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This study explores hydrological and meteorological drought in the Coastal Constantine basin, the Cheffia and Mexa dams in northeast Algeria in particular, using a time series of monthly rainfall and dam inflow data over 54 years (1965–2018). Recently, these dams have experienced recurring droughts, significantly reducing water storage and severely impacting the population's drinking water supply. To better assess the severity and trends of these droughts, we applied a range of methods (Mann-Kendall test, Sen's slope estimator, Standardized Precipitation Index (SPI), Standardized Streamflow Index (SSI), and Innovative Trend Analysis (ITA) method). SPI and SSI were chosen because they can be applied across various timescales, easy to calculate, and have demonstrated accuracy. Additionally, the trend analysis techniques were preferred because they have few assumptions, are resistant to incomplete data, offer the advantage of visual interpretation, and have the ability to capture changes within data subgroups. The findings analysis shows an autumn rainfall increase (1.18 mm/year) and a significant spring decrease (-2.23 mm/year). In addition, an annual rainfall trend magnitude ranging from − 5.26 mm/year to 1.71 mm/year. The Cheffia Dam experiences a summer inflow decrease (-0.05 hm³/year) and an overall annual decline (-0.35 hm³/year), while the Mexa Dam shows a significant spring inflow decrease (-0.17 hm³/year) with a slight annual decline (-0.07 hm³/year). The ITA method revealed severe and prolonged droughts with increasing severity over time, as shown by the SPI and SSI drought trend analysis. These trends affirm the growing impact of reduced precipitation on water resources and emphasize the urgent need for proactive water management strategies in the region. The results highlight the extremely difficult conditions and the severe drought, particularly in central parts of the basin, over the past 54 years, and underscore ongoing challenges in managing water resources amid changing climatic conditions. This situation negatively impacts the Boulatane dam project and the under-construction Boukhroufa dam. Thus, it is necessary to implement necessary measures to mitigate the effects of prolonged droughts on water availability and infrastructure development. The study's findings provide a foundation for spatial and temporal analysis and management of droughts in water-stressed regions globally, in the context of climate change with innovative techniques. The results emphasize the need for policymakers to prepare for droughts and adopt early warning systems. It is recommended that water conservation efforts be strengthened, new water structures be built, and climate change policies be adapted. [ABSTRACT FROM AUTHOR]

Drought is a natural disaster characterised as precipitation much lower than the precipitation reported in actual periods. Many studies characterized drought as meteorological, hydrological, agricultural, or socioeconomic. When there is a long-term shortage of precipitation, deficits in surface and ground waters occur. In this study, a hydrological drought analysis has been performed for Wadi Mina Basin (4900 km2), which is the biggest sub-basin in Cheliff Basin, using the Streamflow Drought Index (SDI) for the time scales of 3, 6, 9, and 12-month. Monthly mean streamflow records for 05 stations are obtained from the National Water Resources Agency. The obtained SDI values were subjected to Run analysis and drought duration and severity values were calculated. According to the analysis, it has been observed that the maximum (duration: 70 months, severity: 92.78) and average (duration: 31 months, severity: 31.28) droughts occurred at the Sidi AEK Djillali station on a 12-month time scale. The average drought severity was 6.34, with a maximum value of 56.71 on a monthly time scale. However, on a 12-month time scale, the average drought severity increased to 31.28, with a maximum value of 92.78. Therefore, it can be said that the drought severity has increased with the increase in the time scale. When the temporal changes of drought indices are evaluated, it is noteworthy that extraordinary droughts prevailed in the basin in 2000 and 2007. When the scatter diagrams of drought characteristics were examined, it was seen that there was a significant linear relationship between drought duration and severity. In addition, the highest correlation was observed at the 9-month time scale at Ain Hamara (R:0.964) and Oued Abtal (R:0.904) stations. In contrast, the highest correlation was observed at the 12-month time scale at Sidi AEK Djillali (R:0.980) and Takhmaret (R:0.969) stations. [ABSTRACT FROM AUTHOR]

Summary: Decades of studies have shown that Bt corn, by reducing insect damage, has lower levels of mycotoxins (fungal toxins), such as aflatoxin and fumonisin, than conventional corn. We used crop insurance data to infer that this benefit from Bt crops extends to reducing aflatoxin risk in peanuts: a non‐Bt crop. In consequence, we suggest that any benefit–cost assessment of how transgenic Bt crops affect food safety should not be limited to assessing those crops alone; because the insect pest control offered by Bt crops affects the food safety profile of other crops grown nearby. Specifically, we found that higher Bt corn and Bt cotton planting rates in peanut‐growing areas of the United States were associated with lower aflatoxin risk in peanuts as measured by aflatoxin‐related insurance claims filed by peanut growers. Drought‐related insurance claims were also lower: possibly due to Bt crops' suppression of insects that would otherwise feed on roots, rendering peanut plants more vulnerable to drought. These findings have implications for countries worldwide where policies allow Bt cotton but not Bt food crops to be grown: simply planting a Bt crop may reduce aflatoxin and drought stress in nearby food crops, resulting in a safer food supply through an inter‐crop "halo effect." [ABSTRACT FROM AUTHOR]

Understanding tree transpiration variability is vital for assessing ecosystem water‐use efficiency and forest health amid climate change, yet most landscape‐level measurements do not differentiate individual trees. Using canopy temperature data from thermal cameras, we estimated the transpiration rates of individual trees at Harvard Forest and Niwot Ridge. PT‐JPL model was used to derive latent heat flux from thermal images at the canopy‐level, showing strong agreement with tower measurements (R2 = 0.70–0.96 at Niwot, 0.59–0.78 at Harvard at half‐hourly to monthly scales) and daily RMSE of 33.5 W/m2 (Niwot) and 52.8 W/m2 (Harvard). Tree‐level analysis revealed species‐specific responses to drought, with lodgepole pine exhibiting greater tolerance than Engelmann spruce at Niwot and red oak showing heightened resistance than red maple at Harvard. These findings show how ecophysiological differences between species result in varying responses to drought and demonstrate that these responses can be characterized by deriving transpiration from crown temperature measurements. Plain Language Summary: Understanding how forests use water, especially during droughts, is crucial for a changing climate. We developed a method using thermal cameras to estimate individual tree water loss (transpiration), something traditional methods lack. These cameras capture temperature data from the tree crown, which is then used to estimate transpiration rates. Tests in two forests showed this method aligns well with existing water use measurements. The study also revealed fascinating differences in how species handle drought. For instance, lodgepole pine outperformed Engelmann spruce in one forest, while red oak proved more resistant than red maple in the other. This shows that the thermal cameras can help assess how different trees resist drought conditions. This thermal camera technique has the potential to become a valuable tool for monitoring forest health as our climate evolves. Key Points: Canopy‐level evapotranspiration derived from thermal cameras agreed well with eddy covariance measurementsCrown temperature measurements facilitated the estimation of individual‐tree transpiration of co‐occurring species using the same approachThe results show interspecific differences in water use and response to drought that align with species traits [ABSTRACT FROM AUTHOR]

