Journal of Hydrology (2006) xxx, xxx–xxx

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ava i lab le a t www.sc iencedi rec t . com

journal homepage: www.elsevier .com/ locate / jhydrol

An application of the distributed hydrologic modelCASC2D to a tropical montane watershed

Matt Marsik *, Peter Waylen

Department of Geography, University of Florida, 3141 Turlington Hall, P.O. Box 117315, Gainesville, FL 32611 USA

Received 11 March 2005; received in revised form 30 March 2006; accepted 3 April 2006

Summary Increased stormflow in the Quebrada Estero watershed (2.5 km2), in the northwest-ern Central Valley tectonic depression of Costa Rica, reportedly has caused flooding of the city ofSan Ramon in recent decades. Although scientifically untested, urban expansion was deemed thecause and remedial measures were recommended by the Programa de Investigacion en DesarrolloHumano Sostenible (ProDUS). CASC2D, a physically-based, spatially explicit hydrologic model,was constructed and calibrated to a June 10th 2002 storm that delivered 110.5 mm of precipita-tion in 4.5 h visibly exceeded the bankfull stage (0.9 m) of the Quebrada flooding portions of SanRamon. The calibrated hydrograph showed a peak discharge 16.68% (2.5 m3 s�1) higher, an aboveflood stage duration 20% shorter, and time to peak discharge 11 min later than the same observeddischarge hydrograph characteristics. Simulations of changing land cover conditions from 1979 to1999 showed an increase also in the peak discharge, above flood stage duration, and time to peakdischarge. Analysis using a modified location quotient identified increased urbanization in lowerportions of the watershed over the time period studied. These results suggest that increasedurbanization in the Quebrada Estero watershed have increased flooding peaks, and durationsabove threshold, confirming the ProDUS report. These results and the CASC2D model offer aneasy-to-use, pragmatic planning tool for policymakers in San Ramon to assess future develop-ment scenarios and their potential flooding impacts to San Ramon.

�c 2006 Elsevier B.V. All rights reserved.

KEYWORDSCosta Rica;Distributed model;GIS;Land cover;Rainfall runoff modeling

0d

Introduction

Unrestricted land cover conversion, without consideration ofpotential degrading repercussions, alters the land-basedhydrologic cycle, often leading to increased frequency and

022-1694/$ - see front matter �c 2006 Elsevier B.V. All rights reservedoi:10.1016/j.jhydrol.2006.04.003

* Corresponding author. Tel.: +1 113523923198x229.E-mail address: mmarsik@ufl.edu (M. Marsik).

magnitude of flooding. This is especially evident in tropicalmontane regions, where intense rainfalls and rugged topogra-phy amplify expected responses. San Ramon, a small town inthe northwestern Central Valley of Costa Rica, has sufferedincreased flooding from the Quebrada Estero (ProDUS, 2000)the local river. In a comprehensive report Programa de Inves-tigacion en Desarrollo Humano Sostenible (ProDUS) qualita-tively singled out urban expansion as the cause and

.

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proposed untested remediation measures to policymakers inSan Ramon. Without scientifically investigating the causes offlooding ProDUS proposed to divert water to an adjacent riverbasin and to enlarge the local lake to impound floodwaters.Additionally, Quesada (2002a) compiled historic accounts offlooding of the Quebrada Estero in San Ramon, where thisproblem has been an issue of concern since the early 1900swith a noticeable increase in flooding during the last threedecades. Suchflooding problems are typical ofmany small cit-ies in Costa Rica (Solıs and Calderon, 1999) and are common indeveloping countries, where unregulated expansion of urbanareas is the rule.

The objectives of this study are as follows: (1) to create ahydrologic model of the Quebrada Estero watershed usingdata collected from field work and the literature sources tobe used as a potential planning tool for San Ramon; (2) to as-sess the hydrologic effects of changing land use/cover condi-tions from 1979 to 1999 in the Quebrada Estero watershed;(3) to investigate potential causes of upstream changes inland use/cover that may increase flood peaks and durationsin downstream urban areas, and (4) to highlight the dynamicchanging land use/cover conditions in the Quebrada Esterowatershed as typical within many rural locations in CostaRica and in other tropical, developing countries.

Impact of changing land cover on thehydrologic cycle

Hillslope hydrologic processes depend on topography, soils,vegetation and climatic characteristics of a watershed(Dunne, 1978). Infiltration excess overland flow (Horton,1933) or Hortonian overland flow (HOF) occurs when rainfallintensities exceed the infiltration capacity of unsaturatedsoils and is particularly critical in humid areas, where thesoils and vegetation have been disturbed. Saturation over-land flow (SOF) (Dunne, 1978) generally prevails in flat bot-tom valleys with gentle slopes and covered by thin soils.Subsurface flow (SSF) through the unsaturated ground com-monly occurs in well drained, deep, and permeable soils lo-cated on steep convex hillslopes bordering narrow valleys.

The spatial distribution and management practices ofland cover affect the response of a watershed. Interceptionand rapid infiltration into deep soils make overland flow rareon undisturbed forested hillslopes in the humid tropics (Bru-ijnzeel, 1990). Increased urban occupation reduces and/oreliminates interception/infiltration since it decreases sur-face roughness thereby increasing peak streamflows (Rog-ers, 1994) and decreasing response times (Urbonas andRoesner, 1993). Disturbance of vegetation in the tropicsand compaction of soil on steep slopes increases HOF andlowers SSF (Bruijnzeel, 1990). HOF also takes place on al-tered and fragmented secondary forests (Giambelluca,2002) and in areas of reduced hydraulic conductivities andinfiltration capacities caused by unpaved roads and foot-paths (Ziegler and Giambelluca, 1997).

