A simplified multi-model statistical approach for predicting the effects of forest management on land surface temperature in Fennoscandia
Forests interact with the local climate through a variety of biophysical mechanisms. Observational and modelling studies have investigated the effects of forested vs. non-forested areas, but the influence of forest management on surface temperature has received far less attention owing to the inhere...
Main Authors: | , , , , , |
---|---|
Format: | Article in Journal/Newspaper |
Language: | English |
Published: |
2023
|
Subjects: | |
Online Access: | https://refubium.fu-berlin.de/handle/fub188/38898 https://doi.org/10.17169/refubium-38614 https://doi.org/10.1016/j.agrformet.2023.109362 |
id |
ftfuberlin:oai:refubium.fu-berlin.de:fub188/38898 |
---|---|
record_format |
openpolar |
spelling |
ftfuberlin:oai:refubium.fu-berlin.de:fub188/38898 2023-06-06T11:53:33+02:00 A simplified multi-model statistical approach for predicting the effects of forest management on land surface temperature in Fennoscandia Huang, Bo Li, Yan Liu, Yi Hu, Xiangping Zhao, Wenwu Cherubini, Francesco 2023 12 Seiten application/pdf https://refubium.fu-berlin.de/handle/fub188/38898 https://doi.org/10.17169/refubium-38614 https://doi.org/10.1016/j.agrformet.2023.109362 eng eng https://refubium.fu-berlin.de/handle/fub188/38898 http://dx.doi.org/10.17169/refubium-38614 doi:10.1016/j.agrformet.2023.109362 https://creativecommons.org/licenses/by/4.0/ Forest management Climate change Surface temperature Machine learning ddc:550 doc-type:article 2023 ftfuberlin https://doi.org/10.17169/refubium-3861410.1016/j.agrformet.2023.109362 2023-04-16T22:24:26Z Forests interact with the local climate through a variety of biophysical mechanisms. Observational and modelling studies have investigated the effects of forested vs. non-forested areas, but the influence of forest management on surface temperature has received far less attention owing to the inherent challenges to adapt climate models to cope with forest dynamics. Further, climate models are complex and highly parameterized, and the time and resource intensity of their use limit applications. The availability of simple yet reliable statistical models based on high resolution maps of forest attributes representative of different development stages can link individual forest management practices to local temperature changes, and ultimately support the design of improved strategies. In this study, we investigate how forest management influences local surface temperature (LSTs) in Fennoscandia through a set of machine learning algorithms. We find that more developed forests are typically associated with higher LST than young or undeveloped forests. The mean multi-model estimates from our statistical system can accurately reproduce the observed LST. Relative to the present state of Fennoscandian forests, fully develop forests are found to induce an annual mean warming of 0.26 °C (0.03/0.69 °C as 5th/95th percentile), and an average cooling effect in the summer daytime from -0.85 to -0.23 °C (depending on the model). On the contrary, a scenario with undeveloped forests induces an annual average cooling of -0.29 °C (-0.61/-0.01 °C), but daytime warming in the summer that can be higher than 1 °C. A weak annual mean cooling of -0.01 °C is attributed to forest harvest from 2015 to 2018, with an increased daytime temperature in summer of about 0.04 °C. Overall, this approach is a flexible option to study effects of forest management on LST that can be applied at various scales and for alternative management scenarios, thereby helping to improve local management strategies with consideration of effects on local climate. Article in Journal/Newspaper Fennoscandia Fennoscandian Freie Universität Berlin: Refubium (FU Berlin) |
institution |
Open Polar |
collection |
Freie Universität Berlin: Refubium (FU Berlin) |
op_collection_id |
ftfuberlin |
language |
English |
topic |
Forest management Climate change Surface temperature Machine learning ddc:550 |
spellingShingle |
Forest management Climate change Surface temperature Machine learning ddc:550 Huang, Bo Li, Yan Liu, Yi Hu, Xiangping Zhao, Wenwu Cherubini, Francesco A simplified multi-model statistical approach for predicting the effects of forest management on land surface temperature in Fennoscandia |
topic_facet |
Forest management Climate change Surface temperature Machine learning ddc:550 |
description |
Forests interact with the local climate through a variety of biophysical mechanisms. Observational and modelling studies have investigated the effects of forested vs. non-forested areas, but the influence of forest management on surface temperature has received far less attention owing to the inherent challenges to adapt climate models to cope with forest dynamics. Further, climate models are complex and highly parameterized, and the time and resource intensity of their use limit applications. The availability of simple yet reliable statistical models based on high resolution maps of forest attributes representative of different development stages can link individual forest management practices to local temperature changes, and ultimately support the design of improved strategies. In this study, we investigate how forest management influences local surface temperature (LSTs) in Fennoscandia through a set of machine learning algorithms. We find that more developed forests are typically associated with higher LST than young or undeveloped forests. The mean multi-model estimates from our statistical system can accurately reproduce the observed LST. Relative to the present state of Fennoscandian forests, fully develop forests are found to induce an annual mean warming of 0.26 °C (0.03/0.69 °C as 5th/95th percentile), and an average cooling effect in the summer daytime from -0.85 to -0.23 °C (depending on the model). On the contrary, a scenario with undeveloped forests induces an annual average cooling of -0.29 °C (-0.61/-0.01 °C), but daytime warming in the summer that can be higher than 1 °C. A weak annual mean cooling of -0.01 °C is attributed to forest harvest from 2015 to 2018, with an increased daytime temperature in summer of about 0.04 °C. Overall, this approach is a flexible option to study effects of forest management on LST that can be applied at various scales and for alternative management scenarios, thereby helping to improve local management strategies with consideration of effects on local climate. |
format |
Article in Journal/Newspaper |
author |
Huang, Bo Li, Yan Liu, Yi Hu, Xiangping Zhao, Wenwu Cherubini, Francesco |
author_facet |
Huang, Bo Li, Yan Liu, Yi Hu, Xiangping Zhao, Wenwu Cherubini, Francesco |
author_sort |
Huang, Bo |
title |
A simplified multi-model statistical approach for predicting the effects of forest management on land surface temperature in Fennoscandia |
title_short |
A simplified multi-model statistical approach for predicting the effects of forest management on land surface temperature in Fennoscandia |
title_full |
A simplified multi-model statistical approach for predicting the effects of forest management on land surface temperature in Fennoscandia |
title_fullStr |
A simplified multi-model statistical approach for predicting the effects of forest management on land surface temperature in Fennoscandia |
title_full_unstemmed |
A simplified multi-model statistical approach for predicting the effects of forest management on land surface temperature in Fennoscandia |
title_sort |
simplified multi-model statistical approach for predicting the effects of forest management on land surface temperature in fennoscandia |
publishDate |
2023 |
url |
https://refubium.fu-berlin.de/handle/fub188/38898 https://doi.org/10.17169/refubium-38614 https://doi.org/10.1016/j.agrformet.2023.109362 |
genre |
Fennoscandia Fennoscandian |
genre_facet |
Fennoscandia Fennoscandian |
op_relation |
https://refubium.fu-berlin.de/handle/fub188/38898 http://dx.doi.org/10.17169/refubium-38614 doi:10.1016/j.agrformet.2023.109362 |
op_rights |
https://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.17169/refubium-3861410.1016/j.agrformet.2023.109362 |
_version_ |
1767959753072312320 |