Standardized Precipitation Evapotranspiration Index Values Research Articles

The Impact of Meteorological Drought at Different Time Scales from 1986 to 2020 on Vegetation Changes in the Shendong Mining Area

The Shendong Mining Area, being the largest coal base in the world, has significant challenges in the intensive development and utilization of coal resources, as well as the impact of a dry climate, which can have serious negative effects on the growth of flora in the region. Investigating the spatial and temporal patterns of how meteorological drought affects vegetation in the Shendong Mining Area at various time scales can offer a scientific foundation for promoting sustainable development and ecological restoration in the region. This study utilizes the Standardized Precipitation Evapotranspiration Index (SPEI) and Normalized Difference Vegetation Index (NDVI) data from 1986 to 2020 in the Shendong Mining Area. It employs Slope trend analysis, a Mann–Kendall test, a Geographic Detector, and other methods to examine the spatiotemporal distribution characteristics of meteorological drought at various time scales. Additionally, the study investigates the influence of these drought patterns on vegetation growth in the Shendong Mining Area. Across the mining area, there was a general decrease in the monthly average SPEI on an annual basis. However, on a seasonal, semi-annual, and annual basis, there was a gradual increase in the annual average SPEI, with a higher rate of increase in the southern region compared to the northern region. When considering the spatial variation trend in different seasons, both positive and negative trends were observed in winter and summer. The negative trend was mainly observed in the western part of the mining area, while the positive trend was observed in the eastern part. In spring, the mining area generally experienced drought, while in autumn, it generally experienced more precipitation. The mining area exhibits a prevailing distribution of vegetation, with a greater extent in the southeast and a lesser extent in the northwest. The vegetation coverage near the mine is insufficient, resulting in a low NDVI value, which makes the area prone to drought. Over the past few years, the mining area has experienced a significant increase in vegetation coverage, indicating successful ecological restoration efforts. Various forms of land use exhibit distinct responses to drought, with forests displaying the most positive correlation and barren land displaying the strongest negative correlation. Various types of landforms exhibit varying responses to drought. Loess ridge and hill landforms demonstrate the most pronounced positive association with monthly-scale SPEI values, whereas alluvial and floodplain landforms display the poorest positive correlation with yearly scale SPEI values. The general findings of this research can be summarized as follows: (1) The mining area exhibits a general pattern of increased humidity, with the pace of humidity increase having intensified in recent times. Seasonal variations exhibit consistent cyclic patterns. (2) There are distinct regional disparities in NDVI values, with a notable peak in the southeast and a decline in the northwest. The majority of the mining area exhibits a positive trend in vegetation recovery. (3) Regional meteorological drought is a significant element that influences changes in vegetation coverage in the Shendong Mining Area. Nevertheless, it displays complexity and is more obviously impacted by other factors at a small scale. (4) It should be noted that forests and barren land exert a more significant influence on SPEI values, despite their relatively lesser spatial coverage. The predominant land use type in most locations is grasslands; however, they have a relatively minor influence on SPEI. (5) A shorter time period, higher elevation, and steeper slope gradient all contribute to a larger correlation with drought.

Assessing the Potential Impacts of Climate Change on Drought in Uzbekistan: Findings from RCP and SSP Scenarios

Future climate change and its impact on drought is critical for Uzbekistan, located in Central Asia, the world’s largest arid zone. This study examines the evolving intensity of climate change and drought events using multi-model ensembles (MMEs) derived from the Coupled Model Intercomparison Project Phase 5 and 6 (CMIP5 and CMIP6) simulated under the Representative Concentration Pathway and Shared Socioeconomic Pathway (RCP and SSP) scenarios. The projections show different rates of increase in temperature and precipitation under the RCPs and SSPs. Projected temperature increases are expected to reach up to 2–2.5 °C under SSP1-2.6, SSP2-4.5, and SSP3-7.0, by mid-century. By 2080–2099, an increase is projected of 2–3 °C in monthly mean temperatures throughout the year (SSP1-2.6), and a more pronounced increase in summer up to 3–4 °C (SSP2-4.5) and 4–6 °C (SSP3-7.0), with a marked contrast in conditions between the mountainous and desert regions of Uzbekistan. Regional changes in precipitation over the study periods show relatively little variability, except for FD, where notable trends are found. Under SSP1-2.6 and SSP2-4.5, the increase in precipitation is relatively modest, whereas the changes in SSP3-7.0 are more substantial, with some regions experiencing variations of up to 10–20 mm per period. The Standardized Precipitation Evapotranspiration Index (SPEI), calculated based on the projected temperature and precipitation, provides an estimate of future drought trends. Our results show increasing aridity under all scenarios by mid-century, with longer-term projections indicating stabilization around different SPEI values by 2100: RCP2.6 and SSP1-1.9 stabilize around −1.0; RCP4.5, RCP6.0, SSP2-4.5, and SSP3-7.0 stabilize around −1.5; while RCP8.5 and SSP5-8.5 scenarios project values of −2 or less by 2100. Notable differences in the SPEI index are found between lowland and foothill regions. In view of Uzbekistan’s heavy reliance on agriculture and irrigation, which are the sectors that are expected to be mostly affected by climate change, our study provides a scientific basis for informed policy decision-making. This includes various aspects such as planning and management water resources, as well as the broader socioeconomic development of the country.

