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Streamflow Characteristics and Trends in New Jersey, Water Years 1903–2017

Amy R. McHugh, Thomas P. Suro, Samantha L. Sullivan, Brianna M. Williams, K.H. Hamed, K.H. Hamed · U.S. Geological Survey

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Prepared in cooperation with the New Jersey Department of Environmental Protection

Streamflow Characteristics and Trends in New Jersey, Water Years 1903–2017

Scientific Investigations Report 2024–5099

U.S. Department of the Interior U.S. Geological Survey

Cover. Upper left: Photograph of base-flow conditions at U.S. Geological Survey streamgage 01403200 Middle Brook at Bound Brook, New Jersey, on August 8, 2022. Photograph by Jonathan T. Suchy, U.S. Geological Survey. Center Right: Photograph of base-flow conditions near U.S. Geological Survey streamgage 01400010 North Branch Raritan River at South Branch, New Jersey, on August 31, 2022. Photograph by Jonathan T. Suchy, U.S. Geological Survey. Bottom left: Photograph of low-flow conditions near U.S. Geological Survey streamgage 01389950 Peckman River at Little Falls, New Jersey, on October 18, 2024. Photograph by Sally A. Carullo U.S. Geological Survey.

Streamflow Characteristics and Trends in New Jersey, Water Years 1903–2017 By Amy R. McHugh, Thomas P. Suro, Samantha L. Sullivan, Brianna M. Williams

Prepared in cooperation with the New Jersey Department of Environmental Protection

Scientific Investigations Report 2024–5099

U.S. Department of the Interior U.S. Geological Survey

U.S. Geological Survey, Reston, Virginia: 2024

For more information on the USGS—the Federal source for science about the Earth, its natural and living resources, natural hazards, and the environment—visit https://www.usgs.gov or call 1–888–ASK–USGS. For an overview of USGS information products, including maps, imagery, and publications, visit https://store.usgs.gov/. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government. Although this information product, for the most part, is in the public domain, it also may contain copyrighted materials as noted in the text. Permission to reproduce copyrighted items must be secured from the copyright owner. Suggested citation: McHugh, A.R., Suro, T.P., Sullivan, S.L., and Williams, B.M., 2024, Streamflow characteristics and trends in New Jersey, water years 1903–2017: U.S. Geological Survey Scientific Investigations Report 2024–5099, 59 p., https://doi.org/10.3133/sir20245099. Associated data for this publication: Williams, B.M., McHugh, A.R., Sullivan, S.L., and Hopple, J.A., 2024, Streamflow characteristics and trends at continuous-record and partial-record streamflow-gaging stations in New Jersey, water years 1903–2017: U.S. Geological Survey data release, https://doi.org/10.5066/P9IGMJGU. ISSN 2328-0328 (online)

iii

Contents Abstract��1 Introduction��1 Purpose and Scope��3 Description of Study Area��3 Previous Studies��4 Methods��4 Continuous-Record Streamgages��4 Flow Durations��7 Base Flow and Runoff��7 Streamflow Variability��8 Peak-Flow Ratio��8 Frequency Analysis��8 Fit of Frequency-Distribution Curve and Outliers��9 Trend Analysis��9 Partial-Record Streamgages��10 Index Site Selection��10 Results��13 Trends in Streamflow at Continuous-record Streamgages��13 Variability—Skew and Kurtosis��17 Base Flow and Low n-day Flows��18 Runoff and High n-day Flows��26 Peak-Flow Ratio��36 Trends in Regression Residuals at Partial-Record Streamgages��36 Trends in Low Flow in Over-Allocated Basins��38 Comparison of Results—2001 and 2017��42 Changes in Trends at Continuous-Record Streamgages��44 Percent Change in Streamflow��44 Continuous-Record Percent Change in Streamflow��45 Partial-Record Streamgage Percent Change in Low Flow��46 Summary and Conclusion��46 Acknowledgments��57 References Cited��57

Figures 1.

Map showing the physiographic provinces and selected U.S. Geological Survey continuous- and partial-record streamgages in and near New Jersey��2 2. Map and bar graph showing distribution of period of record among continuous-record streamgages in New Jersey, 1903–2017��6 3. Line graph showing flow-duration curves for South Branch Raritan River near High Bridge, N.J., 01396500 and Great Egg Harbor River at Folsom, N.J., 01411000��7 4. Map showing index site locations for partial-record streamgages across New Jersey physiographic provinces��12

6. 7.

10. 11.

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Scatterplot of the average computed Hurst coefficient compared to record length for computed for continuous-record streamgages in New Jersey, 1903–2017��15 Boxplots of the Hurst coefficients for various flow statistics at continuous-record streamgages in New Jersey, 1903–2017��15 Pie charts showing the percentage of continuous-record streamgages with statistically significant trends (p<0.05) under an assumption of no serial correlation in New Jersey, 1903–2017��16 Scatterplots of the magnitude of statistically significant trends (p<0.05) in computed continuous-record streamgages in New Jersey, 1903–2017, compared to years of record��17 Bar graph showing the number of continuous-record streamgages in New Jersey with statistically significant trends (p<0.05) for various low-flow characteristics and combinations thereof, 1903–2017��18 Maps showing continuous-record streamgages with statistically significant trends (p<0.05) in low flow in New Jersey, 1903–2017��19 Scatterplot showing the magnitude of statistically significant trends (p<0.05) in low flow at continuous-record streamgages in New Jersey, 1903–2017, compared to years of record��27 Bar graph showing the number of continuous-record streamgages in New Jersey with statistically significant trends (p<0.05) for various high-flow characteristics and combinations thereof, 1903–2017��28 Maps showing continuous-record streamgages with statistically significant trends (p<0.05) in high flow in New Jersey, 1903–2017��29 Plot of the magnitude of statistically significant trends (p<0.05) in high flow at continuous-record streamgages in New Jersey, 1903–2017, compared to years of record��37 Scatterplot of the magnitude of statistically significant trends (p<0.05) in peak-flow ratio to years of record at continuous-record streamgages in New Jersey, 1903–2017��38 Percentage of partial-record streamgages in New Jersey with a statistically significant trend (p<0.05) in regression residuals for the period 1903–2017, versus the total number of partial-record streamgages regressed against each continuous-record streamgage��40 Percentage of continuous-record streamgages indicating a statistically significant trend in regression residuals per partial-record streamgage versus the span of the record, in years, of the partial-record streamgage in New Jersey, 1903–2017��40 Map showing the percentage of index sites indicating a statistically significant trend (p<0.05) in regression residuals per partial-record streamgage versus the span of the record, in years, of the partial-record streamgage in New Jersey, 1903–2017��41 Bar graph showing the number of statistically significant trends (p<0.05) in n-day flow at continuous-records streamgages in New Jersey using data through 2001 versus 2017��44 Histogram of percent change from 2001 to 2017 for 80 continuous-record streamgages in New Jersey��45 Boxplots showing range of percent change in annual flow durations at continuous-record streamgages in New Jersey from 2001 to 2017��47

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Boxplots showing the range of percent change in winter flow durations at continuous-record streamgages in New Jersey from 2001 to 2017��48 Histogram of the frequency statistics percentage of change from 2001 to 2017��49 Boxplots showing range of percent change in annual and winter low-flow frequency statistics at continuous-record streamgages in New Jersey from 2001 to 2017��50 Boxplots showing range of percent change in annual and winter high-flow frequency statistics at continuous-record streamgages in New Jersey from 2001 to 2017��52 Histogram of percent change in low-flow statistics at 462 partial-record streamgages in New Jersey from 2001 to 2017��54 Boxplots showing the range of percent change in low-flow statistics at partial-record streamgages in New Jersey, from 2001 to 2017��55

Tables 1. 2. 3. 4. 5. 6. 7. 8. 9.

10.

11.

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List of statistics computed for continuous- and partial-record streamgages in New Jersey for the period 1903–2017��5 List of USGS streamgages used as index sites in low-flow regression analysis and the associated physiographic province��11 Trend results after adjustment for lag-1 serial correlation in select streamflow characteristics in New Jersey, periods of record spanning 1903–2017��14 Range of computed Hurst coefficients for selected flow characteristics at 97 streamgages in New Jersey, period of record spanning 1903–2017��16 Number of New Jersey continuous-record streamgages indicating a statistically significant trend for various low-flow characteristics, 1903–2017��18 Magnitude of statistically significant trends in low flow at continuous-record streamgages in New Jersey, 1903–2017��26 Number of New Jersey continuous-record streamgages indicating a statistically significant trend for various high-flow characteristics, 1903–2017��28 Magnitude of statistically significant trends in high flow at continuous-record streamgages in New Jersey, 1903–2017��36 Continuous-record streamgages used as index sites in regression analysis with partial-record streamgages and the results of trend tests in New Jersey, 1903–2017��39 Number and direction of trends of computed low flow characteristics at continuous-record streamgages in New Jersey, for which the associated HUC11 allocated water exceeds the low-flow margin��42 Partial-record streamgages in New Jersey with more than half of the index sites used in low-flow regression analysis that had statistically significant trends in residuals among HUC11 where allocated water exceeds the low-flow margin��43 Percent change in computed low-flow statistics at continuous-record streamgages in New Jersey, from 2001 to 2017��54

Conversion Factors U.S. customary units to International System of Units

Multiply

To obtain

Length foot (ft)

0.3048

meter (m)

Area square mile (mi2)

259.0

square mile (mi2)

2.590

hectare (ha) square kilometer (km2)

Flow rate cubic foot per second per square mile ([ft3/s]/mi2)

0.01093

cubic meter per second per square kilometer ([m3/s]/km2)

Supplemental Information A water year is the 12-month period from October 1 through September 30 of the following year and is designated by the calendar year in which it ends. MOVE1 is dominantly used as an acronym in this report to describe the Maintenance of Variance Type 1 method used in the calculations of low-flow frequencies, except for when referring to the publication of Colarullo and others, 2018, where MOVE.1 is used to align with the report title. Both terms refer to the same method.

Abbreviations 1Q10

Annual 1-day low flow with a 10-percent annual nonexceedance probability

7Q10

Annual 7-day low flow with a 10-percent annual nonexceedance probability

ft3/s/mi2/yr

cubic feet per second per square mile per year

HUC11

11-digit hydrologic unit code

IND

identified trend

Kolmogorov-Smirnov test

LPIII

Log-Pearson type III distribution

LTP

long term persistence

Mann-Kendall trend test

MOVE1

Maintenance of Variance method Type 1

NJDEP

New Jersey Department of Environmental Protection

STP

short term persistence

SWToolbox

Surface Water Toolbox

USGS

U.S. Geological Survey

Streamflow Characteristics and Trends in New Jersey, Water Years 1903–2017 By Amy R. McHugh, Thomas P. Suro, Samantha L. Sullivan, Brianna M. Williams

Abstract As New Jersey’s population density remains high, so does its requirements for water management. Understanding the streamflow conditions throughout the state and how they may have changed over time is an important part of managing the water resources within the state. The New Jersey Department of Environmental Protection has many responsibilities related to protecting the environment and natural resources and among them is protecting the waters in the lakes, rivers, and streams of New Jersey for current and future use. To support this mission, the U.S. Geological Survey updated highand low-streamflow statistics for 97 continuous-record streamgages and low-streamflow statistics for 719 partial-record streamgages throughout the state. The continuous-record streamgages included in the study had a minimum of 20 years of record, spanning from 1903 to 2017. This study is an update to previous studies that documented the high- and low-streamflow statistics for New Jersey streams in the 1970s and in 2005. The 1982 report by Gillespie and Schopp documented low-flow characteristics and flow duration for about 400 continuous and partial-record streamgages. The U.S. Geological Survey computed streamflow statistics including, but not limited to, maximum, minimum, and means for period of record, flow durations, nonexceedance high- and low-flow frequencies, base flow, runoff, peak-to-mean flow ratios, and September median streamflow. Overall, both high and low flows are generally increasing in New Jersey, though the results are not uniform across the State. Streamflow trends and changes to duration and frequency statistics can be influenced by local water use, in addition to climate variables. The resulting computations at some streamgages indicated considerable positive change while others showed considerable negative change. Water managers and regulators can use the data provided here and in the companion data release to assess individual stream reaches and watershed management areas to evaluate the available resources and changes, which may have developed during the periods for which streamflow statistics are available.

Introduction As the population continues to increase and New Jersey maintains its status as one of the most densely populated states in the country, the demand for water also increases.

The New Jersey Department of Environmental Protection (NJDEP)’s responsibilities include protecting the environment and natural resources of the state for current and future use, which includes protecting the waters in the lakes, rivers, and streams. Streamflow statistics, utilized by water managers and planners to help them understand flow conditions in the rivers and streams, are computed from published streamflow data at continuous- and partial-record streamgages. Streamflow statistics are also used by planners and regulatory agencies, like NJDEP, to make decisions about minimum passing flows, surface-water withdrawals, and establishing limits for waste load discharges for municipal and industrial facilities. Increases in population and development in New Jersey over the past 120 years has not only increased demands for water but also the need for wastewater discharges to streams. Improvements in water-use efficiency have helped influence decreases in total water use in some areas according to USGS county-level estimates (Falcone, 2017). These human activities, in addition to influences from climatic changes over time, have contributed to alterations of natural streamflow conditions. The U.S. Geological Survey (USGS), in cooperation with other Federal, State, and local agencies, has collected and published daily mean streamflow data for hundreds of streamgages across the state of New Jersey since the late 1890s (U.S. Geological Survey, 2023). This report updates statistics previously published in “Streamflow Characteristics and Trends in New Jersey, Water Years 1897–2003” (Watson and others, 2005) and can be considered the fourth in a series of streamflow statistic reports for New Jersey. Earlier reports published are “Low-flow Characteristics and Flow Duration of New Jersey Streams” (Gillespie and Schopp, 1982) and “Statistical Summaries of New Jersey Streamflow Records—Water Resources Circular 23” (Laskowski, 1970). Daily mean streamflow for 97 continuous-record streamgages and 719 partial-record streamgages in New Jersey were used to compute the statistics presented in this report (fig. 1). The entire period of record available for continuous- and partial-record streamgages was used in this analysis, unless otherwise noted. The range of records span from 1903 to 2017. Detailed tables with statistics for all the continuous- and partial-record streamgages are provided as a separate data release (Williams and others, 2024). This study also produced an interactive mapper to allow users to explore the computed

2 Streamflow Characteristics and Trends in New Jersey, Water Years 1903–2017

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40°45' New York City

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NEW JERSEY EXPLANATION Philadelphia

40°

Physiographic provinces—New Jersey Department of Environmental Protection Bureau of Geographic Information Systems (2002)

DELAWARE

Valley and Ridge New England

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Piedmont Coastal Plain

Limit of Wisconsin glaciation—New Jersey Department of Environmental Protection Bureau of Geographic Information Systems (2022)

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Fall line—New Jersey Department of Environmental Protection Bureau of Geographic Information Systems (2002)

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39°

Streamgages—U.S. Geological Survey (2018)

Delaware Bay

Continuous-record Partial-record

0 0

10 10

20 30

40 MILES

40 KILOMETERS

Figure 1. Map showing the physiographic provinces and selected U.S. Geological Survey continuous- and partial-record streamgages in and near New Jersey.

Introduction 3 statistics for each streamgage graphically (https://geonarrative .usgs.gov/njstreamflowcharacteristics/). Computation of updated statistics provides information to help evaluate if streamflow has statistically changed at these continuous-record and partial-record streamgages. Water managers and regulators, like NJDEP, can use these statistics, along with other detailed information about water demand, to make decisions to protect the State’s water supply and the ecological health of the waters of New Jersey.

Purpose and Scope The purpose of this report is to update and build upon the three previous studies to help assess the quantity of surface water in New Jersey (Laskowski, 1970; Gillespie and Schopp, 1982; Watson and others, 2005). This study computed updated surface-water statistics at USGS streamgages through the 2017 water year; the Watson and others (2005) report used data from continuous-record streamgages through the 2001 water year and from partial-record streamgages through the 2003 water year. This report serves as a single reference for water managers, policy makers, engineers, and consultants to cite the status of New Jersey surface water resources. This update also expands on the previous streamflow characteristics report by adding base flow, runoff, and several measures of annual variability, as well as a trend assessment at partial-record streamgages. Continuous-record streamgages with additional approved data since 2001 and a minimum of 20 years of record through 2017 and partial-record streamgages with a minimum of 10 measurements through 2017 were included in this analysis. For the purposes of this report, the term “partial-record streamgage” describes a stream monitoring location that collected data either as discrete streamflow measurements, or on a continuous basis, for less than 20 years. High, low, and flow-duration statistics were computed using the entire period of record available at each continuous-record streamgage, except those defined as having a known change in the streamflow characteristics that would define a different data population, such as the construction of a reservoir. Low-flow statistics were computed at partial-record streamgages with sufficient data for regression analysis, even if new data were not available beyond the data used in the previous report. The final low-flow statistics estimated for these streamgages were based on a weighted average of regressions with multiple index sites. A total of 97 continuous-record streamgages and 719 partial-record streamgages were included in this study. An assessment of trends and variability in streamflow was also conducted to quantify changes in water resources for the available periods of record at continuous-record streamgages. Trend tests on the residuals of the regressions were computed using the Mann- Kendall trend (MK) test (Hamed and Ramachandra Rao, 1998) to identify possible changes in the statistical relation between continuous- and partial-record streamgages.

