A hydrological model for predicting runoff from different land useareas

T. Karvonena,* , H. Koivusaloa, M. Jauhiainena, J. Palkob, K. Wepplingc

aHelsinki University of Technology, Laboratory of Water Resources, P.O. Box 5300, 02015 Espoo, FinlandbEnvitop Ltd, Oulu, Finland

cNordkalk Ltd, Parainen, Finland

Received 24 November 1997; received in revised form 2 October 1998; accepted 22 October 1998

Abstract

The purpose of this article is to model the influence of land use on catchment runoff. The modeling is based on the sub-division of the catchment into smaller units by generation of the so-called ‘‘hydrologically similar units’’ (HSU) or ‘‘patchtypes‘‘. HSUs aggregate areas of hydrologically similar behavior, e.g., land use, soil, slope, and vegetation. Each HSU isrepresented using a cross-section called a ‘‘characteristic profile‘‘. For the calculation of the water balance of the characteristicprofiles, a mathematical treatment of the key partitions of the hydrograph response was developed. The characteristic profile isthe largest unit that can be handled mathematically still maintaining the idea of a hydrologically similar regime. An agriculturalcharacteristic profile is a cross-section between two parallel open ditches or sub-surface drains. For forest areas the character-istic profile is called hillslope and the length of the hillslope can vary from few meters up to hundreds of meters. The total runofffrom the characteristic profiles is an input to a channel network. In the present model, the channel processes are described usingthe geomorphologic instantaneous unit hydrograph (GIUH). The proposed hydrological model was tested in the Lestijokicatchment (1290 km2) located in western Finland. The catchment was subdivided into 25 characteristic profiles with parametersfixed to typical values measured in Finnish conditions. The model calibration was carried out for the GIUH parameters usingmeasured every-day river flow. The coefficient of determination was 0.74 for a 2 -y calibration period, and 0.70 for a 3 -y testingperiod. The model represented well the extent of variable contributing areas, which was the main reason for the non-linearbehavior of the catchment response.q 1999 Elsevier Science B.V. All rights reserved.

Keywords:Hydrological model; Runoff; Hydrologically similar unit; Scaling

1. Introduction

The main objective of this study is to develop ahydrological modeling system for the prediction ofthe influence of land use on rainfall-runoff processeson a catchment scale. A specific requirement for thehydrological model is to provide input data for a

management system, which helps to identify alterna-tives to decrease acidity load from catchments withacid sulfate (AS) soils. According to the most recentestimation, the extent of AS soils is about 16% of thecultivated field area in Finland (Palko and Weppling,1994). The areas composed of AS soils are flat andrequire efficient drainage for their cultivation. Ifpotential AS soil is drained, it becomes severelyacidic because large quantities of sulfuric acid andiron are released into the soil and drainage system.

Journal of Hydrology 217 (1999) 253–265

0022-1694/99/$ - see front matterq 1999 Elsevier Science B.V. All rights reserved.PII: S0022-1694(98)00280-7

* Corresponding author. Fax:1358-9-451 3827.E-mail address:tkarvone@pato.hut.fi (T. Karvonen)

About 30 major rivers in the western and southwes-tern coast of Finland have severe acidity problemscausing hazards to aquatic life (Palko, 1994; Palkoand Weppling, 1994).

Palko and Weppling (1995) developed a model toestimate the extent and temporal dynamics of runoffacidity in a river or a river basin affected by AS soils.The model was used as a tool to plan mitigationmeasures, such as lime filter drains, to neutralizeriver water (Weppling and Palko, 1994; Weppling,1997). The release of the daily acidity was estimatedusing a hydrological sub-model which predicted theseasonal variation of the depth to the water table. Inthe acidity model proposed by Palko and Weppling(1995), the hydrological sub-model (Karvonen andSkaggs, 1993) was a conceptual model and therefore,a more detailed description of the catchment responseto rainfall and snowmelt needs to be developed. Theinfluence of different land use types has to be takeninto account to evaluate the relative contribution ofrunoff from AS soil areas.

