Regional Drought Research Articles

Divergent Drying Mechanisms in Humid and Non-Humid Regions Across China

Understanding the drying mechanism is critical for formulating targeted mitigation strategies to combat drought impacts. This study aimed to reveal divergent drying mechanisms in humid and non-humid regions across China from the multidimensional perspectives of climate, vegetation, and energy balance. During the period 1982–2012, the Standardized Precipitation Evapotranspiration Index (SPEI) revealed non-significant drying trends across China. Simultaneously, temperature and precipitation indicated a warming and drying pattern in the humid regions, contrasted with a warming and moistening pattern in the non-humid areas. The coupling effects of declined precipitation, increased vegetation coverage, and elevated temperature exacerbated dryness in the humid regions, while pronounced warming dominantly caused dryness in the non-humid regions. The inverse correlations between the actual evapotranspiration (ET) with precipitation and potential ET (PET) highlighted the principal role of moisture availability in divergent drying mechanisms over humid and non-humid regions. Random Forest models recognized precipitation and PET as the primary factors influencing SPEI in the humid and non-humid regions, respectively. Ongoing warming from 2013 to 2022 mitigated dryness in the humid regions due to the increased latent heat at the expense of sensible heat. Conversely, warming, amplified by the heightened sensible heat, exacerbated drought in the non-humid regions. By identifying the contrasting responses of humid and non-humid regions to warming and moisture availability, this study provides crucial insights for policymakers to mitigate drought impacts and enhance resilience in vulnerable non-humid areas.

Exploring the Spatially Compounding Multi‐Sectoral Drought Vulnerabilities in Colorado's West Slope River Basins

AbstractThe state of Colorado's West Slope Basins are critical headwaters of the Colorado River and play a vital role in supporting Colorado's local economy and natural environment. However, balancing the multi‐sectoral water demands in the West Slope Basins while maintaining crucial downstream deliveries to Lake Powell is an increasing challenge for water managers. Internal variability of the hydroclimatic system and climate change complicate future vulnerability assessments. This work contributes a detailed accounting of multi‐sectoral drought vulnerability in the West Slope Basins and the impacts of drought on downstream deliveries. We first introduce a novel multi‐site Hidden Markov Model (HMM)‐based synthetic streamflow generator to create an ensemble of streamflows for all West Slope basins that better characterizes the region's drought extremes. We capture the effects of climate change by perturbing the HMM to generate an ensemble of streamflows reflecting plausible changes in climate. We then route both ensembles through StateMod, Colorado's water allocation model, to evaluate spatially compounding drought impacts across the West Slope Basins. Our results illustrate how drought events emerging from the system's stationary internal variability in the absence of climate change can significantly impact local water uses and deliveries to Lake Powell, exceeding extreme conditions in the historical record. Further, we find that even modest climate change can cause a regime shift where historically low downstream delivery volumes and extreme drought impacts become routine. These results can inform future Colorado River planning efforts, and our methodology can be expanded to other snow‐dominated regions that face persistent droughts.

Enhanced soil moisture–temperature coupling could exacerbate drought under net-negative emissions

Our understanding of drought evolution and land-atmosphere interactions under climate mitigation scenarios remains limited. Here, we analyzed future drought under net-zero and net-negative emission scenarios using the Community Earth System Model version 2, particularly focusing on three atmospheric CO2 states: linearly increases, decreases, and a return to the initial state. Interestingly, results revealed that net-zero emissions are more effective for drought mitigation than net-negative targets. Drying trends and drought characteristics — such as the duration, frequency, intensity, and area expansion are prominently increased under net-negative emissions due to higher potential evapotranspiration (PET). This is because the soil moisture and temperature couplings are stronger over drought regions and years, especially under net-negative forcing, with notable impacts in Central Africa and South Asia. Nevertheless, both target scenarios offer regional benefits, such as weakened dryness. This suggests that mitigating CO2 alone may not be sufficient to manage future droughts, highlighting the need for advanced water management strategies.

Agricultural drought monitoring and early warning at the regional scale using a remote sensing-based combined index.

Early detection of agricultural drought can alert farmers and authorities, enhancing the resilience of the food sector. A framework is proposed for developing a novel regional agricultural drought index (RegCDI) by combining remotely sensed vegetation health, soil moisture and crop water stress via a transparent Shannon's entropy weighting method. The framework consists of the selection of suitable datasets based on their regional performance, the aggregation of selected drought indicators, the validation of the combined index against crop yield, and the testing of predictive capabilities. The creation and performance of RegCDI are demonstrated for the drought prone Indian state of Odisha. MODIS surface reflectance is selected for crop water stress and GLDAS-2 for assessing soil moisture deficits and vegetation health. Three selected indicators (SMCI, TCI, and SIWSI-1) are combined into RegCDI for Odisha. The performance of RegCDI is evaluated (a) against other popular drought indices and (b) by comparing with seasonal crop yields. RegCDI is used to identify drought hotspots based on drought severity, duration, and propensity over the study area. A reforecast evaluation of RegCDI (up to three months ahead) showed that the indicators based on soil moisture deficit and crop water stress could predict drought conditions up to two months ahead with no less than 80% accuracy. This demonstrated the potential of the RegCDI framework and its component indicators for early warning of drought in Odisha.

Integration of SPEI and machine learning for assessing the characteristics of drought in the middle ganga plain, an agro-climatic region of India.

Drought, as a natural and intricate climatic phenomenon, poses challenges with implications for both natural ecosystems and socioeconomic conditions. Evaluating the characteristics of drought is a significant endeavor aimed at mitigating its impact on society and individuals. This research paper explores the integration of the Standardized Precipitation Evapotranspiration Index (SPEI) and machine learning techniques for an assessment of drought characteristics in the Middle Ganga Plain, a crucial agro-climatic region in India. The study focuses on evaluating the frequency, intensity, magnitude, and recurrence interval of drought events. Various drought models, including Random Forest (RF), Artificial Neural Networks (ANN), and an ensemble model combining ANN and RF, were employed to analyze and predict drought patterns at different temporal scales (3-month, 6-month, and 12-month). The performance of these models was rigorously validated using key metrics such as precision, accuracy, proportion incorrectly classified, over-all area under the curve (AUC), mean absolute error (MAE), and root mean square error (RMSE). Furthermore, the research extends its application to delineating drought vulnerability zones by establishing demarcations for high and very high drought vulnerability areas for each model and temporal scale. Results indicate that the south-western part of the middle Ganga plain falls under the highly drought-vulnerable zone, which averagely covers 40% of the study region. The core and buffer regions of drought vulnerability have also been identified. The south-western part of the study area is identified as the core region of drought. Ground verification of the drought-vulnerable area has been done by using soil moisture meter. Validation metrics show that the ensemble model of ANN and RF exhibits the highest accuracy across all temporal scales. This research's findings can be applied to improve drought preparedness and water resource management in the Middle Ganga Plain. By identifying high-risk drought zones and utilizing accurate prediction models, policymakers and farmers can implement targeted mitigation strategies. This approach could enhance agricultural resilience, protect livelihoods, and optimize water allocation in this vital agro-climatic region.

Evaluating and Predicting Meteorological Drought Using Different Climate Reanalysis Datasets over New South Wales, Australia

AbstractDroughts are one of the most disastrous natural hazards, primarily due to their persistence and spatial distribution. Drought prediction is one of the key challenges for effective drought management and to do so, studies often involve the use of station-based data which are effective only in regions with high-gauge density. Therefore, there is growing interest in the use of interpolated climatic grids to predict droughts. In recent decades, drought conditions have been aggravated by climate change and for that reason the use of climatic variables is important to accurately predict droughts. The analysis of any aspect of drought can be affected by the choice of data and drought index. Therefore, this study aims to identify the most suitable dataset and drought index for the New South Wales (NSW) region of Australia. The present study evaluates various precipitation datasets (Climate Research Unit (CRU), ERA-5, and Scientific Information for Land Owners (SILO)) and their corresponding variations on the Standardised Precipitation Index (SPI) at different time scales. Based on the findings, CRU was used to predict meteorological drought using machine learning techniques. The different machine learning models are Support Vector Regression, Random Forest and Artificial Neural Networks. The results suggest SVM to be the best performing model among these models for predicting SPI at short time scales (1 month and 3 month) and ANN to be the best performing model for long-term scales (6 months and 12 months). Such findings depict the capabilities of different models in examining drought characteristics and confirming the use of interpolated climatic grids thereby assisting in regional drought management.

A Coupled Model for Forecasting Spatiotemporal Variability of Regional Drought in the Mu Us Sandy Land Using a Meta-Heuristic Algorithm

Vegetation plays a vital role in terrestrial ecosystems, and droughts driven by rising temperatures pose significant threats to vegetation health. This study investigates the evolution of vegetation drought from 2010 to 2024 and introduces a deep-learning-based forecasting model for analyzing regional spatial and temporal variations in drought. Extensive time-series remote-sensing data were utilized, and we integrated the Temperature–Vegetation Dryness Index (TVDI), Drought Severity Index (DSI), Evaporation Stress Index (ESI), and the Temperature–Vegetation–Precipitation Dryness Index (TVPDI) to develop a comprehensive methodology for extracting regional vegetation drought characteristics. To mitigate the effects of regional drought non-stationarity on predictive accuracy, we propose a coupling-enhancement strategy that combines the Whale Optimization Algorithm (WOA) with the Informer model, enabling more precise forecasting of long-term regional drought variations. Unlike conventional deep-learning models, this approach introduces rapid convergence and global search capabilities, utilizing a sparse self-attention mechanism that improves performance while reducing model complexity. The results demonstrate that: (1) compared to the traditional Transformer model, test accuracy is improved by 43%; (2) the WOA–Informer model efficiently handles multi-objective forecasting for extended time series, achieving MAE (Mean Absolute Error) ≤ 0.05, MSE (Mean Squared Error) ≤ 0.001, MSPE (Mean Squared Percentage Error) ≤ 0.01, and MAPE (Mean Absolute Percentage Error) ≤ 5%. This research provides advanced predictive tools and precise model support for long-term vegetation restoration efforts.

The Spatiotemporal Variations in and Propagation of Meteorological, Agricultural, and Groundwater Droughts in Henan Province, China

As the global climate changes and droughts become more frequent, understanding the characteristics and propagation dynamics of drought is critical for monitoring and early warning. This study utilized the Standardized Precipitation Evapotranspiration Index (SPEI), Vegetation Condition Index (VCI), and Groundwater Drought Index (GDI) to identify meteorological drought (MD), agricultural drought (AD), and groundwater drought (GD), respectively. Sen’s slope method and Mann–Kendall trend analysis were used to examine drought trends. The Pearson correlation coefficient (PCC) and theory of run were utilized to identify the propagation times between different types of droughts. Cross-wavelet transform (XWT) and wavelet coherence (WTC) were applied to investigate the linkages among the three types of droughts. The results showed that, from 2004 to 2022, the average durations of MD, AD, and GD in Henan Province were 4.55, 8.70, and 29.03 months, respectively. MD and AD were gradually alleviated, while GD was exacerbated. The average propagation times for the different types of droughts were as follows: 6.1 months (MD-AD), 4.4 months (MD-GD), and 16.3 months (AD-GD). Drought propagation exhibited significant seasonality, being shorter in summer and autumn than in winter and spring, and there were close relationships among MD, AD, and GD. This study revealed the characteristics and propagation dynamics of different types of droughts in Henan Province, providing scientific references for alleviating regional droughts and promoting the sustainable development of agriculture and food production.

Evolutionary origins, macroevolutionary dynamics, and climatic niche space of the succulent plant syndrome in the Caryophyllales.

The succulent plant syndrome is defined by the coordination of traits that enhance internal water storage within plant tissues. Although distributed globally in different habitats, succulent plants are thought to have evolved to avoid drought in arid regions, due to trait modifications that decrease tissue water deficits. We evaluated the evolution and the ecological significance of the succulent strategy at a global scale by comparing the climatic niche of species displaying succulence within the Core Caryophyllales with their non-succulent relatives. We assembled and curated a worldwide dataset of 201K georeferenced records belonging to 5447 species within 28 families, and analyzed the climatic niche of species along with their origin and evolutionary trajectories using ecological niche modeling, phylogenetic regression, divergence dates and ancestral state estimation. Results indicate the Core Caryophyllales have inhabited drylands since their origin in the Early Cretaceous. However, the succulent syndrome appeared and diversified during later geologic periods. The climatic niche space of succulents is narrower than non-succulent relatives, but no climate niche separation was detected between groups. Our results support alternative interpretations on the environmental and ecological forces that spurred the origin and diversification of the succulent plant syndrome and the radiation of rich succulent lineages.

Multi-scale drought variability over West Africa and the associated large-scale circulation patterns

This study addresses the critical research gap in understanding regional drought dynamics in West Africa (WA) using advanced multivariate wavelet coherence (MWC) techniques, revealing critical insights into the complex interplay between large-scale climate indices and regional drought patterns. Utilizing the Standardized Precipitation Evapotranspiration Index (SPEI) and self-calibrating Palmer Drought Severity Index (scPDSI), we examined drought characteristics across WA from 1950 to 2018. Our analysis revealed significant drying trends across all seasons, with the Arid zone experiencing the most severe and prolonged drought (158 months) from August 1981 to September 1994, affecting 79.77% of the area. The entire WA region faced its most extensive drought during this period, impacting over 60% of the study area. MWC analysis demonstrated enhanced drought predictability when combining climate indices, particularly the Southern Oscillation Index with the Atlantic Multidecadal Oscillation, yielding high Average Wavelet Coherence values of 0.52 (SPEI) and 0.53 (scPDSI), with substantial Percentage Area of Significant Coherence of 40.19% and 43.68%, respectively. These findings highlight the crucial role of oceanic-atmospheric interactions in modulating drought conditions across WA. Our results provide valuable insights for improving drought forecasting and management strategies in the region, emphasizing the need for integrated approaches that consider multiple climate indices and their combined effects on drought dynamics.

Detecting Drought-Related Temporal Effects on Global Net Primary Productivity

Drought has extensive, far-reaching, and long-lasting asymmetric effects on vegetation growth worldwide in the context of global warming. However, to date, few scholars have attempted the systematic quantification of the temporal effects of drought on global vegetation across various vegetation types and diverse climate zones. Addressing this gap, we quantitatively investigated the effects of drought on global vegetation growth under various scenarios, considering lagged and cumulative effects as well as combined effects in the 1982–2018 period. Our investigation was based on long-term net primary productivity (NPP) and two multiple-timescale drought indices: the standardised precipitation index (SPI) and the standardised precipitation and evapotranspiration index (SPEI). Our main findings were the following: (1) SPI and SPEI exhibited lagged effects on 52.08% and 37.05% of global vegetation, leading to average time lags of 2.48 months and 1.76 months, respectively. The cumulative effects of SPI and SPEI were observed in 80.01% and 72.16% of global vegetated areas, respectively, being associated with relatively longer cumulative timescales of 5.60 months and 5.16 months, respectively. (2) Compared to the scenario excluding temporal effects, there were increases in the explanatory powers of SPI and SPEI for variations in vegetation NPP based on the lagged, cumulative, and combined effects of drought: SPI increased by 0.82%, 6.65%, and 6.92%, respectively, whereas SPEI increased by 0.67%, 5.73%, and 6.07%, respectively. The cumulative effects of drought on global vegetation NPP were stronger than the lagged effects in approximately two-thirds (64.95% and 63.52% for SPI and SPEI, respectively) of global vegetated areas. (3) The effects of drought on vegetation NPP varied according to climate zones and vegetation types. Interestingly, vegetation in arid zones was the most sensitive and resilient to drought, as indicated by its rapid response to drought and the longest cumulative timescales. The vegetation NPP in tropical and temperate zones exhibited a relatively stronger response to drought than that in cold and polar zones. The strongest correlation of vegetation NPP with drought occurred in shrubland areas, followed by grassland, cropland, forest, and tundra areas. Moreover, for each vegetation type, the correlations between vegetation NPP and drought differed significantly among most climate zones. (4) The vegetation NPP in warming-induced drought regions displayed a higher correlation to drought than that in non-warming-induced drought regions, with shorter lagged and longer cumulative timescales. Our findings highlight the heterogeneity of the lagged, cumulative, and combined effects of drought across various climate zones and vegetation types; this could enhance our understanding of the coupling relationship between drought and global vegetation.

The spatio-temporal evolution of the Chongzhen drought (1627–1644) in China and its impact on famine

