Standardized Precipitation And Evapotranspiration Index Research Articles

Sensitivity of Standardized Precipitation and Evapotranspiration Index (SPEI) to the Choice of SPEI Probability Distribution and Evapotranspiration Method

Study RegionThe state of Oklahoma located in the Southern Plains region of the United States. Study FocusThe standardized precipitation evapotranspiration index (SPEI) is a widely used meteorological drought index that incorporates potential evapotranspiration (PET) into a precipitation-based index. However, the understanding of the appropriate PET method for SPEI across different temporal scales in non-arid climate conditions remains limited. We compared Thornthwaite (TW), Hargreaves (HG), and Penman-Monteith (PM) equations for SPEI at various accumulations, considering three temporal scales: 1) long-term (25 years), 2) event-based, and 3) monthly. Also, we examined the log-logistic and generalized extreme value distributions to test the normality of SPEI computed from the three PET methods. To do this, we utilized high-quality climate datasets measured at 107 stations across the state of Oklahoma, United States, which has a diverse climate ranging from semi-arid to humid subtropical. New Hydrological Insights for the RegionThe log-logistic distribution was found to be suitable for SPEI. The SPEI-HG showed better agreement with SPEI-PM than SPEI-TW in this region for the analyses of three temporal scales. However, for accumulations of SPEI longer than one year, both TW and HG equations showed no significant differences with SPEI-PM. The findings provide practical guidance for selecting an appropriate PET equation in the Southern Plains region depending on the purpose of study without resorting to data-intensive methods for PET estimation.

Evaluating the impacts of flooding on crop yields by different meteorological indices: A regional case study in the middle-lower reach of the Yangtze River, China

Flooding is a worldwide destructive disaster that severely restricts agricultural production. Meteorological indices are common tools for regionally assessing the impact of flooding on crop yields, but their performances are rarely compared. In this work, six convenient meteorological indices, namely, precipitation (P), standardized precipitation anomaly (PA), China Z index (CZI), standardized precipitation index (SPI), standardized precipitation and evapotranspiration index (SPEI), and standardized antecedent precipitation and evapotranspiration index (SAPEI), were employed to establish correlations between flooding intensity and crop meteorological yield (FL-Ym correlation). Four major crops in the middle-lower reach of the Yangtze River (cotton, oilseed rape, wheat, and maize) were selected as the study crops. The results indicated that the flooding intensities quantified by the SPI, SPEI, and CZI had strong interconnections, whereas those quantified by the P and SAPEI were less related to the others. In the cases with stronger negative FL-Ym correlations, different indices were more likely to yield consistent identifications. In terms of the districts witnessing significant FL-Ym correlations, in only 26%, 41%, 44%, and 75% of them (respectively corresponding to cotton, wheat, maize, and oilseed rape), the correlations were consistently identified as significant by the majority of the indices, demonstrating the nonnegligible influence of index selection. The relative performances of the examined indices varied with the employed calculation periods (whole growth period and single critical stage), whereas the SPEI was generally the best performer. The SAPEI performed best in assessing the flooding impact during crop critical growth stages, in sharp contrast with its mediocre performance over the whole crop growth period. According to the results of multiple indices, flooding during the middle stages of the study crops exerted the greatest negative impact. Additionally, oilseed rape and Anhui Province were identified as the crop and the region most affected by flooding. This work can provide support for flooding disaster assessment and agricultural water management.

Drought Atlas of India, 1901–2020

India has been considerably affected by droughts in the recent past. Despite the considerable impacts of droughts on agriculture and water resources, long-term datasets to examine droughts and their consequences at appropriate spatial and temporal scales have been lacking in India. Datasets that provide drought information are mostly available for a short period and at coarser resolutions, therefore, these do not comprehend the information regarding the major droughts that occurred in the distant past at administrative scales of decision-making. To fill this critical gap, we developed the high-resolution (0.05°) and long-term monthly precipitation and temperature datasets for the 1901–2021 period. We used long-term high-resolution precipitation and temperature to estimate droughts using standardized precipitation and evapotranspiration index (SPEI). As SPEI considers the role of air temperature in drought estimation, it can be used to examine meteorological, agricultural, and hydrological droughts. Using high-resolution SPEI, we developed drought atlas for India (1901–2020) that can provide comprehensive information on drought occurrence, impacts, and risks in India.

