3-month Standardized Precipitation Index Research Articles

Assessing decadal- to centennial-scale nonstationary variability in meteorological drought trends

Abstract. There are indications that the reference climatology underlying meteorological drought has shown nonstationarity at seasonal, decadal, and centennial timescales, impacting the calculation of drought indices and potentially having ecological and economic consequences. Analyzing these trends in meteorological drought climatology beyond 100 years, a time frame which exceeds the available period of observation data, contributes to a better understanding of the nonstationary changes, ultimately determining whether they are within the range of natural variability or outside this range. To accomplish this, our study introduces a novel approach to integrate unevenly scaled tree-ring proxy data from the North American Seasonal Precipitation Atlas (NASPA) with instrumental precipitation datasets by first temporally downscaling the proxy data to produce a regular time series and then modeling climate nonstationarity while simultaneously correcting model-induced bias. This new modeling approach was applied to 14 sites across the continental United States using the 3-month standardized precipitation index (SPI) as a basis. The findings showed that certain locations have experienced recent rapid shifts towards drier or wetter conditions during the instrumental period compared to the past 1000 years, with drying trends generally found in the west and wetting trends in the east. This study also found that seasonal shifts have occurred in some regions recently, with seasonality changes most notable for southern gauges. We expect that our new approach provides a foundation for incorporating various datasets to examine nonstationary variability in long-term precipitation climatology and to confirm the spatial patterns noted here in greater detail.

Machine learning algorithms for the prediction of drought conditions in the Wami River sub-catchment, Tanzania

Study regionThis study refers to the Wami river sub-catchments in Eastern Tanzania. Study FocusThe five-machine learning (ML) algorithms, including long short-term memory (LSTM), multivariate adaptive regression spline (MARS), support vector machine (SVM), extreme learning machine (ELM), and M5 Tree, were used to predict the most widely used drought index, the standard precipitation index (SPI), at six and nine months. Algorithms were established using monthly rainfall data for the period from 1990 to 2022 at five meteorological stations distributed across the Wami River sub-catchment: Barega, Dakawa, Dodoma, Kongwa, and Mandera stations. New hydrological insights for the region. The predicted results of all five ML algorithms were evaluated using several statistical metrics, including Pearson’s correlation coefficient (R), mean absolute error (MAE), root mean square error (RMSE), and Nash Sutcliffe efficiency (NSE). The prediction results revealed that LSTM perform better in predicting drought conditions using SPI6 (6-month SPI) and SPI9 (9-month SPI) with the highest NSE of 0.99 in all five stations, and R of 0.99 in four stations except at Kongwa station, where R range from 0.75 to 0.99. These prediction results will aid decision-makers and planners to develop a drought monitoring and drought early warning system in order to strengthen the governance and resilience to the catchment and people on the impacts of water scarcity and climate change.

Evaluation of Drought in the Ken River Basin in Bundelkhand Region, India

The present study exhibits the analyses of the meteorological drought events in the Ken Basin using various indices viz., Standardized Precipitation Index (SPI) for different time-scale and Rainfall Departure (RD) method. Precipitation data from 1990 to 2020 for six districts falling in the basin have been used to compute SPI and RD values. The computation of SPI series has been carried out for 1-month, 3-month, 6-month, 9-month and 12-month time-scales. The analysis indicated that the most parts of the basin experienced drought events in the years 2007, 2015 and 2017 region during the years. However, the basin was affected partially in the other drought years. The maximum severity of drought has been recorded as -65% and -52% in the year 1997 in Mahoba and Banda districts respectively. However, the up-reach parts of the basin were not affected in the year 1997. The comparative analysis of RD and SPI exhibited a good correlation with 3-month and 6-month SPI time steps. It is found that the SPI-3 identifies the meteorological drought event appropriately followed by SPI-6.

