FIRST RESULTS AND DISCUSSION

Monitoring of precipitation, canopy throughfall and stemflow during three summer seasons showed high variation of these parameters as a function of the characteristics of precipitation events. Rainfall in general is a phenomenon which shows high spatial variability (Lopes, 1996; Hanson et al., 1989). Between the two experimental plots B1 (spruce-fir-beech plot) and F1 (spruce plantation) in the montane zone, gross annual rainfall shows some variability (see Table 1, GR). Interception within the stands (B1 and F1) is highly variable (Durocher, 1990), and each precipitation event shows different interception rates depending on the

intensity of rainfall (Price et al., 1997) and other meteorological variables (such as temperature, wind, etc.).

Due to the high variability of the canopy structure within the mixed stand B1 (spruce-fir-beech plot) in relation to the homogeneous stand in F1 (spruce plantation), the variability of canopy throughfall is also higher in B1 than in F1; the standard deviation of canopy throughfall is therefore higher in B1 than in F1 (Table 1). Stemflow on the beech trees within B1 is responsible for the difference in net precipitation between the mixed stand (B1) and the spruce plantation (F1). It also seems to be obvious that higher values of gross precipitation in wet years (e.g. 1999) lead to higher net precipitation than years with lower values of gross precipitation (e.g. 2001, Table 1). The variation of interception loss for each precipitation event is dependent on the intensity of rainfall. Intensive rainfall leads to lower interception rates than less intense rainfall. In F1 (spruce plantation) interception loss (on an event basis) during the measurement period in 2000 ranged between 8.9 % and 81.3 %. During the measurement period in 2001 the interception loss in F1 ranged between 22.2 and 67.6 %. The years with higher rainfall amounts also show higher net precipitation.

Table 1: Overview for summer canopy throughfall and net precipitation for a mixed spruce-fir-beech stand (B1) and a homogeneous spruce plantation (F1).

B1 F1 deviation of canopy throughfall for each plot, Ste is the stemflow on beech (calculated as the value for the whole plot area of B1), NeP is the net precipitation (which is identical with canopy throughfall in F1 and which is the sum of canopy throughfall and stemflow in B1), and GR is gross rainfall (mm). Values are expressed as a percentage of GR.

In the subalpine zone, interception loss within the krummholz plot (L1 - Pinus mugo) is highly variable.

Interception rates constituted between 19 % and 65 % of gross rainfall, while at the same time one trough received 118 % of gross rainfall. The variations of canopy throughfall within the krummholz plot could be related to stemflow dynamics of the Pinus mugo plants, which have not been measured because of technical obstacles due to the shape of this plant. Pinus mugo grows with a high variation in the angle of its branches, so that locating the place for mounting stemflow gauges is not simple. In the desert of Arizona it was possible to mount stemflow gauges at the base of creosote bushes (Larrea tridentata) (Whitford et al., 1997), by a technique that is not applicable for Pinus mugo. Another reason for the canopy throughfall variations could be the capability of Pinus mugo to intercept moisture from fog and clouds (occult precipitation).

At the edges of the krummholz groups, a higher rate of moisture is likely to be intercepted. In other montane forests of the earth, occult precipitation is an important quantity (Hunzinger, 1997; Flemming, 1993).

Wind-driven rainfall can also have the effect of inhomogeneous distribution of canopy throughfall (Herwitz and Slye, 1995). The canopy throughfall troughs quantify occult precipitation only as a unit together with gross rainfall, therefore it is not possible to measure the percentage of total precipitation which is occult precipitation with this technique.

Soil temperature is influenced by vegetation cover. In the subalpine zone, soil temperature (at a 5 cm depth) on the open subalpine grassland (A1) reaches maxima which are more than 7°C warmer than on the

krummholz area (Pinus mugo plot L1- on 10 June 2000 – Fig 1). The monthly means during the summer season 2000 were also higher in A1 than in L1; the difference, viewed over all measured soil horizons, varied between 0.5°C and 4°C. The trend of higher soil temperatures in A1 during the summer season was inverted during the winter season, where L1 exhibited warmer soil than A1. Within the krummholz, the soil did not freeze during the winter season (e.g. 1999/2000) or froze only slightly in the upper horizon (e.g. 2000/2001), while the soil in A1 froze significantly (less than minus 1°C) and over all measured soil horizons. During the snowmelt period, higher soil temperatures under krummholz vegetation (L1) allowed melting water to percolate into the soil, while on the frozen soil in A1 (open grassland) percolation could not occur on the whole area during the same time.

Fig 1: Soil temperature 5 cm below the soil surface: A1 (subalpine grassland) and L1 (krummholz area, Pinus mugo).

