Onset Of Drought Research Articles

Global Prediction of Flash Drought Using Machine Learning

AbstractFlash droughts are rapidly developing extreme weather events with sudden onset and quick intensification. Global prediction of flash droughts at sub‐seasonal time scales remains a great challenge. Current state‐of‐the‐art dynamic models subject to large errors and demonstrate low skills in global flash drought prediction. Here, we develop a machine learning‐based framework that uses meteorological forecasts as inputs to predict global root‐zone soil moisture and flash droughts from 1 day to 2 week lead times. The results indicate that 33% and 24% global flash drought onset and termination events can be correctly predicted by machine learning at 7 day lead time, versus 19% and 11% fractions by state‐of‐the‐art dynamic model. The developed machine learning model demonstrates substantial improvements over dynamic model in global soil moisture prediction, and thus enhances global flash drought forecasting skills in space and time. The presented framework may benefit global flash drought prediction and early warning at sub‐seasonal scales.

Multivariate Evaluation of Flash Drought Across the United States

AbstractThis study uses the flash drought intensity index (FDII) to develop a multivariate flash drought climatology for the contiguous U.S. using data from 2001 to 2021. The FDII method uses the rate of intensification (FD‐INT) and subsequent drought severity (DRO‐SEV) to determine when a flash drought occurred and the strength of the event. Overall, the results showed that flash drought occurrence and severity varied with season and region and were sensitive to the drought indicator used to compute the FDII. Precipitation‐based indicators identified more flash droughts across the western U.S. whereas soil moisture (SM) and evapotranspiration indicators identified more flash droughts across the central and eastern U.S. When assessed over the entire U.S., the most flash droughts were found when using an evaporative demand indicator. Though FD‐INT was larger than DRO‐SEV across the U.S. for most indicators, regional patterns were also evident in their relative importance. For example, a distinct east‐west gradient was present in the SM and evapotranspiration FD‐INT, with relatively large values in the central and eastern U.S. A combined data set synthesizing information from multiple indicators showed that the strongest flash droughts from a multivariate perspective were located in the central and southeastern U.S. A seasonal analysis revealed a distinct seasonal cycle in flash drought onset across the western and central U.S. Together, the results illustrate the need to use a multivariate framework to identify and characterize the occurrence and severity of flash droughts.

Division Zone Activity Determines the Potential of Drought-Stressed Maize Leaves to Resume Growth after Rehydration.

Drought is one of the most devastating causes of yield losses in crops like maize, and the anticipated increases in severity and duration of drought spells due to climate change pose an imminent threat to agricultural productivity. To understand the drought response, phenotypic and molecular studies are typically performed at a given time point after drought onset, representing a steady-state adaptation response. Because growth is a dynamic process, we monitored the drought response with high temporal resolution and examined cellular and transcriptomic changes after rehydration at 4 and 6 days after leaf four appearance. These data showed that division zone activity is a determinant for full organ growth recovery upon rehydration. Moreover, a prolonged maintenance of cell division by the ectopic expression of PLASTOCHRON1 extends the ability to resume growth after rehydration. The transcriptome analysis indicated that GROWTH-REGULATING FACTORS (GRFs) affect leaf growth by impacting cell division duration, which was confirmed by a prolonged recovery potential of the GRF1-overexpression line after rehydration. Finally, we used a multiplex genome editing approach to evaluate the most promising differentially expressed genes from the transcriptome study and as such narrowed down the gene space from 40 to seven genes for future functional characterization.

