Water Quality Data and Trends Analysis Results for Selected Streams Throughout Puerto Rico, Water Years 1958 - 2022

The U.S. Geological Survey (USGS), in cooperation with the Puerto Rico Department of Natural and Environmental Resources (PRDNER), has been collecting surface water-quality data for over 20 years across the island of Puerto Rico. These data are used for numerous purposes, including to meet the mandates of the Clean Water Act, provide government agencies and public corporations up-to-date data on the quality of surface waters, and maintain a data base of water-quality information to assess the general water-quality at selected sites throughout Puerto Rico. The PRDNER continues progressing in the development of a water-quality management plan to reduce point source and non-point source water pollution across the island of Puerto Rico. To understand how the water-quality conditions have changed over time, the USGS, in cooperation with the PRDNER, conducted an analysis to identify water quality trends for selected sites and parameters (major ions, metals, nutrients, physical and other parameters). Mann-Kendall and Seasonal Kendall trend analyses were done using the open-source R software (R Core Team, 2024) and water quality data obtained from the Water Quality Portal (www. waterqualitydata.us) that were collected between the 1958 and 2022 water years. The trend analysis evaluated the full length of record at each site and included three schemes: the Mann-Kendall and Seasonal Mann-Kendall trend tests using non-flow adjusted data, and the Seasonal Mann-Kendall trend test using flow-adjusted data. The Mann-Kendall trend analysis also included three additional time periods (water years 2008-2022, and 2013-2022). This data release and its child items contain the input data that were used for trend analysis, the output files from the trend analysis, and the accompanying R scripts for data screening and trend analysis. A README file also is available with a description of the files that were included in the data release and steps to follow to run the scripts. Reference Cited: R Core Team, 2024, R – A language and environment for statistical computing: R Foundation for Statistical Computing website, accessed February 9, 2024, at http://www.R-project.org.

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