Previous studies

The application of distributed hydrologic models to investi-gate possible impacts of land cover change on streamflow inthe tropics is rare. However, the lumped model HEC-1 has

been applied widely (Calderon, 1999; Purwanto and Donker,1991; Rojas, 2000; Solıs and Calderon, 1999; Suwanwera-kamtorn, 1994; Wong and Chen, 1994). In the tropicalmontane Tambito watershed, Colombia Mulligan et al.(unpublished report, 1999) employ a continuous simulationhydrologic model to identify critical thresholds of defores-tation while Colby (2001) discovers a sensitivity to thespatial resolution of land cover characteristics in a quasi-distributed continuous model, PRMS. Wooldridge et al.(2001) apply a semi-distributed subsurface flow model inNSW, Australia to simulate streamflow responses to varyingland use conditions and rainfall distributions.

These studies are subjected to some limitations withregard to their geographic region of applicability (not CostaRica or Central America) (Mulligan et al., unpublishedreport, 1999; Wooldridge et al., 2001), and model formula-tion (Colby, 2001). Continuous models employed by Mulliganet al. (unpublished report, 1999) and Wooldridge et al.(2001) require observed, detailed soil, climatic, and vegeta-tion data for modeling SSF and evapotranspiration; a factthat limit their appropriateness.

One model, CASC2D, is viable for application to previ-ously ungauged tropical watersheds with limited data, andexhibiting severe hydrologic effects from land cover conver-sion. The CASC2D model has the advantages of being spa-tially explicit, physically based, and having minimal datarequirements for single event simulations. Downer et al.(2002) offer a comprehensive discussion of the evolutionof CASC2D with a review of its previous applications. Theaforementioned discussion focuses primarily on early modeldevelopment and only Doe et al. (1996) assess watershed re-sponse to changes to land cover/land use with CASC2D.While widely applied within the United States, CASC2D islimited in its application to watersheds outside of the US.

Model description

CASC2D is a spatially explicit, physically-based rainfall–runoff model (Julien et al., 1995) that simulates HOF in a wa-tershed; runoff generated by SOF and SSF is not simulated byCASC2D. Spatial variability within the watershed is modeledusing a raster (cell-based) GIS data format, where amass bal-ance is calculated for every grid cell. Cell outflow equals theamount of precipitation input less interception, infiltration,and surface and channel storage. Precipitation is input fromradar data or from measurements at single or multiple gaugepoints. Interception is modeled empirically (Gray, 1970), andthe Green and Ampt (1911) method models soil infiltration,which allows for continuous infiltration if precipitation inten-sity decreases or precipitation stops whereby upslope HOFwill collect in lower cells and infiltrate, provided the infiltra-tion capacity is not exceeded. Water is routed over a digitalelevation model (DEM) using the diffusive wave solution ofSt. Venant’s equations coupled with Manning’s resistanceequation, and passed to the channel network water and rou-ted to the outlet, via the one-dimensional St. Venant equa-tion of motion (Julien et al., 1995).

Application of CASC2D to the Quebrada Estero

Since CASC2D is a spatially distributed model, it is usefulfor simulating the hydrologic effects of land cover/land

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use change within a watershed (Beven, 1985). Further-more, the physical basis of the model formulation is appro-priate for application to the tropics (Bonell, 1993) sincelong term hydro-meteorological data are not needed formodel calibration in a previously ungauged watershed (Bev-en, 1985). CASC2D was developed for watersheds in botharid and humid environments contingent upon HOF beingthe dominant runoff generation mechanism (Julien et al.,1995). HOF may dominate runoff generation when landcover in a watershed is secondary or tertiary, and no pri-mary forest remains (Giambelluca, 2002) and in watershedswith disturbed land cover and thin vegetation (Dunne,1978). Forest cover in the watershed decreased from 30%in 1979 to 10% in 1999 (Fig. 1) and no primary forest re-mains (Quesada, 2002b). Roads and footpaths, which resultin increased HOF (Ziegler and Giambelluca, 1997), and res-idential areas comprised about 25% of the watershed in1999 compared to 11% in 1979 (Fig. 1). The steep concaveslopes (Fig. 2), and low permeability of the clayey soils alsoencourage HOF, particularly in small watersheds with short

Figure 1 Land cover distributions in

lag times to peak discharge (Dunne, 1978) as are commonin the study basin. The climatic and topographic conditionsalso favor the generation of HOF as tropical rainfall inten-sities regularly exceed the infiltration capacities of clayeysoils.

Model application

The Quebrada Estero watershed occupies 2.5 km2 of topo-graphically rugged, steep slopes on the northwestern fringeof the Central Valley of Costa Rica (Fig. 3). Urban and resi-dential areas have expanded westward into the watersheddue to increasing population and limited urban land avail-ability. No a priori rainfall and stream data exist beforethe current study. A field campaign conducted in August2001, and from June to September 2002 provided fieldobservations for many of the input parameters for modelconstruction and simulation. Precipitation for model inputand for determining interception parameters, land cover

the Quebrada Estero watershed.

Figure 2 Distribution of slopes within the watershed and elevation transects showing concavity of the watershed.

Figure 3 Location map of San Ramon and the Quebrada Estero watershed within Costa Rica, soil sampling points, manual raingauges, and depth recorder within the Quebrada Estero watershed. Automatic tipping bucket rain gauge is located at UCR–SR.Percentages correspond to each subbasin area contributing to the total watershed area.

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characteristics, soil samples for soil texture, stream depth,and channel measurements were collected from field mea-surements. Input parameters were used from the published

literature sources for interception, infiltration, overlandand channel roughness following information gathered fromfield observations.

