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21 records · Page 2Linked to original sources

Characterization and simulation of the quantity and quality of water in the Highland Lakes, Texas, 1983-92

The Highland Lakes, located in central Texas, are a series of seven reservoirs on the Colorado River (Lake Buchanan, Inks Lake, Lake Lyndon B. Johnson, Lake Marble Falls, Lake Travis, Lake Austin, and Town Lake). The reservoirs provide hydroelectric power for the area. In addition, Lake Austin and Town Lake also provide the public water supply for the Austin metropolitan area. Saline water released from Natural Dam Salt Lake during 1987–89 caused increased concern among water managers that high-salinity water entering the Highland Lakes could result in waterquality problems, necessitating additional treatment of the water. The maximum dissolved solids concentrations for the reservoirs after the saline inflow were about two to three times the average concentrations before the inflow. The maximum concentrations of chloride and sulfate after the inflow were about three to five times the average concentrations before the inflow. The concentrations of dissolved solids, chloride, and sulfate in Lake Buchanan, Inks Lake, Lake Lyndon B. Johnson, and Lake Marble Falls were less than the concentrations of the applicable water-quality standards by the end of 1990. Concentrations of these constituents in Lake Travis, Lake Austin, and Town Lake did not decrease to previous levels, which were less than the concentrations of the applicable waterquality standards, until the end of 1991. Constituent concentrations for Lake Buchanan and Inks Lake; for Lake Lyndon B. Johnson and Lake Marble Falls; and for Lake Travis, Lake Austin, and Town Lake were similar because of the relative storage capacities and location of tributary inflows. From the initial increase in constituent concentrations in Lake Buchanan (summer 1987) in response to the saline inflow, the high-salinity water passed through the entire Highland Lakes in about 3.5 years. A mathematical mass-balance model was used to simulate the input and movement of highsalinity water through the Highland Lakes and to estimate monthly mean concentrations of dissolved solids, chloride, and sulfate for wet, average, and dry hydrologic conditions. The simulated median monthly concentrations during the 10-year simulation period for each reservoir generally are larger for the average condition than for the wet condition and generally are larger for the dry condition than for the average condition. The simulated concentrations of dissolved solids, chloride, and sulfate decreased to levels less than the concentrations of the applicable water-quality standards in about 2 to 5 years after the saline water inflow of 1987–89 was simulated for the three hydrologic conditions. Results from the simulations indicate that saline inflows to the Highland Lakes similar to those of the releases from Natural Dam Salt Lake during 1987–89 are unlikely to cause large increases in future concentrations of dissolved solids, chloride, and sulfate in the Highland Lakes. The results also indicate that high-salinity water will continue to be diluted as it is transported downstream through the Highland Lakes, even during extended dry periods.

Texas

A benchmark dataset and workflow for landslide susceptibility zonation

Landslide susceptibility shows the spatial likelihood of landslide occurrence in a specific geographical area and is a relevant tool for mitigating the impact of landslides worldwide. As such, it is the subject of countless scientific studies. Many methods exist for generating a susceptibility map, mostly falling under the definition of statistical or machine learning. These models try to solve a classification problem: given a collection of spatial variables, and their combination associated with landslide presence or absence, a model should be trained, tested to reproduce the target outcome, and eventually applied to unseen data. Contrary to many fields of science that use machine learning for specific tasks, no reference data exist to assess the performance of a given method for landslide susceptibility. Here, we propose a benchmark dataset consisting of 7360 slope units encompassing an area of about 4,100 km 2 "> 4,100 km 2 in Central Italy. Using the dataset, we tried to answer two open questions in landslide research: (1) what effect does the human variability have in creating susceptibility models; (2) how can we develop a reproducible workflow for allowing meaningful model comparisons within the landslide susceptibility research community. With these questions in mind, we released a preliminary version of the dataset, along with a “call for collaboration,” aimed at collecting different calculations using the proposed data, and leaving the freedom of implementation to the respondents. Contributions were different in many respects, including classification methods, use of predictors, implementation of training/validation, and performance assessment. That feedback suggested refining the initial dataset, and constraining the implementation workflow. This resulted in a final benchmark dataset and landslide susceptibility maps obtained with many classification methods. Values of area under the receiver operating characteristic curve obtained with the final benchmark dataset were rather similar, as an effect of constraints on training, cross–validation, and use of data. Brier score results show larger variability, instead, ascribed to different model predictive abilities. Correlation plots show similarities between results of different methods applied by the same group, ascribed to a residual implementation dependence. We stress that the experiment did not intend to select the “best” method but only to establish a first benchmark dataset and workflow, that may be useful as a standard reference for calculations by other scholars. The experiment, to our knowledge, is the first of its kind for landslide susceptibility modeling. The data and workflow presented here comparatively assess the performance of independent methods for landslide susceptibility and we suggest the benchmark approach as a best practice for quantitative research in geosciences.

Earth-Science Reviews

The Sedimentary Geochemistry and Paleoenvironments Project Phase 2 data release: An open data resource for the study of Earth's environmental history

Geochemical data from sedimentary rocks are the primary source of information regarding Earth's surface evolution through time, including its air and water envelopes and interactions with life and deep Earth processes. The Sedimentary Geochemistry and Paleoenvironments Project (SGP) is a scientific consortium centered around open data and community-driven development of cyberinfrastructure tools and resources for sedimentary geochemistry and Earth history. Here we describe the SGP Phase 2 data release, which focused on incorporating Paleoproterozoic and Mesoproterozoic (2500–1000 million years ago) data and better accommodating carbonate data. This data release was built through the involvement of >200 researchers worldwide in academia, government, and industry, and provides the largest available public data resource for our user community in the academic fields of geochemistry, sedimentology, tectonics, paleontology, Earth history, and paleoclimate, as well as the petroleum and minerals industries. The dataset now encompasses 126,006 samples and 4,132,371 geochemical analyses. In addition to direct entry by SGP Team Members, we have ingested and incorporated datasets from the Geoscience Australia OZCHEM database, the Alberta Geological Survey, and the Deep-Time Marine Sedimentary Element Database (DM-SED) compilation. This paper details sampling in the Phase 2 dataset with respect to age, geography, lithology, and other geological characteristics, documents access via our search website and API, discusses possible issues and/or biases in the dataset that could impact analyses, describes plans for governance and stewardship of data from Indigenous lands, and serves as the citable reference paper for the data release.

Chemical Geology