Deforestation reshapes land-surface energy-flux partitioning
Land-use and land-cover change significantly modify local land-surface characteristics and water/energy exchanges, which can lead to atmospheric circulation and regional climate changes. In particular, deforestation accounts for a large portion of global land-use changes, which transforms forests in...
| Main Authors: | , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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IOP Publishing
2021-01-01
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| Series: | Environmental Research Letters |
| Subjects: | |
| Online Access: | https://doi.org/10.1088/1748-9326/abd8f9 |
| _version_ | 1797747816664137728 |
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| author | Kunxiaojia Yuan Qing Zhu Shiyu Zheng Lei Zhao Min Chen William J Riley Xitian Cai Hongxu Ma Fa Li Huayi Wu Liang Chen |
| author_facet | Kunxiaojia Yuan Qing Zhu Shiyu Zheng Lei Zhao Min Chen William J Riley Xitian Cai Hongxu Ma Fa Li Huayi Wu Liang Chen |
| author_sort | Kunxiaojia Yuan |
| collection | DOAJ |
| description | Land-use and land-cover change significantly modify local land-surface characteristics and water/energy exchanges, which can lead to atmospheric circulation and regional climate changes. In particular, deforestation accounts for a large portion of global land-use changes, which transforms forests into other land cover types, such as croplands and grazing lands. Many previous efforts have focused on observing and modeling land–atmosphere–water/energy fluxes to investigate land–atmosphere coupling induced by deforestation. However, interpreting land–atmosphere–water/energy-flux responses to deforestation is often complicated by the concurrent impacts from shifts in land-surface properties versus background atmospheric forcings. In this study, we used 29 paired FLUXNET sites, to improve understanding of how deforested land surfaces drive changes in surface-energy-flux partitioning. Each paired sites included an intact forested and non-forested site that had similar background climate. We employed transfer entropy, a method based on information theory, to diagnose directional controls between coupling variables, and identify nonlinear cause–effect relationships. Transfer entropy is a powerful tool to detective causal relationships in nonlinear and asynchronous systems. The paired eddy covariance flux measurements showed consistent and strong information flows from vegetation activity (gross primary productivity (GPP)) and physical climate (e.g. shortwave radiation, air temperature) to evaporative fraction (EF) over both non-forested and forested land surfaces. More importantly, the information transfers from radiation, precipitation, and GPP to EF were significantly reduced at non-forested sites, compared to forested sites. We then applied these observationally constrained metrics as benchmarks to evaluate the Energy Exascale Earth System Model (E3SM) land model (ELM). ELM predicted vegetation controls on EF relatively well, but underpredicted climate factors on EF, indicating model deficiencies in describing the relationships between atmospheric state and surface fluxes. Moreover, changes in controls on surface energy flux partitioning due to deforestation were not detected in the model. We highlight the need for benchmarking model simulated surface-energy fluxes and the corresponding causal relationships against those of observations, to improve our understanding of model predictability on how deforestation reshapes land surface energy fluxes. |
| first_indexed | 2024-03-12T15:57:03Z |
| format | Article |
| id | doaj.art-c0a06491a8e948959f67f17e81d4b51b |
| institution | Directory Open Access Journal |
| issn | 1748-9326 |
| language | English |
| last_indexed | 2024-03-12T15:57:03Z |
| publishDate | 2021-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | Environmental Research Letters |
| spelling | doaj.art-c0a06491a8e948959f67f17e81d4b51b2023-08-09T14:53:25ZengIOP PublishingEnvironmental Research Letters1748-93262021-01-0116202401410.1088/1748-9326/abd8f9Deforestation reshapes land-surface energy-flux partitioningKunxiaojia Yuan0https://orcid.org/0000-0002-1336-5768Qing Zhu1https://orcid.org/0000-0003-2441-944XShiyu Zheng2Lei Zhao3https://orcid.org/0000-0002-6481-3786Min Chen4William J Riley5Xitian Cai6https://orcid.org/0000-0002-4798-4954Hongxu Ma7Fa Li8https://orcid.org/0000-0002-0625-5587Huayi Wu9Liang Chen10https://orcid.org/0000-0003-1553-2846State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University , Wuhan, Hubei, People’s Republic of China; Climate and Ecosystem Sciences Division, Climate Sciences Department, Lawrence Berkeley National Laboratory , Berkeley, CA, United States of AmericaClimate and Ecosystem Sciences Division, Climate Sciences Department, Lawrence Berkeley National Laboratory , Berkeley, CA, United