Deforestation reshapes land-surface energy-flux partitioning

Abstract

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.

Partitioning of Sensible and Latent Heat Fluxes in Different Vegetation Types and Their Spatiotemporal Variations Based on 203 FLUXNET Sites

“…Albedo was calculated as the outgoing shortwave radiation ( SW_OUT , no gap filling available for the variable) divided by the incoming shortwave radiation ( SW_IN_F ). Since fluxes measured by eddy covariance at night have missing data and large errors (Mahrt, 1999; Yuan et al., 2021), we used the gap–filled half–hourly data of these variables and extracted only the daytime data. Daytime was defined as the local time from 9:00 to 16:00 (Mcgloin et al., 2019; Yuan et al., 2021).…”

“…Since fluxes measured by eddy covariance at night have missing data and large errors (Mahrt, 1999; Yuan et al., 2021), we used the gap–filled half–hourly data of these variables and extracted only the daytime data. Daytime was defined as the local time from 9:00 to 16:00 (Mcgloin et al., 2019; Yuan et al., 2021). We did not use incoming shortwave radiation to define daytime because there were occasional large values at night.…”

Causality Guided Machine Learning Model on Wetland Ch4 Emissions Across Global Wetlands

“…In theory, all potential confounders should be included when identifying causal relationships. However, in practice, too many confounders will cause high dimensionality and statistical instability issues (Runge et al, 2019a;Yuan et al, 2021). For simplicity, previous studies often considered the immediate history of a target variable as the confounder, assuming that it contributes the most confounding information to the target (Ruddell and Kumar, 2009;Yuan et al, 2021).…”

“…However, in practice, too many confounders will cause high dimensionality and statistical instability issues (Runge et al, 2019a;Yuan et al, 2021). For simplicity, previous studies often considered the immediate history of a target variable as the confounder, assuming that it contributes the most confounding information to the target (Ruddell and Kumar, 2009;Yuan et al, 2021). However, wetland F CH4 can be jointly regulated by multiple factors including the history of F CH4 .…”

Observational evidence of regional increasing hot extreme accelerated by surface energy partitioning

“…The increase in hot extremes in various regions may be driven by different physical mechanisms. There are many anthropogenic drivers for changes in regional thermodynamic conditions, such as land use/cover change and agricultural management practices (e.g., irrigation) (Lemordant et al 2018;Liao et al 2018;Yuan et al 2020). Changes in those land surface conditions can cause dynamical variations in the partitioning of surface energy fluxes, which in turn feedback on regional air temperature (Forzieri et al 2020;Gentine et al 2016;Schwingshackl et al 2017).…”