Eur J Forest Res DOI 10.1007/s10342-010-0379-y
ORIGINAL PAPER
Modelling drainage fluxes in managed and natural forests in the Dinaric karst: a model comparison study Ursˇa Vilhar • Michael Starr • Klaus Katzensteiner Primozˇ Simoncˇicˇ • Lucˇka Kajfezˇ-Bogataj • Jurij Diaci
•
Received: 2 October 2008 / Revised: 20 January 2010 / Accepted: 15 March 2010 Ó Springer-Verlag 2010
Abstract Two models for calculating the forest water balance were applied to different development stages of managed and non-managed forests in the Dinaric Karst for two hydrologically contrasting growing seasons. A simple model WATBAL, which calculates water balance on a monthly basis, and the BROOK90 model, which calculates water balance on daily basis, were used. Differences between calculated drainage fluxes between the models were less pronounced in the drier growing season and were lower in the forest stands compared to forest gaps. Average calculated drainage fluxes of the two growing seasons were Communicated by R. Matyssek. U. Vilhar (&) Slovenian Forestry Institute, Vecˇna pot 2, 1000 Ljubljana, Slovenia e-mail:
[email protected] M. Starr Department of Forest Ecology, University of Helsinki, P.O. Box 27, 00014 Helsinki, Finland K. Katzensteiner Department of Forest- and Soil Sciences, Institute of Forest Ecology, University of Natural Resources and Applied Life Sciences, Peter Jordanstr. 82, 1190 Vienna, Austria P. Simoncˇicˇ Slovenian Forestry Institute, Ljubljana, Slovenia L. Kajfezˇ-Bogataj Department of Agrometeorology, Biotechnical Faculty, University of Ljubljana, Jamnikarjeva 101, 1000 Ljubljana, Slovenia J. Diaci Department of Forestry and Renewable Forest Resources, Biotechnical Faculty, University of Ljubljana, Vecˇna pot 83, 1000 Ljubljana, Slovenia
highest in the gaps and lowest in the stand in the virgin forest remnant, followed by the mature stand in the managed forest. According to model fitting, testing the calibration robustness and sensitivity analysis the BROOK90 model was considered best at simulating the water balance of the various research sites. The difference in model behaviour is considered to be mainly the result of the difference in model time step and the inclusion of macropore flow in BROOK90. The greater complexity of the BROOK90 model meant it could be parameterized to describe more fully the complexity of the horizontal and vertical structure of forest stand and soil properties. A disadvantage of the BROOK90 model is the greater need of input data. WATBAL, however, was useful for obtaining rougher estimates of the water balance components and can be applied to areas where there is less data available. Choice of model is therefore determined by data availability. Keywords Water balance Forest development stages WATBAL model BROOK90 model Dinaric silver fir-beech forest Slovenia
Introduction Water supply is a prominent factor affecting the stability, productivity and health of most forest ecosystems (Fischer et al. 1999; Zierl 2001; Briceno-Elizondo et al. 2006). During recent decades, concern about the effects of forest harvesting and regeneration methods on erosion, biodiversity, water balance and nutrient leaching has been discussed intensively (Bormann and Likens 1986; Simoncˇicˇ 2001; Zirlewagen and Von Wilpert 2001; Rothe et al. ´ dor 2002; Katzensteiner 2003; Klimaszewski et al. 2005; O
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et al. 2006). Timber harvesting in the Dinaric Alpine silver fir (Abies alba Mill.)-beech (Fagus sylvatica L.) forests is performed in gaps so as to resemble regeneration in natural forests, with uneven aged and horizontally and vertically structured forests (Boncˇina et al. 2003; Nagel et al. 2006). While a number of ecological factors and processes in forest gaps have been shown to be related to the size of the gap (Gysel 1951; Williamson 1975; Mladenoff 1987; Whitmore 1989; Parsons et al. 1994; Gray et al. 2002; Ritter et al. 2005), little is known about how the water balance is affected, particularly in Dinaric silver fir-beech forests. In karst regions experimental approaches to determine soil water fluxes are not possible, mainly because water pathways are unknown, and drainage can only be assessed by using hydrological models. Various soil hydrological models may be used, ranging from simple budget models to comprehensive mechanistic models (Bouten and Jansson 1995; Federer et al. 2003; Van Der Salm et al. 2004). In this study, we compare the performance of two models for calculating the water balance in Dinaric silver fir-beech forests and gaps. Emphasis is on the simulation of the soil drainage fluxes and differences due to model structure and parameterization. We do not attempt to verify or falsify any of the model concepts used, since the available data sets do not allow such a strict evaluation. The models were applied to stands and gaps in managed and non-managed forests for the 2003 and 2004 growing seasons, which differed in the amount of precipitation received. The two models we used were a simple capacity water balance model, WATBAL, which simulates the water balance on a monthly (end-of-the month) basis (Starr
1999; Vilhar et al. 2005), and BROOK90, which is a process based model but with considerably more parameters and simulates the water balance on a daily basis (Federer 1995a).
