Meteorol Atmos Phys 84, 137–156 (2003)
DOI 10.1007/s00703-002-0596-0
1
Geophysical Institute, University of Alaska Fairbanks, Fairbanks, AK, USA
Staatliche Umweltbetriebsgesellschaft, Brandis, Germany
3
Universit€at Halle-Wittenberg, Institut f€
ur Agrar€
okonomie und Agrarraumgestaltung, Halle=Saale, Germany
2
Long-term investigations on the water budget quantities
predicted by the hydro-thermodynamic soil vegetation scheme
(HTSVS) – Part II: Evaluation, sensitivity, and uncertainty
N. Mölders1 , U. Haferkorn2 , J. Döring3 , and G. Kramm1
With 8 Figures
Received October 8, 2001; revised February 15, 2002; accepted September 20, 2002
Published online: April 10, 2003 # Springer-Verlag 2003
Summary
1. Introduction
Various water budget elements (water supply to the
atmosphere, ground water recharge, change in storage)
are predicted by HTSVS for a period of 2050 days. The
predicted water budget elements are evaluated by routine
lysimeter data. The results show that land surface models
need parameterizations for soil frost, snow effects and
water uptake to catch the broad cycle of soil water budget
elements. In principle, HTSVS is able to simulate the
general characteristics of the seasonal changes in these
water budget elements and their long-term accumulated
sums. Compared to lysimeter data, there is a discrepancy in
the predicted water supply to the atmosphere for summer
and winter which may be attributed to the hardly observed
plant physiological parameters like root depth, LAI,
shielding factor, etc., the lack of measured downward
long-wave radiation, and some simplifications made in the
parameterizations of soil frost and snow effects. The fact
that high resolution data for the evaluation of model
results are missing and evaluation is made on the basis
of the data from routine stations of a network is typical
for the results of long-term studies on climate. Taking
into account the coarse resolution of climate models,
the coarse vertical resolution that is used in their LSMs, and
the lack of suitable parameters needed, it seems that
discrepancies in the order of magnitude found in this study
are a general uncertainty in the results of land surface
modeling on typical spatial and temporal scales of the
climate system.
To investigate the impact of land-use and=or climate changes on future water budget elements
climate models require sophisticated, evaluated
land surface models (LSM). M€olders et al (2003)
introduced the improved version of the hydrothermodynamic soil-vegetation-scheme (HTSVS)
wherein parameterizations of root water uptake,
soil frost and snow effects as well as some minor
changes were included to fulfill the more sophisticated needs of climate modeling as compared
to short-term applications. Results of the new version of HTSVS substantiate the impact of snow
and soil frost effects on predicted soil temperatures and water budget elements, the improvement
gained with respect to soil temperature, and the
importance of the Ludwig-Soret effect (i.e., a
temperature gradient contributes to the water flux
and changes soil volumetric water content) and
Dufour effect (i.e., a moisture gradient contributes
to the heat flux and alters soil temperature) on the
long-term (M€olders et al, 2003).
Based on the observation of mean values of
wind speed, temperature, and humidity as well
as the eddy flux densities (usually denoted as
138
N. M€
olders et al
eddy fluxes) of momentum, sensible heat,
and water vapor performed during the Great
Plains Turbulence Project, GREIV I-1974
(GREnzschicht Instrumentelle Vermessungsphase
I, i.e., first phase of probing the atmospheric
boundary layer, e.g., Kramm, 1995), SANA
(Sanierung der Atmosph€are €
uber den neuen
Bundesl€ander, i.e. recovery of the atmosphere
over the new federal countries, e.g., Spindler
et al, 1996), CASES97 (Cooperative Atmosphere
Surface Exchange Study 1997, e.g., LeMone
et al, 2000), Kramm (1995), and M€olders
(2000) showed that HTSVS is able to simulate
the diurnal course of all these first and second
moments well. Thus, on the short-term scale
HTSVS performs well. To be a suitable tool for
climate modeling the variability on the long-term
has to be caught (e.g., Govindan et al, 2002) by
the LSM and the LSM has to guarantee a sufficient accuracy for the long-term accumulated
sums of the water supply to the atmosphere and
recharge. To give answers on typical questions
directed to climate modeling studies (e.g., on
the impact of changes in land-use and=or climate
on water budget elements, water availability,
etc.), not the hourly values, daily sums or the
diurnal course are of main interest, but the
long-term accumulated sums (e.g., for a season,
year, decade, etc.). This suitability for long-term
integration can only be evaluated by means of
routine data because there is no data set of the
water vapor fluxes and ground water recharge
collected during sophisticated special field
experiments which are continuously performed
over several years. Routine data, however, are
of less accuracy compared to those gained in
such field campaigns. Moreover, some data that
are required to feed the model are not recorded or
are of imperfect quality. Therefore, an evaluation
using routine data will never provide as good
results as those that can be achieved when all
needed data were measured under the special
conditions of field campaigns (e.g., Spindler
et al, 1996; Slater et al, 1998).
Lysimeters (see Fig. 1) are well appropriate
for long-term monitoring of most water budget
quantities like ground water recharge, R, and
change in soil water storage, S. Moreover, they
provide an important process level in up-scaling
because they allow inferring from the laboratory
to field-scale (Keese et al, 1997). Usually, R, S,
Fig. 1. Schematic view of a lysimeter at Brandis
and precipitation, P, are routinely determined on
a daily basis. The percolated water leaving the
lysimeter at its outlet is considered to represent
recharge. By neglecting the small daily change in
weight by plant growth, the change in weight of
the lysimeter is interpreted as the change in
soil water storage. According to the so-called
lysimeter equation, the water supply to the atmosphere is given by (e.g., Marshall et al, 1996),
E ¼ P R S:
ð1Þ
Herein, the residuum E may represent the sum or
some of the following processes: evaporation of
soil water, puddles and intercepted water, transpiration by plants, and=or sublimation of snow.
Choudhury and Idso (1985), for instance,
already used lysimeter data on evapotranspiration
for model evaluation on a short-term scale. Due
to the defined and measurable boundary conditions lysimeter-data are suitable to evaluate
LSMs of climate models under near-natural conditions on time scales much greater than those of
sophisticated field experiments. Thus, such data
may be used to decide whether a LSM is appropriate for long-term integration as needed in climate modeling or not.
