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Accurate drought forecasting is necessary for effective agricultural and water resource management and for early risk warning. Various machine learning models have been developed for drought forecasting. This work developed and tested a fusion-based ensemble model, namely, the stacking (ST) model, that integrates extreme gradient boosting (XGBoost), random forecast (RF), and light gradient boosting machine (LightGBM) for drought forecasting. Additionally, the ST model employs the SHapley Additive exPlanations (SHAP) algorithm to interpret the relationship between variables and forecasting results. Multi-source data that encompass meteorological, vegetation, anthropogenic, landcover, climate teleconnection patterns, and topological characteristics were incorporated in the proposed ST model. The ST model forecasts the one-month lead standardized precipitation evapotranspiration index (SPEI) at a 12 month scale. The proposed ST model was applied and tested in the German federal states of Brandenburg and Berlin. The results show that the ST model outperformed the reference persistence model, XGBboost, RF, and LightGBM, achieving an average coefficient of determination (R2) value of 0.845 in each month in 2018. The spatiotemporal Moran’s I method indicates that the ST model captures non-stationarity in modeling the statistical association between predictors and the meteorological drought index and outperforms the other three models (i.e., XGBoost, RF, and LightGBM). Global sensitivity analysis indicates that the ST model is influenced by a combination of environmental variables, with the most sensitive being the preceding drought indices. The accuracy and versatility of the ST model indicate that this is a promising approach for forecasting drought and other environmental phenomena.

Droughts, depending on their nature, have had devastating consequences, including crop destruction, famine and millions of deaths, particularly in countries like India that heavily rely on rainfall for agriculture. The present study aims to quantify the linkage between meteorological, agricultural and hydrological drought at a high spatial resolution across India. These connections were established by developing various drought propagation metrics followed by subsequent correlation analysis, lag analysis and clustering. Standard Precipitation Index (SPI), Deviation in NDVI (Dev‐NDVI) and GRACE Drought Severity Index (GRACE‐DSI) were used to represent meteorological, agricultural and hydrological droughts. Run theory with thresholds of −1, −0.5 and −0.05 were used to delineate the drought events for meteorological, hydrological and agricultural droughts, respectively. Furthermore, multivariate K‐means clustering based on factors such as drought duration, latitude, longitude, severity, propagation and recovery speeds was done to create spatial clusters having similar drought characteristics. Correlation analysis showed the highest average correlations at a lag of around 7–8 months between meteorological and hydrological drought, a lag of 1–2 months in case of meteorological and agricultural drought and a lag of 3–4 months between agricultural and hydrological drought. The analysis of drought duration indicated that, on average, meteorological drought in India lasted for 2.34 months, while agricultural drought lasted for 3 months, reflecting a 26.5% increase, whereas hydrological drought lasted for 5.22 months, indicating a notable 123% increase. This increase in average drought duration as it propagates from meteorological to agricultural to hydrological drought can be attributed to the lengthening property of drought propagation. Clustering analysis reveals presence of five homogeneous drought clusters. Additionally, cluster analysis reveals that for meteorological and agricultural droughts arid regions showed the highest severity whereas for hydrological droughts north Indian states including Punjab and Haryana showed the highest severity. [ABSTRACT FROM AUTHOR]

The reliability of conventional drought indices is increasingly called into question as the stationarity of hydrologic series undergoes significant alterations and becomes irreversibly impacted by environmental changes. In the extant literature concerning Non‐Stationary Standardized Precipitation Evapotranspiration Indices (NSPEIs), researchers typically construct linear models incorporating location parameters and covariates; however, the simultaneous consideration of both location and scale parameters has been conspicuously infrequent. In this study, we developed a NSPEI using the generalized additive models in location, scale and shape. This index innovatively incorporates location and scale parameters that vary in response to changes in meteorological factors, thereby offering a more reliable and robust means to quantify drought characteristics under changing environmental conditions. The index was employed across representative sub‐watersheds within the western region of Liaoning Province and subjected to a comparative analysis alongside the conventional Standardized Precipitation Evapotranspiration Index (SPEI) to ascertain its reliability. The findings indicate that within the study area, both the precipitation and evapotranspiration series exhibit non‐stationary behaviour. Specifically, the precipitation data reveals an overall declining trend, while the evapotranspiration data demonstrates an ascending trend. Furthermore, NSPEI demonstrates superior performance over SPEI in the reconstruction of historical drought events within the western region of Liaoning Province, thereby providing a more accurate representation of the actual drought conditions experienced. The NSPEI estimated a larger drought peak, whereas the SPEI identified longer durations and greater severity of droughts. These findings provide valuable information for evaluating drought characteristics and managing water resources under changing environmental conditions. [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]

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]

In this study, a stepwise multifactor vegetation regression analysis (SMVRA) approach was proposed to investigate the interaction of multiple climate factors on vegetative growth in the study area from 2000 to 2020. It was developed by integrating the stepwise linear regression method, Standardized Precipitation Evapotranspiration Index (SPEI), Normalized Difference Vegetation Index (NDVI), and Pearson correlation coefficient. SMVRA can be used to intuitively understand the interactive effects of multiple correlated factors (e.g., temperature, precipitation, potential evapotranspiration, and the drought index) upon vegetation. The results show that the resilience of vegetation in the BLR basin is influenced by the severity of drought. Annual changes in SPEI over the BLR basin show an increasing trend, with rates of 3.12 × 10−2. Precipitation and NDVI had a strong positive correlation (p < 0.05), found for 34.93% of the total pixels in the study area. In the BLR basin, vegetation growth is inhibited in the 4 years following a drought event. The area near 800 m is most sensitive to drought events. It provides a theoretical basis for future drought response and effective vegetation restoration in the region. [ABSTRACT FROM AUTHOR]

Gujarat is situated within a climatic spectrum ranging from arid to semi-arid, characterised by periodic and recurrent drought phenomena coupled with an enduring and consistent challenge of water scarcity. Drought events in the Gujarat region place substantial stress on its water resource infrastructure, thereby impacting not only the hydrological aspects but also exerting notable repercussions on the intricate interplay between agriculture, economics, and the broader societal domain. Despite the implementation of numerous legislations and protocols aimed at mitigating the impact of drought and ameliorating the aftermath, the state of Gujarat experiences a recurring pattern of drought, resurfacing approximately every three years and inflicting substantial disruption upon the lives of its residents. This research focuses on analysing the current drought scenarios using the standard precipitation index (SPI) and meteorological drought modelling using autoregressive integrated moving average with exogenous variable (ARIMAX) for identifying future drought conditions, which is a statistical modelling approach. Analysis of reference locations was carried out to identify the best model and the same model used for drought modelling and forecasting in the remaining locations. A meteorological drought risk map and drought frequency map were prepared using forecasting results. Model validation was done by computing RMSE and R2 of multiple locations in the entire region. Research highlights: The study focuses on the recurring drought challenges faced by Gujarat due to its arid to semi-arid climate, leading to persistent water scarcity issues The research explores the intricate effects of droughts across hydrology, agriculture, economics, and society, highlighting their far-reaching consequences Despite mitigation efforts, a consistent drought pattern emerges approximately every three years, posing ongoing challenges to water resource management and societal stability The research employs a two-pronged strategy – utilising the standardized precipitation index (SPI) for assessing current drought conditions and leveraging the ARIMAX model for future predictions The ARIMAX model is carefully chosen and fine-tuned using reference locations to ensure accurate predictions across diverse geographical areas A significant outcome of the research is the development of drought risk maps, providing spatial insights into vulnerability levels and enabling targeted mitigation strategies The research rigorously validates the accuracy of its predictions using metrics such as root mean squared error (RMSE) and coefficient of determination (R2), enhancing the credibility of the findings The study reveals nuanced patterns, indicating that the west coast experiences frequent droughts, while inland areas face less frequent but longer-lasting events Understanding regional differences guides the formulation of customised approaches to address varying degrees of drought impact Through its comprehensive analysis, predictive capabilities, and spatial insights, the research equips stakeholders with actionable strategies to effectively manage and mitigate the recurring challenges posed by drought events [ABSTRACT FROM AUTHOR]

