Current issue: 54(5)
Under compilation: 55(1)
The tree stem volume models of Norway spruce, Scots pine and silver and downy birch currently used in Finland are based on data collected during 1968–1972. These models include four different formulations of a volume model, with three different combinations of independent variables: 1) diameter at height of 1.3 m above ground (dbh), 2) dbh and tree height (h) and 3) dbh, h and upper diameter at height of 6 m (d6). In recent National Forest Inventories of Finland, a difference in the mean volume prediction between the models with and without the upper diameter as predictor has been observed. To analyze the causes of this difference, terrestrial laser scanning (TLS) was used to acquire a large dataset in Finland during 2017–2018. Field-measured predictors and volumes predicted using spline functions fitted to the TLS data were used to re-calibrate the current volume models. The trunk form is different in these two datasets. The form height is larger in the new data for all diameter classes, which indicates that the tree trunks are more slender than they used to be. One probable reason for this change is the increase in stand densities, which is at least partly due to changed forest management. In models with both dbh and h as predictors, the volume is smaller a given h class in the data new data than in the old data, and vice versa for the diameter classes. The differences between the old and new models were largest with pine and smallest with birch.
The carbon reservoir of ecosystems was estimated based on field measurements for forests and peatlands on an area in Finland covering 263,000 km2 and extending about 900 km across the boreal zone from south to north. More than two thirds of the reservoir was in peat, and less than ten per cent in trees. Forest ecosystems growing on mineral soils covering 144,000 km2 contained 10–11 kg C m-2 on an average, including both vegetation (3.4 kg C m-2) and soil (uppermost 75 cm; 7.2 kg C m-2). Mire ecosystems covering 65,000 km2 contained an average of 72 kg C m-2 as peat. For the landscape consisting of peatlands, closed and open forests, and inland water, excluding arable and built-up land, a reservoir of 24.6 kg C m-2 was observed. This includes the peat, forest soil and tree biomass. This is an underestimate of the true total reservoir, because there are additional unknown reservoirs in deep soil, lake sediments, woody debris, and ground vegetation. Geographic distributions of the reservoirs were described, analysed and discussed. The highest reservoir, 35–40 kg C m-2, was observed in sub-regions in central western and north western Finland. Many estimates given for the boreal carbon reservoirs have been higher than those of ours. Either the Finnish environment contains less carbon per unit area than the rest of the boreal zone, or the global boreal reservoir has earlier been overestimated. In order to reduce uncertainties of the global estimates, statistically representative measurements are needed especially on Russian and Canadian peatlands.
Methods involving the use of moving averages, trend surfaces and their combination are compared in deriving local values of monthly mean temperatures and precipitation sums from the observations made by the Finnish Meteorological Office. Correlation between meteorological variables and sea index, lake index and height above sea level were used in the trend surface method and in the combined method. Combined method, with a trend surface calculated from means of a long time period, was the most reliable method to estimate long local time series.
A method to calculate unbiased estimates of effective temperature sums from monthly mean temperatures is presented.
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The paper concerns relationship between climatic factors and annual ring indices mainly in Southern Finland. The studied index series were from papers of different authors and from different localities. The monthly mean temperatures and precipitation sums were derived from the measurements of meteorological stations. Effective temperature sums for different periods of the year were calculated from the monthly mean temperatures.
The autocorrelation functions were estimated for each index series. The autocorrelations at lag I were significant except for one series. Altogether the differences in the structures of the index series were noticeable, especially between the Scots pine (Pinus sylvestris L.) index series. The influence of climatic factors on the annual ring index variation was studied using cross correlation analysis, simple distributed lag models and transfer function-noise models.
The decisive factor for the annual ring index variation of Norway spruce (Picea abies (L.) H. Karst.) appears to be the effective temperature sum of the growing season. Warm periods during latter parts of previous summer had a negative effect on indices. For the variation of the Scots pine indices the most important climatic factors were the effective temperature sum of the latter part of the growing season and, especially on the arid sites, the precipitation sum during May-July.
The PDF includes a summary in Finnish.