Standardized Precipitation Index Drought Research Articles

Comparative analysis of SPI and SPEI drought indices for Montenegro and the impact of teleconnections

ABSTRACT After 2000, Montenegro has been hit by several severe droughts. The results presented in this study were based on the analysis of the standardized precipitation index (SPI) and the standardized precipitation evapotranspiration index (SPEI) for the period 1961–2020. They showed the prevalence of a negative trend in both indices and a significantly higher frequency of drought in the second half of the observed period (1991–2020). The SPEI index is a more representative indicator of drought, because in addition to precipitation, it also takes into account potential evapotranspiration. Using the rescaled adjusted partial sums (RAPS) method, it was determined that the tipping point when the SPEI suddenly fell was the mid-1980s. Out of the 15 analyzed teleconnection variation indicators (atmospheric and oceanic oscillations), 11 oscillations have a significant impact on one or more of the 24 analyzed SPEI time series. The consequences of the drought in Montenegro, as part of the Mediterranean, are most noticeable in the summer, primarily in the form of water shortages, dried vegetation, and frequent fires. Decision-makers in Montenegro should pay attention to this extreme, as drought could pose a serious problem under the projected warmer climate conditions.

Evaluation of the sensitivity of meteorological drought in the Mediterranean region to different data record lengths.

The division and evaluation of data series used in monitoring drought into different time intervals is a practical approach to detecting the spatial and temporal extent of drought spread. This study aimed to determine meteorological drought's spatial and temporal distribution using overlapping and consecutive periods and cycles of the standardized precipitation index (SPI) time series in the Mediterranean region, Turkey. In the scope of the research, SPI values for the SPI12, SPI6 (1), and SPI6 (2) seasons were calculated for consecutive and overlapping hydrological years (1978-1998/21years, 1978-2008/31years, and 1978-2018/41years) at 28 meteorological stations. Autocorrelation, Mann-Kendall, and Sen slope trend tests were applied at a 5% significance level for each season (SPI12, SPI6 (1), and SPI6 (2)) and different time scales (21, 31, and 41years). For each season and period, maps of the SPI drought class, average formation of drought class, Mann-Kendall (MK) trend, and Sen's slope (SS) trend test statistics for the Mediterranean region were obtained, and the spatial distribution rate of trends was determined by drawing hypsometric curves. Changes in drought occurrence at different time scales were thoroughly evaluated with the changing length of data recording.Consequently, it was determined that the mild wet (MIW) and mild drought (MID) classes dominate the study area in the Mediterranean region. Significant and nonstationary changes detected in extreme wet and drought occurrences (extreme wet, EW; severe wet, SW; extreme drought, ED; severe drought, SD) were found to pose a risk in the study area. It was observed that there were spatially and temporally insignificant decreasing drought trends in the Mediterranean basin, considering that the time scales of these trends slowed down. Despite a nonsignificant trend from the MID drought class to the MIW drought class, it is predicted that the MIW and MID classes will maintain their dominance in the Mediterranean region. The central part of the study area (central Mediterranean basin) is the region with the highest drought risk.

Evaluation of Drought Using Meteorological Drought Indices, a Case Study: Alanya (Türkiye)

Drought is one of the most important challenges that many countries, especially countries in the Middle East region, are struggling with. Based on this, the study and monitoring of hydrological and drought factors is an important issue that can have a significant impact on management decisions in the field of water resources, especially in crisis management. Therefore, investigating the drought parameters is very important to understand the drought situation of a region. In this study, Alanya region, which is located on the southern coast of Turkey, was selected as a case study for drought analysis. Four drought indices for the selected region including: China Z-Index (CZI), Standardized Precipitation Index (SPI), Modified China Z-Index (MCZI) and Z-Score Index (ZSI) have been investigated. All these indicators have been investigated and evaluated using time scales of 1, 6, 12 and 24 months, the coefficient of determination (R2) has been calculated for each drought index with a different time scale and their results have been compared. The findings of the research showed that SPI and CZI drought indices performed better than other selected drought indices in identifying and effectively tracking drought severity. In addition to the study of dry events, wet events were also investigated, which indicates the presence of consecutive floods in the last years of the studied period in the region. The results indicated similar very dry events for the selected indicators in the 6-month period. Also, the rainfall trend for the period of 2015-2022 was taken into consideration to examine the rainfall of the last eight years. The results show that precipitation has decreased in recent years and has a downward trend in most months of the period in question, and the possibility of flood events due to sudden showers in the region has increased due to the continuation of droughts experienced in the years before 2015. Investigating soil moisture and vegetation for the selected period in the study area is also important for the evaluation of the drought level. Evaluation of the available land (vegetation) cover maps of the years 1975, 1985, 2000, 2010, 2020 and 2022 show that the vegetation cover has weakened over the years, and it has been evaluated as an indicator that the danger of drought in the region has increased.