Agricultural Drought Monitoring: A Comparative Review of Conventional and Satellite-Based Indices

Drought is a natural hazard that causes significant economic and human losses by creating a persistent lack of precipitation that impacts agriculture and hydrology. It has various characteristics, such as delayed effects and variability across dimensions like severity, spatial extent, and duration, making it difficult to characterize. The agricultural sector is especially susceptible to drought, which is a primary cause of crop failures and poses a significant threat to global food security. To address these risks, it is crucial to develop effective methods for identifying, classifying, and monitoring agricultural drought, thereby aiding in planning and mitigation efforts. Researchers have developed various tools, including agricultural drought indices, to quantify severity levels and determine the onset and evolution of droughts. These tools help in early-stage forecasting and ongoing monitoring of drought conditions. The field has been significantly advanced by remote sensing technology, which now offers high-resolution spatial and temporal data, improving our capacity to monitor and assess agricultural drought. Despite these technological advancements, the unpredictable nature of environmental conditions continues to pose challenges in drought assessment. It remains essential to provide an overview of agricultural drought indices, incorporating both conventional methods and modern remote sensing-based indices used in drought monitoring and assessment.

Contribution of land-atmosphere coupling in 2022 CONUS compound drought-heatwave events and implications for forecasting

Severe compound drought-heatwave events were observed over three regions of the Contiguous United States (CONUS), Northwest (NW), Great Plains (GP), and Northeast (NE) regions, during July and August 2022. In this study, we have found that the developments of these drought-heatwave events were shaped by different land-atmosphere coupling behaviors which are associated with water and energy limitation regimes in these regions. In the NW and GP regions, the surface soil moisture (SM) and evapotranspiration (ET) were coupled through water-limited processes. Heatwaves in these two regions were affected by the decrease of ET and the available SM due to the precipitation deficit. This type of land-atmosphere coupling was especially prominent in the GP. In the NE region, the heatwave governed ET through the increase of potential ET (PET) based on energy-limited coupling, which played a crucial role in the development of drought.The impacts of the different land-atmosphere coupling behaviors on the predictability of the 13-km Geophysical Fluid Dynamics Laboratory (GFDL) System for High-resolution prediction on Earth-to-Local Domains (SHiELD) were also investigated by checking its 10-day forecasts during the same period. The analysis was particularly focused on the GP and NE regions, where different land-atmosphere coupling behaviors were observed. The model's warm bias in the GP region was associated with the overestimated net radiation, and the bias was further amplified through the water-limited coupling. In the NE region, the PET-related variables, including surface air temperature, influenced the predictability of drought onset by limiting ET through the energy-limited coupling. Based on our findings, this study highlights the crucial role of land-atmosphere coupling behaviors and provides a scientific strategy for enhancing the model predictability of compound drought-heatwaves.

A global flash drought inventory based on soil moisture volatility

Flash droughts, characterized by rapid onset and development, present significant challenges to agriculture and climate mitigation strategies. Operational drought monitoring systems, based on precipitation, soil moisture deficits, or temperature anomalies, often fall short in timely detection of these events, underscoring the need for customized identification and monitoring indices that account for the rapidity of flash drought onset. Recognizing this need, this paper introduces a global flash drought inventory from 1990 to 2021 derived using the Soil Moisture Volatility Index (SMVI). Our work expands the application of the SMVI methodology, previously focused on the United States, to a global scale, providing a tool for understanding and predicting these rapidly developing phenomena. The dataset encompasses detailed event characteristics, including onset, duration, and severity, across diverse climate zones. By integrating atmospheric variables through their impact on soil moisture, the inventory offers a platform for analyzing the drivers and impacts of flash droughts, and serves as a large, consistent dataset for use in training and evaluating flash drought prediction models.

Identification of propagation characteristics from meteorological drought to hydrological drought using daily drought indices and lagged correlations analysis