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Precipitation

During an initial site visit in August 2001, a network of man-ual rain gauges (Fig. 3), which included University of CostaRica–San Ramon (UCR–SR), was installed to investigatethe spatial distribution of rainfall with respect to elevationand aspect within the watershed. Precipitation totals mea-sured to 0.1 cm were collected for eight storm events fromAugust to October, 2001. A tipping bucket rain gauge lo-cated at the UCR–SR (installed in April 2002) within the low-er portion of the watershed (Fig. 3) provided rainfall datacollected every two minutes by an attached data logger (re-corded initially in 0.01 in. and converted to mm) for modelcalibration and simulations. The tipping bucket gaugemeasured rainfall data for a large flood producing storm ob-served on June 10, 2002. The tipping bucket gauge providesa good temporal distribution of precipitation, while themanual gauge network provides potential spatial distribu-tions of precipitation events.

Table 1 Proportions of precipitation as calculated from the man

Date Manual network gauge stations

1 2 3

August 9, 2001 0.611 0.778 1.000September 5, 2001 1.250 0.750 0.750September 14, 2001 0.512 0.824 0.549September 28, 2001 0.761 1.109 0.478October 5, 2001 0.605 0.826 0.616October 12, 2001 0.700 0.840 1.020October 17, 2001 0.868 0.984 1.021October 31, 2001 0.832 0.684 0.884

Note that gauge 7 is UCR–SR where the tipping buket rain gauge is lo

Figure 4 Temporal distribution of the June 10th precipitation evethe stations in the manual gauge network.

Since there is only one automated rain gauge due to lo-gistic constraints, the spatial locations of the manual gaugesprovided a template based on the eight observed storms in2001 by which to spatially distribute the precipitation col-lected by the tipping bucket gauge at UCR–SR. Each net-work gauge point was normalized to the precipitationmeasured in the manual gauge at UCR–SR (the subsequentlocation of the tipping bucket gauge) to express the mea-sured at each station (Table 1) relative to that measuredat UCR–SR. The 2002 precipitation data measured atUCR–SR by the tipping bucket gauge are then multipliedby the appropriate normalizing terms proportion for eachstation in the manual gauge network to spatially distributethe precipitation (Fig. 4) according to the eight observeddistributions. These proportions provide the basis uponwhich precipitation subsequently measured by the tippingbucket gauge are spatially distributed about the watershedin order to reflect various potential spatial distributions.This simplistic proportion may not capture the intra-event

ual rain gauge network

4 5 6 7

0.833 0.778 0.833 1.0000.875 1.000 0.500 1.0000.912 0.814 0.735 1.0001.109 1.065 0.913 1.0001.058 1.035 0.942 1.0000.800 0.900 0.840 1.0000.847 1.000 0.868 1.0000.684 0.874 0.895 1.000

cated.

nt recorded at UCR–SR based on the proportion calculated from

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temporal variability, but does provide an idea of possiblespatial distributions of precipitation about the watershed.

Land cover

Land cover data (Fig. 1) are derived from aerial photographsflown in 1979 (scale 1:45,000), 1989 (scale 1:40,000), and1999 (scale 1:20,000) distributed by Instituto GeograficoNacional, San Jose, Costa Rica (Quesada, 2002b). Raster lay-ers using a 25 m cell size are generated for each of land cov-er data. Each land cover is assigned a Manning’s frictioncoefficient (Table 2) based on the mean value provided inthe literature (Hydrologic Engineering Center, 1998). Cellsrepresenting roads and trials are incorporated as importantsources of HOF (Ziegler and Giambelluca, 1997), and aremasked as impervious areas in the soils layers for hydraulicparameters. In addition, a ‘‘lake’’ and a dredged retentionarea are specified as detention storage with 0.5 m depthreflecting their sediment laden, marsh-like condition. Dur-ing simulations, channel depth must exceed this detentiondepth before flow can proceed to the next channel seg-ment. These land cover data provide the template of spatialdistribution for assigning the numerical interception androughness parameters.

Interception

Interception parameters (Table 2) are from the publishedliterature values of interception coefficients and storagecapacity used by Koenigs (1998) following Gray (1970),and from field measurements. In the field precipitation iscollected under two cover types (forest and scrub), and ina nearby open field. Storage capacity and interception coef-ficients are computed using linear regression of precipita-tion in the open and intercepted precipitation (openprecipitation minus precipitation measured under each veg-etation type). Forest and scrub showed a significant linearrelationship indicated by a high adjusted R2 value. Storagecapacity and interception coefficients values are assignedto each 25 m grid cell according to the land covers in1979, 1989 and 1999.

Table 2 Manning’s friction coefficients and interception values

Land cover Manning’s n values

Minimum Mean Maximum

Coffee 0.40 0.60 0.80Forests 0.40 0.60 0.80Ornamental 0.40 0.60 0.80Pasture 0.30 0.39 0.48Pasture/forest 0.30 0.39 0.48Residential 0.01 0.08 0.15Roads 0.01 0.08 0.15Scrub 0.10 0.20 0.30Sugar cane 0.16 0.19 0.22Swamp 0.30 0.39 0.48Urbanizing – 0.10 –Vegetables/grains 0.16 0.19 0.22a Interception values calculated by linear regression.

Soils

In field soil sampling provided data for determining soil tex-ture, by which pertinent hydraulic infiltration parameterswere estimated according to the soil texture published byRawls et al. (1983). Sand, silt and clay mass percentagesare estimated from 22 soil samples (Fig. 3) using a water-based settling technique (Soil Texture Analysis, Sammis,1996 see http://weather.nmsu.edu/Teaching_Material/soil456/soiltexture/soiltext.htm) and texture determinedaccording the USDA soil texture triangle. Clay is the domi-nant soil texture in the watershed. Ranges of possible valuesfor the four soil hydraulic parameters used in the CASC2Dmodel are listed in Table 3. Raster grids of 25 m cell sizeare generated for each of the four soil parameters. For cal-ibration purposes a set of raster grids for initial moistureconditions is generated in one-percent increments fromthe wilting point (0.272 cm3 cm�3) to the effective porosity(0.385 cm3 cm�3), which is complete saturation for clay andrepresents the porosity layer in the model. Additional gridsare generated for saturated hydraulic conductivity (the rec-ommended literature value of 0.06 cm h�1) and for the cap-illary head (the recommended literature value of 31.63 cm).

