Study on the Characteristics of Flash Drought and the Response Regularity of Photosynthesis to Flash Drought in Different Vegetation Ecosystems in the Middle and Lower Reaches of the Yangtze River Basin.

Study on the Characteristics of Flash Drought and the Response Regularity of Photosynthesis to Flash Drought in Different Vegetation Ecosystems in the Middle and Lower Reaches of the Yangtze River Basin. | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Study on the Characteristics of Flash Drought and the Response Regularity of Photosynthesis to Flash Drought in Different Vegetation Ecosystems in the Middle and Lower Reaches of the Yangtze River Basin. Yunjun Zhan, Chuanqi Ma, Yan Yan, Yongsi Luo, Xueting Wang, Senrong Wang, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4961403/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 26 Feb, 2025 Read the published version in International Journal of Biometeorology → Version 1 posted 4 You are reading this latest preprint version Abstract 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. Our current understanding of the spatiotemporal evolution characteristics of flash droughts across different vegetation types remains limited. Moreover, insufficient consideration of the early stress of vegetation during flash droughts has constrained our understanding of the response regularity of vegetation photosynthesis to flash drought events. This study analyzes the spatial and temporal evolution characteristics of flash drought for different vegetation types in the middle and lower reaches of the Yangtze River Basin from 2000 to 2023. Using the flash drought event of 2013 as a case study, solar-induced chlorophyll fluorescence (SIF) and fluorescence yield (ΦF) were employed to further explore the Response Regularity of vegetation photosynthesis to flash drought. The results show that, over the past 24 years, the frequency of flash drought 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 a higher frequency of flash drought. Cropland photosynthesis is the most sensitive to flash drought, responding on the 10th day after the onset and reaching a negative anomaly by the 26th day. Forests and grasslands respond later, beginning on the 20th day and showing a negative anomaly by the 36th day. The findings of this study contribute to a deeper understanding of vegetation photosynthesis responses to flash drought and provide a reference for developing effective flash drought management strategies. Flash drought Soil moisture Photosynthesis response of vegetation Solar-induced chlorophyll fluorescence Different vegetation Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1 Introduction Traditionally, drought has been defined as a slowly developing disaster, typically taking months or years to reach its maximum extent and intensity (Mishra and Singh 2010 ; Zhou et al. 2020 , 2021 ). However, in recent years, the phenomenon of flash drought occurring frequently during the growing season has gradually entered researchers' focus. Unlike traditional slow-onset droughts, these flash drought can develop and reach maximum intensity within a few weeks, leaving insufficient time to mitigate their impacts. This results in severe damage to local vegetation ecosystems and significant economic losses within a short time period (Otkin et al. 2016 ; Wang et al. 2016 ; Mahto and Mishra 2020 ; Zhang and Yuan 2020 ; Zhang et al. 2020 ; Yao et al. 2022 ; An et al. 2024 ). For example, the flash drought in the Midwestern United States in 2012 caused over $ 12 billion in economic losses (Hoerling et al. 2014 ), and the flash drought in the Northern Plains of the United States in 2017 destroyed major farmland crops, resulting in agricultural losses exceeding $ 2.6 billion(Gerken et al. 2018 ). Additionally, there were the flash drought in Southern Africa in 2015/2016 (Yuan et al. 2018 ) and in Eastern Australia in 2017/2018 (Nguyen et al. 2019 ). The severe impacts of these events on entire ecosystems demonstrate our limited understanding of how vegetation responds to flash drought. Therefore, in-depth exploration of the Response Regularity of photosynthesis in different vegetation types to flash drought is crucial for protecting vegetation ecosystems and the terrestrial carbon cycle(Zhu et al. 2024 ). Many studies have shown that the humid regions of southern China are more prone to flash drought compared to northern regions, with longer duration and greater intensity(Wang et al. 2016 ). However, due to the late attention given to flash drought, most previous studies have primarily focused on the spatiotemporal evolution characteristics and driving factors of flash drought across the entire study area (Christian et al. 2021 ; Qing et al. 2022 ; Fu and Wang 2022 ), with limited understanding of the spatiotemporal characteristics of flash drought for different vegetation types. Additionally, when assessing vegetation photosynthesis responses to flash drought, the time lag between the onset of flash drought and the occurrence of the first negative anomaly in vegetation ecological indicators is often used to identify vegetation response time(Yang et al. 2023 ; Zhao et al. 2024 ; Lu et al. 2024 ). A shorter response time indicates that vegetation is more susceptible to flash drought events. However, this approach only provides information on vegetation responses after the vitality of the vegetation ecosystem has already been compromised, neglecting early stress signals in vegetation. This has led to a limited understanding of vegetation photosynthesis responses to flash drought. Drought affects the physiological processes of vegetation, especially photosynthesis. Vegetation indices (VIs) based on remote sensing reflectance, such as the Normalized Difference Vegetation Index (NDVI) and Solar-Induced Chlorophyll Fluorescence (SIF), have been widely used for monitoring vegetation dynamics (Huete et al. 2002 ; Liu et al. 2018 ; Tian et al. 2019 ; Akanbi et al. 2024 ). However, a major drawback of VIs is their noticeable lag in response to drought, making them unable to timely reflect vegetation water stress. Solar radiation absorbed by chlorophyll molecules is either used for photosynthesis, dissipated as heat, or re-emitted as Solar-Induced Chlorophyll Fluorescence (SIF) (Porcar-Castell et al. 2014 ). Therefore, SIF is directly related to vegetation photosynthesis. Additionally, SIF signals contain information on both physiological changes and canopy structure. Studies have shown that the near-infrared reflectance of vegetation(Badgley et al. 2017 , 2019 ) and the product of Photosynthetically Active Radiation (NIRvP) can effectively explain structural and radiative information(Wu et al. 2020 ; Baldocchi et al. 2020 ), while the physiological information in SIF is represented by fluorescence yield (ΦF), a variable not affected by canopy structure. (Dechant et al. 2022 ) proposed an effective method for retrieving ΦF from satellite sensors by standardizing SIF. Since the response of SIF to drought is determined by both canopy structure and physiological changes(Dechant et al. 2020 ), SIF can respond more quickly than VIs when vegetation is under drought stress. Additionally, many studies have used SIF to characterize vegetation photosynthesis and explore its response to flash drought(Yang et al. 2023 ; Lu et al. 2024 ). Therefore, this study also explores the responses of photosynthesis in different vegetation types to flash drought events using Solar-Induced Chlorophyll Fluorescence (SIF). The middle and lower reaches of the Yangtze River Basin are high-incidence areas for flash drought in the humid regions of southern China. In recent years, frequent flash drought events in this area have caused significant economic losses. However, the spatiotemporal evolution characteristics of flash drought for different vegetation types in this region remain unclear. Additionally, there is limited understanding of the Response Regularity of photosynthesis in different vegetation types to flash drought. Therefore, this study focuses on the middle and lower reaches of the Yangtze River Basin to analyze the spatiotemporal evolution characteristics of flash drought for different vegetation types from 2000 to 2023. Using the high-intensity flash drought event that occurred during the summer growing season (June to September) in 2013 as a case study, this research employs SIF and ΦF indices to analyze the Response Regularity of photosynthesis in different vegetation types to flash drought events. This analysis considers both early stress signals and negative anomalies after the vitality of vegetation has been compromised. The findings of this study contribute to a deeper understanding of the Response Regularity of vegetation photosynthesis to flash drought and provide a reference for developing effective flash drought management strategies. 2 Study area The middle and lower reaches of the Yangtze River Basin (29°57′–31°48′N, 108°38′–121°52′E), as shown in Fig. 1 , include six provinces and one municipality: Hubei, Hunan, Jiangxi, Anhui, Jiangsu, Zhejiang, and Shanghai, covering an area of approximately 910,000 km², which accounts for 9.5% of the national land area. This region is located in a humid subtropical monsoon climate zone, with an average annual temperature of 16.8°C and an annual precipitation of 1309.3 mm. It is a key production base for grain, vegetable oil, and cotton in China, and also the area with the richest water resources in the country. Large freshwater lakes such as Dongting Lake, Poyang Lake, Tai Lake, and Chao Lake are concentrated in this region. The study area also includes several internationally important wetlands and national wetland nature reserves, making the middle and lower reaches of the Yangtze River Basin play a crucial role in the overall carbon cycle system. Under the background of climate change, the Yangtze River Basin is more prone to flash drought, which last longer and are more intense, severely impacting the local ecosystem and agricultural production. Therefore, it is urgent to study the characteristics of flash drought changes in this area and their impact on the vegetation ecosystem. 3 Data and methods 3.1 Data 3.1.1 Meteorological and soil moisture data The meteorological data used in this study primarily include temperature (LST), precipitation (PPT), photosynthetically active radiation (PAR), and vapor pressure deficit (VPD). The temperature data comes from the MOD11A2V6 product (available at https://lpdaac.usgs.gov/products/mod11a2v006/ ), which provides 8-day average land surface temperature (LST) with a spatial resolution of 1 km. Precipitation data is sourced from the CHIRPS dataset (available at https://chc.ucsb.edu/data/chirps ), which records daily global rainfall from 1981 to the present. CHIRPS combines 0.05° resolution satellite imagery with in-situ station data to create gridded rainfall time series for trend analysis and seasonal drought monitoring. PAR data is obtained from the Global Land Surface Satellite (GLASS) product (available at http://www.glass.umd.edu/Download.html ), with a spatial resolution of 0.05° and a temporal resolution of 1 day. VPD data is sourced from China's first high-resolution atmospheric humidity index dataset (HiMIC-Monthly)( available at https://data.tpdc.ac.cn/zh-hans/data/6854ebb3-8a60-454a-8d43-4e6a8c0ebd5d ), provided by the National Tibetan Plateau Data Center, with a spatial resolution of 1 km and a temporal resolution of one month. Soil moisture (SM) data comes from the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5-Land dataset, the latest generation product created by the Copernicus Climate Change Service (available at https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-land ), with a spatial resolution of 0.1° and a temporal resolution of 1 hour. Compared to the ERA5 product, ERA5-Land is a reanalysis dataset that provides higher resolution data of various surface parameters over several decades. This reanalysis product combines global surface observation data with model data using physical models to create a globally consistent dataset. Unlike most other SM datasets derived from model outputs, remote sensing, or reanalysis datasets, the ERA5-Land product has higher accuracy. The ERA5-Land reanalysis product provides soil moisture data for four different soil layers: 0–7 cm, 7–28 cm, 28–100 cm, and 100–289 cm. Numerous studies have shown that soil moisture up to 1 m depth from the top can meet the water and nutrient absorption needs of most crop roots. Considering the need to identify flash drought events that have a certain impact on the ecosystem, this study uses soil moisture information from the top three layers of the ECMWF integrated forecast system, i.e., up to 100 cm depth (0–7 cm, 7–28 cm, 28–100 cm) to describe flash drought events. 