We calculated water use efficiency (WUE) using measures of gross primary production (GPP) and evapotranspiration (ET) from five years of continuous eddy covariance measurements (2009–2013) obtained over a primary subtropical evergreen broadleaved forest in southwestern China. Annual mean WUE exhibited a decreasing trend from 2009 to 2013, varying from ~2.28 to 2.68 g C kg H₂O⁻¹. The multiyear average WUE was 2.48 ± 0.17 (mean ± standard deviation) g C kg H₂O⁻¹. WUE increased greatly in the driest year (2009), due to a larger decline in ET than in GPP. At the diurnal scale, WUE in the wet season reached 5.1 g C kg H₂O⁻¹ in the early morning and 4.6 g C kg H₂O⁻¹ in the evening. WUE in the dry season reached 3.1 g C kg H₂O⁻¹ in the early morning and 2.7 g C kg H₂O⁻¹ in the evening. During the leaf emergence stage, the variation of WUE could be suitably explained by water-related variables (relative humidity (RH), soil water content at 100 cm (SWC_100)), solar radiation and the green index (Sgreen). These results revealed large variation in WUE at different time scales, highlighting the importance of individual site characteristics.

Forest soils in the warm-humid tropics significantly contribute to the regional greenhouse gas (GHG) budgets. However, spatial heterogeneity of GHG fluxes is often overlooked. Here, we present a study of N₂O and CH₄ fluxes over 1.5 years, along a topographic gradient in a rainforest catchment in Xishuangbanna, SW China. From the upper hillslope to the foot of the hillslope, and further to the flat groundwater discharge zone, we observed a decrease of N₂O emission associated with an increase of soil water-filled-pore-space (WFPS), which we tentatively attribute to more complete denitrification to N₂ at larger WFPS. In the well-drained soils on the hillslope, denitrification at anaerobic microsites or under transient water-saturation was the potential N₂O source. Negative CH₄ fluxes across the catchment indicated a net soil CH₄ sink. As the oxidation of atmospheric CH₄ is diffusion-limited, soil CH₄ consumption rates were negatively related to WFPS, reflecting the topographic control. Our observations also suggest that during dry seasons N₂O emission was significantly dampened (<10 μg N₂O-N m^-2 h^-1) and CH₄ uptake was strongly enhanced (83 μg CH₄-C m^-2 h^-1) relative to wet seasons (17 μg N₂O-N m^-2 h^-1 and 56 μg CH₄-C m^-2 h^-1). In a post-drought period, several rain episodes induced exceptionally high N₂O emissions (450 μg N₂O-N m^-2 h^-1) in the groundwater discharge zone, likely driven by flushing of labile organic carbon accumulated during drought. Considering the global warming potential associated with both GHGs, we found that N₂O emissions largely offset the C sink contributed by CH₄ uptake in soils (more significant in the groundwater discharge zone). Our study illustrates important topographic controls on N₂O and CH₄ fluxes in forest soils. With projected climate change in the tropics, weather extremes may interact with these controls in regulating forest GHG fluxes, which should be accounted for in future studies.

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.

The distribution of forest vegetation and forest carbon sequestration potential are significantly influenced by climate change. In this study, a map of the current distribution of vegetation in Yunnan Province was compiled based on data from remote sensing imagery from the Advanced Land Observing Satellite (ALOS) from 2008 to 2011. A classification and regression tree (CART) model was used to predict the potential distribution of the main forest vegetation types in Yunnan Province and estimate the changes in carbon storage and carbon sequestration potential (CSP) in response to increasing temperature. The results show that the current total forest area in Yunnan Province is 1.86 × 10⁷ ha and that forest covers 48.63% of the area. As the temperature increases, the area of forest distribution first increases and then decreases, and it decreases by 11% when the temperature increases from 1.5 to 2 °C. The mean carbon density of the seven types of forest vegetation in Yunnan Province is 84.69 Mg/ha. The total carbon storage of the current forest vegetation in Yunnan Province is 871.14 TgC, and the CSP is 1100.61 TgC. The largest CSP (1114.82 TgC) occurs when the temperature increases by 0.5 °C. Incremental warming of 2 °C will sharply decrease the forest CSP, especially in those regions with mature coniferous forest vegetation. Semi-humid evergreen broad-leaved forests were highly sensitive to temperature changes, and the CSP of these forests will decrease with increasing temperature. Warm-hot coniferous forests have the greatest CSP in all simulation scenarios except the scenario of a 2 °C temperature increase. These results indicate that temperature increases can influence the CSP in Yunnan Province, and the largest impact emerged in the 2 °C increase scenario.