States of AmericaSchool of Electrical Engineering, Tsinghua University , Beijing, People’s Republic of ChinaDepartment of Civil and Environmental Engineering, University of Illinois Urbana-Champaign , Champaign, IL, United States of AmericaJoint Global Change Research Institute, Pacific Northwest National Laboratory , College Park, MD, United States of America; Department of Forest and Wildlife Ecology, University of Wisconsin-Madison , Madison, WI, United States of AmericaClimate and Ecosystem Sciences Division, Climate Sciences Department, Lawrence Berkeley National Laboratory , Berkeley, CA, United States of AmericaClimate and Ecosystem Sciences Division, Climate Sciences Department, Lawrence Berkeley National Laboratory , Berkeley, CA, United States of AmericaDepartment of Geography, University of California , Berkeley, CA, United States of AmericaState Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University , Wuhan, Hubei, People’s Republic of China; Climate and Ecosystem Sciences Division, Climate Sciences Department, Lawrence Berkeley National Laboratory , Berkeley, CA, United States of AmericaState Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University , Wuhan, Hubei, People’s Republic of ChinaIllinois State Water Survey, University of Illinois at Urbana-Champaign , Champaign, IL, United States of AmericaLand-use and land-cover change significantly modify local land-surface characteristics and water/energy exchanges, which can lead to atmospheric circulation and regional climate changes. In particular, deforestation accounts for a large portion of global land-use changes, which transforms forests into other land cover types, such as croplands and grazing lands. Many previous efforts have focused on observing and modeling land–atmosphere–water/energy fluxes to investigate land–atmosphere coupling induced by deforestation. However, interpreting land–atmosphere–water/energy-flux responses to deforestation is often complicated by the concurrent impacts from shifts in land-surface properties versus background atmospheric forcings. In this study, we used 29 paired FLUXNET sites, to improve understanding of how deforested land surfaces drive changes in surface-energy-flux partitioning. Each paired sites included an intact forested and non-forested site that had similar background climate. We employed transfer entropy, a method based on information theory, to diagnose directional controls between coupling variables, and identify nonlinear cause–effect relationships. Transfer entropy is a powerful tool to detective causal relationships in nonlinear and asynchronous systems. The paired eddy covariance flux measurements showed consistent and strong information flows from vegetation activity (gross primary productivity (GPP)) and physical climate (e.g. shortwave radiation, air temperature) to evaporative fraction (EF) over both non-forested and forested land surfaces. More importantly, the information transfers from radiation, precipitation, and GPP to EF were significantly reduced at non-forested sites, compared to forested sites. We then applied these observationally constrained metrics as benchmarks to evaluate the Energy Exascale Earth System Model (E3SM) land model (ELM). ELM predicted vegetation controls on EF relatively well, but underpredicted climate factors on EF, indicating model deficiencies in describing the relationships between atmospheric state and surface fluxes. Moreover, changes in controls on surface energy flux partitioning due to deforestation were not detected in the model. We highlight the need for benchmarking model simulated surface-energy fluxes and the corresponding causal relationships against those of observations, to improve our understanding of model predictability on how deforestation reshapes land surface energy fluxes.https://doi.org/10.1088/1748-9326/abd8f9climate changedeforestationcause–effect relationshipearth system model |
| spellingShingle | Kunxiaojia Yuan Qing Zhu Shiyu Zheng Lei Zhao Min Chen William J Riley Xitian Cai Hongxu Ma Fa Li Huayi Wu Liang Chen Deforestation reshapes land-surface energy-flux partitioning Environmental Research Letters climate change deforestation cause–effect relationship earth system model |
| title | Deforestation reshapes land-surface energy-flux partitioning |
| title_full | Deforestation reshapes land-surface energy-flux partitioning |
| title_fullStr | Deforestation reshapes land-surface energy-flux partitioning |
| title_full_unstemmed | Deforestation reshapes land-surface energy-flux partitioning |
| title_short | Deforestation reshapes land-surface energy-flux partitioning |
| title_sort | deforestation reshapes land surface energy flux partitioning |
| topic | climate change deforestation cause–effect relationship earth system model |
| url | https://doi.org/10.1088/1748-9326/abd8f9 |
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