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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]

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

Copyright of Journal of Engineering (17264073) 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.)

Meteorological drought, defined as a decrease in the average amount of precipitation, is among the most insidious natural disasters. Not knowing when a drought will occur (its onset) makes it difficult to predict and monitor it. Scientists face significant challenges in accurately predicting and monitoring global droughts, despite using various machine learning techniques and drought indices developed in recent years. Optimization methods and hybrid models are being developed to overcome these challenges and create effective drought policies. In this study, drought analysis was conducted using The Standard Precipitation Index (SPI) with monthly precipitation data from 1920 to 2022 in the Tromsø region. Models with different input structures were created using the obtained SPI values. These models were then analyzed with The Adaptive Neuro-Fuzzy Inference System (ANFIS) by means of different optimization methods: The Particle Swarm Optimization (PSO), The Genetic Algorithm (GA), The Grey Wolf Optimization (GWO), and The Artificial Bee Colony (ABC), and PSO optimization of Support Vector Machine (SVM-PSO). Correlation coefficient (r), Root Mean Square Error (RMSE), Nash–Sutcliffe efficiency (NSE), and RMSE-Standard Deviation Ratio (RSR) served as performance evaluation criteria. The results of this study demonstrated that, while successful results were obtained in all commonly used algorithms except for ANFIS-GWO, the best performance values obtained using SPI12 input data were achieved with ANFIS-ABC-M04, exhibiting r: 0.9516, NSE: 0.9054, and RMSE: 0.3108. [ABSTRACT FROM AUTHOR]

Drought is a global threat caused by the persistent challenges of climate change. It is important to identify drought conditions based on weather variables and their patterns. In this study, we enhanced the Standardized Precipitation Index (SPI) by integrating ground temperature data to develop a more comprehensive metric for evaluating drought severity: the Multivariate Standardized Drought Index. Our metric offers a dual assessment of drought severity, taking into account both the intensity of the drought and its duration. We employ this evaluation in a primary paddy cultivation region of Sri Lanka, with the aim of shedding light on the prevailing drought conditions affecting paddy crops due to insufficient water supply and prolonged periods of elevated temperatures. Additionally, we calibrate our metric by aligning it with historical drought records and subsequently compare the outcomes with those derived from the conventional SPI. [ABSTRACT FROM AUTHOR]

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

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 this study, a multilevel drought-induced resistance and resilience analysis (MDRRA) approach was developed to investigate the stability of vegetation in the Yellow River Basin (YRB). MDRRA was quantified by utilizing the Normalized Difference Vegetation Index (NDVI). It was applied to YRB to assess vegetation resistance and resilience to various levels of drought by utilizing precipitation and NDVI data from 2000 to 2019. The results reveal that vegetation resistance and resilience in YRB are affected by drought severity. Monthly and annual changes in SPI over the warm–temperate humid zone of the YRB show a decreasing trend, with rates of 0.001 per decade and 0.034 per decade, respectively; however, the other climatic subregions exhibit an increasing trend, with rates ranging from 0.002 per decade to 0.82 per decade. Over 77.56% of the downstream areas show increases in the annual SPI averages. Drought severity differs across subregions in the YRB. More severe drought events occur in its upper and middle reaches, while less severe ones happen in its lower reaches. As the drought severity increases, the arid and semiarid regions of the mesothermal zone exhibit a decrease in the resistance and resilience indices. MDRRA can help improve the stability and resilience of the ecosystem in the YRB. [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]

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

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.

Abstract Rainfall variability and drought events can affect food security globally. This study aimed to assess the spatial and temporal trends and variability of rainfall in Kaduna State, Nigeria. Gridded rainfall data were obtained from the Climate Research Unit (CRU_TS 4.07) (1952–2022) for Birnin Gwari, Kaduna, Kafanchan and Zaria towns. The rainfall data were subjected to the Anderson‒Darling normality test and found to be normally distributed. The data were analysed using the coefficient of variation (CV), rainfall anomaly index (RAI) and precipitation concentration index (PCI). The trend was assessed using the Mann‒Kendall trend test, and finally, meteorological drought was computed using the standardized precipitation index (SPI). The results revealed that all four locations had low to moderate variability, with CVs

