The unresolved energy-unclosed problem in micrometeorology refers to the fact that the sum of turbulent fluxes (sensible and latent heat fluxes, Hs and LE) monitored by eddy covariance (EC) methods tends to be lower than the available energy (net radiation (Rn), soil heat flux (G), and heat storage (S)). The lack of energy balance closure (EBC) increases evapotranspiration-measurement uncertainty. Using EC data from Xishuangbanna, a Southeast Asian tropical seasonal rainforest, we analyzed the energy distribution and closure based on micrometeorological features. We found that: (1) the EBC in the rainy season exceeds that in other seasons and that the seasonal moisture content, frictional wind velocity (u*), and LE contribute to the high seasonal variability in EBC; (2) the annual closure is approximately 65%, and energy non-closure is influenced by turbulence intensity and atmospheric stability. When the atmospheric state is unstable to near neutral, u* is greatest, and EBC can reach nearly 80%. (3) energy is mainly allocated to LE, and energy non-closure leads to LE underestimation, especially in the foggy-cool and hot-dry seasons. (4) Heat storage and large time-scale flux effects on EBC were excluded. The causes of energy non-closure in the tropical calm zone need further investigation.

Pan evaporation (Epan) is a critical measure of the atmospheric evaporation demand. Analyzing meteorological data from the Tropical Rainforest Comprehensive Meteorological Observation Field in the Xishuangbanna Tropical Botanical Garden (XTBG Meteorological Observing Station) based on physical models is helpful to improve our understanding of the state of the hydrological cycle in the Xishuangbanna tropical rainforest region. In this study, we investigated the long-term trend in Epan using the observation data from 1959 to 2021. Moreover, correlation analyses of Epan were performed, such as trend test, assessment of periodic properties and abrupt change analysis. Then, D20 Epan data and related meteorological data from 1979 to 2008 were used to drive Penman‒Monteith and PenPan models for simulating Epan. The partial derivative attribution method was used to analyze the dominant factors affecting Epan. The results showed that Epan exhibits obvious periodic changes, the 19a is the first primary period. In addition, there was a clear ‘evaporation paradox’ phenomenon in Xishuangbanna. Epan showed a decreasing trend during both 1959–2008 and 2009–2018, and the decreasing trend reached a significant level with a rate of −3.404 mm·a−2 during 1959–2008. Through comparative analysis, the PenPan model was considered more suitable for simulating Epan in Xishuangbanna. In order to identify the main meteorological factors influencing Epan, complete data from the D20 pan monitoring period, namely, 1979–2008, were selected for attribution calculations. The variations in the net radiation and saturated vapor pressure deficit are the main triggers that explain the ‘evaporation paradox’ phenomenon in Xishuangbanna.

A multi-technology study of evapotranspiration was conducted on the tropical seasonal forest in Nonggang Karst of Guangxi. From January 2019 to June 2020, three independent methods, including the eddy covariance method (EC), resistance method and Penman–Monteith method (PM), were used to estimate the annual evapotranspiration (ET). We found that the estimated annual ET varied dramatically: with values of 456.66 mm (EC), 292.24 mm (resistance method) and 699.59 mm (PM), respectively. The values were all lower than the reference evapotranspiration (853.26 mm year⁻¹) and potential evapotranspiration (1030.61 mm year⁻¹). The EC method had an energy imbalance problem, with an annual energy closure of 46% at the annual scale. The annual estimate of evapotranspiration after a 100% energy closure correction was 915.03 mm, which was higher than the reference evapotranspiration (853.26 mm), so the corrected annual estimates were considered to be unreasonable. Comparing the resistance method with the EC method, it was found that not only is the annual evapotranspiration (ET) lower in the EC method, but the sensible heat flux is also lower, indicating that the resistivity method has lower energy closure than the EC method, suggesting that this method is not suitable for use in karst forests. When comparing the PM method with the EC method, surface conductivity is the most critical parameter. As the most difficult parameter to quantify in the Penman–Monteith equation, the key influencing factor, maximum stomatal conductance, was carefully explored. In the selection of maximum stomatal conductance, the sensitivity of annual evapotranspiration to maximum stomatal conductance values was first analyzed. It was found that the sensitivity is strong before 0.018 m s⁻¹. When g_smax is 0.0025 m s⁻¹, the annual evapotranspiration (456 mm) is equivalent to that of the EC method, and it slowly decreases after reaching 0.018 ms⁻¹. This indicates that when g_smax is 0.0025 m s⁻¹, the annual evapotranspiration is lower or higher than the critical value of the EC method. Therefore, different maximum stomatal conductance values will result in annual evapotranspiration based on the PM method being higher or lower than the annual evapotranspiration measured by the EC method. In order to obtain a more accurate maximum stomatal conductance, the surface conductance was calculated based on the PM equation, using the maximum stomatal conductance of four key tree species in the study area. The FAO universal fixed surface conductance of 1/70 m s⁻¹ was used to constrain the calculation. The reason for this treatment is that the reference underlying surface of FAO is a uniformly flat and well-watered grassland, with a larger surface conductance than forests. The results showed that the selected maximum stomatal conductance values were all within a reasonable range, and the calculated annual evapotranspiration values were 267.28 mm, 596.42 mm, 699.59 mm and 736.90 mm, respectively. Considering the EC method as the lower limit (456.66 mm), the reference evapotranspiration as the upper limit (853.26 mm) and the specific vegetation in the study area, the estimated annual evapotranspiration of the primary forest in the Nonggang karst area of Guangxi (PM method) falls within the range of 596.42 mm to 736.90 mm, which is relatively reasonable.

Soil moisture content has a direct effect on the growth rate and survival rate of trees. However, previous studies on soil moisture have often focused on the topsoil, lacking effective monitoring of long-term dynamic changes in deep soil layers. In this study, 16 time-domain reflectometer (TDR) probes were installed in the Haikou plantation in Kunming to conduct long-term continuous monitoring of soil moisture within a depth range of 0 to 300 cm. The results indicate that the vertical distribution of soil moisture can be classified into three levels: the active layer from 0 to 70 cm ($\theta =0.23\pm 0.08\mathrm{c}{\mathrm{m}}^3{\mathrm{c}\mathrm{m}}^{-3})$, where the moisture content fluctuates significantly due to precipitation events; the transitional accumulation layer from 70 to 170 cm $(\theta =0.26\pm 0.06\mathrm{c}{\mathrm{m}}^3{\mathrm{c}\mathrm{m}}^{-3})$, where moisture content increases with depth and peaks at 170 cm; and the deep dissipative layer from 170 to 300 cm $(\theta =0.24\pm 0.08\mathrm{c}{\mathrm{m}}^3{\mathrm{c}\mathrm{m}}^{-3})$, where moisture content decreases with depth, forming a noticeable steep drop zone at 290 cm. The Hydrus-1D (Version 4.xx) model demonstrated high simulation capabilities (${\mathrm{R}}^2=0.58$) in shallow (10 to 50 cm) and deep (280 to 300 cm) layers, while its performance decreased (${\mathrm{R}}^2=0.39$) in the middle layer (110 to 200 cm). This study systematically reveals the dynamics of soil moisture from the surface active zone to the deep transition zone and evaluates the simulation ability of the Hydrus-1D model in this specific environment, which is also significant for assessing the groundwater resource conservation function of plantation ecosystems.

Yunnan Province, a key forest region in China, plays a crucial role in the nation’s terrestrial carbon sink. Understanding the spatiotemporal dynamic