Parameterisation and application of a hillslope hydrological model to assess impacts of a forest...

Environmental Modelling & Software 21 (2006) 1324e1339

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Parameterisation and application of a hillslope hydrologicalmodel to assess impacts of a forest clear-cutting

on runoff generation

H. Koivusalo a,*, T. Kokkonen a, A. Lauren b, M. Ahtiainen c, T. Karvonen a,H. Mannerkoski d, S. Penttinen e, P. Seuna f, M. Starr g, L. Finer d

a Laboratory of Water Resources, Helsinki University of Technology, Finlandb Joensuu Research Centre, Finnish Forest Research Institute, Finland

c The North Karelia Regional Environment Centre, Finlandd Faculty of Forestry, University of Joensuu, Finlande Kuopio Office, Geological Survey of Finland, Finland

f Finnish Environment Institute, Finlandg Vantaa Research Centre, Finnish Forest Research Institute, Finland

Received 26 April 2004; received in revised form 21 March 2005; accepted 5 April 2005

Available online 8 September 2005

Abstract

This paper combines results of an empirical paired-catchment method and hydrological simulation techniques in quantifying andexplaining the effect of a clear-cutting on runoff generation. The pair of catchments (56 and 24 ha) is located in the middle boreal

zone in Eastern Finland. In the first part, measured runoff from the two catchments, of which one had been partly clear-cut (35%)and the other had not been treated (control), was analysed to detect differences in runoff preceding and following the cutting. In thenext part, snow and canopy models were calibrated against snow water equivalent data in an open and forested environment,

respectively. Output from the canopy and snow models provided an input to a hillslope hydrological model, which wasparameterised for the two catchments and calibrated against runoff measurements. The hydrological model was applied to identifymechanisms that could explain the observed difference in the hydrological behaviour of the two catchments. According to the modelresults, both the decreased interception in the clear-cut catchment and the increased evapotranspiration in the young, growing forest

in the control catchment contributed to the observed differences in spring flood volumes. During the growing season, most of thechange in runoff generation between the catchments was attributed to the increased evapotranspiration in the young forest.� 2005 Elsevier Ltd. All rights reserved.

Keywords: Evapotranspiration; Forest clear-cut; Hydrology; Interception; Mathematical modelling; Paired-catchment

1. Introduction

Forest clear-cutting can significantly affect thehydrological behaviour of a forested catchment by

* Corresponding author. Laboratory of Water Resources, Helsinki

University of Technology, P.O. Box 5300, FIN-02015 HUT, Finland.

Tel.: C358 9 451 3837; fax: C358 9 451 3856.

E-mail address: [email protected] (H. Koivusalo).

1364-8152/$ - see front matter � 2005 Elsevier Ltd. All rights reserved.

doi:10.1016/j.envsoft.2005.04.020

changing the mass and energy fluxes at the land surface.It is well known that clear-cutting affects the accumu-lation of snow over the winter as there is no longerinterception in the forest canopy and as spatial de-position patterns of precipitation change as a result ofaltered wind conditions (Woo et al., 2000; Lundberget al., 1998; Calder, 1990). Also, the timing and intensityof the spring snowmelt change as the snow coverbecomes exposed to increased solar radiation and

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1325H. Koivusalo et al. / Environmental Modelling & Software 21 (2006) 1324e1339

turbulent energy fluxes (Woo and Giesbrecht, 2000;Hardy et al., 1997; Pomeroy and Dion, 1996). Duringthe growing season both interception and transpirationare affected when the tree stand is removed (Whitakeret al., 2002; Buttle et al., 2000).

The hydrological effects of a forest cutting haveexperimentally been quantified using paired-catchmentstudies, where measurements from time preceding andfollowing the treatment are compared with measure-ments from control catchments (e.g. Seuna, 1988, 1999;Troendle and King, 1987; Whitehead and Robinson,1993). Hydrological effects of a clear-cutting includeincreased accumulation of snow, earlier onset ofsnowmelt and higher snowmelt intensity in the open,and an elevated water table (Whitaker et al., 2002;Troendle, 1983; Paivanen, 1982). Stednick (1996)reported increased runoff volumes after a substantialpart of a catchment had been clear-cut. Also, springflood peaks may decrease in partly harvested catchmentsdue to a longer duration of snowmelt, which resultsfrom an earlier snowmelt in open than in forested areas(Heikurainen and Paivanen, 1970).

When planning forest cutting operations, long timeseries of on-site hydrological data are seldomavailable forpredicting the hydrological impacts of such operations.Coupling of hydrological simulation models with theavailable experimental data constitutes one avenue forpredicting the hydrological effects of cutting in ungaugedcatchments (e.g. Thomas and Megahan, 1998). Anothermotivation for the application of simulationmodels is theneed to distinguish the mechanisms behind the observedbehaviour in runoff generation in catchments subject toforest cuttings. Separation of the influence of climaticvariability, forest growth, and forest cutting on the runoffgeneration becomes particularly challenging when thearea of the studied catchment increases (e.g. Buttle et al.,2000). In many cases the most efficient option to tacklethese issues is the application of a hydrological model.Before a hydrological model can be expected to havecredibility in producing predictions of forestmanagementeffects, it must be tested against measured data. Whendataonly froma treated catchmentareused in amodellingexercise, the effect of a clear-cutting can easily be maskedby errors arising from uncertainties in the input data, themodel structure, and the estimated parameter values.Consequently, it may not be possible to validate themodelled hydrological influence of a forest clear-cutting.Merritt et al. (2005) conducted a sensitivity analysis ofa parsimonious environmental simulation model andshowed howdifferences inmodel results between differentland management options can easily be obscured bypropagation of parameter uncertainty. Uncertainty ofmodel predictions can be significant even for process-oriented, well-validated simulation models and can bereduced only by incorporation of additional field data(e.g. Uhlenbrook and Sieber, 2005).

