the effects of changing land cover on streamflow...

THE EFFECTS OF CHANGING LAND COVER ON STREAMFLOW

SIMULATION IN PUERTO RICO1

Ashley E. Van Beusekom, Lauren E. Hay, Roland J. Viger, William A. Gould, Jaime A. Collazo,

and Azad Henareh Khalyani2

ABSTRACT: This study quantitatively explores whether land cover changes have a substantive impact on simu-lated streamflow within the tropical island setting of Puerto Rico. The Precipitation Runoff Modeling System(PRMS) was used to compare streamflow simulations based on five static parameterizations of land cover withthose based on dynamically varying parameters derived from four land cover scenes for the period 1953-2012. ThePRMS simulations based on static land cover illustrated consistent differences in simulated streamflow across theisland. It was determined that the scale of the analysis makes a difference: large regions with localized areas thathave undergone dramatic land cover change may show negligible difference in total streamflow, but streamflowsimulations using dynamic land cover parameters for a highly altered subwatershed clearly demonstrate theeffects of changing land cover on simulated streamflow. Incorporating dynamic parameterization in these highlyaltered watersheds can reduce the predictive uncertainty in simulations of streamflow using PRMS. Hydrologicmodels that do not consider the projected changes in land cover may be inadequate for water resource manage-ment planning for future conditions.

(KEY TERMS: land use/land cover change; urbanization; surface water hydrology; Precipitation Runoff Model-ing System; geospatial analysis; Caribbean.)

Van Beusekom, Ashley E., Lauren E. Hay, Roland J. Viger, William A. Gould, Jaime A. Collazo, and AzadHenareh Khalyani, 2014. The Effects of Changing Land Cover on Streamflow Simulation in Puerto Rico.Journal of the American Water Resources Association (JAWRA) 1-19. DOI: 10.1111/jawr.12227

INTRODUCTION

The mission of the U.S. Geological Survey (USGS)National Climate Change and Wildlife Science Center(NCCWSC) (http://nccwsc.usgs.gov/) is to provide inte-grated science that is useful to resource managers tounderstand the effect of climate change on a range of

ecosystem responses. The NCCWSC manages the Uni-ted States (U.S.) Department of the Interior (DOI) Cli-mate Science Centers (CSCs) (http://www.interior.gov/csc/index.cfm) which are tasked with prioritizing thedelivery of fundamental scientific information andtools for the resource managers. The DOI launched theLandscape Conservation Cooperatives (LCCs) (http://lccnetwork.org/) to better integrate science and manage-

1Paper No. JAWRA-13-0139-P of the Journal of the American Water Resources Association (JAWRA). Received June 6, 2013; acceptedApril 8, 2014. © 2014 American Water Resources Association. This article is a U.S. Government work and is in the public domain in theUSA. Discussions are open until six months from print publication.

2Geophysicist (Van Beusekom), Department of Biology, North Carolina State University, Box 7617, Raleigh, North Carolina 27695;Research Hydrologist (Hay) and Research Geographer (Viger), National Research Program, United States Geological Survey, Denver,Colorado 80225; Research Ecologist (Gould, Henareh Khalyani), International Institute of Tropical Forestry, United States Department ofAgriculture Forest Service, San Juan, Puerto Rico 00926; and Biologist (Collazo), North Carolina Cooperative Fish and Wildlife ResearchUnit, United States Geological Survey and Department of Applied Ecology, North Carolina State University, Raleigh, North Carolina 27695(E-Mail/Van Beusekom: [email protected]).

JOURNAL OF THE AMERICAN WATER RESOURCES ASSOCIATION JAWRA1

JOURNAL OF THE AMERICAN WATER RESOURCES ASSOCIATION

AMERICAN WATER RESOURCES ASSOCIATION

ment to address climate change and other landscapescale issues. Together, the Southeast CSC (http://www.doi.gov/csc/southeast/index.cfm) and the CaribbeanLCC (http://caribbeanlcc.org/) initiated the developmentof a hydrologic model for the island of Puerto Rico (PR)to provide resource managers with a tool to developmanagement strategies that address the impacts of cli-mate and land cover change on water availability in PR.

The hydrologic impact of land cover change in PR isparticularly important given the growth in populationand urban areas associated with the shift from cropand pasture-based agriculture (Wadsworth, 1950) toindustry (Dietz, 1986). Forest recovery, urban expan-sion, and agricultural decline in PR began around1950 and are well documented (see del Mar L�opezet al., 2001; Helmer et al., 2002; Helmer, 2004; Kenn-away and Helmer, 2007; Martinuzzi et al., 2007; Gouldet al., 2008). This pattern of land cover change is foundin many tropical areas, including El Salvador, theDominican Republic, Vietnam, Argentina, and parts ofIndia (Grau et al., 2003; Kauppi et al., 2006). A tempo-rally varying, spatially explicit, and process-orientedunderstanding of land cover change impact on hydrol-ogy will help to effectively manage water resources forsocietal benefit, environmental preservation, and themitigation of event-based hazard risks.

Streamflow simulation using hydrologic models andcalibration tools can achieve impressive statisticalmatches with measured streamflow, but the applica-tion of these results may be limited to the calibrationperiod if significant anthropogenic changes occur out-side the calibration period and are not accounted for inthe hydrologic model parameterization. According toMilly et al. (2008), the assumption of stationarity —“the idea that natural systems fluctuate within anunchanging envelope of variability” — has long beencompromised by human disturbances; thus, hydrologicmodels are incorrect to assume variables that are timeinvariant. This study quantitatively explores whetherexplicit inclusion of land cover change information inthe parameterization of the Precipitation Runoff Mod-eling System (PRMS) (Leavesley et al., 1983; Mark-strom et al., 2008), a distributed parameter, physicalprocess simulation code, has an impact on simulatedstreamflow within the tropical island setting of PR.Furthermore, this study examines how the incorpora-tion of this dynamic land cover information in PRMSyields a better understanding of hydrologic response inPR than one based on land cover descriptions that areheld static throughout the period of simulation.

Past Studies

The effect of urban expansion (urbanization) onhydrology has long been a topic of study (Leopold,

1968). Alley and Veenhuis (1983) state that “man-made impervious cover has long been known tosignificantly affect the hydrologic response of awatershed.” In addition to altering the ability ofwater to infiltrate into the soil and changing physicalrouting of water across the land surface, urbanizationhas been shown to affect heat budgets and evapo-transpiration (Grimmond and Oke, 1991; Taha, 1997;Dow and DeWalle, 2000). The rate of conversion fromrural to relatively impervious urban land within theU.S. is large (Alig et al., 2004; White et al., 2009).Alig et al. (2004) project that urbanization within thenation will continue for at least the next 25 yearsand that “developed area” will increase by as muchas 79%, resulting in almost 10% of the U.S. landsurface being converted to “developed” land cover.

Applications of hydrologic simulation codes thatfocus on the effect of urbanization on hydrology inthe U.S. have been documented for a variety of geo-graphic regions, such as the Piedmont (Hejazi andMoglen, 2008), Midwest (Tang et al., 2005; Choi andDeal, 2008), coastal New England (Schiff and Benoit,2007), Pacific Northwest (Cuo et al., 2009), SouthernCalifornia (Beighley et al., 2008), Southeastern U.S.(Ferguson and Suckling, 1990; Viger et al., 2011),and Northeastern PR (Wu et al., 2007). In general,these studies either treat land cover as a static quan-tity within the simulation, examining changes inhydrologic response due to different static character-izations of land cover (Wu et al., 2007; Beighley et al.,2008; Choi and Deal, 2008; Cuo et al., 2009) or theytreat the impervious area as a dynamic input (Hejaziand Moglen, 2008; Viger et al., 2011).

Land cover changes that occur over several dec-ades can have substantial impacts on hydrology andneed to be considered to more accurately representactual conditions. In the study by Viger et al. (2011),the potential effects of long-term urbanization andclimate change on the freshwater resources of theFlint River watershed, Georgia, were examined usingPRMS. They demonstrated that land use can bean important moderator in hydrologic response:increases in imperviousness changed the negativetrends in surface runoff seen under climate changeconditions alone to positive ones. Although they trea-ted impervious area as a dynamic input, they notedpotential problems with using temporally constantvalues for vegetation type, vegetation density, inter-ception, and radiation transmissivity. Policy makers,natural resource managers, and the public have theneed to assess impacts of historical, current, and pro-jected anthropogenic changes on the water resourceson which they and ecosystems depend. Withoutaccounting for these dynamic changes it may be diffi-cult to develop hydrologic models for evaluation andpredictive purposes. These dynamically changing

JAWRA JOURNAL OF THE AMERICAN WATER RESOURCES ASSOCIATION2

VAN BEUSEKOM, HAY, VIGER, GOULD, COLLAZO, AND HENAREH KHALYANI

properties occur in all watersheds and often are notaccounted for single simulations at the watershedscale.

Objectives

The objective of this study was to improve theunderstanding of the hydrology of PR by implement-ing PRMS models that reflect the agriculturaldecline, urban expansion, and forest recovery sincethe 1950s for the entire island. The dynamic parame-ter capabilities within PRMS provide the ability tovary landscape characteristics at any location on adaily or longer time step within the model domain,making PRMS an ideal tool to study the effects ofchanging land cover on streamflow. PRMS resultsthat incorporate dynamic land cover parameteriza-tions will be useful in the assessment of historical,current, and projected water resources and hydrologicprocesses in PR. Application of any hydrologic simu-lation code for PR is especially challenging due to thesparse and/or inaccurate information available in PRto drive hydrologic simulations. The following twosections of this study give an overview of the study

area and PRMS, respectively. This is followed by themethodology used to set up PRMS. Results are pre-sented and discussed for both static and dynamicland cover parameterizations in PRMS. The studywill finish with a conclusion to summarize and rein-force the findings of this research.

