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A strong understanding of severe drought conditions is important for its mitigation and damage alleviation. Given the Peninsular Malaysia’s drought vulnerability and its progressively increasing temperatures in the future, this study assessed the significance of temperature for the drought formation through temporal pattern, spatial characteristic and operational accuracy indicated by the Standardized Precipitation Index (SPI) and the Standardized Precipitation Evapotranspiration Index (SPEI) at the timescales of 1-, 3- and 6-month. Temporal analyses of drought frequency and fluctuations of the SPI and SPEI showed similar changes in moisture responsiveness over the increasing timescales. However, in terms of the number of dry months, the two indices showed different trends, consequential of the influence of temperature in the SPEI. The interchangeability of the two indices was confirmed through spatial variation analysis of drought frequency, mean drought duration, mean drought severity and mean drought peak. From an occurrence, duration and onset detection accuracy consideration, the SPI is better for the 1-month short-term drought, while the SPEI is better for the 3-month mid-term and 6-month long-term droughts. This is a result of the increased significance of temperature in drought formations. Further evaluations on drought severity also showed that the SPEI had better description of the long-term drought over Peninsular Malaysia during the 1997/1998 and 2015/2016 El-Nino drought events.

Over the past 50 years, human water use has more than doubled and affected streamflow over various regions of the world. However, it remains unclear to what degree human water consumption intensifies hydrological drought (the occurrence of anomalously low streamflow). Here, we quantify over the period 1960-2010 the impact of human water consumption on the intensity and frequency of hydrological drought worldwide. The results show that human water consumption substantially reduced local and downstream streamflow over Europe, North America and Asia, and subsequently intensified the magnitude of hydrological droughts by 10-500%, occurring during nation- and continent-wide drought events. Also, human water consumption alone increased global drought frequency by 27 (±6)%. The intensification of drought frequency is most severe over Asia (35 ± 7%), but also substantial over North America (25 ± 6%) and Europe (20 ± 5%). Importantly, the severe drought conditions are driven primarily by human water consumption over many parts of these regions. Irrigation is responsible for the intensification of hydrological droughts over the western and central US, southern Europe and Asia, whereas the impact of industrial and households' consumption on the intensification is considerably larger over the eastern US and western and central Europe. Our findings reveal that human water consumption is one of the more important mechanisms intensifying hydrological drought, and is likely to remain as a major factor affecting drought intensity and frequency in the coming decades.

Determining drought indices and characteristics in Algeria is crucial because droughts significantly impact water resources and agricultural production. Additionally, identifying the most suitable drought indicator for the region facilitates effective monitoring of droughts. The study’s main objective is to compare hydro-meteorological droughts, determine their distribution, and assess drought risk. Various drought indices, which are continuous functions of rainfall and other hydro-meteorological variables, are typically used for this purpose. This study calculates seven indices' effectiveness for drought monitoring and assessment in Algeria's Wadi Ouhrane basin. For this purpose, the temporal variation of drought indicators, distribution graphs, weighted Cohen's kappa (Kw), and correlation coefficient values was compared. Among the indices used in the study, four indices, namely SPI, CZI, ZSI, and MCZI, showed high similarity in their behavior. As the time scale increases from 1 to 24 months, the correlation coefficient exceeds 0.97, and Kw becomes greater than 0.57. Furthermore, there is a weak correlation (R < 0.4) between the meteorological and hydrological-based SRI indicators, and the highest correlation was found between the RDI and SRI indices. Therefore, these indices indicate that the precipitation and ET (temperature) ratio is more suitable for hydrological drought studies.

Droughts—major natural disasters with a complex development and evolution process—cause enormous losses for society, especially in the agriculture sector. We analyzed the spatiotemporal evolution of drought frequency in China at grid level during 1981–2019 with the nonparametric Mann-Kendall trend method, using a high temporal resolution vegetation health index dataset at week-scale. Results suggest the that (1) after entering the 21st century, China's drought-affected area has declined, with Northeast China being the least affected region and Northwest China being the most severely affected; (2) the spatial pattern of drought characteristics in China is polarized, and the frequency of droughts has generally declined, with the most prominent intensity and frequency observed in some urbanized and economically developed regions; and (3) although the changes to the drought characteristics and frequency in China provide a generally optimistic picture, drought intensity and frequency in some developed regions have increased significantly, and the future trend predictions for these areas are less positive. This study, focusing on the spatiotemporal characteristics and evolution of drought patterns, with the aim of raising awareness of drought disasters, can help mitigate and prevent the damage caused by droughts to society, and can provide a scientific basis for drought early-warning systems and risk management in China going forward.

