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This research explores the relationship between hydrological drought and agricultural drought in the Corong River Basin, focusing on the Gondang Reservoir Irrigation Area, Indonesia. By employing a Causal Time Series Regression Model, the study uncovers that agricultural drought twelve months prior has a significant impact on current agricultural drought and is influenced by current hydrological drought. Time series regression analysis reveals that 45.86% of agricultural drought is influenced by hydrological drought, with 54.14% influenced by other factors besides rainfall. Further research is needed to investigate these additional factors. These findings have practical implications, serving as a valuable index for assessing drought severity and planning mitigation actions, especially in the irrigation areas of interest. They emphasize the importance of effective irrigation management, appropriate cropping patterns, and a comprehensive understanding of the complex characteristics of drought in agricultural regions through comprehensive monitoring efforts in agricultural drought mitigation. [ABSTRACT FROM AUTHOR]

Groundwater level declines are largely associated with natural processes and human activities. In particular, the drivers of groundwater change can be more complex during meteorological drought owing to human activities. However, disentangling their specific contribution remains poorly understood. By focusing on semiarid ecosystems in the northern Yellow River Basin - the Ordos - here we elucidate the impact of human activities on the propagation of meteorological droughts to groundwater systems. To comprehensively analyze groundwater variations, we employ the K-means, categorizing them into four distinct patterns. Based on the Pearson correlation coefficient analysis between standardized precipitation index (SPI) and groundwater depth (GWD), we found that the majority of lag time for GWD response to SPI is less than 3 months, and the drivers influencing GWD are classified into three categories: SPI, human activities related to SPI, and human activities unrelated to SPI. Our results reveal that both meteorological droughts and human activities jointly influence GWD across the entire region. Notably, human activities unrelated to SPI have the greatest impact in the irrigation district of Ordos, followed by the western part of Ordos and the Mu Us sandy land in central Ordos. Our findings can guide us to formulate effective drought management policies and practices in semiarid regions. [ABSTRACT FROM AUTHOR]

This comprehensive review explores the ecological significance of the Argane stands (Argania spinosa) in southwestern Morocco and the pivotal role of remote sensing technology in monitoring forest ecosystems. Argane stands, known for their resilience in semi-arid and arid conditions, serve as a keystone species, preventing soil erosion, maintaining ecological balance, and providing habitat and sustenance to diverse wildlife species. Additionally, they produce an extremely valuable Argane oil, offering economic opportunities and cultural significance to local communities. Remote sensing tools, including satellite imagery, LiDAR, drones, radar, and GPS precision, have revolutionized our capacity to remotely gather data on forest health, cover, and responses to environmental changes. These technologies provide precise insights into canopy structure, density, and individual tree health, enabling assessments of Argane stand populations and detection of abiotic stresses, biodiversity, and conservation evaluations. Furthermore, remote sensing plays a crucial role in monitoring vegetation health, productivity, and drought stress, contributing to sustainable land management practices. This review underscores the transformative impact of remote sensing in safeguarding forest ecosystems, particularly the Argane forest stands, and highlights its potential for continued advancements in ecological research and conservation efforts. [ABSTRACT FROM AUTHOR]

Wildfires burn annually over 400,000 ha in Mediterranean countries. By the end of the 21st century, wildfire Burnt Area (BA) and associated Green House Gas (GHG) emissions may double to triple due to climate change. Regional projections of future BA are urgently required to update wildfire policies. We present a robust methodology for estimating regional wildfire BA and GHG emissions under future climate change scenarios in the Mediterranean. The Fire Weather Index, selected drought indices, and meteorological variables were correlated against BA/GHG emissions data to create area-specific statistical projection models. State-of-the-art regional climate models (horizontal resolution: 12 km), developed within the EURO-CORDEX initiative, simulated data under three climate change scenarios (RCP2.6, RCP4.5, and RCP8.5) up to 2070. These data drove the statistical models to estimate future wildfire BA and GHG emissions in three pilot areas in Greece, Montenegro, and France. Wildfire BA is projected to increase by 20% to 130% up to 2070, depending on the study area and climate scenario. The future expansion of fire-prone areas into the north Mediterranean and mountain environments is particularly alarming, given the large biomass present here. Fire-smart landscape management may, however, greatly reduce the projected future wildfire BA and GHG increases. [ABSTRACT FROM AUTHOR]

As a significant global source of vegetable oil, the oil palm's ability to withstand abiotic stresses, particularly drought, is crucial for sustainable agriculture. This is especially significant in tropical regions, where water scarcity is becoming more common. Nitrogen, a vital nutrient, plays an essential role in various physiological and biochemical processes in plants, directly influencing growth and stress tolerance. This study investigates the interaction between nitrogen sources (ammonium vs. nitrate) and drought stress in oil palm (Elaeis guineensis) seedlings, which is critical in enhancing productivity in this economically important crop. The experiment evaluated five commercial oil palm genotypes, which were supplied with nitrogen solutions (15 mM NH4+ or NO3−) for 46 days, followed by 30 days of progressive drought. The results showed that drought conditions universally reduced the biomass, with ammonium-fed plants exhibiting greater shoot biomass sensitivity than nitrate-fed plants. Drought also significantly decreased the chlorophyll a, PhiPS2, and root-reducing sugar levels—critical indicators of photosynthetic efficiency and overall plant health. The effects on the root architecture were complex, with ammonium nutrition differentially influencing the lateral root length under well-watered versus drought conditions, highlighting nitrogen forms' nuanced role in root development. Importantly, substantial genotypic variability was observed in most traits, affecting the responses to both the nitrogen source and drought stress. This variability suggests that certain genotypes may be better suited to cultivation in specific environmental conditions, particularly drought-prone areas. In conclusion, this study underscores the intricate interplay between nitrogen nutrition, genotypic variability, and drought tolerance in oil palm seedlings. These findings highlight the need to integrate these factors into agricultural management strategies to improve resilience and productivity in oil palm plantations. [ABSTRACT FROM AUTHOR]

The spatiotemporal continuity characteristics of drought are the basis for analyzing its spatial migration and evolution, which is significant for mitigation and early warning of drought. The aim of this paper is to identify meteorological and vegetation drought events in China from 1982 to 2022, reveal the dynamic changes of typical drought events, and elucidate the propagation characteristics of meteorological and vegetation drought. The results showed that (1) based on a three-dimensional spatiotemporal clustering algorithm, China experienced 138 meteorological drought events and 76 vegetation drought events; (2) the severity of the meteorological drought event No. M138 (2022.03–2022.11) reached 667.58 × 104 km2·month, and the severity of the vegetation drought event No. V68 (2019.06–2020.04) reached 572.89 × 104 km2·month; (3) a total of 40 meteorological-vegetation drought event pairs had been identified, which was divided into three main types: "single", "simple", and "complexity"; and (4) in the typical drought event pair No. P-34, the area difference was 16.45 × 104 km2, and the severity difference was 3.89 × 104 km2. The research results can provide a new perspective for identifying the dynamic changes and propagation characteristics of drought events from a three-dimensional perspective, which is of great significance for predicting vegetation drought and protecting the ecological environment. [ABSTRACT FROM AUTHOR]

