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GMET-4323; No. of Pages 9

Agricultural and Forest Meteorology xxx (2010) xxx–xxx

Contents lists available at ScienceDirect

Agricultural and Forest Meteorology

journa l homepage: www.e lsev ier .com/ locate /agr formet

umerical assessment of the effect of forest structure changes on CO2 fluxootprints for the flux tower in Solling, Germany

. Sogacheva,∗, O. Panferovb, B. Ahrendsc,d, C. Doeringc, H.E. Jørgensena

Wind Energy Division, Risø National Laboratory for Sustainable Energy, Technical University of Denmark, Roskilde, DenmarkBüsgen Institute – Department of Bioclimatology, University of Göttingen, Göttingen, GermanyBüsgen Institute – Department of Soil Science of Temperate and Boreal Ecosystems, University of Göttingen, Göttingen, GermanyNorthwest German Forest Research Station, Göttingen, Germany

r t i c l e i n f o

rticle history:eceived 31 March 2010eceived in revised form 18 October 2010ccepted 25 October 2010

eywords:omplex terrain

a b s t r a c t

There are many natural and anthropogenic reasons why a gap can occur inside the forest. When a gapappears within a studied stand (e.g. near a flux tower which operated for some time, providing infor-mation about the ecosystem–atmosphere exchange), an assessment of new measurement conditionsshould be carried out. Using a three-dimensional approach for footprint estimation based on numericalsolution of Reynolds-Averaged Navier–Stokes (RANS) equations, we investigated possible changes in airflow and CO2 flux footprints resulting from two suggested forest management activities – clear-cut and

orest managementlux footprintlux measurementslosure model

stripe-cut – around the flux tower located in 130-year-old spruce forest in the Solling highland, Germany.The model results show that degree of changes in flux footprints depends on the chosen managementstrategy. The clear-cut strategy produces the largest changes and the stripe-cut leads to weaker changesof investigated characteristics. The role of remote canopy sources increases, while the contribution ofremote soil sources decreases with increased share of removed trees. In general, the investigated charac-

y for
teristics change differentltopography.
. Introduction

Interpreting measurements of the ecosystem–atmospherexchange rate in complex terrain has been a major stimulus to theevelopment of so-called footprint theory (Finnigan, 2004). Quan-itatively, the relation between a source strength Q and the valuef a signal F registered at the location r is described by a “sourceeight function” or “footprint” f (Schuepp et al., 1990; Wilson and

waters, 1991; Horst and Weil, 1992, 1994; see also Pasquill andmith, 1983):

(r) =∫

�Q (r + r′)f (r, r′)dr′, (1)

here r′ is the separation between the point measurement andurface forcing (source location) and � is the integration domain.

Please cite this article in press as: Sogachev, A., et al., Numerical assessmethe flux tower in Solling, Germany. Agric. Forest Meteorol. (2010), doi:10.1

n the words of Schmid (2002), the footprint defines the “field ofiew” of the flux/concentration sensor and reflects the influencef the surface on the measured turbulent flux (or concentration).etailed reviews of the footprint concept and footprint models are

∗ Corresponding author at: Risø National Laboratory for Sustainable Energy, Tech-ical University of Denmark, Frederiksborgvej 399, Building 118, P.O. Box 49, 4000oskilde, Denmark. Tel.: +45 4677 5015; fax: +45 4677 5083.

E-mail address: [email protected] (A. Sogachev).

168-1923/$ – see front matter © 2010 Elsevier B.V. All rights reserved.oi:10.1016/j.agrformet.2010.10.010

summer and winter due to the combined effects of phenology and upwind

© 2010 Elsevier B.V. All rights reserved.

given in Schmid (2002) and Vesala et al. (2008). Once the footprint isproperly estimated for some point (i.e. receptor or sensor location),the accuracy of signal interpretation depends on the accuracy ofmeasured data only. A problem arises when the conditions aroundthe tower are changed due to some distortion, e.g. windthrow ormanagement activity (cuttings and planting). In this case the airflow and consequently the footprint function are changed and itis very difficult to establish any relationships between previouslymeasured and newly established ecosystem–atmosphere exchangerates. Thus, a new estimate of the footprint is immediately required.