Summary Among many mRNA modifications, adenine methylation at the N6 position (N6‐methyladenosine, m6A) is known to affect mRNA biology extensively. The influence of m6A has yet to be assessed under drought, one of the most impactful abiotic stresses. We show that Arabidopsis thaliana (L.) Heynh. (Arabidopsis) plants lacking mRNA ADENOSINE METHYLASE (MTA) are drought‐sensitive. Subsequently, we comprehensively assess the impacts of MTA‐dependent m6A changes during drought on mRNA abundance, stability, and translation in Arabidopsis. During drought, there is a global trend toward hypermethylation of many protein‐coding transcripts that does not occur in mta. We also observe complex regulation of m6A at a transcript‐specific level, possibly reflecting compensation by other m6A components. Importantly, a subset of transcripts that are hypermethylated in an MTA‐dependent manner exhibited reduced turnover and translation in mta, compared with wild‐type (WT) plants, during drought. Additionally, MTA impacts transcript stability and translation independently of m6A. We also correlate drought‐associated deposition of m6A with increased translation of modulators of drought response, such as RD29A, COR47, COR413, ALDH2B, ERD7, and ABF4 in WT, which is impaired in mta. m6A is dynamic during drought and, alongside MTA, promotes tolerance by regulating drought‐responsive changes in transcript turnover and translation. [ABSTRACT FROM AUTHOR]

ABSTRACT Over the past two decades, the frequency of natural hazard incidents has steadily risen, leading to substantial human casualties, infrastructure destruction, societal and economic disruption. The occurrence of disasters, both of natural and human origin, has exhibited an upward trend in frequency over the past few decades on a global scale, posing a significant threat to diverse populations. Over time, remote sensing technologies have proven to be effective in analysing and monitoring diverse natural disasters, including but not limited to droughts, earthquakes, tsunamis, landslides and cyclones. The significant extent of its coverage capacity and the ability to repeat observations make its application economically efficient. This paper aims to elucidate the fundamental contributions and role of remote sensing in disaster management applications. In a comprehensive analysis, this study explores recent practical applications in the context of disaster events. The utilisation of diverse methodologies and functions of remote sensing in disaster monitoring and control is further expanded to encompass the domain of disaster risk management, employing cutting‐edge sensors and satellites from emerging technological advancements. This paper also addresses challenges related to disaster monitoring, detection and management. Emergencies, particularly during typical catastrophe scenarios, often witness partial disruptions in communication networks. Consequently, the role of alternative networks becomes paramount in enabling effective disaster detection and management strategies. Emerging issues are highlighted, and potential directions for future research are outlined and also support sustainable development goal 13 (climate action). [ABSTRACT FROM AUTHOR]

High temperatures and droughts are two natural disasters that cause forest fires. During climate change, the frequent occurrence of high temperatures, droughts, and their coupled effects significantly increase the forest fire risk. To reveal the seasonal and spatial differences in the coupled effects of high temperatures and droughts on forest fires, this study used the Copula method and proposed the compound extremely high-temperature and drought event index ( CTDI ). The results indicated that the study area was subject to frequent forest fires in spring (71.56%), and the burned areas were mainly located in forests (40.83%) and the transition zone between farmland and forests (36.91%). The probability of forest fires in summer increased with high temperatures and drought intensity, with high temperatures playing a dominant role. The highest forest fire hazard occurred in summer (>0.98). The probability of a forest fire occurring under extreme meteorological conditions in summer and fall was more than twice as high as that in the same zone under non-extreme conditions. Droughts play a significant role in the occurrence and spread of forest fires during fall. These results can provide decision-making support for forest fire warnings and fire fighting in the Northeast China forest zone. [ABSTRACT FROM AUTHOR]

Summary: Nicotiana benthamiana is predominantly distributed in arid habitats across northern Australia. However, none of six geographically isolated accessions shows obvious xerophytic morphological features.To investigate how these tender‐looking plants withstand drought, we examined their responses to water deprivation, assessed phenotypic, physiological, and cellular responses, and analysed cuticular wax composition and wax biosynthesis gene expression profiles.Results showed that the Central Australia (CA) accession, globally known as a research tool, has evolved a drought escape strategy with early vigour, short life cycle, and weak, water loss‐limiting responses. By contrast, a northern Queensland (NQ) accession responded to drought by slowing growth, inhibiting flowering, increasing leaf cuticle thickness, and altering cuticular wax composition. Under water stress, NQ increased the heat stability and water impermeability of its cuticle by extending the carbon backbone of cuticular long‐chain alkanes from c. 25 to 33. This correlated with rapid upregulation of at least five wax biosynthesis genes. In CA, the alkane chain lengths (c. 25) and gene expression profiles remained largely unaltered.This study highlights complex genetic and environmental control over cuticle composition and provides evidence for divergence into at least two fundamentally different drought response strategies within the N. benthamiana species in < 1 million years. [ABSTRACT FROM AUTHOR]

