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In this study, a spatiotemporal analysis of drought occurrence and trends across Sicily using ERA50-Land continuous gridded data is carried out. We first use the Standardized Precipitation Index (SPI) and the Standardized Precipitation Evapotranspiration Index (SPEI) to evaluate drought conditions at various time scales from 1950 to 2023. Then, the Modified Mann–Kendall test was employed to detect trends and Sen's slope estimator was used to quantify their magnitude. An analysis of the historical series confirms that 2002 was the most severe drought year, impacting all time scales from short-term to long-term. The spatial analysis revealed that the western regions of Sicily experienced the highest severity and frequency of drought events. In contrast, the northeastern regions were less severely affected compared with the other parts of the island. The analysis detects significant increasing trends in SPI values in the eastern coastal areas of the island, which are related to a possible historical increase in precipitation. On the other hand, the analysis of the SPEI indicates significant decreasing trends in the western part of the island, which are mainly related to increased evapotranspiration rates. These results are partially consistent with previous analyses of future climate change scenarios, where changes in the SPEI values in the island are projected to be way clearer than changes in SPI values. [ABSTRACT FROM AUTHOR]

Drought occurs when there is a sustained decrease in rainfall over an extended period, impacting the socio‐cultural and environmental aspects of humans and other living beings. The geographic distribution and timing of droughts play a crucial role in drought management and mitigation strategies. Identifying and predicting the onset of droughts in specific regions, especially in watershed areas, is a primary concern in the field of hydrology. This study focuses on how the spatiotemporal patterns of drought are developing in Turkish Basins using detailed data on Terrestrial Water Storage (TWS), precipitation, and temperature at the pixel level. GRACE (Gravity Recovery and Climate Experiment), PERSIANN (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks), and WorldClim (World Climate) data sets are employed to assess long‐term changes of drought on a basin‐scale. Spatial analyses are conducted in a Geographic Information System (GIS) environment for the derivation of basinal monthly mean, minimum, and maximum statistics of TWS, precipitation, and temperature anomalies within Turkish Basins. Time series analyses are implemented to investigate the temporal evolution of droughts in these basins, for the basinal monthly mean, minimum, and maximum statistics obtained. The Mann–Kendall trend test and Pettitt change point detection tests are used to assess the statistical significance of the calculated trends and to expose the existence of any change point therein, respectively. The findings of the study indicate that Turkiye faces a significant risk of drought development in nearly all its basins, particularly after 2016. The GRACE dataset provides realistic insights into the temporal behaviour of hydrological droughts. PERSIANN is effective in identifying years with extreme meteorological conditions, and the standardized precipitation index (SPI) shows similar effectiveness, while they are ineffective in exposing significant trends due to the nature of the precipitation data. WorldClim data proves insufficient for modelling the temporal behaviour of droughts in these basins. [ABSTRACT FROM AUTHOR]

In the context of climate change, studying changes in rainfall patterns is a crucial area of research, remarkably so in arid and semi-arid regions due to the susceptibility of human activities to extreme events such as droughts. Employing predictive models to calculate drought indices can be a useful method for the effective characterization of drought conditions. This study applies two type of machine learning methods—long short-term memory (LSTM) and Neural Hierarchical Interpolation for Time Series Forecasting (N-HiTS)—to develop and deploy artificial neural network models with the aim of predicting the regional standardized precipitation index (SPI) in four regions of Zacatecas, Mexico. The predictor variables were a set of climatological time series data spanning from 1964 to 2020. The results suggest that the N-HiTS model outperforms the LSTM model in the prediction and forecasting of SPI time series for all regions in terms of performance metrics: the Mean Squared Error, Mean Absolute Error, Coefficient of Determination and ξ correlation coefficient range from 0.0455 to 0.5472, from 0.1696 to 0.6661, from 0.9162 to 0.9684 and from 0.9222 to 0.9368, respectively, for the regions under study. Consequently, the outcomes revealed the successful performance of the N-HiTS models in accurately predicting the SPI across the four examined regions. [ABSTRACT FROM AUTHOR]

The Black Sea region is the region with the highest rainfall in Türkiye. However, precipitation in the Black Sea region varies considerably both between years and within the region. This situation causes droughts in the region, with dry and wet periods following each other. This study aimed to determine the drought and wet periods in the Black Sea region according to the annual and 12-month SPI values. Standardized Precipitation Index (SPI) values were calculated according to the average precipitation data of 26 meteorology stations selected from the region between 1960 and 2020. The trends of the identified drought and wet periods were determined using Mann-Kendall trend analysis. In addition, using the heat map, the drought and wet periods of the Black Sea Region were determined by dividing coastal and inland areas. The analysis results determined that 1966, 1969, 1974-1977, 1984-1986, 1993-1994, 2006-2007, and 2019-2020 received less rainfall than normal and many stations had severe drought. It shows that 1967, 1988, 1996-1997, 1999, 2009, and 2016 received more precipitation than normal, that is, it has a humid character. According to the Mann-Kendall trend analysis results, it was not found that there was a decreasing trend in the coastal areas of the Western Black Sea Region. However, there was no significance in the decrease. Although there is an increasing trend in the Central and Eastern Black Sea region, this trend was found to be significant in some stations. Due to the landforms of the region, it was determined that there was not a general dry period or a general wet period and that the drought and wet periods differed in the east-west direction and the coastal-inland areas.