3.1.2 SIF and vegetation surface reflectance data SIF is the fluorescence emitted in the 650–800 nm wavelength range by chlorophyll a after absorbing photosynthetically active radiation (PAR). It is directly related to vegetation photosynthesis and represents a new method for monitoring vegetation photosynthesis and productivity(Guanter et al. 2014 ; Frankenberg and Berry 2018 ; Mohammed et al. 2019 ; Jonard et al. 2020 ). Currently, existing SIF data primarily come from GOME-2, OCO-2, and TROPOMI. Each of these sources provides high-fidelity SIF data but has its own advantages and disadvantages. GOME-2 data have the longest recording time and continuous spatial coverage, but their spatiotemporal resolution is low, at 0.5° monthly. In contrast, OCO-2 data offer higher spatiotemporal resolution, providing SIF data at 1.3 km × 2.25 km every 16 days, but the measurements are sparse with large gaps between tracks and the recording time is shorter. TROPOMI, launched in October 2017, addresses some of these issues by offering daily SIF data with a high spatiotemporal resolution of 0.02° and global coverage. However, due to its recent launch, the SIF data recording period is shorter. These challenges pose significant difficulties for long-term continuous research on vegetation ecosystems.(Chen et al. 2022 ), using the XGBoost machine learning model, reconstructed TROPOMI SIF (RTSIF) for the period 2001–2020 under clear sky conditions. The primary inputs were MODIS surface reflectance data, land surface temperature and land type products, CERES reanalysis data, and C3/C4 vegetation coverage data. This dataset (available at https://data.tpdc.ac.cn/zh-hans/data/2b8ffbf4-90ac-4e3d-9ae4-a8be31ae93d4/ ) provides 8-day SIF data at a 0.05° resolution. The RTSIF data were validated against TROPOMI SIF and tower-based SIF observations and compared with other satellite-derived SIF (GOME-2 SIF and OCO-2 SIF), proving its accuracy. RTSIF data have been widely used in vegetation drought monitoring, productivity assessment, and carbon cycle research (Peng and Karimi Sadaghiani 2023 ; Jacobson et al. 2023 ; Miao et al. 2023 ). Therefore, this study uses SIF data from the RTSIF dataset. The surface reflectance data in the red and near-infrared bands are from the MODIS MOD09A1 V6 product (available at https://lpdaac.usgs.gov/products/mod09a1v006/ ), with a spatiotemporal resolution of 500 meters and 8 days. This data is corrected for atmospheric conditions such as gases, aerosols, and Rayleigh scattering. 3.1.3 Drought index data Drought conditions are generally described using drought indices, which mainly include the Standardized Precipitation Evapotranspiration Index (SPEI), the Palmer Drought Severity Index (PDSI), the Standardized Precipitation Index (SPI), the Vegetation Condition Index (VCI), and the Temperature Vegetation Dryness Index (TVDI)(Ji and Peters 2003 ; Gu et al. 2007 ; Rhee et al. 2010 ; Anderson et al. 2011 ). Compared to other drought indices, SPEI takes into account precipitation, temperature, and evapotranspiration, combining the advantages of SPI and PDSI to calculate soil moisture balance(Lewis et al. 2011 ). Due to its real-time nature, SPEI is widely used in drought monitoring and early warning research (Zhao et al. 2017 ). Additionally, SPEI includes classifications based on different timescales to reflect various types of droughts: SPEI01, SPEI02, SPEI03, and SPEI06 typically represent meteorological drought, hydrological drought, agricultural drought, and socio-economic drought, respectively. However, existing SPEI datasets have limitations such as small coverage areas, spatial discontinuities, or low spatial resolution. (Xia et al. 2024 ), using SPEI calculated from meteorological stations, combined GPM precipitation, MODIS land surface temperature (LST), ERA5 land surface shortwave radiation, SRTM digital elevation model (DEM) data, and a random forest regression model to develop a high-precision machine learning algorithm. This algorithm produced a high spatial resolution (1 km) SPEI_RF grid dataset covering multiple timescales (1 month, 3 months, 6 months, 12 months, 24 months) for mainland China from 2001 to 2020 (available at https://www.scidb.cn/en/detail?dataSetId=968592537239420928 ). This dataset exhibits highly consistent spatiotemporal distribution characteristics with the widely recognized SPEIbase v.2.6 dataset (developed by Vicente Serrano). Compared to the SPEIbase v.2.6 dataset, the SPEI_RF dataset offers higher spatial resolution and more accurate identification of local small-scale droughts. Based on this, this study uses the monthly 1 km Standardized Precipitation Evapotranspiration Index (SPEI03) from the SPEI_RF dataset to characterize the spatiotemporal patterns of drought conditions in the study area in 2013. To minimize errors due to mismatched spatiotemporal resolutions, all data were resampled to 5 km to ensure consistency with the SIF product's spatiotemporal resolution. Daily PPT and PAR data were aggregated into 8-day intervals. Hourly soil moisture (SM) data from three layers were depth-weighted and averaged to create daily data, which were then aggregated into pentad (5-day) averages. The pentad average soil moisture data series from 2000 to 2023 were converted to percentiles (Yuan et al. 2023 ; Lu et al. 2024 ) to identify flash drought events. Additionally, the daily soil moisture (SM) data during the 2013 flash drought were aggregated into 8-day intervals to match the SIF spatiotemporal resolution and provide information on soil moisture changes during the flash drought period. 3.2 Methods 3.2.1 Flash drought definition Compared to traditional slow-onset droughts, flash droughts develop rapidly and are difficult to predict. Under the continuous influence of extreme weather, they can form and intensify to their maximum strength within a few weeks, significantly impacting local ecosystems. A rapid decline in soil moisture is the most direct manifestation of flash drought events. According to the U.S. Drought Monitor, a soil moisture percentile of 20% represents the onset of moderate drought, while 40% indicates soil that is drier than normal but not in drought. (Yuan et al. 2019 ) considered the processes of flash drought onset and termination based on the rate of soil moisture (SM) decline and the duration of low SM. They concluded that for a flash drought to form, the SM percentile must drop from 40–20% within 20 to 30 days, with an average pentad (5-day) decline rate of no less than 5%, and last for at least 15 days. A flash drought stops when the percentile rises above 20%. (Yao et al. 2022 ) suggested that the longer the drought duration, the greater the impact on vegetation ecosystems. To better highlight the effects of flash drought events on vegetation ecosystems, we should consider not only the rate and intensity of flash drought development but also the total duration (TD, The time when the SM percentile begins to decline to recover above 20%), If the duration is too short, the drought may not last long enough to negatively impact the ecosystem. Based on this, they defined a flash drought event as a pentad average SM percentile dropping from 40–20% and lasting for at least 20 days, using TD to describe the full profile of the flash drought event. The starting point is defined as the SM percentile being above 40% and then rapidly dropping below 20%, with a pentad decline rate higher than 5%. To accurately reveal the stress Response Regularity of vegetation during flash drought events, this study adopts the flash drought definition method by Yao et al.2022 to identify flash drought events. 3.2.2 SIF decomposition The response of SIF to drought is determined by both canopy structure and physiological changes, and it is highly sensitive to Photosynthetically Active Radiation (PAR). Flash drought events are often accompanied by a significant increase in solar radiation, which leads to higher PAR. The physiological signal \(\:{\:{\upvarphi\:}}_{\text{F}}\:\) of SIF elucidates the effects of solar radiation and canopy structure on SIF and can be used to indicate the effective fluorescence yield and photosynthetic efficiency of vegetation. Calculating \(\:{{\upvarphi\:}}_{\text{F}\:}\) can enhance the understanding of SIF dynamics. Based on this, this study applies an advanced framework to interpret the SIF signal(Zeng et al. 2020 ; Dechant et al. 2020 ), which decomposes the SIF signal into non-physiological and physiological signals, with the specific formula as follows: $$\:\begin{array}{c}SIF=PAR\bullet\:fPAR\bullet\:{\varphi\:}_{F}\bullet\:{f}_{esc}\left(1\right)\end{array}$$ In the formula, the first term, PAR, represents Photosynthetically Active Radiation, which provides energy for vegetation photosynthesis. The second term, \(\:\text{f}\text{P}\text{A}\text{R}\) , is the fraction of absorbed PAR, which is the proportion of PAR absorbed by the crop canopy out of all the PAR it receives. The third term, \(\:{{\upvarphi\:}}_{\text{F}}\) , represents the physiological signal of SIF. The final term, \(\:{\text{f}}_{\text{e}\text{s}\text{c}}\) , is the escape probability, which is the probability that photons re-emitted by PSII reach the sensor. The non-physiological components of SIF, i.e., PAR, \(\:\text{f}\text{P}\text{A}\text{R}\) , and \(\:{\text{f}}_{\text{e}\text{s}\text{c}}\) , can be approximated by NIRvP (non-physiological signal of SIF), as shown in Eq. (2): $$\:\begin{array}{c}NIRvP\approx\:PAR\bullet\:fPAR\bullet\:{f}_{esc}\left(2\right)\end{array}$$ Although SIF has very stringent spectral retrieval requirements, NIRvP can be calculated using only the Normalized Difference Vegetation Index (NDVI), near-infrared surface reflectance of vegetation ( \(\:{{\rho\:}}_{\text{N}\text{I}\text{R}}\) ), and Photosynthetically Active Radiation (PAR). The calculation formula is as follows: $$\:\begin{array}{c}NDVI=\frac{（{\rho\:}_{NIR}-{\rho\:}_{Red}）}{（{\rho\:}_{NIR}+{\rho\:}_{Red}）}\left(3\right)\end{array}$$ $$\:\begin{array}{c}NIRv={\rho\:}_{NIR}\bullet\:NDVI\left(4\right)\end{array}$$ $$\:\begin{array}{c}NIRvP=NIRv\bullet\:PAR\left(5\right)\end{array}$$ NIRv is an approximation of the near-infrared surface reflectance of vegetation. Finally, the SIF physiological signal fluorescence yield \(\:{\:{\upvarphi\:}}_{\text{F}}\:\) is estimated using the ratio of SIF to NIRvP(Dechant et al. 2020 ; Zeng et al. 2022 ): $$\:\begin{array}{c}{\varphi\:}_{F}=\frac{SIF}{NIRvP}\left(6\right)\end{array}$$ 3.2.3 Standardized anomaly To study the changes in various variables during flash drought, this paper calculates standardized anomalies. The standardized anomaly is calculated as the deviation of the current year's value from the multi-year average, and then standardized using the standard deviation. The formula is shown in (7): $$\:\begin{array}{c}{var}^{*}\left(i,t\right)=\frac{var\left(i,t\right)-\stackrel{-}{var}\left(i,t\right)}{\sigma\:\left(var\left(i,t\right)\right)}\left(7\right)\end{array}$$ In the formula, \(\:{\text{v}\text{a}\text{r}}^{\text{*}}\left(\text{i},\text{t}\right)\) is the standardized anomaly of 𝑖 at time 𝑡, \(\:\text{v}\text{a}\text{r}\left(\text{i},\text{t}\right)\) is the original value of 𝑖 at time 𝑡, \(\:\stackrel{-}{\text{v}\text{a}\text{r}}\left(\text{i},\text{t}\right)\) is the multi-year average value of 𝑖 at time 𝑡, and \(\:{\sigma\:}\left(\text{v}\text{a}\text{r}\left(\text{i},\text{t}\right)\right)\) is the multi-year standard deviation of 𝑖 at time 𝑡. 4 Results 4.1 Evolution Characteristics of Flash Droughts in Different Vegetation Types According to the definition of flash drought used in this study, the characteristics of flash drought (total duration and frequency) from 2000 to 2023 in cropland, forest, and grassland areas of the middle and lower reaches of the Yangtze River Basin were detected, as shown in Fig. 2 and Fig. 3 . In the middle and lower reaches of the Yangtze River Basin, cropland, forest, and grassland areas experienced at least one flash drought event per year on average from 2000 to 2023, with the annual total duration of flash drought generally exceeding 30 days(Fig. 3 ). Additionally, from 2000 to 2023, the frequency of flash drought in cropland, forest, and grassland areas of the middle and lower reaches of the Yangtze River Basin showed a non-significant decreasing trend, while the total duration of flash drought showed a slight increasing trend(Fig. 2 ). The statistical results in Fig. 3 a indicate that forests experienced the highest number of flash drought events, followed by grasslands, and croplands had the fewest. The total duration of flash drought in cropland was similar to that in forests, both slightly lower than in grasslands(Fig. 3 b). Moreover, Fig. 2 b shows that the longest average total duration of flash drought in cropland, forest, and grassland areas was detected in 2013, indicating the most severe drought conditions. Therefore, this study selects the flash drought event that occurred in 2013 to further investigate the Response Regularity of photosynthesis in different vegetation types to flash drought events. 