STUDY AREA

PR is the smallest of the Greater Antilles Islands,located in the northeastern Caribbean Sea (Figure 1).The main island is approximately 8,900 square kilo-meters (km2) with a thin strip of coastal plains, 8- to16-km wide, surrounding steep igneous upland. Thesemajor physiographic regions cover approximately 10and 86% of the island area, respectively, with theremainder in a karst area in the north (Boccheciamp,1978). The combination of strong weathering of thevolcaniclastic rock that makes up much of the islandwith relatively steep slopes has resulted in most rivervalleys becoming deeply incised (Murphy and Stal-lard, 2012). Bedrock is exposed to a small degree

Gage 50025155 50092000 50065500 50145395 50049000 50049100

IITF 2000 IITF 1991

IITF 1977 IITF 1951

FIGURE 1. Map of Puerto Rico with Geographic Locations and Land Cover. The land cover snapshots were createdby the USDA Forest Service International Institute of Tropical Forestry (IITF) for years 1951, 1977,

1991, and 2000. The black lines on each map divide the island into four climate regions.


THE EFFECTS OF CHANGING LAND COVER ON STREAMFLOW SIMULATION IN PUERTO RICO

(e.g., Scatena and Lugo, 1995). Soil in the mountainsis a result of the weathering of the volcaniclasticrock, creating clay. Soil on the coastal plains ismostly sand from sedimentary sources, with verylittle loam (USDA, 1994).

PR follows the Caribbean weather pattern createdby the easterly trade winds from the Atlantic Ocean.Average annual rainfall in PR is 1,778 mm with largeinterannual variability due to broad-scale storm pat-terns (Calvesbert, 1970). In general, the Caribbeanrainfall season is bimodal, with an early rainfall sea-son from May through June and a late rainfall seasonfrom August to November. Short-term climatechanges from El Ni~no and La Ni~na occur in the area(Taylor et al., 2002; Angeles et al., 2007). The island-wide dry season is from January to April. Orographiceffects are a major control on temperature and preci-pitation, with heaviest rain in the humid-tropicalcentral mountains and to a lesser degree along thenorthern coasts; dry tropical climates exist along thesouthern coastal plain (Boccheciamp, 1978; Larsenand Simon, 1993). Other mesoscale phenomena, suchas sea breezes, mountain-top convection, and standinggravity waves, affect the local weather with frequentsmaller storms (Carter and Elsner, 1997). Tempera-tures in PR are fairly constant spatially and tempo-rally. Average temperatures range from 24 to 29°Cyear-round (Calvesbert, 1970). Transpiration happensall year since most of the forest is evergreen (Weaveret al., 1973; Lugo et al., 1978; Miller and Lugo, 2009).

There are four mountain ranges within PR (Fig-ure 1). Three of these comprise the Cordillera Cen-tral, which begins about 60 km southwest of SanJuan and extend west through the center of theisland, dominating the landscape of the southerntwo-thirds of the island. Maximum elevation of theCordillera Central is 1,300 m. Annual rainfall inthe upper elevations of the windward slopes (to thenorth) usually falls within the range of 2,000-2,500 mm (Calvesbert, 1970; Helmer et al., 2002).The southern, particularly the southwest, side of theCordillera Central receives much less rain (Helmeret al., 2002). The fourth range, the Luquillo Moun-tains, dominates the geomorphology of the northeast-ern part of the island and is the wettest region of theisland with annual rainfall over 4,500 mm in theupper elevations. Maximum elevation of the LuquilloMountain range is 1,100 m. These mountain rangesnaturally divide PR into four climatic regions: a mod-erate region north of the Cordillera Central, a dryregion south of the Cordillera Central, a wet regioneast of the Cordillera Central surrounding the Luqu-illo Mountains, and a moderate region west of theCordillera Central.

PR has a population density of 438 persons/km2

(Martinuzzi et al., 2007), which is similar to rela-

tively urbanized settings like the state of New Jersey.Most of the population of the island is concentratedalong the coasts. Martinuzzi et al. (2007) define 16%of the island as urban with 960 persons/km2, and48% of the island as sparsely populated rural withless than 195 persons/km2. The largest city is SanJuan, located along the north coast on the easternhalf of the island (see Figure 1). Eight of the 10 mostpopulous cities on the island are located within SanJuan’s greater metropolitan area. Notable exceptionsare Ponce, located in the south, and Mayag€uez, whichis at the west end of the island. Large precipitationevents can create sudden increases in streamflow,resulting in landslides that affect homes, transporta-tion, power and communications in the mountainousinterior, and floods that affect people and infrastruc-ture on the populated coastal plains (Larsen andSimon, 1993).

Prior to European settlement in the 16th Century,PR was likely 95% forested, with broadleaf trees,moist soils, and very dense vegetation. Forest cover inPR reached a low of about 6% in the late 1940sbecause it was plowed over for agricultural develop-ment (Birdsey and Weaver, 1987), specifically planta-tion sugar production. At this time, crops and pasturewere evenly divided covering 84% of PR (Wadsworth,1950). In 1948, tax incentives from PR’s governmentencouraged investment in industry which rapidlychanged the economy from predominantly agriculturebased to industrial based (Dietz, 1986). Althoughsome of the intensively cultivated lands were transi-tioned to hay or intermittently grazed pasture, muchof it was left unmanaged or protected. This allowedforest land cover to increase to 40% of the island(Birdsey and Weaver, 1987) with forest types varyingby elevation. On the rainier northern side of theisland, they range from subtropical moist to wet torain forests. On the south side of the island, lowermontane wet and rain forests are also found. Semide-ciduous forests occur on the coasts (Helmer et al.,2002). The dominant type of forests is a moist broad-leaf evergreen in character (Helmer et al., 2002).

PR land cover changes from 1951 to 2000 areshown cartographically in Figure 1 and are summa-rized in Table 1. The shift to an industrial economybrought residents from rural to urban areas, result-ing not only in rapid urban expansion around thecities but also reversion of former agriculture zonesto forest (del Mar L�opez et al., 2001; Helmer, 2004;Par�es-Ramos et al., 2008). Typical of islands in thehumid tropics, there are few locations in PR with noanthropogenic effects (Wohl et al., 2012). Incorporat-ing these changes into a hydrologic model may notonly decrease the predictive uncertainty, but is cru-cial for planning adaptation of the hydrologicalsystem for future conditions (Buytaert et al., 2009).



HYDROLOGIC MODEL

The USGS PRMS is used to simulate land-surfacehydrologic processes, including evapotranspiration,runoff, infiltration, interflow, snowpack, and soilmoisture on the basis of distributed climate informa-tion, such as temperature, precipitation, and solarradiation (SR), as well as land use and other charac-teristics of the model domain. PRMS is a modular,deterministic, distributed-parameter, physical-processmodel (Figure 2). In addition to runoff, shallow sub-surface, and groundwater fluxes, PRMS simulateshydrologic water budgets at the watershed scale withtemporal scales ranging from days to centuries. Thereader is referred to Leavesley et al. (1983) and

Markstrom et al. (2008) for a complete description ofPRMS.

The distributed parameter capabilities of PRMS areprovided by partitioning the watershed into Hydro-logic Response Units (HRUs). Each HRU is assumedto be homogenous with respect to its hydrologicresponse. PRMS is conceptualized as a series of reser-voirs (soil zone, shallow subsurface, and groundwater;see Figure 2) whose outputs combine to produce run-off. For each HRU, a water balance is computed eachday and an energy balance is computed twice eachday. Surface, subsurface, and groundwater flows fromeach HRU are routed to an associated stream net-work. Once in the network, water is routed to thewatershed outlet (or the coast, in the case of thisstudy). In this study, runoff refers to the local flow

FIGURE 2. Overview of the Precipitation Runoff Modeling System Conceptualizationof Components and Fluxes (taken from Markstrom et al., 2008).

TABLE 1. Land Cover Percentages for the Main Island of Puerto Rico.

Year

Percent Land Cover Type1

Urban Pasture HerbaceousWoody

AgricultureForests

(open/closed)ForestedWetland

HerbaceousWetland Water2

1951 1.7 36.7 22.9 18.6 16.9 0.9 0.2 2.11977 9.8 31.5 8.9 12.6 33.6 0.9 0.5 2.11991 14.3 34.7 3.4 1.6 43.3 0.9 0.8 1.02000 15.4 34.7 1.1 2.2 44.8 0.9 0.8 1.1

1Adapted from Kennaway and Helmer (2007).2Also includes unclassified and natural barren, which are very small amounts.



produced by each HRU and streamflow refers to theaggregated, routed runoff in the stream network.

In addition to a suite of topographic parameters(such as elevation, slope, and aspect), the soils, vege-tation type and density, and imperviousness charac-teristics of each HRU are described by PRMSparameters. Traditionally, PRMS parameters werestatic for the period of simulation. Over long periods,land-cover-related parameters can become obsolete asconditions on the landscape change, creating diffi-culty for appropriate calibration of PRMS (LaFon-taine et al., 2013). This study explores approaches fordealing with the dynamic nature of land cover withinthe period of simulation.

METHODS

The following sections describe the methodologyfollowed in this study which includes: HRU delinea-tion and parameterization using static land cover;interpolation of daily climate values from weatherstations to the HRUs; PRMS calibration and evalua-tion under static land cover conditions; PRMS simu-lations using dynamic land cover parameterization;and methodology used to simulate streamflow for theentire island of PR.

HRU Delineation and Parameterization

Spatial delineations of the HRUs and a routing net-work for PR were taken from the Geospatial Fabric forNational Hydrologic Modeling (GF) (Viger and Bock,2014). These features were created as part of the GFmanufacturing process, which aggregates the flowlines of the NHDPlus Dataset (USEPA and USGS,2008). NHDPlus is based on the 1:100,000 scaleNational Hydrography Dataset. This is chiefly done bydefining a set of Points of Interest (POIs), aggregatingthe minimally sufficient set of flow lines needed to con-nect each hydrologically consecutive pair of POIs intoa single routing segment, and dividing the local con-tributing area associated with the segment into “left-bank” and “right-bank” HRUs. POIs correspond tostream gages, reservoir and lake outflows, and majorriver confluences, among other features. Within PR,there were 243 segments and 489 HRUs.