In this paper, we try to evaluate the water
budget predictions of HTSVS on a long-term
scale by using routine lysimeter data, where
the relevance of the various parameterizations
additionally introduced into HTSVS for its
application in climate models (see M€
olders
Water budget elements predicted by HTSVS
et al, 2003) is pointed out. Since sets of routine
data will always be incomplete with respect to
initial and boundary conditions as well as plantphysiological properties (e.g., distribution, mass
and density of roots, minimum stomatal resistance, albedo and emissivity of the foliage, etc.)
and soil transfer parameters (e.g., heat conductivity, hydraulic conductivity, etc.), the (1)
uncertainty in the water budget predictions due
to such incomplete routine observations, and (2)
possible measurement errors are analyzed. An
additional goal is to demonstrate that HTSVS
simulates the water budget elements in longterm studies to a sufficient degree of accuracy
without calibration. Calibration means that a
large part of a whole data set is used to discover
the optimum (temporally varying) values for
plant-physiological properties and soil transfer
parameters, while the remaining part is only
considered for model evaluation. Applying such
a typical calibration technique would, certainly,
lead to better predictions than those presented
here. There are, however, some aspects that
have to be considered realistically: (1) the landuse type of the lysimeters at Brandis changed
more or less from season to season (see Fig. 1
in M€olders et al, 2003) so that not enough data
were available for calibration and long-term
evaluation, (2) climate prediction with calibrated LSMs would strongly demand unchanged
land-use of landscapes on the long-term scale,
(3) coupling LSMs with biome modules as
indispensable to climate models (e.g., Claussen,
1997; Eastman et al, 2001) seems to be extremely difficult with calibration, and (4) data sets
of consistent plant-physiological properties and
soil parameters are still scarce, but reasonable
values have to be presupposed in climate modeling. Based on all these reasons, we decided to
hold off calibration techniques from evaluation
of HTSVS that is considered to act in climate
models.
2. Brief description of HTSVS
HTSVS (e.g., Kramm, 1995; Kramm et al, 1996;
M€olders et al, 2003) describes the exchange of
momentum, heat, and moisture at the vegetationsoil-atmosphere interface under consideration of
the heterogeneity on the micro-scale by means of
Deardorff’s (1978) mixture approach. Herein, the
139
exchange of energy and matter between the
vegetation-soil-system and the atmosphere as well
as between the soil-snow-system and the atmosphere is parameterized by resistance network
analogy. The impact of ambient air temperature,
photosynthetic active radiation, soil water and
water vapor deficit on transpiration is accounted
for by so-called correction functions (e.g., Jarvis,
1976; Dingman, 1994; Kramm, 1995). HTSVS
considers the coupled heat conduction and water
diffusion within the soil (i.e., Ludwig-Soret
effect and Dufour effect are considered), as well
as the variable ground water depth responding
to the previous meteorological conditions. It
includes parameterizations for infiltration, soil
water extraction by roots, soil frost and thawing,
the insulating effects and melting of snow (see
M€olders et al, 2003).
3. Data description and processing
At Brandis (51.32 N, 12.62 E, 133 m above sea
level, south-east of Leipzig, Saxony), lysimeters
are routinely pursued since 1980. Change in storage and recharge are measured daily. Percolated
water leaving the lysimeters at their outlets in
2.95 m depth is assumed to represent ground
water recharge or subsurface runoff. Water supply to the atmosphere is derived as a residuum
from the lysimeter equation (1).
The lysimeters (Fig. 1) contain natural monoliths (3 m depth, 1 m2 area) that were gained in
1980 in southern East-Germany. As these soils
were deposited during the ice-age, their density
is higher than for fluvially deposited soils. They
are representative for large regions of the southern Baltic region. Porosity, field capacity, fc,
permanent wilting point, pwp, hydraulic conductivity at saturation, soil density, as well as
the percentage of clay, silt, sand, and humus
were determined when installing the lysimeters.
Four methods, namely the granulation-method,
pf-curve-method, a combined granulation-pfcurve-method, and a combined granulation-pfcurve-method under consideration of the soil
skeleton were applied to determine the first three
mentioned quantities (e.g., Keese et al, 1997;
Haferkorn, 2001; see Table 2 in M€olders et al,
2003). Specific heat capacity of soil material is
given by the weighted average of the fraction of
particle size and humus.
140
N. M€
olders et al
Of each soil type three lysimeter are available.
After 14 years of running the lysimeters, soil characteristics were re-determined for one lysimeter
out of a group of three. Comparisons of the characteristics of soils in the lysimeters and those of
the soils at the origin sites determined in 1978
and 1994 showed variations within the range of
natural heterogeneity (Knappe and Keese, 1996;
Keese et al, 1997). Based on those results these
authors concluded that lysimeters can be run on
long-term scales, i.e. the lysimeter fulfill the
criteria to be achieved for climate evaluation
purposes as they are pointed out by Goody et al
(2002).
On May 23, 1992 a climate station was added
to the lysimeter site. Routine data of hourly mean
values of wind, relative humidity, temperature,
global radiation, and precipitation are available
form May 23, 1992 to December 31, 1997. Since
the soil at the climate station is of same type as
one of the lysimeter groups we chose the lysimeters of that group for our study.
The lysimeters are agriculturally managed
according to the surrounding field, i.e. winter
barley (1992), green fallow (1993; 1994), red
clover (1995), potatoes (1996), and summer
wheat (1997) were grown follwed by green
fallow after the harvest of barley, potatoes, and
summer wheat. Harvest and mowing on the lysimeters and their surroundings were performed
concurrently. Routine data of maximum root
length, canopy height, and plant phenology exist
about every ten days (Haferkorn, 2001). Shielding factor and LAI (see Fig. 1 in M€
olders et al,
2003) are deduced from the reported phenologic
characteristics using empirical relationships.
4. Design of the study
4.1 Initialization and data processing
HTSVS is run without prior calibration on the
Brandis site. Simulations start on May 23, 1992,
and are run without restart until December 31,
1997.
In our study, the soil is divided into nine layers
(by logarithmic spacing, see Table 2 in M€olders
et al, 2003) for which moisture and temperature,
the transfer of water and heat, as well as the
water extract by roots are predicted. Even though
the lysimeters extend to a depth of 3 m, the lower
boundary of HTSVS is chosen as zD ¼ 8.25 m
to guarantee proper water flux conditions at their
outlets.
At depth zD, a mean annual course of soil temperature, TS, at that level is prescribed and the
volumetric water content is held constant at fc.
Soil temperatures of the nearby climate station in
0.05 m, 0.1 m, 0.2 m, and 0.5 m depth and climate
data of similar soil properties serve to initialize
TS. Soil volumetric water content is initialized by
making use of the measured water deficit since
the time of total filling of the lysimeters, i.e.
when the soil achieves fc. It is assumed that
the uppermost part of soil lost water by evapotranspiration. Thus, the initial volumetric water
content in the uppermost layers is assumed to be
constant at 70% of fc and the lower layers are at
fc. The thickness of the uppermost layer is
adjusted to agree with the total amount of water
in the lysimeter and the water deficit. In doing so,
the depth-varying soil properties are taken into
account.
The vegetation-type of the lysimeter, maximum root depth, canopy height, h (which is used
to determine roughness length by z0 ¼ 0.1 h and
zero plane displacement by d0 ¼ 0.7 h, in accord
with Oke, 1978; Kramm, 1989; 1995), and LAI
are updated whenever new routine data are available. Although the canopy height hardly varies
among the lysimeters it is considered as observed
for the individual lysimeter.