A lack of high‐density rootzone soil moisture (θRZ) observations limits the estimation of continental‐scale, space‐time contiguous θRZ dynamics. We derive a proxy of daily θRZ dynamics — active rootzone degree of saturation (SRZ) — by recursive low‐pass (LP) filtering of surface soil moisture (θS) within a terrestrial water‐energy coupling (WEC) framework. We estimate the LP filter parameters and WEC thresholds for the piecewise‐linear coupling between SRZ and evaporative fraction (EF) at remote sensing and field scale over the Contiguous U.S. We use θS from the Soil Moisture Active‐Passive (SMAP) satellite and 218 in‐situ stations, with EF from the Moderate Resolution Imaging Spectroradiometer. The estimated SRZ compares well against SMAP Level‐4 estimates and in‐situ θRZ, at the corresponding scale. The instantaneous hydrologic state (SRZ) vis‐à‐vis the WEC thresholds is proposed as a rootzone soil moisture stress index (SMSRZ) for near‐real‐time operational agricultural drought monitoring and agrees well with established drought metrics. Plain Language Summary: Rootzone soil moisture plays a vital role in agricultural, hydrological, and ecosystem processes. The available spaceborne satellites for monitoring soil moisture can only capture variability in a shallow soil layer at the surface, typically limited to the top 5 cm. Hence, spatiotemporally continuous estimation of rootzone soil moisture dynamics typically relies on soil moisture estimates from land‐surface models, which are subject to errors in the surface meteorological forcing data, process formulations, and model parameters. Some studies suggest that the rootzone soil moisture dynamics can be estimated by filtering the high‐frequency variability in the surface soil moisture. However, such "filters" require observed rootzone data (often unavailable at high spatial density) for calibration. This study uses the relationship between surface soil moisture and evaporative fraction derived using spaceborne observations from the Soil Moisture Active Passive mission and the Moderate Resolution Imaging Spectroradiometer to estimate rootzone soil moisture dynamics for the Contiguous U.S. at 9 km grid resolution. We further demonstrate that this approach can be extended into a near‐real‐time agricultural drought monitor to assess drought impacts on vegetation using surface soil moisture observations. Key Points: Terrestrial water‐energy coupling is used to parameterize low‐pass filter to estimate rootzone dynamics from surface soil moistureRootzone degree of saturation and water‐energy coupling thresholds are estimated using evaporative fraction and surface soil moistureSMAP‐based rootzone degree of saturation can used for operational, near‐real‐time agricultural drought monitoring over Contiguous U.S [ABSTRACT FROM AUTHOR]

Rice production within the Tonle Sap basin is a critical driver of economic and social development in Cambodia. This region has been subject to various natural disasters, with increasing attention directed towards drought. This study aims to evaluate the impacts of drought on agriculture and food security through an in-depth case study of the Baribo basin, a sub-basin of the Tonle Sap. The analysis spans the period from 1985 to 2008, a timeframe characterized by relatively high-quality data. Drought assessment was conducted using ground observations and satellite-based products, with the Standardized Precipitation Index (SPI) and Standard Vegetation Index (SVI) employed to assess meteorological and agricultural droughts, respectively. Findings from both the SPI and SVI indicate that drought constitutes a significant natural hazard contributing to food insecurity in the study area. The highest drought intensity (DI) and drought severity (DS) were recorded during the 1993–1994 period, while the most prolonged drought duration (DD) was observed from 2002 to 2006. The year 2004 witnessed the most severe impact on rice production, with approximately 46% of the total cultivated area affected. The analysis further reveals a strong correlation between the drought duration and the extent of rice cultivation affected, as well as the overall food security in the Tonle Sap basin. [ABSTRACT FROM AUTHOR]

Hydrologic models are the primary tools that are used to simulate streamflow drought and assess impacts. However, there is little consensus about how to evaluate the performance of these models, especially as hydrologic modeling moves toward larger spatial domains. This paper presents a comprehensive multi-objective approach to systematically evaluating the critical features in streamflow drought simulations performed by two widely used hydrological models. The evaluation approach captures how well a model classifies observed periods of drought and non-drought, quantifies error components during periods of drought, and assesses the models' simulations of drought severity, duration, and intensity. We apply this approach at 4662 U.S. Geological Survey streamflow gages covering a wide range of hydrologic conditions across the conterminous U.S. from 1985 to 2016 to evaluate streamflow drought using two national-scale hydrologic models: the National Water Model (NWM) and the National Hydrologic Model (NHM); therefore, a benchmark against which to evaluate additional models is provided. Using this approach, we find that generally the NWM better simulates the timing of flows during drought, while the NHM better simulates the magnitude of flows during drought. Both models performed better in wetter eastern regions than in drier western regions. Finally, each model showed increased error when simulating the most severe drought events. [ABSTRACT FROM AUTHOR]

The current study investigated hydroclimatic extremes in Vhembe, Lejweleputswa, and uMgungundlovu District Municipalities based on streamflow data from 21 river gauge stations distributed across the study site for the period spanning 1985–2023. Statistical metrics such as the annual mean and maximum streamflow, as well as trends in annual, maximum, seasonal, and high/low flow, were used to evaluate the historical features of streamflow in each of the three district municipalities. Moreover, the Standardized Streamflow Index (SSI) time series computed from streamflow observations at 3- and 6-month accumulation periods were used to assess hydroclimatic extremes, including drought episodes, proportion of wet/dry years and trends in SSI, drought duration (DD), and drought severity (DS). The results indicate that the three district municipalities have experienced localized and varying degrees of streamflow levels and drought conditions. The uMgungundlovu District Municipality in particular has experienced a significant decline in annual and seasonal streamflow as well as an increase in drought conditions during the 38-year period of analysis. This is supported by the negative trends observed in most of the assessed metrics (e.g., annual, maximum, seasonal, low/high flow, and SSI), whereas DD and DS showed positive trends in all the stations, suggesting an increase in prolonged duration and severity of drought. The Lejweleputswa District Municipality depicted positive trends in most of the assessed metrics, suggesting that streamflow increased, whereas drought decreased in the region over the 38-year period of study. Moreover, the Vhembe District Municipality experienced both negative and positive trends, suggesting localized variations in dry and wet conditions. The results presented in this study contribute towards drought monitoring and management efforts in support of policy- and decision-making that aim to uplift water resources management and planning at local and district municipality levels. [ABSTRACT FROM AUTHOR]