Droughts have become more powerful and frequent, affecting more people for longer periods than any other natural disaster, particularly in eastern Africa. The unprecedented climate change has increased the severity, duration, and frequency of droughts. The objectives of this study were to evaluate performances of different drought indices for spatiotemporal drought characterization in the Bilate river watershed that represents part of the rift valley drylands in Ethiopia. Historical data for 39 years (1981–2019) from seven stations were used for drought analyses using the following indices: Standardized Precipitation Index (SPI), Standardized Precipitation Evapotranspiration Index (SPEI), Reconnaissance Drought Index (RDI), enhanced Reconnaissance Drought Index (eRDI) with different time scale and Self-Calibrated Palmer Drought Severity Index (scPDSI). Among them, SPI, SPEI, RDI and eRDI with 6-month and 9-month time scales were found to be the best correlated drought indices to characterize the historical drought events. Then, using the selected drought indices, temporal drought analysis showed occurrence of major drought events in the years: 1984/85, 1999/2000, 2002/3, and 2009. Some of these years are well known as famine years in some parts of Ethiopia including the study area. The results revealed spatial variation the severity of drought with extreme droughts occurred in the southern part of the Bilate watershed. Application of the theory of run confirmed that the maximum severity and duration of drought were observed at the Bilate Tena station that is located in the southern part of the watershed; the most severe being observed on a 9-month scale during 1984/85. Hossana and Wulbareg stations showed the highest frequency of drought over the study period. The Mann-Kendal trend test statistics showed an increasing trend of drought conditions in the study watershed.

Agricultural drought poses a severe threat to food security in the North China Plain, necessitating accurate and timely monitoring approaches. This study presents a novel drought assessment framework that innovatively integrates multiple remote sensing indices through an optimized random forest algorithm, achieving unprecedented accuracy in regional drought monitoring. The framework introduces three key innovations: (1) a systematic integration of six drought-related factors including vegetation condition index (VCI), temperature condition index (TCI), precipitation condition index (PCI), land cover type (LC), aspect (ASPECT), and available water capacity (AWC); (2) an optimized random forest algorithm configuration with 100 decision trees and enhanced feature extraction capability; and (3) a robust triple-validation strategy combining standardized precipitation evapotranspiration index (SPEI), comprehensive meteorological drought index (CI), and soil moisture verification. The framework demonstrates exceptional performance with R2 values consistently above 0.80 for monthly assessments, reaching 0.86 during autumn and 0.73 during summer seasons. Particularly, it achieves 87% accuracy in mild drought (−1.0 < SPEI ≤ −0.5) and 85% in moderate drought (−1.5 < SPEI ≤ −1.0) detection. The 20-year (2000–2019) spatiotemporal analysis reveals that moderate drought events dominated the region (23.7% of total occurrences), with significant intensification during the 2010–2012 and 2014–2016 periods. Summer drought frequency peaked at 12–15 months in south-central Shandong (37°N, 117°E) and eastern Henan (34°N, 114°E). The framework’s high spatial resolution (1 km) and comprehensive validation protocol establish a reliable foundation for agricultural drought monitoring and water resource management, offering a transferable methodology for regional drought assessment worldwide.

The 2018–2019 drought in north-western and central Europe caused severe damage to a wide range of sectors. It also emphasised the fact that, even in countries with temperate climates, adaptations are needed to cope with increasing future drought frequencies. A crucial component of drought management strategies is to monitor the status of groundwater resources. However, providing up-to-date assessments of regional groundwater drought development remains challenging due to the limited availability of high-quality data. This limits many studies to small selections of groundwater monitoring sites, giving an incomplete image of drought dynamics. In this study, a time series modelling-based method for data preparation was developed and applied to map the spatio-temporal development of the 2018–2019 groundwater drought in the south-eastern Netherlands, based on a large set of monitoring data. The data preparation method was evaluated for its usefulness and reliability for data validation, simulation, and regional groundwater drought assessment. The analysis showed that the 2018–2019 meteorological drought caused extreme groundwater drought throughout the south-eastern Netherlands, breaking 30-year records almost everywhere. Drought onset and duration were strongly variable in space, and higher-elevation areas suffered from severe drought well into 2020. Groundwater drought development appeared to be governed dominantly by the spatial distribution of rainfall and the landscape type. The time series modelling-based data preparation method was found to be a useful tool to enable a spatially detailed record of regional groundwater drought development. The automated time series modelling-based data validation improved the quality and quantity of useable data, although optimal validation parameters are probably context dependent. The time series simulations were generally found to be reliable; however, the use of time series simulations rather than direct measurement series can bias drought estimations, especially at a local scale, and underestimate spatial variability. Further development of time-series-based validation and simulation methods, combined with accessible and consistent monitoring data, will be valuable to enable better groundwater drought monitoring in the future.

