Drought Index Research Articles

Assessment of Basin‐Scale Concurrent Dry and Wet Extreme Dynamics Under Multimodel CORDEX Climate Scenarios

ABSTRACTThe impact of extreme events in risk analysis depends on factors such as magnitude, duration, timing and whether the system recovers fully before the next event occurs. While previous studies have primarily examined the drivers and characteristics of individual extremes, less focus has been given to the concurrent or compounding nature of extremes across adjacent seasons. Thus, understanding the dynamics of such compound extremes, particularly dryness and wetness, is crucial. To address these concerns, a Multi Scalar Drought Index (MSDI) is formulated using precipitation and temperature data from three river basins (Brahmani, Baitarani and Cauvery) of eastern and southern India. The combinations of dryness and wetness, such as Dry‐Dry, Dry‐Wet, Wet‐Dry and Wet‐Wet, between consecutive seasons are analysed across four seasons (summer, rainy, autumn and winter). The prolonged dryness/wetness along with dry/wet year are evaluated from baseline (1979–2018) to projected COrdinated Regional Climate Downscaling Experiment (CORDEX) future period (2020–2099). The spatio‐temporal variations in intra‐annual dry‐wet extremes are identified using the Mann–Kendall test. The results suggest that the eastern Indian river basins, particularly the Brahmani and Baitarani basins, experience more frequent occurrences of compounding dryness‐wetness compared to Cauvery river which is a southern Indian basin. Future scenarios indicate a trend towards dryness during the monsoon season in Brahmani and Baitarani basins, with frequent wet extremes in late autumn and winter. Abrupt transitions between dryness and wetness are prevalent during the Rainy‐Autumn and Autumn‐Winter seasons in Brahmani and Baitarani basins. The increased frequency of compound dry‐wet extremes poses significant socio‐economic risks, including reduced agricultural productivity, water management challenges and heightened vulnerability of local livelihoods dependent on consistent water availability. The results of this study provide a scientific reference for sustainable agriculture and water resource management to predict future seasonal dry and wet alternations and develop effective mitigation strategies.

Prominent Increase in Air Temperatures on Two Small Mediterranean Islands, Lastovo and Lošinj, Since 1998 and Its Effect on the Frequency of Extreme Droughts

The article analyzes the development of air temperatures and precipitation on two remote islands in the Adriatic Sea from 1961 to 2023, examining annual and monthly time scales. Lastovo Island is located in the southern Adriatic, and Lošinj Island is situated 277 km north, and both exhibit a sharp rise in air temperatures since 1998, though precipitation series show no significant trends of an increase or decrease. Using the New Drought Index (NDI) method, this study calculated drought intensities for the period 1961–2023. The analyses conducted in this study undoubtedly indicate a rising frequency and intensity of droughts, with severe droughts doubling and extreme droughts increasing fourfold in the recent period (1998–2023) compared to the previous one (1961–1997). The most pronounced increase in severe and extreme droughts occurs specifically from June to August. This trend is likely applicable to many small Mediterranean Islands, which number over 10,000 and have a permanent population of more than 1.6 million people, with numbers significantly rising during the tourist season. The increased water demand for agriculture and daily use, combined with increased drought risk, not only exacerbates the potential for forest fires but also threatens social structures and ecological conditions. This is particularly critical as the combination of drier conditions and increased fire risk poses a significant challenge, endangering natural landscapes and valuable historical sites that are integral to the islands’ identity and heritage. This study’s findings indicate a dangerous trend likely to persist and worsen with continued increases in air temperatures in the Mediterranean region.

Assessment of Soil Moisture in Vegetation Regions of Mu Us Sandy Land Using Several Aridity Indicators

Drought, a significant calamity in the natural domain, has extensive worldwide repercussions. Drought, primarily characterized by reduced soil moisture (SM), presents a significant risk to both the world environment and human existence. Various drought indicators have been suggested to accurately represent the changing pattern of SM. The study examines various indices related to the Drought Severity Index (DSI), Evaporation Stress Index(ESI), Vegetation Supply Water Index(VSWI), Temperature-Vegetation Dryness Index(TVDI), Temperature Vegetation Precipitation Dryness Index(TVPDI), Vegetation Health Index(VHI), and Temperature Condition Index (TCI). An evaluation was conducted to assess the effectiveness of seven drought indicators, such as DSI, ESI, TVPDI, VSWI, etc., in capturing the changes in SM in Mu Us Sandy Land. The research results indicated that DSI and ESI had the highest accuracy, while TVDI and VSWI showed relatively lower accuracy. However, their smaller fluctuations in the time series demonstrated stronger adaptability to different regions. Additionally, the delayed impact of aridity indices on soil moisture, variable attributes, temperature, and vegetation coverage in sandy land and grassland areas with low, medium, and high coverage all contributed to the effectiveness of the four aridity indices (DSI, ESI, VSWI, and TVPDI) in capturing the dynamics of soil moisture. The primary element that affects the effectiveness of TVDI is the divergence of the relationship curve between Land Surface Temperature (LST) and Normalized Difference Vegetation Index (NDVI), which is a kind of deterioration. This paper presents a very efficient approach for monitoring soil moisture dynamics in dry and semi-arid regions. It also analyzes the patterns of soil moisture changes, offering valuable scientific insights for environmental monitoring and ecological enhancement.

Using human observations with instrument-based metrics to understand changing rainfall patterns.

Shifting precipitation regimes are a well-documented and pervasive consequence of climate change. Subsistence-oriented communities worldwide can identify changes in rainfall patterns that most affect their lives. Here we scrutinize the importance of human-based rainfall observations (collated through a literature review spanning from 1994 to 2013) as climate metrics and the relevance of instrument-based precipitation indices to subsistence activities. For comparable time periods (1955-2005), changes observed by humans match well with instrumental records at same locations for well-established indices of rainfall (72% match), drought (76%), and extreme rainfall (81%), demonstrating that we can bring together human and instrumental observations. Many communities (1114 out of 1827) further identify increased variability and unpredictability in the start, end, and continuity of rainy seasons, all of which disrupt the cropping calendar, particularly in the Tropics. These changes in rainfall patterns and predictability are not fully captured by existing indices, and their social-ecological impacts are still understudied.

Diverging Projections of Future Droughts in High‐End Climate Scenarios for Different Potential Evapotranspiration Methods: A National‐Scale Assessment for Poland

ABSTRACTIt has been broadly reported that future climate change will most likely affect the spatio‐temporal distribution of water resources and consequently droughts. There is a prevailing notion that an increase in temperature and frequency of heat waves are expected to result in more intense droughts in the coming years. In this study, we aimed to evaluate the effect of the potential evapotranspiration (PET) method selection on future drought projections over Poland. In our study, simulations of the Soil and Water Assessment Tool (SWAT) model were conducted, utilising an ensemble of six EURO‐CORDEX projections, spanning the period from 2006 to 2100 under the RCP8.5 scenario. Two model setups with two different PET methods (Penman‐Monteith—PM and Hargreaves—HAR) were used. For drought conditions evaluation we selected the Standardized Precipitation Index (SPI) and Standardized Precipitation‐Evapotranspiration Index (SPEI) for meteorological drought, Standardized Streamflow Index (SSI) for hydrological drought, and Standardized Soil Moisture Index (SMI) for agricultural drought. The meteorological and hydrological droughts were calculated using a 12‐month time aggregation window, while agricultural drought was calculated using a 3‐month window. Climate projections revealed that by 2080s annual mean temperature and precipitation increase is expected by up to +3.4°C and +10.3% respectively. Under future climate conditions duration and severity of meteorological droughts are projected to decrease. PM method leads to a higher PET increases (1.35 mm year−1) than the HAR method (1.1 mm year−1) throughout the century which entail diverging signal of change for agricultural and hydrological droughts. PM‐ and HAR‐based simulations indicate increase in the total duration and cumulative severity of agricultural droughts, buthowever, for HAR‐based projections, the increase is much less. For hydrological droughts the signal of change is similar for both PET methods, but considerably distinct in magnitude. Considering the entire simulation period, by the end of the century cumulative severity of hydrological droughts is projected to decrease, with a much more pronounced decline for HAR (70% reduction) than for the PM method (35% reduction). Our study demonstrated that methodological choices are crucial to the assessment of future drought risk under climate change.