Abstract. Investigations of past extreme climate events offer insights into the interactions between natural forces, ecosystems, and human societies. The Chongzhen drought, which occurred from 1627 to 1644 CE, stands as possibly the most severe drought in central and eastern China over the last 1500 years, remarkable for its duration and extent and the vast number of people affected. Concurrently, a widespread famine emerged, triggering peasant uprisings that are argued as having contributed to the Ming Dynasty's downfall. This study extracted 1802 drought records and 1977 famine records from Chinese historical documents to reconstruct the spatio-temporal progression of the drought and its impact on famine. The records provided drought information of season, duration, and intensity, which enabled a classification of four drought severity levels. Then kernel density estimation reconstructed the spatial pattern of drought and the drought kernel density index (DKDI) series in sub-regions. Between 1627 and 1644, the drought affected most of central and eastern China. The severe drought zone was mainly located north of 29° N, shifting from the northwestern region to the northern region and then expanding to the south. The development of drought in different regions was not synchronized. The northwestern region faced the earliest drought outbreak, which eased in the middle period and peaked in 1640. The northern region's DKDI series forms a single-peaked curve, indicating a gradual aggravation of the drought from 1633 to 1640. The Yangtze–Huai region's DKDI series shows a multi-peaked curve, with repeated cycles of worsening and easing drought, peaking in 1641. Furthermore, the reconstruction of the spatio-temporal progression of famine revealed overlapped ranges and similar development trends to that of the drought. The years marking the peak of the famine kernel density index (FKDI) in the northwestern region, northern region, and Yangtze–Huai region coincided with those of the DKDI. Regression analysis identified drought as the primary factor triggering famine, accounting for approximately 67.3 % of its occurrence. In the northern region, the contribution of drought was higher (73.4 %). Series and correlation analyses indicate a continuity in drought's impacts on famine. This paper further clarifies the dominant pathway of climate impact transmission in this case: extreme drought → declining agricultural harvest → food shortage → famine. Other natural and socio-economic factors, such as locust infestations, nomadic invasions, and economic decline, also played a role in the occurrence of famine. Human response measures were instrumental in regulating the transmission of climate change impacts.

GTDI: a game-theory-based integrated drought index implying hazard-causing and hazard-bearing impact change

Abstract. 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.

A STUDY OF THE STANDARDIZED PRECIPITATION INDEX (SPI) AND MACHINE LEARNING TECHNIQUES FOR DROUGHT PREDICTION IN THE STATE OF PARAÍBA, BRAZIL

This study aimed to identify and analyze droughts in Paraíba, using the Standardized Precipitation Index (SPI) and machine learning algorithms for predicting SPI for the subsequent years (2020-2021) at six rainfall stations distributed across the mesoregions of Paraíba. The Precipitation data were downloaded from the Global Precipitation Climatology Centre (GPCC) and the National Oceanic and Atmospheric Administration (NOAA) database, covering the period from 1991 to 2019. Three machine learning algorithms were selected based on their ability to fit historical SPI data: Extra Trees Regressor, Gradient Boosting Regressor, and Random Forest Regressor. The applied machine learning models yielded satisfactory results, with the Extra Trees Regressor consistently producing the highest R² value across all stations, indicating high data explainability. The predictions were analyzed to determine their accuracy and reliability, providing valuable insights into precipitation variability and drought occurrence in different mesoregions of Paraíba. In conclusion, this study contributed to understanding climate variability and its implications in Paraíba, offering valuable insights into drought occurrence and the importance of adaptive approaches to mitigate adverse impacts. The application of SPI and machine learning techniques proved effective in analyzing and predicting precipitation, providing an objective approach to characterizing drought and rainfall intensity in specific regions.

Rainfall water collection and irrigation via stone bud and karren on karst rocky desertification slopes: Application and benefit analysis

Lack of surface water is a key factor leading to agricultural drought in karst regions and exacerbates the karst rocky desertification, which is characterized by high rate of exposed bedrock. With the characteristics of impermeability, runoff collection, and wide distribution, stone bud and karren (SBK), a unique karst landscape type composed of exposed bedrock and gullies, has a great potential to solve water shortage in agricultural irrigation, especially in the rain-rich karst regions of Guizhou Province, China. Using unmanned aerial vehicle images to identify SBK and measured rainfall–runoff data for validation, we applied SBK as a basic unit to harvest rainfall water on rocky desertification slopes and analyzed the benefits of rainfall water collection and irrigation via SBK. The results showed the following: (1) Karrens on rocky desertification slopes could be identified using a method coupling terrain opening difference and depression filling, and the simulated runoff on SBK could achieved the average NSE and R2 of 0.82 and 0.86, with RMSE and RSR less than 0.03 m3 and 0.51, respectively. (2) The exposed bedrock utilization rate and rainfall water collection coefficient of SBK were 0.62 and 0.55, respectively, such that the area ratio of exposed bedrock to the cultivated land meeting irrigation water demand reached 1:3.1, 1:3.7 and 1:7.0 under 90 %, 80 % and 50 % possibilities of irrigation. (3) Implementing SBK for rainfall water harvesting and irrigation could efficiently avoid crop failure in the karst regions where crops faced drought within only one week after rainfall, and was at an affordable cost to local farmers with limited environmental damage. The use of SBK to harvest rainfall water could also alleviate surface water shortages in karst regions caused by problems such as uneven temporal distribution of rainfall and engineering water shortages. Consequently, SBK demonstrate a strong application potential to alleviate agricultural drought in karst rocky desertification regions.

