Drought Characteristics Research Articles

The spatio-temporal trend of climate and characterization of drought in Borana Zone, Ethiopia

This research assessed the Borana Zone’s climate trends and drought characteristics from 1991 to 2050. Using Innovative Trend Analysis (ITA), we quantified the climate and drought changes. The standard Precipitation and Evaporation index (SPEI) and self calibrated Palmer’s Droguht Severity Index (scPDSI) were selected to compute the drought’s magnitude. Precipitation significantly decreased from 1991 to 2020, with a variation from 2021 to 2050. Drought indices generally showed a decreasing trend. The short-term drought index (SPEI3) in the Mid-Moist (MM) category experienced the greatest decline, with a slope of − 0.055/year during the reference period. Under RCP4.5 scenarios, the SPEI3 for the Extremely-Most (EM) class showed the steepest decline at − 1.827/year. For RCP8.5, MM categories displayed significant reductions, with annual slopes of − 0.073, 0.021, and − 0.14 for SPEI3, SPEI12, and scPDSI, respectively. The moisture intensity also showed a diminishing trend, with scPDSI projected to decrease by 40.47% and 20.77% under the two emission scenarios. During the reference period, Near Normal (NN) moisture frequency varied from 71.39% to 82.81%, but is expected to decrease by 12% and 8% under the two scenarios. The adverse impacts can be mitigated by building the capacity of drought-vulnerable community.

Revealing the Exacerbated Drought Stress Impacts on Regional Vegetation Ecosystems in Karst Areas with Vegetation Indices: A Case Study of Guilin, China

Global warming has exacerbated the impact of regional drought on vegetation ecosystems, especially in typical karst areas with fragile ecosystems that are more severely affected by drought. However, the response mechanisms of vegetation ecosystems in karst areas to drought stress are still uncertain. With drought stress in the summer of 2022, we examined the spatiotemporal patterns of drought in a World Heritage karst site, Guilin, China, and revealed the exacerbated drought impacts on vegetation ecosystems in karst areas with various vegetation indices. Firstly, we analyzed the spatiotemporal characteristics of drought from 2000 to 2022, utilizing the temperature vegetation dryness index (TVDI), highlighting the intra-annual variability of drought in 2022. Additionally, we compared the responses of different vegetation types to drought stress in karst and non-karst areas and explored the exacerbated impacts of drought stress on vegetation ecosystems within the same year with three vegetation indices, namely, the Normalized Difference Vegetation Index (NDVI), Leaf Area index (LAI), and Gross Primary Production (GPP) in karst areas. The results showed that drought started in July and persisted from August to November at moderate to severe levels (with severe drought in September), eventually easing in December. Karst areas exhibited severe drought (TVDI = 0.76), which more significantly impacted regional vegetation ecosystems than those in non-karst areas. Different vegetation types also experienced greater drought stress in karst areas compared to non-karst areas. The vegetation indices increased at the early- to mid-stages of drought (July to September) compared to those in the baseline year (2020–2021), mainly due to the increase in non-karst areas. However, vegetation indices decreased at the late drought stage (October to November), primarily due to the decrease in karst areas, indicating that the karst topography exacerbated the impact of drought on regional vegetation ecosystems. Since LAI and GPP exhibited similar changing patterns to TVDI, with GPP showing particularly strong alignment, they can be used to reveal the response mechanisms of ecosystems to drought stress in karst areas. We emphasize the importance of monitoring the responses of vegetation ecosystems to climate-induced droughts stress and enhancing their resilience to future climatic challenges, particularly in karst areas.

Effects of Inconsistency in Drought Event Definitions on Drought Characteristics

Effects of Inconsistency in Drought Event Definitions on Drought Characteristics

Assessing consistency in drought risks in India with multiple multivariate meteorological drought indices (MMDI) under climate change.