Description of Study Area Familiarity with the basic geology and geography of New Jersey is key to understanding its hydrology. New Jersey includes four primary physiographic provinces. The noncoastal provinces—Valley and Ridge, New England, and Piedmont—comprise the northern part of the state and are located above the Fall Line, a regional geomorphological boundary between Piedmont metamorphic rock and Coastal Plain sedimentary rock (fig. 1). The Coastal Plain covers the southern part of the state and is located below the Fall Line (fig. 1). The Fall Line is a low, east-facing largely buried cliff extending nearly parallel to the Atlantic coastline from New Jersey to the Carolinas (Newell and others, 1998). The Fall Line is the western limit of the coastal sediments separating the hard Paleozoic metamorphic rocks of the Piedmont Physiographic Province to the west from softer, more gently dipping Mesozoic and Tertiary sedimentary rocks of the Coastal Plain to the east. This erosional scarp, in many ways, affects the behavior of streamflow owing to the nature of the underlying geology; for example, whether a stream channel is steeply sloped with swift-moving waters or is more gently sloped with typically lower stream velocities is, to varying degrees, dependent on the underlying geology and streambed material (Watson and McHugh, 2014). The northernmost province, Valley and Ridge is known for long, parallel, northeast to southwest facing ridges and valleys. This region includes the Wallkill River Basin and is composed of faulted and folded sedimentary layers of sandstone, shale, and limestone. The New England Province is generally composed of hilly uplands, divided by steep-sided stream beds. This province includes the Passaic River Basin, as well as several glaciated lakes, and is underlain by erosion resistant rock, including granite, gneiss, and some Precambrian marble. The Piedmont Province is a broad lowland but possesses some ridges. It includes the Raritan River Basin, the largest drainage basin entirely within New Jersey and is underlain by interbedded sandstone, shale, conglomerate, basalt, and diabase. The Coastal Plain Province is a flatland that includes the New Jersey Pine Barrens, a largely uninhabited wilderness. This province includes the Lower Delaware River Basin, comprised of unconsolidated layers of sand, silt, and clay. As the most densely populated state in the country, New Jersey is influenced by urbanization, agriculture, and ongoing land development (U.S. Census Bureau, 2023). The population of New Jersey is variable by region, yet always growing. Population centers include communities that serve as suburbs of New York City and Philadelphia as well as smaller cities such as Newark, Elizabeth, Trenton, and New Brunswick. Seasonally, large populations are drawn to the Jersey Shore. Other regions include large expanses of wilderness, such as the Delaware Water Gap and the New Jersey Pine Barrens. The rest of the state, especially the northern tip and southwest end of the state, are largely agricultural. This diversity of land and population centers plays an important role in water use and streamflow statistics statewide. The streamflow in many

4 Streamflow Characteristics and Trends in New Jersey, Water Years 1903–2017 New Jersey rivers and streams can be regulated by storage and releases from reservoirs, some of which may be used to store drinking water. Another activity affecting streamflow statistics is diversions for a variety of purposes, including wastewater treatment, crop irrigation, and recreation. For example, cranberry bogs in southern New Jersey require large diversions of water at specific times of the year. Accounting for such human-driven influences is crucial for gaining a full understanding of streamflow characteristics statewide.

Previous Studies The USGS has regularly provided statewide, detailed, statistical assessments of all available streamflow records, with each report building and expanding upon previous work. Laskowski (1970) authored “Statistical Summaries of New Jersey Streamflow Records—Water Resources Circular 23” in 1970 to expand on the earlier publication of “New Jersey streamflow records analyzed with electronic computer—Water Resources Circular 6” (Miller and McCall, 1961) with the computation of statistical moments for more than 80 streamflow records and daily low-flow and high-flow frequency statistics to help Federal and State agencies as well as municipalities and local professionals concerned with water availability and conservation. As the population increased, the demand for water to meet the needs of homes, industries, and irrigation was anticipated to increase. Understanding the magnitude and frequency of available streamflow became necessary for water supply planning purposes. In response to this need for better information about streamflow availability, a streamflow statistics report completed in 1981 was published as an update and expansion of the 1970 report (Gillespie and Schopp, 1982). This report analyzed low-flow frequency and flow-duration data for 400 continuous- and partial-record streamgages, some of which had been in operation for over 50 years. Most recently, Watson and others (2005) not only summarized both high- and low-flow data from 1897 to 2003, but also began to assess trends in streamflow in relation to urbanization and increased average precipitation. These updated statistics were instrumental for allocating surface-water withdrawals, setting waste load allocation limits, investigating total maximum daily loads (TMDLs), and studying the effects of contamination sources on the chemical and biological qualities of streams. Other reports have further contributed to the understanding and management of surface water resources in New Jersey. A report by Esralew and Baker (2008) established set time frames for periods of record—baseline and current—which has become important for assessing future changes in streamflow resulting from increased development. Additionally, Watson and McHugh (2014) presented regionalized regression equations for estimation of monthly flow duration and monthly low-flow frequency statistics in ungaged New Jersey streams. Including both baseline and current conditions, these equations estimate 87 different streamflow statistics, including monthly 99th, 90th, 85th, 75th, 50th, and 25th percentile flow durations of the minimum 1-day daily flow, August–September 99th, 90th,

and 75th percentile minimum 1-day daily flow, and monthly 7-day, 10-year low-flow frequencies. More recently, a report by Hammond and Fleming (2021) included trend analysis for a few low-flow statistics for 1950–2018 and 1980–2018 for some streamgages in the Delaware River basin, including New Jersey.

Methods A comprehensive collection of statistics was computed for streamflow records to aid in the evaluation of water resources in New Jersey. Flow frequency and duration were computed for continuous-record streamgages using the same methods as the previous report (Watson and others, 2005). The calculation of base flow and runoff were added to the current study to aid in the assessment of these two basic components of surface-water flow. Trend tests in high and low n-day series, base flow and runoff, peak to 3-day mean flow ratio, as well as annual skew and kurtosis (a description of skew and kurtosis is presented in the “Streamflow Variability” section), were computed to explore possible changes in streamflow and its variability over time. A subset of low-flow frequency, duration statistics, and base flow were computed at partial-record streamgages using regression with continuous-record daily mean flows. Trends in the residuals of those regressions were also determined to explore possible changes in flow over time. The various types of statistics, record types, and trend analysis are listed in table 1. Details on the types and methods of computed statistics and trend analyses are described throughout this report.

Continuous-Record Streamgages Streamgages with a total of at least 20 years of daily streamflow record through 2017, and daily streamflow data since 2001, were used in this analysis. A total of 97 streamgages operated by the USGS fits this criterion (fig. 1). A table of the streamgages used in this study along with additional streamgage information is available in the associated data release (Williams and others, 2024). Most of the streamgages (63) are in the northern half of the State, above the geologic fall line, with varying Valley and Ridge, New England, and Piedmont geology (fig. 1); only 34 of the streamgages are in the southern half of the State, draining Coastal Plain geology (fig. 1). Analyses utilized the entire period of record available at each continuous-record streamgage, except those which were defined in the previous reports as having a known change in the basin which would define a different data population, such as the construction of a reservoir. The previous report included statistics for both pre- and post-event periods. In this study’s analysis, statistics and trends were computed at those streamgages using data only in the latter period of record. Further details about the total 97 streamgages are given in the following list: • 80 streamgages were included in the previous report. (17 streamgages did not previously have 20 years of record.)

Methods 5 Table 1. List of statistics computed for continuous- and partial-record streamgages in New Jersey for the period 1903–2017. [Winter period is defined as November 1 through April 30]

Statistic type

Computed statistic

Gage record type

Trend analysis

Also computed in 2005 report1

Frequency (0.5, 0.2, 0.1, 1-,7-, and 30-day minimum average Continuous Yes 0.05, 0.04) low flow (annual and winter) 1-,7-, and 30-day maximum Continuous Yes average high flow (annual and winter) Frequency (0.1) 1-,7-, and 30-day minimum average Partial No low flow (annual, and winter for 7- and 30-day) Variability Skew Continuous Yes Kurtosis Continuous Yes Peak ratio Continuous Yes Duration 1-, 2-, 5-, 10-, 20-, 25-, 30-, 40-, Continuous No 50-, 60-, 70-, 75-, 80-, 85-, 90-, 95-, 99-percent 50-, 75-percent Partial No Summary Maximum and minimum daily Continuous No mean flow Mean flow Continuous, partial No Mean base flow Continuous, partial Yes (continuous records only) Mean runoff Continuous Yes Median September flow Continuous, partial No Drought-of-record mean base flow Continuous, Partial No (1962–70) Regression residual Partial Yes

Yes Yes

Yes

No No No Yes

Yes Yes Yes No No No No No

1Watson and others, 2005.

• 16 streamgages have a break in the record because of a substantial hydrologic change upstream. • 4 streamgages were discontinued after 2001; therefore, they do not have data completely through 2017. • 20 streamgages are denoted as “regulated” due to controlled releases from upstream reservoirs. The designation does not indicate the presence of general water withdrawals and municipal discharges in the streamgage basin. Of the 97 streamgages analyzed, the periods of record ranged from 20 to 111 years, with 60 of the streamgages having more than 50 years of record (fig. 2). Although most of the statistics included in this study were previously computed in Watson and others (2005), several new statistics were added for a more comprehensive assessment of streamflow. The first four groups of statistics listed below were computed in the previous report, but a percent change in value was determined for the current analysis. The winter season for statistics computed in these groups is defined as November 1 to April 30: • Maximum, minimum, mean for period of record

• Duration of flow (annual and winter) • 50-, 20-, 10-, 5-, and 4-percent annual nonexceedance (or 2-, 5-, 10-, 20-, and 25-year recurrence interval) frequencies of (annual and winter) minimum 1-,7-, and 30-consecutive day average flow (hereafter, referred to as 1-,7-, and 30-day low flows). • 50-, 20-, 10-, 5-, and 4-percent annual exceedance (or 2-, 5-, 10-, 20-, and 25-year recurrence interval) frequencies of (annual and winter) maximum 1-,7-, and 30-consecutive day average flow (hereafter, referred to as 1-,7-, and 30-day high flows). The ratio of peak flow was only computed for a selected subset of streamgages in the previous report; it was computed for all streamgages in this study. • Ratio of peak to 3-day mean flow The statistics listed below were not included in Watson and others (2005) and were added to this study’s analysis to provide a more comprehensive assessment of streamflow in New Jersey: • Drought-of-record base flow (1962–70)

6 Streamflow Characteristics and Trends in New Jersey, Water Years 1903–2017

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Number of streamgages

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NEW JERSEY

Philadelphia

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12 10

6 4 2

DELAWARE

1 91 to 100

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Years of record

ATLANTIC OCEAN

EXPLANATION Continuous-record streamgages, number of years of record— U.S. Geological Survey (2018)

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Greater than 100

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20 to 30 31 to 40 41 to 50 51 to 60 61 to 70 71 to 80 81 to 90 91 to 100 101 to 111

0 0

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Figure 2. Map and bar graph showing distribution of period of record among continuous-record streamgages in New Jersey, 1903–2017.

Methods 7 • Median September flow

Flow Durations

• Annual variability measured as annual skew and kurtosis

• 1-,7-, and 30-day minimum average low flow (annual and winter)

Flow-duration curves define the percentage of time flows were equaled or exceeded for a given period (Searcy, 1959). Specific intervals of the curve (1-, 2-, 5-, 10-, 20-, 25-, 30-, 40-, 50-, 60-, 70-, 75-, 80-, 85-, 90-, 95-, 99-percent) were computed for both annual and winter seasons using the R-program EGRET (Hirsch and DeCicco, 2015) for all 97 continuous-record streamgages for the entire periods of record, when applicable. The percent change in the flow-duration intervals for the 80 streamgages included in Watson and others (2005) were also computed. Flow-duration curves help provide information about streamflow behavior in the basin. A steeper sloped flow-duration curve may represent a basin that is more reactive to events or is generally more variable than a basin with a flatter flow-duration curve (fig. 3). The added level of analysis focusing on the computation of flow-duration curves for just the winter season attempts to provide finer information about the basin response to winter conditions. Understanding the winter component of the composite annual flow-duration curve provides an opportunity to assess the influence of the winter season on the annual statistics. Changes in computed flow durations are presented in the subsection titled “Percent Change in Streamflow” in the “Results” section of this report.

• 1-,7-, and 30-day maximum average high flow (annual and winter)

Base Flow and Runoff

The MK test was computed on several of the annual time series of streamflow statistics using different assumptions of serial correlation to determine if trends were present. The MK test assesses the data to determine if there is a statistical monotonic trend upwards or downwards, that is, if the values are consistently increasing or decreasing over time. Serial correlation is related to how consecutive values or values in a series influence each other or how they are correlated. Sen’s slope, often referred to as part of the Theil-Sen estimator, was also computed to determine the magnitude of change over time for the various statistics. The Theil-Sen regression relies on the median of the slopes between points, and not the mean, making it more robust and less sensitive to outliers (Sen, 1968). Evaluating changes in Sen’s slope was assumed to be a good method to look for changes over time that were less sensitive to outliers or extreme events. Details on the approach and methods is described in the “Trend Analysis” section. The trend tests were applied to the following annual time series:

• Ratio of peak flow to 3-day mean

Streamflow can be separated into the following two basic components: base flow and runoff. Runoff is the portion of streamflow resulting directly from precipitation; base flow is the portion of streamflow sustained by groundwater discharge.

• Base flow and runoff • Skew and kurtosis 1,000

Discharge, in cubic feet per second

EXPLANATION USGS site 01396500 South Branch Raritan River near High Bridge NJ USGS site 01411000 Great Egg Harbor River at Folsom NJ

100

Percentage of the time indicated discharge was exceeded

Figure 3. Line graph showing flow-duration curves for South Branch Raritan River near High Bridge, New Jersey, USGS site 01396500 and Great Egg Harbor River at Folsom, N.J., USGS site 01411000. NJ, New Jersey; USGS, U.S. Geological Survey.

8 Streamflow Characteristics and Trends in New Jersey, Water Years 1903–2017 Separation of these flow components was achieved using the PART base flow separation program (Rutledge, 1998) within the USGS software Groundwater Toolbox (Barlow and others, 2014). The PART program determines base-flow days according to the number of preceding days with continuous recession. The program then linearly interpolates between base-flow days to determine the portion of base flow during runoff events (Rutledge, 1998, p. 34–36; Rutledge, 2007). The mean base flow and runoff was computed for each year of available record, as well as for the entire periods of record for 96 continuous streamgages. The flow components are not able to be separated for canal flow, as measured at Delaware and Raritan Canal at Port Mercer, N.J. (01460440). Analyzing the amounts of each of these pieces of streamflow records aids in the understanding of water availability and variability.

Streamflow Variability Skew and kurtosis are measures of extremes within a data distribution. Skewness can be considered as the extent of the tails (extreme events) or degree symmetry of the distribution, whereas kurtosis can be thought of as the heaviness of the tails or peakedness of the distribution. Annual values of skew and kurtosis were computed for all 97 continuous-record streamgages for the given periods of record included in the study using R statistical software (R Core Team, 2019). The analysis originally followed the previous report in evaluating the variability of streamflow by examining the ratios of flow durations, 10/90 and 25/75. However, this approach was determined to be insufficient because some continuous-record streamgage 90th and 75th percentile flow durations were zero, and, therefore, a ratio could not be calculated. The flow-duration ratios are effective at describing the behavior of the central tendencies of the distribution of flows; however, in the interest of flow variability, the shape and extremes of the distribution were of greater utility. Instead, examination of skew and kurtosis were implemented to supplement trend analysis of high and low flows with changes in streamflow variability. Streamflow variability can be affected by such factors as increased impervious surface, changes in water use, and changes in climate. High and low flows may not change in magnitude alone, but also could have changes in frequency. Understanding both frequency and magnitude of critical or extreme streamflow aids in water management and planning.

Peak-Flow Ratio The peak-flow ratio is the annual maximum instantaneous flow divided by the 3-day mean flow for the day before, day of, and day after the peak (Watson and others, 2005). To examine the high-flow event variability, the peak-flow ratio for each year of record was computed for all continuous-record streamgages in the study. A watershed’s ability to store and attenuate runoff is measured by the peak-flow ratio (Watson and others, 2005). The higher the ratio, the flashier the stream

with quickly rising and receding peaks. A change in the ratio over time can be an indication of changes in land use or vegetation cover in the basin, as well as changes in the intensity and duration of storms.

