Search

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

Drought is a widespread natural hazard resulting from an extended period of reduced rainfall, with significant socioeconomic and ecological consequences. Drought severity can impact food security globally due to its high spatial and temporal coverage. The primary objective of this paper consists of a comparative spatiotemporal analysis of environmental extremes (drought/wetness) through the estimation of a twelve-month Standardized Precipitation Index (SPI12) between three distinct vulnerable agricultural regions in the Mediterranean basin (i.e., Spain, Lebanon and Tunisia), under a climate change environment in the last 38 years (1982–2020). The added value of this paper lies in the simultaneous estimation of temporal and spatial variability of drought and wetness periodic events, paying special attention to the geographical patterns of these extremes both in annual and interannual (seasonal) time scales. The results indicated that Spain and Tunisia (western Mediterranean) exhibit similar patterns over the studied period, while Lebanon demonstrates contrasting trends. Comparing the two extreme dry hydrological years, the Spanish study area faced the highest drought intensity, areal extent and duration (SPI12 = −1.18; −1.84; 28–78%; 9–12 months), followed by the Lebanese (SPI12 = −1.28; −1.39; 37–50%; 7–12 months) and the Tunisian ones (SPI12 = −1.05; −1.08; 10–34%; 8 months). Concerning the wettest hydrological years, the Lebanese study domain has recorded the highest SPI12 values, areal extent and duration (SPI12 = 1.58; 2.28; 66–83%; 8–11 months), followed by the Tunisian (SPI12 = 1.55; 1.79; 49–73%; 7–10 months) and Spanish one (SPI12 = 1.07; 1.99; 21–73%; 4–11 months). The periodicity of drought/wetness episodes is about 20 years in Spanish area and 10 years in the Lebanese area (for drought events), whereas there seems no periodicity in the Tunisian one. Understanding the spatial distribution of drought is crucial for targeted mitigation strategies in high-risk areas, potentially avoiding broad, resource-intensive measures across entire regions. [ABSTRACT FROM AUTHOR]

In arid and semi-arid regions like Iran, sustainable urban and agricultural development is intimately intertwined with the severity, frequency, and duration of meteorological droughts. Prolonged meteorological droughts can trigger hydrological and socio-economic droughts, posing significant challenges to the region's sustainability. Therefore, the primary objective of this study is to perform a comprehensive Regional Frequency Analysis (RFA) for extreme meteorological drought events across Iran. To this end, we meticulously calculated the severity, duration, and magnitude of the Standardized Precipitation Index (SPI) over the 3-, 6-, 9-, and 12-month accumulation periods. The data was derived from monthly precipitation records collected at 106 meteorological stations during the period spanning 1993–2016. Subsequently, we employed a rigorous approach, utilizing Ward's clustering analysis and the test for heterogeneity using L-moment, to effectively classify and categorize the extreme drought characteristics prevalent across the country. The analysis revealed that the generalized Pareto and Pearson type 3 models are the most suitable distribution functions for RFA across almost all the examined clusters. Additionally, the spatiotemporal analysis of the SPI series at various time scales indicated that the extreme events' characteristics, such as severity, duration, and magnitude, remain surprisingly unaffected by the local climate conditions. The results also showed that even in the typically humid regions located in the north, west, and northwest of Iran, areas that receive considerable annual precipitation, severe droughts can still occur. These droughts primarily stem from temporal events rather than being solely dependent on the local climate conditions. Therefore, the study concludes that the drought events can largely be attributed to mismanagement and inadequate regional water allocation. Moreover, these situations might have been further exacerbated by the occurrence of extreme climatic conditions. [ABSTRACT FROM AUTHOR]

This paper presents a comparative analysis of machine learning (ML) models for predicting drought conditions using the Standardized Precipitation Index (SPI) for two distinct stations, one in Shiraz, Iran and one in Tridolino, Italy. Four ML models, including Artificial Neural Network (ANN), Multiple Linear Regression, K-Nearest Neighbors, and XGBoost Regressor, were employed to forecast multi-scale SPI values (for 6-, 9-, 12-, and 24-month) considering various lag times. Results indicated that the ML model with the most robust performance varied depending on station and SPI duration. Furthermore, ANN demonstrated robust performance for SPI estimations at Shiraz station, whereas no single model consistently outperformed the others for Tridolino station. These findings were further validated through the confidence percentage analysis performed on all ML models in this study. Across all scenarios, longer SPI durations generally yielded better model performance. Additionally, for Shiraz station, optimal lag times varied by SPI duration: 6 months for the 6- and 9-month SPI, 4 months for the 12-month SPI, and 2 months for the 24-month SPI. For Tridolino station, on the other hand, no definitive optimal lag time was identified. These findings contribute to our understanding of predicting drought indicators and supporting effective water resource management and climate change adaptation efforts. [ABSTRACT FROM AUTHOR]

This study aimed to analyse the temporal and spatial characteristics of drought in the Mekong Delta region. The influence of Sea Surface Temperature Anomalies (SSTAs) and the Southern Oscillation Index (SOI) in the Pacific and Indian Oceans on drought was also investigated. The drought was calculated using the Standardized Precipitation Index (SPI) and the Standardized Precipitation and Evapotranspiration Index (SPEI) indices at one-month and three-month timescales, respectively. A correlation equation is used to determine the relationships between the SSTA/SOI and the SPI/SPEI. The results showed that drought occurred in several months from 1990–1991, 1992–1993, 1995, 1998, 2002–2003, 2005–2006, 2010–2011, and 2015–2016, consistent with the occurrence of drought in the Mekong Delta. However, the SPEI3 reflected drought events more accurately than the other indices. The El Nino phenomenon had a relatively significant impact on drought in the study area. When an El Nino occurred, drought in the Mekong Delta lasted several months but often occurred later than the El Nino. Examining the relationships between the SSTA/SOI and the SPI/SPEI, we find that when the SST increases in the Nino 3.4 region, the NinoW decreases, and a negative SOI affects all drought events. Particularly when the SST increases in the Nino 3.4 region, there is a high probability of drought occurrence in the Mekong Delta. However, drought occurrence is typically 2 to 3 months later than the El Nino. The results will help managers find appropriate solutions to minimize the effects of drought on the research area. [ABSTRACT FROM AUTHOR]