Drought evaluation using various evapotranspiration models over semi-arid river basins

ABSTRACT Multivariate drought indices including various hydrological processes into account can be more valuable under climatic and anthropogenic changes. Standardized Precipitation Evapotranspiration Index (SPEI) and Standardized precipitation Actual Evapotranspiration Index (SPAEI) are the drought indices used to estimate drought index, considering precipitation, and evapotranspiration (ET) into account. Many studies used empirical, machine learning, and process-based model estimates of ET for the calculation of drought indices of SPEI and SPAEI. However, the sensitivity of ET estimates on drought characteristics at the catchment scale is highly complex. The present study aimed to include and analyze the sensitivity of various approaches of empirical (Budyko, Penman–Monteith, Hargreaves, and Turc), modeled (SWAT), and remote sensing (MODIS) in the drought characterization using SPEI and SPAEI. The present methodology was tested on a dry-sub-humid river catchment of India, the Tunga-Bhadra River catchment for the period of 2000–2012. The performance of statistical indicators (Nash–Sutcliffe Efficiency and R2) for SPEI values by various empirical methods of Potential Evapotranspiration (PET) (i) Penman–Monteith (ii) Hargreaves against remote sensing PET were 0.93 and 0.95, respectively, which are high in comparison with SWAT simulated PET-based SPEI values, which shows NSE values of 0.85 against remote sensing PET-based SPEI.

Meteorological and Agricultural Drought Risk Assessment via Kaplan–Meier Survivability Estimator

Dry periods and drought are inherent natural occurrences. However, due to the increasing pressures of global warming and climate change, these events have become more frequent and severe on a global scale. These phenomena can be traced with various indicators and related indices proposed by various scholars. In general, drought risk assessment is done by modeling these indicators and determining the drought occurrence probabilities. The proposed adaptation introduces the “Kaplan–Meier estimator”, a non-parametric statistic traditionally used in medical contexts to estimate survival functions from lifetime data. The study aims to apply this methodology to assess drought risk by treating past droughts as “events” and using drought indicators such as the Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI). Mapping these results for a better understanding of the drought risks on larger spatial scales such as a river basin is also within the expected outcomes. The adapted method provides the probability of non-occurrence, with inverted results indicating the likelihood of drought occurrence. As a case study, the method is applied to SPI and SPEI values at different time steps (3, 6, and 12 months) across 27 meteorological stations in the Gediz River Basin, located in Western Turkey—a region anticipated to be profoundly affected by global climate change. The results are represented as the generated drought risk maps and curves, which indicate that (i) drought risks increase as the considered period extends, (ii) drought risks decrease as the utilized indicator timescales increase, (iii) locally plotted drought curves indicate higher drought risks as their initial slope gets steeper. The method used enables the generation of historical evidence based spatially distributed drought risk maps, which expose more vulnerable areas within the river basin.

Historical Analysis of the Effects of Drought on Rice and Maize Yields in Southeast Asia

This study performs an empirical orthogonal function (EOF) analysis to study the relationship between rice and maize productivities and the different time scales of the Standardized Precipitation Evapotranspiration Index (SPEI), including 1-month, 6-month, and 12-month time scales in Southeast Asia during the years 1970–2019. For the 1-month time scale, the averaged SPEI values are in the range of −1.0 to 1.0, indicating a moderate degree of dryness and humidity. Both 6-month and 12-month time scales suggest that many countries in Southeast Asia are experiencing mostly wet conditions, with SPEI values reaching up to 1.5. The relationship analysis by using EOF was indicated by the first and second principal components (PC1 and PC2) suggesting that drought events exhibit a positive orientation to crop productivity, especially maize in Southeast Asia. The pattern of climate oscillations, such as the El Niño-Southern Oscillation (ENSO), is likely related to crop productivity in Southeast Asia. The findings emphasize the importance of considering temporal climatic patterns in agricultural decision-making, with implications for enhancing regional climate resilience and ensuring sustainable food security in the face of global climate change.