Based on monthly MODIS EVI and LAI data from 2001 to 2020, combined with the Standardized Precipitation Evapotranspiration Index (SPEI), this study employs Theil–Sen trend analysis, Mann–Kendall (MK) test, Hurst index analysis, and correlation analysis to comparatively analyze the overall vegetation trends, spatial distribution characteristics, and future trends of different vegetation types in Guizhou Province under varying slope conditions. The study also explores the response of vegetation to SPEI at different time scales across different slopes. The results indicate the following: (1) From 2001 to 2020, the average values of EVI (0.34%/a) and LAI (1.4%/a) during the growing season exhibited an increasing trend, with the improved vegetation areas primarily concentrated in the western region of Guizhou, while the degradation areas were mainly located in the central and eastern regions. (2) Under different slope conditions, EVI generally showed slight improvement, while LAI exhibited significant improvement, with dry-lands experiencing the largest changes. Future trends indicate continuous improvement, but the proportion of vegetation improvement area decreases with increasing slope. When the slope is less than 5°, the proportion of vegetation improvement area is the highest. (3) The positive correlation between EVI, LAI, and SPEI at different time scales is stronger than the negative correlation, with the strongest correlations observed when the slope is less than 5°. When the slope exceeds 35°, the relationship between vegetation and drought response is almost unaffected by the slope. These findings provide a scientific basis for vegetation growth monitoring and the study of climate change and vegetation interactions in Guizhou Province. [ABSTRACT FROM AUTHOR]

The Tibetan Plateau, often referred to as Asia's water tower, is a focal point for studying spatiotemporal changes in water resources amidst global warming. Precipitation is a crucial water resource for the Tibetan Plateau. Precipitation information holds significant importance in supporting research on the Tibetan Plateau. In this study, we estimate the performance and applicability of Climate Prediction Center Merged Analysis of Precipitation (CMAP), Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (IMERG), Global Land Data Assimilation System (GLDAS), and Global Precipitation Climatology Project (GPCP) precipitation products for estimating precipitation and different disaster scenarios (including extreme precipitation, drought, and snow) across the Tibetan Plateau. Extreme precipitation and drought indexes are employed to describe extreme precipitation and drought conditions. We evaluated the performance of various precipitation products using daily precipitation time series from 2000 to 2014. Statistical metrics were used to estimate and compare the performances of different precipitation products. The results indicate that (1) Both CMAP and IMERG showed higher fitting degrees with gauge precipitation observations in daily precipitation. Probability of detection, False Alarm Ratio, and Critical Success Index values of CMAP and IMERG were approximately 0.42 to 0.72, 0.38 to 0.56, and 0.30 to 0.42, respectively. Different precipitation products presented higher daily average precipitation amount and frequency in southeastern Tibetan Plateau. (2) CMAP and GPCP precipitation products showed relatively great and poor performance, respectively, in predicting daily and monthly precipitation on the plateau. False alarms might have a notable impact on the accuracy of precipitation products. (3) Extreme precipitation amount could be better predicted by precipitation products. Extreme precipitation day could be badly predicted by precipitation products. Different precipitation products showed that the bias of drought estimation increased as the time scale increased. (4) GLDAS series products might have relatively better performance in simulating (main range of RMSE: 2.0–4.5) snowfall than rainfall and sleet in plateau. G-Noah demonstrated slightly better performance in simulating snowfall (main range of RMSE: 1.0–2.1) than rainfall (main range of RMSE: 2.0–3.8) and sleet (main range of RMSE: 1.5–3.8). This study's findings contribute to understanding the performance variations among different precipitation products and identifying potential factors contributing to biases within these products. Additionally, the study sheds light on disaster characteristics and warning systems specific to the Tibetan Plateau. [ABSTRACT FROM AUTHOR]

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

Agricultural drought affects the regional food security and thus understanding how meteorological drought propagates to agricultural drought is crucial. This study examines the temporal scaling trends of meteorological and agricultural drought data over 34 Indian meteorological sub-divisions from 1981 to 2020. A maximum Pearson's correlation coefficient (MPCC) derived between multiscale Standardised Precipitation Index (SPI) and monthly Standardised Soil Moisture Index (SSMI) time series was used to assess the seasonal as well as annual drought propagation time (DPT). The multifractal characteristics of the SPI time series at a time scale chosen from propagation analysis as well as the SSMI-1 time series were further examined using Multifractal Detrended Fluctuation Analysis (MF-DFA). Results reveal longer average annual DPT in arid and semi-arid regions like Saurashtra and Kutch (~ 6 months), Madhya Maharashtra (~ 5 months), and Western Rajasthan (~ 6 months), whereas, humid regions like Arunachal Pradesh, Assam and Meghalaya, and Kerala exhibit shorter DPT (~ 2 months). The Hurst Index values greater/less than 0.5 indicates the existence of long/short-term persistence (LTP/STP) in the SPI and SSMI time series. The results of our study highlights the inherent connection among drought propagation time, multifractality, and regional climate variations, and offers insights to enhance drought prediction systems in India. [ABSTRACT FROM AUTHOR]

Persistent droughts pose a threat to agricultural production, and the changing environment worsens the risk of drought exposure. Understanding the propagation of drought in changing environments and assessing possible impact factors can help in the early detection of drought, guiding agricultural production practices. The current study cannot reflect the propagation status of drought to the total terrestrial hydrological drought, so this work creatively investigated the atmospheric to hydrological drought propagation time in the Yangtze River Basin under the dynamic and static perspectives based on the Standardized Precipitation Evapotranspiration Index and the Terrestrial Water Storage Anomalous Drought Index, fine-tuned the time scale to the seasonal scale, and explored the contributing capacity of the variable interactions. The results show that: (1) under the dynamic perspective, while the propagation time is decreasing in the annual scale, the spring season shows the opposite trend; and (2) large variability exists in the timing of drought propagation at spatial scales, with elevation playing the most important influential role, and bivariate interactions contributing stronger explanations compared to single variables. This study highlights the importance of considering the impact of variable interactions and contributes to our understanding of the response of secondary droughts to upper-level droughts, providing valuable insights into the propagation of droughts to total terrestrial hydrologic drought. [ABSTRACT FROM AUTHOR]

