Public-supply wells that have been analyzed for trends are displayed when arriving at the site. Sites are color coded according to whether a trend was detected in the water quality record.
The pull-down menu on the left can be used to view concentrations of constituents. The menu can be filtered to show sites with a measured concentration after the selected year.
View trends for constituents. Trends are symbolled and colored to allow multiple trends to be viewed simultaneously.
Use the filter options to limit the display of data to wells with different construction characteristics or trend types. For example, sites with recent and reversal arsenic trends in shallow wells.
Select a site and view information on the well location, well construction, constituent concentrations and a list of trends. Blue text can be clicked to see a graph of the trend.
Groundwater-quality data for 38 inorganic constituents (table 1) can be downloaded from this web map. Data can be downloaded for individual sites. To download data for a site, select either Sites, Concentrations, or Trends from the Display pull down menu and click on a site displayed on the map, as shown below. In the pop-up window for the site, a Download Data button can be clicked to retrieve a zipped folder of the data for that site. If the Sites option is selected or if a constituent is not selected (Concentrations, Trends options), the web map will retrieve all data for the selected site. If a constituent is selected while viewing Concentrations or Trends, only the selected constituent will be retrieved for the download file.
Data can also be downloaded by hydrogeologic province, groundwater unit or public supply code using the Data Downloader. To access the data downloader, navigate to Download Data on the top right corner of the map.
Groundwater-quality data for 38 inorganic constituents (table 1) were compiled from the
California State Water Resources Control Board Division of Drinking Water (SWRCB-DDW) database of water-quality collected for
compliance purposes from 1974 thru 2019 and from data collected by the USGS Groundwater Ambient Monitoring and Assessment –
Priority Basin Project (GAMA-PBP) from 2004 thru 2019. More than 95% of the data used for trends were from the SWRCB-DDW database.
Data from the USGS GAMA program supplements the SWRCB-DDW data, particularly in rural areas of California where water-quality
monitoring is not as frequent. The SWRCB-DDW data are available from the SWRCB’s GAMA-GIS on-line groundwater information system
[CASWRCB, 2018a]; data from the USGS are available on-line from USGS
NWIS [USGS, 2018b] and the USGS GAMA-PBP web mapper [Jurgens et al., 2018].
Groundwater-quality data are from sample points that discharge raw, untreated groundwater. This analysis does not evaluate trends in water
delivered to consumers, which may be treated or blended with other water before delivery to consumers. The data collected by water purveyors
and reported to the State were not evaluated for contamination, bias, or analytical quality. Data reported to the State of California are
from unfiltered samples and laboratory values of pH may not represent ambient groundwater conditions. USGS samples were collected in
accordance with protocols established by the USGS National Field Manual (USGS, variously dated)
and the USGS National Water Quality Assessment (NAWQA) project
[Koterba et al., 1995]. USGS sampling protocols are designed to obtain samples that represent conditions in the aquifer.
Some groundwater-quality data from the state were revised before being combined with data from the USGS. For each water-quality constituent,
data from the SWRCB-DDW were revised to obtain one result per site, sample date, and sample time. Records with missing sample dates and
public-supply codes (unique well identification code used by the state) and records with duplicate results or labeled as a detection but
had missing results were removed. The median value was used when multiple results were reported.
Groundwater-quality parameters from the USGS were mapped to groundwater-quality parameters used by the state based on the chemical name and
units of reporting (table 1). A new parameter for nitrate was created to combine nitrate data reported
as nitrate before 2016 (parameter code 71850) with nitrate data reported as nitrogen beginning in 2016 (parameter code 00618). Data before 2016
were converted from nitrate to nitrogen by multiplying the values by 0.2259. This new parameter (parameter code G0618) provides a single, continuous
record of nitrate that can be used to assess nitrate trends.
Constituent concentrations were compared to Federal and State drinking water-quality benchmarks to provide context (table 1). Benchmarks were selected in the following order of priority: United States Environmental Protection Agency
(EPA) or SWRCB-DDW maximum contaminant level (MCL) or action level (AL), which ever had the lowest concentration (24 constituents), SWRCB-DDW
secondary maximum contaminant levels (SMCL; the upper SMCL was used for constituents with lower and upper recommended values; 5 constituents),
HAL (2 constituent), then SWRCB-DDW notification level – response level, NL-RL (1 constituent)
[EPA, 2018b; SWRCB-DDW, 2018a,b]. Sample concentrations (C) are defined as “high”,
“moderate”, and “low” relative to the benchmark concentration (B): High C > B; Moderate B/2 < C ≤ B; Low C ≤ B/2.