The technology of building CubeSat-class nanosatellites has created a breakthrough in exploring near space for research and education. The availability of commercial solutions for the rapid integration of hardware and software of a CubeSat under development and the launch of such a nano-satellite into Earth orbit as a parasitic payload has made this technology accessible and attractive to universities worldwide. Despite this popularity, the statistics of nanosatellite failures after launch require a systematic approach to their design and construction and the selection of off-the-shelf (COTS) components. The object of the study is to determine the overall efficiency of standardized network interfaces when used in the onboard network of CubeSat nanosatellites. The subject of the article is a method for comprehensively evaluating the effectiveness of standardized network interfaces for data exchange in the onboard network of CubeSat nanosatellites, taking into account typical scenarios for data and command exchange. The aim of the study was to substantiate the method of expert evaluation of the technical efficiency of standardized network interfaces for data exchange in the onboard network of CubeSat nanosatellites, taking into account the following factors: energy efficiency, multi-mastering capability, resistance to electromagnetic interference, redundancy, coverage following the OSI network model, data transmission speed, and complexity of network technology implementation by software. Objectives: To analyze modern methods of organizing onboard avionics networks of nanosatellites of the CubeSat class, formulate an expert comparison model, and compare the selected information onboard networks following the model and the list of selected networks and their protocols. Results: an expert model consisting of a 7-factor comparison was created, modern and most widely used in the industry onboard networks were selected, and each network was compared according to the proposed model. Conclusions: The two most common and efficient models, I2C and CAN, were selected as recommended for use in the avionics of the KhAI1Sat nanosatellite and the “Boryviter”/”Falco” onboard computer being developed by the authors. Although the difference in power consumption between these two types of networks is significant, each has certain advantages in terms of noise immunity, multicasting, and application.

Abstract Evaluating drought parameters at the basin level is one of the fundamental processes for planning sustainable crop production. This study aimed to evaluate both short-term and long-term meteorological drought parameters within the Vaippar Basin, located in southern India, by employing the standardized precipitation index (SPI). Gridded rainfall values developed from 13 rain gauge stations were employed to calculate the SPI values. Drought parameters, encompassing occurrence, intensity, duration, frequency, and trends, were assessed for both short-term and long-term droughts. The study findings indicated that the occurrence of short-term drought was 51.7%, while that of long-term drought was 49.82%. Notably, the basin experienced extreme short-term droughts in 1980, 1998 and 2016 and long-term droughts in 1981, 2013, and 2017. Utilizing an innovative trend identification method for SPI values, a significant monotonic upwards trend was identified in October and December for short-term drought and in December for long-term drought. This study defined the minimum threshold rainfall, which represents the critical amount required to prevent short-term drought (set at 390 mm) and long-term drought (set at 635 mm). The drought severity recurrence curves developed in this study indicate that when the SPI values fall below − 1.0, short-term drought affects 25% of the basin area, while long-term drought impacts 50% of the basin area at a 20-year recurrence interval. Additionally, the drought hazard index (DHI), which combines drought intensity and severity, demonstrated higher values in the northwestern regions for short-term drought and in the southern areas for long-term drought. The study's findings, highlighting areas of drought vulnerability, severity, and recurrence patterns in the basin, direct the attention for timely intervention when drought initiates.

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.

Bioturbation is a key ecosystem function with a fundamental role in mediating major biogeochemical cycles. The intensity and depth of bioturbation is influenced by the taxa present, which is often a function of food supply. The deep sea is generally oligotrophic with sediments composed predominantly of small, shallow burrowing macrofauna (<10 cm). Human activity is increasingly introducing POC to the deep-sea, however, organic enrichment of the deep-sea, and the subsequent response of bioturbators is poorly understood. Here we present data on benthic function in deep-sea systems that have experienced organic enrichment. We show that organic enrichment enhances deep-sea bioturbation through larger, advanced successional taxa, and deeper bioturbation depths. Enhanced bioturbation in the deep-sea should confer positive ecosystem functions (nutrient recycling, microbiological activity, remineralization), but adherence to approaches and interpretations guided by the paradigm of small, shallow-burrowing infauna could significantly underestimate deep-sea benthic processes at a global scale. [ABSTRACT FROM AUTHOR]