Drought analysis based on SPI and RDI drought indices in the Burdur Basin

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

Explainable machine learning for the prediction and assessment of complex drought impacts

Drought is a common and costly natural disaster with broad social, economic, and environmental impacts. Machine learning (ML) has been widely applied in scientific research because of its outstanding performance on predictive tasks. However, for practical applications like disaster monitoring and assessment, the cost of the models failure, especially false negative predictions, might significantly affect society. Stakeholders are not satisfied with or do not “trust” the predictions from a so-called black box. The explainability of ML models becomes progressively crucial in studying drought and its impacts. In this work, we propose an explainable ML pipeline using the XGBoost model and SHAP model based on a comprehensive database of drought impacts in the U.S. The XGBoost models significantly outperformed the baseline models in predicting the occurrence of multi-dimensional drought impacts derived from the text-based Drought Impact Reporter, attaining an average F2 score of 0.883 at the national level and 0.942 at the state level. The interpretation of the models at the state scale indicates that the Standardized Precipitation Index (SPI) and Standardized Temperature Index (STI) contribute significantly to predicting multi-dimensional drought impacts. The time scalar, importance, and relationships of the SPI and STI vary depending on the types of drought impacts and locations. The patterns between the SPI variables and drought impacts indicated by the SHAP values reveal an expected relationship in which negative SPI values positively contribute to complex drought impacts. The explainability based on the SPI variables improves the trustworthiness of the XGBoost models. Overall, this study reveals promising results in accurately predicting complex drought impacts and rendering the relationships between the impacts and indicators more interpretable. This study also reveals the potential of utilizing explainable ML for the general social good to help stakeholders better understand the multi-dimensional drought impacts at the regional level and motivate appropriate responses.

Drought disaster modeling using drought index: a systematic literature review

A prolonged absence of rainfall that results in a temporary reduction or deficit in the amount of natural water available is known as drought. The goal of this article is to examine how drought indices from various nations might be used to simulate the features of drought. Understanding the various drought indexes, as well as their benefits and drawbacks, is crucial. The literature review methodology is employed in this investigation. The Standardized Precipitation Index, or SPI, is the output of the most used modeling technique. Because this technique solely uses the rainfall series, it was discovered that about 57% of the articles utilizing the SPI drought index used the SPI index. The Palmer Drought Severity Index (PDSI), Standardized Precipitation Index (SPI), Standardized Runoff Index (SRI), Standardized Streamflow Index (SSI), and Standardized Precipitation Evapotranspiration Index were used to find additional articles, nevertheless (SPEI). According to several studies, the study methodology often compares drought index approaches using hydrological and meteorological data. It is envisaged that this approach might be applied in other nations to see a place from multiple angles with regard to its hydrology and meteorology.

Prediction of the standardized precipitation index based on the long short-term memory and empirical mode decomposition-extreme learning machine models: The Case of Sakarya, Türkiye

This research predicted the meteorological drought of Sakarya province in northwest Türkiye using long short-term memory (LSTM). This deep learning algorithm has gained popularity in prediction studies. The standardized precipitation index (SPI), which can only be derived using precipitation data, was utilized for 1, 3, and 6-month time scales. SPI-1, SPI-3, and SPI-6-month time scales drought data calculated from the monthly precipitation data of the Sakarya Meteorology Station between 1960 and 2020 were taken as input data in the LSTM model. SPI drought data were used between 1960 and 2005 as training data and 2006–2020 as test data. Drought at t+1 output time was predicted using SPI values at t, t-1, t-2, and t-3 lag times as input variables. In addition, the results were compared with the empirical mode decomposition (EMD)-extreme learning machine (ELM) hybrid model to understand the capabilities of the standalone LSTM prediction model. The LSTM model yielded the best results (MAE = 0.11, NSE = 0.97, R2 = 0.97) for the SPI-1-month time scale and the best results (MAE = 0.18, NSE = 0.92, R2 = 0.94) for 3-month time scale. The EMD-ELM hybrid model yielded the best results (MAE = 0.22, NSE = 0.95, R2 = 0.96) for the SPI-6-month time scale. Due to the high performance of this study's proposed standalone LSTM model, it was concluded that drought time series do not need to be subjected to pre-processing techniques.

