a simplified approach to implement forest eco-hydrological properties in regional hydrological...

Ecological Modelling 187 (2005) 40–59

A simplified approach to implement forest eco-hydrologicalproperties in regional hydrological modelling

Martin Wattenbacha, ∗, Fred Hattermannb, Reinhard Wengb, Frank Wechsungb,Valentina Krysanovab, Franz Badeckb

a University of Aberdeen, School of Biological Sciences, Department of Plant and Soil Science, Cruickshank Building,St. Machar Drive, Aberdeen AB24 3UU, UK

b Potsdam Institute for Climate Impact Research, Department of Global Change and Natural Systems,P.O. Box 60 12 03, 14412 Potsdam, Germany

Available online 16 February 2005

Abstract

The topic of this paper is a simplified model for simulating the hydrological properties of forest stands based on a robustcomputation of the temporal LAI (leaf area index) dynamics. The approach allows the simulation of all hydrologically relevantprocesses. It includes interception of precipitation and transpiration of forest stands with and without groundwater in the rootingzone. The model also considers phenology, mortality and simple management practice. It was implemented as a module in theeco-hydrological model SWIM (Soil and Water Integrated Model). The approach was tested on Scots pine (Pinus sylvestris) andcommon oak (Quercus robur andQ. petraea).

oduction0) withmulationsnd annualandenburgchments

uc-flu-

The results demonstrate a good simulation of annual biomass increase and LAI and satisfactory simulation of litter pr(annual mean value). A comparison of the date of May sprout for Scots pine and leaf unfolding for Oak (1980–199observed data of the DWD (German Weather Service) shows a good reproduction of the temporal dynamic. The daily siof transpiration shows an excellent correlation ofr = 0.81 for the year 1998 but onlyr = 0.65 for 1999. The interception lossewere also simulated and compared with weekly observed data showing satisfactory results in the vegetation periods asums, but worse agreement in autumn and spring time. A regional assessment study was done in the federal state of Br(Germany) to test the applicability and multi-criteria evaluation capabilities of the approach on the landscape and catscale using forest data, daily river discharge and regional water balance.© 2005 Elsevier B.V. All rights reserved.

Keywords: Eco-hydrology; Scots pine;Pinus sylvestris; Common oak;Quercus robur

∗ Corresponding author. Tel.: +44 1224 27 3810;fax: +44 1224 27 2703.

E-mail address: [email protected] (M. Wattenbach).

1. Introduction

The fraction covered by forested areas, their strture and species composition has a fundamental inence on the hydrological behaviour of a landscape.

0304-3800/$ – see front matter © 2005 Elsevier B.V. All rights reserved.doi:10.1016/j.ecolmodel.2005.01.026

M. Wattenbach et al. / Ecological Modelling 187 (2005) 40–59 41

The main reason for this is that trees establish a veryefficient connection between the soil water storage, thegroundwater rising zone and the atmosphere. Trees areable to adapt their root systems to the local soil waterconditions and hence to optimise their water uptake sothat they can very effectively use the plant availablesoil water in their rooting zone. The deep root systemenables them to continue transpiration in dry periodsby taking water from deeper soil layers with adequatewater supply.

In addition, tree stands can intercept amounts of upto 80% (e.g. Douglas fir (Otto, 1994)) of the annualrainfall, which is then subject to evaporation.

The combination of this rooting strategy, intercep-tion losses, high surface roughness of trees, low albedoand great leaf areas lead to higher evapotranspirationrates than any other vegetation type under the sameenvironmental conditions (Mitscherlich, 1970; Baum-gartner, 1971; Lyr et al., 1992; Flemming, 1994; Otto,1994; Feddes et al., 2001).

Beside the great flux of latent heat from forestedsurfaces, trees also change the physical properties ofrain. During the process of temporal interception therain loses part of its kinetic energy and falls then ontoa soft, energy absorbing macro-porous litter layer thatprevents any kind of meso-scale relevant surface runoff.This process is the reason why forests are of high rele-vance as water storage components, especially duringconvective rain events where they reduce peak flows(Lyr et al., 1992; Baumler and Zech, 1999; Feldwisch,1

inet uc-t

turalc is as ;E d( studyi cand ionsf 20ym ciallyi Thes eciesa ,2

Taking this into consideration it seems to be essen-tial to take the temporal and seasonal dynamics of in-terception and transpiration into account when mod-elling landscape change impacts on eco-hydrologicalprocesses related to forest area and structure changes.

There are a number of integrative models whichwere developed to simulate eco-hydrological processesat the catchment scale, taking into account human inter-ferences like land use and water management changes.They were developed to simulate all hydrologicallyrelevant processes in river basins like surface runoff,interflow, return flow, impoundment storage, plant up-take, groundwater recharge, consumption use and de-pletion of groundwater by pumping wells (e.g. HBV(Bergstrom, 1992); SWAT (Arnold et al., 1994); SWIM(Krysanova and Muller-Wohlfeil, 1998); WaSiM-ETH(Schulla, 1997; Gurtz et al., 1998)). Generally, dynamicforest growth is not considered or it is implemented bya simple parameterisation. As a result, forest relatedprocesses such as allocation of biomass, LAI develop-ment and root water uptake and related processes liketranspiration and interception are usually poorly repro-duced and they are not subject to further evaluation.This is problematic because a great number of studieshave confirmed the crucial role of forest structure on thehydrology of catchments and landscapes respectively(Baumler and Zech, 1999; Crockford and Richardson,1999; Finch, 2001; Fohrer et al., 2001; Engel et al.,2002). In the case of the SWIM model,Hattermann etal. (2005)identified the LAI as one of the most sensi-t aterb aterfl

eris-t theg rgea allh e in-fl ess,t in-t ofs rrectr sts.Tf e re-g icalp sc pts,

999).One of the vital attributes of forests that determ

heir hydrological properties is their long term strural development and the seasonal dynamic.

The basic value to reflect and describe the struchanges is the leaf area index (LAI) because ittand density and age dependent value (Finch, 1998ngel et al., 2002). The LAI of a Scots pine stan

the most common tree species in the area undern the federal state of Brandenburg) for exampleecline by natural mortality and management act

rom 6.5 in a 25 year old forest to under 2.0 in a 1ear old stand (Hormann et al., 2003). This forces aassive decrease in interception losses and espe

n the first decades, an increase in transpiration.ame pattern can be observed for deciduous tree sps well (Lyr et al., 1992; Otto, 1994; Hormann et al.003).

ive variables with respect to the discharge and walance at the basin outlet as well as to groundwuctuation.

