Download Mitsuda_Hosoda_Iehara_Matsumoto

yes no Was this document useful for you?
   Thank you for your participation!

* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project

Document related concepts
no text concepts found
Modeling carbon cycles in a Cryptomeria japonica planted forest
Y. Mitsuda1, K. Hosoda1, T. Iehara1, and M. Matsumoto2
Forest Management Division, Forestry and Forest Products Research Institute, 1 Matsunosato,
Tsukuba, Ibaraki, 305-8687 Japan. +81-29-829-8313; [email protected]
Bureau of Climate Change, Forestry and Forest Products Research Institute, 1 Matsunosato,
Tsukuba, Ibaraki, 305-8687 Japan.
Forest managers, policy-makers, and governments require a tool to estimate the carbon dynamics
of forests under various types of forest management (e.g. Kurz et al. 2009). One such tool is a
stand-level process-based carbon cycle model. Japanese cedar (Cryptomeria japonica) is the
dominant planting species in Japan, therefore there is an urgent requirement to develop a carbon
cycle model for this species.
The objective of this study was to develop a stand-level process-based carbon cycle model for
Japanese cedar planted forest using long-term permanent plot data. Using this model, we show
how to make better use of traditional forest monitoring data.
Materials and Methods
Our model is based on the 3-PG (Physiological Principles Predicting Growth) model developed
by Landsberg and Waring (1997). This model consists of four biomass pools (i.e., foliage, stem,
branch, and root) and uses dry matter weight per unit area as the basis for calculating carbon
balance. We simplified the original version to have only six processes: (1) photosynthetically
active radiation absorption; (2) conversion to gross primary production; (3) constraints on
photosynthesis by environmental factors; (4) respiration; (5) litterfall and root turnover; and (6)
biomass partitioning (Figure 1). Photosynthetically active radiation absorbed by stand (APAR) is
calculated by Beer’s law. We adopted a 5-layer canopy structure to substitute for the single-layer
canopy structure used in the original version. We also modified the process of conversion to gross
primary production (GPP). APAR is converted to GPP by a light-response curve (e.g. Hirose and
Werger 1987). The efficiency of this conversion is also constrained by environmental factors,
IUFRO Division 4: Extending Forest Inventory and Monitoring over Space and Time
which were temperature and vapor pressure deficit (VPD) in this study. The respiration of each
biomass pool is calculated in proportion to its biomass. Litterfall and root turnover are calculated
as well. Net primary production (NPP) is calculated as GPP minus respiration and total biomass
growth is calculated as NPP minus litterfall and root turnover. Total biomass growth is partitioned
into four biomass pools using a biomass proportion function for Japanese cedar developed by
Fukuda et al. (2003). We used a monthly time-step for processes 1 to 3, and a yearly time-step for
processes 4 to 6.
Solar Radiation
Radiation absorption
Light-response curve
Biomass partitioning
Figure 1. Flow diagram of a simplified version of the 3-PG model.
We used field measurement data derived from long-term permanent plots and time-series of
climatic data. The Forestry and Forest Products Research Institute and National Forest of Japan
have maintained permanent plots to investigate the growth of Japanese cedar planted forest since
the early 1930’s. Trees in these plots have been identified and their diameter and height have been
measured. We calculated the stand-level biomass of each biomass pool from tree-level field
measurement data, and obtained 136 stand-level observations derived from 20 permanent plots
for parameterization. The 1-km resolution 30-year average climatic data published by the Japan
Meteorological Agency with time-series fluctuations was used as climatic data of radiation,
temperature and VPD. Thus, we prepared time-series stand biomass and climatic data for each
plot (Figure 2).
Using these data, we parameterized the simplified version of the 3-PG by Bayesian calibration
IUFRO Division 4: Extending Forest Inventory and Monitoring over Space and Time
