Research Articles

Simulation of Soil Hydrology

Research Articles

Simulation of Soil Hydrology and Establishment of a Nitrogen Budgetof a Mountain Forest
Robert Jandl1*, Hannes Spögler2, Jiri Simunek2 and Lee K Heng3
1 Institute of Forest Ecology, Forest Research Center, A-1131Vienna, Austria
2 UC Riverside, USDA Soil Salinity Lab, Riverside, CA 92507-4617, USA
3 International Atomic Energy Agency, A-2444 Seibersdorf, Austria* Corresponding author (robert.jandl@fbva.bmlf.gv.at)

Abstract. The water fluxes through the mountainous forest eco-system 'Mühleggerköpfl' were simulated by means of the mechanistic soil physical model Hydrus 1D. The objective was to set
up a nitrogen budget in order to decide if the ecosystem accumulates nitrogen or if nitrogen leaks from the site. The simulated annual loss of N by percolation ranges between 0.4 and 1 g
N m–2 yr–1 and is smaller than the annual input by bulk and occult deposition, which combines to approx 1.2–1.5 g N m–2 yr–1.
Obviously the forest soil presently accumulates N. With an N
input-rate exceeding the N output, the operationally defined
status of N saturation is not yet reached. Comparing the magnitude of the N pool in the soil (several kg N m–2) with the rate of
the annual increase (a few g N m–2 yr–1), the process of N saturation is apparently slow.
Keywords: Alps; forest soil hydrology; N budget; N saturation;
nitrogen; spruce forest

Introduction

Among all biogeochemical nutrient fluxes, that of nitrogenhas been changed by such human activities as combustionof fossil fuel, fertilizer application, livestock, and the burning of biomass to the greatest extent. Ecosystems are expected to become N saturated (Vitousek et al. 1997). There
are, however, ecosystems with a strongly diminished N pool.
Many forests in Austria have a long history of nutrient exploitation and nitrogen is commonly the growth-limiting
element. The continuous addition of excess nitrogen is expected to have a noticeable effect on biogeochemical fluxes
and may in the long run lead to N eutrophication with negative effects on forests (Aber et al. 1995, 1998). Currently,
the productivity of forests in Central Europe is increasing(Spiecker et al. 1996). Evidence for N eutrophication was
found in forests with high rates of N deposition, but it was
proposed that even low rates of N deposition can affect sensitive forest ecosystems (Baron et al. 1994, 2000, Emmett etal. 1998). We wanted to substantiate if the concern of N
eutrophication is valid at our study site and if forests onshallow dolomite-derived soils are particularly sensitive towards high rates of N deposition. It is unclear yet, whether
forests respond to increased nitrogen inputs with higher productivity due to the increased N availability, or the N reten-

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tion proceeds to an N level where the system changes dramatically. Ecological theory suggests that gradual changes
in one factor are buffered for a certain time, dependent on
the system's resilience, but abrupt changes are inevitable(Scheffer et al. 2001).
The Alps form an abrupt barrier and efficiently intercept rainfall. Although nitrogen concentrations in the rain are low, themere quantity assures that the deposition load is high. Moreover, fog can contribute a large part of the nitrogen deposition(Binkley et al. 2000, Fenn et al. 2000, Igawa et al. 1998). At
calcareous sites in the mountain area, there are natural obstacles to overcome when establishing a nutrient budget. Field
measurement of the nutrient output by leaching is impossible
because no defined pathways of the soil water exist. In thecoarse-textured soil, the water flux varies on the smallest spatial scale and streams carry water only temporarily (Hagedorn
et al. 1999). The incoming water is mostly absorbed in the
porous soils and gravitates along variable paths.
We applied the mechanistic soil physical model Hydrus(Simunek et al. 1998) to simulate soil hydrology. It requires
detailed soil physical and meteorological input data. The
steps were to (i) parameterize the models with the existingdata set, (ii) run the simulation, (iii) identify/corroborate themajor factors, (iv) do an error analysis upon unrealistic values of modeling results, (v) refine the parameters, and (vi)obtain answers from the simulation.
1
1.1

Methods
Site and standThe study site Mühleggerköpfl, province of Tyrol (11°38'21"E
47°34'50"N) is located on a N-NE facing slope in 895 m a.s.l.
The bedrock is dolomitic limestone from which a mosaic ofrendzinas and terrae fuscae has developed (Englisch 2001).
The total N deposition is 1.2-1.5 g N m–2 yr–1. The more than
120-year-old stand consists mainly of Norway spruce.
1.2

Water balance

The water balance consists of the incoming precipitation,
the evaporation from the canopy and the surface, the transpiration, and the vertical and lateral output of the system.