Methods Site and stand description The investigated virgin (Rajhenavski Rog) and the managed (Snezˇna jama) silver fir-beech forests are located in the northern part of the Dinaric Alps in SE Slovenia (45°200 N, 14°300 E, 860–890 m a.s.l). The bedrock is Cretaceous limestone, and the soil is generally shallow (leptosolic). The climate of the region is montane Dinaric with an annual precipitation of up to 1,600 mm. The nearest meteorological station, Kocˇevje (45°380 N, 14°520 E, 461 m a.s.l.), has a long-term (1961–1990) mean annual air temperature of 8.4°C (Mekinda-Majaron 1995). Using this value and an environmental lapse rate of 6°C per km elevation (Barry 2001), the long-term mean annual temperature at the study area would correspond to 5.9°C. The growing season (May–October) in 2003 was considerably drier than normal (Fig. 1). Precipitation amounted to 509 mm, which was approximately 60% of the long-term average amount of precipitation for the area. From May to August only half the long-term average amount of precipitation was recorded at Kocˇevje. Precipitation during the 2004 growing season was 888 mm, which was 9% above the long-term average.
Fig. 1 Monthly mean precipitation (P) and monthly mean air temperature (T) at Kocˇevje for the period (a) 1961–1990 and (b) for 2003–2004
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The Rajhenavski Rog virgin forest is located at an elevation of 880 m a.s.l. It is a remnant of a natural Dinaric silver fir-beech forest, and it was officially declared a virgin forest in the first forest management plan of the area in 1892. More precisely, it can be described as a secondary virgin forest (Hartman 1999). It has a complex structure, being a mosaic of stands at different development phases (optimal, terminal, decline, juvenile development phase (Boncˇina and Diaci 1998). Coarse woody debris, which includes standing dead trees (snags), fallen boles (logs), large branches, stumps and roots (sensu Clark et al. 1998) at various stages of decay, give special character to this forest ecosystem. The particular stand in which the study was carried out was classified as an Omphalodo-Fagetum association (Puncer 1980). It is dominated by silver fir (Abies alba Mill.) and European beech (Fagus sylvatica L.). Norway spruce (Picea abies (L.) Karst.), maple (Acer pseudoplatanus L.), elm (Ulmus glabra Huds.) and lime (Tillia cordata Mill.) make up less than 1% of total stem volume. Stand stem volume is 790 m3 ha-1 and total basal area is 52 m2 ha-1. The site is south facing, having a slope of ca. 10%, and the prevailing soil units are Eutric Cambisols and Rendzic Leptosols (FAO 1990; WRB 2007). The thickness of the O horizon averaged 3.4 cm (with standard deviation 1.1 cm), that of the A horizon 10.3 cm (with standard deviation 4.7 cm), and that of the eutric cambic B horizon 35.2 cm (with standard deviation 6.4 cm). The Snezˇna jama managed silver fir-beech forest is located nearby (ca. 1,300 m distance) at an elevation of 880 m a.s.l. The stand is on an east to southeast facing slope of about. 20%. The prevailing soils are also classified as Eutric Cambisols and Rendzic Leptosols. The thickness of the O horizon averaged 3.3 cm (with standard deviation 1.3 cm), that of the A horizon 16.2 cm (with standard deviation 4.0 cm), and that of the B horizon 23.6 cm (with standard deviation 7.1 cm), However, about 30% of the area is bare limestone rock. Up to 40 cm of organic matter can be found accumulated in holes and cracks. The stand composition at the Snezˇna jama site is similar to that at the Rajhenavski Rog virgin forest, but the stem volume is 255 m3 ha-1.
natural forest regeneration up to 0.5 m height in 2004 and the gaps in the virgin forest contained patches of varying development phases.
Study forest and gaps
Hydrological models
In the Rajhenavski Rog virgin forest, a part of the stand (RS) and an existing gap (RV), which had naturally formed in winter 2002–2003, were used as research sites. In the Snezˇna jama managed forest a closed stand (SS), an atypically large gap (SVV) (diameter ca. 45 m) and a small gap (SMV) (diameter ca. 30 m) were selected, both created in winter 2000 and representative of non-traditional ‘‘clearcut management’’. All the trees in the managed forest gaps were harvested and carefully removed by hand skidding. The gaps in the managed forest were partly covered by
WATBAL (Starr 1999) is a single-layer capacity model that calculates the components of the water balance on a monthly (end-of-month) interval and was originally developed for boreal coniferous forests while BROOK90 (Federer 1995b) is a Darcian flow model that simulates the water balance on a daily basis and is therefore more data demanding. To facilitate comparison of the two models, we assumed a common mineral soil depth of 40 cm for all study sites and that this layer contains all roots involved in water uptake. The measured TDR soil moisture values
Meteorological data and soil hydrological measurements The study was restricted to the growing season (May– October) in the years 2003 and 2004. Meteorological data were collected above the tree crowns using an automatic weather station (Vantage Pro wireless, Davis Instruments). Hourly average values of air temperature and humidity, wind direction and speed, and sums of precipitation were recorded (Vilhar et al. 2006). Missing air temperature and humidity data were substituted with data from the Kocˇevje meteorological station and missing global radiation data substituted with data from the Iskrba EMEP station (ARSO archives), using regression functions (ibid.). At each plot, monthly throughfall was measured at 1.3 m height using 9 funnel collectors (each 240 cm2) arranged along a regular grid (spacing: 5 9 5 m). Three similar collectors were placed in an open area in order to obtain open field precipitation, which was assumed to be the same as the precipitation above tree crowns. The available water capacity of the mineral soil horizons was calculated for each research site from pressure plate measurements of field capacity (moisture content at 0.033 MPa) and the permanent wilting point (moisture content at 1.5 MPa) using representative soil samples taken from the prevailing soil units (Vilhar et al. 2005). Saturated soil hydraulic conductivity was determined from soil samples taken from the prevailing soil units. The moisture content of the 0- to 40-cm layer was measured at 3 locations at each research site once a month in the 2003 growing season and twice a month in the 2004 growing season using time domain reflectometry, TDR with the probes extending through the 40-cm layer (Vilhar 2009). The TDR volumetric moisture contents were converted into depths of water (mm) by multiplying the values by the thickness of the soil layer.