Grid cells occupied by residential, roads and urbanizingland cover are deemed impervious and the soil parametervalues set to zero Ogden et al. (2000). Many roads withinthe watershed are paved with some outlying dirt roadsexhibiting compacted condition of clayey soil. High densityresidential areas have little pervious surfaces to infiltrateprecipitation as the house occupy much of the land parceland in urbanizing areas soil compaction is common in theconstruction sites. Given the 25 m cell size of the modelsubpixel heterogeneity cannot be represented in CASC2Das all input data layers must have the 25 m cell size.

Elevation

A DEM is generated from 1:10,000 contour lines using theTOPOGRID (Hutchinson, 1989) interpolation routine with amap-derived stream network to enforce drainage. The gridcell size for the DEM was initially chosen as five meters,

for land cover

Interception values

Storage capacity (mm) Interception coefficient

0.51 0.400.211a 0.18a

0.51 0.410.75 0.460.75 0.460.25 0.050.25 0.050.213a 0.12a

0.51 0.100.75 0.460.25 0.051.02 0.41

Table 3 Infiltration parameters for clay soil from Rawls et al. (1983)

Parameter Minimum Recommended Maximum

Initial moisture (cm3 cm�3)a 0 0.272 0.385Capillary head (cm) 6.39 31.63 156.5Saturated hydraulic conductivity (cm hr�1) 0.05 0.06 0.1Effective porosity (cm3 cm�3) – 0.385 –a The range of values used in model calibration for initial moisture content are from 0 to 0.385 in 5% increments of the upper limit.

Figure 5 Stage–discharge rating curve for the QuebradaEstero.

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which represented the smallest topographic featureidentifiable from the 1:10,000 contours. However, duringpreliminary simulations, this cell size caused numericalinstability in CASC2D due to accumulation of water in DEMdepressions, and, through trial and error, the cell size wasincreased to 25 m whereby model stability was achieved.About 4000 grid cells (25 m · 25 m) comprise the watershed.

Channel network

The channel network is generated from the DEM based oncontributing area, and is represented in CASC2D as links(stream channels segments) and nodes (individual gridcells). Once delineated, the cells comprising the channelsare smoothed to eliminate erroneous elevation pits ordepressions that would interfere with the channel routing.For model parameterization, channel cross-section geome-try is specified as trapezoidal throughout the stream net-work and parameter values for channel depth, width, andside slope are assigned to each channel segment based onfield measurements. Field observations also indicate theappropriate Manning’s friction coefficient assigned to eachsegment based on published values by Bedient and Huber(1988).

Streamflow and rating curve

Observed discharge used to calibrate CASC2D is calculatedusing a depth–discharge relationship. Stage data are mea-sured in (0.01 ft) using a pressure transducer depth recorderinstalled at the western edge of San Ramon where the Queb-rada Estero becomes channelized, and are recorded every2 min by a data logger. The pressure transducer was installedin June 2002 and provided stream depth data for the June10th, 2002 event. Approximately 10 m upstream of the depthrecorder stream velocity (ft s�1) is measured using a velocityprobe at 0.6 of total water depth. Simultaneous measure-ments of stream depth and width (both in cm) are measuredmanually to provide estimates of discharge at this rectangu-lar cross-section (width of 1.3 m and a depth of 0.9 m). Thedepth–discharge relationship between observed stage at thelocation of the automatic depth gauge and the estimateddischarge is represented mathematically as a rating curve(Fig. 5) using the following equation:

Q ¼ 7:98 � S2:444 ðr2 ¼ 0:95Þ ð1Þ

where Q is the discharge in m3 s�1 and S is the logged streamdepth in m. Depth measurements from the depth recorderare converted to discharge for use in model calibrationand simulations.

Calibration to 1999 land cover

The storm on June 10th, 2002, which delivered 110.5 mm ofprecipitation in 4.5 h, visibly exceeded the bankfull stage of0.9 m (discharge 6.17 m3 s�1). As floods are of practicalinterest, only those portions of the hydrograph above thisthreshold are considered. The tipping bucket gauge atUCR–SR provided the temporal distribution of precipitationfor the June 10th. The recorded precipitation from UCR–SRwere temporally distributed using the precipitation propor-tions calculated from the eight spatial distributions mea-sured from the manual gauge network to provide the basisfor generating eight possible temporal and spatial distribu-tions of the June 10th storm. No corresponding stream flowdata were available for the August 2001 events due to thelack of gauging equipment. Although, without radar, thereis no way of defining the true spatial distribution of thisstorm, this approach yields eight potentially realistic distri-butions of precipitation (Fig. 6). Inverse distance weightedinterpolation is specified to spatially distribute the June10th precipitation throughout the watershed based on thespatial distribution of the September 14th, 2001 event mea-sured using the manual gauge network.

Calibration determines the parameters that yield thebest match between the observed and the simulated hydro-graphs. In addition to calibrating CASC2D based the eightspatial configurations of precipitation (Fig. 5), initial soilmoisture content values were allowed to range from thewilting point, 0.272 (cm3 cm�3), to saturation value,0.385, for clay (Table 3). Interception coefficient and stor-age, saturated hydraulic conductivity, capillary head,porosity, and Manning’s n for overland flow are parameter-

1

1

0.611

0.778

0.833

0

(a) (b)

778

0.833

1

1

0.50.75

1.25

0.75

0.875

1

0.549

0.512

0.824

0.735

0.814

0.912

1

0.478

0.761

1.109

0.913

1.065

1.109

1

0.616

0.605

0.826

0.942

1.035

1.058

1

0.7

0.9

0.8

1.02

0.84

0.84

1

1

1.021

0.868

0.984

0.868

0.847

1

0.884

0.832

0.684

0.895

0.874

0.684

(c)

(d) (e) (f)

(g) (h)

Figure 6 Spatial distribution of precipitation proportional to that measured at UCR–SR. Precipitation isohyetal interval is0.05 mm. Letters correspond to observed spatial distribution using the manual rain gauge network: (a) August 9, 2001;(b) September 5, 2001; (c) September 14, 2001; (d) September 28, 2001; (e) October 5, 2001; (f) October 12, 2001; (g) October 17,2001 and (h) October 31, 2001.