The present study assesses the ability of Monsoon Mission CFSv2-T382 initialized at 4-month and 2-month leads (Feb IC and Apr IC hindcast) to simulate the short-term drought during the Indian Summer Monsoon (June to September). The rainfall-based standardized precipitation index (SPI) averaged over the Indian land region showed significant skills for the 1981–2017 hindcast period for both the ICs. However, the spatial distribution of the skill is moderate and is limited to some regions, and varies within the season. Category-wise forecasts indicate that even with strong rainfall bias and moderate false alarm ratio, model hindcasts have a better detection ratio, hit rate, etc., demonstrating the model's usefulness for predicting the all-India drought. The observed SPI over the entire India during the month of June has a strong positive (negative) relationship with soil moisture (surface temperature) over India, along with warm (cold) SST anomalies in the northern Indian Ocean (north tropical Atlantic Ocean). The SST anomalies associated with El Nino Southern Oscillation (ENSO) dominate the drought towards the end of the monsoon season. However, SPI simulated by model hindcasts has a stronger relationship with ENSO throughout the season. The homogenous region-wise skill analysis and teleconnections of SPI explain its scattered skill over Indian land regions. The observed regional surface temperature and soil moisture have a strong 1–2-month lead relationship with SPI in all homogenous regions, and forcing from ENSO is evident for WCI and SPInd only. The model overestimates the simultaneous ENSO relationships for all the homogenous regions, while the lead role of moisture, surface temperature, and north tropical Atlantic SST is under-predicted. WCI and SPInd regions have a moderate lead relationship with soil moisture and surface temperature along with ENSO teleconnection, resulting in improved skill compared to other subdivisions of India. The study shows that further improvement of the regional scale skill of CFSv2 for capturing SPI is possible mainly with the regional scale improvement of land surface processes and its lead teleconnection with SPI in the model. [ABSTRACT FROM AUTHOR]

The Shendong Mining Area, being the largest coal base in the world, has significant challenges in the intensive development and utilization of coal resources, as well as the impact of a dry climate, which can have serious negative effects on the growth of flora in the region. Investigating the spatial and temporal patterns of how meteorological drought affects vegetation in the Shendong Mining Area at various time scales can offer a scientific foundation for promoting sustainable development and ecological restoration in the region. This study utilizes the Standardized Precipitation Evapotranspiration Index (SPEI) and Normalized Difference Vegetation Index (NDVI) data from 1986 to 2020 in the Shendong Mining Area. It employs Slope trend analysis, a Mann–Kendall test, a Geographic Detector, and other methods to examine the spatiotemporal distribution characteristics of meteorological drought at various time scales. Additionally, the study investigates the influence of these drought patterns on vegetation growth in the Shendong Mining Area. Across the mining area, there was a general decrease in the monthly average SPEI on an annual basis. However, on a seasonal, semi-annual, and annual basis, there was a gradual increase in the annual average SPEI, with a higher rate of increase in the southern region compared to the northern region. When considering the spatial variation trend in different seasons, both positive and negative trends were observed in winter and summer. The negative trend was mainly observed in the western part of the mining area, while the positive trend was observed in the eastern part. In spring, the mining area generally experienced drought, while in autumn, it generally experienced more precipitation. The mining area exhibits a prevailing distribution of vegetation, with a greater extent in the southeast and a lesser extent in the northwest. The vegetation coverage near the mine is insufficient, resulting in a low NDVI value, which makes the area prone to drought. Over the past few years, the mining area has experienced a significant increase in vegetation coverage, indicating successful ecological restoration efforts. Various forms of land use exhibit distinct responses to drought, with forests displaying the most positive correlation and barren land displaying the strongest negative correlation. Various types of landforms exhibit varying responses to drought. Loess ridge and hill landforms demonstrate the most pronounced positive association with monthly-scale SPEI values, whereas alluvial and floodplain landforms display the poorest positive correlation with yearly scale SPEI values. The general findings of this research can be summarized as follows: (1) The mining area exhibits a general pattern of increased humidity, with the pace of humidity increase having intensified in recent times. Seasonal variations exhibit consistent cyclic patterns. (2) There are distinct regional disparities in NDVI values, with a notable peak in the southeast and a decline in the northwest. The majority of the mining area exhibits a positive trend in vegetation recovery. (3) Regional meteorological drought is a significant element that influences changes in vegetation coverage in the Shendong Mining Area. Nevertheless, it displays complexity and is more obviously impacted by other factors at a small scale. (4) It should be noted that forests and barren land exert a more significant influence on SPEI values, despite their relatively lesser spatial coverage. The predominant land use type in most locations is grasslands; however, they have a relatively minor influence on SPEI. (5) A shorter time period, higher elevation, and steeper slope gradient all contribute to a larger correlation with drought. [ABSTRACT FROM AUTHOR]

Understanding the transition from meteorological to hydrological drought is essential for accurately predicting hydrological droughts. However, the factors driving this transition are intricate, and a comprehensive understanding of how direct human activities influence this shift in drought is lacking. In this study, we initially explored the spatiotemporal correlation between the occurrence of meteorological and hydrological droughts. Subsequently, we formulated multiple hydrological replenishment scenarios using the soil and water assessment tool (SWAT) model to assess the environmental impact of the transition from meteorological to hydrological droughts. The Xijiang River Basin (XRB), the primary tributary of the Pearl River basin, was selected as the study area. Our results identified 91 meteorological droughts in the XRB, and only 66 hydrological droughts from 1978 to 2018. The transition rate from meteorological to hydrological drought demonstrated large spatial variability, with a basin‐average rate of 56% and the lowest transition rate of 45% in the headstream The transition from meteorological to hydrological drought was mostly rapid between November and December (~2 months), but can be prolonged during spring (about 3–5 months) and winter (around 7–9 months). Additionally, analysis of regeneration scenarios indicated that human activity has mitigated drought severity over recent decades. The primary driver affecting drought duration and frequency during the transition from meteorological to hydrological droughts shows conspicuous spatial disparities in densely populated areas. Although human activity significantly contributes to drought duration and severity compared to climate change during the transition in the headstream, its effects are more pronounced downstream in terms of drought duration and frequency. One plausible explanation is that increased water consumption downstream has considerably prolonged drought progression, whereas water management has counteractive effects on drought progression due to climate change in the headstream. Our findings offer valuable insights into the transition process from meteorological to hydrological droughts in the presence of extensive human activities. [ABSTRACT FROM AUTHOR]

