James M. Lenihan

Climate change effects on vegetation distribution and carbon budget in the United States

The Kyoto protocol has focused the attention of the public and policymarkers on the earths carbon (C) budget. Previous estimates of the impacts of vegetation change have been limited to equilibrium “snapshots” that could not capture nonlinear or threshold effects along the trajectory of change. New models have been designed to complement equilibrium models and simulate vegetation succession through time while estimating variability in the C budget and responses to episodic events such as drought and fire. In addition, a plethora of future climate scenarios has been used to produce a bewildering variety of simulated ecological responses. Our objectives were to use an equilibrium model (Mapped Atmosphere–Plant–Soil system, or MAPSS) and a dynamic model (MC1) to (a) simulate changes in potential equilibrium vegetation distribution under historical conditions and across a wide gradient of future temperature changes to look for consistencies and trends among the many future scenarios, (b) simulate time-dependent changes in vegetation distribution and its associated C pools to illustrate the possible trajectories of vegetation change near the high and low ends of the temperature gradient, and (c) analyze the extent of the US area supporting a negative C balance. Both models agree that a moderate increase in temperature produces an increase in vegetation density and carbon sequestration across most of the US with small changes in vegetation types. Large increases in temperature cause losses of C with large shifts in vegetation types. In the western states, particularly southern California, precipitation and thus vegetation density increase and forests expand under all but the hottest scenarios. In the eastern US, particularly the Southeast, forests expand under the more moderate scenarios but decline under more severe climate scenarios, with catastrophic fires potentially causing rapid vegetation conversions from forest to savanna. Both models show that there is a potential for either positive or negative feedbacks to the atmosphere depending on the level of warming in the climate change scenarios.

CLIMATE CHANGE EFFECTS ON VEGETATION DISTRIBUTION, CARBON, AND FIRE IN CALIFORNIA

The objective of this study was to dynamically simulate the response of vegetation distribution, carbon, and fire to the historical climate and to two contrasting scenarios of climate change in California. The results of the simulations for the historical climate compared favorably to independent estimates and observations, but validation of the results was complicated by the lack of land use effects in the model. The response to increasing temperatures under both scenarios was characterized by a shift in dominance from needle-leaved to broad-leaved life-forms and by increases in vegetation productivity, especially in the relatively cool and mesic regions of the state. The simulated response to changes in precipitation were complex, involving not only the effect of changes in soil moisture on vegetation productivity, but also changes in tree-grass competition mediated by fire. Summer months were warmer and persistently dry under both scenarios, so the trends in simulated fire area under both scenarios were primarily a response to changes in vegetation biomass. Total ecosystem carbon increased under both climate scenarios, but the proportions allocated to the wood and grass carbon pools differed. The results of the simulations underscore the potentially large impact of climate change on California eco- systems, and the need for further use and development of dynamic vegetation models using various ensembles of climate change scenarios.

Forest Processes and Global Environmental Change: Predicting the Effects of Individual and Multiple Stressors

G change involves the simultaneous and rapid alteration of several key environmental parameters that control the dynamics of forests. We cannot predict with certainty, through direct experimentation, what the responses of forests to global change will be, because we cannot carry out the multisite, multifactorial experiments required for doing so. The physical extent, complexity, and expense of even single-factor experiments at the scale of the whole ecosystem challenge our abilities, although several such experiments have been successfully undertaken (e.g., DeLucia et al. 1999, Wright and Rasmussen 1998). To inform policy decisions, however, the scientific community can offer an interdisciplinary synthesis of existing information. When this synthesis takes the form of a computer model, quantitative predictions can be made that integrate what has been learned from single-factor experiments. The success of such an approach depends on the quality and completeness of the information base and on the rigor of the modeling effort. The direct and secondary physiological effects of changes in the physical and chemical climate on plants and soils are relatively well known. We also know which primary environmental drivers—precipitation, temperature, and atmospheric concentrations of carbon dioxide (CO2), ozone (O3), and nitrogen (N), for example—are being altered by human activities, and we can directly measure temporal change in these parameters. Despite this relatively rich information base, predictions of future responses of forests to environmental change show significant variation. This is due in part to differences between the models of ecosystem function derived from the existing database and in part to differences in climate scenarios generated by the general circulation models (GCMs) used to predict future climates. Understanding both the trend in predicted futures and the uncertainties surrounding those trends is critical to policy formation. At this time, the major mechanism for determining the degree of uncertainty in predictions is through comparison of results from runs of different models using identical input parameters. The purpose of this article is to review the state of prediction of forest ecosystem response to envisioned changes in the physical and chemical climate. These results are offered as one part of the forest sector analysis of the National Assessment