The GF provided the static parameters for the HRUs,describing topographic, vegetation, and soils character-istics based on NHDPlus, National Land Cover Data-base (NLCD2001) (Homer et al., 2007), and STATSGO(USDA, 1994), with resolutions of 30, 30, and 1 km,respectively. Parameters derived from NLCD2001

include percent of HRU area that is impervious, sum-mer and winter cover density, and values indicating thedepth of precipitation interception by vegetation forrain in summer and winter. These parameters are allaverage values for the associated HRU. The dominantcover type (bare, grass, shrub, or tree) is also deter-mined for each HRU. Soils parameters related to avail-able water-holding capacity and texture are derivedfrom the STATSGO soils database (Wollock, 1997). Formodel calibration, the value for a given HRU parameterwas held constant for the duration of the simulationperiod, and PRMS was used to simulate the hydrologyof PR under constant land cover conditions.

Climate Inputs

PRMS requires daily inputs of precipitation, maxi-mum temperature, and minimum temperature foreach HRU. A multiple linear regression (MLR)method was used to distribute daily measured preci-pitation and maximum and minimum temperaturedata from a group of climate stations (daily meanvalue) to each HRU in PR (Hay et al., 2000, 2006a, b;Hay and Clark, 2003) based on the longitude (x), lati-tude (y), and elevation (z) of the HRU.

To account for seasonal climate variations, the MLREquation (1) was developed for each month and foreach dependent variable (the climate variables [CV]:precipitation, maximum and minimum temperature)using the independent variables of x, y, and z (xyz). Themonthly MLR equations describe the spatial relationsbetween the monthly dependent CV and the indepen-dent xyz variables. Equation (1) describes a plane inthree-dimensional space with “slopes” b1, b2, and b3intersecting the CV axis at b0. Note that for each monththe best MLR equation for a given CV did not alwaysinclude all the independent variables (i.e., x, y, and z).

CV ¼ b1 xþ b2 yþ b3 zþ b0 ð1Þ

To estimate the daily value of each CV for eachHRU, the following procedure was followed: (1) meandaily values of each CV and corresponding mean x, y,and z values from a set of stations were used withthe “slopes” of the monthly MLRs in Equation (1) toestimate a unique y-intercept (b0est) for that day and(2) Equation (2) was then solved using b1, b2, and b3from Equation (1) and the x, y, and z coordinatesfrom the HRUs:

CVðHRUÞ ¼ b1 xðHRUÞ þ b2 yðHRUÞ þ b3 zðHRUÞ þ b0est

ð2Þ

In past studies (Hay et al., 2000, 2006a, b; Hayand Clark, 2003) the “slopes” in Equation (1) were



determined using nearby climate stations. For thisstudy, daily maximum and minimum temperaturesand precipitation data from 46 climate stations in PRwere compiled from the National Weather Servicecooperative observer network for water years (WYs)1952-2012. The 46 climate stations were found tohave approximately 70% of their data missing from1952 to 2012. Murphy and Stallard (2012) estimatedthe error in measured precipitation in PR to beapproximately 15%. This combination of poor quan-tity and quality of station data made it difficult touse station data to estimate “slopes” of the monthlyMLRs in Equation (1) for each CV. Therefore,“slopes” were calculated using mean monthly CVs cal-culated at each HRU using the Parameter-elevationRegressions on Independent Slopes Model (PRISM)(Daly et al., 2003) maps.

The results from Equation (1) using the PRISMdata produced several dominant MLR relations inPR, which were consistent with the four climaticregions defined by the mountain ranges describedearlier. Therefore, PR was divided into four regions(referred to as the North, South, East, and West) and“slopes” were calculated for each of the four regions(see Figure 1 for geographic outlines of the regions).This resulted in four separate PRMS models for PRthat were parameterized and calibrated. Otherresearchers have found similar climate regions in PRas the ones presented here (e.g., Veve and Taggart,1996; Carter and Elsner, 1997; Harmsen et al., 2004).

The adjusted R2 associated with the MLRs for pre-cipitation in the North, South, East, and Westregions are 0.74, 0.74, 0.86, and 0.72, respectively.The East and West regions have similar precipitation“slopes” in the z direction (elevation); showing highermean monthly precipitation for the same elevationthan the other regions. The South region has the low-est z precipitation “slope” and the North region fallsin the middle. For all regions, the adjusted R2 associ-ated with the MLRs for maximum temperature andminimum temperature are approximately 0.9. Theregions do not have significantly different “slopes” forthe temperature MLRs, indicating that the tempera-ture relations to x, y, and z are similar across theisland.

PRMS Calibration under Static Land CoverConditions

A “default” application of PRMS was developed foreach of the four climate regions using the staticparameterization scheme described earlier. For eachregion, designated “default” parameters, definedthrough a parameter sensitivity analysis, were cali-brated using the Luca software (Hay and Umemoto,

2006). Luca uses a multiple objective, stepwise, auto-mated calibration strategy with the Shuffled ComplexEvolution global search algorithm (Duan et al., 1992,1994) to calibrate parameters in a PRMS model. Thismultiple objective, stepwise calibration procedureassures that intermediate model fluxes as well asthe water balance are simulated consistently withmeasured values (Hay et al., 2006b).

For each of the four climate regions, six steps wereused in the calibration procedure. Table 2 lists thecalibration dataset, objective function, and PRMSparameters calibrated for each step in the process.The parameters calibrated in each step were deter-mined from a single parameter sensitivity analysisconducted using Monte Carlo techniques.

The first two steps in the calibration procedureadjusted the parameters that control the computationof SR and potential evapotranspiration (PET), respec-tively, using mean monthly values as the calibrationdataset. The mean monthly SR values for each regionwere derived from the National Solar Radiation Data-base using measurements at San Juan from 1961 to1990 (NSRDB, 1992). The mean monthly PET valuesfor each region were derived from mean monthlyPET point values based on Harmsen et al. (2004).The sum of the absolute difference of monthly mea-sured and simulated SR and PET was used as theobjective function.

The third step in the calibration procedureadjusted the parameters to match the volume of mea-sured streamflow (Table 2) using monthly streamflowcalculated for the “best” gage in each region as thecalibration dataset. The normalized root mean squareerror of monthly measured and simulated streamflowwas used as the objective function.

Researchers have noted that due to the sparse datain the tropics, poor model performance may be due tothe lack of enough high-quality data rather than alack of understanding (Buytaert et al., 2009; Wohlet al., 2012). It was questionable if any of the stream-flow records in PR would be adequate for PRMS cali-bration. Previous experience by the authors hasshown that if a simple monthly water balance model(MWBM) could not be calibrated accurately withmonthly streamflow information, then a calibrateddaily PRMS model would not be accurate. Therefore,a MWBM was used to identify the “best” gage in eachregion for PRMS model calibration.

Streamflow records from the USGS National WaterInformation System (NWIS) network were pulledusing the USGS Downsizer (Ward-Garrison et al.,2009). The Downsizer retrieved data for 74 gages inPR, 16 of which could be considered relatively free ofanthropogenic effects and would therefore be poten-tial candidates for use in model calibration (seeFalcone, 2011). These 16 gages were identified by



Falcone (2011) to be the least-disturbed watershedsfor PR. Two of these sixteen gages are located in theNorth region, five are in the South, seven are in theEast, and two are in the West.

For each of the 16 gages, a MWBM (McCabe andMarkstrom, 2007) based on the Thornthwaite meth-odology (Thornthwaite, 1948) was run for WYs 1971-2008. Split sample calibration was used to determinewhich gages could be used for model calibration;available data for each gage were separated into a setused for model calibration and an independent set forevaluation of MWBM performance. The coefficient ofdetermination (R2) for the evaluation years was usedto determine how well measured streamflow valueswere simulated by the model and the “best” gage ineach region was determined based on the R2 results.Figure 1 identifies the “best” gage for each region:

50025155 (North region), 50092000 (South region),50065500 (East region), and 50141395 (West region),with R2 values ranging from 0.56 to 0.65. The “best”gage for each region was used to derive the monthlyrunoff volumes for calibration of the water balanceparameters (see Table 2) for all HRUs in a givenregion.

When possible, each region’s PRMS model was cal-ibrated using the middle portion of the record fromthe “best” gage; allowing an evaluation period beforeand after the calibration period. Gage 50025155 inthe North region has anomalously large streamflowfor the year 1996 presumed to be due to a gage mal-function since none of the nearby gages show similarpatterns, therefore WYs 1998-2006 were used for cali-bration. Figure 3 shows the period of record, calibra-tion, and evaluation periods for the “best” gages.

TABLE 2. Parameters Calibrated in Each Step of the Calibration Process.