China is suffering from fewer but more severe Drought to flood abrupt alternation events

Drought to flood abrupt alternation (DFAA) events, as a special category of compound extreme events that suddenly shift from drought to flood conditions, have significantly greater impacts than individual drought or flood events. In this paper, we have utilized a multifactorial drought index and flood index to identify daily DFAA events occurring in mainland China and in major impact areas during the period 1961-2022. Based on drought and flood index, we have used entropy weighting method to measure the intensity of DFAA events. Our findings indicate that China's DFAA events primarily occur in the hotspots of Huang-Huai-Hai River Basin, the middle and lower Yangtze River Basin, the southeastern coastal area, and the southwestern part of the country. The most frequent and intense DFAA events occur from June to September, with varying subseasonal patterns in the frequency and intensity of events in each hotspot. The frequency of DFAA events in mainland China shows a significant decreasing trend declining at a rate of 0.25 pre year in year-round. While DFAA events occurring in the warm season tend to decrease more significantly compared to the year-round at a rate of 0.26 per year. However, the intensity of DFAA events is increasing with a rate of 0.1 per decade in both the year-round and warm season. The evolution of DFAA events and their direct causes vary non-uniformly across regions and months. Subseasonally, frequency and intensity trends diverged monthly across regions, notably with the Huang-Huai-Hai Basin and southeast coast experiencing a July decline in frequency but a surge in intensity. Our research deepens the understanding of changes in DFAA events and provides practical reference for preventing and mitigating drought-to-flood disasters in mainland China.

Practical drought risk assessment and management framework: A step toward sustainable modernization in agricultural water management

A proposed framework to assess and manage risks associated with Agricultural Water Off-farm Conveyance and Distribution Systems (AWOCDS), quantify potential failure due to drought-induced scenarios, and provide short to long-term recovery plans to mitigate drought impacts. It makes an intellectual contribution by creating a new proactive risk assessment focusing specifically on AWOCDS as a crucial part of agricultural water management. The risk assessment comprises two components − risk probability and consequence. The former is calculated by analyzing droughts via the streamflow drought index using the DrinC calculator. The latter is determined through AWOCDS’ performance evaluation using hydraulic simulation via HEC-RAS. Additionally, water distribution is assessed spatiotemporally using adequacy and equity indicators, and an integrated consequence is developed by applying the simple additive weighting method. AWOCDS’ rehabilitation and modernization projects worldwide include three practical practices in its risk management component. These practices involve modifying standard operating procedures (SOPs) by updating maintenance, upgrading manual-based operations, and developing a centralized Model Predictive Controller (MPC) for AWOCDS automatic operation. The proposed framework underwent testing in an arid region. Based on the risk assessment results, the AWOCDS operating system failure rate ranges from 70% to 96% during extreme-severe drought scenarios and 53% to 65% during moderate-mild drought scenarios. Risk management analysis shows that a short-term recovery plan reduces the drought risk by 4% monthly and 3% yearly. However, the risk of failure ranges from 50 to 94% in mild to extreme drought scenarios. Upgrading SOPs decreases impact by 4–11% in extreme, 4–7% in moderate-severe and 7–9% in mild-moderate drought. Automating the operating system with MPC reduces impact by 40–43% and 30–56% in mild-moderate and severe-extreme drought scenarios. This proactive-based strategy can help water managers plan for rehabilitation and improve surface water distribution reliability.

Multivariate Evaluation of Flash Drought Across the United States

AbstractThis study uses the flash drought intensity index (FDII) to develop a multivariate flash drought climatology for the contiguous U.S. using data from 2001 to 2021. The FDII method uses the rate of intensification (FD‐INT) and subsequent drought severity (DRO‐SEV) to determine when a flash drought occurred and the strength of the event. Overall, the results showed that flash drought occurrence and severity varied with season and region and were sensitive to the drought indicator used to compute the FDII. Precipitation‐based indicators identified more flash droughts across the western U.S. whereas soil moisture (SM) and evapotranspiration indicators identified more flash droughts across the central and eastern U.S. When assessed over the entire U.S., the most flash droughts were found when using an evaporative demand indicator. Though FD‐INT was larger than DRO‐SEV across the U.S. for most indicators, regional patterns were also evident in their relative importance. For example, a distinct east‐west gradient was present in the SM and evapotranspiration FD‐INT, with relatively large values in the central and eastern U.S. A combined data set synthesizing information from multiple indicators showed that the strongest flash droughts from a multivariate perspective were located in the central and southeastern U.S. A seasonal analysis revealed a distinct seasonal cycle in flash drought onset across the western and central U.S. Together, the results illustrate the need to use a multivariate framework to identify and characterize the occurrence and severity of flash droughts.