Six constituents did not have a benchmark, but these constituents may contribute to or explain trends of other constituents with benchmarks
(table 1). For example, calcium does not have a benchmark but contributes to total dissolved solids
(TDS) concentrations. Therefore, a calcium trend may help explain TDS trends.
Table 1. List of water-quality constituents analyzed for trends with the number of wells with at least one sample,
the constituent screening level, and water-quality benchmark.
The Mann-Kendall (MK) rank correlation [Kendall, 1975], which is a non-parametric, rank-based
statistical test, and Sen’s slope estimator [Sen, 1968] were used to assess trends in water quality data.
Trends were accepted as statistically significant when MK rank correlation p-values were below a significance level (α) of 0.05 and the
Sen’s slope estimator was not zero. Positive Sen’s slopes indicate increasing concentrations while negative slopes indicate decreasing concentrations.
Tests were computed using the Python scripting language [PSF, 2016] for constituents at wells with 4 or
more unique laboratory analyses (number of analyses minus number of equal values or ties).
Before a statistical test was applied, water-quality data were processed to reduce biases in trend detection caused by serial correlation, changing
reporting levels, and from seasonal patterns. In general, the most common detection level reported with the SWRCB-DDW data was used as a truncation
level such that non-detections and concentrations below the truncation level were recoded to the most common detection level for each constituent
listed in table 1. Non-detect values above the truncation level were removed from the dataset. To reduce
the effects of serial correlation and to test for trends in data that display significant water-quality differences among two pumping seasons,
water-quality data were classified as a Summer sample if the sample date was between May 1st and October 31
st or a Winter sample if the sample dates were outside the Summer date range.
The median concentration for each season was computed when more than one result was measured during the season, and the date associated with the
median concentration was recorded. This method produces at most two data points for each year.
Tests for trends were applied to different periods to identify long-term trends (LTT), recent trends (RT), reversals in trends (TRV) and
trends that have seasonal concentration differences (figure 1). The entire period of recorded data was used to identify
LTTs, while RTs were evaluated with water-quality data collected since the year 2000. LTTs and RTs were computed for datasets with 4 or more unique
Reversals of trends (TRV) show a change in trend direction either from decreasing to increasing or from increasing to decreasing. TRVs were computed
for datasets with at least 8 unique laboratory analyses spanning at least 8 years. TRVs were determined by looking for opposite trends in two
continuous segments; one segment from the oldest data and one segment from the newest data. To determine if a change in slope occurred, the MK
test was computed multiple times by incrementally varying the size of the oldest (Sold=i) and newest
(Sold=N-i) segments, where i goes from 4 to the number of data points (N).
Because this analysis can produce multiple sets of segments with TRVs around the inflexion point, the set of newest data with the largest change in trend
slope was reported (figure 1). This procedure identifies trends that have reversed direction once over the entire period of record
rather than trends with frequent reversals caused by variability over shorter durations (<8 years).
Seasonal trends can result from cyclical periods of pumping and non-pumping that cause changes in the water sampled by a well differences
[Bexfield and Jurgens, 2014]. Trends can be masked, or the rate of change can be over/under-estimated
by seasonal differences in water-quality data [Hirsch et al., 1982; Helsel and Hirsch,
1995]. Seasonality was identified using the Mann-Whitney test for differences between seasonal populations of water-quality data when there were
at least four unique analyses in each season. If differences in concentrations between seasons were significant, MK rank correlation and Sen’s slope
estimator were computed for each set of seasonal data. A seasonal trend was statistically significant if at least one MK test p-value was below
the significance level and the Sen’s Slope estimator was not zero. This approach to seasonal trends is different than the computation by the Seasonal MK
trend test, which is a sum of the individual Kendall’s S statistic among seasons and generally requires trends to be in the same direction for
most seasons to be significant.
Figure 1. Examples if (A) long-term, (B) recent, (C) reversing, and (D) seasonal trends.
Belitz, K.B., Fram, M.S., Johnson, T.D., 2015, Metrics for assessing the quality of groundwater used for public supply, CA, USA: Equivalent-population and Area. Environmental Science and Technology, 49, 14, 8330-8338. DOI: 10.1021/acs.est.5b00265
Bexfield, L.M. and B.C. Jurgens. 2014. Effects of seasonal operation on the quality of water produced by public-supply wells, Groundwater p 15. DOI: 10.1111/gwat.12174.
