MC consists of three linked modules simulating biogeography, biogeochemistry,
and fire disturbance. The main functions of the biogeography module are
to (1) predict the composition of deciduous/evergreen tree and C3/C4 grass
lifeform mixtures and (2) classify the predicted biomass from the biogeochemistry
module into different vegetation classes. The biogeochemistry module simulates
monthly carbon and nutrient dynamics for a given ecosystem. Above- and
below-ground processes are modeled in detail, and include plant production,
soil organic matter decomposition, and water and nutrient cycling. Parameterization
of this module is based on the lifeform composition of the ecosystems,
which is updated annually by the biogeography module. The fire module simulates
the occurrence, behavior and effects of severe fire. Allometric equations,
keyed to the lifeform composition supplied by the biogeography module,
are used to convert above-ground biomass to fuel classes. Fire effects
(i.e., plant mortality and live and dead biomass consumption) are estimated
as a function of simulated fire behavior (i.e., fire spread and fire line
intensity) and vegetation structure. Fire effects feed back to the biogeochemistry
module to adjust levels of various carbon and nutrient pools.
Related Abstracts
Simulating Broad-Scale Fire Severity in a Dynamic Global Vegetation Model. Lenihan, et al. (NW Science 1998 72:91-103)
Simulating the impact of fire in a broad-scale Dynamic Vegetation Model (DGVM) used for global change impact assessments requires components and concepts not part of existing fire modeling systems. The focus shifts from fire behavior and danger at the small scale to the system-specific impacts of fire at the broad scale (i.e., fire severity). MCFIRE, a broad-scale fire severity model we are currently developing as part of our MAPSS-CENTURY DGVM, simulates the occurrence and impacts (i.e., vegetation mortality and fuel consumption) of relatively infrequent and extreme events historically responsible for the majority of fire disturbance to ecosystems. The occurrence of severe fire is strongly related to synoptic-scale climatic conditions producing extended drought, which is indicated in MCFIRE by the low moisture content of large dead fuels. Due to constraints posed by currently available datasets, we have been developing our DGVM model on a relatively fine-scale data grid at a landscape-scale, but we will implement the model at regional to global scales on much coarser data grids. Constraints on the broad-scale impact of severe fire imposed by the fine-scale heterogeneity of fuel properties will be represented in our coarse-scale simulations by sub-grid parameterizations of the fire behavior and effects algorithms for distinct land surface types. Ecosystem structure and function are often constrained by disturbance, so it is critical to include disturbance processes in dynamic vegetation models used to assess the potential broad-scale impact of global change. The ability to simulate the impact of changes in fire severity on vegetation and the atmosphere has been a central focus in the development of the MAPSS-Century Dynamic Global Vegetation Model.
Dynamic Simulation of Tree-Grass Interactions for Global Change Studies. Daly et al. (Ecol. Appl., in press)
The objective of this study was to dynamically simulate the response of a complex landscape, containing forests, savannas and grasslands to potential climate change. It was thus essential to accurately simulate the competition for light and water between trees and grasses. To accurately represent water competition requires simulating the appropriate vertical root distribution and soil water content. The importance of differential rooting depths in structuring savannas has long been debated. In simulating this complex landscape we examined alternative hypotheses of tree and grass vertical root distribution and their impacts on savanna dynamics under historical and changing climates. MC1, a new dynamic vegetation model, was used to estimate the distribution of vegetation and associated carbon and nutrient fluxes for Wind Cave National Park, SD. MC1 consists of three linked modules simulating biogeography, biogeochemistry, and fire disturbance. This new tool allows us to document how changes in rooting patterns may affect production, fire frequency and trace gas emissions, and if current vegetation types and lifeform mixtures can be sustained at the same location or replaced by others. Since climate change may intensify resource deficiencies, it will likely affect allocation of resources to roots and their distribution through the soil profile. By manipulating the rooting depth of two lifeforms -trees and grasses - that are competing for water, and running MC1 for historical climate (1895-1994) and a GCM-simulated future scenario (1995-2094), we document its impact on ecosystem processes and vegetation distribution. Deeply rooting trees causes higher tree productivity, lower grass productivity, and longer fire return intervals. When trees are shallowly rooted, grass productivity exceeds that of trees even if total grass biomass only represents a third to a fourth that of trees. Deeply rooted grasses develop extensive root systems that increase N uptake and the input of litter into soil organic matter pools. Shallowly rooted grasses produce smaller soil carbon pools. Under the climate change scenario, NPP and live biomass increase for grasses and decrease for trees, and total soil organic matter decreases. However, differences between alternative rooting patterns remain similar. Deeply rooted grasses grow larger than shallowly rooted ones and deeply rooted trees outcompete grasses for resources. Consistent changes in fire frequency and intensity are simulated; more fires occur during the climate change scenario, because temperatures are higher, which results in decreased fuel moisture. Fire also increases in the deeply-rooted grass configurations, because grass biomass, which serves as a fine fuel source, is relatively high.
Climate, Fire and Grazing Effects at Wind Cave National Park, SD. Bachelet, et al. (Ecol. Modelling, submitted)
The impacts of climate change, fire and grazing on vegetation distribution and carbon cycling at Wind Cave National Park, SD, were analyzed using a new dynamic vegetation model MC1 that includes biogeography, biogeochemistry and fire disturbance. Warmer climatic conditions predicted by a general circulation model promote the growth of grasses while trees, which rely on the availability of deep water resources, are affected by the possible future drought conditions. The increase in grass biomass produces fuel build-up and promotes higher fire frequency. These constitute negative feedbacks on tree development as seedlings and live foliage are consumed by fire which reduces tree growth and survival. However, grazing reduces grass biomass and thus indirectly reduces fire frequency, enhancing the growth and expansion of forests. To conserve grasslands as a source of forage for local herbivores, Park managers need to prevent forest encroachment into existing grasslands. To do so, they need to reduce grazing pressure and promote a natural fire regime. Future climate projections simulate warmer and drier weather by the end of the next century. A change in management practices might be required to maintain the existence of the forests at Wind Cave when water resources become scarce.