Multiscale Spatiotemporal Dynamics of Drought within the Yellow River Basin (YRB): An Examination of Regional Variability and Trends

Drought, as a recurring extreme climatic event, inflicts diverse impacts on ecological systems, agricultural productivity, water resources, and socio-economic progress globally. Discerning the drought patterns within the evolving environmental landscape of the Yellow River Basin (YRB) is imperative for enhancing regional drought management and fostering ecological conservation alongside high-quality development. This study utilizes meteorological drought indices, the Standardized Precipitation Evapotranspiration Index (SPEI) and the self-calibrating Palmer Drought Severity Index (scPDSI), for a detailed spatiotemporal analysis of drought conditions. It examines the effectiveness of these indices in the basin’s drought monitoring, offering a comprehensive insight into the area’s drought spatiotemporal dynamics. The findings demonstrate the following: (1) SPEI values exhibit distinct fluctuation patterns at varying temporal scales, with more pronounced fluctuations at shorter scales. Drought years identified via the 12-month SPEI time scale include 1965, 1966, 1969, 1972, 1986, 1997, 1999, 2001, and 2006. (2) A modified Mann–Kendall (MMK) trend test analysis of the scPDSI time series reveals a worrying trend of intensifying drought conditions within the basin. (3) Correlation analysis between SPEI and scPDSI across different time scales yields correlation coefficients of 0.35, 0.54, 0.69, 0.76, and 0.62, highlighting the most substantial correlation at an annual scale. Spatial correlation analysis conducted between SPEI and scPDSI across various scales reveals that, within diverse temporal ranges, the correlation peaks at a 12-month time scale, with subsequent prominence observed at 6 and 24 months. This observed pattern accentuates the applicability of scPDSI in the monitoring of medium- to long-term drought phenomena.

Linking drought indices to atmospheric circulation in Svalbard, in the Atlantic sector of the High Arctic

Based on long-term climatological data from Ny-Ålesund, Svalbard Airport—Longyearbyen and the Polish Polar Station at Hornsund, we undertook an analysis of drought indices on Spitsbergen Island, Svalbard, for the period 1979–2019. The features and causes of spatiotemporal variability of atmospheric drought in Svalbard were identified, as expressed by the standardized precipitation evapotranspiration index (SPEI). There were several-year periods with SPEI indicating the dominance of drought or wet conditions. The long-term variability in the annual and half-year (May–October) SPEI values showed a prevalence of droughts in the 1980s and the first decade of the twenty-first century, while wet seasons were frequent in the 1990s and in the second decade of the twenty-first century. The seasonal SPEIs were characteristic of interannual variability. In MAM and JJA, droughts were more frequent after 2000; during SON and DJF of the same period, the frequency of wet seasons increased. The most remarkable changes in the scale of the entire research period occurred in autumn when negative values of SPEI occurred more often in the first part of the period, and positive values dominated in the last 20 years. The long-term pattern of the variables in consecutive seasons between 1979 and 2019 indicates relationships between the SPEI and anomalies of precipitable water and somewhat weaker relationships with anomalies of sea level pressure. The three stations are located at distances of more than 200 km from each other in the northern (Ny-Ålesund), central (Longyearbyen) and southern parts of Svalbard (Hornsund), and the most extreme values of drought conditions depended on the atmospheric circulation which could have been modified by local conditions thus droughts developed under various circulation types depending on the station. However, some similarities were identified in the atmospheric circulation patterns favouring drought conditions at Ny-Ålesund and Hornsund, both having more maritime climates than Longyearbyen. Extremely dry seasons were favoured by anticyclonic conditions, particularly a high-pressure ridge (type Ka) centred over Svalbard, air advection from the eastern sector under an influence of cyclone and negative precipitable water anomalies. During wet seasons anomalies of precipitable water were positive and cyclonic conditions dominated. These results were corroborated by the frequency of regional circulation types during JJA and DJF with the lowest and highest values of SPEI.