Frequency Analysis A frequency curve relates the magnitude of a variable to its frequency of occurrence (Riggs, 1968). The USGS software Surface Water Toolbox (SWToolbox; Kiang and others, 2018) was used to compute mean high and low flows for a given number of consecutive days (n-day) for each year year of record. For this study, the annual highest and lowest 1-, 7-, and 30-day mean-flow time series were determined for both full-year and winter seasons. The annual period used was based on the climatic year (April 1 to March 31) for low-flow frequency analysis (U.S. Interagency Advisory Committee on Water Data, 1982), and the water year (October 1 to September 30) for high-flow frequency analysis. Under natural conditions, the lowest annual flows typically occur in late summer when evapotranspiration is high and groundwater levels are low. Annual low-flow frequency statistics are, therefore, assumed to represent summer conditions. This assumption does not hold true for streamgages below regulated reservoirs where the lowest flow of the year may occur during winter months, depending on operations of the reservoir. Periods were not truncated to determine strictly summer flow frequencies. The frequency calculation within SWToolbox determines the plotting positions of the annual n-day series and fits a probability curve to the data using a Log-Pearson type III (LPIII) statistical distribution. Flow exceedance and nonexceedances are then estimated using that curve. A conditional probability adjustment is applied to records with zero flows, in which the probabilities are first determined without including the zero flows, then adjusted according to occurrence of zero flows. Details on the approach are described in the published SWToolbox Techniques and Methods (Kiang and others, 2018). For high-flow frequency analyses, the probability that a streamflow is exceeded for a given period of time is referred to as the exceedance probability; for low-flow frequency analysis, the probability that streamflow is not exceeded is the nonexceedance probability. The specific high- and low-flow probabilities computed from the curves were the 50-, 20-, 10-, 5-, and 4-percent annual exceedance and nonexceedance probabilities, which correspond to the 2-, 5-, 10-, 20- and 25-year recurrence intervals. The percent change in the high- and low-flow frequency flows for the 80 streamgages included in Watson and others (2005) were also computed and the periods through 2001 were compared against the periods through 2017. Low-flow frequency statistics such as the annual 7-day low flow with a 10-percent annual nonexceedance probability (7Q10) are often used for permitting and regulation of water resources. New Jersey is among the states that utilize the

Methods 9 7Q10 statistic in several regulatory processes including water allocations permitting (Hoffman and others, 2012) High-flow frequency statistics (as opposed to peak-flow frequency statistics) are not typically used in regulation or structural design but could be considered useful for comparing to peak-flow frequencies and changes to storm variability and water availability.

Fit of Frequency-Distribution Curve and Outliers The LPIII distribution is the recommended statistical distribution for use in hydrologic frequency analysis (Riggs, 1972). The shape of the LPIII curve is determined using the first three moments of the log transformed data: mean (µ), standard deviation (σ), and skew (ɣ). The skew is then used in determining the frequency factor (K) for given probabilities (p) to produce the following equation to build the LPIII curve: Q p = μ + Kp  × σ

(1)

Though recommended, the fit of the data to the LPIII curve must still be examined to ensure a reasonable estimate of probability of flow. Using the SWToolbox program, the Kolmogorov-Smirnov (KS) test was applied to evaluate the fit of the annual n-day time series to the LPIII distribution to assess the estimated frequency statistics. The test is applied to nonzero values and determines the probability that the data fit the defined distribution. The LPIII curves that do not have a 95-percent probability of fitting the data are flagged as failing the test. The KS test evaluates the entirety of the curve, but this study focuses on frequencies 4–50-percent probabilities, or the right side of the plotted curve. At times, the KS test may indicate that the data fit the distribution within 95 percent, but visually the data do not appear to fit well at the probabilities of interest in this study. Visual assessments were also used to evaluate the fit of the data points to the LPIII curves at these lower probabilities. Often a less-than-ideal fit is visually noted with shorter periods of record, which reinforces the need for using a statistical distribution—so that information on frequency of events can be produced from a relatively smaller sample of the data population. However, when a sufficient period of record is collected, arbitrarily assumed as more than 50 years, it might also be assumed the fit of the data to the frequency curve would visually improve. When the data do not fit well, it may indicate that the data do not represent a single population, perhaps because of a climate shift or effects from local water use, or that LPIII is not the best distribution to be used. Unexpectedly, when this discrepancy with long-term records was observed in this study, the data seemed to not fit around the 10th percent nonexceedance, the most used return period in low-flow management. The SWToolbox program also identifies outliers based on the K-Ratio. If the ratio is greater than one, then the value is flagged as an outlier. The identification and removal of outliers is common in frequency calculations of flood flows. Peak-flow data populations are considerably different, as they

are instantaneous flow values whereas this study is using daily mean flow values. The daily mean flows have less noise and error than do instantaneous peak flows. Regardless, in pursuit of a better fit, outliers identified by the K-ratio test were removed from the annual n-day series at three long-term streamgages. These outliers and bias-driven values were removed at these streamgages, 01399500, 01396500, and 01377500, to see if a better curve fit could be accomplished at the 10th percent exceedance value. No appreciable difference was made to the curve, as the overall data skew was not greatly affected by only a few values, but rather by the dataset as a whole. The 01377500 streamgage was the only streamgage to fail the KS test. High- and low-flow frequencies were determined for all 97 continuous-record streamgages in the study. If the data had a 95-percent probability of fitting the given distribution, meaning it passed the KS test, then the frequencies were determined using the LPIII curve, even if the data had a visually poor fit. The flow frequencies were computed as simple percentage exceedances when LPIII curves failed the fit test or when the streamgages were noted as having considerable upstream regulation. The method used to compute the flow frequency value is designated in the output as LPIII or percentage exceedances. This differs from methods used in the previous study (Watson and others, 2005), which only used visual assessment and fitted a hand-drawn graphical estimate for curves with a poor fit. This change in methodology between studies for estimating poor or nonfitting data could contribute to misrepresentation of changes in flow when comparing the percentage differences between the two studies.

Trend Analysis The calculation of frequency statistics and various flow components is helpful for permitting and design, as well as documenting the current state of water resources. Determining if and how water resources are changing over time is critical for water resource management planning, especially in a heavily populated state with a changing climate. Trend analysis was used to further evaluate streamflow records; evidence for trends were computed for: base flow, runoff, all n-day series, skew, kurtosis, and peak-flow ratios. The MK test was used for this study as it is appropriate for hydrologic data. It is a nonparametric test which does not require that the data be normally distributed. A trend was considered to be statistically significant when p<0.05. The MK test uses the ranks of values in the dataset to detect the presence of a trend. In using the ranks of values, the test is not subject to influence from outliers or rare events, which are common in hydrologic data in the form of floods and droughts. However, it also assumes that the data are independent (have no serial correlation). Statistical evidence of a trend can be influenced by the presence of serial correlation, which can be present in hydrologic data series in the form of persistent wet and dry climate cycles.

10 Streamflow Characteristics and Trends in New Jersey, Water Years 1903–2017 The presence of serial correlation can be alleviated one of two ways. It can either be removed from the data before applying the trend test, known as “pre-whitening” (Hamed, 2008). The second option is to modify the test to account for the serial correlation. To account for the possibility of serial correlation in the data for this study, the variance of the MK test statistic was adjusted to compensate for an assumed condition of serial correlation. The MK test was used for three different assumptions of serial correlation: independence (meaning no serial correlation), short-term persistence (STP), and long-term persistence (LTP) using variance correction methods from Hamed and Ramachandra Rao, 1998; Hamed, 2008; and Hodgkins and others, 2017. Short term persistence assumes a significant correlation of data lagged by 1 year with itself. Long term-persistence assumes significant correlations of data lagged at intervals greater than 1 year. Measures of serial correlation were computed using the lag-1 correlation coefficient for STP and the Hurst coefficient for LTP (Hamed and Ramachandra Rao, 1998; and Hamed, 2008).

Partial-Record Streamgages A subset of low-flow statistics was estimated at 719 partial-record streamgages for this study. Partial-record streamgages can be either continuous-record streamgages with less than 20 years of record or streamgages that have at least 10 discrete measurements of flow. The low-flow statistics determined at long-term continuous-record streamgages (index sites) were transferred to partial-record streamgages using the Maintenance of Variance Extension Type 1 (MOVE1) method of record extension (Hirsch, 1982) using discrete flow measurements made during base-flow conditions at the partial-record streamgage and concurrent daily mean flows at the index sites. The MOVE1 method derived by Hirsch takes a different regression approach and attempts to preserve the mean and the variance of low-flow estimates rather than minimize the mean and variance, as is typically done with least-squares regression. An example of how the MOVE1 method was applied for this study and the previous study can be found in the report “Streamflow Characteristics and Trends in New Jersey, Water Years 1897–2003” by Watson and others (2005). The estimated low-flow statistics at partial-record streamgages were: • Annual 1-, 7-, and 30-day low flow with a 10-percent annual nonexceedance probability • Winter 7- and 30-day low flow with a 10-percent annual nonexceedance probability • 50th and 75th percent duration flow • September median flow • Mean annual base flow • Drought base flow (1962–70)

The regressions were executed using the R-script documented in “Implementation of MOVE.1, censored MOVE.1, and piecewise MOVE.1 low-flow regressions with applications at partial-record streamgaging stations in New Jersey” by Colarullo and others (2018). The script screened the data for base-flow conditions, which are defined in the program as a change in streamgage height of 0.02 foot (ft) or less at the partial-record streamgage and a change in the daily mean flow at the index site between +10 and −30 percent from the previous day. The script also allowed for the use of multiple continuous-record streamgages (index sites). The use of more than one index site provided more robust estimates rather than over-relying on a single continuous-record streamgage to estimate the low-flow statistics. To incorporate the strength of the multiple individual regressions into a final estimate for the partial-record streamgage, a weighted average flow was determined using the time-sampling error of the streamgage records combined with the error associated with each regression. An average of at least 10 data points among the regressions was required to qualify the partial-record flow estimates for inclusion in this study. For streamgages with measurements of zero flow, the censored MOVE1 regression within the R-script was applied. Colarullo and others (2018) documents details on the script, including base-flow screening, weighting scheme, and associated methodologies. An MK test for trends was also applied to each set of residuals of the individual regressions to determine if the relation between the partial-record streamgage and each index site was changing through time. Each partial-record streamgage, therefore, had multiple tests of trends in residuals, concurrent with the number of associated index sites. If the trend in regression residuals, considered statistically significant when p <0.05, was consistent across all index sites used with a particular partial-record streamgage, greater confidence was given to conclude that a trend in flow exists at the partial-record streamgage. For example, when residuals were increasingly positive relative to the determined regression line over time and the particular index site did not have an identified trend in base flow, the trend indicated that the streamflow was increasing at the partial-record streamgage over time. When this same result was observed at all index sites used for a particular partial-record streamgage, then greater confidence was given to the presumption that a trend in flow was present at the partial-record streamgage. When residuals were repeatedly shown as increasingly negative through time, with no base-flow trend at the index sites, then the trends showed that the flow was decreasing at that partial-record streamgage.

Index Site Selection Although the MOVE1 program incorporates regression error and the use of multiple index sites, the selection of index sites used has a direct influence on the estimated flow trends in residuals at the partial-record streamgages. Knowledge of the local hydrology and index site conditions are important factors

Methods 11 that can influence streamgage selection beyond the criteria of little to no known regulation. Other criteria considered in the index site selection process include: • Length of record greater than 25 years • Identified trend (IND) for 1 or fewer annual low-flow time series • Pass KS test and visual test for fit of LPIII curve

Streamgages were then grouped according to physiographic province (table 2 and fig. 4). Partial-record streamgage basins falling within the same physiographic province all used the same list of index sites. An exception was made to the 25-year requirement for the unglaciated New England Province, as there are not many index sites in this physiographic province. Three index sites were used for more than one physiographic province because of their proximity and mixed basin characteristics.

Table 2. List of USGS streamgages used as index sites in low-flow regression analysis in New Jersey, drainage area, record length, and the associated physiographic province. [USGS, U.S. Geological Survey; NJ, New Jersey; NA, not applicable]

Drainage area, in square miles

Record length, in years

Passaic River near Millington NJ

55.4

Piedmont, unglaciated

Rockaway River at Main Street at Boonton NJ

116

Valley and Ridge, New England, Piedmont; glaciated

01381400

Whippany River near Morristown NJ

New England, unglaciated

01386000

West Brook near Wanaque NJ

11.8

Valley and Ridge, New England, Piedmont; glaciated

01396582

Spruce Run at Main Street at Glen Gardner NJ

12.3

New England, unglaciated

01396660

Mulhockaway Creek at Van Syckel NJ

11.8

Piedmont, unglaciated

01401000

Stony Brook at Princeton NJ

44.5

Piedmont, unglaciated

01408000

Manasquan River at Squankum NJ

Coastal Plain

01408120

North Branch Metedeconk River near Lakewood NJ

34.9

Coastal Plain

01408500

Toms River near Toms River NJ

123

Coastal Plain

01409810

West Branch Wading River near Jenkins NJ

84.1

Coastal Plain

01410150

East Branch Bass River near New Gretna NJ

8.11

Coastal Plain

01411000

Great Egg Harbor River at Folsom NJ

57.1

Coastal Plain

01411456

Little Ease Run near Clayton NJ

9.77

Coastal Plain

01411500

Maurice River at Norma NJ

112

Coastal Plain

01412800

Cohansey River at Seeley NJ

Coastal Plain

01440000

Flat Brook near Flatbrookville NJ

Valley and Ridge, New England, Piedmont; glaciated

01443500

Paulins Kill at Blairstown NJ

126

Valley and Ridge, New England, Piedmont; glaciated

01445500

Pequest River at Pequest NJ

106

Valley and Ridge, New England, New England, unglaciated Piedmont; glaciated

01446000

Beaver Brook near Belvidere NJ

36.7

Valley and Ridge, New England, New England, unglaciated Piedmont; glaciated

01467000

North Branch Rancocas Creek at Pemberton NJ

118

Coastal Plain

01467150

Cooper River at Haddonfield NJ

Coastal Plain

USGS site number

Site name

01379000 01380450

Physiographic province

Secondary physiographic province New England, unglaciated

12 Streamflow Characteristics and Trends in New Jersey, Water Years 1903–2017

75°30'

75°15'

75°

74°45'

74°30'

74°15'

74°

73°45'

73°30'

73°15'

CONNECTICUT

NEW YORK 41°15'

41°

NEW YORK

40°45' New York City

40°30'

PENNSYLVANIA 40°15'

Trenton

NEW JERSEY ATLANTIC OCEAN

Philadelphia

40°

DELAWARE

39°45'

EXPLANATION Physiographic provinces—New Jersey Department of Environmental Protection Bureau of Geographic Information Systems (2002)

39°30'

Valley and Ridge 39°15'

New England

RE AWA

DEL 39°

Piedmont

Delaware Bay

Coastal Plain

Index sites—U.S. Geological Survey (2018) 0 0

10 10

20 30

40 MILES

40 KILOMETERS

Figure 4. Map showing index site locations for partial-record streamgages across New Jersey physiographic provinces.

Results 13 Another exception was made with the Neshanic River at Reaville, N.J., USGS site 01398000 streamgage. Although relied upon in the previous study, it failed the KS test for the 30-day low, and the annual 1-day low flow with a 10-percent annual nonexceedance probability (1Q10) is now zero. Having a 1Q10 frequency magnitude of zero means this streamgage can no longer be used in the MOVE1 regression analysis because an input variable value of zero would always result in an estimate of zero at the partial-record streamgage.

Results The duration, frequency, and summary statistics (table 1) for 97 continuous-record streamgages and 719 partial-record streamgages in New Jersey were computed for each period of record through 2017. Trends for various magnitudes of high flow, low flow, and flow variability were determined for continuous-record streamgages. The magnitude and statistical significance of those trends are presented in this report. For partial-record streamgages, trends in regression residuals were computed and reported by the proportion of streamgages with a statistically significant trend. The percent change in flow at streamgages for which statistics were computed by Watson and others (2005) was also determined to assess changes in streamflow versus trend test results. The computed flow statistics, trends, and percent change in flow are available for download from the associated data release published in Williams and others (2024) as well as through a web map (https://geonarrative.usgs.gov/njstreamflowcharacteristics/).

Trends in Streamflow at Continuous-record Streamgages Seventeen streamflow characteristics were assessed using the MK test under three different assumptions of serial correlation to determine the prevalence and direction of trends within streamflow data in New Jersey. These characteristics included base flow, runoff, skew, kurtosis, and peak-flow ratio, as well as the 1-, 7-, and 30-day high and low flows for both annual and winter-only periods. A total of 1,647 time series of flow characteristics among 97 streamgages were tested. Trends for time series, base flow, and runoff characteristics were not determined for one canal streamgage. Three levels of serial correlation (persistence) were assumed, and adjustments were accordingly applied to the variance of each characteristic’s MK test statistic: an independent test (no adjustment, IND), short-term persistence (STP), and long-term persistence (LTP). The adjusted variance was applied to all data characteristics and all streamgages regardless of whether there was evidence of serial correlation. This was primarily because serial correlation is often present in hydrologic data in the form of persistent wet and dry climate cycles, but records may not be long enough to provide evidence. It was also done to show the range of effect for all three types of assumed serial correlation.

Tests assuming no serial correlation (IND) yielded 314 streamflow characteristics with statistically significant trends, which was the largest group—accounting for about 19 percent of the total. When variance was adjusted for STP, the tests indicated only 241 statistically significant trends, about 15 percent of the total number of characteristics. When the variance was adjusted for LTP, even fewer statistically significant trends (111, or 7 percent) were identified. However, evidence for serial correlation does not appear to be substantial for STP or necessarily clear for LTP and may not justify the variance adjustment for all streamgages. Measures of serial correlation were determined using the lag-1 correlation coefficient for STP and the Hurst coefficient for LTP (Hamed and Ramachandra Rao, 1998; and Hamed, 2008). • For STP, lag-1 correlation values close to 1 indicate strong positive serial correlation; values close to −1 indicate strong negative serial correlation. • For LTP, a Hurst exponent between 0.5 and 1 indicates positive serial correlation, meaning a change (increase or decrease) between observations will probably be followed by another change. A value between 0 and 0.5 indicates a time series with long-term switching between high and low values in adjacent pairs, meaning that a single high value will probably be followed by a low value and that the value after that will tend to be high, with this tendency to switch between high and low values lasting a long time into the future. When examining lag-1 correlation, few streamgage characteristics indicated strong evidence of STP. In all, no time series had a lag-1 correlation coefficient less than –0.6, and only 28 individual times series among 12 streamgages had a lag-1 correlation coefficient greater than +0.6 (table 3). Twenty-seven of the 28 time series were representative of low-flow conditions. None of the high flow n-day series, runoff, winter 30-day low, skew, nor kurtosis indicated evidence of short-term persistence. The annual 1-day low-flow time series had the largest number, with 11 streamgages having a lag-1 correlation coefficient (STP) greater than 0.6. The winter 1-day and annual and winter 7-day time series had a small subset of those 11 streamgages that showed at least some evidence of STP. One of the 11 streamgages also had minor evidence for STP for base flow. One streamgage had minor evidence of STP for peak-flow ratio. All streamgages that indicated some evidence of STP were either below a reservoir with heavy regulation or influenced by water use. In the case of the streamgage at Cedar Creek at Western Blvd near Lanoka Harbor, N.J., USGS site 01408900, the tide likely influences the 1- and 7-day lows, providing attributes of STP. Of the total 28 time series indicating some STP, 22 exhibited a statistically significant trend. After the variance was adjusted to account for STP, 7 of the trends indicated were no longer statistically significant, but 15 of the time series still had p-values <0.05. Assuming lag-1 serial correlation for all sites appears to have been unjustified.

14 Streamflow Characteristics and Trends in New Jersey, Water Years 1903–2017 Table 3. Trend results after adjustment for lag-1 serial correlation in select streamflow characteristics in New Jersey, periods of record spanning 1903–2017. [Where indicated, a lag-1 correlation coefficient less than 0.6 (meaning weak evidence for short-term persistence) is shown for the indicated time series and is shaded gray to help visualize computed trend patterns. Trend persists indicates a statistically significant trend was indicated for an assumption of serial independence, and that the trend remained statistically significant after adjusting the variance for short-term persistence. Trend resolved indicates a statistically significant trend was indicated for an assumption of serial independence, and that the trend was resolved after adjusting the variance for short-term persistence. No trend indicates that no statistically significant trend was indicated for an assumption of serial independence. Winter period is November 1 through April 30. NJ, New Jersey]

USGS site number

Site name

Influence on streamflow

Years of record (through 2017)

Annual lowest 1-day flow

Annual lowest 7-day flow

Annual lowest 30-day flow

Winter lowest 7-day flow

Winter lowest 30-day flow

Base flow Peak ratio

01378500 Hackensack River at New Milford NJ

Upstream reservoircontrolled releases

Trend persists

Lag-1 <0.6

Trend persists

Lag-1 <0.6

01381000 Rockaway River below Reservoir at Boonton NJ

Upstream reservoircontrolled releases

109

Trend persists Trend persists

Lag-1 <0.6

01382500 Pequannock River at Macopin Intake Dam NJ

Upstream reservoircontrolled releases

Trend persists Trend resolved

Lag-1 <0.6

01391000 Hohokus Brook at HoHo-Kus NJ

Diurnal signal from upstream effluent

Trend persists Trend persists Trend persists Trend persists Trend resolved

Lag-1 <0.6

01391500 Saddle River at Lodi NJ Impervious surface

Lag-1 <0.6

Trend persists

01394500 Rahway River near Springfield NJ

Cessation of groundwater withdrawals

Trend persists Trend persists

Lag-1 <0.6

01395000 Rahway River at Rahway NJ

Cessation of groundwater withdrawals

No trend

Lag-1 <0.6

01403400 Green Brook at Seeley Mills NJ

Upstream dam or reservoir

No trend

Lag-1 <0.6

No trend

Lag-1 <0.6

01407500 Swimming River near Red Bank NJ

Upstream dam or reservoir

Trend resolved Trend resolved Trend persists Trend persists

Trend persists

Lag-1 <0.6

01408900 Cedar Creek at Western Upstream dam or Blvd near Lanoka reservoir Harbor NJ

Trend resolved

Lag-1 <0.6

Trend resolved

Lag-1 <0.6

01410500 Absecon Creek at Absecon NJ

Tide

No trend

Lag-1 <0.6

01455500 Musconetcong River at outlet of Lake Hopatcong NJ

Upstream reservoircontrolled releases

Trend resolved

No trend

Lag-1 <0.6

Determining the existence of long-term persistence is not straightforward, as there is a high degree of uncertainty in the Hurst coefficient for records with less than 100 years of data (Khaliq and others, 2009). Only one streamgage in this study had more than 100 years of record. Overall, 398 time series among 79 of the streamgages indicated some evidence of LTP, considerably more than the 28 time series among 12 streamgages which indicated STP. Length of record did not appear to have a considerable influence on the value of the Hurst coefficient (fig. 5). The streamgage with the largest average Hurst coefficient for high flows was the one at East Branch Paulins Kill near Lafayette, N.J., USGS site 01443280, which has 25 years of record. The streamgage with the largest

average Hurst coefficient for low flows was at Rockaway River below Reservoir at Boonton, N.J., USGS site 01381000, which does have the longest record of 111 years; however, the streamgage is heavily regulated, and the persistence pattern is likely a reflection of reservoir operations. It was expected that longer-duration low flows (30-day) might exhibit more persistence than short-duration low flows (1-day), as the longer durations were assumed to better reflect long-term basin storage which was assumed to better represent climate patterns. Although this appears to be true for high flows, there appears to be greater difference according to flow magnitude, with low flows indicating greater evidence of LTP than high flows (fig. 6). The 1-day annual low flows

Results 15

0.80

A. High flow

Hurst coefficient

0.75 0.70 0.65 0.60 0.55 0.50 0.45 0.40 0.80

B. Low flow

Hurst coefficient

0.75 0.70 0.65 0.60 0.55 0.50 0.45 0.40 10

100

110

120

Record length, in years

Figure 5. Scatterplot of the average computed Hurst coefficient compared to record length of continuous-record streamgages in New Jersey, 1903–2017. A, high flows and B, low flows. 1

EXPLANATION

0.9

Hurst coefficient

Outlier—More than 3/2 times of upper quartile 0.8

Maximum—Greatest value, excluding outliers

0.7

Mean

Upper quartile—25 percent of data greater than this value Median—50 percent of data is greater than this value; middle of dataset

0.6

Lower quartile—25 percent of data less than this value

0.5

Winter 30-day minimum average low flows

Winter 7-day minimum average low flows

Winter 1-day minimum average low flows

Annual 30-day minimum average low flows

Annual 7-day minimum average low flows

Annual 1-day minimum average low flows

Baseflow

Winter 30-day maximum average high flows

Winter 7-day maximum average high flows

Winter 1-day maximum average high flows

Annual 30-day maximum average high flows

Annual 7-day maximum average high flows

Annual 1-day maximum average high flows

Runoff

0.4

Streamflow probability statistics

Figure 6. Boxplots of the Hurst coefficients for various flow statistics at continuous-record streamgages in New Jersey, 1903–2017.

16 Streamflow Characteristics and Trends in New Jersey, Water Years 1903–2017 indicated the largest number of streamgages (42) with Hurst coefficients greater than 0.6 indicating a relatively strong indication of LTP (table 4). All other n-day time series had, on average, 23 streamgages with evidence for LTP. Base flow and runoff had 40 streamgages indicating evidence of LTP, again supporting the idea that the more generalized or robust flow characteristics would best display long-term patterns. Peak-flow ratio, skew, and kurtosis had the fewest streamgages with indicated evidence for long-term persistence (9, 5, and 4, respectively), which supports the idea that measures of the extremes or tails of the dataset might be expected to have minimal serial dependence. The number of time series with statistically significant trends decreases considerably as the variance is adjusted with each assumption of serial correlation, from 314 (IND) to 241 (STP) to 111 (LTP), by design. When evidence for serial correlation is considered, rather than simply assuming it exists, the decrease in the number of statistically significant trends is not as large, with 307 after adjustment for STP and 218 after adjustment for LTP. Of the total 398 time series with some evidence of LTP, only 124 indicated a statistically significant trend using the IND assumption. After adjusting the variance for LTP, 28 of the time series still indicated a statistically significant trend. Including adjustments for serial correlation is necessary in terms of recognizing the likelihood of cyclical patterns in hydrologic data and putting trends into context of

long-term climate variability, especially when considering that interpreting the evidence for serial correlation is not straightforward. However, most statistics used in permitting use available record, regardless of when it was collected or how long it is—unless there is a known major change in the basin. Knowing if the available stream record used for planning or permitting is changing can inform water managers of what might be expected in the near term for permit renewals and changes in allocation. In general, for all assumptions of serial correlation, most of the statistically significant trends indicated increasing rather than decreasing flows among the 17 streamflow characteristic time series (table 1). The percentage of continuous-record streamgages indicating statistically significant trends under the basic assumption of no serial correlation for high flow, low flow, and the measures of variability are shown in fig. 7 A, B, C, D, and E. For a broader view, the percentage of trend results at continuous-record streamgages for all flow characteristics are shown in figure 7F. The resulting number, direction, and magnitude of trend tests A. Low-flow trends

B. High-flow trends

Positive 28 percent

Positive 30 percent

Negative 20 percent

No trend 52 percent Negative 6 percent

Table 4. Range of computed Hurst coefficients for selected flow characteristics at 97 streamgages in New Jersey, period of record spanning 1903–2017.

Flow characteristic

Hurst Hurst coefficient coefficient maximum minimum

Number of streamgages greater than |0.6|

0.809058

0.500046

Annual 7-day high flow

0.800765

0.500047

Annual 30-day high flow

0.76268

0.500046

Winter 1-day high flow

0.762424

0.500046

0.833755

0.500044

0.772196

0.500047

Annual 1-day low flow

0.887143

0.500042

Annual 7-day low flow

0.900655

0.500046

Annual 30-day low flow

0.841619

0.500047

Winter 1-day low flow

0.872291

0.500046

Winter 7-day low flow

0.828549

0.500041

Winter 30-day low flow

0.747507

0.500048

Base flow

0.81573

0.500051

Runoff

0.745825

0.500046

Peak ratio

0.716881

0.500043

Skew

0.723112

0.500043

Kurtosis

0.715607

0.500041

Negative 6 percent

Negative 1 percent

Positive 24 percent

No trend 75 percent

E. Peak-flow ratio

F. All flow characteristics

Positive 20 percent No trend 74 percent

Positive 47 percent

No trend 23 percent

e tiv t ga cen Ne per 15

Winter 7-day high flow Winter 30-day high flow

Positive 24 percent

d t ixe n M erce p 12

Annual 1-day high flow

D. Kurtosis

C. Skew

Negative 1 percent

No trend 64 percent

Figure 7. Pie charts showing the percentage of continuous-record streamgages with statistically significant trends (p<0.05) under an assumption of no serial correlation in New Jersey, 1903–2017. A, low-flow trends, B, high-flow trends, C, skew, D, kurtosis, E, annual peak-flow ratio, and F, all flow characteristics.

Results 17 for the various streamflow characteristics are discussed in more detail in the following sections, focused only on the assumption of serial independence, with the knowledge that the results considering STP and LTP have fewer statistically significant trends.

Variability—Skew and Kurtosis Examining the annual distribution of streamflow provided a general assessment of possible changes to stream variability without choosing specific streamflow characteristics, such as the annual 1-day high flow. In using skew and kurtosis, the extremes of the distribution are characterized and assessed for changes. Twenty-four streamgages indicated statistically significant trends in the annual skew of the distribution of daily mean streamflow, all of which were increasing except for one (Saddle River at Lodi, N.J., USGS site 01391500). Of the 24 streamgages, 23 also indicated statistically significant trends for kurtosis; all the kurtosis trends were also increasing except for 01391500. One additional streamgage (Crosswicks Creek at Extonville, N.J., USGS site 01464500) indicated an increase in kurtosis but not skew.

0.08

The distribution of streamflow within a given year is generally skewed to the right, or positively, with high-flow events causing the shape to be “right-tailed.” Increasing skew over time suggests that the shape of the distribution is changing, possibly because of larger flow events (the tail) becoming greater in relation to the bulk of the streamflow record. Increasing kurtosis indicates that the data within the distribution are occurring more often in the tail or that extreme events are occurring more often as time progresses. The Saddle River at Lodi, N.J., 01391500 streamgage is the only exception to this trend, where the data indicate that the highest flow events are not as large and are occurring less often. Record length appears to be a factor in whether skew and kurtosis are significantly changing. Of the 25 streamgages indicating a trend in either skew or kurtosis, 18 (>70 percent) had records longer than 80 years. Meanwhile, streamgages with more than 80 years of record account for 28 percent of the number of streamgages included in this study (fig. 2). Even though more streamgages with longer records had significant positive trends, the magnitude of the trend, as computed by Sen’s slope was less (fig. 8) for those longer record streamgages.

A. Skew

Sen’s slope

0.06 0.04 0.02 0 −0.02 −0.04

0.8

B. Kurtosis

0.6

Sen’s slope

0.4 0.2 0 −0.2 −0.4 −0.6

100

110

120

Years of record

Figure 8. Scatterplots of the magnitude of statistically significant trends (p<0.05) in computed continuous-record streamgages in New Jersey, 1903–2017, compared to years of record. A, skew and B, kurtosis

18 Streamflow Characteristics and Trends in New Jersey, Water Years 1903–2017

Base Flow and Low n-day Flows

for the continuous-record streamgages can be found in the associated date release (Williams and others, 2024). Decreasing flows trends were identified at 19 streamgages, and flow is controlled or regulated at 2 of those streamgages. Increasing flow trends were identified at 27 streamgages, 10 of those streamgages are regulated. Recall from the “Methods, Continuous Records Streamgages” section that for regulated streamgages, periods of record used in the calculations do not include earlier unregulated periods. The same proportion of negative trends were observed in the north and south (about 17 percent of streamgages in each region); positive trends appear to be concentrated in northern New Jersey (fig. 10). Whereas table 5 and figure 10 illustrate the number of trends for each individual low-flow characteristic, as well as the totals for each season, the trends can also be explored in various alternate groupings. At the coarsest measure of low flow considered—base flow—statistically significant trends were identified at 18 streamgages (6 negative, 12 positive). At 6 of those streamgages, base flow was the only low-flow characteristic with evidence of a trend (2 negative, 4 positive). A larger number of streamgages (39) were identified as having a statistically significant trend for at least one of the finer scale

The range or magnitude of the low-flow characteristics tested for trends speaks to possible changes in variability of streamflow. The characteristics tested include annual base flow and n-day series of the lowest 1-, 7-, and 30-day flows during annual and winter seasons. Base flow is the largest or most coarse measure of what is considered “low flow” in this study. The n-day series captures finer scale measurements of low flow, with the lowest 1-day flow representing an extreme event for a given year. Though annual lowest n-day flows series are determined using the full climatic year (April 1–March 31) it is assumed these lowest events occur in the summer, following the typical hydrologic cycle of the northern hemisphere. This assumption does not hold true for streamgages that have controlled or regulated flow. Examining trends and their occurrence of either annual (summer) and winter can inform shifts in climate or seasonal weather patterns. In all, 46 continuous-record streamgages indicated a statistically significant trend for at least 1 or more of the low-flow characteristics computed in this study and are summarized in table 5 and figure 9; more detailed information

Table 5. Number of New Jersey continuous-record streamgages indicating a statistically significant trend for various low-flow characteristics, 1903–2017. [Winter period is defined as November 1 through April 30]

Trend

Any low-flow series

Base flow

Lowest annual 1-day flow

Lowest annual 7-day flow

Lowest annual 30-day flow

All annual n-day series

Lowest winter 1-day flow

Lowest winter 7-day flow

Lowest winter 30-day flow

All winter n-day series

Negative trend

Positive trend

Statistical series

Any low-flow series Baseflow Annual 1-day low flow Annual 7-day low flow Annual 30-day low flow Winter 1-day low flow Winter 7-day low flow Winter 30-day low flow All 3 annual n-day series All 3 winter n-day series 0

100

Number of continuous-record streamgages with low-flow trends

Negative

EXPLANATION Positive

No trend

Figure 9. Bar graph showing the number of continuous-record streamgages in New Jersey with statistically significant trends (p<0.05) for various low-flow characteristics and combinations thereof, 1903–2017.

Results 19

75°30'

75°

74°30'

A. Baseflow

74°

73°30'

NEW YORK

I CT

41°

NEW YORK New York City

40°30'

PENNSYLVANIA

Trenton

NEW JERSEY

Philadelphia

40°

ATLANTIC OCEAN

39°30'

DEL RE AWA

39°

EXPLANATION Trend direction—Williams and others (2024)

Delaware Bay

1 (positive) 0 (neutral) –1 (negative)

0 0

10 10

40 KILOMETERS

40 MILES

Figure 10. Maps showing continuous-record streamgages with statistically significant trends (p<0.05) in low flow in New Jersey, 1903–2017. A, base flow, B, winter 1-day low flow, C, winter 7-day low flow, D, winter 30-day low flow, E, annual 1-day low flow, F, annual 7-day low flow, and G, annual 30-day low flow.