4.2 Evaluation, sensitivity studies,
and uncertainty analysis
To evaluate the model accuracy, statistical measures (e.g., rms-errors, correlation coefficients,
bias, etc.) were determined for the various simulations. If in nature, a process (e.g., water uptake
by roots, soil freezing, etc.) occurs, it is determined by the variables of state and the fluxes
which themselves depend on these variables. To
describe this process in a model, it has to be
parameterized. In the real world, the process
depends on the natural characteristics (e.g., root
distribution, density of snow, etc.). In the model
world, however, these characteristics have to
be prescribed by parameters that are unknown
or not known well (see also Fig. 2). Thus, in
the model world, the effect of a process also
depends on the choice of the parameters, i.e.,
Water budget elements predicted by HTSVS
Fig. 2. Schematic view of processes in the model and in the
real world
source of uncertainty is involved even if the parameters and quantities are determined and chosen
with care and responsibility (see Fig. 2). Since
the stand-alone version of HTSVS is driven by
observations another source of uncertainty in
predicted water budget elements can be related
to errors in measurements (Fig. 2). Therefore, in
addition to the simulations that investigate the
sensitivity to the processes, various uncertainty
studies are carried out to examine the degree of
uncertainty due to parameters and forcing data.
The improvements gained by the parameterizations introduced by M€
olders et al (2003) are evaluated in Sects. 5.3–5.5.
Since the original data set does not include
downward long-wave radiation R1#, this quantity
is optionally parameterized in accord with Bolz
and Falkenberg (1949, denoted as B&F-scheme),
Idso and Jackson (1969, denoted as I&J-scheme),
Eppel et al (1995, denoted as EEA-scheme), and
Croley (1989, denoted as C-scheme) to examine the impact of parameterizing R1# on the
accuracy of the predicted water budget elements
(see Sect. 5.2).
Since measurements are subject to errors, various simulations are carried out to examine the
impact of observational errors on the predicted
water budget elements. Errors in the lysimeter
data may arise from differential heating of the
lysimeter walls by radiative effects and heating
of the lysimeter walls by the air of the cellar. It
has to be expected that these errors may slightly
affect the water fluxes due to the Dufour- and
141
Ludwig-Soret effects. Errors of observed
recharge and storage change amount to 0.1 mm
each. Errors caused by unknown initial and
boundary conditions are addressed in Sect. 5.6.
Since HTSVS is driven by routinely observed
meteorological data, random and unavoidably
systematic errors in these data may also lead to
errors in predicted water budget elements, which
are discussed in Sect. 5.7. Considering the results
of all three lysimeters serves for estimating the
impact of heterogeneity within the soils on the
water budget as performed in Sect. 5.8. Errors
resulting from the uncertainties in soil parameters and the assumptions made on the plant
physiological parameters are evaluated in Sects.
5.8 and 5.9.
5. Results and discussion
The reference simulation (Table 2) is carried out
by applying the parameterizations for water
uptake by roots, snow and soil frost effects
derived by M€olders et al (2003). Herein, the soil
data derived from pf-curve are applied because
data from combined methods are seldom available for large scale areas as required for climate
models. The B&F-scheme is used in the reference because it is independent from predicted
quantities. If not mentioned otherwise, the reference simulation will be discussed exemplary for
lysimeter 5.1 in the following.
Generally, the period from 1992 to 1995 was
relatively wet as compared to that from 1996 to
1997 (e.g., Figs. 3 and 4). Consequently, annual
recharge and evapotranspiration of the former
period exceed that of the latter.
The soils of the three lysimeters examined differ only by their natural heterogeneity that results
from differences in biologic activity, frost heave,
cryoturbation, macro-pores, stones, and different
soil material deposit. Clay lenses or great stones
may delay recharge by storing water on their
surfaces, while macro-pores may accelerate drainage. Comparison of Figs. 3 and 4, exemplary
illustrates differences in recharge, R, and, consequently, in water supply to the atmosphere, E, for
lysimeter 5.1 and 5.2, respectively. Table 1 lists
the statistical material regarding R and E for the
three lysimeters. Differences in R and E between
lysimeter 5.1 and 5.2 do not exceed 15% (R) and
4% (E), respectively. Since lysimeter 5.3 was
142
N. M€
olders et al
Fig. 3. Observed precipitation and comparison of predicted and measured daily recharge and water supply to the atmosphere
for lysimeter 5.1. The letters P, E, and R stand for precipitation, water supply to the atmosphere, and recharge, respectively
stopped in 1994, we also compare the 832-days
period where all three lysimeters run simultaneously. Differences were less than 16% and
10% for R and E, respectively. Based on this
832-days period, the observed water budget elements of lysimeters 5.1 and 5.2 even vary less
than 6% and 1% for the former and the latter
quantity (Figs. 3 and 4, Tables 1 and 2).
Water budget elements predicted by HTSVS
143
Fig. 4. Like Fig. 3, but for lysimeter 5.2. Note that the soil of lysimeter 5.2 has the same soil physical parameters, but it
differs from lysimeter 5.1 by the natural heterogeneity of soils that provides the differences in the observed water budget
elements between lysimeters 5.1 and 5.2 (see text for further discussion)
5.1.1 Recharge
The water fluxes calculated for the outlet level of
the lysimeters are assumed to represent recharge.
Since the HTSVS has no ‘‘solid bottom’’ at this
depth predicted water fluxes, in principal, can be of
both signs. Slight upward directed soil moisture
144
N. M€
olders et al
Table 1. Mean daily values and root mean square errors for recharge and water supply to the atmosphere for the various
lysimeters. Since lysimeter 5.3 was stopped in 1994 to re-determine the soil parameters here statistics of lysimeter 5.1 and 5.2
are given for the 832 days period. Statistics for the 2050-day period are given in brackets
Water supply to the atmosphere
Recharge
5.1
5.2
5.3
rms (5.1:5.2)
rms (5.1:5.3)
rms(5.2:5.3)
1.515 (1.447)
0.592 (0.459)
1.465 (1.392)
0.634 (0.526)
1.543
0.566
0.454 (0.415)
0.313 (0.324)
0.495
0.367
0.593
0.529
fluxes are predicted at some days in the extremely
dry year 1997 when no outflow at the outlets was
observed. An uptake of water through the lysimeter outlets, of course, has not been realized.
On average, predicted and observed recharge
differ from each other about the same percentage
as the recharge observed by the various lysimeters
(Tables 1 and 2). The 2050d-accumulated recharge is over-estimated by 7% for lysimeter 5.1
and under-estimated by the same amount for
lysimeter 5.2 (cf., Fig. 5, Table 2). The rms-errors,
however, are about 1.4 to twice as high as those
between the various lysimeters (Tables 1 and 2).
The predicted temporal development of R broadly
agrees with its observed equivalent (e.g., Figs. 3
and 4). Generally, the predicted recharge responses too quickly to strong precipitation events
(P > 40 mm=d; e.g., Figs. 3 and 4). This shortcoming of the model may result from an insufficient representation of natural soil heterogeneity
with respect to the vertical resolution of soil by
any numerical model. Clay lenses and stones,
which are often of sub-scale size, store water on
their tops until either the water surface tension
cannot hold further water or the stored water is
taken up by roots (see also Sect. 5.8 for a discussion on the effects of soil heterogeneity). Also
observed maximum peaks are usually under-estimated (e.g., Figs. 3 and 4) because macro-pores,
which can accelerate drainage after precipitation
events, are of sub-scale size with respect to the
vertical resolution of soil by any numerical model.
