ISSN 1239-6095 (print),   ISSN 1797-2469 (online)
© Boreal Environment Research 2015

Contents of Volume 20 no. 2

Aalto T., Peltoniemi M., Aurela M., Böttcher K., Gao Y., Härkönen S., Härmä P., Kilkki J., Kolari P., Laurila T., Lehtonen A., Manninen T., Markkanen T., Mattila O.-P., Metsämäki S., Muukkonen P., Mäkelä A., Pulliainen J., Susiluoto J., Takala M., Thum T., Ťupek B., Törmä M. & Arslan A.N. 2015: Preface to the special issue on Monitoring and Modelling of Carbon-Balance-, Water- and Snow-Related Phenomena at Northern Latitudes. Boreal Env. Res. 20: 145–150.
Abstract not available
Full text (pdf format)

Peltoniemi M., Pulkkinen M., Aurela M., Pumpanen J., Kolari P. & Mäkelä A. 2015: A semi-empirical model of boreal-forest gross primary production, evapotranspiration, and soil water — calibration and sensitivity analysis. Boreal Env. Res. 20: 151–171.
Abstract
Full text (pdf format)

Muukkonen P., Nevalainen S., Lindgren M. & Peltoniemi M. 2015: Spatial occurrence of drought-associated damages in Finnish boreal forests: results from forest condition monitoring and GIS analysis. Boreal Env. Res. 20: 172–180.
Abstract
Full text (pdf format)

Härkönen S., Lehtonen A., Manninen T., Tuominen S. & Peltoniemi M. 2015: Estimating forest leaf area index using satellite images: comparison of k-NN based Landsat-NFI LAI with MODIS-RSR based LAI product for Finland. Boreal Env. Res. 20: 181–195.
Abstract
Full text (pdf format)

Peltoniemi M., Markkanen T., Härkönen S., Muukkonen P., Thum T., Aalto T. & Mäkelä A. 2015: Consistent estimates of gross primary production of Finnish forests — comparison of estimates of two process models. Boreal Env. Res. 20: 196–212.
Abstract
Full text (pdf format)

Aalto T., Hatakka J., Kouznetsov R. & Stanislawska K. 2015: Background and anthropogenic influences on atmospheric CO2 concentrations measured at Pallas: Comparison of two models for tracing air mass history. Boreal Env. Res. 20: 213–226.
Abstract
Full text (pdf format)

Kilkki J., Aalto T., Hatakka J., Portin H. & Laurila T. 2015: Atmospheric CO2 observations at Finnish urban and rural sites. Boreal Env. Res. 20: 227–242.
Abstract
Full text (pdf format)

Törmä M., Markkanen T., Hatunen S., Härmä P., Mattila O.-P. & Arslan A.N. 2015: Assessment of land-cover data for land-surface modelling in regional climate studies. Boreal Env. Res. 20: 243–260.
Abstract
Full text (pdf format)

Gao Y., Weiher S., Markkanen T., Pietikäinen J.-P., Gregow H., Henttonen H.M., Jacob D. & Laaksonen A. 2015: Implementation of the CORINE land use classification in the regional climate model REMO. Boreal Env. Res. 20: 261–282.
Abstract
Full text (pdf format)

Ťupek B., Mäkipää R., Heikkinen J., Peltoniemi M., Ukonmaanaho L., Hokkanen T., Nöjd P., Nevalainen S., Lindgren M. & Lehtonen A. 2015: Foliar turnover rates in Finland — comparing estimates from needle-cohort and litterfall-biomass methods. Boreal Env. Res. 20: 283–304.
Abstract
Full text (pdf format)


Peltoniemi M., Pulkkinen M., Aurela M., Pumpanen J., Kolari P. & Mäkelä A. 2015: A semi-empirical model of boreal-forest gross primary production, evapotranspiration, and soil water — calibration and sensitivity analysis. Boreal Env. Res. 20: 151–171.

Simple approaches to predicting ecosystem fluxes are useful in large-scale applications because existing data rarely support justified use of complex models. We developed a model of daily ecosystem gross primary production (P), evapotranspiration (E), and soil water content (θ), which only requires standard weather data and information about the fraction of absorbed radiation. We estimated the parameters of the model for two boreal Scots pine eddy-covariance sites (Hyytiälä and Sodankylä). The model predicted P and E adequately for Hyytiälä for both calibration and additional test years. The model calibrated for Hyytiälä slightly overestimated P and E in Sodankylä, but its performance levelled with the model calibrated for Sodankylä in a dry year. Sensitivity analysis of the model implied that drought prediction is sensitive, not only to key E submodel parameters, but also to P submodel parameters. Further improvement and calibrations of the model could benefit from forest sites with varying canopy and different species structures.
Back to the top

Muukkonen P., Nevalainen S., Lindgren M. & Peltoniemi M. 2015: Spatial occurrence of drought-associated damages in Finnish boreal forests: results from forest condition monitoring and GIS analysis. Boreal Env. Res. 20: 172–180.