This problem is not trivial and is important for long-termmonitoring programs such as FluxNet (Baldocchi et al., 2001)and EuroFlux (Valentini et al., 2000). At some flux sites the forestsurrounding the measuring tower is getting old and/or affectedby insects and strong winds, which results in distortion of theforest structure. For example, large clearings appeared around theFluxNet site Waldstein/Weidenbrunnen, Germany (DE-Bay) dueto the storm “Kyrill” on January, 18th 2008; these prompted anurgent footprint analysis for the site to estimate new metrics forthe spatial quality of measurements (Siebicke, 2008). Footprint

nt of the effect of forest structure changes on CO2 flux footprints for016/j.agrformet.2010.10.010

analysis for the undisturbed situation has previously been done(e.g. Göckede et al., 2004, 2006). Besides natural factors, regular for-est management activities (cutting or planting of trees) change theforest structure. In such cases it is advisable to quantify beforehandwhat effect the management would have on measurements. The

dx.doi.org/10.1016/j.agrformet.2010.10.010


http://www.sciencedirect.com/science/journal/01681923

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IN PRESSG

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2 Forest Meteorology xxx (2010) xxx–xxx

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Fig. 1. The topography of the study area. Isohypses values are given starting fromthe lowest point in the area (440 m AMSL). The distribution of LAI in three grada-

ARTICLEModel


A. Sogachev et al. / Agricultural and

nalysis can help to understand what exactly would be measuredy tower sensors and to decide whether the flux site should beaintained after the implementation of management. A situationhich attracts attention for the forestry management is now

bserved at the flux tower in Solling, Germany. The spruce forestround the tower is about 130 years old and the trees there exhibitellowing of their needles, with the progression of such a situationapidly increasing the risk of wind damage—some damagingvents have already taken place, which according to Panferov andogachev (2008) can result in even greater damage in the followingears. Several methods of management activities were suggestedy the Northwest German Forest Research Centre to deal with theituation (Meesenburg, personal communication). The two mostdvisable methods are the (1) stripe-cut (German—‘Saumschlag’)nd (2) clear-cut of the problem area.

The goal of the present study is to carry out a numerical exper-ment with the site in Solling to evaluate how the two suggested

anagement strategies would affect the “field of view” of the mea-uring tower. As the parts of the forest surrounding the tower areeech stands the changes will have different effects in summer andinter, i.e. during foliated and leafless states of deciduous stands.n additional complication is the irregular topography of the site.e apply an approach for the estimation of the footprint function

ased on numerical solution of Reynolds-Averaged Navier–StokesRANS) equations, as it is the only one capable of taking into accounthese complexities. To stress the differences between managementctivities, footprint analyses are implemented only for the maineasons: summer (full foliage of deciduous species) and winterleafless deciduous species), using the most typical wind direc-ions for those periods. To simplify the problem, we consider theeutrally stratified atmosphere only.

. Measurement site

The site is located on an even plateau (51◦46′N, 9◦35′E, 500 mbove mean sea level (AMSL)) in the Solling highland in centralermany, 80 km south-west of Hannover. A 50 m tower with stan-ard micrometeorological equipment and eddy-covariance systemas erected inside a spruce (Picea abies) stand in 1989. The standas chosen as the research site for International Biological Pro-

ram (IBP) in 1966—so-called F1 stand (Fichte 1, for GermanFichte”—spruce) (Ellenberg et al., 1986). The stand was plantedn 1888 using 4-year-old seedlings. At present the canopy heightaries between 29 and 31 m and the estimated leaf area index,AI, is 7.6 ± 2.4 m2 m−2 (Constantin et al., 1998). The sparse under-tory is Vaccinium myrtillus with LAI less than 0.5. To take intoccount the influence of the surrounding conditions on the mea-urements above this stand an area of 2 km × 2 km was chosen forodelling of wind, turbulence and CO2 fluxes. Fig. 1 shows the

istribution of vegetation around the tower and the site topog-aphy. The time-consuming three-dimensional calculations of theootprint function were carried out for a limited region which isndicated in Fig. 1 by a dashed line. All inflow and border condi-ions were provided from modelling on a large domain. The windose averaged for the period 1990–2000 (Fig. 1 inset) shows that theominant wind direction just above the spruce canopy (zm = 39 m)oth in summer and in winter has a definite westerly component.owever in summer a north-west wind prevails, while in winterost winds come from south-west. The digital forest inventoryaps from the Forest Management Service of Lower Saxony, con-


aining the information about tree species composition, diametert breast height, number of stems and tree height, are employedo describe the stand structure in the modelled region on a regular5 m × 25 m grid. LAI values for the forest were estimated with andapted version (Ahrends et al., 2010a) of the model from Law et al.

tions 0–3, 3–6 and 6–9 (m2 m−2) is shown by different shading intensities. Triangleindicates the flux tower location. The heavy dashed line encloses the area for foot-print estimation. The inset displays summer and winter wind roses for the toweraveraged over the period 1990–2000.