Long-term Hydrometeorological Time-series Analysis over the Central Highland of West Papua

This article presents an innovative data-driven approach for examining long-term temporal rainfall patterns in the central highlands of West Papua, Indonesia. We utilized wavelet transforms to identify signs of a negative temporal correlation between the El Niño-Southern Oscillation (ENSO) and the 12-month Standardized Precipitation Index (SPI-12). Based on this cause-and-effect relationship, we employed dynamic causality modeling using the Nonlinear Autoregressive with Exogenous input (NARX) model to predict SPI-12. The Multivariate ENSO Index (MEI) was used as an attribute variable in this predictive framework. Consequently, this dynamic neural network model effectively captured common patterns within the SPI-12 time series. The implications of this study are significant for advancing data-driven precipitation models in regions characterized by intricate topography within the Indonesian Maritime Continent (IMC).

Monitoring agricultural drought in Peshawar Valley, Pakistan using long -term satellite and meteorological data.

Monitoring agricultural drought across a large area is challenging, especially in regions with limited data availability, like the Peshawar Valley, which holds great agricultural significance in Pakistan. Although remote sensing provides biophysical variables such as precipitation (P), land surface temperature (LST), normalized difference vegetation index (NDVI), and relative soil moisture (RSM) to assess drought conditions at various spatiotemporal scales, these variables have limited capacity to capture the complex nature of agricultural drought and associated crop responses. Here, we developed a composite drought index named "Temperature Vegetation ET Dryness Index" (TVEDI) by modifying the Temperature Vegetation Precipitation Dryness Index (TVPDI) and integrating NDVI, LST, and remotely sensed evapotranspiration (ET) using 3D space and Euclidean distance. Several statistical techniques were employed to examine TVPDI and TVEDI trends and relationships with other commonly used drought indices such as the standardized precipitation index (SPI), standardized precipitation evapotranspiration index (SPEI), and standardized soil moisture index (SSI), as well as crop yield, to better understand how these indices captured the spatial and temporal distribution of agricultural drought in the Peshawar valley between 1986 and 2018. Results indicated that while the temporal patterns of the 3-month SPI, SPEI, and SSI generally align with those of TVEDI and TVPDI, TVEDI was more strongly correlated with these indices (e.g., correlation coefficient, r = 0.78-0.84 from TVEDI and r = 0.73-0.79 from TVPDI). Moreover, the crop yield, a measure of crop response to agricultural drought, demonstrated a significant positive correlation with TVEDI (r = 0.60-0.80), much higher than its correlation with TVPDI (r = 0.30-0.48). These outcomes indicate that the inclusion of ET in TVEDI effectively captured changes in soil moisture, crop water status, and their impact on crop yield. Overall, TVEDI exhibited enhanced capability to identify drought impacts compared to TVPDI, showing its potential for characterizing agricultural drought in regions with limited data availability.

Response of vegetation to SPI and driving factors in Chinese mainland

Considering the sensitivity and vulnerability of vegetation to precipitation changes, exploring the response of vegetation to standardized precipitation index (SPI) is essential for ecosystem stability. Based on observed data from 624 stations and Moderate Resolution Imaging Spectroradiometer (MODIS) dataset from 2000 to 2018, Spearman's rank correlation coefficient (rs) of SPI and vegetation condition index (VCI) in Chinese mainland was analysed, response time of vegetation to SPI was investigated, and random forest models were constructed to explore the drivers of vegetation response. The results revealed that: (1) SPI and VCI exhibit a loop distribution. The correlation between VCI and 1-month SPI is mostly positive, accounting for 77.58% of the total; rs shows an increasing-decreasing distribution pattern, and the response time of VCI to 1-month SPI indicates an overall decreasing-increasing-decreasing trend. (2) Precipitation, temperature, altitude, vegetation, land use, landform and soil types all have influence on the correlation between 1-month SPI and VCI. Under various factors, rs is concentrated in the range of -0.1 ∼ 0.2, and the response time of VCI to SPI under various factors primarily falls into the ranges of 0 ∼ 1 month and 6 ∼ 9 months. Meanwhile, rs generally decreases with the increasing latitude, ranging between -0.1 and 0.2, whereas the mean response time increases with latitude, within a 0 ∼ 6 months range. (3) Precipitation, temperature and altitude are the top three important factors that affect both maximum rs of VCI and 1-month SPI and response time of VCI to 1-month SPI. The rs is positive in areas with low precipitation, high temperature and low altitude, whereas response time is generally long in areas with low precipitation, low temperature and high altitude. This study is significant for better understanding the interactions between vegetation and SPI changes, and provides reference for vegetation irrigation and vegetation management.