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

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

Underestimation of meteorological drought intensity due to lengthening of the drought season with climate change

Abstract Meteorological drought may lead to water shortages, which has negative impacts on water-dependent sectors. Whilst there is a wealth of studies on changing drought intensity or frequency due to climate change, much less is known regarding potential shifts in the timing of drought. The purpose of this study is to analyse the timing of the drought season in the Netherlands and climatic changes therein, with a special focus on the onset of the drought season. Based on an analysis of meteorological observations in the Netherlands over the period 1965-2023, we conclude that the Dutch meteorological drought season has extended forward in time. On average, the drought season starts 16 days earlier in the period 1994-2023 compared to 1965-1993. This is mostly the result of an increase in potential evapotranspiration, while the amount of precipitation does not show a clear change at the start of the growing season. Using three climate model ensembles, we show that a forced climate change signal exists, but that natural variability also plays a role. Following this assessment of trends in meteorological variables, we analyse the consequences for the operational monitoring of meteorological drought. In the Netherlands, this is done by means of the `precipitation deficit’-indicator, based on a fixed-in-time starting point (1 April) of the drought season. The combination of this fixed starting point and the observed earlier onset of the drought season, means that in some years the indicator underestimates drought intensity, and that climatic trends are underestimated. We therefore advocate for an update of the operational drought indicator, such that meteorological drought occurring before 1 April will not be missed.

Multi-Index Approach to Assess and Monitor Meteorological and Agricultural Drought in the Mediterranean Region: Case of the Upper Oum Er Rabia Watershed, Morocco

Drought is a severe disaster, increasingly exacerbated by climate change, and poses significant challenges worldwide, particularly in arid and semi-arid regions like Morocco. This study aims to assess and monitor drought using a multi-index approach to provide a comprehensive understanding of its spatio-temporal dynamics at both meteorological and agricultural levels. The research focuses on the Upper Oum Er Rabia watershed, which spans 35,000 km2 and contributes approximately a quarter of Morocco’s renewable water resources. We propose a methodology that combines ERA5 temperature data from remote sensing with ground-based precipitation data to analyze drought characteristics. Three meteorological indices were utilized: the Standardized Precipitation Index (SPI), the Standardized Precipitation Evapotranspiration Index (SPEI), and the Reconnaissance Drought Index (RDI). Additionally, three remote-sensing indices were employed to capture agricultural drought: the Normalized Difference Vegetation Index (NDVI), the Enhanced Vegetation Index (EVI), and the Crop Water Stress Index (CWSI), with a total of 528 NDVI and EVI images and 1016 CWSI images generated through Google Earth Engine (GEE), using machine-learning techniques. Trend analyses were conducted to monitor drought patterns spatio-temporally. Our results reveal that the three-month interval is critical for effective drought monitoring and evaluation. Among the indices, SPEI emerged as the most effective for capturing drought in combination with remote-sensing data, while CWSI exhibited the highest correlation with SPEI over the three-month period, outperforming NDVI and EVI. The trend analysis indicates a significant precipitation deficit, alongside increasing trends in temperature and evapotranspiration over both the short and long term. Furthermore, all drought indices (SPI, SPEI, and RDI) demonstrate an intensification of drought conditions. Adaptation strategies are essential for managing water resources in the Upper Oum Er Rabia watershed under these evolving climate conditions. Continuous monitoring of climate variables and drought indices will be crucial for tracking changes and informing future water management strategies.

Wavelet-Entropy Enhanced Clustering: A Comprehensive Analysis of Drought Patterns in the Southern Plains, USA

Abstract Droughts may exhibit spatiotemporal heterogeneity at regional scale. Effective drought assessment and management necessitates identifying homogeneous areas. However, previous studies often simplified clustering analysis by focusing only on a single variable. In this study, we present a novel drought risk map for the Southern Plains (SP) region of the United States by integrating the wavelet-entropy approach with k-means clustering algorithm to capture spatio-temporal patterns of drought-related variables across various resolutions while eliminating redundant information. We considered multiple drought indicators and indices including gridded precipitation (P), potential evapotranspiration (PET), normalized difference vegetation index (NDVI), and Standardized Precipitation Evapotranspiration Index (SPEI) as well as geographical coordinates and topography map. Through evaluating five different combinations of input datasets, we selected the one demonstrating optimal results based on Davies-Bouldin and Calinski-Harabasz criteria. In addition to P, PET, and NDVI, including the coordinates and elevation as secondary variables significantly enhanced the clustering performance. Using these variables, the region was subdivided into 21 clusters. The Pearson’s correlation coefficients for the SPEI between centroid members and corresponding cells within clusters averaged between 0.84 to 0.94. Comparison with an existing cluster map (DRA) for the region revealed that our proposed cluster map showed higher variability between clusters for P, PET, and NDVI, confirming the robustness of the clustering results for drought conditions in the SP. The new clustering framework is expected to provide valuable insights for understanding and addressing drought dynamics in the SP region.