In boreal forests, some growing sites are more vulnerable to decreased soil moisture than others, which might result in stress symptoms in trees and thus affect their growth. We combined Finnish forest health data (ICP Level 1) with GIS data describing growing conditions, soil properties and soil water conditions to find out ways to identify the most vulnerable risk areas. The summer of 2006 was extremely dry, and the relative soil water index (SWI) in August relative to the 30-year average was only about 25%. This led to a higher percentage (24%) of sites (603 in total) where trees showed drought-damage symptoms. Our study shows that the risk of drought damages differs spatially depending on climatic conditions and soil properties. The most important variables to identify risk areas are the proportion of bare-rock areas, topographic wetness index (TWI), soil water indices (absolute and relative) and the spatial location on the north–south axis.
Back to the top

Härkönen S., Lehtonen A., Manninen T., Tuominen S. & Peltoniemi M. 2015: Estimating forest leaf area index using satellite images: comparison of k-NN based Landsat-NFI LAI with MODIS-RSR based LAI product for Finland. Boreal Env. Res. 20: 181–195.

Leaf area index (LAI) is a key variable for many ecological models, but it is typically not available from basic forest inventories. In this study, we (1) construct a high-resolution LAI map using k nearest-neighbor (k-NN) imputation based on National Forest Inventory data and Landsat 5 TM images (Landsat-NFI LAI), and (2) examine a moderate-resolution LAI map produced based on reduced simple ratio derived from MODIS reflectances (MODIS-RSR LAI). The maps cover all the forested areas in Finland. Country-level averages of Landsat-NFI and MODIS-RSR LAI were at same level, but several geographical and land-use related differences between them were detected. Difference was the largest in the lake district of Finland and in northern Finland, and it increased with decreasing share of forests and increasing share of deciduous trees. As MODIS-RSR LAI does not take into account the sub-pixel variation in land use, Landsat-NFI LAI was found to produce more reliable estimates.
Back to the top

Peltoniemi M., Markkanen T., Härkönen S., Muukkonen P., Thum T., Aalto T. & Mäkelä A. 2015: Consistent estimates of gross primary production of Finnish forests — comparison of estimates of two process models. Boreal Env. Res. 20: 196–212.

We simulated Gross Primary Production (GPP) of Finnish forests using a landsurface model (LSM), JSBACH, and a semi-empirical stand-flux model PRELES, and compared their predictions with the MODIS GPP product. JSBACH used information about plant functional type fractions in 0.167° pixels. PRELES applied inventory-scaled information about forest structure at high resolution. There was little difference between the models in the results aggregated to national level. Temporal trends in annual GPPs were also parallel. Spatial differences could be partially related to differences in model input data on soils and leaf area. Differences were detected in the seasonal pattern of GPP but they contributed moderately to the annual totals. Both models predicted lower GPPs than MODIS, but MODIS still showed similar south–north distribution of GPP. Convergent results for the national total GPP between JSBACH and PRELES, and those derived for comparison from the forest ghg-inventory, implied that modelled GPP estimates can be realistically up-scaled to larger region in spite of the fact that model calibrations may not originate from the study region, or that a limited number of sites was used in the calibration of a model.
Back to the top

Aalto T., Hatakka J., Kouznetsov R. & Stanislawska K. 2015: Background and anthropogenic influences on atmospheric CO2 concentrations measured at Pallas: Comparison of two models for tracing air mass history. Boreal Env. Res. 20: 213–226.

The FLEXTRA and SILAM models were utilized in estimating the influence regions (IR) for the measured CO2 concentration ([CO2]) at Pallas together with tracers for anthropogenic emissions. The models produced similar synoptic features and associated background [CO2] with marine IR and elevated [CO2] with continental IR, but there were also differences which affected the interpretation of measurements. The background, i.e. marine boundary layer (MBL) signal, was compared to the NOAA MBL reference. Both models performed well, with monthly mean deviations from the reference usually inside 1 ppm. The FLEXTRA MBL signal had some seasonality in the difference, however, only very few cases were associated with anthropogenic emissions. We used [CO] and fossil fuel [CO2] simulations by the TM5 (CarbonTracker CT2011_oi) model as emission tracers. The model and [CO] captured well the timing of high [CO2] in measurements. The anthropogenic influence was more pronounced in winter than in summer, and it had a large inter-annual variation.
Back to the top

Kilkki J., Aalto T., Hatakka J., Portin H. & Laurila T. 2015: Atmospheric CO2 observations at Finnish urban and rural sites. Boreal Env. Res. 20: 227–242.