The inadequate reproduction of forest charactics in eco-hydrological models is not so crucial ifoal of the model run is to reproduce river dischat the basin outlet. River flow dynamics integrateydrological processes in river catchments, and thuence of a single process is limited. Neverthelhe local water balance inside basins is of primeerest in many model applications and modellingite-specific processes is impossible without a coeproduction of the hydrological processes in forehe European Water Framework Directive (EC, 2000),

or example, claims that water management at thional scale should integrate all relevant hydrologrocesses.Hattermann et al. (2004)mentioned in thiontext the need for multi-criteria evaluation conce

42 M. Wattenbach et al. / Ecological Modelling 187 (2005) 40–59

pointing out that each structural component has to bevalidated separately to make the model behaviour morereliable when these models are used as tools to manageriver catchments and to evaluate the impacts of humanintervention.

The aim of this study was to develop a model ap-proach that considers forest specific eco-hydrologicalcharacteristics in time and space, making it suitable forevaluation purposes by using an appropriate amountof data to be applicable for landscape and catchmentscales. The structure of the model was based on thephilosophy ofLindstrom et al. (1997):

- the model shall be based on a sound scientific foun-dation,

- data demand must be met in typical basins,- the model complexity must be justified by model per-

formance,- the model must be properly validated,- the model must be understandable by users.

Based on these criteria we decided to develop an in-dependent approach instead of using an existing forestgrowth model.

There are a number of forest growth models, whichare able to simulate growth and development of sin-gle trees or forest stands under different environmen-tal condition and on different scales of complexity(Chertov et al., 1999; Badeck et al., 2001; Pretzsch,2001). However, all of them have a forest science fo-cus aiming to simulate environmental impact on forestg g ati nd-s

delso romt m-p g,1 cor-p les tor

a meti ot bef

e aref im-p co-h AI

because it is linked to age and stand density. We as-sume that it is possible to simulate transpiration andinterception based on a realistic LAI development. Inorder to test the applicability of the approach a regionalstudy in the federal State of Brandenburg (Germany)was performed after evaluating the model behaviour atthe point scale on two plots of the LEVEL II forestmonitoring network in Europe (Hormann et al., 2003).

2. Methods and data

2.1. Methods

2.1.1. The eco-hydrological modelThe eco-hydrological model SWIM was used for the

implementation because of its suitability for simulatingthe annual growth of a wide range of crops and naturalvegetation types in Central Europe and their hydro-logical interactions under current and climate changeconditions (Krysanova et al., 1999; Wechsung et al.,1999; Hattermann et al., 2004, 2005). In addition it is aSWAT sibling, which opens the opportunity to use thenew module within this model too. The SWIM modelintegrates hydrology, vegetation, erosion, and nutrientdynamics at the watershed scale and was developedto simulate hydrology and water quality in meso-scaleand large river basins (100–10.000 km2). It follows athree level disaggregating scheme ‘basin – sub – hy-drotope’ and is coupled to the Geographic InformationS calR tiono dW eda im-p

detailbef as-p

them e thep luef ken.

a edr rion

rowth and ecology and, to a lesser extent, lookinnteraction of forests with the environment at the lacape scale.

As a second point, forest models, even the mof lower process resolution, are rather complex f

he catchment modeller’s point of view. As an exale, ForestBGC (Running and Gower, 1991; Runnin994) a model of moderate process resolution inorates 48 parameters and needs 11 initial variabun.

Thus, these models are all failing theLindstrom etl. (1997)requirement that data demand must be

n typical basins, because such data demand cannulfilled in regional applications in most cases.

Therefore, the basic concept of the approach wollowing in this study is an as much as possible slified forest growth description integrated in the eydrological model SWIM. The key variable is the L

ystem GRASS. A hydrotope or HRU (Hydrologiesponse Unit) is defined by its unique combinaf sub-basin, land use and soil type (Krysanova anechsung, 2000). The vegetation growth is comput

t this spatial level with a daily time step using a slified EPIC (Williams et al., 1989) approach.

The implemented approaches are discussed iny Krysanova and Muller-Wohlfeil (1998), Krysanovat al. (1999)andHattermann et al. (2004, 2005). There-

ore, the following explanations will focus on theects relevant for the forest module approach.

As a measure of the model quality in respect ofeasured river discharge at the catchment scalercent deviation (WB) of the mean simulated va

rom the mean observed discharges in the river is taAdditionally the efficiency (EF) as defined byNash

nd Scutcliff (1979)for simulated against measuriver discharge on a daily time step is used as a crite


to analyse the dynamic model performance in terms ofriver discharge.

2.1.2. The forest module approachThe central element of the approach we propose

here is the use of an allometric relation for the ratio ofleaf biomass to total biomass that is given by an age-dependent exponential function (see below) (Burger,1947, 1948; Mitscherlich, 1970; Whittaker and Marks,1975; Bugmann, 1994; King and Grant, 1996; Pret-zsch, 2001). Additionally, we simplified this approachby assuming that the age-dependent relation is inde-pendent of the environmental conditions. An overviewof the parameter set used in the approach is given inTable 1.

To initiate the forest stands of a catchment, any hy-drotope is defined by a uniform forest stand with aninitial total aboveground biomass and age value. Theaboveground biomass is calculated based on inventorydata using the total stand volume to compute the woodybiomass and the DBH (diameter at breast height) to es-timate the leaf biomass (Burger, 1947, 1948; Bugmann,1994; Cannell and Dewar, 1994).

The age-dependent species-specific allometric func-tion (Eq.(1)) is used to estimate the allocation param-eterkfoli , whereka, kb andkc are species specific pa-

rameters andxageis the age of the stand in years:

kfoli = ka e(kbxage) + kc (1)

The parameterska, kb and kc can be estimated fromlong term measurements likeBurger (1947, 1948).

The daily potential maximum LAImax,i for eachstand is calculated based on total aboveground biomassbioi (kg ha−1):

LAI max,i =(

kfafw

kwdr(bioi · kfoli )

)fc (2)

where LAImax,i denotes the maximum LAI at dayi andkfafw is the specific leaf area (m2 kg−1), kwdr indicatesthe ratio of wet to dry biomass (%) and the abovegroundbiomass bioi (kg m−2), fc (0.00005 for oak, 0.00004 forpine) converts all sided leaf area to LAI and ha to m2.

The minimum LAImin,i for Scots pine depends onthe number of needle generations on the tree and is set totwo third of maximum LAImax,i assuming three needlegenerations as an average value in summer (Kallweit,2001) and the loss of one during autumn. For oakLAI min,i is set to zero.

Management actions like thinning or harvest as wellas natural biomass losses like litter fall and mortalityare modelled as a reduction in aboveground biomasswhich forces a change in LAIi by changing LAImax,i.

