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Flash drought is an extreme phenomenon, characterized by unusually rapid intensification of drought severity, with strong impacts on plant growth especially for crops. However, it's unclear how hydrometeorological changes contribute to flash drought and how vegetation physiology, greenness, and productivity respond to flash drought. In this study, we developed an multivariate integrated framework for flash drought identification using a regression model based on principal-components analysis (PCA): the PCA regression flash drought (PRFD) model. Three criteria are specified to emphasize the rapid intensification of drought and its impacts on vegetation growth and water resources. We applied our new model in two geographical units with different climates and hydrology: one is dominated by agriculture and subject to natural drought (the Loess Plateau) and the other, a natural region with infrequent drought (the Qinling Mountains). We found that high frequency of flash droughts is most likely to occur in the eastern and central Loess Plateau and part of the Qinling Mountains. However, in relatively humid areas, flash drought shows strong intensity such as the Qinling Mountains and western and eastern edges of the Loess Plateau. Trend analysis indicates that PRFD has increased frequency, longer duration, and stronger intensity since the 1990s in both regions. From an ecological perspective, PRFD also showed a spatial pattern consistent with values of vegetation-related proxies that were below the long-term average, demonstrating vegetation transpiration, normalized-difference vegetation index (NDVI), gross primary productivity have obvious feedback on flash drought events. The Loess Plateau's NDVI responded immediately to flash drought, versus a 1-month lag in the Qinling Mountains. Because our proposed framework integrates multiple aspects of drought information, it can be applied in areas outside the study region according to regional hydrometeorological conditions. This has significant implications for improving agricultural management and forecasting future severe impacts of flash drought on plant growth. • A new integrated framework is proposed to identify flash drought (FD) events. • FD with greater frequency, longer pentads, and stronger intensity since 1990s. • Vegetation structure and physiological function respond rapidly to FD. • The response time of plant activity to FD is different in diverse climate-water regions. [ABSTRACT FROM AUTHOR]

As the most frequent and longest-lasting climate-related disasters, droughts are complex processes caused by hydrological imbalance, and have significant effects on ecosystem function and socioeconomic development. However, most conventional satellite-based drought indices and combined drought indices are mechanically constrained by the fact that they were developed and evaluated for a specific climate or critical ecological region, especially in China. A comprehensive drought index should be developed for extensive application in different climate-hydrology regions. To address this problem, an optimised principal component analysis method (PCA-RF) was proposed to support drought assessment across the transitional area between the humid and arid regions in central China, including the Loess Plateau (LP) and Qinling Mountains (QL). This method eliminated the causal relationships of non-linear changes for multi-aspect parameters such as land surface temperature, precipitation and soil moisture, and was used in developing a new index named "Meteorological drought index by PCA-RF methods (PMDI)". The results showed that PMDI had higher correlations and lower root mean square error than other drought indices (established using the empirical-weights method) with the 1-month standardized precipitation evapotranspiration index (SPEI-1). That means PMDI performed well in meteorological drought monitoring. Additionally, the normalized difference vegetation index (NDVI) and solar-induced chlorophyll fluorescence (SIF) were utilised to evaluate PMDI effectiveness in detecting vegetation response to drought, which represents agricultural drought. The results indicated that correlations between PMDI and vegetation indices (VIs) were stronger when severe or extreme droughts occurred, and that their relationships were affected by the high spatiotemporal heterogeneity in vegetation change over the LP and QL. Overall, PMDI was shown here to be a very suitable index for monitoring meteorological drought (short-term) and has effective action in capturing the response of vegetation signals to agriculture drought events (long-term). Given the excellent performance of PMDI in diverse climate-hydrology conditions of our study regions, we concluded that the PMDI has considerable potential as a drought monitoring methodology on a larger spatial scale. • A new drought index was developed (PMDI), which integrated multiple sources of information describing different aspects. • PMDI performed well in different climate-hydrology regions for short- and long-term drought monitoring. • PMDI has reasonably good potential applicability in drought monitoring on a larger spatial scale. [ABSTRACT FROM AUTHOR]