Precipitation variation and water balance evaluation using different indices

The study evaluates the variability of water balance using different indices for the period 1961–2018 in Cotnari and surroundings, in the middle part of the Moldavian Plateau (MP), Romania. With the aid of statistical analysis and remote sensing, we discovered that the Cotnari's SPEI (Standardized Precipitation and Evapotranspiration Index) variability is characterized by severe values that alternate between significant excess and significant deficits. According to SPEI, between 57.2 and 61.4% of the months were near normal in terms of water balance. There were between 19.3 and 25.1% months with water excess and between 17.1 and 20.8% with water deficit. The links between NDVI and SPEI become stronger as SPEI reaches extreme values (above 1.5 units or below −1.5 units). The water balance indicates a decrease in available water resources.

Hydrological Impacts of Climate Change (Rainfall and Temperature) and Characterization of Future Drought in the Aga Foua Djilas Watershed

Studying climate change's impact on runoff and drought is crucial for sustainable society and ecosystems. The extent of drought evolution and how droughts would affect society and the environment are not sufficiently considered in Senegal. This study assesses hydrological impacts and future drought using three global climate models (ACCESS-ESM1-5, BCC-CSM2-MR, and MRI-ESM2-0) as part of the Coupled Model Intercomparison Project (CMIP6) in the Aga-Foua-Djilas Basin. To this end, the hydrological impacts of climate change over 20-year periods (2021-2040; 2041-2060; 2061-2080; 2081-2100) at a resolution of 2.5 under four emission scenarios (SSP 126; 245; 370; 585), were investigated, and the drought characteristics are shown below, the SSP 245 and 585 scenarios over the 2021-2100 period. The results highlight a decrease in runoff potential given the drop in rainfall, which fell from 25.2 mm over the 2021-2040 period and under SSP 126 to 2.4 mm towards the end of the century (2081-2100) and under the SSP 585 scenario, changes in the standardized precipitation index (SPI) and the standardized precipitation and evapotranspiration index (SPEI) were first compared, and the SPEI showed larger changes due to its inclusion of temperature effects. The drought zone in the basin is likely to increase at the end of the 21st century with values approaching 80% for SPI and 90% for SPEI under the SSP 245 and SSP 585 scenarios if drought mitigation and adaptation mechanisms are inadequate. The results provide important guidance for improving the identification of causes, minimizing impacts, and building resilience to droughts in Senegal.

Drought limits vegetation carbon sequestration by affecting photosynthetic capacity of semi-arid ecosystems on the Loess Plateau

Drought is the driver for ecosystem production in semi-arid areas. However, the response mechanism of ecosystem productivity to drought remains largely unknown. In particular, it is still unclear whether drought limits the production via photosynthetic capacity or phenological process. Herein, we assess the effects of maximum seasonal photosynthesis, growing season length, and climate on the annual gross primary productivity (GPP) in vegetation areas of the Loess Plateau using multi-source remote sensing and climate data from 2001 to 2021. We found that maximum seasonal photosynthesis rather than growing season length dominates annual GPP, with above 90 % of the study area showing significant and positive correlation. GPP and maximum seasonal photosynthesis were positively correlated with self-calibrating Palmer Drought Severity Index (scPDSI), standardized precipitation and evapotranspiration index (SPEI) in >95 % of the study area. Structural equation model demonstrated that both drought indices contributed to the annual GPP by promoting the maximum seasonal photosynthesis. Total annual precipitation had a positive and significant effect on two drought indices, whereas the effects of temperature and radiation were not significant. Evidence from wood formation data also confirmed that low precipitation inhibited long-term carbon sequestration by decreasing the maximum growth rate in forests. Our findings suggest that drought limits ecosystem carbon sequestration by inhibiting vegetation photosynthetic capacity rather than phenology, providing a support for assessing the future dynamics of the terrestrial carbon cycle and guiding landscape management in semi-arid ecosystems.