(2001). The litterfall parameters were taken from Ahrends et al.(2010b). The resulting LAI distribution within the small study areaaround the tower is shown in Figs. 2 and 3 for summer and winter,respectively.

As mentioned in Section 1, two management methods weresuggested for the F1 spruce forest: the stripe-cut and a clear-cut (Meesenburg, personal communication). Figs. 4–7 respectivelyshow the resulting distribution of LAI and flux footprint func-tions estimated in summer and winter for both methods. The firstmethod, stripe-cut, is the cutting of regular stripes having widthsapproximately equal to mean tree height and oriented perpendic-ular to the main wind direction. The method is used to provide asheltering effect for younger trees. In the present study the stripesof 25 m width were simulated in the model. Because the orientationof stripes in the stripe-cut method is chosen to be perpendicularto the prevailing winds, it was set as north-south in the simula-tions here, i.e. perpendicular to westerly winds (Fig. 1 inset). Notethat the tower (x, y) = (0, 0) falls within a cut stripe (Figs. 4 and 5).For safety reasons the Forestry Management Service does not con-sider any strip-cutting parallel to the prevailing wind direction. Thesecond and simplest management method, where the F1 stand iscompletely cut except for the existing understorey with a very lowLAI, resulted in almost zero LAI around the tower (Figs. 6 and 7).All suggested cuttings would be performed mostly upwind of theF1-tower, which will cause a significant distortion in the air flowaround the tower—and consequently changes of registered signalin the future. This would complicate the interpretation of measureddata and the comparison of old and new datasets.

3. Method

3.1. Footprint modelling


Several approaches for the estimation of the footprint functionthat have been developed over the previous decades can be classi-fied into four categories:


ARTICLE IN PRESSG Model

AGMET-4323; No. of Pages 9

A. Sogachev et al. / Agricultural and Forest Meteorology xxx (2010) xxx–xxx 3

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ig. 2. Calculations for summer (full foliage): wind vector plots and vertical wind sodel domain used for footprint estimation at heights of 10 m (a) and of 30 m abo

egetation canopies (d) for the sensor point (x, y, z) = (0, 0, zm = 39 m) denoted by trianhe contribution of the model domain to the registered signal. The distribution of L

analytical models;Lagrangian stochastic (LS) particle dispersion models;large-eddy simulation (LES); andensemble-averaged (RANS) closure models.

Each of them includes models with different levels of complexitynd numerical demands. Advantages and disadvantages of existingodels are given in details in the recent review on this subject (e.g.esala et al., 2008).

Thorough inspection of the different types of models showshat not all of them are suitable for the goal of this study. Annalytical approach implying horizontally homogeneous flow con-itions ignores horizontal diffusion and the lateral component ofhe flow and, therefore, is not applicable in the present study. Ofhe two existing types of LS footprint models, forward (pre-definedources) and backward (a pre-defined sensor location), only theatter can be efficiently employed for horizontally inhomogeneousonditions. However, in our case the transported particles are CO2olecules and therefore the question arises: what fraction of par-

icles tracked backwards in time are absorbed by the vegetationanopy, and where? Lacking this information the implementationf the backward LS approach for canopy footprint estimation is notdvisable; to our knowledge no such attempts are found in thecientific literature. Moreover, the LS models need a pre-definedind field usually obtained from RANS or LES models, which means


ather high computing costs for three-dimensional calculations.p to now there had been no real attempts to estimate footprintssing the data obtained by an LES model itself, mainly due to highomputational costs. The approach is listed in a separate categorybove only because LES provides turbulent statistics for LS models.

(denoted by contours: solid for positive and dashed for negative values) (m s ) inund (b), and flux footprint functions for sources located on ground (c) and inside

he values of footprints are given per grid cell area. The percentage numbers indicatem−2) used in calculations is given in the same way as in Fig. 1.

RANS models can also be used as a basis for statistical approaches,but at present there is also a methodology to estimate footprintsusing the RANS model solution itself. The method proposed bySogachev et al. (2002) and Sogachev and Lloyd (2004) (actu-ally applicable to LES models also) has been designed and testedusing the computationally economical RANS model SCADIS (seeSection 3.2).