Step Calibration Dataset Objective FunctionPRMS Parameters Usedto Calibrate Model State Parameter Description

1 Observed mean monthlysolar radiation

Sum of the absolutedifference

dday_intcp_hru Intercept in temperature degree-dayrelationship

dday_slope_“month” Slope in temperature degree-dayrelationship

2 Observed mean monthlypotentialevapotranspiration

Sum of the absolutedifference

jh_coef_hru_“month” Monthly air temperature coefficient used inJensen-Haise computations

3 Monthly measured flowwith 15% error bounds

NRMSE adjust_rain Precipitation adjust factor for rain dayspsta_nuse Binary indicator for using station in

precipitation distribution calculationspsta_freq_nuse Binary indicator for using station in

precipitation frequency calculations4 Monthly measured flow

with 15% error boundsNRMSE K_coef Travel time of flood wave from one segment

to the next downstream segmentslowcoef_lin Linear coefficient in equation to route

gravity-reservoir storage down slope foreach HRU

soil_moist_max Maximum available water-holding capacityof soil profile

soil_rechr_max Maximum available water-holding capacityfor soil recharge zone

tsta_nuse Binary indicator for using station intemperature distribution calculations

5 Daily high flows with15% error bounds

NRMSE fastcoef_lin Coefficient to route preferential flow storagedown slope

pref_flow_den Fraction of the soil zone in whichpreferential flow occurs

sat_threshold Water-holding capacity of the gravity andpreferential flow reservoirs

smidx_coef Coefficient in nonlinear surface runoffcontributing area algorithm

6 Daily low flows with 15%error bounds

NRMSE gwflow_coef Linear coefficient to compute groundwaterdischarge from each groundwater reservoir

soil2gw_max Maximum amount of capillary reservoirexcess routed directly to the groundwaterreservoir

ssr2gw_rate Linear coefficient used to route water fromthe gravity reservoir to the groundwaterreservoir

Note: HRU, Hydrologic Response Unit; NRMSE, normalized root mean square error; PRMS, Precipitation Runoff Modeling System.



Eight years of record were selected for the calibrationperiod (Figure 3) to ensure robustness in the results(Yapo et al., 1996).

The streamflow measurements for “best” gages ineach region are rated as “fair” in the NWIS database,indicating that approximately 95% of the dischargesare within 15% of the true value (see http://wdr.water.usgs.gov). To account for this measurementerror in the calibration, the PRMS models were cali-brated with an error range of 15% in the calibrationdatasets.

The fourth step in the calibration procedure cali-brates the PRMS parameters associated with stream-flow timing (Table 2). The PRMS models werecalibrated with an error range of 15% in the calibra-tion datasets based on reported streamflow measure-ment estimates. The normalized root mean squareerror of measured and simulated streamflow wasused as the objective function.

The fifth and sixth calibration steps adjust theparameters that influence the high and low flows,respectively (Table 2). The high and low flows weredetermined using the Indicators of Hydrologic Alter-ation software (Richter et al., 1996). All consideredthe general 15% streamflow measurement error. Thenormalized root mean square error of measured andsimulated high (low) streamflow was used as theobjective function.

PRMS Evaluation under Static Land CoverConditions

The PRMS models for the four climatic regionswere calibrated and evaluated for the periods shownin Figure 3 using the static parameterization schemedescribed earlier. Calibration of SR and PET parame-ters was the first two steps in the stepwise calibra-tion procedure. Mean monthly simulations of SR andPET for the calibration and evaluation periods werenearly identical to those produced from the calibra-tion datasets. This is similar to previous studiesusing this calibration methodology (see figures 6-7 in

Hay et al., 2006b; figure 6 in Markstrom et al., 2012;and figure 13 in LaFontaine et al., 2013).

Steps 3-6 calibrated parameters associated withstreamflow volume, timing, and high and low values.Measured and simulated annual streamflow (WYs1953-2012) from the calibration and evaluation peri-ods at the “best” gages in the North, South, East, andWest regions are shown in Figure 4. Annual stream-flow values were simulated accurately for the calibra-tion periods with the exception in the West region.Simulation accuracies during the evaluation periodswere variable.

The monthly biases in simulated streamflow foreach region are summarized in Figure 5 for the cali-bration and evaluation periods. A positive monthlybias indicates that the simulated was greater thanthe measured streamflow. The West region has nodata outside the calibration period for evaluation (seeFigure 3 for dates of the calibration and evaluationperiods). In general, the median monthly bias for thecalibration periods tends to be close to zero, withexception for some of the months in the South andWest regions. In the North and West regions, themean magnitude of the median biases in the driermonths (January through April, and July and Decem-ber) and the mean magnitude of the median biases inthe wetter months (May through June and Augustthrough November) are approximately equal to themean magnitude of the median biases in all months,indicating the models perform similarly in dry andwet conditions. In the South and East regions, themodels perform better in the drier months, with onlyone-quarter of the mean magnitude of the medianbiases coming from the drier months in the Southregion, and one-tenth of the mean magnitude of themedian biases coming from the drier months in theEast region.

The biases for the pre-calibration evaluation period(dark gray) are generally larger and have greaterabsolute medians than the post-calibration evaluationperiod (light gray), especially in the northern region.As noted earlier, forest recovery, urban expansion,and agricultural decline in PR began around 1950.

North (50025155)

South (50092000)

East (50065500)

West (50145395)

Water Year

Evaluation Period Calibration Period

FIGURE 3. Period of Record for Streamflow Gages Used for Model Calibration.



This land cover change is not represented by the sta-tic parameterization used in the PRMS model calibra-tions. The PRMS PR models all used static land coverderived from NLCD2001 for parameter derivation,which is more similar to the actual land cover in thepost-calibration evaluation period. Land-cover-basedparameters that change through the period of simula-tion may result in an overall better model, especiallyprior to the 1990s in PR (see Figure 1 and Table 1for qualitative and quantitative description of landcover changes prior to the 1990s, respectively). Thesedynamically changing properties occur in all water-sheds and are generally not accounted for in singlehydrologic model simulations at the watershed scale.

PRMS Simulations Using Dynamic Parameterization

PRMS simulations using dynamic parameters pro-vide the capability of varying landscape characteris-tics at any location and on any day of a simulation.The dynamic land cover parameters for PR werederived from four United States Department of Agri-culture (USDA) Forest Service International Instituteof Tropical Forestry (IITF) (shown in Figure 1) landcover maps with dates of 1951, 1977, 1991, and 2000.Parameter sets derived from the IITF 1951, 1977,

1991, and 2000 datasets were used to create staticparameter sets representing land cover for each ofthose four years. To create dynamic parameter setsin which the parameters changed on a yearly basis,parameter values for interceding years were linearlyinterpolated yearly (and linearly extrapolated afteryear 2000) for each HRU.

The 1951 land cover map is a digitization of a1:150,000 scale paper map of land cover in 1951(Brockman, 1952; Kennaway and Helmer, 2007). The1977 land cover map was created from non-orthorecti-fied air photo mosaics of 1977 and 1978, which weremanually interpreted to derive land cover maps. TheUSDA IITF team mosaicked the quadrangles andgeneralized them to PR land cover types (Ramos andLugo, 1994). The 1992 land cover map is from theLandsat 5 TM images of 1991 and 1992. Missingareas in the imagery were filled from the 1977 landcover map (Helmer et al., 2002). The 2000 land covermap was made from 18 Landsat 7 ETM+ images col-lected during 1999-2003. Multiple years were usedfor the 2000 land cover map to overcome issues withcloud cover and image availability. There are twoscenes from 1999, eight from 2000, seven from 2001,and one from 2003 (Martinuzzi et al., 2007; Gouldet al., 2008). The spatial distribution of the land covertypes for each of the mapped points in time is shown

1960 1970 1980 1990 2000 2010

0.5

1.0

1.5

Measured Annual Simulated Calibrated

North Region

1960 1970 1980 1990 2000 2010

1

2

3

4

5 South Region

1960 1970 1980 1990 2000 2010

1.0

1.5

2.0East Region

1960 1970 1980 1990 2000 2010

0.4

0.6

0.8

1.0 West Region

Water Year

(gage 50025155)

(gage 50092000)

(gage 50065500)

(gage 50145395)

Ann

ual S

tream

flow

(c

ms)

A

nnua

l Stre

amflo

w

(cm

s)

Ann

ual S

tream

flow

(c

ms)

A

nnua

l Stre

amflo

w

(cm

s)

FIGURE 4. Annual Measured and Simulated Streamflow during the Calibration and Evaluation Periodsat the “Best” Gages in the North, South, East, and West Regions of Puerto Rico.



in Figure 1 and a numerical summary of the mapsfor the main island of PR is given in Table 1.

The PRMS parameters affected by changing landcover describe the land cover type, seasonal canopydensity, seasonal rain-storage capacity, and impervi-ous-land percent for each HRU (see Table 3). Thecanopy leaf loss in winter, cover type, and depth ofprecipitation interception in summer and winter wereassigned per cover type, and not calculated. The IITF

datasets do not provide the impervious-land percentor seasonal canopy density found in the NLCD2001products. Impervious area was assumed to be 100%wherever the urban land cover type existed. Canopydensities were calculated by comparing theNLCD2001 cover density per IITF 2000 cover typeand are listed in Table 4. The per-cover type densitieswere assumed to be constant for all years in the IITFsequence. After calculating mean canopy density for

Evaluation Earlier Period Calibration Period Evaluation Later Period

J F M A M J J A S O N D-100

-500

50100150200250

Mon

thly

Bia

s (P

erce

nt) North Region


-500

50100150200250

Mon

thly

Bia

s (P

erce

nt) South Region


-500

50100150200250

Mon

thly

Bia

s (P

erce

nt) East Region


-500

50100150200250

Mon

thly

Bia

s (P

erce

nt) West Region

Month

(gage 50025155)

(gage 50092000)

(gage 50065500)

(gage 50145395)

FIGURE 5. Monthly Bias (percent) between Measured and Simulated Streamflow CalculatedUsing the Early (dark gray) and Later (light gray) Evaluation Periods and the Calibration Period(medium gray) for the “Best” Gages in the North, South, East, and West Regions of Puerto Rico.

TABLE 3. PRMS Parameters Affected by Changing Land Cover.

PRMS Parameter Parameter Description Source

cov_type Dominant cover type of each HRU (bare, grass,shrub, or tree)

PRMS cover type per land cover type that is dominant ineach HRU

covden_sum Plant canopy density during summer as a fractionof HRU area

Canopy density per land cover type from NLCD2001 or frommerge of NLCD2001 density and year 2000 IITF land cover

covden_win Plant canopy density during winter as a fraction ofHRU area

Canopy density multiplied by canopy leaf-loss percent perland cover type

srain_intcp Depth of precipitation interception by vegetationfor rain in summer in each HRU

Depth of precipitation interception in summer per landcover type

wrain_intcp Depth of precipitation interception by vegetationfor rain in winter in each HRU

Depth of precipitation interception in winter per land covertype

hru_percent_impv Fraction of each HRU area that is impervious NLCD2001 imperviousness surface or percent of urban landcover type in each HRU

Note: PRMS, Precipitation Runoff Modeling System; HRU, Hydrologic Response Unit; NLCD, National Land Cover Database; IITF, Interna-tional Institute of Tropical Forestry.



each IITF cover type, the area-weighted average ofthese cover type densities was determined per HRU.Any holes or unclassified areas in the IITF land covermaps are filled in from the surrounding areas.

PRMS Simulations for Puerto Rico

The relative changes in the adjusted parametersfrom the calibration procedure described above wereused to adjust the parameters in the HRUs located inthe ungaged areas of each region. This resulted in aPRMS model for each of the four PR regions with cal-ibrated parameters based on the “best” gage in eachregion. Each of these PRMS models was run for WYs1953-2012 (with 1952 for initialization) with staticland cover parameter sets based on the (1)NLCD2001 dataset and (2) each of the four IITF landcover datasets. PRMS was then run using dynamicland cover parameters (based on an interpolationderived from the IITF land cover datasets over time).No parameter calibration was carried out for thesealternate scenarios in addition to that described inthe Hydrologic Model calibration section.

RESULTS

The following sections describe the results fromthe PRMS simulations for each region in PR withland cover parameters based on the (1) NLCD2001

dataset and (2) each of the four IITF land cover data-sets. In these PRMS models, all parameter valuesremain static for the specified time period. Use of sta-tic parameter values over broad temporal and spatialscales may be insufficient to evaluate the hydrologicresponse when changes in climate or landscape aresubstantial. Therefore, results from a PRMS modelsimulation using dynamic land cover parameters areshown to illustrate that accounting for these dynamicchanges is important for hydrologic model evaluationand predictive purposes.

Simulations Based on Static Land Cover

The PRMS model for each of the four regions wasrun for the period of record (WYs 1953-2012, with1952 for initialization) using land cover parametersderived from NLCD2001. Figure 6 shows the annualsimulated runoff for each region. Overall, the Eastregion has the most runoff and the South andNorth regions have the least. Although the West andEast regions have similar precipitation elevation“slope” relations from the MLRs (increasing precipita-tion with elevation), the West is lower in averageelevation than the East. As a result, the West regionreceives much less precipitation than the East andin general less runoff. The total drainage areas forthe North, South, East, and West regions are3,700, 2,625, 1,265, and 1,632 km2, respectively.Therefore, changes in the North region’s streamflowwill have the largest contribution to total streamflowfor PR.

TABLE 4. IITF and NLCD Land Cover Type Comparison.

IITF Land CoverType

NLCD LandCover Type

Canopy Densityfrom IITFLand Cover

(%)1

Canopy Densityfrom NLCDLand Cover

(%)2

Canopy LeafLoss in

Winter (%)

PRMSCoverType

Depth ofPrecipitationInterception inSummer (mm)

Depth ofPrecipitationInterception inWinter (mm)

Urban Urban 7.6 2.9 100 Bare 0 0Pasture Pasture 12.5 0 100 Grass 0.5 0.5Herbaceousagriculture

Agriculture 3.1 3.9 100 Grass 0.5 0.5

Woody agriculture N/A 39.2 N/A 0 Tree 0.5 0.5Open forest Shrub 35.4 15.8 60 Shrub 1.3 1.3Evergreen closedforest

Evergreenforest

55.5 65.9 0 Tree 1.3 1.3

Semideciduous closedforest

N/A 59.9 N/A 60 Tree 1.3 0.5

Forested wetland Forestedwetland

42.6 53.6 60 Tree 1.3 1.3

Herbaceous wetland Herbaceouswetland

3.1 0 100 Shrub 1.3 1.3

Water Water 6.1 0 100 Bare 0 0

Notes: IITF, International Institute of Tropical Forestry; NLCD, National Land Cover Database; PRMS, Precipitation Runoff ModelingSystem.

1From merging the NLCD2001 density with the year 2000 IITF land cover map.2From merging the NLCD2001 density with the NLCD2001 land cover map.



The PRMS model for each of the four regions wasrun for the period of record (WYs 1953-2012, with1952 for initialization) using each of the four IITFland cover datasets as a static land cover parameteri-zation (16 PRMS simulations). Figure 7 shows thepercent difference in the annual streamflow simu-lated using each of the IITF land cover parameteriza-tions relative to values simulated using NLCD2001

land cover. Positive values indicate that the IITFland cover parameterization resulted in more stream-flow than the parameterization based on NLCD2001land cover. A consistent pattern is shown in all fourregions with negative values using the earliest IITFland cover (1951, dashed gray lines) and positive val-ues using the 1977, 1991, and 2000 IITF land covers(solid black, solid gray, and dashed black lines,

1960 1970 1980 1990 2000 2010

1

2

3

4

5

6

7

North Region South Region East Region West Region

Ann

ual R

unof

f (m

m/d

ay)

Water Year

FIGURE 6. Annual Simulated Runoff for Each Region Using Static Land CoverParameters Derived from National Land Cover Database 2001.

1960 1970 1980 1990 2000 2010-10

-5

0

5

10

15

IITF 1951 IITF 1977 IITF 1991 IITF 2000

North Region

1960 1970 1980 1990 2000 2010-2-1012345 South Region

1960 1970 1980 1990 2000 2010-2-1012345 East Region

1960 1970 1980 1990 2000 2010-2-1012345 West Region

Water Year

Per

cent

Diff

eren

ce in

Ann

ual S

tream

flow

(NLC

D-II

TF)

FIGURE 7. Percent Difference in Annual Streamflow Simulated Using Parameters Based on National Land CoverDatabase (NLCD) 2001 Land Cover and Each of the International Institute of Tropical Forestry (IITF)

Land Covers (1951, 1977, 1991, and 2000) for the North, South, East, and West Regions.



respectively). This may reflect the large increase inurban land cover between 1951 and 1977, and contin-ued state of greater urban land cover after 1977 (seeFigure 1 and Table 1).

Note that the NLCD2001 HRU percent imperviousvalues consistently fell between those calculatedusing the IITF 1951 and 1977 snapshot. Greenfieldet al. (2009) report that the methodology by whichNLCD2001 estimates percent impervious can under-estimate the value by as much as 5.7% in the conter-minous U.S. In the IITF and NLCD2001, designatedurban areas are considered 100% impervious; greenspaces, such as parks and lawns, on which infiltra-tion occurs within urban areas are not recognized inthe current study. Including a more detailed interpre-tation of the IITF and NLCD2001 output could revealthat the increases in streamflow are too steepbecause the hru_percent_imperv parameter values(Table 3) were consistently overestimated.

The most drastic urbanization occurred in theNorth region, which contains the city of San Juan(Figure 1). This is reflected in Figure 7 with signifi-cantly larger percent differences in streamflow in theNorth vs. the South, East, and West regions. Forestrecovery occurred in the North, East, and Westregions between 1951 and 1977, and continuedbetween 1977 and 1991 when the urbanization slowed(Figure 1 and Table 1). This results in less streamflowwith the 1991 land cover parameterization than withthe 1977 land cover parameterization in all regionsbut the South. In contrast, the South region experi-

enced much less forest recovery (it is a drier area withless inclination to be forested) and was still principallyinfluenced by urbanization changes from 1977 to 1991.PRMS simulations in the South region produce morestreamflow with the 1991 land cover parameterization(Figure 7 South region, solid gray line) vs the 1977one (Figure 7 South region, solid black line). From1991 to 2000, the effect of urbanization was greaterthan the effect of reforestation in all areas but theSouth, increasing the streamflow in the North, East,and West regions with the 2000 land cover parameteri-zation over the 1991 land cover parameterization.

Simulations Based on Dynamic Land Cover

Based on the PRMS simulations using static landcover parameterizations for the four regions, onemight interpret that using dynamic land coverparameterizations would not have a large impact onstreamflow simulations for a large region (percent dif-ferences in Figure 7 are very small) unless the entireregion has undergone extensive land cover change(North region being closest to this). In an attempt todetermine if the uncertainty in simulated streamflowis reduced using dynamic parameterization, the rangeof PRMS simulations was compared at a stream gagein the North region that has experienced extensiveland cover change over the period of simulation (WYs1953-2012). The gage for the R�ıo Piedras watershed(USGS Gage 50049100) was selected because the

1960 1970 1980 1990 2000 2010

0.5

1.0

1.5

2.0

2.5

Measured Synthesized Upstream Gage

Water Year

Ann

ual S

tream

flow

(c

ms)

FIGURE 8. Measured and Synthesized Annual Streamflow at R�ıo Piedras (USGS Gage 50049100)and Measured Annual Streamflow at Upstream Gage (USGS Gage 50049000).

1960 1970 1980 1990 2000 2010

0.5

1.0

1.5

2.0

2.5

'Measured'Annual Simulated Dynamic Land Cover Range of Static Land Covers

Water Year

Ann

ual S

tream

flow

(c

ms)

FIGURE 9. Annual Streamflow at R�ıo Piedras (USGS Gage 50049100) Using “Measured,” Range of Simulatedwith Static Land Cover Parameters, and Simulated with Dynamic Land Cover Parameters.



watershed has had a large degree of urbanizationover the simulation period being entirely containedwithin the current boundary of the San Juan munici-pality.

