Spatiotemporal variability and uncertainty in wood decay estimates across Europe and Scandinavia using a data-driven moisture content model with multi-scale weather datasets
Hosseini, Hasan LU ; Iannacone, Leandro LU
; Brischke, Christian
and Niklewski, Jonas
LU
(2026)
In Results in Engineering
29.
- Abstract
The use of remote sensing and reanalysis weather data has not been adequately acknowledged for large-scale built environment applications, primarily regarding engineering analyses that traditionally rely on computationally expensive numerical models. This study employs a parsimonious data-driven approach concerning service life assessment of rain-exposed wooden components, enabling mapping and assessment of related risks across Europe and Scandinavia from gridded data. Wood moisture content is the primary vector for fungal decay and accurate modeling is essential for the design of wooden commodities. Numerical models of moisture flow within woody material provide accuracy but are impractically slow for large-scale applications. We... (More)
The use of remote sensing and reanalysis weather data has not been adequately acknowledged for large-scale built environment applications, primarily regarding engineering analyses that traditionally rely on computationally expensive numerical models. This study employs a parsimonious data-driven approach concerning service life assessment of rain-exposed wooden components, enabling mapping and assessment of related risks across Europe and Scandinavia from gridded data. Wood moisture content is the primary vector for fungal decay and accurate modeling is essential for the design of wooden commodities. Numerical models of moisture flow within woody material provide accuracy but are impractically slow for large-scale applications. We explore high-resolution decay risk assessments using a neural network trained on existing numerical predictions of moisture content. Specifically, the model used time-lagged gridded weather data as inputs to calculate moisture content at grids of 5.5 (1.0) km resolution over 21–36 (6) years across Europe (Scandinavia), compared with similar calculations from 890 weather stations. The moisture data were subsequently used to predict decay rates and quantify uncertainties stemming from temporal and spatial variation in weather. The results present high-resolution decay hazard maps alongside the associated spatiotemporal variations. Both spatial and temporal variations were identified as significant contributors to uncertainty in decay assessments, with their impacts varying considerably across regions in Europe. The study is part of an effort to integrate uncertainty propagation in service life prediction of wood, to enhance the resilience and reliability of wood in outdoor applications and to optimize its potential for sustainable and enduring construction solutions.
(Less)
- author
- Hosseini, Hasan
LU
; Iannacone, Leandro
LU
; Brischke, Christian
and Niklewski, Jonas
LU
- organization
- publishing date
- 2026-03
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Durability, Gridded weather data, Numerical, Spatiotemporal uncertainty, Wood
- in
- Results in Engineering
- volume
- 29
- article number
- 108983
- publisher
- Elsevier
- external identifiers
-
- scopus:105027937199
- ISSN
- 2590-1230
- DOI
- 10.1016/j.rineng.2026.108983
- language
- English
- LU publication?
- yes
- id
- 18b4b386-f898-4be0-86c4-5d94f6228100
- date added to LUP
- 2026-02-18 13:27:15
- date last changed
- 2026-02-18 13:27:27
@article{18b4b386-f898-4be0-86c4-5d94f6228100,
abstract = {{<p>The use of remote sensing and reanalysis weather data has not been adequately acknowledged for large-scale built environment applications, primarily regarding engineering analyses that traditionally rely on computationally expensive numerical models. This study employs a parsimonious data-driven approach concerning service life assessment of rain-exposed wooden components, enabling mapping and assessment of related risks across Europe and Scandinavia from gridded data. Wood moisture content is the primary vector for fungal decay and accurate modeling is essential for the design of wooden commodities. Numerical models of moisture flow within woody material provide accuracy but are impractically slow for large-scale applications. We explore high-resolution decay risk assessments using a neural network trained on existing numerical predictions of moisture content. Specifically, the model used time-lagged gridded weather data as inputs to calculate moisture content at grids of 5.5 (1.0) km resolution over 21–36 (6) years across Europe (Scandinavia), compared with similar calculations from 890 weather stations. The moisture data were subsequently used to predict decay rates and quantify uncertainties stemming from temporal and spatial variation in weather. The results present high-resolution decay hazard maps alongside the associated spatiotemporal variations. Both spatial and temporal variations were identified as significant contributors to uncertainty in decay assessments, with their impacts varying considerably across regions in Europe. The study is part of an effort to integrate uncertainty propagation in service life prediction of wood, to enhance the resilience and reliability of wood in outdoor applications and to optimize its potential for sustainable and enduring construction solutions.</p>}},
author = {{Hosseini, Hasan and Iannacone, Leandro and Brischke, Christian and Niklewski, Jonas}},
issn = {{2590-1230}},
keywords = {{Durability; Gridded weather data; Numerical; Spatiotemporal uncertainty; Wood}},
language = {{eng}},
publisher = {{Elsevier}},
series = {{Results in Engineering}},
title = {{Spatiotemporal variability and uncertainty in wood decay estimates across Europe and Scandinavia using a data-driven moisture content model with multi-scale weather datasets}},
url = {{http://dx.doi.org/10.1016/j.rineng.2026.108983}},
doi = {{10.1016/j.rineng.2026.108983}},
volume = {{29}},
year = {{2026}},
}