The Karst region is a typical ecologically vulnerable area. In recent years, influenced by global climate change, short-term droughts have frequently occurred in the Karst region. The study of vegetation response patterns under drought stress holds significant importance for ecological environment restoration and management in the Karst region. An improved vegetation condition index (VCINDVIRE ) was created based on the red-edge band of the wide field of view (WFV) imagery from the high-resolution satellite GF6. It was utilized to monitor vegetation responses to short-term droughts. Additionally, the standardized precipitation index (SPI) was computed using monthly precipitation data to identify occurrences of drought. Taking the typical Karst region of Guilin as an example from September to December 2022, the focus of the study was on vegetation responses to short-term drought. The results indicate that in the central, southern, and northeastern regions of Guilin, vegetation exhibits the highest sensitivity to drought due to poor soil water retention capacity and a lack of irrigation facilities. In the study area, vegetation underwent continuous drought, reaching a critical point, leading to a sustained and irreversible withering response, taking approximately two months. Different types of vegetation showed varying degrees of response to shortterm drought, with cropland being more sensitive than grassland and woodland. GF6-WFV data demonstrated excellent performance in monitoring short-term droughts, providing crucial references for ecological protection and management in the Karst region. [ABSTRACT FROM AUTHOR]

An accurate analysis of climate extremes is essential for impact assessment and devising appropriate adaptation measures. There is an urgent need to assess precipitation products in capturing the increasing occurrence of climate extremes. This study evaluates the ability of 20 observational datasets, including gauge-based, satellite-based and reanalyses, in representing different drought severity (moderate, severe and extreme drought) over Africa and its nine sub-regions at varying time scales (3-, 6- and 12-months) during 1983–2014. Drought is represented using the Standardized Precipitation Index (SPI). The results demonstrate that while most datasets are suitable for drought studies over the continent, the African Rainfall Climatology version 2 (ARC2) and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks Climate Data Records (PERSIANN_CDR_v1r1) are less fitted for such investigations. Moreover, regions such as the Sahara (SAH), Central Africa (CAF) and North Eastern Africa (NEAF) show a larger disparity among the datasets, requiring more caution when selecting a dataset for use in such areas. Generally, the datasets present low agreement toward the lower end of the range (5–30%) because the individual datasets estimate varying drought severities at different grids and months. This is observed in the coefficient of variation of 20–25% of the datasets falling outside the ± 1 standard deviation range. Therefore, using an ensemble to represent the datasets remains an indispensable tool. The datasets present better agreement in the timing of drought events than the spatial distribution. The findings provide valuable insights into the complexity of drought assessment using diverse precipitation datasets. Furthermore, the results highlight the significance of considering spatial and temporal dimensions, as datasets may capture drought events at varying locations and times, revealing subtle variations in drought impact. [ABSTRACT FROM AUTHOR]

The frequency and severity of extreme climatic phenomena, including droughts, have increased across the globe. The aim of this study is to detect changes in the severity and trends of short- and medium- term droughts (3 and 12 months) across Iran (1990–2019) and project the near (2059–2030) and far (2095–2066) future changes. SPI and SPEI indices were used for this purpose. The non-parametric Mann–Kendall test was used for trend analysis, and the G * (Geties and Ord) index was exerted for hot spot analysis. The results revealed that in 1990–2020, except for 3 month SPI (SPI3) index, the SPI and SPEI trends tended to be negative and statistically significant at the 0.05 significance level. During the same period, specified and significant hot spots of the indices were formed in the northern and northwestern Iran, and significant cold spots were concentrated only in Zabol and Birjand stations in eastern Iran. Near and far future minimum and maximum monthly precipitation projections for four selected stations show increases in monthly maximum and minimum temperatures and decreases in precipitation at all stations, though the decrease in precipitation for Esfahan station only with RCP8.5. The Projected trends of 3 and 12 months SPI and SPEI (SPI3, SPI12, SPEI3 and SPEI12) in near and far future show the dominance of significant negative trends. Projected trends of SPI and SPEI for the near future was negative for all stations, though it was positive and significant for far future. [ABSTRACT FROM AUTHOR]

Drought is, among natural hazards, one of the most harmful to humanity. The forecasting of droughts is essential to reduce their impact on the economy, agriculture, tourism and water resource systems. In this study, drought forecast in the central region of the state of Zacatecas, a semi-arid region of Mexico, is explored by means of artificial neural networks (ANNs), forecasting numerical values of three drought indices—the standardized precipitation index (SPI), the standardized precipitation and evapotranspiration index (SPEI) and the reconnaissance drought index (RDI)—in an effort to establish the most suitable index for drought forecasting with ANNs in semi-arid regions. Records of 52 years of monthly precipitation and temperature were used. The indices were calculated in three different time scales: 3, 6 and 12 months. The analyzed models showed great capacity to forecast the values of the three drought indices, and it was found that for the trial set, the RDI was the drought index that was best fitted by the models, with the evaluation metrics R2 (determination coefficient), RMSE (root mean square error), MAE (mean absolute error) and MBE (Mean Bias Error) showing ranges of 0.834–0.988, 0.099–0.402, 0.072–0.343 and 0.017–0.095, respectively. For the validation set, the evaluation metrics were slightly better. [ABSTRACT FROM AUTHOR]

Droughts have negative impacts on agricultural productivity and economic growth. Effective monitoring and accurate forecasting of drought occurrences and trends are crucial for minimizing drought losses and mitigating their spatial and temporal effects. In this study, trend dynamics in monthly total rainfall time series measured at Cape Town International Airport were analyzed using the Mann–Kendall (MK) test, Modified Mann–Kendall (MMK) test and innovative trend analysis (ITA). Additionally, we utilized a hybrid prediction method that combined the model with the complementary ensemble empirical mode decomposition with adaptive noise (CEEMDAN) technique, the autoregressive integrated moving average (ARIMA) model, and the long short-term memory (LSTM) network (i.e., CEEMDAN-ARIMA-LSTM) to forecast SPI values of 6-, 9-, and 12-months using rainfall data between 1995 and 2020 from Cape Town International Airport meteorological rainfall stations. In terms of trend analysis of the monthly total rainfall, the MK and MMK tests detected a significant decreasing trend with negative z-scores of −3.7541 and −4.0773, respectively. The ITA also indicated a significant downward trend of total monthly rainfall, especially for values between 10 and 110 mm/month. The SPI forecasting results show that the hybrid model (CEEMDAN-ARIMA-LSTM) had the highest prediction accuracy of the models at all SPI timescales. The Root Mean Square Error (RMSE) values of the CEEMDAN-ARIMA-LSTM hybrid model are 0.121, 0.044, and 0.042 for SPI-6, SPI-9, and SPI-12, respectively. The directional symmetry for this hybrid model is 0.950, 0.917, and 0.950, for SPI-6, SPI-9, and SPI-12, respectively. This indicates that this is the most suitable model for forecasting long-term drought conditions in Cape Town. Additionally, models that use a decomposition step and those that are built by combining independent models seem to produce improved SPI prediction accuracy. [ABSTRACT FROM AUTHOR]