DYNAMIC SIMULATION OF TREE–GRASS INTERACTIONS FOR GLOBAL CHANGE STUDIES

The objective of this study was to simulate dynamically the response of a complex landscape, containing forests, savannas, and grasslands, to potential climate change. Thus, it was essential to simulate accurately the competition for light and water between trees and grasses. Accurate representation of water competition requires simulating the appropriate vertical root distribution and soil water content. The importance of different 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 the importance of fire as a disturbance, as they influence 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, South Dakota, USA. 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 whether or not current vegetation types and life-form mixtures can be sustained at the same location or would be replaced by others. Because climate change may intensify resource deficiencies, it will probably affect allocation of resources to roots and their distribution through the soil profile. We manipulated the rooting depth of two life-forms, trees and grasses, that are competing for water. We then assessed the importance of variable rooting depth on eco- system processes and vegetation distribution by running MC1 for historical climate (1895- 1994) and a GCM-simulated future scenario (1995-2094). Deeply rooted trees caused higher tree productivity, lower grass productivity, and longer fire return intervals. When trees were shallowly rooted, grass productivity exceeded that of trees even if total grass biomass was only one-third to one-fourth that of trees. Deeply rooted grasses developed extensive root systems that increased N uptake and the input of litter into soil organic matter pools. Shallowly rooted grasses produced smaller soil carbon pools. Under the climate change scenario, NPP and live biomass increased for grasses and decreased for trees, and total soil organic matter decreased. Changes in the size of biogeochemical pools produced by the climate change scenario were overwhelmed by the range of responses across the four rooting configurations. Deeply rooted grasses grew larger than shallowly rooted ones, and deeply rooted trees outcompeted grasses for resources. In both historical and future scenarios, fire was required for the coexistence of trees and grasses when deep soil water was available to trees. Consistent changes in fire frequency and intensity were simulated during the climate change scenario: more fires occurred because higher temperatures resulted in decreased fuel moisture. Fire also increased in the deeply rooted grass configurations because grass biomass, which serves as a fine fuel source, was relatively high.

Comparison of the sensitivity of landscape-fire-succession models to variation in terrain, fuel pattern, climate and weather

The purpose of this study was to compare the sensitivity of modelled area burned to environmental factors across a range of independently-developed landscape-fire-succession models. The sensitivity of area burned to variation in four factors, namely terrain (flat, undulating and mountainous), fuel pattern (finely and coarsely clumped), climate (observed, warmer & wetter, and warmer & drier) and weather (year-to-year variability) was determined for four existing landscape-fire-succession models (EMBYR, FIRESCAPE, LANDSUM and SEM-LAND) and a new model implemented in the LAMOS modelling shell (LAMOS(DS)). Sensitivity was measured as the variance in area burned explained by each of the four factors, and all of the interactions amongst them, in a standard generalised linear modelling analysis. Modelled area burned was most sensitive to climate and variation in weather, with four models sensitive to each of these factors and three models sensitive to their interaction. Models generally exhibited a trend of increasing area burned from observed, through warmer and wetter, to warmer and drier climates with a 23-fold increase in area burned, on average, from the observed to the warmer, drier climate. Area burned was sensitive to terrain for FIRESCAPE and fuel pattern for EMBYR. These results demonstrate that the models are generally more sensitive to variation in climate and weather as compared with terrain complexity and fuel pattern, although the sensitivity to these latter factors in a small number of models demonstrates the importance of representing key processes. The models that represented fire ignition and spread in a relatively complex fashion were more sensitive to changes in all four factors because they explicitly simulate the processes that link these factors to area burned.

Relative importance of fuel management, ignition management and weather for area burned: evidence from five landscape–fire–succession models

The behaviour of five landscape fire models (CAFE, FIRESCAPE, LAMOS(HS), LANDSUM and SEM- LAND) was compared in a standardised modelling experiment. The importance of fuel management approach, fuel management effort, ignition management effort and weather in determining variation in area burned and number of edge pixels burned (a measure of potential impact on assets adjacent to fire-prone landscapes) was quantified for a standardised modelling landscape. Importance was measured as the proportion of variation in area or edge pixels burned explained by each factor and all interactions among them. Weather and ignition management were consistently more important for explaining variation in area burned than fuel management approach and effort, which were found to be statistically unimportant. For the number of edge pixels burned, weather and ignition management were generally more important than fuel management approach and effort. Increased ignition management effort resulted in decreased area burned in all models and decreased number of edge pixels burned in three models. The findings demonstrate that year-to-year variation in weather and the success of ignition management consistently prevail over the effects of fuel management on area burned in a range of modelled ecosystems.