Study regionThe Juam Dam area in Suncheon, Jeollanam-do, South Korea Study focusThis study aims to analyze drought propagation characteristics using time-lagged correlation analysis based on daily drought index, determining the lag time from meteorological drought to propagation into hydrological drought. The target period for correlation analysis is the onset date of the dry spell preceding the occurrence of hydrological drought and the termination date of the dry spell following the termination of the drought for each drought event. The Standardized Precipitation Index (SPI) at various time-scales for meteorological drought and the Standardized Reservoir Supply Index (SRSI) for hydrological drought were applied. New hydrological insights for the regionThis study presents the objectivity and accuracy of drought onset and termination date for drought propagation analysis through daily lagged correlation analysis. Through ROC analysis, SPI90, SPI180, and SPI365 are shown to increase by an average of 16.0 %, 8.8 %, and 6.0 %, respectively. From 1993–2023, long-term hydrological droughts lasting 2 years occurred 5 times, with a maximum duration of 408 days, magnitude −629, and severity −1.76. The daily lag between the multi-scale SPIs and SRSI of individual drought events presents the possibility of predicting hydrological drought through multiple regression analysis. This research provides insights for improving hydrological drought monitoring, prediction, and response strategies through results of individual propagation characteristics of drought events.

Unraveling Subseasonal Drought Dynamics in India: Insights from NCMRWF Extended Range Prediction System

Abstract Drought, a prolonged natural event, profoundly impacts water resources and societies, particularly in agriculturally dependent nations like India. This study focuses on subseasonal droughts during the Indian summer monsoon season using standardized precipitation index (SPI). Analyzing hindcasts from the National Centre for Medium Range Weather Forecasting (NCMRWF) Extended Range Prediction (NERP) system spanning 1993–2015, we assess NERP’s strengths and limitations. NERP replicates climatic patterns well but overestimates rainfall in the Himalayan foothills and the Indo-Gangetic Plain while underestimating it in the core monsoon zone and western coastline. Nonetheless, the NERP system demonstrates its ability to predict subseasonal drought conditions across India. Our research explores the model’s dynamics, emphasizing tropical and extratropical influences. We evaluate the impact of monsoon intraseasonal oscillation (MSIO) and Madden–Julian oscillation (MJO) on drought onset and persistence, noting model performance and discrepancies. While the model consistently identifies MSIO locations, variations in phase propagation affect drought severity in India. Remarkably, NERP excels in predicting MJO phases during droughts. The study underscores the robust response in the near-equatorial Indian Ocean, a crucial factor in subseasonal drought development. Furthermore, we explored upper-level dynamic interactions, demonstrating NERP’s ability to capture subseasonal drought dynamics. For example, unusual westerly winds weaken the tropical easterly jet, and a cyclonic anomaly transports cold air at midlevels and upper levels. These interactions reduce thermal contrast, weakening monsoon flow and favoring drought conditions. Hence, the NERP system demonstrates its skill in assessing prevailing drought conditions and associated teleconnection patterns, enhancing our understanding of subseasonal droughts and their complex triggers.

Effects of atmospheric CO2 concentration on transpiration and leaf elongation responses to drought in Triticum aestivum, Lolium perenne and Festuca arundinacea.

Leaf elongation is vital for Poaceae species' productivity, influenced by atmospheric CO2 concentration ([CO2]) and climate-induced water availability changes. Although [CO2] mitigates the effects of drought on reducing transpiration per unit leaf area, it also increases total leaf area and water use. These complex interactions associated with leaf growth pose challenges in anticipating climate change effects. This study aims to assess [CO2] effects on leaf growth response to drought in perennial ryegrass (Lolium perenne), tall fescue (Festuca arundinacea) and wheat (Triticum aestivum). Plants were cultivated in growth chambers with [CO2] at 200 or 800ppm. At leaf six to seven unfolding, half of the plants were subjected to severe drought treatment. Leaf elongation rate (LER) was measured daily, whereas plant transpiration was continuously recorded gravimetrically. Additionally, water-soluble carbohydrate (WSC) content along with water and osmotic potentials in the leaf growing zone were measured at drought onset, mid-drought and leaf growth cessation. Elevated [CO2] mitigated drought impacts on LER and delayed growth cessation across species. A positive correlation between LER and soil relative water content (SRWC) was observed. At the same SRWC, perennial grasses exhibited a higher LER with elevated [CO2], likely due to enhanced stomatal regulation. Despite stomatal closure and WSC accumulation, CO2 did not influence nighttime water potential or osmotic potential. The marked increase in leaf area across species resulted in similar (wheat and tall fescue) or higher (ryegrass) total water use by the experiment's end, under both watered and unwatered conditions. In conclusion, elevated [CO2] mitigates the adverse effects of drought on leaf elongation in three Poaceae species, due to its impact on plant transpiration. Overall, these findings provide valuable insights into CO2 and drought interactions that may help anticipate plant responses to climate change.

