Simulating vegetation response to climate change in the Blue Mountains with MC2 dynamic global vegetation model

Kim, John B.; Kerns, Becky K.; Drapek, Raymond J.; Pitts, G. Stephen; Halofsky, Jessica E.

doi:10.1016/j.cliser.2018.04.001

Cited by 30 publications

(18 citation statements)

References 52 publications

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“…MC2 (short for MC version 2) is a new implementation of the MC1 dynamic global vegetation model (Daly et al 2000;Bachelet et al 2001a), which has been proved to provide reasonable vegetation dynamics for many studies from regional (Bachelet et al 2000;Kim et al 2018), national (Bachelet et al 2003;Mills et al 2015) to global scales (Daly et al 2000;Kim et al 2017). MC2 can simulate potential vegetation distributions, and the movement of carbon, nitrogen, and water through ecosystems (Lenihan et al 2008).…”

Section: Mc2 Model Simulation and Validationmentioning

confidence: 99%

Assessing the vulnerability of ecosystems to climate change based on climate exposure, vegetation stability and productivity

Wang

Jiang

et al. 2020

For. Ecosyst.

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Background: Global warming has brought many negative impacts on terrestrial ecosystems, which makes the vulnerability of ecosystems one of the hot issues in current ecological research. Here, we proposed an assessment method based on the IPCC definition of vulnerability. The exposure to future climate was characterized using a moisture index (MI) that integrates the effects of temperature and precipitation. Vegetation stability, defined as the proportion of intact natural vegetation that remains unchanged under changing climate, was used together with vegetation productivity trend to represent the sensitivity and adaptability of ecosystems. Using this method, we evaluated the vulnerability of ecosystems in Southwestern China under two future representative concentration pathways (RCP 4.5 and RCP 8.5) with MC2 dynamic global vegetation model. Results: (1) Future (2017-2100) climate change will leave 7.4% (under RCP 4.5) and 57.4% of (under RCP 8.5) of areas under high or very high vulnerable climate exposure; (2) in terms of vegetation stability, nearly 45% of the study area will show high or very high vulnerability under both RCPs. Beside the impacts of human disturbance on natural vegetation coverage (vegetation intactness), climate change will cause obvious latitudinal movements in vegetation distribution, but the direction of movements under two RCPs were opposite due to the difference in water availability; (3) vegetation productivity in most areas will generally increase and remain a low vulnerability in the future; (4) an assessment based on the above three aspects together indicated that future climate change will generally have an adverse impact on all ecosystems in Southwestern China, with non-vulnerable areas account for only about 3% of the study area under both RCPs. However, compared with RCP 4.5, the areas with mid-and highvulnerability under RCP 8.5 scenario increased by 13% and 16%, respectively. Conclusion: Analyses of future climate exposure and projected vegetation distribution indicate widespread vulnerability of ecosystems in Southwestern China, while vegetation productivity in most areas will show an increasing trend to the end of twenty-first century. Based on new climate indicators and improved vulnerability assessment rules, our method provides an extra option for a more comprehensive evaluation of ecosystem vulnerability, and should be further tested at larger spatial scales in order to provide references for regional, or even global, ecosystem conservation works.

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Section: Mc2 Model Simulation and Validationmentioning

confidence: 99%

Assessing the vulnerability of ecosystems to climate change based on climate exposure, vegetation stability and productivity

Wang

Jiang

et al. 2020

For. Ecosyst.

View full text Add to dashboard Cite

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“…Dynamic vegetation models (DVMs) in particular simulate establishment, growth, competition, and mortality, making them a useful tool for understanding how vegetation species distributions and associated carbon stocks respond to climate forcings. DVMs, being process‐based, can be applied at global (Friedlingstein et al, ; Sitch et al, ) to local scales (Kim et al, ) and have been used to project changes in vegetation distributions and associated carbon consequences in the western United States (Bachelet et al, ; Jiang et al, ; Kerns et al, ; Rehfeldt et al, ; Shafer et al, ).…”

Section: Introductionmentioning

confidence: 99%

Parametric Sensitivity of Vegetation Dynamics in the TRIFFID Model and the Associated Uncertainty in Projected Climate Change Impacts on Western U.S. Forests

