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5 results for “Subsidence modeling”
Dataset related to Balazs et al. The dynamics of forearc – back-arc basin subsidence: numerical models and observations from Mediterranean subduction zones
<p>Additional model data to publication by Balazs et al. The dynamics of forearc – back-arc basin subsidence: numerical models and observations from Mediterranean subduction zones</p>
Data observed at Kawajima land subsidence observatory (Japan) and ensemble model
<p>This data set contains all data necessary to reproduce the results of the manuscript submitted by Akitaya & Aichi. The data set includes hydraulic head and land subsidence data observed at the Kawajima land subsidence observatory (Japan), and an ensemble model parameter set constructed using the evolutionary multimodal algorithm, ensemble simulation results, and predictive uncertainty analysis results obtained through ensemble model output statistics. Please refer to the "readme.txt" file provided in each individual folder for detailed instructions on how to read the data.</p>
Results from groundwater pumping-induced subsidence model, San Joaquin Valley
<p>The San Joaquin Valley, California has experienced dramatic subsidence over the past 100 years, but the regions with the most subsidence have shifted dramatically over this time period, from west (Kettleman City/Los Banos) to south (Tulare/Pixley/Corcoran). To date, no study has done an in-depth analysis of the mechanisms driving this shift in subsidence. We analyze head records, utilizing a novel approach that assimilates change in head data from multiple overlapping time periods, to produce an 80-year record of change in head over both the historical and modern regions of greatest subsidence. We then calibrate a deformation model to fit both historical (measured with leveling surveys) and modern (measured with Interferometric Synthetic Aperture Radar, or InSAR) datasets. We find that the stress history of the Kettleman City/Los Banos region with historically high subsidence plays a large role in reducing modern subsidence in that region, while declining heads in both regions are likely to result in major subsidence over the next several decades. This study highlights the need for active groundwater management to mitigate ongoing and future subsidence. One key dataset needed in this effort is accurate long-term head histories to reconstruct the stress history of aquifers for accurate deformation modeling.</p>
Results from groundwater pumping-induced subsidence model, San Joaquin Valley
Open the record for dataset details and reuse information.
Development and Validation of Interpretable Machine Learning Models Incorporating Paraspinal Muscle Quality for to Predict Cage Subsidence Risk Followingposterior Lumbar Interbody Fusion
ClinicalTrials.gov study NCT06888739. IPD Sharing: YES. Countries: 1. Publications: 0.
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