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Accurate estimation of regional and global patterns of ecosystem respiration (ER) is crucial to improve the understanding of terrestrial carbon cycles and the predictive ability of the global carbon budget. However, large uncertainties still exist in regional and global ER estimation due to the drawbacks of modeling methods. Based on eddy covariance ER data from 132 sites in China from 2002 to 2020, we established Intelligent Random Forest (IRF) models that integrated ecological understanding with machine learning techniques to estimate ER. The results showed that the IRF models performed better than semiempirical models and machine learning algorithms. The observed data revealed that gross primary productivity (GPP), living plant biomass, and soil organic carbon (SOC) were of great importance in controlling the spatiotemporal variability of ER across China. An optimal model governed by annual GPP, living plant biomass, SOC, and air temperature (IRF‐04 model) matched 93% of the spatiotemporal variation in site‐level ER, and was adopted to evaluate the spatiotemporal pattern of ER in China. Using the optimal model, we obtained that the annual value of ER in China ranged from 5.05 to 5.84 Pg C yr−1 between 2000 and 2020, with an average value of 5.53 ± 0.22 Pg C yr−1. In this study, we suggest that future models should integrate process‐based and data‐driven approaches for understanding and evaluating regional and global carbon budgets. Plain Language Summary: With China already committing to achieve carbon neutrality before 2060, an accurate assessment of land carbon sink and its flux rate in China is an increasingly important area in global change ecology. In this essay, a high‐efficiency and accurate simulation method was introduced in this field; This method is particularly useful in the assessment of carbon sink and its flux rate in China by combining with reliable observation flux data. Using this new method, a reliable and reasonable value of carbon flux (ecosystem respiration) was obtained. Meanwhile, that method provides a better understanding of the mechanism governing the spatiotemporal variability of carbon flux. Therefore, this present study has gone some way toward enhancing our understanding of a comprehensive assessment and analysis of land carbon sink in China. Key Points: A model integrating ecological knowledge and machine learning was established to estimate ecosystem respiration (ER) in ChinaThe spatiotemporal patterns of ER are significantly affected by productivity, plant biomass, soil organic carbon, and air temperatureChina's ER was estimated to be 5.53 ± 0.22 petagrams of carbon per year (Pg C yr−1) on average for the years 2000–2020 [ABSTRACT FROM AUTHOR]

Ecosystem light use efficiency (LUE) is a critical parameter in estimating CO2 uptake by vegetation from climatological and satellite data. However, the spatiotemporal dynamics and biophysical regulations of ecosystem‐level LUE are not well understood, resulting in large uncertainties in the estimation of gross primary productivity (GPP) using LUE‐based models. In this study, we used eddy covariance to explore spatiotemporal variations and controls of LUE in four contrasting forest ecosystems (savanna, tropical rainforest, subtropical evergreen forest, and subalpine coniferous forest). Based on 27 site years of data, we found that (1) the multiyear mean LUE was 0.063, 0.251, 0.247, and 0.140 g C· mol photon−1 in the four contrasting ecosystems, respectively; (2) the LUE in the wet season (May–October) was higher than that in the dry season in all studied ecosystems; (3) the leaf area index controlled GPP and LUE significantly and explained 74%, 29%, 54%, and 36% of the variation in GPP and 51%, 19%, 41%, and 54% of the variation in LUE in the four contrasting ecosystems, respectively; (4) path analysis revealed the critical roles of GPP and vapor pressure deficit in controlling LUE in these four forest ecosystems; and (5) under warming scenarios, LUE may decrease in savanna but increase in the other three ecosystems, while decreasing precipitation (P) may reduce LUE in the ecosystems studied. This study improves our understanding of the influence of biophysical factors on LUE and demonstrates how LUE changes with variations in temperature, soil moisture, and leaf area index, thereby improving estimations of large‐scale carbon exchange/cycling. Key Points: Evergreen broad‐leaved forest (EBF) ecosystem shows higher LUE than subalpine coniferous/savanna ecosystemsThe vapor pressure deficit (VPD) and leaf area index (LAI) largely determine the variability in LUEWarming might decrease LUE in the savanna ecosystem and may increase LUE in the other three ecosystems [ABSTRACT FROM AUTHOR]

Evapotranspiration (ET) was observed over a 5-year period at a primary subtropical evergreen forest in southwest China. The study used the eddy covariance method and quantified the precipitation, throughfall, stemflow, soil evaporation, and routine meteorological parameters to analyze the contributions of stand-level transpiration and canopy interception loss to the total ET. The annual ET ranged between 785 and 901 mm. The average ratios ET-potential evapotranspiration were 0.45 and 0.87 during the dry and wet seasons, respectively. The relative contributions of soil evaporation, stand-level transpiration, and canopy interception loss to ET were quantified in order to understand their roles in today's climate and in a potential future climate. Solar radiation was a driver for ET, although the occurrence of drought limited the tree transpiration and thus ET. Specifically, deep soil moisture was an important driver of ET during the dry season. During the wet season, the vapor pressure deficit became one of the main drivers. Although the current study did not collect specific data for the effect of fog on ET, fog likely plays an important role in the ecohydrologic system and deserves further investigation. Although the hydrological system is currently stable, it is anticipated that the groundwater recharge from the ecosystem may be largely reduced in the future, likely through climate change and an associated positive temperature-ET feedback loop. [ABSTRACT FROM AUTHOR]