One of the most common natural hazards that can endanger life and property is drought, which can happen under a variety of weather and environmental circumstances. This study aims to monitor the agricultural and metrological drought in the Wasit Province using remote sensing data. Landsat 8 images were used to create the agriculture drought maps based on the NDVI for the years 2013, and 2023. Additionally, SPI-12 was used for the same years to evaluate the meteorological drought. The findings demonstrated that while SPI readings in 2023 were lower, the SPI-12 in 2013 indicated near-normal drought types. Two types of drought have been identified: moderate and slight. The result shows that, for the year 2013 the percentage of moderate drought is 31%, slight drought 64%, and no drought 3.9% from the total area. While, in 2023 the percentage of moderate drought is 47%, slight drought 49%, and no drought 3.2% from the total area. It is noticed that in 2023, the moderate drought class increased by about 16%. Government planners may find this results valuable when developing and managing drought consequences.

Drought is a natural hazard that affects environmental and socio-economic development. In this study, the Water Surplus Variability Index (WSVI), which is based on both precipitation and Penman-Monteith (PM) evapotranspiration (ET) estimates, was used to quantify drought. The application of the WSVI is not possible in some locations because of the missing data for estimating ET. The key objectives of this research were to 1. compare the different temperature-based WSVIs for the 1-, 3-, 6- and 12-month timescales with the original PM-based WSVI; 2. improve the WSVI by replacing the full-set PM equation with the corresponding temperature-based ET equation, such as Hargreaves, adjusted Hargreaves, Thornthwaite, modified Thornthwaite or temperature-based PM; and 3. compare the best temperature-based WSVI with the Standardized Precipitation Index (SPI). The set of meteorological data was obtained from several stations from Pannonian Basin (Budapest, Debrecen, Novi Sad and Sombor). According to statistical indicators, the Hargreaves-based WSVI (WSVIH) was ranked first or second for each time period at all locations. The WSVIH was additionally tested using the SPI for each location and each timescale. The obtained results indicated the capability of the WSVIH to quantify drought frequency, duration and severity identified by the SPI. Finally, the WSVI can be recommended using the Hargreaves equation instead of the full-set PM equation in future WSVI implementations in the Pannonian Basin. [ABSTRACT FROM AUTHOR]

The standardized precipitation index (SPI) is a fundamental indicator of meteorological, hydrological, and agricultural droughts in the world. This study aims to evaluate different timescales, 3 months (SPI-3), 6 months (SPI-6), 9 months (SPI-9), and 12 months (SPI-12) indices from meteorological data in quantifying drought characterization in lower eastern counties of Kenya from 1990 to 2018 for observed data and from 1986 to 2018 for Climatic Research Unit Time Series (CRU) data. Precipitation in-situ data (annual) and high-resolution (0.5 x 0.5 degree grid) monthly-scale precipitation data were sought from Kenya Meteorological Department and CRU TS, respectively. Z-Score (SPI) was computed for each year (in-situ data) and month (CRU TS data) using the SPI algorithm, expressed as the departure from the mean in standard deviation units. Quality control of CRU TS data was done by checking outlier values and comparing the data with precipitation data obtained from the meteorological department as well as ERA5 reanalysis data. Results showed that extreme to mild drought was experienced across the Kenyan counties for both annual in-situ and monthly gridded data. Machakos county experienced a year of extreme drought, while Makueni and Taita-Taveta have had 2 and 4 years of severe droughts, respectively. The monthly SPI indices of 3, 6, 9, and 12 months showed a remarkably consistent behavioral pattern detecting extreme droughts across the counties. Considering the uncertainties, unpredictability, and shifting of the long and short rainy seasons in Kenya, results were obtained related to dry and wet episodes and to their relationship with agricultural production as well as water availability and environmental management. [ABSTRACT FROM AUTHOR]

One of the most common natural hazards that can endanger life and property is drought, which can happen under a variety of weather and environmental circumstances. This study aims to monitor the agricultural and metrological drought in the Wasit Province using remote sensing data. Landsat 8 images were used to create the agriculture drought maps based on the NDVI for the years 2013, and 2023. Additionally, SPI-12 was used for the same years to evaluate the meteorological drought. The findings demonstrated that while SPI readings in 2023 were lower, the SPI-12 in 2013 indicated near-normal drought types. Two types of drought have been identified: moderate and slight. The result shows that, for the year 2013 the percentage of moderate drought is 31%, slight drought 64%, and no drought 3.9% from the total area. While, in 2023 the percentage of moderate drought is 47%, slight drought 49%, and no drought 3.2% from the total area. It is noticed that in 2023, the moderate drought class increased by about 16%. Government planners may find this results valuable when developing and managing drought consequences. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Natural Hazards & Environment (JNHE) / Doğal Afetler ve Çevre Dergisi (DACD) is the property of Artvin Coruh University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Certain impacts of climate change could potentially be linked to alterations in rainfall patterns, including shifts in rainfall intensity or drought occurrences. Hence, predicting droughts can provide valuable assistance in mitigating the detrimental consequences associated with water scarcity, particularly in agricultural areas or densely populated urban regions. Employing predictive models to calculate drought indices can be a useful method for the effective characterization of drought conditions. This study applied an Auto-Machine-Learning approach to deploy Artificial Neural Network models, aiming to predict the Standardized Precipitation Index in four regions of Zacatecas, Mexico. Climatological time-series data spanning from 1979 to 2020 were utilized as predictive variables. The best models were found using performance metrics that yielded a Mean Squared Error, Mean Absolute Error, and Coefficient of Determination ranging from 0.0296 to 0.0388, 0.1214 to 0.1355, and 0.9342 to 0.9584, respectively, for the regions under study. As a result, the Auto-Machine-Learning approach successfully developed and tested Artificial Neural Network models that exhibited notable predictive capabilities when estimating the monthly Standardized Precipitation Index within the study region. [ABSTRACT FROM AUTHOR]