This study investigates the spatio-temporal consistency of different MMDI formulations and their role in meteorological drought characterization uncertainty under historic and future climates using ERA5 reanalysis, and outputs from eight Coupled Model Intercomparison Project Phase 6 models, respectively, across different climate zones and shared socioeconomic pathways (SSP) in the Indian subcontinent. Six MMDI formulations namely the Standardized Precipitation Evaporation Index (SPEI), Reconnaissance Drought Index (RDI), and self-calibrated Palmer Drought Severity Index (scPDSI), Standardized Palmer Drought Index (SPDI), Standardized Moisture Anomaly Index (SZI) and Supply Demand Drought Index (SDDI) are used. A suite of analysis including agreement mapping, category difference analysis and uncertainty contribution analysis using global sensitivity analysis (GSA) are employed to quantify the consistency of MMDIs and uncertainty in drought characterization due to the MMDI formulation. The variation in MMDI consistency due to different reference evapotranspiration (ETo) methods is also studied. Results demonstrate strong agreement among the MMDIs under historic climate. Under climate change scenarios our findings demonstrate broad agreement among majority of the MMDIs across the study domain, but in substantial areas where MMDI not agree, especially for higher emission scenarios and arid zones. Increased uncertainty under climate change is due to SDDI and SPEI projecting dryer conditions while scPDSI projecting wetter conditions in the far future period owing to varying degrees of sensitivity of MMDIs to its constituent variables (Precipitation and ETo). Results also show that the uncertainty due to MMDIs varied considerably based on ETo methods as well. Finally, based on GSA analysis, the most significant sources of uncertainty in drought projections under climate change are attributed to MMDI-GCM interactions and MMDIs for the Penman-Monteith method. Discrepancies in drought estimates caused by the MMDI selection highlight the need for careful evaluation of drought indices before adopting for climate change impact assessment.

Planted Forests in China Have Higher Drought Risk Than Natural Forests.

To improve the environment and mitigate climate change, China has implemented ambitious projects for natural forest protection and expanded planted forests. However, increased climate variability has led to more frequent and severe droughts, exacerbating the decline of these forests. The drought risk of planted forests is rarely assessed by considering both resistance and resilience, and comparative analyses between natural and planted forests are lacking. Here, we compared drought resistance and resilience in natural and planted forests across China using satellite observations from 2001 to 2020 to understand which forests were at higher risk of drought. The results showed that planted forests exhibited lower drought resistance and resilience compared to natural forests, particularly in subtropical broad-leaved evergreen forests and warm temperate deciduous broad-leaved forests. Moreover, drought resistance in planted forests significantly increased, while resilience decreased during 2011-2020 compared to 2001-2010, suggesting a shift in the strategies of planted forests to cope with drought stress. The higher drought risk in planted forests compared to natural forests was mainly attributed to lower forest canopy height and poorer soil nutrients, which limited resistance, and lower canopy height and severe drought characteristics (severity, duration, and frequency), which reduced resilience. These results underscore the higher potential risk of drought exposure in planted forests. To mitigate future drought impacts on planted forests under climate change, enhanced management strategies, including the preservation of natural forests and augmentation of structural diversity in planted forests, are imperative.

Spatial and temporal variability of soil moisture active and passive (SMAP) droughts and their impacts on vegetation in the Central Highlands of Vietnam.

Drought is a reoccurring natural phenomenon that presents significant challenges to agricultural production, ecosystem stability, and water resource management. The Central Highlands of Vietnam, a major region of industrial crops and vegetation ecosystems, has become increasingly vulnerable to drought impacts. Despite this vulnerability, limited research has explored the specific characteristics of drought and its seasonal effects on vegetation ecosystems in the region. This study addressed these gaps by providing a detailed analysis of recent soil moisture drought characteristics and their seasonal impacts on vegetation from 2015 to 2023 using weekly soil moisture active passive (SMAP) and moderate resolution imaging spectroradiometer (MODIS) satellite time series observations. This analysis derived the soil moisture anomaly index as a proxy to assess drought characteristics and used correlation analysis to quantify their impacts on seasonal vegetation dynamics. Our spatial analysis identified the most significant drought years in 2015 and 2019 in the study region, while the wettest conditions were detected in 2017 and 2022 over the study period. Notably, significant soil moisture deficits were observed in August and October throughout the study period, even though these months typically fall within the rainy season. On average, nearly 25 drought events were detected in the region from 2015 to 2023 due to soil moisture deficits, each lasting approximately 6 weeks. The impact of drought events on the vegetation ecosystem was seasonally pronounced in spring and winter, where droughts were notably higher. Our results provide valuable insights into informed decision-making and sustainable agricultural practices in the region. Understanding the spatial and temporal patterns of drought and its seasonal effects on vegetation can help policymakers and farmers develop targeted strategies to mitigate the adverse impacts, enhance water management practices, and promote drought-resistant crop varieties, thereby maintaining agricultural productivity and ecosystem health amidst increasing climate variability.

