Parallel estimates of gross primary production of Finnish forests

2. PRELES applied forest inventory-scaled information
about forest structure at 100 m resolution.
3. PRELES applied a simple soil water model with one
pool for root-available water and one pool for surfacial
water.
2. JSBACH used information about plant functional type
fractions in 0.167 degree pixels, based on the
European Corine Landcover
3. A new five-layer soil moisture scheme (Hagemann and
Stacke, 2013) where the soil information is not merely
PFT specific but partly based on soil texture data.
1 Raddatz, T. J., C. J. Reick, W. Knorr, J. Kattge, E. Roeckner, R. Schnur, K.-G. Schnitzler, P. Wetzel, and J. Jungclaus, 2007. Will the tropical
land biosphere dominate the climate-carbon cycle feedback during the twenty-first century?, Climate Dynamics, 29, 565–574,
doi:10.1007/s00382-007-0247-8.
2 Peltoniemi, Mikko, Kalliokoski, Tuomo, Lindroos, Antti-Jussi, Beuker, Egbert & Mäkelä, Annikki. 2012. User guide for PRELES, a simple
model for the assessment of gross primary production and water balance of forests. Metlan työraportteja / Working Papers of the Finnish
Forest Research Institute 247. 23 s. ISBN ISBN 978-951-40-2395-8 (PDF). http://www.metla.fi/julkaisut/workingpapers/2012/mwp247.htm.
3 Running, S., R. R. Nemani, F. A. Heinsch, M. Zhao, M. Reeves, and H. Hashimoto (2004). A continuous satellite-derived measure of global
terrestrial primary production BioScience, 54(6): 547-560
4 Zhao, M., F. A. Heinsch, R. R. Nemani, and S. W. Running (2005). Improvements of the MODIS terrestrial gross and net primary production
global data set. Remote Sensing of Environment. 95: 164–176.
5. Annual Modis GPP product downloaded from th website of the Numerical Terradynamic Simulation Group of the University of Montana
(http://www.ntsg.umt.edu/project/mod17).
With the contribution of the LIFE+ financial instrument of the
European Union. LIFE09 ENV/FI/000571 Climate change induced
drought effects on forest growth and vulnerability (Climforisk,
www.metla.fi/life/climforisk), and LIFE07 /ENV/FIN/000133
(SnowCarbo)
1. Semi-empirical model of GPP, evapotranspiration, and
water balance of stand. GPP-prediction is based on lightuse efficiency based model, and evapotranspiration is
predicted based on water use efficiency.
1. Photosynthesis of trees, is described with Farquhar et
al. (1980) model, which has two PFT-specific
parameters in JSBACH.
References:
PRELES
Fig. 4
Fig. 2
Fig. 5
Fig. 6
different approaches, and calibration data
sets, PRELES currently calibrated with eddycovariance data from only two sites.
JSBACH parameters, on the other hand, draw
from generic PFT-parameters that originate
outside the study region.
Fig. 3
04.2014 Metla/AnSi
Finnish Forest Research Institute, P.O. Box 18, FI‐01301 Vantaa, Finland, *[email protected]
Agreement of the modelled GPP is
remarkable as all these estimates are based on
Remaining differences in GPP predictions
between JSBACH and PRELES can be
reconciled with differences in LAI data used
(Fig. 5, Fig. 6), and their different sensitivity
to soil water. Differences to MODIS GPP can
be partially reconciled by the fact that we
simulated here only the GPP of trees.
The consistency between PRELES and
JSBACH largely stems from the fact that
their respective predictions for conifers
coincided well with each other (and with
data), as this group of species dominates the
forest area in Finland.
Discussion
Models predicted coiciding patterns of GPP at sites for conifers (Fig 4). Further improvement of models for deciduous species and their seasonality is needed.
JSBACH
Models predicting GPP
The objective of this study was was to screen the total annual production of forests and its temporal and spatial distribution in the approaches, so as to analyse the causes of possible discrepancies between the models. Especially, we aim to assess to what degree the possible differences are related to either forest structure or to the response of GPP to climate in the models.
Modelled spatial patterns of annual GPP with JSBACH and PRELES were close to each other, and MODIS showed higher GPP (Fig 1).
We simulated Gross Primary Production (GPP) of Finnish forests using a land‐surface model JSBACH1, and a semi‐empirical stand flux model PRELES2. We compared model’s GPP predictions to MODIS GPP product3,4,5.
Annual total GPP (Fig. 2), and the latitudinal gradient of the mean GPP predictions (Fig 3) of JSBACH and PRELES agreed.
Results
What we did
Fig. 1
Mikko Peltoniemi*, Tiina Markkanen, Sanna Härkönen, Petteri Muukkonen, Tea Thum, Tuula Aalto and Annikki Mäkelä
Parallel estimates of gross primary production of Finnish forests – comparison of two process models

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