The study focused on the prediction of the Temperature Vegetation Dryness Index (TVDI), an agricultural drought index, for a Mango orchard in Tamale, Ghana. It investigated the temporal relationship between the meteorological drought index, Standardized Precipitation Index (SPI), and TVDI. The SPI was calculated based on utilizing precipitation data from the World Meteorological Organization (WMO) database (2010–2022) and CMIP6 projected precipitation data (2023–2050) from 35 climate models representing various Shared Socioeconomic Pathway (SSP) climate change scenarios. Concurrently, TVDI was derived from Landsat 8/9 satellite imagery, validated using thermal data obtained from unmanned aerial vehicle (UAV) surveys. A comprehensive cross-correlation analysis between TVDI and SPI was conducted to identify lag times between these indices. Building on this temporal relationship, the TVDI was modeled as a function of SPI, with varying lag times as inputs to the Wavelet-Adaptive Neuro-Fuzzy Inference System (Wavelet-ANFIS). This innovative approach facilitated robust predictions of TVDI as an agricultural drought index, specifically relying on SPI as a predictor of meteorological drought occurrences for the years 2023–2050. The research outcome provides practical insights into the dynamic nature of drought conditions in the Tamale mango orchard region. The results indicate significant water stress projected for different time frames: 186 months for SSP126, 183 months for SSP245, and 179 months for both SSP370 and SSP585. This corresponds to a range of 55–57% of the projected months. These insights are crucial for formulating proactive and sustainable strategies for agricultural practices. For instance, implementing supplemental irrigation systems or crop adaptations can be effective measures. The anticipated outcomes contribute to a nuanced understanding of drought impacts, facilitating informed decision-making for agricultural planning and resource allocation. [ABSTRACT FROM AUTHOR]

Climate change occurs due to global warming, resulting in irregular seasonal shifts and ecological disasters such as floods or droughts that cause substantial losses. This research aims to determine the influence of climate variability on drought conditions by analyzing drought using the Standardized Precipitation Index (SPI) method. The data used consists of monthly rainfall over 40 years, from 1981 to 2020, obtained from CHIRPS satellite and observation data located in Sleman District. From the 40-year data divided into ten-year periods, severe droughts occurred in the second and fourth periods, specifically from 1991 to 2000 and from 2011 to 2020. The highest frequency of drought occurrences from the entire dataset also happened during the 1991-2000 period. Climate variability, based on the Schmidt-Ferguson climate types, shows climate changes by calculating the Q value. Initially, from 1981 to 1990, the Q value ranged from 33.3 to 60, categorized under type C climate. However, from 1991 to 2000, the Q value ranged from 33.3 to 100, with climate types categorized as C and D. Climate pattern changes have occurred from 2001 to 2010, with climate types ranging from B to D. Spatial analysis scale changes were observed from 2011 to 2020. These conditions indicate climate pattern changes in Sleman District over the 40-year period. The relationship between drought index and SPI method with climate variability (Q) values indicates a weak to moderate relationship with a significance level of 5%. This suggests climate change affects rainfall patterns and drought levels. The correlation coefficient values indicate a moderate relationship, with correlation coefficient values ranging from 0.3 to 0.5 highest during period II (1991-2000) and period IV (2011-2020). [ABSTRACT FROM AUTHOR]

Copyright of Journal of Kirkuk University for Agricultural Sciences is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Maize (Zea mays L.) is an essential crop in South Africa serving as a staple food; however, agricultural drought threatens its production, resulting in lower yields. This study aimed to assess the impact of agricultural drought on maize yield in the major areas (Bethlehem, Bloemfontein, and Bothaville) that produce maize in the Free State Province from 1990 to 2020. The study used the Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI) to examine drought occurrences and severity during the maize growing season (October–March). The Standardized Yield Residuals Series (SYRS), Crop Drought Resilient Factor (CDRF), Spearman's Rank Correlation (rs), and yield loss rate were employed to emphasize agricultural drought impact on maize yield. The results based on the SPI and SPEI show that drought frequently occurred in Bethlehem, followed by Bloemfontein and Bothaville. Drought severity indicated that moderate droughts were prevalent in Bethlehem, while severe droughts were in all areas (Bethlehem, Bloemfontein, and Bothaville) and extreme droughts in Bloemfontein. The agricultural drought's impact on maize varied across growth seasons and areas. Notably, the lowest SYRS value of −2.38 (1991/92) was observed in Bethlehem. An extremely strong significant correlation (rsSPEI-6 vs SYRS = 0.83, p = 1.07 × 10−8) was observed between the SPEI and SYRS in Bloemfontein during the October–November–December–January–February–March (ONDJFM) season. The CDRF indicated that maize yield was severely non-resilient (CDRF < 0.8) to drought in Bethlehem (CDRF = 0.27) and Bloemfontein (CDRF = 0.33) and resilient (CDRF = 1.16) in Bothaville. The highest maize yield loss of −88.62% was observed in Bethlehem due to extreme agricultural drought. The results suggest that, historically, agricultural drought was a threat to maize production in the studied areas, particularly in Bethlehem and Bloemfontein. This underscores the implementation of sustainable agricultural practices, such as drought-resistant varieties in these areas, to mitigate the impacts of climate change, especially drought, and ensure food security. This is a step toward achieving Sustainable Development Goal 2 (Zero Hunger). [ABSTRACT FROM AUTHOR]