The influence of the acidity mitigation measures onthe magnitude and timing of runoff peaks must becalculated using a model which can handle differentscales. The scales range from microscale to transitionrange, which is between mesoscale and macroscaleaccording to the definition of scales as proposed byBecker (1992). In our model the microscale corre-sponds both to a lime filter drain and to a cross-sectionbetween two sub-surface drains, and the transitionscale corresponds to a catchment. The catchmentscale runoff must be calculated to evaluate the needfor acidity neutralization in the river.

1.1. Concept of modeling

Different modeling approaches are available forcalculating the catchment response to rainfall andsnowmelt. In choosing between the approaches, oneshould keep in mind that the model must be capable oftaking into account both the influence of land use andthe areal diversity on catchment hydrology. It is atleast in theory possible to reach a high level of under-standing of catchment hydrology using a fully distrib-uted model, which separately describes each smallsub-area of the catchment through physically consis-tent formulations and parameters related to measuredcatchment properties. Examples of this type of models

are the SHE-model (Abbott et al., 1986; Bathurst,1986) and IHDM-model (Beven et al., 1987).However, as pointed out by Beven (1989) and Kirkby(1993), this goal has so far been unattainable. Practi-cal difficulties appear in the implementation of thesystem and the data availability. An adequate data-base is costly to assemble and may be unavailablefor large catchments.

A possibility of another extreme is to use a lumpedhydrological model, which considers the wholesystem (catchment, sub-catchment, aquifer, etc.) as asingle unit. Examples of conceptual rainfall-runoffmodels are the Stanford model (Linsley, 1976),SSARR (USCE, 1975), and HBV (Bergstro¨m andForsman, 1973). The deficiency of this modelingapproach is that the lumped models are not able tohandle different land use types and the areal diversityof the hydrological process. According to Becker(1992), application of lumped models for large areasas a whole (e.g. GCM grid units), can only be toler-ated until more suitable distributed or semi-distributedmodels become available. Moreover, small scalefactors, such as the influence of a lime filter on runofffrom AS soils, are impossible to be described mathe-matically in conceptual models.

Kirkby (1993) stated that at present, the best hopefor reaching an overall understanding of the hydro-graph response at a level between the lumped anddistributed approaches seems to be the treatment ofthe response in a number of partitions. The key parti-tions are the unsaturated infiltration zone, the satu-rated sub-surface and surface zones, the channelnetwork and groundwater in aquifers.

The ideas given by Becker (1992) and Schultz(1993b) are in close accordance with Kirkby’s state-ment. Becker and Schultz propose a sub-division ofthe catchment into smaller units by a generation of theso-called ‘‘hydrologically similar units’’, whichBecker refers to as ‘‘patch types’’. Patch types aggre-gate the areas of hydrologically similar behavior, e.g.,land use, soil, slope and vegetation. Instead of havinga completely distributed structure, the model can beconsidered semi-distributed (Becker, 1992). Success-ful examples of this modeling technique to riverbasins have been given, e.g., by Knudsen et al.(1986), Becker and Pfu¨tzner (1986), Avissar andPielke (1989) and Flu¨gel (1995).

In the development of the modeling system

T. Karvonen et al. / Journal of Hydrology 217 (1999) 253–265254

described in this article the semi-distributed conceptwas chosen because the areal diversity of the hydro-logical processes can be taken into account in this typeof models. Here the methodology of the catchmentsub-division into smaller Hydrologically SimilarUnits is called the HSU-concept. Each HSU is repre-sented using a cross-section called a ‘‘characteristicprofile’’, which is a two-dimensional model of waterbalance in vertical and horizontal directions. Thegeneral outline of the modeling system and the

concept of a characteristic profile are explained inthe next few sections.

2. General description of the modeling system

The general principle of the hydrological model isillustrated in Fig. 1. The basic idea in the system isthat the response from characteristic profiles togetherwith direct rainfall on the channel area provide the

T. Karvonen et al. / Journal of Hydrology 217 (1999) 253–265 255

Fig. 1. Illustration of the hydrological model sub-dividing the catchment into characteristic profiles.

