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Environmental Pollution 159 (2011) 3204e3214

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Environmental Pollution

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Quantification of nitrate leaching from German forest ecosystemsby use of a process oriented biogeochemical model

Ralf Kiese a,*, Christoph Heinzeller a,b, Christian Werner a,c, Sandra Wochele a,Rüdiger Grote a, Klaus Butterbach-Bahl a

aKarlsruhe Institute of Technology, Institute for Meteorology and Climate Research, IMK-IFU Gramisch-Partenkirchen, GermanybDepartment of Geography, Ludwig-Maximilians University Munich, Germanyc LOEWE Biodiversity and Climate Research Centre (BIK-F), Frankfurt, Germany

a r t i c l e i n f o

Article history:Received 2 May 2011Accepted 4 May 2011

Keywords:Seepage waterNitrate leachingForest ecosystemsBiogeochemical modelModel evaluationUncertainties

* Corresponding author.E-mail address: ralf.kiese@kit.edu (R. Kiese).

0269-7491/$ e see front matter � 2011 Elsevier Ltd.doi:10.1016/j.envpol.2011.05.004

a b s t r a c t

Simulations with the process oriented Forest-DNDC model showed reasonable to good agreement withobservations of soil water contents of different soil layers, annual amounts of seepage water andapproximated rates of nitrate leaching at 79 sites across Germany. Following site evaluation, Forest-DNDCwas coupled to a GIS to assess nitrate leaching from German forest ecosystems for the year 2000. At na-tional scale leaching rates varied in a range of 0e>80 kg NO3eNha�1 yr�1 (mean 5.5 kg NO3eNha�1 yr�1).A comparison of regional simulations with the results of a nitrate inventory study for Bavaria showed thatmeasured and simulated percentages for different nitrate leaching classes (0e5 kg N ha�1 yr�1:66% vs.74%, 5e15 kg N ha�1 yr�1:20% vs. 20%, >15 kg N ha�1 yr�1:14% vs. 6%) were in good agreement. Meannitrate concentrations in seepage water ranged between 0 and 23 mg NO3eN l�1.

� 2011 Elsevier Ltd. All rights reserved.

1. Introduction

Since several decades, natural ecosystems inWestern Europe areexposed to high atmospheric deposition of pollutants such assulphur and nitrogen. For sulphur deposition major progress hasbeen achieved in the framework of the Gothenburg Protocol: themajority of European countries have reduced their emissions e

mainly originating from energy production e by more than 60%between 1990 and 2004, with one quarter of countries havingreduced sulphur emission by>80% (Vestreng et al., 2007). However,atmospheric nitrogen deposition has remained at high levelsand decreasing trends are not visible yet. In Germany totalnitrogen deposition to forest ecosystems are averaging approx.25 kg N ha�1 yr�1. In intensively used agricultural areas in theNorthwest and South of Germany, annual N deposition may even behigher than 50 kg N ha�1 yr�1 (Gauger et al., 2002). In naturally Nlimited forest ecosystems, elevated atmospheric deposition ofnitrogen can have various adverse environmental effects (Matsonet al., 2002), including eutrophication and acidification of terres-trial ecosystems with related impacts on plant and faunal biodiver-sity (Bobbinket al.,1998), eutrophicationof surface andgroundwater

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due to nitrate leaching (Dise and Wright, 1995; MacDonald et al.,2002; Gundersen et al., 2006) as well as forcing of global warmingdue to enhanced emissions of the primarily and secondarily activeN-trace gases N2O and NO (Butterbach-Bahl et al., 2002; Pilegaardet al., 2006; Venterea et al., 2004), but also increases in C seques-tration due to accelerated forest growth (De Vries et al., 2009).

In Germany diffuse water pollution by nitrate leaching is notexclusively limited to intensively used agricultural ecosystems, buthas also been reported for forest ecosystems exposed to high loadsof atmospheric N deposition. Borken and Matzner (2004) found intheir analysis of 57 German forest sites nitrate leaching rates of upto 26 kg N ha�1 yr�1, with 30% of the sites already showing nitrateleaching rates> 5 kg N ha�1 yr�1. Having water quality in mind thisis of uppermost importance since drinking water in Germany isoften gained from “clean” forest watersheds or used for dilution ofnitrate polluted water from other areas.

Due to the outlined various environmental impacts of chronicalN deposition for environmental health, international protocolshave been established such as the Gothenburg protocol under theUNECE convention on Long Range Transboundary Air Pollution(LRTAP) aiming at e among others e reducing environmental Nthreats and, thus N inputs to (semi-) natural ecosystems. Under theLRTAP convention several monitoring programmes have beeninitiated such as ICP Forests, which stands for the “International Co-operative Programme on Assessment and Monitoring of Air

[sl] Specific soil layer

outWater[sl] Percolation (m/day)water[sl] water content (m3/m3)fs[sl] water content at field capacity (m3/m3)wp[sl] water content at wilting point (m3/m3)sks[sl] saturated hydraulic conductivity (m/s)

R. Kiese et al. / Environmental Pollution 159 (2011) 3204e3214 3205

Pollution Effects on Forests”, which started a “Pan e EuropeanProgramme for Intensive and Continuous Monitoring of ForestEcosystems” (the so-called level II programme) in 1995. It includesmonitoring of crown condition, forest growth, chemical status ofsoil and foliage, deposition, meteorology, soil solution and groundvegetation (De Vries et al., 2003a). These monitoring data enabledEuropean wide assessments of atmospheric deposition and itsimpacts on soil solution chemistry (De Vries et al., 2003b), andinput output budgets of nitrogen, sulphur, aluminium and basecations using a common methodological approach (De Vries et al.,2007a,b). The data also allowed validation of various statisticaland process-based model approaches to assess N depositionimpacts on growth and carbon sequestration (e.g. Solberg et al.,2009; Wamelink et al., 2009) and element leaching (Reinds et al.,2009).

To direct and test European N emission policy with respect totheir effectiveness in reducing the environmental threats of Ndeposition e such as nitrate leaching e a set of methodologicaltools have been developed ranging from statistical methods(Gundersen et al., 1998; MacDonald et al., 2002), stratified block-Kriging (Pebesma and de Kwaadsteniet, 1997) to process-basedmodels of varying complexity (De Vries et al., 1995; Kros et al.,2004; Li et al., 2006). Statistical models generally have the disad-vantage that they cannot be applied out of the parameter spacethey have been developed for. Thus, they are not suitable to predicteffects of changes in climate and/or magnitude of N deposition onforest health and nitrate leaching. Process oriented biogeochemicalmodels however have the advantage to evaluate and assessa potentially broad range of environmental impacts of N depositionacross a wide range of spatial and temporal scales because they dospecifically address site differences in climate, vegetation and soilproperties and consider their importance for the biogeochemicalcycling of water, carbon and nitrogen.

In the framework of this paper the process oriented Forest-DNDC model was tested for its suitability to simulate NO3 leach-ing from forest ecosystems in Germany. The Forest-DNDC modelapplied in this study originated from the PnET-N-DNDC model,which was designed for simulation of C and N turnover in forestecosystems in particular with focus on formation and emission ofC and N-trace gases (e.g. Butterbach-Bahl et al., 2001; Kesik et al.,2005; Kiese et al., 2005; Werner et al., 2007). Though the agri-cultural version of the DNDC model was applied for simulation ofNO3 leaching from arable soil systems (Tonitto et al., 2007, 2010; Liet al., 2006) this paper for the first time deals with simulatingnitrate leaching below the rooting zone of forest ecosystems. Firstthe Forest-DNDC model was tested and validated on site scaleusing data from the German ICP forests Level II forest monitoringprogram. Since a correct representation of water fluxes isa precondition for the description of nitrogen export by seepagewater, model testing concentrated first on the evaluation of foreststand hydrology, and in the following step on the evaluation ofnitrate leaching at site scale. The tested Forest-DNDC model wasthen linked to a GIS database to estimate the magnitude of NO3leaching from forest ecosystems in entire Germany.

2. Material and methods

2.1. Forest-DNDC model as embedded into the MoBiLE framework

The Forest-DNDC model used in this study is a follow-up version of the PnET-N-DNDC model, which was initially developed to predict soil carbon and nitrogenbiogeochemistry, with focus on N-trace gas emissions from temperate and tropicalforest ecosystems (Li et al., 2000; Stange et al., 2000; Butterbach-Bahl et al., 2001;Kiese et al., 2005; Kesik et al., 2005; Werner et al., 2007). Within recent years, theformer PnET-N-DNDC model was restructured and integrated into a ModularBiosphere SimuLation Environment MoBiLE (Grote et al., 2009). MoBiLE combinesdifferent modules for describing microclimatic conditions (e.g. temperature in the

soil and vegetation canopy), water cycle (moisture contents in canopy, soil layer andecosystem water dynamics), physiology (vegetation pools and dynamics of carbonand nitrogen) soil chemistry (soil pools and dynamics of carbon, nitrogen) andvegetation structure (changes in vegetation properties). In contrast to the originalversion the new Forest-DNDC model applied in this study considers a layer specificdistribution of climatic conditions and foliage within the canopy (Grote, 2007) andcan be flexibly initialized for any number and specific height of soil layers.

The Forest-DNDC model consists of sub-modules for the simulation of soilclimate, decomposition and forest growth.Within the soil climate andwater balancesub-module, daily climate data is used to calculate soil temperature and moistureprofiles from physical soil properties and one-dimensional thermal-hydraulic flowwhich then affect oxygen profiles according to gas diffusion equations and respi-ratory O2 consumption by microbes and plant roots (Li et al., 2000). Forest biomassdevelopment including nitrogen uptake and litterfall is calculated depending onsolar radiation, temperature, water-, and nitrogen availability. Litter production,water and nitrogen demand (NH4 and NO3) of plants and root respiration is linkedwith the soil climate and the decomposition submodel. Decomposition of organicmatter increases concentrations of dissolved organic carbon (DOC), ammonium(NH4

þ), and carbon dioxide (CO2). Decomposition is based on soil environmentalconditions and specific decay rates for a series of organic matter pools (Li et al.,2000). Soil NH4 and NO3 concentrations are further affected by N turnoverdescribed in the submodels of nitrification and denitrification. The processes arebased on simulated soil microbial activities, which depend on soil environmentalconditions and a series of biochemical and geochemical reactions determining thetransport and transformation of C and N components (Li et al., 2000). Aerobicnitrification (autotrophic and heterotrophic) and anaerobic denitrification aresimultaneously calculated using the concept of a dynamic ‘anaerobic balloon’. Byusing this approach, substrates (DOC, NH4 and NO3) are allocated into aerobic andanaerobic soil compartments on the basis of the oxygen concentration in therespective soil layer (Li et al., 2000).

