In recent decades, flash drought events have frequently occurred in the humid regions of southern China. Due to the sudden onset and rapid intensification of these droughts, they often cause severe damage to vegetation photosynthesis. However, our understanding of the spatiotemporal evolution characteristics of flash droughts across different vegetation types, as well as the response regularity of photosynthesis to flash droughts, especially early responses, remains limited. This study analyzes the spatiotemporal evolution characteristics of flash droughts for different vegetation types in the Middle and Lower Reaches of the Yangtze River Basin from 2000 to 2023. It uses solar-induced chlorophyll fluorescence (SIF) and fluorescence yield (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\:{{\upvarphi\:}}_{\text{F}\:}$$\end{document}) to explore the response regularity of vegetation photosynthesis to flash droughts, with a systematic analysis of the 2013 flash drought event. The results show that, over the past 24 years, the frequency of flash droughts for different vegetation types in the Middle and Lower Reaches of the Yangtze River Basin has decreased, but the total duration has increased, with forests experiencing the highest frequency of flash droughts, while cropland experiences the least. Cropland photosynthesis is the most sensitive to flash drought, showing an early response 8-16 days after the onset and reaching a negative anomaly between 24 and 32 days. Forests mainly show an early response between 16 and 24 days and a negative anomaly response between 32 and 40 days. During the 2013 flash drought, cropland showed an early response on the 10th day after the onset and a negative anomaly on the 26th day, while forest responses were later, with early responses on the 20th day and negative anomalies on the 36th day. These results align with long-term statistical data. This study contributes to a deeper understanding of vegetation photosynthesis response regularity to flash droughts and provides insights for developing effective flash drought management strategies.

Flash droughts tend to cause severe damage to agriculture due to their characteristics of sudden onset and rapid intensification. Early detection of the response of vegetation to flash droughts is of utmost importance in mitigating the effects of flash droughts, as it can provide a scientific basis for establishing an early warning system. The commonly used method of determining the response time of vegetation to flash drought, based on the response time index or the correlation between the precipitation anomaly and vegetation growth anomaly, leads to the late detection of irreversible drought effects on vegetation, which may not be sufficient for use in analyzing the response of vegetation to flash drought for early earning. The evapotranspiration-based (ET-based) drought indices are an effective indicator for identifying and monitoring flash drought. This study proposes a novel approach that applies cross-spectral analysis to an ET-based drought index, i.e., Evaporative Stress Anomaly Index (ESAI), as the forcing and a vegetation-based drought index, i.e., Normalized Vegetation Anomaly Index (NVAI), as the response, both from medium-resolution remote sensing data, to estimate the time lag of the response of vegetation vitality status to flash drought. An experiment on the novel method was carried out in North China during March-September for the period of 2001-2020 using remote sensing products at 1 km spatial resolution. The results show that the average time lag of the response of vegetation to water availability during flash droughts estimated by the cross-spectral analysis over North China in 2001-2020 was 5.9 days, which is shorter than the results measured by the widely used response time index (26.5 days). The main difference between the phase lag from the cross-spectral analysis method and the response time from the response time index method lies in the fundamental processes behind the definitions of the vegetation response in the two methods, i.e., a subtle and dynamic fluctuation signature in the response signal (vegetation-based drought index) that correlates with the fluctuation in the forcing signal (ET-based drought index) versus an irreversible impact indicated by a negative NDVI anomaly. The time lag of the response of vegetation to flash droughts varied with vegetation types and irrigation conditions. The average time lag for rainfed cropland, irrigated cropland, grassland, and forest in North China was 5.4, 5.8, 6.1, and 6.9 days, respectively. Forests have a longer response time to flash droughts than grasses and crops due to their deeper root systems, and irrigation can mitigate the impacts of flash droughts. Our method, based on cross-spectral analysis and the ET-based drought index, is innovative and can provide an earlier warning of impending drought impacts, rather than waiting for the irreversible impacts to occur. The information detected at an earlier stage of flash droughts can help decision makers in developing more effective and timely strategies to mitigate the impact of flash droughts on ecosystems.

Droughts are among the most devastating natural hazards, occurring in all regions with different climate conditions. The impacts of droughts result in significant damages annually around the world. While drought is generally described as a slow-developing hazardous event, a rapidly developing type of drought, the so-called flash drought has been revealed by recent studies. The rapid onset and strong intensity of flash droughts require accurate real-time monitoring. Addressing this issue, a Generative Adversarial Network (GAN) is developed in this study to monitor flash droughts over the Contiguous United States (CONUS). GAN contains two models: (a) discriminator and (b) generator. The developed architecture in this study employs a Markovian discriminator, which emphasizes the spatial dependencies, with a modified U-Net generator, tuned for optimal performance. To determine the best loss function for the generator, four different networks are developed with different loss functions, including Mean Absolute Error (MAE), adversarial loss, a combination of adversarial loss with Mean Square Error (MSE), and a combination of adversarial loss with MAE. Utilizing daily datasets collected from NLDAS-2 and Standardized Soil Moisture Index (SSI) maps, the network is trained for real-time daily SSI monitoring. Comparative assessments reveal the proposed GAN's superior ability to replicate SSI values over U-Net and Na & iuml;ve models. Evaluation metrics further underscore that the developed GAN successfully identifies both fine- and coarse-scale spatial drought patterns and abrupt changes in the SSI temporal patterns that is important for flash drought identification. A new deep learning-based model using a generative adversarial network (GAN) is developed for real-time flash drought detection and monitoring Remote sensing maps are used as inputs to encompass the entire regions within the CONUS The proposed GAN is able to capture abrupt changes in drought patterns

The Tarim River, the largest inland river in China, sits in the Tarim River Basin (TRB), which is an arid area with the ecosystem primarily sustained by water from melting snow and glaciers in the headstream area. To evaluate the pressures of natural disasters in this climate-change-sensitive basin, this study projected flash droughts in the headstream area of the TRB. We used the variable infiltration capacity (VIC) model to describe the hydrological processes of the study area, Markov chain Monte Carlo to quantify the parameter uncertainty of the VIC model. Ten downscaled general circulation models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) were used to drive the VIC model, and the standardized evaporative stress ratio was applied to identify flash droughts. The results demonstrated that the VIC model after Bayesian parameters uncertainty analysis can efficiently describe the hydrological processes of the study area. In the future (2021-2100), compared with the plain region, the alpine region has higher flash drought frequency and intensity. Compared with the historical period (1961-2014), the frequency, duration, and intensity of flash droughts tend to increase throughout the study area, especially for the alpine area. Moreover, based on variance decomposition, CMIP6 model is the most important uncertainty source for flash drought projection, followed by the shared socioeconomic pathway of climate change scenario and VIC model parameters.