Historical drivers and future streamflow variations of the Kura River in the Western Transcaucasia region of Georgia: Analysis of tree-ring chronologies from 1720 to 2021 CE

In this paper, we conduct a dendrochronological study in the Western Transcaucasia region of Georgia to elucidate the relationship between tree growth and climatic variables (temperature, precipitation, and streamflow) in order to reconstruct the paleohydrology of the Kura River and to assess the impacts of climate change on water resources. We analyzed tree-ring chronologies from 1720 to 2021 CE, and analysis of July–August streamflow revealed five wet phases (1739–1753, 1764–1807, 1811–1831, 1962–1988 and 2000–2019 CE ranging in duration from 11 to 49 years) when streamflow exceeded the long-term mean, and four dry phases (1720–1738, 1832–1881, 1936–1961 and 1989–1999 CE) when streamflow was below average. The most extreme wet and dry years were 1771 and 1851 CE, respectively. Spatial correlation patterns of Kura River reconstructed runoff against sea level pressure (SLP) are negative in the Arctic and positive in mid- to high-latitude Eurasia. The Arctic Oscillation (AO)/North Atlantic Oscillation (NAO), and solar activity are important drivers of hydroclimatic conditions in the Mediterranean region, by modifying the location of high-pressure areas and large-scale atmospheric circulations. Comparison of our Kura streamflow reconstructions and data in the Old World Drought Atlas (OWDA) reveals a moderately positive correlation (0.40) throughout the study period, being strongest over the Southern Caucasus and Eastern Turkey. This validates the drought signals and emphasizes the importance of using multi-proxy approaches, supported by spatially resolved data, to enhance understanding of regional hydroclimatic variability and drought patterns. We additionally conducted future streamflow scenarios, most notably under the high-emission SSP585 scenario, in line with global trends in CMIP6 projections. Our streamflow results indicate a trend towards increasing drought severity, which highlights the need for urgent adaptation and mitigation strategies in water resource management in this region. Ultimately, this study provides valuable information on historical hydroclimatic conditions in the Kura River Basin, which help to develop strategies to mitigate the hydrologic impacts of climate change in the Western Transcaucasia Caucasus region. We further highlight the importance of regional and global teleconnections for understanding regional hydrological dynamics.

The mechanism of urban agglomeration causing the enhancement of regional extreme heat and drought events

Urban agglomerations formed by rapid urbanization have been found to be important ways through which human activities influence regional weather and climate. However, the role of urban agglomerations in the formation of extreme heat and drought events remains unclear. We perform numerical simulations using RegCM 5.0 on an August 2022 extreme heat event in the Yangtze River Basin in China. The results show that urban agglomerations lead to significantly increased temperature in both urban (0.6 °C increase) and non-urban (0.2 °C increase) areas. The land feedback formed by urban agglomerations significantly increases the surface sensible heat, turbulence activity, urban upward movement, and urban heat island effect. As a result, the sinking airflow is enhanced, the surface wind is increased, and a dome-shaped wind field anomaly is formed around the urban agglomerations. This widens the geographic scope of the heat event and heightens its intensity.

The flash droughts across the south-central United States in 2022: Drivers, predictability, and impacts

A rare subseasonal-to-seasonal phenomenon – two consecutive flash drought events interrupted by a period of recovery – occurred across eastern Oklahoma, Arkansas, and southern Missouri, spanning the summer and early fall of 2022. These flash drought events (the first in June–July, the second in August–September) led to severe (D2) and extreme (D3) drought conditions via the United States Drought Monitor across much of the region following the first period of rapid drought intensification, and extreme (D3) and exceptional (D4) drought conditions by the end of the second event. A notable driver of both flash drought events included a persistent upper-level ridge either centered over or shifted west and upstream of the flash drought region, leading to broad-scale subsidence and reduced mid-level moisture which acted to limit precipitation development and increase evaporative demand. In addition, several heatwave events developed during the warm season in 2022 and either (1) acted to drive flash drought development via increased evaporative demand or (2) were enhanced by land surface desiccation and land-atmosphere feedbacks following rapid drought intensification. Furthermore, S2S composite forecasts predicted drought development for both events. However, only 20% of the ensembles predicted rapid drought development associated with flash drought for the first event and 16% of the ensembles predicted rapid drought development during the second event. This result highlights a key challenge in S2S prediction of rapidly developing drought conditions versus that of more conventional and slower drought development. The ensembles that did predict rapid drought intensification were associated with the forecasting of positive 500 hPa geopotential height anomalies over the south-central United States (first event) or an amplified wave pattern centered over the west-central United States (second event). Lastly, the compounding effects of two flash droughts in a single warm season led to substantial impacts on agricultural, environmental, and hydrologic sectors across the region.