2.2. Water balance in Forest-DNDC

The forest ecosystem water balance is calculated based on daily climatic inputdata, though internally an hourly time step for all calculated fluxes is realized.Depending on air temperature, daily precipitation in form of rain or snow is dividedinto site specific hourly events (default 5mm/hour). Throughfall is calculateddepending on the interception capacity derived from vegetation biomass (leaf andwood) and Leaf Area Index (LAI). Intercepted water evaporates from the canopyaccording to evaporation demand. The latter is related to daily potential evapora-tion, which is derived by amodified Thornthwaite equation that uses daily andmeanmonthly temperatures as basis of calculations. Soil infiltration is limited by theinfiltration capacity of the top soil layer (organic layer). If rainfall exceeds theinfiltration capacity, water can accumulate at the surface and depending on slope,surface runoff occurs. The percolation of water within the soil profile is described bya cascading bucket model. Percolation between soil layers depends on layer specificphysical properties i.e. field capacity, wilting point, and saturated hydraulicconductivity, as well as the actual water content of two adjacent soil layers. Thewater balance submodel thereby differs between percolation under unsaturated(Eq. (1)) and saturated conditions (Eq (2)):

If water[sl] > wp[sl] & water[sl] < fc[sl]

outWater½sl� ¼�water½sl�fc½sl� �water½slþ 1�

fc½slþ 1��*water½sl�*

0B@1:0� exp

�1:0logðsks½sl�Þ

1CA (1)

If water[sl] > fc[sl]

outWater½sl� ¼��

1� fc½sl�water½sl�

��2

*water½sl�*

0B@1:0� exp

�1:0logðsks½sl�Þ

1CA (2)

with:

Besides leaching to deeper soil layers, soil water content is reduced by plant tran-spiration and soil evaporation. Plant transpiration is calculated from the plant waterdemand resulting from photosynthesis and species specific water-use-efficiency andis limited by the available water throughout all rooted soil layers. Soil evaporation isdetermined from the residual of potential evaporation demand and the available soilwater up to a pre-defined soil depth (0.3 m).

R. Kiese et al. / Environmental Pollution 159 (2011) 3204e32143206

2.3. Calculation of soil NO3 concentrations and NO3 leaching

Calculations of soil NO3 concentrations in a given layer are complex and considerthe major biological production and consumption processes (mineralisation, nitri-fication and denitrification) as well as loss processes due to leaching. For the firstlayer also atmospheric NO3 input is considered (see below). Nitrate production inthe soil is exclusively due to nitrification, which can be autotrophic or in acid forestsoils predominantly heterotrophic. The magnitude of nitrification is a function ofavailable substrate e i.e. NH4 for autotrophic and NH4 as well as DOC for hetero-trophic nitrification -, microbial activity e which depends on moisture andtemperature conditions - and aerobic status of the given soil layer. Consumptionprocesses for NO3 include denitrification, chemo-denitrification, plant uptake andmicrobial metabolism. The magnitude of the mentioned processes will depend onaeration status, availability of DOC and size and activity of the denitrifier populationfor denitrification, pH for chemo-denitrification, and demand and root distributionfor plant N uptake (for details see Li et al., 2000; De Bruijn et al., 2009). NO3

concentrations in soil water and its amount will also depend on leaching losses. NO3

leaching is calculated proportionally to the ratio of percolated and actual amount ofwater of a given layer reduce by a retention factor (Eq. (3)). Please note that inForest-DNDC, NH4 leaching is assumed to be negligible due to the strong binding ofNH4 at clay minerals and organic matter.

leach NO3½sl� ¼ outWater½sl�=water½sl�*NO3½sl�*RETNO3 (3)

TS

leach_NO3[sl]

able 1ite characteristics of

Site/Characteristics

Dominant tree specAnnual precipitatioMean annual tempeAnnual N-depositio

(period 1996e20Soil typeHumus typeBulk density in topSOC in top soil [%]c

Clay content in top

a Period 1990�200b Bavarian Forest Inc 0e0.1 m soil dept

leached NO3 out of a specific soil layer

five Level II forest sites in Bavaria used for deta

Altdorf

ies Pinus sylvestrisn [mm]a 795.4rature [�C]a 9.1n [kg ha�1]b

01)16.5

PodzoleRaw-Humus

soila [g cm3]c 1.20.8

soil [%]c 0.9

0.stitute.h of the mineral soil.

(kg N me2)

NO3[sl] NO3 content of a specific soil layer (kg N me2) RETNO3 fraction of NO3 which is free for leaching (e)

2.4. Atmospheric N deposition in Forest-DNDC

Daily atmospheric N deposition is calculated from annual values of total Ndeposition (kg N m�2 year�1) in the form of oxidized (NOx) and reduced (NHy)compounds and the annual sum of rainfall (l m�2). From this mean N concentrationsin precipitation (mg N l�1) for NOx and NHy are calculated. Thus, in the modelatmospheric N deposition only occurs at rainfall days. For those days the calculatedmean N concentrations are multiplied by the amount of rain and added as NH4 (kgN m�2) and NO3 (kg N m�2) to the pools of inorganic N in the first soil layer.

2.5. Input and validation data for Forest-DNDC site simulations

In this study data from 74 German ICP Level II forest monitoring sites (http://www.icp-forests.org/MonLvII.htm), covering the major forest and soil types fordifferent climatic regions in Germany, were used for model set-up and testing(Haußmann and Lux, 1997; Block et al., 2000). Data for model initialization anddriving included soil and vegetation characteristics as well as climate and N depo-sition data. Due to incompleteness and inconsistencies of the climate datasetprovided with the ICP Level II dataset we took climate data from the nearest weatherstation as operated by the Germanweather service. For the same reason simulated Ndeposition data of Gauger et al. (2002) were used. These data are calculated forentire Germany with 1 km2 grid resolution, since the provided measured N depo-sition in the throughfall was significantly under-representing dry deposition.