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.

Prediction of meteorological drought and standardized precipitation index based on the random forest (RF), random tree (RT), and Gaussian process regression (GPR) models.

Agriculture, meteorological, and hydrological drought is a natural hazard which affects ecosystems in the central India of Maharashtra state. Due to limited historical data for drought monitoring and forecasting available in the central India of Maharashtra state, implementing machine learning (ML) algorithms could allow for the prediction of future drought events. In this paper, we have focused on the prediction accuracy of meteorological drought in the semi-arid region based on the standardized precipitation index (SPI) using the random forest (RF), random tree (RT), and Gaussian process regression (GPR-PUK kernel) models. A different combination of machine learning models and variables has been performed for the forecasting of metrological drought based on the SPI-6 and 12months. Models were developed using monthly rainfall data for the period of 2000-2019 at two meteorological stations, namely, Karanjali and Gangawdi, each representing a geographical region of Upper Godavari river basin area in the central India of Maharashtra state which frequently experiences droughts. Historical data from the SPI from 2000 to 2013 was processed to train the model into machine learning model, and the rest of the 2014 to 2019-year data were used for testing to forecast the SPI and metrological drought. The mean square error (MSE), root mean square error (RMSE), adjustedR2, Mallows' (Cp), Akaike's (AIC), Schwarz's (SBC), and Amemiya's PC were used to identify the best combination input model and best subregression analysis for both stations of SPI-6 and 12. The correlation coefficient ([Formula: see text]), mean absolute error (MAE), root mean square error (RMSE), relative absolute error (RAE), and root relative squared error (RRSE) were used to perform evaluation for SPI-6 and 12months of both stations with RF, RT, and GPR-PUK kernel models during the training and testing scenarios. The results during testing phase revealed that the RF was found as the best model in forecasting droughts with values of [Formula: see text], MAE, RMSE, RAE (%), and RRSE (%) being 0.856, 0.551, 0.718, 74.778, and 54.019, respectively, for SPI-6 while 0.961, 0.361, 0.538, 34.926, and 28.262, respectively, for SPI-12 scales at Gangawdi station. Further, the respective values of evaluators at Karanjali station were 0.913 and 0.966, 0.541 and 0.386, 0.604 and 0.589, 52.592 and 36.959, and 42.315 and 31.394 for PUK kernel and RT models, respectively, during SPI-6 and SPI-12. Machine learning models are potential drought warning techniques because they take less time, have fewer inputs, and are less sophisticated than dynamic or scientific models.

The role of drought conditions on the recent increase in wildfire occurrence in the high Andean regions of Peru

Wildfire occurrence has increased sharply in the last two decades in the Peruvian Andes. There is, however, little research on wildfires and their impacts. This study explores the conditions conducive to wildfire during 2020. MODIS images were collected to estimate the development of vegetation. In addition, ground-based monthly and satellite-based daily precipitation data were collected. Daily precipitation regularity was evaluated using a concentration index (CI), while monthly precipitation was used to estimate the Standard Precipitation Index (SPI). We used also the Global Vegetation Moisture Index (GVMI), which is a useful indicator of vegetation dynamics based on vegetation moisture. Our results do not indicate a direct link between rainfall regularity (lowest CI values) and development of vegetation. Although the SPI drought analysis using seasonal rainfall indicated nearly normal conditions during 2019–2020, analysis of dry-day frequency (DDF) suggests that the dry period played an important role between September and November 2020, producing conditions similar to the droughts of 2005, 2010 and 2016. GVMI also showed below-average values from April to November. We corroborate the usefulness of DDF for monitoring the potential increase in wildfire conditions. A controlled burn policy could offer a more useful way to reduce the impacts of wildfire.