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]

Previously, we showed that water extract (soymilk, except pH was increased to 8 from 6.5) of whole soybean could be used directly as a raw material for producing edible soy films by deposition of the film‐forming solution (soy extract with enhancers). However, the strength of such soy films needed improvement because they were weak. The purpose of this study was to investigate how transglutaminase (TG) cross‐linking reactions and film enhancers, including pectin (low‐ and high‐methoxyl pectin), whey protein isolate (WPI), and soy protein isolate (SPI), improve the physical properties of soy films. Soy films prepared with TG had tensile strength (TS) of 3.01 MPa and puncture strength (PS) of 0.78 MPa, which were higher by as much as 51% and 30% than that of soy films without TG treatment, respectively. Pectin showed significant effects on the mechanical properties of TG‐added soy films in terms of TS, PS, and % elongation. On the other hand, only TS and PS were increased by the addition of WPI or SPI. Heat curing had a significant effect on soy film's physical properties. TG treatment significantly reduced film solubility when soaked in water and various levels of acid (vinegar) and base (baking soda) solutions. Under the experimental conditions of 35 unit TG and 28 min of reaction, the degrees of cross‐linking were evidenced by the disappearance of individual protein subunits, except the basic subunit of glycinin, and the reduction of 21% of lysine residues of the proteins. Highlights: Edible soy films were made with transglutaminase and about 21% lysine cross‐linked.The mechanical strength of soy films was increased by incorporating film enhancers.Transglutaminase enhanced the mechanical properties of soy films. [ABSTRACT FROM AUTHOR]

Rainfall variability and drought events can affect food security globally. This study aimed to assess the spatial and temporal trends and variability of rainfall in Kaduna State, Nigeria. Gridded rainfall data were obtained from the Climate Research Unit (CRU_TS 4.07) (1952–2022) for Birnin Gwari, Kaduna, Kafanchan and Zaria towns. The rainfall data were subjected to the Anderson‒Darling normality test and found to be normally distributed. The data were analysed using the coefficient of variation (CV), rainfall anomaly index (RAI) and precipitation concentration index (PCI). The trend was assessed using the Mann‒Kendall trend test, and finally, meteorological drought was computed using the standardized precipitation index (SPI). The results revealed that all four locations had low to moderate variability, with CVs < 15%. The rainfall anomaly index revealed that 1983 was the driest year for Birnin Gwari, Kaduna and Kafanchan, with RAI values of − 4.863, − 4.474 and − 4.316, respectively, while in Zaria, the driest year was 1974, with a RAI value of − 4.065. The PCI showed that there was a high concentration of rainfall in Birnin Gwari, Kaduna, and Kafanchan. However, Zaria experienced 41 years of high concentrations and 30 years of very high concentrations of precipitation. The Mann‒Kendall trend test revealed that all four locations experienced a significant downward trend in rainfall within the study period. The Z statistics for Birnin Gwari, Kaduna, Kafanchan and Zaria were − 1.112, − 0.357, − 0.556 and − 0.764, respectively, all indicating decreasing rainfall amounts. Finally, the SPI Birnin Gwari and Kaduna experienced 18 months of severe drought, with maximum intensities of − 2.671 and − 3.111, respectively. Kafanchan experienced 25 months of drought with a maximum intensity of − 2.643, while Zaria experienced 30 months of drought with a maximum intensity of − 3.069. These severe droughts occurred from the 1970s to the 1990s. The study concluded that rainfall concentrations were mostly high and very high, and severe, catastrophic drought events occurred in the 1970s and 1980s. [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]