Hawkins

Rupp

McNeall

et al. 2019

J Adv Model Earth Syst

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Changing climate conditions impact ecosystem dynamics and have local to global impacts on water and carbon cycles. Many processes in dynamic vegetation models (DVMs) are parameterized, and the unknown/unknowable parameter values introduce uncertainty that has rarely been quantified in projections of forced changes. In this study, we identify processes and parameters that introduce the largest uncertainties in the vegetation state simulated by the DVM Top‐down Representation of Interactive Foliage and Flora Including Dynamics (TRIFFID) coupled to a regional climate model. We adjust parameters simultaneously in an ensemble of equilibrium vegetation simulations and use statistical emulation to explore sensitivities to, and interactions among, parameters. We find that vegetation distribution is most sensitive to parameters related to carbon allocation and competition. Using a suite of statistical emulators, we identify regions of parameter space that reduce the error in modeled forest cover by 31±9%. We then generate large initial atmospheric condition ensembles with 10 improved DVM parameterizations under preindustrial, contemporary, and future climate conditions to assess uncertainty in the forced response due to parameterization. We find that while most parameterizations agree on the direction of future vegetation transitions in the western United States, the magnitude varies considerably: for example, in the northwest coast the expansion of broadleaf trees and corresponding decline of needleleaf trees ranges from 4 to 28% across 10 DVM parameterizations under projected future climate conditions. We demonstrate that model parameterization contributes to uncertainty in vegetation transition and carbon cycle feedback under nonstationary climate conditions, which has important implications for carbon stocks, ecosystem services, and climate feedback.

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“…The first step of the method is to generate projections of the volume of surface water runoff by decade through the 21 st century. These projections are generated using the MC2 dynamic global vegetation model (Bachelet et al 2001), which has been successfully applied at the regional scale (Case et al 2018; Kim et al 2018). MC2 was calibrated to Southern California using multiple observation data sets, including net primary productivity, carbon stock estimates, and wildfire regimes.…”

Section: Methodsmentioning

confidence: 99%

How Will Climate Change Affect the Provision and Value of Water from Public Lands in Southern California Through the 21^st Century?

Srivastava¹,

Hand²,

Kim³

et al. 2020

Agric. Resour. Econom. Rev.

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We estimate the ecosystem service value of water supplied by the San Bernardino National Forest in Southern California under climate change projections through the 21st century. We couple water flow projections from a dynamic vegetation model with an economic demand model for residential water originating from the San Bernardino National Forest. Application of the method demonstrates how estimates of consumer welfare changes due to variation in water supply from public lands in Southern California can inform policy and land management decisions. Results suggest variations in welfare changes over time due to alterations in the projected water supply surpluses, shifting demand limited by water supply shortages or surpluses, and price increases. Results are sensitive to future climate projections—in some cases large decreases in welfare due to supply shortages—and to assumptions about the demand model.

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Simulating vegetation response to climate change in the Blue Mountains with MC2 dynamic global vegetation model

Cited by 30 publications

References 52 publications

Assessing the vulnerability of ecosystems to climate change based on climate exposure, vegetation stability and productivity

Assessing the vulnerability of ecosystems to climate change based on climate exposure, vegetation stability and productivity

Parametric Sensitivity of Vegetation Dynamics in the TRIFFID Model and the Associated Uncertainty in Projected Climate Change Impacts on Western U.S. Forests

How Will Climate Change Affect the Provision and Value of Water from Public Lands in Southern California Through the 21^st Century?

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Simulating vegetation response to climate change in the Blue Mountains with MC2 dynamic global vegetation model

Cited by 30 publications

References 52 publications

Assessing the vulnerability of ecosystems to climate change based on climate exposure, vegetation stability and productivity

Assessing the vulnerability of ecosystems to climate change based on climate exposure, vegetation stability and productivity

Parametric Sensitivity of Vegetation Dynamics in the TRIFFID Model and the Associated Uncertainty in Projected Climate Change Impacts on Western U.S. Forests

How Will Climate Change Affect the Provision and Value of Water from Public Lands in Southern California Through the 21st Century?

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Product

Resources

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How Will Climate Change Affect the Provision and Value of Water from Public Lands in Southern California Through the 21^st Century?