The purpose of this research is to identify changes in the spatiotemporal distribution and severity of meteorological droughts in Ukraine between 1946 and 2020. In order to determine the role of precipitation and atmospheric evaporative demand on drought severity, two drought indices were applied for comparative analysis: the standardized precipitation index (SPI) and the standardized evaporation precipitation index (SPEI). Both indices were calculated on 3‐ and 12‐month timescales. The gridded dataset of monthly air temperature and atmospheric precipitation for Ukraine with high spatial resolution (0.1° × 0.1°), developed in the Ukrainian Hydrometeorological Institute, was used. Results of time series analysis of drought indices showed at least 20 episodes, in which more than 25% of the territory of Ukraine was affected by drought of varying intensity. Trends show an increase in drought severity determined mostly by a strong observed increase in the atmospheric evaporative demand. Tendencies towards an increase in the duration and intensity of droughts were observed mainly in the southwestern, central and northern regions of Ukraine. The main cause of the increase was droughts in spring and summer. The temporal analysis of the spatial distribution of areas affected by drought during the identified main drought episodes showed different patterns in drought propagation across the territory of Ukraine. [ABSTRACT FROM AUTHOR]

Drought, a major phenomenon impacting water resources, viability, sustainability, and the economy, has been one of the most important hydrological concerns. In the literature, it has been classified into four groups that are meteorological, agricultural, hydrological, and socio-economic. Meteorological drought expresses the precipitation deficits when they are significantly below those recorded normal times. In this study, using the Standard Precipitation Index (SPI) and mean monthly precipitation records of 17 stations which have been obtained from the General Directorate of Meteorology of Türkiye, a drought monitoring analysis has been conducted for Kızılırmak Basin, which is the second largest water basin of the country and water source of many provinces allowing for the time scales of 1, 3, 6, 9, 12, and 24 months considering the cases of "dry" (SPI ≤ − 1.5) and "wet" (SPI ≥ 1.5). To detect the possible trends in two categories of Severe and Extreme Drought, SED (SPI ≤ − 1.5), and Severe and Extreme Wet, SEW (SPI ≥ 1.5) of all time scales, a developed form of innovative trend analysis (ITA) is performed by adding two vertical lines. In addition, a traditional the Mann–Kendall test is applied to the SPI series. The findings indicate that dry occurrences tend to outnumber wet occurrences across various time scales. Analysis reveals that a significant majority of results across all time scales exhibit a consistent trend (89%), with a notable increase in the SEW category (62.74%) and a decrease in the SED category (60.78%). Mann–Kendall analysis demonstrates that 67% of the observed trends show a decrease, while 33% show an increase across all time scales. [ABSTRACT FROM AUTHOR]

Morocco is currently facing significant challenges due to the ever–changing climate, with its critical water sources crucial for agriculture, economy, and daily life being greatly affected. In order to thoroughly understand the impact of climate change on the Ghis–Nekor watershed, an in–depth study spanning 38 years (1978–2016) was conducted. This involved examining meteorological data from three stations and utilizing advanced indices such as SPI, RDI, and DI. The findings of this study reveal prominent shifts in precipitation patterns, indicating a vulnerability in the region. While there was a general increase in annual rainfall during the specified time period, a sharp decline was observed post–2008. Further analysis of drought confirmed the presence of persistent dry spells and recurring episodes, highlighting the urgent need for effective water management strategies. These crucial findings must be considered by decision–makers for successful climate adaptation, emphasizing the crucial role played by this study in mitigating the effects of climate change.

Copyright of Pearl River is the property of Pearl River Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This study evaluated the impacts of climate change on hydro-meteorological droughts in the Chao Phraya River Basin (CPRB), Thailand under two Representative Concentration Pathway (RCP) scenarios (RCP4.5 and RCP8.5). We used three Reginal Climate Models (RCMs) of the Southeast Asia Regional Climate Downscaling/Coordinated Regional Climate Downscaling Experiment—Southeast Asia (SEACLID/CORDEX-SEA), which are bias corrected. The Soil and Water Assessment Tool (SWAT) was used to simulate streamflow for future periods. The Standardized Precipitation Index (SPI) and Standardized Streamflow Index (SSI) were estimated and used for drought characterization at three time scales (3, 6, and 12 months). The lag time between meteorological and hydrological droughts is approximately 1–3 months. The results suggest that the CPRB is likely to experience less frequent hydro-meteorological drought events in the future. The meteorological drought is projected to be longer, more severe, and intense. The severity of hydrological drought tends to decrease, but the intensity could increase. Climate change has been discovered to alter drought behaviors in the CPRB, posing a threat to drought monitoring and warning because droughts will be less predictable in future climate scenarios. The characterization of historical and future droughts over the CPRB is therefore valuable in developing an improved understanding of the risks of drought. [ABSTRACT FROM AUTHOR]

