Details

Autor(en) / Beteiligte

• Longo, Marcos
• Knox, Ryan G
• Medvigy, David M
• Levine, Naomi M
• Dietze, Michael C
• Kim, Yeonjoo
• Swann, Abigail L. S
• Zhang, Ke
• Rollinson, Christine R
• Bras, Rafael L
• Wofsy, Steven C
• Moorcroft, Paul R

Titel

The biophysics, ecology, and biogeochemistry of functionally diverse, vertically and horizontally heterogeneous ecosystems: the Ecosystem Demography model, version 2.2 – Part 1: Model description

Ist Teil von

• Geoscientific Model Development, 2019-10, Vol.12 (10), p.4309-4346

Ort / Verlag

Katlenburg-Lindau: Copernicus GmbH

Erscheinungsjahr

2019

Quelle

EZB Electronic Journals Library

Beschreibungen/Notizen

• Earth system models (ESMs) have been developed to represent the role of terrestrial ecosystems on the energy, water, and carbon cycles. However, many ESMs still lack representation of within-ecosystem heterogeneity and diversity. In this paper, we present the Ecosystem Demography model version 2.2 (ED-2.2). In ED-2.2, the biophysical and physiological processes account for the horizontal and vertical heterogeneity of the ecosystem: the energy, water, and carbon cycles are solved separately for a series of vegetation cohorts (groups of individual plants of similar size and plant functional type) distributed across a series of spatially implicit patches (representing collections of micro-environments that have a similar disturbance history). We define the equations that describe the energy, water, and carbon cycles in terms of total energy, water, and carbon, which simplifies the differential equations and guarantees excellent conservation of these quantities in long-term simulation (< 0.1 % error over 50 years). We also show examples of ED-2.2 simulation results at single sites and across tropical South America. These results demonstrate the model's ability to characterize the variability of ecosystem structure, composition, and functioning both at stand and continental scales. A detailed model evaluation was conducted and is presented in a companion paper (Longo et al., 2019a). Finally, we highlight some of the ongoing model developments designed to improve the model's accuracy and performance and to include processes hitherto not represented in the model.