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Eur J Forest Res Table 1 An overview of the basic input data requirements of the models Input data and parameters
WATBAL
BROOK90
Meteorological data
Long-term maximum and minimum air temperature for the warmest month of the year (°C)
Daily solar radiation (MJ m-2)
Monthly air temperature (°C)
Daily maximum air temperature (°C)
Monthly precipitation (mm)
Daily minimum air temperature (°C)
Monthly average cloud cover (tenths)
Average daily vapour pressure (kPa) Average daily wind speed (m s-1) Daily or hourly precipitation (mm)
Vegetation-related parameters
Canopy cover fraction Kc
MAXLAI MAXH GLMAX CVPD PSICR FRINTL FRINTS CINTRL CINTRS DENSEF
Soil parameters
SMfc
THETAF
SMpwp
THSAT
Matrix Loss
BEXP
Infiltration/by-pass fraction during snowmelt
QFFC
Kc, crop coefficient; SMfc, soil moisture content of 0- to 40-cm layer at field capacity (mm); SMpwp, soil moisture content of 0- to 40-cm layer at permanent wilting point (mm); Matrix Loss, fraction of plant available soil water lost through matric potential gradients (redistribution); MAXLAI, maximal leaf area index; MAXH, maximal height (m); GLMAX, maximal leaf conductance when stomates are fully open; CVPD, vapour pressure deficit at which conductance is halved (kPa); PSICR, critical water potential at which stomates close (MPa); FRINTL, intercepted fraction of rain per unit of projected leaf area index; FRINTS, intercepted fraction of rain per unit of projected stem area index; CINTRL, maximal interception storage of rain per unit of projected leaf area index; CINTRS, maximal interception storage of rain per unit of projected stem area index; DENSEF, canopy density multiplier, used to simulate thinned or spaced plants when compared to the original canopy; THETAF, volumetric soil water content at field capacity; THSAT, volumetric soil water content at saturation; BEXP, exponent in ‘‘matric soil water potential–soil water content’’ power curve relationship (Clapp and Hornberger 1978); QFFC, fraction of quick flow at field capacity
were corrected for stone content. An overview of the basic input data and parameter requirements of the two models is given in Table 1. Evapotranspiration and interception Evapotranspiration in WATBAL is based on the JensenHaise alfalfa-reference radiation method (Thompson 1999). For convenience, this crop reference evapotranspiration is referred to as potential evapotranspiration (PET) in WATBAL. A crop coefficient, Kc, is used to convert this alfalfa PET rate, calculated from simulated global radiation, into a PET rate for the vegetation in question. The crop coefficient integrates the differences in physiology, aerodynamics, interception capacity and albedo between the reference crop in standard condition and the vegetation in question. In the version of WATBAL used, the same crop coefficient is applied each month; it therefore does not take into account the phenological changes of leaf cover. Evaporation of intercepted precipitation (Interception), soil
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evaporation and transpiration components of PET are not calculated separately by WATBAL. Interception is assumed to be evaporated at the PET rate and soil evaporation is considered negligible as the soil is covered by vegetation. When there is a transpiration demand on soil water in the rooting zone, it takes place at the PET rate until the critical soil moisture content, SMcrit, is reached; further depletion then takes place at a reduced rate, SMrate. The values of SMcrit and SMrate are parameters in WATBAL and vary with soil texture (porosity and permeability), as described by Zahner (1967). In BROOK90, soil evaporation and potential transpiration are estimated separately using the Shuttleworth– Wallace method (Shuttleworth and Wallace 1985) modified to separate day-time and night-time evaporation (Federer 1995a). The surface-dependent potential transpiration estimation uses a canopy resistance approach that is dependent on maximum leaf conductance and a light and vapour pressure deficit at reference height. BROOK90 calculates PET as the sum of interception, soil evaporation
Eur J Forest Res