Forecasting of SPI and Meteorological Drought Based on the Artificial Neural Network and M5P Model Tree

Climate change has caused droughts to increase in frequency and severity worldwide, which has attracted scientists to create drought prediction models to mitigate the impacts of droughts. One of the most important challenges in addressing droughts is developing accurate models to predict their discrete characteristics, i.e., occurrence, duration, and severity. The current research examined the performance of several different machine learning models, including Artificial Neural Network (ANN) and M5P Tree in forecasting the most widely used drought measure, the Standardized Precipitation Index (SPI), at both discrete time scales (SPI 3, SPI 6). The drought model was developed utilizing rainfall data from two stations in India (i.e., Angangaon and Dahalewadi) for 2000–2019, wherein the first 14 years are employed for model training, while the remaining six years are employed for model validation. The subset regression analysis was performed on 12 different input combinations to choose the best input combination for SPI 3 and SPI 6. The sensitivity analysis was carried out on the given best input combination to find the most effective parameter for forecasting. The performance of all the developed models for ANN (4, 5), ANN (5, 6), ANN (6, 7), and M5P models was assessed through the different statistical indicators, namely, MAE, RMSE, RAE, RRSE, and r. The results revealed that SPI (t-1) is the most sensitive parameters with highest values of β = 0.916, 1.017, respectively, for SPI-3 and SPI-6 prediction at both stations on the best input combinations i.e., combination 7 (SPI-1/SPI-3/SPI-4/SPI-5/SPI-8/SPI-9/SPI-11) and combination 4 (SPI-1/SPI-2/SPI-6/SPI-7) based on the higher values of R2 and Adjusted R2 while the lowest values of MSE values. It is clear from the performance of models that the M5P model has higher r values and lesser RMSE values as compared to ANN (4, 5), ANN (5, 6), and ANN (6, 7) models. Therefore, the M5P model was superior to other developed models at both stations.

Interactions between precipitation, evapotranspiration and soil-moisture-based indices to characterize drought with high-resolution remote sensing and land-surface model data

Abstract. The Iberian Peninsula is prone to drought due to the high variability in the Mediterranean climate with severe consequences for drinking water supply, agriculture, hydropower and ecosystem functioning. Because of the complexity and relevance of droughts in this region, it is necessary to increase our understanding of the temporal interactions of precipitation, evapotranspiration and soil moisture that originate from drought within the Ebro basin, in northeastern Spain, as the study region. Remote sensing and land-surface models provide high-spatial-resolution and high-temporal-resolution data to characterize evapotranspiration and soil moisture anomalies in detail. The increasing availability of these datasets has the potential to overcome the lack of in situ observations of evapotranspiration and soil moisture. In this study, remote sensing data of evapotranspiration from MOD16A2 and soil moisture data from SMOS1km as well as SURFEX-ISBA land-surface model data are used to calculate the evapotranspiration deficit index (ETDI) and the soil moisture deficit index (SMDI) for the period 2010–2017. The study compares the remote sensing time series of the ETDI and SMDI with the ones estimated using the land-surface model SURFEX-ISBA, including the standardized precipitation index (SPI) computed at a weekly scale. The study focuses on the analysis of the time lags between the indices to identify the synchronicity and memory of the anomalies between precipitation, evapotranspiration and soil moisture. Lag analysis results demonstrate the capabilities of the SPI, ETDI and SMDI drought indices computed at a weekly scale to give information about the mechanisms of drought propagation at distinct levels of the land–atmosphere system. Relevant feedback for both antecedent and subsequent conditions is identified, with a preeminent role of evapotranspiration in the link between rainfall and soil moisture. Both remote sensing and the land-surface model show capability to characterize drought events, with specific advantages and drawbacks of the remote sensing and land-surface model datasets. Results underline the value of analyzing drought with dedicated indices, preferably at a weekly scale, to better identify the quick self-intensifying and mitigating mechanisms governing drought, which are relevant for drought monitoring in semi-arid areas.

