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Environmental Pollution 159 (2011) 1257e1265

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Environmental Pollution

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Salting our landscape: An integrated catchment model using readily accessibledata to assess emerging road salt contamination to streams

Li Jin a,b,*, Paul Whitehead b, Donald I. Siegel a, Stuart Findlay c

a Earth Sciences Department, Syracuse University, Syracuse, NY 13210, USAb School of Geography and the Environment, University of Oxford, Oxford, OX1 3QY, UKcCary Institute of Ecosystem Studies, 2801 Sharon Turnpike, Millbrook, NY 12545, USA

A newly developed integrated catchment model for salinity can be use

d to manage and forecast the inputs and transport of chloride to streams.

a r t i c l e i n f o

Article history:Received 26 July 2010Received in revised form18 January 2011Accepted 18 January 2011

Keywords:INCAChlorideSalinityRoad saltWater softener

* Corresponding author.E-mail addresses: li.jin@ouce.ox.ac.uk, ljin@syr.edu

0269-7491/$ e see front matter � 2011 Elsevier Ltd.doi:10.1016/j.envpol.2011.01.029

a b s t r a c t

A new integrated catchment model for salinity has been developed to assess the transport of road saltfrom upland areas in watersheds to streams using readily accessible landscape, hydrologic, and mete-orological data together with reported salt applications. We used Fishkill Creek (NY) as a representativewatershed to test the model. Results showed good agreement between modeled and measured streamwater chloride concentrations. These results suggest that a dominant mode of catchment simulation thatdoes not entail complex deterministic modeling is an appropriate method to model salinization and toassess effects of future applications of road salt to streams. We heuristically increased and decreased saltapplications by 100% and results showed that stream chloride concentrations increased by 13% anddecreased by 7%, respectively. The model suggests that future management of salt application can reduceenvironmental concentrations, albeit over some time.

� 2011 Elsevier Ltd. All rights reserved.

1. Introduction

Increasing chloride concentrations in surfacewaters indicate theemergence of a new and widespread threat to drinking watersupplies, aquatic species and ecosystem functioning (Demers,1992;Green and Cresser, 2008; Riva-Murray et al., 2002). Concentrationsof chloride in surface waters and private drinking water wells canapproach levels harmful to humans and aquatic biota e.g., (Jacksonand Jobbagy, 2005; Kaushal et al., 2005). Although increasingsalinity in surface waters can be caused by road salt application e.g.,(Jackson and Jobbagy, 2005), there are other potential chloridesources, including salt used in homewater-softening systems, septicfield discharges, waste treatment plants and natural brines (Kellyet al., 2008). These sources have different ratios of sodium to chlo-ride, allowing them to be identified (Mullaney et al., 2009). Forexample, the molar ratio of sodium and chloride is unity (1:1 ratio)in the vast majority of de-icing salts whereas septic systems oftenhave ratios less than unity. The U.S. Geological Survey recentlypublished a synthesis report that showed how multiple sourcesof salinity now are compromising surface waters and shallow

(L. Jin).

All rights reserved.

groundwaters throughout the glaciated regions of the north-centraland northeastern United States (Mullaney et al., 2009). However,the major source for this salinity still appears to be road salt appli-cation to roads and parking lots in the winter, at the rate tons perlane mile for many roads e.g., Kelly et al., 2008.

Watershed managers have to assess the degree to which water-shedsmay be sensitive to road salt application. Themost susceptiblewatersheds to salt contamination logically are those in rural areaswith shallow soils that cover hillslopes. Even in the protected NewYork City watersheds in the Catskill Mountains and Croton Riverbasin east of the Hudson River, road salt has contaminated surfacewaters in small watersheds (Heisig, 2000). In hillslope dominatedwatersheds, the “front” of stored salinity can break through faster tostreams and shallow groundwaters compared to where water tableslopes are shallow in deeper soils and groundwater travel times areconsequently slower, at least as a first approximation. On hillslopes,ubiquitous preferential flow paths e.g., (McDonnell et al., 2007;McMahon and Chapelle, 2008) enhance salinity transport fromuplands to streams.

Kelly et al. (2008) produced a simplified mass balance ona hillslope dominated watershed in eastern New York State wherea 20 year record of increasing salinity was available, along withdemographic changes and daily streamflow measurements. Theirsimple mass balance model showed that steady upland chloriderelease from road salt does cause steady increases in stream

L. Jin et al. / Environmental Pollution 159 (2011) 1257e12651258

chloride concentrations. They also noted that stream salinity maylevel off in time. In contrast, long-term increases in stream chlorideconcentrations have been observed in other New York streamsdespite stable rates of salt application in watersheds (Kelly et al.,2008). The model by Kelly et al. (2008) projected chlorideconcentration that match observations reasonably well althoughsome fluctuations were missed.

Howard and Maier (2007) recently used a deterministicnumerical model that incorporated directly the physics of waterand solute transport to heuristically evaluate how long it wouldtake chloride in ground water near Toronto to exceed drinkingwater standards. Using deterministic models requires substantialspatial data on the subsurface as well as salt application rates, andknowledge of surface wateregroundwater interactions. There isstill uncertainty over the best method to estimate the degree towhich road salt contamination will reach and contaminate surfacewaters.

What is needed is a relatively simple model to be used for thewatershed management and the results of this type of simplemodels can be effectively used to assess “what if” questions such ashow will salinity change in streams in hillslope dominated water-sheds given additional developments and salting? Or, conversely, ifsalting decreases or stops, how long would it take for the salinity todecrease? In addition, such a modeling approach can be readilyrecalibrated and validated against field data. Although academicresearchers often use numerical models that involve large amountsof field data on watershed characteristics, these models can becostly, cumbersome to use, and do not necessarily lead to any betterpredictive value than simpler models. Moreover, the success ofthese complex numerical models to accurately predict contami-nation fate and transport has been equivocal (see abstracts; http://gsa.confex.com/gsa/2007AM/finalprogram/session_19371.htm).Simpler models, while not directly modeling all physical processesin the watershed from first principles, do capture the dominantmodes of behavior.

The Integrated Catchment Model (INCA), was first developed byWhitehead and others in 1998 for the assessment of multiplesources of nitrogen in catchments (Whitehead et al., 1998a, 1998b).The model is process-based and uses reaction kinetic equations tosimulate the principal mechanisms operating. This dynamic, massbalance model attempts to track the temporal variations in thehydrological flowpaths, transformations and stores, in both theland and in stream components of a river system. The model isapplied to catchments as a semi-distributed simulation and has aninbuilt multi-reach structure for river systems. A suite of IntegratedCatchment Models for hydrology and water quality, such asnitrogen, phosphorus and carbon, have been applied widely in thepast decade for assessing different sources of nutrients/solutes andalso the impacts onwater quality from climate change and land usechange (Futter et al., 2007; Langusch and Matzner, 2002; Limbricket al., 2000; Wade et al., 2007, 2002; Whitehead et al., 2002, 2006;Wilby et al., 2006).

In this paper, we used the INCA model structure and developeda new INCA-Cl model to give high quality estimates of chlorideconcentration variations based on a dynamic, daily and process-based equation of behavior and various input sources. We presentthe results of a readily accessible and available semi-distributedintegrated catchment model to evaluate road salt contamination inthe Fishkill Creek watershed (Dutchess and Putnam Counties, NewYork). Our study also aims to evaluate, heuristically, future salinitycontamination under various conditions. This basin typifies manyhillslope watersheds in moderate sized watersheds on the order ofhundreds of square kilometers. Results from this study would bepotentially transferred to many other watersheds in similar envi-ronmental settings.

2. Study area

Fishkill Creek is about 50 km in length and drains southwesttowards the Hudson River, in the State of New York of the UnitedStates (Fig. 1a). Its watershed covers approximately 500 km2 inDutchess and Putnam counties and is a suburbanizing watershedtypical of conversion from small-scale agriculture to mediumdensity residential land use. Fishkill Creek, the basin’s main stream,begins in the center of Union Vale and flows in southwest directionand enters the Hudson River at Beacon (Fig. 1b). The watershed hassubstantial topographic relief and several tributaries, includingSprout and Whortlekill creeks (Fig. 1c). The land uses are domi-nated by forest, arable and suburban areas. The Fishkill Creek isprimarily underlain by dolomite and limestone of the WappingerGroup.

Cary Institute of Ecosystem Studies (ISE) in Millbrook (NY)within the watershed has been a leader in watershed studies forover half a century. In 2008, the Institute discovered that chlorideconcentrations in the basin approached 250 mg/L at some loca-tions, the maximum contaminant level of concern to human healthand ecological effects (MCL, NY Department of EnvironmentalConservation). In the Fishkill Creek, summer baseflow chlorideconcentrations in 2008 was as high as 150 mg/L, increasingdownstream as the creek passes through regions with higherhuman population density.

Based on this record, we selected to investigate this creek asa model case study where a practical approach may be used tomanage salinity in watersheds. That is, most watershed managersin the Northeastern States have access to all kinds of basic map GISdata we used, can easily obtain measurements of stream water forsalinity using our methods, and can find a USGS stream gauge aspart of the National Stream Information Program or State streamgauge, if not on the stream in the watershed in question, then closeenough to it to use in this modeling approach.

The Fishkill Creek watershed typifies many watersheds insouthern New York and elsewhere in the northern “snowbelt”withfairly rapid suburbanization but still having a predominant relianceon private and small drinking water wells. Data necessary tosupport the modeling are readily available including land-cover,topography, and stream network, as well as total road length andgeneral population demography and house density. These data arenow available for watersheds all much of the US through theNational Map (http://nationalmap.gov/) and state GIS data sources.

Fishkill Creek once had a USGS gauge near its mouth, and a smallcontinuous data set has been obtained. A nearby USGS gauge onWappinger Creek (Fig. 1b), which has been continuously recordingstreamflow data since 1928. No direct information on soil hydraulicconductivity or porosity are available, which is typical formost watersheds in hillslope areas unless they are part of largescale research initiatives (e.g., Hubbard Brook; http://www.hubbardbrook.org/). Most of the uplands in the watershed areunderlain by shallow, well drained to moderately well drainedmedium textured soils that formed from glacial till. Minor poorlydrained soils can be found along the creek. Annual precipitation isabout 110 cm/year and is relatively evenly distributed throughoutthe year.

3. Methods

3.1. Water quality

We collected water samples at the mouths of selected tributaries in the FishkillCreek watershed longitudinally down the main stem to span a gradient of land usedevelopment (Fig. 1c). Samples were collected both during base flow and duringstorms and analyzed for Cl-, Br-, Ca2þ, Mg2þ and Naþ at the Rachel Carson analyticallaboratory of the Cary Institute of Ecosystem Studies. The analytical laboratory

Fig. 1. a) Map of the Fishkill Creek watershed showing its location in the New York state; b) Fishkill Creek discharges to Hudson River and its adjacent watershed, Wappinger Creek;c) Topographic map of the Fishkill Creek watershed including the main stream and tributaries; Seven sub-catchment boundaries were shown in grey lines.

L. Jin et al. / Environmental Pollution 159 (2011) 1257e1265 1259

participates regularly in inter-laboratory comparisons, laboratory round-robins anduses certified reference material as QA/QC checks. Cation and anion concentrationswere determined by Dionex Ion Chromatography and specific conductance using anelectrode meter (YSI 6920 Water Quality Sonde). We plotted solute ratios todetermine the extent to which road salt adds salinity to Fishkill Creek. Sources ofsalinity were evaluated using chloride/bromide ratio and cation ratios. We corre-lated chloride against specific conductance and then used specific conductance asa surrogate for chloride on a daily measurement basis at two locations within thewatershed, one at Beacon, near the mouth of the creek and the second one atHopewell Junction, about 25 km upstream from the creek mouth.

3.2. Integrated catchment model setup and model inputs

3.2.1. INCA-Cl modelThere are three main components in the INCA-Cl model:

1. A GIS interface that defines the sub-catchment boundaries, and calculates thearea of each land use type within each and decomposes the area of each sub-catchment into a maximum of six land use classes;

2. A land-phase hydrological model that calculates the flow of effective rainfallthrough soil water and groundwater stores, and as direct runoff. This compo-nent will drive the chloride fluxes through the catchment;

3. The chloride input model which calculates the total chloride inputs from allsources to each sub-catchment. The main chloride sources identified in theFishkill Creek watershed and considered in the INCA-Cl model here include:precipitation input, home water-softening waste discharge and winter roadsalt application.

The INCA-Cl hydrology and chloride flux models constitute two main compo-nents: a catchment model (Fig. 2a) and a multi-reach river model (Fig. 2b). In thecatchment hydrology model, the hydrological effective rainfall enters the soil zoneand forms the soil flow, direct runoff flowand discharge to groundwater zone, whichbecomes groundwater flow. The multi-reach structure shows that water flows fromthe previous sub-catchment into the river reaches and the river equations are solvedto maintain a mass balance along the river. Additional inputs from sewagedischarges can also be incorporated into the reach structure mass balance.

The hydrology within the catchment is modeled using a simple two boxapproach, representing two key reservoirs, the soil zone and groundwater zone(Whitehead et al., 1998a). The residence time in these zones as well as the flow ratesthrough soil and groundwater are essential for flow and chloride flux simulation.Chloride can enter the river system via the soil zone lateral flow or the groundwaterreservoir. The flow models for these two zones are:

Soil zone :dx1dt

¼ 1T1

ðU1 � x1Þ (1)

Groundwater :dx2dt

¼ 1T2

ðU2x1 � x2Þ (2)

where x1 and x2 are output flows (m3/s) for soil and groundwater zones,respectively; U1 (m3/s) is the input driving hydrologically effective rainfall; T1 and T2(days) are residence time constants associated with soil and groundwater zones; U2

is the baseflow index i.e., proportion of water being transferred to the lowergroundwater zone (Fig. 2a).

The multi-reach river flow model is based on mass balance of flow and usesa multi-reach description of the river system (Whitehead et al., 1979, 1998a). Withineach reach, flow variation is determined by a non-linear reservoir model. The

Fig. 2. a) Catchment model with hydrological and chemical components; b) multi-reach structure with hydrological and chemical components. Parameters are describedin the main text with mathematical equations.

Table 1INCA reach and sub-catchment land use information.

Reach/Sub-catchment

Reachlength(km)

Sub-catchmentarea (km2)

Land use class percentage

Urban(%)

Forest(%)

Shortvegetation (%)

Arable (%)

1-headwater 10.1 41.6 4 65 11 212 4.1 54.0 8 69 5 183 12.6 50.6 16 52 9 244 11.8 265.5 15 53 9 235 3.1 4.4 47 40 4 86 6.7 72.2 20 71 4 47-mouth 3.5 11.3 47 48 1 4

L. Jin et al. / Environmental Pollution 159 (2011) 1257e12651260

relationship between inflow (I), outflow (Q) and storage (S) in each reach isexpressed as:

dSðtÞdt

¼ IðtÞ � QðtÞ (3)

SðtÞ ¼ TðtÞ � QðtÞ (4)

where T is a travel time parameter, which can be determined by:

TðtÞ ¼ LvðTÞ (5)

L is the reach length (m); v is the mean flow velocity in the reach (m/s) and isrelated to discharge Q as expressed below

vðtÞ ¼ aQðtÞb (6)

Constants a and b can bedetermined fromflowevelocity relationships in the river.The flow and mass balance equations in both soil and groundwater zones are

solving simultaneously. Chloride is treated as a conservative element, therefore nochemical reaction or transformation is considered in this model. As the soil zoneleaches water to the groundwater zone and the river, a mass balance of chloride inthe catchment is calculated as:

Soil zone :dc1dt

¼ 1V1

ðU3 � x1c1Þ (7)

Groundwater zone :dc2dt

¼ 1V2

ðU2x1c1 � x2c2Þ (8)

c1 and c2 are the daily chloride concentrations (mg/L) in the soil zone andgroundwater zone, respectively; V1 and V2 are the equivalent water volumes(V1 ¼ T1x1 and V2 ¼ T2x2) for the soil and groundwater zones, respectively. U3 is thedaily loading of chloride entering the soil zone, which constitutes the additional dryand wet deposition.

The catchment chloride model calculates the flux in the soil and groundwaterzones as described above and the river chloridemodel combines this output flux andthe direct discharge of sewage effluent (or other point source) to calculate the totalchloride flux in the river. The mass balance for each reach includes the upstreamchloride, the catchment chloride flux and input of effluents.

The equations for flow and chloride flux in the river reaches are as follows:

Flow :dQd

dt¼ 1

T3ðQu � QdÞ (9)

Chloride :dCddt

¼ 1V3

ðQuCu � QdCdÞ (10)

Where V3 ¼ T3Qd and T3 ¼ Lv¼ L

aQb(11)

T3 is the reach residence time and is derived from non-linear velocity flow rela-tionship as described previously. As seen in Fig. 2b,Qu and Cu are the sumof upstreamflows (m3/s) and the flowweighted upstream chloride concentration (mg/L) from theprevious reach, sub-catchment runoff and sewage discharge, respectively. Qd is thedownstream flow rate (m3/s). Cd is the downstream (reach output) chloride concen-trations (mg/L).

All the equations in the model are first order differential equation modelsrequiring numerical integration. The numerical method for solving the equations isbased on the fourth-order Runge-Kutta technique with a merson variable steplength, since this allows stable simultaneous solution of the model equations andthereby ensures that no single process represented by the equations, takes prece-dence over another (Whitehead et al., 1998a).

3.2.2. INCA-Cl setup for the Fishkill CreekWe divided the entire Fishkill watershed into seven sub-catchments (Fig. 1c,

Table 1). Sub-catchment/reach boundaries were selected at confluences and waterquality monitoring stations. The sub-catchment boundaries were derived using theDigital Terrain Model (DTM). We obtained land use data from the National LandCover (NLCD 2001), the national map Seamless Server, Earth Resources Observationand Science (EROS). TheNLCD 2001 covers all 50 states and Puerto Rico using Landsat5 and 7 data. The 21 land use classes fromNLCD 2001 have been aggregated into fourclasses for running INCA-Cl in Fishkill Creek, which are forest, short vegetation,arable and urban. These land classes have been superimposed onto the watershedmap and the percentage land use calculated for each sub-catchment. Table 1 showseach reach and sub-catchment information including the reach length, sub-catch-ment area and different land use percentages. Main land uses are urban and forest.

The daily time series of hydrological effective rainfall (HER) and soil moisturedeficit (SMD) have been derived using a simple excel spreadsheet model using themeteorological data (Limbrick, 2002). The other two inputs for the model are thedaily average temperature and actual precipitation. HER and SMD are used to drivehydrological models to calculate flows.

For each sub-catchment, the model works as a lumped parameter model,however the effects of land surface and topography on flow are simulated througha semi-distributed approach to incorporate the dynamics and characteristics of eachsub-catchment (Whitehead et al., 1998a).

The baseflow index (U2) in the Fishkill Creek is small because the flow dischargeis primarily driven by the surface water flowpaths (Burns et al., 2005). The historicalflow measurement and velocity calculation gives constants a and b values of 0.27and 0.65, respectively, in equations (6) and (11).

3.2.3. Chloride inputs to INCA-ClThe sources of chloride input to Fishkill watershed are natural atmospheric

deposition, winter road salt application and water softener usage. INCA uses landuse and GIS census 2000 data to calculate chloride inputs to each sub-catchmentfrom wet and dry deposition, road salt application and point source discharge.

For natural atmospheric deposition, Orange County station in NY (NY99), nextto Dutchess and Putman counties, has recorded annual chloride deposition of4.13 kg/ha/year (data from National atmospheric deposition program-NTN) and thisdeposition rate has been used in the INCA model.

For road salt application, we used GIS techniques to estimate the total roadlength for each sub-catchment, then multiplied the standard salt application rate tocalculate the bulk amount of road salts that were used during the modeling period.Street lengths data are created by the New York State Office of Cyber Security &Critical Infrastructure Coordination (CSCIC) and are available via http://www.nysgis.state.ny.us/gisdata/inventories/details.cfm?DSID¼932. The New York State Depart-ment of Transportation reported that on average 16.6 tons/lane mile of road salt isused every year. Assuming winter lasts from mid-Nov to the end of March (127days), we can calculate the daily chloride application rate from the annual road saltloading for each sub-catchment (Table 2). In the current INCA-Cl setup, road salt wastreated as non-point source pollution and incorporated into the model as a timeseries wet/dry deposition (kg Cl/ha/day).

Table 2INCA input for road salt application to each sub-catchment.

Reach/Sub-catchment

Roadlength (km)

Salt application(tons/year)

Cl(kg/ha/day)

Cl(kg/ha/winter)

1-headwater 59 1218 1.4 178.02 135 2792 2.5 314.23 161 3313 3.1 398.04 803 16 562 3.0 378.75 27 555 6.1 768.46 213 4395 2.9 369.77-mouth 74 1517 6.4 813.9

L. Jin et al. / Environmental Pollution 159 (2011) 1257e1265 1261

We estimated water softener salt by assuming that all the houses in the Fishkillwatershed had water softeners, which is likely to be an overestimate. However,houses in the Wappinger watershed outside of the Millbrook village receive waterfrom individual wells, usually drilled deep into the bedrock, which often supply hardwater making water softeners probably necessary for most households (Kelly et al.,2008). To assess the uncertainty of chloride inputs fromwater softeners, we also ranthe model assuming 50% houses use water softeners and compared results with theone using the 100% assumption. We determined the number of houses in thewatershed using GIS census 2000 block maps and used the number of 125 kg peryear of water softener salt used per house based on an average family size of 2.6people (US Census Bureau) and 380 L of water per person per day from United StatesEnvironmental Protection Agency report (Indoor water use in the United States, USEnvironmental Protection Agency Water Sense, EPA832-F-06-004). Thus, chlorideconcentrations and the amount of water discharged into the creek from the watersoftener usage for each sub-catchment were calculated as model inputs (Table 3).

3.2.4. Data collection for model calibrationThe daily simulations for Fishkill Creek using INCA-Cl was set up for a 365-days

period from 2008/6/1 to 2009/5/31. We calibrated the model against streamdischarge data and the spatial/temporal patterns in surface water chloride. Themodel computes the daily flows and chloride concentrations at all reaches along theriver system and these values can then be compared to the observed chloride andstreamflow for model calibration. Once calibrated, the model can be used to explorealternative strategies for salinity management. The models can also be used tocalculate annual chloride budgets to determine how much chloride might besequestered in the watershed soils to be released later.

For stream flow data, currently there is no operational USGS gauge in the Fishkillbasin but the adjacent watershed (Wappinger Creek 01372500, see Fig. 1b) hasa functional, real-time gauge at Red Oaks Mill. TheWappinger Creek and the FishkillCreek are located in similar geological and climatic environment. We correlateddischarge at Wappinger gauge to the streamflow record at Fishkill Creek during theperiod it was operating (1945e1965) and then used the correlation to estimatedischarge at Fishkill Creek during the period of study. Meteorological data(precipitation and temperature) are from a weather station at Millbrook at the IESfacilities (central Dutchess County). We sampled streamwater samples periodicallyat several locations within the watershed. Two specific conductance probes wereinstalled at Hopewell (bottom of reach 3) from 11/19/2008 to 5/31/2009 and atBeacon, the mouth of the creek (bottom of reach 7) from 12/8/2008 to 3/14/2009 torecord streamwater conductivity every 15 min, which were then converted to dailychloride concentrations based on a laboratory standard curve for model calibration.

4. Results and discussion

4.1. Streamflow and geochemistry of chloride

Streamflow at Fishkill Creek (FC) correlated well against that atWappinger Creek (WC) producing a least squared regression of

Table 3INCA input for water softener usage for reach sub-catchment.

Reach/Sub-catchment

Household # Population # Salt used in(tons/year)

Wastedischarge(L/day)

Cl (mg/L)

1-headwater 1113 3224 139 1 225 120 188.82 3532 10 772 442 4 093 360 179.33 3928 12 080 491 4 590 400 177.84 15 312 46 045 1914 17 497 100 181.95 1690 4368 211 1 659 840 211.66 5032 14 755 629 5 606 900 186.57-mouth 3991 13 231 499 5 027 780 165.0

FC ¼ 0.997 � WC with an r2 of 0.93 (Fig. 3) (1945e1965 meanmonthly flow). During most years, the highest flow occurs in theearly spring, whilst low flows occur in summer and are ground-water flow dominated.

Chloride concentrations measured during 2009 in Fishkill Creekwatershed ranged from 36 to 102 mg/L, and bromide concentra-tions were all less than 0.1 mg/L with most below the detectionlimit of 0.02 mg/L. Calcium and magnesium concentrations rangedfrom about 4 to 16 and 20e70 mg/L, respectively (Table 4). Thesource of salinity to streams can be determined from the ratio ofchloride to bromide (Mullaney et al., 2009). When seawater evap-orates and halite (NaCl) precipitates, bromide is excluded from thecrystal structure. Therefore, halite contains small amounts ofbromide. When it dissolves, the ratio of chloride to bromideexceeds 2000, which is the case for all samples collected at Fishkill(Table 4). The main source for salinity to the creek is road salt.Moreover, a plot of sodium versus chloride forms a linear trendconsistent with road salt, with a few outliers (Fig. 4). Chlorideto bromide and sodium to chloride ratios constitute a powerful toolto allow modelers to focus on the major salinity sources inwatersheds.

Based on our estimation and calculation (Table 5), road saltconstituted the primary source of stream chloride (87%), whereaschloride from home water softener usage is the second largestsource (12%). Natural rainfall deposited a small amount of chlorideinto the watershed of only 1%.

Chloride concentrations estimated from ISE specific conduc-tance meter correlated well with chloride values measured byDionex Ion Chromatography (IC) with a least squared linearregression of IC Cl ¼ 0.80 � ISE probe þ 2.44 with r2 of 0.54 (Fig. 5).Using specific conductance provides a cheap and simple way tocontinuously monitor the salinity change and can be used for INCA-Cl model calibration.

4.2. INCA-Cl model simulation and scenario applicationfor Fishkill Creek

4.2.1. Flow and chloride simulation resultsThe principal objective of this model is to simulate the observed

behavior of flow and chloride in the river systems. The modelgenerates daily simulation at any of the seven reaches in the FishkillCreek catchment. Fig. 6 shows an example of daily flow simulationat the headwater and mouth of the river from 2008 June to 2009May. The simulated flow agreed well with the observed data foreach reach (r2 ranges from 0.70 to 0.80). However, the extreme flowevents such as high flow were sometimes overestimated and lowflow were sometimes underestimated.

Fig. 3. Streamflow correlation between Fishkill Creek and Wappinger Creek.

Table 4The chemical analysis of major cations and anion for all samples collected in the Fishkill Creek watershed.

Collection date Site Reach numberin Fig. 1

Creek Calcium (mg/L) Magnesium(mg/L)

Sodium(mg/L)

Chloride(mg/L)

Bromide(mg/L)

08/27/08 Clove Valley 1 Fishkill Creek 32.3 10.6 12.5 20.2 <0.0208/27/08 Clove Valley 1 Fishkill Creek 32 10.5 12.2 20.2 <0.0211/24/08 Clove Valley 1 Fishkill Creek 29.2 9.27 11.7 18.8 <0.0211/24/08 Clove Valley 1 Fishkill Creek 29.2 9.34 11.4 18.8 <0.0212/18/08 Greenhaven 2 Fishkill Creek 24.4 8.25 19.9 32 <0.0212/18/08 Greenhaven 2 Fishkill Creek 24.3 8.17 19.8 32 <0.0208/27/08 Hopewell 3 Fishkill Creek 39.2 14.3 40.1 65.5 <0.0208/27/08 Hopewell 3 Fishkill Creek 39.3 14.2 39.9 65.5 <0.0209/25/08 Hopewell 3 Fishkill Creek 37.6 13.9 35 57.4 <0.0209/25/08 Hopewell 3 Fishkill Creek 37.7 14 34.9 57 <0.0212/18/08 Hopewell 3 Fishkill Creek 37 10.8 47.3 80.8 0.0212/18/08 Hopewell 3 Fishkill Creek 37.4 10.9 48.2 81.2 <0.0208/27/08 Route 52 4 Fishkill Creek 55.8 15.2 125 101 0.0808/27/08 Route 52 4 Fishkill Creek 55.6 15 125 101 0.0911/24/08 Route 52 4 Fishkill Creek 38.8 11.6 53.9 68.6 0.0211/24/08 Route 52 4 Fishkill Creek 39 11.5 54.6 68.6 0.0308/27/08 Walmart 5 Fishkill Creek 57.2 15.9 125 105 0.0808/27/08 Walmart 5 Fishkill Creek 57 15.5 125 105 0.0809/25/08 Walmart 5 Fishkill Creek 49.7 13.6 79.1 88.2 0.0509/25/08 Walmart 5 Fishkill Creek 49.4 13.5 78.5 88 0.0511/24/08 Walmart 5 Fishkill Creek 39.9 11.8 53.9 69.7 0.0311/24/08 Walmart 5 Fishkill Creek 39.8 12 54.1 69.7 0.0309/25/08 USGS 6 Fishkill Creek 49 13.4 84 97.2 0.0509/25/08 USGS 6 Fishkill Creek 48.7 13.3 83.6 96 0.0512/18/08 USGS 6 Fishkill Creek 28.7 8.84 42 65.3 <0.0212/18/08 USGS 6 Fishkill Creek 28.4 8.74 42 65.3 <0.0212/08/08 M. Brett Park 7 Fishkill Creek 35.3 10.6 45.3 62.3 <0.0212/08/08 M. Brett Park 7 Fishkill Creek 35.2 10.7 45.4 62.1 0.0212/18/08 M. Brett Park 7 Fishkill Creek 28.1 8.7 42.6 65.8 <0.0212/18/08 M. Brett Park 7 Fishkill Creek 28.4 8.73 42.4 65.8 <0.0201/12/09 M. Brett Park 7 Fishkill Creek 37.2 11.3 63.3 102 0.0201/12/09 M. Brett Park 7 Fishkill Creek 36.9 11.1 63.3 102 0.0209/24/08 Nelsonville n.a. Clove Creek 33.3 11.1 23.1 44.7 <0.0209/24/08 Nelsonville n.a. Clove Creek 32.9 11 23.4 44.6 <0.0212/18/08 Fishkill n.a. Clove Creek 12.8 3.93 15.1 24.9 <0.0212/18/08 Fishkill n.a. Clove Creek 12.8 3.91 15 24.9 <0.0209/24/08 Hopewell n.a. Whortlekill Creek 64.6 12.1 73.2 138 0.0309/24/08 Hopewell n.a. Whortlekill Creek 65 12.1 72.7 137 0.0309/24/08 Poughquag n.a. Whaley Lake Stream 31.6 12.2 28.7 48.8 <0.0209/24/08 Poughquag n.a. Whaley Lake Stream 31.5 12.3 28.8 48.9 <0.0211/24/08 Poughquag n.a. Whaley Lake Stream 22.1 8.05 21 36 <0.0211/24/08 Poughquag n.a. Whaley Lake Stream 21.9 7.99 20.7 36.3 <0.0209/24/08 Jackson Creek n.a. Jackson Creek 32.9 6.44 37.8 70.4 0.0209/24/08 Jackson Creek n.a. Jackson Creek 32.9 6.43 38.1 70.1 <0.0209/24/08 Jackson Creek n.a. Jackson Creek 30.7 6.49 38.5 69.5 <0.0209/24/08 Jackson Creek n.a. Jackson Creek 30.4 6.5 38.7 69.4 <0.0212/18/08 Swartoutville n.a. Sprout Creek 24.3 4.65 34.7 59.9 <0.0212/18/08 Swartoutville n.a. Sprout Creek 24.5 4.64 34.7 59.9 <0.02

Fig. 4. The bivariate plot of sodium verses chloride. The 1:1 line indicates the salinityorigin is from the road salt.

Table 5Estimated average input from different sources of chloride (tons/year) to each sub-catchment of Fishkill Creek during 2008 June to 2009 June.

Reach/Sub-catchment

Chloride loading to the Fishkill Creek watershed

from saltapplication(tons/year)

from watersoftener usage(tons/year)

from naturaldeposition(tons/year)

1-headwater 749 84 172 1718 268 223 2039 298 214 10 193 1161 1105 342 128 26 2705 382 307-mouth 934 303 5

Sum 18 681 2624 206Relative contributions 86.8% 12.2% 1.0%

Total input 21511

L. Jin et al. / Environmental Pollution 159 (2011) 1257e12651262

Fig. 5. The linear relationship of chloride concentration measurements between theIon Chromatography (IC) and Ion Specific Electrode (ISE) probe.

Fig. 7. Times series of chloride concentration at Hopewell (reach 3) and Beacon (reach7). Solid points are measured chloride concentrations in mg/L and grey line shows themodeled daily chloride values in mg/L.

L. Jin et al. / Environmental Pollution 159 (2011) 1257e1265 1263

INCAmodel also generates daily chloride concentration for eachreach. The simulation gave reasonable estimates of chlorideconcentrations compared to observed ones at reach 2, 4, 6(r2 ranges from 0.30 to 0.62), where several point measurementswere available from 2008 August to 2009 March. Fig. 7 shows thesimulated and observed chloride concentrations at Hopewell(reach 3) and Beacon (reach 7), where continuous daily observa-tions were available. The chloride concentration is mainlycontrolled by the mixing between low chloride rainfall or snow-melt with high chloride groundwater. At both locations, the effectof snowmelt dilution and recoverywas simulatedwell in thewintermonths, when the flowwas the highest and chloride concentrationwas the lowest during the year (Figs. 6 and 7). Chloride concen-tration reached at the lowest of 40 mg/L in mid-January andincreased by over three-folds afterwards. The modeled chlorideconcentrations fluctuated in correspondence to the flow change.However, the measured chloride concentrations were damped. Inaddition, measured chloride concentrations were lower thanmodeled values after the spring snowmelt at Hopewell (Fig. 7). Thismight suggest there are processes for attenuating chloride inputsbefore it enters the stream, such as soil retention in the microporesand chloride build-up in the groundwater, which could lead to netaccumulation of chloride within the catchment (Bastviken et al.,

Fig. 6. Times series of Fishkill stream flow change at the headwater (reach 1) and mouth (rem3/s and grey line shows INCA modeled daily stream flow values in m3/s.

2006; Kincaid and Findlay, 2009; Mason et al., 1999; Neal andRosier, 1990). Ground water may potentially represent a long-term source of chloride to surface water and contribute to theupward trend in salinity.

Although the chloride from home water softener usage wasa small portion of inputs to the watershed (12%), to assess theuncertainty, we have performed the model runs assuming allhousehold and only half of the households have water softeners.Results showed the differences of daily chloride concentrationsbetween two runs are small (6.2 � 3.3%).

We also calculated the stream water chloride export at themouth of the creek. During the modeling time, 1.44 � 104 tons ofchloride was exported from Fishkill Creek at the mouth, whichconstitutes 67% of the total input to thewatershed (2.15�104 tons).Long-term monitoring of salinity is needed for future research tobetter understand the annual and seasonal variations of streamwater chloride concentration and chloride input and export in thewatershed. Model simulation would be improved significantlywhen longer monitoring records are available.

ach 7). Solid points are flow estimated from the relationship with Wappinger Creek in

Fig. 8. Times series of chloride concentration at Beacon (reach 7) with two scenariosshowing the stream water chloride concentration changes corresponding to doublingand halving the road salt application rate.

L. Jin et al. / Environmental Pollution 159 (2011) 1257e12651264

4.2.2. Scenario analysisThe INCA-Cl model can be used as a management tool by

assessing different scenarios and outcomes from major sourcechanges such as road salt application. Fig. 8 shows the simulationsgiven a decrease (scenario 1) and increase (scenario 2) in road saltapplication by 100%. Results indicate that decreasing the road saltby 100% could lead to the decrease in chloride concentration onaverage by 7% at the mouth of the creek; doubling the road saltapplication could increase the chloride concentration in the riverup to 13%. The change in streamwater chloride concentration is notas large as the input change, but themodel clearly shows the effectson stream water chloride concentration from the road salt usagewithin the watershed. In the longer term, assuming similar mete-orological and hydrologic conditions, the annual mean chlorideconcentration will continue to rise if no action is taken. Even if saltapplications are stopped immediately, it will take 10 years for theannual chloride concentration to decrease by approximately one-third. Therefore, source control of road salts may be necessary overa long period of time to significantly improve river water quality.

5. Conclusion

In Northeast United States, chloride concentrations areincreasing at a rate which would potentially threaten the freshwater availability and the habitat of aquatic communities in thenear future. The majority of the salinity is from the intensive roadsalt use. A dynamic Integrated Catchment (INCA) model wasapplied to Fishkill Creek watershed, New York to simulate thechloride behavior. The model used readily accessible topographic,land use, hydrological and meteorological data and incorporatesthree main chloride inputs to the watershed, including road salt,water softener usage and atmospheric deposition. The resultsshowed generally good agreement of simulated flow and chlorideconcentration with observed values. However, the observed chlo-ride concentration variation was damped and did not have some ofthe fluctuations which modeled chloride indicated. This mayindicate some attenuation process in the watershed before chlorideenters the stream. Monitoring the salinity change by specificconductance also proved to be a cheap but reasonably goodapproach to obtain continuous chloride record for INCA-Cl modelcalibration. We then used the model to assess outcomes underdifferent road salt application scenarios. Our study shows that thismodeling approach may provide an effective tool for watershedmanagers to manage and forecast the input and transport of chlo-ride input to the rivers, which certainly deserves more attentionespecially in northeast United States.

Acknowledgement

We like to thank Syracuse Center of Excellence in Environmentaland Energy Systems to award this Collaborative Activities for

Research and Technology Innovation (CARTI) grant and fund ourresearch. We also like to thank two anonymous reviews for theirconstructive comments and suggestions, which greatly improvedour work.

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