and potential transpiration. Actual transpiration is determined from potential transpiration by applying a stress factor (\1) as soon as the leaf potential falls below a certain critical threshold defined by the parameter PSICR. The stomata remain fully open until PSICR is reached and then they close as much as necessary to prevent a further drop of the leaf water potential. Leaf water potential is calculated from soil matrix potential, inner plant resistances and tree height. Internal plant resistance is parameterized by bulk plant conductivity, defined by the ratio of transpiration to the leaf water potential when the soil water potential is effectively zero. BROOK90 allocates a fixed fraction of the bulk plant conductivity to the above-ground xylem; this fraction was set to 0.5 for forest canopies. The remaining bulk conductivity is distributed among soil layers in proportion to the fine root length in each layer and is combined with the rhizosphere resistance in each layer. The supply rate is obtained by integration over the soil layers, assuming a constant supply rate through the day and a halfsine potential transpiration during daytime. BROOK90 assumes that interception catch rates are a constant fraction of rainfall or snowfall until the canopy reaches a storage capacity. The potential intercepted rain evaporation is obtained by using the Shuttleworth–Wallace surface wetness equations for a single-layered plant canopy. Intercepted rain and snow evaporation are calculated using an interception fraction of rain (snow) per unit of projected leaf area index and the projected stem area index. Drainage All forms of drainage (saturated flow through the soil matrix and macropores and unsaturated flow) constitute the difference between precipitation and evapotranspiration plus any changes in soil water storage. In WATBAL, water supply (precipitation and any snowmelt) in excess of the evapotranspiration demand is first used to replenish the plant available storage capacity of the soil layer of interest (i.e. take it to field capacity). If there is still an excess of water, it then drains from the soil layer in question as gravitational flow through soil matrix. During snowmelt (high flow conditions) a fraction of the excess water entering the soil is allocated to by-pass flow (this could be shallow runoff and/or macropore flow). This fraction is defined by an infiltration coefficient, which is related to soil texture and degree of stratification of soils (Soveri 1995). However, since our model runs were confined to the growing season there is no snowmelt and no bypass flow is simulated by WATBAL. In addition to gravitational-driven drainage and snowmelt related bypass flow, a fraction of the infiltrating water and soil water content is lost from the soil along matric potential gradients (redistribution movement) in WATBAL. This fraction is defined by the matric loss parameter.
BROOK90 can be run for up to 25 soil layers (3 layers in this study), each with its own thickness, root density, and hydraulic properties. The relationships between volumetric water fraction, water potential and hydraulic conductivity are parameterized according to the procedures outlined by Clapp and Hornberger (1978). The vertical soil water flux is calculated according to Darcy’s law, using the geometric mean interlayer conductivity and a gradient. Sharp wetting fronts are thought to uniformly penetrate homogenous soils, but in case of heterogeneities, like shrinkage cracks or old root channels, quick macropore flow to deeper layer occurs (Federer et al. 2003). BROOK90 can allow for macropore-assisted vertical infiltration down to a specified depth by setting an empirical value. Infiltrated water is portioned immediately into all layers above this depth, using the fraction of infiltration going into each selected layer, which is again defined by an empirical value. Model fitting and testing for calibration robustness BROOK90 was fitted for the managed forest stand (SS) and the virgin forest remnant stand (RS) comparing the simulated and the measured monthly throughfall. Additional fitting of both models was performed for each plot by comparing the simulated and measured (TDR) soil moisture contents of the 0- to 40-cm layer (stone content corrected), using every second measurement in the measurement period (growing seasons 2003 and 2004) in order to include both hydrological contrasting growing seasons. To test the calibration robustness of the models, the remaining soil moisture measurements were used. The goodness of fit was evaluated by examining the linear correlation coefficient (r), which describes the degree of correspondence between measured and simulated values, the index of agreement (D) (Thompson 1999), which is a descriptive measure of relative error, and the root mean square error (RMSE), which expresses the error between measured and simulated values (ibid.). For both models, a sensitivity analysis was performed by testing the influence of a 10% increase or decrease in the value of the input and calibrated parameters on the simulated drainage fluxes in the managed forest stand (SS) for the growing season 2003.
