Simulating seasonal and inter‐annual variations in energy and carbon exchanges and forest dynamics using a process‐based atmosphere–vegetation dynamics model

Abstract

The present paper shows simulated results of seasonal and inter-annual variations in energy and carbon exchanges and forest dynamics in a sub-boreal deciduous forest using a fully coupled atmospherevegetation interaction model [multilayered integrated numerical model of surface physics-growing plants interaction (MINoSGI)]. With careful adjustment of site-specific eco-physiological parameters, MINoSGI reproduced successfully stand biomass-tree density relationship based on the forest inventory data for 7 years (1999)(2000)(2001)(2002)(2003)(2004)(2005) and seasonal and inter-annual variations in energy and CO 2 fluxes measured by means of eddy covariance technique for 3 years (2003)(2004)(2005) in the sub-boreal forest, northern Japan. In addition, MINoSGI estimated annual evapotranspiration (E vt ) at 328.6 ± 25.8 mm year À1 , net primary production (NPP) at 372.1 ± 31.5 gC m À2 year À1 and net ecosystem exchange (NEE) at À224.2 ± 32.2 gC m À2 year À1 . We found the estimate of annual NEE in our site lies among the estimates at other forest stands with the almost same climatic conditions in northern Japan, although the tree species and stand age of these forests are different from those of our site. Overall, MINoSGI was found useful to present simultaneous simulations of forest dynamics, surface energy, and carbon exchanges of a forest stand in the future from micro-meteorological and ecophysiological points of view.

Predicting ecosystem dynamics at regional scales: an evaluation of a terrestrial biosphere model for the forests of northeastern North America

“…Re-parametrization in HF-OPT decreased the impact of shading on mortality, though figure 7b suggests that it has not yet been reduced enough in the highest productivity sites. One way to address this problem would be to reduce leaf nitrogen for understorey trees, which would reduce leaf respiration and thus improve the carbon balance of these trees [33]. More realistic representations of crown shape and accounting for phototropism would also be likely to reduce mortality of shaded individuals [34].…”

Photosynthetic recovery of foliage after wind disturbance activates ecosystem CO2uptake in cool temperate forests of northern Japan

“…The annual mean air temperature, specific humidity and precipitation are 2.7°C, 4.8 g kg −1 and 1237 mm, respectively. The area is widely covered with snow from November to May, and the snow season length averaged 209 days from 1999 to 2005 [ Toda et al , 2011]. The plant growing season length is largely affected by the annual snow conditions.…”

“…The 600 m 2 (20 m × 30 m) stand was established in 1998 as a study plot (44°20′N, 142°15′E, 550 m above sea level). The stand was composed of five deciduous species mainly dominated by broad‐leaved Betula ermanii (40 years old in 2009) [ Toda et al , 2011]. The canopy structure of the stand was homogeneous and a representative canopy height of the young‐growth forest was estimated using the cumulative basal area inflection (CuBI) height, defined as the height of the inflection point of the plot between tree height and cumulative basal area [ Nakai et al , 2010].…”

“…Values of w p and w np were determined based on the allometric relationships between w np and dbh and tree height h and between w p and the leaf amount and dbh of individual trees [ Watanabe et al , 2004]: where θ and γ are allometric parameters and SLA is specific leaf area. Four B. ermanii trees of different heights near the stand were cut and these parameters were estimated as γ = 0.025 kg cm −2 m −1 and θ = 0.013 kg cm −2 based on the measured w p , w np , h , dbh , and SLA (SLA = 15 m 2 kg −1 ) of the four trees [ Takahashi et al , 2004; Toda et al , 2011]. The stand biomass was estimated using these parameters and h and dbh for all trees in the stand.…”

“…In , TER was modeled using the following empirical exponential temperature regression: where R 10 is the base level of respiration (i.e., respiration at 10°C), Q 10 is the temperature sensitivity (i.e., the slope of the apparent temperature response of respiration), and T is the mean of air and soil temperature. In the present study, we used Q 10 = 2.20 for the young‐growth forest [ Toda et al , 2011] and Q 10 = 2.04 for the old‐growth forest, based on tentative soil respiration measurements during the various plant growth stages (Table 1). Of the respiration measurements conducted in the forest stands during 2003–2007, most data from 2003 to 2005 could not be utilized because of poor quality.…”