Evaluating drought parameters at the basin level is one of the fundamental processes for planning sustainable crop production. This study aimed to evaluate both short-term and long-term meteorological drought parameters within the Vaippar Basin, located in southern India, by employing the standardized precipitation index (SPI). Gridded rainfall values developed from 13 rain gauge stations were employed to calculate the SPI values. Drought parameters, encompassing occurrence, intensity, duration, frequency, and trends, were assessed for both short-term and long-term droughts. The study findings indicated that the occurrence of short-term drought was 51.7%, while that of long-term drought was 49.82%. Notably, the basin experienced extreme short-term droughts in 1980, 1998 and 2016 and long-term droughts in 1981, 2013, and 2017. Utilizing an innovative trend identification method for SPI values, a significant monotonic upwards trend was identified in October and December for short-term drought and in December for long-term drought. This study defined the minimum threshold rainfall, which represents the critical amount required to prevent short-term drought (set at 390 mm) and long-term drought (set at 635 mm). The drought severity recurrence curves developed in this study indicate that when the SPI values fall below − 1.0, short-term drought affects 25% of the basin area, while long-term drought impacts 50% of the basin area at a 20-year recurrence interval. Additionally, the drought hazard index (DHI), which combines drought intensity and severity, demonstrated higher values in the northwestern regions for short-term drought and in the southern areas for long-term drought. The study's findings, highlighting areas of drought vulnerability, severity, and recurrence patterns in the basin, direct the attention for timely intervention when drought initiates. [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]

Drought can be considered an atmospheric condition, which rapidly goes beyond to affect multiple fields of the environment and human activities. The persistence of these atmospheric conditions can affect the recharge of surface and groundwater bodies due to a decrease in the volume of runoff and seepage, thus affecting human and environmental activities. In this context, the main aim of this work is to characterize the occurrence and variability of future droughts in Galicia over the twenty-first century. The methodology followed is based on the calculation and analysis of the Standardized Precipitation Index (SPI) in three-time scales (3, 6, and 12 months), using daily outputs of precipitation data from one RCM provided by the EURO-CORDEX project for different periods (reference from 1971 to 2005, and future from 2025 to 2060 and 2061 to 2096) and scenarios (RCP 4.5 and RCP 8.5). Using SPI-3, SPI-6, and SPI-12 projections, drought trends and potential changes in their characteristics were explored under RCP 4.5 and RCP 8.5 scenarios. In both scenarios, throughout the twenty-first century, a decreasing trend in SPI indicates an intensification of drought conditions over Galicia. Moreover, at the beginning of the century, under RCP 4.5, drought events will increase and will be slightly more intense but less persistent, while under RCP 8.5, the number of events will be almost the same, but shorter and less severe. Toward the end of the century, drought events are expected to be more numerous, less durable, and more intense under both scenarios. [ABSTRACT FROM AUTHOR]

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

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]

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Evaluating drought is paramount in water resources management and drought mitigation plans. Drought indices are essential tools in this evaluation, which mainly depends on the time period of the original datasets. Investigating the effects of time periods is critical for a comprehensive understanding and evaluation of drought. Also, It holds particular significance for regions facing data availability challenges. The existing literature reveals a gap in drought assessment and comparison analysis using conventional methods based on drought indices only. This research proposes an innovative drought classification matrix to compare drought indices and spatial and temporal scenarios; the proposed matrix depends on any drought classification for comparison procedure. Furthermore, it aims to investigate the differences between several time period scenarios based on the proposed matrix and several statistical metrics (R2, CC, RMSE, HH, and RB) and determine the acceptable/minimum time period. The application of the proposed matrix and selection of an acceptable/minimum time period is presented to three different climates: Durham station in the United Kingdom, Florya station in Türkiye, and Karapinar station in Türkiye. The results show that the time period scenarios are able to catch the reference time period (RTP) scenario reasonably, with strong correlation and negative relative bias. The 10-year time period is sufficient as an acceptable/minimum time period for short timescales, such as meteorological drought. Conversely, for longer timescales, such as hydrological drought, a 20-year time period is the acceptable/minimum time period. The proposed matrix demonstrates a robust and powerful framework for comparison, making it applicable to various drought assessment scenarios globally. [ABSTRACT FROM AUTHOR]

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]

