Study area
The West-Siberian Plain is the largest peatland region of the world, with mires covering over 10,00,000 km2 (Yefremov and Yefremova 2001; Tanneberger et al. 2003; Smith et al. 2004). The plain is situated between the Ural Mountains to the west, the Yenisey River to the east and the Altai Mountains to the south. The south-eastern part of the West-Siberian Plain consists of an alluvial plain that slightly dips in a north-western direction. Paleozoic rock was covered with layers of loam, silt and clay up to 250 m thick during the Mesozoic and Cenozoic. The area was never glaciated during the Quaternary (Franz 1973), but was dissected by fluvial erosion when the Ob River cut stepwise into the alluvial surface. The total width of the Ob valley reaches 30–60 km and includes four to five river terraces.
The climate of the area is continental, with cold winters (mean January temperature: −19.6°C) and warm summers (mean July temperature: 18.0°C) (Evseeva 2001). Mean annual precipitation is 415 mm, 70% of which falls in the frost-free period (May–September). The average snow cover is 60 cm during a period of 180 days (Atlas of the USSR 1983); permafrost does not occur (Walter and Breckle 1994).
The study site (56°30′N 84°01′E; Fig. 1) is located approximately 10 km south of Melnikovo, 50 km west of Tomsk and 250 km north of Novosibirsk, in the floodplain of the Ob River. Gyttja accumulation started in oxbow lakes from 9000 to 8000 BP; mire development began in 7000 BP (Lapshina 1987; Blyakharchuk 2003). The current surface of the mire, which is bordered by a steep terrace scarp to the west, slopes down over a width of approximately 3 km from the foot of the scarp (ca. 95.0 m a.s.l.) to the banks of the Sargatka River (ca. 91.5 m a.s.l.). The Sargatka River is a left-bank tributary of the Ob River, flowing between the study site and the levees of the Ob River and discharging superfluous mire water (Fig. 1). Spring mounds have formed along the steep scarp (Lapshina 1987). The western part of the study site contains open fen vegetation with sparse growth of shrubs and trees. In the central part, peat ridges of varying length and with a sparse tree cover of Pinus sylvestris, Betula pubescens and Picea obovata run parallel to the Ob River. The mire is forested in the vicinity of the Ob River. In large parts of the mire, the vegetation and root layer almost floats on a watery layer with little dead organic matter. The transition between this watery top layer and the deeper, denser peat cannot be defined clearly everywhere. The uplands are characterized by a mosaic of Betula pendula-Populus tremula forests and agricultural fields or pastures that are largely abandoned since the collapse of the collective farms. In earlier times, floods from the Ob River occasionally reached the terrace scarp (personal communication with residents, 2002). Since 1969, the Novosibirsk hydroelectric power plant reservoir, located 300 km upstream from the study area, has prevented flooding of the site (Lapshina 1987).
Data collection
For detailed study of the relations between vegetation, hydrology and hydrochemistry, a transect (length: 3.2 km) was selected from the terrace scarp to the levee of the Sargatka tributary (Fig. 1), crossing the main variation in vegetation types. Along the transect, surface elevation was measured with a water-level device. To determine peat stratigraphy, we made 23 corings using a chamber peat corer (diameter: 4.5 cm; length: 50 cm) in steps of 200 m (maximum) . Field identification of peat and sediments followed Boden (1996), Stegmann et al. (2001) and Succow and Stegmann (2001). The core from sampling location 1 (Fig. 1) was analysed for macrofossils following Michaelis (2002).
Using a modified Braun-Blanquet scale (Wilmanns 1998), in July 2002 we recorded 105 vegetation relevés (25 m2; cf. Dierschke 1994) that were located within a radius of 25 m from the centres of the sampling locations (indicated as 0–9 on Fig. 1). The nomenclature of the angiosperm species followed Rothmaler (2002) and Krasnoborov (2000) (Salix rosmarinifolia); that of moss species was according to Frahm and Frey (1992). To estimate actual biomass production, we harvested the above-ground standing crop of vascular plants at the peak of the growing season (July 4–12) at sampling locations 0–5. Because representative biomass production samples could not be obtained from sites with substantial tree cover, the forested part of the mire was not sampled (sampling locations 6–9). For each biomass sample, three separate plots were selected of 0.16 m2 each; these were located within a radius of 10 m from the centres of the sampling locations. After removal of the dead parts, the above-ground vascular plant material was stored in polyethylene bags, dried for 48 h at 70°C and weighed immediately after drying. Kjeldahl destruction (Allen 1989) was carried out on the dried and homogenized plant material in order to determine the contents of nitrogen (N) (with an auto-analyser, AA), phosphorus (P) and potassium (K) (with the inductively coupled plasma technique, ICP).