This study focused on assessing and analyzing meteorological characteristics based on rainfall and temperature data from eight stations in Australia over the past three decades (1991–2000, 2001–2010, and 2011–2020). A Mann–Kendall test, followed by Sen's slope analysis, was conducted to evaluate the spatial and temporal patterns of the most common indicators of climate change. Several drought indices were used to monitor and detect drought occurrences in the study area. It was found that from 2011 to 2020, most stations recorded negative rainfall trends, with no significant trends overall. Northern Australia saw slight rainfall increases, the northeast had notable increases, and the southwest and southeast showed drying trends. Minimum temperatures increased in northern and northeastern Australia, with significant positive trends at some stations. Maximum temperatures showed slight increases, with several stations having significant positive trends. PET values were higher at stations with low rainfall and high temperatures. The results of the drought indices show a normal drought cycle that recurs every ten years. Additionally, the driest conditions were recorded in the most recent decade, with extreme dryness noted in 2011–2020 compared to previous decades, highlighting the complex and changing climate patterns in Australia with varying regional impacts. [ABSTRACT FROM AUTHOR]

Hydrological droughts occur as a result of various hydrometeorological conditions, such as precipitation deficits, reduced snow cover, and high evapotranspiration. Droughts caused by precipitation deficits and occurring during warm seasons are usually longer in duration. This important observation raises the question that climate change associated with global warming may increase drought conditions. Consequently, it is important to understand changes in the processes leading to dry periods in order to predict potential changes in the future. This study is a scientific analysis of the impact of climate change on drought conditions in the Zhaiyk–Caspian, Tobyl–Torgai, Yesil, and Nura–Sarysu water management basins using the standardized precipitation index (SPI) and streamflow drought index (SDI). The analysis methods include the collection of hydrometeorological data for the entire observation period up to and including 2021 and the calculation of drought indices to assess their intensity and duration. The results of this study indicate an increase in the intensity and frequency of drought periods in the areas under consideration, which is associated with changes in climatic conditions. The identified trends have serious implications for agriculture, ecological balance, and water resources. The conclusions of this scientific study can be useful for the development of climate change adaptation strategies and the sustainable management of natural resources in the regions under consideration. [ABSTRACT FROM AUTHOR]

Global climate change has increased the events and intensity of extreme events. Tamil Nadu is located in the southern peninsula region of India, which has benefitted both from the south-west monsoon (SWM) and the north-east monsoon (NEM). Although variations in the monsoon pattern increased events of drought. The IMD gridded (1991–2020) and projected global climate model data (EC_Earth3_Veg_LR) were used to anticipate the drought-risk-prone zones over Tamil Nadu by using the standardized precipitation index. During both the SSP245 and SSP585 scenarios of the near (2021–2050) and mid (2051–2080) century periods, an increase in rainfall amount was expected with a high coefficient of variation (CV) across the region. The CVs of the future SWM and NEM seasons were expected to range from 20% to 60% and 25% to 50% respectively. Increased variability often leads to an increase in number of drought events. Regardless of scenarios, the southern zone was expected to experience more drought events, followed by the northwestern zone during SWM. Drought events during the NEM were expected to increase in northeastern zone districts. Changes in cropping patterns and policymaking for future risk-prone areas were undertaken as a proactive response to mitigate potential agricultural challenges. [ABSTRACT FROM AUTHOR]

This study analyzes drought conditions in the Aegean region using monthly precipitation data from nine stations between 1972 and 2020. The Standardized Precipitation Index (SPI) was calculated for 1-, 3-, 6-, 9-, and 12-month periods to evaluate drought conditions at different timescales and station-specific conditions. The results indicate that short-term droughts are more frequent but shorter in duration, while longer periods exhibit fewer but more prolonged droughts. The relative frequency of drought across all periods ranges between 9% and 27%. The İzmir and Denizli stations were highlighted due to their representation of coastal and inner regions, respectively. The findings show that coastal stations, like İzmir, experience more frequent wet years compared to inner stations like Denizli, which have more dry years. Time series linear autoregressive (AR) models, using SPI-12 data, were developed to represent long-term drought trends and forecasts. The best-fitting models were determined using AIC, AICC, FPE, and Var(e) criteria, with AR(2) generally being the most suitable, except for Denizli. This integrated analysis of SPI and AR models provides a robust basis for understanding regional precipitation regimes and predicting future droughts, aiding in the development of effective drought mitigation strategies and water resource management. Future research is anticipated to extend this analysis to encompass all of Turkey and explore various time series models' applicability. [ABSTRACT FROM AUTHOR]

This study attempts to develop a composite index by integrating meteorological, hydrological and agricultural droughts over semi-arid Banas River basin, Rajasthan, India. To affect this, the standardized precipitation index (SPI), streamflow drought index (SDI), and vegetation condition index (VCI) have been used at 1-, 3-, 5-, 9- and 12-month time scales using station and remote sensing-based data for the period 2000 to 2020. To identify the occurrence of above-stated droughts and most vulnerable drought period at different time scales (1-, 3-, 5-, 9- and 12-month) regarding SPI, SDI and VCI has been validated with foodgrains produced and occurrence of historical drought years. This validation has been found significant with SPI-3 (r = − 0.81), SDI-3 (r = − 0.78) and VCI-5 (r = − 0.80) time scales. Subsequently, these time scales have been coalesced using weights obtained from principal component analysis (PCA) to develop the composite drought index (CDI). The annual CDI developed this way has been further validated with foodgrains produced and occurrence of historical drought years. The results of CDI demonstrate the maximum area under mild drought (73 percent) followed by moderate (21 percent) and severe (4 percent), whereas minuscule area has been detected under wet conditions (2 percent). Finally, this study suggests that individual drought types (meteorological, hydrological, agricultural) do not appropriately arrest the drought severity, hence, the usage of multiple droughts based composite index can be more realistic for effective drought assessment and monitoring in hydrologic systems. [ABSTRACT FROM AUTHOR]

Copyright of Arid Zone Research / Ganhanqu Yanjiu is the property of Arid Zone Research 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.)