Unfortunately, there are substantial gaps in thestreamflow measurement record for USGS gage50049100. As noted earlier, there is a notable lack ofhigh-quality streamflow data in PR. This has beennoted as a growing concern; hydrologic data collectionin the tropics is in rapid decline despite the pressingneed for more and higher quality measurements(Wohl et al., 2012). The missing streamflow recordswere filled using a correlation established with anupstream gage (USGS 50049000) to provide stream-flow for evaluation that covers the IITF snapshots ofland cover (years 1951, 1977, 1991, and 2000). Thisupstream gage contains 18% of the R�ıo Piedras drain-age (gage locations are shown in Figure 1). Bothgages recorded measurements from WYs 1988 to1993. A best-fit coefficient describing the monthlypercent increase in streamflow from the upstreamgage streamflow to the downstream gage was calcu-lated and applied to produce synthesized data atUSGS gage 50049100 from 1972 to 1982. The mea-surements for gages 50049100 and 50049000 andsynthesized data based on the correlation are shownin Figure 8.

Figure 9 shows the “measured” and PRMS simu-lated streamflow using the five static (IITF 1951,IITF 1977, IITF 1991, IITF 2000, and NLCD2001)and one dynamic land cover parameterization in theNorth region’s R�ıo Piedras watershed. In the Northregion, the percent impervious area (a reflection ofurbanization in the region) was lowest in the IITF1951 snapshot and peaked in the IITF 1977 snapshot.While there is no measured streamflow for compari-son prior to 1973, the effects of urbanization on simu-lated streamflow are clearly evident in the dynamicsimulations. The range in the streamflow resultingfrom the five static parameterizations is shown bythe gray band in Figure 9 and demonstrates therange of uncertainty associated with different landcover parameterization. The streamflow resultingfrom the dynamic parameterization falls within thisgray band and accurately matches the measuredstreamflow, with the exception of WYs 1988-1997.

DISCUSSION

PRMS models were developed for four climaticregions covering PR. Each of these PRMS models wasrun for WYs 1952-2012 with five static land coverparameterizations based on snapshots in time (IITF

1951, 1977, 1991, 2000, and NLCD2001). A simula-tion using dynamic land cover parameterization wasrun for a highly altered watershed in the Northregion to illustrate the reduction in uncertainty thatmay be achieved when using the appropriate landcover. The following sections discuss the: (1) implica-tions of data scarcity; (2) impervious area parameteri-zation effects on streamflow; (3) human impacts onwater supply; and (4) effects of scale when analyzingsimulated streamflow.

Data Scarcity

The effects of land cover on streamflow simulationsin the outer islands of PR were not assessed dueto the lack of land cover data. The outer islands ofVieques, Culebra, and the U.S. Virgin Islands wereincluded in the South region (see Figure 1) becausetheir climates and land cover are most similar to thatof the South (Veve and Taggart, 1996). Vieques andCulebra have IITF land cover for the year 2000 (seeFigure 1). The U.S. Virgin Islands do not have anyIITF land cover and are assumed to be similar toVieques and Culebra in the year 2000. Therefore, theland cover parameters for the outer islands are fromNLCD2001 in all of the IITF models in Figure 7 (andtherefore do not contribute to South region IITFmodel bias from NLCD2001), except the year 2000IITF land cover model in which all outer islands haveparameters from Vieques and Culebra 2000 IITF landcover. Kennaway et al. (2008) mapped land cover inthe U.S. Virgin Islands for 2000, but it was not partof the IITF land cover change dataset. Future hydro-logic model applications could benefit from utilizingthese data and future land cover change studies inthe outer islands.

While data scarcity has been a consistent problemin developing every aspect of this modeling study inPR, the results indicate that hydrologic models devel-oped for historic or future conditions that do not con-sider the past or projected changes in land cover maybe inadequate. Finding land cover data sources todevelop accurate dynamic parameterization can beproblematic, but there are data sources becomingavailable that may alleviate this problem in someareas. For example, the completion of the USGSLandCarbon project (http://www.usgs.gov/climate_landuse/land_carbon/) will result in a continuous(1938-2100) annual set of land cover maps at 250-mresolution, for 16 land cover classes, for the contermi-nous U.S. Products like this, which are developed tobe consistent with the NLCD data source, will makedevelopment of dynamic land cover parameterizationdatasets for PRMS relatively seamless within theconterminous U.S. PRMS models based on future cli-



mate projections, such as those made across the U.S.by Hay et al. (2011) and Markstrom et al. (2012), thatalso incorporate future land cover projections are cru-cial when planning adaptation of the hydrologicalsystem for future conditions (Buytaert et al., 2009).

Impervious Area

The handling of impervious area within PRMS isparticularly important in light of the physical growthof urban areas in PR and throughout the nation.Urbanization has long been held to result in anincrease in the velocity and volume of surface runoffdue to increasing impervious area which tends to besmoother, accelerating the runoff, and impeding theprecipitation infiltration into the soil (Urbonas andRoesner, 1992). Past studies using hydrologic simula-tion models have demonstrated this phenomenon(e.g., Wu et al., 2007; Viger et al., 2011). Wu et al.(2007) examined the influence of land cover changeson streamflow in a northeastern PR watershed andconcluded that complete urbanization in thiswatershed would result in increased surface runoffdue to storm runoff going directly to the stream chan-nels. Viger et al. (2011) examined the influence oflong-term urbanization on streamflow in a watershedin Georgia and concluded that the impact of increas-ing impervious surfaces was to increase surface run-off. This phenomenon was demonstrated in thecurrent study by comparing the PRMS simulationsusing the 1951 IITF land cover parameterization(least urbanized) with the later snapshots in Fig-ure 7. PRMS simulations using the 1951 IITF landcover parameterization produced less streamflow inall regions than all other static land cover parameterdatasets (Figure 7). This phenomenon has been rec-ognized in PR and dams have been built to increasewater storage capacity and help control the watersupply (Wu et al., 2007).

The current study did not consider the water stor-age capacity from dams or other small water bodies;there was no detention of surface runoff from imper-vious surfaces or storage ascribed to the newlyimpervious areas by PRMS. Many researchers (e.g.,Alley and Veenhuis, 1983; Lee and Heaney, 2003;Wissmar et al., 2004; Olivera and DeFee, 2007;Schueler et al., 2009; Viger et al. 2011; LaFontaineet al., 2013) have demonstrated that the presence ofsmall water bodies can detain runoff from impervioussurfaces, reducing peaks in streamflow response thatmight otherwise be expected to accompany anincrease in impervious area within a watershed. Thisrelatively fine-scale feature was not identified in theIITF land cover snapshots. An extension of PRMSdeveloped in Viger et al. (2010) accounts for the effect

of a large number of water-holding depressions in theland surface on the hydrologic response of awatershed. Properly characterizing this geographicfeature in PR, and any change in this feature overtime, is important for supporting effective naturalresource management.

Human Impacts

The model bias shown in Figure 9, especially inWYs 1988-1997, could have many probable causessuch as model error, precipitation under catch, and/orhuman impacts on the supply of water. In the R�ıoPiedras watershed, a large amount of water is trans-ferred into the watershed for human consumption(Lugo et al., 2011). These transfers were not consid-ered in PRMS, therefore PRMS should underpredictthe measured streamflow. In addition, a very large(42%) water loss from leaky pipes was noted over aperiod of years (1991-1995) (Larsen, 2000), whichmay have contributed to increasing the volume ofstreamflow in the R�ıo Piedras to well above whatwould be expected to be generated by precipitationthat actually occurred within the watershed. The per-cent water loss went down drastically in the followingyears, to 15% in 2005 due to pipe repair (Molina-Rivera, 1998; Molina-Rivera and G�omez-G�omez,2008; Lugo et al., 2011). Understanding the impactsof water use on the hydrologic system for the pastand potential future uses is crucial for waterresources management planning.

Effects of Scale

The PRMS simulations based on static land coverillustrated the effect different land cover parameteri-zations can have on streamflow in PR. Notably,scale makes a difference: simulated streamflow for alarge region with localized areas that have under-gone dramatic land cover change may show negligi-ble difference in total streamflow (note the smallpercent changes in Figure 7). But PRMS simulationsusing dynamic land cover parameterization for ahighly altered watershed within the San Juanmunicipality in the North region (R�ıo Piedraswatershed) clearly demonstrates the effects of urban-ization on simulated streamflow at the finer scale.The incorporation of dynamic land cover parameteri-zation in highly altered watersheds has the abilityto reduce the uncertainty in the PRMS simulations.A hydrologic model for historic or future conditionsthat does not consider the past or projected changesin land cover may produce significantly biasedstreamflow.



CONCLUSIONS

PRMS models were developed for PR, taking intoconsideration the large anthropogenic impacts, poor-quality climate data, and sparse streamflow records.This was accomplished by dividing PR into four cli-matic regions and using the least-altered and mostreliable streamflow measurements to calibrate PRMSby region. The PRMS model for each region was usedto simulate streamflow for WYs 1953-2012 using fivestatic land cover parameterizations based on differentsnapshots in time (IITF 1951, 1977, 1991, 2000, andNLCD2001). The PRMS simulations based on staticland cover illustrated consistent differences in simu-lated streamflow across the island. PRMS was thenrun using a dynamic land cover parameterization fora highly altered watershed located within the SanJuan municipality. The PRMS simulations based ondynamic land cover parameterization illustrated thepossible reduction in uncertainty that could beachieved in highly altered watersheds. Hydrologicmodels for historic or future conditions that do notconsider the past or projected changes in land covermay be inadequate.

ACKNOWLEDGMENTS

We would like to thank Andy Bock at the USGS for help withthe monthly water balance model (MWBM). We would also like tothank our anonymous reviewers. Any use of trade, product, or firmnames is for descriptive purposes only and does not imply endorse-ment by the U.S. government. All research at the US FS Interna-tional Institute of Tropical Forestry is done in collaboration withthe University of Puerto Rico.

LITERATURE CITED

Alig, R.J., J.D. Kline, and M. Lichtenstein, 2004. Urbanization onthe US Landscape: Looking Ahead in the 21st Century. Land-scape and Urban Planning 69(2):219-234.