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Research Articles

Simulation of Soil Hydrology

The 'precipitation above the canopy' was measured bi-weekly
at a small, clear cut adjacent to the stand. Measured precipitation data were harmonized with the daily measurements of ameteorological station and were converted to daily data.

1.4

In the field, we encountered a considerable small-scale variability of the soil profile morphology. A reference profile waschosen as representative soil for the site (Englisch 2001). The
root distribution was assessed by visual inspection of the reference profile. This parameter partitions the water uptake,which equals the calculated transpiration, over the profile. It
reflects the shallow rooting system of Norway spruce: the layer
0–6 cm contains 60% of the roots; 30% of the roots are found
at 6–25 cm and the remaining 10% between 25 and 40 cm. A
water retention function was obtained by calibrating a waterflow model against measured data (Table 2).

The throughfall was collected bi-weekly. A linear regressionfunction with zero intercept between measured amounts ofprecipitation above and below the canopy was calculated.
The regression was used to derive values for the daily precipitation below the canopy. Curvilinear models did not
improve the data quality and could be ignored.
Evapotranspiration was calculated with the simulation program
Brook-90 that uses the Penman-Monteith formula (Federer
1995). Climate data (precipitation above the canopy [mm],minimal, maximal, mean air temperature Tm [°C], solar radiation [W m–2], relative humidity rH [%]) were taken from thelocal climate station at the experimental site. The vapor pressure deficit Dp [hPA] was calculated from the saturated vapor
pressure Es [hPa] and actual vapor pressure e [hPa], respectively,
and the mean air temperature, and relative humidity (Eq. 1).
Dp = Es –e

The soil physical characteristics, derived from retention
curves that had been generated from undisturbed soil samples in the lab, did not satisfyingly reflect the site (Feichtinger& Scheidl 2001).
1.5

Simulation model

For modeling the water and solute transport, we used the model
HYDRUS 1D Version 2.01 (Simunek et al. 1998). HYDRUS1D uses the Richards' equation for simulating variably-saturated flow and Fickian-based convection-dispersion equations
for heat and solute transport. The water flow equation incorporates a sink term to account for water uptake by plant roots.
The solute transport equations consider convective-dispersive
transport in the liquid phase, as well as diffusion in the gaseous
phase. The transport equations also include provisions fornonlinear non-equilibrium reactions between the solid and liquid phases, linear equilibrium reactions between the liquid andgaseous phases, zero-order production, and two first-order degradation reactions: one which is independent of other solutes,
and one which provides coupling between solutes involved in
the sequential, first-order decay reactions. The model includes
modules for the discretization of the soil profile into finite elements, and definition of the vertical distribution of hydraulicand other parameters characterizing the soil profile. It is a tool
for predicting water and solute movement in the vadose zone,
and extrapolating information from a limited number of field
experiments to different soil and climatic conditions. HYDRUS1D does not handle preferential water flow and may fail for
extremely nonlinear flow and transport problems.

(1)

Es = 6.1054*EXP(17.27*Tm/[237+Tm])
e = Es * rH / 100
The climatological input data set for HYDRUS requires thedistinction of root and soil respiration. The calculated
evapotranspiration was assigned to root respiration, if theminimum air temperature was above freezing point, because
Norway spruce starts to transport water as soon as positive
temperatures are reached (Körner 1999). If the minimal temperature was below 0°C, it was assumed that only evaporation from the soil, but no transpiration, took place.
1.3

The soil profile

Soil moisture / soil solution chemistry

At four soil profiles, the water content was recorded at 3
soil depths every 30 min by means of thermocouples. Samples of the soil solution were collected every two weeks bymeans of suction cups and were analyzed. The technicalequipment of the site is described in Feichtinger & Scheidl(2001), the chemical analysis in Smidt (2001).

Table 1: Summary of key values of the water balance at the Mühleggerköpfl site

Year

Precipitation [mm]Throughfall [mm]Evapotranspiration [mm]Infiltration [mm]Outflux [mm]

1998

1691

1489

702

1300

787

1999

1976

1729

817

1510

873

2000 (IXI)

1818

1577

792

1460

800

Table 2: Parameters of the van Genuchten (1980) model derived from field measurements for individual layers. Θr and Θs are residual and saturated watercontents (1=100%), respectively, α and n are dimensionless shape parameters, K is the hydraulic conductivity of the saturated soil (cm day–1)