Monitoring and Assessment of Drought using Remote Sensing and association rules

Drought is a natural threat that exists in all climatic zones around the globe. There is a need to categorize drought events and the probability of occurrence for better planning and management of relief and rehabilitation. In this study, drought monitoring indices namely the Standard Precipitation Index (SPI) and Vegetation Condition Index (VCI) were used to analyse the observed variability of monsoon droughts over Andhra Pradesh State. Precipitation data between 1991-2019 was used to evaluate the SPI and to evaluate the VCI from NDVI data collected from 2011 to 2019 using multi-temporal Terra MODIS Vegetation Indices Product (MOD13Q1). In this analysis, more often drought events occurred in 3 and 6 months SPI during monsoon season. In this study, data mining techniques (such as the Association Rules) are used to explain the association between VCI and SPI to predict the probability of occurrence of drought. The association rules formed by the VCI and the 3-month SPI with 77 percentage of confidence and 1.11 of lift indicate the higher accuracy of the rules and the effect on vegetation ford rainfall accumulation. This research incorporated the various software and dataset levels used to predict the probable occurrence and severity of drought using the current situation. The analysis revealed the advantages of NDVI and rainfall for indices of spatial and multitemporal drought to identify and forecast the characteristics of drought.

Evaluating Markov chains and Bayesian networks as probabilistic meteorological drought forecasting tools in the seasonally dry tropics of Costa Rica

Meteorological drought is a climatic phenomenon that affects all global climates with social, political, and economic impacts. Consequently, it is essential to develop drought forecasting tools to minimize the impacts on communities. Here, probabilistic models based on Markov chains (first and second order) and Bayesian networks (first and second order) were explored to generate forecasts of meteorological drought events. A Ranked Probability Score (RPS) metric selected the best-performing model. Long-term precipitation data from Liberia Airport in Guanacaste, Costa Rica, from 1937 to 2020 were used to estimate the 1-month Standardized Precipitation Index (SPI-1) characterizing four meteorological drought states (no drought, moderate drought, severe drought, and extreme drought). The validation results showed that both models could reflect the climatic seasonality of the dry and rainy seasons without mistaking 4–5 months of the rain-free dry season for a drought. Bayesian networks outperformed Markov chains in terms of the RPS at both reproducing probabilities of drought states in the rainy season and when compared to the months in which a drought state was observed. Considering the forecasting capability of the latter method, we conclude that these models can help predict meteorological drought with a 1-month lead time in an operational early warning system.

Meteorological Drought Characterization in the Calabria Region (Southern Italy)

Due to the important role of water resources in the growth of the world’s economy, drought causes global concern for its severe worldwide implications on different sectors, such as biodiversity, farming, public water supply, energy, tourism, human health, and ecosystem services. In particular, drought events can have strong environmental and socioeconomic impacts in countries depending on rain-fed agriculture such as the ones in the Mediterranean region, which, due to a detected increase in warming and precipitation decrease, is considered a climate change hotspot. In this context, in this paper, meteorological drought in the Calabria region (southern Italy) has been characterized considering the Standardized Precipitation Index (SPI) evaluated at different timescales. First, the temporal distribution of the most severe dry episodes has been evaluated. Then, a trend analysis has been conducted considering the different seasons, the wet (autumn and winter) and dry (spring and summer) periods, and the annual scale. Finally, the relationship between drought and some teleconnection patterns (the North Atlantic Oscillation—NAO, the El Niño–Southern Oscillation—ENSO, and the Mediterranean Oscillation—MO) has been investigated. Results show that the majority of the severe/extreme drought events have been observed between 1985 and 2008. Moreover, a decrease in SPI values has been observed in winter and spring, in both the wet and dry periods, and upon the annual scale considering the 12-month SPI and the 24-month SPI. Finally, a link between the drought episodes in the Calabria region and the NAO phases and the MO has been identified. Since drought episodes can severely impact water resources and their uses, the findings presented in this work can be useful to plan and manage the water supply for household, farming, and industrial uses.