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This study uses Multifractal Detrended Fluctuation Analysis to examine the scaling behavior and spatiotemporal variability of drought indicators. We employ two hybrid drought indicators—the GRACE Root Zone Soil Moisture Percentile (SMI) and Evaporative Demand Drought Index (EDDI)—to elucidate agricultural drought characteristics, and we quantify meteorological drought using the Standardized Precipitation Index (SPI). Our investigation was carried out in the United States in the California and Mississippi watersheds, each characterized by distinct agricultural climate conditions and planting systems. The changes in drought indicators exhibit multifractality primarily derived from the long-term memory embedded within the time series. Spatially, the increment sequence of SPI displays pronounced dependence in almost all areas. Specifically, the changes in SPI demonstrate persistence, as the Hurst exponents changed from 0.53 to 0.58 in California, revealing the possibility of a degree of trend continuation (albeit, not very strong) in the future. A regular summary cannot be obtained for Mississippi, since the Hurst exponent ranges from 0.46 to 0.51 for the increment sequence of SPI in that watershed. However, the increment sequences for the agricultural drought indicators exhibit obvious short-term persistence (anti-persistence), indicating that future drought trends may be discontinuous. The variations in the strength of anti-persistence among agricultural drought indicators can be attributed to geographical differences, diverse irrigation strategies, and varying cropping systems. No significant evolution pattern is evident for the scaling structure of drought processes due to minor variations in external controlling factors, such as rainfall and temperature, within the drought process. [ABSTRACT FROM AUTHOR]

It is not clear to what extent anthropogenic activities increase meteorological drought based on regional-scale observations. This study provides a detection and attribution (D&A) analysis of external forcing on meteorological drought using the standard precipitation index for a 12-month time scale (SPI-12) on a regional scale, particularly in the Ajichay basin, from 1972 to 2020, based on models from the Coupled Model Intercomparison Project Phase 6 (CMIP6). The Regularized Optimal Fingerprinting (ROF) method is performed for D&A analyses on two SPI-12 time series (inter-annual/decadal and long-term), which are decomposed by the Ensemble Empirical Mode Decomposition (EEMD). Observed annual precipitation, greenhouse gas (GHG) forcing, and anthropogenic-plus-natural (ALL) forcing show 62%, 33%, and 17% upward trends, respectively, based on the Mann-Kendall test. Additionally, the EEMD method reveals that the long-term trends of observed SPI-12, GHG, and ALL forcings exhibit nonlinear trends that have 7%, 3.5%, and 4.5% variance contribution rates of components, respectively. The scaling factor (β) presents the responses SPI-12 to external forcing using total least squares regression estimates in the ROF method. External forcing is detectable and attributable should β and an uncertainty range be greater than zero and spanning unity. The results show that for inter-annual/decadal SPI-12, greenhouse gas can be detected and separated from natural (NAT) and other anthropogenic forcings ( β =0.96 with 95% confidence interval of 0.64–1.2) in single, two, and three-signal analyses. In long-term evaluations, greenhouse gas forcing ( β =1.27 with a 95% confidence interval of 0.95–1.59) can be detected and separated from natural and other anthropogenic forcing in single, two, and three-signal analyses. [ABSTRACT FROM AUTHOR]

Abstract The Tibetan Plateau, often referred to as Asia’s water tower, is a focal point for studying spatiotemporal changes in water resources amidst global warming. Precipitation is a crucial water resource for the Tibetan Plateau. Precipitation information holds significant importance in supporting research on the Tibetan Plateau. In this study, we estimate the performance and applicability of Climate Prediction Center Merged Analysis of Precipitation (CMAP), Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (IMERG), Global Land Data Assimilation System (GLDAS), and Global Precipitation Climatology Project (GPCP) precipitation products for estimating precipitation and different disaster scenarios (including extreme precipitation, drought, and snow) across the Tibetan Plateau. Extreme precipitation and drought indexes are employed to describe extreme precipitation and drought conditions. We evaluated the performance of various precipitation products using daily precipitation time series from 2000 to 2014. Statistical metrics were used to estimate and compare the performances of different precipitation products. The results indicate that (1) Both CMAP and IMERG showed higher fitting degrees with gauge precipitation observations in daily precipitation. Probability of detection, False Alarm Ratio, and Critical Success Index values of CMAP and IMERG were approximately 0.42 to 0.72, 0.38 to 0.56, and 0.30 to 0.42, respectively. Different precipitation products presented higher daily average precipitation amount and frequency in southeastern Tibetan Plateau. (2) CMAP and GPCP precipitation products showed relatively great and poor performance, respectively, in predicting daily and monthly precipitation on the plateau. False alarms might have a notable impact on the accuracy of precipitation products. (3) Extreme precipitation amount could be better predicted by precipitation products. Extreme precipitation day could be badly predicted by precipitation products. Different precipitation products showed that the bias of drought estimation increased as the time scale increased. (4) GLDAS series products might have relatively better performance in simulating (main range of RMSE: 2.0–4.5) snowfall than rainfall and sleet in plateau. G-Noah demonstrated slightly better performance in simulating snowfall (main range of RMSE: 1.0–2.1) than rainfall (main range of RMSE: 2.0–3.8) and sleet (main range of RMSE: 1.5–3.8). This study’s findings contribute to understanding the performance variations among different precipitation products and identifying potential factors contributing to biases within these products. Additionally, the study sheds light on disaster characteristics and warning systems specific to the Tibetan Plateau.

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

Abstract Agricultural drought affects the regional food security and thus understanding how meteorological drought propagates to agricultural drought is crucial. This study examines the temporal scaling trends of meteorological and agricultural drought data over 34 Indian meteorological sub-divisions from 1981 to 2020. A maximum Pearson's correlation coefficient (MPCC) derived between multiscale Standardised Precipitation Index (SPI) and monthly Standardised Soil Moisture Index (SSMI) time series was used to assess the seasonal as well as annual drought propagation time (DPT). The multifractal characteristics of the SPI time series at a time scale chosen from propagation analysis as well as the SSMI-1 time series were further examined using Multifractal Detrended Fluctuation Analysis (MF-DFA). Results reveal longer average annual DPT in arid and semi-arid regions like Saurashtra and Kutch (~ 6 months), Madhya Maharashtra (~ 5 months), and Western Rajasthan (~ 6 months), whereas, humid regions like Arunachal Pradesh, Assam and Meghalaya, and Kerala exhibit shorter DPT (~ 2 months). The Hurst Index values greater/less than 0.5 indicates the existence of long/short-term persistence (LTP/STP) in the SPI and SSMI time series. The results of our study highlights the inherent connection among drought propagation time, multifractality, and regional climate variations, and offers insights to enhance drought prediction systems in India.