Meteorological and agricultural droughts are inherently correlated, whereas the propagation mechanism between them remains unclear in Northwestern China. Investigating the linkages between these drought types and identifying the potential influencing factors is crucial for effective water resource management and drought mitigation. This study adopted the Standardized Precipitation Evapotranspiration Index (SPEI) and Standardized Soil Moisture Index (SSMI) to characterize the meteorological and agricultural droughts from 1960 to 2018. The propagation time between these droughts was detected using the Pearson correlation analysis, and the cross-wavelet transform and wavelet cross-correlation were utilized to describe their linkages across the time–frequency scales. The grey relational analysis was applied to explore the potential factors influencing the propagation time. The results revealed that the agricultural drought typically lagged behind the meteorological drought by an average of 6 months in Northwestern China, with distinct seasonal and regional characteristics. The shortest propagation time occurred in the summer (3 months), followed by the autumn (4 months), and the propagation time was longer in the winter (8 months) and spring (9 months). Additionally, the average propagation time was longer in the plateau climate zone (8 months) than in the southeastern climate zone (6 months) and the westerly climate zone (4 months). There was a multi-timescale response between the meteorological and agricultural droughts, with a relatively stable and significant positive correlation over long timescales, whereas the correlation was less clear over short timescales. The key factors influencing the propagation time were soil moisture, elevation, precipitation, and potential evapotranspiration. Furthermore, the wavelet cross-correlation between agricultural and meteorological droughts was relatively high, with a lag of 0 to 3 months; as the timescale increased, the fluctuation period of their cross-correlation also increased. [ABSTRACT FROM AUTHOR]

Solar-induced chlorophyll fluorescence (SIF) serves as a proxy indicator for vegetation photosynthesis and can directly reflect the growth status of vegetation. Using SIF for drought monitoring offers greater potential compared to traditional vegetation indices. This study aims to develop and validate a novel approach, the improved Temperature Fluorescence Dryness Index (iTFDI), for more accurate drought monitoring in Henan Province, China. However, the low spatial resolution, data dispersion, and short temporal sequence of SIF data hinder its direct application in drought studies. To overcome these challenges, this study constructs a random forest SIF downscaling model based on the TROPOspheric Monitoring Instrument SIF (TROPOSIF) and the Moderate-resolution Imaging Spectroradiometer (MODIS) data. Assuming an unchanging spatial scale relationship, an improved SIF (iSIF) product with a temporal resolution of 500 m over the period March to September, 2010–2022 was obtained for Henan Province. Subsequently, using the retrieved iSIF and the surface temperature difference data, the iTFDI was proposed, based on the assumption that under the same vegetation cover conditions, lower soil moisture and a greater diurnal temperature range of the surface indicate more severe drought. Results showed that: (1) The accuracy of the TROPOSIF downscaling model achieved coefficient of determination (R2), mean absolute error (MAE), and root mean square error (RMSE) values of 0.847, 0.073 mW m−2 nm−1 sr−1, and 0.096 mW m−2 nm−1 sr−1, respectively. (2) The 2022 iTFDI drought monitoring results indicated favorable soil moisture in Henan Province during March, April, July, and August, while extensive droughts occurred in May, June, and September, accounting for 70.27%, 71.49%, and 43.61%, respectively. The monitored results were consistent with the regional water conditions measured at ground stations. (3) The correlation between the Standardized Precipitation Evapotranspiration Index (SPEI) and iTFDI at five stations was significantly stronger than the correlation with the Temperature Vegetation Dryness Index (TVDI), with the values −0.631, −0.565, −0.612, −0.653, and −0.453, respectively. (4) The annual Sen's slope and Mann–Kendall significance test revealed a significant decreasing trend in drought severity in the southern and western regions of Henan Province (6.74% of the total area), while the eastern region showed a significant increasing trend (4.69% of the total area). These results demonstrate that the iTFDI offers a significant advantage over traditional indices, providing a more accurate reflection of regional drought conditions. This enhances the ability to identify drought trends and supports the development of targeted drought management strategies. In conclusion, the iTFDI constructed using the downscaled iSIF data and surface temperature differential data shows great potential for drought monitoring. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This study examines the global health implications of extreme drought events from 2000 to 2023. Utilizing data from the International Disaster Database (EM-DAT), we analyzed the number of people affected and the total deaths attributed to drought. Our findings reveal that over 1.6 billion people have been impacted by drought globally, with Southern Asia and Sub-Saharan Africa being the most severely affected regions. India and China account for a significant portion of the affected population, with 688.2 million and 327.35 million impacted people, respectively. Drought-related mortality has also been substantial, with over 24,000 deaths recorded globally, including more than 20,000 in Somalia alone. The study highlights the uneven distribution of drought impacts, underscoring the need for targeted interventions and comprehensive drought preparedness strategies. Our analysis also reveals the critical role of socio-economic factors in exacerbating the health impacts of drought, particularly in regions with inadequate healthcare infrastructure and limited access to resources. This study provides novel insights into the specific health impacts of drought, including the correlation between drought frequency and mortality rates, and offers actionable recommendations for improving future emergency responses and health system preparedness. These recommendations are tailored to address the unique challenges faced by the most vulnerable regions, emphasizing the importance of context-specific strategies to enhance resilience against the growing threat of climate-induced droughts. [ABSTRACT FROM AUTHOR]

Liaoning Province, a crucial agricultural region in Northeast China, has endured frequent drought disasters in recent years, significantly affecting both agricultural production and the ecological environment. Conducting drought research is of paramount importance for formulating scientific drought monitoring and prevention strategies, ensuring agricultural production and ecological safety. This study developed a Comprehensive Joint Drought Index (CJDI) using the empirical Copula function to systematically analyze drought events in Liaoning Province from 1981 to 2020. Through the application of MK trend tests, Morlet wavelet analysis, and run theory, the spatiotemporal variation patterns and recurrence characteristics of drought in Liaoning Province were thoroughly investigated. The results show that, compared to the three classic drought indices, Standardized Precipitation Index (SPI), Evaporative Demand Drought Index (EDDI), and Standardized Precipitation Evapotranspiration Index (SPEI), CJDI has the highest accuracy in monitoring actual drought events. From 1981 to 2020, drought intensity in all regions of Liaoning Province (east, west, south, and north) exhibited an upward trend, with the western region experiencing the most significant increase, as evidenced by an MK test Z-value of −4.53. Drought events in Liaoning Province show clear seasonality, with the most significant periodic fluctuations in spring (main cycles of 5–20 years, longer cycles of 40–57 years), while the frequency and variability of drought events in autumn and winter are lower. Mild droughts frequently occur in Liaoning Province, with joint and co-occurrence recurrence periods ranging from 1.0 to 1.8 years. Moderate droughts have shorter joint recurrence periods in the eastern region (1.2–1.4 years) and longer in the western and southern regions (1.4–2.2 years), with the longest co-occurrence recurrence period in the southern region (3.0–4.0 years). Severe and extreme droughts are less frequent in Liaoning Province. This study provides a scientific foundation for drought monitoring and prevention in Liaoning Province and serves as a valuable reference for developing agricultural production strategies to adapt to climate change. [ABSTRACT FROM AUTHOR]

Drought can cause significant disruption to forest ecosystems and may have long‐term impacts on the structure and function of ecosystems after the end of drought. This is the key to quantifying the ability of ecosystem to respond to disturbance events by comprehensively analyzing the impact of drought on vegetation, the lagged effect, and ecosystem resilience to drought. This article takes broad‐leaved forests and coniferous forests in multiple temperature zones of China as the object of study, using distributed lagged nonlinear model (DLNM) to construct a systematic method. Our results show that the main sensitive lagged time for coniferous forests and broad‐leaved forests is the first 3 months in various temperature zones, with the strongest lagged effect in the month when the drought incidents occur. Coping capacity represents ecosystems to remain stable during droughts, and we quantified the indicator by the ratio of the resistance (the difference between NDVI value before the drought and during the drought) to recovery (the difference between NDVI value after the drought and during the drought). When dealing with intensive drought events, the coping capacity of subtropical broad‐leaved forests (−0.67) and tropical broad‐leaved forests (−0.88) exhibit the strongest coping capacity (value tends to −1). Overall, vegetation growth in subtropical and tropical regions is less affected by drought compared to temperate and cold temperate zones. The research results help us understand the comprehensive impact of drought on vegetation and the strategies for different vegetation to cope with drought. [ABSTRACT FROM AUTHOR]