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

Drought is the most complex natural hazard that can occur over large spatial scales and during long time periods. It affects more people than any other natural hazard, particularly in areas with a dry climate, such as the semiarid region of the Brazilian Northeast (NEB), which is the world's most populated dry area. In this work, we analyzed trends and the spatial distribution of drought characteristics (frequency, affected area, and intensity) based on the Standardized Precipitation Index (SPI) on annual (SPI-12) and seasonal (SPI-3) scales. The study used monthly precipitation data recorded between 1962 and 2012 at 133 meteorological stations in Pernambuco State, Brazil, which is located in the eastern part of the NEB and has more than 80% of its territory characterized by a semiarid climate. The regions of Sertão, Agreste, and Zona da Mata of Pernambuco were considered for comparison. The Mann–Kendall and Sen's slope tests were used to detect the trend and determine its magnitude, respectively. The results indicated that annual droughts in the state of Pernambuco became more frequent from the 1990s onwards, with summer having the greatest spatial coverage, followed by winter, autumn, and spring. Sertão presented a greater number of stations with a significant positive trend in drought frequency. Regarding the drought-affected area, global events occurred in a greater number of years on an annual scale and during the summer. Trend analysis pointed to an increase in areas with drought events on both scales. As for the drought intensity, the entire state of Pernambuco experienced drought events with high intensity during the autumn. The relationship between drought characteristics indicated an increase in the affected area as the result of an increase in drought intensity. [ABSTRACT FROM AUTHOR]

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

Study region: The Juam Dam area in Suncheon, Jeollanam-do, South Korea Study focus: This study aims to analyze drought propagation characteristics using time-lagged correlation analysis based on daily drought index, determining the lag time from meteorological drought to propagation into hydrological drought. The target period for correlation analysis is the onset date of the dry spell preceding the occurrence of hydrological drought and the termination date of the dry spell following the termination of the drought for each drought event. The Standardized Precipitation Index (SPI) at various time-scales for meteorological drought and the Standardized Reservoir Supply Index (SRSI) for hydrological drought were applied. New hydrological insights for the region: This study presents the objectivity and accuracy of drought onset and termination date for drought propagation analysis through daily lagged correlation analysis. Through ROC analysis, SPI90, SPI180, and SPI365 are shown to increase by an average of 16.0 %, 8.8 %, and 6.0 %, respectively. From 1993–2023, long-term hydrological droughts lasting 2 years occurred 5 times, with a maximum duration of 408 days, magnitude −629, and severity −1.76. The daily lag between the multi-scale SPIs and SRSI of individual drought events presents the possibility of predicting hydrological drought through multiple regression analysis. This research provides insights for improving hydrological drought monitoring, prediction, and response strategies through results of individual propagation characteristics of drought events.

Study region: Taiwan Study focus: This study examines the relationship between typhoon parameters and droughts in Taiwan, particularly following the severe drought of 2020–2021. Using tropical cyclone best-track and satellite-based precipitation datasets from 1981 to 2020, we analyzed anomalies, correlation matrices, and wavelet coherence. Seasonal variations and long-term trends were also detected. New hydrological insights for the region: We found a positive correlation between typhoon characteristics (count, duration, length, wind speed) in Taiwan and drought occurrence and severity, especially over 2–4 year periods. Conversely, negative relationships were observed between typhoon duration and length in the Western North Pacific (WNP) and drought indices in Taiwan, influenced by large-scale atmospheric patterns. Typhoon duration and length in the WNP had a greater impact on Taiwan's drought than typhoon quantity, showing significant coherence with long-term drought over multi-year to decadal timescales. Seasonally, drought intensity peaked in central and southeastern Taiwan during late winter and early spring when typhoons were absent, in contrast to the rainy summer typhoon season. Spatially, increasing drought trends were identified in central and southern Taiwan, while northern regions exhibited decreasing dryness, potentially linked to the concentration of typhoon landing points in the north. This study underscores the complex relationships between drought severity in Taiwan and typhoon behavior in both the vicinity of Taiwan and the WNP.

Understanding the relationship between hydrological and meteorological drought in drought-prone regions is critical for proper reservoir management. This study presents a novel multiscale framework for investigating the associations between hydrological and meteorological drought based on the Time-Dependent Intrinsic Correlation (TDIC) method. Firstly, the characteristics of short, medium and long term standardized precipitation index (SPI) and the standardized runoff index (SRI) of the Wadi Mina basin (Algeria) have been analyzed based on data from 6 rainfall and hydrometric stations. Then an Improved Complete Ensemble Empirical Mode Decomposition with adaptive noise (ICEEMDAN) method is used to decompose the most correlated SPI and SRI series to different scales. A stronger association between the two types of droughts is evident in the low-frequency trend component regardless of the station, but their evolution pattern does not remain the same. Subsequently, a TDIC based running correlation analysis is performed between the modes to examine the SPI–SRI associations over the time domain and across the time scales. TDIC analysis has proven the dynamic behavior in the SPI–SRI associations bearing frequent alterations in nature and strength across the process scales and along the time domain. In general, at the intra-annual scales the SPI–SRI correlations are mostly weak positive with localized alterations to negative along the time domain, whereas the relationship is dominantly strong positive and long range at inter-annual scales up to 4 years. This dynamic behavior in the SPI–SRI association and the evolution pattern of trend decipher that the rainfall processes are not directly transferred to streamflow drought, but it also gets controlled by many other local meteorological processes. [ABSTRACT FROM AUTHOR]

The impacts of climate change increasingly show themselves in many forms in our everyday lives such as heatwaves and droughts. Drought is one of the critical events today for increasing drought frequency. This study focuses on meteorological drought because it directly affects other drought types. Hence, this study focuses on how the future drought conditions will vary under climate change effects in the Mediterranean basin (Turkey). In doing so, this study utilizes precipitation data from three General Circulation Models (GCMs) and three Regional Circulation Models (RCMs). The GCMs are CNRM-CM6, GFDL-CM4, and MPI-ESM1, while the RCMs are (RCA4)-CNRM-CM5, (Reg CM4)-GFDL-ESM2M, and (RCA4)-MPI-ESM-MR. Mitigating biases of the climate models, this study utilizes four statistical downscaling methods (SD), linear scaling (LS), local intensity scaling (LOCI), power transformation (PT), and distribution mapping (DM). Here, the study has two purposes. The main aim of the paper here is to compare the performance of SD methods in improving the representation of observed climate variables in climate models. In addition, the study shows how different methods will affect the spatial drought distribution in the area under the SSP2 4.5 and SSP5 8.5 scenarios. Consequently, the study uses the standardized precipitation index (SPI) and Z-score index (ZSI) to quantify future drought conditions and reaches the following results. The study reveals that mild drought conditions are prevalent in the basin for future periods, and drought indices go down to − 0.55. The study also shows that different SD methods affect the results obtained by each climate model diversely. For example, while the LS method causes the most drought conditions on the results based on CNRM-CM5 and CNRM-CM6, the DM method has a similar impact on outcomes based on GFDL-CM4 and GFDL-ESM2M and causes the most drought conditions. [ABSTRACT FROM AUTHOR]