20 Streamflow Characteristics and Trends in New Jersey, Water Years 1903–2017

75°30'

75°

74°30'

B. Winter 1-day low flow

74°

73°30'

NEW YORK

I CT

41°

NEW YORK New York City

40°30'

PENNSYLVANIA

Trenton

NEW JERSEY

Philadelphia

40°

ATLANTIC OCEAN

39°30'

RE AWA DEL

39°

EXPLANATION Trend direction—Williams and others (2024)

Delaware Bay

1 (positive) 0 (neutral) –1 (negative)

Figure 10.—Continued

0 0

10 10

40 KILOMETERS

40 MILES

Results 21

75°30'

75°

74°30'

C. Winter 7-day low flow

74°

73°30'

NEW YORK

I CT

41°

NEW YORK New York City

40°30'

PENNSYLVANIA

Trenton

NEW JERSEY

Philadelphia

40°

ATLANTIC OCEAN

39°30'

RE AWA DEL

39°

EXPLANATION Trend direction—Williams and others (2024)

Delaware Bay

1 (positive) 0 (neutral) –1 (negative)

Figure 10.—Continued

0 0

10 10

40 KILOMETERS

40 MILES

22 Streamflow Characteristics and Trends in New Jersey, Water Years 1903–2017

75°30'

75°

74°30'

D. Winter 30-day low flow

74°

73°30'

NEW YORK

I CT

41°

NEW YORK New York City

40°30'

PENNSYLVANIA

Trenton

NEW JERSEY

Philadelphia

40°

ATLANTIC OCEAN

39°30'

RE AWA DEL

39°

EXPLANATION Trend direction—Williams and others (2024)

Delaware Bay

1 (positive) 0 (neutral) –1 (negative)

Figure 10.—Continued

0 0

10 10

40 KILOMETERS

40 MILES

Results 23

75°30'

75°

74°30'

E. Annual 1-day low flow

74°

73°30'

NEW YORK

I CT

41°

NEW YORK New York City

40°30'

PENNSYLVANIA

Trenton

NEW JERSEY

Philadelphia

40°

ATLANTIC OCEAN

39°30'

RE AWA DEL

39°

EXPLANATION Trend direction—Williams and others (2024)

Delaware Bay

1 (positive) 0 (neutral) –1 (negative)

Figure 10.—Continued

0 0

10 10

40 KILOMETERS

40 MILES

24 Streamflow Characteristics and Trends in New Jersey, Water Years 1903–2017

75°30'

75°

74°30'

F. Annual 7-day low flow

74°

73°30'

NEW YORK

I CT

41°

NEW YORK New York City

40°30'

PENNSYLVANIA

Trenton

NEW JERSEY

Philadelphia

40°

ATLANTIC OCEAN

39°30'

RE AWA DEL

39°

EXPLANATION Trend direction—Williams and others (2024)

Delaware Bay

1 (positive) 0 (neutral) –1 (negative)

Figure 10.—Continued

0 0

10 10

40 KILOMETERS

40 MILES

Results 25

75°30'

75°

74°30'

G. Annual 30-day low flow

74°

73°30'

NEW YORK

I CT

41°

NEW YORK New York City

40°30'

PENNSYLVANIA

Trenton

NEW JERSEY

Philadelphia

40°

ATLANTIC OCEAN

39°30'

RE AWA DEL

39°

EXPLANATION Trend direction—Williams and others (2024)

Delaware Bay

1 (positive) 0 (neutral) –1 (negative)

Figure 10.—Continued

0 0

10 10

40 KILOMETERS

40 MILES

26 Streamflow Characteristics and Trends in New Jersey, Water Years 1903–2017 1-, 7-, or 30-day low-flow series for either annual, winter, or both periods. Only 18 of those 39 streamgages indicated trends during both annual (summer) and winter low flow (3 negative, 15 positive). For any annual (summer) n-day low-flow series, a total of 32 streamgages indicated statistically significant changes in flow (12 negative, 20 positive). Of the total 32 streamgages, more than half (18) indicated a trend for all 3 n-day series (7 negative, 11 positive). For any winter n-day low-flows, 25 streamgages indicated statistically significant changes in flow (6 negative, 19 positive). Of the 25 total streamgages, only 7 indicated a trend for all 3 n-day series (2 negative, 5 positive). Sen’s slope was then divided by the streamgage drainage area so that the degree of indicated change could be compared among the streamgages for each statistic. Note, however, that this comparison does not necessarily infer that the trend is the result of basin-wide changes but could also be the result of a point-source discharge or withdrawal at any point in the basin, including immediately upstream of the given streamgage. The largest statistically significant negative trend in cubic feet per second per square mile per year (ft3/s/mi2/yr) in base flow was −0.0429 ft3/s/mi2/yr at Whippany River near Pine Brook, N.J., USGS site 01381800. However, none of the n-day flow series showed evidence of a statistically significant trend at this location. This streamgage has a relatively short period of record of 21 years. Another streamgage located further upstream on the Whippany River (Whippany River near Morristown, N.J., USGS site 01381400) had the second largest statistically significant negative trend in base flow at −0.0358 ft3/s/mi2/yr. The data at this streamgage also did not show evidence for statistically significant trends in any of the n-day low-flow series. The period of record at Morristown is similar at 22 years. The streamgage Whippany River at Morristown (01381500) is located between the streamgage in Pine Brook (01381800) and the streamgage near Morristown (01381400) and has 96 years of record. At this streamgage, all low-flow characteristics except for the winter 30-day low flow indicated statistically significant positive trends. The different trends indicated at each streamgage are likely influenced not only by length of record, but also by local water use upstream of each streamgage. The largest positive trend in base flow was 0.0136 ft3/s/mi2/yr at the streamgage at Hohokus Brook at Ho-Ho-Kus, N.J., USGS site 01391000. At this location, the n-day series for both annual and winter flows indicated statistically significant positive trends and account for the largest positive trends in the n-day flows, with the highest at 0.0191 for the 30-day low-flow series

(table 6). Streamflow at Hohokus Brook at Ho-Ho-Kus, N.J., 01391000 receives contribution from several point-source dischargers upstream of the streamgage. The largest statistically significant negative trend in ft3/s/mi2/yr for the n-day low-flows was −0.0142 at East Branch Paulins Kill near Lafayette, N.J., 01443280, for the 1-day annual low-flow (table 6). The period of record for this streamgage is relatively short at 25 years, beginning only in 1993. This streamgage did not have any other flow characteristics that showed a statistically significant trend. In examining the daily flow record at this streamgage, the statistically significant decrease in 1-day low flows appears to be attributable to a few extreme occurrences which appear to be outliers and likely anthropogenic in nature. A quarry on an upstream tributary may be contributing to the change in flow. Examining the magnitude and direction of trends versus record length can inform the influence of cyclical wet and dry periods on the statistics, as opposed to assuming the existence of and adjusting for serial correlation (fig. 11). Generally, streamgages with less than 60 years of data (record beginning after 1956) were associated with the greatest range of low-flow Sen’s slope. The overall range of Sen’s slope per drainage area is listed in table 6 (−0.0429 to 0.0191 ft3/s/mi2/yr). For records between 20 and 40 years in length (beginning after 1976), most trends were negative (10 of 13 streamgages); for records between 40 and 60 years in length (beginning between 1957 and 1976), most trends were positive (8 of 11). Streamgages with greater than 60 years of record (beginning prior to 1956) have smaller ranges of low-flow Sen’s Slopes (−0.0095 to 0.0048), and most trends were positive (17 of 22).

Runoff and High n-day Flows The high-flow characteristics examined for trends in this report mirror the range and magnitude of low-flow statistics, with runoff (the counterpart to base flow) as the coarsest measure of the upper half of the hydrograph and the highest 1-, 7-, and 30-day high flows informing possible changes in duration and magnitude of large rainfall events. Examination of n-day high flows can inform water managers of more common flooding conditions as well as extreme events without the large uncertainty and variation that can be associated with frequency analysis of annual peak flows. Highest n-day flows typically occur during the winter. Although it may seem redundant, differences in trends of annual compared to winter high flows can still inform shifts in seasonal climate if differing numbers of trends are observed in each season,

Table 6. Magnitude of statistically significant trends in low flow at continuous-record streamgages in New Jersey, 1903–2017. [Winter season is November 1 through April 30. Measurements are given in cubic feet per second per square mile per year]

Sen’s slope per drainage area

Base flow

Lowest annual 1-day flow

Lowest annual 7-day flow

Lowest annual 30-day flow

Lowest winter 1-day flow

Lowest winter 7-day flow

Lowest winter 30-day flow

Minimum

−0.0429

−0.0142

−0.0089

−0.0093

−0.0068

−0.0103

−0.0095

Maximum

0.0136

0.0190

0.0184

0.0191

0.0162

0.0124

0.0133

0.03

Sen’s slope divided by drainage area, in cubic feet per second per square mile per year

0.02

0.01

−0.01

−0.02

−0.03

−0.04

−0.05

100

120

Years of record EXPLANATION Sen’s slope of the baseflow divided by the drainage area Sen’s slope for the winter period 1-day minimum average low flow divided by the drainage area Sen’s slope for the winter period 7-day minimum average low flow divided by the drainage area Sen’s slope for the winter period 30-day minimum average low flow divided by the drainage area Sen’s slope for the annual 1-day minimum average low flow divided by the drainage area Sen’s slope for the annual 7-day minimum average low flow divided by the drainage area Sen’s slope for the annual 30-day minimum average low flow divided by the drainage area

Results 27

Figure 11. Scatterplot showing the magnitude of statistically significant trends (p<0.05) in low flow at continuous-record streamgages in New Jersey, 1903–2017, compared to years of record.

28 Streamflow Characteristics and Trends in New Jersey, Water Years 1903–2017 and winter n-day values. Only one of the streamgages which had both annual and winter n-day high-flow trends indicated decreasing flow. For any annual n-day high-flow series, a total of 28 streamgages indicated statistically significant changes in flow (2 decreasing, 26 increasing). Of the 28, only 8 indicated a trend for all 3 n-day series (all positive). For any winter n-day high-flow series, fewer (19) streamgages indicated statistically significant changes in flow (1 decreasing, 14 increasing). Of the 19 streamgages, only 4 indicated a trend for all 3 n-day high flow series (all positive). With a greater number of streamgages having evidence for trends in the annual versus winter series, it suggests that highest flows may be shifting to later in the spring (after April 30). As with the magnitudes of low-flow trends, Sen’s slope was divided by drainage area so comparisons could be made among streamgages. Unlike the low-flow characteristics, high-flow trends are not likely attributable to point-source discharges and withdrawals, which typically account for a much smaller proportion of the highest n-day flow or base

though not ideal. The use of annual and winter high values in this study were implemented, in part, to replicate Watson and others (2005). A total of 35 continuous-record streamgages indicated a statistically significant trend for at least 1 or more of the high-flow characteristics computed in this study (table 7 and fig. 12). Most of the trends indicated increasing flows (6 negative, 29 positive). Of the negative trends, flow is regulated or controlled at 1 streamgage; of the positive trends, 6 streamgages are regulated (fig. 13). All but a few of the positive high-flow trends were in northern New Jersey. At the coarsest measure of high flow—runoff—statistically significant trends were identified at 17 streamgages (5 negative, 12 positive). Four southern New Jersey streamgages indicated a trend only for runoff (all negative). As was the case for low flow, a larger number of streamgages (31) indicated statistically significant trends at the finer-scale 1-, 7-, or 30-day high flow series for either annual, winter, or both seasons. In the case of high-flow characteristics, approximately half of those streamgages (16) indicated changing flow for both annual

Table 7. Number of New Jersey continuous-record streamgages indicating a statistically significant trend for various high-flow characteristics, 1903–2017. [Winter period is defined as November 1 through April 30].

Trend

Any high-flow Runoff series

Highest annual 1-day flow

Highest annual 7-day flow

All annual n-day series

Highest annual 30-day flow

Highest winter 1-day flow

Highest winter 7-day flow

All high annual n-day series

Highest winter 30-day flow

Negative trend

Positive trend

Total

Statistical series

Any high-flow series Runoff Annual 1-day high flow Annual 7-day high flow Annual 30-day high flow Winter 1-day high flow Winter 7-day high flow Winter 30-day high flow All 3 annual n-day series All 3 winter n-day series 0

100

Number of streamgages with high-flow trends

Negative

EXPLANATION Positive

No trend

Figure 12. Bar graph showing the number of continuous-record streamgages in New Jersey with statistically significant trends (p<0.05) for various high-flow characteristics and combinations thereof, 1903–2017.

110

Results 29

75°30'

75°

74°30'

A. Runoff

74°

73°30'

NEW YORK

I CT

41°

NEW YORK New York City

40°30'

PENNSYLVANIA

Trenton

NEW JERSEY

Philadelphia

40°

ATLANTIC OCEAN

39°30'

DEL RE AWA

39°

EXPLANATION Trend direction—Williams and others (2024)

Delaware Bay

1 (positive) 0 (neutral) –1 (negative)

0 0

10 10

40 KILOMETERS

40 MILES

Figure 13. Maps showing continuous-record streamgages with statistically significant trends (p<0.05) in high flow in New Jersey, 1903–2017. A, runoff, B, winter 1-day low flow, C, winter 7-day low flow, D, winter 30-day low flow, E, annual 1-day low flow, G, annual 30-day low flow.

30 Streamflow Characteristics and Trends in New Jersey, Water Years 1903–2017

75°30'

75°

74°30'

B. Winter 1-day high flow

74°

73°30'

NEW YORK

I CT

41°

NEW YORK New York City

40°30'

PENNSYLVANIA

Trenton

NEW JERSEY

Philadelphia

40°

ATLANTIC OCEAN

39°30'

RE AWA DEL

39°

EXPLANATION

Delaware Bay

Trend direction—Williams and others (2024) 1 (positive) 0 (neutral)

Figure 13.—Continued

0 0

10 10

40 KILOMETERS

40 MILES

Results 31

75°30'

75°

74°30'

C. Winter 7-day high flow

74°

73°30'

NEW YORK

I CT

41°

NEW YORK New York City

40°30'

PENNSYLVANIA

Trenton

NEW JERSEY

Philadelphia

40°

ATLANTIC OCEAN

39°30'

RE AWA DEL

39°

EXPLANATION Trend direction—Williams and others (2024)

Delaware Bay

1 (positive) 0 (neutral) –1 (negative)

Figure 13.—Continued

0 0

10 10

40 KILOMETERS

40 MILES

32 Streamflow Characteristics and Trends in New Jersey, Water Years 1903–2017

75°30'

75°

74°30'

D. Winter 30-day high flow

74°

73°30'

NEW YORK

I CT

41°

NEW YORK New York City

40°30'

PENNSYLVANIA

Trenton

NEW JERSEY

Philadelphia

40°

ATLANTIC OCEAN

39°30'

RE AWA DEL

39°

EXPLANATION Trend direction—Williams and others (2024)

Delaware Bay

1 (positive) 0 (neutral) –1 (negative)

Figure 13.—Continued

0 0

10 10

40 KILOMETERS

40 MILES

Results 33

75°30'

75°

74°30'

E. Annual 1-day high flow

74°

73°30'

NEW YORK

I CT

41°

NEW YORK New York City

40°30'

PENNSYLVANIA

Trenton

NEW JERSEY

Philadelphia

40°

ATLANTIC OCEAN

39°30'

RE AWA DEL

39°

EXPLANATION

Delaware Bay

Trend direction—Williams and others (2024) 1 (positive) 0 (neutral)

Figure 13.—Continued

0 0

10 10

40 KILOMETERS

40 MILES

34 Streamflow Characteristics and Trends in New Jersey, Water Years 1903–2017

75°30'

75°

74°30'

F. Annual 7-day high flow

74°

73°30'

NEW YORK

I CT

41°

NEW YORK New York City

40°30'

PENNSYLVANIA

Trenton

NEW JERSEY

Philadelphia

40°

ATLANTIC OCEAN

39°30'

RE AWA DEL

39°

EXPLANATION

Delaware Bay

Trend direction—Williams and others (2024) 1 (positive) 0 (neutral)

Figure 13.—Continued

0 0

10 10

40 KILOMETERS

40 MILES

Results 35

75°30'

75°

74°30'

G. Annual 30-day high flow

74°

73°30'

NEW YORK

I CT

41°

NEW YORK New York City

40°30'

PENNSYLVANIA

Trenton

NEW JERSEY

Philadelphia

40°

ATLANTIC OCEAN

39°30'

RE AWA DEL

39°

EXPLANATION Trend direction—Williams and others (2024)

Delaware Bay

1 (positive) 0 (neutral) –1 (negative)

Figure 13.—Continued

0 0

10 10

40 KILOMETERS

40 MILES

36 Streamflow Characteristics and Trends in New Jersey, Water Years 1903–2017 flow values. In addition to shifts in climate, regulation and changes in land cover may contribute to changes in n-day high flows. The largest statistically significant negative trend in runoff was −0.0129 ft3/s/mi2/yr at the Hackensack River at New Milford, N.J., USGS site 01378500 (table 8), a heavily regulated streamgage located immediately downstream from Oradell Reservoir, with 96 years of record. In addition to runoff, the annual 30-day, winter 7-day, and winter 30-day high-flows also indicated negative trends. The largest statistically significant positive trend in runoff was 0.0051 ft3/s/mi2/yr at the Pequannock River at Macopin Intake Dam, N.J., USGS site 01382500 (table 8), also a heavily regulated streamgage with 56 years of record. All n-day high-flow series also indicated positive trends. The largest statistically significant negative trend for the n-day high-flow series was −0.1167 ft3/s/mi2/yr at the Whippany River near Pine Brook, N.J., 01381800, the annual 30-day high-flow (table 8). No other n-day high-flow statistics, either annual or winter, showed evidence of a trend. Recall however, that base flow at this streamgage also had the largest negative trend in base flow and it only has 21 years of record. The largest statistically significant positive trend for the n-day high-flow series was 0.3071 ft3/s/mi2/yr at Mantua Creek at East Holly Avenue at Pitman, N.J., USGS site 01475001, for the annual 1-day high-flow (table 8). All the n-day high-flows, as well as runoff, indicated statistically significant increases. The period of record at 01475001 stretches back to 1940; however, there are two gaps in the record, 1976–2003 and 2006–09, for a total of 45 years of record. This streamgage is occasionally regulated from gates at Kressey Lake. As was observed in the low-flow trends, the largest range of Sen’s slopes for statistically significant high-flow characteristics were associated with streamgages having less than 60 years of record (−0.1167 to 0.3071; fig. 14). Only two streamgages with record length between 20 and 40 years indicated a statistically significant high-flow trend. The Whippany River near Pine Brook, N.J., 01381800 streamgage showed a negative trend in the 30-day high, whereas Pequest River at Huntsville, N.J., USGS site 01445000, showed a positive trend in 1-day high-flows. For streamgages with records greater than 60 years, the range in Sen’s slope for high-flow trends was −0.0197 to 0.1281 ft3/s/mi2/yr, with decreasing runoff only accounting for negative trends at 4 of the 6 streamgages with negative trends.

