This repository includes code and results used in development of a new machine learning approach to modeling salinity yield modeling for Upper Colorado River Basin. This work is currently in in revision with the journal Water Resources Research in a paper entitled "Salinity yield modeling of the Upper Colorado River Basin using 30-meter resolution soil maps and machine learning".

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.

The Beta release for peer review is available at https://doi.org/10.5281/zenodo.2636706. This link will be removed pending final peer review and completion of the official USGS data release which will be available as the following.

Nauman, T.W., 2019, Salinity yield modeling spatial data for the Upper Colorado River Basin, USA: U.S. Geological Survey data release, https://doi.org/10.5066/P9QSFDJN (pending)

The predictive soil maps used for creating many of the model inputs are documented at https://github.com/usgs/Predictive-Soil-Mapping/tree/master/SoilSurvReconstrProperties. Links to all the original soil maps are provided in that repository.