Climate change has become a prominent concern in recent years, with extensive research revealing a range of adverse impacts linked to ongoing global warming. The non-linear dynamics of precipitation represent a significant implication, often resulting in region-specific droughts and flooding events. Droughts, in particular, lead to numerous negative consequences. The drought index quantifies drought characteristics. Recognizing the unique attributes of each region, there is a need to adopt a suitable drought index for accurate regional drought analysis. This study compared multiple time scale drought indices-SPI, EDI, and MCZI-from 1980 to 2020, determining the 6-month SPI as the most consistent and justifiable index. To enhance drought forecasting using the identified SPI-6 index, deep learning architectures such as RNN, GRU, and LSTM were employed. Model performance was evaluated using metrics including correlation (r), root-mean-square error (RMSE), mean absolute error (MAE), and Nash-Sutcliffe efficiency (E). Notably, LSTM demonstrated superior performance, exhibiting the lowest RMSE (0.61) and MAE (0.4). Among the three models (LSTM, RNN, and GRU), LSTM achieved the highest correlation coefficient (r) of 0.85 during testing and validation phases. LSTM outperformed RNN and GRU across all datasets, exhibiting lower RMSE and MAE values, indicating higher prediction accuracy with fewer errors. These findings suggest that combining 6-month SPI with LSTM can be employed as a new, reliable integrated approach. Additionally, this study examined drought occurrences between 1980 and 2020 with a specified 6-month SPI. [ABSTRACT FROM AUTHOR]

Copyright of Tikrit Journal of Engineering 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.)

Understanding drought evolution and its driving factors is crucial for effective water resource management and forecasting. This study enhances the analysis of drought probability by constructing bivariate distributions, providing a more realistic perspective than single-characteristic approaches. Additionally, a meteorological drought migration model is established to explore spatiotemporal paths and related characteristics of major drought events in the Choushui River alluvial fan. The results reveal a significant increase in the probability of southward-moving drought events after 1981. Before 1981, drought paths were diverse, while after 1981, these paths became remarkably similar, following a trajectory from north to south. This is primarily attributed to the higher rainfall in the northern region of the Choushui River alluvial fan from February to April, leading to a consistent southward movement of drought centroids. This study proposes that climate change is a primary factor influencing changes in the spatiotemporal paths of drought. It implies that changes in rainfall patterns and climate conditions can be discerned through the meteorological drought migration model. As a result, it provides the potential for simplifying drought-monitoring methods. These research findings provide further insight into the dynamic process of drought in the Choushui River alluvial fan and serve as valuable references for future water resource management. [ABSTRACT FROM AUTHOR]

Several indices based on both single and multiple hydro-climatic factors have been established to monitor and evaluate droughts. However, no single drought indicator can accurately describe the density of the overall drought due to constraints caused by a number of factors. Therefore, combining many drought indices more properly into one instrument to better depict and manage droughts would be beneficial. This study differentiated and established two merging methods through Triple Collocation (TC) and Multi-scalar Weighted Amalgamated Drought Index (MWADI). Therefore, three individual standardized drought indices (SDIs), including the Standardized Precipitation Index (SPI), Standardized Precipitation Temperature Index (SPTI), and Standardized Precipitation Evaporation Index (SPEI), were used to fabricate a composite Merged Drought Index (MDI) based on TC and MWADI across six meteorological stations of Pakistan from 1971 to 2017. Their performance was analyzed through correlation, Taylor diagram, Kling Gupta Efficiency Criteria (KGE), Root Mean Square Error (RMSE), and Sen's slope. In addition, the performance of TC is also evaluated through Correlation Coefficient (CC's) and Fixed Residual Mean Square Error (FRMSE) through Extended Triple Collocation (ETC). The exploratory analysis reveals that individual indices SPI and SPTI are closely related to both MDI relative to SPEI across the study area. However, TC-based MDI has a stronger correlation with the individual indices than MWADI. Sen's slope indicates a similar trend with a slight divergence in magnitude from both approaches. The estimated values of KGE suggest TC exhibits better efficiency than MWADI. RMSE shows a minute difference under both approaches. Overall, TC executes well with individual series in contrast with MWADI. Furthermore, the performance of TC is also evaluated through Correlation Coefficients (CC's) and Fixed RMSE (FRMSE), where it shows high sensitivity. However, implicit presumptions place restrictions on the accuracy of TC comparison. MDI has the potential to be a useful tool for collaborating and making decisions concerning multiple kinds of droughts. [ABSTRACT FROM AUTHOR]

The changing climate has a serious bearing on agriculture, particularly livestock production in Botswana. Therefore, studying the relationship between climate and livestock, which at present is largely missing, is necessary for the proper formulation of government policy and interventions. This is critical in promoting the adoption of relevant mitigation strategies by farmers, thereby increasing resilience. The aim of this research is to establish associations between climate variability and livestock production in Botswana at the national level. The paper employs time series data from 1970 to 2020 and the Vector Autoregression with Exogenous Variables (VARX) model for statistical analysis. The trend shows that both cattle and goat populations are decreasing. The VARX model results reveal that cattle and goat populations are negatively associated with increasing maximum temperatures. Cattle respond negatively to increased minimum temperatures as well, while goats tend to respond positively, implying that livestock species react differently to climatic conditions due to their distinct features. The results of the roots of the companion matrix for cattle and goat production meet the stability condition as all the eigenvalues lie inside the unit circle. The study recommends further intervention by the government to deal with increasing temperatures, thereby addressing the dwindling populations of goats and cattle, which have significant contributions to the household economies of smallholders and the national economy, respectively. [ABSTRACT FROM AUTHOR]