Fish Health Altered by Contaminants and Low Water Temperatures Compounded by Prolonged Regional Drought in the Lower Colorado River Basin, USA.

The goal of this study was to assess health of male Common Carp (carp, Cyprinus carpio) at four sites with a wide range in environmental organic contaminant (EOC) concentrations and water temperatures in Lake Mead National Recreation Area NV/AZ, US, and the potential influence of regional drought. Histological and reproductive biomarkers were measured in 17-30 carp at four sites and 130 EOCs in water per site were analyzed using passive samplers in 2010. Wide ranges among sites were noted in total EOC concentrations (>10Xs) and water temperature/degree days (10Xs). In 2007/08, total polychlorinated biphenyls (tPCBs) in fish whole bodies from Willow Beach (WB) in the free-flowing Colorado River below Hoover Dam were clearly higher than at the other sites. This was most likely due to longer exposures in colder water (12-14 °C) and fish there having the longest lifespan (up to 54 years) for carp reported in the Colorado River Basin. Calculated estrogenicity in water exceeded long-term, environmentally safe criteria of 0.1-0.4 ng/L by one to three orders of magnitude at all sites except the reference site. Low ecological screening values for four contaminants of emerging concern (CEC) in water were exceeded for one CEC in the reference site, two in WB and Las Vegas Bay and three in the most contaminated site LVW. Fish health biomarkers in WB carp had 25% lower liver glycogen, 10Xs higher testicular pigmented cell aggregates and higher sperm abnormalities than the reference site. Sperm from LVW fish also had significantly higher fragmentation of DNA, lower motility and testis had lower percent of spermatozoa, all of which can impair reproduction. Projections from a 3D water quality model performed for WB showed that EOC concentrations due to prolonged regional drought and reduced water levels could increase as high as 135%. Water temperatures by late 21st century are predicted to rise between 0.7 and 2.1 °C that could increase eutrophication, algal blooms, spread disease and decrease dissolved oxygen over 5%.

Cascading spatial drought network: A complex networks approach to track propagation of meteorological droughts to agricultural droughts

Meteorological droughts often propagate to agricultural (and other) droughts, both spatially and temporally. The present study proposes a novel complex networks-based cascading spatial drought network to examine the spatial propagation of meteorological droughts in a region to agricultural droughts in other regions. This is done through: (1) establishing stable homogeneous drought communities; (2) investigating inter-community drought propagation; (3) locating drought sources; and (4) evaluating drought connections within major crop belts. The approach is implemented to study droughts in the Indian-subcontinent during the period 1948–2022. Monthly precipitation and root-zone soil moisture data from GLDAS (Global Land Data Assimilation System) are used to compute the standardized precipitation index (SPI) for meteorological droughts and standardized soil moisture index (SSI) for agricultural droughts. Primarily, the drought network is demarcated into several subsets of network communities within which clusters of localized propagation take place. Multi-community subgraphs combining different communities are also formed to understand the long-distance inter-community drought linkages. Using network centrality measures, such as degree, closeness, and clustering coefficient, network properties of scale-freeness, small-worldness, and presence of rich-clubs are checked. Although the overall network does not exhibit any of these properties, certain subgraphs have significant small-worldness, rich-clubs, and partial scale-freeness. Some of the crucial nodes that support these network properties lie in the monsoon pathways (in the Western Ghats), and others have a strong association with El Niño Southern Oscillation (ENSO) teleconnections, thus validating the ability of the drought network to capture seasonal and climatic features. Additionally, subgraphs of nodes with high productivity of different food crops are created to study drought propagation within crop belts. Barring potential shortcomings related to data dependencies, the cascading spatial drought network helps identify an impending agricultural drought that could strengthen our ability to forecast droughts.