Flood and drought are natural phenomena whose probability of occurrence increases with changing weather conditions. Climate change has affected the hydrologic cycle's parameters and led to changes in temperature and precipitation pattern, which may increase the probability of occurrence of floods and droughts. Arid and semi‐arid areas are subject to extreme weather conditions and water resources scarcity. This study aims to assess drought and flood in Syria using remote sensing data. Monthly precipitation data have been collected from 10 land stations in the western part of Syria for the period (1991–2009) and satellite images for monthly precipitation from 1983 to 2020 were collected. The land stations data were used to correct the satellite images and correction equation was developed for each station. Standardized Precipitation Index (SPI) is used for analysis of drought and flood. Also, the main parameters for drought and flood, severity, intensity, duration, and the average return time were estimated. The results showed extreme flood events were recorded in 1988, 2002, 2012, and 2019 and extreme drought events were recorded in 1990, 2014, and 2016. The highest severity of drought and flood were recorded in 2014 and 1988 respectively. However, the highest intensity of droughts and floods was recorded in 2016 and 1988, respectively. If land station data are unavailable, as in the case of Syria owing to the protracted war, satellite images may be a useful source of data for drought and flood analysis after correction. This methodology can help in drought/flood analysis and estimate severity, intensity, duration, and the average return time of flood and drought in other locations of the world. The results proved that SPI is a useful tool to predict the time of occurrence of both flood and drought that could help decision‐makers for putting efficient plans for flood and drought risk management that could mitigate the impacts of such risks. [ABSTRACT FROM AUTHOR]

Morocco is currently facing significant challenges due to the ever-changing climate, with its critical water sources crucial for agriculture, economy, and daily life being greatly affected. In order to thoroughly understand the impact of climate change on the Ghis-Nekor watershed, an in-depth study spanning 38 years (1978-2016) was conducted. This involved examining the meteorological data from three stations and utilizing advanced indices, such as SPI, RDI, and DI. The findings of this study revealed prominent shifts in precipitation patterns, indicating a vulnerability in the region. While there was a general increase in annual rainfall during the specified time period, a sharp decline was observed post-2008. Further analysis of drought confirmed the presence of persistent dry spells and recurring episodes, highlighting the urgent need for effective water management strategies. These crucial findings must be considered by decision-makers for successful climate adaptation, emphasizing the key role played by this study in mitigating the effects of climate change. [ABSTRACT FROM AUTHOR]

Drought is one of the main natural hazards affecting large areas' economies and the environment. Therefore, it is necessary to study the different aspects of the drought on the land with several indices like the Standard Precipitation Index (SPI) index which can clarify the existing conditions for decisions and planning. The objective of this study was to analyze the spatial pattern of drought by SPI index in the Fars Province located in the Southern part of Iran. In this paper, according to the data from 42 stations in Fars Province, during 1990-2019, the pattern of drought hazard is evaluated. In the presented model, several drought hazard criteria were used including the maximum severity of drought in the period, the trend of drought, and the maximum number of sequential arid years. The final drought risk map was obtained with an arithmetic mean of 3 criteria: intensity, continuity, and trend. The three criteria maps and final hazard map were interpolated by the Inverse Distance Weightage (IDW) method and classified into five hazard classes none, slight, moderate, severe, and very severe. The obtained maps showed that the intensity, trend, and continuity of drought increases from the southeast to the northwest. The final vulnerability map shows that moderate hazard areas (36% of the region) observed in the Southern parts of the region are less widespread than areas under severe and very severe hazard (64% of the region) observed in the northern and central parts of the region. Preparation of these hazard maps may prove to be helpful for regional managers, and policymakers for environmental and agricultural strategies, not only in Iran but also in other countries facing this hazard. [ABSTRACT FROM AUTHOR]

Climate change is the most prominent issue in this decade. The expected impacts of this change have become a reality, with rising sea levels and long, intense heat waves occurring globally. The present study examines the characteristics of drought in Nineveh Governorate and its impact on the wheat crop. Two drought indices were used in the study: (1) the Standardised Precipitation Index (SPI) and (2) the Chinese Z Index (CZI). The study used climate data on rainfall and temperature for the period 1990-2020 from eight meteorological stations distributed in the study area. Results showed a nonsignificant decrease in rainfall and an increase in maximum and minimum temperatures. Results also showed that the highest recurrence rates of drought (recorded at the Tal-Abta) are 51.2% and 56% for SPI and CZI, whereas the lowest rates (recorded at Mosul, Sheikhan and Sinjar) are 45.1% and 50.8% for SPI and CZI, respectively. The two drought indices match in identifying wet and dry periods, with R2 values ranging from 0.84 to 0.94. Furthermore, a relationship was found between productivity and climatic drought indices and it was concluded that wheat productivity is more significantly influenced by the distribution of rainfall than by drought. The current study can help demonstrate the extent of climate change's impacts on wheat productivity. Thus, the results can contribute to planning and managing agricultural production according to climate change. [ABSTRACT FROM AUTHOR]