Results Model fitting and testing for calibration robustness WATBAL The parameter values used in the final simulations with the WATBAL model are summarised in Table 2. The
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Eur J Forest Res Table 2 WATBAL selected parameter values used for Snezˇna jama managed forest stand (SS), small gap (SMV), large gap (SVV) and Rajhenavski Rog virgin forest remnant stand (RS) and gap (RV) Study plot
SVV
SMV
SS
RV
RS
173.2
171.6
156.7
171.8
145.1
SMpwp 137.0 Calibrated parameters
150.2
98.0
156.0
95.8
Input parameters SMfc
Kc 2003
1.0
1.8
1.1
1.4
1.1
Kc 2004
1.2
1.5
1.4
1.7
1.1
Matrix Loss 2003
0.05
0.20
0.20
0.25
0.20
Matrix Loss 2004
0.20
0.14
0.13
0.11
0.23
SMfc, soil moisture content of 0- to 40-cm layer at field capacity (mm); assessed by soil hydrological measurements; SMpwp, soil moisture content of 0- to 40-cm layer at permanent wilting point (mm); assessed by soil hydrological measurements; Kc, crop coefficient; assessed by calibration; Matrix Loss, fraction of plant available soil water lost through matric potential gradients (redistribution); assessed by calibration
Table 3 Linear regression (y = a ? bx), correlation coefficients (r), number of measurements (n), root mean square error (RMSE) and indices of agreement (D) between the WATBAL simulated (y) and measured (x) values for the soil moisture contents of the 0- to 40-cm layer (mm) Study plot
a
b
r
D
RMSE
n
7
Model fitting SS
2.45
0.99
0.990
1.000
2.938
SVV
-4.24
1.05
0.930
1.000
5.919
7
SMV
23.91
0.87
0.827
1.000
4.901
7
RS
-2.89
1.05
0.950
0.999
4.690
8
RV
55.70
0.68
0.634
1.000
3.944
7
Model testing for calibration robustness SS
-7.52
1.07
0.967
1.000
5.456
6
SVV
-15.15
1.11
0.951
1.000
3.908
6
SMV RS
-11.64 -0.91
1.08 1.04
0.908 0.936
1.000 1.000
3.888 5.010
6 7
5.59
0.97
0.999
1.000
0.441
5
RV
Snezˇna jama managed forest stand (SS), small gap (SMV), large gap (SVV) and Rajhenavski Rog virgin forest remnant stand (RS) and gap (RV)
simulated monthly soil moisture values from WATBAL were in good agreement with the measured values (Table 3; Fig. 2). The plot average r value for model fitting was 0.871 and the index of agreement (D) was 1, for model testing the plot average r value was 0.948 and the index of agreement (D) was 1. BROOK90 The parameters used in the final simulations with the BROOK90 model are summarised in Table 4. Monthly
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Fig. 2 Soil moisture contents (mm) during the 2003 and 2004 growing seasons calculated from TDR measurements and simulated with the WATBAL and BROOK90 models for the managed forest stand (SS) (a) and large gap (SVV) (b) in the Snezˇna jama managed forest
throughfall and daily soil moisture content were well simulated in time and magnitude for all plots. The plot average correlation coefficient (r) for throughfall was 0.792 and the index of agreement (D) was 0.882 (Table 5). For the soil moisture content, the plot average r value for model fitting was 0.853 and the index of agreement (D) was 0.999, indicating good agreement between the modelled and the measured values (Table 6; Fig. 2). For model testing the plot average r value was 0.881 and the index of agreement (D) was 0.999. Sensitivity analysis The fit of WATBAL drainage flux values was most sensitive to Kc and SMfc parameters (Table 7). Decreasing the value of these parameters, respectively, by 10% resulted in a 30% and a 25% increase in the drainage flux for the growing season. A 10% decrease in the matrix loss parameter resulted in an increase in the seasonal drainage flux by 19%. The agreement between measured and simulated soil moisture contents was lower for all applied changes of the tested parameters in the sensitivity analysis when compared to the calibrated simulation. The correlation coefficients (r) and the
Eur J Forest Res Table 4 Values for selected parameters used in the water balance simulation with the BROOK90 model Parameters
SVV 2003
SMV 2004
2003
SS 2004
RV
2003
2004
2003
RS 2004
2003
2004
Input parameters MAXLAI
3.00
4.00
4.00
3.00
7.00
6.00
2.00
2.00
7.00
6.00
MAXH
0.30
0.50
0.30
0.75
20.00
20.00
0.25
0.50
28.00
28.00
GLMAX
0.80
0.80
0.80
0.80
0.53
0.53
0.80
0.80
0.53
0.53
CVPD
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
PSICR
-1.90
-1.90
-1.90
-1.90
-1.90
-1.90
-1.90
-1.90
-1.90
-1.90
STONEF L1a
0.15
0.15
0.15
0.15
0.10
0.10
0.15
0.15
0.10
0.10
b
0.25
0.25
0.25
0.25
0.20
0.20
0.25
0.25
0.20
0.20
L3c
0.45
0.45
0.45
0.45
0.30
0.30
0.45
0.45
0.30
0.30
L1
0.42
0.42
0.41
0.41
0.38
0.38
0.42
0.34
0.34
0.36
L2
0.28
0.29
0.28
0.28
0.24
0.24
0.28
0.33
0.22
0.24
L3
0.29
0.31
0.29
0.29
0.25
0.25
0.29
0.33
0.23
0.25
L1
0.42
0.42
0.42
0.46
0.71
0.71
0.42
0.36
0.74
0.74
L2
0.40
0.40
0.30
0.34
0.60
0.60
0.40
0.34
0.62
0.62
L3
0.37
0.37
0.30
0.30
0.57
0.57
0.37
0.34
0.57
0.57
L1
6.75
7.75
9.50
11.50
6.75
6.75
9.10
11.50
7.10
7.10