4.2 Climate Conditions and Flash Drought Detection in 2013 During the summer growing season of 2013 (June to September), the middle and lower reaches of the Yangtze River Basin experienced extreme high temperatures exceeding the long-term average, accompanied by below-normal precipitation levels, as shown in Fig. 4 . In June 2013, surface temperatures were generally similar to the long-term average. However, from July to August, surface temperatures in the middle and lower reaches of the Yangtze River Basin were above the long-term average for two consecutive months, followed by temperatures below the average in September(Fig. 4 c). Precipitation remained below average for three consecutive months from June to August, with a brief recovery in September(Fig. 4 d). Figure 5 illustrates that in June, most areas of the middle and lower reaches of the Yangtze River Basin had hydro-meteorological elements that were generally normal. Starting from July, temperatures increased, precipitation decreased, soil moisture declined, and VPD (Vapor Pressure Deficit) rose across most areas. This situation persisted into August, when almost the entire region was affected by extreme weather conditions. The SPEI (Standardized Precipitation Evapotranspiration Index) indicated drought conditions across nearly the entire area. By September, environmental conditions in most areas had improved, gradually returning to normal. Due to the occurrence and persistence of extreme weather, soil moisture in the middle and lower reaches of the Yangtze River Basin rapidly declined in a very short time. As shown in Fig. 6 , the soil moisture percentile of cropland in the middle and lower reaches of the Yangtze River Basin rapidly declined from 52% starting on July 10, dropping to 12% within one pentad, with an average pentad decrease rate of 40% per pentad, marking the onset of the flash drought in cropland. Although the soil moisture percentile slightly increased afterwards, it remained below 20%. From August 5 to August 20, the soil moisture percentile remained at its lowest value of 4%, indicating severe drought. Subsequently, due to a decrease in temperature and an increase in precipitation, the environment improved. The soil moisture percentile of cropland began to rise from August 20, recovering to 21% by September 5, after three pentads, and then remained above 20% for a long time, indicating the end of the flash drought in cropland. The soil moisture percentiles of forests and grasslands began to decline rapidly from June 30, dropping from 68% and 60–12% and 16%, respectively, within two pentads, with average pentad decrease rates of 28% and 22% per pentad, respectively, marking the onset of the flash drought in forests and grasslands. Although the soil moisture percentiles slightly increased afterwards, they remained below 20%. From August 10 to August 15, the soil moisture percentiles remained at their lowest value of 4%, indicating the most severe drought. Then, from August 15, the soil moisture percentiles began to rise, recovering to 32% and 21%, respectively, by August 31, after three pentads, marking the end of the flash drought in forests and grasslands. According to the definition of the total duration of flash drought used in this study, the total duration of the flash drought in cropland was 57 days, from July 10 to September 5, and the total duration of the flash drought in forests and grasslands was 62 days, from June 30 to August 31. Additionally, Fig. 7 shows that the SPEI drought index also detected drought in the middle and lower reaches of the Yangtze River Basin during July and August. 4.3 Response of photosynthesis of different vegetation to flash drought This paper analyzes the hydrometeorological conditions of different vegetation types (cropland, forest, grassland) in the middle and lower reaches of the Yangtze River Basin from June 18 to September 14, as shown in Fig. 8 . The average temperature (LST) during this period follows the order: cropland > forest > grassland(Fig. 8 a). The average precipitation (PPT) from June 18 to August 5 remains cropland > grassland > forest, and from August 5 to September 14, it changes to forest > grassland > cropland(Fig. 8 b). The average photosynthetically active radiation (PAR) from June 18 to September 14 generally remains cropland > forest > grassland(Fig. 8 c). The average soil moisture (SM) during this period follows the order: forest > grassland > cropland(Fig. 8 d). Additionally, the soil moisture in cropland shows a brief increase followed by a rapid decrease before the onset of flash drought in forest and grassland, while the soil moisture in forest and grassland continuously decreases during this time. The trends and standardized anomalies of SIF and \(\:{\varphi\:}_{F}\) for different vegetation types (cropland, forest, grassland) in the middle and lower reaches of the Yangtze River from June 18 to September 14 are shown in Fig. S1 and Fig.S2. The results of the standardized anomalies indicate that cropland SIF showed its first negative anomaly on the 26th day (August 5) after the start of the flash drought (July 10). The negative anomaly reached its maximum on the 8th day (August 13) of the sustained minimum soil moisture percentile and then began to recover, returning to a positive anomaly on the 9th day (September 14) after the end of the flash drought (September 5)(Fig. S1 d). For forest and grassland, SIF first showed a negative anomaly on the 36th day (August 5) after the start of the flash drought (June 30). The negative anomaly reached its maximum on the 3rd day (August 13) of the sustained minimum soil moisture percentile and then began to recover, returning to a positive anomaly on the 14th day (September 14) after the end of the flash drought (August 31) (Fig. S1 e and f). The \(\:{\varphi\:}_{F}\) for cropland, forest, and grassland showed negative anomalies before the start of their respective flash droughts and continued until August 29, when it returned to positive anomalies, nearly spanning the entire flash drought event(Fig.S2d,e and f). The long-term trend results show that cropland SIF from June 18 to September 14 exhibited a trend of first increasing and then decreasing. In 2013, due to the impact of the flash drought, SIF showed a stress response with an early decline on the 10th day (July 20) after the start of the flash drought (July 10), which was 16 days earlier than the normal year's decline time (August 5) (Fig. S1 a). The trends for forest and grassland were similar, both showing an initial increase followed by a decrease from June 18 to September 14. Due to the impact of the flash drought, SIF in 2013 showed a stress response with a rapid decline on the 20th day (July 20) after the start of the flash drought (June 30), with a decline rate exceeding the average of previous years(Fig. S1 b and c). Cropland, forest, and grassland \(\:{\varphi\:}_{F}\) from June 18 to September 14 exhibited large fluctuations and was not as significant in its trend changes as SIF(Fig.S2a,b and c). 5 Discussion The analysis results of this study indicate that from 2000 to 2023, the occurrence frequency of flash droughts in cropland, forest, and grassland regions in the middle and lower reaches of the Yangtze River Basin all show a non-significant decreasing trend. This observation is corroborated by the findings of Xiong et al. (2023), which also confirm this viewpoint. However, the total duration of flash droughts shows a slight increasing trend. It is generally understood that longer drought durations lead to greater damage to local vegetation ecosystems. Despite the decreasing trend in occurrence frequency of flash droughts across different vegetation types, their total durations are gradually increasing. Therefore, the impact of flash droughts on the vegetation ecosystems of the middle and lower reaches of the Yangtze River Basin cannot be overlooked. Moreover, forests in the middle and lower reaches of the Yangtze River Basin experience the highest frequency of flash droughts, followed by grasslands, with croplands experiencing the fewest occurrences. However, the total duration of flash droughts in croplands is similar to that in forests and slightly less than in grasslands. Therefore, special attention needs to be paid to flash drought prevention in forest ecosystems. Research indicates that vegetation with shallow root systems is more susceptible to drought impacts. Thus, despite the lower frequency of flash droughts in grasslands and croplands compared to forests, their durations of occurrence are similar to forests and are more susceptible to drought impacts. Therefore, while emphasizing forest flash drought prevention, we must also not neglect the prevention and control of flash droughts in croplands and grasslands. The Response Regularity of photosynthesis to flash droughts exhibit significant variation across different vegetation types. For cropland SIF, it took only 10 days from the onset of the flash drought (July 10) to the initiation of response (July 20). It then took until the 26th day (August 5) to show a negative anomaly, peaking with the maximum negative anomaly on August 13 during the height of the flash drought. Subsequently, it returned to a positive anomaly by the 9th day (September 14) after the flash drought ended (September 5). Forest and grassland SIF starting from the onset of the flash drought (June 30) to the onset of response (July 20), required 20 days. By the 36th day (August 5) they exhibited a negative anomaly, reaching its peak negative anomaly on August 13 during the peak of the flash drought. They then recovered to a positive anomaly by the 14th day (September 14) after the flash drought ended (August 31). Since \(\:{\:\varphi\:}_{F}\:\) primarily reflects vegetation physiological information and is related to the physiological status of the plants, while SIF is influenced by both structural and physiological factors, studies indicate that \(\:\:{\varphi\:}_{F}\:\) is more sensitive to temperature compared to SIF (Kimm et al. 2021 ). Therefore, during the early stages of a flash drought, when temperatures sharply rise, \(\:{\varphi\:}_{F\:}\) for cropland, forest, and grassland quickly exhibits negative anomalies. These anomalies tend to recover to positive anomalies soon after meteorological conditions slightly improve, even before the flash drought ends (around August 29). The current retrieved SIF remote sensing data has limited spatial and temporal resolution, constraining the precise detection of terrestrial vegetation photosynthesis. Future efforts should focus on progressively enhancing SIF data accuracy to improve the observation of land vegetation ecosystems. 