A process-based modelling of forest ecosystems hasrecently advanced, as field campaigns, such as NOPEXeWINTEX (Halldin et al., 2001) and BOREAS (Sellerset al., 1995), have produced detailed data for the processstudies. Field efforts have contributed to the deve-lopment and validation of mathematical models thatcharacterise the influence of a forest stand on snow pro-cesses and evapotranspiration. Such descriptions areessential when simulating the effect of a clear-cuttingon hydrological fluxes. This study relies on the canopyand snow process descriptions reported in Koivusaloand Kokkonen (2002) and Wigmosta et al. (1994), andutilises the output of these schemes to drive the charac-teristic profile model (CPM) presented in Karvonenet al. (1999) and Koivusalo and Kokkonen (2003). TheCPM is a runoff generation model that allows therelative share of different forest stand types, and theiraverage distance from an outlet stream, to be describedin a vertical two-dimensional hillslope.

The aim of the current paper is to combine theempirical paired-catchment method with hydrologicalsimulation techniques in quantifying and explaining theinfluence of a forest clear-cutting on runoff generation.At the first stage, 10 years of runoff data from twoadjacent catchments, of which one has been partlyclear-cut (35%) and the other has acted as a controlcatchment, are analysed. Data analysis is complicatedby the fact that 65% of the control catchment is coveredwith a young, growing forest. At the second stage, thehydrological model is calibrated against snow andrunoff data from both catchments. Based on thesimulations, the hydrological effects of the clear-cuttingand the forest growth are quantified. At the final stage,the paired-catchment analysis conducted with the runoffdata is repeated for the simulated runoff series. Theresults are discussed to address the relative importanceof mechanisms that could explain the observed differ-ences in the behaviour of the two catchments.

2. Site description and data

The studied Kangasvaara (56 ha) and Kangaslampi(24 ha) catchment pair are located in Eastern Finland(63 �51#N, 28 �58#E, Fig. 1) and were monitored as a partof the VALU project commenced in 1992 (Finer et al.,1997). Meteorological, snow, and runoff data wereavailable from January 1992 to December 2001.

While the dominant tree species in Kangasvaara ismatureNorway spruce (Picea abiesL.Karst.), both Scotspines (Pinus sylvestris L.) and deciduous trees (mostlyBetula sp.) are also found in the area. The mean timbervolume in Kangasvaara in 1992 was 266 m3 ha�1

(Piirainen et al., 1998). In SeptembereOctober 1996,35% of the catchment area was clear-cut. A total of260 m3 ha�1 of stemwoodwas removed from the clear-cut

1326 H. Koivusalo et al. / Environmental Modelling & Software 21 (2006) 1324e1339

Fig. 1. Location of Kangasvaara and Kangaslampi catchments, and Iso-Kauhea and Valtimo meteorological stations (a). Map of the catchments (b).

area and a buffer zone was left between the clear-cut areaand the outlet stream. In August 1998 the soil wasprepared with disc ploughing in strips to enhance forestregeneration, and in spring 1999 Scots pine seedlings wereplanted in the area. Forests in the Kangaslampi catch-ment are both young (65% of the area) and matureconiferous forests (35% of the area). The mature forest issimilar to the forest in Kangasvaara and the young forestis mainly composed of Scots pines (P. sylvestris L.). Thetimber volume in the young forest was 10 m3 ha�1 in 1992.The field layer in the forest is mainly of the Vaccinium-Myrtillus type (Cajander, 1949), and it is dominated bydwarf shrubs (Vaccinium vitis-ideaea L. and Vacciniummyrtillus L.). Feather mosses (Pleurozium schreberi Brid.andHylocomnium splendens (Hedw.) B.S.&G.) dominatethe bottom layer.

Kangasvaara and Kangaslampi catchments comprise92% and 91% glacial till soils, respectively, and theremaining catchment area, mostly surrounding theoutlet streams, is covered by forested peat soils.Elevation in the catchments ranges from 184 to 238 mabove mean sea level. Spatial data on the topographyand soil depths were available in a 10 ! 10 m2 grid.Hydraulic characteristics of the till soils reported inMottonen (2000) were available for the study. Long-term mean annual precipitation and air temperature inthe area are 700 mm and 1.5 �C, respectively.

Hourly meteorological data to drive the simulationmodels were compiled using the weather stationmeasurements in Kangasvaara and the records obtainedfrom the Valtimo weather station (ca. 20 km fromKangasvaara) operated by the Finnish MeteorologicalInstitute. Meteorological data were also compiled fromrecords of the Iso-Kauhea weather station in order to

apply the snow model for the open mire situated in Iso-Kauhea.

The following meteorological variables were mea-sured at 20 min intervals both at Kangasvaara andIso-Kauhea weather stations: air temperature, relativehumidity, global radiation, and wind speed throughoutthe year, and rainfall during the growing season (Fineret al., 1997). Furthermore, precipitation during thewinter was gathered in Kangasvaara with four collectorsthat were monitored every four weeks (Piirainen et al.,2002). An estimate of the daily precipitation wascompiled by scaling the daily precipitation availablefrom Valtimo to match with the on-site collector data.The hourly precipitation was created by dividing thedaily value over the hours from 9 am to 12 am. Thisimplies that the mean duration of a precipitation eventwas assumed to be 4 h. The average wind speed atKangasvaara and Iso-Kauhea weather stations de-creased during the 10-year period, presumably due tothe growth of the forest surrounding the stations.Decrease in the wind speed leads to an increasedcollecting efficiency of the precipitation collectors, whichwas considered in this study with the precipitationcorrection method suggested by Førland et al. (1996). Inaddition to this correction, the correction factor forsnowfall was multiplied by a constant value, which wascalibrated against the mean snow water equivalent in theopen area. The decreasing trend in the wind speed wasremoved before wind speed data were used as an inputfor the canopy and snow models. Systematic errors andoutliers in the data, which could be found particularlyduring the winter months, were corrected by comparingthe meteorological data between the three stations.Downward long-wave radiation was estimated as


a function of the cloudiness index and the airtemperature following Satterlund (1979).

Areal snow water equivalent (SWE) values for openand forested environments were estimated from snowcourse data. Each snow course had five measurementpoints for the snow depth and one point for the snowdensity. One snow course was located in the open mirein Iso-Kauhea, eight snow courses in the mature forestin Kangasvaara and Kangaslampi, and four snowcourses in the young forest in Kangaslampi. Snowcourses were monitored one to two times a month, andthe data cover the period from autumn 1993 to spring2001. One of the eight snow courses in the mature forestwas located in the area clear-cut in 1996.