Alley, W.M. and J.E. Veenhuis, 1983. Effective Impervious Area inUrban Runoff Modeling. Journal of Hydraulic Engineering109(2):313-319.

Angeles, M.E., J.E. Gonz�alez, D.J. Erickson, and J.L. Hern�andez,2007. Predictions of Future Climate Change in the CaribbeanRegion Using Global General Circulation Models. InternationalJournal of Climatology 27(5):555-569.

Beighley, R.E., T. Dunne, and J.M. Melack, 2008. Impacts of Cli-mate Variability and Land Use Alterations on Frequency Distri-butions of Terrestrial Runoff Loading to Coastal Waters inSouthern California. Journal of the American Water ResourcesAssociation 44:62-74.

Birdsey, R.A. and P.L. Weaver, 1987. Forest Area Trends in PuertoRico. US Department of Agriculture Forest Service ResearchNote SO-331.

Boccheciamp, R.A., 1978. Soil Survey of San Juan Area of PuertoRico. US Department of Agriculture Soil Conservation Service.

Brockman, V.M., 1952. The Rural Land Classification Program ofPuerto Rico. Northwestern University Studies in Geography,Evanston, Illinois.

Buytaert, W., R. C�elleri, and L. Timbe, 2009. Predicting ClimateChange Impacts on Water Resources in the Tropical Andes:Effects of GCM Uncertainty. Geophysical Research Letters 36:L07406.

Calvesbert, R.J., 1970. Climate of Puerto Rico and the US VirginIslands. US Department of Commerce, Climatography of the US60-52.

Carter, M. and J. Elsner, 1997. A Statistical Method for Forecast-ing Rainfall over Puerto Rico. Weather and Forecasting 12(3):515-525.

Choi, W. and B.M. Deal, 2008. Assessing Hydrological Impact ofPotential Land Use Change through Hydrological and Land UseChange Modeling for the Kishwaukee River Basin (USA).Journal of Environmental Management 88(4):1119-1130.

Cuo, L., D.P. Lettenmaier, M. Alberti, and J.E. Richey, 2009.Effects of a Century of Land Cover and Climate Change on theHydrology of the Puget Sound Basin. Hydrological Processes23(6):907-933.

Daly, C., E.H. Helmer, and M. Qui~nones, 2003. Mapping the Cli-mate of Puerto Rico, Vieques and Culebra. International Jour-nal of Climatology 23(11):1359-1381.

del Mar L�opez, T., T. Aide, and J. Thomlinson, 2001. Urban Expan-sion and the Loss of Prime Agricultural Lands in Puerto Rico.AMBIO: A Journal of the Human Environment 30(1):49-54.

Dietz, J., 1986. Economic History of Puerto Rico: InstitutionalChange and Capitalist Development. Princeton UniversityPress, Princeton, New Jersey.

Dow, C.L. and D.R. DeWalle, 2000. Trends in Evaporation andBowen Ratio on Urbanizing Watersheds in Eastern UnitedStates. Water Resources Research 36(7):1835-1843.

Duan, Q., S. Sorooshian, and V. Gupta, 1992. Effective and Effi-cient Global Optimization for Conceptual Rainfall-RunoffModels. Water Resources Research 28(4):1015-1031.

Duan, Q., S. Sorooshian, and V. Gupta, 1994. Optimal Use of theSCE-UA Global Optimization Method for Calibrating WatershedModels. Journal of Hydrology 158(3):265-284.

Falcone, J., 2011. GAGES-II: Geospatial Attributes of Gagesfor Evaluating Streamflow. USGS Unnumbered Series unde-fined URL. http://pubs.er.usgs.gov/publication/70046617, accessedDecember 2013.

Ferguson, B.K. and P.W. Suckling, 1990. Changing Rainfall-RunoffRelationships in the Urbanizing Peachtree Creek Watershed,Atlanta, Georgia. Journal of the American Water ResourcesAssociation 26(2):313-322.

Gould, W.A., C. Alarc�on, B. Fevold, M. Jim�enez, S. Martinuzzi, G.Potts, M. Qui~nones, M. Sol�orzano, and E. Ventosa, 2008. ThePuerto Rico Gap Analysis Project. Volume 1: Land Cover, Verte-brate Species Distributions, and Land Stewardship. US Depart-ment of Agriculture Forest Service, International Institute ofTropical Forestry GTR-39:165. http://www.fs.fed.us/global/iitf/pubs/iitf_gtr39.pdf, accessed December 2013.

Grau, H.R., T.M. Aide, J.K. Zimmerman, J.R. Thomlinson, E. Hel-mer, and X. Zou, 2003. The Ecological Consequences of Socioeco-nomic and Land-Use Changes in Postagriculture Puerto Rico.BioScience 53(12):1159-1168.

Greenfield, E.J., D.J. Nowak, and J.T. Walton, 2009. Assessment ofthe 2001 NLCD Percent Tree and Impervious Cover Estimates.Photogrammetric Engineering and Remote Sensing 75(11):1279-1287.

Grimmond, C.S.B. and T.R. Oke, 1991. An Evapotranspiration-Interception Model for Urban Areas. Water Resources Research27(7):1739-1755.

Harmsen, E.W., A. Gonz�alez-P�erez, and A. Winter, 2004. Re-Evalu-ation of Pan Evaporation Coefficients at Seven Locations in



Puerto Rico. Journal of Agriculture of the University of PuertoRico 88(3-4):109-122.

Hay, L. and M. Clark, 2003. Use of Statistically and DynamicallyDownscaled Atmospheric Model Output for Hydrologic Simula-tions in Three Mountainous Basins in the Western UnitedStates. Journal of Hydrology 282(1):56-75.

Hay, L.E., M. Clark, M. Pagowski, G. Leavesley, and W. Gutowski,Jr., 2006a. One-Way Coupling of an Atmospheric and a Hydro-logic Model in Colorado. Journal of Hydrometeorology 7(4):569-589.

Hay, L.E., G.H. Leavesley, M. Clark, S.L. Markstrom, R.J. Viger,and M. Umemoto, 2006b. Step Wise, Multiple Objective Calibra-tion of a Hydrologic Model for a Snowmelt Dominated Basin.Journal of the American Water Resources Association 42:877-890.

Hay, L.E., S.L. Markstrom, and C. Ward-Garrison, 2011.Watershed-Scale Response to Climate Change through theTwenty-First Century for Selected Basins across the UnitedStates. Earth Interactions 15(17):1-37.

Hay, L.E. and M. Umemoto, 2006. Multiple-Objective Stepwise Cal-ibration Using Luca. USGS Open-File Report 2006-1323. ftp://brrcrftp.cr.usgs.gov/pub/mows/pubs/hay_pubs/hay_pdf/luca_manual.pdf, accessed December 2013.

Hay, L.E., R.L. Wilby, and G.H. Leavesley, 2000. A Comparison ofDelta Change and Downscaled GCM Scenarios for Three Moun-tainous Basins in the United States. Journal of the AmericanWater Resources Association 36(2):387-397.

Hejazi, M.I. and G.E. Moglen, 2008. The Effect of Climate andLand Use Change on Flow Duration in the Maryland PiedmontRegion. Hydrological Processes 22(24):4710-4722.

Helmer, E., 2004. Forest Conservation and Land Development inPuerto Rico. Landscape Ecology 19(1):29-40.

Helmer, E., O. Ramos, T. del Mar L�opez, M. Qui~nonez, and W.Diaz, 2002. Mapping the Forest Type and Land Cover of PuertoRico, a Component of the Caribbean Biodiversity Hotspot.Caribbean Journal of Science 38(3/4):165-183.

Homer, C., J. Dewitz, J. Fry, M. Coan, N. Hossain, C. Larson, N.Herold, A. McKerrow, J.N. VanDriel, and J. Wickham, 2007.Completion of the 2001 National Land Cover Database for theConterminous United States. Photogrammetric Engineering andRemote Sensing 73(4):337.

Kauppi, P.E., J.H. Ausubel, J. Fang, A.S. Mather, R.A. Sedjo, andP.E. Waggoner, 2006. Returning Forests Analyzed with theForest Identity. Proceedings of the National Academy ofSciences of the United States of America 103(46):17574-17579.

Kennaway, T. and E.H. Helmer, 2007. The Forest Types and AgesCleared for Land Development in Puerto Rico. GIScience andRemote Sensing 44(4):356-382.

Kennaway, T.A., E.H. Helmer, M.A. Lefsky, T.A. Brandeis, andK.R. Sherrill, 2008. Mapping Land Cover and Estimating ForestStructure Using Satellite Imagery and Coarse Resolution Lidarin the Virgin Islands. Journal of Applied Remote Sensing2:023551.

LaFontaine, J.H., L.E. Hay, R.J. Viger, S.L. Markstrom, R.S.Regan, C.M. Elliott, and J.W. Jones, 2013. Application of thePrecipitation-Runoff Modeling System (PRMS) in the Apalachi-cola–Chattahoochee–Flint River Basin in the SoutheasternUnited States. USGS Scientific Investigations Report 2013–5162:118. http://pubs.usgs.gov/sir/2013/5162/pdf/sir2013-5162.pdf.

Larsen, M.C., 2000. Analysis of 20th Century Rainfall and Stream-flow to Characterize Drought and Water Resources in PuertoRico. Physical Geography 21(6):494-521.

Larsen, M.C. and S. Simon, 1993. A Rainfall Intensity-DurationThreshold for Landslides in a Humid-Tropical Environment,Puerto Rico. Geografiska Annaler. Series A, Physical Geography75(1/2):13-23.

Leavesley, G.H., R.W. Lichty, B.M. Troutman, and L.G. Saindon,1983. Precipitation-Runoff Modeling System—User’s Manual.USGS Water-Resources Investigations Report 83–4238. http://pubs.usgs.gov/wri/1983/4238/report.pdf.

Lee, J.G. and J.P. Heaney, 2003. Estimation of Urban Impervious-ness and Its Impacts on Storm Water Systems. Journal of WaterResources Planning and Management 129:419-426.

