An enhanced forest classification scheme for modeling vegetation–climate interactions based on national forest inventory data

Majasalmi, Titta; Eisner, Stephanie; Astrup, Rasmus; Fridman, Jonas; Bright, Ryan M.

doi:10.5194/bg-15-399-2018

Cited by 17 publications

(7 citation statements)

References 30 publications

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“…The specific focus of our study is the boreal region, both because of the importance of these ecosystems in the climate system and because of the data availability of vegetation-type DM and the field-based reference dataset (AR). However, we believe that the improved DGVM parameters resulting from our sensitivity experiments may be applicable to other DGVMs such as Terrestrial Ecosystem Model (TEM) and LPJ-GUESS (Lund-Potsdam-Jena General Ecosystem Simulator) (Euskirchen et al, 2009;Miller and Smith, 2012). Also, the results from this study are likely to be transferable to other high-latitude areas in the circumboreal region.…”

Section: Sensitivity Experimentsmentioning

confidence: 76%

“…Most notably, we recognize that the implementation of precipitation seasonality (bioclim_15 < 50) as a threshold for the establishment of NET, which has not yet been used in DGVM, improves the distribution of high-latitude PFTs simulated by DGVM. This adds to the environmental thresholds for the establishment of a PFT previously used in DGVMs to restrict the predicted distribution of PFTs to realistic geographic regions (Miller and Smith, 2012). Even though our sensitivity experiments focus on a limited number of additional thresholds across three PFTs, this approach shows promising results and is worth exploring more extensively in future studies.…”

Section: Sensitivity Experimentsmentioning

confidence: 99%

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Improving the representation of high-latitude vegetation distribution in dynamic global vegetation models

et al. 2021

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Abstract. Vegetation is an important component in global ecosystems, affecting the physical, hydrological and biogeochemical properties of the land surface. Accordingly, the way vegetation is parameterized strongly influences predictions of future climate by Earth system models. To capture future spatial and temporal changes in vegetation cover and its feedbacks to the climate system, dynamic global vegetation models (DGVMs) are included as important components of land surface models. Variation in the predicted vegetation cover from DGVMs therefore has large impacts on modelled radiative and non-radiative properties, especially over high-latitude regions. DGVMs are mostly evaluated by remotely sensed products and less often by other vegetation products or by in situ field observations. In this study, we evaluate the performance of three methods for spatial representation of present-day vegetation cover with respect to prediction of plant functional type (PFT) profiles – one based upon distribution models (DMs), one that uses a remote sensing (RS) dataset and a DGVM (CLM4.5BGCDV; Community Land Model 4.5 Bio-Geo-Chemical cycles and Dynamical Vegetation). While DGVMs predict PFT profiles based on physiological and ecological processes, a DM relies on statistical correlations between a set of predictors and the modelled target, and the RS dataset is based on classification of spectral reflectance patterns of satellite images. PFT profiles obtained from an independently collected field-based vegetation dataset from Norway were used for the evaluation. We found that RS-based PFT profiles matched the reference dataset best, closely followed by DM, whereas predictions from DGVMs often deviated strongly from the reference. DGVM predictions overestimated the area covered by boreal needleleaf evergreen trees and bare ground at the expense of boreal broadleaf deciduous trees and shrubs. Based on environmental predictors identified by DM as important, three new environmental variables (e.g. minimum temperature in May, snow water equivalent in October and precipitation seasonality) were selected as the threshold for the establishment of these high-latitude PFTs. We performed a series of sensitivity experiments to investigate if these thresholds improve the performance of the DGVM method. Based on our results, we suggest implementation of one of these novel PFT-specific thresholds (i.e. precipitation seasonality) in the DGVM method. The results highlight the potential of using PFT-specific thresholds obtained by DM in development of DGVMs in broader regions. Also, we emphasize the potential of establishing DMs as a reliable method for providing PFT distributions for evaluation of DGVMs alongside RS.

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Section: Sensitivity Experimentsmentioning

confidence: 76%

Section: Sensitivity Experimentsmentioning

confidence: 99%

Improving the representation of high-latitude vegetation distribution in dynamic global vegetation models

et al. 2021

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“…Here land and PFT cover fractions within the vegetated land unit remain fixed across the three scenarios, and only the four structural parameters LAI, stem area index (SAI), canopy top height ( z top ), and canopy bottom height (z bottom ) are altered. Using national forest inventory information, Majasalmi et al (2018) recently enhanced the 2015 forest classification of the European Space Agency's Land Cover product (ESA's CCI‐LC) for Fennoscandia. The classification differentiates between dominant tree genera or phenology and between forest development stages.…”

Section: Methodsmentioning

confidence: 99%

“…In All DC4, although not constrained by observation, the prescribed structural changes are meant to capture a broad range of management interventions that could potentially enhance stand volume densities in the future, such as more optimal planting densities, precommercial thinning regimes, and fertilization. In All DC4, the prescribed LAI and canopy heights correspond to the most developed classes of Majasalmi et al (2018).…”

Section: Representation Of Forest Management Proxies and Data Preparationmentioning

confidence: 99%

Quantifying Regional Surface Energy Responses to Forest Structural Change in Nordic Fennoscandia

et al. 2020

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In a climate model, surface energy and water fluxes of the vegetated ecosystem largely depend on important structural attributes like leaf area index and canopy height. For forests, management can greatly alter these attributes with resulting consequences for the surface albedo, surface roughness, and evapotranspiration. The sensitivity of surface energy and water budgets to alterations in forest structure is relatively unknown in boreal regions, particularly in Nordic Fennoscandia (Norway, Sweden, and Finland), where the forest management footprint is large. Here we perform offline simulations to quantify the sensitivity of surface heat and moisture fluxes to changes in forest composition and structure across daily, seasonal, and annual time scales. For the region on average, it is found that broadleaved deciduous forests cool the surface by 0.16 K annually and 0.3 K in the growing season owed to higher year‐round albedo and lower Bowen ratio, yet in some locations the local cooling can be as much as 2.4 K and 3.0 K, respectively. Moreover, fully developed forests cool the surface by 0.04 K annually in our domain owed to higher evapotranspiration, reaching up to 0.4 K locally in some locations, whereas undeveloped forests warm annually by 0.14 K owed to much lower evapotranspiration reaching up to 0.8 K for some locations. If regional forests are ever to be managed for the local climate regulation services that they provide, our results are an important first step illuminating the potential adverse impacts or benefits across space and time.

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“…Likewise, forest carbon budgets and assessments of sustainable forest management practices rely on data from the Finnish NFI (e.g. Härkönen et al 2011;Majasalmi et al 2018).…”

Section: Forest Monitoring Information Needsmentioning

confidence: 99%

Improving capacity for large-area monitoring of forest disturbance and recovery

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2019

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An enhanced forest classification scheme for modeling vegetation–climate interactions based on national forest inventory data

Cited by 17 publications

References 30 publications

Improving the representation of high-latitude vegetation distribution in dynamic global vegetation models

Improving the representation of high-latitude vegetation distribution in dynamic global vegetation models

Quantifying Regional Surface Energy Responses to Forest Structural Change in Nordic Fennoscandia

Improving capacity for large-area monitoring of forest disturbance and recovery

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