Fig. 2. Characteristic profile for an agricultural area is a cross-section between two parallel drains.

inflow to the channel network. This implies that thechannel routing is completely separated from the rain-fall–runoff model. Water balance over each charac-teristic profile is computed independently and theinfluence of a specific profile on the total runoff isobtained by taking into account the surface area ofthe corresponding HSU.

The characteristic profile is the largest unit that canbe handled mathematically still maintaining the ideaof hydrologically similar regime. An agriculturalcharacteristic profile is a cross-section between twoparallel sub-surface drains or open ditches (Fig. 2). Atypical spacing between the drains is 10–30 m, whichis the length of the characteristic profile. The verticalwater movement in the cross-section is calculatedusing the unsaturated–saturated module described inSection 3. In the agricultural profile, the horizontalmovement towards sub-surface drains or open ditchesis calculated using the Hooghoudt’s equation (e.g.Feddes et al., 1978; Skaggs, 1978; Karvonen, 1988).Lime filter drains affect the water balance in twoways: A high hydraulic conductivity of the fillingmaterial in the lime filter increases both the effectiveradius of the drains and the infiltration capacity of thesoil surface. The lime filter decreases the productionof surface runoff, because surface runoff flowing overthe filter infiltrates effectively.

For forested areas the characteristic profile is calleda hillslope and the length of the hillslope can vary

from few meters up to hundreds of meters (Fig.. 3).The water balance of the hillslope was representedusing the calculation modules described in Section3. During excessive rainfall or snowmelt the lowestsection of the profile becomes completely saturated(exfiltration areas) and a certain fraction of the hill-slope area contributes to direct overland flow.

Bogs and mires are represented using the character-istic profile of a forested hillslope with a small slopeand peat soil water retention curve. The peat miningareas are treated mathematically in a similar way asthe agricultural cross-sections with open ditches.Lakes, reservoirs, stream channels and imperviousareas comprise a characteristic profile which instantlyproduces runoff.

3. Key partitions of hydrograph response

Mathematical tools are needed for the key parti-tions, such as the unsaturated and saturated zones,and the channel network. In Finnish conditions snowaccumulation and melt can be considered as an addi-tional partition that has to be taken into account.

3.1. Calculation of vertical flows in unsaturated–saturated zones

The flow of water in unsaturated–saturated soil–root system is described using the Richards equation

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Fig. 3. Characteristic profile for a forest area is a hillslope strip sub-divided into vertical soil profiles.

(Richards, 1931).

C�h� 2h2t� 2

2zKz�h� 2h

2z

� �2

2Kz�h�2z

2 S�h�2 Q�h�;�1�

whereh is the soil water potential (m),Kz(h) is soilhydraulic conductivity (m d21), z is the position of thevertical dimension (m) (positive downward from thesurface),t is time (d),C(h) is defined as the differen-tial water capacity (m21), i.e. C(h) � dw/dh, wherewis volumetric soil water content.S(h) represents thevolume of water taken up by roots from the unitvolume of soil in unit time (m3 m23d21) andQ(h) isused to take into account the influence of additionalsinks or sources, which include flow to sub-surfacedrains or open ditches and horizontal flow in andout of a vertical soil column.

The Richards equation has to be solved numericallyand in this study standard finite difference solution isused (e.g. Feddes et al., 1978). The surface boundarycondition is formulated using a maximum infiltrationcapacityimax:

imax� 2Ksurfh1 2 hsurf

0:5 Dz12 1

� �; �2�

whereKsurf is a geometric average between the surfaceand top node hydraulic conductivities,h1 is thehydraulic head at the top node,hsurf equals to zero,andDz1 is the thickness of the top layer. The modelallows the generation of infiltration excess overlandflow (Horton, 1933), if rainfall or snowmelt intensityexceeds the soil surface infiltration capacity. Thebottom boundary condition is a no-flow interface atthe assumed depth of an impervious bedrock. Thesame unsaturated module is used for both agriculturaland forested characteristic profiles.

3.2. Calculation of horizontal flows in characteristicprofiles

The horizontal flow is assumed to take place only inthe saturated part of the soil profile and is modeledseparately for agricultural, forested and bog areas. Forthe agricultural characteristic profile, the horizontalwater flow is derived using the Hooghoudt’s formula(e.g. Feddes et al., 1978).

For forest profiles, horizontal flow in the saturatedzone is described along a hillslope by sub-dividing it

into vertical soil columns (see Fig. 3). For eachcolumn, the vertical soil water fluxes are calculatedusing the unsaturated–saturated module presented inSection 3.1. Darcy’s law is used to calculate thedownslope water movement between the verticalsoil columns.

qlat � 2KsDdhdl

; �3�

whereqlat is the horizontal flow between two columns(m2 d21), Ks is the saturated hydraulic conductivity(m d21), D is the thickness of the saturated zone(m), and dh/dl is the water table gradient betweentwo vertical columns. A no-flow boundary conditionfor the horizontal flow is used at the top of the hill-slope, and the lower boundary is a fixed hydraulichead at the stream located next to the hillslope. Noflow from the stream back to the hillslope is allowed.

The characteristic profile for forested areas is aquasi-two-dimensional model, because the verticaland horizontal flows are not solved simultaneously.Vertical water fluxes in the unsaturated–saturatedzone and the depth to the ground water table arecomputed first, following with the estimation ofDarcian flux between the vertical columns. In thisway, the horizontal flows in and out of a column areincluded as additional sink/source-terms in the solu-tion of Eq. (1). At the lowest part of the hillslope,intensive rain or snowmelt may raise the water tablenear the the soil surface resulting in a complete satura-tion of a vertical soil column. Any water input on sucha soil column generates saturation excess overlandflow (Dunne and Black, 1970).

The response of a characteristic profile is dividedinto sub-surface flow and surface runoff. Sub-surfaceflow contributes to the recession part of the hydro-graph, and saturation excess overland flow to thefast responding part. Infiltration excess overlandflow rarely exists over forested areas because of thehigh infiltration capacity of the soil surface layers.Surface runoff is not routed over a characteristicprofile, but is discharged instantly to the channelnetwork.

3.3. Snow accumulation and melt

The springtime runoff in Finland is caused bysnowmelt and rainfall during the melt period. The

T. Karvonen et al. / Journal of Hydrology 217 (1999) 253–265 257

prediction of the runoff acidity during springrequires an accurate method to model the dilutingeffect of snowmelt waters on the daily runoff acid-ity. Energy balance snow models (e.g. Anderson,1976; Price and Dunne, 1976; Jordan, 1991;Koivusalo and Burges, 1996) cannot be used asthe regional values of the meteorological variablesneeded in this type of models – global radiation,wind speed and relative humidity – are not available.Therefore, the sub-model for snow accumulation andsnowmelt is based on the degree–day concept usingdaily average air temperature and precipitation asinput data. The description of the snow model hasbeen given by Karvonen (1988). Canopy processesare largely ignored in the snow routine, except thatthe effect of interception is assumed to be included inthe correction factor for the measured rain orsnowfall.

3.4. Influence of channel network

A river basin includes two interrelated systems: thechannel network and the characteristic profiles. Thecharacteristic profiles control the production of melt-water or stormwater runoff which, in turn, is

transported through the channel network towards thebasin outlet. The runoff-contributing areas of the hill-slopes are both the cause and the effect of the drainagenetwork growth and development (Rodrı´guez-Iturbe,1993). According to Kirkby (1993), the longest delaysfor small catchments are usually associated with thehillslopes, but for catchments of 50 km2 or larger,travel time through the channel network generallybecomes more important and ultimately dominatesthe delay and the shape of the hydrograph.

Catchment hydrology comprises the interaction of alarge number of individual components, but accordingto Rodrıguez-Iturbe (1993) a large number of degreesof freedom in the dymanic equations of the compo-nents reduce to a few independent variables governingthe response of the system at the macroscopic scale. Inthe modeling system described here, the concept ofgeomorphologic instantaneous unit hydrograph(GIUH) is utilized in calculating the influence of thechannel network on the delay and the shape of thehydrograph. A detailed derivation of the GIUH-method has been given by Rodrı´guez-Iturbe andValdes (1979), Valde´s et al. (1979) and Rodrı´guez-Iturbe (1993) and only a brief description is givenhere.

