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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.

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.

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