Peak-Flow Ratio As a way to assess both a high-flow characteristic and flow variability, the annual peak-flow ratio was examined. An increasing peak-flow ratio over time would indicate that the annual instantaneous peak is becoming larger in relation to the mean daily flow one day prior, the day of, and one day after the peak. This circumstance suggests that more intense rainfall events are occurring, or that the streamflow is becoming “flashier,” or occurring during times of relatively low base flow in the summer. A decreasing peak-flow ratio over time would indicate that the peak is becoming smaller in relation to associated 3-day mean flow. This circumstance suggests that peaks may be prolonged or increasing in duration, or perhaps occurring on already saturated soils with higher prepeak mean daily flow or possibly smaller peaks happening more frequently on saturated soils. Twenty-five streamgages indicated a statistically significant trend in peak-flow ratio (Williams and others, 2024). More than 75 percent of the trends indicated an increase in the peak-flow ratio over time. These results coincide with the premise that 1-day high flows may be occurring more often in the annual (summer) series than in the winter season. The magnitude or the number of statistically significant trends does not appear to be influenced by length of record (fig. 15). Streamgage West Branch Middle Brook near Martinsville, N.J., USGS site 01403150, has an exceptionally high increasing Sen’s slope. This streamgage has the smallest drainage area of all those included in this study, 1.99 square miles.

Trends in Regression Residuals at Partial-Record Streamgages The residuals of the MOVE1 regressions used to estimate low-flow statistics at partial-record streamgages were assessed for trends using the MK test. Because the test is applied to the regression residuals and not a continuous time series of data, a statistically significant (p<0.05) trend indicates a change in the relation between the partial-record streamgage and the continuous-record index site over time. The assumption was made that changes in the relation are likely due to changing flows at the partial-record streamgage, as streamgages with little or no trends in the computed low-flow characteristics were one of the criteria for use of an index site.

Table 8. Magnitude of statistically significant trends in high flow at continuous-record streamgages in New Jersey, 1903–2017. [Winter season is November 1 through April 30. Measurements are given in cubic feet per second per square mile per year]

Runoff

Highest annual 1-day flow

Highest annual 7-day flow

Highest annual 30-day flow

Highest winter 1-day flow

Highest winter 7-day flow

Highest winter 30-day flow

Minimum

−0.0129

0.0291

0.0231

−0.1167

0.0272

−0.0256

−0.0197

Maximum

0.0051

0.3071

0.1086

0.0561

0.2692

0.1344

0.0612

Sen’s slope per drainage area

0.35

Sen’s slope divided by drainage area, in cubic feet per second per square mile per year

0.30

0.25

0.20

0.15

0.10

0.05

−0.05

−0.10

−0.15

100

120

Years of record EXPLANATION Sen’s slope of the runoff divided by the drainage area Sen’s slope for the winter period 1-day maximum average high flow divided by the drainage area Sen’s slope for the winter period 7-day maximum average high flow divided by the drainage area Sen’s slope for the winter period 30-day maximum average high flow divided by the drainage area Sen’s slope for the annual 1-day maximum average high flow divided by the drainage area Sen’s slope for the annual 7-day maximum average high flow divided by the drainage area

Figure 14. Plot of the magnitude of statistically significant trends (p<0.05) in high flow at continuous-record streamgages in New Jersey, 1903–2017, compared to years of record.

Results 37

Sen’s slope for the annual 30-day maximum average high flow divided by the drainage area

38 Streamflow Characteristics and Trends in New Jersey, Water Years 1903–2017 0.6

A. Full range of values

0.5

Sen’s slope

0.4 0.3 0.2 0.1 0 −0.1 0.1

B. Zoomed to ±0.10 scale

0.08

Sen’s slope

0.06 0.04 0.02 0 −0.02 −0.04 −0.06 −0.08

100

120

Years of record

Figure 15. Scatterplot of the magnitude of statistically significant trends (p<0.05) in peak-flow ratio to years of record at continuous-record streamgages in New Jersey, 1903–2017. A, full range of values, and B, zoomed to plus or minus 0.10 scale.

A different number of index sites were used per partial-record streamgage, depending on 1) physiographic province as noted in the “Methods” section, and 2) available data. To summarize identified trends in the residuals, a ratio or percentage of the number of index sites indicating a statistically significant trend was determined for each partial-record streamgage. Only the direction of the trend was determined, not the magnitude. It is assumed that as a greater proportion of index sites per partial-record streamgage are identified, the likelihood that flows are changing at the partial-record streamgage increases. Among the 719 partial-record streamgages, 321 indicated a trend in residuals with at least 1 index site (Williams and others, 2024). The single index site trends were split close to even, with 145 negative and 176 positive. Additionally, of the 321 streamgages with a trend in residuals, 53 had more than 50 percent of index sites indicating a statistically significant (p<0.05) trend in the residuals (26 negative, 27 positive). Having more than 50 percent of the index sites showing a trend in residuals increases the likelihood that the partial-record streamgage has a similar trend. Table 9 lists the index sites used in the regressions and accompanying information on the number of partial-record streamgages that used each index site and the proportion of trends in residuals indicated for each

index site. The table is sorted by which index site indicated the greatest number of trends, to determine if any particular index site(s) may have had undue influence in designating a trend. It appears that the proportion (percentage) of trends identified increased with the number of partial-record streamgages which used each index site; in other words, the more often an index site was used, the greater the likelihood it would indicate change at a partial-record streamgage (fig. 16). The span of the partial-record streamgage data (end year minus begin year of discrete flow measurements) also did not seem to influence the number of index site regression residuals that indicated a trend (fig. 17). Trends at partial-record streamgages are most likely due to localized effects of water use. A map showing the direction and spatial distribution of low-flow trends at partial-record streamgages, based on the proportion of statistically significant trends in regression residuals, is presented in figure 18.

Trends in Low Flow in Over-Allocated Basins The New Jersey Geological and Water Survey Technical Memorandum 13-3 (Domber and others, 2013) established an approach for determining water availability in the State

Table 9. Continuous-record streamgages used as index sites in regression analysis with partial-record streamgages and the results of trend tests in New Jersey, 1903–2017. [USGS, U.S. Geological Survey; NJ, New Jersey]

USGS site number

Site name

Drainage Record area, in length, in square years miles

Partial-record Count of Number and partial-record streamgages Number of Negative Negative Positive Positive direction of with partial-record streamgages trend trends, in trend trends, in low-flow indicated with trends streamgages count percent count percent trends at index trends, in in regression regressed site percent residuals

01467150 Cooper River at Haddonfield NJ

374

01411500 Maurice River at Norma NJ

112

374

01410150 East Branch Bass River near New Gretna NJ

8.11

348

01408000 Manasquan River at Squankum NJ

372

01411000 Great Egg Harbor River at Folsom NJ

57.1

373

01408500 Toms River near Toms River NJ

123

373

01408120 North Branch Metedeconk River near Lakewood NJ

34.9

349

01380450 Rockaway River at Main Street at Boonton NJ

116

213

01467000 North Branch Rancocas Creek at Pemberton NJ

118

373

01445500 Pequest River at Pequest NJ

106

258

01409810 West Branch Wading River near Jenkins NJ

84.1

328

01411456 Little Ease Run near Clayton NJ

9.77

326

01440000 Flat Brook near Flatbrookville NJ

214

01443500 Paulins Kill at Blairstown NJ

126

214

01386000 West Brook near Wanaque NJ

11.8

210

01412800 Cohansey River at Seeley NJ

311

01396660 Mulhockaway Creek at Van Syckel NJ

11.8

171

01379000 Passaic River near Millington NJ

55.4

−1

195

36.7

258

44.5

151

01396582 Spruce Run at Main Street at Glen Gardner NJ

12.3

01381400 Whippany River near Morristown NJ

−1

100

Results 39

01446000 Beaver Brook near Belvidere NJ 01401000 Stony Brook at Princeton NJ

40 Streamflow Characteristics and Trends in New Jersey, Water Years 1903–2017

Percentage of streamgages with trend

Trend line 15

R ² = 0.27

100

150

200

250

300

350

400

Number of partial-record streamgages per index site

Figure 16. Percentage of partial-record streamgages in New Jersey with a statistically significant trend (p<0.05) in regression residuals for the period 1903–2017, versus the total number of partial-record streamgages regressed against each continuous-record streamgage. 100

Percentage of index sites that indicated a trend

80 60 40 20 0 −20 −40 −60 −80 −100

Span of partial-record site record, in years

Figure 17. Percentage of continuous-record streamgages indicating a statistically significant trend in regression residuals per partial-record streamgage versus the span of the record, in years, of the partial-record streamgage in New Jersey, 1903–2017.

100

Results 41

75°30'

75°

74°30'

74°

73°30'

NEW YORK

T EC

41°

NEW YORK

40°30'

PENNSYLVANIA ATLANTIC OCEAN 40°

NEW JERSEY

39°30'

EXPLANATION Percent of index streamgages indicating trend in residuals at partial record streamgages—Williams and others (2024) Less than or equal to 100 Less than or equal to 75 Less than or equal to 50 Less than or equal to 25

RE AWA

DEL 39°

Delaware Bay

0 Greater than or equal to −25 Greater than or equal to −50 Greater than or equal to −75 Greater than or equal to −100

0 0

10 10

40 KILOMETERS

40 MILES

Figure 18. Map showing the percentage of index sites indicating a statistically significant trend (p<0.05) in regression residuals per partial-record streamgage versus the span of the record, in years, of the partial-record streamgage in New Jersey, 1903–2017.

42 Streamflow Characteristics and Trends in New Jersey, Water Years 1903–2017 using a low-flow margin, defined as the difference between the 7-day 10-year low flow, herein referred to as the 7Q10, and the September median flow. The low-flow margin was determined for subwatersheds at the 11-digit hydrologic unit code (HUC11) scale (Domber and others, 2013). The NJDEP then identified HUC11s for which allocated water exceeded the low-flow margin. Both positive and negative trends in low-flow characteristics at continuous-record streamgages were identified in over-allocated basins, as defined by NJDEP (table 10). Negative trends were identified for at least one of the computed low-flow characteristics at eight continuous-record streamgages among seven of those identified HUC11s. Positive trends in low-flow characteristics were identified at 11 streamgages among 9 HUC11s. Trends in flow at partial-records sites were defined by the percentage of index sites which had statistically significant trends in the residuals of the regressions with each partial-record site. Just as with the continuous-record streamgages, partial-record

streamgages indicate both increasing and negative trends in HUC11s where allocation exceeded the low-flow margin. Sites where the percentage of index sites with residual trends greater than ±50 percent are listed in table 11. Recall that the trend tests were applied to the full periods of record for both continuous and partial-record streamgage analysis. Current or changing water use may not be reflected in the trend test results or the allocated amounts from the NJDEP assessment.

Comparison of Results—2001 and 2017 This study used 97 continuous-record streamgages and 719 partial-record streamgages with data through 2017. A subset of 80 continuous-record streamgages and 462 partial-record streamgages were included as part of the previous report (Watson and others, 2005), which used data through 2001. Trends were assessed for the same n-day flows (high and low, annual, and

Table 10. Number and direction of trends of computed low-flow characteristics at continuous-record streamgages in New Jersey, for which the associated HUC11 allocated water exceeds the low-flow margin. [USGS, U.S. Geological Survey; regulated flow, a reservoir or flow control structure upstream of the streamgage; HUC11, 11-digit hydrologic unit code; NJ, New Jersey]

USGS site number

Number and direction of low HUC11 HUC11 name flow trends −4 02030103140 Saddle River −3 02040202100 Pennsauken Creek

Drainage area

Regulated flow

Saddle River at Ridgewood NJ South Branch Pennsauken Creek at Cherry Hill NJ Crosswicks Creek at Extonville NJ

21.6 8.98

No No

81.5

−2

11.3

Yes

−1

25.3

Yes

−1

01379000

South B Rockaway Creek at Whitehouse Station NJ Musconetcong River at outlet of Lake Hopatcong NJ Passaic River near Millington NJ

55.4

−1

01381400 01381800 01412800

Whippany River near Morristown NJ Whippany River near Pine Brook NJ Cohansey River at Seeley NJ

14 68.5 28

No No No

−1 −1 1

01405030 01410500 01377000

Lawrence Brook at Westons Mills NJ Absecon Creek at Absecon NJ Hackensack River at Rivervale NJ

44.9 17.9 58

No No Yes

1 2 2

01377500

Pascack Brook at Westwood NJ

29.6

Yes

01378500

Hackensack River at New Milford NJ

113

Yes

01400000

North Branch Raritan River near Raritan NJ Passaic River near Chatham NJ

190

100

Whippany River at Morristown NJ Pequannock River at Macopin Intake Dam NJ Hohokus Brook at Ho-Ho-Kus NJ

29.4 63.7

No Yes

6 7

02040105150 Musconetcong River (above Trout Brook) 02030103010 Passaic River Upr (above Pine Bk br) 02030103020 Whippany River 02030103020 Whippany River 02040206080 Cohansey River (above Sunset Lake) 02030105130 Lawrence Brook 02040302020 Absecon Creek 02030103170 Hackensack R (above Hirshfeld Brook) 02030103170 Hackensack R (above Hirshfeld Brook) 02030103170 Hackensack R (above Hirshfeld Brook) 02030105070 Raritan River NB (SB to Lamington) 02030103010 Passaic River Upr (above Pine Bk br) 02030103020 Whippany River 02030103050 Pequannock River

16.4

02030103140 Saddle River

01390500 01467081 01464500 01399670 01455500

01379500 01381500 01382500 01391000

Site name

02040201050 Crosswicks Ck (Doctors Ck to New Egypt) 02030105050 Lamington River

Results 43 Table 11. Partial-record streamgages in New Jersey with more than half of the index sites used in low-flow regression analysis that had statistically significant trends in residuals among HUC11 where allocated water exceeds the low-flow margin. [USGS, U.S. Geological Survey; HUC11, 11-digit hydrologic unit code; NJ, New Jersey]

Percent and direction of trends in Drainage index site area regression residuals, in percent

USGS site number

Site name

01412500

West Branch Cohansey River at Seeley NJ

2.58

−100

2040206080 Cohansey River (above Sunset Lake)

0140940365 Sleeper B diversion channel near Atsion NJ

None

−91

2040301160 Mullica River (above Basto River)

01377370

Pascack Brook at Park Ridge NJ

13.4

−83

2030103170 Hackensack R (above Hirshfeld Brook)

01390450

Saddle River at Upper Saddle River NJ

10.9

−83

2030103140 Saddle River

01391485

Sprout Bk at Rochelle Park NJ

5.56

−83

2030103140 Saddle River

01464583

NB Barkers Brook near Jobstown NJ

1.72

−73

2040201100 Assiscunk Creek

01403330

Bound Brook at South Plainfield NJ

9.55

−67

2030105120 Raritan R Lower (Lawrence to Millstone)

01409375

Mullica River near Atco NJ

3.22

−64

2040301160 Mullica River (above Basto River)

01455350

Weldon Bk near Woodport NJ

3.63

−60

2040105150 Musconetcong River (above Trout Brook)

01467140

Cooper R at Lawnside NJ

12.7

−55

2040202110 Cooper River

01405340

Manalapan Brook at Federal Road near Manalapan NJ

20.9

−55

2030105140 Manalapan Brook

01475031

Chestnut Branch at Glassboro NJ

0.36

−55

2040202130 Mantua Creek

01410789

Great Egg Harbor R tr 2 at Winslow Crossing NJ

0.52

−50

2040302030 Great Egg Harbor R (above HospitalityBr)

0140940250 Cooper Branch near Chesilhurst NJ

1.93

−50

2040301160 Mullica River (above Basto River)

01389738

Molly Ann Brook tributary near Franklin Lakes NJ

0.33

−50

2030103120 Passaic River Lower (Saddle to Pompton)

01399820

Chambers Bk near North Branch NJ

4.71

−50

2030105070 Raritan River NB (SB to Lamington)

01383600

Hewitt Brook at Hewitt NJ

3.24

−50

2030103070 Wanaque River

01368950

Black Creek near Vernon NJ

17.3

2020007040 Pochuck Creek

01382000

Passaic River at Two Bridges NJ

361

2030103040 Passaic River Upr (Pompton to Pine Bk)

HUC11

HUC11 name

01410784

Great Egg Harbor R near Sicklerville NJ

15.1

2040302030 Great Egg Harbor R (above HospitalityBr)

01409408

Pump Branch near Waterford Works NJ

9.78

2040301160 Mullica River (above Basto River)

01382910

Morsetown Bk at West Milford NJ

1.31

2030103070 Wanaque River

01405285

Barclay Bk near Englishtown NJ

4.94

2030105150 Matchaponix Brook

01405300

Matchaponix Brook at Spotswood NJ

43.9

2030105150 Matchaponix Brook

01378900

Black Brook near Meyersville NJ

11.7

2030103010 Passaic River Upr (above Pine Bk br)

01389100

Singac Brook at Singac NJ

11.1

2030103120 Passaic River Lower (Saddle to Pompton)

01390700

Hohokus Brook at Wyckoff NJ

5.31

2030103140 Saddle River

01397800

Neshanic R near Flemington NJ

11.4

2030105030 Neshanic River

01403385

Bound Brook at Route 28 at Middlesex NJ

23.9

2030105120 Raritan R Lower (Lawrence to Millstone)

01411035

Hospitality Branch at Blue Bell Road near Cecil NJ Papakating Creek at Sussex NJ Green Brook near West Milford NJ Matchaponix Brook near Englishtown NJ Dead River near Millington NJ

4.51

2040302040 Great Egg Harbor R (Lk Lenape to HospBr)

59.4 1.85 29.1 20.8

80 80 90 100

2020007020 2030103070 2030105150 2030103010

01367910 01382960 01405240 01379200

Papakating Creek Wanaque River Matchaponix Brook Passaic River Upr (above Pine Bk br)

44 Streamflow Characteristics and Trends in New Jersey, Water Years 1903–2017

Changes in Trends at Continuous-Record Streamgages

Percent Change in Streamflow The determination of trends in streamflow noted in the previous sections evaluates the full periods of record for statistically significant changes. To quantify changes in streamflow since the previously computed statistics through 2001 (Watson and others, 2005), the percent change in individual streamflow statistics were computed. This determination can provide information of possible impacts of the updated statistics on streamflow permits and prior management strategies, regardless of whether the data show a statistically significant trend. Comparisons in continuous-record streamgage-flow statistics include changes in computed durations (1-, 2-, 5-, 10-, 20-, 25-, 30-, 40-, 50-, 60-, 70-, 75-, 80-, 85-, 90-, 95-, 99-percent) and all high and low flow frequencies (1-, 7-, and 30-day high and low flows at 50-, 20-, 10-, 5-, and 4-percent exceedance and nonexceedances, for annual and winter periods). For partial-record streamgage statistics, percent change in the 1-, 7-, 30-day annual, and 7- and

10 40

2001 Positive 2017 Positive 2001 Negative 2017 Negative

0 2001 Positive 2017 Positive 2001 Negative 2017 Negative

2001 Positive 2017 Positive 2001 Negative 2017 Negative

Number of streamgages with statistically significant trends

Among the 80 continuous-record streamgages that were included in both the current and previous (Watson and others, 2005) studies, trends in the annual 1-, 7-, and 30-day high and low, annual, and winter flow series were computed using the MK trend test under the assumption of serial independence. Trend test results indicated a considerable increase in the number of statistically significant positive trends, in both high and low flows, for both seasons (fig. 19). The total number of positive n-day high-flow trends more than doubled from 37, using data through 2001, to 82, using data through 2017. The largest increase in number of positive

trends was observed for the annual 30-day high flow. The total number of negative or decreasing n-day high-flow trends dropped from 5 to 3 for the same datasets. A similar change in the number of trends in n-day low flow was observed, but to a lesser degree, with positive trends ranging from 61 to 82 between the two datasets. The total number of negative trends in n-day low flows declined from 37 to 25 (fig. 19).

1-Day

7-Day

30-Day

1-Day

7-Day

30-Day

1-Day

7-Day

30-Day

1-Day

7-Day

30-Day

Annual High Flow

Winter High Flow

Annual Low Flow

Number of streamgages with no statistically significant trend

winter) for continuous-record streamgages in both studies; however, Watson and others (2005) did not assess regression residuals at partial-record streamgages. The same streamflow characteristics for flow durations and n-day high and low, annual, and winter flow were computed in both studies. The statistics for streamgages included in both studies were compared to help users develop a better understanding of changes in high- and low-streamflow trends, as well as how much the computed streamflow characteristics may have changed. This type of comparison may help to inform possible effects on water resource management.

Winter Low Flow

EXPLANATION 2001 No trend 2017 No trend

Figure 19. Bar graph showing the number of statistically significant trends (p<0.05) in n-day flow at continuous-records streamgages in New Jersey using data through 2001 versus 2017.

Results 45 30-day winter low flows at 10-percent annual nonexceedance probability, the 75th and 50th percentile flow, and the annual mean flow were computed.

Continuous-Record Percent Change in Streamflow Most changes to computed streamflow characteristics for both flow durations and frequencies were generally within ±10 percent using the additional data collected among the 80 continuous-record streamgages that were included in both this and the previous report. The lower flow characteristics typically accounted for changes greater than ±10 percent from the previous report, with a full range of change from −100 to +500 percent. A greater number of both annual and winter flow duration values indicated decreases in flow durations, though the change was mostly minimal—between 0 and −5 percent. Histograms demonstrating the distribution of the annual and winter flow

percent change among the 1,360 individual computed durations (17 durations among the 80 sites) are shown in figure 20. Of those computed annual flow durations, 61 percent (830) indicated decreases and 38 percent (514) indicated increases in flow from 2001 to 2017; 1 percent (16) indicated zero change. For winter flow durations, 57 percent (778) indicated decreases and 39 percent (525) indicated increases; 4 percent (57) indicated zero change. Only 13 and 9 percent of computed annual and winter durations indicated a change in flow greater than 10 percent. The histograms do not include values of zero change because the zeroes would fall into the −5 to 0 percentage bin, making it appear there were more negative changes than were actually computed. Percent change was not consistent in either the positive or negative direction for all 17 computed durations for a given site, indicating some changes to the distribution of flow in the duration curves. The 50th through 5th percentile flows indicated

700 600

A. Annual

622

400 321 300 200

142 90 8

6 Greater than 50

45.01 to 50

40.01 to 45

35.01 to 40

30.01 to 35

25.01 to 30

20.01 to 25

100

15.01 to 20

Count

500

700 600

B. Winter

583

400

374

300 200

140 79 0

−44.99 to −40

−39.99 to −35

−34.99 to −30

−29.99 to −25

−24.99 to −20

−19.99 to −15

34 10.01 to 15

0.01 to 5

−4.99 to 0

−9.99 to −5

34 −14.99 to −10

−49.99 to −45

Less than or equal to −50

100

5.01 to 10

Count

500

Ranges of percentage change

Figure 20. Histogram of percent change from 2001 to 2017 for 80 continuous-record streamgages in New Jersey. A, annual streamflow duration values, and B, winter streamflow duration values. Durations computed were the 1-, 2-, 5-, 10-, 20-, 25-, 30-, 40-, 50-, 60-, 70-, 75-, 80-, 85-, 90-, 95-, and 99-percent flows. Winter period is November 1 through April 30. A total of 16 annual and 57 winter zero values for duration flow percent change are not included in the graphs.

46 Streamflow Characteristics and Trends in New Jersey, Water Years 1903–2017 mostly negative changes. Lowest flows (99th to 95th percentiles) indicated greatest percent change with most outliers greater than ±50 percent, but, overall, leaned more positive. Boxplots of the distribution of percent change of each of the 17 annual and winter flow durations are shown in figure 21 and figure 22. The greatest decreases in the lower flow durations (99th to 90th) were observed at both regulated and unregulated sites (USGS sites 01378500, 01407500, 01382500, 01401650, 01381000, and 01396800). The outliers of percent change ranged from −47 to −100 percent. The greatest increases in the lower flow durations were at mostly unregulated sites (USGS sites 01396800, 01401000, 01455500, 01403150, 01466500, 01443900, and 01403540). The outlier changes ranged from +22 to +289 percent. The computed frequency statistics, which are generally associated with the rarest occurring flow conditions, indicated more increases than decreases for both low and high flows from 2001 to 2017. Histograms demonstrating the distribution of the low and high flow computed percent changes among the 1,920 individual computed frequency statistics (12 n-day frequencies for both annual and winter flows among the 80 sites) are shown in figure 23. The histograms do not include values of zero change. Of the low-flow frequency statistics, 7 percent (125) indicated zero change; for high flow, 1 percent (11) indicated zero change. Generally, most changes in low and high frequency statistics were slightly greater than the changes in durations statistics, ranging mostly from −10 to +15 percent. A greater proportion indicated positive changes in both low and high flow frequencies, at 58 and 75 percent, respectively. A histogram plot showing the percent change in low-flow statistics was generally between −10 to +15 percent, and high-flow percent change was generally between -5 and +15 percent change for the majority of streamgages. The outliers and ranges of percent change do not indicate a clear pattern or bias for a particular n-day or recurrence interval, as shown in the boxplots in fig. 24 and fig. 25. As mentioned in the “Methods” and “Frequency Analysis” sections, some of the changes in frequency flow statistics at regulated streamgages in this study may be a result of the different approaches used in analysis of data that did not fit the LPIII distribution; that is, using a graphical or visual fit compared to computing a percentage of the flows. The largest increases in low-flow frequency statistics were at the streamgage below Musconetcong River at outlet of Lake Hopatcong, N.J., USGS site 01455500. The site is heavily regulated with a minimum passing flow. The second largest overall increase was observed at Rockaway River below Reservoir at Boonton, N.J., 01381000, which is also heavily regulated with a minimum passing flow. Increases were between 0 and +500 percent. The streamgage with the largest decreases in low-flow frequency statistics was Absecon Creek at Absecon, N.J., USGS site 01410500. Flow at this site is unregulated but is affected by tidal fluctuations. The site with the second most decreases in flow frequencies was at Neshanic River at Reaville, N.J., 01398000. This site is not regulated (has no controlled flow upstream). Decreases were between 0 and −100 percent.

For high-flow frequencies, the two streamgages with the highest increase were Pequest River at Huntsville, N.J., 01445000, and Mantua Creek at East Holly Avenue at Pitman, N.J., 01475001. Neither streamgage is regulated. Changes were between 2 and 50 percent for the two streamgages. The streamgages with the greatest decrease in high-flow frequencies were West Branch Middle Brook near Martinsville, N.J., 01403150, and Cedar Creek at Western Blvd near Lanoka Harbor, N.J., 01408900. The Martinsville streamgage has a very small drainage area (1.99 square miles), and the Lanoka Harbor streamgage is affected by tide. Neither site is regulated. The decreases were relatively minimal, ranging from −2 to −19 percent.

Partial-Record Streamgage Percent Change in Low Flow Of the 719 partial-record streamgages in the current analysis using data through 2017, 462 of the streamgages were also computed in the 2005 report, which used data through 2001, and were available for comparison. As indicated earlier, partial-record streamgage statistics were computed using a MOVE1 relation with 22 continuous-record streamgages (index sites), 19 of which were also included in the previous report. The percent change in the computed statistics at the index sites from 2001 to 2017 ranged from −15 to +25 percent, but only about 9 percent of those changes were greater than 10 percent (table 12). Overall, the changes in the low-flow statistics at the partial-record streamgages were greater than those observed at the continuous-record streamgages. Like the continuous-record low-flow frequency statistics, the change skewed slightly toward more increases in flow. Most percent changes in partial-record streamgages low-flow statistics ranged from −15 to +20 percent (fig. 26). Of the possible 3,696 values of change (8 computed statistics for 462 streamgages), about 17 percent (634) of values indicated zero change and are excluded from the histogram. Percentage change could not be computed for 6 percent (217) of values, as the 2001 computed value was a zero. A histogram plot of percent change in low-flow statistics at 462 partial-record streamgages in New Jersey shows a broad distribution of streamgages between -30 and +50 percent change. The range of percent change among the individual low-flow statistics is consistent, as seen in the boxplots in figure 27. The greatest changes were observed as outliers at the lower flows, the 1-day annual and 1- and 7-day winter, 10-year statistics. The annual median flow indicated the least amount of change among the partial-record streamgage statistics.

Summary and Conclusion Streamflow data collected in New Jersey from 1903 to 2017 were compiled and analyzed to produce a collection of statistical characteristics to describe the frequency, duration, variability, and trends in streamflow. The information generated from this study serves as a repository and reference to enhance the understanding and support management of water resources in the State.

Summary and Conclusion 47

300

250 20

200

Percent

150

100

50 −10

B 0

−20 −50

1st

5th

2nd

10th

20th

30th

25th

50th

40th

60th

70th

80th

75th

95th

90th

98th

1st

5th

2nd

10th

20th

25th

30th

40th

50th

60th

70th

75th

80th

90th

95th

98th

99th

Streamflow percentile

99th

−30

−100

Streamflow percentile EXPLANATION

Mean

Outlier—More than 3/2 times of upper quartile Maximum—Greatest value, excluding outliers Upper quartile—25 percent of data greater than this value Median—50 percent of data is greater than this value; middle of dataset Lower quartile—25 percent of data less than this value Minimum—Least value, excluding outliers Outlier—Less than 3/2 times of lower quartile

Figure 21. Boxplots showing range of percent change in annual flow durations at continuous-record streamgages in New Jersey from 2001 to 2017. A, with statistical outliers and B, without statistical outliers.

48 Streamflow Characteristics and Trends in New Jersey, Water Years 1903–2017

200

100

Percent

150

Streamflow percentile

1st

5th

2nd

10th

20th

25th

30th

40th

50th

60th

70th

75th

80th

90th

95th

98th

1st

5th

2nd

10th

20th

25th

30th

40th

50th

60th

70th

75th

80th

90th

−20 95th

−50 98th

−10

99th

Streamflow percentile EXPLANATION

Mean

Figure 22. Boxplots showing the range of percent change in winter flow durations at continuous-record streamgages in New Jersey from 2001 to 2017. A, with statistical outliers and B, without statistical outliers. Winter period is from November 1 through April 30.

Summary and Conclusion 49 600

A. Low flow 500

487 451

Count

400

300 261

200 136

132

7 40.01 to 45

8 45.01 to 50

10 35.01 to 40

23 30.01 to 35

25.01 to 30

20.01 to 25

15.01 to 20

−24.99 to −20

10.01 to 15

−29.99 to −25

5.01 to 10

−34.99 to −30

0.01 to 5

−4.99 to 0

−9.99 to −5

−14.99 to −10

−19.99 to −15

−39.99 to −35

Less than or equal to −50

−44.99 to −40

11 0

−49.99 to −45

Greater than 50

100

800

B. High flow

744

700

600

Count

500 419 400

300

281

200 146 112

25.01 to 30

30.01 to 35

35.01 to 40

40.01 to 45

45.01 to 50

Greater than 50

20.01 to 25

10.01 to 15

5.01 to 10

0.01 to 5

−4.99 to 0

−9.99 to −5

15.01 to 20

−14.99 to −10

14 −19.99 to −15

4 −25 to −20

100

Percentage change

Figure 23. Histogram of the frequency statistics percentage of change from 2001 to 2017. A, low flow, and B, high flow. Frequencies computed were the 1-, 7- and 30- day high and low flows at 50-, 20-, 10-, 5-, and 4-percent exceedance and nonexceedance for annual and winter periods. Winter period is from November 1 through April 30. A total of 125 and 11 zero values for low- and high-flow frequency percent change are not included in the graphs.

400

EXPLANATION 300

Outlier—More than 3/2 times of upper quartile Maximum—Greatest value, excluding outliers

200

Percent

Mean

Upper quartile—25 percent of data greater than this value Median—50 percent of data is greater than this value; middle of dataset

100

Lower quartile—25 percent of data less than this value Minimum—Least value, excluding outliers

Outlier—Less than 3/2 times of lower quartile

−100

30-day, 20-year low flow, winter

30-day, 10-year low flow, winter

30-day, 5-year low flow, winter

30-day, 2-year low flow, winter

7-day, 20-year low flow, winter

7-day, 10-year low flow, winter

7-day, 5-year low flow, winter

7-day, 2-year low flow, winter

1-day, 20-year low flow, winter

1-day, 10-year low flow, winter

1-day, 5-year low flow, winter

1-day, 2-year low flow, winter

30-day, 20-year low flow

30-day, 10-year low flow

30-day, 5-year low flow

30-day, 2-year low flow

7-day, 20-year low flow

7-day, 10-year low flow

7-day, 5-year low flow

7-day, 2-year low flow

1-day, 20-year low flow

1-day, 10-year low flow

1-day, 5-year low flow

1-day, 2-year low flow

−200

Low-flow frequency

Figure 24. Boxplots showing range of percent change in annual and winter low-flow frequency statistics at continuous-record streamgages in New Jersey from 2001 to 2017. A, with statistical outliers and B, without statistical outliers. Winter period is from November 1 to April 30.

50 Streamflow Characteristics and Trends in New Jersey, Water Years 1903–2017

500

Low-flow frequency 30-day, 20-year low flow, winter

30-day, 10-year low flow, winter

30-day, 5-year low flow, winter

30-day, 2-year low flow, winter

7-day, 20-year low flow, winter

7-day, 10-year low flow, winter

7-day, 5-year low flow, winter

7-day, 2-year low flow, winter

1-day, 20-year low flow, winter

1-day, 10-year low flow, winter

1-day, 5-year low flow, winter

1-day, 2-year low flow, winter

30-day, 20-year low flow

30-day, 10-year low flow

30-day, 5-year low flow

30-day, 2-year low flow

7-day, 20-year low flow

7-day, 10-year low flow

7-day, 5-year low flow

7-day, 2-year low flow

Mean

−30

Summary and Conclusion 51

Figure 24.—Continued 1-day, 20-year low flow

1-day, 10-year low flow

1-day, 5-year low flow

1-day, 2-year low flow

Percent 40

EXPLANATION Maximum—Greatest value, excluding outliers

Upper quartile—25 percent of data greater than this value

Median—50 percent of data is greater than this value; middle of dataset

Lower quartile—25 percent of data less than this value

Minimum—Least value, excluding outliers

−10

−20

EXPLANATION

Outlier—More than 3/2 times of upper quartile Maximum—Greatest value, excluding outliers

Mean

Percent

Upper quartile—25 percent of data greater than this value Median—50 percent of data is greater than this value; middle of dataset

Lower quartile—25 percent of data less than this value Minimum—Least value, excluding outliers Outlier—Less than 3/2 times of lower quartile

−20

30-day, 25-year high flow, winter

30-day, 10-year high flow, winter

30-day, 5-year high flow, winter

30-day, 2-year high flow, winter

7-day, 25-year high flow, winter

7-day, 10-year high flow, winter

7-day, 5-year high flow, winter

7-day, 2-year high flow, winter

1-day, 25-year high flow, winter

1-day, 10-year high flow, winter

1-day, 5-year high flow, winter

1-day, 2-year high flow, winter

30-day, 25-year high flow

30-day, 10-year high flow

30-day, 5-year high flow

30-day, 2-year high flow

7-day, 25-year high flow

7-day, 10-year high flow

7-day, 5-year high flow

7-day, 2-year high flow

1-day, 25-year high flow

1-day, 10-year high flow

1-day, 5-year high flow

1-day, 2-year high flow

−40

Streamflow percentile

Figure 25. Boxplots showing range of percent change in annual and winter high-flow frequency statistics at continuous-record streamgages in New Jersey from 2001 to 2017. A, with statistical outliers and B, without statistical outliers. Winter period is from November 1 to April 30.

52 Streamflow Characteristics and Trends in New Jersey, Water Years 1903–2017

100

Streamflow percentile 30-day, 25-year high flow, winter

30-day, 10-year high flow, winter

30-day, 5-year high flow, winter

30-day, 2-year high flow, winter

7-day, 25-year high flow, winter

7-day, 10-year high flow, winter

7-day, 5-year high flow, winter

7-day, 2-year high flow, winter

1-day, 25-year high flow, winter

1-day, 10-year high flow, winter

1-day, 5-year high flow, winter

1-day, 2-year high flow, winter

30-day, 25-year high flow

30-day, 10-year high flow

30-day, 5-year high flow

30-day, 2-year high flow

7-day, 25-year high flow

7-day, 10-year high flow

7-day, 5-year high flow

7-day, 2-year high flow

Mean

−20

Summary and Conclusion 53

Figure 25.—Continued 1-day, 25-year high flow

1-day, 10-year high flow

1-day, 5-year high flow

1-day, 2-year high flow

Percent 40

EXPLANATION Maximum—Greatest value, excluding outliers

Upper quartile—25 percent of data greater than this value

Median—50 percent of data is greater than this value; middle of dataset

Lower quartile—25 percent of data less than this value

Minimum—Least value, excluding outliers

−10

54 Streamflow Characteristics and Trends in New Jersey, Water Years 1903–2017 Table 12. Percent change in computed low-flow statistics at continuous-record streamgages in New Jersey, from 2001 to 2017. The streamgages were used to estimate low-flow statistics at partial-record streamgages in New Jersey, through 2017. [Data are given in percentages. USGS, U.S. Geological Survey; NJ, New Jersey. Blue shading indicates changes greater than 10 percent, red shading indicates changes less than −10 percent]

USGS station number

1-day, 7-day, 30-day, 10-year 10-year 10-year low low low flows flow flow

Station name

30-day, 10-year 75th 50th low percentile percentile flow, winter

7-day, 10-year low flow, winter

01379000

Passaic River near Millington NJ

−8.0

−15.4

−9.1

8.3

10.4

−4.2

0.0

01380450

Rockaway River at Main Street at Boonton NJ

−7.2

−5.9

−1.2

−0.5

−3.7

−1.2

−0.8

01408500

Toms River near Toms River NJ

−6.7

−5.8

−6.0

−2.2

−2.8

−1.9

−0.9

01396660

Mulhockaway Creek at Van Syckel NJ

−3.4

−4.3

0.4

−4.5

−3.7

0.3

−3.4

01411000

Great Egg Harbor River at Folsom NJ

−2.9

−3.1

−2.0

−0.4

−3.2

−0.5

−1.4

01467150

Cooper River at Haddonfield NJ

−2.3

0.0

−2.0

0.3

3.4

−2.2

−5.3

01408000

Manasquan River at Squankum NJ

−2.0

−4.5

−0.5

−4.6

−1.6

−2.8

−3.0

01467000

North Branch Rancocas Creek at Pemberton NJ

0.0

−1.6

−3.1

−2.5

−2.3

−2.9

−2.0

01408120

North Branch Metedeconk River near Lakewood NJ

0.8

−6.0

−4.5

−1.2

1.7

−2.6

−2.2

01409810

West Branch Wading River near Jenkins NJ

1.1

1.4

0.4

−4.3

−8.4

−2.6

−0.3

01411500

Maurice River at Norma NJ

1.7

−1.9

−1.3

0.8

0.6

0.8

0.7

01440000

Flat Brook near Flatbrookville NJ

2.0

0.5

0.1

0.2

1.1

1.2

01443500

Paulins Kill at Blairstown NJ

3.8

−2.3

3.0

3.4

2.1

1.2

01445500

Pequest River at Pequest NJ

4.4

2.5

−0.2

0.9

0.1

−0.3

−1.1

01386000

West Brook near Wanaque NJ

8.7

1.7

−1.1

8.1

10.4

1.8

−2.1

01410150

East Branch Bass River near New Gretna NJ

9.1

7.0

7.1

3.3

1.2

−0.1

−5.3

01446000

Beaver Brook near Belvidere NJ

12.5

11.1

13.6

0.6

−0.8

1.2

1.7

01396582

Spruce Run at Main Street at Glen Gardner NJ

16.3

14.9

16.2

5.9

7.8

13.0

4.1

01401000

Stony Brook at Princeton NJ

25.0

0.0

20.7

6.2

8.9

6.9

0.6

350

326 289

300 264

200

187

111 73

65.01 to 70

Greater than 70

23 55.01 to 60

50.01 to 55

10 45.01 to 50

40.01 to 45

60.01 to 65

73 49

35.01 to 40

20.01 to 25

15.01 to 20

5.01 to 10

10.01 to 15

0.01 to 5

−4.99 to 0

−9.99 to −5

−14.99 to −10

−19.99 to −15

141

−24.99 to −20

−39.99 to −35

−59.99 to −55

13 −49.99 to −45

13 −54.99 to −50

7 −64.99 to −60

Less than or equal to −70

−44.99 to −40

73 44

−69.99 to −65

−29.99 to −25

100

183

30.01 to 35

122

199

197

25.01 to 30

150

−34.99 to −30

Count

250

Percentage change

Figure 26. Histogram of percent change in low-flow statistics at 462 partial-record streamgages in New Jersey from 2001 to 2017. Computed statistics were the 1-, 7-, 30-day annual, and 7- and 30-day winter low flows at 10 percent annual nonexceedance probability, the 75th and 50th percentile flow, and the annual mean flow. The data in the graph do not include values of zero or those percent changes that could not be computed because of a zero value in 2001.

Summary and Conclusion 55 100

800

A 700

600 60

500 40

400

Percent

20 300 0 200

−20 100

B −40

−60

−100

Mean daily

Median

75th percentile

30-day-10-year low flow, winter

7-day, 10-year low flow, winter

30-day, 10-year low flow

7-day, 10-year low flow

Mean daily

Median

75th percentile

30-day-10-year low flow, winter

7-day, 10-year low flow, winter

30-day, 10-year low flow

7-day, 10-year low flow

1-day, 10-year low flow

Low-flow statistics

1-day, 10-year low flow

−80

−200

Low-flow statistics EXPLANATION

Mean

Figure 27. Boxplots showing the range of percent change in low-flow statistics at partial-record streamgages in New Jersey, from 2001 to 2017. A, with statistical outliers and B, without statistical outliers.

56 Streamflow Characteristics and Trends in New Jersey, Water Years 1903–2017 Overall, the direction of statistical trends in low-flow characteristics were somewhat balanced, with 28 percent of the continuous-record streamgages tested indicating a positive trend, and 20 percent indicating a negative trend. The same was not true for high-flow characteristics, with 30 percent of the continuous-record streamgages tested indicating a positive trend, but only 6 percent indicating a negative trend. Decreasing flow trends were more often observed in southern New Jersey. Fewer streamgages indicated increased variability. About 25 percent of the continuous-record streamgages tested indicated increased variability by analyzing results of annual skew, kurtosis, and peak flow ratio. The partial-record streamgages showed slightly different results, with about 45 percent showing a trend in at least 1 index site, but only about 7 percent had more than 50 percent of the reference index sites that showed a trend. A total of 97 continuous-record streamgages and 719 partial-record streamgages were included in the analysis. Computed continuous-record streamflow characteristics included the following: • Maximum, minimum, mean for period of record • Duration of flow (annual and winter) • 50-, 20-, 10-, 5-, and 4-percent annual nonexceedance (or 2-, 5-, 10-, 20-, and 25-year recurrence interval) frequencies of 1-,7-, and 30-consecutive day minimum average low flow (annual and winter) • 50-, 20-, 10-, 5-, and 4-percent annual exceedance (or 2-, 5-, 10-, 20-, and 25-year recurrence interval) frequencies of 1-,7-, and 30-consecutive day maximum average high flow (annual and winter) • Ratio of peak to 3-day mean flow • Mean annual base flow and runoff • Drought-of-record base flow (1962–70) • Median September flow • Annual variability measured as annual skew and kurtosis Low-flow characteristics were computed at partial-record streamgages using MOVE1 regressions of flow with continuous-record streamgages. The computed low-flow characteristics included the following: • Annual 1-, 7-, and 30-day low flow with a 10 percent annual nonexceedance probability • Winter 7- and 30-day low flow with a 10 percent annual nonexceedance probability • 50th and 75th percent duration flow • September median flow • Mean annual base flow • Drought-of-record base flow (1962–70)

Mann-Kendall trend tests were applied to n-day low and high flows, base flow, runoff, peak-flow ratio, skew, and kurtosis annual time series using independent, lag-1, and long-term persistence assumptions of serial correlation for all streamgages. A Sen-Thiel slope was computed to determine the magnitude of change over time for the various statistics. An MK test for trends was also applied to the residuals of the individual regressions of partial-record streamgages to determine if the relation between index site and the partial-record streamgages was changing through time. For both continuous- and partial-record streamgages, the percent change in streamflow characteristics from those reported in Watson and others (2005) was also determined. The number of statistically significant trends decreased for all flow characteristics when variance was adjusted to account for assumptions of short term (lag-1) and long-term persistence. However, evidence for serial correlation does not appear to be substantial for STP or necessarily clear for LTP and may not justify the variance adjustment for all streamgages. Most statistics used in water use permitting use all available record, regardless of when it was collected or how long it is—unless there is a known major change in the basin. Knowing if the available stream record used for planning or permitting is changing or has an identified trend can inform water managers of what might be expected in the near term for permit renewals and changes in allocation. Although the trend tests of all three assumptions of serial correlation were computed for this study and are available in the data release (Williams and others, 2024), the discussion focused on the results of the independent assumption, with the knowledge that the other results have fewer statistically significant trends. About a quarter of streamgages indicated increasing variability as measured by annual skew, kurtosis, and peak-flow ratio. This finding is supported by both the annual 1-day low- and high-flow time series indicating the greatest number of trends among all flow characteristics. The Saddle River at Lodi, N.J., 01391500 streamgage is the only exception to this trend, where the data indicate that the highest flow events are not as large and are occurring less often. Lodi is the only streamgage with decreasing skew and kurtosis. There was also a greater number of streamgages having evidence for trends in the annual versus winter high-flow series, suggesting that the highest flows may be shifting to later in the spring (after April 30). Examining the magnitude and direction of trends versus record length can inform the influence of cyclical wet and dry periods on the statistics, as opposed to assuming the existence of and adjusting for serial correlation. The magnitude of statistically significant trends in this study was influenced by length of record; the largest trends occurring between 40 and 60 years of record for both high and low flows, as well as variability. Though less in magnitude, a greater number of trends were identified in skew and kurtosis at streamgages with records longer than 80 years. These results reflect that the longer the record is collected, the more likely it is to have captured a comparatively rare drought or flood event.

References Cited 57 The magnitude or number of trends in peak-flow ratio does not appear to be influenced by record length. Twenty-five streamgages indicated a statistically significant trend in peak-flow ratio. More than 75 percent of the trends indicated an increase in the peak-flow ratio over time. These results coincide with the premise that 1-day high flows are occurring more often in the annual (summer) series than in the winter season, which would also indicate an increased peak-flow ratio. Trends at partial-record streamgages are most likely due to localized effects of water use. Of the 719 partial-record streamgages, 321 indicated a trend in residuals with at least 1 index site (145 decreasing, 176 increasing). Of the 321 partial-record streamgages, 53 had more than 50 percent of index sites indicating a statistically significant (p<0.05) trend in the residuals (26 decreasing, 27 increasing). The span of the partial-record streamgage data (end year minus begin year of discrete flow measurements) did not seem to influence the number of index site regression residuals that indicated a trend. Both positive and negative trends in low-flow characteristics at continuous-record streamgages were identified in over-allocated basins, as defined by New Jersey Department of Environmental Protection (NJDEP). The same was true for trends in residuals of partial-record streamgage regressions. Recall that the trend tests were applied to the full periods of record for both continuous- and partial-record streamgage analysis. Current or changing water use may not be reflected in the trend test results or the allocated amounts from the NJDEP assessment. When compared to the previous assessment of New Jersey streamflow characteristics and trends (Watson and others, 2005), which used data through 2001, the number of statistically significant positive trends is growing among the 80 long-term continuous streamgages that were included in both studies. A greater number of positive trends in both annual and winter, low and high n-days flows are in this study as compared to the previous analysis. The number of negative trends appears to be declining for both high and low flows at these older streamgages. When assessing the percent change in the computed streamflow characteristics from the previous report, most differences for both flow durations and frequencies were generally within ±10 percent using the additional data collected. Percent change was not consistent in either the positive or negative direction for all 17 computed durations for a given streamgage, indicating some changes to the distribution of flow in the duration curves. Generally, most changes in low- and high-flow frequency statistics were slightly greater as compared with the changes in durations statistics, ranging from −10 to +15 percent. Some of the changes in frequency flow statistics at regulated streamgages in this study may be a result of the different approaches used for data that did not fit the LPIII distribution; that is, using a graphical or visual fit compared to computing a percentage of the flows. Most percent changes in partial-record streamgage low-flow statistics ranged from −15 to +20 percent. Less than 5 percent of all computed changes in flow were greater than ±50 percent.

Streamflow trends and changes to duration and frequency statistics can be influenced by water use, in addition to climate variables. This study did not attempt to parse out the distinction of those influences, but rather focused on reporting updates and potential changes at selected streamgages. Despite trends in streamflow appearing to be mostly increasing in New Jersey, the magnitude of changes in streamflow appears somewhat stable or increasing, but only slightly. However, the results are not necessarily consistent or uniform, with some individual stream locations indicating considerable change in either direction. Water managers and regulators can use the data provided here to more effectively assess individual reaches and watershed management areas and to better understand the resources available and the potential streamflow changes from water years 1903 to 2017.

Acknowledgments The authors would like to extend their sincere appreciation to several USGS colleagues, without whom this report would not have been completed. Robert G. Reiser (retired) contributed to the initial planning and development of the project. Glenn Hodgkins of the New England Water Science Center provided guidance and R code to help assess trends in the context of serial correlation. Dean Riddle and Robert Palumbo completed the initial delineation for over 800 drainage basins for the associated web mapper. Steve Cauller assisted with mapping of the data results. Lucas Pick (former employee) contributed to data summarization. Jessica Hopple (retired) assembled, organized, and prepared the very large dataset that was produced from this study. Toby Feaster and John Hammond peer-reviewed the report.

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For more information about this report, contact: Director, New Jersey Water Science Center U.S. Geological Survey 3450 Princeton Pike, Suite 110 Lawrenceville, NJ 08648 Or visit our website at https://www.usgs.gov/centers/new-jersey-water-science-center. Publishing support provided by the Baltimore Publishing Service Center.

McHugh and others—Streamflow Characteristics and Trends in New Jersey, Water Years 1903–2017—SIR 2024–5099

ISSN 2328-0328 (online) https://doi.org/10.3133/sir20245099

Streamflow Characteristics and Trends in New Jersey, Water Years 1903–2017

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