(e.g. Van Oijen et al. 2005). Parameters of respiration and litterfall and turnover processes were
fixed to the values used in Chiba (1998). Parameters of Beer’s law, light-response curve, and the
constraint functions for temperature and VPD were estimated by Bayesian calibration.
Stem biomass [ton/ha]
a) Stem biomass
Mean temperature [ C]
0 5 10 15 20 25
b) Monthly mean temperature
PAR [MJ m ]
0 50
c) Monthly photosynthetically active radiation
VPD [kPa]
0.5 1.0 1.5
d) Monthly mean vapor pressure deficit
Calendar Year
Figure 2. An example of time-series data of model parameterization. Field measurements
were conducted at the years indicated by the dotted line.
IUFRO Division 4: Extending Forest Inventory and Monitoring over Space and Time
All parameters converged in the Bayesian calibration. Posterior distributions of some parameters
showed sharp peakes, which indicated that these parameters were well conditioned by the data
through Bayesian calibration; however, others were not so informative.
The comparison of measured and predicted stem biomass is shown in Figure 3. We used medians
of the posterior distribution of each parameter for simulating stem biomass. Model calculations
reproduced the observed stand stem biomass to some degree, but there were large errors in some
Measured Stem Biomass [ton ha]
100 150 200 250 300
100 150 200 250 300
Predicted Stem Biomass [ton ha]
Figure 3. Comparison of measured stem biomass and predicted stem biomass.
A simplified version of the 3-PG model for Japanese cedar was parameterized by Bayesian
calibration using tree measurement data derived from long-term permanent plots and monthly
climatic data of radiation, temperature, and VPD. Although our model is relatively simple and
uses only three climatic factors, it can represent the growth patterns of various stands with
various climatic conditions to some degree. Because 1-km resolution grid climatic data as used in
this study is available for the whole of Japan, this model offers broad applicability. It would also
be possible to assess the long-term effects of climate change on Japanese cedar planted forest
using the carbon cycle model developed in this study.
IUFRO Division 4: Extending Forest Inventory and Monitoring over Space and Time
Process-based models usually require ecophysiological experiments for parameterization,
whereas we parameterized this kind of model using tree measurement data, which did not contain
any ecophysiological values. The recently-developed Bayesian approach may allow us to use a
large amount of accumulated field survey data for various applications.
The stand-level process-based carbon cycle model for Japanese cedar planted forest developed in
this study could help forest managers and policy-makers to make decisions on forest management.
This success in parameterization of a stand-level process-based model using Bayesian calibration
also shows the way to make better use of traditional forest monitoring data.
Literature Cited
Chiba, Y. 1998. Simulation of CO2 budget and ecological implications of sugi (Cryptomeria
japonica) man-made forests in Japan. Ecol. Model. 111(2-3): 269–281.
Fukuda, M., T. Iehara, and M. Matsumoto. 2003. Carbon stock estimates for sugi and hinoki
forests in Japan. For.Ecol. Manage. 184(1-3): 1–16.
Hirose, T. and M.J.A. Werger. 1987. Maximizing daily canopy photosynthesis with respect to the
leaf nitrogen allocation pattern in the canopy. Oecologia 72(4): 520–526.
Kurz, W., C. Dymond, T. White, G. Stinson, C.H. Shaw G. Rampley, C. Smyth, B.N. Simpson,
E.T. Neilson J.A. Trofymow, J. Metsaranta, and M.J. Apps. 2009. CBM-CFS3: A model of
carbon-dynamics in forestry and land-use change implementing IPCC standards. Ecol. Model.
220(4): 480-504.
Landsberg, J. and R. Waring. 1997. A generalised model of forest productivity using simplified
concepts of radiation-use efficiency, carbon balance and partitioning. For. Ecol. Manage. 95(3):
Van Oijen, M., J. Rougier, and R. Smith. 2005. Bayesian calibration of process-based forest
models: bridging the gap between models and data. Tree Physiol. 25: 915–927.
IUFRO Division 4: Extending Forest Inventory and Monitoring over Space and Time