L2
6.75
7.75
9.50
11.50
6.75
6.75
9.10
11.50
7.10
7.10
L3 7.75 Calibrated parameters
8.75
10.50
12.50
7.75
7.75
10.10
12.50
8.10
8.10
FRINTL
0.00
0.01
0.00
0.01
0.07
0.01
0.05
0.05
0.05
0.07
FRINTS
0.00
0.01
0.00
0.01
0.06
0.01
0.05
0.05
0.06
0.07
CINTRL
0.10
0.20
0.10
0.20
0.25
0.15
0.05
0.10
0.35
1.00
CINTRS
0.10
0.20
0.10
0.20
0.25
0.15
0.05
0.10
0.35
1.00
DENSEF
0.40
0.30
0.05
0.05
1.00
1.00
0.05
0.05
1.00
1.00
QFFC
0.10
0.01
0.10
0.00
0.30
0.20
0.30
0.25
0.50
0.25
L2
THETAF
THSAT
BEXP
Snezˇna jama managed forest stand (SS), small gap (SMV), large gap (SVV) and Rajhenavski Rog virgin forest remnant stand (RS) and gap (RV) MAXLAI, Maximal leaf area index; assessed by litter collectors (Vilhar 2006), according to ECE–ICP Forest methodology (Bastrup-Birk and Breda 2004); MAXH, Maximal height (m); assessed by stand inventory measurements (Vilhar 2006); GLMAX, Maximum leaf conductance when stomates are fully open; default values for deciduous broadleaved forest in SS, RS and default values for grass in SVV, SMV, RV (Federer 1995a); CVPD, Vapour pressure deficit at which conductance is halved (kPa); default value (Federer 1995a); PSICR, Critical water potential at which stomates close (MPa); default value (Federer 1995a); STONEF, Stone fraction; assessed by soil analysis (Vilhar 2006); THETAF, Volumetric soil water content at field capacity; assessed by soil hydrological measurements; THSAT, Volumetric soil water content at saturation; assessed by soil hydrological measurements; BEXP, Exponent in ‘‘matric soil water potential–soil water content’’ power curve relationship (Clapp and Hornberger 1978); assessed by soil hydrological measurements; FRINTL, Intercepted fraction of rain per unit of projected leaf area index; assessed by calibration; FRINTS, Intercepted fraction of rain per unit of projected stem area index; assessed by calibration; CINTRL, Maximal interception storage of rain per unit of projected leaf area index; assessed by calibration; CINTRS, Maximal interception storage of rain per unit of projected stem area index; assessed by calibration; DENSEF, Canopy density multiplier, used to simulate thinned or spaced plants when compared to the original canopy; assessed by calibration; QFFC, Fraction of quick flow at field capacity; assessed by calibration a
L1, Soil layer 1 (0–10 cm)
b
L2, Soil layer 2 (10–30 cm)
c
L3, Soil layer 3 (30–40 cm)
indices of agreement (D) were lower whereas RMSE was higher for the simulation runs analysing the model sensitivity.
The fit of BROOK90 drainage flux values was much less sensitive to input parameters. It was most sensitive to changes in QFFC parameter, which describes the quick
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Eur J Forest Res Table 5 Linear regression (y = a ? bx), correlation coefficients (r), number of measurements (n), root mean square error (RMSE) and indices of agreement (D) between the BROOK90 simulated (y) and the measured (x) throughfall (mm) Study plot
a
b
r
D
RMSE
n
SS
0.88
15.53
0.771
0.875
37.649
12
RS
0.88
23.73
0.813
0.890
31.716
12
Table 7 Sensitivity analysis of the parameters, used in the WATBAL simulation, testing the influence of a 10% increase or decrease in the value of the parameters on the simulated drainage fluxes (D DF) in the managed forest stand (SS) for the growing season 2003 and linear regression (y = a ? bx), correlation coefficients (r), number of measurements (n), root mean square error (RMSE) and indices of agreement (D) between the WATBAL simulated (y) and the measured (x) soil moisture contents of the of the 0- to 40-cm layer (mm) D DF (%) a
Snezˇna jama managed forest stand (SS) and Rajhenavski Rog virgin forest remnant stand (RS)
7.9
SMpwp
a
b
r
D
RMSE
n
Model fitting SS
27.80
0.84
0.837
0.998
13.076
9
SVV
12.83
0.92
0.732
0.999
9.424
10
SMV
-5.78
1.02
0.891
1.000
4.697
9
RS
12.42
9.57
0.905
0.997
11.585
10
RV
-86.00
1.10
0.860
1.000
5.534
9
D
RMSE n
-28.01 1.28 0.951 0.933 12.699 6
15.5
21.98 0.87 0.956 0.960
7.936 6
4.9
-4.51 1.01 0.926 0.954
9.257 6
Matrix Loss 14.1
-2.31 1.00 0.942 0.967
7.728 6
Kc
Study plot
r
Parameter values ? 10% SMfc
Table 6 Linear regression (y = a ? bx), correlation coefficients (r), number of measurements (n), root mean square error (RMSE) and indices of agreement (D) between the BROOK90 simulated (y) and the measured (x) soil moisture contents of the 0- to 40-cm layer (mm)
b
Parameter values - 10% SMfc
24.9
SMpwp
25.96 0.75 0.936 0.927 10.148 6
8.0
-27.84 1.19 0.967 0.968
8.304 6
29.5
3.00 1.01 0.965 0.972
7.189 6
Matrix Loss 18.9
1.29 1.01 0.969 0.980
6.063 6
Kc
SMfc, soil moisture content of 0- to 40-cm layer at field capacity (mm); SMpwp, soil moisture content of 0- to 40-cm layer at permanent wilting point (mm); Kc, crop coefficient; Loss, fraction of plant available soil water lost through matric potential gradients (redistribution); D DF (%), changes in seasonal drainage flux (%) in the managed forest stand (SS) for the growing season 2003