Analisis dan Mitigasi Bencana Alam Kekeringan terhadap Kebutuhan Air Baku di Perumahan Antasari Permai Sukabumi Bandar Lampung

This research aims to carry out a drought analysis of raw water needs in these settlements, as well as designing mitigation strategies to overcome this problem. The research method involves field surveys and drought index analysis using the Standardized Precipitation Index (SPI) method. The results of analysis using the SPI-3 and SPI-12 methods show that in the last 20 years the nature of the drought index in the Antasari Permai Housing Complex has been dominated by "Normal" characteristics. The times that should receive more attention are June to November because in these months the drought shows the characteristics of "Extreme Dry" compared to other months. As a response, the proposed mitigation strategies can be implemented in the short term or long term. In the short term, mitigation is carried out by supplying clean water using water tankers, PDAM or by building rainwater reservoirs. Meanwhile, the long term is carried out in an integrated and sustainable manner by considering natural phenomena, weather anomalies, groundwater cycles. Maintaining the groundwater cycle by means of reforestation and groundwater conservation planning in the form of biopores or infiltration wells. Implementation of this strategy is expected to increase the resilience of settlements to drought and ensure the availability of adequate raw water for community needs.

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

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

The Relationship Standardized Precipitation Evapotranspiration Index (SPEI) and Forage Value of Rubus Species Collected from Türkiye’s Flora

The increasing drought caused by climate change makes it difficult for rural areas reliant on pasture-based livestock farming to sustain their agricultural practices. Blackberries, a spreading species, have been selected as the material for this study due to their perennial shrub nature and their tolerance to adverse environmental conditions. Türkiye, the gene center for blackberries, is part of the Mediterranean belt and is located at the intersection of three flora regions—Mediterranean, Euro-Siberian, and Irano-Turanian. This study aims to determine the forage value of the Rubus species and other shrub/tree species (Quercus, Pistacia, and Rosa) collected and identified from these flora regions. Furthermore, a linear regression analysis established a relationship between the forage values and the SPEI, a drought index, considering the combined effects of rainfall and temperature in the collection regions. Among the Rubus species, the highest organic matter content (887.8 g kg−1) was obtained from R. pruinosus. In comparison, the highest protein content (240.1 g kg−1) was found in cultivated blackberries in the Euro-Siberian flora region. P. lentiscus had the highest Ca content (14.4 g kg−1) and offered feed at the “Prime” level with 154 of RFV. The SPEI explained 87% of the variation in fructan, 89% in Mg, and 92% in ADF. Due to the perennial nature of the species studied, a strong relationship was found between their growth and forage values over a 48-month time scale using the SPEI. Consequently, the equations related to ADF, Mg, and fructan content obtained in this study can be recommended for woody species.

Google Earth Engine application for mapping and monitoring drought patterns and trends: A case study in Arkansas, USA

Drought is a prolonged dry period that can have severe impacts on the environment, human health, economies, agriculture, and energy resources. It can lead to water shortages, ruin crops, dry out forests, and reduce the availability of food and water for wildlife and livestock. The primary objectives of this study are to: 1) quantify the variability and distributions of drought patterns in Arkansas, United States (US), 2) use remotely sensed indices to investigate the correlation between drought and vegetation cover in the area, and 3) develop a cloud-based framework (user-friendly app) to facilitate the assessment of drought impact in Arkansas over the past decades. A correlation analysis was also performed between the Vegetation Health Index (VHI) and meteorological indices to better understand the impact of meteorological drought on vegetation stress. In addition, Mann-Kendall trend analysis was used to assess trends in meteorological drought indices. The results indicate that drought is most prevalent during March and August months. The results of this study revealed that approximately 31% of the study area fell under the four drought classes (i.e., 1% Extreme drought, 4% Severe drought, 9% moderate drought, and 19% mild drought), with spring and the growing season experiencing moderate drought, particularly in agricultural areas, most notably within the Mississippi Alluvial Valley Plain at both state and county levels. In August, approximately 31% of the study area fell under the Four drought classes (i.e., 1% Extreme drought, 4% Severe drought, 9% moderate drought, and 19% mild drought), with spring and the growing season experiencing moderate drought, particularly in agricultural areas, most notably within the Mississippi Alluvial Valley Plain at both state and county levels. This study provides an essential foundation for policymakers, environmental scientists, and agricultural stakeholders aiming to mitigate drought impacts and safeguard against future climate uncertainties.