Layer

Depth

Θr

Θs

α

N

Ks

Forest floor

50 cm

1E-005

0.57

0.003

1.147

985.3

A

05 cm

0.08

0.293

0.0025

2.919

27.99

C1

525 cm

0.19

0.409

0.0023

1.547

23.13

C2

2550 cm

0.013

0.179

0.0425

1.374

21.39

C3

>50 cm

0.001

0.239

0.016

1.106

22.203

ESPR – Environ Sci & Pollut Res • Special Issue 2 (2002)

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Simulation of Soil Hydrology

Research Articles

For simulation purposes, the soil profile down to 75 cm depth
is discretized into a series of 100 finite elements. We set thedensity of the elements to a high value in the upper part ofthe soil profile, where we expect larger pressure gradients,
and gradually increased the element size downwards.
1.6

Calibration of model parameters, validation of simulated
results, setting up a nitrogen budget

For the calibration of the model using the soil physical properties from Table 2, we took data of the first year of measured water contents. The parameters were optimized in order to provide the best possible fit between measured andsimulated values for three depths and one year. The parameters were validated by comparison of simulated and observed water contents of the two remaining years and wererefined until the extent and the temporal pattern of changes
in the soil water content were reflected. In general, the measured data showed a larger amplitude than simulated watercontents. The correlation between measured and simulated
water contents varies between r=0.35 and r=0.40 and is significant (α <0.1%) (Fig. 1).

Fig. 2: Water fluxes into and out of the soil at the experimental site Mühleggerköpfl, displayed as cumulative functions for the individual years of
the experiment1999 and 2000 were considerably wetter than 1998. The rateof the outflux of water shows more variability than the water
input, because the water flux density quickly responds to heavy
rain. Single intense precipitation events (e.g. May 1999) had astrong influence on the total outflux from the system. A numberof heavy rain periods in the second half of the year 2000 led tosimilarly high seepage rates.
The temporal trend of the water retained in the soil profile, the'pressure head', shows only small differences between the upper and lower parts of the soil (data not shown). The pressure
head is somewhat smaller at the top of the soil because roots
tap the water resource. Despite some temporal fluctuation ofpressure heads, the trees never experience a water deficit. The
amount of rainfall is sufficiently large to supply the plants, eventhough the water-holding capacity of the soil is relatively low.
Small infiltration events (i.e. rain reaching the soil surface) areretained in the soil profile, whereas intensive rain almost immediately leads to the formation of water outflux from the soil.
Water saturation is reached at approx. 220 L m–3. The simulated water content in the entire soil profile varies between 180
and 210 L m–3 most of the time.
For the estimation of the N output of the soil, we used thenitrate concentrations in 50 cm soil depth and the simulated
outflux of the soil (Fig. 3; see also Fig. 2). The nitrate concentrations were considerably higher in 1998 than in thesubsequent years.
Multiplying the nitrate concentration in the soil solution withthe flux density of water yields an estimate of the annual N
output. The calculated output of NO3-N is 1.2 and 0.4 g m–2

Fig. 1: Calibration of soil physical parameters with field measurements ofthe soil water content in the year 1998. Note different scales for individual
soil depths at the y-axis

2

Results

The main water transport processes, i.e. cumulative infiltration,transpiration, and outflux (=seepage flux) are shown in Fig. 2.
At any time, the amount of water reaching the soil surface islarger than the water demand of the forest. Obviously, there isan almost constant supply of water to the soil surface. The episodes of outflow are less frequent, because the soil temporarily
retains water. The annual rainfall varies less than the rate of thethroughfall that ultimately reaches the soil surface. The years

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Fig. 3: Temporal trend of nitrate concentrations in the soil solution for years
1999 and 2000; depth 50 cm

ESPR – Environ Sci & Pollut Res • Special Issue 2 (2002)

Research Articles

Simulation of Soil Hydrology
References

Fig. 4: Nitrate-N flux out of the soil at the experimental site Mühleggerköpfl,displayed as cumulative functions for the years 1999 and 2000

for the years 1999 and 2000, respectively (Fig. 4). The value
for the year 2000 reflects January to October only. Nevertheless, only initially (1998) high rates of leaching have been observed, but the values are low in the years 1999 and 2000.
3

Discussion

Precipitation events at the experimental site Mühleggerköpfl
are frequent and the entire soil profile stays moist all year
round. The pore space of the soil profile down to 1 m allows
it to retain about 220 L water. This is a high volume, considering the high content of coarse material. The high frequencyof rain causes the quick displacement of soil water. Events ofheavy rain quickly fill the soil pores with water and frequently
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The simulated N output varies between 0.4 and 1 g N m–2.
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ESPR – Environ Sci & Pollut Res • Special Issue 2 (2002)

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