Assessment and prediction of meteorological drought using machine learning algorithms and climate data

Monitoring drought in semi-arid regions due to climate change is of paramount importance. This study, conducted in Morocco’s Upper Drâa Basin (UDB), analyzed data spanning from 1980 to 2019, focusing on the calculation of drought indices, specifically the Standardized Precipitation Index (SPI) and the Standardized Precipitation Evapotranspiration Index (SPEI) at multiple timescales (1, 3, 9, 12 months). Trends were assessed using statistical methods such as the Mann-Kendall test and the Sen’s Slope estimator. Four significant machine learning (ML) algorithms, including Random Forest, Voting Regressor, AdaBoost Regressor, and K-Nearest Neighbors Regressor, were evaluated to predict the SPEI values for both three and 12-month periods. The algorithms’ performance was measured using statistical indices. The study revealed that drought distribution within the UDB is not uniform, with a discernible decreasing trend in SPEI values. Notably, the four ML algorithms effectively predicted SPEI values for the specified periods. Random Forest, Voting Regressor, and AdaBoost demonstrated the highest Nash-Sutcliffe Efficiency (NSE) values, ranging from 0.74 to 0.93. In contrast, the K-Nearest Neighbors algorithm produced values within the range of 0.44 to 0.84. These research findings have the potential to provide valuable insights for water resource management experts and policymakers. However, it is imperative to enhance data collection methodologies and expand the distribution of measurement sites to improve data representativeness and reduce errors associated with local variations.

Climate-adaptive strategies for enhancing agricultural resilience in southeastern coastal Bangladesh: Insights from farmers and stakeholders.

Climate change impacts crop production worldwide, and coastal regions are particularly vulnerable to its adverse effects. Given the projected rise in temperature and shifting precipitation patterns, it is crucial to examine the current challenges faced by farmers in coastal Bangladesh. Using Focus Group Discussions (FGDs) and Key Informant Interviews (KIIs), we assessed the perceptions and experiences of farmers and stakeholders regarding the existing agricultural practices, the challenges they face in crop cultivation, and the adoption of climate-adaptive practices in 2 sub-districts in the southeastern coastal region of Bangladesh. Moreover, using the Standardized Precipitation Evapotranspiration Index (SPEI) and the Standardized Terrestrial Water Storage Index (STI), we assessed the frequency and intensity of different climatic conditions in these two sub-districts. Results show that 100% of the respondents reported an increase in dry climatic conditions, the occurrence of untimely precipitation, and a decline in irrigation water during the cropping season. All the respondents in the FGDs expressed a loss of crop production because of these climate-induced disturbances. Despite these challenges, farmers have been implementing several climate-adaptive practices. Among the 9 mentioned climate-adaptive practices, 50% of FGD respondents utilize organic fertilizers, 42% cultivate heat- and drought-resilient crop varieties, use improved irrigation and harvest rainwater, and 25% cultivate integrated crops. The results of quantitative analysis of 3- and 6-month SPEI and STI values show that this region experienced frequent and intense dry climatic conditions during the growing-season, which supports the farmers' and stakeholders' concern about the increasing occurrence of droughts during crop growing periods. The results suggest that despite adopting climate-resilient practices under increasing growing-season droughts, farmers require support from the government and NGOs in capacity-building training and input support (e.g., stress-resilient seeds). This study holds practical implications for government, NGOs, and policymakers for ensuring sustainable agricultural productivity in the coastal region of Bangladesh.

Drought projection from CMIP6 Climate models over Morocco in the 21st century using the Standardized Precipitation Evapotranspiration Index (SPEI)

Quantifying how climate change drives drought in the 21st century is a priority to inform policy and adaptation planning in Morocco. SPEI drought index calculated from precipitation and temperature at 12 months’ time-scale covering the agricultural year September-August was carried out for each of the five models over the future period 2023-2099. The average changes across Morocco were obtained by comparing between the averages of SPEI values and the drought zone percentages (Light, Moderate, Severe and Extreme). Also, by comparing drought characteristics for the different 11-year time horizons 2023-2033, 2034-2044, 2045-2055, 2056-2066, 2067-2077, 2078-2088 and at the end 2089-2099. The study of future drought projections based on the SSP2-4.5 scenario of the CMIP6 models indicates a worsening of drought in Morocco during the second half of the century. Moderate drought is expected to predominate, with a sharp increase in the area affected by drought, even reaching 90% over six years. These results are essential for decision-makers in water resources management, highlighting the need to put in place strategies to mitigate the adverse effects of drought, including the efficient use of water resources.