Identifikasi Kekeringan Meteorologi menggunakan SPI 3 Bulanan dan 6 Bulanan di DAS Brantas Hulu

Drought is a state of water availability that is far below the need for water for survival, agriculture, economic activity, and the environment. Drought in the Brantas Hulu watershed has been recorded as hampering the function of the Sutami Reservoir. This study used the Standardized Precipitation Index (SPI) method of 3 monthly and 6 monthly to determine the distribution of drought in the form of duration, severity, and maximum intensity of the Brantas Hulu watershed. This study also analyzed the relationship between the 3-monthly and 6-monthly SPI index to the sutami reservoir inflow discharge in the Brantas Hulu watershed using the Pearson Correlation method including correlations without lag, lag 1, and lag 2. The data used are monthly rainfall data for 30 years for the period 1991 - 2020 from 10 Rain Posts in Malang City, Batu City, and Malang Regency as well as monthly inflow debit data for 10 years for the period 2011 - 2020 from Perum Jasa Tirta 1. In general, the distribution of the maximum drought duration occurs for 15 – 17 months, while the SPI of 6 months occurs for 25 – 29 months. The maximum drought severity occurs for 5 - 6 months, while the 6-month SPI occurs for 9 – 11 months. The maximum drought intensity of SPI is 3 months and 6 months is categorized as Dry. Otherwise, the correlation of 3 monthly SPI and sutami reservoir inflow discharge without lag and lag 1 is worth 0,2 to 0,3, while for lag 2 it is worth 0,1 to 0,2. The correlation value of the 6 -month SPI index and the inflow discharge of Sutami Reservoir without lag is worth 0,3 to 0,4, while the correlation of lag 1 and lag 2 is worth 0,2 to 0,3.

Spatiotemporal characterization of droughts and vegetation response in Northwest Africa from 1981 to 2020

Drought has become one of the most devastating natural risks of agricultural production and the environment in almost all climate regions. Thus, understanding the spatiotemporal characteristics of drought and its associated impacts is crucial in drought early warning management and adaptation efforts. In this study, we used the 3-month Standardized Precipitation Index (SPI-3) obtained from Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) to investigate the space–time characteristics of drought conditions. Also, this study examined the impact of the SPI-based drought on vegetation health conditions using the Advanced Very High Resolution Radiometer (AVHRR) Normalized Difference Vegetation Index (NDVI) time-series data from 1981 to 2020. The results revealed that the region experiences drought primarily between July and September, with the most prolonged drought events lasting up to five months. Morocco suffered from more frequent droughts than other countries in the region. The Mann-Kendall test showed that the trend of drought became drier over the last decade, whereas the period from 1981 to 2010 witnessed either wetting or no trends. This study also found that the response of crops and grasslands showed higher correlation with the SPI-3 and that the response of vegetation to droughts was higher during the dry season. The findings of this study provide useful information to support local and regional drought planning and adaptation programs and enhance the understanding of drought development in the region.

Spatiotemporal analysis of meteorological drought across China based on the high-spatial-resolution multiscale SPI generated by machine learning