Using Integrated Geodetic Data for Enhanced Monitoring of Drought Characteristics Across Four Provinces and Municipalities in Southwest China

This paper presents an analysis of regional terrestrial water storage (TWS) changes and drought characteristics in Southwest China, encompassing Sichuan Province, Chongqing Municipality, Yunnan Province, and Guizhou Province. Existing geodetic datasets, such as those from the Gravity Recovery and Climate Experiment (GRACE) and its successor satellites (GRACE Follow-On), as well as Global Navigation Satellite System (GNSS) data, face significant challenges related to limited spatial resolution and insufficient station distribution. To address these issues, we propose a novel inversion method that integrates GNSS and GRACE/GFO data by establishing virtual stations for GRACE/GFO data and determining the weight values between GNSS and GRACE/GFO using the Akaike Bayesian Information Criterion (ABIC). This method allows for estimating the TWS changes from December 2010 to June 2023 and monitoring drought conditions in conjunction with hydrometeorological data (precipitation, evapotranspiration, and runoff). The results show strong correlations between TWS changes from the joint inversion and GNSS (0.98) and GRACE/GFO (0.69). The Joint Drought Severity Index (Joint-DSI) indicates five major drought events, with the most severe occurring from July 2022 to June 2023, with an average deficit of 86.133 km³. Extreme drought primarily impacts Sichuan and Yunnan, driven by abnormal precipitation deficits. The joint inversion methodology presented in this study provides a practical approach for monitoring TWS changes and assessing drought characteristics in Southwest China. This paper leverages the complementary strengths of GNSS and GRACE/GFO data and offers new insights into regional water resource management and drought detection.

Investigation of Hydrological Drought in Central Dry Zone, Myanmar

This study presents the investigation of hydrological drought in Myanmar’s Central Dry Zone using the Streamflow Drought Index (SDI) across various timescales (3, 6, 9, and 12 months) to assess its impact on water resources and agricultural productivity. The Central Dry Zone, which includes the Ayeyarwaddy River and encompasses the regions of Sagaing, Mandalay, and Magway, shows significant vulnerability of hydrological extremes due to its semi-arid climate and dependence on water resources. Monthly discharge data from selected hydrological stations from 1993 to 2022 is analyzed using DrinC 1.7 software to derive SDI values and drought characteristics. The results show that critical drought events in 2005-2006, 2013-2015, and 2019-2021 for all stations are marked by high severity and extended duration. Short-term SDI (SDI 3 and SDI 6) values capture rapid, intense droughts, while long-term SDI (SDI 9 and SDI 12) highlight extended water shortages. From this result, Monywa station shows the highest severity and duration of droughts across all SDI timescales compared to other stations. The results underscore the necessity of strategic water management and drought mitigation measures to protect agriculture and guide planning, establish early warning systems, and support sustainable development in the Central Dry Zone, Myanmar.

Analysis of the Propagation Characteristics of Meteorological Drought to Hydrological Drought and Their Joint Effects on Low-Flow Drought Variability in the Oum Er Rbia Watershed, Morocco

Analysis of the temporal relationship between meteorological drought and hydrological drought is crucial in monitoring water resource availability. This study examined the linear and lagged relationships of the spread of meteorological drought to hydrological drought and their joint effects on low-flow drought variability in the Oum Er-Rbia (OER) watershed. To this end, random forest (RF) model and statistical methods were used to study the characteristics of the temporal relationships between meteorological and hydrological drought indices at monthly, seasonal, and annual scales. The various analyses revealed that the relationship between hydrological and meteorological drought is mainly a function of the time scale considered, the choice of indices to describe each type of drought and the season considered. The hydrological drought of surface water and snow cover is synchronized with the meteorological drought at the monthly, seasonal, and annual scales. In contrast, the transition from meteorological drought to groundwater drought has a lag time of 1 month and is statistically significant up to t − 5 and t + 5, i.e., 6 months. The linear correlation between the annual rainfall deficit and the monthly groundwater storage index was the lowest (0.15) in December and the highest (0.83) in March. This suggests a seasonal response of groundwater drought to the cumulative effects of precipitation deficits. The RF analysis highlighted the importance of the cumulative characteristics of meteorological drought regarding the severity of low-flow drought. The meteorological drought indices at longer time scales have a greater impact on the severity of low-flow drought, with a contribution of approximately 10% per index. However, the relative contributions of meteorological factors and hydrological indices rarely exceed 5%. Thus, by exploring for the first time the complex interactions among the severity of low-flow regimes, meteorological and hydrological drought indices and meteorological factors, this study provides a new perspective for understanding the characteristics of propagation from meteorological to severe hydrological drought.