Influence of climate variability on fire generation

This study evaluates the occurrence of dry and wet events and their relationship with fires in southern Pampas, Argentina. The intensity and magnitude of dry and wet events were determined based on the regional series of the Standardized Precipitation and Evapotranspiration Index (SPEI) for the 2000-2021 period. The data obtained were related to the El Niño Oceanic Index (ONI) to analyze the incidence of El Niño and La Niña events in generating them. Fires in the region were detected using remote sensing techniques, considering the number of events, their intensity, extent, and duration. The southern Pampas experiences marked rainfall variability, with 15 dry events, 11 wet years, and 2 standard years recorded for the period analyzed. Extreme dry years were, on average, more intense (SPEI = -2.14) and occurred mainly during the negative ONI phase. In contrast, extreme wet years exhibited lower intensity (SPEI = 1.98), and only the most intense ones were related to neutral ONI phases. We analyzed a representative extremely dry (ED) and an extremely wet events (EW) to interpret the relationship between climate variability and the spatiotemporal variability of fires in the region. It was observed that during the EW event (2014-2015, SPEI = 1.52, and El Niño event until 2015) the number of fires was higher compared to an ED event (2008-2009, SPEI = -2.22, and La Niña event during 2008), with 460 and 205 fires, respectively. The intensity was higher in the EW (302.6 and 31.5 MW), while the area presented considerable differences (1722 and 815.5 km2, respectively). Finally, the duration of the fires was shorter in ED than in EW (6 and 8 months, respectively). These results were related to vegetation health (NDVI = 0.29 and 0.41 and EVI = 0.15 and 0.21 in ED and EW, respectively) and changes in land covers. This study provides a solid database for future research efforts and sustainable land management plans.

Standardized Precipitation and Evapotranspiration Index Approach for Drought Assessment in Slovakia—Statistical Evaluation of Different Calculations

In the conditions of rising air temperature and changing precipitation regimes in Central Europe and Slovakia over the last two decades, it is necessary to analyse drought, develop high-quality tools for drought detection, and understand its reactions to the emerging drought situation. One of the frequently used meteorological drought indices is the Standardized Precipitation and Evapotranspiration Index (SPEI). Several parameters can be modified in different steps of the calculation process of SPEI. In the article, we analyse the influence of selected adjustable parameters on the index results. Our research has shown that the choice of a statistical distribution (Log-logistic, Pearson III, or Generalized Extreme Value) for fitting water balance can affect the feasibility of calculating distribution parameters (and thus the index) from the provided input data, as well as lead to either underestimation or overestimation of the index. The normality test of SPEI can be used as a tool for the detection and elimination of highly skewed indices and cases when the indices were not well determined by the distribution function. This study demonstrated improved results when using the GEV distribution, despite the common use of the Log-logistic distribution. With the Pearson III distribution, unusually high or low SPEI values (|SPEI| > 6) were detected.

VMD-GP: A New Evolutionary Explicit Model for Meteorological Drought Prediction at Ungauged Catchments

Meteorological drought is a common hydrological hazard that affects human life. It is one of the significant factors leading to water and food scarcity. Early detection of drought events is necessary for sustainable agricultural and water resources management. For the catchments with scarce meteorological observatory stations, the lack of observed data is the main leading cause of unfeasible sustainable watershed management plans. However, various earth science and environmental databases are available that can be used for hydrological studies, even at a catchment scale. In this study, the Global Drought Monitoring (GDM) data repository that provides real-time monthly Standardized Precipitation and Evapotranspiration Index (SPEI) across the globe was used to develop a new explicit evolutionary model for SPEI prediction at ungauged catchments. The proposed model, called VMD-GP, uses an inverse distance weighting technique to transfer the GDM data to the desired area. Then, the variational mode decomposition (VMD), in conjunction with state-of-the-art genetic programming, is implemented to map the intrinsic mode functions of the GMD series to the subsequent SPEI values in the study area. The suggested model was applied for the month-ahead prediction of the SPEI series at Erbil, Iraq. The results showed a significant improvement in the prediction accuracy over the classic GP and gene expression programming models developed as the benchmarks.