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ized based on the mean literature value from their respec-tive sources.

The resulting calibrated and observed hydrographs arecompared quantitatively using equations for: (1) the per-cent error in peak, Zp,

Zp ¼ 100 � ðqS � qOÞqO

�������� ð2Þ

where qO and qS are the observed and simulated peak dis-charges, and (2) the mean sum of squared errors (MSSE)for the above threshold hydrograph:

ZE ¼1

NQ

XNQi¼1½qOðiÞ � qSðiÞ�

2 ð3Þ

where ZE is the mean sum of square errors between the ob-served and calibration hydrograph qO(i) and qS(i), and NQ is

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the number of computed hydrograph ordinates. The percenterror in peak measures the fit between observed and simu-lated peaks, and the MSSE gives a comparison of the magni-tudes of observed and simulated hydrographs of the peakdischarges, volumes, and peak timing (Hydrologic Engineer-ing Center, 1998). Additionally, an assessment is made ofthe simulated hydrographs using the time to peak discharge(from the start of the rising limb), and the time abovethreshold.

Sensitivity analysis

A sensitivity analysis was performed to identify the degree ofsensitivity to the manual calibration of parameter values forinitial moisture, saturated hydraulic conductivity, capillaryhead, Manning’s overland roughness, and the spatial distribu-tion of precipitation. Values for saturated hydraulicconductivity, and capillary head, and Manning’s overlandroughness, with the exception of urbanizing areas as onevalue was given in the literature, were perturbed in 5% incre-ments from the minimum to maximum recommended values(Tables 2 and 3). Initial soil moisture values were perturbedby 1% increments for fromwilting point to effective porosity.The eight potential spatial distributions for precipitationmeasured from the manual gauge network were included inthe sensitivity analysis. Following Senarath et al. (2000) theresults of the sensitivity analysis were quantified using thepercent change in peak discharge (PCPD), percent change inrunoff volume (PCRV), and the percent change in root meansquare error (PCRMSE) between the manually calibrated hyd-rograph and the hydrographs resulting from the perturbationof the five parameters. PCRMSE is included to provide insightinto the sensitivity to changes in differences in timingbetween the manual and perturbed hydrographs (Senarathet al., 2000). PCPD is calculated as the following:

PCPD ¼ Q s � Qm

Q s

� 100 ð4Þ

Figure 7 Calibration hydrograph (observed and simulated hydrogrJune 10 storm.

where Qs and Qm refer to peak discharges of the hydro-graphs with perturbed parameters and the manually cali-brated hydrograph, respectively. PCRV is calculated as thefollowing:

PCPD ¼ V s � Vm

V s� 100 ð5Þ

where Vs and Vm refer to peak discharges of the hydrographswith perturbed parameters and the manually calibratedhydrograph, respectively. PCRMSE is calculated as thefollowing:

PCRMSE ¼ RMSEs � RMSEm

RMSEs� 100 ð6Þ

where RMSE and RMSEm refer to the root mean square errorof the observed and manually calibrated hydrographs,respectively. RMSE is calculated as the following:

RMSE ¼

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiXni¼1

ðQ S � QOÞ2

n

vuut ð7Þ

where Qs and Qo refer to the hydrograph ordinates of thehydrograph with perturbed parameter values and the ob-served hydrograph, respectively, n refers to the total num-ber of hydrograph ordinates used in the calculation, and i isthe index denoting individual hydrograph ordinates. RMSEmused in Eq. (6) is calculated by substituting Qm for Qs inEq. (7).

Results and discussion

Calibration

Given the required input model parameters and that theQuebrada Estero watershed is previously ungauged, CASC2Dreproduces the flood hydrograph (Fig. 7) of June 10th, 2002under the 1999 land cover conditions. Flood duration and

aphs) and observed storm hyetograph for the simulation of the

Table 4 Statistical characteristics of observed and simulated discharge hydrographs

Discharge Hydrographs

Observed 1979 1989 1999

Peak discharge (m3 s�1) 14.87 14.49 16.25 17.35Percent error of peak discharge – 10.72 6.46 16.68MSSE of peak discharge – – – 10.98Time to peak discharge 16:27 16:28 16:28 16:18Time of threshold upcrossing 15:02 15:48 15:33 15:18Time of threshold downcrossing 17:22 17:18 17:13 17:08Duration above threshold 2:20 1:30 1:40 1:50

Time is in h:min.

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timing are simulated despite the temporal lagging andabbreviated duration. The quick response to the June 10precipitation event is reproduced by the ascending limb ofthe calibrated hydrograph and the calibrated peak corre-sponds in timing and shape with the second and largest peakof the observed hydrograph. Simulated peak discharge (Ta-ble 4) is slightly greater (16.68%) than the observed peakdischarge. The calibrated hydrograph crosses the floodingthreshold about 15 minutes later than the observed butpeaks about 10 min earlier. Overall the calibrated floodevent is about 20% shorter and peaks at about 2.5 m3 s�1

higher than the observed.The overall MSSE is 10.98 primarily due to CASC2D’s

inability to reproduce the multi-modal character of the ob-served hydrograph. CASC2D has difficulty in reproducing thepeakedness and tri-modality of the observed hydrographthat may result from short pulses of high intensity precipita-tion, and from the quick response generated by urban runofftriggered by road tops and open channel road gutters,allowing rapid routing of overland flow into the QuebradaEstero. In the current simulations the shallow runoff onroadways is distributed over the 25 m width. Roads couldbe simulated as channels, with greater depth of flow, andmay depict a faster stream response of the Quebrada Es-tero, thus potentially better capturing the triple peaks ofthe observed hydrograph. Discrepancy between observedand calibrated hydrographs may arise also from the extrap-olation of the rating curve beyond the observed upper limitof 0.5 m (a discharge of about 1.5 m3 s�1), which may befurther accentuated by the potential change in flow regimeonce channel capacity is exceeded.