6 Conclusion This study, using the middle and lower reaches of the Yangtze River Basin as a case study, analyzed the spatiotemporal evolution characteristics of flash droughts in different vegetation types in the humid southern regions of China. Simultaneously, it further explored the Response Regularity of photosynthesis of different vegetation types to flash drought events. The results indicate that from 2000 to 2023, the frequency of flash droughts in croplands, forests, and grasslands in the middle and lower reaches of the Yangtze River Basin shows a non-significant decreasing trend, while the total duration of flash droughts shows a slight increasing trend. Each vegetation type experiences at least one flash drought event annually, with the average annual total duration of flash droughts generally exceeding 30 days. Forests experience the highest frequency of flash drought outbreaks, followed by grasslands, with croplands experiencing the fewest. However, the total duration of flash droughts in croplands is similar to that in forests and slightly less than that in grasslands. There are significant differences in the photosynthetic responses of different vegetation types to flash drought events; specifically, croplands exhibit the highest sensitivity, followed by forests and grasslands. Cropland, forest, and grassland ФF exhibit negative anomaly responses right at the beginning of flash drought events. Cropland SIF shows a response within the first 10 days after the onset of the flash drought, reaching a negative anomaly by the 26th day. Forest and grassland SIF exhibit responses by the 20th day after the onset of the flash drought and reach negative anomalies by the 36th day. Declarations Data Availability Data will be made available on request. Funding We would like to express our sincere thanks to all data supporters and websites. Meanwhile, the authors thank the project of Remote Sensing Data and Related Parameters Processing in Southwest China (612106241). Thank you for the Special research Assistant project, Chinese Academy of Sciences. Ethics declarations Compliance with ethical standards Informed consent. 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Clim Dyn. https://doi.org/10.1007/s00382-024-07266-3 Supplementary Files SupplementaryInformation.docx Cite Share Download PDF Status: Published Journal Publication published 26 Feb, 2025 Read the published version in International Journal of Biometeorology → Version 1 posted Reviewers agreed at journal 01 Sep, 2024 Reviewers invited by journal 27 Aug, 2024 Editor assigned by journal 26 Aug, 2024 First submitted to journal 24 Aug, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4961403","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":346025348,"identity":"2b45fecd-b5a9-44ad-8ca1-ae01d632a7c4","order_by":0,"name":"Yunjun Zhan","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Yunjun","middleName":"","lastName":"Zhan","suffix":""},{"id":346025349,"identity":"67c0d9a2-2275-4f31-bceb-2ca034a1bbce","order_by":1,"name":"Chuanqi Ma","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Chuanqi","middleName":"","lastName":"Ma","suffix":""},{"id":346025350,"identity":"40c9f113-0ec1-4b99-a685-bbf345c9c63d","order_by":2,"name":"Yan Yan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAt0lEQVRIiWNgGAWjYLCCDyTrYJxBshZmHpKUG9xIfvbYdo+dHD//AcYPPxjs8ojQkmZunPMs2VhyRgKzZA9DcjERWhLMpHMOHEjccIOBQZqB4UBiA2Et6d+kLQ4cqN9//gDzbyK15JhJMxw4kGDAkMBGnC2SZ96USfYcSDaccSOxzbLHIJmwFr7j6dskfhywk+fvP3z4xo8KO8JaFC4kwJiMQMUGhNQDgXz/ASJUjYJRMApGwcgGAIyCPLIq6lM7AAAAAElFTkSuQmCC","orcid":"","institution":"RCEES: Research Centre for Eco-Environmental Sciences Chinese Academy of Sciences","correspondingAuthor":true,"prefix":"","firstName":"Yan","middleName":"","lastName":"Yan","suffix":""},{"id":346025351,"identity":"f1aec407-61f6-4b2c-93f2-b02e64de812f","order_by":3,"name":"Yongsi Luo","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Yongsi","middleName":"","lastName":"Luo","suffix":""},{"id":346025352,"identity":"739008c1-9270-44a5-a7f9-04330e1345fb","order_by":4,"name":"Xueting Wang","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Xueting","middleName":"","lastName":"Wang","suffix":""},{"id":346025353,"identity":"dbf8f8ea-6619-4673-bcf8-be029089a365","order_by":5,"name":"Senrong Wang","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Senrong","middleName":"","lastName":"Wang","suffix":""},{"id":346025354,"identity":"021589a9-9e9a-4169-8b5b-e3709d8e4f7b","order_by":6,"name":"Yuejing Rong","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Yuejing","middleName":"","lastName":"Rong","suffix":""}],"badges":[],"createdAt":"2024-08-23 04:33:54","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4961403/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4961403/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00484-025-02878-8","type":"published","date":"2025-02-26T15:56:57+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":66867119,"identity":"c7f6c573-a5a6-4e87-93f2-5b06f9fe8e58","added_by":"auto","created_at":"2024-10-17 09:15:16","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":286974,"visible":true,"origin":"","legend":"\u003cp\u003eLand use types and distribution of rivers and lakes in the middle and lower reaches of the Yangtze River Basin\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4961403/v1/2e5933c51973fb3de29e6fc2.png"},{"id":66867122,"identity":"a94fbf68-0b2d-4b7b-801e-b7978f18f139","added_by":"auto","created_at":"2024-10-17 09:15:16","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":314625,"visible":true,"origin":"","legend":"\u003cp\u003eFlash drought (a) total degree and (b) total duration trend for different vegetation types from 2000 to 2023 (CROP: Cropland; FOR: Forest; GRA: Grassland)\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4961403/v1/b69475ea61eb3b2cc8423d4b.png"},{"id":66867847,"identity":"13749e67-7ab9-4f46-ae37-d196bbb8d2f2","added_by":"auto","created_at":"2024-10-17 09:23:16","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":34583,"visible":true,"origin":"","legend":"\u003cp\u003eBox plot of flash drought (a) total degree and (b) total duration for different vegetation types from 2000 to 2023 (CROP: Cropland; FOR: Forest; GRA: Grassland)\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4961403/v1/dc00902b334c331b973f49b3.png"},{"id":66867124,"identity":"ac957046-8785-4c2a-80b1-90841b09d70f","added_by":"auto","created_at":"2024-10-17 09:15:16","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":409047,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Land surface temperature, (b) Precipitation, (c) Land surface temperature standardized anomaly and (d) Precipitation standardized anomaly in the middle and lower reaches of the Yangtze River in 2013 (red curve represents multi-year average Land surface temperature and Precipitation, black curve represents 2013 Land surface temperature and Precipitation)\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4961403/v1/b371b3f65a906f62834e63ca.png"},{"id":66867126,"identity":"73aea9c7-b4c7-4a18-a021-06c9c2b41844","added_by":"auto","created_at":"2024-10-17 09:15:16","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":377063,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial distribution of Land surface temperature, Precipitation, Vapor pressure deficit, Soil moisture, and SPEI anomalies in the middle and lower reaches of the Yangtze River Basin from June to September 2013\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-4961403/v1/a13cad5ecf34c58f0492441e.png"},{"id":66867121,"identity":"c747719a-c8a2-4873-9183-835c54b9db91","added_by":"auto","created_at":"2024-10-17 09:15:16","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":199933,"visible":true,"origin":"","legend":"\u003cp\u003eDepicts the pentad-average soil moisture changes across different vegetation types in the middle and lower Reaches of the Yangtze River Basin in 2013, along with the identification of flash drought events (red shaded areas indicate the total duration of flash droughts; CROP: Cropland; FOR: Forest; GRA: Grassland)\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-4961403/v1/dbc3437adb67b510aadbc60a.png"},{"id":66867120,"identity":"7823ebf4-4eef-491b-b3c3-94dd57dfc540","added_by":"auto","created_at":"2024-10-17 09:15:16","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":12966,"visible":true,"origin":"","legend":"\u003cp\u003eMonthly mean value of SPEI03 in the middle and lower reaches of the Yangtze River basin in 2013\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-4961403/v1/927bedf35c1fa41e210b2ab4.png"},{"id":66867848,"identity":"54adfed3-eb02-4f21-801c-734ce3ef03e1","added_by":"auto","created_at":"2024-10-17 09:23:16","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":668228,"visible":true,"origin":"","legend":"\u003cp\u003eChanges in hydrometeorological factors of different vegetation types during the flash drought in the middle and lower reaches of the Yangtze River in 2013: (a) Land surface temperature, (b) Precipitation, (c) Photosynthetically Active Radiation, (d) Soil Moisture (red lines indicate the duration of flash drought for cropland, blue lines indicate the duration of flash drought for forest/grassland)\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-4961403/v1/d77b5b140c6f6dc97dee1362.png"},{"id":77622300,"identity":"e38b4d94-9772-44ef-a04d-cc0321c35fd0","added_by":"auto","created_at":"2025-03-03 16:02:16","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2483094,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4961403/v1/36748734-aa97-4681-860b-284e2f96deec.pdf"},{"id":66867127,"identity":"ff0bdd34-1acb-42ce-aa89-08602e5b3446","added_by":"auto","created_at":"2024-10-17 09:15:16","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":706619,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryInformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-4961403/v1/5994228556e46b2e34abe47b.docx"}],"financialInterests":"","formattedTitle":"Study on the Characteristics of Flash Drought and the Response Regularity of Photosynthesis to Flash Drought in Different Vegetation Ecosystems in the Middle and Lower Reaches of the Yangtze River Basin.","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eTraditionally, drought has been defined as a slowly developing disaster, typically taking months or years to reach its maximum extent and intensity (Mishra and Singh \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Zhou et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2020\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). However, in recent years, the phenomenon of flash drought occurring frequently during the growing season has gradually entered researchers' focus. Unlike traditional slow-onset droughts, these flash drought can develop and reach maximum intensity within a few weeks, leaving insufficient time to mitigate their impacts. This results in severe damage to local vegetation ecosystems and significant economic losses within a short time period (Otkin et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Wang et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Mahto and Mishra \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Zhang and Yuan \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Zhang et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Yao et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; An et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). For example, the flash drought in the Midwestern United States in 2012 caused over \u003cspan\u003e$\u003c/span\u003e12\u0026nbsp;billion in economic losses (Hoerling et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), and the flash drought in the Northern Plains of the United States in 2017 destroyed major farmland crops, resulting in agricultural losses exceeding \u003cspan\u003e$\u003c/span\u003e2.6\u0026nbsp;billion(Gerken et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Additionally, there were the flash drought in Southern Africa in 2015/2016 (Yuan et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) and in Eastern Australia in 2017/2018 (Nguyen et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The severe impacts of these events on entire ecosystems demonstrate our limited understanding of how vegetation responds to flash drought. Therefore, in-depth exploration of the Response Regularity of photosynthesis in different vegetation types to flash drought is crucial for protecting vegetation ecosystems and the terrestrial carbon cycle(Zhu et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMany studies have shown that the humid regions of southern China are more prone to flash drought compared to northern regions, with longer duration and greater intensity(Wang et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). However, due to the late attention given to flash drought, most previous studies have primarily focused on the spatiotemporal evolution characteristics and driving factors of flash drought across the entire study area (Christian et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Qing et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Fu and Wang \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), with limited understanding of the spatiotemporal characteristics of flash drought for different vegetation types. Additionally, when assessing vegetation photosynthesis responses to flash drought, the time lag between the onset of flash drought and the occurrence of the first negative anomaly in vegetation ecological indicators is often used to identify vegetation response time(Yang et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Zhao et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Lu et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). A shorter response time indicates that vegetation is more susceptible to flash drought events. However, this approach only provides information on vegetation responses after the vitality of the vegetation ecosystem has already been compromised, neglecting early stress signals in vegetation. This has led to a limited understanding of vegetation photosynthesis responses to flash drought.