Spatiotemporal Dynamics of Drought in the Huai River Basin (2012–2018): Analyzing Patterns Through Hydrological Simulation and Geospatial Methods

As climate change intensifies, extreme drought events have become more frequent, and investigating the mechanisms of watershed drought has become highly significant for basin water resource management. This study utilizes the WRF-Hydro model in conjunction with standardized drought indices, including the standardized precipitation index (SPI), standardized soil moisture index (SSMI), and Standardized Streamflow Index (SSFI), to comprehensively investigate the spatiotemporal characteristics of drought in the Huai River Basin, China, from 2012 to 2018. The simulation performance of the WRF-Hydro model was evaluated by comparing model outputs with reanalysis data at the regional scale and site observational data at the site scale, respectively. Our results demonstrate that the model showed a correlation coefficient of 0.74, a bias of −0.29, and a root mean square error of 2.66% when compared with reanalysis data in the 0–10 cm soil layer. Against the six observational sites, the model achieved a maximum correlation coefficient of 0.81, a minimum bias of −0.54, and a minimum root mean square error of 3.12%. The simulation results at both regional and site scales demonstrate that the model achieves high accuracy in simulating soil moisture in this basin. The analysis of SPI, SSMI, and SSFI from 2012 to 2018 shows that the summer months rarely experience drought, and droughts predominantly occurred in December, January, and February in the Huai River Basin. Moreover, we found that the drought characteristics in this basin have significant seasonal and interannual variability and spatial heterogeneity. On the one hand, the middle and southern parts of the basin experience more frequent and severe agricultural droughts compared to the northern regions. On the other hand, we identified a time–lag relationship among meteorological, agricultural, and hydrological droughts, uncovering interactions and propagation mechanisms across different drought types in this basin. Finally, we concluded that the WRF-Hydro model can provide highly accurate soil moisture simulation results and can be used to assess the spatiotemporal variations in regional drought events and the propagation mechanisms between different types of droughts.

Spatiotemporal Dynamics of Drought and the Ecohydrological Response in Central Asia

Due to the influences of climate change and human activities, the resources and environments of the “One Belt and One Road” initiative are facing severe challenges. Using drought indicators, this study aimed to analyze the spatiotemporal characteristics of the drought environment and the response of vegetation cover in the area to drought conditions. The Gravity Recovery and Climate Experiment (GRACE) drought severity index (GRACE-DSI), GRACE water storage deficit index (GRACE-WSDI) and standardized precipitation index (SPI) were calculated to measure hydrological drought. Additionally, based on GRACE and Global Land Data Assimilation System (GLDAS) data, groundwater data in Central Asia was retrieved to calculate the groundwater drought index using the GRACE Standardized Groundwater Level Index (GRACE-SGI). The findings indicate that, from 2000, Central Asia’s annual precipitation decreased at a rate of 1.80 mm/year (p < 0.1), and its annual temperature increased slightly, at a rate of 0.008 °C/year (p = 0.62). Water storage decreased significantly at a rate of −3.53 mm/year (p < 0.001) and showed an increase-decrease-increase-decrease pattern. During the study period, the aridity in Central Asia deteriorated, especially on the eastern coast of the Caspian Sea and the Aral Sea basin. After 2020, most of Central Asia experienced droughts at both the hydrological and groundwater droughts levels and of varying lengths and severity. During the growing season, there was a substantial positive association between the Normalized Difference Vegetation Index (NDVI) and drought indicators such as GRACE-DSI and GRACE-WSDI. Nonetheless, the NDVI of cultivated land and grassland distribution areas in Central Asia displayed a strong negative correlation with GRACE-SGI. This study concludes that the arid environment in Central Asia affected the growth of vegetation. The ecological system in Central Asia may be put under additional stress if drought conditions continue to worsen. This paper explores the drought characteristics in Central Asia, especially those of groundwater drought, and analyzes the response of vegetation, which is very important for the ecological and environmental protection of the region.