Multiscale investigations on RDI-SPI teleconnections of Çoruh and Aras Basins, Türkiye using time dependent intrinsic correlation

This paper proposes a multiscale dynamic correlation framework based on time-dependent intrinsic correlation (TDIC) to investigate the teleconnections between reconnaissance drought index (RDI) and standardized precipitation index (SPI). The SPI and RDI indices were calculated at 3-, 6-, and 12-month time scales using data from six stations in the Çoruh and Aras (CA) basins in Turkey for the 1969–2020 period. The spatial variability of the evaluation of the drought class demonstrated that, with the exception of the northwestern region (Bayburt station), the correlation between RDI and SPI exceeded 97% and the difference in occurrence between drought classes is found to be marginal. In the multiscale analysis of the two indices, firstly, the Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (I-CEEMDAN) was employed to decompose the series. The modes of RDI and SPI at different process scales exhibited a strong positive linear correlation, however, the association in their long-term trends may not be of the same nature. The TDIC analysis captured the long-range correlations between the modes of RDI and SPI at diverse process scales, and the newly proposed average significant correlation (ASC) measure could quantify their strength. Within the basin, there exists strong interconnections between SPI and RDI, with the exception of the observed alterations in their nature and strength at short time spells in some inter-annual scales. The proposed TDIC based framework is a generic and robust alternative for multiscale investigation studies while ASC can quantify the strength of non-linear associations at distinct process scales.

Anthropogenic Exacerbation in Dry‐Hot Probability and Consequential Record‐Shattering Droughts in the Middle and Lower Reaches of the Yangtze River

AbstractIn the year 2019, the middle and lower reaches of the Yangtze River (MLRYR) experienced an unprecedented summer‐autumn drought (SAD) driven by dry‐hot conditions [high near‐surface air temperatures (T) and low precipitation (P)], causing substantial agricultural and economic losses. However, the influence of anthropogenic climate change (ACC) on these dry‐hot conditions and their impacts on SAD occurrences remains uncertain. Here, both observations and simulations show that an ACC‐driven T increase led to the greater likelihood of dry‐hot conditions from August to November 1901–2020 in MLRYR. Using the self‐calibrating Palmer drought index (scPDSI) to assess SAD severity, we find an increasing likelihood of SAD occurrence (from 33.3% in 1901–2000 to 85.7% in 2001–2020) in MLRYR associated with more frequent dry‐hot conditions. Under a business‐as‐usual scenario, future dry‐hot association is projected to be stronger, with exceptional dry‐hot conditions to increase by +10% per century. ACC‐induced increase in dry‐hot conditions would elevate the likelihood of SAD events like the 2019 event from 1.59% (1961–2020) to 17.82% (2041–2100). Therefore, effective measures are needed in MLRYR to adapt to increasing dry‐hot conditions and associated SAD occurrences under anthropogenic warming.

A Multiscalar Standardized Vapor Pressure Deficit Index for Drought Monitoring and Impacts

ABSTRACTVapour pressure deficit (VPD) is a critical measure of the atmospheric demand for water and can be used to assess short‐term and seasonal drought. To provide for probabilistic comparisons of VPD across space and time, we develop a Standardized Vapor Pressure Deficit Index (SVPDI). Similar to the way that other standardised drought indices are used, SVPDI allows for the analysis and comparison of changes in VPD across regions with different base level VPD values. It also should be useful for analysing impacts on vegetation that has varying levels of adaptation to high VPD. We use 1‐, 3‐, 6‐ and 12‐month timescales for the development of SVPDI and show that the gamma distribution is superior to other zero‐limited probability distributions for analysing VPD and, therefore, for calculating SVPDI. Then, focusing on the short‐term variations at the 1‐ and 3‐month timescales, we show how SVPDI has changed globally from 1958 to 2023 and how those changes differ from those of the commonly used Standardized Precipitation Evaporation Index (SPEI). We find that SVPDI shows more widespread drying conditions that also are larger in magnitude compared to those of SPEI. Although the two indices are moderately well correlated across the terrestrial surface, we discover that they are more decoupled in humid and arid regions compared to dry sub‐humid and semi‐arid regions. Using four locations that have recently experienced severe drought, we find that SVPDI generally showed longer drought duration and more severe drought events in the last decade when compared to SPEI.

Remote sensing based Multivariate Hierarchical Agricultural Drought Index (MHADI) for India

Remote sensing based Multivariate Hierarchical Agricultural Drought Index (MHADI) for India