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Many areas worldwide are known to experience land subsidence due to groundwater extraction. It is traditionally assumed that subsidence extent and rates are controlled by groundwater extraction locations and volumes. Here, we reevaluate this assumption in the Mexico City metropolitan area by combining stratigraphic, hydrologic, geodetic, and demographic datasets. Integration of 115 years of leveling with 24 years of Interferometric Synthetic Aperture Radar (InSAR) and 14 years of GPS data reveals that subsidence rates have been mostly constant in Mexico City since at least 1950 and reach 50 cm/year. Analysis of InSAR and GPS data shows that no significant elastic deformation exists, demonstrating that the subsidence is almost fully irreversible. In Mexico City, no direct relationships exist between groundwater level fluctuations and subsidence rates or between pumping rates and subsidence rates. In contrast, a strong positive linear relationship is isolated between subsidence rates and the thickness of the upper aquitard. Through the integration of these long‐term datasets, we forecast that it will take ∼150 years to reach total compaction of the upper aquitard, which may lead to additional subsidence up to 30 m. With the potentiometric surface now deeper than most of the aquitard, clay's porewater rich in salts, chemical constituents, and pollutants is now flowing downward into the productive aquifer, hence decreasing water quality. Finally, our work shows that the consequences of land subsidence greatly influence the socioeconomic landscape in the Mexico City metropolitan area.

 

The massive surge in the amount of observational field data demands richer and more meaningful collab- oration between data scientists and geoscientists. This document was written by members of the Working Group on Case Studies of the NSF-funded RCN on Intelli- gent Systems Research To Support Geosciences (IS-GEO, https://is-geo.org/) to describe our vision to build and enhance such collaboration through the use of specially- designed benchmark datasets. Benchmark datasets serve as summary descriptions of problem areas, providing a simple interface between disciplines without requiring extensive background knowledge. Benchmark data intend to address a number of overarching goals. First, they are concrete, identifiable, and public, which results in a natural coordination of research efforts across multiple disciplines and institutions. Second, they provide multi- fold opportunities for objective comparison of various algorithms in terms of computational costs, accuracy, utility and other measurable standards, to address a particular question in geoscience. Third, as materials for education, the benchmark data cultivate future human capital and interest in geoscience problems and data science methods. Finally, a concerted effort to produce and publish benchmarks has the potential to spur the development of new data science methods, while provid- ing deeper insights into many fundamental problems in modern geosciences. That is, similarly to the critical role the genomic and molecular biology data archives serve in facilitating the field of bioinformatics, we expect that the proposed geosciences data repository will serve as “catalysts” for the new discicpline of geoinformatics. We describe specifications of a high quality geoscience bench- mark dataset and discuss some of our first benchmark efforts. We invite the Climate Informatics community to join us in creating additional benchmarks that aim to address important climate science problems.