Climate change is expected to have long-term impacts on drought and wildfire risks in Oregon as summers continue to become warmer and drier. This paper investigates the projected changes in drought characteristics and drought propagation in the Umatilla River Basin in northeastern Oregon for mid-century (2030–2059) and late-century (2070–2099) climate scenarios. Drought characteristics for projected climates were determined using downscaled CMIP5 climate datasets from ten climate models and Soil and Water Assessment Tool to simulate effects on hydrologic processes. Short-term (three months) drought characteristics (frequency, duration, and severity) were analyzed using four drought indices, including the Standardized Precipitation Index (SPI-3), Standardized Precipitation-Evapotranspiration Index (SPEI-3), Standardized Streamflow Index (SSI-3), and the Standardized Soil Moisture Index (SSMI-3). Results indicate that short-term meteorological droughts are projected to become more prevalent, with up to a 20% increase in the frequency of SPI-3 drought events. Short-term hydrological droughts are projected to become more frequent (average increase of 11% in frequency of SSI-3 drought events), more severe, and longer in duration (average increase of 8% for short-term droughts). Similarly, short-term agricultural droughts are projected to become more frequent (average increase of 28% in frequency of SSMI-3 drought events) but slightly shorter in duration (average decrease of 4%) in the future. Historically, drought propagation time from meteorological to hydrological drought is shorter than from meteorological to agricultural drought in most sub-basins. For the projected climate scenarios, the decrease in drought propagation time will likely stress the timing and capacity of water supply in the basin for irrigation and other uses. [ABSTRACT FROM AUTHOR]

In the semi-arid Marrakech-Safi (MS) region of southwest Morocco, climate change has amplified drought occurrences, posing significant threats to water resources and agriculture. A comprehensive understanding of drought patterns is imperative to manage these risks and enhance resilience effectively. Precipitation from 18 pluviometer stations was employed to analyze meteorological drought using the standardized precipitation index (SPI) from 1980 to 2018. Additionally, agricultural drought is quantified using three remote sensing-driven indices: vegetation condition index (VCI), temperature conditions index (TCI), and vegetation health index (VHI) from 2000 to 2018. These indices are correlated with SPI to evaluate their performance and gauge vegetation sensitivity to meteorological drought. The Mann–Kendall test assesses trends in drought events and their severity. The results demonstrated that SPI, VCI, TCI, and VHI experienced alternating dry and wet periods with an overall upward trend, especially in mountainous areas, plateau zones, and Haouz plain of the MS region. The correlation analysis establishes a significant relationship between remote sensing-based indices and SPI-6, with mean correlation coefficients exceeding 0.6. The findings underscore the importance of considering multiple time scales to comprehensively assess climate's impact on vegetation. Seasonal drought trends analysis indicates no significant negative trends in winter and spring but positive trends in autumn. HIGHLIGHTS Use the SPI to study meteorological droughts between 1980 and 2018.; Use Mann–Kendall and Sen's slope estimators to analyze seasonal and yearly SPI trends.; Spatio-temporal characteristics of agricultural droughts using the VCI, TCI, and VHI drought indices from 2000 to 2018.; Assess and analyze the correlations of SPI (3, 6, and 12 months) with VCI and VHI.;

In this study, a spatiotemporal analysis of drought occurrence and trends across Sicily using ERA50-Land continuous gridded data is carried out. We first use the Standardized Precipitation Index (SPI) and the Standardized Precipitation Evapotranspiration Index (SPEI) to evaluate drought conditions at various time scales from 1950 to 2023. Then, the Modified Mann–Kendall test was employed to detect trends and Sen’s slope estimator was used to quantify their magnitude. An analysis of the historical series confirms that 2002 was the most severe drought year, impacting all time scales from short-term to long-term. The spatial analysis revealed that the western regions of Sicily experienced the highest severity and frequency of drought events. In contrast, the northeastern regions were less severely affected compared with the other parts of the island. The analysis detects significant increasing trends in SPI values in the eastern coastal areas of the island, which are related to a possible historical increase in precipitation. On the other hand, the analysis of the SPEI indicates significant decreasing trends in the western part of the island, which are mainly related to increased evapotranspiration rates. These results are partially consistent with previous analyses of future climate change scenarios, where changes in the SPEI values in the island are projected to be way clearer than changes in SPI values.

The frequency of winter drought episodes has been marked frequently in the recent decades. This study examines the time series indices of drought variability over Nepal using historical data of 42 years (1977–2018) from 107 stations using the standardized precipitation index (SPI). Monthly rainfall was used as an input variable to generate the output for SPI time scales of each station. SPI threshold was used to identify severity, frequency, duration, and spatial extent of the drought episodes. The SPI3 output showed occurrence of major eight drought episodes. Among these years, dryness signals identified 2006 as the worst drought episode year. However, from regional perspective, the western region observed extreme drought episodes in 2009. There were distinct drought dynamics in each major drought event over the western, central and eastern Nepal. Spatial variability for SPI3 time scale was interpolated to depict spatial patterns of major drought episodes with their severities. The areas of Nepal affected by extreme, severe and moderate drought in winter were 4, 21 and 37%. [ABSTRACT FROM AUTHOR]