Increasing tree diversity is considered a key management option to adapt forests to climate change. However, the effect of species diversity on a forest's ability to cope with extreme drought remains elusive. In this study, we assessed drought tolerance (xylem vulnerability to cavitation) and water stress (water potential), and combined them into a metric of drought–mortality risk (hydraulic safety margin) during extreme 2021 or 2022 summer droughts in five European tree diversity experiments encompassing different biomes. Overall, we found that drought–mortality risk was primarily driven by species identity (56.7% of the total variability), while tree diversity had a much lower effect (8% of the total variability). This result remained valid at the local scale (i.e within experiment) and across the studied European biomes. Tree diversity effect on drought–mortality risk was mediated by changes in water stress intensity, not by changes in xylem vulnerability to cavitation. Significant diversity effects were observed in all experiments, but those effects often varied from positive to negative across mixtures for a given species. Indeed, we found that the composition of the mixtures (i.e., the identities of the species mixed), but not the species richness of the mixture per se, is a driver of tree drought–mortality risk. This calls for a better understanding of the underlying mechanisms before tree diversity can be considered an operational adaption tool to extreme drought. Forest diversification should be considered jointly with management strategies focussed on favouring drought‐tolerant species. [ABSTRACT FROM AUTHOR]

Droughts are among the most costly hazards in Tay Nguyen (known as Vietnamese Central Highlands), causing significant threats to agriculture and vegetation ecosystems. This study investigated the spatial and temporal dynamics of vegetation-based drought in the Tay Nguyen Provinces of Dak Lak and Dak Nong, using a long-term series of Moderate Resolution Imaging Spectroradiometer (MODIS) Vegetation Condition Index (VCI) from 2000 to 2023. The results exhibited a high positive correlation (R=0.73) between VCI and soil moisture-based drought index in drought-detected areas. Monthly analysis revealed severe drought events during the dry months, notably in 2005, 2010, 2013, 2016, and 2019. In contrast, wetter conditions were primarily observed during 2017-2018 and 2022-2023. Despite temporal variability of drought, larger trends of decreasing and increasing vegetation-based drought were detected during the dry season. These trends remained a relatively stable during the rainy season. Among vegetation types, shrubland exhibited the lowest VCI trends. This research offers valuable insights for stakeholders and policymakers to develop targeted strategies for sustainable land management and regional drought resilience. [ABSTRACT FROM AUTHOR]

Changes in rainfall and drought significantly impact agriculture and water management, making it vital for effective planning and management. This study aimed to analyze rainfall trends and drought conditions in the Awash River Basin, Ethiopia. Twenty meteorological (1985–2021) and nine streamflow (1985–2014) station data were used to analyze rainfall trends, variability, and drought conditions based on the Mann–Kendall test, innovative trend analysis, standardized precipitation index, agricultural standardized precipitation index, reconnaissance drought index, effective reconnaissance drought index, and streamflow drought index. Based on Mann–Kendall's test results rainfall during the Bega season showed a decreasing trend at all stations while Tsedey and Kiremt seasons showed an increasing trend at the majority of the stations. However, the Belg season and annual rainfall showed no clear trend at the majority of the stations. A significant (p < 0.05) increase at Debre Berhan and a decrease at Awash7kilo and Ginchi were observed in annual rainfall by 44.1, 102.4, and 116.4 mm per decade, respectively. The innovative trend analysis revealed the Tsedey and Bega seasons showed increasing and decreasing trends in the majority of the stations for all rainfall categories, respectively. However, the annual, Belg, and Kiremt rainfall showed no clear trend in the majority of the stations for different rainfall categories. Annual rainfall showed increasing (Debre Berhan, Mojo, and Sheno) and decreasing (Awash7kilo, Dire Dawa, and Ginchi) trends for all rainfall categories. Generally, there is high variability in rainfall during Tsedey, Bega, and Belg, moderate and low variability during Kiremt, and annual with moderate and irregular rainfall distribution for the majority of the stations. The drought analysis revealed that 15.7, 17.3, 30.7, and 16.3% of drought periods were detected with annual standardized precipitation, agricultural standardized precipitation, reconnaissance drought, and effective reconnaissance drought indices, respectively. Hydrological drought conditions also showed a high probability of occurrence amounting to 47.6 and 48.2% for annual and three-month with severe indices of about -2.58 and -4.26 found at Awash Melka Sedi and Metehara gauge stations, respectively. Moderate to extreme hydrometeorological droughts have occurred approximately every six to eight years, with significant drought events recorded in 1987/88, 1991/92, 1996/97, 2001/02, 2003/04, 2014/15, and 2016/17. The results could have paramount importance for water resource policies and planning for rainfall variability and drought management and adaptation strategies in the Awash River basin. [ABSTRACT FROM AUTHOR]

Drought is one of the significant natural disasters that has a profound impact on human societies, particularly in arid places such as Balochistan, Pakistan. Geographic information system and remote sensing has played a major role in predicting the effect of drought events and mitigate. Therefore, the purpose of this study was firstly to evaluate the spatiotemporal patterns of drought in Balochistan, Pakistan, utilizing MODIS based satellite data and validate the PMD stations data with CHIRPS data. Secondly the objective of this research to quantify the influence of drought on vegetation anomalies and comparison between droughts patterns with vegetation response. Drought conditions in Balochistan by integrating remote sensing (RS) drought indices (RSDI).RSDI was calculated through Hargreaves method using monthly data. The following remaining indices were the main focus of the study i.e., Standardized Precipitation Evapotranspiration Index (SPEI), Vegetation Condition Index (VCI), Vegetation Health Index (VHI), Temperature Vegetation Dryness Index (TVDI), and Precipitation Condition Index (PCI). These indices offered differing perspectives, emphasizing the value of a comprehensive strategy. Approximately 60% of the area was significantly affected by drought conditions, with SPEI values for the period being less than -1.5.SPEI and TVDI performed better in identifying droughts. TVDI values ranged from 0.63 to 0.88, indicating agricultural dryness. For instance, the East experienced a severe drought between 2001 and 2022, according to SPEI. Significant drought events occurred in 2001, 2004, 2009, 2014, and 2022, allowing comparative analysis. TVDI proved more effective than VCI in predicting drought. RDI and TVDI localized drought better than PCI. SPEI, RDI, and TVDI contributed significantly to understanding drought (73.63%, 74.15%, and 72.30% respectively). Considering diverse indices is vital for long-term drought mitigation strategies. RDI, especially valuable with limited temperature data, aids in understanding drought dynamics. This analysis aids in predicting future droughts and mitigating agricultural losses in Balochistan, informing decision-making and adaptive measures. [ABSTRACT FROM AUTHOR]

An exponential rise in the atmospheric vapour pressure deficit (VPD) is among the most consequential impacts of climate change in terrestrial ecosystems. Rising VPD has negative and cascading effects on nearly all aspects of plant function including photosynthesis, water status, growth and survival. These responses are exacerbated by land–atmosphere interactions that couple VPD to soil water and govern the evolution of drought, affecting a range of ecosystem services including carbon uptake, biodiversity, the provisioning of water resources and crop yields. However, despite the global nature of this phenomenon, research on how to incorporate these impacts into resilient management regimes is largely in its infancy, due in part to the entanglement of VPD trends with those of other co‐evolving climate drivers. Here, we review the mechanistic bases of VPD impacts at a range of spatial scales, paying particular attention to the independent and interactive influence of VPD in the context of other environmental changes. We then evaluate the consequences of these impacts within key management contexts, including water resources, croplands, wildfire risk mitigation and management of natural grasslands and forests. We conclude with recommendations describing how management regimes could be altered to mitigate the otherwise highly deleterious consequences of rising VPD. Summary statement: Rising atmospheric vapour pressure deficit (or VPD) is one of the most widespread and significant consequences of climate warming for terrestrial ecosystems. This article reviews the mechanistic bases of these usually deleterious impacts and synthesises that information into a set of management recommendations to mitigate them. [ABSTRACT FROM AUTHOR]