Drought is the primary driver of wildfires in humid regions, and the main drought drivers for wildfire occurrence and spread vary across different humid climatic areas. This study explores the suitability of different drought descriptions for wildfires under various humid temperate climates in Japan. Based on wildfire data from 1995 to 2012, statistical and correlation analyses were conducted to examine the performance of effective humidity (EH) and soil moisture (SM) as indicators of atmospheric and soil drought. EH is used for nationwide wildfire and drought warnings in Japan. The results show that EH is significantly influenced by seasonal and regional factors, with its ability to assess drought for wildfire varying accordingly, whereas SM demonstrates a more consistent ability to assess drought across different seasons and regions. Correlation analysis revealed that atmospheric drought better explains the drought conditions for wildfire ignition in 11 prefectures, mainly concentrated in the northern regions along the Sea of Japan. In contrast, the correlation coefficients for SM were higher in 33 prefectures, particularly along the Pacific coast, indicating that soil drought better explains the drought conditions for burned areas in these prefectures. [ABSTRACT FROM AUTHOR]

In this review, we discuss current research on forest carbon risk from natural disturbance under climate change for the United States, with emphasis on advancements in analytical mapping and modeling tools that have potential to drive research for managing future long-term stability of forest carbon. As a natural mechanism for carbon storage, forests are a critical component of meeting climate mitigation strategies designed to combat anthropogenic emissions. Forests consist of long-lived organisms (trees) that can store carbon for centuries or more. However, trees have finite lifespans, and disturbances such as wildfire, insect and disease outbreaks, and drought can hasten tree mortality or reduce tree growth, thereby slowing carbon sequestration, driving carbon emissions, and reducing forest carbon storage in stable pools, particularly the live and standing dead portions that are counted in many carbon offset programs. Many forests have natural disturbance regimes, but climate change and human activities disrupt the frequency and severity of disturbances in ways that are likely to have consequences for the long-term stability of forest carbon. To minimize negative effects and maximize resilience of forest carbon, disturbance risks must be accounted for in carbon offset protocols, carbon management practices, and carbon mapping and modeling techniques. This requires detailed mapping and modeling of the quantities and distribution of forest carbon across the United States and hopefully one day globally; the frequency, severity, and timing of disturbances; the mechanisms by which disturbances affect carbon storage; and how climate change may alter each of these elements. Several tools (e.g. fire spread models, imputed forest inventory models, and forest growth simulators) exist to address one or more of the aforementioned items and can help inform management strategies that reduce forest carbon risk, maintain long-term stability of forest carbon, and further explore challenges, uncertainties, and opportunities for evaluating the continued potential of, and threats to, forests as viable mechanisms for forest carbon storage, including carbon offsets. A growing collective body of research and technological improvements have advanced the science, but we highlight and discuss key limitations, uncertainties, and gaps that remain. [ABSTRACT FROM AUTHOR]

Summary Plant physiological responses to increasing atmospheric CO2 concentration (iCO2), including enhanced photosynthesis and reduced stomatal conductance, impact regional and global climate. Here, I describe recent advances in understanding these effects through Earth system models (ESMs). Idealized simulations of a 1% annual iCO2 show that despite fertilization, CO2 physiological forcing contributes to 10% of warming and at least 30% of future precipitation decline in Amazonia. This reduces aboveground vegetation carbon storage and triggers positive carbon–climate feedback. ESM simulations indicate that reduced transpiration and increased heat stress from iCO2 could amplify meteorological drought and wildfire risks. Understanding these climate feedbacks is essential for improving carbon accounting in natural climate solutions, such as avoiding deforestation and reforestation, as iCO2 complicates assessing their climate benefits. [ABSTRACT FROM AUTHOR]

Drought is a major limiting factor in the cultivation of Sophora alopecuroides in China's arid and semi-arid regions. This study aimed to identify drought-resistant S. alopecuroides ecotypes and explore key drought tolerance indicators during germination by simulating drought conditions with a 10% PEG-6000 solution, using pure water as a control. Determination of seven germination indicators for S. alopecuroides, including germination rate (GR), germination energy (GE), germination index (GI), vigor index (VI), promptness index (PI), fresh weight (FW), and dry weight (DW), was conducted. Principal component analysis (PCA), membership function, cluster analysis, and linear regression were employed to comprehensively evaluate the drought resistance of thirty-five S. alopecuroides ecotypes. The results showed that drought stress caused reductions in six of the seven indicators across all ecotypes, except for DW, compared to the control. Correlation analysis revealed varying relationships among the indicators, with most showing significant or highly significant correlations. PCA reduced the seven indicators to two independent comprehensive factors, with a cumulative contribution rate of 83.99%. Based on the D-value and cluster analysis, the thirty-five ecotypes were ranked for drought resistance and classified into four categories. The top five drought-tolerant genotypes during the germination stage were identified as Yutian, Alar, Jinghe, Baoding, and Guyuan. Moreover, the stepwise regression model was established and demonstrated that GR, GE, PI, FW, and DW are key indicators for screening and identifying drought-resistant S. alopecuroides ecotypes. This study offers a comprehensive and reliable method for evaluating drought resistance in S. alopecuroides ecotypes and provides a reference for selecting ecotypes for artificial cultivation in Northwestern China. [ABSTRACT FROM AUTHOR]

Ordos drought impacts are complex; the Geodetector model is able to explore the interaction between impact factors. Based on the drought severity index (DSI), this study explored the spatio-temporal dynamics and changing trends of drought, and analyzed the driving factors of DSI spatial differentiation by using the Geodetector model. The results show that: the evapotranspiration (ET) and normalized difference vegetation index (NDVI) in Ordos showed a significant increasing trend (p < 0.05). The increasing rates were ET (4.291 mm yr−1) and NDVI (0.004 yr−1). In addition, the interannual variation of the DSI also showed a significant increase, with a trend change rate of 0.089. The spatial pattern of ET and the NDVI was low in the southwest and high in the northeast, and the spatial pattern of potential evapotranspiration (PET) was high in the southwest and low in the northeast, while the distribution of the DSI was dry in the west and wet in the east. The spatial differentiation of the DSI was mainly affected by five factors: air temperature, precipitation, land use type, soil type, and the digital elevation model (DEM), with q exceeding 0.15, which were the main driving factors of drought in the Loess Plateau. Under the interaction of multiple factors, the four combinations of temperature and the DEM, precipitation and the DEM, sunshine duration and the DEM, and relative humidity and the DEM jointly drive drought, in which precipitation (0.156) ∩ DEM (0.248) has the strongest influence on drought occurrence, and q reaches 0.389. This study directly informs specific drought management strategies or ecological conservation efforts in the region. [ABSTRACT FROM AUTHOR]