Drought refers to a meteorological disaster that results in insufficient soil moisture due to a long-term lack of rainfall and disrupts the moisture balance of crops. Yinshanbeilu in Inner Mongolia is an arid and semi-arid region, and the onset of drought and its subsequent transmission is a key challenge in water resource management. This study takes Yinshanbeilu as the study area, analyses the changing characteristics of multi-timescale drought from 1971 to 2020 based on the Standardised Precipitation Index (SPI), and analyses the stochasticity and stability of the drought by using a cloud model. Finally, the cross-wavelet transform method and Pearson's test are used to explore the correlation between atmospheric circulation factors, PRE and PET, and drought. The results indicate that (1) on the annual scale, the frequency of drought in Yinshanbeilu mainly ranges from 22% to 28%, with a high frequency of light droughts, a low frequency of severe droughts, a high frequency of droughts in the east and west, and a low frequency of droughts in the north and south; on the seasonal scale, the frequency of droughts in winter is the highest, with a rate of 34.6%, and the lowest frequency of droughts is in autumn, with a rate of 28.3%. (2) There is a decreasing trend in Entropy (En) and Hyper-Entropy (He), and an increasing trend in Expectation (Ex) for the inter-annual SPI-12 cloud model. Spatially, Ex and He are negatively correlated, while En and He are positively correlated. The inter-annual variation in cloud eigenvalues is greater than the inter-site variation, so the cloud model better reflects the spatial stochasticity and stability of regional inter-annual SPI. For the seasonal-scale SPI-3 cloud model, Ex is smaller in all seasons, En is also smaller, and He is larger. (3) Sunspot, PRE (precipitation), and PET (Potential Evapotranspiration) are all positively correlated with SPI and have the highest correlation. This study reveals the characteristics and causes of variations of drought in Yinshanbeilu, which can be applied to future research areas related to regional drought risk management. [ABSTRACT FROM AUTHOR]

Several periods of drought in the 21st century have severely affected large parts of Europe and caused considerable economic losses, particularly in the agricultural and energy sectors. While rainfall deficits are one of the main causes of droughts, high temperatures in the summer months exacerbate their development, which can have devastating consequences. This was the case in 2022, when long dry spells were accompanied by several heat waves. In this study, climatological drought monitoring using the Standardized Precipitation Evapo-transpiration Index (SPEI) is analyzed with the primary goal of finding the best theoretical distribution for adjusting the water balance (defined as the difference between precipitation and potential evapotranspiration) in Croatia before the index is officially introduced into the operational drought monitoring. Although the SPEI is widely used for drought monitoring, the underlying theoretical distribution of the water balance may differ between regions. The study is based on monthly precipitation amounts and monthly mean values of daily minimum and maximum temperatures observed at 31 main meteorological stations for the period 1961--2022. The analysis is carried out for different time scales, ranging from 1 to 24 months. Among the five distributions considered, a generalized logistic distribution with three parameters (GLO) proves to be the most appropriate. There is general agreement between SPEI and SPI time series, both in terms of sign and intensity. However, in periods with a light to moderate lack of precipitation and high air temperatures, a tendency towards higher drought intensity prevails in the SPEI. A comparative analysis of the 2022 drought in Croatia confirmed the ability of the SPEI to detect a drought earlier than the SPI, which also suggests that a larger area of the country was affected by the drought due to consistent air temperature excesses. [ABSTRACT FROM AUTHOR]

Copyright of Jurnal Teknologi dan Rekayasa Sumber Daya Air is the property of Brawijaya University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Jurnal Teknologi dan Rekayasa Sumber Daya Air is the property of Brawijaya University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Jurnal Teknologi dan Rekayasa Sumber Daya Air is the property of Brawijaya University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Drought is the most complex of the recurrent extreme weather events and is defined as a natural disaster with severe environmental, economic, and agricultural impacts resulting from a significant decrease in the average rainfall recorded in an area and the average rainfall recorded in the same place. Droughts have become more frequent and severe in many parts of the world, including Türkiye, due to global warming and climate change (increasing temperatures and changing precipitation patterns). Water resources and the agricultural sector are most severely affected by droughts. In this study, drought analyses of the Burdur Basin, located in the Aegean region, one of Türkiye's seven geographical regions, were carried out. For drought analysis, annual average total precipitation, annual maximum temperature, annual minimum temperature, and annual average temperature data of 17238 Burdur and 17892 Tefenni meteorological observation stations were used. Both meteorological and agricultural drought analyzes are included in the analysis of droughts. Standard Precipitation Index (SPI) and Reconnaissance Drought Index (RDI) methods were used to determine meteorological and agricultural drought, respectively. SPI and RDI values were obtained for 1-, 3-, 6- and 12-month time periods, and the severity, size, and distribution of dry and humid periods were determined for both stations separately. When the results of both methods were examined, severe droughts were observed in the study area in 1973, 1978, 1981, and 2017. [ABSTRACT FROM AUTHOR]