Table 1Overview of the parameters used in the forest module

P

k

A

S

MP

BOABI

arameter Symbol (unit)

llocation ka (no unit)kb (no unit)kc (no unit)

pecific leaf area kfafw (m2 kg−1)kwdr (%/100)

anagement/mortality Bm (kg ha−1)henology a (◦C)

b (◦C)Tb,st (◦C)Tb,cd (◦C)

ase temperature Tb (◦C)ptimum temperature T0 (◦C)ttenuation coefficient k (no unit)iomass energy ratio BE (kg MJ−1)

nterception f (no unit)Rint,pine (mm)LAI b (m2 m−2)c (no unit)

Value

Scots pine Common oa

1.792 0.1026−0.109 −0.0569

0.0249 0.00596.0 12.00.45 0.35

2500 18001394.5225 3175.68222.7066 579.73

5 5.699 19.440.0 3.0

15.0 20.00.65 0.65

16 16– 2.02 –4 –0.4 0.7


The start of the growing season is simulated by us-ing the empirical phenological model developed byCannell and Smith (1983)in a version modified byMenzel (1997a). For oak, the Germany-specific param-eterisation ofSchaber (2002)was used. The model isbased on the assumption that an increasing number ofchill days in winter reduces the temperature sum thatis required as stimulus in spring. The model postulatesa logarithmic reduction of a critical temperature sum(TTcrit) by chill days (CD) (Eq.(3)):

TTcrit = a + b ln(CD) (3)

wherea undb denotes species specific parameters.The chill days are the number of days starting from

the first of November (i1 = 305) when temperature fallsbelow a threshold (Tb,cd) given as a plant specific pa-rameter (Eq.(4))

CD =imax∑i1

1 if Taver,i ≤ Tb,cdin ◦C (4)

Taver,i indicates the daily average temperature (◦C) andimax denotes the day of growing season start.

After the first of February (i2 = 32), a thermal time iscomputed according to the temperature sum model asby Arnold et al. (1994). The temperature surplus (TT)(Eq. (5)) is computed for days when the temperatureexceeds the base temperature (Tb,st)

TT =imax∑

(Taver,i − Tb,st) if Taver,i ≥ Tb,st in◦C (5)

T sumo

AIa ture( tureT

andp for-m

L

L

LAI i = max

{LAI i−1 −

(1

1 + e(Taver,i−Tb)

); LAI min,i

},

i > 280 (8)

T0 is the species specific optimum growth temperature.Tb denotes the species specific base temper ature (◦C).

The concept of biomass accumulation is based onthe EPIC (Williams et al., 1989) approach. The amountof daily intercepted radiation is calculated using theformula ofMonsi and Saeki (1953)(Eq.(9)):

PARi = α · RADi(1 − e(−k·LAI i)) (9)

where PARi denotes the absorbed photosyntheticallyactive radiation (MJ m−2) RADi is the solar radiation(MJ m−2), k denotes the attenuation coefficient (set to0.65 for all trees) andα stands for the conversion factorfrom global radiation to PARi (0.5).

The daily potential biomass increment is estimatedwith theMonteith (1977)formulation of the radiationuse efficiency (Eq.(10)):

�Bi = BE · PARi (10)

where�Bi symbolizes the potential increase in totalplant biomass (g d−1) and BE is biomass energy ratio(g MJ−1).

The actual biomass increment is adjusted by waterand temperature stress (Eqs.(11)and(12))

�Ba,i = �Bi · y (11)

y

w andy tedat

tiento(a spi-r

W

tem-po as ac for

i2

he growing season starts when the temperaturef TT exceeds the critical temperature sum (TTcrit).

After the start of the growing season the actual Lit dayi increases, driven by daily average temperaTaver,i) and the species specific optimum tempera0 up to its maximum value (LAImax,i) (Eq.(6)).

The LAIi declines, described by a daily averagelant specific minimum growth temperature drivenula, down to its minimum level (LAImin,i) (Eq.(8)):

AI i = min

{LAI i−1 + 0.1 + 1

1 + e(T0−Taver,i);

LAI max,i

}, i < 180 (6)

AI i = LAI max,i, 180< i < 280 (7)

= min{WSi; TSi} (12)

here�Ba,i denotes the actual biomass increaseindicates the plant growth factor (0.0–1.0) calculas the minimum of the stress factor for water (WSi) or

emperature (TSi).The water stress factor is estimated as the quo

f the sum of the actual water in the soil profileWua,z,imm) and the uptake from groundwaterWua,gw,i avail-ble for actual transpiration and the potential tranation (Et,i) (mm) at the dayi:

Si =∑zmax

z=1Wua,z,i + Wua,gw,i

Et,i(13)

The temperature stress factor is estimated foreratures above plant base temperatureTb by a functionf the daily average temperature. It is computedombination of two functions which are different


temperatures higher and lower than the plant optimumtemperatureT0 (◦C). The base temperatureTb is givenas a species specific parameter (Krysanova and Wech-sung, 2000; Neitsch et al., 2001) (Eqs.(14)–(17)):

TSi = e[ln(0.9)(Ctl,i(T0−Taver,i)/(Taver+1×10−6))2],

Taver,i ≤ T0; Taver,i > Tb (14)

Ctl,i = T0 + Tb

T0 − Tb(15)

TSi = e[ln(0.9)((T0−Taver,i)/(Cth,i+1×10−6))2],

Taver,i > T0; Taver,i > Tb (16)

Cth,i = T0 − Taver,i − Tb (17)

2.1.3. Root water uptake and interception ofprecipitation

The simulation of interception is done by a modifiedversion of theMenzel (1997b)approach that was testedfor a broad range of vegetation types (Menzel et al.,1998). The basic advantage is its ability to simulate thedifferent characteristics of deciduous and coniferoustree species with a distinct number of parameters on adaily time step.

For the description of the interception of precipi-tation the maximum storage capacityRint,pot,i (mm) isc ing al

R

w-

t n ofs gt f theL e of0 unkw

a-p ,1 forR st in-c ,

1985; Larcher, 1994; Otto, 1994; Jenssen, 1997):

Rint,pot,i = Rint,pine(14LAI i) (19)

whereRint,pine is the maximum storage capacity (mm).The maximum storage capacity is only filled dur-

ing a rain event with a clearly higher amount of rain.The reason is that an increased saturation of the canopyleads to increase in the part of rain that is only tempo-rally stored and later drops-through or occurs as stemflow. Based on those findings, the model postulates anexponential relation between precipitation and actualstorage (Menzel, 1997b, Hormann et al., 2003) (Eq.(20))

Rint,i = Rint(f ),i + (Rint,pot,i − Rint(f ),i)[1 − e(−cpi)]

(20)

whereRint,i represents the actual storage (mm),Rint(f),iis the storage residue of the day before (mm) andpi de-notes the precipitation (mm) andc represents a speciesspecific parameter.