Natural springs, recognized as biodiversity hotspots and keystone ecosystems, exert positive ecological influences beyond their immediate extent, particularly in dryland environments. The water feeding these springs, largely governed by natural climatic conditions, is susceptible to anthropogenic impacts. The objective of this study was to determine the factors that cause fluctuations in water availability to springs of the hyper-arid Arava Valley (Israel/Jordan). Using the Standard Precipitation Index, we statistically classified the historical record of yearly rainfall for the past four decades into clusters of dry and wet sub-periods. We assessed changes in vegetation cover around the springs using the Landsat-derived Normalized Difference Vegetation Index (NDVI) for each sub-period. To assess the anthropogenic effects, we examined the correlations between vegetation cover, water extraction from the aquifer, and the status of adjacent agricultural plots that share a hydrological connection with the springs. Our findings revealed fluctuations between wet and dry sub-periods over the last four decades. We observed high responsiveness of vegetation cover around the springs to these fluctuating sub-periods. Of the 25 studied springs, 12 were directly influenced by anthropogenic factors—7 experienced a decline in vegetation, which we attributed to water extraction from the aquifers, while vegetation increase in 5 springs was attributed to water seepage from agricultural areas upstream. In conclusion, addressing vital habitats such as natural springs in arid drylands requires a holistic approach that integrates long-term climatic, ecological, and anthropogenic observations. [ABSTRACT FROM AUTHOR]

West Bengal is a key producer of raw jute fiber in the country. Identifying and managing dry spells during the jute growing period is crucial, necessitating contingency crop planning for enhanced productivity. Keeping this view in mind, standardized precipitation index (SPI) was calculated over five locations, representing five different districts of southern West Bengal. These locations are Barrackpore (North 24 Parganas District), Panagarh (Burdwan District), Bagati (Hooghly District), Krishnanagar (Nadia District) and Uluberia (Howrah District). This rainfall dependent dryness index (SPI) was calculated in 1 month and 3 months interval to identify short term dryness as well as mid-term dryness, applicable for seasonal crops. The trend analysis of the SPI values indicated that North 24 Parganas and Nadia experienced increased dryness during vegetative phase of Jute. Nadia district showed a significant increase in both short term and long-term dryness. The yield reduction index is well correlated with SPI values in all the study locations except Howrah. Arrangement of irrigation during the early stages of Jute can help the crop to cope up with the break of monsoon in this region. [ABSTRACT FROM AUTHOR]

Jatiluhur dam, located in West Java province, is one of the most extensive reservoirs in Indonesia and is used to secure the water supply for 240,000 ha of rice fields to prevent agricultural drought in the downstream Citarum watershed. During the last two decades, several areas in the downstream Citarum watershed have been experiencing agricultural drought following meteorological drought events. This study aimed to examine the spatiotemporal patterns of agricultural drought and their correlation with meteorological drought occurrence by utilizing a remote sensing image processing technique. The Normalized Difference Drought Index (NDDI) and Standardized Precipitation Index (SPI) derived from MODIS and CHIRPS data were used to estimate agricultural and meteorological drought events, respectively. The results showed that the magnitude of agricultural drought in the downstream Citarum watershed was varied and related to earlier meteorological drought. The agricultural drought mainly occurred after a meteorological event with the magnitude mostly related to the magnitude of the ONI and DMI. A strong El Nino and positive IOD will cause a higher magnitude of agricultural drought. The time lag between the two drought occurrences was found to be related to the distance of the paddy fields from the main irrigation network and the proximity of the main irrigation network to the Jatiluhur Dam spillway.

This paper presents a comparative analysis of machine learning (ML) models for predicting drought conditions using the Standardized Precipitation Index (SPI) for two distinct stations, one in Shiraz, Iran and one in Tridolino, Italy. Four ML models, including Artificial Neural Network (ANN), Multiple Linear Regression, K-Nearest Neighbors, and XGBoost Regressor, were employed to forecast multi-scale SPI values (for 6-, 9-, 12-, and 24-month) considering various lag times. Results indicated that the ML model with the most robust performance varied depending on station and SPI duration. Furthermore, ANN demonstrated robust performance for SPI estimations at Shiraz station, whereas no single model consistently outperformed the others for Tridolino station. These findings were further validated through the confidence percentage analysis performed on all ML models in this study. Across all scenarios, longer SPI durations generally yielded better model performance. Additionally, for Shiraz station, optimal lag times varied by SPI duration: 6 months for the 6- and 9-month SPI, 4 months for the 12-month SPI, and 2 months for the 24-month SPI. For Tridolino station, on the other hand, no definitive optimal lag time was identified. These findings contribute to our understanding of predicting drought indicators and supporting effective water resource management and climate change adaptation efforts.

Drought is a widespread natural hazard resulting from an extended period of reduced rainfall, with significant socioeconomic and ecological consequences. Drought severity can impact food security globally due to its high spatial and temporal coverage. The primary objective of this paper consists of a comparative spatiotemporal analysis of environmental extremes (drought/wetness) through the estimation of a twelve-month Standardized Precipitation Index (SPI12) between three distinct vulnerable agricultural regions in the Mediterranean basin (i.e., Spain, Lebanon and Tunisia), under a climate change environment in the last 38 years (1982–2020). The added value of this paper lies in the simultaneous estimation of temporal and spatial variability of drought and wetness periodic events, paying special attention to the geographical patterns of these extremes both in annual and interannual (seasonal) time scales. The results indicated that Spain and Tunisia (western Mediterranean) exhibit similar patterns over the studied period, while Lebanon demonstrates contrasting trends. Comparing the two extreme dry hydrological years, the Spanish study area faced the highest drought intensity, areal extent and duration (SPI12 = −1.18; −1.84; 28–78%; 9–12 months), followed by the Lebanese (SPI12 = −1.28; −1.39; 37–50%; 7–12 months) and the Tunisian ones (SPI12 = −1.05; −1.08; 10–34%; 8 months). Concerning the wettest hydrological years, the Lebanese study domain has recorded the highest SPI12 values, areal extent and duration (SPI12 = 1.58; 2.28; 66–83%; 8–11 months), followed by the Tunisian (SPI12 = 1.55; 1.79; 49–73%; 7–10 months) and Spanish one (SPI12 = 1.07; 1.99; 21–73%; 4–11 months). The periodicity of drought/wetness episodes is about 20 years in Spanish area and 10 years in the Lebanese area (for drought events), whereas there seems no periodicity in the Tunisian one. Understanding the spatial distribution of drought is crucial for targeted mitigation strategies in high-risk areas, potentially avoiding broad, resource-intensive measures across entire regions.

