Abstract
Accurate estimation of forest net primary productivity (NPP), biomass, and their sensitivity to changes in temperature and precipitation is important for understanding the fluxes and pools of terrestrial carbon resulting from anthropogenically driven climate change. The objectives of this study were to (1) estimate potential forest NPP and biomass for New England using a regional ecosystem model, (2) compare modeled forest NPP and biomass with other reported data for New England, and (3) examine the sensitivity of modeled forest NPP to historical climatic variation. We addressed these objectives using the regional ecosystem model LPJ‐GUESS implemented with eight plant functional types representing New England forests. We ran the model using 30‐arc second spatial resolution climate data in monthly time‐steps for the period 1901–2006. The modeled forest NPP and biomass were compared to empirically‐based MODIS and FIA estimates of NPP and U.S. forest biomass. Our results indicate that forest NPP in New England averages 428 g C·m−2·yr−1 and ranges from 333 to 541 g C·m−2·yr−1 for the baseline period (1971–2000), while forest biomass averages 135 Mg/ha and ranges from 77 to 242 Mg/ha. Modeled forest biomass decreased at a rate of 0.11 Mg/ha (R2 = 0.74) per year in the period 1901–1949 but increased at a rate of 0.25 Mg/ha (R2 = 0.95) per year in the period 1950–2006. Estimates of NPP and biomass depend on forest type: spruce‐fir had the lowest mean of 395 g C·m−2·yr−1 and oak forest had the highest mean of 468 g C·m−2·yr−1. Similarly, forest biomass was highest in oak (153 Mg/ha) and lowest in red‐jack pine (118 Mg/ha) forests. The modeled NPP for New England agrees well with FIA‐based estimates from similar forests in the mid‐Atlantic region but was smaller than MODIS NPP estimates for New England. Nevertheless, the modeled inter‐annual variability of NPP was strongly correlated with the MODIS NPP data. The modeled biomass agrees well with U.S. forest biomass data for New England but was less than FIA‐based estimates in the mid‐Atlantic region. For the region as a whole, the modeled NPP and biomass are within the ranges of MODIS‐ and FIA‐based estimates. Forest NPP was sensitive to changes in temperature and precipitation: NPP was positively related to temperatures in April, May and October but negatively related to summer temperature. Increases in precipitation in the growing season enhanced forest NPP.
Highlights
Forest net primary productivity (NPP) is a key component of the global carbon cycle and an important link between the biosphere and the atmosphere, influencing water fluxes, nutrient cycles, and climate variation (e.g., Prentice et al 2000)
We suggest that LPJ-GUESS might better quantify forest NPP for New England than MODIS derived NPP estimates based on our comparison to forest inventory and analysis (FIA)-based estimates of NPP (e.g., Jenkins et al 2001)
The modeled annual variability of forest NPP is significantly correlated with MODIS NPP estimates
Summary
Forest net primary productivity (NPP) is a key component of the global carbon cycle and an important link between the biosphere and the atmosphere, influencing water fluxes, nutrient cycles, and climate variation (e.g., Prentice et al 2000). Process-based vegetation models, including terrestrial biogeochemistry models (TBMs) like Biome-BGC (Running and Coughlan 1988) and dynamic global vegetation models (DGVMs) like LPJ-DGVM (Sitch et al 2003), have been widely used in estimating forest NPP and biomass at broad spatial scales. These models include mechanistic representations of the ecosystem carbon cycle and its dynamic responses to external disturbances, including plant photosynthesis and the allocation of assimilated carbon to leaves, roots, sapwood and heartwood, and are useful for quantifying forest NPP and biomass (Cramer et al 2001, Morales et al 2007). The utility of these models can be improved by (1) creating finer definition of forest types at regional scale (Jenkins et al 2001), and (2) increasing the quality of model input data (e.g., the resolution of climate data)
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