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GRTD

Groundwater Residence Time Distributions

Groundwater residence-time distributions (RTDs) are critical for assessing lag times between activities at the land surface and the emergence of related solutes in the baseflow of streams. However, RTDs can not be measured directly, they must be inferred from an analysis of data using models. Glacial aquifers present challenges to modeling approaches because they are spatially discontinuous and have highly variable properties. An innovative approach by the USGS uses machine learning techniques in conjunction with numerical models that results in a rapid and robust way of generating RTDs. The main idea is to simulate groundwater flow in subregional type locales (for example at the HUC8 scale) and then apply machine learning to extract variables to predict RTDs across a region. The technique has been applied to glacial aquifers in the U.S. and compared to tracer data in public water-supply wells. This work is currently in review.

This software is preliminary or provisional and is subject to revision. It is being provided to meet the need for timely best science. The software has not received final approval by the U.S. Geological Survey (USGS). No warranty, expressed or implied, is made by the USGS or the U.S. Government as to the functionality of the software and related material nor shall the fact of release constitute any such warranty. The software is provided on the condition that neither the USGS nor the U.S. Government shall be held liable for any damages resulting from the authorized or unauthorized use of the software.

To run these Jupyter Notebooks, place the Notebooks directory in a directory alongside the unzipped Data, Figures, Models, and Executables directories. The directory Models has been broken into 3 parts for file transfer but it should be unzipped into one directory called Models. See GRTD Readme.pdf for details on Notebook contents. The appropriate Python environment will have to be installed first.

An alternative to running the notebooks is to run the batch-mode scripts in Scripts. The Windows batch file run.bat will do this. Figures that show individual model results have to be run from the notebooks so that the appropriate model can be selected using the drop-down menus in the notebooks.

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  • Jupyter Notebook 58.2%
  • Python 41.6%
  • Batchfile 0.2%