Leopold, L.B., 1968. Hydrology for Urban Land Planning – AGuidebook on the Hydrologic Effects of Urban Land Use. Geo-logical Survey Circular 554, U.S. Department of the Interior,Washington, D.C.

Lugo, A., J. Gonz�alez-Liboy, B. Cintron, and K. Dugger, 1978.Structure, Productivity, and Transpiration of a Subtropical DryForest in Puerto Rico. Biotropica 10(4):278-291.

Lugo, A., O. Ramos Gonz�alez, and C. Pedraza, 2011. The R�ıo Pie-dras Watershed and Its Surrounding Environment. U.S. Depart-ment of Agriculture Forest Service, International Institute ofTropical Forestry FS-980. http://www.fs.fed.us/global/iitf/pubs/RioPiedras_FNLrvsd.pdf, accessed December 2013.

Markstrom, S.L., L.E. Hay, C.D. Ward-Garrison, J.C. Risley, W.A.Battaglin, D.M. Bjerklie, K.J. Chase, D.E. Christiansen, R.W.Dudley, R.J. Hunt, K.M. Koczot, M.C. Mastin, R.S. Regan, R.J.Viger, K.C. Vining, and J.F. Walker, 2012. Integrated Watershed-Scale Response to Climate Change for Selected Basins across theUnited States. USGS Scientific Investigations Report 2011–5077:143. http://pubs.usgs.gov/sir/2011/5077/SIR11-5077_508.pdf.

Markstrom, S.L., R.G. Niswonger, R.S. Regan, D.E. Prudic, andP.M. Barlow, 2008. GSFLOW, Coupled Ground-Water and Sur-face-Water Flow Model Based on the Integration of the Precipi-tation-Runoff Modeling System (PRMS) and the ModularGround-Water Flow Model (MODFLOW-2005). USGS Tech-niques and Methods 6-D1:240. http://pubs.usgs.gov/tm/tm6d1/pdf/tm6d1.pdf.

Martinuzzi, S., W.A. Gould, and O.M. Ramos Gonz�alez, 2007. Cre-ating Cloud-Free Landsat ETM+ Data Sets in Tropical Land-scapes: Cloud and Cloud-Shadow Removal. U.S. Department ofAgriculture Forest Service, International Institute of TropicalForestry GTR-32:12. http://www.fs.fed.us/global/iitf/pubs/iitf-gtr32.pdf, accessed December 2013.

McCabe, G.J. and S.L. Markstrom, 2007. A Monthly Water-BalanceModel Driven by a Graphical User Interface. USGS Open-FileReport 2007-1008:6. http://pubs.usgs.gov/of/2007/1088/pdf/of07-1088_508.pdf.

Miller, G.L. and A.E. Lugo, 2009. Guide to the Ecological Systemsof Puerto Rico. Gen. Tech. Rep. IITF-GTR-35. U.S. Departmentof Agriculture, Forest Service, International Institute of Tropi-cal Forestry, San Juan, Puerto Rico, 437 pp.

Milly, P.C.D., J. Betancourt, M. Falkenmark, R.M. Hirsch, Z.W.Kundzewicz, D.P. Lettenmaier, and R.J. Stouffer, 2008. Sta-tionarity Is Dead: Whither Water Management? Science319:573-574.

Molina-Rivera, W.L., 1998. Estimated Water Use in Puerto Rico,1995. USGS Open-File Report 98-276:28. http://pr.water.usgs.gov/public/online_pubs/ofr98_276/ofr98_276.pdf.

Molina-Rivera, W.L. and F. G�omez-G�omez, 2008. Estimated WaterUse in Puerto Rico, 2005. USGS Open-File Report 2008-1286:37.http://pubs.usgs.gov/of/2008/1286/OFR_2008_1286.pdf.

Murphy, S. and R. Stallard (Editors), 2012. Water Quality andLandscape Processes of Four Watersheds in Eastern PuertoRico. USGS Professional Paper 1789. http://pubs.usgs.gov/pp/1789/PP1789.pdf.

NSRDB (National Solar Radiation Database), 1992. National SolarRadiation Database 1961–1990 User’s Manual. National Renew-able Energy Laboratory, Golden, Colorado.

Olivera, F. and B.B. DeFee, 2007. Urbanization and Its Effect onRunoff in the Whiteoak Bayou Watershed, Texas. Journal of theAmerican Water Resources Association 43:170-182.



Par�es-Ramos, I.K., W.A. Gould, and T.M. Aide, 2008. AgriculturalAbandonment, Suburban Growth, and Forest Expansion inPuerto Rico between 1991 and 2000. Ecology and Society13(2):1.

Ramos, O. and A. Lugo, 1994. Mapa de la Vegetaci�on de PuertoRico. Acta Cient 8:63-66.

Richter, B.D., J.V. Baumgartner, J. Powell, and D.P. Braun, 1996.A Method for Assessing Hydrologic Alteration within Ecosys-tems. Conservation Biology 10(4):1163-1174.

Scatena, F.N. and A.E. Lugo, 1995. Geomorphology, Disturbance,and the Soil and Vegetation of Two Subtropical Wet SteeplandWatersheds of Puerto Rico. Geomorphology 13(1-4):199-213.

Schiff, R. and G. Benoit, 2007. Effects of Impervious Cover at Mul-tiple Spatial Scales on Coastal Watershed Streams. Journal ofthe American Water Resources Association 43(3):712-730.

Schueler, T.R., L. Fraley-McNeal, and K. Cappiella, 2009. Is Imper-vious Cover Still Important? Review of Recent Research. Jour-nal of Hydrologic Engineering 14:309-315.

Taha, H., 1997. Urban Climates and Heat Islands: Albedo, Evapo-transpiration, and Anthropogenic Heat. Energy and Buildings25(2):99-103.

Tang, Z., B.A. Engel, B.C. Pijanowski, and K.J. Lim, 2005. Fore-casting Land Use Change and Its Environmental Impact at aWatershed Scale. Journal of Environmental Management 76(1):35-45.

Taylor, M.A., D.B. Enfield, and A.A. Chen, 2002. Influence of theTropical Atlantic Versus the Tropical Pacific on Caribbean Rain-fall. Journal of Geophysical Research 107(C9):3127.

Thornthwaite, C., 1948. An Approach toward a Rational Classifica-tion of Climate. Geographical Review 38:55-94.

Urbonas, B.R. and L.A. Roesner, 1992. Hydrological Design forUrban Drainage & Flood Control. In: Handbook of Hydrology,D.R. Maidment (Editor). McGraw-Hill, New York City, NewYork, pp. 21.1-21.2.

USDA, 1994. State Soil Geographic Database (STATSGO) DataUsers Guide. Natural Resources Conservation Service 1492:107.

USEPA and USGS, 2008. National Hydrography Dataset Plus,NHDPlus User Guide. NHDPLUS UserGuide.pdf. http://www.horizon-systems.com/NHDPlus/NHDPlusV2_documentation.php,accessed December 2013.

Veve, T.D. and B.E. Taggart (Editors), 1996. Atlas of Ground-WaterResources in Puerto Rico and the US Virgin Islands. USGSWater Resources Investigations Report 94-4198. http://vi.water.usgs.gov/public/online_pubs/wri94_4198/wri94_4198.pdf.

Viger, R.J. and A. Bock, 2014. The Geospatial Fabric Feature Setfor National Hydrologic Modeling. U.S. Geological Survey DataSet, doi: http://dx.doi.org/doi:10.5066/F7542KMD.

Viger, R.J., L.E. Hay, S.L. Markstrom, J.W. Jones, and G.R. Buell,2011. Hydrologic Effects of Urbanization and Climate Changeon the Flint River Basin, Georgia. Earth Interactions 15(20):1-25.

Wadsworth, F.H., 1950. Notes on the Climax Forests of Puerto Ricoand Their Destruction and Conservation Prior to 1900. Carib-bean Forester 11(1):38-56.

Ward-Garrison, C., S.L. Markstrom, and L.E. Hay, 2009. Down-sizer – A Graphical User Interface-Based Application for Brows-ing, Acquiring, and Formatting Time-Series Data for HydrologicModeling. USGS Open-File Report 2009-1166:27. http://pubs.usgs.gov/of/2009/1166/pdf/OF09-1166.pdf.

Weaver, P.L., M.D. Byer, and D.L. Bruck, 1973. TranspirationRates in the Luquillo Mountains of Puerto Rico. Biotropica5:123-133.

White, E.D., G.W. Feyereisen, T.L. Veith, and D.D. Bosch, 2009.Improving Daily Water Yield Estimates in the Little RiverWatershed: SWAT Adjustments. Transactions of the ASABE52(1):69-79.

Wissmar, R.C., R.K. Timm, and M.G. Logsdon, 2004. Effects ofChanging Forest and Impervious Land Covers on DischargeCharacteristics of Watersheds. Environmental Management34:91-98.

Wohl, E., A. Barros, N. Brunsell, N.A. Chappell, M. Coe, T. Giam-belluca, S. Goldsmith, R. Harmon, J.M.H. Hendrickx, J. Juvik,J. McDonnell, and F. Ogden, 2012. The Hydrology of the HumidTropics. Nature Climate Change 2(9):655-662.

Wolock, D.M., 1997. STATSGO Soil Characteristics for the Conter-minous United States Open-File Report 656.

Wu, W., C.A. Hall, and F.N. Scatena, 2007. Modelling the Impactof Recent Land-Cover Changes on the Stream Flows in North-eastern Puerto Rico. Hydrological Processes 21(21):2944-2956.

Yapo, P.O., H.V. Gupta, and S. Sorooshian, 1996. AutomaticCalibration of Conceptual Rainfall-Runoff Models: Sensitivity toCalibration Data. Journal of Hydrology 181(1):23-48.



the effects of changing land cover on streamflow...

Documents