Although able to simulate the quick ascending hydro-graph limb, CASC2D was unable to simulate the extendedrecession limb of the observed hydrograph. An explanationfor the observed sustained recession limb may be the slowrelease of subsurface flow. This is plausible since the dom-inant soil type is clay, which has low transmissivity of waterespecially under saturated conditions. Depending on thetype of clay swelling may occur during saturation furtherrestricting the flow of water along the hillslope. Field mea-surements of subsurface flow are necessary to verify thecontributions of subsurface flow. SOF and SSF may be impor-tant in the watershed after initial HOF has dissipated.

Furthermore, it could furnish an explanation of the roleof SOF and SSF. The areal extent and importance of otherpotential runoff generating processes can only be estab-lished by field monitoring of the watershed.

Calibration accuracy depends on the parameters of ini-tial soil moisture (Downer et al., 2002; Senarath et al.,2000) and the spatial distribution of precipitation. The se-lected value of initial soil moisture content (0.272), reflect-ing the wilting point of vegetation suggested by Rawls et al.(1983), is appropriate as the June 10th storm occurs at theonset of the rainy season, following a six-month period ofalmost total lack of precipitation. The choice of initial soilmoisture values is guided by the factual observation ofincreasing soil moisture conditions in the watershed as thewet season progressed. Values specified for initial soil mois-ture conditions are approximations using reference sources;therefore, it is extremely difficult to know the exact pastmoisture conditions since the response of watershed toeach precipitation event is unique depending on the ante-cedent moisture conditions. Field measurements of soilmoisture content or analysis of remotely sensed imagescould provide better initial estimates for future calibrationsand also for different periods through out the year (dry ver-sus wet seasons). The selected spatial distribution of theSeptember 14th, 2001 precipitation event (Fig. 6) used inthe simulation allocates more rainfall to the lower subba-sins of the watershed. This spatial distribution of precipita-tion within the watershed may exacerbate the floodingdepending on the allotment of increased precipitation with-in the lower part of the watershed. The issue of perceivedincrease in flooding of San Ramon has become more rele-vant in recent decades as the lower part of the watershedhas experienced an increase in impervious urban and resi-dential surfaces (Fig. 1) and a decrease in naturally vege-tated areas.

Sensitivity analysis

The model sensitivity to changes in saturated hydraulic con-ductivity (Ks), initial moisture content (hi), capillary head(Hc), Manning’s overland roughness (no), and precipitationdistribution are shown in Fig. 8. In descending order of sen-sitivity the model is most sensitive to changes in value ofsaturated hydraulic conductivity, capillary head, initialmoisture content, and the distribution of precipitation.The model is relatively insensitive to changes in the Man-ning’s overland roughness coefficient. Using the approachfor calculating sensitivity analysis measures outlined bySenarath et al. (2000), the expected signs of percentchange values would be positive, indicating Qm/Vm is great-er than Qs/Vs meaning discharge and runoff volume are

Figure 8 Scatter and vertical point plots from the sensitivity analysis. Saturated hydraulic conductivity (Ks), initial moisturecontent (hi), capillary head (Hc), Manning’s overland roughness (no), and precipitation distribution.

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overpredicted, or negative, indicating the opposite condi-tions. If the percent change values are at or near zero fora given parameter value, then alternative optimal modelparameter values could exist and be used for model calibra-tion. The dashed lines at ±5% on Fig. 8 are defined as arbi-trary cutoff thresholds indicating the limit of minimizedvalues for the percent change measures. Multiple parame-ter sets are possible as shown by the sensitivity analysis be-cause there are groups of parameter values that minimizethe values of the percent change measures. Peak dischargeis most affected by (in descending order) by the spatialdistribution of precipitation, initial moisture content, satu-rated hydraulic conductivity, capillary head, and Manning’soverland roughness. Runoff volume is most affected by cap-illary head, saturated hydraulic conductivity, initial mois-ture content, spatial distribution of precipitation, and

Manning’s overland roughness. The RMSE is most affectedby the spatial distribution of precipitation, initial moisturecontent, saturated hydraulic conductivity, Manning’s over-land roughness, and capillary head. The scatter plots forPCPD and PCRMSE show non-continuous curvilinear trends,perhaps due to averaging of output values over a five min-ute interval. PCRV shows more continuous curvilinear trend(except for the spatial distribution of precipitation) likelydue to cumulative summation during the model simulationwhereby short term perturbations (like those affectingPCPD and PCRMSE) in output are muted. As evidenced bythe results from the sensitivity analysis of tested parame-ters, multiple parameter sets exists which could satisfacto-rily match the observed hydrograph from the June 10thstorm. Beven (2001) labels this as the problem ofequifinality.

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Simulations

After calibration, the model is rerun using the land coverconditions from 1979 and 1989, holding all other inputparameters constant. Earlier land covers (Fig. 9) generallyproduce similar shapes to the 1999 hydrograph but displaysmaller peaks and are slightly delayed in both the rising limband the time to peak (Table 4). Simulated responses showan 11% increase in peak discharge from 1979 to 1989, anda 6% rise from 1989 to 1999. The storm of June 10, 2002could have produced smaller, above threshold floods underboth earlier land cover scenarios. The simulated response tochanging land cover shows increased flooding from urbani-zation comparable to those noted in other studies (Colby,2001; Doe et al., 1996; Purwanto and Donker, 1991;Suwanwerakamtorn, 1994; Wong and Chen, 1994). Increasesin peak discharges and time above flooding threshold areconsistent with anecdotal information (Quesada, 2002a;ProDUS, 2000).