Indicators are important tools for tracking the socio‐environmental impacts of droughts and building resilience to climate change. We begin with an overview of metrics used for water planning and drought management, with particular emphasis on the UK. We explain how considerations of cost, immediacy, access, consistency, relevance, reliability and others denote the suitability of information for developing new indicators. We then demonstrate the potential of Google Trends (GT) online search data as drought indicators for England and Ireland. We show that search terms such as 'drought', 'water butt' and 'hosepipe ban' correlate significantly with conventional hydroclimatic data as well as with newspaper reports of various drought impacts during the period 2011–2022. GT data also show evidence of rising interest in water saving technologies, especially for outdoor water use. Meanwhile, online searches for 'Defra' and 'Environment Agency' have declined and are more often associated with flood episodes than droughts. Interest in water companies in England is more likely around hosepipe bans than water leakage (although this varies by company). We discuss the implications of these findings for targeting information campaigns, plus prospects for monitoring drought impacts and public sentiment in near real‐time. [ABSTRACT FROM AUTHOR]

Drought poses a serious threat to the productivity of hop, an important perennial crop. However, the precise physiological mechanisms that make it highly susceptible to drought are not yet fully understood. In this study, we investigated stomatal regulation and xylem vulnerability to embolism, which are important traits closely associated with plant drought resistance. In a glasshouse cultivation experiment, we monitored changes in leaf water potential, stem elongation rates, and leaf gas exchange, including net photosynthetic rates, stomatal conductance, and intrinsic water use efficiency, on relatively young hop plants (traditional Saaz - Osvald's clone 31) exposed to declining soil water availability. The transpiration rate and stem elongation of plants decreased significantly with a small decline in substrate water potential (ΨSUB), indicating a highly sensitive stomata response during early phases of soil dehydration. The stem elongation was completely halted, and the transpiration rate dropped to less than 50% of its maximum at ΨSUB levels below − 0.8 MPa. In well-watered hop plants, xylem in stems operates near the initial point of embolization and is highly vulnerable to embolism, with a water potential corresponding to a 50% loss of xylem conductivity at -1.6 MPa. The sensitive stomatal response to declining ΨSUB likely helps to mitigate the risk of hydraulic failure, albeit at the cost of impaired growth. Scheduled irrigation, particularly during the sensitive stem elongation stage, may be a promising approach to mitigate the detrimental effects of reduced soil water availability on hop growth and yield while also conserving water resources. [ABSTRACT FROM AUTHOR]

• Identified the severity and frequency of drought in Singbhum Protocontinent, India. • Meteorological and remote sensing indices are used to identify drought dynamics. • 1.67 to 1.05 percent extreme drought frequency shows in the western part. • Poor aquifer properties and extreme climate are the main reason for droughts. • Sustainable watershed management is highly required. Drought is an extremely hazardous situation that predominantly affects ecosystems, hydrological systems, and agricultural mechanisms in different pockets of the world. Management of water resources in dry and semi-arid environments has become a critical challenge in the last few decades. The principal objectives of this study are (i) to categorize the severity and frequency of drought events using the Reconnaissance Drought Index and Rainfall Deviation Index and ii) to evaluate the spatial extension of drought in particular drought times of severe and extreme droughts through Remote Sensing Drought Indices (RSDIs). This drought monitoring index is commonly used index for its high degree of resilience and sensitivity to droughts. The result shows that severe to extreme drought occurred in Bero, Tulin, Sirkabad, Manbazar and Bundwan which are 22, 28, 23, 14 and 25 months correspondingly based on RDI3 whereas RDI12 values indicated 26, 47, 42, 22 and 38 months in the study period (1981 to 2021). Bundwan area faced the most extreme drought occurrence in May month of 1996 with −3.58 values followed by Sirkabad, Bero, Manbazar and Tulin. Manbazar is going through severe to extreme drought months (for short time) whereas 'extreme drought' frequency (1.67 % to 1.05 %) is higher in the western part mainly in Jhalda I, Jhalda II, Bagmundi, and Arsha blocks of Purulia district (part of Manbhum Singhbhum Plateau). Additionally, the recurrence of 'moderate drought' is much greater in Balarampur along with some portions of Purulia I block. The whole study can be summarized as the northeastern portion being challenged with moderate or severe drought conditions while mild or severe drought is a considerable recurrence in the northern section. The outcome of this study will be helpful for local planners and management officials during implementation strategies related to water resource management. [ABSTRACT FROM AUTHOR]

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Managing aquatic ecosystems for people and nature can be improved by collaboration among scientists, managers, decision‐makers, and other stakeholders. Many collaborative and interdisciplinary approaches have been developed to address the management of freshwater ecosystems; however, there are still barriers to overcome. We worked as part of a regional stakeholder group comprising municipal water utility operators, conservation organizations, academic partners, and other stakeholders to understand the effects of low‐flow and drought on ecological functions of the upper Flint River, Georgia (USA), a free‐flowing river important for municipal water supply, recreation, and native biota. We used published literature and locally targeted studies to identify quantitative flow targets that could be used to inform water management and drought planning. Drawing from principles of Translational Ecology, we relied on an iterative process to develop information needs for the group and maintained communication and engagement throughout data collection, analysis, and synthesis. We identified three quantitative flow benchmarks to evaluate the ecological impacts of drought in the river. The results were valuable to both the water utilities represented in the working group and State regional water planning, which is used to guide water management strategies and permitting for the basin. We identified principles that were important for the successful engagement in the working group and helped to overcome the challenge of working across sectors and without direct authority guiding the implementation of our work. Interdisciplinary work and creative solutions are crucial to plan for and adapt to greater pressure on our water resources. [ABSTRACT FROM AUTHOR]

In recent decades, abnormal rainfall and temperature patterns have significantly impacted the environment and human life, particularly in East Nusa Tenggara. The region is known for its low rainfall and high temperatures, making it vulnerable to drought events, which have their own complexities due to being random and changing over time. This study aimed to analyze the trend of short-term meteorological drought intensity in Timor Island, East Nusa Tenggara. The analysis was carried out by utilizing the standardized precipitation evapotranspiration index (SPEI) for a 1-month period to characterize drought in intensity, duration, and severity. A power law process approach was used to model the intensity of the event, which is inversely proportional to the magnitude of the drought event. Intensity parameters of the power law process were estimated using the maximum likelihood estimation (MLE) method to predict an increase in the intensity of drought events in the future. The probability of drought was calculated using the non-homogeneous Poisson process. The analysis showed that "extremely dry" events in Timor Island are less frequent than "very dry" and "dry" events. The power law process model's estimated intensity parameter showed a beta value greater than 1, indicating an increase in future drought events. In the next 12 months, two months of drought are expected in each region of Timor Island, East Nusa Tenggara, with the following probabilities for each region: 0.264 for Kupang City, 0.25 for Kupang, 0.265 for South Central Timor, 0.269 for North Central Timor, 0.265 for Malaka, and 0.266 for Belu. This research provides important insights into drought dynamics in vulnerable regions such as East Nusa Tenggara and its potential impact on future mitigation and adaptation planning. [ABSTRACT FROM AUTHOR]