[Objective] This study was aimed to explore the response mechanism of vegetation to drought at different times scales and clarify the frequent occurrence of extreme drought events in southwest China on vegetation growth. [Methods] Based on vegetation net primary productivity (NPP) data from 2001 to 2019 and standardized precipitation evapotranspiration index (SPEI) data on a continuous scale from 1-24 months in southwest China, correlation analysis, maximum synthesis method, significance test were used to analyze the responses of NPP of different geomorphic types and vegetation types to multi-scale SPEI in southwest China. [Results] (1) On the annual scale, seasonal scale and growing season, the responses of vegetation NPP and SPEI at 1-24 months were dominated by the January-March scale, vegetation NPP had a good response to short-term drought changes, but a relatively poor response to medium and long-term drought changes. (2) The response of NPP to SPEI at 1-24 month scale showed significant spatial heterogeneity and seasonal differences. The interannual correlation showed a trend of significant positive correlation to significant negative correlation from southeast to northwest. The seasonal correlation showed that significant negative correlation was dominant in summer, significant positive correlation was dominant in spring, autumn, and winter. This indicated that in the southwest China, the summer temperature was high and humid, which limited vegetation growth the area with the largest positive correlation was Guangxi Hills, while the area with the largest negative correlation was Zoige Plateau. (3) The responses of NPP to SPEI from 1-24 months were different among different vegetation types. Although the response characteristics of grassland, scrub and forest land to SPEI were basically similar, the trend of negative correlation between NPP and SPEI became stronger with the scales of SPEI decreased in summer for all types of vegetation. This suggested that all types of vegetation are more susceptible to drought under high temperature and drought conditions in summer. [Conclusion] The research results provide effective scientific support for the protection and restoration of the ecosystem in the southwest China, provide an important theoretical basis for disaster prevention and reduction and coping with climate change, help formulate more targeted policies and measures to promote the sustainable development of southwest China.

Understanding the propagation characteristics and driving factors from meteorological drought to hydrological drought is essential for alleviating drought and for early warning systems regarding drought. This study focused on the Weihe River basin (WRB) and its two subregions (the Jinghe River (JRB) and the middle reaches of the Weihe River (MWRB)), utilizing the Standardized Precipitation Index (SPI) and Standardized Runoff Index (SRI) to characterize meteorological and hydrological drought, respectively. Based on Copula theory and conditional probability, a quantification model for the propagation time (PT) of meteorological–hydrological drought was constructed. The dynamic characteristics of PT on annual and seasonal scales were explored. Additionally, the influences of different seasonal meteorological factors and underlying surface factors on the dynamic changes in PT were analyzed. The main conclusions were as follows: (1) The PT of meteorological–hydrological drought was characterized by faster propagation during the hot months (June–September) and slower propagation during the cold months (December to March of the following year); (2) Under the same level of hydrological drought, as the level of meteorological drought increases, the PT of the drought shortens. The propagation thresholds of meteorological to hydrological drought in the WRB, the JRB, and the MWRB are −0.69, −0.81, and −0.78, respectively. (3) In the dynamic changes in PT, the WRB showed a non-significant decrease; however, both the JRB and the MWRB exhibited a significant increase in PT across different drought levels. (4) The influence of the water and heat status during spring, summer, and winter on PT was more pronounced, while in autumn, the impact of the basin's water storage and discharge status was more significant in the JRB and the MWRB. [ABSTRACT FROM AUTHOR]

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

The economy and welfare of Iranians are heavily reliant on crops, thus the detrimental effects of drought on crops can have far-reaching consequences for society. In this research, we evaluated the occurrence of meteorological drought and its consequential effect on crop yield (barley, lentils, chickpea, and wheat) in rainfed cultivation within diverse regions of Iran from 2006 to 2020. The results demonstrate that a severe drought affected the majority of regions in Iran in both 2007 and 2016–2017, resulting in a negative impact on crop yields specifically in regions R1 and R4. The findings from the analysis of crop performance using the SYRS index indicated that in the year 2007, there were significant losses observed in rainfed crops. Furthermore, the regions exhibiting values of -2.38 and − 2.35 within the barley crop are the regions characterized by the most significant decline in yield. In 2008, the highest losses in chickpea production were observed in the region R8, with a value of -2.08. Lentils have recorded the highest casualties in the regions of R3 and R7, with − 2.71 and − 2.65 respectively. From 2007 to 2012, there has been a decline in wheat yield, with R4 experiencing higher losses compared to other regions. Based on the findings of SYRS, it can be concluded that wheat and chickpeas experience greater losses compared to lentils and barley. A study investigating the effect of climatic parameters and drought on rainfed crop yield found that rainfall had a greater influence on the yield of most crops. Moreover, the yield of barley is more significantly influenced by drought compared to other crops in 3 and 7 regions. The findings derived from this research can inform the implementation of suitable measures to address future drought challenges in regions susceptible to drought and regions crucial for agricultural production. [ABSTRACT FROM AUTHOR]

The division and evaluation of data series used in monitoring drought into different time intervals is a practical approach to detecting the spatial and temporal extent of drought spread. This study aimed to determine meteorological drought's spatial and temporal distribution using overlapping and consecutive periods and cycles of the standardized precipitation index (SPI) time series in the Mediterranean region, Turkey. In the scope of the research, SPI values for the SPI12, SPI6 (1), and SPI6 (2) seasons were calculated for consecutive and overlapping hydrological years (1978–1998/21 years, 1978–2008/31 years, and 1978–2018/41 years) at 28 meteorological stations. Autocorrelation, Mann–Kendall, and Sen slope trend tests were applied at a 5% significance level for each season (SPI12, SPI6 (1), and SPI6 (2)) and different time scales (21, 31, and 41 years). For each season and period, maps of the SPI drought class, average formation of drought class, Mann–Kendall (MK) trend, and Sen's slope (SS) trend test statistics for the Mediterranean region were obtained, and the spatial distribution rate of trends was determined by drawing hypsometric curves. Changes in drought occurrence at different time scales were thoroughly evaluated with the changing length of data recording. Consequently, it was determined that the mild wet (MIW) and mild drought (MID) classes dominate the study area in the Mediterranean region. Significant and nonstationary changes detected in extreme wet and drought occurrences (extreme wet, EW; severe wet, SW; extreme drought, ED; severe drought, SD) were found to pose a risk in the study area. It was observed that there were spatially and temporally insignificant decreasing drought trends in the Mediterranean basin, considering that the time scales of these trends slowed down. Despite a nonsignificant trend from the MID drought class to the MIW drought class, it is predicted that the MIW and MID classes will maintain their dominance in the Mediterranean region. The central part of the study area (central Mediterranean basin) is the region with the highest drought risk. [ABSTRACT FROM AUTHOR]

In this study, we propose a probabilistic metric, the formative drought rate (FDR), to quantify drought propagation. It is the probability that a meteorological drought in precipitation forms a hydrological drought in streamflow. Drought events were identified based on the standardized precipitation index and streamflow drought index, respectively, at 1‐, 3‐, 6‐ and 12‐month timescales. The method was tested in three river basins in Turkey (Kucuk Menderes, Gediz and Ergene). In each river basin, meteorological stations were coupled with streamflow gauging stations to form pairs of stations depending on their distance from each other and the length of their common record periods. The FDR was calculated across all timescales for each pair of stations. It was found capable to describe the river basin‐specific spatial and temporal variability of drought propagation. As the FDR is defined in the form of probability, it is expected to be a useful metric for quantifying propagation from meteorological to hydrological drought. Thus, it carries a potential for scientific research and practice in water resources management. [ABSTRACT FROM AUTHOR]