Model testing for calibration robustness SS
-13.33
1.15
0.889
0.998
12.552
9
SVV
-51.85
1.31
0.893
0.999
8.185
9
SMV
-26.77
1.17
0.850
1.000
5.044
9
RS
-5.01
1.11
0.904
0.998
12.208
9
RV
21.71
0.88
0.842
1.000
4.229
8
Snezˇna jama managed forest stand (SS), small gap (SMV), large gap (SVV) and Rajhenavski Rog virgin forest remnant stand (RS) and gap (RV)
Table 8 Sensitivity analysis of the parameters, used in the BROOK90 simulation, testing the influence of a 10% increase or decrease in the value of the parameters on the simulated drainage fluxes (D DF) in the managed forest stand (SS) for the growing season 2003 and linear regression (y = a ? bx), correlation coefficients (r), number of measurements (n), root mean square error (RMSE) and indices of agreement (D) between the BROOK90 simulated (y) and the measured (x) soil moisture contents of the 0- to 40-cm layer (mm) D DF (%)
flow fraction at field capacity (Table 8). An increase in QFFC by 10% increased the seasonal drainage flux by 3% and a 10% decrease in QFFC resulted in a decrease in the seasonal drainage flux by 3%. A 10% decrease in the DENSEF parameter, which is a canopy density multiplier, used to simulate thinned or spaced plants when compared to the original canopy, caused a 2% increase in the seasonal drainage flux. Another sensitive parameter in the BROOK90 model was MAXLAI, which corresponds to the maximal leaf area index. A 10% increase or decrease resulted in 2% increase or decrease in the seasonal drainage flux. Changing of any other parameters by 10% resulted in a maximum change of 1% in the seasonal drainage flux. The agreement between measured and simulated soil moisture contents was lower for all applied parameter changes in the sensitivity analysis. The correlation coefficients (r) and the indices of agreement (D) were lower
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a
b
r
D
RMSE
n
38.63
0.69
0.603
0.758
17.598
8
QFFC 3.0 38.91 Parameter values - 10%
0.67
0.607
0.762
17.433
8
Parameter values ? 10% MAXLAI
-2.2
DENSEF
/
MAXLAI
2.2
39.92
0.69
0.601
0.756
17.590
8
DENSEF
2.4
40.90
0.68
0.597
0.754
17.640
8
QFFC
-2.9
39.34
0.70
0.608
0.753
17.674
8
MAXLAI, maximal leaf area index; DENSEF, canopy density multiplier, used to simulate thinned or spaced plants when compared to the original canopy; QFFC, fraction of quick flow at field capacity; D DF (%), changes in seasonal drainage flux (%) in the managed forest stand (SS) for the growing season 2003
whereas RMSE was higher for the simulation runs analysing the model sensitivity than for the calibrated simulation.
Eur J Forest Res Fig. 3 Simulated drainage fluxes in % of the open field precipitation in the growing seasons 2003 and 2004, simulated with the models WATBAL and BROOK90 in the Snezˇna jama managed forest stand (SS), large gap (SVV), small gap (SMV) and the Rajhenavski Rog virgin forest stand (RS) and gap (RV)
Fig. 4 Simulated actual evapotranspiration in % of the open field precipitation in the growing seasons 2003 and 2004, simulated with the models WATBAL and BROOK90 in the Snezˇna jama managed forest stand (SS), large gap (SVV), small gap (SMV) and the Rajhenavski Rog virgin forest stand (RS) and gap (RV). Penman–Monteith potential evapotranspiration values (PET) from Kocˇevje meteorological station are given as a reference (archive ARSO)
Seasonal drainage fluxes and (evapo)transpiration estimates The drainage flux values for the study plots of both models were more similar for the relatively dry 2003 growing season than for the wetter 2004 growing season (Fig. 3). BROOK90 calculated higher seasonal drainage fluxes for the gaps than the forest plots in both cases, the virgin as well as the managed forest. There was hardly any difference in the drainage fluxes between large and small gap in the managed forest. Drainage fluxes were higher in the wetter season 2004 than in the drier season 2003. With WATBAL the differences between gaps and forest plots were not consistent. The large gap in the managed forest had a lower seasonal drainage flux than the forest plot in the managed forest in 2003, as was the drainage flux of the gap in the virgin forest in 2004. The drainage fluxes for the wetter season 2004 were not consistently greater than those in the drier season 2003. With the exception of the virgin forest gap (RV) for 2003, WATBAL seasonal drainage flux values were lower than BROOK90 values. Correspondingly, WATBAL
actual evapotranspiration fluxes (AET) were greater than BROOK90 estimates (Fig. 4). The agreement between the two simulated series of AET with WATBAL and BROOK90 was better in the growing season 2003 (r = 0.681, P \ 0.01) than in 2004 (r = 0.563, P \ 0.01) and the results were more similar to each other for the forest stands than for the gaps.