In fall, predicted R is about a month too early
(e.g., Figs. 3 and 4). Besides storing water on subscale sized clay lenses and stones, an under-estimation of E may contribute to this discrepancy
(cf., e.g., fall 1995). As a consequence of this
discrepancy, less recharge is predicted in late winter. In late winter and early spring, discrepancies
between predicted and observed values of R can
be explained by frost heave and cryoturbation that
cause macro-pores through which water may
more rapidly drain than through smaller pores.
Sediments fill the macro-pores when the soil collapses as soil ice melts and may reduce drainage.
Such changes in soil matrix, however, cannot be
considered in LSMs because of unavailable data
on soil properties that are changing during the
frost-thaw-cycles.
After snow events predicted recharge often
occurs earlier than observed (e.g., Figs. 3 and 4).
The reasons are manifold. In nature, the diurnal
course of thawing, percolation of melt-water
within the snow pack, and re-freezing as well
as other processes of snow metamorphism (e.g.,
water vapor diffusion from convex to concave
surfaces and from warm to cold layers, gravity)
delay infiltration and recharging. In the simulations, however, it is assumed that all snow melts
immediately when no snow is reported any
longer. Moreover, the snow captured by a rain
gauge may differ from that actually fallen onto
a lysimeter. Rain gauge catch deficiencies nonlinearly increase with wind speed and may
exceed 30% for a wind of 3 m=s (e.g., Dingman,
1994). The influence of catch deficiencies is
addressed to in Sect. 5.7.
Some discrepancies may also result from the
use of hourly mean meteorological data to force
the model. Strong, but short precipitation events,
for instance, may lead to ponded water that partly
evaporates, while the whole precipitation may
infiltrate if it is distributed equally over the entire
hour. In such a case, applying hourly data means
substituting showers by slight, but one hour lasting precipitation. Consequently, the partition of
precipitation between water supply to the atmosphere and infiltration disagrees with its natural
equivalent.
5.1.2 Storage
Obviously, precipitation, recharge, and water
supply to the atmosphere lead to a change in soil
(5.1)
E
1.550
1.810
1.971
1.674
1.561
1.516
1.418
1.398
1.436
1.567
1.549
1.346
1.530
1.556
1.549
1.550
1.552
1.527
1.821
1.547
1.542
1.538
1.615
1.447
1.468
1.518
1.100
1.550
1.544
1.543
1.447
1.565
1.471
1.686
1.822
1.285
1.124
Simulation
Reference simulation
Rl# Idso and Jackson (1969)
Rl# Croley (1989)
Rl# Eppel et al (1995)
Without roots
zd ¼ 0.3 m
Without soil frost
Without soil frost, granulation
Without frost, granulation, pf-c.
Without snow effects
snow ¼ 400 kg=m3
Without new parameterizations
TS( 8.25 m) ¼ 282 K
TS( 8.25 m) ¼ 295 K
Initial moisture profile 0.6fc
Initial moisture profile 0.8fc
Initial moisture profile fc
Initial moisture profile pwp
Variation of precipitation
Vari. of temperature ( 0.2 K)
Vari. of wind speed ( 0.5 m=s)
Vari. of global radiation ( 10%)
Vari. of humidity ( 0.5 g=kg)
Vari. of cloud cover ( 10%)
Granulation
pf-curve and granulation
pf-curve, granulation, skeleton
Soil torsion T ¼ 0.5
S ¼ f
"f ¼ 1
f ¼ 1.1 f (Tab. 1 in part I)
f ¼ 0.9 f (Tab. 1 in part I)
LAI ¼ 1
LAI ¼ 7
f ¼ 0 (bare soil)
f ¼ 1
Grassland all the time
1.105
1.099
1.254
1.051
1.125
1.066
0.977
1.091
1.076
1.105
1.104
0.985
1.100
1.106
1.104
1.105
1.108
1.085
1.325
1.101
1.093
1.089
1.092
1.103
1.070
1.086
0.985
1.105
1.119
1.102
1.094
1.113
1.062
1.240
1.325
1.101
1.028
rmsE(5.1)
0.491
0.333
0.337
0.417
0.654
0.466
0.453
0.435
0.445
0.431
0.492
0.468
0.497
0.474
0.478
0.490
0.506
0.483
0.278
0.512
0.501
0.499
0.449
0.560
0.403
0.447
0.526
0.492
0.500
0.495
0.498
0.483
0.518
0.429
0.278
0.670
0.821
(5.1)
R
0.755
0.736
0.710
0.746
0.906
0.741
0.734
0.831
0.867
0.755
0.756
0.785
0.761
0.746
0.751
0.758
0.819
0.730
0.726
0.748
0.752
0.757
0.737
0.791
0.854
0.901
0.674
0.755
0.758
0.756
0.757
0.751
0.772
0.729
0.726
0.831
0.945
rmsR(5.1)
1.547
1.969
1.512
1.671
1.555
1.515
1.416
1.394
1.430
1.563
1.544
1.344
1.526
1.552
1.547
1.546
1.548
1.526
1.409
1.543
1.538
1.533
1.538
1.440
1.464
1.512
1.085
1.547
1.541
1.539
1.531
1.562
1.469
1.679
1.824
1.280
0.944
(5.2)
E
1.121
1.288
1.097
1.069
1.141
1.079
0.986
1.093
1.081
1.120
1.122
0.972
1.117
1.122
1.121
1.122
1.125
1.103
1.151
1.118
1.108
1.106
1.114
1.119
1.080
1.097
1.015
1.122
1.137
1.118
1.110
1.133
1.063
1.268
1.374
1.096
1.046
rmsE(5.2)
0.491
0.329
0.452
0.409
0.632
0.480
0.459
0.431
0.444
0.433
0.480
0.463
0.495
0.486
0.490
0.491
0.509
0.487
0.192
0.517
0.503
0.486
0.508
0.557
0.405
0.452
0.507
0.491
0.504
0.495
0.499
0.484
0.511
0.436
0.281
0.671
0.873
(5.2)
R
0.802
0.788
0.950
0.805
0.921
0.794
0.790
0.863
0.908
0.805
0.799
0.825
0.808
0.802
0.803
0.804
0.865
0.784
1.117
0.795
0.799
0.800
0.801
0.810
0.898
0.949
0.752
0.803
0.802
0.803
0.805
0.801
0.812
0.794
0.802
0.855
1.056
rmsR(5.2)
1.627
2.029
1.662
1.716
1.640
1.614
1.560
1.671
1.675
1.812
1.625
1.585
1.607
1.632
1.626
1.627
1.630
1.590
1.442
1.625
1.621
1.618
1.620
1.543
1.662
1.670
1.134
1.627
1.594
1.618
1.603
1.645
1.598
1.787
2.127
1.279
0.961
(5.3)
E
1.047
1.185
1.118
0.978
1.085
1.043
0.982
1.115
1.117
1.180
1.048
1.030
1.049
1.045
1.045
1.046
1.055
1.003
1.068
1.046
1.045
1.041
1.044
1.067
1.118
1.119
0.933
1.047
1.050
1.045
1.037
1.055
1.046
1.159
1.459
1.030
1.052
rmsE(5.3)
0.667
0.406
0.624
0.527
0.745
0.672
0.651
0.630
0.640
0.310
0.648
0.357
0.682
0.662
0.666
0.672
0.709
0.627
0.062
0.668
0.640
0.671
0.668
0.742
0.624
0.657
0.680
0.667
0.693
0.673
0.646
0.656
0.654
0.578
0.375
0.881
1.093
(5.3)
R
0.876
0.852
1.041
0.854
0.921
0.877
0.866
0.993
1.037
1.365
0.869
1.382
0.881
0.873
0.876
0.880
1.006
0.834
1.335
0.876
0.865
0.878
0.876
0.911
1.041
1.102
0.822
0.876
0.883
0.879
0.868
0.870
0.873
0.849
0.869
0.971
1.181
rmsR(5.3)
, R
, and root mean square errors, rmsE, rmsR, for water supply to the atmosphere and recharge as obtained for various simulations. The averages of
Table 2. Mean daily values, E
observed water supply to the atmosphere and recharge are 1.447 mm=d and 0.459 mm=d for lysimeter 5.1, 1.392 mm=d and 0.526 mm=d for lysimeter 5.2, 1.543 mm=d and
0.566 mm=d for lysimeter 5.3. Lysimeter 5.3 was stopped on 9 May 1994. See text for description of simulations. Mean values given in bold fall within the margin of error of