The RANS footprint method is based on the estimation of thecontribution from each model cell to a vertical flux at the pointof interest, e.g. sensor location. This is carried out by means ofa comparative analysis of vertical flux fields that are formed bythe sources consecutively activated within the model domain. Thesuperposition of all such fields is normalized then, and thus, thecontribution of each cell to the flux at the point of interest – anormalized contribution function – is obtained. The normalizedcontribution function estimated by the model is effectively equiva-lent to the strict definition of footprint only in the case of horizontalhomogeneity of sources (in our case it is only for soil sources). How-ever, it should be noted, that independent of estimation methodthe real footprint for canopy sources can be derived only in a caseof horizontally homogeneous vegetation and flat terrain (for dis-cussion see Sogachev et al., 2005). Thus for simplicity we referto the contribution function of canopy sources as the footprint inpresent study. For further details, verification of the method andits restrictions (see Sogachev and Lloyd, 2004). In the present work


the footprints at the height of sensor were estimated separately forsoil, f0, and canopy sources/sinks, fc. Knowing these footprints andthe strengths of both canopy sinks, Sc, and soil sources, S0, it is pos-sible to obtain the contribution function for a net flux at the pointof interest fn(x, y) according to the expression (Markkanen et al.,




4 A. Sogachev et al. / Agricultural and Forest Meteorology xxx (2010) xxx–xxx

n Fig.

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003; Sogachev and Lloyd, 2004):

n(x, y) = S0f0(x, y) + Scfc(x, y)S0 + Sc

. (2)

.2. SCADIS model

The atmospheric boundary layer (ABL) model SCADIS is basedn a so-called one-and-a-half order closure scheme. It was ini-ially described in Sogachev et al. (2002). The model providesdequate descriptions of flows over ecosystems having wide rangesf vegetation architectures and species compositions, without anydditional place- or canopy-specific modifications. It is capable ofaking into account complex vertical variations in both foliage den-ity and in intensities of CO2 sources and sinks (Sogachev andanferov, 2006; Klaassen and Sogachev, 2006; Sogachev et al.,008). The lack of experimental data does not allow testing of thehree-dimensional model (SCADIS or any other) for complex terrainully. However, where it was possible the validation has been done.ecently the model was tested against the air flow observed overhe Askervain hill (Taylor and Teunissen, 1985). The good agree-

ent between model results and the experimental speed-up overhe hill (Sogachev et al., 2009) suggests that SCADIS is able to per-orm a good estimation of wind flow over undulating terrain. Theill with a forest imposes additional drag on the wind flow, which

eads to increased turbulence and eventual flow separation (Belchernd Hunt, 1998); these effects were reconstructed well by SCADIS


Sogachev et al., 2004). The results were also recently confirmed byheoretical and experimental investigations (Finnigan and Belcher,004; Katul et al., 2006; Feigenwinter et al., 2008). In the presenttudy the three-dimensional version of SCADIS is used, with closurecheme described as in Sogachev (2009).

2 except for winter.

3.3. Numerical details

The basic air flow was estimated inside a computational spaceof 2 km × 2 km × 2 km divided into cells using a 81 × 81 × 50 gridin two horizontal (x, y) and vertical (z) directions, respectively.Equidistant horizontal steps were set as 25 m. An irregular verti-cal grid with minimum step of 1 m at the surface and maximum of100 m at the upper boundary was used. The aerodynamic rough-ness at the surface was taken as 0.03 m. The geotrophical wind atthe upper boundary was set as 10 m s−1 with directions chosento provide agreement with prevailing wind directions observedat the tower: 320◦ and 285◦ for summer and winter, respec-tively.