An accurate gridded Standardized Precipitation Index (SPI) at high spatial resolution is important for meteorological drought monitoring and assessment. However, the spatial estimation of the SPI solely derived from gridded precipitation products or interpolation of meteorological station-based data is subject to low accuracy and large uncertainty. Here, we developed a Gaussian process regression-based method by blending multiple precipitation products (GPR-BMP) with information from meteorological stations and topographical characteristics to improve the resolution and accuracy of spatial SPI. GPR-BMP was applied over China in this study, using five widely used precipitation products and meteorological station data, as well as elevation, slope, longitude and latitude. The gridded multiscale (i.e., 1-month, 3-month, 6-month, 9-month and 12-month) SPI datasets over China for 1984–2020 (GPR-BMP-SPI) were generated. Results showed that GPR-BMI-SPIs have higher accuracy than the SPIs derived from single precipitation products at all time scales. Additionally, GPR-BMP-SPIs can relatively accurately identify historical drought events. By analysing the drought spatiotemporal variation over China during 1984–2020, it was found that the annual drought frequency, severity and duration were relatively high in southeast China during 1984–2020. The annual trends of drought frequency, severity and duration showed a significant decrease on the Qinghai-Tibet Plateau. Winter drought in 1984–2020 exhibited the most distinctive features between the east and the west. Additionally, the drought area percentage showed an overall decreasing trend over the whole region of China on both annual and seasonal scales. It was also found that drought frequency contributed more to drought severity than drought duration. The GPR-BMP-SPI datasets generated in this study can serve as fundamental data support for future studies. The proposed model can be applied to other regions globally for drought monitoring and mitigation.

Analysis of Drought Characteristic of Sichuan Province, Southwestern China

Drought is a widespread and destructive natural hazard and is projected to occur more frequently and intensely, with more severe impacts in a changing environment. In this study, we used the standardized precipitation index (SPI) at various time scales (i.e., 3, 6, and 12 months) to provide an overall view of drought conditions across Sichuan Province, southwestern China, from 1961 to 2016. Then, the relationship between the SPI and the soil moisture anomalies was analyzed. Furthermore, the causes of SPI drought from the perspective of large-scale atmospheric circulation were assessed in the study area. The results showed that most stations with decreasing trends were located in the eastern part of Sichuan Province, while most stations with increasing trends were located in the northwestern part, indicating that the eastern region presented a drying trend, while the northwestern part exhibited a wetting trend. The specific analysis focused on extreme drought indicated an increasing occurrence the probability of extreme drought events, which could induce a high potential drought risk in the study area. The SPI values had a strong relationship with the soil moisture anomalies, and the linear correlation coefficients decreased as the time scale increased. This result indicated that SPI3 (3-month SPI) could be regarded as a good predictor of soil moisture drought. The cross wavelet analysis revealed that the Southern Oscillation Index (SOI) had statistically significant correlations with the SPIs in Sichuan Province. The results of this study are useful for assessing the change in local drought events, which will help reduce the losses caused by drought disasters in Sichuan Province.

Spatiotemporal evaluation of drought characteristics based on standard drought indices at various timescales over Uttar Pradesh, India.

Prolonged and repeated drought, as seen in India and other parts of South Asia, is a symptom of climate change, which is partially the result of human interventions. The performance of the widely used drought metrics Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI) are evaluated for 18 stations in Uttar Pradesh state for the period 1971 to 2018 in this study. Drought characteristics such as intensity, duration, and frequency of different categories are estimated and compared based on SPI and SPEI. In addition, station proportion is estimated at a different timescales, providing a better insight into temporal variability drought of a specific category. Spatiotemporal trend variability of SPEI and SPI was investigated at a significance level of 0.05 using the non-parametric Mann-Kendall (MK) test. SPEI adds the effect of temperature rise and deficit change on the drought occurrences of different classes. SPEI provides a better estimation of drought characteristics due to its consideration of temperature change in the drought severity. The more significant number of drying events accounted for a timescale of 3months and 6months, reflecting the higher variability of the seasonal fluctuation of water balance over the state. At 9-month and 12-month timescales, SPI and SPEI fluctuate gradually with considerable differences between the duration and severity of the drought event. This study reveals that there have been a substantial number of drought events over the state during the last two decades (2000 to 2018). The results conclude that the study area is at risk of erratic meteorological drought conditions where the western part of the study is worst affected compared to the eastern part of Uttar Pradesh (India).