During the sowing operation, the consistency and stability of the row dispenses of a seeder are important indicators for evaluating sowing performance. A multi-channel real-time weighing device was designed to study and analyze the consistency of row dispenses in seeder under identical conditions, as well as the stability of dispenses from a seed dispenser. This device utilized two Arduino boards as controllers for data acquisition, processing, and transmission. Based on the Serial Peripheral Interface (SPI), data exchange between multiple Arduinos was achieved in a master-slave configuration, allowing for data acquisition from 14 pressure sensors in a one-master, one-slave setup. Pressure data was collected using pressure sensors, HX711 converter chips, and Arduino. Lab VIEW was utilized as the upper computer to read data from the Arduino host serial port and provide real-time display and storage. This paper presents the structural and working principles of the device. Experimental tests on the weighing unit were conducted using a test bench to evaluate measurement errors. The absolute error mean ranged from 0.143 g to 0.262 g. Additionally, the impact of the seed impact force on the error was simulated using EDEM. The device was used to evaluate a six-row wheat seeder under the experimental conditions of the groove wheel having a length of 2.5 cm and a groove wheel speed of 25 r/min. The maximum coefficient of variation for seeding stability (Y1) was 2.38%, the minimum was 0.83%, and the mean was 1.21%. The coefficient of variation for consistency of seeding in rows (Y2) was 1.86%. [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]

Drought is a climatic event that has negative environmental effects. Detecting the drought in a region enables more effective water resources management in that region. This study was designed to carry out drought analysis for 98 months in the period of February 2010 and November 2020 in designated areas within the region determined in Turkey which is located in the temperate zone of the northern hemisphere. The analyzed data included terrestrial water storage anomaly (TWSA) values derived from Gravity Recovery and Climate Experiment (GRACE) and GRACE-FO (Follow On) satellites and monthly total precipitation values obtained from the PERSIANN system (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks). Drought analysis was identified by utilizing the water storage deficit index (WSDI) and standardized precipitation index (SPI). The drought index values were compared with 5 different (The Arctic Oscillation (AO), The North Atlantic Oscillation (NAO), The Multivariate ENSO (El-Nino/Southern Oscillation) Index (MEI), The Oceanic Nino Index (ONI) and The Southern Oscillation Index (SOI)) atmospheric/oceanic climate indexes. The results of this study indicate that the years with the highest water deficit are 2014, 2018 and 2020. From 2010 to 2020, the equivalent water thickness (EWT) has decreased from + 1.5 to − 11.1 cm. Whereas wetness was observed throughout Turkey between the years 2010–2013, it was revealed that droughts occurred between the years 2014–2020. The findings of this study also showed that there was a significant correlation (at a significance level) among WSDI and AO, NAO and ONI indexes. [ABSTRACT FROM AUTHOR]

Non-sustainable land use results in induced climate change and severe hydrologic hazards all over the world. Now a days, floods play a significant role in destructing the environment and human lives. The present study uses geospatial data with thirteen flood conditioning criteria to identify the flood risk in a deltaic region, Kuttanad. Eight physical- geological criteria such as elevation, drainage density, geology, flow accumulation, slope, Stream Power Index (SPI), Topographic Wetness Index (TWI) and Standard Curvature (SC) were used for vulnerability studies. Three socio-economic factors such as population density, Land Use and Land Cover (LULC) and road density were included to identify the flood exposure. Precipitation and river density were considered to identify the flood hazard. The above criteria were integrated by the method of spatial integration in the ArcGIS environment to identify the area subjected to flood risk. Subjective judgements made using the above criteria were converted into quantitative data based on Analytical Hierarchy Process (AHP) scale. Essential judgements of this study demonstrated that a critical percentage of the region (68%) comes under high flood vulnerability. 34% of the area comes under high hazardous regions and they need immediate attention. Integrating vulnerability, exposure and hazard, a flood risk map was prepared and found that nearly 72% of the region comes under high-risk. Efficiency of the flood risk and flood vulnerability map tested by Area Under Curve (AUC) indicates a higher precision, by obtaining 89% accuracy for the flood risk map and 93% for the flood vulnerability map. The mean value of the flood risk index in 6 agro-ecological zones has been computed and distributed evenly by the method of zonal statistics to identify the flood risk zone. Flood risk index ranging from 2.84 to 3.10 is classified under Zone I, 2.72–2.78 under Zone II, 2.71 under Zone III and 2.61 is classified as Zone IV respectively. The results obtained from this study enable prioritization of flood risk areas, offering valuable insights for planners, decision makers to control and alleviate flood events along the deltaic region, Kuttanad. [ABSTRACT FROM AUTHOR]