Drought is the most important abiotic stress that restricts the genetically predetermined yield potential of the crops. In the present study, four tomato varieties: Kashi Vishesh, Kashi Aman, Kashi Abhiman, and Kashi Amrit, were used to study the effect of PGPMs (plant growth-promoting microorganisms). PGPM strains, Bacillus megaterium BHUPSB14, Pseudomonas fluorescens BHUPSB06, Pseudomonas aeruginosa BHUPSB01, Pseudomonas putida BHUPSB0, Paenibacillus polymixa BHUPSB17, and Trichoderma horzianum, were used as the consortium. The control group was irrigated up to 80% of field capacity, while 7-, 14-, and 21-day water-deficit-exposed (DWD) plants' pot soil moisture was maintained to 40, 25, and 15% of the field capacity, both with and without the PGPM inoculation condition. The physiological parameters, such as electrolyte leakage, relative water content, photosynthetic efficiency, and chlorophyll color index, were significantly improved by PGPM application under progressive drought stress, compared to the control. PGPM application enhanced the proline accumulation and reduced the formation of hydrogen peroxide and lipid peroxidation under drought stress. The plant growth attributes were significantly increased by PGPM application. The Kashi Amrit variety showed the highest fruit yield among the four varieties under all the treatments. The PGPM consortium application also improved the soil physico-biological properties and nutrient availability in the soil. The PGPM consortium used in this study can potentially mitigate drought stress on tomato in drought-prone regions and act as a biofertilizer. The present study will open a new avenue of drought stress management in tomato. [ABSTRACT FROM AUTHOR]

High-frequency drought hazards have presented persistent challenges for environmental management and sustainable development in the South Hebei Plain, China. In this paper, the assessment of meteorological droughts in the South Hebei Plain was conducted using a multifaceted approach to ensure a comprehensive analysis. Our results demonstrated that distinct timescale cycles, ranging from centennial–semicentennial to interdecadal variations, can be identified over the past few centuries. These cycles aligned with patterns observed in the middle Yangtze basin and corresponded to regional climatic conditions. The drought cycles in the South Hebei Plain showed significant correlations with variations in the monsoon climate, sunspot activity, global changes, and human disturbances. Changes in the frequency, duration, and intensity of droughts have notably impacted hydrological variations. Extreme droughts, in particular, have heightened concerns about their effects on river systems, potentially increasing the risk of channel migration. This study enhanced our understanding of meteorological hazard patterns in the South Hebei Plain and provided valuable insights into different stages of drought management. It thus can offer lessons for improving drought preparedness and resilience and for formulating adaptive measures to mitigate future droughts and promote regional sustainability. [ABSTRACT FROM AUTHOR]

Drought poses a serious threat to Sudan, causing water shortages, crop failures, hunger, and conflict. The relationships between Indo-Pacific teleconnections and drought events in Sudan are examined based on the Standardized Precipitation Index (SPI), anomalies, Empirical Orthogonal Function (EOF), correlation, composite analysis, sequential Mann–Kendall test, and MK-trend test during the period of 1993–2022. The results of the SPI-1 values indicate that the extreme drought in Sudan in 2004 was an exceptional case that affected the entire region, with an SPI-1 value of −2 indicating extremely dry conditions. In addition, Sudan experienced moderate to severe drought conditions for several years (1993, 2002, 2008, 2009 and 2015). The Empirical Orthogonal Function showed that the first EOF mode (42.2%) was the dominant variability mode and had positive loading over most of the country, indicating consistent rainfall variation in the central, eastern, and western regions. Correlation analysis showed a strong significant relationship between June–September rainfall and Indian Ocean sea surface temperature (SST) (r ≤ 0.5). Furthermore, a weak positive influence of the Indian Ocean Dipole (IOD) on JJAS precipitation was observed (r ≤ 0.14). Various time lags in the range of ±12 months were examined, with the highest correlation (0.6) found at 9 month among the time lags of ±12 months. This study contributes to a better understanding of drought dynamics and provides essential information for effective drought management in Sudan. Further research is needed to explore the specific mechanisms driving these interactions and to develop tailored strategies to mitigate the impacts of drought events in the future. [ABSTRACT FROM AUTHOR]

Droughts have a significant impact on surface water resources, especially in arid and semi-arid regions. Computational and data handling limitations have constrained previous time-series analyses. However, advances in cloud computing services and remote sensing technology allow for a more detailed examination. This study integrates multi-source satellite-derived data with a cloud computing platform to assess the impact of long-term drought on surface water and water balance in Iran from 2000 to 2021. Given the varying effects of drought on highlands and lowlands, the analysis was conducted at three levels: the entire country, the highlands, and the lowlands. The results of this study reveal imbalances between water balance from 2000 to 2021, with notable disparities observed during 2000–2007, 2009–2014, and 2016–2019. The results also show varying drought trends (e.g., −1.22 in 2000 and −0.73 in 2021), with severe conditions captured in 2008 (SPI: −1.92). Additionally, our analysis illustrated that lowlands were more impacted by droughts compared to highlands. Long-term drought and permanent surface water had correlation values of 0.33 across the country, 0.33 in the highlands, and 0.31 in the lowlands. For seasonal surface water, coefficients were 0.18 for the entire country, 0.16 for the highlands, and 0.18 for the lowlands. Overall, long-term drought had minimal effect on reducing surface water. These findings show that drought is only part of the explanation for the decrease in surface water resources. [ABSTRACT FROM AUTHOR]