10%. Simulations with purposely unrealistic assumptions are in italic
Water budget elements predicted by HTSVS
145
146
N. M€
olders et al
Fig. 5. Accumulated recharge (for May 23, 1992 to December 31, 1997) as obtained by various sensitivity and uncertainty studies (see text for discussion). Note that the
measurements of lysimeter 5.3 were stopped on 9 September 1994 (see Sect. 3 for further explanation)
water storage (see Eq. (1)). Generally, soil storage is filled in fall and winter and is reduced in
spring and summer. Great positive changes are
associated with strong precipitation events
(P > 40 mm=d). Negative changes do not exceed
18 mm=d. They are associated with snowmelt
events or occur some time after a heavy precipitation event. Predictions, on average, over-estimate
the storage variability except in summer when
soil water storage changes too slowly. Note that
the heating of the lysimeters by the cellar (that is
of different temperature than the soil in the adjacent field) may contribute to the observed relatively quick change in soil water storage. The
neglecting of detailed snow metamorphism processes may cause the too strong response of the
Water budget elements predicted by HTSVS
soil water storage to water input in winter.
Nevertheless, the correlation coefficient of
predicted versus observed water storage exceeds 0.96 for all simulations. The prediction
efficiency of water storage exceeds 92%, i.e.
HTSVS is suitable for long-term studies on water
availability.
5.1.3 Water supply to the atmosphere
As pointed out above, HTSVS is able to predict
the diurnal course of the fluxes of momentum,
sensible and latent heat as well as the corresponding mean profile quantities well (e.g.,
Kramm, 1995; Kramm et al, 1996; M€olders,
2000). Water vapor flux data from eddy-correlation techniques are more suitable for evaluating
the water supply to the atmosphere predicted by a
LSM than the daily values of E derived as a residuum from routine lysimeter data (see Eq. (1)).
Since the technical effort for eddy correlation
techniques and the unavoidable computer data
management are considerable, a continuous data
base gained from performing such techniques
over several years is still lacking (e.g., Twine
et al, 2000; Flage et al, 2001). To overcome this
handicap, we prefer lysimeter data rather than
flux values indirectly derived from micrometeorological methods like Bowen-ratio method or
aerodynamic profile techniques (see also
Choudhury and Idso, 1985) that have to be evaluated first. As discussed in the introduction, the
residuum E of Eq. (1) should be suitable for evaluating the predicted water supply to the atmosphere with respect to its long-term overall
behavior, i.e., its accumulated seasonal or total
sums, its seasonal behavior and annual course.
The total accumulated water supply to the
atmosphere provided by HTSVS is slightly
higher (less than 7% for lysimeter 5.1) than the
observed equivalent (Fig. 6; see also Table 2).
For most of the sensitivity studies, it ranges
between the margins of a 10% error (see also
Fig. 6, Table 2). In 1992 and 1997, the annual
accumulated value of E is over-estimated compared to its observed equivalent (e.g., Figs. 3–6).
In 1992, this over-estimate may result from the
spin-up of the model and uncertainties in the
initial distributions of soil moisture and temperature (see also Sect. 5.6). After the water storage
has been completely filled in winter 1992=1993
147
the impact due to errors in the initial soil water
distribution was removed (Fig. 5).
Sometimes upward-directed water fluxes are
predicted for the outlet level in the extremely
dry summer of 1997. This upward transport of
water may contribute to the overestimation of E
in 1997. Note that the solid lysimeter bottom at
the depth of 3 m causes an uncoupling of soil
moisture within the lysimeter from the ground
water and, hence, a suppression of the natural
process of water suction. Consequently, the predictions may be more sufficient than they seem in
comparison with observations.
Overall, HTSVS is able to predict the annual
course of water supply to the atmosphere (e.g.,
Figs. 3–5). However, in summer, on average,
HTSVS tends to over-estimate E, while the opposite is true under snow-free conditions in winter.
Based on the uncertainty studies discussed later
(Sects. 5.7–5.9) soil heterogeneity, improper estimates of shielding factors, and errors in measured air temperature due to the insufficient
natural ventilation of dry- and wet-bulb thermometers, especially during free-convective conditions of sunny and calm weather, may be
responsible for the discrepancies between predicted and observed values of E. The overestimation may also be a consequence of the artificial
heating of the lysimeter by the cellar because of
the complex coupling between soil temperature
and the water supply to the atmosphere.
5.2 Sensitivity studies on downward
directed long-wave radiation
In winter, the downward directed long-wave
radiation, R1#, provided by the B&F-scheme is,
on average, about 50 Wm 2 lower than that
determined with the optionally used parameterizations that include surface properties (cf. Fig. 4
in M€olders et al, 2003). Depending on the parameterization of R1# used, other soil temperature
and (see, e.g., Fig. 5 in M€olders et al, 2003) soil
moisture states (e.g., Fig. 7) are established.
These soil conditions lead to fluxes that again
affect the former.
The simulations with I&J-, C-, and EEAscheme all underestimate accumulated water
recharge (e.g., about 28%, 27%, 9% for lysimeter
5.1) and over-estimate accumulated water supply
to the atmosphere (about 25%, 36%, 16%; see
148
N. M€
olders et al
Fig. 6. Like Fig. 5, but for accumulated water supply to the
atmosphere
also Table 2). Only the simulation with the Cscheme succeeds in predicting the peak of
recharge occurring in spring 1997 (e.g., Fig. 8).
Note that the C-scheme predicted the frequency
olders et al,
of TS < 273.15 K the best (cf. M€
2003). The simulations using the C- or EEAscheme more strongly over-estimate recharge in
fall 1995 than all others.
Comparing Figs. 3 and 8 provides that the kind
of radiation scheme can cause differences in the
Water budget elements predicted by HTSVS
149
are responsible occur in summer 1993, early
summer 1994, late winter and spring as well as
late fall and winter 1995, winter 1996, late winter
and early spring 1997 for recharge and summer
1997 for evapotranspiration.