From a modeling perspective, the increases in peak dis-charge from 1979 to 1999 are indeed simulated as all inputmodel parameters are held constant expect for the landcover configuration and distribution. The increase in peakdischarges reflects the numerical response of CASC2D tothe changing land cover and its spatial distribution withinthe watershed, and not to erroneous choices in the calibra-tion parameters. If inappropriate calibration parameterswere used for soil moisture, soil hydraulic parameters, Man-ning’s n (overland and stream flows), and precipitation dis-tribution, changing the land cover spatial distribution duringsimulations would not change the output dischargemarkedly.

In light of potential uncertainty in model parameters,calibration and simulations we acknowledge that the suit-ability of CASC2D, as applied to this particular study wa-tershed, may be extended. Specifically, the validity maybe questioned of the reported increase in simulated dis-charges resulting from changing land use/cover conditions,in which the discharge values are within the error of model

Figure 9 Simulated hydrographs and observed storm h

calibration. Without a data intensive uncertainty analysisrequiring non-existent long term hydrologic climatic data,it is difficult to ascertain the validity of the simulation re-sults. The objective of this study was not to assess thehydrologic suitability of the CASC2D model or to accuratelydiscern the runoff generating mechanisms at work withinthe watershed. The CASC2D model and simulation resultsare meant to provide the policymakers and citizens of SanRamon a planning tool to investigate potential causes of in-creased flooding resulting from changing land use/cover.

Prior to this study the ProDUS report qualitatively pro-posed the notion that expanding urban areas are responsiblefor increased flooding. They suggested remediation of theflooding without any scientific investigation into the actualcauses of flooding or the feasibility of the proposed solu-tions of diverting flood waters to an adjacent watershedor the dredging of a local lake for impoundment. In develop-ing his model testing system Klemes (1986) states threeimportant considerations of hydrologic model testing, thefirst of which is most relevant to the results of this study.In certain situations, hydrologic model results are used tomake operational decisions about hydrologically relevantand useful information; i.e. in certain contexts model useis not meant to further hydrologic knowledge, but ‘‘to em-ploy already available hydrologic knowledge to generateoutputs that can be useful for a particular applied purpose’’(Klemes, 1986, p. 16). The ProDUS did highlight the in-creased urban areas near the Quebrada Estero stream withinSan Ramon as a potential cause of increased flooding,although it did not investigate this scientifically.

The simulation results presented here in a modeling con-text coupled with spatial analysis of, where urban areas ex-panded from 1979 to 1999, corroborate the ProDUS reportthat the increase of urban areas, particularly near the wes-tern edge of San Ramon, have contributed to the increase inflood peaks and durations noted from storms like that ofJune 10th, 2002. Oreskes et al. (1994) indicate the useful-ness of numerical models to ‘‘corroborate a hypothesis byoffering evidence to strengthen what may be already partly

yetograph for the simulation of the June 10 storm.

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established through other means’’ (p. 645). Again thehydrologic response of the CASC2D to the changing land cov-er conditions fulfills a more pragmatic purpose of hydrologicmodeling by corroborating the untested ProDUS assertions.A public presentation at the University of Costa Rica, SanRamon of the study results described here, has enabledthe citizens of San Ramon to make more informed decisionsfor themselves about the potential cause of flooding andappropriate remediation measures.

Although able to simulate the streamflow response tochanging land cover conditions over time, CASC2D has somelimitations in this study, and does not simulate all hydrologicprocesses at work in the watershed, as evidenced by thesustained recession limb and multimodal peaks of the mea-sured hydrograph. SOF and SSF, both of which could play arole, although possibly minor, in generating storm runoff,are not simulated in this version CASC2D. The long termcontinuous simulation abilities of CASC2D coupled withsoil moisture redistribution could yield better modelcalibration, but were not employed here due to necessary,demanding model requirements for additional hydro-climatic, vegetative, and soil data, which require furtherfield survey and data collection. Furthermore, CASC2D is un-able to model urban hydrology (retention ponds, culverts,and runoff from impervious surfaces). The successor toCASC2D, the gridded surface/subsurface hydrologic analysis(GSSHA) model incorporates non-HOF runoff generation,linkage with groundwater for improved modeling of soil infil-tration along with the allowance for culverts in channel rout-ing to better simulate urban hydraulics (Downer et al.,2002). These additions could better represent the hydrologicprocesses at work in the Quebrada Estero watershed duringthe simulation of flood producing precipitation events.

The limitations of the CASC2D simulation and lack of fielddata are a known constraint of this study. Oreskes et al.(1994) note that modeling results, can be useful to guidefurther study and can illuminate components of the systemmost in need of additional study. The CASC2D model may beproducing calibration results from the June 10th storm sim-ilar to the observed discharge by coincidence. The GSSHAmodel, which the authors have recently acquired, will beused for future model simulations of the Quebrada Estero.More intensive field work campaigns are planned to measureimportant soil properties (saturated hydraulic conductivity,soil water tension, and initial soil moisture) at multipledepths in the soil profile, overland surface roughness valuesfrom in situ measures of actual land cover types, and depthto water table. These field measures coupled with the mul-tiple runoff generation mechanism capabilities of GSSHAwill better elucidate the hydrologic characteristics of theQuebrada Estero watershed and the resulting simulationsto changing land cover will have greater credibility, partic-ularly when coupled with the Alternative Blueprint andGLUE approach (Beven, 2002) for model uncertaintyestimation.