\u003c/p\u003e \u003cp\u003eDrought affects the physiological processes of vegetation, especially photosynthesis. Vegetation indices (VIs) based on remote sensing reflectance, such as the Normalized Difference Vegetation Index (NDVI) and Solar-Induced Chlorophyll Fluorescence (SIF), have been widely used for monitoring vegetation dynamics (Huete et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Liu et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Tian et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Akanbi et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). However, a major drawback of VIs is their noticeable lag in response to drought, making them unable to timely reflect vegetation water stress. Solar radiation absorbed by chlorophyll molecules is either used for photosynthesis, dissipated as heat, or re-emitted as Solar-Induced Chlorophyll Fluorescence (SIF) (Porcar-Castell et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Therefore, SIF is directly related to vegetation photosynthesis. Additionally, SIF signals contain information on both physiological changes and canopy structure. Studies have shown that the near-infrared reflectance of vegetation(Badgley et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2017\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) and the product of Photosynthetically Active Radiation (NIRvP) can effectively explain structural and radiative information(Wu et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Baldocchi et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), while the physiological information in SIF is represented by fluorescence yield (ΦF), a variable not affected by canopy structure. (Dechant et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) proposed an effective method for retrieving ΦF from satellite sensors by standardizing SIF. Since the response of SIF to drought is determined by both canopy structure and physiological changes(Dechant et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), SIF can respond more quickly than VIs when vegetation is under drought stress. Additionally, many studies have used SIF to characterize vegetation photosynthesis and explore its response to flash drought(Yang et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Lu et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Therefore, this study also explores the responses of photosynthesis in different vegetation types to flash drought events using Solar-Induced Chlorophyll Fluorescence (SIF).\u003c/p\u003e \u003cp\u003eThe middle and lower reaches of the Yangtze River Basin are high-incidence areas for flash drought in the humid regions of southern China. In recent years, frequent flash drought events in this area have caused significant economic losses. However, the spatiotemporal evolution characteristics of flash drought for different vegetation types in this region remain unclear. Additionally, there is limited understanding of the Response Regularity of photosynthesis in different vegetation types to flash drought. Therefore, this study focuses on the middle and lower reaches of the Yangtze River Basin to analyze the spatiotemporal evolution characteristics of flash drought for different vegetation types from 2000 to 2023. Using the high-intensity flash drought event that occurred during the summer growing season (June to September) in 2013 as a case study, this research employs SIF and ΦF indices to analyze the Response Regularity of photosynthesis in different vegetation types to flash drought events. This analysis considers both early stress signals and negative anomalies after the vitality of vegetation has been compromised. The findings of this study contribute to a deeper understanding of the Response Regularity of vegetation photosynthesis to flash drought and provide a reference for developing effective flash drought management strategies.\u003c/p\u003e"},{"header":"2 Study area","content":"\u003cp\u003eThe middle and lower reaches of the Yangtze River Basin (29\u0026deg;57\u0026prime;\u0026ndash;31\u0026deg;48\u0026prime;N, 108\u0026deg;38\u0026prime;\u0026ndash;121\u0026deg;52\u0026prime;E), as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, include six provinces and one municipality: Hubei, Hunan, Jiangxi, Anhui, Jiangsu, Zhejiang, and Shanghai, covering an area of approximately 910,000 km\u0026sup2;, which accounts for 9.5% of the national land area. This region is located in a humid subtropical monsoon climate zone, with an average annual temperature of 16.8\u0026deg;C and an annual precipitation of 1309.3 mm. It is a key production base for grain, vegetable oil, and cotton in China, and also the area with the richest water resources in the country. Large freshwater lakes such as Dongting Lake, Poyang Lake, Tai Lake, and Chao Lake are concentrated in this region. The study area also includes several internationally important wetlands and national wetland nature reserves, making the middle and lower reaches of the Yangtze River Basin play a crucial role in the overall carbon cycle system. Under the background of climate change, the Yangtze River Basin is more prone to flash drought, which last longer and are more intense, severely impacting the local ecosystem and agricultural production. Therefore, it is urgent to study the characteristics of flash drought changes in this area and their impact on the vegetation ecosystem.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"3 Data and methods","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Data\u003c/h2\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e3.1.1 Meteorological and soil moisture data\u003c/h2\u003e \u003cp\u003eThe meteorological data used in this study primarily include temperature (LST), precipitation (PPT), photosynthetically active radiation (PAR), and vapor pressure deficit (VPD). The temperature data comes from the MOD11A2V6 product (available at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://lpdaac.usgs.gov/products/mod11a2v006/\u003c/span\u003e\u003cspan address=\"https://lpdaac.usgs.gov/products/mod11a2v006/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), which provides 8-day average land surface temperature (LST) with a spatial resolution of 1 km. Precipitation data is sourced from the CHIRPS dataset (available at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://chc.ucsb.edu/data/chirps\u003c/span\u003e\u003cspan address=\"https://chc.ucsb.edu/data/chirps\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), which records daily global rainfall from 1981 to the present. CHIRPS combines 0.05\u0026deg; resolution satellite imagery with in-situ station data to create gridded rainfall time series for trend analysis and seasonal drought monitoring. PAR data is obtained from the Global Land Surface Satellite (GLASS) product (available at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.glass.umd.edu/Download.html\u003c/span\u003e\u003cspan address=\"http://www.glass.umd.edu/Download.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), with a spatial resolution of 0.05\u0026deg; and a temporal resolution of 1 day. VPD data is sourced from China's first high-resolution atmospheric humidity index dataset (HiMIC-Monthly)( available at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://data.tpdc.ac.cn/zh-hans/data/6854ebb3-8a60-454a-8d43-4e6a8c0ebd5d\u003c/span\u003e\u003cspan address=\"https://data.tpdc.ac.cn/zh-hans/data/6854ebb3-8a60-454a-8d43-4e6a8c0ebd5d\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), provided by the National Tibetan Plateau Data Center, with a spatial resolution of 1 km and a temporal resolution of one month. Soil moisture (SM) data comes from the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5-Land dataset, the latest generation product created by the Copernicus Climate Change Service (available at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-land\u003c/span\u003e\u003cspan address=\"https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-land\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), with a spatial resolution of 0.1\u0026deg; and a temporal resolution of 1 hour. Compared to the ERA5 product, ERA5-Land is a reanalysis dataset that provides higher resolution data of various surface parameters over several decades. This reanalysis product combines global surface observation data with model data using physical models to create a globally consistent dataset. Unlike most other SM datasets derived from model outputs, remote sensing, or reanalysis datasets, the ERA5-Land product has higher accuracy. The ERA5-Land reanalysis product provides soil moisture data for four different soil layers: 0\u0026ndash;7 cm, 7\u0026ndash;28 cm, 28\u0026ndash;100 cm, and 100\u0026ndash;289 cm. Numerous studies have shown that soil moisture up to 1 m depth from the top can meet the water and nutrient absorption needs of most crop roots. Considering the need to identify flash drought events that have a certain impact on the ecosystem, this study uses soil moisture information from the top three layers of the ECMWF integrated forecast system, i.e., up to 100 cm depth (0\u0026ndash;7 cm, 7\u0026ndash;28 cm, 28\u0026ndash;100 cm) to describe flash drought events.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e3.1.2 SIF and vegetation surface reflectance data\u003c/h2\u003e \u003cp\u003eSIF is the fluorescence emitted in the 650\u0026ndash;800 nm wavelength range by chlorophyll a after absorbing photosynthetically active radiation (PAR). It is directly related to vegetation photosynthesis and represents a new method for monitoring vegetation photosynthesis and productivity(Guanter et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Frankenberg and Berry \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Mohammed et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Jonard et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Currently, existing SIF data primarily come from GOME-2, OCO-2, and TROPOMI. Each of these sources provides high-fidelity SIF data but has its own advantages and disadvantages. GOME-2 data have the longest recording time and continuous spatial coverage, but their spatiotemporal resolution is low, at 0.5\u0026deg; monthly. In contrast, OCO-2 data offer higher spatiotemporal resolution, providing SIF data at 1.3 km \u0026times; 2.25 km every 16 days, but the measurements are sparse with large gaps between tracks and the recording time is shorter. TROPOMI, launched in October 2017, addresses some of these issues by offering daily SIF data with a high spatiotemporal resolution of 0.02\u0026deg; and global coverage. However, due to its recent launch, the SIF data recording period is shorter. These challenges pose significant difficulties for long-term continuous research on vegetation ecosystems.