The species-specific parameterc describes the slopeof the saturation curve. The slope represents the com-plex process of the partitioning of water into through-fall and stem flow in a simplified way. Based onOtto(1994)we set the value ofc at 0.4 for pine and 0.7 foroak respectively. The evaporation of intercepted rainis computed in the same time-step as storage filling,assuming that evaporation of intercepted rain occursduring the day of rain

E

R

E

R

w tion( tedr n-s

b ,1

t dt thec ative

alculated on a daily basis. For Oak we are assumogarithmic relation:

int,pot,i = f [log(1 + LAI i)] (18)

heref denotes a species specific parameter.The species-specific parameterf describes the rela

ionship between increasing LAI and the extensiotorage capacity. The value off is set to 2.0 assuminhat the storage capacity reaches 1.8 at a LAI of 7. IAI i declines to zero in autumn we assume a valu.5 forRint,pot as storage capacity of the leafless trith twigs and branches (Larcher, 1994).A linear relation of LAI development to storage c

acity is assumed for Scots pine (Eq.(19)) (Jenssen997). The values used in the model are 2 mmint,pine at a LAI of four. If the actual LAIi exceed

he value of four the storage capacity continues torease linearly (Rutter et al., 1971; Schroder, 1984

a,i = Ecan,i = Eo,i if Rint(f ),i > Eo,i (21)

int(f ),i = Rint,i − Ecan,i (22)

can,i = Rint,i if Rint(f ),i < Eo,i (23)

int(f ),i = 0 (24)

hereEa,i corresponds to the actual evapotranspiramm),Ecan,i denotes the evaporation of the intercepain (mm) andEo,i symbolizes the potential evapotrapiration (mm).

The potential evapotranspiration (Eo,i) is providedy the relevant SWIM modules (Krysanova and Luik989).

If the potential evapotranspirationEo,i is less thanhe actual canopy storageRint,i, the residue is addeo the storage of the next day. Once the water inanopy storage is removed, the remaining evapor


water demand is partitioned between transpiration andevaporation from the soil (Menzel, 1997b; Neitsch etal., 2001).

The root water uptake is estimated based on theSWAT2000 approach (Neitsch et al., 2001). As a mod-ification for forest, it is postulated that the root dis-tribution is not necessarily correlated with root wateruptake. Thus potential water use for each soil layer canbe equal to the potential transpiration (Eq.(25)). Thisallows compensation of low water supply in the upperlayers by uptake from deeper layers down to the maxi-mum root depth of 2 m (Tolle, 1967; Riek et al., 1994;Adar et al., 1995; Plamboeck et al., 1999).

The potential daily uptakeWup,z,i (mm) for each soillayerz is computed based on the plant available soil wa-ter content AWCz,i (mm), starting from the uppermostlayerz = 1 down to the layer of the maximum root depth.If the total soil water content SWz,i (mm) in one layerfalls below 75% of the plant available water content,the uptake is reduced exponentially. If the actual wateruptake (Wua,z,i) (mm) from one layer is lower than thepotential water uptake (Wup,z,i) (mm) the residue can betaken from the next deeper layer until the atmosphericwater demandEt,i (mm) is satisfied or the total availablewater of the soil profile is used (Eqs.(25)–(29)):

Et,i = Eo,i − Ecan,i (25)

Wup,z,i = Et,i, SWz,i > 0.75 · AWCz,i (26)

W [5(SWz,i/(0.75·AWCz,i))]

S

W

E

w m),Ei

bes en-d f thes

If the sum of available soil water is lower than theplant water demand the model allows the uptake fromgroundwater if the actual groundwater level is withinthe rooting zone of 2 m (Riek et al., 1994).

The ground vegetation and litter layer is only rep-resented as additional interception storage where thesame processes of storage and evaporation take placeas described in Eqs.(21)–(24)for canopy water storage(Menzel, 1997b). The assumed storage capacity for apine forest is 2 mm and for deciduous forest 0.5 mm.

2.2. The area under study – the federal state ofBrandenburg

2.2.1. Geo-morphogenesis and landscapeThe federal state of Brandenburg is situated in the

eastern part of Germany (Fig. 7). It is characterised byits glacial formed landscape. The area can by dividedinto three zones that are unique in landscape and for-mation. The southern part of Brandenburg was formedin the Warthe-stage of the Saale glaciation. It is charac-terised by a strong relief and eastwards passing rangeof ice pushed ridges. Northwards the country is domi-nated by three great Weichsel glaciation runoff valleys.They form a flat boggy landscape that is only partly in-terrupted by plateaus of different genesis (pushed byice, fluviatil or wind borne sand). The area is rich inlakes and is drained by the Havel–Spree river systeminto the Elbe River. Part of this drainage system is theNuthe basin (Fig. 7). It is situated directly at the bordero

rderd nd-s ich-s wnyg kes.

a de-c re-g int ndya

no-c -i stedaQ r-t uthc d

up,z,i = Et,i e ,

Wz,i ≤ 0.75 · AWCz,i (27)

ua,z,i = min{Wup,z,i − Wua,z−1,i; SWz,i − WPz}(28)

ac,i =zmax∑z=1

Wua,z,i, Eac,i ≤ Et,i (29)

hereEt,i symbolizes the potential transpiration (mac,i the actual transpiration (mm), and WPz is the wilt-

ng point (mm).A general problem of the approach that will

olved in the next version of the module is its depency of water uptake on the layer discretisation ooil profile.

f the two mentioned glacial formations.Only small parts of the state at the eastern bo

rain into the Oder river system. Further North the lacape was formed during the latest glaciations (Weel ice age). The dominant surface forms are doround and end moraines incised by valleys and la

Over the whole area of the state, the soils havereasing fertility from the north to the south of theion only interrupted by the organic and fluviatil soils

he great valleys. The terrestrial soils are mostly sand sandy loam.

The pure sandy soils are normally covered by moultures of Scots pine (Pinus sylvestris). It is the domnant species and covers 79% of the state’s forerea only enriched by common oak (Quercus robur and. petraea) with a portion of 4%. Only on more fe

ile soils in the north and on some spots in the soommon beech (Fagus sylvatica) stands occupy aroun


Table 2Total area and land use distribution in the Nuthe basin and the Federal State of Brandenburg

Gauge station Settlement/industry(%)

Arable land(%)

Grassland(%)

Forest(%)

Other surfaces(%)

Area (km2)

Nuthe Babelsberg 5 43 13 36 3 1993Brandenburg Havelberg 7 45 11 36 2 29477

2% of the area. On waterlogged soils black alder (Al-nus glutinosa) and common ash (Fraxinus excelsior)are dominant (Muller-Stoll, 1955; Scholz, 1962; LFE,2000; MLUR and S.f.S. Berlin, 2002). A summary ofthe land use derived from the biotope map (FederalEnvironmental Agency) in the Nuthe basin and in thefederal state is given inTable 2.

2.2.2. ClimateThe climate is characterised by the transition from

sub-oceanic in the north-western part of the country tosub-continental conditions at the south-east. This cli-matic gradient is represented in a decrease of annual av-erage precipitation (500–600 mm) to less than 500 mmand an increase in average temperature and ampli-tude from the west to the east (Muller-Stoll, 1955).As an example the climatic conditions at the stationBerlin/Potsdam are presented inTable 3.