Daily runoff data measured using v-notch weirs wereavailable for five-year-periods before (1992e1996) andafter the clear-cutting (1997e2001). Table 1 showsstatistics of annual runoff in Kangasvaara and Kangas-lampi.

3. Methods and model parameterisation

3.1. Canopy model

The canopy model is largely based on the schemecharacterising overstorey and understorey vegetationprocesses in the Distributed Hydrology, Vegetation, andSoil Model (DHVSM, Wigmosta et al., 1994). Applica-tions of the DHVSM over a range of climatic conditionsand forest species have been published, e.g. by Thyeret al. (2004), Whitaker et al. (2002), Nijssen andLettenmaier (2002), and Nijssen et al. (1997).

The canopy model simulates at an hourly time-stepsolar radiation, long-wave radiation, wind speed, andthroughfall beneath the forest canopy from input datacharacterising the meteorological conditions above thecanopy. Relative humidity and air temperature areassumed not to be affected by the canopy. The processdescriptions are given in detail in Koivusalo andKokkonen (2002) and Wigmosta et al. (1994), and inthis section only a brief summary of the model ispresented.

The canopy model accounts for the interception ofrainfall and snowfall in the overstorey vegetation (trees),and for the interception of rainfall in the understoreyvegetation (field layer). Whenever the ground is

Table 1

The mean annual runoff (Qmean) and the coefficient of variation (Cv) in

Kangasvaara and Kangaslampi for the periods preceding and

following the logging

Qmean (mm/a) Cv

Kangasvaara 1992e1996 308 0.09

Kangasvaara 1997e2001 328 0.22

Kangaslampi 1992e1996 328 0.08

Kangaslampi 1997e2001 288 0.25

snow-covered, the interception in the understorey isdisregarded. The forest density cd gives the fraction ofthe ground that is covered by the overstorey. Thedensity of the understorey canopy is set to the value of 1.The efficiency of interception is described according tothe method proposed by Aston (1979), and the value ofthe parameter ki describing the interception efficiencyhas been adopted from Koivusalo and Kokkonen(2002). The same parameter value is used for snowfallinterception in the overstorey and for rainfall intercep-tion in both the overstorey and the understorey. Themaximum overstorey interception capacities were relatedto the leaf area index (LAI), and were 4 mm ! LAIfor snow interception and 1 mm ! LAI for rainfall inter-ception. Themaximumunderstorey interception capacityfor rainfall was 0.1 mm! LAI as reported in Wigmostaet al. (1994).

Potential evapotranspiration is computed separatelyfor the overstorey and understorey vegetation accordingto a combination equation of the PenmaneMonteith typewhere the stomatal resistance is set to zero. Computationof the aerodynamic resistances is similar to that reportedin Koivusalo and Kokkonen (2002) when the LAI in theoverstorey is higher than or equal to 4.0. When theoverstorey LAI is less than 4.0 the method of Shuttle-worth and Wallace (1985) is used to determine theaerodynamic resistance (see Wigmosta et al., 1994). Theparameters defining the extinction of the wind speedwithin the canopy, n, and the multiplier of the aero-dynamic resistance in snow conditions fr, were calibrated(see Section 4.2.1). The radiation budget for the over-storey, understorey, and the soil surface follow theprocedure used in DHVMS. The solar radiation attenu-ation coefficient, ak, was calibrated. Evaporation of theintercepted water occurs at the potential rate until allintercepted water is depleted. Transpiration, which isinitiated after the canopy has become dry, is computedfollowing themethods given inWigmosta et al. (1994) andNijssen et al. (1997). The stomatal resistance is a functionof the LAI, the soil temperature, the water vapourpressure deficit, the photosynthetically active radiation(PAR), and the soil moisture. The parameters of thefunctions controlling stomatal resistance are fixed tothe values reported in Nijssen et al. (1997), except for theparameter defining the minimum stomatal resistance forthe overstorey, rsmino, which was calibrated (see Section4.2.2). In this study, evaporation from the soil surfacewasneglected, because the moss and humus layers coveringthe ground were assumed to block evaporation from themineral soil surface.

For the purposes of this study, an estimate of the LAIand its evolution in time was derived on the basis of theavailable forest inventory data from compartments ofdifferent ages. Firstly, the Chapman-Richards growthfunctions (e.g., Clutter et al., 1983) were fitted to theforest inventory data to compute the temporal evolution


of the mean height of the tree stand and the meandiameter at the breast height (DBH). Growth of Scotspine and growth of Norway spruce were consideredseparately. Secondly, the evolution of the LAI wascomputed with aid of the Marklund (1988) regressionequations that relate the LAI to the tree height, the DBH,and the stem density. Parameters for the Marklundequations were adopted from Nilson et al. (2000), whohave determined values for these parameters for differenttree species using data from Estonian forests. Finally,Pomeroy et al. (2002) have published a relationshipbetween the effective winter leaf area index and the forestdensity (cd) for data measured in a coniferous forest inCanada (Fig. 2). The effective winter leaf area index isdefined as the total horizontal area of stems and needlesper unit area of ground. The Marklund estimate of theLAI was multiplied with a factor of 0.62 to get values ofthe effective winter LAI that are representative ofconditions in Eastern Finland. In Fig. 2 the arrowsindicate how the forest density in the young and matureforests changes as a function of effective winter LAI overthe study period from 1992 to 2001. The estimated valuesof the effective winter LAI and the forest density wereused in the parameterisation of the overstorey vegetation.The understorey vegetation LAI was fixed to the value of2.0 according to Nijssen et al. (1997). Table 2 presentsvalues of those canopy model parameters that arerelevant to the current study.

3.2. Snow model

The snow model operates in one-dimension andsimulates at an hourly time-step heat conductionthrough the snow into the soil, snowmelt, liquid waterretention in snow, melt water discharge out of thesnowpack, and compaction of snow. Time series ofwater equivalent, depth, and temperature of snow areproduced as an output. Computation procedures arebased on the ideas from Tarboton et al. (1995) and

Karvonen (1988). In the model the snowesoil domain issplit into two layers of snow and one of soil. Separationof the snow into two layers restricts the heat exchangewith the atmosphere only to the upper part of thesnowpack, and inclusion of the soil layer enablesconsideration of the ground freezing, which both aidin obtaining a realistic estimate of the snow surface andsnowpack temperatures. The heat content of each layeris used to determine the average temperature and theliquid water content of a layer. The snow model usedhas been described in detail in Koivusalo et al. (2001),except for the correction of aerodynamic resistance instable and unstable atmospheric conditions, which hasbeen adopted from Storck (2000).