Spatio-temporal characteristics of meteorological and agricultural drought indices and their dynamic relationships during the pre-monsoon season in drought-prone region of Bangladesh

The global water crisis has become a rising concern against the background of climate change, which is responsible for increasing drought frequency, a devastating natural hazard that severely affects the agricultural sector of a nation. Due to the geographical position, spatial variation in seasonal precipitation patterns, and groundwater table, the northwestern part of Bangladesh is one of the most drought-prone regions with extensive irrigation practices. In recent decades, the frequency of meteorological drought has significantly increased, but its dynamic relationship with agricultural drought is still unclear. In this research, we aim to study the meteorological and agricultural drought conditions and their dynamic relationships during the pre-monsoon season (March-May) in northwest Bangladesh. For this, we analyzed the 3-month standardized precipitation index (SPI), Palmer drought severity index (PDSI), and 3-month standardized precipitation evapotranspiration index (SPEI) to study the meteorological drought, and the normalized difference vegetation index (NDVI), land surface temperature (LST), vegetation condition index (VCI), temperature condition index (TCI), and vegetation health index (VHI) using MODIS to study the agricultural drought. Results revealed that SPI-3 and SPEI-3 during the pre-monsoon increased, while PDSI decreased. Agricultural drought indices showed a better VHI status in 2021. The Pearson correlation among these indices represented no correlation between meteorological and agricultural drought. This research identified that the relationship between VHI and SPI-3 might not be positive in all cases, especially in extensively irrigated regions. Therefore, it is important to consider hydrological drought indices to improve the understanding and accuracy of drought assessment.

Characterization of hydrological droughts in Brazil using a novel multiscale index from GNSS

Drought severity is not only related to drought intensity but also depends on drought duration. To accurately characterize short- and long-term droughts and comprehend drought propagation mechanisms, it is essential to ascertain the multiscale features of drought extremes. Here, we estimate terrestrial water storage changes (TWSC) using Global Navigation Satellite System (GNSS) vertical crustal displacement and then develop a novel Multiscale GNSS-based Drought Index (MGDI) to characterize hydrological droughts in Brazil from 2010 to 2021. MGDI is a probability-based drought index, and we employ a normal distribution to fit the TWSC result. The comparisons of temporal variations indicate that the MGDI is similar to other multiscale drought indices (i.e., standardized precipitation index (SPI), standardized precipitation evapotranspiration index (SPEI), and standardized terrestrial water storage index (STI)) at different time scales. Due to the longer response time to meteorological variables, hydrological drought indicators (i.e., MGDI and STI) generally lag behind meteorological drought indicators (i.e., SPI and SPEI) but with less noise on the short scale. The spatial distribution of MGDI-derived drought frequency reveals that hydrological droughts are most frequent in northeastern Brazil and near the Parana River Basin, with a frequency of up to 0.38 (about 55 months) on the 1-month scale. The spatiotemporal evolutions of the 2015–2016 drought event imaged by 3-month MGDI and 12-month SPI are generally consistent, and 3-month MGDI shows good temporal continuity of evolution. Investigation of drought propagation in various basins shows that 1-month MGDI correlates well with SPI on the specific time scales (e.g., 6-month, 11-month, 13-month, and 14-month), with all correlation coefficients above 0.55. In the Amazon Basin, we also find that MGDI on the 1.5–3 years scale is strongly correlated with El Niño-Southern Oscillation (ENSO) events, suggesting hydrological droughts are likely to be modulated by ENSO. Our findings imply that MGDI can characterize hydrological deficits more comprehensively and offer some critical insights for multiscale drought research.

Drought Analysis with Two Different Indices in Yeşilırmak Basin

Reducing the negative effects of drought disaster, which is one of the most important parameters affecting the planning and management of water resources, has become very important today. Many methods have been proposed in the literature for the calculation of drought, which is used to express the periods when precipitation is significantly less than the average, and two of these methods, "Standardized Precipitation Index" and "Reconnaissance Drought Index" were applied to the Yeşilırmak basin in this study.
 12-month standardized precipitation index and reconnaissance drought index analyzes were made and compared by using the monthly precipitation and evapotranspiration totals of 8 stations in the Yeşilırmak Basin for the period 1991-2020. The results show us that standardized precipitation index and reconnaissance drought index methods generally give similar signals for wet periods, but reconnaissance drought index method defines more extreme dry periods than standardized precipitation index method in extreme dry periods. While the reconnaissance drought index method defines more extreme dry periods than the standardized precipitation index method; It was observed that the standardized precipitation index method defined more extreme wet periods than the reconnaissance drought index method. In drought analysis, using an index based on more than one meteorological parameter (such as reconnaissance drought index) will give more reliable results instead of using an index based on a single parameter (i.e, standardized precipitation index). It has shown in this study that the effect of evapotranspiration values in drought calculation is very important by comparing it with reconnaissance drought index analysis and standardized precipitation index analysis.