2.2.3. Stand level dataBecause of their fundamental role in environmen-

tal services, as renewable resources and as recreationzones, forests in Europe are the objects of intensivemonitoring. The data used in the context of the modelevaluation are results of the intensively continuouslymonitored so-called LEVEL II forest observation sites(Hormann et al., 2003). From the total number of 860measurement plots that are scattered over the EU andassociated countries two plots in the federal state ofB eo lotsa e them ata

TL

P

AAA

were available. The data used were gathered on mea-surement plots in the federal state of Brandenburg un-der comparable environmental conditions of soil andclimate (Lutzke, 1970, 1991b; Simon, 1984) or takenfrom literature (Lyr et al., 1992; Otto, 1994). Datafor the LEVEL II plots were available for 4 years(1996–1999). To extend the evaluation of the model re-sults, forest science publications, which provided mea-surements under comparable environmental stand con-ditions (Kunstle et al., 1979; Lutzke, 1991a, 1991b) orstandard textbooks (Lyr et al., 1992; Otto, 1994) wereused.

The comparison of forest growth and interception ofprecipitation was done for the LEVEL II plot at Kien-horst (13.59◦E 53.01◦N). The forest is a 91-year-oldScots pine on sandy podzol soil. The climate informa-tion for temperature, precipitation and solar radiationwere gathered from local measurements. The averageannual temperature was 8.3◦C and the average annualprecipitation sum 585 mm. Soil information was takenfrom the profile description of the stand, the initialbiomass and age from the stand description (Kallweit,2001). The model run was then performed for the wholeperiod without calibration of the model parameters.

For further evaluation of the hydrological proper-ties we used the sap flow measurements of 10 Scotspine trees (Luttschwager, 2001) for the years 1998 and1999 of the LEVEL II stand Beerenbusch (12.58◦E,53.08◦N, mean annual precipitation 532 mm, meanannual temperature 9.6◦C) and interception measure-m ta pines de-s tandd ds

2bil-

i rest

randenburg are used in this study (Fig. 7). Becausf the mentioned dominance of Scots pine all pre situated in such stands. Therefore, to evaluatodel behaviour for common oak only literature d

able 3ong term average climate conditions for Potsdam

otsdam (Brandenburg, Germany)

nnual yearly precipitation (mm) 590.5nnual average temperature (◦C) 8.8nnual daily temperature difference (◦C) 8.3

ents of the observation site Kienhorst (Jochheim el., 2001). The Beerenbusch stand is also a Scotstand but younger (62 years). The available standcriptions also provided detailed climate, soil and sata that were used to run the model over a 4 year periotarting in 1996.

.2.4. Spatial dataA regional study to prove the spatial applica

ty and to understand the influence of dynamic fo


growth on the regional hydrology/water balance wasperformed. The forest data were captured from a bian-nual report published by the Federal Forest Agency(LFE – Landesforstanstalt Eberswalde). The reportprovided average ages, DBHs and average stand vol-umes for the dominant tree species Scots pine (Pinussylvestris), common oak (Quercus robur andpetreae)and beech (Fagus sylvatica). The management infor-mation was taken from yield table data (Schober, 1987).Based on this statistical information we distributed theage classes following the given age class distributionsusing the biotope map provided by the Federal Envi-ronmental Agency (Landesumweltamt Brandenburg).The high class resolution of this map was aggregatedto the SWIM land use classes evergreen forest, decid-uous forest and wetland forested. The mixed forestareas were converted based on their portion of ever-green species. Forests with more than 50% coniferswere transferred to evergreen and vice versa for de-ciduous dominated forests. The soil map was providedby the national agency for geo-sciences and resources(Bundesanstalt fur Geowissenschaften und Rohstoffe)with a scale of 1:1.000.000 together with soil pro-file descriptions providing the basic physical char-acteristics like saturated conductivity and bulk den-sity for 72 different soil types. Areas with shallowgroundwater were taken into account when definingthe HRUs by using an interpolated groundwater levelmap.

The digital elevation model was aggregated to a5 liedb es-s

ser-v manw ter-

polated for the individual sub-basins using the inversedistance method.

3. Results and discussion

3.1. Plot scale evaluation

To validate the model on the plot scale, the dataof the 1994 inventory (age and aboveground biomass)of the LEVEL II sites Kienhorst and Beerenbusch(see Section2.2.3) were used for initialisation. Thestandard management/mortality of 2500 kg ha−1 lossin total biomass assumed per year in the modulewas changed to the documented management andmortality (Kallweit, 2001) for the stands. Conse-quently, the annual biomass loss in the SWIM mod-ule was adjusted to 1400 kg ha−1 per year as theloss of twigs, cones and other litter except leaves.In addition, at the plot Beerenbusch 3355 kg ha−1

and at Kienhorst 980 kg ha−1 were removed in1999.

The model-run was performed for the period(1994–1999) using the default parameters for forestgrowth given inTable 1and compared with measure-ments from 1996–1999 (seeTable 4).

3.1.1. The LEVEL II stand Beerenbusch3.1.1.1. Biomass and LAI. The simulated averageannual woody biomass increase for the period of1 thtT easeb

hes 999,

TC ts and

C lot Bee

asured ted

L 1.85W 05L 71

719431

0 m grid resolution based on a 25 m grid suppy the federal land surveying office (Landesvermungsamt Brandenburg).

The climate data as well as phenological obations were gathered from stations of the Gereather service (DWD). The climate data were in

able 4omparison of modelled and measured biomass compartmen

ompartment Year P

Me

AI (autumn) 1998oody biomass increase (kg ha−1) 1998 48

itter (kg ha−1) 1996 171997 201998 211999 22

994–1999 of 4866 kg ha−1 is in good agreement wihe measured rate of 4805 kg ha−1 (Kallweit, 2001).he model slightly overestimates the biomass incry 61 kg ha−1 (1.2%).

The simulated LAI for autumn 1998 (1.85) is tame as the measured value (1.85) (Kallweit, 1

LAI at the LEVEL II plots Beerenbusch and Kienhorst

renbusch Plot Kienhorst

Simulated Measured Simula

1.85 1.7 1.34866 2610 43302198 1571 15342229 1697 15852247 2119 16182265 1882 1649


personal communication). The corresponding simu-lated leaf biomass (5112 kg ha−1) for the same timeis 67 kg ha−1 (1.3%) lower than the calculated value(5179 kg ha−1) using the formula ofBurger (1948)and the DBH of 1999. Thus, the underestimation inleaf biomass may explain the overestimation in woodybiomass increase because the difference is counted aswood in the SWIM model.

The comparison of the simulated LAI with averagemeasured LAI values for the whole period (1996–1999)published byJochheim et al. (2001)for the Beeren-busch plot is in acceptable agreement. The 4 year aver-age LAI simulated by the model is 2.2 and 2.6 for thewhole year average and for the vegetation season (Maysprout – litter fall), respectively. The highest simulatedLAI of 2.7 is in the range of the uncertainty of mea-sured values of 2.6–2.9. Where as the lower measuredvalue was determined by single measurement 1999 us-ing a LI-COR LAI 2000 the higher value was based onlitter measurements (average 1996–1999) (Jochheimet al., 2001). Because of the lack of continuous sea-sonal measurements of LAI on the LEVEL II plots thecomparison of the temporal dynamic was done in thecatchment study (see Section3.2).