Values of the following parameters were calibrated inthis study: the snowfall scaling factor, the liquid waterretention capacity, the windless exchange coefficient forthe sensible heat flux, the depth of the thermally activesoil layer, and the average wintertime soil moisture (seeSection 4.2.1). The remaining snow model parameterswere fixed at values reported in Koivusalo et al. (2001).Values for those parameters that are relevant to thisstudy are listed in Table 3.

3.3. Characteristic profile model

The characteristic profile model (CPM, Karvonenet al., 1999; Koivusalo and Kokkonen, 2003) describessoil water movement and runoff generation processesalong a typical longitudinal section (hillslope) froma water divide to a stream. It takes as an input dailyseries of throughfall/snowmelt and transpiration, whichhave been aggregated from the hourly output of thecanopy and snow models. The CPM is a quasi-two-dimensional model in the sense that vertical and lateralwater fluxes are computed alternately. The characteristicprofile is divided into vertical soil columns, which arefurther divided into soil layers. Vertical fluxes in allcolumns are computed by approximating the Richards

0

0.2

0.4

0.6

0.8

1

0 0.5 1 1.5 2 2.5 3 3.5 4

Leaf Area Index

For

est

dens

ity

(ove

rsto

rey)

Growth of young stand in Kangaslampi

Growth of Kangasvaara forest

Fig. 2. Relationship between the effective winter LAI and forest density calculated according to Pomeroy et al. (2002). Arrows indicate how the LAI

and forest density change in response to the growth of a mature forest in Kangasvaara, and to the growth of a young forest in Kangaslampi.


equation with successive steady-state solutions of thepressure head distribution (Skaggs, 1980). Infiltrationinto a soil column is controlled by the available airvolume in the column. Water that cannot infiltrate istransported downslope as surface runoff and it eitherreaches a stream, or infiltrates if the air volume furtherdown the profile allows it.

After the vertical fluxes and the resulting groundwaterlevels have been resolved, the lateral groundwater flowbetween vertical soil columns is computed according toDarcy’s law (e.g. Rawls et al., 1992). Groundwater flowfrom the column next to the stream constitutes the slowlyresponding baseflow component. When the groundwaterlevel in any column rises above the soil surface, themodel generates exfiltration which e similarly to surfacerunoff e is transported downslope. The sum of all runoffcomponentse surface runoff, exfiltration, andbasefloweis passed through a linear storage, which describes thedelay of water flowing in a stream.

In this study both Kangasvaara and Kangaslampicatchments were described with a single characteristicprofile. The method for identifying the profile shape wasmodified from the procedure reported in Kokkonen et al.(2001). Fig. 3 plots the elevation difference as a function ofthe distance from the outlet stream for both catchments.The total length of the profile was taken as the longestdistance to the stream, the width distribution was

Table 2

Canopy model parameters

Parameter Symbol Value

Efficiency of interception ki 0.39

Snowfall interception capacity

in the overstorey

isnow,o 0.004 m ! LAI

Rainfall interception capacity

in the overstorey

irain,o 0.001 m ! LAI

Rainfall interception capacity

in the understorey

irain,u 0.0001 m ! LAI

Canopy albedo ac 0.18

Canopy attenuation coefficienta ak 0.7

Extinction coefficienta n 0.9

Multiplier of the aerodynamic

resistance in snow conditionsafr 4

Lower temperature limit for the

form of precipitation

Tl 0 �C

Upper temperature limit for the

form of precipitation

Th 2 �C

Minimum stomatal resistance for

the overstorey (mature forest)brsmin,o 900 s/m


the overstorey (young forest)brsmin,o 100 s/m


the understoreybrsmin,u 200 s/m

Maximum stomatal resistance for

the overstorey

rsmax,o 5000 s/m

Maximum stomatal resistance for

the understorey

rsmax,u 5000 s/m

a Calibrated against SWE data.b Calibrated against runoff data.

calculated according to the number of pixels residing ata given distance, and the shape of the soil surface along theprofile was determined as the mean elevation of pixelslocated at a given distance. Depth to the bedrock alongthe profile was estimated in a similar fashion to theestimation of the surface topography using the spatialdistribution of the soil thickness. Ground penetratingradar surveys were used in estimating the soil thickness.Forest and clear-cut areas were assigned on the Kangas-vaara profile according to their relative share at a givendistance from the stream. In this model application,ploughing of the clear-cut areas was assumed not to havean impact on the hydrological response.Mire andmineralsoil areas along the profile were assigned based on thespatial distribution of these two soil types at a givendistance from the stream. The profile was discretised into30 m long columns, which yielded 40 columns forKangasvaara and 28 columns for Kangaslampi. Figs. 4and 5 illustrate parameterisation of the characteristicprofile for Kangasvaara and Kangaslampi, respectively.Table 4 summarises themain features of theKangasvaaraand Kangaslampi characteristic profiles. Vertical distri-bution of the soil hydraulic properties was adopted fromMottonen (2000). The hydraulic conductivities in the rootzone (0.3 m) and the retention coefficient of the routingprocedure were adjusted in the calibration. Values for thesoil hydraulic parameters are listed in Table 5.

3.4. Performance criteria

The goodness of fit between the modelled andmeasured time series was assessed in terms of theefficiency ENS (Nash and Sutcliffe, 1970) and the mass

Table 3

Snow model parameters

Parameter Symbol Value

Emissivity of snow 3s 0.99

Von Karman constant K 0.41

Maximum limit for the

Richardson number

Rimax 0.2

Snow surface roughness length zos 0.005 m

Liquid water holding capacity of snowa Lc 0.1

Maximum SWE of upper snow layer SWEact 0.02 m

Saturated hydraulic conductivity of snow Ksat 20 m/h

Specific heat of soil particles Cg 0.84 kJ/kg/ �C

Density of soil particles rg 2650 kg/m3

Depth of soil layera Dg 0.9 m

Soil porosity f 0.5

Soil water contenta w 0.4

Windless exchange coefficient

for sensible heat flux

EHO 0 kJ/m2/h

Windless exchange coefficient

for latent heat flux

EEO 0 kJ/Pa/h

Snowfall scaling factora csnow 1.08

a Calibrated against SWE data.


