Jiquan Chen

TL;DR: An estimate of global land evapotranspiration from 1982 to 2008 is provided using a global monitoring network, meteorological and remote-sensing observations, and a machine-learning algorithm, which suggests that increasing soil-moisture limitations on evapOTranspiration largely explain the recent decline of the global land-evapotranpiration trend.

TL;DR: In this paper, the authors discuss the use of principles from disturbance ecology and natural stand development to create silvicultural approaches that are more aligned with natural processes, including the role of disturbances in creating structural legacies that become key elements of the post-disturbance stands.

TL;DR: In this paper, the magnitude and distance of edge influence are a direct function of the contrast in structure and composition between adjacent communities on either side of the edge, and local factors such as climate, edge characteristics, stand attributes, and biotic factors affect patch contrast.

TL;DR: In this paper, the authors upscaled FLUXNET observations of carbon dioxide, water, and energy fluxes to the global scale using the machine learning technique, model tree ensembles (MTE), to predict site-level gross primary productivity (GPP), terrestrial ecosystem respiration (TER), net ecosystem exchange (NEE), latent energy (LE), and sensible heat (H) based on remote sensing indices, climate and meteorological data, and information on land use.

TL;DR: In this article, the effects of disturbance history, climate, and changes in atmospheric carbon dioxide (CO2) concentration and nitrogen deposition (Ndep) on carbon and water fluxes in seven North American evergreen forests are assessed using a coupled water-carbon-nitrogen model, canopy-scale flux observations, and descriptions of the vegetation type, management practices, and disturbance histories at each site.