Radial Increment of Beech (Fagus sylvatica L.) Is under a Strong Impact of Climate in the Continental Biogeographical Region of Croatia.

European beech (Fagus sylvatica L.) is an important component of forests in the alpine and continental biogeographical regions of Croatia. This study aimed to (1) analyze the long-term response of beech to climate, (2) identify potentially critical climatic conditions that could negatively affect the radial increment (RI) and vitality of beech, and (3) evaluate differences in the response of beech between the two biogeographical regions in Croatia. We used the 16 × 16 km Croatian ICP Forests Level 1 network. On a total of 25 plots, we cored between 5 and 24 trees for dendrochronological analysis. Tree-ring widths (TRW) were measured and standardized using cubic spline. TRW chronologies for the two regions were calculated and correlated to the temperature and precipitation data and Standardized Precipitation and Evapotranspiration Index (SPEI) using bootstrapped correlations. Continental region precipitation from April to August and alpine region precipitation from June to August were significantly important for RI. Temperature was less important for RI than precipitation in both regions, but the importance of the negative impact of above-average temperatures in the continental region and the positive impact of above-average precipitation in the alpine region has increased over the last two decades. A comparison with the 3-month SPEI confirmed the significant influence of high temperatures and the lack of precipitation in August on the RI of beech trees in both regions.

Spatiotemporal Features and Time-Lagged Effects of Drought on Terrestrial Ecosystem in Southwest China

With global warming, the increase in the frequency and intensity of droughts have severely affected the balance of terrestrial ecosystems. Although the immediate effects of drought on vegetation growth have been widely studied, the time-lagged effects have been neglected, particularly in ecologically fragile karst areas. We examined the vegetation growth trends and abrupt changes in southwest China from 1990 to 2018 by reconstructing the normalized difference vegetation index (NDVI); we then used the standardized precipitation and evapotranspiration index (SPEI) to explore the drought evolution characteristics and the time-lagged effect of drought on vegetation growth. The results showed that 97% of the study area exhibited a greening trend, which accelerated after 1995. Spring drought increased noticeably. We demonstrated that drought had a time-lagged effect on vegetation growth; 27.28% of the vegetation lands had a lag time of less than 3 months, and the mean lagged time in karst areas was shorter than that in non-karst areas. Compared to other vegetation types, the cultivated vegetation had weaker drought resistance, while the mixed-forest had stronger tolerance to drought. This study contributes to a further understanding of the drought–vegetation relationship and has important implications for optimizing vegetation conservation strategies in southwest China while coping with climate change.

Effects of climate change and drought attributes in Nigeria based on RCP 8.5 climate scenario

Historical observatory and future simulated datasets based on representative concentration pathway (RCP) 8.5 scenario covering from 1951 to 2100 were analysed. Four well accepted drought indices: the standardized precipitation index (SPI), standardized runoff index (SRI), reconnaissance drought index (RDI) and standardized precipitation and evapotranspiration index (SPEI) were used in drought characterization. The ensemble of the seven GCMs using RCA-4 captured some pragmatic features of the climatology of Nigeria in the historical period. The correlations between SRI and SPEI/SPI are significant. The major peaks can be divided into three drought cycles with 1–2, 2–4 and 8–16 periods (years). On a general note, there is a projection of an increase in both the frequency and intensity of drought in the five classified agroecological zones in Nigeria. Considering the output of drought characteristics as measured by SPI, future climates were predicted to be more wet when compared to the historical period as indicated in the study. In contrast, SPEI and RDI forecasted dryer climates. The prediction of SPEI and RDI must have been predicated on the influence of increasing temperature due to global warming as captured by RCP 8.5, which would in turn affect the rate of evapotranspiration. In view of drought analysis using the RCP 8.5 scenario, the frequency of severe/extreme drought will likely become more intense especially towards the end of the 21st century.