The dynamic variations of agricultural drought can reflect the water shortage situation of the agricultural system, and there is a progressive relationship in the response of agricultural drought to meteorological drought on a spatiotemporal scale. In this study, the vegetation health index and the standardized precipitation evapotranspiration index were adopted as agricultural and meteorological drought indicators, respectively. Additionally, using the three‐dimensional spatiotemporal clustering technology, the dynamic evolutions of typical drought events were clarified, and the spatiotemporal response characteristics of agricultural drought to meteorological drought were revealed. The results indicated that: (a) there were 81 agricultural drought events in the North China Plain (NCP) during 1982–2020, with a largest drought severity (12.82 × 104 month km2), a 6‐month duration, and a 23.24 × 104 km2 affected area occurring in the No. 4 event; (b) from the 1980s to the 2010s, the agricultural drought gradually decreased and large‐scale droughts mainly concentrated in the border areas of Hebei, Shandong, and Henan; and (c) a total of 13 drought event pairs were successfully matched in the NCP, including 7 pairs of "one‐to‐one," 4 pairs of "one‐to‐many," 1 pair of "many‐to‐one," and 1 pair of "many‐to‐many." The spatiotemporal responses of agricultural drought were elucidated in a three‐dimensional perspective, which can propose a new approach for establishing drought propagation model, predicting future agricultural drought conditions, improving ecological environment quality, and can also be applied for the investigation of other drought types. Plain Language Summary: There is a certain delay effect and time lag between agricultural drought and meteorological drought, thus, it is crucial to investigate the dynamic variation of agricultural drought and its response to meteorological drought to understand the impact of climate change on vegetation, which has important scientific significance and application value for improving ecological environment quality and restoring ecosystem functions. However, the dynamic variation of agricultural drought and its response to meteorological drought are still unclear from a three‐dimensional perspective. Based on the Center for Satellite Applications and Research and Famine Early Warning Systems Network Land Data Assimilation System data set, the vegetation health index and the standardized precipitation evapotranspiration index were adopted as agricultural and meteorological drought indicators, respectively. Additionally, using the three‐dimensional spatiotemporal clustering technology, the temporal variations and spatial patterns of drought were explored, the dynamic evolutions of typical drought events were clarified, and the spatiotemporal response characteristics of agricultural drought to meteorological drought were revealed from a three‐dimensional perspective. This study sheds new viewpoints into the identification of agricultural drought variation and its relationships with meteorological drought, which can also be applied in other areas. Key Points: There were 81 agricultural drought events in the North China Plain during 1982–2020The drought gradually decreased from the 1980s to the 2010sA total of 13 drought event pairs were successfully matched [ABSTRACT FROM AUTHOR]

Drought is a natural and complex climatic hazard. It has both natural and social connotations. The purpose of this study is to use machine learning methods (MLAs) for drought vulnerability (DVM) in Uttar Pradesh, India. There were 18 factors used to determine drought vulnerability, separated into two groups: physical drought and meteorological drought. The study found that the eastern part of Uttar Pradesh is high to very highly prone to drought, which is approximately 31.38% of the area of Uttar Pradesh. The receiver operating characteristic curve (ROC) was then used to evaluate the machine learning models (artificial neural networks). According to the findings, the ANN functioned with AUC values of 0.843. For policy actions to lessen drought sensitivity, DVMs may be valuable. Future exploration may involve refining machine learning algorithms, integrating real-time data sources, and assessing the socio-economic impacts to continually enhance the efficacy of drought resilience strategies in Uttar Pradesh. [ABSTRACT FROM AUTHOR]

This study evaluated the accuracy of two new generation satellite rainfall estimates (SREs): Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) and Integrated Multi-satellite Retrieval for GPM (IMERG) over Bolivia's complex terrain. These SREs were compared against rainfall data from rain gauge measurements on a point-to-pixel basis for the period 2002–2020. The evaluation was performed across three regions with distinct topographical settings: Altiplano (Highland), Valles (Midland), and Llanos (Lowland). IMERG exhibited better accuracy in rainfall detection than CHIRPS, with the highest rainfall detection skills observed in the Highland region. However, IMERG's higher rainfall detection skill was countered by its higher false alarm ratio. CHIRPS provided a more accurate estimation of rainfall amounts across the three regions, exhibiting low random errors and relative biases below 10%. IMERG tended to overestimate rainfall amounts, with marked overestimation by up to 75% in the Highland region. Bias decomposition revealed that IMERG's high false rainfall bias contributed to its marked overestimation of rainfall. We showcase the utility of long-term CHIRPS data to investigate spatio-temporal rainfall patterns and meteorological drought occurrence in Bolivia. The findings of this study offer valuable insights for choosing appropriate SREs for informed decision-making, particularly in regions of complex topography lacking reliable gauge data. [ABSTRACT FROM AUTHOR]

Drought, as a widespread natural calamity, leads to the most severe agricultural losses among all such disasters. Alterations in the yield of major global agricultural products are pivotal factors influencing food prices, food security, and land use decisions. China's rapidly expanding demand for sustenance will persist over the forthcoming decades, emphasizing the critical need for an accurate assessment of drought's impact on food production. Consequently, we conducted a comprehensive evaluation of the drought risk in China and its repercussions on agricultural output. Additionally, we delved into the underlying factors driving changes in yield for three primary grain crops (wheat, corn, and rice), which hold particular relevance for shaping effective strategies to mitigate future drought challenges. The findings divulge that both the number of drought months (DM) and the drought magnitude index (DMI) have displayed an upward trajectory over 60 years with a correlation coefficient of 0.96. The overall severity of meteorological drought has escalated across China, and it is particularly evident in regions such as the southwest and central parts of the Huang-Huai-Hai region, the northwestern middle region, and the Xinjiang region. Conversely, there has been some relief from drought conditions in southern China and the Yangtze River Delta. Shifts in the total grain output (TGO) during this period were compared: it underwent three stages, namely "fluctuating growth" (1961–1999), then a "sharp decline" (2000–2003), followed by "stable growth" (2004–2018). Similarly, changes in the grain planting area (GPA) experienced two stages, "continuous reduction" (1961–2003) succeeded by "stable growth" (2004–2018), while maintaining an upward trend for grain yield per unit area (GY) throughout. Furthermore, it was revealed that the drought grade serves as a significant constraint on continuous expansion within China's grain output—where the drought damage rate's influence on the TGO outweighs that from the GY. Our research outcomes play an instrumental role in deepening our comprehension regarding how drought impacts agricultural production within China while furnishing the scientific groundwork to devise efficacious policies addressing these challenges. [ABSTRACT FROM AUTHOR]

