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Accurate estimations of forest evapotranspiration (ET) and its components, transpiration (T) and evaporation (E), are important for deep understanding and predicting the responses of forest water cycles to climate change. In this study, the improved Shuttleworth-Wallace model (SWH) was applied to estimate ET, T, and E during 2003–2014 in a subtropical planation, and the modeled results were verified using in situ measurements by the eddy covariance technique, sap flow, and micro-lysimeter method. The study aimed to clarify whether it is feasible and reliable to use the SWH model to estimate and partition ET in forests. In addition, depending on the long-term data, the specific performances in modeling ET under different climatic backgrounds were investigated, and the underlying mechanisms were explored. The results verified that the SWH performed relatively well in the subtropical forest, and the modeled ET, T and E could track the seasonal variations, although overestimations were found in the peak seasons. However, the model was relatively weaker in estimating the interannual variabilities. It performed well in modeling ET in normal years but showed larger model residuals in years with obvious climatic anomalies. In the severe summer-drought (2003) and cold-spring (2005) years, the model greatly overestimated ET. It also overestimated ET in summer since 2010, which may be ascribed to the less dependency of ET on VPD induced by the more humid microclimate in forest accompanied with forest development. For the ET partitioning results, the modeled and measured E and T values were all in reasonable ranges. The possible reasons for underestimations (overestimations) of E and T by measurements (SWH model) were discussed. In this study, the data obtained using different methods and from different scales matched each other and could be cross validated, and the discussion on discrepancies would be beneficial for understanding the advantages and flaws of different methods and could be the basis for optimizing the measurement and model methods. In sum, this study verified that it is feasible to use the SWH model in forests and provided a basis for further improving and optimizing the modeled results under different climate backgrounds.

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Aims Fisher discriminant analysis can comprehensively take multiple factors into consideration and effectively conduct separations between two classes. If it can be used to detect the occurrences of drought, drought can be detected more effectively and accurately. Methods Based on 9-year carbon flux and corresponding meteorological data, soil water content (SWC) and vapor pressure deficit (VPD) were selected as the discriminant factors. Drought occurrences were detected by applying the Fisher discriminant analysis method in an alpine ecosystem in Tibet. Important Findings Fisher discriminant analysis was successfully applied to detect drought occurrence in an alpine meadow ecosystem. The soil water deficit and atmospheric water deficit were comprehensively taken into consideration. Consequently, this method could detect the onset and end date of droughts more accurately and reasonably. Based on the characteristics of drought and non-drought samples, the discriminant equation was constructed as y = 24.46 SWC – 4.60 VPD. When y>1, the days were distributed above the critical line. In addition, when y was greater than one for more than 10 days, it was labeled as one drought event. If the interval between two drought processes was less than 2 days, it was considered one drought event. With increasing the study period and continued accumulation of observation data, the discriminant equation could be further optimized in the future, resulting in more accurate drought detection.

Drought plays a prominent role in affecting ecosystem stability and ecosystem productivity. Based on eddy covariance and climatic observations during 2012–2020, the Fisher discriminant analysis method was employed to accurately detect drought occurrences. Furthermore, the ecosystem water sensitivity and its resistance to drought were quantified to evaluate the ecosystem stability. The results showed that the alpine meadow suffered drought most frequently at the beginning of the growing seasons. However, drought during the peak growing seasons reduced the gross primary productivity (GPP) the most, by 30.5 ± 15.2%. In the middle of the peak growing seasons, the ecosystem water sensitivity was weak, and thus, the resistance to drought was strong, which resulted in high ecosystem stability. At the beginning and end of the peak growing seasons, the ecosystem stability was relatively weak. Ecosystem stability was positively related to the corresponding multiyear average soil water content (SWC ave ). However, drought occurring during high SWC ave periods led to larger reductions in GPP, which indicated that the inhibitory effects of drought on ecosystems were more dependent on the occurrence time of droughts than on ecosystem stability.