5.3 Effects due to roots
5.3.1 Sensitivity studies
Considering root water uptake leads, on average,
to slightly drier soils in the entire root space as
compared to the simulation without root water
uptake, except for the uppermost soil layer
(e.g., Fig. 6 in M€olders et al, 2003). Nevertheless,
inclusion of root effects improves the prediction
of the accumulated sums of the water budget
elements (Table 2). Simulations without water
uptake by roots over-estimate accumulated
recharge and water supply to the atmosphere by
about 43% and 8% (see also Figs. 5 and 6).
5.3.2 Uncertainty studies
Fig. 7. Comparison of relative soil volumetric water content
as obtained by the simulations with the B&F-scheme
(reference simulation) to those gained with the simulations
using (a) the EEA-scheme, (b) the I&J-scheme, and (c) the
C-scheme
predicted water budget elements that are of same
magnitude than those caused by soil heterogeneity (compare e.g., Figs. 3 and 4). The most evident differences for which the radiation schemes
In nature, root distribution with depth, among
other things, depends on the types of soil and
vegetation, the vertical distribution of soil water
deficit, soil density, and fertilizer. A parameterization of root distribution allows either more
roots in the upper or lower root zone. To examine
the impact of the depth of the boundary between
these different root zones zd, a model run has
been carried out wherein zd is set equal to
0.3 m instead of the 0.1 m assumed in the reference prediction. As compared to the reference
simulation, assuming zd ¼ 0.3 m better predicts
the accumulated sums of recharge and water supply to the atmosphere by about 5% (e.g., Figs. 5
and 6; Table 2). In addition, predicted and
observed daily values of recharge as well as of
water supply to the atmosphere correlate better
for an upper root zone depth of 0.3 m than 0.1 m.
However, most of the time maximum root depth
was less than 0.3 m (cf. Fig. 1 in M€olders et al,
2003) so that an upper root zone depth of 0.3 m is
unrealistic.
Based on these results, we recognize that the
vertical distribution of roots can play an important role in the prediction of water budgets. Thus,
global data sets on the annual course of the root
distributions for various biomes are indispensable for climate modeling purposes.
150
N. M€
olders et al
Fig. 8. Like Fig. 3, but for the simulation with the C-scheme. Differences between the simulated water budget quantities in
Fig. 3 and those shown here result from the parameterization of downward directed long-wave radiation chosen (see text for
further details)
Water budget elements predicted by HTSVS
5.4 Effects due to frost
5.4.1 Sensitivity studies
As shown by M€
olders et al (2003), soil water
freezing still affects soil temperature long after
the occurrence of soil frost (cf. their Fig. 9).
Freeze-thaw cycles influence the thermal and
hydrological properties of the soil because phase
transition processes are accompanied by the
release of latent heat and consumption of energy.
Frost reduces the mobility of soil-water so that
capillary action, infiltration and percolation are
rather inefficient (M€
olders et al, 2003). Soil frost
affects the water supply to the atmosphere by
several mechanisms. The reduced mobility of
soil water is responsible for the decreasing evaporation during soil frost events, which prevents
the soil from losing water. After the soil frost
event the wetter soil provides more water vapor
to the atmosphere as compared to the case without inclusion of soil frost effects. Due to the
higher volumetric water content also recharge
changes. The direction of change depends on
the weather and soil conditions after soil thawing. Depending on the lysimeter considered, the
inclusion of soil frost improves either the prediction of the recharge at the cost of the water supply to the atmosphere or vice versa (Table 2).
5.4.2 Uncertainty studies
Results of the simulations with and without
inclusion of soil frost processes that are performed with the alternatively determined soil
parameters suggest that uncertainties in soil physical parameters (besides soil heterogeneity) may
also affect the accumulated sums of predicted
water budget elements by margins of error of
about 10% (Table 2). This is due to the fact that
the maximum liquid water that can be present at
temperatures below freezing point depends on
these soil parameters (cf. Fig. 3 in M€olders
et al, 2003). Depending on the determination of
soil parameters the same soil frost parameterization leads to either better or worse results for
accumulated recharge and water supply to the
atmosphere than the simulation without soil frost
parameterization (e.g., Figs. 5 and 6, Table 2).
Note that the simulations with I&J-, C-, or
EEA-schemes, which provide the better prediction
151
of the soil frost frequency (cf. M€olders et al,
2003), yield to a worse prediction of recharge
and water supply to the atmosphere than the
model run with the B&F-scheme (Table 2, Figs.
5 and 6).
5.5 Effects due to snow
5.5.1 Sensitivity studies
Snow was reported on 76 days. M€olders et al
(2003) showed that the insulating effect of snow
reduces soil cooling up to about 1 m depth, and
that soil frost occurs quite more often when snow
insulating effects are ignored. In the simulations
without snow effect, all precipitation is subject to
infiltration, while in the simulation with snow
effects, solid precipitation will be infiltrated later
when no snow is reported any longer. Infiltration,
soil volumetric water content, recharge, water supply to the atmosphere (more energy is required
for sublimation than evaporation), and energy
budget (by the higher albedo of snow as compared to the simulations without snow effects)
differ whether or not snow is present.
Considering snow effects increases the variability of recharge and slightly improves the prediction of the temporal evolution of recharge as
compared to the simulation without them. Predicted and observed recharge still differs due to
the neglecting of snow metamorphism. Neglecting snow metamorphism and the assumption of
immediate snow-melt, when no snow is reported,
ignore that melt-water percolates through the
snow-pack and is available for infiltration. Thus,
in HTSVS, more water is available at the end of
the snow coverage as in the natural equivalent.
An influence of snow on the accumulated sums
of the water budget elements is clearly visible
(Table 2, Figs. 5 and 6). Predicted accumulated
water supply to the atmosphere is slightly reduced
(about 1%) by consideration of snow effects and
better agrees with observation (Table 2). The snow
effects slightly delay recharge, and increase accumulated recharge by about 12% (Table 2). Without their consideration HTSVS underestimates
accumulated recharge by about 6% (Table 2).
For lysimeters 5.2 and 5.3, however, the prediction
of the temporal evolution of recharge and its accumulated amount is improved.
152
N. M€
olders et al
5.5.2 Uncertainty studies
Wind blowing effects and snow metamorphism
increase snow density (e.g., Dingman, 1994).
An increase of snow density, snow, means a
decrease of the thickness of a snow pack. Consequently, the insulating effect is reduced and
soil temperature is less than for a snow pack of
lower density. Since soil temperature and soil
volumetric water content are coupled (see Eqs.
(33) and (34) in M€
olders et al, 2003) we examined the impact of snow on the water budget elements. Snow density hardly affects recharge and
water supply to the atmosphere (Table 2). This
means that the impact of the delayed input of
water due to snow on the water budget elements
is greater than that of snow density. Based on
these findings we expect that retention and
percolation of melt-water in the snow-pack may
influence predicted recharge if snow metamorphism processes are considered.
5.6 Uncertainty studies on the initial
and boundary conditions
5.6.1 Soil volumetric water content
The measured water deficit of the lysimeter on
May 23, 1992 does not provide information on
the distribution of the volumetric water content,
. To examine the impact of the initial soil moisture distribution, the initial volumetric water content in the uppermost layers is assumed to be
constant at 60 and 80% of fc, respectively, using
the same procedure as described in Sect. 4.1.