The performance of the Standardized Precipitation Index (SPI) is affected by the choice of an incorrect probability distribution function, which can skew the values of the index, exaggerating or minimizing the severity of drought. This study aims to test data fitability of ten statistical distribution functions (gamma, weibull, exponential, lognormal, gumbel, cauchy, logistic, chi-square, burr, pareto) for SPI computation at time scales (TSs) of 3, 6, 9, 12, 15, 18, 21 and 24 months, and to quantify the errors made if the gamma function were used by default as is the case in general. Monthly precipitation data collected at 24 meteorological stations distributed in the five Agro-Ecological Zones (AEZs) of Cameroon were used for the period 1951-2005. The parameters of the distribution functions were estimated with the Maximum Likelihood (ML) method, and the Kolmogorov-Smirnov (K-S) test was applied to assess how well these functions fit the data. The results show that the logistic and burr distributions provide for several stations of the five AEZs the best data fits. A comparative study between the SPIs from the appropriate distribution and the gamma functions shows a significant qualitative and quantitative difference in several stations and the root mean square error (RMSE) increases with the TS and with the severity of drought. [ABSTRACT FROM AUTHOR]

The objective of this research is to select the best orbital sensor for rainfall estimates (monthly and annual scales) and to analyze the frequency and magnitude of extreme rainfall events and their trends and disruptions based on the use of satellite rainfall product data for the Cananeia–Iguape Coastal System (CICS). Data from four satellite rainfall products were used to identify the correspondence with seven points on the surface of the study area. Statistical metrics were used to identify the best satellite rainfall product. After identifying the sensor with the best performance in estimating orbital precipitation, extreme events were identified by the Standardized Precipitation Index (SPI) on a one-month (SPI-1), three-month (SPI-3), and twelve-month (SPI-12) scale. Trend and rupture detection in the time series were performed using different statistical techniques (Mann–Kendall, Pettitt, Standard Normal Homogeneity Test, or Buishand test). Among the satellite rainfall products, CHIRPS had the best measurements for the analyzed points on the surface. The year 1983 was characterized as very rainy, also marked by the occurrence of El Niño, and was marked by the rupture of the rains at all points (IDs 1, 2, 3, 4, 5, 6, and 7) analyzed in the month of June. The decrease in monthly rainfall was more significant in the months of February (at points IDs 1, 2, 3, 5, and 7) and April (IDs 1, 3, 5, and 7). Decreased rainfall may cause CICS mangrove shrinkage. These results showed the importance of studying rainfall in an area with mangroves in order to understand the dynamics of vegetation in the face of climate change. [ABSTRACT FROM AUTHOR]

A drought-flood abrupt alternation (DFAA) event is an extreme combination of an overlapping drought and flood resulting in adverse impacts on ecosystems and human society. As precipitation variability increases due to climate change, the occurrence and magnitude of DFAA events are expected to increase. Using daily precipitation data for 1966–2018, we calculated the daily standardized precipitation index for middle-sized watersheds and evaluated the severity of DFAA events in South Korea. The severity of DFAA events was evaluated in two ways: the severity of whole period (SWP) represents the total magnitude of DFAA event, and the severity of transition period (STP) indicates the instantaneous severity of DFAA event during the transition from drought to flood. The severities were also graded by the quantiles to identify risk regions, and we used precipitation concentration degree (PCD) and precipitation concentration period (PCP) to analyze the relationship between DFAA events and precipitation trends in South Korea. The results showed that the STP in the Yeongsan River basin and SWP in the Han River and the Nakdong River basin were higher than in the other basins. In addition, considering patterns and trends of the PCF and PCP in South Korea, this study found that the frequency and severity of DFAA events were high during flood seasons when precipitation was concentrated. [ABSTRACT FROM AUTHOR]

A drought is a prolonged natural disaster with numerous economic, social, and environmental consequences; it occurs when the natural water supply in a given region falls below normal levels. Precautions must be taken to mitigate the negative impacts that drought can cause. Drought probabilities can be estimated by carefully analyzing variables such as precipitation, river flow, and soil moisture with the help of various indices. In the literature, many studies have been conducted to estimate drought indices over time. In this study, drought forecasts were made for the Seyhan and Ceyhan Basins in 3-, 4-, 6-, and 12-month periods with the Standardized Precipitation Index (SPI) using precipitation data between January 1989 and July 2020. The success rates of the forecasts made in the models created with the Random Forest (RF) Algorithm, Support Vector Machine (SVM), and Artificial Neural Network (ANN) machine learning methods were statistically compared. The SVM drought forecasting model was performed in 3-month forecasts in the study. Among the machine learning methods, the ANN method was more successful than the other methods in terms of performance in 4, 6, and 12-month drought forecasts. [ABSTRACT FROM AUTHOR]

Drought is one of the most critical environmental hazards for the viability and productive development of crops, especially in a climate change environment. To this end, drought assessment is a process of paramount importance to make vulnerable agricultural regions more resilient. The primary aim of this paper is an integrated drought assessment through time and space in one of the most susceptible (in terms of water availability limitations) and agriculturally productive regions in Greece and the Mediterranean, namely, the Thessaly region. Supplementary objectives consist of the determination of the two most extreme years in terms of drought and wetness, so that we may reveal any potential climatological cycles/patterns from 1981 to 2020. Additionally, the methodology includes the annual and seasonal analysis using one of the most widely used drought indices, namely, the Standardized Precipitation Index (SPI), so that consistent measurements are available across a large study area, avoiding the possible scarcity/deficiency of data coming from a sparse land weather network. The innovative element of this paper is the integrated spatiotemporal drought assessment in multiple time scales through the estimation of the SPI making use of remotely sensed data, such as CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data). The outcomes highlight that the study area faced two severe years of drought in 1988 and 1989, which led to moderate and extreme drought conditions, respectively. In contrast, extremely wet conditions were observed in 2002–2003, whereas 2009–2010 experienced moderately wet conditions. The central and western part of the region tends to suffer the most in terms of drought severity, especially at the most extreme years. The validity of the results has been confirmed by the adoption of R2 where the index is approaching 0.67 despite the large size of the pixels (5 × 5 km). In this context, the mapping of spatial and seasonal variability across the study area permits more targeted measures (e.g., precision farming) instead of horizontal policies. [ABSTRACT FROM AUTHOR]