Based on vegetation heights h provided by the forest inventorymap and derived LAI, the vertical distribution of leaf area density(LAD, m2 m−3) was approximated for each stand in the domain, byusing the beta probability density function (e.g. Meyers and Paw,1986; Markkanen et al., 2003):

f (z/h; ˛, ˇ) = (z/h)˛−1(1 − (z/h))ˇ−1

∫ 10

(z/h)˛−1(1 − (z/h))ˇ−1d(z/h). (3)

Two parameters of this function, ˛ and ˇ, determine the shapeof the vertical LAD distribution. When ˛ > ˇ, the maximum valueoccurs where z/h > 0.5, and therefore represents foliage concen-trated in the upper part of the canopy. For any set of non-negative

˛ and ˇ,∫ z/h

0f (z/h)d(z/h) equals unity, thus the real leaf area den-

sity is the product of (3) and LAI/h. ˛ was taken as 7 for coniferous


(mostly spruce) and as 10 for matured deciduous (mostly beech)species. For vegetation species having heights less than 5 m, ˛ wasset to 3. The ˇ parameter was set to 3 for all tree species. Thecanopy sources were described in a simplified way according toSogachev et al. (2008). In all presented numerical experiments, the




A. Sogachev et al. / Agricultural and Forest Meteorology xxx (2010) xxx–xxx 5

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ig. 4. Flux footprint functions for sources located on ground in the case of stripe-cutompared to footprints derived for unmanaged stand (solid contours for positive any triangle. The values of footprints are given per grid cell area. The percentage numf LAI (m2 m−2) used in calculations is given in the same way as in Fig. 1.

oncentration of carbon dioxide was initially assumed constant andqual to 360 �mol mol−1 within the ABL. For canopy footprint cal-ulations the CO2 soil flux at the surface was set to zero, and foroil footprint calculations it was set to 8 �mol m−2 s−1. The cal-ulations were carried out until the condition of small variabilityf turbulence coefficient and of CO2-concentration was fulfilled.


he latter condition was validated against the CO2-field withoutaking into account the sinks in vegetation cover (Sogachev et al.,005). Footprints with horizontal resolution of 25 m were then esti-ated for a rectangular domain with the size of 750 m × 575 m (see

ig. 1).

ig. 5. Flux footprint functions for sources located inside vegetation canopies in the case, z) = (0, 0, zm = 39 m), denoted by triangle. The values of footprints are given per grid ceegistered signal. The distribution of LAI (m2 m−2) used in calculations is given in the sam

gement in summer (a) and in winter (c), and their differences (b and d, respectively)ed ones for negative values), for the sensor point (x, y, z) = (0, 0, zm = 39 m) denoted

ndicate the contribution of model domain to the registered signal. The distribution

4. Results

4.1. Unmanaged stand (present situation)

Figs. 2 and 3 show the results of calculations performed forthe unmanaged stand referred to as “present situation” for the


summer and winter, respectively. Wind fields, given in each fig-ure as vectors, illustrate the structures of air flow and thus theformation of footprints. To represent the transport of signal fromsources located on the ground, in Figs. 2 and 3 the wind compo-nents are given at a height of 10 m in the panels on the left; for

of stripe-cut management in summer (a) and in winter (b) for the sensor point (x,ll area. The percentage numbers indicate the contribution of model domain to thee way as in Fig. 1.




6 A. Sogachev et al. / Agricultural and Forest Meteorology xxx (2010) xxx–xxx

excep

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Fig. 6. The same as in Fig. 4

he sources within the canopy, the wind is given at a height of0 m above the ground in the right-hand panels. Both different pre-ailing wind directions and different LAI distributions in summernd in winter result in different wind fields around the tower, andubsequently mediate the transport of signal from sources to theensor.

Thus in summer the flow convergence region around (x,) = (−400, 50) seen at the height of 10 m (Fig. 2a), and even strongerelow 10 m (not shown), causes a local maximum in the soil foot-


rint function at almost the same location (Fig. 2c). Another localaximum of the soil footprint is due to positive vertical velocity

long the border of stands with different LAI around x = −300 to200, y = 50–100. In winter the leaf senescence in beech stands

Fig. 7. The same as in Fig. 5 excep

t for clear-cut management.

results in increasing wind speed within the beech canopy and inhigher vertical velocity at the edge between beech stand and denserspruce stand (Fig. 3b). This leads to the formation of a local max-imum of footprint around the point of (−200, −150). The value ofthis local maximum is more than two times higher than the one insummer due to increased turbulent exchange in this region, trans-porting the signal from the soil to the atmospheric surface layer.In general, the complex field of turbulent flow creates a compli-cated configuration of soil footprint, which can hardly be described


by any analytical method. One of the issues is the turning of windinside the vegetation canopy (e. g. Smith and Carson, 1972), so thatthe wind direction recorded on the towers above the forest tellsus little about the actual extent and orientation of, e.g. the “central

t for clear-cut management.


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xis” of the soil footprint (e.g. Steinfeld et al., 2008; Vesala et al.,010).

The footprint structure for sources situated within the treeanopy is quite clear. Because the sensor is usually located notar from the canopy sources and the air flow above the treetopss highly turbulent, only a short time is needed for a signal from theource to reach the sensor. Thus, the footprint function is concen-rated around the tower for both seasons (Figs. 2d and 3d) with thecentral axis” oriented in the direction of the oncoming flows—NWn summer and SW in winter. Due to higher vertical velocity at theorder between beech and spruce stands, the canopy footprint ininter is slightly different from its usually assumed oval form.

Considering the overall contribution from the chosen small areao the signal measured at the tower it is interesting to note that inummer the contribution from soil sources within the area is lesshan in winter. In winter the 94.1% of the information comes fromithin the area, while in summer only 61.7% (i.e. almost 40% of sig-al comes from “remote areas”). The difference exists also for theanopy footprints but not that significant: 84.1% in summer and7.6% in winter. Thus in summer a considerable part of the informa-ion comes from outside the domain used for footprint estimation.t is primarily due to the wind–topography interaction. As one canee in Fig. 1 in summer the air flow with prevailing NW-directionrriving at the sensor comes over the hill and has complicated struc-ure. This facilitates the delivery of information from more remoteources, in particularly from those located at the upwind ridge. Ininter the signal is transported from the SW—up the gentle slope.

Analysis of the wind and turbulent characteristics modelledith cut stripes and clear-cut at the F1-stand showed that they

re not so remarkably different from the unmanaged stand to bellustrated separately. I.e. the fields are changed of course, but thehanges occur within a small area in the immediate vicinity ofhe tower. Therefore only the overall effect of these changes onootprints were analysed in this study.

.2. Stripe-cut

Fig. 4a and c shows the footprint for the sources located onhe ground in the case of stripe-cut management for summernd winter, respectively. The differences of these footprints fromnmanaged ones are also given in Fig. 4b and d. The smallestootprint changes occur in the summer. As the maximum of con-ribution was outside the F1-stand for the unmanaged situationnd the stripe-cut management did not alter the flow structureuch, the local contribution maxima remained at the same posi-

ion around (x, y) = (−400, 50). The share of information collectedrom the study area (61.8%) also remained almost unchanged. Thenly difference is the slight deformation of the area with the great-st influence. The contribution of sources situated at long fetch200–400 m) increases and the contribution of sources situated athe short fetch affected by cutting (0–200 m) decreases (Fig. 4b).pparently this is because the overall effect of alternating positivend negative vertical velocities caused by strip cutting is detrimen-al to transporting the signal from the ground to the sensor. Theffect of stripe-cutting on ground footprint is stronger in winter.he local maximum of contribution shifted to the north for morehan 100 m (Fig. 4c). However the contribution from the area oftudy remains almost as high (91.3%) as in unmanaged stand.

Fig. 5a and b shows canopy footprints for summer and winter,espectively. One can see that the alternation of sources leads tostreaky type of footprint (i.e. contribution function, to be exact)


ith “central axis” oriented along leading wind directions. As inhe case of unmanaged stand, the greatest contribution from theonsidered area is for winter (97%) compared to a summer value of6.8%. Differences between the canopy footprint from the originali.e. unmanaged) and stripe-cut cases are not informative; the effect

PRESSt Meteorology xxx (2010) xxx–xxx 7

of the remaining trees would increase, and cut stripes will indicatethe areas of greatest reduction. Thus, canopy footprint differencesare not represented here, though differences between footprintsfor ground-based sources are found in Fig. 4b and d.

4.3. Clear-cut

Footprints for sources located on the ground in the case of clear-cutting of the F1-stand are presented in Fig. 6. In general it is evidentthat the region of highest contribution is shifted to the area of log-ging. In summer (Fig. 6a) the effect of convergence of air flow near(x, y) = (−400, 50) is still pronounced, and in winter (Fig. 6c) theeffect of the vertical velocity at far fetches (around (−500, −50)) isalso noticeable. However, for both seasons the area with the max-imum contribution lies around x = −150, y = 0–50), i.e. within themanaged stand. The differences between the clear-cut case andthe original footprints (Fig. 6b and d) illustrate the redistributionof the source contribution rate. In summer the difference due toclear-cutting showed the opposite tendency than under the stripedcutting: the contribution from short fetches increases. In winterthe local maximum observed in undisturbed stand remained andis shifted to the north-east. Similarly to the present unmanagedcase, the signal registered at the tower has 92.4% of its informationcoming from the study area in winter and only 69% in summer.