In a first step, simulations of soil water and soil NO3 concentration wereextensively validated at five Level II sites in Bavaria. These five sites have differentforest and soil types and are exposed to different climatic conditions (Altdorf,Ebersberg, Flossenbuerg, Mitterfels, Freising, Table 1). For all sites a detailed fielddataset of measured soil water (five different soil depths) and soil NO3 concentra-tions (four soil depths) were provided by the Bavarian Forest Institute (LWF). Further

iled testing of Fores

Ebersberg

Picea abies1088.98.822.4

LuvisolModer1.61.828.8

model test datasets included calculated annual seepage water and nitrate leachingdata from the 74 level II sites of Germany as provided by the central nationaldatabase of Level II sites run by Johann Heinrich von Thünen Institute (vTI)Braunschweig/Germany. In this dataset annual seepage water and nitrate leachingbelow the rooting zone was calculated after Block et al. (2000) by use of a massbalance approach (Eq. (4)) using chloride as an inert tracer (CMB):

Seepage water ¼ ðAnnual precipitation*Mean Clconc precipitationÞ*Fact NaMean Clconc seepage water

(4)

Annual nitrate leaching was estimated by:

NO3 leaching ¼ Seepage water*Mean NO3conc seepage water (5)

with fact_Na being a dimensionless factor that takes into account the enrichment ofCl in throughfall compared to open field conditions. The units used are l m�2 yr�1 forseepage water and annual precipitation (open field), mmol l�1 for mean Clconc inprecipitation and mean Clconc in seepage water, kg N m�2 yr�1 for NO3_leaching andkg l�1 for mean NO3conc in seepage water.

2.6. Input data for Forest-DNDC regional simulations

In the framework of the regional application of Forest-DNDC for upscaling ofnitrate leaching under forest ecosystems of Germany a GIS database holding allrelevant input data for climate, soil and vegetation properties was set up. Infor-mation on soil types and properties were obtained from the Soil Survey MapGermany 1:1,000,000 (Federal Institute for Geosciences and Natural Resources).Layered soil attribute data were given for 69 distinct soil classes including infor-mation about soil texture, pH, soil organic carbon, skeleton rate, wilting point andfield capacity. Forest coverage was derived from the CORINE land-use map bymerging all forest subclasses (Federal Statistical Office, Destatis). Forest standinformation (age and species composition) was provided by a range of sources (seeFig. 1), as no concise source of information was present at the time of GIS devel-opment. Information on the area of the former Federal Republic of Germany wastaken from the National Forest Inventory 1 (Bundeswaldinventur 1), which is basedon survey grids of 2 � 2 kme4 � 4 km resolution. Information for the eastern stateswas not homogenized by the time of GIS development and thus local statistics andinventories for each state were used (for details see Fig. 1). In order to link state-onlystatistics, administrative boundaries were also included into the database (ESRI mapof the world). Species and age classes were filtered for relevance and no more thansix age-species classes were allowed for each polygon. All discarded classes weremerged with the most related dominant age-species class. Data collectives of theGerman Meteorological Service (DWD) provided daily weather data (precipitation,minimal and maximal temperature) for the simulation year 2000. Station data werespatially filtered to avoid unnecessary local clustering and mapped spatially bycalculating Thiessen polygons. Total N deposition data was derived from 1 � 1 kmsimulations from Gauger et al. (2002) and also linked to the station polygons. Alldatasets were merged and filtered to remove polygons of insignificant area (<1 ha).Information about spatial extent of data was scaled by statistical data whereappropriate. For each resulting individual polygon with specific properties indi-vidual model runs were performed. Mixed forests were dealt with by simulatingfractions of a polygon with different forest age and species setups. This informationwas finally aggregated for the calculation of rates of NO3 leaching (approximately at1 m soil depth depending on specific site characteristics) from forest soils inGermany (Fig. 1) for which in total 17,149 simulation runs were done.

2.7. Sensitivity of Forest-DNDC on input parameters

Sensitivity of Forest-DNDC on input parameters was tested on a regional scale. Forthis a subset of 1000 simulation units (total 12,000)were randomly selected across the

t-DNDC on site scale.

Flossenbuerg Mitterfels Freising

Picea abies Fagus sylvatica Fagus sylvatica993.1 984.6 923.66.9 8.5 8.919.5 21.9 20.2

Cambisol-Podzole Cambisol-Podzole Luvisol/CambisolModer Mull Mull1.6 1.1 1.56.0 5.5 3.018.2 36.0 14.7

Fig. 1. Scheme showing data sources and processing structure of regional input information of Forest-DNDC for simulating regional patterns of NO3 leaching from forest soils inGermany.

R. Kiese et al. / Environmental Pollution 159 (2011) 3204e3214 3207

simulation area covering main climate, forest types and soil types of Germany. Modelsensitivity (Friend et al., 1993) was evaluated as variation of predicted annual NO3

leaching rates to changes in all major input and driving parameters (P):

b ¼ NO13 � NO0

3

NO03

=P1 � P0

P0(6)

Each parameter P was individually increased (P1) or decreased (P0) in a rangewhich represents the site/regional uncertainty of the respective parameter (�30%: Ndeposition, precipitation, clay content and soil organic carbon (SOC); �3 �C airtemperature; �1 unit pH). The distance of the b value from zero is proportional tothe sensitivity of a given parameter and the sign of b indicates if the correlation ispositive or negative.

2.8. Statistical evaluations

Model performance was documented using the coefficient of determination(r2) and model efficiency (r2eff) which are calculated using the followingequations:

r2 ¼ ðPðxmod � xmodÞ*ðxmeas � xmeasÞÞ2Pðxmod � xmodÞ2*Pðxmeas � xmeasÞ2

�0 � r2 � 1

�(7)

r2eff ¼ 1� Pðxmod � xmeasÞ2Pðxmeas � xmeasÞ2

! ��N < r2eff � 1

�(8)

where xmod is the simulated value, xmod is the average of all simulated values, xmeas

is the value obtained from field data, and xmeas is the average of measured field data.

The value of r2 was used in simple regression relation forcing the intercept to beequal to zero and model over or underestimation was assessed by the slope of theregression line.

3. Results

3.1. Forest-DNDC site simulations

For an overall evaluation of the capability of Forest-DNDC forsimulating nitrate leaching under forest ecosystems of Germanya stepwise testing and validation procedure on site scale wasapplied, focusing first on water cycling and formation on seepagewater fluxes and second on a comparison of simulated andmeasured NO3 concentrations in the soil profile. For this a subset offive forest sites in Bavaria, varying in forest and soil types as well asclimatic conditions, were selected (Table 1). Fig. 2 showsa comparison of simulated and measured water content for theyears 2001e2003 for two of the five forest sites in Bavaria, i.e.Mitterfels and Freising. Model performance measures of r2 (coef-ficient of determination) and r2eff (model efficiency) variedbetween 0.45 and 0.85. Simulations tended to slightly underesti-mate (<10%) soil water contents in a given layer. This is mostlyrelated to differences in the top soil in the summer months.However, it has to be stated that simulations even for the

Fig. 2. Simulated (blue) and measured (red) soil water content in five different soil layers (soil depths: 5e140 cm) at two Level II forest sites in Bavaria (A Mitterfels; B Freising) forthe years 2001e2003 including results of a linear regression of the modelled (y) versus measured (x) water content and the coefficient of determination (r2) as well as modelefficiency (r2eff). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

0 200 400 600 800 1000

0

200

400

600

800

1000 r² = 0.57; r 2eff = 0.51 n = 252

y = 0.84 * x p = 0.001

sim

ulat

ed s

eepa

ge w

ater

[mm

a-1]

measured seepage water [mm a-1]

Fig. 3. Comparison of simulated and measured (chloride mass balance approach)seepage water below the rooting zone of 74 level II forest sites across Germany. Givenare the results of a linear regression and the 95% confidence intervals and predictionbands.

R. Kiese et al. / Environmental Pollution 159 (2011) 3204e32143208

exceptionally dry year 2003 were mostly matching the dynamics ofthe field measurements. Also for the other sites, model perfor-mance measures were comparable (data not shown), whichdemonstrates that Forest-DNDC was generally capable to simulatesite specific water cycling and percolation of infiltrated rain waterthrough the soil profile.

In a next step Forest-DNDC was applied to simulate seepagewater fluxes at 74 ICP forest level II sites across Germany. Onaverage more than three years of data were available and used foreach site (period 1996e2001). Simulations were compared withdata of stand water balances as obtained by the chloride massbalance (CMB) approach provided by the central level II databasefor Germany (hosted by von Thünen Institute (vTI), Braunschweig,Germany). In dependency of climatic and soil conditions, theamount of annual seepage water below the rooting zone variedsignificantly between 0 and approx. 1000 mm. Model simulationsin average were 16% lower than measured values but could explainabout 60% of their variation (Fig. 3). In evaluating the results, itshould be considered that “measured” seepage fluxes based on theCMB approach are also uncertain specifically when the period isrelatively short.

Results, demonstrating the overall capability of Forest-DNDC toreproduce the variation of NO3 concentrations in different soildepths and across the five different sites in Bavaria for whichdetailed informationwere available are summarized in Fig. 4. Meansimulated and measured soil NO3 concentrations varied between0.1 and 2.7 mg NO3eN l�1 (0.4 and 11.8 mg NO3 l�1). The simula-tions could explain more than 50% of the variation of themeasurements. Measured values tended to be underestimated bymodel simulations by on average 22% of the considered combina-tions of sites and soil depths. Fig. 4 clearly indicates that the modeloverestimates the means of soil solution NO3 concentration at

the low end, and underestimates the higher values. However, soilconcentration differences across sites e lowest at Altdorf andhighest at Ebersberg e followed the pattern of site specific loads ofatmospheric N-deposition (see Table 1).

After testing Forest-DNDC on site scale the model was appliedfor estimating the annual amount of nitrate leached out of the rootzone of Level II forest stands of Germany. In total 187 annual datapoints from 74 forest stands within the period 1996e2001 wereavailable to test model performance. Thereby annual leaching rates

0 1 2 3 40

1

2

3

4r² = 0.52; r2

eff = 0.65p = 0.01y = 0.78 x

Altdorf Ebersberg Flossenbuerg Mitterfels Freising

sim

ulat

ed N

O3-N

[mg

l-1]

measured NO3-N [mg l-1]

Fig. 4. Comparison of mean measured and simulated nitrate-concentrations (mean1996e2001 in mg NO3eN l�1) at site Altdorf, Eberberg, Flossenbuerg, Mitterfels andFreising.