(Chen et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), using the XGBoost machine learning model, reconstructed TROPOMI SIF (RTSIF) for the period 2001\u0026ndash;2020 under clear sky conditions. The primary inputs were MODIS surface reflectance data, land surface temperature and land type products, CERES reanalysis data, and C3/C4 vegetation coverage data. This dataset (available at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://data.tpdc.ac.cn/zh-hans/data/2b8ffbf4-90ac-4e3d-9ae4-a8be31ae93d4/\u003c/span\u003e\u003cspan address=\"https://data.tpdc.ac.cn/zh-hans/data/2b8ffbf4-90ac-4e3d-9ae4-a8be31ae93d4/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) provides 8-day SIF data at a 0.05\u0026deg; resolution. The RTSIF data were validated against TROPOMI SIF and tower-based SIF observations and compared with other satellite-derived SIF (GOME-2 SIF and OCO-2 SIF), proving its accuracy. RTSIF data have been widely used in vegetation drought monitoring, productivity assessment, and carbon cycle research (Peng and Karimi Sadaghiani \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Jacobson et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Miao et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Therefore, this study uses SIF data from the RTSIF dataset. The surface reflectance data in the red and near-infrared bands are from the MODIS MOD09A1 V6 product (available at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://lpdaac.usgs.gov/products/mod09a1v006/\u003c/span\u003e\u003cspan address=\"https://lpdaac.usgs.gov/products/mod09a1v006/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), with a spatiotemporal resolution of 500 meters and 8 days. This data is corrected for atmospheric conditions such as gases, aerosols, and Rayleigh scattering.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e3.1.3 Drought index data\u003c/h2\u003e \u003cp\u003eDrought conditions are generally described using drought indices, which mainly include the Standardized Precipitation Evapotranspiration Index (SPEI), the Palmer Drought Severity Index (PDSI), the Standardized Precipitation Index (SPI), the Vegetation Condition Index (VCI), and the Temperature Vegetation Dryness Index (TVDI)(Ji and Peters \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Gu et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Rhee et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Anderson et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Compared to other drought indices, SPEI takes into account precipitation, temperature, and evapotranspiration, combining the advantages of SPI and PDSI to calculate soil moisture balance(Lewis et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Due to its real-time nature, SPEI is widely used in drought monitoring and early warning research (Zhao et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Additionally, SPEI includes classifications based on different timescales to reflect various types of droughts: SPEI01, SPEI02, SPEI03, and SPEI06 typically represent meteorological drought, hydrological drought, agricultural drought, and socio-economic drought, respectively. However, existing SPEI datasets have limitations such as small coverage areas, spatial discontinuities, or low spatial resolution. (Xia et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), using SPEI calculated from meteorological stations, combined GPM precipitation, MODIS land surface temperature (LST), ERA5 land surface shortwave radiation, SRTM digital elevation model (DEM) data, and a random forest regression model to develop a high-precision machine learning algorithm. This algorithm produced a high spatial resolution (1 km) SPEI_RF grid dataset covering multiple timescales (1 month, 3 months, 6 months, 12 months, 24 months) for mainland China from 2001 to 2020 (available at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.scidb.cn/en/detail?dataSetId=968592537239420928\u003c/span\u003e\u003cspan address=\"https://www.scidb.cn/en/detail?dataSetId=968592537239420928\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). This dataset exhibits highly consistent spatiotemporal distribution characteristics with the widely recognized SPEIbase v.2.6 dataset (developed by Vicente Serrano). Compared to the SPEIbase v.2.6 dataset, the SPEI_RF dataset offers higher spatial resolution and more accurate identification of local small-scale droughts. Based on this, this study uses the monthly 1 km Standardized Precipitation Evapotranspiration Index (SPEI03) from the SPEI_RF dataset to characterize the spatiotemporal patterns of drought conditions in the study area in 2013.\u003c/p\u003e \u003cp\u003eTo minimize errors due to mismatched spatiotemporal resolutions, all data were resampled to 5 km to ensure consistency with the SIF product's spatiotemporal resolution. Daily PPT and PAR data were aggregated into 8-day intervals. Hourly soil moisture (SM) data from three layers were depth-weighted and averaged to create daily data, which were then aggregated into pentad (5-day) averages. The pentad average soil moisture data series from 2000 to 2023 were converted to percentiles (Yuan et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Lu et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) to identify flash drought events. Additionally, the daily soil moisture (SM) data during the 2013 flash drought were aggregated into 8-day intervals to match the SIF spatiotemporal resolution and provide information on soil moisture changes during the flash drought period.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Methods\u003c/h2\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e3.2.1 Flash drought definition\u003c/h2\u003e \u003cp\u003eCompared to traditional slow-onset droughts, flash droughts develop rapidly and are difficult to predict. Under the continuous influence of extreme weather, they can form and intensify to their maximum strength within a few weeks, significantly impacting local ecosystems. A rapid decline in soil moisture is the most direct manifestation of flash drought events. According to the U.S. Drought Monitor, a soil moisture percentile of 20% represents the onset of moderate drought, while 40% indicates soil that is drier than normal but not in drought. (Yuan et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) considered the processes of flash drought onset and termination based on the rate of soil moisture (SM) decline and the duration of low SM. They concluded that for a flash drought to form, the SM percentile must drop from 40\u0026ndash;20% within 20 to 30 days, with an average pentad (5-day) decline rate of no less than 5%, and last for at least 15 days. A flash drought stops when the percentile rises above 20%.\u003c/p\u003e \u003cp\u003e(Yao et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) suggested that the longer the drought duration, the greater the impact on vegetation ecosystems. To better highlight the effects of flash drought events on vegetation ecosystems, we should consider not only the rate and intensity of flash drought development but also the total duration (TD, The time when the SM percentile begins to decline to recover above 20%), If the duration is too short, the drought may not last long enough to negatively impact the ecosystem. Based on this, they defined a flash drought event as a pentad average SM percentile dropping from 40\u0026ndash;20% and lasting for at least 20 days, using TD to describe the full profile of the flash drought event. The starting point is defined as the SM percentile being above 40% and then rapidly dropping below 20%, with a pentad decline rate higher than 5%. To accurately reveal the stress Response Regularity of vegetation during flash drought events, this study adopts the flash drought definition method by Yao et al.2022 to identify flash drought events.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e3.2.2 SIF decomposition\u003c/h2\u003e \u003cp\u003eThe response of SIF to drought is determined by both canopy structure and physiological changes, and it is highly sensitive to Photosynthetically Active Radiation (PAR). Flash drought events are often accompanied by a significant increase in solar radiation, which leads to higher PAR. The physiological signal\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\:{\\upvarphi\\:}}_{\\text{F}}\\:\\)\u003c/span\u003e\u003c/span\u003eof SIF elucidates the effects of solar radiation and canopy structure on SIF and can be used to indicate the effective fluorescence yield and photosynthetic efficiency of vegetation. Calculating \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{{\\upvarphi\\:}}_{\\text{F}\\:}\\)\u003c/span\u003e\u003c/span\u003ecan enhance the understanding of SIF dynamics. Based on this, this study applies an advanced framework to interpret the SIF signal(Zeng et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Dechant et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), which decomposes the SIF signal into non-physiological and physiological signals, with the specific formula as follows:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:\\begin{array}{c}SIF=PAR\\bullet\\:fPAR\\bullet\\:{\\varphi\\:}_{F}\\bullet\\:{f}_{esc}\\left(1\\right)\\end{array}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eIn the formula, the first term, PAR, represents Photosynthetically Active Radiation, which provides energy for vegetation photosynthesis. The second term,\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{f}\\text{P}\\text{A}\\text{R}\\)\u003c/span\u003e\u003c/span\u003e, is the fraction of absorbed PAR, which is the proportion of PAR absorbed by the crop canopy out of all the PAR it receives. The third term,\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{{\\upvarphi\\:}}_{\\text{F}}\\)\u003c/span\u003e\u003c/span\u003e, represents the physiological signal of SIF. The final term,\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{f}}_{\\text{e}\\text{s}\\text{c}}\\)\u003c/span\u003e\u003c/span\u003e, is the escape probability, which is the probability that photons re-emitted by PSII reach the sensor. The non-physiological components of SIF, i.e., PAR,\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{f}\\text{P}\\text{A}\\text{R}\\)\u003c/span\u003e\u003c/span\u003e, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{f}}_{\\text{e}\\text{s}\\text{c}}\\)\u003c/span\u003e\u003c/span\u003e, can be approximated by NIRvP (non-physiological signal of SIF), as shown in Eq.