T. Karvonen et al. / Journal of Hydrology 217 (1999) 253–265258

Fig. 4. Location of Lestijoki river and extent of areas containing acid sulfate soils at the coastal side of southern and western Finland.

The primary idea of the method is to derive expli-citly the instantaneous unit hydrograp (IUH) from theHorton’s laws of basin composition, which describethe architecture of the channel network. The resultingresponse function is called a geomorphologic instan-taneous unit hydrograph. The geomorphologic para-meters include the bifurcation ratioRB, the lengthratio RL and the area ratioRA whose values varynormally between 3 and 5 forRB, between 1.5 and3.5 for RL and between 3 and 6 forRA. Additionalparameters needed are the mean length of streamsLw and the average streamflow velocity in the catch-ment v. The mathematical equation for GIUH givenby Rodrıguez-Iturbe (1993) reads:

GIUH�t� � �t=k�a21e2t=k=�kG�A��; �4�

a� 3:29�RB=RA�0:78R0:07L ; �5�

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Table 1Classification of land use in the Lestijoki catchment

Land use type Area (km2) Relative area (%)

Lakes 80 6.2Agricultural field 120 9.3Sparse forest 268 20.8Treeless bog, peat mining area 131 10.2Swampy coniferous forest 74 5.7Swampy deciduous forest 44 3.4Swamp 132 10.2Pine forest 77 6.0Spruce forest 54 4.2Deciduous forest 79 6.1Mixed forest 226 17.5Urban areas 5 0.4Total 1290 100

Table 2Parameters of characteristic profiles in Lestijoki catchment. The soil types for agricultural profiles are: 2� Loamy sand, 3� Fine sandy loam,6 � Loess loam and 7� Light clay. The hydraulic conductivity represents a value for saturated soil

Profile type Area (km2) Soil type Hydraulic cond. (m d21) Length (m) Depth (m) Slope (m m21)

Agricultural profilesAgr./1 15 7 0.03 14 0.9 0.005Agr./2 15 7 0.05 14 0.9 0.005Agr./3 15 7 0.07 14 0.9 0.005Agr./4 15 6 0.09 18 0.9 0.005Agr./5 15 3 0.15 15 0.9 0.005Agr./6 15 3 0.25 15 0.9 0.005Agr./7 15 2 0.35 20 0.9 0.005Agr./8 15 2 0.45 20 0.9 0.005Forest profilesDec./1 40 OMaT 0.4 50 1 0.01Dec./2 40 OMaT 0.7 50 1 0.01Pine/1 39 CT 0.7 50 1 0.03Pine/2 38 CT 1 80 1 0.03Spruce/1 27 MT 0.4 80 1 0.015Spruce/2 27 MT 0.7 80 1 0.015Mixed/1 75 OMT 0.3 20 1 0.025Mixed/2 75 OMT 0.5 30 1 0.025Mixed/3 76 OMT 0.8 40 1 0.025KORPI/1 59 MT 0.3 20 1 0.025KORPI/2 59 MT 0.6 40 1 0.025BogsBogs/1 106 Peat 0.6 30 0.6 0.005Bogs/2 106 Peat 0.8 40 0.6 0.005Bogs/3 106 Peat 1.1 60 0.6 0.005Bogs/4 106 Peat 1.1 80 0.6 0.005Bogs/5 106 Peat 0.8 100 0.6 0.005Lakes 85 2 2 2 2 2

k � 0:70�RA =�RBRL�0:48Lwv21; �6�

whereG(a) is the gamma function andk, v andLw arein consistent units.