R. Kiese et al. / Environmental Pollution 159 (2011) 3204e3214 3209

of the central database were estimated by multiplication of themean NO3 concentration of the lowest soil layer with the annualseepage water derived with the CMB approach. Forest-DNDCsimulated and CMB estimated leaching rates varied between0 and up to 25 kg NO3eN ha�1 yr�1. Forest-DNDC simulations ofNO3 leaching were slightly, by approx. 10%, lower as those by theCMB approach (Fig. 5). As with soil solution NO3 concentrations, themodel tends to overestimate N leaching at the low end, andunderestimates the higher N leaching values (Fig. 5). Over 50% ofthe variability of estimated values was covered by model simula-tions. Highest relative deviations between estimated and simulatedNO3 leaching rates occurred in the range of<5 kg NO3eN ha�1 yr�1.

0 5 10 15 20 25

0

5

10

15

20

25

0 5 10 15 20 25

sim

ulat

ed N

O3 le

achi

ng [k

g N

ha-1

yr-1

]

measured NO3 leaching [kg N ha-1 yr-1]

y = 0.9 * xr2 = 0.51; r2

eff = 0.32p< 0.0001

Fig. 5. Comparison of mean measured (CMB) and simulated nitrate leaching[kg N ha�1 a�1] for Level II forest sites of Germany. Given are the results of a linearregression and the 95% confidence intervals as well as prediction bands.

3.2. Forest-DNDC regional simulations

Results of the more than 17,000 Forest-DNDC regional simula-tions are presented in Fig. 6. Annual nitrate leaching rates in theyear 2000 varied between 0 and 85 kg NO3eN ha�1, with an areaweighted mean value of 5.5 kg NO3eN ha�1 yr�1 (Fig. 7, Table 2).High annual NO3 leaching rates of up to >30 kg NO3eN ha�1 yr�1

were simulated mainly for regions in the NeNE of Germany, all lowmountain ranges and the pre-alpine area in the south, which are allcharacterized by high N-deposition and high seepage water fluxes(data not shown). For all other areas, leaching rates were mostly<5 kg NO3eN ha�1 yr�1. Cumulative NO3eN leaching via seepagewater from forest ecosystems in Germany amounted to 54.6 kt yr�1,equalling approx. 20% of the total N-deposition. On state levelhighest total export rates and thus, highest contribution to thenational export were simulated for Bavaria (30%), having by far thehighest forest area, followed by Baden-Wurttemberg (17.9%), NorthRhine Westphalia (11.2%) and Lower Saxony (9.6%). However,relating nitrate leaching rates to the state area (numbers < 1 ¼smaller than German average; numbers >1 ¼ higher than Germanaverage) relative contributions were highest for Saarland (1.84),North RhineWestphalia (1.23) and Thuringia (1.23). Forests at all ofthe mentioned states are receiving in average disproportionallyhigh loads of atmospheric N deposition as compared to forests inother states (Table 2 and Fig. 6). Applying a multiple regressionanalysis (N ¼ 17,149) total N deposition could explain most of thevariability of NO3 leaching rates (r2 ¼ 0.45) followed by annualrainfall amount and soil organic carbon values which increased r2

to 0.55 and 0.61 respectively.Similar results were obtained in the framework of the model

sensitivity test on basis of 1000 randomly selected simulation units.Beside pH (b¼ 2.3) annual simulated NO3 leaching rates were mostsensitive to changes in precipitation (b ¼ 2.8), SOC (b ¼ 1.3) anddeposition (b ¼ 0.92), whereas temperature (b ¼ 0.6) and claycontent (b ¼ �0.03) were of minor importance.

4. Discussion

4.1. Hydrology

Before running Forest-DNDC for simulating nitrate leachingrates, emphasis was given for testing and validation of the watercycle simulations. Taking into account uncertainty of modelinitialization and parameters, but also uncertainty in measure-ments, simulation of Forest-DNDC showed a good agreement withobservations of soil water content of different soil layers coveringthe whole soil profile (r2 and r2eff ranging between 0.45 and 0.85 at5 highly investigated Bavarian sites). Deviations of measured andsimulated values occurred mainly in the extreme dry summer2003. Simulations clearly underestimated decreasing soil watercontent especially in the top soil layers, which might be attributedto underestimation of soil evaporation and/or more likely inuncertainties with model parameterization with respect to rootdistribution and thus to underestimation of the transpirationprocess.

Applying the Forest-DNDC model for simulations of annualseepage water at 74 level II sites was quite robust (r2 ¼ 0.57) andmostlymatching site differences in annual seepagewater formationranging between 0 and 1000 mm yr�1, respectively. Model perfor-mance for forest simulations is in the same range as reported byTonitto et al. (2010, r2 ¼ 0.55) for DNDC simulations of drainage ina U.S. agroecosystem. Except for model uncertainties discussedabove, deviation between measurements and simulations can befurther addressed to uncertainties in climate data derived fromnearby weather stations of the German weather service (max.

Fig. 6. Regional pattern of total N-Deposition and nitrate leaching rates [kg N ha�1 a�1] of forest ecosystems in Germany for the year 2000.

0-10

10-15

15-20

20-25

25-30

30-35

35-40 >4

00

10

20

30

NO

3-leac

hing

[kg

N h

a-1 y

r-1]

N-deposition classes [kg N ha-1 yr-1]

Fig. 7. Relationship between the magnitude of atmospheric N deposition and simu-lated nitrate leaching from forest ecosystems in Germany for the entire dataset.

R. Kiese et al. / Environmental Pollution 159 (2011) 3204e32143210

distance 10 km, data from Level II sites were not completely avail-able) and uncertainty involved in the CMB approach used to deriveannual seepage water by changes of the mean chloride concentra-tion in seepage water and rainfall (Eq. (5)). Comparing our simula-tion results of seepagewater formation for entireGermany resultingfrom the regional application of the Forest-DNDC model with theones given by the Hydrological Atlas of Germany (BMU, 2003)showed good agreement in the spatial pattern as well as in themagnitude of water fluxes (data not shown). This further demon-strates the overall capability of Forest-DNDC to realistically describesite and regional differences in forest water cycling.

4.2. Nitrate leaching

Simulation of nitrate concentrations in different soil layerscovering the whole soil profile was restricted to five sites in Bavariadue to the fact, that for most other Level II sites model input dataand/or measurements were rather incomplete or did not cover longenough periods to allow a reasonable model evaluation. For thesefive sites, Forest-DNDC was able to explain 66% of the variability ofmeasured NO3 concentrations, demonstrating the overall capabilityof the model to mimic concentration differences between soillayers with higher concentration in the top soil and differences inconcentration levels across sites which could be explained bydifferences in site specific atmospheric loads of N-deposition (seealso Fig. 4 and further discussion of results).

Model evaluation on annual nitrate leaching rates at the 74 level IIforestmonitoring relied on estimates based on the CMB approach by

Table 2Simulated nitrate leaching rates and contribution of states to total nitrate leaching from forests in Germany.

State Area Total NO3 leaching Mean NO3 leaching rate %-Contribution to totalNO3 leaching

[1000 ha] [t N yr�1] [kg N ha�1 yr�1] % Areaa weighted

Schleswig-Holstein 296 684.5 2.3 1.3 0.42Hamburg 15 15.5 1.0 0.0 0.19Lower Saxony 1185 5235.7 4.4 9.6 0.80Bremen 1 1.6 1.6 0.0 0.29North Rhine Westphalia 903 6091.2 6.7 11.2 1.23Hesse 914 2775.8 3.0 5.1 0.55Rhineland Palatinate 613 3031.2 4.9 5.6 0.90Baden-Wurttemberg 1620 9780.6 6.0 17.9 1.10Bavaria 2523 16383.0 6.5 30.0 1.18Saarland 95 963.1 10.1 1.8 1.84Berlin 10 63.9 6.4 0.1 1.16Brandenburg 578 2576.5 4.5 4.7 0.81Mecklenburg-West Pomerania 266 1275.2 4.8 2.3 0.87Saxony 344 2154.4 6.3 3.9 1.14Saxony Anhalt 228 1304.0 5.7 2.4 1.04Thuringia 331 2240.5 6.8 4.1 1.23Total 9922 54576.8 Avg. 5.5 100.0

a (Total NO3 leaching state/Total NO3 leaching Germany)/(Area state/Total area Germany).

R. Kiese et al. / Environmental Pollution 159 (2011) 3204e3214 3211

multiplying the amount of annual seepagewater calculated from theCMBwith themeannitrate concentrationof the lowest soil layer. Theelement flux could have been better derived by multiplying thecalculated daily water fluxes with measured weekly or biweeklyelement concentrations that are interpolated to daily values (see DeVries et al., 2010) but unfortunately, we did not have such resultsavailable. Due to temporal variations in soil NO3 concentrationsacross the year (Kristensen et al., 2004) and a rather episodicoccurrence of leaching events, the mean annual soil NO3 concen-tration might not be fully representative, thus causing a highuncertainty for the approximated annual leaching rates at the level IIsites by theCMBapproach. Following this hypothesismultiplying themean daily simulated NO3 concentration of the lowest soil layer andthe simulated seepage water indeed resulted in significantlydifferent leaching rates as compared to daily, and thus “event driven”simulation runs. Beside model uncertainty further uncertainty canbe related again to the use of climate data fromnearby stations of theGerman weather service and the approach we used to estimateatmospheric N deposition for given sites using the Gauger et al.(2002) dataset rather than the throughfall concentration dataprovided with level II sites. This was done since use of throughfalldata will result in a significant underestimation of the local Ndeposition situation due to canopy exchange. Despite these uncer-tainties, Forest-DNDC was able to explain more than 51% of thevariability of the measured data, which is comparable to the DNDCmodeling study of nitrate leaching from a U.S. agroecosystem(Tonitto et al., 2010, r2 ¼ 0.74). Furthermore, a comparison of mean(2.8/2.3), median (0.9/0.5), 25% (0.2/0.1), and 75% (3.0/4.2) percen-tiles of simulated and CBM approximated nitrate leaching demon-strated a goodagreement betweenapproacheswith regard to annualnitrate leaching rates (kg N ha�1 yr�1). It also shows good agreementwith the evaluation of Borken and Matzner (2004), who founda weak relationship between N deposition and NO3 leaching for thesame level II monitoring dataset. They could explain only 30% of thevariability of CMB approximated N leaching rates. This indicates thatother factors such as percolation, nitrification, denitrification,immobilization and plant N-uptake are playing an even moreimportant role in explaining site differences in nitrate leaching rates.These factors, which are differing across sites, and which may besummarized as N status of a forest site (MacDonald et al., 2002) aretaken into account by process models such as Forest-DNDC.