The most radical changes in the case of the clear-cutting occurfor canopy footprints (Fig. 7). While for unmanaged stands the sig-nal originates almost entirely from the F1-stand around the tower,the local maximum contribution areas for clear cutting are shiftedto the areas of nearest upwind vegetation (spruce stand to the NWin summer and SW in winter). For both seasons, due to redistribu-tion of sources (smaller LAI of vegetation equates to a lower heightof the effective source), the footprints are spatially more extended.Through cutting of the “main contributor,” the contribution of studyarea to measured signal decreased in summer from 84.1% to 67.1%and in winter from 97.6% to 88.2% (see also Figs. 2d and 3d); i.e.,the role of remote sources increased.

5. Discussion and conclusion

In our attempt to assess the impact of different forest manage-ment methods – which cause abrupt forest structure changes –upon flux footprints, we used a 3D approach based on numeri-cal solution of the RANS equations. It was once again explainedthat for footprint assessment over complex landscapes, there arecurrently no equally feasible alternatives to this approach. Whenapplied to the complex landscape investigated, the model provideduseful information about the flux footprint both for summer (fullfoliage) and for winter (leafless deciduous species) seasons. Thusinformation about the current distribution of land-use types andseasonal variations of vegetation structure – as well as variations intopography – were taken into account, which is nearly impossiblewith any other modelling approach. Considering seasonal effectswe have found that in summer the contribution of informationfrom the modelled domain to the signal measured at the tower isalways lower than in winter, and the role of remote sources includ-ing surrounding vegetation and soils is consequently higher. Themain reason however is not the phenology, but the change of pre-vailing wind directions between summer and winter and thus thedifferences in upwind topography and land use. Although this con-clusion is based on model results and is valid for the study area only,


it is advisable that the seasonal differences should be estimatedseparately for each particular flux site.

The investigation of forest management effects shows thatgenerally the air flow over the managed landscape (both clear-cut and the stripe-cut) will not change strongly at the landscape


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evel but the small-scale changes are significant. The clear-cuttingill of course have the strongest effect on the flux footprint as

ir flow characteristics change strongly and the main contribu-or is removed. The tower will register signal from surroundingegetation, with the primary contributions changing constantlyepending on the wind direction. The contribution of soil from theut area will also increase considerably. The stripe-cutting will haveess impact on both footprints mostly because the flow statistics doot change as dramatically. The effect of such cutting is mostly lim-

ted by the stripes orientation being perpendicular to the directionf prevailing winds. Recently, Vesala et al. (2005) showed that thin-ing had no significant effect on the total photosynthesis activityf stands, because the increase of solar radiation penetration intohe canopy and better ventilation compensates for the loss of LAI.hus the total canopy source and the canopy footprint itself willot undergo significant changes, i.e. the signal measured at towerill still characterize the remained stand. For rough estimation ofet footprint, Eq. (2) describing the net contribution function cane used.

Abrupt changes in forest structure affect not only footprints, butlso the measured signals. The authors are well aware that the pre-ented numerical experiments do not describe all possible effectsnd changes caused by proposed management activities. Both sug-ested thinning methods will increase solar radiation input to theoil and, consequently, increase the soil temperature which willesult in changes of soil source intensity. In the case of clear-utting the thermal effect is expected to be stronger than undertripe-cutting. Flux footprint, which is a function of the air veloc-ty field only, is unable to characterize these changes. However,he footprint allows identification of areas requiring closer studyf the sources, in both summer and winter. Consideration of atmo-pheric stratification, which tends to have a different mean impactn summer and winter, is a challenging and more time consumingask which will also be investigated by authors in the studies toollow.

cknowledgements

O. Panferov, B. Ahrends and C. Doering were supported byhe grant from the Federal Ministry of Education and ResearchBMBF), Germany within the frames of DSS-WuK project of kli-azwei program and by the grant for project KLIFF of NMWK,

ower Saxony. We gratefully acknowledge this support. Our spe-ial thanks to Dr. Mark C. Kelly from Risø-DTU for his valuableomments which improved the manuscript considerably and to Dr.enning Meesenburg from the Northwest German Forest Research

nstitute (NW-FVA) for consulting on forest management activitiesxpected in the study area.

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