Changing land cover

In 1979, 85% of the watershed was under natural and agri-cultural land cover (Fig. 1) with limited residential areasand roads, thus the flood peak is reduced, but the durationand timing above flood threshold are not substantially

attenuated. More vegetation allows greater interceptionand higher values of Manning’s friction, which slow overlandflow, permitting greater infiltration. The more fragmentedand diverse landscape of 1989 (Fig. 1) reveals peak dis-charges comparable to those seen in 1999. HOF is morelikely to occur in a fragmented landscape in combinationwith low hydraulic conductivity of soils Giambelluca(2002). Potential for low infiltration capacities within thesefragments, as indicated by the clay soil sampled throughoutthe watershed, suggests the model formulation of CASC2Dmight be appropriate to apply to the Quebrada Estero wa-tershed. Land cover fragmentation decreases in 1999 asthe percentage of impervious area increases. The concen-tration of residential areas and roads nearest the basin out-let is believed to be the primary cause for the observedquick response and flooding. Reduced infiltration increasessurface runoff, which is further accelerated by a decreasein surface friction. The road network has expanded intoareas with steeper slopes, but residential expansion is con-fined to gentler slopes near the channel (Fig. 1). The roadnetwork also act as collectors that quickly route overlandflow into the stream.

The importance of expanding impervious surfaces inincreasing the simulated peak discharges can be further sub-stantiated using the proportion of percentage growth of res-idential areas and roads in each subbasin compared to thepercentage of subbasin area (Fig. 3) of the total watershed,as a relative measure similar to a location quotient, in orderto determine which subbasin is most affected by urbaniza-tion. A proportion of one indicates even growth within a ba-sin, while values greater than one denote greater urbanexpansion in a subbasin relative to its size. Compared tothe middle and upper subbasins, for both roads and residen-tial areas (Fig. 10), the lower basin has proportions fromabout 1.25–2 indicating relative increased outward urbanexpansion of San Ramon in the past 30 years. This outwardexpansion observed in the lower part of watershed nearestthe western urban fringe coupled with the increased peakdischarges noted from the simulations substantiates the no-tion that increased urban areas (ProDUS, 2000), includingresidential areas and roads, have augmented the magnitudeof flooding of the Quebrada Estero.

During multiple field campaigns to the watershed during2002 and 2003, observations were made of two areas withinthe watershed that were undergoing conversion from vege-tation to intensive housing construction. One importantarea, a fallow field in the initial stages of housing construc-tion, was directly north of UCR–SR along the Quebrada Es-tero stream. The vegetation was removed, exposing baresoil subject to reduced infiltration capacity from loss of rootstructure of the grasses, and increased soil crusting fromrainfall impact, subsequently increasing overland runoff. Asecond urbanizing area was southwest of UCR–SR in theheadwaters of the Quebrada Estero, near the end of thelarge arm of the marshy area. Here, a fallow sugar cane fieldhad been completely cleared of vegetation, and the slopingground terraced for construction of high density housing. Ofhydrologic importance is the complete loss of any vegetativeroot structure and the compaction of soil by constructionequipment, further reducing the infiltration capacity ofthe soil and increasing overland runoff. The area is particu-larly important as headwaters of the Quebrada Estero and

Figure 10 Proportion of expanding residential and roads in each subbasin compared to the total watershed area.

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may serve to exacerbate flooding in the middle areas of thewatershed, specifically in the small village of Alfaro, asstorm water drains discharged directly into the Quebrada.Although these observations are qualitative, they highlightconsequences of urbanizing areas in potentially hydrologi-cally sensitive areas of a watershed. Further field monitor-ing is needed to quantify infiltration capacities andinterception capacities before and after vegetation removaland land clearing in areas prone to urbanization.

Conclusions

The results presented here are in agreement with othermodeling studies conducted in tropical watersheds. Specif-ically the reduction of forests (Suwanwerakamtorn, 1994;Purwanto and Donker, 1991), coupled with increased expan-sion of impervious surfaces (urban, residential, and roads)(Wong and Chen, 1994) within a watershed, increases down-stream discharge.

Simulations using CASC2D show increased peak dis-charges and above threshold flood durations with changingland cover conditions from 1979 to 1999 associated withthe precipitation event recorded on June 10th 2002. Theincreasing contribution of the lower subbasin to the outputdischarge as a whole provides evidence that CASC2D simu-lates the response to the changing land cover. The resultspresented here corroborate the ProDUS report that urbanareas have expanded in the lower part of the watershedas evidenced by the location quotient analysis. The modelsimulations, although not perfect, adequately replicatethe dominant runoff process during the extreme, flood pro-ducing event within the Quebrada Estero watershed.

The physical basis of the CASC2D model along with itsaffordable computer resources and minimal data require-ments make it well suited for operational use in tropicalwatersheds like theQuebrada Esterowhere the basic assump-tions of HOF aremore appropriate evidenced by climate, veg-

etation, topography, and soils (Dunne, 1978). CASC2D can beused in anoperational sense as a planning tool to assess hydro-logic responses in theQuebrada Esterowatershed to changingland use/cover conditions and/or spatial configurations. Thiswas not possible before the current study.

Acknowledgements

The Tropical Conservation and Development Program at theUniversity of Florida and the Tinker Foundation supportedfieldwork for this research. Marvin Quesada and the Univer-sity of Costa Rica, Sede de Occidente, San Ramon extendedlogistic and field support, and data access. Billy Johnson,USACE-WES, and Michael Binford supplied and provided ac-cess to the CASC2D v1.19c model and code. Julio Moranga,UNA, Costa Rica; and the Instituto Geografica Nacional, SanJose, Costa Rica supplied the GIS data. Trino and Ana Bar-rantes-Jimenez are gratefully acknowledged for their sup-port during the extent of this research. Comments fromCesar Caviedes and two anonymous reviewers improvedthe quality of the manuscript.

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