Linking models and data on vegetation structure

Abstract

[1] For more than a century, scientists have recognized the importance of vegetation structure in understanding forest dynamics. Now future satellite missions such as Deformation, Ecosystem Structure, and Dynamics of Ice (DESDynI) hold the potential to provide unprecedented global data on vegetation structure needed to reduce uncertainties in terrestrial carbon dynamics. Here, we briefly review the uses of data on vegetation structure in ecosystem models, develop and analyze theoretical models to quantify model-data requirements, and describe recent progress using a mechanistic modeling approach utilizing a formal scaling method and data on vegetation structure to improve model predictions. Generally, both limited sampling and coarse resolution averaging lead to model initialization error, which in turn is propagated in subsequent model prediction uncertainty and error. In cases with representative sampling, sufficient resolution, and linear dynamics, errors in initialization tend to compensate at larger spatial scales. However, with inadequate sampling, overly coarse resolution data or models, and nonlinear dynamics, errors in initialization lead to prediction error. A robust model-data framework will require both models and data on vegetation structure sufficient to resolve important environmental gradients and tree-level heterogeneity in forest structure globally.

Characterizing 3D vegetation structure from space: Mission requirements

“…Recent studies 1066 using forest dynamics computer simulation models suggests that models initialized with 1067 data that is too coarse to resolve the distribution in vegetation height (and how it is 1068 correlated to underlying environmental gradients) incur substantial initialization and flux 1069 prediction error. Operationally, model prediction errors over complex mountainous 1070 terrain increase rapidly at data scales > 1 ha (Thomas et al 2008, Hurtt et al 2010). 1071 1072…”

“…Chave et al (2004) regions, plots may be allocated even more sparsely than in the US and worse, not 365 necessarily allocated in an unbiased manner. The resulting biomass and structure maps 366 from a probability sample framework are generally not fine enough spatially to allow a 367 mechanistic understanding of the biomass variation with topographic, edaphic and 368 climatic gradients, which can vary at scales of km and finer (Brown and Lugo 1992, 369 Fearnside 1992, DeFries et al 2002, Achard et al 2004, Brown et al 1993, Iverson et al 370 1994, Myneni et al 2001, Baccini et al 2004, Saatchi et al 371 2007, Hurtt et al 2010. 372…”

“…2002, Hurtt et al 2004, Hurtt et al 2010). For each patch in a landscape the rates of 388 structural and biomass change following disturbance depends on the (1) operationally about 1 ha in complex environments (Hurtt et al 2008(Hurtt et al , 2010. 413…”

“…Based on the initial conditions, models simulate forest 385 succession and estimate carbon stocks and associated, time-dependent fluxes of carbon 386 between the atmosphere and the surface (Hurtt, et al 1998, Moorcroft et al 2001, Hurtt et 387 al. 2002, Hurtt et al 2004, Hurtt et al 2010. For each patch in a landscape the rates of 388 structural and biomass change following disturbance depends on the (1) vegetation state 389 pre-disturbance (2) type of disturbance (3) lapsed time since disturbance, (4) composition 390 of the regenerating vegetation (5) its physiognomy (primarily soils and topography) and 391 (6) extant climate conditions.…”

“…According to FAO (2006) operationally about 1 ha in complex environments (Hurtt et al 2008(Hurtt et al , 2010. 413…”