Four new ground-based atmospheric monitoring stations in Finland were examined for local and large-scale signals in carbon dioxide mole fraction, and the results were compared with the corresponding values obtained from WMO/GAW site Pallas, northern Finland and NOAA/ESRL marine boundary layer reference time series. The measurements were filtered with knowledge of local weather and air composition. Periods of a presumably well-mixed boundary layer and relatively pollutant-free air were close to the Pallas time series in mole fraction, particularly in the winter. Wintertime mole fractions were 5–10 ppm higher than the signal in the marine boundary layer at all stations. The seasonal amplitude was 18–24 ppm, and diurnal amplitude was 03 ppm in winter and 3–20 ppm in the summer months. All stations, with the possible exception of the urban site in Helsinki, showed potential for observing a large-scale carbon dioxide signal.
Back to the top

Törmä M., Markkanen T., Hatunen S., Härmä P., Mattila O.-P. & Arslan A.N. 2015: Assessment of land-cover data for land-surface modelling in regional climate studies. Boreal Env. Res. 20: 243–260.

We studied the land-cover data used by the regional climate model REMO and the land surface model JSBACH, for Finland and surrounding areas. To date, the land-cover data determining REMO's surface parameterisations have originated from the Global Ecosystem classification of the Global Land Cover Characteristics (GLCC-GEC) database. The same database has also been used as basis for prescribed plant functional type distribution with JSBACH. We showed that the GLCC-GEC does not represent the Finnish landscape particularly well, and there are large errors in the land cover type distributions. Furthermore, we have inspected the values of the land surface parameters forest ratio and leaf area index, which were assigned to land-cover types, and found them to typically be too large for Finland. Different revised land-cover data sets were created using GlobCover and different versions of Corine Land Cover (CLC) classifications. The benefits of the new land-cover data sets were much more spatial detail and thematic content which corresponded better to the Finnish environment, unlike in the GLCC-GEC. For example, there are wetlands and they are correctly located. Although no definite reference exists to assess the qualification of the land-cover data in the light of the model results, modelling benefits from the use of land-cover data that is more spatially accurate and recent. Even though regionally the differences are not great, at a more local level they become substantial.
Back to the top

Gao Y., Weiher S., Markkanen T., Pietikäinen J.-P., Gregow H., Henttonen H.M., Jacob D. & Laaksonen A. 2015: Implementation of the CORINE land use classification in the regional climate model REMO. Boreal Env. Res. 20: 261–282.

Regional climate models provide an effective way to study the effects of land use changes on regional climate conditions. A precise land cover map is a precondition for land use change studies. We introduce a more realistic high-resolution land cover map, CORINE Land Cover (CLC), to replace the Global Land Cover Characteristics Database (GLCCD), which is used as a standard land cover map in the regional climate model REMO. In this study, present-day climate simulations over northern Europe are performed by using REMO at 18-km resolution with both CLC and GLCCD. Simulated maximum and minimum 2-m air temperatures, diurnal temperature range and precipitation are assessed with the observation-based E-OBS data. The updated CLC enhances the realism of the description of present-day land surface. However, biases from simulated climate conditions to observations are only marginally reduced while more improvements are expected to be achieved by further developments in model physics.
Back to the top

Ťupek B., Mäkipää R., Heikkinen J., Peltoniemi M., Ukonmaanaho L., Hokkanen T., Nöjd P., Nevalainen S., Lindgren M. & Lehtonen A. 2015: Foliar turnover rates in Finland — comparing estimates from needle-cohort and litterfall-biomass methods. Boreal Env. Res. 20: 283–304.

Soil carbon models serving national greenhouse gas (GHG) inventories need precise litter input estimates that typically originate from regionally-averaged and species-specific biomass turnover rates. We compared the foliar turnover rates estimated from long-term measurements by two methods: the needle-cohort based turnover rates (NT; 1064 Scots pine and Norway spruce stands), used in Finnish GHG inventory, and litterfall-biomass based turnover rates (LT; 40 Scots pine, Norway spruce, and silver and downy birch stands). For evergreens, regionally averaged NT values (± SD) (0.139 ± 0.01, 0.1 ± 0.009 for spruce south and north of 64°N, and 0.278 ± 0.016, 0.213 ± 0.028 for pine, respectively) were greater than those used in the GHG inventory model in Finland (0.1, 0.05 for spruce in the south and north, and 0.245, 0.154 for pine, respectively). For deciduous forests, averaged LT values ± SD (0.784 ± 0.162, 0.634 ± 0.093 for birch in the south and north) were close to that (0.79) currently used for the whole of Finland.
Back to the top