Tuning ANN Hyperparameters by CPSOCGSA, MPA, and SMA for Short-Term SPI Drought Forecasting

Modelling drought is vital to water resources management, particularly in arid areas, to reduce its effects. Drought severity and frequency are significantly influenced by climate change. In this study, a novel hybrid methodology was built, data preprocessing and artificial neural network (ANN) combined with the constriction coefficient-based particle swarm optimisation and chaotic gravitational search algorithm (CPSOCGSA), to forecast standard precipitation index (SPI) based on climatic factors. Additionally, the marine predators algorithm (MPA) and the slime mould algorithm (SMA) were used to validate the performance of the CPSOCGSA algorithm. Climatic factors data from 1990 to 2020 were employed to create and evaluate the SPI 1, SPI 3, and SPI 6 models for Al-Kut City, Iraq. The results indicated that data preprocessing methods improve data quality and find the best predictors scenario. The performance of CPSOCGSA-ANN is better than MPA-ANN and SMA-ANN algorithms based on various statistical criteria (i.e., R2, MAE, and RMSE). The proposed methodology yield R2 = 0.93, 0.93, and 0.88 for SPI 1, SPI 3, and SPI 6, respectively.

Regional frequency analysis of drought severity and duration in Karkheh River Basin, Iran using univariate L-moments method.

Drought is one of the natural disasters that causes a great damage to human life and natural ecosystems. The main differences are in the gradual effect of drought over a relatively long period, impossibility of accurately determining time of the beginning and end of drought, and geographical extent of the associated effects. On the other hand, lack of a universally accepted definition of drought has added to the complexity of this phenomenon. In the last decade, due to increasing frequency of drought in Iran and reduction of water resources, its consequences have become apparent and have caused problems for planners and managers. So in this research, regional frequency analysis using L-moments methods was performed to investigate severity and duration of Standardized Precipitation Index (SPI), Standardized Evapotranspiration Index (SEI), Standardized Runoff Index (SRI), and Standardized Soil Moisture Index (SSI) and to study of meteorological, agricultural, and hydrological droughts in Karkheh River Basin in Iran. Using K-means clustering method, basin was divided into four homogeneous areas. Uncoordinated stations in each cluster were removed. The best regional distribution function was selected for each homogeneous region, and it was found that Pearson type (3) has the highest fit on the data set in the basin. Based on Hosking and Wallis heterogeneity test, Karkheh Basin with H1 < 1 was identified as acceptable homogeneous in all clusters. The results showed that hydrological drought occurs with a very short time delay in Karkheh River Basin after the meteorological drought, and two indicators show meteorological and hydrological drought conditions well. Agricultural drought occurs after meteorological and hydrological drought, respectively, and its severity and duration are less than the other indicators. Meteorological, hydrological, and agricultural droughts do not occur at the same time in all of the years. In general, the SPI drought index shows the most severe droughts compared with the other three indices. By this way, in 5- to 20-year return period with severity of 3SPI and in 20- to 100-year return period with severity of 7SPI, region IV or the western and northwestern areas of the basin has been affected by severe meteorological drought. By using the regional standardized quantities, it is possible to estimate the probability of drought in any part of the catchment that does not have sufficient data for hydrological studies.

Quantification of groundwater storage variation in Himalayan &amp; Peninsular River basins correlating with land deformation effects observed at different Indian cities

Groundwater is a significant resource that supports almost one-fifth population globally, but has been is diminishing at an alarming rate in recent years. To delve into this objective more thoroughly, we calculated interannual (2002–2020) GWS (per grid) distribution using GRACE &amp; GRACE-FO (CSR-M, JPL-M and SH) Level 3 RL06 datasets in seven Indian river basins and found comparatively higher negative trends (−20.10 ± 1.81 to −8.60 ± 1.52 mm/yr) in Basin 1–4 than in Basin 5–7 (−7.11 ± 0.64 to −0.76 ± 0.47 mm/yr). After comparing the Groundwater Storage (GWS) results with the CHIRPS (Climate Hazards Group Infrared Precipitation with Stations) derived SPI (Standardized Precipitation Index) drought index, we found that GWS exhausts analogously in the same period (2005–2020) when SPI values show improvement (∼1.89–2), indicating towards wet condition. Subsequently, the GWSA time series is decomposed using the STL (Seasonal Trend Decomposition) (LOESS Regression) approach to monitor long-term groundwater fluctuation. The long term GWS rate (mm/yr) derived from three GRACE &amp; GRACE-FO solutions vary from −20.3 ± 5.52 to −13.19 ± 3.28 and the GWS mass rate (km3/yr) lie in range of −15.17 ± 4.18 to −1.67 ± 0.49 for basins 1–3. Simultaneously, in basin 4–7 the GWS rate observed is −8.56 ± 8.03 to −0.58 ± 7.04 mm/yr, and the GWS mass rate differs by −1.71 ± 0.64$ to −0.26 ± 3.19 km3/yr. The deseasonalized GWS estimation (2002–2020) states that Himalayan River basins 1,2,3 exhibit high GWS mass loss (−260 to −35.12 km3), with Basin 2 being the highest (−260 km3). Whereas the Peninsular River basin 4,6,7 gives moderate mass loss value from −26.72 to −23.58 km3. And in River basin 5, the GWS mass loss observed is the lowest, with a value of −8 km3.