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Drought events are a major cause of large crop yield losses with implications for food security and farmers' incomes. Growing multiple crops simultaneously during a cropping season is a well‐known on‐farm risk management strategy to cope with these drought risks. However, the effectiveness of this crop diversification under different severity levels of drought and how this effectiveness is influenced by the crop composition is unclear. This article provides new methodological and empirical insights to assess the effectiveness of such diversification, in particular to cope with extreme drought. We apply and evaluate nested Archimedean copulas and elliptical copulas to assess simultaneous farm‐level yield losses of different cash crops in German agriculture (winter wheat, winter barley, winter rapeseed, sugar beet, and grain maize) under different drought severity levels (N = 249,756; regionally pooled farm‐level crop‐yield pairs, 1995–2019). We show that on‐farm crop diversification contributes to cope with drought risks, but its effectiveness varies considerably across regions, crop pairs, and drought severity. Our results underline that cropping system diversification alone is often not sufficient to cope with drought risks, but that the right crop combinations are needed. For example, during a severe drought (one in 20 years event), 26.4% of farmers in eastern Germany suffered simultaneous yield losses of at least 20% in winter wheat and winter barley, while 19.1% of farmers in eastern Germany suffered simultaneous yield losses of at least 20% in winter wheat and sugar beet. Farmers should therefore be encouraged to grow crops with more diverse phenological requirements throughout the year. [ABSTRACT FROM AUTHOR]

Droughts are often long-lasting phenomena, without a distinct start or end and with impacts cascading across sectors and systems, creating long-term legacies. Nevertheless, our current perceptions and management of droughts and their impacts are often event-based, which can limit the effective assessment of drought risks and reduction of drought impacts. Here, we advocate for changing this perspective and viewing drought as a hydrological–ecological–social continuum. We take a systems theory perspective and focus on how "memory" causes feedback and interactions between parts of the interconnected systems at different timescales. We first discuss the characteristics of the drought continuum with a focus on the hydrological, ecological, and social systems separately, and then we study the system of systems. Our analysis is based on a review of the literature and a study of five cases: Chile, the Colorado River basin in the USA, northeast Brazil, Kenya, and the Rhine River basin in northwest Europe. We find that the memories of past dry and wet periods, carried by both bio-physical (e.g. groundwater, vegetation) and social systems (e.g. people, governance), influence how future drought risk manifests. We identify four archetypes of drought dynamics: impact and recovery, slow resilience building, gradual collapse, and high resilience–big shock. The interactions between the hydrological, ecological, and social systems result in systems shifting between these types, which plays out differently in the five case studies. We call for more research on drought preconditions and recovery in different systems, on dynamics cascading between systems and triggering system changes, and on dynamic vulnerability and maladaptation. Additionally, we advocate for more continuous monitoring of drought hazards and impacts, modelling tools that better incorporate memories and adaptation responses, and management strategies that increase societal and institutional memory. This will help us to better deal with the complex hydrological–ecological–social drought continuum and identify effective pathways to adaptation and mitigation. [ABSTRACT FROM AUTHOR]

Drought indices, such as the Standardized Precipitation Index (SPI) and the Stream Flow Drought Index (SDI), are mathematical indicators that represent an overall decrease in average amounts of rainfall over a specific period of time. The changing values of SPI and SDI can be determined by trend analysis and can help decision makers in estimating and managing the future values of water resources on issues such as dam management and energy production. In this study, in addition to SPI12 and SDI12, trend analyzes of monthly precipitation and stream flow data affecting drought were also conducted. Mann–Kendall Test (MK), Spearman's Rho test, Sen-Innovative Trend Analysis (ITA) were chosen as trend analysis. As a result of the analysis, in precipitation performed with MK, an increasing trend at 95% significance level was detected in January, while no trend was found in the other months. While increasing trends were found in all months using SPI12, no trend was detected in SDI12. In Spearman's Rho test, no trend was detected in SDI12 and precipitation for all months, whereas increasing trend for January, February and April were detected for SPI12 and January for streamflow data. The analysis made with ITA was evaluated in two parts, graphically and statistically. The graphical method was carried out for monthly data. In statistical evaluation of ITA for SPI12 and SDI12, increasing trends were detected for all monthly data, however, in the graphical analysis, different results were obtained for each month, which did not fully support the results of the statistical analysis. [ABSTRACT FROM AUTHOR]

The area of Sia Kouanza in the Sahel of southwestern Niger is a potential location for expanding agriculture through irrigation with groundwater. Agriculture is key to supporting smallholders and promoting food security. As plans proceed, questions include how much water is available, how is groundwater replenished, many hectares to develop, and where to locate the wells. While these questions can be addressed with a model, it is difficult to find detailed procedures, especially when data are scarce. How can we use existing information to develop a model of a natural system where groundwater development will take place? We describe an approach that can be employed in data-scarce areas where similar questions are being asked. The approach includes setting details; conceptual model development; water balance; numerical code MODFLOW; model construction, calibration, and statistics; and result interpretation. Conceptual model component estimates are derived from field data: recharge, evapotranspiration, wetlands discharge, existing extraction, and river stages. When field data are not available or scarce, we employ other sources and describe how they are validated with field data or analog sites. The calibrated steady-state model gives a water balance of 22 × 106 m3/yr with inflows (recharge 22 × 106 m3/yr) and outflows (extraction 7.2 × 105 m3/yr, wetlands 5.7 × 106 m3/yr, evapotranspiration 11.9 × 106 m3/yr). The model is a point of departure; approaches for transient and predictive models, which can be used to simulate changes in irrigation pumping volumes and drought, for example, will be described subsequently. [ABSTRACT FROM AUTHOR]

Kern County is one of the most valuable agricultural counties in the nation. This, however, is being put in jeopardy with the recently implemented Sustainable Groundwater Management Act (SGMA) in response to the ongoing California drought and extensive groundwater pumping for irrigation. The Groundwater Sustainability Agencies (GSAs) are responsible for developing Groundwater Sustainability Plans to address the new SGMA policies. The objective of this paper is to examine the Kern Groundwater Sustainability Plan (KGSP), determine its strengths and weaknesses, and provide recommendations for the updated plan released in 2025. The plan performs well in defining criteria that must be met, but fails to address how these goals will be implemented. Based on our policy analysis, our recommendations include standardizing groundwater management terms across sub-basins, defining clear measurements of undesirable results, utilizing financial (dis)incentives to encourage groundwater users to manage water sustainably, and increasing interconnections between local and state organizations. Importantly, improving this policy process for the SGMA may be an example for critically overdrafted groundwater basins globally on how to more sustainably manage their groundwater. [ABSTRACT FROM AUTHOR]

Accurate monitoring of river discharge variations is essential for managing floods and droughts and understanding the response of global river systems to climate change. Remote sensing of discharge (RSQ) offers a timely and efficient alternative for widespread monitoring, particularly in ungauged areas. Current methods often struggle with accuracy, especially when estimating the width of narrow rivers from medium‐resolution images. We first observe that, although estimating the width variation of narrow rivers can be challenging from medium‐resolution satellite imagery, river discharge still correlates with river surface color or reflectance. However, existing methods can only correlate river surface reflectance with discharge in gauged rivers. Here, we introduce a novel method employing an advanced Transformer architecture to map river discharge variations directly from time‐series reflectance imagery. Our model, trained on quality‐checked data from 2,036 discharge gauges, outperforms existing methods in discharge estimation accuracy and is less affected by the need for precise river width estimation. The proposed model yields positive Kling‐Gupta Efficiency (KGE) in 68.6% of ungauged rivers, a substantial improvement over the BAM and geoBAM methods, which show positive KGEs in only 28.4% and 33.1% of rivers, respectively. Notably, this performance is achieved despite two‐thirds of the rivers being less than 100 m wide, a range where traditional RSQ methods typically struggle, and the RSQ performance does not show degradation for braided rivers. Our approach suggests a significant shift toward more efficient, extensive, and adaptable space‐based river discharge monitoring. Plain Language Summary: Monitoring river discharge is crucial for managing water‐related disasters like floods and droughts and for understanding how rivers respond to climate change. Traditional methods that use remote sensing to monitor discharge (RSQ) often face challenges, especially with accurately measuring the width of narrow rivers using satellite images. However, we've noticed that the color or reflectance of a river's surface, which can be seen from space, still tells us something about the river's discharge, even if the width is hard to measure accurately. Our study introduces a new method using a Transformer architecture that learns to estimate river discharge directly from how the river looks in time‐series satellite images. We trained our model using data from 2,036 river gauges and found that it performs better than existing methods. It is particularly effective in areas where we don't have direct river discharge measurements, achieving good results in 68.6% of these cases‐a big improvement over the 28.4% and 33.1% success rates of previous methods. This is impressive because two‐thirds of the rivers we studied are less than 100 m wide, where older methods usually fail. Key Points: Deep learning model maps river discharge from satellites in ungauged rivers, improving accuracy for rivers over 30 m wideThe model uses Transformer architecture, correlating river surface reflectance data with discharge when width changes are minimalValidated with positive Kling‐Gupta Efficiency in 68.6% of ungauged rivers, median width 67 m, significantly better than existing Mass Conserved Flow Law Inversion (McFLI) methods [ABSTRACT FROM AUTHOR]