This article reviews the main recent applications of multi-sensor remote sensing and Artificial Intelligence techniques in multivariate modelling of agricultural drought. The study focused mainly on three fundamental aspects, namely descriptive modelling, predictive modelling, and spatial modelling of expected risks and vulnerability to drought. Thus, out of 417 articles across all studies on drought, 226 articles published from 2010 to 2022 were analyzed to provide a global overview of the current state of knowledge on multivariate drought modelling using the inclusion criteria. The main objective is to review the recent available scientific evidence regarding multivariate drought modelling based on the joint use of geospatial technologies and artificial intelligence. The analysis focused on the different methods used, the choice of algorithms and the most relevant variables depending on whether they are descriptive or predictive models. Criteria such as the skill score, the given game complexity used, and the nature of validation data were considered to draw the main conclusions. The results highlight the very heterogeneous nature of studies on multivariate modelling of agricultural drought, and the very original nature of studies on multivariate modelling of agricultural drought in the recent literature. For future studies, in addition to scientific advances in prospects, case studies and comparative studies appear necessary for an in-depth analysis of the reproducibility and operational applicability of the different approaches proposed for spatial and temporal modelling of agricultural drought.

Portions of group ranches of northern Kenya communally held by pastoralists have been removed from grazing to support wildlife and encourage tourism and the resources that follow. These community-based conservancies (CBCs) were designed to benefit CBC members through regular payments, potential for wages, improved security, etc. We used a coupled-systems simulation approach to quantify potential changes in livestock numbers and pastoral well-being associated with the presence of CBC core and buffer areas, and we did so under the current frequency of droughts and increased frequency associated with climate change. The interannual precipitation coefficient of variation (CV) for our focal CBCs in Samburu County was 22% (706 mm average precipitation). We altered precipitation variability to span from 10% to 60% CV while maintaining the average. Compared to a simulation with observed precipitation and all rangelands available, when herders did not use the CBC core areas and seasonally avoided buffer areas, there was an 11% decline in tropical livestock units supported. More predictable precipitation patterns supported more livestock and improved pastoral well-being. At CVs above 30%, dramatic declines in livestock populations were simulated. When drought was made moderately more frequent (i.e., CV from 22% to 27%) there was a 15% decline in the number of livestock. Members receive a variety of benefits as part of CBC communities, but payments are small for these CBCs, and most households do not receive payments. Our results suggest that, from an economic perspective alone, payments must be raised to make membership of residents in conservancies more tenable. Additional adaptive pathways and perhaps external supports will be needed in the future as the frequency of drought increases and livestock populations decrease.

Droughts can be considered as multidimensional hazardous phenomena characterised by three attributes: severity, duration and areal extent. Conventionally, drought events are assessed for their severity, using drought indices such as SPI (Standardised Precipitation Index), RDI (Reconnaissance Drought Index), PDSI (Palmer Drought Severity Index) and many others. This approach may be extended to incorporate the modelling of an additional dimension, the duration or the areal extent. Since the marginal distributions describing these dimensions of drought are often different, no simple mixed probability distribution can be used for the bivariate frequency analysis. The copula approach seems to be sufficiently general and suitable for this type of analysis. It is the aim of this paper to analyse droughts as two-dimensional phenomena, including drought severity and areal extent. In this paper, the Gumbel-Hougaard copula from the Archimedean family is used for this two-dimensional frequency analysis. Annual data on historical droughts from Eastern Crete are analysed for their severity and areal extent, producing copula-based probability distributions, incorporating Gumbel marginal probability functions. Useful conclusions are derived for estimating the «OR» return period of drought events related to both severity and areal extent.