Identification

Title

The predictability of near-term forest biomass change in boreal North America

Abstract

Climate change is driving substantial changes in North American boreal forests, including changes in productivity, mortality, recruitment, and biomass. Despite the importance for carbon budgets and informing management decisions, there is a lack of near-term (5-30 year) forecasts of expected changes in aboveground biomass (AGB). In this study, we forecast AGB changes across the North American boreal forest using machine learning, repeat measurements from 25,000 forest inventory sites, and gridded geospatial datasets. We find that AGB change can be predicted up to 30 years into the future, and that training on sites across the entire domain allows accurate predictions even in regions with only a small amount of existing field data. While predicting AGB loss is less skillful than gains, using a multi-model ensemble can improve the accuracy in detecting change direction to >90% for observed increases, and up to 70% for observed losses. Higher stem density, winter temperatures, and the presence of temperate tree species in forest plots were positively associated with AGB change, whereas greater initial biomass, continentality (difference between mean summer and winter temperatures), prevalence of black spruce (Picea mariana), summer precipitation, and early warning metrics from long-term remote sensing time series were negatively associated with AGB change. Across the domain, we predict nondisturbance-induced declines in AGB at 23% of sites by 2030. The approach developed here can be used to estimate near-future forest biomass in boreal North America and inform relevant management decisions. Our study also highlights the power of machine learning multi-model ensembles when trained on a large volume of forest inventory plots, which could be applied to other regions with adequate plot density and spatial coverage.

Resource type

document

Resource locator

Unique resource identifier

code

https://n2t.org/ark:/85065/d7k35z4q

codeSpace

Dataset language

eng

Spatial reference system

code identifying the spatial reference system

Classification of spatial data and services

Topic category

geoscientificInformation

Keywords

Keyword set

keyword value

Text

originating controlled vocabulary

title

Resource Type

reference date

date type

publication

effective date

2016-01-01T00:00:00Z

Geographic location

West bounding longitude

East bounding longitude

North bounding latitude

South bounding latitude

Temporal reference

Temporal extent

Begin position

End position

Dataset reference date

date type

publication

effective date

2024-01-11T00:00:00Z

Frequency of update

Quality and validity

Lineage

Conformity

Data format

name of format

version of format

Constraints related to access and use

Constraint set

Use constraints

Limitations on public access

None

Responsible organisations

Responsible party

contact position

OpenSky Support

organisation name

UCAR/NCAR - Library

full postal address

PO Box 3000

Boulder

80307-3000

email address

opensky@ucar.edu

web address

http://opensky.ucar.edu/

name: homepage

responsible party role

pointOfContact

Metadata on metadata

Metadata point of contact