Evaluating Satellite-Observed Ecosystem Function Changes and the Interaction with Drought in Songnen Plain, Northeast China

Drought is considered one of the devastating natural disasters worldwide. In the context of global climate change, the frequency and intensity of drought have increased, thereby affecting terrestrial ecosystems. To date, the interactions between ecosystem change and drought, especially their mutual lag and cumulative effects is unclear. The Songnen Plain in northeastern China is one of the three major black soil areas in the world and is highly sensitive to global change. Herein, to quantify the interaction between drought and ecosystem function changes in the Songnen Plain, integrating with time-series moderate resolution imaging spectroradiometer (MODIS), leaf area Index (LAI), evapotranspiration (ET), and gross primary productivity (GPP) data, we calculated the standardized precipitation and evapotranspiration index (SPEI) based on the meteorological data, diagnosed the causal relationship between SPEI and the ecosystem function indicators i.e., LAI, ET, and GPP, and analyzed the time-lag and cumulative effects between the degree of drought and three ecosystem function indicators using impulse response analysis. The results showed that the trend of SPEI (2000–2020) was positive in the Songnen Plain, indicating that the drought extent had eased towards wetness. LAI showed insignificant changes (taking up 88.34% of the total area), except for the decrease in LAI found in some forestland and grassland, accounting for 9.43%. The pixels showing a positive trend of ET and GPP occupied 24.86% and 54.94%, respectively. The numbers of pixels with Granger causality between LAI and SPEI (32.31%), SPEI and GPP (52.8%) were greater at the significance 0.05 level. Impulse responses between each variable pair were stronger mainly between the 6th and 8th months, but differed significantly between vegetation types. Grassland and cropland were more susceptible to drought than forest. The cumulative impulse response coefficients values indicated that the mutual impacts between all variables were mainly positive. The increased wetness positively contributed to ecosystem function, and in turn enhanced ecosystem function improved regional drought conditions to some extent. However, in the northeastern forest areas, the SPEI showed a significant negative response to increased ET and GPP, suggesting that the improved physiological functions of forest might lead to regional drought. There were regional differences in the interaction between drought conditions and ecosystem function in the Songnen Plain over the past 21 years.

Global Assessment of Cumulative and Time-Lag Effects of Drought on Land Surface Phenology

ABSTRACT Increased frequency and intensity of droughts under climate change will have a significant impact on land surface phenology, however, the drought-phenology interactions that are associated with complex temporal effects are not well understood. This study examined the response of land surface phenology to drought cumulative and time-lag effects by using the standardized precipitation and evapotranspiration index (SPEI) and explored the influence of hydrothermal and plant physiological factors on the phenology-drought relationship. We used the maximum correlation (rmax-cml ) between phenology and cumulative SPEI (1- to 12-month) to determine the cumulative effect. The maximum correlation (rmax-lag ) between phenology and lagged SPEI (1-month) was utilized to analyze the time-lag effect. Overall, the cumulative effect affected 25.36% of the vegetated area at the start of the growing season (SOS), 26.43% of the end of the growing season (EOS), and 26.57% of the length of the growing season (LOS). SOS was negatively affected by long-term SPEI, whereas EOS and LOS had positive correlations with short-term SPEI. The rmax-cml for shrubland was the largest, and the SOS and LOS response time scales of the forest were the shortest. The rmax-cml was larger in arid and semi-arid (AR and SAR) than in humid and semi-humid (HU and SHU). Meanwhile, the response time scales were longer in HU and SHU than in AR and SAR. The time-lag effect had a larger area of impact on land surface phenology than the cumulative effect, the areas were 46.12%, 47.93%, and 50.45% for SOS, EOS, and LOS, respectively, and the lagged time scales were longer. The phenology-SPEI correlation was dominantly driven by hydrological conditions, and the time scales were mainly affected by thermal factors. Moreover, the onset of phenology and the growth/senescence rate of plants influenced the relationship, suggesting that hydrothermal conditions may shift the time of phenology by regulating the growth/senescence rate and may thus modulate the phenology–drought interaction.