Research the drought characteristics of Ninh Thuan province through the Rainfall Anomaly Index (RAI), Standardized Precipitation Index (SPI), and the Moisture Index (MI)

This article studies the drought characteristics of the Ninh Thuan province in the past through meteorological data for the period 1986-2016. The study used three main indicators to study drought, namely, the Rainfall Anomaly Index (RAI), Standardized Precipitation Index (SPI), and Moisture Index (MI). Besides, the study also used non-parametric analysis to study trends and Spearman's rho to study the correlation between the indexes. The results of the study showed the occurrence of drought in the period 1986-2016 with different developments and frequencies, in which the period 1986-97 drought appeared continuously. According to RAI's drought index, the drought occurs 15 times with a frequency of severe drought of 12.5% and mild drought of 34.4%. SPI's drought index shows the drought happened 26 times with a severe drought in 1988. Drought occurred 31 times during the study period, 77% of the time, and the severe drought only occurred in 1998, according to the drought index MI. Additionally, the study also identified the trend of changes in drought indicators in the future and showed a trend of reducing drought in Ninh Thuan. The trend results show an average annual increase in RAI and MI of 0.01 and SPI of 0.08. To consolidate and evaluate the significance, the study also evaluated the correlation between indicators, and the results met the requirements with a value of &gt; 0.84. The three indexes have Cronbach's Alpha reliability is 0.78, in which, the SPI index correlates most strongly with RAI and MI at 0.92 and 0.94, respectively. This result will help local authorities and policymakers have more methods to study future droughts and develop solutions to socio-economic development in Ninh Thuạn in the context of drought.

Potential impacts of stratospheric aerosol injection on drought risk managements over major river basins in Africa

Most socio-economic activities in Africa depend on the continent’s river basins, but effectively managing drought risks over the basins in response to climate change remains a big challenge. While studies have shown that the stratospheric aerosol injection (SAI) intervention could mitigate temperature-related climate change impacts over Africa, there is a dearth of information on how the SAI intervention could influence drought characteristics and drought risk managements over the river basins. The present study thus examines the potential impacts of climate change and the SAI intervention on droughts and drought management over the major river basins in Africa. Multi-ensemble climate simulation datasets from the Stratospheric Aerosol Geoengineering Large Ensemble (GLENS) Project were analysed for the study. The Standardized Precipitation Evapotranspiration Index (SPEI) and the Standardized Precipitation Index (SPI) were used to characterize the upper and lower limits of future drought severity, respectively, over the basins. The SPEI is a function of rainfall and potential evapotranspiration, whereas the SPI is only a function of rainfall, so the difference between the two indices is influenced by atmospheric evaporative demand. The results of the study show that, while the SAI intervention, as simulated in GLENS, may offset the impacts of climate change on temperature and atmospheric evaporative demand, the level of SAI that compensates for temperature change would overcompensate for the impacts on precipitation and therefore impose a climate water balance deficit in the tropics. SAI would narrow the gaps between SPEI and SPI projections over the basins by reducing SPEI drought frequency through reduced temperature and atmospheric evaporative demand while increasing SPI drought frequency through reduced rainfall. The narrowing of this gap lowers the level of uncertainty regarding future changes in drought frequency, but nonetheless has implications for future drought management in the basins, because while SAI lowers the upper limit of the future drought stress, it also raises the lower limit of the drought stress.