Evaluating the cumulative and time-lag effects of vegetation response to drought in Central Asia under changing environments

Central Asia (CA) is an arid region sensitive to climate change, and with the increase in drought events due to global warming, droughts are posing more severe threats to the vegetation in CA. However, the response of vegetation in CA to the cumulative and time-lag effects of prolonged drought remains unclear. This has limited our understanding of the mechanisms by which vegetation responds to meteorological drought in arid regions. To clarify the response of vegetation to drought in CA, this study analyzed the effects of drought and meteorological factors on vegetation growth dynamics. The extent of vegetation response to the cumulative and time-lag effects from 2001 to 2020 was evaluated using three satellite-derived vegetation indices (NDVI, EVI, NIRv) and the solar-induced chlorophyll fluorescence (SIF) and standardized precipitation evapotranspiration index (SPEI). We used the Copula-Bayes conditional probability method to assess the drought thresholds for vegetation under two scenarios: mild and severe losses. The effects of soil moisture (SM) and vapor pressure deficit (VPD) on vegetation dynamics were analyzed based on an improved partial wavelet coherence (PWC). The results show that the average response time of vegetation to the cumulative and lagged effects of drought was mostly concentrated within 4–6 months, and the lagged effect was stronger than the cumulative effect. The vegetation response to drought is nonlinear along the precipitation gradient. The SPEI values for triggering mild vegetation loss (below the 40th percentile) drought threshold range from −0.7 to −1.0, while the SPEI values for triggering severe vegetation loss (below the 10th percentile) drought threshold range from −2.0 to −2.5. The SM was the main driver affecting vegetation dynamics. This study provides deeper insight into the response of vegetation to drought in the arid zone of CA, which will further provide theoretical support for addressing global climate change and extreme drought events.

Remote Sensing Monitoring of Drought in Southwest China Using Random Forest and eXtreme Gradient Boosting Methods

A drought results from the combined action of several factors. The continuous progress of remote sensing technology and the rapid development of artificial intelligence technology have enabled the use of multisource remote sensing data and data-driven machine learning (ML) methods to mine drought features from different perspectives. This method improves the generalization ability and accuracy of drought monitoring and prediction models. The present study focused on drought monitoring in southwest China, where drought disasters occur frequently and with a high intensity, especially in areas with limited meteorological station coverage. Several drought indices were calculated based on multisource satellite remote sensing data and weather station observation data. Remote sensing data from multiple sources were combined to build a reconstructed land surface temperature (LST) and drought monitoring method using the two different ML methods of random forest (RF) and eXtreme Gradient Boosting (XGBoost 1.5.1), respectively. A 5-fold cross-validation (CV) method was used for the model’s hyperparameter optimization and accuracy evaluation. The performance of the model was also assessed and validated using several accuracy assessment indicators. The model monitored the results of the spatial and temporal distributions of the drought, drought grades, and influence scope of the drought. These results from the model were compared against historical drought situations and those based on the standardized precipitation evapotranspiration index (SPEI) and the meteorological drought composite index (MCI) values estimated using weather station observation data in southwest China. The results show that the average score of the 5-fold CV for the RF and XGBoost was 0.955 and 0.931, respectively. The root-mean-square error (RMSE) of the LST values reconstructed using the RF model on the training and test sets was 1.172 and 2.236, the mean absolute error (MAE) was 0.847 and 1.719, and the explained variance score (EVS) was 0.901 and 0.858, respectively. Furthermore, the correlation coefficients (CCs) were all greater than 0.9. The RMSE of the monitoring values using the XGBoost model on the training and test sets was 0.135 and 0.435, the MAE was 0.095 and 0.328, the EVS was 0.976 and 0.782, and the CC was 0.982 and 0.868, respectively. The consistency rate between the drought grades identified using SPEI1 (the SPEI values of the 1-month scale) based on the observed data from the 144 meteorological stations and the monitoring values from the XGBoost model was more than 85%. The overall consistency rate between the drought grades identified using the monitoring and MCI values was 67.88%. The aforementioned two different ML methods achieved a high comprehensive performance, accuracy, and applicability. The constructed model can improve the level of dynamic drought monitoring and prediction for regions with complex terrain and topography and formative factors of climate as well as where weather stations are sparsely distributed.

Quantitative analysis of input data uncertainty for SPI and SPEI in Peninsular Malaysia based on the bootstrap method

ABSTRACT Drought assessment has attracted attention in the research community, especially regarding the accuracy of drought indices due to input data uncertainty. This study addressed the impacts of input data uncertainty on the estimation of the Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI) for drought assessment in Peninsular Malaysia. The Kolmogorov-Smirnov test recommended the gamma distribution function for the SPI and the log-logistic distribution function for the SPEI. The bootstrap method was used to estimate SPIU and SPEIU, which account for input data uncertainty, and provided estimates for SPI and SPEI values. However, the standard deviation indicated significant input data uncertainty, with values ranging from 0.1038 to 0.1378. The two drought indices exhibited similar classifications of drought categories, but SPIU showed greater uncertainty for very dry and extremely dry events. The findings emphasize the importance of input data uncertainty, especially when dealing with extreme drought events.

Application of Informer Model Based on SPEI for Drought Forecasting

To increase the accuracy of drought prediction, this study proposes a drought forecasting method based on the Informer model. Taking the Yellow River Basin as an example, the forecasting accuracies of the Autoregressive Integrated Moving Average (ARIMA), Long Short-Term Memory (LSTM), and Informer models on multiple timescales of the Standardized Precipitation Evapotranspiration Index (SPEI) were compared and analyzed. The results indicate that, with an increasing timescale, the forecasting accuracies of the ARIMA, LSTM, and Informer models improved gradually, reaching the best accuracy on the 24-month timescale. However, the predicted values of ARIMA, as well as those of LSTM, were significantly different from the true SPEI values on the 1-month timescale. The Informer model was more accurate than the ARIMA and LSTM models on all timescales, indicating that Informer can widely capture the information of the input series over time and is more effective in long-term prediction problems. Furthermore, Informer can significantly enhance the precision of SPEI prediction. The predicted values of the Informer model were closer to the true SPEI values, and the forecasted SPEI trends complied with the actual trends. The Informer model can model different timescales adaptively and, therefore, better capture relevance on different timecales. The NSE values of the Informer model for the four meteorological stations on SPEI24 were 0.968, 0.974, 0.972, and 0.986.

Modified Standardized Precipitation Evapotranspiration Index: spatiotemporal analysis of drought

Drought monitoring is a complicated issue as it requires multiple meteorological variables to monitor and anticipate drought accurately. Therefore, developing a method that enables researchers, data scientists, and planners to comprehend drought mitigation policies more accurately is essential. In this research, based on the concepts behind the calculation of the Standardized Precipitation Evapotranspiration Index (SPEI), a new drought index is proposed for regional drought monitoring: the Modified Standardized Precipitation Evapotranspiration Index (MSPEI). The potential of the proposed index is based on the estimation of Reference Evapotranspiration (ETo). Therefore, the Modified Hargreaves-Samani (MHS) equation based on fuzzy logic calibration is used to estimate ETo. The proposed index is validated on ten meteorological stations in Pakistan at a one-month time scale. Afterward, based on the Pearson correlation, the performance of the proposed index is compared with the commonly used drought index (SPEI). Results showed a significant correlation (r > 0.7) between the quantitative values of MSPEI and SPEI for all ten stations. Moreover, a modified Tjostheims coefficient is used to estimate and test the spatial correlation between SPEI and MSPEI for different drought classes. According to our findings, the association between the SW, ND, ED, EW, MW, and SD patterns of MSPEI and SPI is 0.74, 0.834, 0.673, 0.592, 0.393, and 0.434, respectively. Meanwhile, considering the significance of future drought trend detection, this research is further extended to detect the future trend of MSPEI by using the Hurst index. In accordance with the results, Bahawalnagar, Sialkot, Lahore, Kotli, and Gilgit all have HI values greater than 0.5 (0.63, 0.58, 0.56, 0.55, and 0.53, respectively). In contrast, Muzaffarabad, Skardu, and Jhelum have HI values 0.47, 0.45 and 0.38, respectively; however, HI values of 0.5 are observed at Dera Ismail Khan (DIK) and Islamabad. Therefore, this research provides a basis for developing and enhancing drought hazard characterization, encouraging researchers and policymakers to monitor and forecast regional droughts using a more accurate drought index.

