Dataset Information

Fire, CO2, and climate effects on modeled vegetation and carbon dynamics in western Oregon and Washington.

ABSTRACT: To develop effective long-term strategies, natural resource managers need to account for the projected effects of climate change as well as the uncertainty inherent in those projections. Vegetation models are one important source of projected climate effects. We explore results and associated uncertainties from the MC2 Dynamic Global Vegetation Model for the Pacific Northwest west of the Cascade crest. We compare model results for vegetation cover and carbon dynamics over the period 1895-2100 assuming: 1) unlimited wildfire ignitions versus stochastic ignitions, 2) no fire, and 3) a moderate CO2 fertilization effect versus no CO2 fertilization effect. Carbon stocks decline in all scenarios, except without fire and with a moderate CO2 fertilization effect. The greatest carbon stock loss, approximately 23% of historical levels, occurs with unlimited ignitions and no CO2 fertilization effect. With stochastic ignitions and a CO2 fertilization effect, carbon stocks are more stable than with unlimited ignitions. For all scenarios, the dominant vegetation type shifts from pure conifer to mixed forest, indicating that vegetation cover change is driven solely by climate and that significant mortality and vegetation shifts are likely through the 21st century regardless of fire regime changes.

SUBMITTER: Sheehan T

PROVIDER: S-EPMC6347276 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Fire, CO2, and climate effects on modeled vegetation and carbon dynamics in western Oregon and Washington.

Sheehan Tim T Bachelet Dominique D Ferschweiler Ken K

PloS one 20190125 1

To develop effective long-term strategies, natural resource managers need to account for the projected effects of climate change as well as the uncertainty inherent in those projections. Vegetation models are one important source of projected climate effects. We explore results and associated uncertainties from the MC2 Dynamic Global Vegetation Model for the Pacific Northwest west of the Cascade crest. We compare model results for vegetation cover and carbon dynamics over the period 1895-2100 as ...[more]

PMID: 30682107

Similar Datasets

Project description:Future vegetation shifts under changing climate are uncertain for forests with infrequent stand-replacing disturbance regimes. These high-inertia forests may have long persistence even with climate change because disturbance-free periods can span centuries, broad-scale regeneration opportunities are fewer relative to frequent-fire systems, and mature tree species are long-lived with relatively high tolerance for sub-optimal growing conditions. Here, we used a combination of empirical and process-based modeling approaches to examine vegetation projections across high-inertia forests of Washington State, USA, under different climate and wildfire futures. We ran our models without forest management (to assess inherent system behavior/potential) and also with wildfire suppression. Projections suggested relatively stable mid-elevation forests through the end of the century despite anticipated increases in wildfire. The largest changes were projected at the lowest and uppermost forest boundaries, with upward expansion of the driest low-elevation forests and contraction of cold, high-elevation subalpine parklands. While forests were overall relatively stable in simulations, increases in early-seral conditions and decreases in late-seral conditions occurred as wildfire became more frequent. With partial fire suppression, projected changes were dampened or delayed, suggesting a potential tool to forestall change in some (but not all) high-inertia forests, especially since extending fire-free periods does little to alter overall fire regimes in these systems. Model projections also illustrated the importance of fire regime context and projection limitations; the time horizon over which disturbances will eventually allow the system to shift are so long that the prevailing climatic conditions under which many of those shifts will occur are beyond what most climate models can predict with any certainty. This will present a fundamental challenge to setting expectations and managing for long-term change in these systems.

Project description:Forest understory plant communities in the United States harbor most of the vegetation diversity of forests and are often sensitive to changes in climate and atmospheric deposition of nitrogen (N). As temperature increases from human-caused climate change and soils recover from long term atmospheric deposition of N and sulfur (S), it is unclear how these important ecosystem components will respond. We used the newly developed US-PROPS model - based on species response functions for over 1,500 species - to evaluate the potential impacts of atmospheric N deposition and climate change on species occurrence probability for a case study in the forested ecosystems of the Great Smoky Mountains National Park (GRSM), an iconic park in the southeastern United States. We evaluated six future scenarios from various combinations of two potential recoveries of soil pH (no change, +0.5 pH units) and three climate futures (no change, +1.5, +3.0 deg C). Species critical loads (CLs) of N deposition and projected responses for each scenario were determined. Critical loads were estimated to be low (< 2 kg N/ha/yr) to protect all species under current and expected future conditions across broad regions of GRSM and these CLs were exceeded at large spatial extents among scenarios. Northern hardwood, yellow pine, and chestnut oak forests were among the most N-sensitive vegetation map classes found within GRSM. Potential future air temperature conditions generally led to decreases in the maximum occurrence probability for species. Therefore, CLs were considered "unattainable" in these situations because the specified level of protection used for CL determination (i.e., maximum occurrence probability under ambient conditions) was not attainable. Although some species showed decreases in maximum occurrence probability with simulated increases in soil pH, most species were favored by increased pH. The importance of our study is rooted in the methodology described here for establishing regional CLs and for evaluating future conditions, which is transferable to other national parks in the U.S. and in Europe where the original PROPS model was developed.

Project description:Forests play a crucial role in the global carbon (C) cycle by storing and sequestering a substantial amount of C in the terrestrial biosphere. Due to temporal dynamics in climate and vegetation activity, there are significant regional variations in carbon dioxide (CO2) fluxes between the biosphere and atmosphere in forests that are affecting the global C cycle. Current forest CO2 flux dynamics are controlled by instantaneous climate, soil, and vegetation conditions, which carry legacy effects from disturbances and extreme climate events. Our level of understanding from the legacies of these processes on net CO2 fluxes is still limited due to their complexities and their long-term effects. Here, we combined remote sensing, climate, and eddy-covariance flux data to study net ecosystem CO2 exchange (NEE) at 185 forest sites globally. Instead of commonly used non-dynamic statistical methods, we employed a type of recurrent neural network (RNN), called Long Short-Term Memory network (LSTM) that captures information from the vegetation and climate's temporal dynamics. The resulting data-driven model integrates interannual and seasonal variations of climate and vegetation by using Landsat and climate data at each site. The presented LSTM algorithm was able to effectively describe the overall seasonal variability (Nash-Sutcliffe efficiency, NSE = 0.66) and across-site (NSE = 0.42) variations in NEE, while it had less success in predicting specific seasonal and interannual anomalies (NSE = 0.07). This analysis demonstrated that an LSTM approach with embedded climate and vegetation memory effects outperformed a non-dynamic statistical model (i.e. Random Forest) for estimating NEE. Additionally, it is shown that the vegetation mean seasonal cycle embeds most of the information content to realistically explain the spatial and seasonal variations in NEE. These findings show the relevance of capturing memory effects from both climate and vegetation in quantifying spatio-temporal variations in forest NEE.

Dataset Information

Fire, CO2, and climate effects on modeled vegetation and carbon dynamics in western Oregon and Washington.

Publications

Fire, CO2, and climate effects on modeled vegetation and carbon dynamics in western Oregon and Washington.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets