High-resolution Canada domain disturbance forcings suitable for land surface modeling applications

Spatially explicit fire and harvest data are useful for driving land surface model (LSM) simulations of the carbon cycle. From 1985-present, numerous Canadian disturbance datasets exist. However, before the launch of Landsat-4 (1984), few are available. We create spatially explicit LSM disturbance drivers for Canada for 1740-2018. We catalog and harmonize spatial and aspatial datasets and develop a novel algorithm that reconstructs disturbance far back in time using stand age. Based on possible historical scenarios, we reconstruct 283-394 Mha of fire and 3.42 Mha of harvest in total Canada-wide from 1740-1918. After 1918, when spatial records are available, we supplement them by reconstructing 25.79-60.30 Mha of fire and 24.75 Mha of harvest. After 1984, we exclusively use spatially explicit records. We verify the algorithm by comparing the inputs and resultant drivers and examine diagnostic metrics to disentangle the contribution of spatial, aspatial, and stand-age data. The resulting drivers primarily capture stand-replacing disturbance on forested land. Our forcings and algorithm will improve the representation of disturbance-mediated impacts on Canada's terrestrial carbon cycle and possibly in other regions.

Authors

Institutions

Publication Details

Journal
Scientific Data
Published
2025-08-22
DOI
https://doi.org/10.1038/s41597-025-05123-4
Citations
1
Primary Topic
Cryospheric studies and observations
Type
article
Field-Weighted Citation Impact
1.30

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

High-resolution Canada domain disturbance forcings suitable for land surface modeling applications

Txomin Hermosilla, Michael A. Wulder, Joe R. Melton, Salvatore R. Curasi et al.
1 citations
Scientific Data
Cryospheric studies and observations
1.30
article

High-resolution Canada domain disturbance forcings suitable for land surface modeling applications

Txomin Hermosilla, Michael A. Wulder, Joe R. Melton, Salvatore R. Curasi, Elyn Humphreys, Jason Beaver
article en
1 citations

Abstract

Spatially explicit fire and harvest data are useful for driving land surface model (LSM) simulations of the carbon cycle. From 1985-present, numerous Canadian disturbance datasets exist. However, before the launch of Landsat-4 (1984), few are available. We create spatially explicit LSM disturbance drivers for Canada for 1740-2018. We catalog and harmonize spatial and aspatial datasets and develop a novel algorithm that reconstructs disturbance far back in time using stand age. Based on possible historical scenarios, we reconstruct 283-394 Mha of fire and 3.42 Mha of harvest in total Canada-wide from 1740-1918. After 1918, when spatial records are available, we supplement them by reconstructing 25.79-60.30 Mha of fire and 24.75 Mha of harvest. After 1984, we exclusively use spatially explicit records. We verify the algorithm by comparing the inputs and resultant drivers and examine diagnostic metrics to disentangle the contribution of spatial, aspatial, and stand-age data. The resulting drivers primarily capture stand-replacing disturbance on forested land. Our forcings and algorithm will improve the representation of disturbance-mediated impacts on Canada's terrestrial carbon cycle and possibly in other regions.

Scientific DataVol. 12(1)
Canadian Forest Service (CA), Natural Resources Canada (CA), Environment and Climate Change Canada (CA), Carleton University (CA)
Natural Sciences and Engineering Research Council of Canada
Life in Land
Openalex Percentile: Top 19%
Cryospheric studies and observations
1.30
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.