0

5

10

15

20

25

30

35

40

45

50

0 300 600 900 1200

Distance to Stream [m]

Ele

vati

on [

m]

PixelsMean

PixelsMean

a)

0

5

10

15

20

25

30

35

40

45

50

0 300 600 900 1200

Distance to Stream [m]

Ele

vati

on [

m]

b)

Fig. 3. Elevation above the stream level for all pixels as a function of distance from the outlet stream, and the surface topography of the characteristic

profile (mean) for Kangasvaara (a) and Kangaslampi (b).

balance error EVol. In mathematical terms the perfor-mance criteria were:

ENSZ1�

Pi

ðxi�miÞ2

Pi

ðmi�mÞ2ð1Þ

EVolZ

Pi

ðxi�miÞPi

mi

100% ð2Þ

where xi is the simulated variable at time i, mi is themeasured variable, and m is the mean of the measuredvariable.

4. Results and discussion

4.1. Comparison of observed runoff in Kangasvaaraand Kangaslampi

Daily runoff series from the partly clear-cut Kangas-vaara catchment and from the Kangaslampi controlcatchment were examined to detect changes in thehydrological behaviour following the cutting inKangasvaara. Two half-yearly periods, the first oneextending from January to June and the second fromJuly to December, were studied separately. The firstperiod includes the spring flood and its recession, and thesecond period accounts for the late summer and autumnrunoff events. Fig. 6 shows the observed cumulativerunoff from Kangasvaara plotted against the cumulative

0 200 400 600 800 1000 1200

Distance to stream [m]

Ele

vati

on [

m]

Surface

Bottom

Column interface

a)

Wid

th

Forest Logged Peatb)

5

15

25

35

45

Fig. 4. Longitudinal cross-section (a) and plan view (b) of the characteristic profile for Kangasvaara. Soil surface and bedrock surface topography,

and length of the profile are shown in (a). Relative width, and vegetation and soil types of the profile are shown in (b).


0 100 200 300 400 500 600 700 800

Distance to stream [m]

Ele

vati

on [

m]

a)

Wid

th

b)

5

15

25

35

45Surface

Bottom

Column interface

Forest Young Forest Peat

Fig. 5. Longitudinal cross-section (a) and plan view (b) of the characteristic profile for Kangaslampi. Soil surface and bedrock surface topography,

and length of the profile are shown in (a). Relative width, and vegetation and soil types of the profile are shown in (b).

runoff from Kangaslampi before and after the treat-ment, for the half-yearly periods. Clearly, the relation-ship between the volumes of runoff changed after theclear-cutting. For the years after the clear-cutting thegraphs are mostly above the graphs plotted for yearspreceding the cutting, which indicates that runoff fromKangasvaara in relation to runoff from Kangaslampiincreased from the pre- to the post-treatment period.

The difference in runoff between the two catchmentswas accumulated over time in order to detect when thedifference in the behaviour of these catchments is mostvisible. In mathematical terms, the cumulative differenceDt at day t of any particular year was calculated asfollows:

DtZXt

iZi0

�qKv;i� qKl;i

�ð3Þ

where qKv,i is the runoff in Kangasvaara at day i, qKl,i isthe runoff in Kangaslampi, and i0 is the first day of theperiod (January 1 or July 1) of any particular year.Averaging the differences (Dt) gained for the five-yearperiod preceding the clear-cut, and the five-year periodfollowing the clear-cut, produced the graphs shown inFig. 7.

Results indicate that after the clear-cut runoff fromKangasvaara has increased in relation to runoff fromKangaslampi. The increase in the first half-yearly periodoccurred during the few weeks of the spring flood, whilein the second half-yearly period the deviation between

the difference curves gradually increased from July toDecember. Before the clear-cut the spring flood seemedto commence a little earlier in Kangaslampi than inKangasvaara. After the cutting the difference in theoccurrence time of the spring flood was smaller.

4.2. Hydrological simulations

4.2.1. Simulation of snow water equivalentThe snow model was calibrated to a time period from

autumn 1993 to spring 1997. Snow processes in an openarea were calibrated against SWE data from an openmire in Iso-Kauhea (16 km from Kangasvaara) usingthe climatic data for the Iso-Kauhea catchment. Thecalibration parameters included the snowfall correctionfactor, the liquid water retention capacity, the windlessexchange coefficient, the depth of thermally active soillayer, and the average wintertime soil moisture (seeTable 3). SWE data from autumn 1997 until spring 2001were used for model validation. Measured and computedSWE values in the mire in Iso-Kauhea and in the clear-cut in Kangasvaara are presented in Fig. 8 for thecalibration and validation periods. The meteorologicaldata for the Kangasvaara catchment were used whensimulating snow processes in the clear-cut area.Performance criteria for the SWE simulations shownin Table 6 indicate that the model performed equallywell for both calibration and validation periods.