Water deficiency has become a serious ecological problem facing all mankind, and drought has become a key factor constraining the development of agricultural production. Gaining insights into drought response mechanisms is vital for crop adaptation. We present, here, a multifactorial study from germination to vegetative drought stages in the model plant Brachypodium hybridum. A total of 200 genotypes representing 10 natural populations (Fayedh, Enfidha, Jbel Zaghouan, Haouaria, Raouad, Sejnen, Ain Drahem, El Kef, Daouar El Hej Wniss and Djerba) which represent almost all the bioclimatic regions in Tunisia were used. High variation in germination capacity under drought stress was found between studied populations. It reaches 100% at 250 mV for Djerba population and decrease up to 40% at 100 mV. Drought caused significant increase in oxidative stress. Interestingly, genotype 8/14 originated from El Kef exhibited the highest leaf H2O2 content, resulting in higher leaf MDA accumulation. Consistently, no significant changes were observed in-SOD, CAT and GPX activities in both leaves and roots, relatively to unstressed state. Different profile was recorded in 6/9 originated from Raoued in which the most pronounced levels of H2O2 and MDA were observed in drought stressed roots. Concurrently, root CAT activity increased by 22-fold relatively to unstressed plants. Drought stress resulted in a decrease in H2O2 content, mitigation of lipid peroxidation in both leaves and roots of 6/5 genotype originated from Raoued, with a simultaneous increase in leaf and root SOD activity. This was associated with enhanced GPX activity where 75 and 40% of increase were recorded in genotype 6/5 in leaves and roots. [ABSTRACT FROM AUTHOR]

Copyright of Environmental & Social Management Journal / Revista de Gestão Social e Ambiental is the property of Environmental & Social Management Journal and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Background: As fire regimes continue to evolve in response to climate change, understanding how fire characteristics have responded to changes in the recent past is vital to inform predictions of future fire events. Aims and methods: Using Fourier Transform Infrared (FTIR) spectroscopy, we assessed how fire intensity has changed in two fire-prone landscapes in south-eastern Australia: (1) the Blue Mountains; and (2) Namadgi National Park during the past 3000 years. Key results: Higher aromatic/aliphatic ratios suggest increased high-intensity fire frequency in sediments at the surface of both cores. Increases in the frequency of extreme drought periods, coupled with the change in vegetation and anthropogenic ignitions following colonisation, could have increased the frequency of high-intensity fires in the past ~200 years. Conclusions: FTIR spectroscopy can be used in sediment deposits to infer that the frequency of high-intensity fire events has increased in the past 200 years compared to the previous ~3000 years. Implications: These results are important for understanding how past fire regimes have responded to climate, people and vegetation shifts in the past ~3000 years and can be used to inform models for future predictions and management strategies. Fourier Transform Infrared (FTIR) spectroscopy was applied to sediment deposits in southeastern Australia. The aromatic/aliphatic ratio was used as a proxy for high-intensity fires. The increased frequency of high-intensity fire events could result from interactions between climate, people, and vegetation. The existing fire record was extended by centuries to millennia. [ABSTRACT FROM AUTHOR]

Copyright of China Rural Water & Hydropower is the property of China Rural Water & Hydropower Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The impacts of drought and climate change on agriculture have become increasingly apparent, and affect smallholder livestock farmers. Farmers' livelihoods rely on agriculture; thus, they are vulnerable to the primary and secondary impacts of climate change. In South Africa, policies for increasing the resilience of smallholder livestock farmers who have suffered from agricultural drought have not paid sufficient attention to the level of livelihood vulnerability. This study assessed the level of livelihood vulnerability of smallholder livestock farmers in the drought-stricken Frances Baard District Municipality in the Northern Cape Province of South Africa. The livelihood vulnerability of 217 randomly selected farmers from the municipality were determined using the Livelihood Vulnerability Index (LVI) and Livelihood Vulnerability Index of the Intergovernmental Panel on Climate Change (LVI-IPCC), which includes seven components and 34 subcomponents addressing livelihood. A high level of livelihood vulnerability, with an LVI score of 0.436, was determined and attributed to high-risk livelihood strategies, food, social networks, health, water, sociodemographics, natural disasters, and climate change. The LVI-IPCC of 0.04 also showed moderate vulnerability due to high exposure, high sensitivity, and low adaptive capacity, especially for the Phokwane, Dikgatlong, and Magareng districts in the Frances Baard municipality. Given continued drought recurrences, it is crucial for the government and other stakeholders to implement strategic and targeted sustainable interventions. The resilience of smallholder livestock farmers should be enhanced by increasing their adaptive capacity through diversified livelihood options while decreasing exposure and sensitivity to agricultural drought risks. [ABSTRACT FROM AUTHOR]

Scenarios have emerged as valuable tools in managing complex human‐natural systems, but the traditional approach of limiting focus on a small number of predetermined scenarios can inadvertently miss consequential dynamics, extremes, and diverse stakeholder impacts. Exploratory modeling approaches have been developed to address these issues by exploring a wide range of possible futures and identifying those that yield consequential vulnerabilities. However, vulnerabilities are typically identified based on aggregate robustness measures that do not take full advantage of the richness of the underlying dynamics in the large ensembles of model simulations and can make it hard to identify key dynamics and/or storylines that can guide planning or further analyses. This study introduces the FRamework for Narrative Storylines and Impact Classification (FRNSIC; pronounced "forensic"): a scenario discovery framework that addresses these challenges by organizing and investigating consequential scenarios using hierarchical classification of diverse outcomes across actors, sectors, and scales, while also aiding in the selection of scenario storylines, based on system dynamics that drive consequential outcomes. We present an application of this framework to the Upper Colorado River Basin, focusing on decadal droughts and their water scarcity implications for the basin's diverse users and its obligations to downstream states through Lake Powell. We show how FRNSIC can explore alternative sets of impact metrics and drought dynamics and use them to identify drought scenario storylines, that can be used to inform future adaptation planning. Plain Language Summary: Scenario analysis is a useful tool for assessing the impacts of future conditions or alternative strategies. Focusing on a small number of predetermined scenarios can, however, limit our understanding of key uncertainties, and fail to represent diverse stakeholder impacts. Approaches such as exploratory modeling have been developed to address these issues by exploring a wide range of possible futures and system perspectives. These approaches often involve large simulation experiments with their own interpretability challenges. So, on one hand, we recognize the need to utilize large ensembles of hypothesized changes, but on the other hand, each additional dimension considered makes it more difficult to convey actionable insights. We introduce the FRamework for Narrative Storylines and Impact Classification (FRNSIC; pronounced "forensic"), a scenario discovery framework that helps users identify scenario storylines that capture key system dynamics and as well as important outcomes. We demonstrate its application to the Upper Colorado River Basin, focusing on decadal droughts and their water scarcity implications for the basin's diverse users and its obligations to downstream states through Lake Powell. We explore alternative impact metrics and dynamics, identifying scenario storylines with significant impacts, which can be used in future planning efforts to adapt to these stressed conditions. Key Points: Introduce framework for discovery of scenario storylines describing diverse stakeholder impacts and consequential dynamicsDemonstrate the framework in the Upper Colorado River Basin with hundreds of stakeholders and complex human‐natural system interactionsThe framework improves understanding and selection of drought scenario storylines through their effects on user‐ and basin‐scale impacts [ABSTRACT FROM AUTHOR]