4. Model application

4.1. Site description and data

The Lestijoki catchment (1290 km2) is located onthe western coast of Finland as shown in Fig. 4. TheLestijoki catchment is characterized by a smoothtopography with an average slope of 1.3%. The riverruns through the coastal area containing AS soils. Asmost of the agricultural fields in the catchment are

generally located close to the rivers and streams, theriver water quality is affected by the acid runoff fromthe cultivated areas.

The land use data for the Lestijoki catchment areawere obtained from the Finnish Environment Insti-tute. The classification is based on Landsat data andthe original land use categories are shown in Table 1.

Altogether 25 characteristic profiles were used tosimulate the runoff production in the Lestijoki catch-ment area: eight agricultural profiles, 11 forestedprofiles, five profiles for bogs and mires and oneprofile for impervious areas (lakes and urban areas).The parameters of the profiles are shown in Table 2.The areas classified as bogs include sparse forests andswamps which are assumed to be composed of peatsoils. In the computation of the agricultural areas, aconventional drainage was assumed. No lime filterdrains were in application in the area during the calcu-lation period, and therefore, their influence could notbe modeled in the present case study.

The soil water retention curves for agriculturalprofiles are shown in Fig. 5 and the correspondingcurves for forest soils are presented in Fig. 6. Thewater retention characteristics in Figs. 5 and 6 arebased on measurements carried out for differenttypes of Finnish soils. For example, the curves forforest soils in Fig. 6 are based on a laboratory analysisof soil samples taken in coniferous forests in centralFinland, and in broadleaf forest in southern Finland.Using the forest type classification from Cajander(1949), the broadleaf forest corresponds toOxalis-Majanthemum(OMaT) type. Mixed, spruce, andpine forests are ofOxalis-Myrtillus (OMT) type,Myrtillus (MT) type, andCalluna (CT) type, respec-tively. The water retention curves for different soilswere parameterized using an analytic function (vanGenuchten, 1980) which was fitted to the measuredseries.

Daily data of precipitation, air temperature, andcloud cover were available from September 1991 toDecember 1996 at Kokkola and Nivala, where theFinnish Meteorological Institute had climatic stations(Fig. 4). As the data were insufficient to estimate thespatial distribution of the meteorological variables,average values between the two stations were usedin the model application. Global radiation, whichwas needed to approximate potential evapotranspira-tion (PET), was calculated as a function of daily

T. Karvonen et al. / Journal of Hydrology 217 (1999) 253–265260

Fig. 5. Water retention curves characterizing typical agriculturalsoils.

Fig. 6. Water retention curves characterizing typical forest soils.The solid lines represent the van Genuchten (1980) function fitted tothe measurements.

clear-sky radiation and cloud cover index (Feddes etal., 1978; Karvonen, 1988). An estimate of PET wasobtained from an empirical Makkink equation (e.g.Aslyng and Hansen, 1982). The calculated PET-value was further reduced linearly at air temperaturesless than 108C. Zero PET was assumed when dailyaverage temperature was below 08C. Daily measure-ments of Lestijoki river flow were available from theSaarenpa¨a gauging station located about 15 km fromthe mouth of the river.

4.2. Model calibration

The model was calibrated against the daily riverflow measurements for the 2-y period from September1991 to August 1993. The calibration was not carriedout for the characteristic profiles but for the channelrouting parameters only. The profile parameters, suchas soil hydraulic conductivity, profile length, ditchdepth, and average slope, were intuitively set to valuesshown in Table 2 to represent typical Finnish condi-tions. Therefore, these parameters were not calibratedbut fixed to the selected values. There was no consis-tent way to verify the values of the parameters,because detailed measurements of the catchmentspatial properties were not available.

In addition to the assumed profile parameterspresented in Table 2, the following assumptionswere adopted for individual characteristic profiles.The forest and bog hillslopes were sub-divided into10 vertical soil columns, which were linked togetheras described in the previous sections. The root depthwas assumed a constant value of 0.40 m for

agricultural areas and 0.6 m for forest profiles. Maxi-mum ponding storage on the soil surface was 0.5 mmfor all characteristic profiles. For the agriculturalcross-sections, the depth to sub-surface drains wasset to 0.9 m.