Drought is a global problem that affects particularly agricultural and water resources. The spatiotemporal drought characteristics and their possible drivers over Upper Awash Basin (UAB) were assessed in magnitude, duration, frequency, and intensity. Gridded data and a statistical downscaling model (SDSM) were used for historical projection. The Standardized Precipitation Index (SPI) and Standardized Evapotranspiration Index (SPEI) at 4- and 12-month timescales were used to compute the drought. The SPI 4-and 12-months indicate 1984, 1987, 2002, 2015, and 2016 years of dominant drought. Persistent dry events were observed in the west, northwestern, and some eastern parts of the study area in Representative Concentration Pathways (RCP) 4.5 and 8.5 scenarios. Trend analysis of seasonal shows that a statistically significant (P < 0.05) increasing trend during the main cropping seasons and annual drought events were detected in almost all basin parts. Near future projections in the two (RCP4.5 and RCP8.5) scenarios exhibited the continuation of drought up to 2030s and mid-2040s extreme and severe dryness, respectively. The seasonal and annual analysis projection indicates a decrease in dry events from 2050 onwards. The detected periodicity of dryness/wetness agreed with the negative/positive phase of SOI/ ENSO (Nino3.4) during Belg and the positive/negative phase during Kiremt and annual (SPI4/SPEI4). The possible driving forces of these drought events were land use/cover changes such as land degradation and urbanization. Global indices IOD, SOI, and ENSO (NINO3.4) are drivers that caused the seasonal droughts. These findings are useful for better preparedness priorities that suggest developing basin-wide targeted interventions. [ABSTRACT FROM AUTHOR]

Water resources in Morocco have been severely influenced by climate change and prolonged drought, particularly in the pre-Saharan zone. The Ziz watershed faces increasing pressure due to the high demographic growth, increased demand for water, excessive groundwater consumption, and investment in agriculture. But how long will water resources withstand these problems? This study, therefore, enters into the context of the assessment of water resources and estimates their vulnerability using the Standardized Precipitation Index (SPI), Standardized Precipitation Evapotranspiration Index (SPEI), and Standardized Groundwater Index (SGI), on data from the Ziz watershed from 1986 to 2016. Additionally, climate projections were utilized to simulate the future SGI from 2017 to 2100. The Water Evaluation and Planning System (WEAP) was employed to evaluate changes in Land Use and Land Cover (LULC) during the period of 1992–2020, and to generate future scenarios for land class inflows and outflows from 2017 to 2100, in comparison to the reference period of 1986–2016, thereby incorporating the SSP climate scenarios. The results indicate that the Ziz Basin experienced significant drought events in 1986–1989 and 2000–2003. The SPI and SPEI significantly correlated with SGI in some monitoring wells and with specific accumulation periods. The LULC analysis showed an increase in agricultural land and urban land and a decrease in barren or sparse land. Climate data analysis and scenarios predict that under SSP5-8.5, minimum and maximum temperatures will increase by 2.61 °C and 2.93 °C, respectively, and precipitation will decrease by 30% over this century. This substantial shift in climate conditions is reflected in the decline in SGIs, especially in the long term under SSP5-8.5. Water availability will decrease during this century under SSP3-7.0 and SSP5-8.5, as reflected in reduced land class inflows and increased outflows. These findings emphasize the need for stakeholders to implement integrated water governance for sustainability in the Ziz watershed. [ABSTRACT FROM AUTHOR]

Understanding how climate change is likely to impact on meteorological droughts is key to informing adaptation planning. For Ireland, little research has examined how droughts are likely to change in future. Here we examine changes in the monthly climate water balance, aridity index and changes in magnitude, frequency and duration of droughts using standardized drought indices for the island of Ireland derived from bias‐adjusted CORDEX simulations for the 2080s (2070–2099), forced with a high (RCP8.5) and moderate (RCP4.5) emissions pathway. Findings highlight that increases in potential evapotranspiration, driven by increasing temperature, together with changing seasonal rainfall patterns increase aridity in summer with water deficits extending into spring months, especially in the east and midlands by the end of the century. Increases in drought frequency and magnitude are also evident, with greatest increases for RCP8.5. Critically, increases are considerably greater for the standardized precipitation evapotranspiration index (SPEI) than for the standardized precipitation index (SPI), emphasizing the importance of using metrics that capture potential evapotranspiration in monitoring and assessing future drought risk. Summer and spring show the greatest increase in drought magnitude and frequency, most marked in the east of the island. By contrast, multiseasonal droughts, assessed using 6‐month accumulation periods show more modest changes in magnitude and duration. The resultant impacts of climate change on drought for Ireland would require considerable adaptation given the vulnerabilities exposed by the 2018 drought. [ABSTRACT FROM AUTHOR]

Study region: United Kingdom (UK). Study focus: A regional investigation of the Standard Precipitation Index (SPI) trends and abrupt changes in the UK has been carried out. The K-means algorithm was employed to partition the study area into six homogeneous regions, each distinguished by specific SPI characteristics. Subsequently, the seasonal Mann-Kendall (MK) test and the Bayesian Changepoint Detection and Time Series Decomposition (BEAST) algorithm were used to evaluate the overall trends for each cluster and SPI time scale, as well as to identify abrupt changes in trend and seasonality along the SPI time series, respectively. New hydrological insights for the region: The seasonal MK test revealed statistically significant increasing SPI trends for all clusters, except for the southeastern area of the UK, where decreasing, but not statistically significant, SPI trends were observed. Moreover, despite a scenario suggesting an increasingly humid UK, the BEAST analysis allowed the detection of decreasing abrupt changes in trends, resulting in sudden changes from wet to dry conditions, that cannot be identified using the MK test. Alongside these, the BEAST analysis has also revealed positive abrupt changes in trends across all UK, as well as positive or negative variations in seasonality, which are followed by longer or shorter wet or dry periods, respectively. Overall, the study approach provides a detailed picture of the SPI trends and abrupt changes, in light of the impact of climate change on the different areas of the UK.