As warmer and drier conditions associated with global warming are projected to increase in southern Europe, the Mediterranean countries are currently the most prone to wildfire danger. In the present study, we investigated the statistical relationship between drought and fire weather risks in the context of climate change using drought index and fire weather-related indicators. We focused on the vulnerable and long-suffering area of the Attica region using high-resolution gridded climate datasets. Concerning fire weather components and fire hazard days, the majority of Attica consistently produced values that were moderately to highly anti-correlated (−0.5 to −0.9). This suggests that drier circumstances raise the risk of fires. Additionally, it was shown that the spatial dependence of each variable on the 6-months scale Standardized Precipitation Evapotranspiration Index (SPEI6), varied based on the period and climate scenario. Under both scenarios, an increasing rate of change between the drought index and fire indicators was calculated over future periods versus the historical period. In the case of mean and 95th percentiles of FWI with SPEI6, abrupt changes in linear regression slope values were observed, shifting from lower in the past to higher values in the future periods. Finally, the fire indicators' future projections demonstrated a tendency towards an increasing fire weather risk for the region's non-urban (forested and agricultural) areas. This increase was evident from the probability distributions shifting to higher mean and even more extreme values in future periods and scenarios. The study demonstrated the region's growing vulnerability to future fire incidents in the context of climate change. [ABSTRACT FROM AUTHOR]

The Journal of Experimental Botany has published guidelines for designing and interpreting drought experiments in controlled conditions. The guidelines aim to address flaws in research on drought tolerance and plant responses to water deficit, such as flawed experimental designs and inconsistent terminology. The recommendations include using precise terminology, accounting for genotype-environment interactions, considering whole-plant transpiration, and measuring soil and plant water status. The article discusses the challenges and pitfalls of designing and conducting plant stress experiments, specifically focusing on drought and heat stress. The authors emphasize the importance of considering factors such as spatial and temporal variations, feedback mechanisms, and the pot effect in experimental design. They recommend researchers to recognize and report the limitations of their experiments, assess complex environmental interactions, and account for genotype-by-environment interactions when interpreting data. The article also highlights the need for accurate measurement of plant stress levels and the selection of appropriate variables and time points for measurement. The authors aim to provide guidance for researchers in designing rigorous and reproducible stress experiments. [Extracted from the article]

Drought is deemed a major natural disaster that can lead to severe economic and social implications. Drought indices are utilized worldwide for drought management and monitoring. However, as a result of the inherent complexity of drought phenomena and hydroclimatic condition differences, no universal drought index is available for effectively monitoring drought across the world. Therefore, this study aimed to develop a new meteorological drought index to describe and forecast drought based on various artificial intelligence (AI) models: decision tree (DT), generalized linear model (GLM), support vector machine, artificial neural network, deep learning, and random forest. A comparative assessment was conducted between the developed AI-based indices and nine conventional drought indices based on their correlations with multiple drought indicators. Historical records of five drought indicators, namely runoff, along with deep, lower, root, and upper soil moisture, were utilized to evaluate the models' performance. Different combinations of climatic datasets from Alice Springs, Australia, were utilized to develop and train the AI models. The results demonstrated that the rainfall anomaly drought index was the best conventional drought index, scoring the highest correlation (0.718) with the upper soil moisture. The highest correlation between the new and conventional indices was found between the DT-based index and the rainfall anomaly index at a value of 0.97, whereas the lowest correlation was 0.57 between the GLM and the Palmer drought severity index. The GLM-based index achieved the best performance according to its high correlations with conventional drought indicators, e.g., a correlation coefficient of 0.78 with the upper soil moisture. Overall, the developed AI-based drought indices outperformed the conventional indices, hence contributing effectively to more accurate drought forecasting and monitoring. The findings emphasized that AI can be a promising and reliable prediction approach for achieving better drought assessment and mitigation. [ABSTRACT FROM AUTHOR]

Drought is a complex natural hazard that occurs when a region experiences a prolonged period of dry conditions, leading to water scarcity and negative impacts on the environment. This study analyzed the recurrence of drought and wet spells in Baluchistan province, Pakistan. Reconnaissance Drought Index (RDI), Standardized Precipitation Evapotranspiration Index (SPEI), and Vegetation Condition Index (VCI) were used to analyze droughts in Baluchistan during 1986–2021. Statistical analysis i.e. run theory, linear regression, and correlation coefficient were used to quantify the trend and relationship between meteorological (RDI, SPEI) and agricultural (VCI) droughts. The meteorological drought indices (1, 3, 6, and 12-month RDI and SPEI) identified severe to extreme drought spells during 1986, 1988, 1998, 2000–2002, 2004, 2006, 2010, 2018–2019, and 2021 in most meteorological stations (met-stations). The Lasbella met-station experienced the most frequent extreme to severe droughts according to both the 12-month RDI (8.82%) and SPEI (15.38%) indices. The Dalbandin met-station (8.34%) follows closely behind for RDI, while Khuzdar (5.88%) comes in second for the 12-month SPEI. VCI data showed that Baluchistan experienced severe to extreme drought in 2000, 2001, 2006, and 2010. The most severe drought occurred in 2000 and 2001, affecting 69% of the study region. A positive correlation was indicated between meteorological (RDI, SPEI) and agricultural drought index (VCI). The multivariate indices can provide valuable knowledge about drought episodes and preparedness to mitigate drought impacts. [ABSTRACT FROM AUTHOR]

Trunk injection applies more nutrients in the correct volume and composition, delivering ideal results with fewer fertilizer treatments under drought-stress conditions. We used a completely randomized block design to examine the effects of trunk injection therapy on the development of drought stress tolerance in olive trees in arid climates. Treatments included seaweed extract (SWE), potassium amino acid chelate (PAC), calcium silicate (CS), and potassium silicate (PS) at five different concentrations: 0.25, 0.5, 1, 2, and 4% for each treatment. Each treatment consisted of four replications – four dry replications for each treatment and four irrigation controls – each receiving weekly irrigation. The findings showed that, in comparison to drought control, trunk injection of PAC at a concentration of 2% enhances the relative water content of leaf and proline and decreases electrolyte leakage (EL) and malondialdehyde (MDA) content. Chlorophyll a, carotenoids, and proline content are all significantly higher in the trunk after CS injection at a 4% concentration than in the control treatment. MDA and EL were also reduced. Injecting PS leads to an increase in proline and photosynthetic pigments. The SWE treatment improved the proline, relative leaf water content, and photosynthetic pigments. To sum up, trunk injection treatments under drought conditions improved plant photosynthetic pigments, increased proline content, prevented relative water content reduction, and prevented an increase in MDA and EL. In conclusion, among the several treatments, the use of 2% PS was the most effective at reducing the adverse effects of drought stress on olive plants. [ABSTRACT FROM AUTHOR]