Standardized precipitation evapotranspiration index (SPEI) for Canada: assessment of probability distributions

Candidate probability distributions for the Standardized Precipitation Evapotranspiration Index (SPEI) for Canada were examined using the Canadian Gridded (CANGRD) temperature and precipitation dataset and CMIP5 projections. The probability distribution is a core component to the calculation of standardized values. For SPEI, a continuous probability distribution is fitted to the water balance time series so that the resulting transformed index follows a standard normal distribution. Selection of an appropriate distribution is therefore important as an inappropriate distribution may lead to biased values and subsequently affect the interpretation of SPEI results. Candidate distributions considered for SPEI included the generalized logistic (GLO), generalized extreme value (GEV), Pearson Type III (PE3) and normal (NOR) distributions. A range of goodness of fit tests were used to assess how well the distributions fit SPEI. Differences in CANGRD SPEI time series between three pairs of distributions, GLO-GEV, GLO-PE3, and GEV-PE3, showed that there were larger differences in SPEI values between GLO and the other two distributions than the difference between GEV and PE3. SPEI results fitted with GLO resulted in lower threshold exceedances of extreme SPEI (-/+2) than GEV and GLO. A comparison between CMIP5 SPEI values fitted with GLO (SPEI-GLO) and PE3 (SPEI-PE3) showed that there were seasonal and spatial variations in results, although the use of multi-model ensembles reduced these differences. As was the case for CMIP5 SPEI-GLO projections, projected annual changes in CMIP5 SPEI-PE3 show an increase in drying at the surface in central/southern Canada. Overall, the study recommends using PE3 or GEV for SPEI analysis for Canada.

Assessment of the relationship of the salt-covered area and the groundwater storage/drought indicators in the disappearing Lake Tuz in Turkey (1985–2021)

This study aims to reveal the relationship of the expansion of the salt-covered area of Lake Tuz in Turkey with the drought in the region and the change of the groundwater storage. The changes in the salt-covered area in Lake Tuz between 1985 and 2021 (37years), which have been obtained by August satellite images, are compared with those in groundwater and the 12-month Standardized Precipitation-Evapotranspiration Index (SPEI) variations in the lake. The results show that the salt-covered area on the lake surface was most strongly correlated with the SPEI spring (-0.78, t test; p < 0.01) during the period of August 1985-2021, implying that the drought conditions in spring determines 62% of the salt-covered area on the lake in August. The groundwater storage (GWS) in June and July and the salt-covered area in August in Lake Tuz also depict a moderate correlation of -0.60 at 1% significance level from 1985 to 2021. The results illustrate that the SPEI and GWS values decreased, while the salt-covered area on the lake surface expanded in the Lake Tuz Basin over the past 37years. The trends of the salt-covered area, SPEI, and GWS became especially pronounced after 1999, during which the SPEI change and similar changing trends appeared in the GWS and salt-covered area. These patterns of increase in drought severity and decreasing groundwater storage are expected to increasingly compromise the future of Lake Tuz and cause ecological and environmental problems in the near future.

Assessment of Meteorological and Agricultural Drought Analysis in Kırklareli province

The Global warming in the world cause to the climate change in this century. Agriculture is one of the sectors that can be most affected by climate change. Agricultural production is affected by precipitation and temperature variables. Due to the changes in these two parameters, the increase in the temperatures and the changes in the amount of precipitation in parallel with this disrupt the ecological balance and bring along the problems of desertification and drought. Agricultural production is highly dependent on meteorological factors in Thrace region. For this reason, it is expected that changes in climatic factors will affect the agriculture of the region positively or negatively. The aim of this study is to determine the frequency and severity of meteorological and agricultural drought with two indexes between 1963-2019 years in Kırklareli province. Standardized Precipitation Evapotranspiration Index (SPEI) was used to determine monthly, seasonal, six monthly and annual agricultural drought. According to annual agricultural drought results, it was calculated ten years mild arid (1969, 1985, 1986, 1990, 1991, 1992, 1993, 2011, 2016, and 2019), five years moderate arid (1983, 1989, 1996, 2008, and 2015), one year severe arid (1994), and two years extreme arid (2000 and 2001). Another drought index which is Standardized Precipitation Index (SPI) was used to determine meteorological drought. According to annual meteorological drought results, Eight years mild arid (1964, 1969, 1982, 1985, 1986, 1990, 1991, and 2011), seven years moderate arid (1983, 1989, 1992, 1993, 1994, 1996, and 2008), and two years extreme arid (2000, 2001) was figured out. According to linear correlation analysis, a good correlation was obtained for SPI and SPEI values, between annual, six monthly (Spring-Summer), and three monthly only winter and spring periods (R2=0.871, R2=0.901, R2=0.974, and R2=0.919), respectively. In the 57-year observation period; 18 years were determined as the arid year with SPEI index and 67% of these total arid years (12 arid years) occurred and 17 years were determined as the arid year with SPI index and 76% of these total arid years (13 arid years) occurred, between 1982 and 2002 years. The SPEI drought index which use precipitation, temperature and evapotranspiration data could give much good results to policy makers in drought mitigation policies in terms of giving results that are more accurate in agricultural production and drought assessments.