Evaluation of the performance of meteorological drought indices in Morocco: A case study of different climatic zones

AbstractUnderstanding drought occurrence and evolution is important in minimizing the impacts associated with it. This work assesses the performance of 10 commonly used meteorological indices to measure drought in Morocco. The studied indices are Deciles Index (DI), Percent of Normal Index (PNI), Z‐Score Index (ZSI), China‐Z Index (CZI), Rainfall Anomaly Index (RAI), Standardized Precipitation Index (SPI), Standardized Precipitation Evapotranspiration Index (SPEI), Palmer Drought Severity Index (PDSI), its self‐calibrated variant (scPDSI) and Palmer Z Index (PZI). Rainfall and temperature gridded data is sourced from PERSIANN‐CDR and ERA5, respectively, for the period 1983–2021. The study area exhibits three main climatic regimes; subhumid, semi‐arid and arid, with a drying and warming climate, as depicted by the rainfall and temperature trends analysis. Results show that most rainfall‐based indices perform relatively poorly in drought monitoring in the study area. DI and PNI appear to be inconsistent and abnormally responsive to rainfall. RAI reports droughts 56.5% more frequently and slightly underestimate drought intensity compared to other indices. Similarly, ZSI and CZI largely underestimate drought intensity. PDSI and scPDSI are computationally demanding, often underestimate drought intensity and overestimate drought duration by at least 115% compared to SPI and SPEI. Conversely, PZI can be used for drought onset detection as it reported droughts early compared to the other indices. SPI and SPEI perform overall better regarding their consistent drought identification and severity assessment. However, SPEI is found to be more suitable than SPI in the arid and semi‐arid regions and performed better considering the warming climate of the country.

Differences in drought characteristics, progression, and recession across ecosystem types in the pantropical region of the Lancang-Mekong River Basin

Exploring the development and impacts of drought across different ecosystems can offer new insights for mitigating the adverse effects of drought events. Using the pantropical Lancang-Mekong River Basin as the study region, we investigated the agricultural, ecological, and hydrological drought characteristics and explored their drought progression and recession rates across four vegetation ecosystem types: tropical forests, subtropical forests, shrubs, and crops. We utilized newly developed drought indices based on the ERA5-Land reanalysis dataset, GOSIF chlorophyll fluorescence data, and modified Moderate Resolution Imaging Spectroradiometer (MODIS) land cover data. The results showed that agricultural and hydrological droughts exhibited increasing trends from 2001 to 2021, whereas ecological drought displayed a decreasing trend over the same period. The cropland region experienced the fewest drought events, shortest drought durations, slowest progression rates, and lowest recession rates. By contrast, the two evergreen, broadleaf forest ecosystems (subtropical and tropical forests) experienced the highest number of drought events and fastest progression and recession rates. The findings suggest a trade-off relationship between vegetation resistance and recovery, where faster drought onset is associated with faster drought recession for ecological drought. Given the more severe challenges posed by agricultural and hydrological droughts, the riparian countries in the Lancang-Mekong River Basin should adopt proactive financial and management measures to mitigate the adverse impacts of these drought types. The insights gained from this study can inform the development of targeted strategies for drought monitoring, preparedness, and response across diverse ecosystems.