\u0026nbsp;(2):\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:\\begin{array}{c}NIRvP\\approx\\:PAR\\bullet\\:fPAR\\bullet\\:{f}_{esc}\\left(2\\right)\\end{array}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eAlthough SIF has very stringent spectral retrieval requirements, NIRvP can be calculated using only the Normalized Difference Vegetation Index (NDVI), near-infrared surface reflectance of vegetation (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{{\\rho\\:}}_{\\text{N}\\text{I}\\text{R}}\\)\u003c/span\u003e\u003c/span\u003e), and Photosynthetically Active Radiation (PAR). The calculation formula is as follows:\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$\\:\\begin{array}{c}NDVI=\\frac{（{\\rho\\:}_{NIR}-{\\rho\\:}_{Red}）}{（{\\rho\\:}_{NIR}+{\\rho\\:}_{Red}）}\\left(3\\right)\\end{array}$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equd\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equd\" name=\"EquationSource\"\u003e\n$$\\:\\begin{array}{c}NIRv={\\rho\\:}_{NIR}\\bullet\\:NDVI\\left(4\\right)\\end{array}$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Eque\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Eque\" name=\"EquationSource\"\u003e\n$$\\:\\begin{array}{c}NIRvP=NIRv\\bullet\\:PAR\\left(5\\right)\\end{array}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eNIRv is an approximation of the near-infrared surface reflectance of vegetation. Finally, the SIF physiological signal fluorescence yield\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\:{\\upvarphi\\:}}_{\\text{F}}\\:\\)\u003c/span\u003e\u003c/span\u003eis estimated using the ratio of SIF to NIRvP(Dechant et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Zeng et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2022\u003c/span\u003e):\u003cdiv id=\"Equf\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equf\" name=\"EquationSource\"\u003e\n$$\\:\\begin{array}{c}{\\varphi\\:}_{F}=\\frac{SIF}{NIRvP}\\left(6\\right)\\end{array}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e3.2.3 Standardized anomaly\u003c/h2\u003e \u003cp\u003eTo study the changes in various variables during flash drought, this paper calculates standardized anomalies. The standardized anomaly is calculated as the deviation of the current year's value from the multi-year average, and then standardized using the standard deviation. The formula is shown in (7):\u003cdiv id=\"Equg\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equg\" name=\"EquationSource\"\u003e\n$$\\:\\begin{array}{c}{var}^{*}\\left(i,t\\right)=\\frac{var\\left(i,t\\right)-\\stackrel{-}{var}\\left(i,t\\right)}{\\sigma\\:\\left(var\\left(i,t\\right)\\right)}\\left(7\\right)\\end{array}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eIn the formula, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{v}\\text{a}\\text{r}}^{\\text{*}}\\left(\\text{i},\\text{t}\\right)\\)\u003c/span\u003e\u003c/span\u003e is the standardized anomaly of \u0026#119894; at time \u0026#119905;, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{v}\\text{a}\\text{r}\\left(\\text{i},\\text{t}\\right)\\)\u003c/span\u003e\u003c/span\u003e is the original value of \u0026#119894; at time \u0026#119905;, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\stackrel{-}{\\text{v}\\text{a}\\text{r}}\\left(\\text{i},\\text{t}\\right)\\)\u003c/span\u003e\u003c/span\u003e is the multi-year average value of \u0026#119894; at time \u0026#119905;, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\sigma\\:}\\left(\\text{v}\\text{a}\\text{r}\\left(\\text{i},\\text{t}\\right)\\right)\\)\u003c/span\u003e\u003c/span\u003e is the multi-year standard deviation of \u0026#119894; at time \u0026#119905;.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"4 Results","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Evolution Characteristics of Flash Droughts in Different Vegetation Types\u003c/h2\u003e \u003cp\u003eAccording to the definition of flash drought used in this study, the characteristics of flash drought (total duration and frequency) from 2000 to 2023 in cropland, forest, and grassland areas of the middle and lower reaches of the Yangtze River Basin were detected, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. In the middle and lower reaches of the Yangtze River Basin, cropland, forest, and grassland areas experienced at least one flash drought event per year on average from 2000 to 2023, with the annual total duration of flash drought generally exceeding 30 days(Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Additionally, from 2000 to 2023, the frequency of flash drought in cropland, forest, and grassland areas of the middle and lower reaches of the Yangtze River Basin showed a non-significant decreasing trend, while the total duration of flash drought showed a slight increasing trend(Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The statistical results in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea indicate that forests experienced the highest number of flash drought events, followed by grasslands, and croplands had the fewest. The total duration of flash drought in cropland was similar to that in forests, both slightly lower than in grasslands(Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb). Moreover, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb shows that the longest average total duration of flash drought in cropland, forest, and grassland areas was detected in 2013, indicating the most severe drought conditions. Therefore, this study selects the flash drought event that occurred in 2013 to further investigate the Response Regularity of photosynthesis in different vegetation types to flash drought events.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Climate Conditions and Flash Drought Detection in 2013\u003c/h2\u003e \u003cp\u003eDuring the summer growing season of 2013 (June to September), the middle and lower reaches of the Yangtze River Basin experienced extreme high temperatures exceeding the long-term average, accompanied by below-normal precipitation levels, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. In June 2013, surface temperatures were generally similar to the long-term average. However, from July to August, surface temperatures in the middle and lower reaches of the Yangtze River Basin were above the long-term average for two consecutive months, followed by temperatures below the average in September(Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec). Precipitation remained below average for three consecutive months from June to August, with a brief recovery in September(Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ed). Figure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e illustrates that in June, most areas of the middle and lower reaches of the Yangtze River Basin had hydro-meteorological elements that were generally normal. Starting from July, temperatures increased, precipitation decreased, soil moisture declined, and VPD (Vapor Pressure Deficit) rose across most areas. This situation persisted into August, when almost the entire region was affected by extreme weather conditions. The SPEI (Standardized Precipitation Evapotranspiration Index) indicated drought conditions across nearly the entire area. By September, environmental conditions in most areas had improved, gradually returning to normal.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eDue to the occurrence and persistence of extreme weather, soil moisture in the middle and lower reaches of the Yangtze River Basin rapidly declined in a very short time. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, the soil moisture percentile of cropland in the middle and lower reaches of the Yangtze River Basin rapidly declined from 52% starting on July 10, dropping to 12% within one pentad, with an average pentad decrease rate of 40% per pentad, marking the onset of the flash drought in cropland. Although the soil moisture percentile slightly increased afterwards, it remained below 20%. From August 5 to August 20, the soil moisture percentile remained at its lowest value of 4%, indicating severe drought. Subsequently, due to a decrease in temperature and an increase in precipitation, the environment improved. The soil moisture percentile of cropland began to rise from August 20, recovering to 21% by September 5, after three pentads, and then remained above 20% for a long time, indicating the end of the flash drought in cropland.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe soil moisture percentiles of forests and grasslands began to decline rapidly from June 30, dropping from 68% and 60\u0026ndash;12% and 16%, respectively, within two pentads, with average pentad decrease rates of 28% and 22% per pentad, respectively, marking the onset of the flash drought in forests and grasslands. Although the soil moisture percentiles slightly increased afterwards, they remained below 20%. From August 10 to August 15, the soil moisture percentiles remained at their lowest value of 4%, indicating the most severe drought. Then, from August 15, the soil moisture percentiles began to rise, recovering to 32% and 21%, respectively, by August 31, after three pentads, marking the end of the flash drought in forests and grasslands.\u003c/p\u003e \u003cp\u003eAccording to the definition of the total duration of flash drought used in this study, the total duration of the flash drought in cropland was 57 days, from July 10 to September 5, and the total duration of the flash drought in forests and grasslands was 62 days, from June 30 to August 31. Additionally, Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e shows that the SPEI drought index also detected drought in the middle and lower reaches of the Yangtze River Basin during July and August.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Response of photosynthesis of different vegetation to flash drought\u003c/h2\u003e \u003cp\u003eThis paper analyzes the hydrometeorological conditions of different vegetation types (cropland, forest, grassland) in the middle and lower reaches of the Yangtze River Basin from June 18 to September 14, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e. The average temperature (LST) during this period follows the order: cropland\u0026thinsp;\u0026gt;\u0026thinsp;forest\u0026thinsp;\u0026gt;\u0026thinsp;grassland(Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ea). The average precipitation (PPT) from June 18 to August 5 remains cropland\u0026thinsp;\u0026gt;\u0026thinsp;grassland\u0026thinsp;\u0026gt;\u0026thinsp;forest, and from August 5 to September 14, it changes to forest\u0026thinsp;\u0026gt;\u0026thinsp;grassland\u0026thinsp;\u0026gt;\u0026thinsp;cropland(Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eb). The average photosynthetically active radiation (PAR) from June 18 to September 14 generally remains cropland\u0026thinsp;\u0026gt;\u0026thinsp;forest\u0026thinsp;\u0026gt;\u0026thinsp;grassland(Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ec). The average soil moisture (SM) during this period follows the order: forest\u0026thinsp;\u0026gt;\u0026thinsp;grassland\u0026thinsp;\u0026gt;\u0026thinsp;cropland(Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ed). Additionally, the soil moisture in cropland shows a brief increase followed by a rapid decrease before the onset of flash drought in forest and grassland, while the soil moisture in forest and grassland continuously decreases during this time.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe trends and standardized anomalies of SIF and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\varphi\\:}_{F}\\)\u003c/span\u003e\u003c/span\u003e for different vegetation types (cropland, forest, grassland) in the middle and lower reaches of the Yangtze River from June 18 to September 14 are shown in Fig.\u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e and Fig.S2. The results of the standardized anomalies indicate that cropland SIF showed its first negative anomaly on the 26th day (August 5) after the start of the flash drought (July 10). The negative anomaly reached its maximum on the 8th day (August 13) of the sustained minimum soil moisture percentile and then began to recover, returning to a positive anomaly on the 9th day (September 14) after the end of the flash drought (September 5)(Fig.\u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003ed). For forest and grassland, SIF first showed a negative anomaly on the 36th day (August 5) after the start of the flash drought (June 30). The negative anomaly reached its maximum on the 3rd day (August 13) of the sustained minimum soil moisture percentile and then began to recover, returning to a positive anomaly on the 14th day (September 14) after the end of the flash drought (August 31) (Fig.\u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003ee and f). The \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\varphi\\:}_{F}\\)\u003c/span\u003e\u003c/span\u003e for cropland, forest, and grassland showed negative anomalies before the start of their respective flash droughts and continued until August 29, when it returned to positive anomalies, nearly spanning the entire flash drought event(Fig.S2d,e and f).\u003c/p\u003e \u003cp\u003eThe long-term trend results show that cropland SIF from June 18 to September 14 exhibited a trend of first increasing and then decreasing. In 2013, due to the impact of the flash drought, SIF showed a stress response with an early decline on the 10th day (July 20) after the start of the flash drought (July 10), which was 16 days earlier than the normal year's decline time (August 5) (Fig.\u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003ea). The trends for forest and grassland were similar, both showing an initial increase followed by a decrease from June 18 to September 14. Due to the impact of the flash drought, SIF in 2013 showed a stress response with a rapid decline on the 20th day (July 20) after the start of the flash drought (June 30), with a decline rate exceeding the average of previous years(Fig.