Estimates of nitrate leaching from forest ecosystems at the scaleof Germany for the year 2000 by coupling Forest-DNDC to a GIS

varied between 0 and 85 kg NO3-N ha�1. Spatial patterns of nitrateleaching could be explained mostly by atmospheric N depositionand annual amount of rainfall as well as SOC. The average rate ofnitrate leaching from forest ecosystems in Germany was 5.5 kgNO3eN ha�1 yr�1. This is twice as high as simulations and CBMapproximated nitrate leaching rates for the 74 Level II sites, indi-cating, that the selection of sites within the forest monitoringacross Germany at least with respect to nitrogen inputeoutputstudies may not be representative. Despite the uncertainties inmodel simulations this demonstrates that process-based modelsmay help for identification of potential hotspots which could thenbe integrated or help to adapt existing monitoring programs.Relating simulated nitrate leaching rates (N ¼ 17,149) to classes oftotal atmospheric N-deposition (Fig. 7) revealed that significantleaching rates (>2.5 kg N ha�1 yr�1) occur at atmosphericN-depositions > 15e20 kg ha�1 yr�1 and highest leaching ratesoccur at forest floor C/N ratios < 25 indicating potential nitrogensaturation (data not shown). This agrees well with findings of theNITREX EU-Program (e.g. Dise and Wright, 1995; Gundersen, 1995;Gundersen et al., 2002), an evaluation of the German level II datasetby Borken andMatzner (2004), a detailed review of ICP Level II sitesalong Europe (Van der Salm et al., 2007; Dise et al., 2009) andfindings for N-affected forest ecosystems in the North-Eastern US(Aber et al., 2003).

Model validation on regional scale is always problematic sinceavailability of data on regional scales is quite scarce. However,Mellert et al. (2005) developed a nitrate leaching inventory fromforest soils for Bavaria, so that we were able to compare ourregional simulation results for Bavaria with the mentioned study.On basis of soil NO3 concentrationmeasurements at 399 forest sitesin the years 2001 and 2002 Mellert et al. (2005) estimated nitrateleaching rates and classified them into three categories (0e5, 5e15and>15 kg N ha�1 yr�1). According to their evaluation 66% of forestecosystems in Bavaria show annual nitrate annual leaching rates of0e5 kg N ha�1 yr�1, 20% of 5e15 kg N ha�1 yr�1. Fourteen percent offorest ecosystems may show nitrate leaching rates >15 kg Nha�1 yr�1. These values are in very good agreement with our esti-mates for Bavaria (0e5 kg N ha�1 yr�1:74%; 5e15 kg N ha�1

yr�1:20%; >15 kg N ha�1 yr�1:6%).The results of the application of Forest-DNDC for entire

Germany were also used to derive mean nitrate concentrations inthe seepage. Seepage nitrate concentrations may be used asa conservative (since concentrations in the top soil are even higher)

R. Kiese et al. / Environmental Pollution 159 (2011) 3204e32143212

indicator for evaluating the nutrient status of forest ecosystems assuggested by De Vries et al. (2007a,b). The mean nitrate concen-tration in the seepage water of forest ecosystems in Germany in theyear 2000 was 1.3 mg N l�1 (5.8 mg NO3 l�1). The regional patternshows generally higher concentrations in regions with comparablelow seepage water formation and thus, enrichment of NO3 in soilwater solution and again in regions with high N-deposition (Fig. 8).A more detailed analysis of the results for the state of Bavariarevealed that 15% of forest ecosystems showedmean seepagewater

Fig. 8. Simulated mean nitrate concentrations [mg N l�1] in seepage

nitrate concentrations exceeding the former German threshold fordrinking water of 25 mg NO3 l�1 and 8% of all forest ecosystemsexceeding the EU threshold of 50 mg NO3 l�1. Taking into accountthe range of 3e6 mg N l�1 given in a review of critical loads ofnitrogen for terrestrial ecosystems in Europe by De Vries et al.,(2007a,b), our study demonstrates that a potential risk not onlyfor groundwater pollution but also with regard to nutrient imbal-ances and shifts in plant biodiversity exists in German forestecosystems if atmospheric N deposition remains on current levels.

water under forest ecosystems in Germany for the year 2000.

R. Kiese et al. / Environmental Pollution 159 (2011) 3204e3214 3213

4.3. Model adequacy for risk assessment

Here we showed an approach where we tested and used thebiogeochemical Forest-DNDC model e which has been applied ina series of studies to simulate gaseous N losses from systems fromregional to continental scales (Butterbach-Bahl et al., 2001; Kesiket al., 2005; Kiese et al., 2005; Werner et al., 2007) e for its capa-bility to simulate nitrate leaching from forest ecosystems. Theresults found are stimulating. In combination with the provenadequacy of the model to predict N2O and NO fluxes, we areproviding evidence that the model is developing in a direction thatallows quantification of N balances at regional scales. This impliesthat it can be used for an assessment of environmental risks of Ndeposition with regard to hydrosphereeatmosphere N lossprocesses. Themodel may also be used to explore critical limits of Ndeposition to minimize N losses or to better understand howclimate change may impact biosphereeatmosphereehydrosphereexchange processes, even though this will require a further evalu-ation of uncertainties. However, in accordance with Tonitto et al.(2010) this can only be realized if more quality assured long termdatasets covering all aspects of climate, deposition, water andnutrient cycling and export as well as plant growth are availablesince they are still scarce but essential for understanding thelimitations of model outcomes and guidance of further modeldevelopments.

Acknowledgements

This study received support by the Nitroeurope IP and theUmweltbundesamt (UBA, FE 205 85 209, FE 205 85 239) and isa contribution to Modelling and mapping of spatial differentiatedimpacts of nitrogen input to ecosystems within the framework ofthe UNECE e Convention of Air Pollution Prevention. We aregrateful to the Forest Research Institutes of the German states foragreeing to use their data from Level II sites. We also thank W. Luxand W. Seitling from vTI Braunschweig for data compilation andaccess to the central Level II database. In particular we are thankfulfor the supply of detailed data of the forest climate stations oper-ated by the Bavarian Forest Institute (LWF), Freising, Germanynamely S. Raspe, Ch. Schulz H. P. Dietrich and W. Grimmeisen.

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