The canopy model was calibrated against the meanareal SWE in the mature forest. All snow model

Table 4

Summary of the main features of Kangasvaara (after clear-cut) and Kangaslampi characteristic profiles

Profile

length (m)

Max. elevation

difference (m)

Mature forest

area (%)

Young forest

area (%)

Forested

mire (%)

Clear-cut

area (%)

Kangasvaara 1200 37.6 56 0 9 35

Kangaslampi 810 33.0 30 63 7 0


Table 5

Soil hydraulic parameters

Soil type (depth

range, m)

Porosity Residual water

content

Van Genuchten

parameter a

Van Genuchten

parameter b

Vertical saturated

hydraulic conductivity

(cm/h)

Horizontal saturated

hydraulic conductivitya

(cm/h)

Mineral (0e0.1) 0.55 0.1 0.0584 1.3553 5 50a

Mineral (0.1e0.2) 0.55 0.1 0.0584 1.3553 4 40a

Mineral (0.2e0.4) 0.45 0.08 0.0675 1.3996 3 30a

Mineral (0.4e0.6) 0.45 0.08 0.0675 1.3996 0.33 10.3

Bottom soil (0.6e2.1) 0.289 0 0.024 1.23 0.11 0.11

Peat (0e0.1) 0.9 0 0.123 1.28 5 50a

Peat (0.1e0.2) 0.9 0 0.123 1.28 4 40a

Peat (0.2e0.4) 0.9 0 0.123 1.28 3 30a

Peat (0.4e0.6) 0.9 0 0.123 1.28 0.33 10.3

a Calibrated against runoff data.

parameter values fixed above using the SWEdata from anopen mire were kept unchanged. The calibration andvalidation periods were the same as before. The canopyattenuation coefficient ak, the extinction coefficient nuand the multiplier of aerodynamic resistance in snowconditions fr were adjusted in the calibration (Table 2).Calibration andvalidation results shown inFig. 9, and theefficiencies presented in Table 6, suggest that there was nonotable difference in the model performance between thecalibration and validation periods. Comparison of thesnow simulation results for the forest and the clear-cut(Fig. 9) clearly demonstrates a greater accumulation ofsnow, and a higher snowmelt intensity in the clear-cut.

After parameter values for both snow and canopymodels had been calibrated, SWE was simulated ina young forest by changing the values of the tree heightand the DBH, and as a consequence, the values of theLAI and the forest density as described in Section 3.1.

The efficiencies for the simulation results in the youngforest, listed in Table 6, reveal that while the modelfitted well to the data measured in the period from 1998to 2001, the model results for the young forest in theperiod from 1993 to 1997 were the poorest of all SWEsimulation results. In general, the model performedbetter for the validation period, which is likely to be anindication of an inferior accuracy of the estimate ofsnowfall during the calibration period.

4.2.2. Simulation of runoffFor the period preceding the clear-cut the CPM was

driven with the simulation output of the canopy andsnow models parameterised to represent the matureforest. And for the period following the clear-cut, thecanopy model included only the understorey vegetationin the clear-cut part of the profile. The hydrologicalmodel was calibrated against measured runoff in

January-June

0 50 100 150 200 250 300

Cum. Runoff from Kangaslampi [mm]

Cum

. Run

off

from

Kan

gasv

aara

[m

m]

Before Logging

After Logging

a)July-December

b)

0

50

100

150

200

250

300

0 50 100 150 200 250 300

Cum. Runoff from Kangaslampi [mm]

Cum

. Run

off

from

Kan

gasv

aara

[m

m]

Before Logging

After Logging

0

50

100

150

200

250

300

Fig. 6. Measured cumulative runoff from Kangasvaara (clear-cut) plotted against cumulative runoff from Kangaslampi (control) for the first (a) and

second (b) half-yearly periods. Plots are shown separately for the individual years both before and after the clear-cut.


a)Period from January to June

01-Jan 01-Mar 01-May 30-Jun

Dif

fere

nce

betw

een

cum

ulat

ive

runo

ffs

[mm

] Measured before treatmentMeasured after treatment

Measured before treatmentMeasured after treatment

b)Period from July to December

-60

Dif

fere

nce

betw

een

cum

ulat

ive

runo

ffs

[mm

]

-60

-40

-20

0

20

40

60

-40

-20

0

20

40

60

01-Jul 31-Aug 01-Nov 01-Jan

Fig. 7. Average differences in the cumulative runoff between Kangasvaara and Kangaslampi catchments (see Eq. (3)) for the first (a) and second (b)

half-yearly periods. Average differences are shown separately for the time preceding and following the clear-cut.

Kangasvaara for a period from January 1992 to October1996. The minimum stomatal resistance for the over-storey, rsmino (Table 2), was adjusted to match themeasured cumulative runoff in the calibration period.Lateral hydraulic conductivities in the root zone (Table 5)and the retention coefficient of the routing procedure(kretZ 0.11 d�1) were also adjusted in calibration.

Fig. 10 presents measured and computed runoff forKangasvaara for the periods preceding and following

the harvest. According to the performance criteriapresented in Table 7, the model fit was slightly betterin the calibration period. The deteriorated performancein the prediction period mainly resulted from the under-estimation of peak flows in springs 1997 and 2000(Fig. 10a), and from mass balance errors in years 1997and 2001 (Fig. 10b). The hydrological model was alsorun to simulate a case where no clear-cut had beencarried out, and Table 8 summarises the calculated effect

0

100

200

300

Jan-92 Dec-92 Jan-94 Jan-95 Jan-96 Jan-97

SWE

[m

m]

0

100

200

300

SWE

[m

m]

a)Calibration period

Sep-97 Sep-98 Sep-99 Sep-00 Sep-01

Measured (Open Bog)Calculated (Open Bog)

Measured (Clear-cut)

Measured (Open Bog)Calculated (Open Bog)

Measured (Clear-cut)

Calculated (Clear-cut)

Validation periodb)

Fig. 8. Measured and calculated SWE in an open mire in Iso-Kauhea for the calibration (a) and validation (b) periods. The measured and calculated

SWE in the clear-cut area in Kangasvaara is also shown.


of the cutting on the simulated evapotranspirationcomponents and the runoff for the two half-yearlyperiods.

Table 8 shows that in the first half-yearly periodrunoff increased by 9% in response to the cutting, whichwas explained by the reduced interception evaporation(�22%). According to the simulation results, thetranspiration increased at the same time by 10%, whichpartly compensated for the decreased interception. Theincrease in transpiration was explained by a smallervalue of the minimum stomatal resistance for theunderstorey than for the overstorey (see Table 2). Whenthe overstorey is removed, the energy previously avail-able to the overstorey becomes now available to theunderstorey, which leads to an increased transpirationdue to the smaller resistance of the transpiring surface.Whitaker et al. (2002) have reported a similar resultfrom a mountainous catchment in Western Canada,

Table 6

Efficiencies, ENS, between measured and computed SWE for the

calibration (1994e1997) and validation periods (1998e2001)

Period ENS

1994e1997 1998e2001

Calibration

Open mire in Iso-Kauhea 0.85

Mature forest in

Kangasvaara/Kangaslampi

0.86

Validation

Open mire in Iso-Kauhea 0.91

Clear-cut in Kangasvaara 0.89

Mature forest in

Kangasvaara/Kangaslampi

0.88

Young forest in Kangaslampi 0.71 0.93

where they applied DHVSM to study the effect of aclear-cutting on runoff volumes. In the second half-yearly period the cutting had hardly any impact onrunoff (2%). As explained above, the decreased in-terception was compensated by the increased transpira-tion.