Climate change modifies monsoon rainfall patterns, leading to significant impacts on future meteorological droughts. This study investigated the spatiotemporal pattern of future precipitation, drought features, and the dynamic mechanisms governing their evolution induced by teleconnected atmospheric circulation in the Haihe River Basin, situated in the north China monsoon region. The investigation was conducted under SSP1‐2.6, SSP2‐4.5, and SSP5‐8.5 scenarios of CMIP6, utilizing the Standardized Precipitation Index and an improved run theory at seasonal and annual scales. The findings revealed that high‐emission scenarios cause a wetter climate trend, particularly in autumn and winter, related to the attenuation of the East Asian Trough from climate warming. The uneven intra‐annual precipitation patterns within the basin, induced by the Asian monsoon climate, are expected to improve in the far future, particularly under the SSP2‐4.5 scenarios. The drought frequency, intensity, and duration of future meteorological drought are expected to improve compared to historical conditions under all three scenarios. SSP2‐4.5 shows the shortest duration and lightest intensity of annual droughts. Extreme droughts are primarily driven by strong subsidence with low‐level moisture divergence. However, in the far future, the eastward shift of the western Pacific subtropical high results in reducing moisture transport from the western Pacific, leading to increasing contribution of advection term to atmospheric drying, particularly under the SSP1‐2.6. In winter, the increase in radiative forcing leads to enhanced evapotranspiration from the Eurasian continent, providing additional moisture and alleviating drought, especially in the far future under the SSP1‐2.6 and SSP2‐4.5 scenarios. Plain Language Summary: Climate change is causing changes in rainfall patterns during monsoon season, which will affect droughts in the future. This study looked at how rainfall, wet and dry trends, and droughts will change in the Haihe River Basin in north China from 2025 to 2099 under different climate scenarios. The study found that models predicting high greenhouse gas emissions show more rainfall and a trend toward wetter conditions, particularly in autumn and winter. The scenario with moderate emissions shows the shortest and lightest droughts. The study also found that droughts are caused by subsidence and low‐level moisture divergence, and in the future, the reduction of moisture transport from the western Pacific will contribute to more atmospheric drying. Finally, during winter, increased radiative forcing causes significant moisture supply from evapotranspiration originating from the Eurasian continent during the drought period. Overall, this study shows how climate change will affect droughts in the future and provides important information for policymakers and stakeholders to plan for the future. Key Points: High radiative forcing cause attenuation of East Asia Trough and increased precipitation in north China monsoon regionSSP2‐4.5 scenario shows the shortest duration and lightest intensity of annual droughtsIn far future, eastward shift of western Pacific subtropical high enhances advection's role in atmospheric drying, especially in SSP1‐2.6 [ABSTRACT FROM AUTHOR]

The relationship between biodiversity and the stability of ecosystem functioning over time has been widely studied. The current global context has refloated this topic for biodiversity's role in buffering the effects of different disturbances. In general, the results of these studies show that ecosystem functioning is more stable over time in more diverse systems. However, these results are derived from empirical research on small‐scale studies, where species and disturbances conditions are manipulated. In this work, we used climate and floristic information data obtained from surveys over an extended period on Flooding Pampa grasslands (Argentina) with a remotely sensed indicator of the stability of net primary productivity at a regional scale over a broad temporal range to evaluate the relationship between species diversity and the stability of ecosystem functioning under different water conditions. We found a close correlation between Normalized Difference Vegetation Index responses of natural grasslands and climate variability in the study area. Besides, grasslands with higher species richness and diversity showed greater stability in ecosystem functioning at different water conditions. The results obtained could be relevant in natural resource management for the close relationship between diversity–stability in a local and regional productive context characterized by a simplified landscape of space and time. [ABSTRACT FROM AUTHOR]

This study aims to assess the performance of three drought indices for drought monitoring to study the characteristics of meteorological drought in Iraq, which are the Standard Precipitation Index (SPI), the China-Z Index (CZI), and the Modified China-Z Index (MCZI) on 1-, 3-, 6-, 9-, and 12-month timescales, using monthly precipitation data from 1980 to 2021. These indices were used to analyze the spatiotemporal dynamics of droughts using the rainfall data collected from five meteorological stations scattered across the four climatic zones as classified by Köppen in Iraq. According to the study, the Pearson correlation coefficient (r) values among the indices increase with increasing time scale and give similar drought characteristics at the 9-month and 12-month time scales. SPI generally indicated the drought event earlier and with more severe characteristics than the other indices. MCZI showed wetness earlier than the other two indices, and it also described the drought categories similarly to SPI and CZI, especially in the northern regions of Iraq, but it was less official in describing it as we headed down to the south. While CZI described the drought as having less severity than SPI, it frequently gives the same classifications of drought as SPI. CZI could be used as a good meteorological drought monitor, depending on the month, the length of the drought duration, and the climatic conditions of the region. It might be an alternative to the SPI, which needs long rainfall records and has a complicated structure. SPI and CZI can be considered good indices for describing drought in all regions of Iraq. The result shows that 1980, 1993, 1995, and 2019 were the most wet years; 2007–2010 was the most severe drought event; and Iraq's climate was normal to moderate drought during the studied period for all considered stations.

The environment of the Mu Us Sandy Land, northern Shaanxi, is fragile, so the temporal and spatial evolution of drought can provide a reference for ecological construction and agricultural production. Based on the daily precipitation data of five meteorological stations from 1967 to 2020, the spatial and temporal evolution characteristics of drought in the Mu Us Sandy Land of northern Shaanxi were analyzed using the methods Standardized Precipitation Index (SPI) and run-length theory. The results show that 1) the smaller the time scale, the higher the sensitivity of the SPI to primary precipitation. 2) The annual, summer, autumn, and winter SPI showed an upward and a wetting trend, and the fastest wetting speed is observed in summer, while spring showed an increasing trend of drought. 3) In the past 54 years, the duration, degree, and intensity of drought events at SPI-3 and SPI-12 scales in the Mu Us Sandy Land of northern Shaanxi showed an insignificant decreasing trend, but the decreasing rate at the SPI-12 scale was faster than that at the SPI-3 scale. The serious periods of drought are November 2018 to May 2019 and April 1999 to July 2021. 4) The duration of drought events at two timescales in each region showed a decreasing trend. The longest durations of drought were in Yulin and Jingbian; Shenmu and Dingbian are the regions with the fastest reduction rate of drought degree at the two scales. Hengshan shows rapid reduction of drought intensity at the two scales and also the region with large average drought intensity. These results are helpful in understanding and describing drought events for drought risk management under the condition of global warming.