Note that the value 70% was chosen to be consistent with the root length observed at begin of
our study. A reduction to 60% means that the soil
layer, for which < fc, is thinner, but drier than
in the reference simulation. On the contrary,
using 80% of field capacity means that the upper
soil layer, having < fc, is thicker, but moister
than in the reference simulation. The results of
the simulations using these initial profiles substantiate that the accumulated water supply to
the atmosphere is insensitive to the initial soil
moisture distribution (Fig. 5). Although initializing the uppermost layers with 60% of fc reduces
over-estimation of recharge by about 3% (to an
over-estimation of only 4%; Table 2), these
initial conditions are improbable because of the
unrealistically sharp soil moisture gradient at the
boundary dry to wet soil. Initializing the uppermost layers with 80% of fc reduces accumulated
recharge by less than 1%.
Simulations assuming either ¼ fc or
¼ pwp for the entire lysimeter provide large
differences in recharge in the first 500 days of
integration. Initializing with ¼ fc, however,
yields to similar accumulated 2050 d sums as
using the initial profile of the reference run
(Table 2). According to these findings the correct
total initial water content or its vertical distribution will play a minor role on the long-term scale
if the initial values of are chosen lower, but not
too far from fc.
5.6.2 Soil temperature
To examine the error resulting from the uncertainty in soil temperature at zD ¼ 8.25 m these
values are alternatively held constant at 282 K
and 295 K, respectively. Doing so affects the soil
temperatures in the deeper layers (z < 1 m).
The constant deep soil temperature of 282 K
reduces the accumulated water supply to the
atmosphere by about 13% and enhances recharge
by less than 1% (Table 2). The opposite is true
for increase in deep soil temperature (Table 2).
This means that predictions of ground water
recharge need knowledge of the mean annual
course of deep soil temperature. These results
again confirm the effect of the Dufour- and
Luwig-Soret effects on the long-term scale.
5.7 Uncertainty due to errors
in measured forcing data
The margins of errors that typically arise when
air temperature, wind, humidity, global radiation,
and cloud fraction are routinely observed amount
to 0.2 K, 0.5 m=s, 0.5 g=kg (WMO
1971), 35 W=m2 (Raabe, 1999; private communication), and about 10%, respectively.
Simulations were, therefore, performed wherein
alternatively a random error within the typical
margins mentioned before were superimposed
on the observed meteorological data.
As expected the superimposed disturbances
may cause slight changes, i.e., the change of a
water budget element may be positive for one
and negative for the other lysimeter (Table 2).
The greatest differences in predicted water
Water budget elements predicted by HTSVS
budget elements occur for variations in precipitation and specific humidity (Table 2). Disturbing
humidity affects 2050d-accumulated recharge by
about 9% and water supply to the atmosphere by
about 5%, as compared to the reference simulation. Variation of air temperature or global radiation slightly affects recharge (4%, and 2%,
respectively) and hardly affects the water supply
to the atmosphere as compared to the reference
simulation (Figs. 5 and 6). Based on these findings we conclude that all predictions covering the
range between the upper and lower margins of
the reference case of 10% have to be considered
as excellent because this range is the uncertainty
caused by the meteorological forcing data.
5.8 Uncertainty due to the natural
heterogeneity of the soil
As pointed out above, frost heave, cryoturbation
and related macro-pores filled by sediments, biologic activity, stones and differences in the soil
deposit lead to the natural heterogeneity of soils.
Soil heterogeneity causes great differences
between the observations in amount of recharge,
maximum recharge, temporal evolution and peak
of recharge in July and fall 1993, spring and fall
1994, December 1994, fall and winter 1995,
December 1996, spring and December 1997
(compare Figs. 3 and 4). The differences in the
water supply to the atmosphere observed for the
two lysimeters that result in response to the different soil heterogeneity are the most obvious in
fall (Figs. 3 and 4). The different heterogeneity
and resulting soil moisture lead to the differences
in plant growth and, hence, height. These altered
heights slightly affect the roughness length and
the depending quantities. In the model world,
only the differences in vegetation height and precipitation can be considered as soil heterogeneity
is of subgrid-scale with respect to the model
resolution and as there is no information about
the heterogeneity. The accumulated water supply
to the atmosphere simulated for the lysimeters
hardly changes in response to the aforementioned
differences (cf. also Table 2). The temporal evolution of the water budget elements observed
by the three lysimeters differs less than those
between simulation and observation (e.g., Figs.
3–6). The differences demonstrate the strong
impact of sub-scale soil heterogeneity on the
153
onset of recharge (Figs. 3 and 4). The daily scatter between the values observed by the lysimeters
occasionally exceeds several millimeter. We conclude from comparing Figs. 3 and 4 that the predictability of the water budget elements is limited
by the unknown heterogeneity of soils.
In the determination of the molecular diffusion
coefficient for water vapor in air within the soil,
the torsion of the soil by roots and worms is
considered by an empirical factor, T ¼ 0.67
(e.g., Kramm, 1995). Simulations assuming other
values for this quantity substantiated that this
factor hardly influences the water budget elements (Table 2).
Using the parameters derived by the combined
pf-curve and granulation method (see Table 2 in
M€olders et al, 2003 for values) leads to a slight
underestimation of accumulated recharge (2%),
while the accumulated water supply to the atmosphere is over-estimated less than 4% (Table 2).
These accumulated sums better agree with the
observed sums than in the case of the parameters
deduced by the pf-curve method, but the predicted temporal development of recharge and
water supply to the atmosphere agrees less with
the observations. Applying the parameters gained
by the granulation method also leads to an excellent prediction of accumulated water supply to
the atmosphere. Recharge is under-estimated by
about 12%. Here, again applying the parameters
derived from the pf-curve method yields the better agreement of temporal course of predicted
and observed quantities. Taking granulation, pfcurve and soil skeleton into account leads to
worse results except for recharge of lysimeter
5.2 (Table 2). Probably the soil skeleton determined is more representative for the monolith
of this lysimeter than for the others. Based on
these findings we conclude that soil heterogeneity is a great source of uncertainty in predicted
water budget elements, and predicted accumulated sums that fall within the 12% error are
acceptable.
5.9 Uncertainty due to plant
physiological data
Studies are performed wherein the albedo of foliage was either reduced=enhanced by 10% or the
soil albedo (index s) was set equal to that of the
foliage (index f) as assumed in many LSMs (e.g.,
154
N. M€
olders et al
Dickinson et al, 1986; Noilhan and Planton,
1989; Chen and Dudhia, 2001). Water supply to
the atmosphere is slightly influenced by the
altered albedo, . Within a vegetation period,
there are times where the higher=lower albedo
leads to slightly better results than the albedo
used in the reference simulation. These results
can be explained by the change of during
the growing of vegetation so that the respective
higher=lower value can be more representative
than that used in the reference simulation.
A huge number of models take the value 1 for
the surface emissivity, " (cf., e.g., Pielke, 1984).
Like for assuming " ¼ 1 for the foliage
(index f) leads to better or worse results within
the annual course for the same reasons. Thus, we
conclude that results of climate models can be
improved when data sets of and " are available
in a high temporal resolution for the various
vegetation types or when albedo is diagnosed
by plant evolution models.