Spatiotemporal analysis of drought patterns and trends across Africa: a multi-scale SPEI approach (1960–2018)

ABSTRACT This study provides unprecedented insights into water scarcity dynamics across Africa's diverse climatic zones, utilizing the Standardized Precipitation-Evapotranspiration Index (SPEI) across multiple timescales from 1960 to 2018. Employing the Modified Mann-Kendall test, Sen's Slope estimator, and copula functions, we analyzed drought characteristics across eight African subregions using the CRU TS4.05 dataset. Key findings reveal significant spatial heterogeneity in drought trends, with increasing dryness particularly evident in the Mediterranean and Sahara regions. A pronounced shift towards drier conditions emerged post-1990, with mean drought duration expanding from 1.5 months (SPEI-3) to over 6 months (SPEI-24). Drought intensity increased by 0.2 unit annually, while frequency decreased by 20%. The Sahara region demonstrated the highest increasing drought trend across timescales. Return period analyses showed most sub-regions experiencing short-term droughts with low severity, while extreme events remained less frequent. These findings underscore the complex nature of African drought patterns, emphasizing the critical need for region-specific, adaptive climate strategies to address unique ecological challenges.

Trends and Spatiotemporal Patterns of the Meteorological Drought in the Ili River Valley from 1961 to 2023: An SPEI-Based Study

Drought presents significant challenges in arid regions, influencing local climate and environmental dynamics. While the large-scale climatic phenomena in Xinjiang, northwest China, are well-documented, the finer-scale climatic variability in subregions such as the Ili River Valley (IRV) remains insufficiently studied. This knowledge gap impedes effective regional planning and environmental management in this ecologically sensitive area. In this study, we analyze the spatiotemporal evolution of drought in the IRV from 1961 to 2023, using data from ten meteorological stations. The SPEI drought index, along with Sen’s trend analysis, the Mann–Kendall test, the cumulative departure method, and wavelet analysis, were employed to assess drought patterns. Results show a significant drying trend in the IRV, starting in 1995, with frequent drought events from 2018 onwards, and no notable transition year observed from wet to dry conditions. The overall drought rate was −0.09 per decade, indicating milder drought severity in the IRV compared to broader Xinjiang. Seasonally, the IRV experiences drier summers and wetter winters compared to regional averages, with negligible changes in autumn and milder drought conditions in spring. Abrupt changes in the drying seasons occurred later in the IRV than in Xinjiang, with delays of 21 years for summer, and over 17 and 35 years for spring and autumn, respectively, indicating a lagged response. Spatially, the western plains are more prone to aridification than the central and eastern mountainous regions. The study also reveals significant differences in drought cycles, which are longer than those in Xinjiang, with distinct wet–dry phases observed across multiple time scales and seasons, emphasizing the complexity of drought variability in the IRV. In conclusion, the valley exhibits unique drought characteristics, including milder intensity, pronounced seasonal variation, spatial heterogeneity, and notable resilience to climate change. These findings underscore the need for region-specific drought management strategies, as broader approaches may not be effective at the subregional scale.

The relationship between prenatal drought exposure and the diversity and composition of gut microbiome in pregnant women and neonates

Drought induced by climate change poses a serious threat to human health. The gut microbiome also plays a critical role in human health. However, no studies have explored the effect of drought on the human gut microbiome. Therefore, our study aimed to investigate the relationship between drought and gut microbiome. Our study included 59 mothers and 38 neonates in our study. 16S rRNA V3-V4 sequencing was used to profile the gut microbiome. The Standardized Precipitation Evapotranspiration Index (SPEI) was used to represent drought characteristics. KEGG pathway level 3 was employed for functional analysis. Generalized linear models were used to explore the effect of drought on the gut microbiome. Mothers and neonates were divided into the LSPEI (Lower SPEI) group or HSPEI (Higher SPEI) group by calculating the average levels of prenatal SPEI levels. The maternal and neonatal gut microbiome exhibited similar diversities in terms of alpha and beta diversity between the LSPEI and HSPEI groups. However, notable differences were observed in their composition. We found that in the neonatal gut microbiome, Sediminibacterium and Thermovirga were positively associated with SPEI after controlling for PM2.5 in linear regression models. Additionally, SPEI was significantly associated with phenylpropanoid biosynthesis and cyanoamino acid metabolism in neonates. This study identified that prenatal SPEI levels were correlated with specific maternal and neonatal gut microbial taxa, as well as neonatal gut microbial functional pathways. Future studies should further investigate the mechanisms by which drought exposure influences maternal and neonatal gut microbial diversities, composition, and functional pathways.