The hydrological model was also calibrated againstmeasured runoff in Kangaslampi for the same period asin Kangasvaara. The minimum overstorey resistancersmino for the young growing forest (Table 2), and theretention coefficient of the routing procedure(kret Z 0.22) were adjusted in calibration. In order toillustrate the effect of forest growth on runoff, the modelwas also run with a parameterisation where the treeheight and the DBH and as a result, the LAI and theforest density in the young forest were fixed to theirinitial values. Table 9 shows that the modelled effect offorest growth can be seen as a decrease in the totalvolumes of runoff. When compared with the scenariowhere the young forest did not grow, the simulatedforest growth resulted in a 1% and 5% decrease inrunoff in the period preceding the cutting for the first(JanuaryeJune) and second (JulyeDecember) half-yearly periods, respectively. In the period following thecutting, the runoff decreased 8% and 29%, respectively,for the first and second half-yearly periods. Decrease inrunoff was explained by increase in both interceptionevaporation and transpiration (Table 9).

The half-yearly mass balances shown in Table 9indicate a systematic overprediction of runoff in the firsthalf of the year, and a systematic underprediction in thesecond half of the year. This suggests that the absolutemagnitude of the evapotranspiration in the growingforest is not accurately estimated. In the first half-yearly

0

100

200

300

Jan-92 Dec-92 Jan-94 Jan-95 Jan-96 Jan-97

SWE

[m

m]

0

100

200

300

SWE

[m

m]

Measured (Mature Forest)Calculated (Mature Forest)Calculated (Clear-cut)

Measured (Mature Forest)Calculated (Mature Forest)Calculated (Clear-cut)

a) Calibration period

Sep-97 Sep-98 Sep-99 Sep-00 Sep-01

b) Validation period

Fig. 9. Measured and calculated SWE in the mature forest in Kangasvaara/Kangaslampi for the calibration (a) and validation (b) periods. The

calculated SWE in the clear-cut in Kangasvaara is also shown.


0

2

4

6

8

10

12

14

Jan-92 Jan-93 Jan-94 Jan-95 Jan-96 Jan-97 Jan-98 Jan-99 Jan-00 Jan-01 Jan-02

Run

off

[mm

/d]

MeasuredCalculated

MeasuredCalculated

a) Calibration period Prediction periodCutting time

Calibration period Prediction periodCutting time

0

100

200

300

400

500

Jan-92 Jan-93 Jan-94 Jan-95 Jan-96 Jan-97 Jan-98 Jan-99 Jan-00 Jan-01 Jan-02

Cum

. Run

off

[mm

]

b)

Fig. 10. Measured and calculated daily runoff (a) and annually accumulated runoff (b) in Kangasvaara for the calibration period preceding the clear-

cut and for the prediction period following the clear-cut.

period, however, both the measured runoff and thecomputed runoff showed only little change between thepre- and the post-treatment periods, although pre-cipitation input increased by 19%. This indicates thatthe modelled change in the evapotranspiration arisingfrom the forest growth was in line with the runoffobservations. Similarly, in the second half-yearly periodthe decrease in measured and computed runoff volumesis almost the same.

4.3. Comparison of simulated runoff in Kangasvaaraand Kangaslampi

Simulated cumulative runoff from the clear-cutcatchment, Kangasvaara, is plotted against runoff fromthe Kangaslampi control catchment in Fig. 11. Theresults show the same behaviour as the observed runoffvalues graphed in Fig. 6, i.e. runoff from Kangasvaarahas increased in relation to runoff from Kangaslampi inboth of the half-yearly periods. However, the graphs

Table 7

Performance of the hydrological model for calibration (1992e1996)

and prediction (1997e2001) periods

Period 1992e1996 Period 1997e2001

Efficiency Mass balance

error (%)

Efficiency Mass balance

error (%)

Kangasvaara 0.86 �0.5 0.81 2.6

Kangaslampi 0.67 0.2 0.71 �0.6Performance statistics are computed from the daily series of measured

and simulated runoff.

computed from the simulation results do not formas distinct groups for the time periods preceding andfollowing the logging as the graphs derived from theobserved flows do.

Fig. 12 shows average differences between thesimulated cumulative runoff from Kangasvaara andKangaslampi (see Eq. (3)). First it was examined howthe modelled runoff changed in response to the loggingin the first half of the year (Fig. 12a). The change in thedifference curves in response to the treatment was clearlyvisible for both observed and simulated runoff results.The model explains the changed behaviour by both thedecreased interception in Kangasvaara due to the clear-cut, and the increased evapotranspiration in Kangas-lampi resulting from the growth of the young forest.

Results for the second half of the year (Fig. 12b) showthat according to both the observations and the simu-lations the average difference in the late summer/autumnrunoff between the treated catchment and the controlcatchment increased after the clear-cut. In the model theincrease in the difference is explainedonly by the increasedevapotranspiration in Kangaslampi, which was due tothe growth of the young forest, while the clear-cutin Kangasvaara had hardly any impact on the runoffvolumes in late summer and autumn (see Tables 8 and 9).