Assessing drought in Turkish basins through satellite observations

AbstractDrought occurs when there is a sustained decrease in rainfall over an extended period, impacting the socio‐cultural and environmental aspects of humans and other living beings. The geographic distribution and timing of droughts play a crucial role in drought management and mitigation strategies. Identifying and predicting the onset of droughts in specific regions, especially in watershed areas, is a primary concern in the field of hydrology. This study focuses on how the spatiotemporal patterns of drought are developing in Turkish Basins using detailed data on Terrestrial Water Storage (TWS), precipitation, and temperature at the pixel level. GRACE (Gravity Recovery and Climate Experiment), PERSIANN (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks), and WorldClim (World Climate) data sets are employed to assess long‐term changes of drought on a basin‐scale. Spatial analyses are conducted in a Geographic Information System (GIS) environment for the derivation of basinal monthly mean, minimum, and maximum statistics of TWS, precipitation, and temperature anomalies within Turkish Basins. Time series analyses are implemented to investigate the temporal evolution of droughts in these basins, for the basinal monthly mean, minimum, and maximum statistics obtained. The Mann–Kendall trend test and Pettitt change point detection tests are used to assess the statistical significance of the calculated trends and to expose the existence of any change point therein, respectively. The findings of the study indicate that Turkiye faces a significant risk of drought development in nearly all its basins, particularly after 2016. The GRACE dataset provides realistic insights into the temporal behaviour of hydrological droughts. PERSIANN is effective in identifying years with extreme meteorological conditions, and the standardized precipitation index (SPI) shows similar effectiveness, while they are ineffective in exposing significant trends due to the nature of the precipitation data. WorldClim data proves insufficient for modelling the temporal behaviour of droughts in these basins.

Streamflow drought onset and severity explained by non‐linear responses between climate‐catchment and land surface processes

AbstractKnowledge of drought onset and its relationship with drought severity (deficit volume) is crucial for providing timely information for reservoir operations, irrigation scheduling, devising cropping choices and patterns and managing surface and groundwater water resources. An analysis of the relationship between drought onset timing and deficit volume can help in drought hazard assessments and associated risks. Despite its importance, little attention has been paid to understand the drought onset timing and its potential linkage with deficit volume for effective drought monitoring and its impact assessment. Further, only a few studies have explored the role of environmental controls, encompassing the interaction between climate, catchment and land‐surface processes in influencing streamflow droughts and associated characteristics such as onset time and severity. This study leverages quality‐controlled streamflow observations from 1965 to 2018 to unveil regional patterns of streamflow drought onset, at‐site trends in deficit volume and detect non‐linear relationships between onset timing and deficit volume across 82 rain‐fed catchments in peninsular India (8°–24° N, 72°–87° E). We show that around 12% of catchments show an earlier onset of streamflow droughts in conjunction with a decreasing trend in deficit volume. Further, approximately one‐third of the catchments show a significant non‐linear dependency between drought deficit volume and onset time. Among catchment controls, such as soil and topographic properties, we found soil organic carbon stock and stock as dominant drivers controlling the streamflow drought onset time. Likewise, sand content and vertical distance to channel network control the streamflow deficit volume. Finally, the linkages between inferred dominant low‐flow generation mechanisms and the specific combinations of environmental controls are synthesized in a conceptual diagram that might assist in developing appropriate models for low‐flow simulations and predictions, especially across ungauged sites. The new insights add value to understanding the chain of physical processes linking climatic and physiographic controls on streamflow droughts, which can support drought forecasting and climate impact assessment efforts.