Spatiotemporal drought analysis in Bangladesh using the standardized precipitation index (SPI) and standardized precipitation evapotranspiration index (SPEI)

Countries depending on small-scale agriculture, such as Bangladesh, are susceptible to climate change and variability. Changes in the frequency and intensity of drought are a crucial aspect of this issue and the focus of this research. The goal of this work is to use SPI (standardized precipitation index) and SPEI (standardized precipitation evapotranspiration index) to investigate the differences in drought characteristics across different physiognomy types in Bangladesh and to highlight how drought characteristics change over time and spatial scales when considering different geomorphologies. This study used monthly precipitation and temperature data from 29 metrological stations for 39 years (1980–2018) for calculating SPI and SPEI values. To determine the significance of drought characteristic trends over different temporal and spatial scales, the modified Mann–Kendall trend test and multivariable linear regression (MLR) techniques were used. The results are as follows: (1) Overall, decreasing dry trend was found in Eastern hill regions, whereas an increasing drought trends were found in the in the rest of the regions in all time scaless (range is from − 0.08 decade−1 to − 0.15 decade−1 for 3-month time scale). However, except for the one-month time scale, the statistically significant trend was identified mostly in the north-central and northeast regions, indicating that drought patterns migrate from the northwest to the center region. (2) SPEI is anticipated to be better at capturing dry/wet cycles in more complex regions than SPI. (3) According to the MLR, longitude and maximum temperature can both influence precipitation. (4) Drought intensity increased gradually from the southern to the northern regions (1.26–1.56), and drought events occurred predominantly in the northwestern regions (27–30 times), indicating that drought meteorological hotspots were primarily concentrated in the Barind Tract and Tista River basin over time. Findings can be used to improve drought evaluation, hazard management, and application policymaking in Bangladesh. This has implications for agricultural catastrophe prevention and mitigation.

Droughts Are Not the Likely Primary Cause for Abies sibirica and Pinus sibirica Forest Dieback in the South Siberian Mountains

Background. Since the mid-20th century, massive dieback of coniferous forests has been observed in the temperate and boreal zones across North America and Northern Eurasia. The first hypotheses explaining forest dieback were associated with industrial air pollution (acid rain). At the end of the century, new hypotheses emerged that supported critical climate-induced aridization to explain forest dieback. Many studies were based on the SPEI (Standardized Precipitation Evapotranspiration Index) drought index. Our goals were to investigate if the SPEI drought index was a suitable metric to reflect drought conditions in wet and moist dark-needled forests in the South Siberian Mountains (Mts) and if droughts trigger the dieback of those forests. Methods. We calculated the SPEI drought index, the annual moisture index AMI, potential evapotranspiration PET, and water balance dynamics for the period 1961–2019 for four transects in the South Siberian Mts. where decline/dieback of dark-needled Siberian pine and fir forests were identified in situ. Climate data from nine weather stations located at lower and upper elevations of each transect were used to calculate climatic index dynamics for the 1961–2019 period to identify dry and wet phases of the period. Results. Our findings showed that climatic moisture/dryness indices have rarely gone down to high risk levels during the last 60 years (1961–2019). AMI did not reach the critical limit, 2.25, characteristic of the lower border for the dark-needled taiga. SPEI values &lt; −1.5 represent drought stress conditions for dark-needled conifers at the lower border, and these conditions occurred 3–4 times during the 60-year period. However, the annual water balance stayed positive in those years in wet and moist forests at mid-to-high elevations. Trees are known to survive occasional (1–2) dry years. We found that dark-needled conifer dieback often occurs in wet years with plentiful rain rather than in drought years. We found forest dieback was associated with the westerlies that bring atmospheric pollution from the west at 50–56 N latitudes, where the air masses cross populated regions that have widespread industrial complexes. Conclusions. We concluded that the observed decline of dark-needled conifers at middle-to-high elevations across the South Siberia’s Mts was conditioned by several plausible causes, among which air pollution seems to be more credible, rather than dry climatic conditions, as cited in recent literature. Results are essential for understanding these ecosystems and others as our planet changes. Other causes and mechanisms should be further investigated, which would necessitate creating infrastructure that supports multi-disciplinary, inter-agency teamwork of plant physiologists, foresters, chemists, etc.