Summary: Recent droughts have strongly impacted forest ecosystems and are projected to increase in frequency, intensity, and duration in the future together with continued warming. While evidence suggests that tree diversity can regulate drought impacts in natural forests, few studies examine whether mixed tree plantations are more resistant to the impacts of severe droughts.Using natural variations in leaf carbon (C) and nitrogen (N) isotopic ratios, that is δ13C and δ15N, as proxies for drought response, we analyzed the effects of tree species richness on the functional responses of tree plantations to the pan‐European 2018 summer drought in seven European tree diversity experiments.We found that leaf δ13C decreased with increasing tree species richness, indicating less drought stress. This effect was not related to drought intensity, nor desiccation tolerance of the tree species. Leaf δ15N increased with drought intensity, indicating a shift toward more open N cycling as water availability diminishes. Additionally, drought intensity was observed to alter the influence of tree species richness on leaf δ15N from weakly negative under low drought intensity to weakly positive under high drought intensity.Overall, our findings suggest that dual leaf isotope analysis helps understand the interaction between drought, nutrients, and species richness. [ABSTRACT FROM AUTHOR]

Accurately evaluating drought and its effects on the natural environment is difficult in regions with limited climate monitoring stations, particularly in the hyper-arid region of the Sultanate of Oman. Rising global temperatures and increasing incidences of insufficient precipitation have turned drought into a major natural disaster worldwide. In Oman, drought constitutes a major threat to food security. In this study, drought indices (DIs), such as temperature condition index (TCI), vegetation condition index (VCI), and vegetation health index (VHI), which integrate data on drought streamflow, were applied using moderate resolution imaging spectroradiometer (MODIS) data and the Google Earth Engine (GEE) platform to monitor agricultural drought and assess the drought risks using the drought hazard index (DHI) during the period of 2001–2023. This approach allowed us to explore the spatial and temporal complexities of drought patterns in the Najd region. As a result, the detailed analysis of the TCI values exhibited temporal variations over the study period, with notable minimum values observed in specific years (2001, 2005, 2009, 2010, 2014, 2015, 2016, 2017, 2019, 2020, and 2021), and there was a discernible trend of increasing temperatures from 2014 to 2023 compared to earlier years. According to the VCI index, several years, including 2001, 2003, 2006, 2008, 2009, 2013, 2015, 2016, 2017, 2018, 2020, 2021, 2022, and 2023, were characterized by mild drought conditions. Except for 2005 and 2007, all studied years were classified as moderate drought years based on the VHI index. The Pearson correlation coefficient analysis (PCA) was utilized to observe the correlation between DIs, and a high positive correlation between VHI and VCI (0.829, p < 0.01) was found. Based on DHI index spatial analysis, the northern regions of the study area faced the most severe drought hazards, with severity gradually diminishing towards the south and east, and approximately 44% of the total area fell under moderate drought risk, while the remaining 56% was classified as facing very severe drought risk. This study emphasizes the importance of continued monitoring, proactive measures, and effective adaptation strategies to address the heightened risk of drought and its impacts on local ecosystems and communities. [ABSTRACT FROM AUTHOR]

In 2022, a severe drought and heatwave occurred in the middle and lower reaches of the Yangtze River Basin. Previous studies have highlighted the severity of this event, yet the relevance of soil moisture (SM), as well as vapor pressure deficit (VPD) and vegetation damage, remained unclear. Here, we utilized solar-induced chlorophyll fluorescence (SIF) and various flux data to monitor the impact of drought on vegetation and analyze the influence of different environmental factors. The results indicated a severe situation of drought and heatwave in the Yangtze River Basin in 2022 that significantly affected vegetation growth and the ecosystem carbon balance. SIF and NDVI have respective advantages in reflecting damage to vegetation under drought and heatwave conditions; SIF is more capable of capturing the weakening of vegetation photosynthesis, while NDVI can more rapidly indicate vegetation damage. Additionally, the correlation of SM and SIF are comparable to that of VPD and SIF. By contrast, the differentiation in the severity of vegetation damage among different types of vegetation is evident; cropland is more vulnerable compared to forest ecosystems and is more severely affected by drought and heatwave. These findings provided important insights for assessing the impact of compound drought and heatwave events on vegetation growth. [ABSTRACT FROM AUTHOR]

Drought events have been linked with the enhancements of organic aerosols (OAs), but the mechanisms have not been comprehensively understood. This study investigates the relationships between the monthly standardized precipitation–evapotranspiration index (SPEI) and surface OA in the contiguous United States (CONUS) during the summertime from 1998 to 2018. OA under severe drought conditions shows a significant increase in mass concentrations across most of the CONUS relative to non-drought periods, with the Pacific Northwest (PNW) and Southeast United States (SEUS) experiencing the highest average enhancement of 1.79 µgm-3 (112 %) and 0.92 µgm-3 (33 %), respectively. In the SEUS, a linear regression approach between OA and sulfate was used to estimate the epoxydiol-derived secondary organic aerosol (IEPOX SOA), which is the primary driver of the OA enhancements under droughts due to the simultaneous increase in emissions of biogenic volatile organic compounds (VOCs; such as isoprene and monoterpene) and sulfate. The rise in sulfate is mainly caused by the reduced wet deposition because of the up to 62 % lower precipitation amount. In the PNW, OA enhancements are closely linked to intensified wildfire emissions, which raise OA mass concentrations to be 4–8 times higher relative to non-fire conditions. All 10 Earth system models participating in the sixth phase of the Coupled Model Intercomparison Project (CMIP6) can capture the slopes between SPEI and OA in the PNW, with CESM2-WACCM and GFDL-ESM4 performing the best and worst in predicting the OA enhancement under severe droughts. However, all models significantly underestimate the OA increase in the SEUS, with Nor-ESM2-LM and MIRCO6 showing relatively better performance. This study reveals the key drivers of the elevated OA levels under droughts in the CONUS and underscores the deficiencies of current climate models in their predictive capacity for assessing the impact of future droughts on air quality. [ABSTRACT FROM AUTHOR]

Drought may facilitate the invasion process of invasive plants, mainly because invasive plants can obtain a stronger growth competitiveness than native plants under drought. It is therefore imperative to illuminate the mechanisms underlying the successful invasion of invasive plants under drought, with a particular focus on the differences in the resistance of invasive and native plants to drought. This study aimed to elucidate the differences in the resistance between the invasive plant Amaranthus spinosus L. and the native plant A. tricolor L. to drought under a gradient of drought. The resistance of co-cultivated A. spinosus to drought was significantly higher than that of co-cultivated A. tricolor under light drought. Hence, A. spinosus may obtain a stronger competitive advantage than A. spinosus under co-cultivation conditions when treated with light drought. The resistance of the two plants to drought may be predominantly influenced by their height and biomass. This present study also defines a method for evaluating the stress resistance of a given plant species to stress by calculating the stress resistance index. This present study offers a robust theoretical foundation for determining the stress resistance of a given plant species and the environmental management of A. spinosus under drought. [ABSTRACT FROM AUTHOR]

Drought stress has serious effects on the growth and yield of wheat in both productivity and quality and is an abiotic factor. Traditional methods of crop drought stress monitoring have some deficits. This work has been conducted in order to enhance these conventional methods by proposing a new deep learning approach. This paper has presented a deep learning-based model customized for monitoring drought stress in winter wheat during the critical growth stages. Drought-afflicted winter wheat images were captured at three crucial phases: rising–jointing, heading–flowering, and flowering–maturity. These images are correlated against soil moisture data to construct a comprehensive dataset. DenseNet121 was chosen as the network model since it extracts features from images relating to phenotypes. Several factors, like training methods, learning rate adjustment, and addition of the attention mechanism, are optimized in eight sets of experiments. This provided the final DenseNet-121 model with an average recognition accuracy of 94.67% on the test set, which means that monitoring drought stress during wheat growth's key periods is feasible and effective. [ABSTRACT FROM AUTHOR]