The effect of soil temperature during the winter season has been estimated by a comparative analysis of soil moisture and soil temperature dynamics during the snowmelt period in the spring season 2000 in the subalpine zone. Melting water or precipitation water percolates more easily into soils without ground frost than into frozen soils (Shanley and Chalmers, 1999). In the montane zone, soil temperature also shows the influence of vegetation cover. Peaks in soil temperature in the upper soil horizons differ on radiation days by 2°C to 7°C during the day time between the mixed forest stand B1 and the open regeneration plot B2, with higher soil temperatures occurring in B2. The homogeneous spruce plantation (F1) exhibits slightly higher soil temperatures than the mixed stand in B1. Soil temperature is a key parameter for many geo-chemical processes, which start to be evident after using the clearcutting technique of harvesting (Likens and Bormann, 1995; Reynolds et al., 1992; v. Wilpert et al., 2000; Martin et al., 2000).

Soil moisture dynamics showed different behaviour for each vegetation type. In the subalpine zone for example, soil moisture remained higher under krummholz vegetation (L1 - Pinus mugo) than under subalpine grassland vegetation (A1) after a winter season with a high level of snow accumulation (e.g. 1999/2000). During long lasting dry spells, which occurred in the year 2000 during the month of August, soil moisture dropped more quickly at L1 than at A1. In June 2000, soil moisture at every measurement plot in L1 was higher than in A1. In August the soil moisture levels in L1 were lower than in A1 at two plots. Soil moisture is, especially during dry periods, controlled by the soil type and vegetation (Gautam et al., 2000). During the dry spell in August 2000, soil moisture dropped more quickly under the krummholz vegetation than under subalpine grassland vegetation, which can be explained by a higher transpiration demand of Pinus mugo compared to grassland vegetation (Dirnböck and Grabherr, 2000).

The high soil moisture content within the krummholz area in the summer months of 2000 can be related to high snow accumulation during the winter season of 1999/2000. The snowmelt water percolated

with higher ease into the soils which were covered by krummholz vegetation. Variations of soil moisture conditions are higher within the krummholz area (L1) than within the subalpine grassland area (A1).

This can be related to higher variability of precipitation distribution due to the growing shape of the Pinus mugo plants. Variations of soil moisture conditions due to the variation of soils within the subalpine zone of the Rax are not substantial, because the soils on A1 and L1 are identical (chromic cambisols).

Variations due to differing depths of groundwater table can also be excluded since groundwater is not relevant for karstic sites at this altitude, but variations due to groundwater do occur in watersheds with crystalline bedrocks (Beldring et al., 1999).

The analysis of the seepage water, which has been extracted by the lysimeters, showed the highest nitrate concentrations on the spruce plot F1 (8,7 mg NO^3-/l as the highest value). The regeneration area B2 had the lowest nitrate concentrations due to mobilisation and erosion and/or leaching of the nutrients which had been stored in humus and root biomass after the clearcut period. The clearcut was made after wind blowdown, which took place 25 years ago. On the plot in B1 the seepage water shows varying nitrate concentrations, which are generally lower than in F1. After clearcutting, the nitrate concentration in seepage water can reach high values (v. Wilpert et al., 2000; Likens and Bormann, 1995; Reynolds et al., 1992; Martin et al., 2000).

In general, nitrate concentrations in seepage water in the Rax area are low, also in comparison with the karstic research area in the Northern Calcareous Alps of Tyrol at the Mühleggerköpfl (Smidt, 2001;

Feichtinger et al., 2002). This may be due to lower atmospheric N inputs in the Rax area. The pH value and the electric conductivity of the seepage water generally increase with soil depth. The highest concentrations of nitrate and ammonium in precipitation water (crown throughfall) have been found beneath the crowns of spruce (in F1 and B1). The tendency of spruce to filter higher amounts of pollutants is also highlighted in other studies (Rothe et al., 1998; v. Wilpert et al., 2000; Adamson et al., 1993; Robertson et al.; 2000).

CONCLUSIONS

The installation of a comparable set of measuring instruments on five experimental plots in the water protection zone of the City of Vienna made it possible to monitor some hydrological processes within these karstic headwaters. The five experimental plots represent characteristic vegetation types within the water protection area. The setting of the instrumentation is suited for the operation of the sensors during the summer season as well as during the winter season. The first results of this project show hydrological differences between the monitored vegetation types. These differences can be used in order to elaborate more specific forest and land use management concepts for water protection purposes.

ACKNOWLEDGEMENTS

The project has been funded by the Viennese Water Works (Municipal Department 31), by the Forestry Office and Urban Agriculture (Municipal Department 49) and by the University of Agricultural Sciences, Vienna, Austria (Internal Research Stimulation Program). The continuation of the monitoring processes has been funded by the Austrian Federal Ministry for Education, Science and Culture and by the Municipal Departments 31 and 49 of the City of Vienna. The authors want to express their acknowledgements to the representatives of these institutions.

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