In general, both predicted and measured changes ofrunoff volumes in response to the cutting and the forestgrowth represented a relatively small fraction of thetotal runoff. Although the combined effect of the foresttreatment in Kangasvaara, and the forest growth inKangaslampi, was clearly visible in the measured runoff,quantification of the relative importance of these twofactors is subject to significant uncertainties. It is likely


Table 8

Precipitation, runoff and evapotranspiration components (mm/6 months) in Kangasvaara for the periods preceding and following the clear-cut

Before cutting After cutting No cutting Effect of cutting

1992e1996 1997e2001 1997e2001 1997e2001

Period JanuaryeJune

Precipitation 243 290 290

Measured runoff 186 212

Calculated runoff 184 221 203 18 (9%)

Error in runoff computation �2 (�1%) 9 (4%)

Interception evaporation 79 66 85 �19 (�22%)

Transpiration 67 68 62 6 (10%)

Period JulyeDecember

Precipitation 419 376 376


Calculated runoff 117 121 119 2 (2%)

Error in runoff computation 1 (1%) 0 (0%)

Interception evaporation 112 85 101 �16 (�16%)

Transpiration 97 92 84 8 (10%)

Modelled effect of the clear-cut is also shown.

that errors in the meteorological input and runoff datacould have a significant impact on the interpretationspresented in this study. In the field campaign the mainfocus has been on the growing season, and themeasurement activity in the winter has consequentlybeen lower. As approximately half of the annual flow inthe study site occurs during the spring flood, it would beessential to concentrate more field effort to the winter-and springtime. Furthermore, forest growth appears tohave a significant role in altering the water budget inthe Kangaslampi control catchment over the studyperiod. Also, empirical evidence shedding light on theevolution of the evapotranspiration in a young, growing

forest would be needed to validate evapotranspirationsimulations.

5. Conclusions

Comparison of observed runoff from the clear-cutKangasvaara catchment and the Kangaslampi catch-ment used as a control suggests that runoff volumesfrom Kangasvaara in relation to runoff from Kangas-lampi have increased for both of the studied half-yearlyperiods after the cutting. The increase is most clearlyseen during a few weeks period of the spring flood.

Table 9

Precipitation, runoff and evapotranspiration components (mm/6 months) in Kangaslampi for the periods preceding and following the clear-cut in

Kangasvaara

Before cutting After cutting No growth No growth Growth effect Growth effect

1992e1996 1997e2001 1992e1997 1997e2001 1992e1997 1997e2001

Period JanuaryeJune

Precipitation 243 290 243 290


Calculated runoff 225 221 228 241 �3 (�1%) �20 (�8%)

Error in runoff

computation

20 (10%) 18 (9%)

Interception evaporation 47 60 45 49 2 (4%) 11 (22%)

Transpiration 93 100 90 86 3 (3%) 14 (16%)

Period JulyeDecember

Precipitation 419 376 419 376


Calculated runoff 96 75 101 106 �5 (�5%) �31 (�29%)

Error in runoff

computation

�20 (�17%) �19 (�20%)

Interception evaporation 69 73 65 59 4 (6%) 14 (24%)

Transpiration 132 126 129 118 3 (2%) 8 (7%)

Modelled effect of the growth of the young forest is also shown.


January-June

0

50

100

150

200

250

300

0 50 100 150 200 250 300 0 50 100 150 200 250 300

Cum. Runoff from Kangaslampi [mm] Cum. Runoff from Kangaslampi [mm]

Cum

. Run

off

from

Kan

gasv

aara

[m

m] Before Logging

After Logging

a)C

um. R

unof

f fr

om K

anga

svaa

ra [

mm

]

0

50

100

150

200

250

300

Before LoggingAfter Logging

July-Decemberb)

Fig. 11. Simulated cumulative runoff from Kangasvaara (clear-cut) plotted against cumulative runoff from Kangaslampi (control) for the first (a) and

second (b) half-yearly periods. Plots are shown separately for the individual years both before and after the clear-cut.

In the model the change in runoff volumes followingthe cutting was attributed in the first half of the yearboth to the decreased interception losses in the clear-cutin Kangasvaara, and to the increased evapotranspira-tion losses in the young, growing forest in Kangaslampi.Clear-cutting and forest growth were equally importantin explaining how the difference in the hydrologicalbehaviour of the catchments changed from the pre- tothe post-treatment period. In the second half-yearly

period the model explains the change in the differenceonly by a decrease in runoff in Kangaslampi, which isa consequence of the growth of the young forest, whilethe clear-cut in Kangasvaara has hardly any impact onthe runoff volumes in late summer and autumn.Absence of the effect of cutting on runoff in the secondhalf of the year is, according to the model, due to theincreased transpiration in the clear-cut, which compen-sates for the decreased interception resulting from the

a)Period from January to June

-80

-70

-60

-50

-40

-30

-20

-10

0

10

20

1-Jan 1-Mar 1-May 30-Jun

Dif

fere

nce

betw

een

cum

ulat

ive

runo

ffs

[mm

]

Measuredbefore treatmentMeasured aftertreatmentCalculatedbefore treatmentModelled aftertreatment

29 mm

42 mm

b)Period from July to December

-40

-30

-20

-10

0

10

20

30

40

50

60

1-Jul 31-Aug 1-Nov 1-Jan

Dif

fere

nce

betw

een

cum

ulat

ive

runo

ffs

[mm

]

Measured before treatmentMeasured after treatmentModelled before treatment Modelled after treatment

21 mm

31 mm

Fig. 12. Differences in the cumulative runoff between Kangasvaara and Kangaslampi catchments for the first (a) and second (b) half-yearly periods.

Curves derived from both the simulation results and the measurements are shown.


clear-cut. In the current study, identification of clear-cutting effects was significantly complicated by thepresence of a young forest in the control catchment.

Finally, both measured and simulated changes ofrunoff volumes in response to the cutting and the forestgrowth represent a relatively small fraction of the totalrunoff. As a consequence, moderate errors (5e10%) inthe meteorological or the runoff data have a potential tochange the relative importance of those mechanisms thataccording to the model explain the observed changes inrunoff.

Acknowledgements

This work was funded by the Academy of Finlandproject FEMMA (No 52740), which develops tools forassessing environmental impacts of forest managementpractises. On-site hydro-meteorological data were col-lected in the VALU project, which studies effects offorest management on water and nutrient fluxes.Additional meteorological data were kindly providedby the Finnish Meteorological Institute. Inspiringdiscussions with Prof. Pertti Vakkilainen, Dr. KeijoNenonen, Dr. Pekka Hanninen, and Dr. Sirpa Piirainenare greatly acknowledged. Constructive comments bythe two anonymous reviewers are appreciated.

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Parameterisation and application of a hillslope hydrological model to assess impacts of a forest...

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