\u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eb and c). Cropland, forest, and grassland \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\varphi\\:}_{F}\\)\u003c/span\u003e\u003c/span\u003e from June 18 to September 14 exhibited large fluctuations and was not as significant in its trend changes as SIF(Fig.S2a,b and c).\u003c/p\u003e \u003c/div\u003e"},{"header":"5 Discussion","content":"\u003cp\u003eThe analysis results of this study indicate that from 2000 to 2023, the occurrence frequency of flash droughts in cropland, forest, and grassland regions in the middle and lower reaches of the Yangtze River Basin all show a non-significant decreasing trend. This observation is corroborated by the findings of Xiong et al. (2023), which also confirm this viewpoint. However, the total duration of flash droughts shows a slight increasing trend. It is generally understood that longer drought durations lead to greater damage to local vegetation ecosystems. Despite the decreasing trend in occurrence frequency of flash droughts across different vegetation types, their total durations are gradually increasing. Therefore, the impact of flash droughts on the vegetation ecosystems of the middle and lower reaches of the Yangtze River Basin cannot be overlooked. Moreover, forests in the middle and lower reaches of the Yangtze River Basin experience the highest frequency of flash droughts, followed by grasslands, with croplands experiencing the fewest occurrences. However, the total duration of flash droughts in croplands is similar to that in forests and slightly less than in grasslands. Therefore, special attention needs to be paid to flash drought prevention in forest ecosystems. Research indicates that vegetation with shallow root systems is more susceptible to drought impacts. Thus, despite the lower frequency of flash droughts in grasslands and croplands compared to forests, their durations of occurrence are similar to forests and are more susceptible to drought impacts. Therefore, while emphasizing forest flash drought prevention, we must also not neglect the prevention and control of flash droughts in croplands and grasslands.\u003c/p\u003e \u003cp\u003eThe Response Regularity of photosynthesis to flash droughts exhibit significant variation across different vegetation types. For cropland SIF, it took only 10 days from the onset of the flash drought (July 10) to the initiation of response (July 20). It then took until the 26th day (August 5) to show a negative anomaly, peaking with the maximum negative anomaly on August 13 during the height of the flash drought. Subsequently, it returned to a positive anomaly by the 9th day (September 14) after the flash drought ended (September 5). Forest and grassland SIF starting from the onset of the flash drought (June 30) to the onset of response (July 20), required 20 days. By the 36th day (August 5) they exhibited a negative anomaly, reaching its peak negative anomaly on August 13 during the peak of the flash drought. They then recovered to a positive anomaly by the 14th day (September 14) after the flash drought ended (August 31). Since\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\:\\varphi\\:}_{F}\\:\\)\u003c/span\u003e\u003c/span\u003eprimarily reflects vegetation physiological information and is related to the physiological status of the plants, while SIF is influenced by both structural and physiological factors, studies indicate that\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\:{\\varphi\\:}_{F}\\:\\)\u003c/span\u003e\u003c/span\u003eis more sensitive to temperature compared to SIF (Kimm et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Therefore, during the early stages of a flash drought, when temperatures sharply rise, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\varphi\\:}_{F\\:}\\)\u003c/span\u003e\u003c/span\u003efor cropland, forest, and grassland quickly exhibits negative anomalies. These anomalies tend to recover to positive anomalies soon after meteorological conditions slightly improve, even before the flash drought ends (around August 29).\u003c/p\u003e \u003cp\u003eThe current retrieved SIF remote sensing data has limited spatial and temporal resolution, constraining the precise detection of terrestrial vegetation photosynthesis. Future efforts should focus on progressively enhancing SIF data accuracy to improve the observation of land vegetation ecosystems.\u003c/p\u003e"},{"header":"6 Conclusion","content":"\u003cp\u003eThis study, using the middle and lower reaches of the Yangtze River Basin as a case study, analyzed the spatiotemporal evolution characteristics of flash droughts in different vegetation types in the humid southern regions of China. Simultaneously, it further explored the Response Regularity of photosynthesis of different vegetation types to flash drought events. The results indicate that from 2000 to 2023, the frequency of flash droughts in croplands, forests, and grasslands in the middle and lower reaches of the Yangtze River Basin shows a non-significant decreasing trend, while the total duration of flash droughts shows a slight increasing trend. Each vegetation type experiences at least one flash drought event annually, with the average annual total duration of flash droughts generally exceeding 30 days. Forests experience the highest frequency of flash drought outbreaks, followed by grasslands, with croplands experiencing the fewest. However, the total duration of flash droughts in croplands is similar to that in forests and slightly less than that in grasslands. There are significant differences in the photosynthetic responses of different vegetation types to flash drought events; specifically, croplands exhibit the highest sensitivity, followed by forests and grasslands. Cropland, forest, and grassland ФF exhibit negative anomaly responses right at the beginning of flash drought events. Cropland SIF shows a response within the first 10 days after the onset of the flash drought, reaching a negative anomaly by the 26th day. Forest and grassland SIF exhibit responses by the 20th day after the onset of the flash drought and reach negative anomalies by the 36th day.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData will be made available on request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to express our sincere thanks to all data supporters and websites. Meanwhile, the authors thank the project of Remote Sensing Data and Related Parameters Processing in Southwest China (612106241). Thank you for the\u0026nbsp;Special research Assistant project, Chinese Academy of Sciences.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompliance with ethical standards\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInformed consent.\u003c/p\u003e\n\u003cp\u003eAuthor information\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors and Affiliations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSchool of Resources and Environmental Engineering, Wuhan University of Technology, Wuhan, 430070, China\u003c/p\u003e\n\u003cp\u003eYunjun Zhan , Chuanqi Ma , Yongsi Luo , Xueting Wang \u0026amp; Senrong Wang\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eState Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing, 100089, China\u003c/p\u003e\n\u003cp\u003eYan Yan \u0026amp; Yuejing Rong\u003c/p\u003e\n\u003cp\u003eCorresponding author\u003c/p\u003e\n\u003cp\u003eCorrespondence to Yan Yan.\u003c/p\u003e\n\u003cp\u003e*Correspondence: State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing, 100089, China, e-mail: [email protected]\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAkanbi OD, Bhuvanagiri DC, Barcelos EI, et al (2024) Integrating Multiscale Geospatial Analysis for Monitoring Crop Growth, Nutrient Distribution, and Hydrological Dynamics in Large-Scale Agricultural Systems. 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Water Resources Research 57:e2021WR030028. https://doi.org/10.1029/2021WR030028\u003c/li\u003e\n\u003cli\u003eZhu Y, Yang P, Huang H, et al (2024) How is about the flash drought events and their impacts on vegetation in Central Asia. Clim Dyn. https://doi.org/10.1007/s00382-024-07266-3\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"international-journal-of-biometeorology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ijbm","sideBox":"Learn more about [International Journal of Biometeorology](http://link.springer.com/journal/484)","snPcode":"484","submissionUrl":"https://www.editorialmanager.com/ijbm/default2.aspx","title":"International Journal of Biometeorology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Flash drought, Soil moisture, Photosynthesis response of vegetation, Solar-induced chlorophyll fluorescence, Different vegetation","lastPublishedDoi":"10.21203/rs.3.rs-4961403/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4961403/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn 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. Our current understanding of the spatiotemporal evolution characteristics of flash droughts across different vegetation types remains limited. Moreover, insufficient consideration of the early stress of vegetation during flash droughts has constrained our understanding of the response regularity of vegetation photosynthesis to flash drought events. This study analyzes the spatial and temporal evolution characteristics of flash drought for different vegetation types in the middle and lower reaches of the Yangtze River Basin from 2000 to 2023. Using the flash drought event of 2013 as a case study, solar-induced chlorophyll fluorescence (SIF) and fluorescence yield (ΦF) were employed to further explore the Response Regularity of vegetation photosynthesis to flash drought. The results show that, over the past 24 years, the frequency of flash drought 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 a higher frequency of flash drought. Cropland photosynthesis is the most sensitive to flash drought, responding on the 10th day after the onset and reaching a negative anomaly by the 26th day. Forests and grasslands respond later, beginning on the 20th day and showing a negative anomaly by the 36th day. The findings of this study contribute to a deeper understanding of vegetation photosynthesis responses to flash drought and provide a reference for developing effective flash drought management strategies.\u003c/p\u003e","manuscriptTitle":"Study on the Characteristics of Flash Drought and the Response Regularity of Photosynthesis to Flash Drought in Different Vegetation Ecosystems in the Middle and Lower Reaches of the Yangtze River Basin.","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-17 09:15:11","doi":"10.21203/rs.3.rs-4961403/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2024-09-02T00:53:33+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-08-28T02:23:05+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-08-26T17:39:38+00:00","index":"","fulltext":""},{"type":"submitted","content":"International Journal of Biometeorology","date":"2024-08-24T06:57:43+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"international-journal-of-biometeorology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ijbm","sideBox":"Learn more about [International Journal of Biometeorology](http://link.springer.com/journal/484)","snPcode":"484","submissionUrl":"https://www.editorialmanager.com/ijbm/default2.aspx","title":"International Journal of Biometeorology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"23822753-d8dd-49d8-8d3d-711d615dfa8b","owner":[],"postedDate":"October 17th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-03-03T15:58:57+00:00","versionOfRecord":{"articleIdentity":"rs-4961403","link":"https://doi.org/10.1007/s00484-025-02878-8","journal":{"identity":"international-journal-of-biometeorology","isVorOnly":false,"title":"International Journal of Biometeorology"},"publishedOn":"2025-02-26 15:56:57","publishedOnDateReadable":"February 26th, 2025"},"versionCreatedAt":"2024-10-17 09:15:11","video":"","vorDoi":"10.1007/s00484-025-02878-8","vorDoiUrl":"https://doi.org/10.1007/s00484-025-02878-8","workflowStages":[]},"version":"v1","identity":"rs-4961403","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4961403","identity":"rs-4961403","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}