Developing an effective and reliable integrated drought index is crucial for tracking and identifying droughts. The study employs game theory to create a spatially variable weight drought index (game-theory-based drought index, GTDI) by combining two single-type indices: an agricultural drought index (standardized soil moisture index, SSMI), which implies drought hazard-bearing conditions, and a meteorological drought index (standardized precipitation evapotranspiration index, SPEI), which implies drought hazard-causing conditions. In addition, the entropy-theory-based drought index (ETDI) is introduced to incorporate a spatial comparison with GTDI to illustrate the rationality of gaming weight integration, as both entropy theory and game theory belong to linear combination methods in the development of the integrated drought index and entropy theory has been applied in related research. Leaf area index (LAI) data are employed to confirm the reliability of GTDI in identifying drought by comparing it with SPEI, SSMI, and ETDI. Furthermore, a comparative analysis is conducted on the temporal trajectories and spatial evolution of the GTDI-identified drought to discuss GTDI's level of advancement in monitoring changes in hazard-causing and hazard-bearing impacts. The results show that GTDI has a very high correlation with single-type drought indices (SPEI and SSMI), and its gaming weight integration is more logical and trustworthy than that of ETDI. As a result, it outperforms ETDI, SPEI, and SSMI in recognizing drought spatiotemporally and is projected to replace single-type drought indices to provide a more accurate picture of actual drought. Additionally, GTDI exhibits the gaming feature, indicating a distinct benefit in monitoring changes in hazard-causing and hazard-bearing impacts. The case studies show that drought events in the Wei River basin are dominated by a lack of precipitation. The hazard-causing index, SPEI, dominates the early stages of a drought event, whereas the hazard-bearing index, SSMI, dominates the later stages. This study surely serves as a helpful reference for the development of integrated drought indices as well as regional drought prevention and monitoring. [ABSTRACT FROM AUTHOR]

The Poyang Lake Wetland, an internationally significant ecosystem, frequently experiences drought during the flood season. However, the total impact of extreme drought on wetland vegetation remains poorly understood. This study determined the standardised precipitation evapotranspiration index (SPEI) and analysed drought trends within the Poyang Lake Basin. Additionally, spatiotemporal variations in wetland vegetation under drought conditions were examined by analysing the mean normalised difference vegetation index (NDVI) values and categorising NDVI classifications. The key factors affecting wetland vegetation and its respective thresholds were determined. The Poyang Lake Basin has experienced increasing aridity over the past 3 years. In response to this trend, the wetland vegetation area in Poyang Lake expanded, whereas vegetation greenness declined. Notably, in the year following an extreme drought, Poyang Lake's vegetation greenness was lower than that during the same period in previous years. Regardless, the correlation analysis showed no significant relationship between the SPEI values and the wetland vegetation greenness; however, water level changes significantly impacted the wetland vegetation, with a correlation coefficient of −0.89 (p < 0.001). A critical water level of 14 m was identified as the threshold at which sudden changes in the mean NDVI were observed. This research offers valuable insights into hydrological management strategies to protect Poyang Lake Wetland's vegetation under drought conditions. Future studies should enhance the differentiation of drought tolerance among different wetland plant species, thereby achieving differentiated hydrological management. [ABSTRACT FROM AUTHOR]

Intensifying weather events are key characteristics of climate change that are fundamentally changing ecological disturbance regimes. Intensifying drought is a particular threat to species, ecosystems, and ecosystem services worldwide. Proactive drought adaptation measures are acutely needed, but without a better understanding of drought vulnerability at the appropriate scale and geography, such measures may not be effective, or even anticipated as potential options. A recent conceptual framework for ecological drought aligns a holistic suite of potential drivers with the key components of climate change vulnerability (exposure, sensitivity, and adaptive capacity). We leverage the ecological drought framework and components of vulnerability to introduce a six‐step process for developing a drought vulnerability assessment (DVA) that (1) is place‐based and avoids mismatches between assessment geography and management action, (2) uses existing empirical datasets and leverages machine learning techniques and remotely sensed data from a recent drought, (3) emphasizes the inclusion of stakeholders and the importance of data visualization and science communication, and (4) is flexible and adaptable to a wide range of planning contexts. We illustrate the DVA process with a case study for forested watersheds in the Missouri Headwaters (MH), Montana, USA, that is focused on the impact of an early 2000s drought event on forest health. We show how the DVA provides insights on drought vulnerability that are helpful starting points for co‐developing region‐specific management actions to prepare for the next drought, including strategies to enhance ecologically available water, reduce competition for water, promote ecosystem persistence under drought conditions, and prioritize sites for forest restoration, transition, or protection. The work described here provides a model for developing a DVA in other places that, when used in a participatory adaptation planning process, supports the implementation of effective adaptation strategies. [ABSTRACT FROM AUTHOR]

Assessing drought is crucial for effective water resources management and the development of mitigation strategies. Drought indices serve as indispensable tools in this evaluation process, and choosing an appropriate index is vital for accurate drought assessment. The characteristics and classification of drought depend entirely on the chosen index. Based on the existing literature, the Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI) are the most commonly and widely used indices, and there is a significant need for a comprehensive comparison between SPI and SPEI to understand their differences and implications for drought assessment. This research aims to compare SPI and SPEI based on drought indices, characteristics, and classifications using the innovative drought classification matrix (IDCM) for spatiotemporal drought evaluation, and the comparison process is done for events and monthly scales. Also, it aims to investigate the comparison between SPEI obtained from in situ meteorological stations and from the SPEI database. The application and the comparison are presented for Istanbul city between 1951 and 2020. The results show similar variations and high correlation (more than 0.65) between SPI and SPEI. For drought characteristics, there is no consistent relationship between SPI and SPEI at the drought event scale. The outcomes revealed that approximately 60% of the months exhibit consistent drought classifications between the two indices. Finally, it shows a significant difference between SPEI based on in situ meteorological stations and satellite data. The comparison between SPI and SPEI based on different aspects is necessary and essential for drought studies and water resources management. [ABSTRACT FROM AUTHOR]