LAI and shielding factor, f, had to be derived
from phenological data. Assuming an unrealistically total coverage (f ¼ 1) for the entire simulation time decreases accumulated water supply
to the atmosphere by about 39% and increases
recharge by about 36%, respectively (Table 2).
For f ¼ 0, which corresponds to bare soil, the
water supply to the atmosphere grows and
recharge diminishes even more than f ¼ 1 (Table
2). Consequently, climate models need actual
data of vegetation fraction or modules to predict
vegetation evolution. Using LAI ¼ 1 increases
accumulated recharge less than 1% and decreases
accumulated water supply by 5%, while LAI ¼ 7
works in the opposite direction (Table 2).
Results of sensitivity studies assuming grass
all the time substantiate that the predicted water
budget elements are highly sensitive to the correct vegetation type. Thus, dynamic vegetation
models should be included into climate models
(e.g., Martin, 1990; Claussen, 1997) to predict
vegetation type and evolution.
6. Conclusion
A further-developed version of the hydro-thermodynamic soil-vegetation-atmosphere transfer
scheme HTSVS (see M€
olders et al, 2003) is evaluated by means of routinely measured lysimeterdata. The improved HTSVS is applied without
calibration to the site. Simulations are carried
out without restart for 2050 days to evaluate
the model performance in calculating the water
budget elements on a long-term scale, and to
examine the effects of root water uptake, frost,
and snow effects on the water budget. In doing
so, HTSVS is driven by routinely measured
meteorological data. Uncertainty studies on
initial conditions, various parameters and the
errors of the forcing data serve to evaluate the
possible accuracy in modeling water budgets on
long-term scale like in climate modeling. Moreover, the improvement in predicted water budget
elements gained by the inclusion of parameterizations for root water uptake, soil frost, and snow
effects is examined.
It has to be admitted that the obtained results
would agree better with observations if (1) data of
field experimental quality were available instead
of the routinely measured data, which are the only
that exist on the long-term scale, (2) if complete
data sets of all forcing quantities and all required
parameters would be available (e.g., data of
measured long-wave downward radiation, LAI,
shielding factor, albedo, emissivity, etc.), (3)
initial and boundary conditions were measured,
and (4) if we would have calibrated HTSVS.
Investigations show that the predicted water
budget elements are more sensitive to measurement errors in precipitation and humidity than in
air temperature, wind or global radiation, i.e.,
sophisticated state-of-the-art cloud parameterization schemes are an urgent need in climate
models to appropriately simulate future water
availability and budget components. Sensitivity
studies on plant physiological and soil physical
parameters, initial and lower boundary conditions substantiate that HTSVS will provide reasonable results if these parameters and conditions
are chosen reasonably.
Note that calibration of HTSVS was not an
aim of this study because LSMs of climate models cannot be calibrated due to lack of global data
sets with the sufficient resolution of all quantities
required. It was the aim to demonstrate that
HTSVS is able to simulate the water budget elements on a long-term scale. The results show that
HTSVS runs without drift. On a long-term point
of view, simulated water budget elements agree
reasonably with those determined by the lysimeters. The slight temporal offset in recharge
Water budget elements predicted by HTSVS
between simulations and routine observations
may be explained by retention of water on clay
lenses within the soil that are of sub-scale with
respect to the model resolution. The effect of
such sub-scale heterogeneity was examined by
comparison of routine data of three lysimeters
of same soil profile type (cf. e.g., Figs. 3 and
4). In general, HTSVS will perform better if precipitation is approximately equally distributed
between April to September (e.g., 1993; 1996)
than in years of long times without precipitation
followed by extreme precipitation events (e.g.,
1994; 1995; 1997).
Comparison of simulation results with and
without the parameterizations of soil frost, snow
effects, and root water uptake show that inclusion
of these processes improves the prediction of the
temporal behavior of the water budget elements
slightly. Simulations alternatively performed
with and without snow and soil frost emphasize
the great impacts of insulating by snow-pack and
freezing of soil on the soil water budget.
Discrepancies still occurring in winter may be
attributed to the simplification made in the frostparameterization and dealing with snow effects.
In future, it has to be examined whether the
inclusion of snow metamorphism (e.g., Fr€ohlich
and M€olders, 2002) may improve the predictions.
In the literature, a closed theory to parameterize
the physical processes in frozen soil still remains
an outstanding problem and requires further
research including more observational work.
Since the freezing front releases latent heat, the
heat can be conducted towards colder layers and
cause melting. The melted water requires a redistribution of soil temperature to increase in
volume and to maintain thermal equilibrium.
The failure to simulate these processes in full
complexity may yield to poor results in later seasons in any LSM.
Although the daily differences between quantities predicted with and without root parameterization are small, simulations, on average, will
meet slightly better the observations if root
effects are considered. Thus, climate models
should apply a LSM that possesses parameterizations of soil frost, snow, and root water uptake.
Based on our findings we conclude that
HTSVS is suitable to serve as a LSM in climate
modeling with an acceptable degree of accuracy
(with a long-term error of about 10% for the
155
2050-days accumulated sums of the water budget
elements) proposed that the plant physiological
and soil physical parameters are chosen reasonably. One of the next tasks to be addressed is to
systematically perform uncertainty analysis on
the dependence of the prediction on the choice
of the plant physiological properties and soil
physical parameters in combination to point out
the most sensitive parameters or interactions.
Global fine resolved maps of these sensitive
parameters – eventually with a temporal resolution for plant physiological parameters – could
be gained to improve climate modeling.
Acknowledgements
We would like to express our thanks to J. Rehnert and
F. Weisse for their help in data exchange. Thanks also to
H. Wanner, J. E. Walsh, and the anonymous reviewers for
helpful discussion and fruitful comments. Our thanks also
include K. E. Erdmann and C. T. Qu. We thank BMBF,
DFG and NSF for financial support under contracts
01LA98394, 07ATF30, Mo770=2-1, and OPP=0002239,
respectively.
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Authors’ addresses: N. M€
olders (E-mail: molders@gi.
alaska.edu) and G. Kramm (E-mail: kramm@gi.alaska.edu),
Geophysical Institute, University of Alaska Fairbanks,
903 Koyukuk Drive, Fairbanks, AK 99775-7320, USA;
U. Haferkorn, Staatliche Umweltbetriebsgesellschaft,
Kleinsteinberger Str. 13, 04821 Brandis, Germany; J. Döring,
Universität Halle-Wittenberg, Institut für Agrarökonomie und
Agrarraumgestaltung, Adam-Kuckhoff-Straße 15, 06108
Halle=Saale, Germany
Verleger: Springer-Verlag KG, Sachsenplatz 4–6, A-1201 Wien. – Herausgeber: Prof. Dr. Reinhold Steinacker, Institut für Meteorologie und Geophysik, Universität
Wien, Althanstraße 14, A-1090 Wien. – Redaktion: Innrain 52, A-6020 Innsbruck. – Satz und Umbruch: Thomson Press (India) Ltd., Chennai. – Druck und
Bindung: Grasl Druck&Neue Medien, A-2540 Bad Vöslau. – Verlagsort: Wien. – Herstellungsort: Bad Vöslau. – Printed in Austria.