Changing Causes of Drought in the Urmia Lake Basin—Increasing Influence of Evaporation and Disappearing Snow Cover

The water level of the Urmia Lake Basin (ULB), located in the northwest of Iran, started to decline dramatically about two decades ago. As a result, the area has become the focus of increasing scientific research. In order to improve understanding of the connections between declining lake level and changing local drought conditions, three common drought indices are employed to analyze the period 1981–2018: The Standard Precipitation Index (SPI), the Standard Precipitation-Evaporation Index (SPEI), and the Standardized Snow Melt and Rain Index (SMRI). Although rainfall is a significant indicator of water availability, temperature is also a key factor since it determines rates of evapotranspiration and snowmelt. These different processes are captured by the three drought indices mentioned above to describe drought in the catchment. Therefore, the main objective of this paper is to provide a comparative analysis of drought over the ULB by incorporating different drought indices. Since there is not enough long-term observational data of sufficiently high density for the ULB region, ECMWF Reanalysis data version 5(ERA5) has been used to estimate SPI, SPEI, and SMRI drought indicators. These are shown to work well, with AUC-ROC &gt; 0.9, in capturing different classes of basin drought characteristics. The results show a downward trend for SPEI and SMRI (but not for SPI), suggesting that both evaporation and lack of snowmelt exacerbate droughts. Owing to the increasing temperatures in the basin and the decrease in snowfall, drought events have become particularly pronounced in the SPEI and SMRI time series since 1995. No significant SMRI drought was detected prior to 1995, thus indicating that sufficient snowfall was available at the beginning of the study period. The study results also reveal that the decrease in lake water level from 2010 to 2018 was not only caused by changes in the water balance components, but also by unsustainable water management.

Changes in drought features at the European level over the last 120 years

Abstract. In this study we analyze drought features at the European level over the period 1901–2019 using three drought indices: the standardized precipitation index (SPI), the standardized precipitation evapotranspiration index (SPEI), and the self-calibrated Palmer drought severity index (scPDSI). The results based on the SPEI and scPDSI point to the fact that Central Europe (CEU) and the Mediterranean region (MED) are becoming dryer due to an increase in the potential evapotranspiration and mean air temperature, while North Europe (NEU) is becoming wetter. By contrast, the SPI drought does not reveal these changes in the drought variability, mainly due to the fact that the precipitation does not exhibit a significant change, especially over CEU. The SPEI12 indicates a significant increase both in the drought frequency and area over the last three decades for MED and CEU, while SPI12 does not capture these features. Thus, the performance of the SPI may be insufficient for drought analysis studies over regions where there is a strong warming signal. By analyzing the frequency of compound events (e.g., high temperatures and droughts), we show that the potential evapotranspiration and the mean air temperature are becoming essential components for drought occurrence over CEU and MED. This, together with the projected increase in the potential evapotranspiration under a warming climate, has significant implications concerning the future occurrence of drought events, especially for the MED and CEU regions.

Accuracy Assessment of the SPEI, RDI and SPI Drought Indices in Regions of Iran with Different Climate Conditions

Considering the impact of drought on agricultural products and human food security, the selection of the appropriate drought index to assess drought conditions is very important. Therefore, in this research, based on the relationship between the percent annual yield loss (AYL) of winter wheat (Triticum sativum) and three commonly used drought indices, i.e. Standardized Precipitation Evapotranspiration Index (SPEI), Reconnaissance Drought Index (RDI) and Standardized Precipitation Index (SPI), the accuracy of these indices was evaluated at 1-, 3-, 6- and 12-month time scales. Results showed that the average AYL at Ahvaz, Babolsar, Esfahan, Gorgan, Kerman, Mashhad, Ramsar, Rasht, Shiraz and Zabol was 66.64, 5.42, 97.52, 10.20, 98.57, 84.99, 2.47, 3.84, 77.03 and 97.07%, respectively. At stations with hyper-arid, semi-arid, Mediterranean, humid and hyper-humid type A climate conditions such as Zabol, Esfahan, Mashhad, Shiraz, Gorgan, Babolsar, Ramsar and Rasht stations, the calculated values of SPEI demonstrated the highest CC with AYL in winter wheat. At Kerman and Ahvaz, with arid climate conditions, the calculated values of the RDI had the greatest CC with AYL in winter wheat. Thus, in general, among the SPI, RDI and SPEI, the use of the SPEI is recommended for the assessment of drought characteristics.