Projected changes to drought characteristics in Tehran under CMIP6 SSP-RCP climate change scenarios.

Drought represents one of the most devastating natural hazards, significantly impacting economies, societies, and the environment. Climate change is expected to alter future drought characteristics and may increase the severity of droughts. To mitigate these effects, it is essential to identify the characteristics of future droughts influenced by climate change using appropriate methods. This study aims to assess the climate change impacts on the frequency, duration, and magnitude of droughts in Tehran, the capital of Iran, which has a high concentration of the country's population and industrial activities and is currently facing water stress. Using the Modified Long Ashton Research Station Weather Generator (M-LARS-WG), capable of reproducing inter-annual variability, future projections from four GCMs under four SSP-RCP scenarios from the latest CMIP6 were downscaled. The impacts of climate change on droughts were then assessed using the SPEI and SPI drought indices. The findings suggest that M-LARS-WG was capable of accurately reproducing historical drought characteristics and performed significantly better than LARS-WG. Based on the SPEI, the duration, magnitude, and frequency of future droughts are expected to increase significantly across nearly all GCM projections. Therefore, developing proactive drought risk frameworks and mitigation strategies is essential for reducing damages from future droughts.

DroughtStats: A comprehensive software for drought monitoring and analysis

The significance of drought monitoring and prediction systems has grown substantially due to the escalating impacts of climate change. However, existing tools for drought analysis face several limitations, including restricted functionality to single-variable indices, reliance on predefined probability distributions, lack of flexibility in choosing distributions, and the need for advanced programming expertise. These constraints hinder comprehensive and accurate drought assessments. This study introduces DroughtStats, a novel, user-friendly software designed to overcome these challenges and enhance drought analysis capabilities. DroughtStats integrates advanced statistical tools to analyze hydrometeorological data, compute both single-variable and multivariable drought indices using empirical and parametric methods, and evaluate drought characteristics with improved accuracy. Notably, it supports a broader range of probability distributions, performs copula-based analyses, and estimates potential evapotranspiration using multiple methods, including Penman–Monteith. Additionally, DroughtStats can analyze the relationship between different datasets using techniques like copula-based Kendall’s tau. By addressing the limitations of existing tools, DroughtStats provides a more flexible and comprehensive approach to drought monitoring. Its versatility and global applicability are demonstrated through a case study in Turkey’s Çoruh River Basin (CRB), where drought indices based on precipitation and streamflow are calculated to characterize drought conditions. The results show that DroughtStats can successfully identify and characterize drought events at various time scales, providing valuable insights into drought severity, frequency, and recovery, and offering a reliable tool for ongoing drought monitoring and management.

Developing a multivariate drought index to assess drought characteristics based on the SWAT-Copula method in the Poyang Lake basin, China

Developing a multivariate drought index to assess drought characteristics based on the SWAT-Copula method in the Poyang Lake basin, China

Global Evaluation of Optimal Probability Distribution Functions for RDI Assessments

