Strain Models for Mapping Extensional Fissures and Constraining Rock Mechanical Properties in the San Joaquin Valley, California.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets