________________________________________________________________________

A Thesis Presented

By

Koosha Kalhor

to

The Department of Civil and Environmental Engineering

in partial fulfillment of the requirements for the degree of

Master of Science

in the field of

Civil Engineering

Northeastern University Boston, Massachusetts

December 2017

Assessment and Modeling of Groundwater Flow and Nitrate

Contamination within Coastal Karst Aquifer of Puerto Rico

ii

ABSTRACT

Karst aquifers, capable of store and transmit large amount of water, are the main source of drinking

water in many regions worldwide. Their excessive permeability leads to enhanced vulnerability to

contamination accordingly. In the first section of this study, a comprehensive overview of hydrological

processes and concepts, assessment methods and governing equations regarding groundwater flow

and contaminant transport in karst aquifers is presented. Moreover, surface water and groundwater

interaction and recent groundwater remediation techniques in karst terrains are discussed. Due to the

complexity of karst aquifers, different approaches are developed by researchers for investigating and

predicting karst processes and groundwater behavior. Modeling techniques are among the most

beneficial and powerful methods for assessing groundwater flow and contaminant transport in karst

aquifers, as hydrogeological systems with complicated and unpredictable behavior. Hence, several

modeling approaches, are reviewed and assessed. Moreover, associated research works conducted for

northern Puerto Rico are discussed to complement ongoing hydrogeologic investigations in this island.

In the second section, groundwater Nitrate contamination, as a result of agricultural, industrial and

urban development, is assessed for north-central part of Puerto Rico. Using collected field samples and

historical data, a Nitrate fate and transport simulation was conducted using MODFLOW and MT3D

models. The calculated results of the regional-scale simulation showed high correlation with observed

values and hence, the calibrated model was used for prediction purposes. Using land cover data and

by assessing agricultural development trend in the island, spatiotemporal pattern of groundwater

Nitrate concentration was predicted for the next two decades. It was predicted that agricultural

activities will rise dramatically after economic damages of Hurricane Maria and this will negatively

impact the groundwater quality. Based on the model prediction results, recommended management

plans for each municipality were presented for the use of policy makers and authorities.

iii

TABLE OF CONTENTS

Abstract………………………………………………………………….............................................................................………….… ii

Chapter 1. Overview ……………………………………………….................................................................................………… 1

Chapter 2. Quantitative and Qualitative Assessment of Groundwater in Karst Aquifers: A Review ….. 4

2.1. Karst Aquifers ……………………………………………………………………………………...…………………..…….…… 4

2.1.1. Governing Equations ……………………………………………………………………………………….….…..…..… 9

2.1.2. Means of Studying Karst Aquifers ……………………………………………....…………………………....…… 10

2.2. Groundwater Contamination and Remediation Techniques …………………….………………...……...… 12

2.2.1. Contamination sources ………………………………………………………………………………….……...…..… 12

2.2.2. Remediation strategies in karst aquifers …………………………………...………………….……….....…… 13

2.2.2.1. Remediation by addressing source zones ……………………………………………..……………...…… 13

2.2.2.2. Remediation by mitigating exposure pathways …………………….………………….................…… 14

2.2.2.3. Remediation by managing contaminated groundwater…………..……….………………………… 15

2.2.3. Groundwater Contamination in Puerto Rico ………………………………………………….…………..…… 15

2.3. Surface Water and Groundwater Interactions (SWGWI………………………………...........................….… 17

2.3.1. SWGWI Assessment Methods ………………………………………………………………………………….…..… 17

2.3.2. Surface Water.Groundwater Interaction in Karst …………………………………….....................……..… 22

2.4. Modeling Methods …………………………………………………….……………………………………….……...…....… 22

iv

2.4.1. Model Parameters and Development ……………………………………………………………...………..…..… 23

2.4.2. Spatially lumped models and distributed parameter models ………………………..…………..……… 24

2.4.3. Computer Models and Programs ………………………………………………………………………..…..…....… 26

2.4.4. Equivalent Porous Media (EPM) method ……………………………………………………………………..… 34

2.4.5. How Remote Sensing Can Improve Karst Assessment and Modeling? ……………………….........… 34

2.5. Conclusion …………………………………………………………………………………………………….………….….…… 36

3. Assessment and Modeling of Groundwater Nitrate Contamination within a Coastal Karst Aquifer 38

3.1. Introduction …………………………………………..……………………………………………….……………...…….…… 38

3.1.1. Site Description ………………………………………………………………….………………..…………..…….…… 38

3.1.1.1. Geographical Location …………………………………………………………………..………...……..……… 38

3.1.1.2. Geology ……………….…………………………………………………………………………………..….…...….… 39

3.1.1.3. Climate …………………….………………………………………………………….………………….….…....…… 41

3.1.1.4. Hydrology ……………………………………………………………………………………………..….…...….…… 41

3.1.1.5. Land Cover ………………………………………………………………..…………………………..……..…...…… 42

3.1.2. Occurrence of Nitrate in GW ………………………………….…………………………………………..…..…..… 43

3.1.3. GW Nitrate modeling and prediction …………….….………………….………………………..………...…… 46

3.2. Materials and Methods ……………………………………………………………..…….………………..…………..…… 49

3.2.1. Model Setup …………………………………………………………………………………..…………….………...…… 49

3.2.1.1. GW Flow Model ……….………………………………………………..………………………..…….……….…… 49

3.2.1.2. Contaminant Transport Model ……………………………………………………………...……………...… 51

3.2.2. Prediction of Nitrate Concentration ………………………………………………...………………..………..… 55

v

3.3. Results and Discussion …………………………………………….……………………………………….……………..… 55

3.3.1. GW Flow Model ………………………………………………………………………………….……..…………..…..… 55

3.3.2. Nitrate Transport Model ………………………………………………………………..………….………………… 57

3.3.3. Prediction of GW Nitrate contamination …………………………………………..…..………….…..….…… 58

3.4. Conclusion …………………………………………………………………………………………….……………………..…… 63

References ………………………………………………………...……………………………………………..………………….…… 64

vi

ACKNOWLEDGMENTS

I would first like to thank my research advisor Prof. Akram Alshawabkeh of the Department

of Civil and Environmental Engineering at Northeastern University. He steered me in the right

direction whenever he thought I needed it. Without his mentorship and valuable comments

and guidance, this MS thesis could not have been written.

I also would like to express my very profound gratitude to my parents and my brother who

supported me and trusted in me all the time from thousands miles away. Indeed, no words

can describe the deep love between us. Hence, this thesis is dedicated to them. Moreover, my

appreciation goes to my grandmother, aunts, uncle and other family members for their

endless support and deep kindness.

I would also like to acknowledge Prof. Philip Larese-Casanova ad Prof. Loretta Fernandez of

the Department of Civil and Environmental Engineering at Northeastern University as the

reviewers of this thesis, and I am gratefully indebted for their very valuable comments.

Furthermore, I am grateful for Prof. Ingrid Padilla and her team at University of Puerto Rico

for providing me with their field sampling data.

Graduate students, researchers and staff at PROTECT center need to be acknowledged for

making my research experience truly pleasurable and also for offering continuous assistance

during my research experience. I would like to especially thank Dr. Ljiljana Rajic, Shadi

Hamdan and Shirin Hojabri for their support and kindness. In addition, I truly appreciate the

kind regard and consideration of Dr. Reza Ghasemizadeh, a former PhD student and

researcher at PROTECT center, for his valuable comments on the second chapter of this

thesis.

vii

Finally, I must express my deepest gratitude to my dear friends, especially Masoud

Mahdisoltani and Newsha Emaminejad for providing me with unfailing support and

continuous encouragement throughout my graduate studies at Northeastern.

Support of this MS thesis is provided through Award Number P42ES017198 from the

National Institute of Environmental Health Sciences to the PROTECT research project. The

content is solely the responsibility of the author and does not necessarily represent the

official views or policies of the National Institute of Environmental Health Sciences, the

National Institutes of Health, or the US Environmental Protection Agency.

1

Chapter1:

Overview

Sustainablewaterresourcesmanagementisacrucialconcerninmostcountriesacrosstheglobe.Only

3%oftotalwaterontheEarthisconsideredasfreshwaterresourcesandapproximately30%ofthese

areaccessibleasgroundwater,whichisvitalforhumanhealth,ecosystem,energyindustryandother

water‐dependent topics (Shiklomanov, 1993).Karst aquifers are responsible forprovidingpotable

waterfor40%and25%oftheUSandworld’spopulation,respectively(Ghasemizadehetal.,2012).

The increasing demand by residential, industrial and agricultural uses have caused groundwater

depletionandhaveaffectedwaterqualityinmanyregions.

With particular reference to karst aquifers, the first part of this study (Chapter 2) provides a

comprehensive review of hydrological concepts and novel investigation andmodeling techniques

followedbyashortdiscussionofgroundwatercontaminationandremediationtechniques.Itistried

topresentandreviewtheworkofotherresearchersintherecentyears(especiallyafter2010)andto

discuss the improvements that have been occurred regarding groundwater quality and quantity

assessment. In each section, the associated research work that has been done for Puerto Rico is

presented tobetterunderstandwhatresearchworkshavebeenconductedandcanbedone in the

islandregardingkarstgroundwater.

PuertoRico(8,937km2),asthecasestudylocation,isconsideredaterritoryoftheUnitedStates(US).

The island is located innortheasternsideofCaribbeanSeaandhasanestimatedpopulationof3.6

million(Castro‐Prietoetal.,2017).Severalsurfacewaterandgroundwaterresourcesacrosstheisland

provide residents with fresh water and are used for agricultural, industrial and energy‐based

purposes.Figure1.1exhibitsthegeographicallocationofPuertoRicoanditsaltituderangebasedon

DigitalElevationModel(DEM)databaseofUnitedStatedGeologicalSurvey(USGS).

2 

Figure1.1.Geographicallocation(modifiedfromGoogleEarth‐upper)andelevationrange(based

onDEMdata)andstreamsinPuertoRico(lower)

Duetothepresenceofkarstaquiferswithhighlevelofheterogeneityandanisotropyinnortherncoast

oftheisland,rainfallwatercaneasilypercolateintothegroundandthisrapidmovement,makeskarst

aquiferultimatelyvulnerabletocontamination.Severalsourcesofcontaminationsuchasagricultural

and industrial activities in addition to proximity to urban areas are responsible for groundwater

contaminationintheregion(Cherry,2001).Moreover,highlyheterogeneousandkarsticaquiferswith

conduitscancausehighrateofwaterlevelfluctuationeveninsmalltemporalscaling(Yuetal.,2016).

TherearedifferenttypesofGWpollutants, includinginorganiccontaminantssuchasheavymetals,

Nitrate andchloride;organic contaminants suchas volatile organic compounds (VOCs),pesticides,

plasticizers,chlorinatedsolvents,pharmaceuticalsandpersonalcareproducts(PPCP);andmicrobial

3

contaminantssuchasColiformbacteria(Galitskayaetal.,2017;Kačaroğlu,1999;Lapworthetal.,2012;

Suietal.,2015).HighconcentrationsofNitrate(NO3)isoneofthemostcommonconcernsregarding

GWcontaminationworldwide.Industrialsites,landfills,agriculturalactivities,urbanwastewateretc.

areamongmajorsourcesofGWNitratecontamination(AlmasriandKaluarachchi,2004;Eshtawiet

al., 2016; Wang et al., 2016). In the second part of this study (Chapter 3), groundwater Nitrate

contaminationinNorthcoastlimestoneaquiferofPuertoRicoisassessed.Thescopeoftheresearch

work in this part is to predict the spatiotemporal distribution of Nitratewithin karst aquifers by

developinganumericalmodelandbyassessingagriculturaldevelopmentcapacityof theregion. In

fact, groundwater flow and Nitrate transport simulations were done usingMODFLOW andMT3D

models, respectively using historical observations and field data. After successful calibration and

validationofthetransportmodel,itwasusedforpredictionpurposesfortheyears2025and2035.

Finally, recommendedmanagement actions, regarding sustainable agricultural development,were

presentedfordifferentmunicipalitiesinthearea.

4 

Chapter2:

QuantitativeandQualitativeAssessmentofGroundwaterinKarst

Aquifers:AReview

2.1.KarstAquifers

Comprisingofchemicallysolublerockswithlargepassagesornetworkofconduitsandcavesinside,

karstaquifersareverypermeableandcapableofstoreandtransmitlargeamountofwater.Limestone,

dolomite, gypsum and anhydrite are the most common materials that form karst aquifers and

carbonaterocks.Similartoothergroundwaterresources,regionsinwhichkarstaquifersexistarevery

popular for people to reside in because of their potential of providing habitantswith freshwater

(Quinnetal.,2006).Millionsofpeopleliveinareaswheretherekarstaquifersexistand20‐25%ofthe

world’spopulationdependsonwatersuppliesfromkarstaquiferdirectlyorindirectly.Approximately

10%oftheworld’slandsurfaceareashavekarstaquiferbeneaththem.Thispercentageishigherin

someareassuchasinEuropewhereitisroughly35%(FordandWilliams,2007).Untilrecently,the

boundaries of karst aquifers around the world were not recognized accurately. Hence, by taking

advantageofGIStools,recentexplorationofkarstaquifers,GlobalLithologicalMapthatwasdeveloped

before,Chenetal.havealmostcompletedthe firstWorldKarstAquiferMap(WOKAM).Theirmap

distinguishescontinuouscarbonaterocksanddiscontinuouscarbonaterocksandincludemajorkarst

springs,wellsandcaves(Chenetal.,2017).Figure2.1demonstratesthedistributionofkarstaquifers

withintheUnitedStatesanditsterritories.

5 

Figure2.1.DistributionofkarstaquifersintheUnitedStatesanditsterritories–Compiledfromopen

filesassociatedwiththeUSGSreportof(WearyandDoctor,2014)

Karstaquifersareindividuallydifferentwithuniquework‐frameandcharacteristicsandtheyshould

be studied case by case (Stevanović, 2015). Two important characteristics of karst aquifers are

heterogeneity and anisotropywhichmake it hard for hydrogeologists and researchers to develop

modelsusingsimplifyingassumptions.Basically,theyhavethemostcomplexsystemamongstkarst

terrainsand thiswill causea lotofuncertaintiesanderrors indevelopedmodels forstudyingand

predictingtheirbehavior(Bakalowicz,2005).Also,therechargeanddischargerateofkarstsprings

canvaryalotduetoseveralreasonssuchasfluctuationsinwatertablelevelcausedbyhydrological

events or seasonal variations (Gárfias‐Soliz et al., 2009). Table 2.1 elaborates hydrogeological

characteristicsofthreemainaquifertypes,porousmedia,fracturedrockandkarstsystembasedon

ASTM D 5717–95 Standard: Guide for Design of Ground‐Water Monitoring Systems in Karst and

FracturedRockAquifers.

CarbonateRocksEvaporiteRocksSedimentaryRocksQuartzSandstoneVolcanicRocksEvaporiteBasins

Legend

6 

Table2.1.Hydrogeologicalcharacteristicsofthreemainaquifertypes,porousmedia,fracturedrock

andkarstsystembasedonASTMD5717–95Standard(RosenberryandLaBaugh,2008)

Aquifer

characteristics

Aquifertype

Porous(Granular) FracturedRock Karst

Effective

porosity

Mostlyprimary,

through

intergranularpores

Mostlysecondary,

throughjoints,

fractures,andbedding

planepartings

Mostlytertiary(secondary

porositymodifiedby

dissolution);throughpores,

beddingplanes,fractures,

conduits,andcaves

Isotropy Moreisotropic Probablyanisotropic Highlyanisotropic

Homogeneity Morehomogeneous Lesshomogeneous Heterogeneous

Flow Slow,laminarPossiblyrapidand

possiblyturbulentLikelyrapidandturbulent

Flow

predictions

Darcy'slawusually

applies

Darcy'slawmaynot

applyDarcy'slawrarelyapplies

StorageWithinsaturated

zoneWithinsaturatedzone

Withinbothsaturatedzone

andepikarst

Recharge Dispersed

Primarilydispersed,

withsomepoint

recharge

Rangesfromalmost

completelydispersed‐to

almostcompletelypoint‐

recharge

Temporalhead

variationLowvariation Moderatevariation Moderatetohighvariation

Temporalwater

chemistry

variation

LowvariationLowtomoderate

variationModeratetohighvariation

BasedontheinformationinTable2.1,akarstaquifersystemcomprisesseveralelementssuchascaves,

conduits,sinkholesandsprings.Limestonekarstaquifersarecommoninmanyareasaroundtheworld

including Puerto Rico (Cherry, 2001; Rafael et al., 2016), Florida (Xu et al., 2016)Mexico (Bauer‐

Gottweinetal.,2011),China(Luoetal.,2016)etc.Basically,theyusuallyareevolvedfromfractured

orfractured‐porousrocknetworksafterseveralyearsandbycarbonatedissolution, largepassages

and caves are created. It should be noted that severalmodelingmethods have been employed to

simulatetheevolutionofkarstaquifersfromfracturedorporous‐fracturedrocksystems(Kaufmann,

2003,2016;Kiraly,2003).Figure2.2depictssinkholeplainintheBarcelonetamunicipalityofPRwith

7 

anindustrialfacilityinthebackgroundandsurroundedbyresiduallimestonehills(a)andablockof

vuggylimestonefromthenorthcoastkarstaquiferofPR(b).Industrialfacilitiesintheareahavefaced

withseveralissuesduetofrequentcreationofnewsinkholes(Field,2017).

Figure2.2.SinkholeplainintheBarcelonetamunicipalityofPRwithanindustrialfacilityinthe

backgroundandsurroundedbyresiduallimestonehills(a)andablockofvuggylimestonefromthe

northcoastkarstaquiferofPR(b)–(Field,2017)

Figure 2.3, modified from iasmania.com/karst‐topography‐limestone‐chalk, depicts a conceptual

model of a limestone coastal aquifer in a karstic area. Several sources of contamination such as

agricultural and industrial activities in addition to proximity to an urban area are responsible for

groundwater contamination in the region. The graphics of urban and industrial areas have been

capturedfromSanJuan(CapitalofPuertoRico)areausingGoogleEarthsoftware.

8 

Figure2.3.Conceptualmodelofalimestonecoastalaquiferinakarsticareabeingexposedtoseveral

sourcesofcontamination

AsitappearsinFigure2.3andbasedontheinformationinTable2.1,akarstaquifersystemcomprises

severalelementssuchascaves,conduits,sinkhole,springetc.Limestonekarstaquifersarecommonin

manyareasaroundtheworldincludingPuertoRico(Cherry,2001;Rafaeletal.,2016;Zack,1994),

Florida(DufresneandDrake,1999;Xuetal.,2016),Mexico(Bauer‐Gottweinetal.,2011),China(Luo

etal.,2016)etc.Basically,theyusuallyareevolvedfromfracturedorfractured‐porousrocknetworks

afterseveralyearsandbycarbonatedissolution,largepassagesandcavescanbecreated.Itshouldbe

notedthatseveralmodelingmethodshavebeenemployedtosimulatetheevolutionofkarstaquifers

fromfracturedorporous‐fracturedrocksystems(Kaufmann,2003,2016;Kiraly,2003;Siemersand

Dreybrodt,1998).

Ingroundwaterhydrology,hydraulicconductivityquantifiestheabilityofsoilintransferringwater.

Basedupondifferenttypesandpropertiesofaquifermaterial,hydraulicconductivitycanrangefrom

10 cm/s for gravel to 10‐10 cm/s for shale. Figure 2.4, modified from (Freeze and Cherry, 1979),

demonstratestherangeofhydraulicconductivity(K)fordifferenttypesofrock.

Debris (soil, rock etc.)

Sinkholes

River

Urban area

Well drilling in residential area

Groundwater table

Caves Stream disappears

Stream disappears

and appears from

underground

Industrial area

Agricultural area

Seawater intrusion Confining unit

9 

Figure2.4.Hydraulicconductivity(K)rangefordifferenttypesofrock

Commonly, laboratory, field and numericalmethods are 3mainmethods formeasuring hydraulic

conductivity. Numerical and finite element‐based methods are used for determining vertical and

horizontalhydraulicconductivities(Kalbusetal.,2006;Smithetal.,2016).Usually,inkarstaquifers

where subsurface heterogeneity exists, determining hydraulic parameters such as K requires a

complicated analysis because this parameter is spatially and temporally variable throughout the

aquifer.Hydraulictomographyisanovelmethodthatcanbeusedforimagingtheheterogeneityin

karstic terrains (Illman et al., 2007).Moreover, in karst aquifers, the average range of K can vary

dependingonseveralfactorssuchasgeology,slope,levelofheterogeneityandkarstification.Angulu

et al. studiedhydraulic conductivity in karst areas by applyingwater injection tests and electrical

resistivitylogging(Anguloetal.,2011).Similarly,differentresearchersreportedexperimentalvalues

forhydraulicconductivitybasedontheirresearchapproachandcasestudyarea(Chenetal.,2011;Fu

etal.,2015;Sudickyetal.,2010).AsitisshowninFigure2.4,Koflimestonekarstaquiferwhichis

dominant in northern coast of Puerto Rico, can be assumed in the range of 10‐4 to 5 cm/swhich

demonstrates high level of permeability in karst aquifers. The estimated values of K in different

locationsofnorthcoastkarstaquiferofPuertoRicocanbefoundinthewaterresourcesinvestigation

reports(Rodriguez‐Martinez,1995)orsimilarsourcesformodelingpurposes.

2.1.1.MeansofStudyingKarstAquifers

Based on the complex characteristics of karst, several techniques and methods associated with

modifiedandreformedconventionalhydrogeologicalmethodshavebeenemployedforunderstanding

thebehaviorofkarstaquifers.Hydrologicandhydraulicmethods,geophysicalandgeologicalmethods,

modelingtechniquesandtracertestsareamongthemostcommonmeansofdescribingkarsticsystems

(GoldscheiderandDrew,2007;Stevanović,2015).

-11 -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1Log K (cm/s)

Karst limestone

Permeable basalt

fractured metaphoric and igneous rocks

Limestone and dolomite

Sandstone

Unfractured metaphoricand igneous rocks

10 

(Giudicietal.,2012;Huetal.,2009)studiedkarstaquifersbytakingadvantageofmodelingmethods.

Inspiteof their limitations(sufficientdatarequirement),advantagesofusingmodelingtechniques

havemadethempopular.Variableparameterscanbeusedandthemodelcanbegeneralizedforother

aquifers.More importantly,otherassessmentmethodssuchasremotesensingtoolsandgeological

methodscanbecoupledwithmodelingtechniquesforabetterdescriptionofkarstsystems.Remote

sensing tools, either coupled with modeling methods or be used separate, can be very beneficial

becauseoftheirstrongdataanalysisandmanagementcapabilitywhichallowsassessmentofseveral

datasets and layers simultaneously.However, lack of high resolution data for local studies can be

problematicinsomecases(MandaandGross,2006;Theilen‐Willigeetal.,2014).Inaddition,taking

advantage of geological methods, which help understanding the aquifer geometry and hydraulic

propertiessuchaspermeabilityinadditiontoorientationandcharacteristicsofpotentialflowpaths,

canboosttheaccuracyofmodelingresults(GoldscheiderandDrew,2007).Geophysicaltechniques

canalsobeemployedinconjunctionwithgeologicalmethodstounderstandgeologicstructuresand

overburdenthicknessoftheaquifer(Chalikakisetal.,2011;FordandWilliams,2007;Goldscheider

andDrew,2007).

Moreover,understandingkarstaquiferscanbeachievedbyusinghydrologicalandhydraulicmethods.

By using thesemethods,water balance dynamics are assessed and spring hydrographs, hydraulic

parameters, boundary conditions, flow directions and water table variations are identified to

characterize karst behavior. Sometimes, because of unknown and complex catchment boundaries,

waterbudgetsareoftenproblematic(GoldscheiderandDrew,2007;Hartmannetal.,2014;Kovácset

al.,2005).

Inmanycases,isotropictechniquesandartificialtracersareusedfordeterminingresidencetimeand

waterageandunderstandingthemovementofwaterthroughconduits.Themainadvantagesofthese

techniques are determining linear flow velocities and information on contaminant transport and

delineatingcatchmentareas.Althoughobtainedinformationanddatafromtracersareoftenreliable

andunequivocal,limitedapplicabilityinlargeareaswithlongtransittimesandalsochangeofcolor

andtoxicityconcernsaresomeofthedisadvantagesofusingisotropictechniquesandartificialtracers

(Goldscheideretal.,2008;JonesandBanner,2003;Moralesetal.,2017)

2.1.2.GoverningEquations

Takingadvantageof thegeneral formofDarcy’svelocity,ChengandChendescribedthegoverning

equationof groundwater flow in conduits. The hydraulic conductivity of karst conduit flowunder

laminarandnon‐linearsituationcanbeexpressedasKlcandKncrespectively(ChengandChen,2004).

Klc=d γ 32μ⁄ (1)

11 

Knc=2gd uf⁄

WhereuisthemeanvelocityandfisfrictionfactorthatdependsonReynoldsnumberandrelative

roughnessofthekarstconduit.

Fordescribing steady flow in anopen‐channel andclosed‐channel,Mannings equation (eq. 3) and

DarcyWeisbachequation(eq.4)canbeemployedrespectively(Ghasemizadehetal.,2012).

V= R / S /

Q= 2πg / r / f .⁄ i1/2

WherenisManning’sroughnessfactor[T/L1/3],Rishydraulicradiusofthechannel[L],Sisthechannel

slope[L/L],fistheempiricalDarcy‐Weisbachfrictionfactor,gisthegravitationalacceleration[LT−2],

ristheradius[L],iisthehydraulicgradient[L/L],ACistheconduitcrosssectionalarea[L2].

However,forthepurposeofmodelingunsteadyandnon‐uniformflowinkarstaquifersanddiscrete

conduitsystems,Reimannetal.presentedrelatedequationsbyconsideringgroundwaterflowasfree‐

surface flow(open‐channel)andpipeflow(flowin fully filledconduits).Hence,unsteadyandnon‐

uniformflowhydraulicsinanopen‐channelsituationcanbeexpressedbyequationofcontinuity(eq.

5)andequationofmotion(eq.6)(Reimannetal.,2011).

∂Q∂x

W∂h∂t

q 0

∂Q∂t

∂∂x

QA

gA∂h∂x

gA s s 0

Where

s n Q|Q|A R /

andQisdischarge[L3/T],Wisconduitwidth[L],hCisconduithead[L],xisspatialcoordinateinflow

direction[L], t istime[T],q is lateraldischargeperunit lengthofchannel[L2/T],g isgravitational

acceleration[L/T2],Aiscross‐sectionalarea[L2],s0 ischannelslope,sf isthefrictionslope[L],nis

Manningcoefficient[T/L1/3]andRisthehydraulicradius[L].

Furthermore, Li mathematically described the 1D solute transport in conduits by introducing a

formulawhichisdisplayedhereasequation7(Li,2009).

∂C∂t

∂∂x

V C D∂C∂x

2rjC q

(3)

(4)

(5)

(6)

(2)

(7)

12 

WhereCistheisthesoluteconcentrationintheconduit[M/L3],ristheconduitradius[L],VCisthe

meanspeedof conduit flow [L/T],DCis thecoefficientofdispersion in theconduit [L2/T],q is the

Darcianflowfrommatrixintoconduit[L/T],C0isthesoluteconcentrationinthematrix[M/L3]andj

is thespecific fluxof soluteat thewallwhich isequal to1 for contaminatedwaterand0 fornon‐

contaminated water. Although this equation works for mean velocity of flow inside the conduits,

variablevelocityduetoflowincreaseinthedownstreamdirectioncanbeexpressedas:

V x V 0

WhereV 0 istheaveragespeedatx=0.

2.2.GroundwaterContaminationandRemediationTechniques

2.2.1.Contaminationsources

Karstaquiferswhicharecharacterizedwithhighpermeabilitywithmanycavesandfracturesinside

and also recharged by sinkholes, rivers etc., have shown high vulnerability to contamination

(Kačaroğlu, 1999). The formation of solution channels and sinkholes facilitates the intrusion of

seawaterandcontaminated stormwaterandwastewater into theaquifer.Coastal aquifers, suchas

northcoastlimestoneaquiferofPuertoRico,aresusceptibletoseawaterintrusionwhichcanincrease

thesalinityofgroundwater(Arfibetal.,2007).Havingahydraulicconnectiontothesea,karstic‐coastal

aquiferscanbecharacterizedbyhavinggroundwaterflowinconduits,sub‐marinefreshwatersprings

and intrusion of seawater through the aquifer via conduit networks (Fleury et al., 2007) and are

exposedtocontaminationbyNaCl‐basedbrackishwaterfromtheseaortheoceannearby(Mongelli

etal.,2013).Itwasfoundoutthatpharmaceuticalandpersonalcareproducts(PPCP),pesticidesand

a fewmore contaminants have caused groundwater contamination (Metcalfe et al., 2011). Hence,

because coastal aquifers are susceptible to seawater intrusion and municipal wastewater‐based

contamination,developingasustainableplanbyusingintegratedmodelsformanagingandmonitoring

waterresourcesisessential(SreekanthandDatta,2015).

Anthropogenic operations such as agricultural, industrial, residential, commercial and municipal

activitieshaveshownresponsibilityforgroundwaterresourcespollutioninrecentdecades(Fetter,

2001;WakidaandLerner,2005).Leakageof storagetanks,chemical spills, landfills, fertilizersand

pesticides,sanitationsystems,untreatedwastedischargeandsewageetc.aresomeofthemainsources

ofcontaminationduetoanthropogenicactivities(ElAlfyandFaraj,2017).Generally,regardlessofthe

cause of contamination, organic compounds (Lapworth et al., 2012), Metals (Yao et al., 2012),

Pathogens and Chemical compounds and elements such as Nitrate, Chloride and Fluoride, are

consideredasfourmaincategoriesofcontaminationsource(PanagiotakisandDermatas,2017;Vidal

Montesetal.,2016).Nowadays,newchemicalcompoundsmainlyoriginatedfrompharmaceuticaland

(8)

13 

personalcareproducts(PPCP)areabigconcernbecausetreatingwaterthatcontainstheseproducts

ismoredifficult.ConcentrationofPPCPssuchasAntibiotics,Anti‐inflammatories,Lipid regulators,

Psychiatricdrugs,Stimulants,InsectRepellants,Sunscreenagentsetc.wasobservedtobehigherthan

regulatorycriterion insomeareas.Usually,Wastewaterandcontaminatedsurfacewater,Landfills,

Septic systems and Sewer leakages are considered as common sources of PPCP contamination

especiallyinkarsticareas(Dodgenetal.,2017;Suietal.,2015).

2.2.2.Remediationstrategiesinkarstaquifers

KarstGWremediationtechnologiesusuallytakeadvantageofacombinedsetoftreatmentmechanisms

forachievinghigherefficiency.Asanexample,Xankeetal.proposedacombinedprotectionplanfora

large‐scaleaquiferrecharge intoakarstaquifersystemin Jordan.Theirsuggestedcombinedsetof

actionplanswasnotabletopreventcontaminationbutitwasabletoabatetheextentofpollutionand

lowertheremediationcosts(Xankeetal.,2017).Duetohighlevelofcontaminationatsuperfundsites

inproximityofkarstaquifer,manyremediationmethodshavebeenemployedbyagencies.Gaining

moreknowledgeaboutefficacyof remediation techniquesandbehaviorofcontaminantsandkarst

aquifershaveledtovariationinuseofremedialtechnologies(Pariseetal.,2015).

2.2.2.1.Remediationbyaddressingsourcezones

Thisstrategyisusedinordertodecreasethemassfluxintotheaquifer.Themostcommonremediation

techniques associated with this strategy are soil excavation, mass reduction by NAPL and vapor

removal,physical,chemicalandhydrauliccontainmentandin‐situremediationmethods.Inspiteof

their advantages, these techniques are associates with some challenges as well. For example,

contaminant mass may remain in epikarst zones and consequently not accessible to excavation.

Moreover,capturezonescannotbereliablysimulatedusingpumpingwellsdataandnumericalmodels

such as MODFLOW. These techniques are often expensive and tricky to build any hydrogeologic

barriersinkarstaquifers.

In‐situthermaltreatment,in‐situchemicaloxidation(ISCO),andin‐situbioremediationarethreemain

in‐situ remediationmethods thathavecommonlybeenusedatkarst aquifers (Pariseet al., 2015).

However,therearesomelimitationsthatcandecreasetheefficiencyofthesetreatmentmethods.As

anexample,inthermaltreatmentmethod,preferentialpathwayswithinkarstaquifersthatleadtohigh

seepagevelocity,cancauseheatloss.Electricalresistanceheating(ERH)hasbeenusedforthermal

remediationofGWinakarstaquiferinAlabama.Itwasreportedthatthistechniquewassuccessfulin

removingDNAPLsinthecasestudyarea(Hodgesetal.,2014).Thepreferentialflowpathwayswithin

karstsystemthatcandisperseinjectedmaterials,usuallyareproblematicforothertreatmentmethods

such as ISCO and bioremediation as well. Hence, identifying the location of conduits and major

fracturesisnecessaryforanefficientremedialtreatment.

14 

RegardingISCOremediationmethod,byassumingthatthecontaminatedareaiswellidentifiedand

theinjectionfluidhastherightdosageandresidencetime,thepossibilityofdeliveringinjectionfluid

to the contaminated area withminimal error is the major challenge, similar to other fluid‐based

remediation methods in karst aquifers. Moreover, for achieving the highest efficiency in treating

contaminantsdiffusedintotherockmatrixormovingwithslowadvectivetransport,oxidizingagents

shouldremaininthecontaminatedarea.However,rapidmovementofwaterthroughpreferentialflow

pathwaysdilutestheoxidizingagents.Thus,itcanbeassertedthatapplicationofISCOinkarstaquifers

islimited.Furthermore,itwasshownthatpersistentreducingconditionsinhighflowsettingscannot

beachievedduetoGWandnativeelectronacceptorflux.Hence,usingbioremediationtechniquesfor

treating GW in karst aquifers may not lead to acceptable results. However, in some cases, using

bioremediation is recommended if tracer studies and sample collection can be done diligently.

Regardless of limitations in applying treatment methods for karst aquifers, Randrianarivelo et al

pointedoutchemicaloxidationasabeneficialtechniquesforGWremediation(Randrianariveloetal.,

2017).As it appears inFigure2.5, source zone treatment at superfund siteswere associatedwith

inconsistentpreferenceofusingGWremedialtechnologies.Nevertheless,soilvaporextractionwas

remainedasthemostcommonremediationmethodduring2005‐2011.

Figure2.5.Preferredin‐situremediationtechniquesforsourcezonetreatmentchosenatsuperfund

sites–modifiedfrom(USEPA,2013)

2.2.2.2.Remediationbymitigatingexposurepathways

Exposurepathwaysoftenplayacrucialroleinspreadingthecontaminationthroughkarstaquifers.

Mainly,remediationbymitigatingexposurepathwayscanbedonebytreatingat thetap,replacing

0

10

20

30

40

50

60

2005 2006 2007 2008 2009 2010 2011

Per

cent

age

of in

-sit

u so

urce

trea

tmen

t de

cisi

on

docu

men

ts

Year

Soil Vapor Extraction Chemical TreatmentBioremediation Thermal TreatmentSolidification/Stabilization Multy-Phase Extraction

15 

drinkingwater supplies, treatingspring flowusingactiveandpassivemethods, landcovercontrol

using fences, signage, deed restriction and local law enforcement. In spite of their high remedial

capability, these techniques often require long‐term operation and maintenance costs

(Randrianariveloetal.,2017).

2.2.2.3.Remediationbymanagingcontaminatedgroundwater

SeveralmethodsfortreatingandmanagingcontaminatedkarstGWarebeingusedworldwide.Pump

andtreat,permeablereactivebarriers,chemicaloxidation,bioremediationandthermalremediation

areamongmostcommontechniquesfortreatingimpactedGW.

Themainchallengeassociatedwiththesetechniquesistoidentifythezonethatrequirestreatment.

Based on site conditions and the type of contaminants, the most effective technology should be

employed.Assessmentofremediationtechniquerequiresanappropriatemonitoringapproach(for

locations consist of springs, streams, extraction systems, and previously tested wells) and

comprehensivehydrogeologicalandwaterqualitysamplingdata(Randrianariveloetal.,2017).

2.2.3.GroundwaterContaminationinPuertoRico

Several researchershaveassessed theGWcontamination innorthcoast limestonekarstaquiferof

PuertoRicoandhavesuggestedbeneficial remediation techniquesandgroundwatermodelingand

managementapproaches(Biaggi,1995).Historicalstudiessince1980showthatmainly,contaminants

with chlorinated solvents including TCE, Dichloroethene, Chloroform, Carbon tetrachloride,

Tetrachloroethene,Tetrachloroethane,Dichloroethaneandmethylenechloride,werefoundtohave

high concentrations causing public health concerns (Padilla et al., 2011). Several wells and sites

(Figure 2.6)were considered as theNational Priority List (NPL) Superfund Sites and remediation

actionsfortreatingwaterinthesesiteshavebegun.Regardingstudyingthegroundwaterpollutionand

understandingthepotentialexposurepathwaysofcontaminants,somemethodssuchasusingtracers

andGISwasemployed(Steele‐ValentínandPadilla,2009).Theabundancyofsuperfundsitesandhigh

concentrationofcontaminantsinGWrecoursesofPuertoRicohavecausedincreasingrateofpre‐term

birth (highest amongst US states and territories) in the island (Mathews andMacDorman, 2011).

However,since2006,thisratehasbeendeclinedfrom20%to11.4%duetoremediationtechniques

thatwereemployedandawarenessofhabitant thatwasenhancedregardingwater‐bornediseases

(MarchofDimesWebsite,2016;Rutigliano,2016).

Hydraulic and hydrogeological properties of the aquifer are important in studying contaminant

transport.Inkarstaquifersanalysisofcontaminanttransportrequiresmoresophisticatedapproaches

(Huetal.,2009).FateandtransportofNon‐aqueousPhaseLiquids(NAPLs),chlorinatedcompounds

suchasCVOCsandPhthalatesinkarsticaquifersofPuertoRicowasstudiedrecently.Yuetal.assessed

16 

the concentration of CVOC in northern Puerto Rico based on historical data They stated that the

hydrogeological conditions of the karst aquifer were greatly associated with the spatiotemporal

distribution patterns of the CVOCs.Water resources pollution in northernPuertoRico has caused

negativesocial,economicandenvironmentalimpacts.Hence,long‐termandconsistentmonitoringof

waterqualityintheareaswithhighconcentrationofcontaminantsweresuggested.(Yuetal.,2015).

Furthermore,Yuetal.studiedthehistoricalvariationsinconcentrationofCVOCssuchasTCE,PCE,

CT,TCM,andDCMinnorthcoastkarstaquiferofPuertoRico.Bydevelopingamodelandanalyzing

data,theyreportedthattheaquiferishighlycontaminatedandfurtherremediationprocessesshould

beundertaken.Figure2.6whichismodifiedfromtheirpaperdepictsthelocationofwells,Resource

ConservationandRecoveryAct(RCRA)andNationalPriorityList(NPL)superfundsites.(Yuetal.,

2015).

Figure2.6.Casestudylocation(upperpicture),RCRA,NPLSuperfundsites,aquifers,andthe

samplingwells(lowerpicture)basedonthestudyofYuetal.2015

AsitappearsinFigure2.6,thereisabundanceofsuperfundsitesinnorthernPuertoRicomainlydue

to industrial activities, improper management of landfills, accidental spills, unidentified waste

disposals,orresidentialsepticsystems.MostofthesesitesarelocatedinupperaquiferofNorthCoast

17 

Limestoneaquifersystem.OntheborderofAreciboandBarceloneta,thereare3superfundsiteswhich

indicatedhighlevelofcontaminationingroundwaterofthatarea.Thespatiotemporaldistributionof

theCVOCsinthekarstaquiferswerereportedtobelargelyassociatedwithhydrogeologicalconditions

ofthekarst(intrinsicpropertiesandthebiologicalenvironment)inadditiontothesourceorigin.

2.3.SurfaceWaterandGroundwaterInteractions(SWGWI)

Interaction of surface water and groundwater plays a critical role in understanding hydrological

behavior of a basin. This interconnectivity incorporates the topographical, geological and

morphological characteristics of terrains. Generally, water recharge from inflow of GW into the

riverbed,waterdischargefromriverbedtoaquiferandalsolosingandgainingwaterforbothSWand

GWinsomeriversegmentsarethreemainprocessesthatcanoccurinSWGWI.

2.3.1.SWGWIAssessmentMethods

Hydrochemicalmethodssuchasenvironmentalisotopes,hydrochemistryandtracersandnumerical

modelingareamongcommontechniquesthatcanbeusedtoassessSWGWI(Fleckensteinetal.,2010).

Also,fieldobservations,seepagemeasurementandalsohydrogeological,hydrographic,hydrometric

andgeophysicalanalysisarebeneficialtoolsthatcanbeusedindescribingSWGWI(González‐Pinzón

etal.,2015;Martinezetal.,2015).Temperaturechangeanalysisandwaterbudgetassessmentcanbe

coupledwithothermethodstoachievemoreaccurateandvalidateresults(Brodieetal.,2007).Despite

thevarietyinSWGWIanalysistools,tracershavebeenwidelyusedduetotheircapabilityofproviding

independentwaysofvalidatingorrefutingconventional‐traditionalmethodsofanalyzingdataand

describingSWGWI(Baskaranetal.,2009;Jankowski,2007).Acomprehensiveassessmentofdifferent

meansandmethodsofdescribingSWGWIispresentedinTable2.2(Brodieetal.,2007).

18

Table2.2.Sum

maryofmostpow

erfultoolsfordescribingSWGWI–Modifiedfrom

(Brodieetal.,2007)

Method

Description

Easeof

Use

Advantages

Disadvantages

Application

DesktopTools

Hydrographic

Analysis

Monitoringtime‐series

stream

flow

define

baseflow

(GW

discharge)com

ponent

High

Usesexistingflow

monitoringdata.Canbe

undertakenasadesktop

studypriortodetailedfield

investigations.Provides

informationofseepage

changesthroughtime

Applicabletogainingstream

conditionsonly.Assum

ption

thatbaseflowis

groundwaterdischargemay

notbevalid.

Commonlyapplied

methodfor

unregulated

catchm

ents

Hydrogeological

Mapping

MappingofGW

system

sincluding

flowpaths,GWquality,

aquiferpropertiesand

geom

orphology.

Lowto

Medium

Providescom

prehensive

understandingofGW

system

saroundstreamand

itsrelatedhydrogeological

system

s

Compilingandinterpreting

hydrogeologicaldatacanbe

timeconsum

ingandcomplex.

Limitedboreholedatacan

leadtomisinterpretation

DescribingGWflow

system

,surface

geologicaland

hydrogeological

propertiesatacoarse

scale(Gleesonetal.,

2014).

Modeling

Simulatingwaterflow

regimearoundstream

usingmathematical

equations

Lowto

Medium

Predictiveandusefultool

forpolicy‐makers.Transient

3‐Dmodelscan

spatiotemporallyestimate

seepagechanges

Oversimplifiedmodelsmay

notbevalidenough.Over‐

complex

modelsneedmoredataand

arecostlyandtime‐

consum

ing

Easysimulationof

SWGWIthatcanmake

predictionsfora

hydrologicalsystem

(Guayetal.,2013).

FieldTools

FieldIndicators

Visualindicationsof

seepagesuchaswater

clarity,springs,aquatic

plantspeciesand

chem

icalprecipitates

Medium

toHigh

Canidentifyseepage

hotspotsquickly.Return

visitscanprovide

informationonseasonal

changesinseepageflux.

Limitedinquantifying

seepageflux.Effectiveness

varieswithobserver’s

know

ledgeoffieldindicators

(e.g.plantoraquaticbiota).

Usedinspecific

settingssuchasacid

groundwater(e.g.iron

precipitates)and

karsticstream

s(e.g.

travertinedeposits)

Tracers

Monitoringmovem

ent

ofintroducedtracers

suchasfluorescentdye

orchemical

constituentsofw

ater

(suchasmajorions,

stableisotopes,radon)

totrackwaterflow

Medium

Canprovideevidenceof

waterflow

from

streaminto

aquifer.Aquiferparameters

suchasrechargeand

dischargeandfluid

transportpropertiescanbe

quantified.

Tracerstudiesrequire

carefulplanningincluding

meetingenvironmental

regulatorycontrols.

Processessuchas

degradation,precipitationor

sorptioncanaffecttracer

performance.Therecanbea

timegapbetweensample

collectionandfinalresults

analysis.

Karsticaquifersor

investigationsof

contam

inatedsites

(Wardetal.,2013),

Groundw

aterseepage

tostreams(Martinez

etal.,2015).

Geophysicsand

Rem

ote

Sensing

Useofgeophysics(e.g.

resistivity,EM,

radiom

etrics)or

remotesensing(e.g.

Landsat)tomap

landscapefeaturesthat

Low

Allowsrapid,non‐invasive

mappingoflandscape

parameterswithgood

spatialresolution.Som

e

techniquesprovide

informationatdepth.

Requiresspecificequipm

ent,

technicalexpertiseand

logisticalsupport.Can

requirecomplexdata

processingandcalibration

withotherdatasets.Ground

Opportunitiesexistto

usegeophysicaldata

collectedforother

purposese.g.Mineral

exploration.Satellite

imagerycommercially

19

indicateorcontrol

connectivity

surveyscanencounter

obstaclessuchasrough

terrain,vegetationcoveretc.

available,som

efreein

publicdom

ain.

Hydrometrics

Measurementof

hydraulicgradient

betweenaquiferand

stream

andthe

hydraulicconductivity

ofinterveningaquifer

material.Basedon

Darcy’sLaw

.

Medium

toHigh

Comparisonofstreamand

groundwaterlevelsasimple

guidetoseepagedirection.

Installationof

minipiezometersinstream

bedallowsdirectlocal

measurementofpotential

seepagedirection.

Reliesonreasonable

estimateofhydraulic

conductivitytoquantify

seepageflux.

Assum

ptionofsimple

groundwaterflow

conditions

maynotbevalid.Point

measurement.Needto

correctfordensityeffects.

Comparisonofstream

levelswithnearby

groundwaterlevels

commonlyusedto

definedirectionof

potentialseepage.

WaterBudgets

Quantificationof

stream

reachwater

balancetodefine

seepagecomponent

Medium

toHigh

Simplewaterbalances

estimatedrapidlyusing

existingstreamflow

monitoring.Provides

estimateofaggregate

seepagealongreach.

Measurementerrorsin

stream

flow

datacanbe

significant,hencemore

suitedtolongreaches.Canbe

misleadingifwaterbalance

component(e.g.extraction)

isnotadequatelyaccounted

for.

Routinelyapplied,

particularlyfor

regulatedriversor

irrigationchannels

(Gebreyohannesetal.,

2013).

20

21 

DuetocomplexityofdescribingSWGWI,numerousresearchershavetriedtousedifferenttypesof

techniquessuchasmodeldevelopmenttounderstandthe interconnectivitybetweensurfacewater

andgroundwater.UsingGSFLOWmodel,Wuetal. showed that for largeriverbasins,asystematic

uncertainty analysis is important in SWGWI modeling. They took advantage of a probabilistic

collocation method or PCM‐based approach and were able to improve the model accuracy by

calibration. They also suggested using a stochastic simulation rather than a deterministic one in

uncertaintyanalysis(Wuetal.,2014).Fleckensteinetal.discusseddevelopmentofnewapproaches

andmodels,includingnumericalmethodstospatiotemporallyquantifySWGWpatterns(Fleckenstein

etal.,2010).Sulisetal.comparedtwophysical‐based,spatially‐distributednumericalmodels,ParFlow

andCATHYthatcanbeemployedforSWGWIanalysis.Despitesomeminordifferencesduetousing

differentdiscretizationschemes(finitedifferenceandfiniteelement),theyfoundbothmodelstobein

goodagreementwithdata(Sulisetal.,2010).SuttonandScreatonexplainedSWGWIofakarstaquifer

basininFloridabyanalyzingriverdischargeandatransientnumericalgroundwaterflowmodeling.

Theirmodelingresultshighlighttheprominenceofspatiotemporalvariationsinheadgradientsthat

canaffectstreamsandkarstaquifersconnectionsandaquifermartialdissolution(Suttonetal.,2014).

Takingadvantageofalumpedhydrologicalmodel,Wandersetal.studiedSWGWIinacatchmentin

Netherlands. They used Lowland Groundwater‐Surfacewater Interactionmodel (LGSI‐model) and

came into conclusion that this model shows very promising results and can generate excellent

simulationsofdischargeandgroundwaterdepthsinadditiontodescribingSWGWIsinthecasestudy

area(Wandersetal.,2011).

Understanding the SWGWI in coastal aquifers is vital for water resources management; because

SWGWIdynamicallycontrolswaterregimesandsalinityincoastalwetlandsandaquifers.SWGWIin

coastalwetlandsisinfluencedbycomplexityofhydrologicalandecologicalprocesses(Langevinetal.,

2005). Researchers have developed physically‐based and fully‐integrated models such as

hydrogeosphere (Brunner and Simmons, 2012), MIKE SHE (McMichael et al., 2006), InHM

(VanderKwaakandLoague,2001),MODHMS(PandayandHuyakorn,2004)andsomeothermethods

thatwasreviewedanddiscussedby(Sebbenetal.,2013).Mostofthedevelopedmodelsarebasedon

theassumptionthatdensityof fluid isconstant.Nevertheless, incoastalwetlands,duetoseawater

intrusion,thisassumptionmaynotbecompletelytrue(LiuandMou,2014).

DespitethefactthatnumerousmethodshavebeendevelopedfordescribingSW‐GWinterrelationship,

there are still uncertainties and lack of sufficient knowledge for fully understanding the time lag

betweenGWpumpinganditsinfluenceonSW,relationshipbetweenGWpumpingandriverlossesand

alsoexactrechargeanddischargepointsinstreams(Jankowski2007).Wuetal.assesseduncertainties

inSWGWImodelingandemployingaprobabilisticcollectionmethod,theyevaluatedtheapplicability

of the frame‐work through an integrated SW‐GWmodel for a basin in China and asserted that in

describing complex SWGWIs, modeling uncertainties depend on the output and have significant

22 

spatiotemporal variability. Hence, employing a systematic uncertainty analysis can be extremely

helpful in understanding SWGWI (Wu et al., 2014) and also in groundwater model development

process(Engelhardtetal.,2014).

2.3.2.SurfaceWater‐GroundwaterInteractioninKarst

Highpermeabilityandlowattenuationcapacityofkarstaquifers,makemixtureofsurfacewaterand

GWproblematic for freshwater use in the karstic terrains. Usually, surfacewater refers towaste

surfacewaters,seaandbrackishwater,lakewatersandriverwaterswhicharealreadycontaminated

oruntreated.Attemptscanbeenmadetominimizetheinteractionbetweenpollutedsurfacewaterand

GWinkarsticareasbyplacinganimpermeablesealalongcanalbottomsorriverbeds,constructing

smalldams/weirs,buildinggroutingcurtains,changingtheflowdirectionofsurfacewaters,plunging

ponors,creatingreactivebarriersbypumpingfreshwaterintoaquifer(Milanovićetal.,2015).

Furthermore,manyresearchershavestudiedSWGWIparticularlyinkarstaquifers(Chuetal.,2016;

Katzetal.,1997;Rugeletal.,2016).Forexample,(Bailly‐Comteetal.,2009)studiedthehydrodynamic

interactionsbetweenGWandsurfacewaterinakarstwatershedinsouthernFrance.Theirfocuswas

ontheeffectofGWonthegenesisandpropagationofsurfacefloods.Theyshowntheroleofinitial

waterlevelinakarstaquiferinpredictingthetypeofhydraulicconnectionbetweensurfacewaterand

GWduringfloodevents.Moreover,theanalysisofsurfacewaterandkarstGWinteractionconducted

by(Baylessetal.,2014)showsthatanalyticalmethodssuchashydrographseparationandhysteretic

loops can be used for identifying bounding conditionswithin thewatershed. In north coast karst

aquiferofPuertoRico,whererivers,lagoons,intenseprecipitationandalsoseawaterintrusionexists,

SWGWIcanhaveamajorimpactinthequalityoffreshwaterinkarstaquifersoftheregion.However,

wefailedinourattemptstofindanydetailedresearchworksassociatedwithSWGWIinkarstaquifers

ofnorthernPR.Hence,collectingfielddata,doingresearchanddevelopingmodelsinordertofully

understandthemechanismofSWGWIinnorthpartoftheislandisstronglyrecommended.

2.4.ModelingMethods

Inordertopredictthebehaviorofanaquiferbasedonhydrologicalvariations,groundwatermodels

havebeendevelopedbyhydrogeologistsandwaterresourcesscientists.Inaddition,somemodelsare

developedtochemicallyanalyzethewaterqualityandtosimulatefateandtransportofcontaminants.

A groundwater flowmodel is able to exhibit precise representationof hydrological and geological

systemsandalsoitcangivearealinsightintorelationshipandinteractionsbetweensystemelements.

Modelingusingcomputerprogramscanbetrulybeneficialwhentherearekarstaquifersinthecase

studylocation.Thisismainlyduetothefactthatkarstaquifersareveryheterogeneousandanisotropic

andhaveacomplexstructure.Ergo,developingarelativelysophisticatedmodelisthebestoptionfor

simulatingthesetypesofaquifers.

23 

2.4.1.ModelParametersandDevelopment

Dependingonthesoiltypeandwatertablelevel,thepercolationrateregardingthemovementofwater

fromsaturatedzonetogroundwaterdiffers(RitcheyandRumbaugh,1996).Additionally,theimpact

of human interferences with natural water cycle which can be caused mostly by irrigation and

pumpingwaterfromwells,shouldbetakenintoaccount.Actually,agroundwatermodelcandetermine

howmuchitisperilousforanaquiferandalsofortheecosystemifcertainlevelofhumaninterference

exists.Thiscanhelpdevelopingwaterresourcesmanagementplansthatcannotonlyhelpoptimizing

water extraction, but also can preserve the environment and natural resources (Drew and Hötzl,

1999).Otherphysicalparameterssuchastopographicalandgeologicalinformationoftheregionthat

isgoingtobemodeledshouldbegiventothemodelingplatform(PetersonandWicks,2006).

Anisotropy of aquifers regarding hydraulic conductivity, which is a parameter that can have a

dissimilar value in each direction, can only be considered in two or three‐dimensional models.

Nowadays,bydevelopingcomputerprogramsandalsoduetotheneedofacquiringmorevalidresult

astheoutputofmodeling,three‐dimensionalmodelsaremoreacceptabledespitethepossiblecomplex

procedureofsettingthemup(Andersonetal.,2015).Whileone‐dimensionalmodelscanbeapplied

for vertical flow inmultiple horizontal layers, two‐dimensionalmodels considers water flow in a

verticalplainandthis isrepeated inmultipleparallelverticalplainsaswell.Nevertheless,a three‐

dimensionalmodelsubdividestheflowregionintosmallercellsthateachofthemcanhaveadifferent

propertiesregardingaquifercondition,soilcharacteristics,waterflowetc.(Ebrahim,2014).

Mostly, numerical analysis and tools should be used to solve complex differential equations of

groundwater flow. In fact,amathematicalgroundwater flowmodel isabletorepresentconceptual

modelofanaquifermathematicallyandthismathematicalrepresentationenablesresearcherstosolve

the governing equations numerically by computers (Ebrahim, 2014; World Meteorological

Organization,2009).Usingnumerical solutions for solvinggroundwater flowequations ina three‐

dimensionalscaleisbeneficialformodelsthatfollowtheflowdomaindiscretizationapproach.Usually,

inagroundwaterflowmodel,hydraulicheadateachcellcenterandgroundwaterflowratebetween

cellscanbeconsideredasoutcomes.Moreover,impactsonstreamflowbecauseofpumpingorlong‐

termimpactsofcurrentpumpingcanbeassessed.Additionally,checkingtheconsistencyofdatasets

andparametersandalsodefiningeframeworkforfuturestudiesrelatedtogroundwaterandaquifer

conditionaretwoprominentproductsofagroundwatermodel(Hartmannetal.,2014;Ritcheyand

Rumbaugh,1996).

Researchers and hydrogeologists have tried to develop groundwatermodels that can predict and

simulatethegroundwaterflowinthekarstaquifersinaregionalorlocalscale.Regionalgroundwater

modelingareusually large‐scale transientgroundwatermodelscapableofoptimizinggroundwater

24 

resourcesdevelopmentplans,analyzingwaterbudgetofaquifersandassessingregionalflowsystems.

ZhouandLipublishedareviewpaperregardingregionalgroundwatermodelinganddiscussedtheir

characteristics and associates drawbacks (Zhou and Li, 2011). Moreover, regional groundwater

modeling was studied by a few researchers. Sauter assessed the quantification and prediction of

regionalgroundwaterflowandtransportinakarstaquiferinGermany.Hediscussedhowthemost

appropriatemodelingtoolcanbeselectedandhowitcanbeusedforsimulationforaspecificcase

studylocation.Byanalyzingspringflow,spatiotemporalvariationsofgroundwaterlevels,hydraulic

parametersetc.,hismodelwasabletosuccessfullydescribethekarstaquifersysteminthestudied

location (Sauter, 1992). Figure 2.7 depicts the schematic diagram of the process of developing a

groundwatermodel.

Figure2.7.Schematicdiagramrepresentingtheprocessofdevelopingagroundwatermodel–

Modifiedfrom(WorldMeteorologicalOrganization,2009)

2.4.2.Spatiallylumpedmodelsanddistributedparametermodels

Ghasemizadehetal.categorizedgroundwatermodelsintodifferentgroupsbasedontheircapabilities

and characteristics.Due tohigh level ofheterogeneity andanisotropy inkarst aquifers, accurately

understanding of their behavior and distribution has always been challenging. This has forced

25 

modelers to employ approximate‐based approaches and consequently consider the impact of the

uncertaintiescausedbytheseapproachesintheirmodels.Hence,SpatiallyLumpedModels(SLM)and

DistributedModels(DM)orSpatiotemporalDistributedModels(SDM)wereintroducedastwogeneral

approachesinmodelingkarstaquifers(Ghasemizadehetal.,2012;WorldMeteorologicalOrganization,

2009).

Spatiallylumpedmodelscomprisesofconcentratedelementsatspatiallysingularpoints;whereas,the

elements are spatially distributed in distributed models. Hence, in distributed systems, physical

quantitiesarespatiallyandtemporallydependent.Spatiallylumped(orglobal)modelsdonotconsider

spatial alternation of flow patterns and are supposed to simulate a global chemical‐hydrological

responseattheaquiferoutputpoint(forexamplespringdischargepoint)withregardtoinputsofthe

aquifer(e.g.rivers,groundwaterrechargepoints,netrunoffetc.)(Ghasemizadehetal.,2012;Singh,

2014).Assessingtemporalalternationsisanapproachthatspatiallylumpedmodelstaketodescribe

the global water balance and hydrological behavior of an aquifer. Moreover, in spatially lumped

models, some factors that cause complexity in calculations and simulating are neglected due to

simplifying assumptions and hence, using only the global parameters in simple ordinary linear

differential equations and also low data requirements, are some of their properties that can be

consideredwhen trying to select thebestmodeling approach for groundwater flowand transport

simulation.Althoughthesemodelscannotproduceaccurateresults,especiallyinkarsticareas,they

havebeenwidelyusedbyresearchersintheareasthatlessdataisavailableoronlythepredictionof

groundwaterflow,springdischargeandgroundwaterlevelsisnecessary(Long,2015;Panagopoulos,

2012).Hydrograph‐ChemographAnalysis(Dewandeletal.,2003),LinearStorageModels(orRainfall‐

DischargeModels)(ButscherandHuggenberger,2008)andSoftComputingTechniquessuchasFuzzy

Logic (MohdAdnan et al., 2013;Rezaei et al., 2013), GeneticAlgorithm (McKinney and Lin, 1994;

Nicklowetal.,2010)andArtificialNeuralNetwork(ANN)(Huetal.,2008),arethreemainapproaches

withregardtospatiallylumpedmodelsthathavebeenadoptedbyhydrologicalmodelers.

Incontrast,distributedmodelstakecomplexparametersinvolvedingroundwaterflowandtransport

intoaccount. In thesemodels,dependenthydrologicalparametersandboundaryconditionscanbe

spatiotemporallyvariableandthiswillrequiretheequationstobesolvednumericallyandbasedon

partial differential equations (Asher et al., 2015; Kuniansky, 2016). Also, due to the fact that all

variablesshouldbedefiedtothesystem,collectingmoredataandpayingcarefulattentiontodetails

inthistypeofmodelingisdemandedwhichcanmakeitmorechallenging.(Dongetal.,2012;Longand

Gilcrease,2009).Forkarstaquifermodeling,differenttypeofdistributedmodelsbasedonthelevelof

simplifiedassumptionshavebeenused.Severalmethodshavebeendevelopedthateachofthemtreats

complexityofkarstaquiferdifferentlyandsimulatesgroundwater flowbasedon itsown logicand

assumptions. Equivalent Porous Medium (EPM), Double Porosity (or Continuum) Method (DPM),

DiscreteFractureNetwork(DFN),DiscreteChannel(orConduit)Network(DCN)andHybridModels

26 

(HM)arefivecommonmodelingapproachesindistributedsystemsthathavetheirowncharacteristics

which will be discussed shortly in the following (Ghasemizadeh et al., 2012). DFN can be

subcategorizedintoDiscretesingularfracturesetapproach(DSFS)andDiscretemultiplefractureset

(DMFS) approaches. Sometimes, EPM and DPM are also mentioned as Single continuum porous

equivalentapproach(SCPE)andDoubleContinuumporousequivalentapproach(DCPE)respectively

in the literature. It is worth mentioning that employing Hybrid Models, which are the result of

integrating discrete models and EPM approach and are also called coupled continuum pipe flow

models,canbebeneficial inmanycasesregardingmodelingcomplexhydrologicalsystemssuchas

karstaquifers(Kiraly,1998;Liedletal.,2003).Figure2.8demonstratedschematicconfigurationofthe

aforementioneddistributedmodelingapproaches.

Figure2.8.Distributedparametermodelingmethodsforkarstaquifers–Modifiedfrom(Kuniansky,

2016)

Baueretal.developedanumericalmodeltodescribetheinfluenceofexchangeflowbetweenconduits

and fissured system. They found out that under conditions of early karst evolution, conduit

development is faster. Hence, exchange flow plays an important role in developing early karst

evolution in limestone aquifers (Bauer et al., 2003). Also, some researchers employed numerical

modelingapproaches todescribegroundwater flowandtransport inrough fractures (Briggsetal.,

2014)andkarstaquifers (Faulkneretal.,2009).However,modelingkarstaquifers cannotonlybe

carriedoutbynumericalapproaches(BarrettandCharbeneau,1998).Furthermore,forsimulatingthe

genesis of karst aquifer systems, a numerical couple reactive networkmodel, comprising of a 2D

porous continuum flowmodule, a discrete pipe network for modelling flow and transport in the

conduitsandacarbonatedissolutionmodulewasdevelopedby(ClemensT.,1997).

2.4.3.ComputerModelsandPrograms

MODFLOWisthemostcommongroundwatermodelingcodethathasbeenusedduetoitscapability

of simulating complex groundwater flows in a three‐dimensional scale. Working based on finite

27 

differencemethodandblock‐centeredapproach,MODFLOWsimulates thegroundwaterwithin the

aquiferbyconsideringdifferenttypeoflayersunderground(i.e.confined,unconfinedorboth)andalso

different recharge or discharge sources such as areal recharge, groundwater flow towells, runoff

caused by rainfall, flow to riverbeds, spring flow etc. (Harbaugh, 2005). The initial version of

MODFLOW(MODFLOW‐2000)wasreleasedintheyear2000andfiveyearslater,theupdatedversion

(MODFLOW‐2005) startedgaining attentions fromgroundwatermodelers andhydrogeologists.To

enhancetheapplicationofMODFLOW‐2000,twomodelswereintroducedbyUSGSwhichareVSFand

MF2K‐GWT.Basically,VSFisaversionofMODFLOW‐2000thatinadditiontotheabilityofMODFLOW‐

2000tomodelgroundwaterflowusingafinite‐differencemethodina3‐Dscale,canbeapplicablefor

variablysaturatedflow(VSF)(Thomsetal.,2006).Furthermore,MF2K‐GWTisanintegratedmodel

withMODFLOW‐2000thathavetheabilitytosimulategroundwaterflowandsolutetransport(U.S.

GeologicalSurveyWebsite,2012).Nevertheless,someprogramsthatwereindependenttoMODFLOW

butdevelopedbyUSGSwerereleasedaswellsuchasHST3D(3‐DHeatandSoluteTransportModel)

thatisabletosimulateground‐waterflowandassociatedheatandsolutetransportina3Dscale.Its

capabilitiescanbeusedinanalyzingproblemsassociateswithlandfill leaching,seawaterintrusion,

hot‐watergeothermalsystemsetc.(Kipp,1997).

The most updated version of MODFLOW program (MODFLOW 6) was released recently. In this

program,anynumberofmodelscanbeusedforsimulation.Thesemodelscanhaveinter‐connection

witheachotherandthiscanhelpsolvingcomplexhydrogeologicalproblemsinmanycasessuchasthe

conditions inkarstaquifers.Also,withinthisframework,multiple localGWmodelscanbecoupled

withregionalscalemodels(Langevinetal.,2017).Moreover,ConduitFlowPackage(CFP),whichcan

becoupledwithMODFLOW‐2005,canfacilitatesimulationofkarsticgeometryandGWmovementand

consequently,increasetheaccuracyofGWflowmodelinginconduits(Shoemakeretal.,2007).

After releasing MODFLOW‐2005, several associated models and packages were introduced and

released basedon numerous approaches and techniques. As an example,MT3Dmodel,which is a

modular, comprehensive, numerical three‐dimensional solute transport model, was developed by

USGS.Thismodelisdesignedtoworkverywellregardingsimulationofsolutetransportandreactive

solutetransportincomplexhydrologicalsystems.BeingconnectedtoMODFLOW,whichistheUSGS

groundwaterflowsimulator,MT3Disabletosimulateandanalyzeadvection‐dominatedtransport,

especiallysolutetransport,withoutrefiningnewmodels(Bedekaretal.,2016).LautzandSiegelused

MT3DandMODFLOWtosimulategroundwaterandsurfacewatermixinginthehyporheiczone.They

tookadvantageofthismodeldueto itsabilitytosimulateadvectivetransportandsourceandsink

mixingofsolutes(LautzandSiegel,2006).

TakingadvantageofthefeaturesinMODFLOWandMT3D,anewcomputerprogram,SEAWAT,was

released to assist hydrogeologists in simulating three‐dimensional, variable‐density and transient

28 

groundwaterflowthatcanbecoupledwithsolutetransport.InthelastversionofSEAWAT(version

4),theeffectoffluidviscosityanddensityfluctuationscanbeconsideredinsimulationofgroundwater

flowandsolutetransport.Thiswillallowtheuserstorecognizethismodelasatoolthatcanbeused

inawide rangeof simulationpractices includingseawater intrusion in coastalaquifers (Langevin,

2009;Langevinetal.,2008).XuemployedSEAWATinhisdissertationtostudyseawaterintrusioninto

acoastalkarsticaquiferinFlorida.Itisworthmentioningthatseawaterintrusioncanbeconsidered

asasubstantialsourceofbrackishwater incoastalaquiferssuchaskarstaquifer innorthcoastof

PuertoRico(Xu,2016).

In addition, FEFLOW (Finite Element Subsurface Flow System) is a finite‐element package for

simulating3Dand2Dfluiddensity‐coupledflow,contaminantmass(salinity)andheattransportin

the subsurface. It has several applications including regional groundwatermanagement, saltwater

intrusion, seepage through dams and levees, land use and climate change scenarios, groundwater

remediationandnaturalattenuationandalsogroundwater‐surfacewaterinteraction.Asanexample,

astudywasconductedtosimulategroundwaterdynamicsinanirrigationanddrainagenetworkin

Uzbekistan using FEFLOW. After model calibration and validation, the results show high level of

accuracyandcanbeusedforhydrogeologicalmanagementplans(Diersch,2014;KhalidAwanetal.,

2015).

SUTRAisanothermodel thatwasreleasedforsimulating2‐Dsaturated‐unsaturated, fluid‐density‐

dependentflowwithenergytransportorchemically‐reactivesingle‐speciessolutetransportcapable

of analyzing saltwater intrusion and energy transport. It uses a 2D hybrid finite‐element and

integratedfinite‐differenceapproachtoapproximatethegoverningflowandtransportequationsthat

explainthetwointerdependentprocesses.Itshouldbenotedthatthe3Dversionofthismodelwas

alsoreleasedrecently.InSUTRA’sVersion2.2specificationoftime‐dependentboundaryconditions

canbe identifiedwithoutprogrammingFORTRANcode.SUTRA,canalsodescribechemicalspecies

transport including absorption, production and decay processes and assesswell performance and

pumpingtestdata(VossandProvost,2002).Forinstance,Hussainetal.usedSUTRAintheirpaperto

studycoastalaquifersystemsthataresubjectedtoseawaterintrusion(Hussainetal.,2015).

Furthermore, Visual MODFLOW Flex model, an integrated modeling environment that connects

MODFLOWandMT3D,isabletosimulatecomplex3Dgroundwaterflowandcontaminanttransport.

Its graphical user interface and 3D visualization capabilities in addition to its ability to simulate

groundwater flow and contaminant transport can gain attention of hydrological and groundwater

modelers.Asanexampleofwork,Varghese,RaikarandPurandarasuccessfullydevelopedaVisual

MODFLOWFlexmodelforsimulationofgroundwaterflowinaregioninIndia(KumarandSingh,2015;

Vargheseetal.,2015).

29 

CHEMFLO‐2000, which is interactive software for simulating water and chemical movement in

unsaturated soils, enables users to simulate groundwater flow and chemical fate and transport in

vadosezones.Themodelcanbeusedasatoolthatcanenhancetheunderstandingofunsaturatedflow

andtransportprocesses.Inthismodel,watermovementandchemicaltransportaremodeledusing

the Richards and the convection‐dispersion equations, respectively. The equations are solved

numericallyusingthefinitedifferencesapproach(NofzigerandWu,2003).

Another3Dfinite‐elementbasedmodelforsimulatingflowandtransportis3DFEMFAT.Thismodel

works for saturated/unsaturated heterogeneous and anisotropic media. Its typical applications

includeinfiltration,agriculturepesticides,sanitarylandfill,hazardouswastedisposalsites,density‐

inducedflowandtransport,saltwaterintrusion,etc.Itsflexibilityandfeasibilityinsimulatingawide

range of practical problems especially by employing its transport module, has made it valuable

softwareforresearchersandtransportmodelers.Alsoitsapplicationinstudyingseawaterintrusion

incoastalaquiferwasverifiedbysomescientists(LathashriandMahesha,2016;Parketal.,2012).

Regardingsurfacewaterandgroundwaterinteractionwhichwasdiscussedintheprevioussectionsin

detail, GSFLOW (Groundwater and Surface‐water FLOW) was released by USGS in 2008 as an

integratedtoolthatisabletocouplegroundwaterandsurfacewaterflowmodelsbytakingadvantage

oftheapproachesusedinUSGSPrecipitation‐RunoffModelingSystem(PRMS)andtheUSGSModular

GroundwaterFlowModel(MODFLOWandMODFLOW‐NWT).Meteorologicalandhydrologicaldata

such as rainfall, sunny hours and temperature in addition to groundwater stresses and

initial/boundaryconditionsareinvolvedasinputsfortheprocessofsimulationinthismodel.GSFLOW

canalsotakeintoaccounttheimpactoflandcoverchange,climatechangeandgroundwaterextraction

onsurfacewaterandgroundwater flow forspatiotemporallyvariable situations (Markstrometal.,

2008).However,regardingitslimitations,itwasassertedbyresearchersthatitsabilitytosimulate

surfacewaterandgroundwaterinkarstaquiferswithhighlevelofheterogeneityisnotguaranteed

(Fultonetal.,2015).

Bytakingadvantageofacontrolvolumefinite‐differencemethod,MODFLOW‐USG(Un‐SaturatedGrid

versionofMODFLOW)isabletosimulategroundwaterflowanditsrelatedprocesses.Thisversionof

MODFLOWsupportsdifferenttypesofstructuredandunstructuredgrids.Thiscapabilityisextremely

usefulwhenhigh resolution along rivers and aroundwells is needed. In addition,MODFLOW‐USG

couplesConnectedLinearNetwork(CLN)processtoGroundwaterFlow(GWF)process,whichwas

introducedinMODFLOW‐2005,toanalyzeandsimulatetheinfluenceofkarstconduitsandmulti‐node

wells.Hence,thisversioncanhelpmodelerstogainadeeperunderstandingaboutkarstsystemsand

conduitnetworks(Pandayetal.,2013).Moreover,forthepurposeofgeneratinglayeredquadtreegrids

that canbeused inMODFLOW‐USGorother similarnumericalmodels, a new computerprogram,

GRIDGEN,wasdevelopedbyLienetal.in2015.Afterreadinga3‐Dbasegrid,GRIDGENwillcontinue

30 

dividing intorefinement features,which isprovidedbyuser,until reachingthedesiredrefinement

level.Afterfinishingtheprocessofgridding,atreestructurefilewillbecreatedandcanbeusedin

numericalmodelssuchasMODFLOW‐USG.ThismodelwasusedforassessingtheBiscayneaquiferin

southernFloridainwhichkarstaquifersareabundant(Lienetal.,2014).

DevelopingaNewton‐RaphsonFormulationforMODFLOW‐2005forofferinganenhancedsolutionfor

problemsrelatedtogroundwaterflowinunconfinedaquifers,MODFLOW‐NWTwasintroducedand

developedbyNiswongeretal.ItsmainapplicationinadditiontoSurface‐WaterRouting(Hughesetal.,

2012)andSeawaterIntrusion(Bakkeretal.,2013)canbedescribedasitsabilitytosolveproblems

thatarecoupledwithdryingandrewettingnonlinearitiesinequationsthatgoverngroundwaterflow

inunconfinedaquifers.

ArecentlydevelopedmodelsimilartoMODFLOWbutwithawiderrangeofapplicabilityindescribing

hydrological systems isRainfall‐ResponseAquifer andWatershedFlowModel (RRAWFLOW).This

lumped‐parametermodelreceiveshydrologicalinputssuchasrainfall,rechargeanddischargeetc.and

isabletosimulategroundwaterlevel,streamflowandspringflow.Italsocanbeusedformodeling

solutetransportinaquifersandassessingsystemresponsetohydrologicalevents(Long,2015).For

classificationofkarstaquifersandcharacterizingtime‐variantsystems,LongandMahlerdeveloped

and used thismodel in 2013. Thismodelwas used to predict and classify hydraulic responses to

rechargeintwokarstaquifersinTexasandSouthDakota,USA(LongandMahler,2013).

Usually,groundwaterflowandcontaminanttransportmodelsareusedsimultaneouslyusingsoftware

platforms such as GMS. Several researchers conducted flow and transport analysis (e.g. using

MODFLOWandMT3D)andachievedaccurateandvalidresults(AbdallaandKhalaf,2015;Boraand

Borah,2016).Also,fewscientistsstudiedthegroundwaterflowandcontaminanttransportinkarstic

aquiferofnorthernPuertoRicousingGMSandtheirmodelingresultsshowitscapabilityinanalyzing

anddescribinghydrologicalsystemswithcomplexpropertiessuchashighlevelofheterogeneityand

anisotropy (Ghasemizadeh, 2015; Ghasemizadeh et al., 2016; Maihemuti et al., 2015). Table 2.3

elaboratesthecharacteristicsandapplicationofaforementionedmostcommonlyusedgroundwater

modelingcodes.

Ta ble2.3.Prevalentgroundw

atermodelsthatwereusedforsimulatinggroundwaterflow

andcontaminanttransport.FEandFDrepresentFinite

Elem

entandFiniteDifferencerespectively.

Model/Software

Modeling

technique

Focus

ApplicationandAdvantages/Reference

EaseofUseand

Accuracyfor

KarstModeling

GWFlow

Solute

Transport

Heat

Transport

3DFEMFAT

FE

**

GWmodelinginsaturated/unsaturatedheterogeneousand

anisotropicmedia,simulationofinfiltration,agriculture

pesticides,sanitarylandfill,hazardouswastedisposalsites,

density‐inducedflowandtransport,seaw

aterintrusion

etc.(LathashriandMahesha,2016;Parketal.,2012)

High

AQUA3D

FE

**

*

3Dgroundw

aterflow

andtransportsimulationfor

homogeneousandanisotropicflow

conditions,simulationof

heatandcontaminanttransportbytakingintoaccountthe

effectofdispersion

MediumtoHigh

CHEM

FLO

FD

**

Simulationofwatermovem

entandchemicalfateand

transportinvadosezonesandlayeredsoilbyemploying

improvednum

ericalmethods(NofzigerandWu,2003)

Low

FEFLOW

FE

**

*

regionalgroundw

atermanagem

ent,saltwaterintrusion,

seepagethroughdamsandlevees,landuseandclimate

changescenarios,groundw

aterrem

ediationandnatural

High

31

attenuation,groundw

ater‐surfacewaterinteraction

(Diersch,2014;KhalidAwanetal.,2015)

GSFLOW

FD

*

CoupledGroundw

aterandsurfacewatermodelwhichcan

assessthehydrologicalbehaviorbasedonlandusechange,

climatevariabilityandgroundw

aterwithdrawals

(Markstrom

etal.,2008)

Medium

HST3D

FD

**

*

sub‐surface‐wasteinjection,landfillleaching,saltwater

intrusion,freshw

aterrechargeandrecovery,radioactive‐

wastedisposal,hotw

atergeothermalsystems,and

subsurface‐energystorage(Kipp,1997)

Medium

MODFLOW

FD

*

Simulationofsteadyorunsteadyflowincom

plexflow

system

withirregulargeom

etry,Simulationofflow

from

externalstressesinaconfinedorunconfinedaquifer

(Harbaugh,2005),Highapplicabilityforkarstaquifersifit

coupleswithCFPpackage

High

MODFLOW‐NWT

FD

*

Surfacewaterandgroundw

aterinteractions,seawater

intrusionandsolvingproblemsrelatedtodryingand

rewettingnonlinearitiesoftheunconfinedGWflow

equation(Niswongeretal.,2011)

Medium

MODFLOW‐

OWHM

FD

*

Simulation,analysis,andmanagem

entofhum

anandnatural

watermovem

entw

ithinaphysically‐basedsupply‐and‐

demandfram

ework,seawaterintrusion,conjunctiveuseof

groundwaterandsurfacewater(Hansonetal.,2014)

LowtoMedium

32

MODFLOW‐USG

FD

*

UnstructuredgridversionofM

ODFLOWforsimulatingGW

flowandotherrelatedprocesses,simulationoftheeffectsof

multi‐nodewells,karstconduitsandtiledrains(Pandayet

al.,2013)

High

MT3D

FD

*

simulationofsolutetransportandreactivesolutetransport

incom

plexhydrologicalsystemsandanalyzingadvection‐

dominatedsolutetransport(Bedekaretal.,2016)

High

SEAWAT

FD

**

*

3Dsimulationofvariabledensity,transientgroundw

ater

flowinporousmediacoupledwithmulti‐speciessoluteand

heattransport,seaw

aterintrusionincoastalaquifers

(Langevin,2009;Langevinetal.,2008;Post,2011)

High

SURTA

FE

**

*

Simulationofsaturated‐unsaturated,fluid‐densit y‐

dependentgroundw

aterflow

withenergytransportor

chem

ically‐reactivesingle‐speciessolutetransport(Voss

andProvost,2002)

Medium

33

34 

2.4.4.EquivalentPorousMedia(EPM)method

Severalapproacheshavebeenfollowedtoachieveaccurateandvalidresultswithacceptableefficiency

atthesametime.Forsomecases,simulationofgroundwaterhydraulicandcontaminanttransportin

karst aquifers is carriedoutbyemployingEquivalentPorousMedia (EPM)method (Scanlonetal.,

2003). Basically, using EPM approach formodeling a karst aquifermeans considering simplifying

assumptionsinordertomakethemodelmorepracticalandapplicable.Ghasemizadehetal.developed

theirmodelbasedinEPMapproachandfoundoutthat itsresult isacceptableforpredictingwater

table fluctuations. Although their EMP‐basedmodel was not supposed to be accurate enough for

contaminant transport, they found good agreement between their model output and actual data

regardingspreadingTCE(Ghasemizadehetal.,2015).Furthermore,inanotherstudy,byemploying

drainagefeaturesinregionalgroundwaterflowmodelinginkarsticaquiferofnorthernPuertoRico,

Ghasemizadehetal.assertedthattheywereabletoimprovetheirsimulationbyassigningarraysof

adjacentmodelcellswithdrainstosimulateconduits.Theysuggestedthatusingthisfeaturecanbe

truly helpful especially when there is not sufficient data for conduit characteristics. Similarly,

Maihemuti et al. developed a regionalmodel for assessing karst aquifer system and groundwater

resourcesforacasestudylocationinnorthernPuertoRico.Theycameintoconclusionthatalthough

thereishighpotentialofconduitdominatedflow,theresultoftheirEPM‐basedapproachisreliablein

representing thehydrodynamicsof thekarstaquifer in their casestudy location(Maihemutietal.,

2015).Moreover,Maihemutietal.simulatedaregionalkarstaquifersystemtoevaluategroundwater

systeminnorthernPuertoRico.TheydevelopedthismodelusingEPMapproachtopredictthekarst

systemresponsetorainfalleventsandhighpumpingdemandsandalsotodescribethehydrological

behavioroftheaquifer.Theyassertedthatthismodelcanbeusedforpredictionofgroundwaterlevel

fluctuationsundervariousexploitationscenarios(Maihemutietal.,2015).

2.4.5.HowRemoteSensingCanImproveKarstGWAssessmentandModeling?

UsingGeographic Information System (GIS) as a tool in groundwatermodelingprocedure, is truly

beneficial; because all parameters such as distribution of rainfall, groundwater recharge and

discharge,landcoveretc.aredefinedwithinaspatialcontext(SinghandFiorentino,1996).Several

researchers took advantage of this powerful tool directly or as a parallel method in integrated

approaches(Daretal.,2010;Nampaketal.,2014).(Alonso‐Contes,2011)usedremotesensingand

advanced digital image processing techniques to delineate karst features which can enhance the

understanding with regard to hydrogeology of the Tanamá River and Rio Grande de Arecibo

catchments located in thenorthcoast tertiarybasinofPuertoRico.Basically, remotesensing tools

assistedtheauthorinlineamentmappingforGWexploration.

35 

Also,MandaandGrossemployedGISanalysistocharacterizesolutionconduitsinkarsticareas.Based

ontheirstudy,theyshowedthatGIS‐basedmethodscanbeusedfordeterminingdepths,dimensions,

shapes, apertures and connectivity of potential conduits and also for describing physical

characteristicsthathaveaneffectonthegroundwaterflowinkarstaquifers(MandaandGross,2006).

Inaddition,Theilen‐Willigeetal.employedGISandremotesensingmethodsbyanalyzingsatellitedata

inordertodetectofnear‐surfacefaultsandfracturezonesthatcanleadtodissolutionprocessesin

conduitsofkarstaquifers(Theilen‐Willigeetal.,2014).

Numerousresearcherstookadvantageofthistoimprovetheirmodelsandsolvesomeun‐answered

andcomplexproblemsbyincreasingtheaccuracyofpredictionandalsobytakingintoaccountother

hydrologicalphenomena(AshrafandAhmad,2012;Machiwaletal.,2012;Thakuretal.,2016;Xuet

al., 2011). Table 2.4 elaborates the application of GIS and remote sensing in different phases of

groundwatermodeling.

Table2.4.ThepotentialroleofGISandremotesensingindifferentstepsofgroundwatermodeling

procedure–Modifiedfrom(AshrafandAhmad,2012)

Phase GISfunctions ModelingSteps

DataCollection

andAnalysis

Datainput,Digitization,Dataconversion

(import/export,

Coordinatetransformation,Mapretrieval

Groundwaterandhydrological

datacollection

Developing

Conceptual

Model

Conversionofvectorandrasterlayers,Data

integration,Imageprocessing,buffering,

Surfacegeneration,Linkingofspatialand

attributedata

Developingconceptualmodel

ModelDesign

Mapcalculations,Neighborhoodoperations,

Interpolation,Theissenpolygons,buffering,

Surfacegeneration

Delineatingboundary

conditions,Meshgeneration,

3Dlayeringoftheaquifer

Model

Calibration

DatalayersintegrationParameterzonation,Recharge

estimation,Waterbalance

OverlayanalysisSteady‐stateandTransient‐

statesimulations

Statisticalanalysis Parametersestimation

36 

Model

Generalization,

Predictionsand

Result

Presentation

DataretrievalPrediction,Assessingdifferent

scenarios

Datavisualization,Presentationofsimulated

resultsMapcomposition

2.5.Conclusion

Employingacomprehensiveandefficientapproachformanagingwaterresourcesinregionswhere

groundwateristhekeysourceofwatersupplyandvulnerabletocontamination(e.g.karstaquifers)is

vital. Limestone karstic aquifer of northern Puerto Rico has been experiencing high level of

contaminationandthishasresultedinacceleratingrateofpretermbirthsintheislandinadditionto

otherhumanhealthrelateddisorders.InPR,severalwellsandsiteswereconsideredastheNational

PriorityList (NPL) Superfund Sites and remediation action for treatingwater in these sites are in

process.Byconsideringnorthernpartoftheislandasthecasestudylocationandalsobyfocusingon

karstaquiferswithconduitnetworkasthemostcomplicatedandhard‐to‐analyzeformsofaquifers,a

reviewstudytoassessGWresourceswaspresented.Afterashortexplanationofkarstsystemsand

their associated studymethods, a brief reviewdiscussionongroundwater contaminationand risk

assessmentwas carried out. Potential contamination threats in karst aquiferswere discussed and

differentremediationtechniqueswereevaluated.

SurfacewaterandGWinteraction(SWGWI),asamajorsourceofGWcontaminationandwaterlevel

fluctuation,wasalsoreviewed.DesktopandfieldtoolsassociatedwithSWGWIwereintroducedand

assessed as key approaches for understanding interconnectivity between GW and surface water.

DespitethefactthatnumerousmethodshavebeendevelopedfordescribingSWGWI,therearestill

uncertainties and lack of sufficient knowledge for fully understanding the time lag between GW

pumpinganditsinfluenceonSW,relationshipbetweenGWpumpingandriverlossesandalsoexact

rechargeanddischargepointsinstreams(Jankowski,2007).Multiplefeaturesandsoftwarepackages

forsimulatingSWGWIcanbeemployed;however,asystematicuncertaintyanalysis isessentialfor

achievingvalidandreliableresults.

Furthermore,acomprehensivediscussiononexistinggroundwatermodelingmethodswithregardto

their application, advantages and disadvantages was presented. Mostly, numerical modeling

approachesare takenbymodelerstosimulatecomplexgroundwatersystems.Numericalmodeling

tools often take advantage of finite‐difference and finite‐element techniques to solve complicated

37 

equationsthatgovernshydrologicalsystemdynamicsinanaquifer.Forkarstaquifers,lumpedmodels

andspatiallydistributedmodelsareconsideredastwogeneralmodelingapproachesthatcanbeused

forcertainconditions.Spatiallylumpedmodelscanbeusedevenwhenheterogeneousstructureof

karst aquifers is unknown and hydraulic data is not sufficient. Thesemodels are usually used for

regionalgroundwaterqualitypredictions.Ontheotherhand,spatiallydistributedmodelsareoften

usedwhenspatiallyassessmentofgroundwaterqualityandquantityisthepurpose.Obviously,these

models,requiremoredataasinput,havemoreaccuracyandarecapableofsimulatingfine‐scalelocal

groundwaterflow.Inaddition,variouscomputer‐basedmodelshavebeenexplainedandevaluated.

MODFLOW,as themostpopulargroundwater flowmodeling code in addition toFEFLOW,HST3D,

SEAWATandAQUA3Dhavebeen introduced in this studyaspowerful tools forgroundwater flow

simulation. For solute and contaminant transport, usually,MT3D code is used.Also, usingHST3D,

SEAWAT,SUTRAandothersimilarcodes,researchershavesuccessfullydevelopedcontaminantand

solute transport models. A combination of the aforementioned models can be used to simulate

groundwaterflowandcontaminanttransporteitherinsteadystateortransientform.

38 

Chapter3:

AssessmentandModelingofGroundwaterNitrateContamination

withinaCoastalKarstAquifer

3.1.Introduction

There are several approaches for assessment of contaminant fate and transport in GW.Modeling

methods offer valuable capability of for accurate simulation and assessment of GW flow and

contaminants transport in aquifers (Conan et al., 2003;Molénat andGascuel‐Odoux, 2002). These

methods, however, are challenging to implement in aquifers within karst regions because of the

significantheterogeneityofsuchaquifers.ThescopeofthisworkistostudyGWNitratecontamination

inkarstaquiferofNorthCoastLimestoneaquiferofPuertoRicobyemployinganumericalmodels.The

applicabilityofmodelingtoolsinquantitativeandqualitativeevaluationofcomplexhydrogeological

systemsinkarstisexamined.Also,predictionofspatiotemporaldistributionofNitratecontamination

inadditiontoimplicationsandrecommendationarepresented.

3.1.1.SiteDescription

3.1.1.1.GeographicalLocation

PuertoRicoisland(8937km2),aterritoryoftheUnitedStates(US),islocatedinnortheasternsideof

CaribbeanSeaandhaveanestimatedpopulationof3.7million(Castro‐Prietoetal.,2017).Thereare

manysurfacewaterandGWresourcesacrosstheislandthatprovidefreshwaterandalsoareusedfor

agriculturaland industrialdevelopment.Thecasestudy location is innorthernpartofPuertoRico

(PR), comprising Arecibo, Barceloneta,Manati, Vega Baja, Vega Alta, Dorado and small portion of

FloridaandToaBajamunicipalities.Figure3.1exhibitsthegeographicallocationandelevationrange

(BasedononlineDigitalElevationModelorDEMdata)ofthecasestudyarea.

39 

Figure3.1.Geographicallocationandelevationrange(BasedononlineDigitalElevationModelor

DEMdata)ofthecasestudyarea

3.1.1.2.Geology

AlongthenortherncoastofPR,widespreadsolution‐basedactivitieshaveinfluencedthelimestone

and this has led tokarst topography formation in the area.Karst terrains are themost important

physiographic features in Northern PR (NPR). These terrains consist of common solution feature

landforms(e.g.sinkholesandcockpits)andresidualtowerkarstfeatures(i.e.landformswhichhave

elongatedplainssurroundedbysteephills)(Gómez‐Gómezetal.,2014).

Thereare2majoraquifers(Figure3.2)innorthcoastlimestoneaquiferofPR:1‐Theupperaquifer

which has connection to the surface throughout most of its outcrop area and is associated with

AymamónandAguadalimestoneandalluvialdepositsalongthecoastalareasand2‐Theloweraquifer

which is associatedwith various locations of the Cibao formation and Lares limestone and also is

confinedtowardthecoastalzoneandoutcropstothesouthoftheupperaquifer,whereitisrecharged

(Maihemutietal.,2015;Renkenetal.,2002).

40 

Figure3.2.GeneralizedsurficialgeologyoftheNorthCoastLimestoneaquiferofPuertoRico(Vertical

scaleinthelowerpictureisexaggerated)–Modifiedfrom(Gómez‐Gómezetal.,2014)

InnorthernPR(NPR),GWflowsthroughanetworkofpreferentialflow‐pathssuchasclosely‐spaced

conduits and faults due to existence ofmanymajor springs and limestone rocks containingwater

(Giusti, 1978). Due to presence of limestone karst aquifers with high level of heterogeneity and

anisotropyinthisarea,rainfallwatercaneasilypercolateintothegroundandthisrapidmovement,

makeskarstaquifervulnerabletocontamination.Infact,limestoneaquifersinhumidareas(similarto

PR)havebeenreportedtobemorevulnerablecomparedtootheraquifersinsub‐humidareas(Kreitler

andBrowning,1983).Moreover,highlyheterogeneousandkarsticaquiferswithconduitscancause

highrateofwaterlevelfluctuationeveninsmalltemporalscaling(Yuetal.,2016).Behaviorofkarst

conduit system plays a more important role than hydraulic conductivity of matrix in assessing

contaminant transport within karst aquifers (Ghasemizadeh et al., 2016). Hence, more complex

approachesshouldbeemployedforquantitativeandqualitativeassessmentofGWinkarstaquifers.

Basedonhydrogeologicalstudies,innorthwesternPR,betweenAguadillaandRioCamuyarea,water‐

containingconduitsarepresent.Thereare3majorspringsbetweenRioGrandedeManatiandRio

41 

IndioareawhicharecategorizedasconduittypespringsbyRodrfguez‐Martmez.Thisconfirmsthat

intense fluctuations in transmissivity data is probably because of fracture zones and dissolution

channelsinthatarea(Ghasemizadehetal.,2016;Rodrfguez‐Martmez,1997).

3.1.1.3.Climate

Theairtemperatureintheislandfallswithinarelativelyshortrangeduetoconstantsolarradiation

andseawatertemperature.AugustandFebruaryarethehottestandcoolestmonths,respectively.In

the central north coast, on average, maximum and minimum temperatures were recorded as

approximately30and21°C(86and70°F),respectively,basedonthreedecadesofNationalOceanic

and Atmospheric Administration (NOAA) data from 1981 to 2010 (Figure 3.3 shows temperature

trendsince1995).BecauseoftheexistenceofCordilleraCentralandSierradeCayeymountains,the

uppertwothirdoftheisland(includingourcasestudylocation)hasahumidclimatewhilethelower

one third is semi‐arid. Along the north coast, prevailing winds blow from the northeastern side

(Gómez‐Gómezetal.,2014).

3.1.1.4.Hydrology

Historical rainfall data from National Oceanography and Atmospheric Administration (NOAA)

demonstratesthatNPRhasrelativelydryandwetseasonsinDecembertoAprilandMaytoNovember

periods,respectively.Inparticular,basedonmonthlyprecipitationdata,MayandFebruaryarewettest

anddriestmonths,respectively.Hurricanesintheregionoccurmostlyinthewetseason.Accordingto

thescientificinvestigationsandhistoricaldata,infiltrationduetoprecipitationisconsideredthemain

sourceofaquiferrechargewhilethereareseveralstreamsinthearea.Theinfiltrationandpercolation

occurthroughthelimestoneoutcropsviarunofftosinkholesandexistingdepressionsassociatedwith

topography(Maihemutietal.,2015).Onaverage,theamountofprecipitationandevapotranspiration

intheislandareroughly1,825and1,189mm/yrespectively.Fromtheremaining636mm/yofwater

on/intheground,theportionsofstreamflowandGWare583mm(161m3/s)and53mm(14.6m3/s)

annually,respectively(Gómez‐Gómezetal.,2014).MajorstreamsinthestudyareaareRiodelaPlata

(thelongestriverwiththelargestwatershedareaintheisland),RioCibuco,RioGrandedeManatiand

RioGrandedeArecibofromEasttoWest(Figure3.8).Figure3.3showsrainfallandtemperaturetrends

inManati (NOAAMANATI 2 E (66‐5807) station, Elevation: 250 ft, Latitude: 18.43°N, Longitude:

66.45°W)andalsodepth towater table fromgroundsurface (USGS182549066304300USGS166

ObservationWell,Latitude:18.43°N,Longitude:66.51°W)

42 

Figure3.3.Rainfall,TemperaturerangeanddepthtoGWlevelfromgroundsurfacefor1995‐2015

inManati,PR

3.1.1.5.LandCover

PuertoRicohasbeensubjectedtourban,industrialandagriculturaldevelopmentforafewdecades.

Thishasbeentheresultofpopulationgrowthanditsconsequencessuchasdemandforfood,jobsetc.

(Castro‐Prietoetal.,2017;Martinuzzietal.,2007).PRisatropicalislandwithextensiverainfalland

greenareas(i.e.forest,shrublands,grasslandsandvegetatedfields).Themainurbandevelopedarea

isintheSanJuancity(theCapital).AlongthestudyareainNPR,extensiveagriculturaldevelopment

andindustrialactivitiesduringpastdecadeshaveresultedindeteriorationofGWquality(Yuetal.,

43 

2015).Figure3.4,createdfromLandCoverNationalDataset,depictslandcovermapofnorth‐central

municipalitiesoftheisland.

Figure3.4.Majorlandcoversinnorth‐centralmunicipalitiesinPuertoRico–DatafromUnitedStates

GeologicalSurvey(USGS)NationalLandCoverDatabase(NLCD)2001

Moreover,Figure3.5,demonstratesagriculturalcapacityofsoilinmunicipalitiesofnorth‐centralpart

ofPR.ThismapwasgeneratedusingSoilSurveyGeographic(SSURGO)Database,preparedbyNatural

ResourcesConservationServiceatUnitedStatesDepartmentofAgriculture.

Figure3.5.Agriculturalcapabilityofsoilinmunicipalitiesofnorth‐centralpartofPR

3.1.2.OccurrenceofNitrateinGW

TheconcentrationofNitrate(NO3),Nitrite(NO2)andAmmonia(NH3+),ascommonformsofNitrogen,

aretypicallymeasuredinGW(Almasri,2007).SinceNitriteexistsinamuchsmallerconcentrationthat

44 

Nitrateduetoitsinstability,thecombinationofthesetwoissometimesreportedastheconcentration

ofNitrate.ItwasreportedthatthepresenceofAmmoniaandorganicNitrogeninGWisrarebecause

ofthelowlevelofdemandedbiologicalactivitiesinaquifersthatresultintheirproduction(Burkartaus

andStoner,2008).Moreover,NitrousOxide(N2O),whichisamajorgreenhousegas,isanotherform

ofNitrogeninGWandisaccumulatedwithintheaquifermostlybecauseofdenitrification(Juradoet

al.,2017).Additionally,differentisotopesofNitrateinGWsystemshavebeendiscussedby(Kendall

andAravena,2000).

Becauseofitssolubilityandnegativecharge,Nitrateisverymobileandcaneasilyleachfromground

surfaceandunsaturatedzone.HighconcentrationofNitrateindrinkingwatercanpotentiallycause

healthproblemssuchasmethemoglobinemia (adecrease in the capacityof theblood to transport

oxygen,alsoknownas"bluebabysyndrome")ininfantsandstomachcancerinadults(Halletal.,2001;

WolfeandPatz,2002).Moreover,(Galaviz‐Villaetal.,2010)and(MajumdarandGupta,2000)reported

otherhealthproblemssuchasthedysfunctionofthethyroidgland,productionofnitrosamines(which

commonly leads to cancer), gastric cancer, goiter and hypertension. Consequently, the US

Environmental Protection Agency (USEPA) has determined 10 mg/l NO3‐N as the maximum

contaminantlevel(MCL)ofNitrateindrinkingwater.(Almasri,2007;KendallandAravena,2000)

SpatiotemporalchangesinNitrateleachingfromtheunsaturatedzonetieswithuncertaintiesandalso

complexinteractionsandparameters.Landcover,pointsourcesofNitrogen,rainfallandinfiltration,

behaviorofNissoil,geologicalsettingandwatertablelevelareamongmostsignificantfactorsthat

contributetooccurrenceofNitrateinGW.(Almasri,2007).Moreover,uncertaintiessuchaspresence

of multiple Nitrogen loading sources in a certain area, point and non‐point Nitrogen source

overlappingandoccurrenceofbiogeochemicalprocesseswithinthesoil(KendallandAravena,2000)

increasethecomplexitylevelofNitrogen‐GWinterconnectivity.Hence,thoroughunderstandingofthe

relationship between the amount of on‐groundNitrogen loading andNitrate concentration in GW

systems requires complicated analysis and careful consideration. As presented in Figure 3.6,

spatiotemporal occurrence of Nitrate in GW depends on on‐ground Nitrogen loading, soil

characteristics/behaviorandGWproperties.ItcanalsobeassertedthatNitratefollowsanadvective

anddispersivemovementwithintheaquifer.

45 

Figure3.6.Schematicdiagramofon‐groundNitrogenloadingsourcesandpossibleinteractionof

Nitrogen‐basedcompoundsinunsaturatedandsaturatedzones(Almasri,2007)

Often, land cover can be a good indicator and predictor of Nitrate concentration in the aquifers

(Gardner and Vogel, 2005). However, there are several uncertainties and factors such as rainfall,

temperature, and soil properties in each region that can undermine the prediction of Nitrate

contaminationmerelybasedon landcoverdata (McLayetal.,2001;Wicketal.,2012).Keelerand

PolaskyhaveestimatedthattheincreasedcostforaddressingGWNitratecontaminationduetoland

coverchangeandagriculturaldevelopmentinSoutheasternMinnesotacanbeupto$12millionfora

20‐yearperiod(KeelerandPolasky,2014).Urbanandruralaquiferscanhaveadifferentresponseto

climaticvariationswithregardtoNitrateconcentrationandstudyingtheresponseofanaquifer to

Nitratedynamics(especiallyinkarstaquifers)requiresmorein‐depthunderstandingandanalysisdue

toseveralcomplexconditions(Opsahletal.,2017).ItwasalsoreportedthatGWNitratecontamination

canbeaffectedadverselybyclimatechange(Stuartetal.,2011).

46 

Inmanygeographicallocations,elevatedconcentrationsofNitratemainlyduetohumanactivitieshave

been reported (Buvaneshwari et al., 2017; Elisante and Muzuka, 2015). Point sources of

Nitrogen/Nitrateleachatesuchasoldsepticsystems,landfills,wastewaterholdingpondsandleaks

from cracks in sewer pipelines can cause GW Nitrate contamination (Almasri, 2007; Kendall and

Aravena, 2000;Wakida and Lerner, 2005). Additionally, non‐point sources of N leaching, such as

fertilizeruseinagriculturalareas,playaverysignificantroleinincreasingGWNitratecontamination.

Strong correlation between agricultural activities (use of fertilizers andmanure) and GW Nitrate

contamination was reported (Babiker, 2004; Burkartaus and Stoner, 2008; Carey and Cummings,

2013).Guetal.assessedsourcesofGWcontaminationNitrateinChinaandassertedthatagricultural

activitiesfollowedbylandfillleachatearetwomainsourcesofNitratepollution(Guetal.,2013).Ina

USGS report with focus on Manati and Vega Baja municipalities in NPR, Nitrate occurrence and

contaminationwereassessed.ItwasidentifiedthatthemajorsourcesofNitratecontaminationinthe

karst aquifer of the region are use of fertilizers for cultivation of pineapples and also septic tank

effluentinruralandun‐sewered(nosewersystem)areas(Conde‐CostasandGómez‐Gómez,1999).

AlthoughPuertoRicoislocatedinahumidandhotregionwhichintensifiesdenitrificationinsoilasa

naturalcontaminantattenuationprocess,elevatedlevelsofNitratehavebeenconstantlyreporteddue

toexcessiveagriculturalactivities(SpaldingandExner,1972).Itisreportedthattheuseoffertilizers

foragriculturalactivitiesisincreasingeveryyearintheUS.Hence,basedonthefactthatlargeamount

offertilizershasastrongcorrelationwithelevatedNitrateconcentrationinGW,usageoffertilizers

shouldbelimitedatleastintheregionswithhighlypermeableandvulnerableaquifers(Kumarasamy,

2007).Itwasshownby(Kurtzmanetal.,2013)that50%reductioninuseofnitrogenfertilizeradded

totheirrigationwaterinIsrael,resultsin70%mitigationofaverageNO3‐NfluxtoGWinadditionto

20%reductioninrootNuptakeandasignificantdecreaseinconcentrationofNO3‐Ninporewater

withinvadosezone.AcomprehensivestudybyBurowetal.impliesthatGWNitrateconcentrationin

theUS ishigher inshallowandoxicaquifersespeciallybeneaththeareaswith intenseagricultural

activities,highsoilpermeabilityandoxicgeochemicalconditions.Itwasassertedthattheexistenceof

dissolved Iron followed by manganese, calcium, farm N fertilizer inputs, percentage of highly

permeablesoilanddissolvedoxygen(DO),isabletojustifythefluctuationsinNitrateconcentration.

Additionally, themost important factors influencing GWNitrate concentrationswere identified as

redoxconditions,non‐pointNloadings,otherwaterqualityindexesandphysicalvariables(Burowet

al.,2010).

3.1.3.GWNitratemodelingandprediction

SeveralresearchershavedevelopedaccurateandvalidmodelstoassessGWNitratecontaminationin

manygeographicallocations.Forinstance,usingordinaryandindicatorkrigingtechniques,Arslanet

al.assessedspatiotemporaldistributionandvariationofGWNitrate(Arslanetal.,2016).Akhavanet

47 

al. used Soil and Water Assessment Tool (SWAT) to assess Nitrate leaching and pollution in a

watershedinIran(Akhavanetal.,2010).Lakeetal.developedseriesofmodelsbymergingspatialdata

of on‐ground loading, soil properties, drift cover and aquifer type to evaluate factors affecting

vulnerabilityofGWinaquifersofEnglandandWalestoNitratecontamination(Lakeetal.,2003).

Moreover,byanalyzingtheNitrogeninputsanddynamicsinanarea,anapproximatepredictionof

Nitrate concentration can be achieved by assuming that N in inputs is equal to N in output plus

variations in theN contents of the soil, livestock and other elements (Goss andGoorahoo, 1995).

Maintainingsoilfertilitywhileminimizingenvironmentalcontaminationwithregardtotheamountof

Ninputandoutputdependsonseveralfactors. ItwasdeterminedbyJuetal.that100kgha‐1y‐1of

excessNisthebaselineofleachingNO3intoGWonaregionalscale(Juetal.,2006).

Usingamodularneuralnetworkapproach,AlmasriandKaluarachchidevelopedamodeltopredictthe

concentrationofNitrateinanagricultural‐basedterrainandaquifer(AlmasriandKaluarachchi,2005).

Kotir et al. developed a system dynamic simulation model to assess the influence of agricultural

activitiesandpopulationgrowthonsurfacewaterandGWresourcesqualityandquantityinaregion

inGhana.Uponsuccessfulmodeldevelopmentwithhighlevelofaccuracyandvalidity,theyconsidered

a few more scenarios (development of the water infrastructure, cropland expansion and dry

conditions)andpredictedthefuturebehaviorofwaterresourcessystems(Kotiretal.,2016).

Furthermore,byemployingMODFLOWandMT3Dmodels forGWflowandcontaminant transport

simulationsrespectively,Almasrietal.assessedtheNitratecontaminationinanaquiferinWashington

state(AlmasriandKaluarachchi,2007).Usingthesameapproach,Lasserreaetal.developeda“GIS‐

transport”model toassess theNitratecontamination inawatershed inFranceusingminimaldata

(Lasserreaetal.,1999).Levyetal.andEshtawietal.assessedtheNitratecontaminationbydeveloping

an integrated MODFLOW‐MT3D model in Israel and Ghaza Strip respectively (as Mediterranean

regions)andmadepredictionsbasedondifferentscenarios(Eshtawietal.,2016;Levyetal.,2017).

Likewise,Conanetal.employedMODFLOW,MT3DandSWATmodels foraNitrate fateanalysis in

France(Conanetal.,2003).Bystudyingtheliterature,itwasrealizedthatintegrationofMODFLOW

and MT3D models is the most popular and common method that were used by researchers for

assessmentofGWNitratecontamination(Baalousha,2010;Guseetal.,2015;Lametal.,2010;Narula

and Gosain, 2013; Prommer et al., 2003; Zhang andHiscock, 2016). Table 3.1 tabulates themost

commonGWflowandcontaminanttransportmodelsforsimulatingNitrateconcentration.

48 

Table3.1.SummaryofotherresearchworksregardingGWNitratecontaminationmodeling

SourceModels

LocationMODFLOW MT3D SWAT Others

(JiangandSomers,2008) * * PrinceEdward

Island,Canada

(Karatzasand

Psarropoulou,2014)* * Corinth,Greece

(MolénatandGascuel‐

Odoux,2002)* * MODPATH Brittany,France

(Pisciottaetal.,2015) DRASTIC;

SINTACS

Canicattìandsub‐

urbanareas,Italy

(RoelsmaandHendriks,

2014) ANIMO Netherlands

(Wheeleretal.,2015) Random

forestIowa,USA

(Almasriand

Kaluarachchi,2007)* * Washingtonstate

(Levyetal.,2017) * * Israel

(Eshtawietal.,2016) * * GhazaStrip,

Palestine

(Lasserreaetal.,1999) * * Self‐

developedFrance

(Conanetal.,2003) * * * France

(Guseetal.,2015) * Northern

Germany

(Baalousha,2010) * PMPATH;

DRASTIC

GhazaStrip,

Palestine

(Prommeretal.,2003) * * PHT3D N/A

(Lametal.,2010) * Northern

Germany

(NarulaandGosain,2013) * * * NorthernIndia

(ZhangandHiscock,

2016)* * UnitedKingdom

49 

3.2.MaterialsandMethods

3.2.1.ModelSetup

3.2.1.1.GWFlowModel

GW flow in the regionwas simulatedusingMODFLOWmodelwithinGMSsoftware interface.This

modelisbasedonapreviousmodeldevelopedby(Ghasemizadehetal.,2016).Uponaccurateresult

of that model, in this study, some minor changes and improvements (e.g. adding data, adjusting

parametersetc.)wereimplementedtomakeregionalGWflowmodelforthecasestudyareaevenmore

accurateandvalid.Due tocomplexityofconduitnetworkwithinkarstaquifers,usuallyEquivalent

PorousMedium(EPM)approach,asasimplifyingmethod,isusedforGWflowmodeldevelopmentin

karsticterrains(Ghasemizadehetal.,2015).Incurrentstudy,usingDrainagefeature,morecomplexity

wasbroughtintothemodelingschemeinordertosimulatekarstsystemofNPRinmoredetail.

Data,usedformodeldevelopment,calibrationandvalidation,wascollectedfromliterature,historical

studies and USGS database. Model boundaries extend 10.9 and 54.9 km in the north/south and

east/westdirectionsrespectivelywithatotalareaof545.6km2.Thedevelopedmodelcomprisesof30

rowsand151columns,makingauniformly‐spacedblock‐centeredgridnetworkwithcellsizeof358.9

x 363.3m. Aquifer recharge, as an input parameter, varies throughout the aquifer based on areal

topographyandhydrogeologicalconditions.Thespatiotemporalvaluesofrechargewereestimated

basedonrainfalldataofastationinManatimunicipalityandbyassuminganevapotranspirationof

approximately 60%. This water recharge comes from streams, limestone outcrops, sinkholes and

enclosedtopographicdepressions.Moreover,LagunaTortuguero,whichisacoastallagoonlocatedin

northern side ofManati/VegaBaja boundary, is consideredas a regional drainage feature inNPR.

Figure 3.7, demonstratesmodeling steps followed in current study for GW flow and contaminant

transportmodel.

50 

Figure3.7.SchematicdiagramofGWflowandNitratetransportmodelingprocessusingMODFLOW

andMT3Dcodesincurrentstudy

51 

Springs,sinkholes,dipdirections,dryvalleys,strikes,andpartiallymappedsurfacelineamentswere

initiallyconsideredasdrainagefeaturesthatcancontributetoregionalgroundwaterflow.However,

onlydrainlinesconnectingsinkholestospringswereidentifiedtohaveasignificantimpactandhence,

wereaddedtothemodel(Figure3.8–brownlines).Additionally,RioSantiago,RioTanama,RioGrande

deArecibo,RioGrandedeManati,RioIndio,RioCibuco,andRiodelaPlatastreamswereaddedtothe

modelasatransferboundaryconditionwithconstantwaterlevelandriverbedconductancevalues.

Finally, the model was calibrated using parameter estimation tool (PEST) and during calibration

process,parametervalueswereadjustedwithinpredefinedrangesuntil thesimulatedheadvalues

matchedtheobserveddata.Becauseofdatalimitationandregionalscaleofthemodel,uniformvalues

fortheeffectiveporosity(0.3),specificyield(0.05),andstoragecoefficient(10‐5m‐1)weredefinedin

thetransientcalibration.Furthermore,automaticallycalibratedhydraulicconductivitiesofdiscrete

zoneswereusedtoincreasetheaccuracyofmodeling.Itshouldbenotedthatdrainpropertiesherein

do not represent the actual locations, roughness, diameter, tortuosity, and lumpedmatrix conduit

exchangecoefficientsoftheconduits.Theybasicallysimulatethedrainageeffectofconduitsonthe

regionalGWflowtoenhancetheaccuracyoftheEPMmethod(Ghasemizadehetal.,2016).Figure3.8

depicts the model boundary, location of streams, drain features, conduits, observation wells and

springsthatweredefinedintheGWflowmodel

Figure3.8.Locationofstreams,drainfeatures,conduits,observationwells,pumpingwellsand

springsinGWflowmodel

3.2.1.2.ContaminantTransportModel

After successful development of GW flowmodel usingMODFLOW code for both steady‐state and

transientconditions,NitratetransportmodelwasdevelopedusingMT3DcodewithinGMSsoftware

interfaceandwaslinkedtotheflowmodel.MT3DMSisamodularthree‐dimensionaltransportmodel

for the simulation of advection, dispersion, and chemical reactions of dissolved constituents in

52 

groundwater systems (Zheng et al., 2012). This model uses a modular structure similar to the

structureutilizedbyMODFLOW,andisusedinconjunctionwithMODFLOWinatwo‐stepflowand

transport simulation. In this process, the heads and cell‐by‐cell flux terms initially computed by

MODFLOWduringtheflowsimulationswereusedastheflowinputforthetransportportionofthe

simulation.Stressperiodsandtimestepsforthetransportmodelwereconsideredasthesameasthose

usedfortheflowmodel.TheinitialNitrateconcentrationandotherinputparameterssuchastransient

rechargeconcentrationofNitratebasedonlandcovertypeandfielddataweregiventothetransport

model. InadvectionpackageofMT3Dmodel,ThirdorderTVDscheme(ULTIMATE)waschosenas

solutionscheme.Forcalibrationofthetransportmodel,Nitrateconcentrationdataofcertainwellsin

NPRsince1992wasused.Datacollectioninthestudyarea(2000‐2016)wascarriedoutatdifferent

locationsanddates.Nitratesamplingdata,USGSdataandotherhistoricalobservationswereusedto

setuptransientobservationpointsinthemodel.

Inastudyconductedby(Conde‐CostasandGómez‐Gómez,1999),Nitrateloadinginasmallareawithin

ManatiandVegaBajamunicipalitieswasdeterminedbasedonlandcovertype.Infact,Agricultural

areas(useofmanureandfertilizers)andun‐seweredruralcommunities(effluentofseptictanks)were

knownasthemainsourcesofNitrateleachateintothekarstaquiferoftheregionforeachofthem

transient recharge concentration was specified. Rural communities without sewer service were

responsible for an estimated nitrogen loading of approximately 200 Kg per hectare per year

(kgN/ha.y).Thisestimationwasbasedonthe followingassumptions:1‐ totalnitrogenexcretedby

humanis17g/dpercapita,2‐thereare36personsper10housingunits(hu)onaverageinPRand3‐

anaverageruralhousingdensityis9hu/ha.Byapplyinganestimateddomesticwastewaterdischarge

ontothesubsurfaceofabout0.71m3/dperhousingunitsorabout0.20m3/dperperson,thisload

translatedtoanapproximateeffluentnitrogenconcentrationof85mg/L/haoverruralcommunity

areasnothavingsewersystem.Domesticwastewaterdischargefromun‐seweredruralcommunities

wasestimatedbasedonwater‐usedataof1982whichindicates4,160m3/dofwatersupplyto5,852

householdsthroughtheun‐seweredpublicwatersupplydistributionsystem.

ThepotentialNitrateloadingcomingfromagriculturallandsvariesthroughouttheyearbecauseofthe

variability in rainfall, runoff and fertilizer use rates. Nitrate load based on fertilizer use rate in

agriculturalareasofNPRwasestimatedas760kg‐N/ha.ybetween1992to1995.However,only550

kg/ha.yofNitrogenmaybeavailableforleachingorvolatilizationduetoincorporationbyplantsor

mineralization insoil.ForManati, itwascalculatedthatNitrate load fromagriculturalareas to the

upperaquiferis45kg‐N/ha.y.Finally,dilutedNitrateconcentrationcausedbyaquiferrechargeyields

arechargeconcentrationofapproximately110mg/L.Itshouldbenotedthatduring1992to1995,

relativelyhighconcentrationofNitrate(above10mg/LasMCL)wasobservedintheselectedwells.

53 

Nitraterechargeratecalculationwasrepeatedforothermunicipalitiesandfordifferentyearsbased

onvariationsinNitrateconcentrationatdifferentobservationwells.Thiswasmainlybecauseafter

2000, agricultural activities in a fewareaswere reducedor ceasedaccording to local farmersand

unofficialsources.Infact,itwasobservedthatGWNitratecontaminationhasbeenmitigatedduring

last 15 years. Accordingly, different amounts of Nitrate recharge concentration associated with

agriculturalareasweregiventothemodelasinput.Figure3.9showslandcovertypesassociatedwith

GWNitratecontaminationandlocationofsamplingwellswheredatacollectionwasconducted

Figure3.9.SelectedlandcovertypesandlocationofNitratesamplingwellsinNPR

Table 3.2 elaborates the annualmaximumGWNitrate concentration inmg/l formunicipalities of

north‐central PR based on field data collection. In addition, Figure 3.10 depicts spatiotemporal

distributionofNitratesamplingsitesandamountofNitrateconcentrationusinggradualsymbols.

Table3.2.AnnualmaximumGWNitrateconcentration(mg/l)formunucipalitisofNPR–Dataof

1992‐1995wasderivedfromaUSGSstudy(Conde‐CostasandGómez‐Gómez,1999)

YearMunicipality

Arecibo Barceloneta Manati VegaBaja VegaAlta Dorado ToaBaja

1992‐

1995‐ ‐ 18 9 ‐ ‐ ‐

2005 4.64 3.6 7.55 9.04 6.11 4.85 ‐

2006 4.49 3.05 6.73 8.24 4.11 4.31 3.84

2007 5.19 3.66 8.65 8.95 2.94 6.87 2.84

2008 4.11 3.57 5.35 9.23 2.48 3.45 ‐

2009 5.02 2.81 5.41 8.26 2.96 4.43 ‐

54 

2010 5.64 2.74 7.05 8.64 5.97 4.22 ‐

2011 5.07 10.6 4.99 7.52 4.95 10.7 3.38

2012 4.69 9.13 4.25 6.84 6.53 4.3 ‐

2013 4.51 3.9 2.04 7.91 5.7 3.06 ‐

2014 4.04 2.85 3.63 7 5.05 3.19 ‐

2015 3.86 2.94 3.51 7.17 5.3 3.14 ‐

2016 3.95 2.79 3.18 6.92 4.75 2.23 ‐

Figure3.10.SpatiotemporaldistributionofNitratesamplingsitesandamountofNitrate

concentration

55 

3.2.2.PredictionofNitrateConcentration

NitratetransportmodelcanbeusedforpredictionofNitrateconcentrationbasedonhydrologicaland

meteorologicalconditionsandalsobyconsideringurban,industrialandagriculturaldevelopmentin

NPR for the next 20 years. This helps authorities to make policies accordingly to minimize the

environmental impacts of economic and agricultural growth and move toward sustainable

development.

InordertopredicttheGWNitrateconcentrationinNPR,thesamemodeldevelopedfortheperiodof

1983‐2015wasemployedfortheperiodof2015‐2035basedonafewassumption.First,precipitation

data was assumed as the average rainfall data of 2005‐2015 period. This rainfall variation was

repeated for every year of the 2015‐2035 period. This assumption may not be accurate enough

becauseofmanyuncertaintiessuchasoccurringmajorhurricanesortheimpactofclimatechange.

However,becausethegoalofthisstudyistopredicttheGWNitrateconcentrationfortheyears2025

and 2035, neglecting those uncertainties seems to be acceptable. Moreover, although GWNitrate

concentrationwasobservedtobemitigatedinthepastyearsduetoreducedagriculturalactivitiesand

propermanagement of landfills, it is predicted that after hitting HurricaneMaria in 2017 and its

negativeeconomicconsequences,agriculturalactivitiesintensify.Agriculturaldevelopmentisoneof

the possible ways of economic growthwithin the island. Thus, if this assumption is correct, it is

expectedthatusingfertilizersinexistingagriculturallandswillexacerbateGWNitrateconcentration

again.Moreover,itwasassumedthatsomeareasthathavenotbeenusedforagriculturalactivitiesyet

buthavethepotentialtogrowcultivatedcrops,willalsobeusedforagriculturalactivities.Theseareas

areidentifiedasHay/PastureinFigure3.4.UsingthelandcoverdataofFigure3.4(cultivatedcrops

andhay/pasture)andalsothedatainFigure3.5regardingagriculturalcapabilityofsoil,areaswith

highpotentialofbecomingcultivatedlandsinthefuturewereidentifiedandusedinmodelingprocess.

3.3.ResultsandDiscussion

3.3.1.GWFlowModel

AftersuccessfulcalibrationandvalidationoftransientMODFLOWmodel,calculatedGWheadlevels

werecomparedtoobservedheadlevels.TheresultsledtoR2valueof0.97andRootMeanSquareError

(RMSE)of1.3m(Figure3.11).

56 

Figure3.11.Scatterdiagramdepictingsimulatedversusobservedhydraulicheadvaluesforsteady‐

statecalibrationoftheflowmodel

ItwasfoundoutthatthepresenceofconduitscanaffecttheGWflowoftheregionsignificantly.Anovel

methodforsimulatingtheheterogeneities inkarstaquifersbyassigningarraysofadjacentcellsas

conduitswasintroduced.Moreover,precipitationfollowedbyriverleakagethroughstreambedsand

fromunconfinedpartsoftheloweraquifer inthesouthwereidentifiedasthemainsourcesofGW

rechargeintheregion.Modeloutflowsarespringdischarges,dischargetotheoceananddischarge

intowetlandareas.Majorsinksinthemodearegroundwaterwithdrawalsinwells.Thewaterbudget

ofthemodelshowsthatapproximately11%oftheGWrechargetieswithconduitsordiffuseflowat

thesprings.Thesteady‐stateGWbudgetfortheyear1992andsurfacewater‐GWinterconnectivityis

tabulatedinTable3.3(Ghasemizadehetal.,2016).

R²=0.9754

0.1

1

10

100

0.1 1 10 100

CalculatedGWHeadLevel(ma.s.l.)

ObservedGWHeadLevel(ma.s.l.)

57 

Table3.3.Steady‐stategroundwaterbudgetsfor1992hydrologicconditionsinthecentralNPR

(Ghasemizadehetal.,2016)

Source/Sink Discharge(m3/d) Percentage

Inflows

Recharge 270,650 76.8

Riverleakage 62,090 17.6

Subsurfacecontributions 19,600 5.6

Totalinflows 352,340 100

Outflows

Withdrawals 149,500 42.4

Springs 30,120 8.6

Oceandischarge 72,030 20.4

Wetlanddrainage 93,700 26.6

Lake 6,990 2.0

Totaloutflows 352,340 100

3.3.2.NitrateTransportModel

After calibrationprocess of the transportmodel andminimizing the errors by adjusting the input

parameters, the outputof themodelwas compared to the observed values. The simulatedNitrate

concentration values were observed to have high correlation with observed values. For each

municipality, 3 statistical indexes namely, mean absolute error (MAE), root mean squared error

(RMSE)andcoefficientofdetermination(R2)werecalculated.Onaverage,thevaluesofMAE,RMSE

andR2werecalculatedas0.92mg/L,0.89mg/Land91.4%respectively.

Although thestatistical indexesprovesatisfactory resultsofourmodeling, theerrorvaluescanbe

decreased evenmore ifmore data and information are available. Themain source of GWNitrate

contaminationwasidentifiedasagriculturalactivitiesandeffluentofseptictanksinun‐seweredrural

communities. However, it was observed that in some areas where the dominant land cover is

“Evergreen Forest” or “Herbaceous”, GW Nitrate concentration is relatively high in some years

comparedtootherlocations.Thiscanbeduetopresenceofabandonedlandfillsinthosefields.Not

muchdatawasavailableforpresenceorstatusofthelandfillsinNPR;hence,thiscanbeconsideredas

aweaknessinourmodeling.Moreover,animalwasteonthegroundsurface(mixedwithstormrunoff

orrainfall)canbeconsideredasasourceofNitraterechargeinthoseareas.Ontheotherhand,for

someareaswithagricultural landuse, thevalue forGWNitrate concentrationwasobserved tobe

lowerthanexpected.Thisismainlybecauseofthefollowingreasons:1‐Agriculturalactivitiesinthose

58 

areasarelimitedorlessintensecomparedtoothercultivatedfields2‐Useoffertilizersandmanureis

limitedinthoseareas3‐Soilpermeabilityislessthanotherareas.ForexampleinArecibo,agricultural

fieldsaremainlylocatedinareaswithlowerhydraulicconductivityandasitcanbeobservedinFigure

3.10, GW Nitrate concentrations in Arecibo are generally lower compared to areas with higher

hydraulicconductivitysuchasVegaBajaandVegaAlta.

3.3.3.PredictionofGWNitratecontamination

ThemodelwasexpandedtopredicttheGWNitrateconcentrationinNPRfortheyears2025and2035

basedonestimateduseoffertilizersandmanureapplicationincultivatedareas.Althoughduringlast

years, agricultural activities were reduced and many cultivated croplands were abandoned, it is

predicted that agricultural industry will rise again and the use of fertilizers and manure will be

escalatedwhichismainlybecauseoftheunacceptableeconomicstatusoftheisland.Thisprediction

becamemore valid after hittingHurricaneMaria, a category 4hurricane, in September 2017. The

hurricanehasdevastatedfarmlandsofPRandhasresultedin$780millionofcroplosses(80%ofthe

value of the crops in a matter of hours). This financial damage is approximately $45 million for

HurricaneIrmathathittheislandafewweeksbeforeMaria.Beforethesehurricanes,theislandused

toimport85%ofitsfood.Thisimportrateispredictedtobecomeevenhigherforthenext1‐2years.

Hence,agriculturaldevelopmentandgrowthseemstobeapriorityforpolicymakersandauthorities

of the island (Abbott,2017;Perroni,2017).FloresOrtega, secretaryof agriculture inPR, said that

agriculture,asoneofthemajoreconomicsectorsofPR,willberecuperatedinanearfuture(McGrory,

2017).Additionally,RicardoL.Fernández,PresidentandCEOofPuertoRicoFarmCredit,saidthatthe

islandisplanningtohavebiggerfarmsinthefuture(Fernández,2017).

Accordingly,ourmodelpredictedtheGWNitrateconcentrationinaregionalscalefornorth‐central

partofPR.ThepredictedresultsshowthatareaswithexistinghighNitrateconcentration(suchas

Vega Baja and Vega Alta) will remain vulnerable to contamination in the next 2 decades; but

agriculturaldevelopmentinArecibowillnotleadtointenseGWNitratecontaminationcomparedto

other locations. Hence, forArecibo, focusing on existing cultivated fields and also areaswith high

potential of agricultural development in their proximity (i.e. land cover of hay/pasture) is

recommended.Moreover,ourpredictionresults indicatesGWNitrateconcentrationof lessthan10

mg/L (RecommendedMCLofEPA) throughout thenorth‐centralpartof the island for thenext20

years.Thispredictionistiedwithalotofuncertaintiesandunknownfactorsandmaynotbeaccurate

enough. However, it gives an overall understanding of the spatiotemporal trends of Nitrate

contamination within karst aquifer of NPR. Figure 3.12 illustrates the prediction results of our

modelingforthenext2decades.ItisworthmentioningthattheoutputresultfromGMSsoftwarewas

importedintoArcGISforinterpolationandfordepictingasmoothertransitionbetweencounters.

59 

Figure3.12.SpatialdistributionofsimulatedGWNitrateconcentration(mg/L)fortheyears2015,

2025and2035innorth‐centralpartofPuertoRico

Inaddition,usingourcollecteddataandhistoricalobservations,Nitrateconcentrationvariationtrend

since2005wasassessedfordifferentlocationsthroughoutNPR(Figure3.13).Asitappearsfromthis

figure, a generally declining trend canbe observed for all sampling locations. The averageNitrate

concentration in some areas such as Vega Baja and Vega Alta seems to be higher than other

municipalities.However,thishighconcentrationhasalwaysbeenlessorslightlyhigherthanMCLof

10mg/L.

60

Figure3.13.GWNitrateconcentrationtrendforsamplingsitesineachmunicipalitywithhighest

observedNitrateconcentrationsince2005

Basedontheavailabledata,informationandhistoricaltrendofGWNitrateconcentrationandalsoour

predictionresult,recommendedwaterresourcesmanagementactionsfordifferentmunicipalitiesof

NPRaretabulatedinTable3.4.Inadditiontotheserecommendedactions,therearesomemethods

(suchasbiogeochemicalcontrollingprocesses)thatcanbeusedtomitigateNitratecontaminationin

GW(Rivettetal.,2008;Thayalakumaranetal.,2008).

61

Table3.4.Recom

mendedmanagem

entactionsforcontrollingGWNitratecontaminationinmunicipalitiesofNPR

Municipality

ProtectionPriority

andVulnerabilityto

contamination

Recom

mendedManagem

ent

Action

DataCollectionPriority

Recom

mended

Monitoring

Frequency

Arecibo

Low

Usemodernagriculturalequipment

andtechnologyfornewfarms

Medium–butmoredatais

neededforagriculturalareasin

northw

esternside

Yearly

Barceloneta

Low

Usemodernagriculturalequipment,

CollectNitratesam

plesatw

ellsevery

3months

Low–Butmoredataisneeded

forthenorthernside

Seasonal

Manati

MediumtoHigh

Usemodernagriculturalequipment

andtechnology,Reducetheuseof

fertilizerandmanure,CollectNitrate

samplesatw

ellseverymonth

High–Comparedtotheareaof

themunicipality,moredata

pointsespeciallyforagricultural

landsareneeded

Monthly

VegaBaja

High

Usemodernagriculturalequipment

andtechnology,Reducetheuseof

fertilizerandmanure,CollectNitrate

samplesatw

ellseverymonth

High–Moredatafor

northeasternandwesternsideis

needed

Weeklyto

Monthly

62

VegaAlta

MediumtoHigh

Usemodernagriculturalequipment

andtechnology,Reducetheuseof

fertilizerandmanure,CollectNitrate

samplesatw

ellseverymonth

Medium

Monthly

Dorado

Medium

Usemodernagriculturalequipment;

CollectNitratesam

plesatw

ellsevery

3months

LowtoMedium

Seasonal

63

3.4.Conclusion

InnorthernPuertoRico,highpermeabilityofsoil/rockinkarsticaquifers,asoneofthemostaccessible

andproductivefreshwaterresources,hasincreasedtheirvulnerabilitytocontamination.Inthisstudy,

groundwaterNitratecontamination,asaresultofagricultural,industrialandurbandevelopment,was

assessedandsimulatedfornorth‐centralpartoftheisland.Usingcollectedfieldsamples(since2005)

andhistoricaldata(since1992),aNitratefateandtransportsimulationwasdoneusingMODFLOW

and MT3D models within GMS software interface. The calculated results of the regional‐scale

simulation showed relatively high correlation with observed values and hence, the calibrated

transportmodelwasusedforpredictionpurposes.Usingsoiltypedata(agriculturalcapabilityofsoil),

landcoverdata,andbyassessingagriculturalandeconomicdevelopmenttrendintheislandespecially

afterhittingHurricaneMaria,spatiotemporaldistributionofgroundwaterNitrateconcentrationwas

projectedforthenexttwodecades.ItwaspredictedthatalthoughgroundwaterNitrateconcentration

has been reduced generally during last decade due to mitigated use of fertilizers or cultivation,

agriculturalactivitieswill riseagaindramaticallyaftereconomicdamagesofHurricaneMaria.This

agriculturaldevelopment,ifnotmanagedproperly,willnegativelyimpactthegroundwaterqualityand

quantity especially inManati, Vega Baja and Vega Altamunicipalities. Hence, based on themodel

prediction results, recommended management plans for controlling groundwater Nitrate

contaminationineachmunicipalitywerepresentedfortheuseofpolicymakersandauthorities.

64 

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