Being susceptible to natural disasters, Bangladesh is one of the most disaster-prone countries. Among the various natural calamities, droughts are a frequent occurrence in the western region of the country. Hence, this study first compared the efficacy of five bias-corrected Regional Climate Models (RCMs) - MIROC, NOAA, MPI, IPSL, and CCCma - for the Western region of Bangladesh with observed monthly precipitation and subsequent SPI values. Various evaluation methods- RMSE, Taylor Diagram, Mann–Whitney U Test, and t-test, were applied to precipitation and SPI3 values for the historical base period. Through these analyses, the MIROC model exhibited the highest level of accuracy. Accordingly, future projections for short-term droughts (SPI-3) and their characteristics were conducted using the ensemble of top three climate model under the RCM 8.5 W/m2 scenario. Short-term droughts are anticipated to become less frequent and severe in the 2060s and 2080s compared to the 2020s. Nonetheless, North-West region is projected to be more drought-prone than South-West until 2100. This research shows the importance of evaluating the better-fitting RCMs for assessing historical droughts and making reliable projections for the future. The methodology and findings can be employed in evidence-based decision-making and applied in other drought-prone areas to understand future drought risks.

Drought is a dangerous situation that lowers individual standards of living. It is considered a devastating natural event. This study intends to thoroughly examine the occurrence and characteristics of drought events in the Sulaymaniyah Governorate from 1985 to 2015. The study employed three standardized indices: the standardized precipitation evapotranspiration index (SPEI), the standardized precipitation index (SPI), and the Reconnaissance Drought Index (RDI). These indices were utilized at different periods, namely 3, 6, 9, and 12 months. According to findings, the maximum percent of extremely dry highlighted in Sulaymaniyah and Dukan stations for SPI index and time scale three months was 48% and 30%, respectively; the maximum percent of severely dry highlighted using RDI index and time scale three months with 41%, 32%, 44% in Sulaymaniyah, Dukan, and Darbandikhan stations respectively. Results revealed that the years 1998, 1999, 2000, 2007, 2008, 2009, and 2010 saw a drought for the used indices, Dukan station has the maximum severity of 112.25 for SPEI12 for the event happened from Nov. 2007 to Sep 2015. The maximum durations were observed at Dukan station using SPI and SPEI as well, and Darbandikhan station has the highest drought frequency. The correlations of drought indices, using the coefficient of determination ( ) showed the lowest and highest agreement between SPI-SPEI and SPI-RDI, respectively. The findings revealed that water resources in the study area are at risk of depletion due to drought. Therefore, water management techniques are required to mitigate drought in the study area.

Extreme precipitation events, especially climatic heavy precipitation-induced (CHP-induced) floods, pose substantial negative impacts on a myriad of socio-economic drivers. Incidents related to extreme precipitation events result in revenue loss, thereby negatively affecting national economies, and there is a growing need to advance studies that promote early warning and rapid resilience building across West Africa. Despite the region’s vulnerability to climate change, studies on flood events have barely compared multiple indices in order to provide a true representation of meteorological floods in the region Using multiple precipitation indices (Standardized Rainfall Anomaly, Standardized Precipitation Index and Rainfall Anomaly Index), CHP-induced flood events were assessed both historically and into the far-future. The projections were estimated under three Shared Socioeconomic Pathways (SSPs; SSP1-2.6, SSP3-7.0 and SSP5-8.5) and over three demarcated zones of West Africa (Guinea Coast [GC], Savanna [SAV] and Sahel [SAH]). The findings from the study indicate an expected intensification in future flood potential, particularly in the Savanna and Sahel. Contrastingly, flood potential in the Guinea Coast is projected to remain nearly similar to the historical records. On the impacts of emissions scenarios on projected CHP-induced floods, an increase in the mean flood count and maximum flood could potentially result from rising emission concentrations. The findings of the study are useful for advancing current understanding of CHP-induced floods over West Africa, and planning effective mitigation and adaptation strategies to build resilience in the face of current to future episodes.