Drought, being one of the most devastating natural disasters, has a far- reaching impact. In the context of global warming, it becomes crucial to quantitatively and scientifically assess the effects of anthropogenic climate change on meteorological drought in China. This assessment not only enhances our understanding of anthropogenic climate change but also aids in formulating more effective strategies for managing the risks associated with meteorological drought. This study employs the standardized precipitation evapotranspiration index (SPEI) to compute drought events by utilizing both observational data and counterfactual climate data (i.e., detrended observations). Subsequently, it analyzes the impact of anthropogenic climate change on the characteristics of drought, including frequency, intensity, duration, and affected area, in China as well as nine major river basins from 1960 to 2019. The outcomes of the analysis reveal that based on observational data, there is a discernible upward trend in the frequency, intensity, duration, and affected area of meteorological droughts in China. Notably, the regions experiencing an increase in frequency, intensity, and duration are primarily situated in the northeastern part of the Northwestern Rivers basin, the central and western parts of the Yellow River basin, the central and northern parts of the Yangtze River basin, the western part of the Southeastern River basins, and the eastern part of the Pearl River basin. Conversely, when considering a counterfactual climate scenario, the frequency and intensity of meteorological droughts in China demonstrate an upward trend, while the duration and affected area exhibit a downward trend. The impact of anthropogenic climate change on China has been evident in the increased frequency, intensity, duration, and affected area of droughts. Specifically, regions located in the northeastern parts of Northwest River basins, the southern part of the Songliao River basin, the northern part of the Haihe River basin, the central- northern part of the Yangtze River basin, the eastern part of the Pearl River basin, and the western part of the Southwest River basins have experienced amplified levels of drought. Anthropogenic climate change is highlighted as the primary factor influencing the observed drought characteristics changes in China, with contribution rates of 84.67%, 75.25%, 190.32%, and 133.99% for changes in the increased drought frequency, intensity, duration, and affected area, respectively. These changes have significant implications for water resource management and agricultural practices in the affected regions. [ABSTRACT FROM AUTHOR]

The evaluation and monitoring of drought, a complex disaster, are essential issues for the planning of agricultural activities and the operation and management of water resources. In this study, the meteorological drought of the Black Sea region in northern Turkey is evaluated with the Standardized Precipitation Index (SPI) and Reconnaissance Drought Index (RDI) methods at 3-, 6-, and 12-month time scales. Meteorological parameters obtained between 1965 and 2019 from 28 meteorological observation stations are used to analyze the drought. Drought-wet period percentages, percentages of occurrence of drought classes, critical period, and negative peak index values are investigated. As a result of the study, it is observed that the percentage of drought occurrence at all time scales is higher than 50% at two stations in the study region, which received precipitation above the average of Turkey. The sum of the extreme drought (ED) and severe drought (SD) values throughout the region is approximately 15% for the whole area. There is a high correlation coefficient, 97.8, 97.6, and 97.3 for the 3-, 6-, and 12-month time scales, respectively, between the SPI and RDIst methods in the evaluation of meteorological drought in the Black Sea region, which is generally a rainy region. It is determined that with the increase in the time scale, the percentage of ED occurrence increased and the risk level of the region around RDI and 17089 station is the highest. It has been determined that the correlation between oscillation indices and meteorological drought indices for the Black Sea region is almost non-existent. The highest correlation value between the oscillation and meteorological drought indices in the region is obtained with the Atlantic Multidecadal Oscillation (AMO). [ABSTRACT FROM AUTHOR]

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The drought is a prominent disaster the world is facing currently. The drought identification, its characterization, and monitoring are, thus, very important to mitigate this calamity. Many drought indices have been developed for this purpose in the past; however, their evaluation is necessary to select the best suited drought index for the specific area. This study aims to analyze the meteorological drought risk across the Raigarh district of Chhattisgarh state by evaluating five popular drought indices, namely, Reconnaissance drought index (RDI) rainfall anomaly index, standardized precipitation evapotranspiration index (SPEI), standardized precipitation index (SPI), and standardized anomaly index (SAI) considering all the tehsils of the district and proposes the methodology to select the best suited index for the region. The methodology has been selected in such a manner that it deals with all aspects of meteorological drought and the most suitable drought index for the region can be identified by comparing the drought indices based on the actual drought occurrence in the region which are identified by different agencies. These drought indices were computed for different assessment periods of 1, 3, 6, 9, and 12 months using the historical precipitation and temperature data derived from (NASA-POWER) for the period 1981–2021. The variation in severity in terms of extreme, severe, and moderate drought events has been observed with these drought indices in the district. Maximum correlation is observed between RAI and SAI across all assessment periods. Further, the SPI is seen to have fairly good agreement with all other drought indices and it rises with increasing assessment periods. Modified Mann–Kendall test shows a significant positive trend for all drought indices (except SPEI across all assessment periods, RDI-1 and RDI-3) across all assessment periods. The SPI performs exceptionally well in capturing the actual drought conditions reported by different agencies in the Raigarh region among all other indices considered in this study. It is also observed that although SPEI which comes out to be the second most suitable index for drought identification in the study region, it was not able to capture the drought events before 1990. However, SPEI identified most of the drought years post 1990 which shows the prevalent effect of temperature on the formulation of index driven by heavy industrialization in the region post 1990. Overall results suggest that the SPI is the best-suited meteorological drought index for the Raigarh region. [ABSTRACT FROM AUTHOR]

Study Region: Continental Argentina, South America. Study Focus: This study integrates multiple data sources to analyze the response of vegetation to meteorological and groundwater drought events in Argentina. It utilizes GRACE satellite data to monitor water storage variations, the GLDAS hydrological model to track soil moisture levels, and NDVI to assess vegetation health, categorized by different vegetation types. Meteorological data facilitated the computation of drought severity using the Standardized Precipitation-Evapotranspiration Index (SPEI). Anomalies in groundwater storage, derived from these data, informed the calculation of the Grace Groundwater Drought Index (GGDI). A comprehensive statistical analysis was conducted to examine the impact of meteorological (SPEI) and groundwater (GGDI) droughts on various vegetation types. New Hydrological Insights for the Region: The study reveals that from 2002 to 2022, GRACE-TWSA and GLDAS-TWSA displayed consistent trends in Argentina. Meteorological droughts, characterized by higher frequency and intensity, predominantly affect croplands and grasslands. Compared to lagged effects, the cumulative impact of droughts was found to be more significant. Shrublands demonstrated the greatest resistance to drought, whereas vegetation in humid regions showed lower drought tolerance. Notably, the duration associated with the lag effects of meteorological droughts decreased from 2.16 months in 2002–1.52 months in 2020, suggesting an increasing sensitivity of vegetation to these droughts. This underscores the urgency of developing targeted drought resilience and water resource management strategies.

Different drought types exhibit propagation phenomena and feedback through the hydrological cycle. Therefore, this study utilized the convergent cross mapping to identify the causality among meteorological drought(MD), agricultural drought(AD), hydrological drought(HD), and ecological drought(ED) in the Nenjiang River Basin from 1980 to 2020, constructing a causal relationship network. The results indicate that different types of droughts are more prone to spread and influence each other at shorter time scales. Although there exist bidirectional causality between MD and other droughts at the monthly scale, no clear causal relationship is observed between AD and ED. Contrasting the direction and strength of causality, presenting the order MD-AD-HD-ED. This finding is consistent with the hydrological cycle pattern. The intensity of drought causality varies significantly across different regions spatially. This study delves into the mechanisms and intensities of mutual influence among different types of droughts by establishing the causal relationship network, offering essential scientific basis for drought monitoring, early warning, and emergency response.