In the snow model the degree–day factor was set to2.5 mm8C21 d21 for open areas and 2.0 mm8C21 d21

for forest areas. Precipitation correction factor forsnowmelt was 1.3 for open areas, and for forestareas a lower value 1.1 was used to take into accountthe influence of canopy interception.

The GIUH routing parameters were calibratedusing Gauss2 Newton2 Levenberg2 Marquardt-algorithm (Sun, 1994). A coefficient of efficiency(Nash and Sutcliffe, 1970), which represented thegoodness of fit between the measured and calculatedcatchment outflow, was used as an optimization criter-ion. Fig. 7 shows the results for the calibration period,and Table 3 summarizes the cumulative measured andcalculated runoff and fitness coefficients. The coeffi-cient of determination for the entire calibration periodwas 0.74, and the calculated cumulative runoff wasclose to the sum of the measured runoff. The opti-mized GIUH parameter values were:RA � 4.50,RB � 3.80, RL � 1.96. Lw � 60 km and v �0.36 m s21.

4.3. Model testing with independent data set

The calibrated model was tested by comparing themeasured and calculated daily river flow fromSeptember 1993 to December 1996. The model para-meters and assumptions were the same as listed in

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Fig. 7. Measured and calculated flow from Lestijoki catchment during model calibration period of 1991–1993.

Section 4.2. Fig. 8 and Table 3 show the results oftesting, which indicate a similar goodness of fitcompared to the calibration. The calculated cumula-tive runoff for the entire test period was about 4%higher than the measured value, but the results forindividual water years were less satisfactory.

Fig. 9 shows the partitioning of monthly inflow tothe channel network from the areas of different landuse. The runoff contribution from forested, agricul-tural, bog, and lake areas are shown separately sothat the sum of different partitions equals to the totalflow from the land areas to the channel network. Therelative contributions from the bog and forest areaswere the highest, because these were the mostcommon land types in the catchment. Fig. 10 showsan individual spring melt period and the contributionof runoff from each four land use types. Surface runoffand sub-surface or drainage flow are identified sepa-rately. The spring maximum runoff is dominated byfast responding runoff from bog areas, whereas sub-surface flow from forest areas contributes to flowduring the recession.

5. Discussion and conclusion

The HSU-concept allowed us to calculate sepa-rately runoff from agricultural, forest and bog areas.A characteristic profile was used as a basic componentto represent various sizes of homogeneous land useareas. The hydraulic properties were defined at theprofile scale and were applied as such for areas ofdifferent sizes. The profile parameters were scale-independent, which is important in maintaining the

hydrological soundness. In this type of a semi-distrib-uted model it is possible to avoid the commonproblem encountered in many large scale modelswhere physical model parameters, e.g. saturatedhydraulic conductivity, become ‘‘tuning variables’’(Becker, 1992).

Kirkby (1993) stated that the use of key partitionsof hydrograph response enables a simpler descriptionof a catchment as a set of interacting components. Intemperate humid areas, such as Finland, catchmentresponse to rainfall and snowmelt is non-linearowing to dynamic variation of the extent of contribut-ing areas. The partition to unsaturated and saturatedzones, and the dynamic change of the boundariesbetween these partitions play a major role in thegeneration of fast responding runoff. The proposedmodel takes into account the above mentioned parti-tions, and therefore, was successful in simulating thecatchment runoff.

In the present model, some simplifications wereneeded to apply the model in a medium size catch-ment, such as the Lestijoki River basin. The charac-teristic profiles of forested areas were connected to thechannel network using the assumption of a constantwater table in the channels. This assumption allowedus to separate the channel routing from the calculationof the water balance of the forested hillslopes. A moreconsistent way would be to model the dynamic inter-action between water level in channels and sub-surface and surface flows from the characteristicprofiles. This interaction cannot be modeled unlessdetailed information is available about the hydraulicproperties of the entire channel network. As suchinformation does not exist from the Lestijoki

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Table 3Results of model calibration and testing during 1991–1996. The fitness coefficients are defined, e.g., in Franchini et al. (1996, p. 307)