Subseasonal Forecast Skill of Evaporative Demand, Soil Moisture, and Flash Drought Onset from Two Dynamic Models over the Contiguous United States

Abstract Flash droughts are rapidly developing subseasonal climate extreme events that are manifested as suddenly decreased soil moisture, driven by increased evaporative demand and/or sustained precipitation deficits. Over each climate region in the contiguous United States (CONUS), we evaluated the forecast skill of weekly root-zone soil moisture (RZSM), evaporative demand (ETo), and relevant flash drought (FD) indices derived from two dynamic models [Goddard Earth Observing System model V2p1 (GEOS-V2p1) and Global Ensemble Forecast System version 12 (GEFSv12)] in the Subseasonal Experiment (SubX) project between years 2000 and 2019 against three reference datasets: Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA-2), North American Land Data Assimilation System, phase 2 (NLDAS-2), and GEFSv12 reanalysis. The ETo and its forcing variables at lead week 1 have moderate-to-high anomaly correlation coefficient (ACC) skill (∼0.70–0.95) except downwelling shortwave radiation, and by weeks 3–4, predictability was low for all forcing variables (ACC < 0.5). RZSM (0–100 cm) for model GEFSv12 showed high skill at lead week 1 (∼0.7–0.85 ACC) in the High Plains, West, Midwest, and South CONUS regions when evaluated against GEFSv12 reanalysis but lower skill against MERRA-2 and NLDAS-2 and ACC skill are still close to 0.5 for lead weeks 3–4, better than ETo forecasts. GEFSv12 analysis has not been evaluated against in situ observations and has substantial RZSM anomaly differences when compared to NLDAS-2, and our analysis identified GEFSv12 reforecast prediction limit, which can maximally achieve ACC ∼0.6 for RZSM forecasts between lead weeks 3 and 4. Analysis of major FD events reveals that GEFSv12 reforecast inconsistently captured the correct location of atmospheric and RZSM anomalies contributing to FD onset, suggesting the needs for improving the dynamic models’ assimilation and initialization procedures to improve subseasonal FD predictability. Significance Statement Flash droughts are rapidly developing climate extremes which reduce soil moisture through enhanced evaporative demand and precipitation deficits, and these events can have large impacts on the ecosystem and crop health. We evaluated the subseasonal forecast skill of soil moisture and evaporative demand against three reanalysis datasets and found that evaporative demand skill was similar between forecasts and reanalyses while soil moisture skill is dependent on the reference dataset. Skill of evaporative demand decreases rapidly after week 1, while soil moisture skill declines more slowly after week 1. Case studies for the 2012, 2017, and 2019 United States flash droughts identified that forecasts could capture rapid decreases in soil moisture in some regions but not consistently, implying that long-lead forecasts still need improvements before being used in early warning systems. Improvements in flash drought predictability at longer lead times will require less biased initial conditions, better model parameterizations, and improved representations of large-scale teleconnections.

Onset of flash drought based on the WRF in the Poyang Lake Basin of China

The occurrence of flash droughts rapidly triggers soil water deficits, easily causing significant impacts on agricultural production, water resources, and ecosystems. As a critical phase in the formation and widespread outbreaks of flash drought, the rapid onset increases the difficulty of monitoring and forecasting. However, the evolutionary mechanisms of flash drought have not been given sufficient attention. In this study, we identified flash drought based on soil moisture from 1993 to 2022 to deepen the understanding of flash drought and presented simulations of three typical flash drought events (occurring in 2019, 2021, and 2022) to explore drought processes by the Weather Research and Forecasting (WRF) model over the largest flesh lake basin of China, Poyang Lake Basin (PLB). The driving factors and short-term atmospheric characteristics of flash drought were used to illustrate the rapid onset. The results show that (1) the PLB experienced increasements in flash drought. The frequency of flash droughts was approximately 4–10 times per decade, with more flash droughts occurring in the northern part of the PLB than in the south. The average duration of flash drought onset was between 9 and 13 days. The types of flash drought onset with two pentads had the highest ratio (70%). (2) The meteorological drivers (precipitation, temperature, evapotranspiration) had strong impacts on the soil evolution in the central and northern parts of the PLB. Precipitation always appeared before or at the very beginning of onset and almost disappeared during the onset of flash drought. (3) The atmosphere over the PLB was relatively dry during the onset. Different atmospheric conditions were observed to follow flash drought onset, which were likely to result in different recovery periods.