In addition, they simulated the LAI (Jochheim et al.,2001) for the same plot using the forest model Forest-BGC (Running and Gower, 1991; Running, 1994).They found a 4-year average LAI of 2.9. A compar-ison with the 4C model (Bugmann et al., 1997) at thesame plot shows a higher value with 3.7 as average overt

,w andl ultst wasc hev alueg fora ivenba riodi them .5 iso hes s aret entsa lts,w av-

erage of 2235 kg ha−1 versus measured 2067 kg ha−1

(Table 4).

3.1.1.2. Transpiration. The comparison of the dailyvalues for sap flow measurements during the vegetationperiod at the Beerenbusch plot (22 June 1998–11 Oc-tober 1998) with the tree transpiration simulations forthe same period shows a good agreement (r = 0.81) (seeFig. 1a–c). However,Fig. 1demonstrates that the modelslightly but systematically calculates higher transpira-tion rates. The analysis of the residuals (Fig. 1c) showsno non-linear relationship but acknowledges the over-estimation especially of values close to the mean sim-ulated values.

The agreement of the simulated transpiration withthe observations of the sap flow rate for the period 27March 1999 until 15 November 1999 (Fig. 2a–c) is notas good as in 1998. The correlation is onlyr = 0.65. Themodel also overestimates the transpiration especially inthe spring period from the start of measurements on the27th of March until the 17th June. The analysis of theresiduals (Fig. 2c) shows no trend indicating no needfor major model refinement.

The greatest difference between the two measuredtime series for 1999 and 1998, and a possible explana-tion for the lower model performance in 1999, is thedifferent correlation of the transpiration measurementsto solar radiation. The transpiration measurements of1998 are highly correlated (r = 0.89) with the solar ra-d pira-t n ofr spi-r u-l ofr ureds

sim-u weenm ion isi untilt ns.T ofm easeow esentt uneu rela-

he total period.The values given byKallweit (2001)for leaf areas

hich are based on estimation tables for dry matteritter measurements, confirm the SWIM model resoo. The all sided leaf area given by the authorsonverted into the one sided LAI by dividing by 3. Talue 3 was chosen based on the one-sided LAI viven by Kallweit (1999, personal communication)utumn 1998 of 1.8 and the double-sided values gy him for the same year of 5.5 (Kallweit, 2001). Theverage of the annual one-sided LAI for this pe

s then 2.1 in comparison to 2.2 as calculated byodel. The observed average summer value of 2nly slightly lower than the simulated LAI of 2.6. Timulated and measured winter average LAI valuehe same (1.8). The needle litter mass measuremre in good agreement with the SWIM model resuhich are slightly higher with an annual 4-year

iation. As opposed to this, the measured transion series for the year 1999 has a lower correlatio= 0.71. This indicates a possible restriction in tranation capability, which is not considered in the simation. This is underlined by the higher correlation= 0.81 between simulated transpiration and measolar radiation for 1999.

The period of the highest divergence betweenlated and measured transpiration as well as beteasured solar radiation and measured transpirat

n spring, from the beginning of the measurementshe 17th of June, with normally low water restrictiohe following very strong jump in the time serieseasured transpiration was explained by the incrf LAI during May sprout (Luttschwager, 2001). Thus,e have to assume that the model is not able to repr

his effect in an accurate way. After the 17th of Jntil the end of the measurement period the cor


Fig. 1. (a) Correlation of simulated and measured transpiration, (b) the daily simulated values for transpiration (black line) in comparison withmeasured data (grey line), (c) comparison of the residuals with predicted values for the year 1998 (LEVEL II Beerenbusch).

Fig. 2. (a) Correlation of simulated and measured transpiration, (b) the daily simulated values for transpiration (black line) in comparison withmeasured data (grey line), (c) comparison of the residuals with predicted values for the year 1999 (LEVEL II Beerenbusch).


tion of simulated and measured transpiration is higher(r = 0.73) than for the total period (r = 0.65).

An additional comparison of cumulative annual val-ues for soil water content provided by LFE with valuessimulated by the SWIM model shows a mean under-estimation of 20% at 30 cm depth and 15% at 70 cmdepth for the period 1997–1999. This may indicate aproblem in reproduction of the depth allocation of thewater uptake. Nevertheless, there is still a high degreeof uncertainty because of the simple multilayer stor-age approach used in the SWIM model. The maximumsimulated water content reaches field capacity whereasthe TDR probe values can exceed this value or fallbelow during frost periods (Jochheim et al., 2001). Amore comprehensive evaluation will be subject of fur-ther model studies.

3.1.2. The LEVEL II stand Kienhorst3.1.2.1. Biomass and LAI. The comparison ofthe woody biomass increment at Kienhorst gives2610 kg ha−1 (Kallweit, 2001) measured and4330 kg ha−1 simulated by the model. The modelconsequently considerably overestimates the woodbiomass increase by 66%. This large difference is a re-sult of a low validity of the allocation parameterisationfor the specific stand resulting in an underestimationof leaf biomass. The model underestimates the averagesummer leaf biomass given byKallweit (2001) of6908 kg ha−1 average per year by 2118 kg ha−1,w assi ated

using the formula ofBurger (1948)and based onthe DBH of 1999 identified the reason. The formulayields 3874 kg ha−1 in comparison to 4947 kg ha−1

calculated by the SWIM model. Both are lower thanthe values given byKallweit (2001)also indicating alower validity of the module parameterisation for thisstand.

A comparison of litter measurements and simulatedvalues given inTable 5shows a good agreement, ex-cept for 1998, but also underlines the above-mentionedeffect of a systematic underestimation of leaf biomass.Nevertheless, we can conclude that the model repro-duces the annual variability of leaf biomass.

The underestimation in needle litter and biomasscorresponds with an underestimation of LAI. Themodel underestimates the measured LAI (1.7) for au-tumn 1998 by 0.4 (23%).

An evaluation of the LAI simulation using the re-sults ofJochheim et al. (2001)confirmed the underes-timation of LAI by the SWIM model. The comparisonfor Kienhorst gives a SWIM simulated mean LAI of1.6 over all years and yields 1.9 for the vegetation sea-son with a maximum of 2.0 in relation to 2.3–2.6 givenby the authors as measured values (the lower value wasmeasured by LI-COR LAI 2000 the higher one resultsfrom litter measurements) (Jochheim et al., 2001).