Interactions between fire, grazing and climate change at Wind Cave National Park, SD

Projected changes in global climate have important ramifications for the future of national parks and other reserves set aside to conserve ecological uniqueness. We explored potential implications of climatic changes on lifeform distribution and growth at Wind Cave National Park (WCNP), South Dakota, which lies on a climatically determined ecotone between grassland and forest. Fire, promoted by healthy grasslands, is a negative feedback limiting tree development because it kills seedlings and consumes live foliage thus reducing tree growth and survival. Historical records show that fire suppression has enhanced forest expansion. On the other hand, livestock grazing reduces grass biomass and fuel loads thus indirectly reducing fire frequency and enhancing the expansion of forests or woodlands. Natural fires and moderate grazing by native herbivores have maintained the coexistence of trees and grasses but climatic variations affecting the areas water resources can lead to dominance by either lifeform. We used a dynamic vegetation model (DVM) MC1 to simulate the interactions between climatic changes, natural fire regime, and grazing pressure and their impact on the biogeographical and biogeochemical characteristics of the park. We used one future climate projection (HADCM2SUL) which simulates warmer weather by the end of the next century: the temperature increase would constrain the growth of trees that rely on the availability of deep water, favor shrub and grass development and promote a shift from forests to woodlands. Woody encroachment of shrubs in grasslands areas, enhanced by grazing, was only held in check by frequent natural fires in the simulation.

Canadian vegetation sensitivity to projected climatic change at three organizational levels

The potential equilibrium response of Canadian vegetation under two doubled-CO2 climatic scenarios was investigated at three levels in the vegetation mosaic using the rule-based, Canadian Climate-Vegetation Model (CCVM) and climatic response surfaces. The climatic parameters employed as model drivers (i.e., degree-days, minimum temperature, snowpack, actual evapotranspiration, and soil moisture deficit) have a more direct influence on the distribution of vegetation than those commonly used in equilibrium models. Under both scenarios, CCVM predicted reductions in the extent of the tundra and subarctic woodland formations, a northward shift and some expansion in the distributions of boreal and the temperate forest, and an expansion of the dry woodland and prairie formations that was especially pronounced under one of the scenarios. Results of the response surface analysis suggest the potential for significant changes in the probability of dominance for eight boreal tree species. A dissimilarity coefficient was used to identify forest-types under the future climatic scenarios that were analogous to boreal forest-types derived from cluster analysis of the current probabilities of species dominance. All of the current forest-types persisted under the doubled-CO2 scenarios, but ‘no-analog’ areas were also identified within which an empirically derived threshold of the distance coefficient was exceeded. Maps showing the highest level in the vegetation hierarchy where change was predicted suggest the relative impact of the response under the two climatic scenarios.

A rule-based vegetation formation model for Canada

A rule-based, equilibrium vegetation model was developed for predicting the distribution of vegetation forma- tions in Canada under current and projected climatic conditions. The Canadian Climate-Vegetation Model (CCVM) relies on climatic parameters with an inferred mech- anistic relationship to the distribution of vegetation. Model inputs are monthly temperature, precipitation and potential evapotranspiration, from which degree-days, absolute mini- mum temperature, snowpack, actual evapotranspiration and soil moisture deficit are calculated. Splitting rules in a binary decision tree classify the potential natural vegetation at grid cells in a spatially distributed data base. The rules are critical climatic thresholds which physiologically constrain the distri- butions of different vegetation life-forms. Under current climatic conditions, CCVM produces an accurate simulation

Strategy for a Fire Module in Dynamic Global Vegetation Models

Disturbance plays a major role in shaping and maintaining many of the Earthsterrestrial ecosystems. In fact, many ecosystems depend on fire for theirvery existence. Global Change is expected to result in changed distributionof current ecosystems, changed composition of those ecosystems, and increation of new ecosystems. The International Geosphere Biosphere Program(IGBP), through the Core Projects Biospheric Aspects of the HydrologicalCycle, International Global Atmospheric Chemistry, Global Change andTerrestrial Ecosystems and International Global Atmospheric Chemistry,Biomass Burning Working Group, recognized that disturbances need to beincluded in the modeling efforts of each project. Disturbance from fire, landuse and other factors may be as important as climate change in shaping futurelandscapes (Weber and Flannigan 1998). Three main themes were recognized:impact of disturbance on carbon pools, vegetation change, and feedbacks to theatmosphere. In June 1998, a workshop was held in Potsdam, Germany to developa strategy to introduce disturbance into dynamic global vegetation models.This strategy was based on the fact that vegetation burning influencesatmospheric chemistry, that feedbacks of energy, water and trace gases tothe atmosphere are influenced by vegetation, and that changes in thecomposition of ecosystems have direct impact on the carbon pool, onbiodiversity, and on health and productivity of the land. Disturbanceincludes fire, insect, disease, drought and flooding, land conversion,land use, air pollution, and introduction of exotic species. While it willbe necessary to ultimately include all disturbances, the Potsdam workshoplimited itself to fire. This strategy is based on the fact that there areno process driven models for all disturbances, and that fire has a numberof reliable models with which to begin the process of introducing disturbanceinto dynamic global vegetation models. While this workshop limited itself tofire, a great deal of consideration was given to the fact that the modelshell must be able to include other disturbances in the future. As a result,the strategy was to focus on a hazard function which would lead to effectsof disturbance. The hazard function is basically a probability statement ofrisk of effects. This approach seems equally valid for all forms ofdisturbance.