Catalogue of drought events in peninsular Spanish along 1916–2020 period

We leveraged the most extensive and detailed gridded database of monthly precipitation data across the Spanish mainland (MOPREDAScentury), encompassing 1916–2020 time period, to pinpoint the most severe drought events within this timeframe and analyse their spatio-temporal dynamics. To identify these events, we employed the Standardized Precipitation Index (SPI) at a 12-month timescale. Drought events were identified as periods of at least three months where significantly dry conditions affected 20% or more of the study area, defined as grid cells with SPI values lower than −0.84. Our analysis revealed a total of 40 major drought events. Our catalogue contains detailed information on each episode’s spatial extent, duration, severity, and spatio-temporal dynamics. The analysis of the propagation patterns of the events unveils substantial heterogeneity, implying that droughts stem from diverse atmospheric mechanisms, further influenced by complex local topography. The open-licensed drought database serves as a valuable resource. It not only facilitates exploration of drought onset and evolution mechanisms but also aids in assessing drought impact on agricultural and other socio-economic sectors.

High Throughput Image-Based Phenotyping for Determining Morphological and Physiological Responses to Single and Combined Stresses in Potato.

High throughput image-based phenotyping is a powerful tool to non-invasively determine the development and performance of plants under specific conditions over time. By using multiple imaging sensors, many traits of interest can be assessed, including plant biomass, photosynthetic efficiency, canopy temperature, and leaf reflectance indices. Plants are frequently exposed to multiple stresses under field conditions where severe heat waves, flooding, and drought events seriously threaten crop productivity. When stresses coincide, resulting effects on plants can be distinct due to synergistic or antagonistic interactions. To elucidate how potato plants respond to single and combined stresses that resemble naturally occurring stress scenarios, five different treatments were imposed on a selected potato cultivar (Solanum tuberosum L., cv. Lady Rosetta) at the onset of tuberization, i.e. control, drought, heat, waterlogging, and combinations of heat, drought, and waterlogging stresses. Our analysis shows that waterlogging stress had the most detrimental effect on plant performance, leading to fast and drastic physiological responses related to stomatal closure, including a reduction in the quantum yield and efficiency of photosystem II and an increase in canopy temperature and water index. Under heat and combined stress treatments, the relative growth rate was reduced in the early phase of stress. Under drought and combined stresses, plant volume and photosynthetic performance dropped with an increased temperature and stomata closure in the late phase of stress. The combination of optimized stress treatment under defined environmental conditions together with selected phenotyping protocols allowed to revealthe dynamics of morphological and physiological responses to single and combined stresses. Here, a useful tool is presented for plant researchers looking to identify plant traits indicative of resilience to several climate change-related stresses.

Developing a Multivariate Agro‐Meteorological Index to Improve Capturing Onset and Persistence of Droughts Utilizing Vapor Pressure Deficit and Soil Moisture

AbstractDrought is associated with adverse environmental and societal impacts across various regions. Therefore, drought monitoring based on a single variable may lead to unreliable information, especially about the onset and persistence of drought. Previous studies show vapor pressure deficit (VPD) data can detect drought onset earlier than other drought indicators such as precipitation. On the other hand, soil moisture (SM) is a robust indicator for assessing drought persistence. This study introduces a nonparametric multivariate drought index Vapor Pressure Deficit Soil moisture standardized Drought Index (VPDSDI) which is developed by combining VPD with SM information. The performance of the multivariate index in terms of drought onset detection is compared with the Standardized Precipitation Evapotranspiration Index (SPEI) and the Standardized Precipitation Index (SPI) for six major drought events across the United States including three rapidly developing drought events (this term refers to flash droughts that develop on monthly scales) and three conventional drought events. Additionally, the performance of the proposed index in detecting drought persistence is compared with the Standardized Soil moisture Index (SSI), which is an agricultural drought index. Results indicate the multivariate index detects drought onset always earlier than SPI for conventional events, but VPDSDI detects drought onset earlier than or about the same time as SPEI for rapidly developing droughts. In terms of persistence, VPDSDI detects persistence almost identical to SSI for both rapidly developing and conventional drought events. The results also show that combining VPD with SM reduces the high variability of VPD and produces a smoother index which improves the onset and persistence detection of drought events leveraging VPD and SM information.