Dynamic-LSTM hybrid models to improve seasonal drought predictions over China

Accurate drought prediction is essential for drought resilience and water resources management. The skill of seasonal drought prediction from dynamical and statistical models has limitations. This study combines dynamical models and deep learning to construct hybrid (dynamical-statistical) models. We use the random forest to extract typical grids based on the geopotential height, sea-level pressure, and 2-m temperature. The long short-term memory (LSTM) is used to construct the statistical models, with atmospheric variables as predictors and the 3-month Standardized Precipitation Index (SPI3) as the predictand. The hindcasts of atmospheric variables from the European Centre for Medium-Range Weather Forecasts (ECMWF) SEAS5 model are processed as predictors to force the statistical models. The hybrid model, constructed using the dynamical models and the LSTM, is named dynamic-LSTM (“D-LSTM”). The results suggest that the LSTM models are of information-extraction capability and robustness. When the lead time exceeds one month, the prediction skills are significantly improved by the D-LSTM models, especially in the East, Northwest, Southwest, and Tibet. In most regions, the D-LSTM models are more skillful across all seasons for lead times exceeding 30 days and are reliable in predicting droughts in spring and summer when the ECMWF SEAS5 loses skills at the seasonal scale. Furthermore, the D-LSTM models are more accurate in drought onset prediction.

Investigation of meteorological drought characteristics of the great man-made river region: a case study of drought in Libya

Drought is a hidden natural hazard that involves complex climatic systems and has significant environmental and social consequences. Due to the current state of catastrophic climatic occurrences, there has been an increased interest in monitoring droughts in recent years. This study conducted meteorological drought analysis for five monitoring stations in the Great Man-Made River region located in Libya. The Standardized Precipitation Index (SPI) and Reconnaissance Drought Index (RDI) methods were used to determine meteorological droughts utilizing monthly total precipitation data, and mean monthly temperatures and monthly total precipitation data, respectively. The drought analysis of the Great Man-Made River region using DrinC software for 1-, 3-, 6-, and 12-month SPI values were researched. According to the SPI-12-month index values, the driest period was determined by 86% in the Tripoli Airport and Nalut station, and the least dry period was determined in the Sirt station by 39%. It was found that the year 2000–2001 was one of the driest years in all studied stations, and the other years with high drought rates were 1981–1982, 1984–1985, and 1992–1993.

رصد ظاهرة الجفاف باستخدام الاستشعار عن بعد ونظم المعلومات الجغرافية في محافظة واسط / العراق

Drought considered being the most vulnerable natural hazard that affects a large number of people. Drought has nonstructural effects that spread across a vast geographic area, endangering human lives. The Normalized difference vegetation index (NDVI) based on Landsat 8 imaging and the meteorological-based Standardized Precipitation Index (SPI) were used to monitor the drought in Wasit governorate eastern Iraq for 2013 and 2018. To accurately assess the drought-affected area and provide appropriate drought-proofing, the Arc GIS Desktop 10.5 program was utilized to examine the spatial and temporal variance of drought across the study area. Using NDVI analysis in the study area, the vegetation area decreased by 7 %. Drought classification based on 12-month SPI indicated the drought of the near-normal type. The SPI data analysis data show that in 2013, SPI values were lower in Badrah station's northern part of the study area. While in 2018, the lower SPI value - was 0.54033 in Ali al Gharbi terrestrial station. This result is considered near normal according to the SPI usual range. These findings could aid drought management programs and reveal the genuine drought situation in the area.