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From The Stand to the Regional Scale: An Assessment of Modelling Managed Forest Ecosystems in Sweden | OMICS International| Abstract
ISSN: 2157-7625

Journal of Ecosystem & Ecography
Open Access

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  • Editorial   
  • J Ecosys Ecograph 2023, Vol 13(1): 373
  • DOI: 10.4172/2157-7625.1000373

From The Stand to the Regional Scale: An Assessment of Modelling Managed Forest Ecosystems in Sweden

Susanne Sparks*
Department of Biological Sciences, University of Essex, United Kingdom
*Corresponding Author : Susanne Sparks, Department of Biological Sciences, University of Essex, United Kingdom, Email: Sus33@hotmail.com

Received Date: Jan 03, 2023 / Published Date: Jan 30, 2023

Abstract

The dynamic vegetation model LPJ-GUESS now includes a forest management module that enables analysis and forecasting of management treatment impacts on the carbon cycle and forest ecosystem structure. In this study, LPJ-GUESS is compared to observational data from the Swedish National Forest Inventory at the regional level. For the four most prevalent forest types in the nation, observed standing volume and simulations of standing volume are contrasted. Furthermore, to assess model predictions of net ecosystem exchange (NEE), gross primary production (GPP), and ecosystem respiration (Reco) at the site scale, eddy-covariance flux measurements from the Integrated Carbon Observation System (ICOS) are employed. For regional simulations in southern and Eastern Europe, the model results point to a sufficient depiction of standing volume in Norway spruce and Scots pine monocultures. The modified parameter values overestimated the standing volume in Norway spruce monocultures for northern Sweden. When compared to eddy-covariance data from two sites, one in central Sweden and one in the south, the model’s predictions for carbon fluxes at the stand scale were inconsistent.

Citation: Sparks S (2023) From The Stand to the Regional Scale: An Assessment of Modelling Managed Forest Ecosystems in Sweden. J Ecosys Ecograph 13: 373. Doi: 10.4172/2157-7625.1000373

Copyright: © 2023 Sparks S. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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