Patterns of Meteorological Drought Using Standardized Precipitation Evapotranspiration Index for Massili Basin, Burkina Faso

As a Semi-arid country, Burkina Faso is highly vulnerable to climate-related disasters such as drought and flood. Analyzing drought signature is therefore a key factor in advocating climate change adaptation at the local scale. In this study, Spatiotemporal trends of drought were conducted for the period 1960–2021 within Massili Basin using the standardized precipitation and evapotranspiration index (SPEI) package in R. The non-parametric method (Mann–Kendall) was then used to test for a monotonic trend, whereas the magnitude was estimated using Sen's method. Accordingly, the result revealed that during the period 1960-1979, the mean value of SPEI varies from 0.06 to 0.71; over 1980 to 2009, the mean value of the SPEI varies from -0.08 to -0.88 and for the last decades (2010 to 2021), the mean value of the SPEI ranges from 0.05 to 0.75. Normal to middle wet conditions is thus observed over the periods 1960-1979 and 2010-2021 while the period (1980-2009) depicts a middle drought condition. The Mann-Kendall test results show a decreasing trend of SPEI-3 and SPEI-24 with a Z value of - 0.784 and -0.530 respectively. A slightly increasing trend is observed for SPEI-6 and SPEI-12 with Z ranging from 0.598 to 1.917 respectively. The magnitude of the decrease is indicated by the sens’ slope value, which is -0.0014 for SPEI-3 and -0.00010 for SPEI-24 while the magnitude of the increase ranges from 0.00011 for SPEI-6 to 0.00037 for SPEI-12. This study highlights the importance of examining past drought features to obtain essential information to assist in designing and implementing efficient water resources management strategies over the Massili Basin.

How standard are standardized drought indices? Uncertainty components for the SPI & SPEI case

Standardized drought indices such as the Standardized Precipitation Index (SPI) or the Standardized Precipitation and Evapotranspiration Index (SPEI) are frequently used around the world to assess drought severity across a continent or a larger region covering different meteorological regimes. But how standard are these standardized indices? In this paper we quantify the uncertainty of SPI and SPEI based on an Austrian dataset to shed light on what are the main sources of uncertainty in the study area. Five factors that either defy the control of the analyst (record length, observation period), or need to be subjectively decided during the steps of the calculation (choice of the distribution, parameter estimation method, and GOF-test of the fitted distribution) are considered. We use the root mean squared error ( E R M S ) for estimating the typical error for different calculation algorithm of SPI and SPEI. The total and relative uncertainty components for each factor are analysed by a linear mixed model (LMM) and significance of each model parameter are tested by the Akaike information criterion (AIC) and the restricted likelihood ratio test. The E R M S indicates that computational variations of standardized drought indices lead to highly variable results. From the LMM, the choice of the distribution and the observational window are the most important sources of uncertainty. They, on average, control between 19% and 63% (choice of distribution) and 24% to 70% (observation period) of the total variance of the SPI across all stations and month of the year, with similar values observed for the SPEI. The parameter estimation method and the GOF-tests, however, have almost no effect on the standardized indices. Total errors and observation period uncertainty are typically decreasing with the record length as one would expect, while the distribution uncertainty is almost independent from the record length. An additional assessment shows that the uncertainties are similar at the pan-European scale leading to uncertain characterizations of major events such as the drought of 2015. Overall, the uncertainty of standardized drought indices is substantial. Alternative approaches as nonparametric methods, ensemble approaches or probability-based indices based on established methods of extreme-value statistics should be considered, to make the indices more accurate. • A novel error model sheds light on the accuracy of drought indices SPI and SPEI. • Main sources of uncertainty are observation period and choice of distribution. • Parameter estimation methods and GOF tests have almost no effect. • Errors are substantial and may yield to false classifications of drought events. • Concepts are discussed to make drought indices more accurate.

Spatial-temporal evolution of meteorological and groundwater droughts and their relationship in the North China Plain

Understanding the propagation dynamics from meteorological droughts to groundwater droughts and their spatial–temporal evolution is essential for monitoring and assessing groundwater drought risk. In this study, the Standardized Precipitation and Evapotranspiration Index (SPEI) and Gravity Recovery and Climate Experiment (GRACE) Groundwater Drought Index (GDI) were used for assessing meteorological and groundwater droughts, respectively, in the North China Plain (NCP). The Directed Information Transfer Index (DITI) was used to identify the propagation from meteorological droughts to groundwater droughts. The verification results indicated good reliability of GRACE data for assessing drought events in the NCP. From the view of temporal variation, the SPEI-1 was first decreased and then increased, and the minimum value was in 2011. The GDI was first increased and then decreased, and the maximum was in 2008. In terms of spatial distribution, the SPEI-1 significantly decreased in the central part of the NCP before 2011. The 2011–2020 period revealed an increasing trend in SPEI-1 in many parts of the NCP. According to the GDI results, an increasing trend was observed in the central and western parts of the NCP before 2008, and many parts of the study region revealed a decreasing trend in GDI during the 2008–2020 period. The propagation time of SPEI-1 to GDI was long in spring and winter and short in summer. The maximum DITI values ranged from 0.4 to 0.9 and the most spread time was about 18 to 24 months in the northern and southern parts of Hebei and the northern part of Henan province, covering 36.40% of the total surface area of the NCP. Regarding the drought sensitivity and propagation rate, the dominated distribution was in a pattern of high in the northern and low in southern parts of the NCP, with the rates of 38.44% and 34.17%, respectively.

The Trends of the Standardized Precipitation and Evapotranspiration Index (SPEI) in Turkey

The Trends of the Standardized Precipitation and Evapotranspiration Index (SPEI) in Turkey

Groundwater Drought Analysis under Data Scarcity: The Case of the Salento Aquifer (Italy)

Salento is a regional coastal karst aquifer located in Southern Italy with a highly complex geological, geomorphological, and hydrogeological structure. High and unruly exploitation of groundwater from licensed and unlicensed wells for irrigation and drinking purposes affects groundwater, with consequent degradation of its qualitative and quantitative status. The increased frequency of meteorological droughts and rising temperatures may only worsen the already compromised situation. The absence of complete and enduring monitoring of groundwater levels prevents the application of some methodologies, which require long time series. The analysis of climate indexes to describe the groundwater level variation is a possible approach under data scarcity. However, this approach may not be obvious for complex aquifers (in terms of scale, intrinsic properties, and boundary conditions) where the response of the groundwater to precipitation is not necessarily linear. Thus, the proposed research deals with the assessment of the response of the Salento aquifer to precipitation variability based on correlations between the Standardized Precipitation Index (SPI) and Standardized Precipitation and Evapotranspiration Index (SPEI) and groundwater levels for nine monitoring wells from July 2007 to December 2011. The study aims at evaluating the ability of the above indicators to explain the behavior of groundwater on complex aquifers. Moreover, it has the general aim to verify their more general reliable application. Results of three different correlation factors outline direct and statistically significant correlations between the time series. They describe the Salento aquifer as a slow filter, with a notable inertial behavior in response to meteorological events. The SPI 18-months demonstrates to be a viable candidate to predict the groundwater response to precipitation variability for the Salento aquifer.

Drought monitoring and prediction using SPI, SPEI, and random forest model in various climates of Iran

Abstract The aim of this study is to select the best model (combination of different lag times) for predicting the standardized precipitation index (SPI) and the standardized precipitation and evapotranspiration index (SPEI) in next time. Monthly precipitation and temperature data from 1960 to 2019 were used. In temperate climates, such as the north of Iran, the correlation coefficients of SPI and SPEI were 0.94, 0.95, and 0.81 at the time scales of 3, 12, and 48 months, respectively. Besides, this correlation coefficient was 0.47, 0.35, and 0.44 in arid and hot climates, such as the southwest of Iran because potential evapotranspiration (PET) depends on temperature more than rainfall. Drought was predicted using the random forest (RF) model and applying 1–12 months lag times for next time. By increasing the time scale, the prediction accuracy of SPI and SPEI will improve. The ability of SPEI is more than SPI for drought prediction, because the overall accuracy (OA) of prediction will increase, and the errors (i.e., overestimate (OE) and underestimate (UE)) will reduce. It is recommended for future studies (1) using wavelet analysis for improving accuracy of predictions and (2) using the Penman–Monteith method if ground-based data are available.