However, the comparison of the SWIM model per-formance in respect of the simulation of the meanannual LAI with the forest model FOREST-BGC(Running and Gower, 1991; Running, 1994) used byJ er

TC s of dif

V dified

L)

)A

)

A) )

T mentaiomas

hich is in good agreement with the error in biomncrease. A comparison of the leaf biomass calcul

able 5omparison of modelled and measured data of annual biomas

ariable SWIM original SWIM mo

eaf biomass (t/ha)Scots pine 1.9 (±0.7) 4.8 (±0.8)

Oak 2.3 (±0.1) 2.5 (±0.02nnual woody biomass increase (t/ha)Scots pine 2.2 (±0.7)a 3.6 (±1.6)a

Oak 2.9 (±1.0)a 3.7 (±1.3)a

nnual total biomass (t/ha)Scots pine 4.1 (±1.3) 10.1 (±1.6

Oak 5.3 (±1.0) 6.2 (±1.3)

he measurements where captured under comparable environa Not modelled – calculated difference of leaf and aboveground b

ochheim et al. (2001)at the same stand, shows a low

ferent tree compartments

Data from literature Source

4.2 Kunstle et al. (19794.6 Simon (1984)2.6 Simon (1984)



l conditions (values in brackets are deviation from mean).s.


Fig. 3. Simulated value weekly intercepted rainfall aggregated from daily simulated values (black line) in comparison with measured data for1996–1998 (LEVEL II Kienhorst).

divergence (SWIM: 0.3–0.6 versus 0.6–0.9). In con-trast, the FOREST-BGC model overestimates the LAI(3.3), where the 4C model (Bugmann et al., 1997) is inthe range of the measurements by simulating a LAI of2.3 as average over the total period.

The explanation for the systematic error of theSWIM model is as mentioned before, a result of thelow validity of the allocation parameterisation for thatspecific stand. That is reasonable because the param-eterisation was done for an average stand. Thus, thedeviation is more a measure for the variation of theKienhorst plot from that average than for the quality ofthe parameterisation.

3.1.2.2. Interception of precipitation. The intercep-tion values for the Scots pine stand at Kienhorst weresimulated for 3 years (1996–1998) with a daily timestep and then summarized on a weekly and annualbase to compare them with the measured data. Theweekly aggregated simulation results show a goodagreement with the measurements during the vegeta-tion period (Fig. 3 a–c). Nevertheless, there are very

strong discrepancies during early spring and late au-tumn.

The reasons are unclear. One reason might be thewind speed, which is not explicitly considered in theactual model version. In this matter,Crockford andRichardson (1999)mentioned the uncertainties in theinterception measurement methods when consideringthe position of the rain gauge in relation to the canopyand the influence of wind. They mention that an es-timate of zero for interception and an overestimationof rainfall by 2.4 mm only would already lead to anoverestimation of interception by 100%.

3.2. The catchment case study

To evaluate the behaviour of the hydrological com-ponents at the catchment scale the classical approach ofcalibrating the model using the runoff at the basin outletof the Nuthe river was applied (Fig. 7). The Nuthe basinincorporates the typical landscape elements of Bran-denburg (Table 2) and shows the typical behaviour withlow summer discharge and high runoff peaks in winter.


The runoff is mostly fed by groundwater (Hattermannet al., 2004).

Table 5 provides a compilation of literature data(source see Section2.2.3) of annual biomass increaseof different tree components. They are compared witha 10-year average over the appropriate age class simu-lated by the modified model and simulated values forthe land use classes evergreen and deciduous forest ofthe SWIM base version in the Nuthe basin. The resultsof the modified model are of the same magnitude as themeasured data. The differences between measurementsand model results in this case may be mainly inducedby the lack of detailed information of the stand condi-tions because an average over the local conditions inthe Nuthe basin had to be used in the modelled values.Nevertheless, they are a clear improvement in compar-ison to the results of the model base version.

A compilation of literature data (Section2.2.3) forthe different components of the water cycle are givenin Table 6.

The observations agree with the modelled valuesdemonstrating the improvement in respect of the for-mer model approach for the land use classes evergreenand deciduous forest. The model results are again 10-year averages of the Nuthe basin using appropriate ageclass averages for comparison.

As an additional regional measure of model qualityin respect of forest simulation as well as to evaluate

the seasonal dynamic, we used phenology data from63 stations around and within the basin.

The results calculated for the day of the year (DOY)of leaf unfolding of common oak (Fig. 4) are again inacceptable agreement with the observations, demon-strating the ability of the model to represent the annualvariability. The comparison with the simple tempera-ture sum approach used in the former model versionshows the significant improvement.

For Scots pine (Fig. 5) the model systematically un-derestimated the date of May sprout but demonstrateda acceptable representation of the inter-annual variabil-ity.

The results for the simulation of Scots pine phenol-ogy are based on a parameterisation ofMenzel (1997a)where data observed in phenological gardens were usedfor parameterisation. Unfortunately, no parameterisa-tion point was located close enough to the area understudy to represent the local conditions. Thus, the possi-ble source for the underestimation in the simulation ofMay sprout is the non-adapted model parameterisation.This is underlined by the good model performance forthe calculation of leaf unfolding of common oak basedon the parameterisation ofSchaber (2002), who vali-dated the parameters in the study area Brandenburg.

The results for the simulation of river discharge(Fig. 6) are in acceptable agreement with the mea-sured runoff at the Nuthe basin outlet (gauge Babels-

TS litera rackets ared

V D

A83868886–

T473935

I36 )422812–10

besidLutzk

able 6imulated hydrological components in comparison to data fromeviation from mean)

ariable SWIM original SWIM modified

ctual evapotranspiration (%)Scots pinea 39 (±5) 77 (±13)

Oak 52 (±10) 67 (±7)

ranspiration (%)Scots pinea 45 (±3)

Pinus nigranterception (%)

Scots pinea 25 (±3)

Oak 13 (±1.4)

a No separation of ground vegetation and tree by the authors

ture, data given in percent of annual precipitation (values in b

ata from literature Source

Kunstle et al. (1979)Lutzke (1991a)Lutzke (1970)andLyr et al. (1992)

99 Lutzke (1970)

Kunstle et al. (1979)Lyr et al. (1992)Rutter et al. (1975)

Kunstle et al. (1979)andLyr et al. (1992Gash (1979)Lutzke (1991b)

44 Otto (1994)–30 Otto (1994)

ee (1991b).


Fig. 4. Comparison of calculated values (triangles) for the day of the year (DOY) of may sprout of Scots pine (Pinus sylvestris) with means ofmeasured data (stars) of the German weather service (lines are the standard deviation) and modelled data (squares) of the simple temperaturesum approach in the original SWIM version.

berg, EF = 0.54, WB =−5). The results are in the rangeof comparable studies in lowland catchments (Fohreret al., 2001; Hattermann et al., 2005). The high levelof human interference (water management like im-

plementation of drainage systems and weir plants)in this catchment makes it difficult to reproduce therunoff better without detailed information about theseactions.

Fig. 5. Comparison of calculated values (triangles) for the day of the year (DOY) of leaf unfolding of common oak (Quercus robur) with meansof measured data (stars) of the German weather service (lines are the standard deviation) and modelled data (squares) of the simple temperaturesum approach in the original SWIM version.