Reliable monitoring and thorough spatiotemporal prediction of meteorological drought are crucial for early warning and decision-making regarding drought-related disasters. The utilisation of multiscale methods is effective for a comprehensive evaluation of drought occurrence and progression, given the complex nature of meteorological drought. Nevertheless, the nonlinear spatiotemporal features of meteorological droughts, influenced by various climatological, physical and environmental factors, pose significant challenges to integrated prediction that considers multiple indicators and time scales. To address these constraints, we introduce an innovative deep learning framework based on the shifted window transformer, designed for executing spatiotemporal prediction of meteorological drought across multiple scales. We formulate four prediction indicators using the standardized precipitation index and the standard precipitation evaporation index as core methods for drought definition using the ERA5 reanalysis dataset. These indicators span time scales of approximately 30 d and one season. Short-term indicators capture more anomalous variations, whereas long-term indicators attain comparatively higher accuracy in predicting future trends. We focus on the East Asian region, notable for its diverse climate conditions and intricate terrains, to validate the model's efficacy in addressing the complexities of nonlinear spatiotemporal prediction. The model's performance is evaluated from diverse spatiotemporal viewpoints, and practical application values are analysed by representative drought events. Experimental results substantiate the effectiveness of our proposed model in providing accurate multiscale predictions and capturing the spatiotemporal evolution characteristics of drought. Each of the four drought indicators accurately delineates specific facets of the meteorological drought trend. Moreover, three representative drought events, namely flash drought, sustained drought and severe drought, underscore the significance of selecting appropriate prediction indicators to effectively denote different types of drought events. This study provides methodological and technological support for using a deep learning approach in meteorological drought prediction. Such findings also demonstrate prediction issues related to natural hazards in regions with scarce observational data, complex topography and diverse microclimate systems.

Abstract This study aims to quantify meteorological–hydrological drought propagations and examine the potential impacts by climatic variability, LULC change (LULC), and human regulations. An integrated observation-modeling framework quantifies drought propagation intervals and assesses mechanisms influencing hydrological droughts. Meteorological droughts are characterized using the Standardized Precipitation Evapotranspiration Index (SPEI), and hydrological droughts are assessed through the Standardized Streamflow Index (SSI) across diverse climatic zones. Cross-correlation analysis between SPEI and SSI time series identifies the lag time associated with the highest correlation as the drought propagation interval. Mechanisms are investigated via a coupled empirical-process modeling framework incorporating the Soil and Water Assessment Tool (SWAT). Discrepancies between simulated and observed SSI time series help quantify the extent of human regulation impacts on hydrological drought characteristics and propagation. The Yellow River Basin (YRB), divided into six subzones based on climate characteristics, is selected as the case study. Key findings include: (1) Meteorological droughts were extremely severe across most YRB during the 1990s, while the 2000s showed some mitigation primarily due to precipitation increases. (2) Hydrological droughts and propagation times from meteorology to hydrology demonstrated substantial spatiotemporal variability. In general, summer propagation times were shorter than other seasons. (3) Propagation times were shorter in arid regions with cropland or built-up land cover versus grassland and woodland, while the reverse held for humid regions. (4) Human regulations prolonged propagation times, likely due to reservoir regulations designed to overcome water deficits. While the YRB is the focus of this paper, the methodologies and findings are applicable to other regions worldwide to enhance drought forecasting and water resource management. In various hydrological and climatic contexts worldwide.

【Objective】 Drought is an abiotic stress detrimentally affecting crop production in many countries. Understanding its occurrence in a region is crucial to improving its water and agricultural management. In this paper, we analyzed the spatiotemporal variation in drought from 1961—2022 in Huaibei located in central China. 【Method】 The analysis is based on meteorological data measured from 1961—2022 from 21 weather stations across the region. Monthly, seasonal, and annual standardized precipitation evapotranspiration index (SPEI) in each station were calculated, from which we calculated the spatiotemporal variation in drought across the region. 【Result】 ① Monthly, seasonal and annual SPEI in Huaibei had been in decrease from 1961—2022. ② The frequency and duration of persistent droughts had both been in increase from 1961—2022, with Huaibei station showing the highest drought frequency at 0.73 times/year, and Sixian station showing the longest drought duration at 4.61 months. ③ Annual change in droughts in spring and autumn did not show any identifiable trend. Frequency and intensity of droughts in winter were the highest, reaching 34.43% and 21.86%, respectively. Spatially, spring drought was more frequent in the center, east, and southwest of the region; summer drought was less frequent in the southeast of the region; autumn drought was more frequent in the east and north of the region; winter drought was more frequent in the west, northeast and southeast of the region. ④ On average, annual change in drought was increasing at a rate of 0.01/10 a, but not at significant levels. Drought areas had been in increase from 1961 to 2022 at a rate of 1.4%/10 a. The north has a high tendency for occurrence of droughts, while drought intensity tends to be high in the west and southeast of the region. Drought is more frequent in the northeast and southwest. 【Conclusion】 Overall, there are noticeable trends in spatiotemporal variation in drought in the Huaibei region, with drought frequency and intensity varying between different regions and showing an increase trend from 1961 to 2022.

[Objective] Intensive research on the spatial and temporal response characteristics of meteorological drought to vegetation at different time scales in the Yellow River Basin provides valuable information for ecological conservation, water resource management, and climate change adaptation, and can mitigate the environmental and economic problems caused by drought. [Methods] Based on the standardized precipitation evapotranspiration index (SPEI) and normalized vegetation index (NDVI) for the Yellow River Basin during the period of 1982—2020, image-by-image metric linear regression models, Sen’s trend analysis and Mann-Kendall test were used to study the spatial and temporal distribution characteristics of meteorological droughts at different time scales in the Yellow River Basin, as well as their impacts on vegetation. [Results] (1) From 1982 to 2020, the Yellow River Basin showed an insignificant increasing trend in aridity, with regions exhibiting negative trends in SPEI mainly concentrated in the western region from Longyangxia to Lanzhou, the southern part of the Yellow River Basin, and the surrounding areas of the Hetao Plain. (2) The spatial distribution pattern of average vegetation coverage in the Yellow River Basin demonstrated a decreasing trend from south to north and from southeast to northwest. The proportion of regions with a significant increase in vegetation coverage (61.94%) was greater than that of those with a significant decrease (5.43%). (3) In most areas of the Yellow River Basin, drought conditions were positively correlated with vegetation status. Regions exhibiting significant positive correlations were mainly concentrated in the northwest and northernmost areas of the Yellow River Basin, indicating that vegetation in these areas was severely affected by drought. (4) Grassland and cultivated land showed the strongest response to drought at a six-month time scale, while forests and unused land were more vulnerable to long-term drought compared to other land types. All four landform types were susceptible to medium- to long-term water shortages, with vegetation in hilly areas being the most affected by drought. [Conclusion] The results of the study provide an effective scientific basis for disaster prevention and mitigation as well as ecological environment construction in the Yellow River Basin, and provide important information for ecological protection, water resource management and climate change adaptation in the Yellow River Basin, which will help to formulate more precise policies and measures to mitigate the potential impacts of drought on the environment and the economy, and to promote sustainable development.