Peat samples were collected from the vegetation relevé sites at a depth of 10–15 cm below the peat surface and stored in polyethylene bags at 5°C. After 2–4 weeks, samples were dried first for 1–2 days under direct sunlight, followed by 12 h in an oven at 105°C. After 20 weeks, samples were analysed for pH and CaCO3 content. pH was determined in rewetted samples in 0.01 M CaCl2 using WTW-pH 96 (Rowell 1997). CaCO3 content (%) was determined in ground samples with 10% HCl using a Scheibler-apparatus (Schlichting et al. 1995). Total carbon (C) and total N were measured by Dumas digestion with a C/N-analyser (Elementar Vario-EL, Germany). Corg/N ratios were calculated after subtracting the inorganic C – determined with the Scheibler-device – from the total C.
Piezometers were placed at each of sampling locations 1–9, with filters (length: 30 cm) at depths of 0.5, 2 and – when possible – 4 and 6 m below the mire surface. Between June 1 and August 25, 2002, eight sampling campaigns were carried out to measure piezometric heads and surficial mire water levels and to collect water samples. Piezometric heads and surficial mire water levels were measured relative to the mire surface and related to each other with the elevation data of the levelling survey. Water samples were taken from the piezometers, from the surficial mire water at all sampling locations and from the Ob River, using a hand pump connected to a polyethylene Erlenmeyer. Piezometers were emptied and allowed to refill with fresh groundwater once before sampling. Water samples were collected in polyethylene bottles (50 ml) that were filled to the brim to limit aeration. Electrical conductivity (EC) and pH were measured in the field using WTW-LF91 with an Ag/AgCl-electrode and WTW-pH96, respectively. HCO3 concentrations were determined within 24 h after sampling by acidimetric titration to pH 4.3 (Aquamerck Alkalinity field set; Merck, Germany). Samples were conserved by acidification to pH 1 and stored for a maximum of 15 weeks before being analysed for Ca, Fe, K, Mg, Mn, Na, Si and SO4 concentrations using the ICP technique and for concentrations of Cl, NH4, NO3 and PO4 using the AA. Net precipitation in the study area was estimated by measuring water level changes in open-water pans (diameter: 12 cm). Saturated hydraulic conductivity of the peat was determined by slug tests following Hvorslev (1951; in Domenico and Schwarz 1998).
Data processing
Plant communities were classified by ordination of the relevés into non-hierarchical species groups based on resemblances and dissimilarities in floristic composition and dominance of species (Koska et al. 2001); these were then named on the basis of two or three characteristic species. Nutrient limitation was determined by calculating N:P, N:K and K:P ratios in the above-ground biomass of vascular plants (Olde Venterink et al. 2003). Peat and water chemical data of locations characterized by the same floristic vegetation type were combined, and mean values were calculated.
Surficial mire water level measurements and piezometric heads were used to reveal water flow directions in the mire. To determine the water balance of the mire, we constructed a two-dimensional steady-state numerical groundwater model (cell size: 20 m) with the MODFLOW code (Harbaugh et al. 2000). The groundwater body in the terrace plateau was simulated as a constant head boundary at a distance of 700 m from the scarp using estimates of the phreatic water level in a small pond in the plateau to determine the head level. At the eastern model border, the Sargatka River was simulated by applying a constant head of 89.2 m a.s.l. in accordance with the water level recorded during the surface levelling measurements. Precipitation data from a nearby climate station (hydrological year 2001–2002) and evapotranspiration rates calculated according to the Penman-Monteith model were used to determine net precipitation input. The model includes a sand layer (K
h = 25 m d−1; K
v = 10 m d−1) overlain by a clay-loam layer (K
h = K
v = 0.1 m d−1), a peat layer (K
h = 2 m d−1; K
v = 1 m d−1), a root zone layer (K
h = 100 m d−1; K
v = 50 m d−1) and, on top, an air layer with high conductivity (K
h = K
v = 50,000 m d−1). For the terrace plateau, conductivity values of K
h = 10 m d−1 and K
v = 5 m d−1 were assumed. The model was calibrated on piezometric heads and surficial mire water levels. Based on the model calibration, the hydraulic conductivity of the peat layer was set slightly higher than the value measured in the field (i.e. K = 0.695 (±0.96) m d−1; n = 6).
Reliability of the hydrochemical data was tested by charge balance calculations. Deviations from electro-neutrality were within 10% for 92% of the water samples, indicating that the reliability of the data is sufficient to explore differences between main water types (cf. Schot and Van der Wal 1992). Water types were distinguished by agglomerative hierarchical clustering of the water samples using the between-groups linkage method as clustering algorithm with the Euclidian distance as measure for similarity (SPSS ver. 14.0; SPSS, Chicago, Ill.). Since pH was measured less frequently than the other hydrochemical variables, this variable was discarded. Clustering is thus based on Ca, Cl, EC, Fe, HCO3, K, Mg, Mn, Na, NH4, NO3, PO4, Si and SO4.