Fig. 6. Comparison of observed (grey) and simulated discharge (black) at the Nuthe basin outlet gauge Babelsberg.

After calibrating the model a sensitivity study wasdone to demonstrate the necessity of using forestgrowth measurements as an additional evaluation cri-terion to validate the model: the day of the year for leafunfolding and May sprout was set to a date 6 days later,10 days earlier and 44 days earlier as calculated by theSWIM model. The value 10 days earlier was chosenbased on the findings ofMyneni et al. (1997), Menzeland Fabian (1999)andSchaber (2002)who observed aprolongation of the vegetation season in these ranges.The shortening of the vegetation season by 6 days wasdone to test the reaction of the model. The change of44 days earlier is in the error range of the base versionof SWIM. The results demonstrate the influence of thephenology on the 10 year modelled average water bal-ance which is changed by 2% (6 days later),−1% (10days earlier) and−5% (44 days earlier) respectively.The simple temperature sum approach used by the base

version of the SWIM model leads to a systematic un-derestimation of runoff.

The sensitivity study shows the influence of phe-nology on the landscape water balance, which was ne-glected by the former approach. Considering the find-ings ofMyneni et al. (1997), Menzel and Fabian (1999)andSchaber (2002), which illustrate an extension of thevegetation season in Europe by 6–10 days as a responseto climate change, it seems to be reasonable to assumea real world effect on the water balance that should beinvestigated (Table 7).

3.3. Regional case study

The goal of the regional case study in which the totalfederal state area was modelled was to test the applica-bility of this approach on this scale and to evaluate it us-ing the long term relative water balance (mm m−2 y−1),

Table 7Comparison of simulated and measured 10 year water balance for the federal state of Brandenburg

Spree–Havel river system (1981–1991),gauge Havelberg

SWIM simulated (1981–1991),total area Brandenburg

Mean precipitation (mm a−1) 608 608Mean discharge (mm a−1) 140 145Mean evapotranspiration (mm a−1) 468a 473

a Difference of precipitation and discharge.


Fig. 7. The federal State of Brandenburg, the Nuthe basin (dark grey), The Spree–Havel-basin (light grey) and the gauge stations (grey triangles)as well climate and precipitation stations (black triangles and black squares, respectively) and the LEVEL II plots (dots). The natural landscapeunits are according toScholz (1962). Potsdam is the closest climate station to the gauge Babelsberg at the outlet of the Nuthe basin.

which can be obtained from the official hydrologi-cal yearbook (LUA, 1991). The observed values givenin Table 6are calculated for the basin outlet of theSpree–Havel-river system, which drains most of thestate area at the gauge station Havelberg (Fig. 7). Thecomparison with the simulated values for the same pe-riod shows only slight differences in all components ofthe water balance indicating a good reproduction of thelong-term conditions.

4. Conclusions and summary

The forest module has proven to be able to simulateScots pine and partly common oak growth and hydro-logical properties as well as discharge at the basin out-let and the landscape water balance within the SWIMmodel framework. A reasonable description of forest

growth (LAI and phenology) and forest related hydro-logical processes (interception, transpiration, and rootwater uptake) is crucial in catchment modelling, be-cause changes in land use as well as climate changeswill also change the forest distribution and tree com-position. This will affect the regional water balance(Baumler and Zech, 1999; Crockford and Richardson,1999; Finch, 2001; Fohrer et al., 2001; Engel et al.,2002) and has to be considered in relevant model appli-cations. Another advantage of the approach describedis the possibility of a multi-criteria model evaluation incases where forest data are available. The European-wide available LEVEL II network is a good base forevaluation as shown in this study. The overall perfor-mance of the forest extended SWIM model is satis-factory as shown in the results section. In addition, themodel agrees with the criteria ofLindstrom et al. (1997)as mentioned in Section1:


- The model shall be based on a sound scientific foun-dation.

The approaches that are used in the forest mod-ule are approved methods that are widely validatedand it was possible to use and evaluate them underBrandenburg conditions too. It was possible to re-produce the biomass on LEVEL II stands in Bran-denburg in a sophisticated manner. The computationof intercepted rain was good in respect of the sum-mer season. Transpiration was modelled for the year1998 with a good agreement with measured data aswell as with acceptable results for the year 1999.

- Data demand must be met in typical basins.The parameters (Table 2) for the core function of

biomass allocation and initial data for biomass andage can be obtained from long-term measurementslike in Burger (1947), Burger (1948). For applica-tions in other regions,Bugmann (1994)gives a com-pilation of parameters to calculate leaf biomass basedon DBH. Forest inventory data incorporating DBHand standing volume are available in most Europeancountries. If there is no detailed spatial informationfor age class distribution the use of local statisticsand land use maps is possible as shown in the re-gional study for the federal state of Brandenburg. Forthe calculation of the phenological events,Menzel(1997a)andSchaber (2002)published the parame-ters of theCannell and Smith (1983)model for anumber of tree species for Europe and Germany, re-spectively.

- del

si-latees.d one the

-areutredif-

-sicaly toions

and other tree species. This paper gives an overviewof the approaches used and enables the reader to ap-ply it if needed.

There are some open points related to the module’sbehaviour that cannot be answered in this paper andwill be object of further investigations.

The discrepancies between the simulated intercep-tion and the measurements in winter and early springare not fully understood as well as the problems in mod-elling transpiration in 1999. In addition, long-term eval-uations in terms/scales of a tree’s life time are missing.In the case of common oak, an extensive evaluation likefor Scots pine is needed. In addition, a wider evaluationof the root water uptake on soil water measurements isnecessary.

Because of the strong similarities between theSWIM and the SWAT model (Hattermann et al., 2004),the module should be applicable within the SWATmodel too.

Acknowledgements

The authors like to thank the Volkswagen founda-tion who financed the “MESSAGE” Project of whichthis work was part. Special thanks for supporting thework over the last years to all the colleagues at Pots-dam Institute for Climate Impact Research especiallyto the developer team of the 4C model especially Pe-t o tot ber-s idedt m-p

R

A Uti-nti-ater

HS

A AT,rvice,X.

B ,001.

par-

The model complexity must be justified by moperformance.

The model uses a simplified but phycally/physiologically based approach to calcuforest growth and the related hydrological fluxDue to this approach, the model can be appliethe regional scale, the scale relevant to calculatwater and nutrient balance of river basins.The model must be properly validated.

The evaluations shown in the current papera first step in the validation of the model bwith promising results. Additional applications aneeded to investigate the model performance inferent regions and for other tree species.The model must be understandable by users.

The parameters used in the module have a phyor plant-physiological background making it easadapt the calculations to specific regional condit

ra Lasch and Felicitas Succow. Many thanks alshe colleagues of the LFE (Landesforstanstalt Ewalde). Special thanks to Marc Zebisch who provhe groundwater level map and Ylva Hauf for the coilation ofFig. 7.

eferences

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a simplified approach to implement forest eco-hydrological properties in regional hydrological...

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