This study investigates the stability of hydrological drought trends in the Euphrates Basin from 1960 to 2020 using three-dimensional (3D) graphical representations based on innovative trend analysis (ITA) and triple Wilcoxon test (WT) methods. Unlike traditional ITA and WT, which are widely used for trend identification but do not inherently provide trend stability information, this study employs a novel approach to assess and visualize trend stability. The Triple WT method divides the data into three equal segments, examining differences without altering the time series. Drought indices are calculated for 3-month, 6-month, and 12-month time scales using historical streamflow data from five stations. The research identifies trends and their stabilities across three distinct periods: 1967–1984, 1985–2002, and 2003–2020. Results show that as the time scale increases, trend differences between extreme drought conditions diminish. One station consistently exhibits significantly decreasing trends, while three stations show unstable trends with notable variations in the standardized streamflow index (SSFI). The use of 3D-ITA and Triple WT effectively captures the dynamics and stability of drought trends, offering a deeper understanding of hydrological drought in the Euphrates Basin. These findings provide a reference for future studies on drought trend mechanisms in various climatic regions. [ABSTRACT FROM AUTHOR]

Accurate detection of drought stress in plants is essential for water use efficiency and agricultural output. Hyperspectral imaging (HSI) provides a non-invasive method in plant phenotyping, allowing the long-term monitoring of plant health due to sensitivity to subtle changes in leaf constituents. The broad spectral range of HSI enables the development of different vegetation indices (VIs) to analyze plant trait responses to multiple stresses, such as the combination of nutrient and drought stresses. However, known VIs may underperform when subjected to multiple stresses. This study presents new VIs in tandem with machine learning models to identify drought stress in wheat plants under varying nitrogen (N) levels. A pot wheat experiment was set up in the glasshouse with four treatments: well-watered high-N (WWHN), well-watered low-N (WWLN), drought-stress high-N (DSHN) and drought-stress low-N (DSLN). In addition to ensuring that plants were watered according to the experiment design, photosynthetic rate (Pn) and stomatal conductance (gs) (which are used to assess plant drought stress) were taken regularly, serving as the ground truth data for this study. The proposed VIs, together with known VIs, were used to train three classification models: support vector machines (SVM), random forest (RF), and deep neural networks (DNN) to classify plants based on their drought status. The proposed VIs achieved more than 0.94 accuracy across all models, and their performance further increased when combined with known VIs. The combined VIs were used to train three regression models to predict the stomatal conductance and photosynthetic rates of plants. The random forest regression model performed best, suggesting that it could be used as a stand-alone tool to forecast gs and Pn and track drought stress in wheat. This study shows that combining hyperspectral data with machine learning can effectively monitor and predict drought stress in crops, especially in varying nitrogen conditions. [ABSTRACT FROM AUTHOR]

Drought monitoring is a critical environmental challenge, particularly in regions where irrigated agricultural intensification and urban expansion pressure water resources. This study assesses the impact of these activities on drought dynamics in Morocco's Oum Er-Rbia (OER) watershed from 2002 to 2022, using the newly developed Watershed Integrated Multi-Drought Index (WIMDI), through Google Earth Engine (GEE). WIMDI integrates several drought indices, including SMCI, ESI, VCI, TVDI, SWI, PCI, and SVI, via a localized weighted averaging model (LOWA). Statistical validation against various drought-type indices including SPI, SDI, SEDI, and SMCI showed WIMDI's strong correlations (r-values up to 0.805) and lower RMSE, indicating superior accuracy. Spatiotemporal validation against aggregated drought indices such as VHI, VDSI, and SDCI, along with time-series analysis, confirmed WIMDI's robustness in capturing drought variability across the OER watershed. These results highlight WIMDI's potential as a reliable tool for effective drought monitoring and management across diverse ecosystems and climates. [ABSTRACT FROM AUTHOR]

Effective drought management requires precise measurement, but this is challenging due to the variety of drought indices and indicators, each with unique methods and specific uses, and limited ground data availability. This study utilizes remote sensing data from 2001 to 2020 to compute drought indices categorized as meteorological, agricultural, and hydrological. A Gaussian kernel convolves these indices into a denoised, multi-band composite image. Further refinement with a Gaussian kernel enhances a single drought index from each category: Reconnaissance Drought Index (RDI), Soil Moisture Agricultural Drought Index (SMADI), and Streamflow Drought Index (SDI). The enhanced index, encompassing all bands, serves as a predictor for classification and regression tree (CART), support vector machine (SVM), and random forest (RF) machine learning models, further improving the three indices. CART demonstrated the highest accuracy and error minimization across all drought categories, with root mean square error (RMSE) and mean absolute error (MAE) values between 0 and 0.4. RF ranked second, while SVM, though less reliable, achieved values below 0.7. The results show persistent drought in the Sahel, North Africa, and southwestern Africa, with meteorological drought affecting 30% of Africa, agricultural drought affecting 22%, and hydrological drought affecting 21%. [ABSTRACT FROM AUTHOR]

This study highlights drought forecasting for understanding the semi-arid area in India, where drought phenomena play vital role in the irrigation, drinking water supplies, and sustaining the ecological with economic balance for every nation. Therefore, drought forecasting is important for the future drought planning based on the machine learning (ML) models. Hence, The Standardized Precipitation Index (SPI) at 3- and 6-month periods have been selected and used for future drought forecasting scenarios in area. The combinations of ten inputs SPI-1- and SPI-10 were used for predicting modeling for SPI-3 and SPI-6 timescales, that modeling developed based on the historical SPI datasets from 1989 to 2019 years. The SPI-3 and SPI-6 maximum and minimum values are shown SPI-3 (2.03 and -5.522) and SPI-6 (1.94 and -6.93). The SPI is a popular method for estimating the drought analysis and has been used everywhere at global level. The developed models have been compared with each other, with the best combination of input variables selected using subset regression models and sensitivity studies. After that, the active input parameters were used for forecasting of SPI-3 and SPI-6 values to understanding of drought in semi-arid area. The finest input variables combination have been used in the Ml models and established the novel five models such as robust linear regression, bagged trees, boosted trees, support vector regression (SVM-Linear), and Matern Gaussian Process Regression (Matern GPR) models. Such kind of models first time has been applied for the forecasting of future drought conditions. Whole models were fine and improved modeling by using hyperparameters tuning, bagging, and boosting models. Entire ML models' accuracy was compared using different statistical metrics. Compared with five ML models accuracy, we have found that the Matern GPR model better accuracy than other ML models. The best model accuracy is R2 = 0.95 and 0.93, RMSE, MSE, MAE, MARE, and NSE values, respectively, for predicting SPI-3 and SPI-6 values in the area. Therefore, the Matern GPR model was identified as the finest ML algorithm for predicting SPI-3 and SPI-6 associated with other algorithms. This research demonstrates the Matern GPR model's efficacy in predicting multiscale SPI-3 and SPI-6 under climate variations. It can be helpful in soil and water resource conservation planning and management and understanding droughts in the entire basin areas of the country India. [ABSTRACT FROM AUTHOR]