Total meas.runoff (mm)

Total calc.runoff (mm)

Total uncorrectedprecip. (mm)

Coefficient ofdetermination

Correlationcoefficient

Explained variancecoefficient

CalibrationSep 91–Aug 92 320 338 606 0.539 0.734 0.543Sep 92–Aug 93 353 343 539 0.855 0.925 0.855Total calibr. Period 673 681 1145 0.740 0.860 0.740TestingSep 93–Aug 94 268 215 397 0.782 0.884 0.805Sep 94–Aug 95 272 297 552 0.638 0.799 0.641Sep 95–Dec 96 220 280 646 0.725 0.851 0.751Total test period 760 792 1595 0.697 0.835 0.698

catchment, a simple conceptual routing procedure(GIUH) was adopted in the model. Another simplifi-cation was that overland flow was taken as a directinput to the channels, and therefore, the channel rout-ing dominated the delay and shape of the hydrograph.

A deficiency of the routing procedure (GIUH) is theassumption of a constant velocity parameter, which isused in the original model proposed by Rodrı´guez-Iturbe and Valde´s (1979). This problem could be alle-viated by sub-dividing the channel network into smal-ler networks and modeling each of them usingdifferent GIUH. Several GIUHs are also needed ifthe relative location of the land use types within thecatchment is important.

The model was calibrated using the measured riverflow at one location only, which is not adequate forthe calibration of semi-distributed models. As pointed

out by Beven (1989), the model performance shouldbe checked using internal variables, such asmeasurements of areal snow cover and water tabledepth. Small representative basins of relatively homo-geneous land use would also provide data for thechecking of internal variables. In the present applica-tion, the parameters of the characteristic profiles wereset more or less arbitrarily to fixed values. Neither theselected values or the proposed model structure wereverified. Detailed measurements from characteristicprofiles, such as individual field sections and foresthillslopes, would provide valuable but still not suffi-cient data to verify the model structure and the para-meters. Further research is needed to testmathematical models for different land use typesand to develop simple but still reliable water balancemodels for the characteristic profiles.

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Fig. 8. Measured and calculated flow from Lestijoki catchment during model testing period of 1993–1996.

Fig. 9. Partition of monthly river flow to contributions from areas of different land use type during 1993–1996.

Even if the number of possible characteristic profilesis infinite and it is impossible to calculate all of them, thestrong averaging effect of a large catchment enables thecomputation of the hydrograph response using only afew characteristic profiles for each land use type. Thenumber of characteristic profiles could be furtherreduced by using a statistical distribution (e.g. gammadistribution) to represent the variability of profile para-meters in each land use type. The sub-division of theLestijoki catchment into hydrologically similar unitswas approximate because it was based on a rough clas-sification of land use. The rapid advancement of remotesensing technologies (e.g.Schultz, 1993a, 1993b),geographic information systems and digital terrainanalysis (e.g. Moore et al., 1991; Band, 1993; Jolma,1993) possibly provide a systematic way to identify thecharacteristic profiles of different slope and length.Digital elevation models also enable the estimationof the geomorphologic parameters instead of calibrat-ing the parameters as was done in this study.

The proposed model uses the characteristic profileas the basic unit in modeling the catchment hydrol-ogy. The size of the unit corresponds to the actuallength of the travel path to the nearest stream, whichis on the order of 10–100 m. This scale is consider-ably smaller than e.g. that Flu¨gel (1995) used in theapplication of the concept of hydrological responseunits (HRU) (3.2–23.4 km2). In our approach, thecharacteristic profile is not fixed to a specific locationin the catchment but to a specific HSU. The HSU isdefined according to land use and soil type and coverssub-areas that can be located at any part of the catch-ment area.

Acknowledgements

This study was conducted with the funding from theAcademy of Finland and LIFE-project ‘‘ManagementSystem for Preventing Acidification due to Land Usein Acid Sulfate Soils Areas: Demonstration Project inthe Lestijoki River Catchment’’ organized by theRegional Council of Central Ostrobothnia/Authorityfor Regional Development, Kokkola, Finland.

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