Alignment between water inputs and vegetation green‐up reduces next year's runoff efficiency

AbstractIn the western United States, water supplies largely originate as snowmelt from forested land. Forests impact the water balance of these headwater streams, yet most predictive runoff models do not explicitly account for changing snow‐vegetation dynamics. Here, we present a case study showing how warmer temperatures and changing forests in the Henrys Fork of the Snake River, a seasonally snow‐covered headwater basin in the Greater Yellowstone Ecosystem, have altered the relationship between April 1st snow water equivalent (SWE) and summer streamflow. Since the onset and recovery of severe drought in the early 2000s, predictive models based on pre‐drought relationships over‐predict summer runoff in all three headwater tributaries of the Henrys Fork, despite minimal changes in precipitation or snow accumulation. Compared with the pre‐drought period, late springs and summers (May–September) are warmer and vegetation is greener with denser forests due to recovery from multiple historical disturbances. Shifts in the alignment of snowmelt and energy availability due to warmer temperatures may reduce runoff efficiency by changing the amount of precipitation that goes to evapotranspiration versus runoff and recharge. To quantify the alignment between snowmelt and energy on a timeframe needed for predictive models, we propose a new metric, the Vegetation‐Water Alignment Index (VWA), to characterize the synchrony of vegetation greenness and snowmelt and rain inputs. New predictive models show that in addition to April 1st SWE, the previous year's VWA and summer reference evapotranspiration are the most significant predictors of runoff in each watershed and provide more predictive power than traditionally used metrics. These results suggest that the timing of snowmelt relative to the start of the growing season affects not only annual partitioning of streamflow, but can also determine the groundwater storage state that dictates runoff efficiency the following spring.

Flash droughts in a hotspot region: Spatiotemporal patterns, possible climatic drivings and ecological impacts

Northeast China, recognized as a global flash drought hotspot and a region of nationally important commercial grains and ecological fragility, is highly susceptible to the profound impacts of droughts on both food security and ecological safety. However, the regional-scale characteristics, possible causes and impacts of flash droughts across Northeast China are rarely investigated. Soil moisture data from 2000 to 2022 were utilized to identify the onset, frequency and duration of flash droughts using the quantile method. The spatial trajectories of flash droughts were determined based on the patch-scale centroid transfers. Further, the possible drivings and ecological impacts of flash droughts were analyzed using datasets of climatic variables and gross primary productivity. We found that flash drought coverage with short onset (1- pentad onset) was generally larger than that with long onset (2-, 3- and 4-pentad onset). The affected area of flash droughts generally decreased, while the onset speed increased over the past two decades. Flash droughts frequently occurred the intersection of western Jilin Province, southern Inner Mongolia Autonomous Region, and northern Liaoning Province but with short duration. Compared with 2001–2011, the start, middle and end centroids of flash droughts in 2012–2021 showed more spatial dynamic changes, mainly shifting towards the northeast-southwest and southeast-northwest directions. The onset of flash drought due to potential evapotranspiration, temperature, precipitation deficit, and vapor pressure anomalies account for an average of 33%, 28%, 22%, and 16%, respectively. However, the contributions of dominant meteorological factors and their combinations varied remarkably in different sub-regions. Long onset flash droughts exhibited larger impact on GPP than short onset flash droughts. This study highlights that due to climate change, the affected area of flash droughts in hotspot regions decreased, but the onset speed and spatial dynamics increased, and followed by a more severe ecological impact of short-onset flash drought. Therefore, it is imperative to incorporate the increasing impacts of flash droughts, which is critical to ensuring regional food security and ecological safety in the Northeast China.