A Forest Model Intercomparison Framework and Application at Two Temperate Forests Along the East Coast of the United States

Abstract

State-of-the-art forest models are often complex, analytically intractable, and computationally expensive, due to the explicit representation of detailed biogeochemical and ecological processes. Different models often produce distinct results while predictions from the same model vary with parameter values. In this project, we developed a rigorous quantitative approach for conducting model intercomparisons and assessing model performance. We have applied our original methodology to compare two forest biogeochemistry models, the Perfect Plasticity Approximation with Simple Biogeochemistry (PPA-SiBGC) and Landscape Disturbance and Succession with Net Ecosystem Carbon and Nitrogen (LANDIS-II NECN). We simulated past-decade conditions at flux tower sites located within Harvard Forest, MA, USA (HF-EMS) and Jones Ecological Research Center, GA, USA (JERC-RD). We mined field data available from both sites to perform model parameterization, validation, and intercomparison. We assessed model performance using the following time-series metrics: Net ecosystem exchange, aboveground net primary production, aboveground biomass, C, and N, belowground biomass, C, and N, soil respiration, and species total biomass and relative abundance. We also assessed static observations of soil organic C and N, and concluded with an assessment of general model usability, performance, and transferability. Despite substantial differences in design, both models achieved good accuracy across the range of pool metrics. While LANDIS-II NECN showed better fidelity to interannual NEE fluxes, PPA-SiBGC indicated better overall performance for both sites across the 11 temporal and two static metrics tested (HF-EMS R 2 ¯ = 0.73 , + 0.07 , R M S E ¯ = 4.68 , − 9.96 ; JERC-RD R 2 ¯ = 0.73 , + 0.01 , R M S E ¯ = 2.18 , − 1.64 ). To facilitate further testing of forest models at the two sites, we provide pre-processed datasets and original software written in the R language of statistical computing. In addition to model intercomparisons, our approach may be employed to test modifications to forest models and their sensitivity to different parameterizations.

Modeling Management-Relevant Urban Forest Stand Characteristics to Optimize Carbon Storage and Sequestration

“…Given a set of parameters, the model can be used to simulate and predict how the trees of the stand will grow and interact over time, providing insights about forest structure and composition. Given its favorable balance between simplicity and realism, the PPA is widely used [32][33][34][35][36][37][38][39][40][41][42][43][44][45][46][47][48].…”

Scenario Analysis of Renewable Energy–Biodiversity Nexuses Using a Forest Landscape Model

“…Currently, large accumulated datasets of traits (e.g., TRY, 2020) and occurrence maps of plant species (see, for example, GBIF, 2020; Tanaka and Matsui, 2007; Forestry Agency of Japan, 2019; Ministry of the Environment, Japan [MOE], 2020; Long Term Ecological Research [LTER], 2020) enable us to systematically prepare the standard parameters required for forest landscape simulation models. These standard datasets facilitate forest model intercomparison practice (Erickson and Strigul, 2019) and reduce uncertainties when simulating the vegetation dynamics of region-specific tree species under climate change.…”

Time Series Analysis of Forest Dynamics at the Ecoregion Level

“…The forest gap models, or their approximations, such as the ecosystem demography model [10] and the perfect plasticity approximation model [11], can be scaled up directly to the large scales using spatially-explicit computer simulations of forest stands and large spatial units [29,30]. This approach allows one to link forest dynamics with biogechemistry models [17,31]. Analytically tractable approaches for scaling up mosaic of forest patches include continuous and discrete conservation law models.…”

“…Due to computational efficiency and analytical tractability, the developed time series models can be particularly useful for the large scale biogeochemical modeling and global climate change modeling [17], where the species-level community dynamics are usually not taken into account or averaged. These time series models can be also linked to the large scale models of forest tolerance in order to quantify environmental disturbance regimes and forecast overall basal area and biomass dynamics in resilience modeling [41].…”

“…Similarly to Markov chains, time series models can be parameterized from irregularly collected forest inventory data using Bayesian techniques [14][15][16]. Autoregressive models are computationally efficient and analytically tractable, and, therefore, are especially suitable for large scale carbon cycle and biogeochemistry forecasting models, which do not require species-level predictions of forest dynamics [17].…”