ABSTRACTDrought is caused by an imbalance between precipitation and evapotranspiration. A prolonged lack of precipitation and/or excess evapotranspiration results in insufficient replenishment of runoff and groundwater. Choosing an appropriate drought index is crucial for managing water resources effectively. The Reconnaissance Drought Index (RDI) which considers both precipitation and potential evapotranspiration is recommended for identifying droughts in a changing climate. Standardising the index involves using a probability distribution, and choosing the correct distribution is important for accurate assessments of drought characteristics. Furthermore, identifying the optimal distributions for RDI assessments ensures reliable evaluations of subsequent hydrological processes. Based on a regional study, the index developers suggest using gamma or log‐normal probability distributions to compute the index using real observations. Furthermore, there is a lack of research on suitable distributions for RDI calculation using GCMs projections (simulated data) in drought projection studies. This global study aims to address these gaps in research by evaluating the performance of probability distributions in calculating RDI. The study consists of two phases: The first phase involves identifying the appropriate distribution for historical observed data, whilst the second phase does the same for future projections from GCMs. To achieve this, 17 probability distributions are applied. The 0.5° × 0.5° gridded CRU data from 1950 to 2018 and projections of 18 GCMs from 2006 to 2080 are utilised. The analysis identified the log logistic, inverse Gaussian and gamma distributions as the best fits for the historical period. For future projections, the gamma, inverse Gaussian and Nakagami distributions are recommended. Finally, the findings revealed for both periods, Fitting to the Best Distribution of any Grid (FBDG) performs the best for large‐scale drought studies using gridded data.

Attribution and Risk Projections of Hydrological Drought Over Water‐Scarce Central Asia

AbstractCentral Asia (CA), a typical arid and semiarid region, has experienced worsening droughts, adversely impacting agricultural production and socioeconomic development. However, the evolution of hydrological droughts in CA remains unclear. Here, we used instrumental streamflow and reanalysis to demonstrate a decline in surface runoff in CA since the 1990s, with 44.6% and 33.2% of the area dominated by reductions in snowmelt and precipitation, respectively. We found that global warming contributes to the long‐term decrease in surface runoff, while short‐term fluctuations in surface runoff are caused by the El Niño‐Southern Oscillation, such as southern CA drying induced by decreasing precipitation during La Niña. We project the future hydrological drought characteristics based on state‐of‐the‐art global hydrological simulations and found increasing duration and severity of hydrological droughts in CA, especially in the Amu Darya basin, and the Caspian Sea East Coast basin. These increasing droughts are exacerbated by higher anthropogenic emissions, posing high‐level risks to 39.01% of land area and 35.9% of human population under an extremely high emissions scenario. These findings highlight the need for improved water conservation technologies and concerted development strategies should be considered by national policy makers in this water‐scarce and climatically sensitive region.

Projected Effects of Climate Change on Meteorological Droughts over China: A Study Based on High-Resolution NEX-GDDP Data

Abstract While historical observations reveal increases in meteorological drought frequency across China during 1960–2021, future drought characteristics at the regional scale are uncertain. We evaluate historic and twenty-first-century-projected dryness changes using the NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) 0.25° resolution dataset for representative concentration pathways (RCPs) 4.5 and 8.5. The 20-model NEX-GDDP ensemble mean precipitation and derived potential evapotranspiration, which is calculated by utilizing the Thornthwaite approach, are consistent with gridded station data over China from 1961 to 2005. Results show that the projections of occurrence frequency of moderate drought based on the standardized precipitation evapotranspiration index (SPEI) increase remarkably under the RCP4.5 scenario, and a significant increasing frequency is seen for severe drought under the RCP8.5 scenario, while few droughts of extreme dryness occur during the twenty-first century under both emission scenarios. More moderate drought projections are found over northwestern, northern, and southeastern regions under the RCP4.5 scenario, but all of China will face severe droughts for RCP8.5. The areas affected by moderate droughts begin to extend rapidly around 2030s at both national and regional scales for the RCP4.5 scenario, and the substantial increases in drying area extension for RCP8.5 appear around the 2050s. Particularly, the severe drought areas are found to grow much more sharply after the 2070s under the RCP8.5 scenario, implying that the occurrences of severe droughts over China are more sensitive to greenhouse gas emissions. Significance Statement Both temperature and rainfall are closely associated with drought variability; therefore, this study examines the changes in frequency and intensity of projected droughts across China based on the standardized precipitation index (SPI) and standardized precipitation evapotranspiration index (SPEI). Severe droughts are projected to increase over most of the regions according to SPEI. The substantial increases in moderate and severe droughts are seen over all of China under the RCP4.5 and RCP8.5 scenarios, respectively. At the temporal scale, most of the areas are projected to experience more severe droughts by the end of twenty-first century, while the areas of moderate droughts are projected to exhibit fluctuating variations during the twenty-first century. These results indicate more future droughts, which may increase the vulnerability of more areas of China from exposure to the consequences of global warming.