The prevalence of drought events worldwide emphasizes the importance of screening and cultivating drought-adapted crops. In this study, 206 germplasm resources were used as materials, dry weight as target trait, and two genotyping methods as criteria to evaluate drought adaptability at the seedling establishment stage. The results showed a significant decrease in average dry weight of the tested germplasm resources (from 746.90 mg to 285.40 mg) and rich variation in the responses of dry weight among each genotype to drought (CV=61.14%). In traditional evaluation method, drought resistance coefficient (DC), geometric mean productivity index (GMP), mean productivity index (MP), stress susceptibility index (SSI), stress tolerance index (STI), and tolerance index (TOL) also exhibited diversity in tested genotypes (CV>30%). However, these indices showed varying degrees of explanation for dry weight under stress and nonstress environments and failed to differentiate drought adaptability among genotypes clearly. In new evaluation method, four stress indices were developed to quantify barley seedling production and stability capacities. Compared to traditional stress indices, the stress production index (SI) explained dry weight more comprehensively under stress conditions (R² = 0.98), while the ideal production index (II) explained dry weight better under non-stress conditions (R² = 0.89). Furthermore, the potential index (PI) and elasticity index (EI) eliminated disparities in traditional stress indices and comprehensively clarified the contribution of elasticity and potential to production capacity under drought stress. Ultimately, through grading evaluation and cluster analysis, the tested germplasm resources were effectively categorized, and 11 genotypes were identified as suitable for cultivation in arid areas. Overall, the comprehensive evaluation method based on the newly developed stress indices surpasses the traditional method in screening drought adaptability of crops and serves as a vital tool for identifying high-stability and high-production capacities genotypes in various environments, which is expected to provide practical guidance for barley planting and breeding in arid areas. [ABSTRACT FROM AUTHOR]

Drought is a major global challenge causing significant socio-economic and environmental impacts. A paradigm shift from crisis to risk management is advocated to reduce the impacts of droughts, and to build the resilience of societies, and water and environmental systems against drought. A number of drought policy and planning guidelines are developed and used to support the transition from crisis to risk management and enhancing resilience. However, research is lacking on critical reflection, evaluation and update of the available drought guidelines. For example, there is no study on assessing the correspondence of the available guidelines to the contemporary disaster risk reduction agenda. Therefore, this study evaluates twelve drought policy and planning guidelines for their alignment with the four priority areas of the SENDAI framework for disaster risk reduction 2015–2030. A qualitative evaluation matrix was developed and used in the assessment. The examined priorities and associated thematic elements were scored in the range 0–100, and classified under Very Low (0–10), Low (11–30), Medium-Low (31–50), Medium-High (51–70), High (71–90), and Very High (91–100) categories. Most guidelines achieved (medium) high to very high scores on data and information, risk assessment, and communication and dissemination elements associated with priority 1 (understanding disaster risk). Whereas, mostly very low to low coverage was found for science-policy-practice dialogue, local knowledge and practices, and research and development. Strengthening disaster risk governance to manage disaster risk (priority 2) earned high scores on most elements, notably for strategies and plans, coordination mechanisms, community representation, and policy and governance. In contrast, most elements under priority 3 (investing in disaster risk reduction) were classified under low to medium categories, which include financial allocation, risk transfer, and mainstreaming drought risk reduction into land use and rural development planning, business resilience and protection of livelihoods, and health and safety. Most elements under priority 4 (enhancing disaster preparedness) scored under medium low to medium high ranges, as sufficient information was lacking on multi-hazard early warning systems, post-disaster recovery, rehabilitation, and reconstruction, and resilience of critical infrastructure. Furthermore, the study outlined several strengths, weaknesses, opportunities and threats pertaining to the examined guidelines. In general, the study reveals an urgent need to better align drought policy and planning guidelines with the contemporary disaster risk reduction agenda outlined in the SENDAI Framework. The findings of this study can be instructive in designing the next generation of drought guidelines in support of an accelerated transition towards drought risk management, and building resilient societies and ecosystems under a changing climate and increasing anthropogenic pressures. [ABSTRACT FROM AUTHOR]

This study examines the spatiotemporal variability of drought and associated physical processes over the Arabian Peninsula (AP). For this purpose, we computed the standardized precipitation evapotranspiration index (SPEI) for the period 1951–2020 using the Climate Research Unit and fifth generation ECMWF atmospheric reanalysis datasets. By applying rotated empirical orthogonal function analysis on the SPEI data, we identified four homogeneous and coherent drought regions. The droughts in the northern regions follow a relatively similar temporal evolution as compared to those in the southern region. All four sub-regions of the AP exhibit a significant drying trend (p < 0.01) with an abrupt acceleration in drought frequency and intensity over the last two decades. The increase in droughts is associated with the reduction of synoptic activity and an increase in the high pressure over the AP. Seasonally, potential evapotranspiration is the dominant driver of summer droughts in the AP, whereas both precipitation and temperature are important for driving winter droughts. The summer droughts, mainly over the northern AP, are due to the occurrence of an anomalous equivalent barotropic high associated with anomalous dry and hot conditions. However, anomalous dry conditions in winter are a result of an anomalous paucity of winter storms caused by the weakening of the sub-tropical jets. [ABSTRACT FROM AUTHOR]

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]

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]

• 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]

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]