California Department of Water Resources. California’s Groundwater—Bulletin 118, Update 2003; California Department of Water Resources: Sacramento, CA, 2003; http://www.water.ca.gov/groundwater/bulletin118/index.cfm.
California State Water Resources Control Board – Division of Drinking Water (SWRCB-DDW), 2016; EDT Library and Water Quality Analyses Data and Download Page; http://www.waterboards.ca.gov/drinking_water/certlic/drinkingwater/EDTlibrary.shtml.
California State Water Resources Control Board, 2018. Division of Drinking Water, Chemicals and Contaminants in Drinking Water Website, accessed February 21, 2018 at: https://www.waterboards.ca.gov/drinking_water/certlic/drinkingwater/Lawbook.shtml.
California State Water Resources Control Board, 2018. GAMA – Groundwater Ambient Monitoring and Assessment Program Website; https://www.waterboards.ca.gov/water_issues/programs/gama/online_tools.html
Helsel, D.R., Hirsch, R.M., 1995. Statistical Methods in water resources. Elsevier Science, p. 529.
Hirsch, R. M., Slack, J. R., Smith, R.A., 1982, Techniques of trend analysis for monthly water quality data. Water Resources Research, 18 (1), 107-121. DOI: 10.1029/WR018i001p00107.
Jurgens, B., Jasper, M., Nguyen, D.H., and Bennett, G.L., 2018, USGS CA GAMA-PBP Groundwater-Quality Results--Assessment and Trends: U.S. Geological Survey website, at https://ca.water.usgs.gov/projects/gama/water-quality-results/.
Kendall, M.G. 1975. Rank correlation methods. Griffin. London.
Koterba, M.T., F.D. Wilde, and W.W. Lapham. 1995. Ground-water data-collection protocols and procedures for the National Water-Quality Assessment Program—Collection and documentation of water-quality samples and related data. U.S. Geological Survey Open-File Report 95-399, 113. Reston, Virginia: USGS.
PSF, 2016. Python Software Foundation. Python Language Reference, version 3.5. Available at http://www.python.org
Sen, P.K. 1968. Estimates of the regression coefficient based on Kendall’s tau. Journal of American Statistical Association, 63, 1379-1389.
U.S. Geological Survey, variously dated, National field manual for the collection of water-quality data: U.S. Geological Survey Techniques of Water-Resources Investigations, book 9, chaps. A1-A10, available online at http://pubs.water.usgs.gov/twri9A/.
U.S. Geological Survey, 2018a, National Water Information System data available on the World Wide Web (USGS Water Data for the Nation) at URL [http://waterdata.usgs.gov/nwis/ ].
U.S. Geological Survey, 2018b, National Water Information System: Web Interface Website, accessed February 21, 2018 at: https://www.waterboards.ca.gov/gama/geotracker_gama.shtml.
U.S. Environmental Protection Agency. 2018a. Safe Drinking Water Act Amendments of 1996; Office of Ground Water and Drinking Water; Washington, DC, 1996. https://www.epa.gov/sdwa (accessed March 2018).
U.S. Environmental Protection Agency. 2018b. Drinking water standards and regulations: National Primary Drinking Water Regulations. Website, accessed February 21, 2018 at: https://www.epa.gov/dwstandardsregulations.U.S.
AL: action level
GAMA-PBP:USGS Groundwater Ambient Monitoring and Assessment – Priority Basin Project (GAMA-PBP)
GAMA-GIS:USGS Groundwater Ambient Monitoring and Assessment – Geographic Information System
LTT: long-term trends
MCL: maximum contaminant level
NAWQA: USGS National Water Quality Assessment
NL-RL: notification level – response level
PSWs: public-supply wells
RT: recent trends
SWRCB: State Water Regional Control Board
SWRCB-DDW: California State Water Resources Control Board Division of Drinking Water
TDS: total dissolved solids
TRV: reversals in trends
EPA: United States Environmental Protection Agency
USGS: United States Geological Survey
NWIS: National Water Information System
Dupuy, D.I., Nguyen, D.H., and Jurgens, B.C., 2019, California GAMA Priority Basin Project: Trends in water-quality for inorganic constituents in California public-supply wells (1st ed.): U.S. Geological Survey website,
Jurgens, B.C., Fram, M.S., Rutledge, J., Bennett V., G.L., 2020. Identifying areas of degrading and improving groundwater-quality conditions in the State of California, USA, 1974–2014. Environ Monit Assess 192, 250. https://doi.org/10.1007/s10661-020-8180-y
Table 1. List of water-quality constituents analyzed for trends with the number of wells with at least one sample, the constituent screening level, and water-quality benchmark.