Discussion The drainage fluxes from the forest stands and gaps in our Dinaric silver fir-beech forest simulated by BROOK90 and WATBAL differed considerably, particularly for the wetter season 2004. While the simulations with WATBAL resulted in higher evapotranspiration and lower drainage fluxes, BROOK90 indicated lower evapotranspiration and higher drainage fluxes. We attribute this difference in model behaviour to the difference in how macropore or bypass flow is handled and to the difference in time step used. Short intense rainfall events, especially when combined with small soil water storage capacities (as is the case in
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Eur J Forest Res
this study), can generate macropore flow and surface runoff (Petricˇ 2002). Infiltration experiments under similar conditions confirm the assumption (Katzensteiner 2000). Such rapid bypass flow is implemented in BROOK90 according to the QFFC parameter, the fraction of infiltrating water that is lost as quick (macropore) flow at field capacity (Table 1). The shallowness soil and presence of trees roots would favour the formation of macropore, and these would more likely to conduct water during the wetter 2004 growing season. In WATBAL, such bypass flow is set to occur only when there is snowmelt. At this time of high water supply to the soil, the fraction of water entering the soil matrix pore system and that entering large fissures, pipes or forming surface runoff (collectively bypass flow) is set by the infiltration coefficient (Table 1). However, since this study was carried out during the growing season, when there was no snowmelt, no bypass (macropore) flow was simulated with WATBAL and all rainfall in excess of ET enters the soil matrix pore system. If this excess water is sufficient to satisfy the matrix loss component (Table 1) and to take the soil matrix to field capacity, then any excess forms the drainage flux. The priority given to satisfying the ET demand in WATBAL combined with using a monthly time step tends to accentuate evapotranspiration losses over drainage losses compared to using a daily time step. The crop coefficient, Kc, in WATBAL is used to describe the difference in the potential evapotranspiration between a reference alfalfa crop and the vegetation cover at the study site. It integrates the effects of all the physiological and morphological vegetation factors affecting evapotranspiration into a single coefficient. While crop coefficients for low crops are readily available (Allen et al. 1998), few exist for trees and forests (Guidi et al. 2008). However, values greater than 1 can be expected for trees because of their significant interception capacity compared to low crops, although this effect is countered to some extent during transpiration by the greater stomatal control exhibited by woody plants compared to non-woody plants. The more complex situation in the gaps regarding vegetation cover complicates the use of a single crop coefficient further. ET in BROOK90 is controlled by a number of parameters related to the tree stand, including tree height, leaf water potential, soil matrix potential, inner plant resistance, etc. It is therefore better able to be parameterized to the complex situation in the gaps. The greater number of parameters makes BROOK90 less sensitive to individual parameters. For example, the sensitivity to soil hydraulic properties in BROOK90 is spread out over several layers while in WATBAL there is only one soil layer. But the price for the better resolution and performance of BROOK90 is the substantial increase in input data and
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parameter needs compared to WATBAL. Daily meteorological data are needed as well as many parameters describing the site, the canopy, the soil and the water flow through the soil. Many of the parameters are rarely measured in the field and values have to be obtained from literature. Thus although providing less accurate estimates of water balance components, particularly the balance between ET and drainage, the WATBAL model can be applied to areas where there are few input data available (Anonymous 2002, Starr 2004). The 2003 and 2004 seasons were, respectively, drier and wetter than average. While long-term climatic conditions could have been used, the two contrasting study seasons allowed us to compare model behaviour under a range of meteorological conditions.
Conclusions For a simulation of the water balance of stands and gaps in complex forest systems (e.g. strongly structured forest stands, complex karst morphology, heterogeneous soils, small soil water storage capacity), the more flexible BROOK90 model with several parameterization options for site and stand was found more suitable than the simpler capacity water balance WATBAL model. Part of the increase in performance of BROOK90 was considered to be due to the use of a daily time step compared to the monthly time step used in WATBAL. The result of a longer time step is to favour evapotranspiration losses over drainage losses. However, the greater drainage fluxes simulated with BROOK90 are also due to the inclusion of macropore flow. In the version of WATBAL used, macropore flow occurs, but only when there is snowmelt. The disadvantage of BROOK90 is that it requires a substantially greater number of input parameters than WATBAL, many of which are rarely available or measured in field studies. Differences in calculated drainage fluxes between the two models were less pronounced in the dry 2003 growing season than wetter 2004 growing season and were lower in the forest stands compared to forest gaps. WATBAL performance was satisfactory in the homogeneous forest stand and in the dry vegetation period with clear depletion of soil moisture of the rooting zone in summer months. In conclusion, choice of model is determined by data availability. Acknowledgments The study was part of the Ph.D. study and Program group Forest biology, ecology and technology (0404-501), financed by Ministry of Education, Science and Sport, Republic of Slovenia. We thank two anonymous reviewers for their helpful comments on the manuscript.
Eur J Forest Res
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