Standardized Precipitation Evapotranspiration Index Research Articles

Performances of different yield-detrending methods in assessing the impacts of agricultural drought and flooding: A case study in the middle-and-lower reach of the Yangtze River, China

In evaluating the impacts of drought and flooding disasters on crop yields, accurately calculating meteorological yield (i.e., detrended yield) is an important procedure. The present work aimed to compare various yield-detrending methods in terms of characterizing the regression relationships between meteorological yield and the intensities of drought and flooding. Taking the middle-and-lower reach of the Yangtze River (MLRYR) as the study region, the intensities of drought and flooding during the growing seasons of four study crops (cotton, oilseed rape, wheat, and maize) were quantified using the standardized precipitation evapotranspiration index. Nine popular yield-detrending methods and the first-difference method were employed to determine meteorological yields. The results indicated that the examined methods consistently identified the cases with very significant yield-reducing impacts of drought and flooding. The first-difference method identified 58 significant regression relationships, outperforming the yield-detrending methods (identifying 39–44 significant regression relationships). The 20-yr moving average and the linear fitting methods performed best at the provincial level, while the cubic smoothing spline and the quadratic polynomial fitting methods performed best at the district level. Based on these best-performing methods, the average method was proposed and exhibited wider applicability and better performance than the individual methods. In terms of meteorological yield losses, cotton was the most affected crop (35% of all districts experienced severe cotton yield loss) and Anhui was the most affected region (with an average crop yield loss of 14.06%). The yield-reducing impact of flooding was significant in 41 districts; in comparison, the impact of drought was significant only in 7 districts. Additionally, oilseed rape was the crop most affected by drought and flooding. These results can provide guidance for assessing agricultural drought and flooding disasters under climate change.

Projected climatic drought events in drought-prone cities: Insights from high-resolution downscaled CMIP6 data

This study investigated the drought trends, frequency, and duration in three drought-prone cities in Northwest China under various Shared Socioeconomic Pathways (SSPs) climate scenarios from 2015 to 2099. Utilizing a two-step downscaling method, we generated high-resolution (1 km) temperature and precipitation projections for the cities using multiple global climate model (GCM) data. The Standardized Precipitation Evapotranspiration Index (SPEI) was then calculated to assess drought conditions. Our results revealed that under the influence of global climate change, the 12-month SPEI exhibited a significant increasing trend for all three cities, indicating a shift towards a more humid climate. The influence of climate change on drought trends was more pronounced at higher latitudes. Additionally, the occurrence and duration of severe droughts (3-month SPEI < -1.5) were higher in the near future (2015–2056) compared to the far future (2057–2099), suggesting time-lagged and cumulative effects of global climate change on drought. Spatial variations within each city were also observed, with the western mountainous regions of Xi'an showing a more pronounced decrease in drought frequency under higher emission scenarios. Under the influence of different projected scenarios, the variations in drought characteristics across cities exhibited significant spatial heterogeneity. This study highlights the importance of understanding local and inter-regional drought characteristics in the context of climate change, and the need for targeted management policies to effectively cope with climate anomalies and associated disasters in different regions.

Evaluation of models and drought-wetness factors contributing to predicting the vegetation health index in Dak Nong Province, Vietnam

Monitoring and predicting vegetation health are essential for agricultural activities and food security. This study aimed to select a model and evaluate the factors contributing to predicting the vegetation health index (VHI) in the Dak Nong Province, Vietnam. Machine learning algorithms were evaluated, including multiple linear regression, xGBoost, and artificial neural networks (ANN). The input variables of the models included the standardized precipitation evapotranspiration index (SPEI), soil moisture (SM), and VHI in the previous periods. Research results showed that the ANN model gave the best prediction results. The accuracy of prediction results depended on the season of the year, in which the dry season gave a result with high accuracy. The results also indicated that SM from one to two previous months, SPEI1 from one to three previous months, SPEI3 and SPEI5 from three to six previous months, and VHI from one previous month contributed mainly to the prediction model. The relative contribution of SM and SPEI ranged from 42% to 52% in the last 4 months of the dry season. In addition, land use type also affected prediction quality.

Delineating the predominant impact of rising temperature on the enhancement of severity in compound drought-hot events in China: An empirical Copula and path analysis-based approach

Study regionChina Study focusCompound drought-hot events (CDH) inflict serious socio-economic damages on our society and the natural environment. Given the inverse relationship between average summer temperature and precipitation, this investigation introduces an innovative empirical copula-based compound drought-hot index (CDHI). This index is crafted from the joint distribution of the standardized precipitation evapotranspiration index (SPEI) and standardized temperature index (STI). While previous research has documented a rising trend in these complex events on both regional and global stages, scrutiny into their escalating severity remains limited. To highlight the critical role of climbing temperatures in the increasing severity of CDH events within China, our research utilizes the CDHI in tandem with a path analysis to precisely assess severity shifts in CDH occurrences during the warm season from 1901 to 2022. New hydrological insights for the regionThis study used an empirical copula-based CDH index and implemented the path analysis to quantify the response of severity changes of compound drought-hot events to shifts of summer mean temperature and water deficit from the historical perspective. Our findings reveal a marked escalation in CDH severity across much of China. Path analysis divulges that the influence of rising summer temperatures on CDH severity has seen a significant uptick in the last 60 years (1962–2022), displaying a more considerable contribution rate than the earlier 60-year span (1901–1961). This points to the changing impact of temperature on CDH events over recent decades. During the initial interval (1901–1961), we saw a 0.7% and 1.7% per-decade increase in areas affected by severe and moderate CDH events, respectively. Contrastingly, the subsequent period (1962–2022) experienced a more significant rise, with the area affected by severe and moderate events expanding over twice as much. Totally speaking, this exploration enhances our comprehension of the intensification in CDH event severity and the role of climatic drivers. These insights can contribute to improved risk assessments and the development of tailored adaptation and mitigation strategies in the face of ongoing climate change.

Evaluation and Impact Assessment of Meteorological Drought Characteristics over West Africa

When a region receives little precipitation for an extended period of time, droughts occur. The environmental and socioeconomic effects it has make it one of the most devastating natural disasters that can happen to a location. For the purpose of enhancing early warning systems and lowering the risk of disasters caused by drought, research into the effects of meteorological drought characteristics on a location is crucial. Therefore, this study uses the Standard Precipitation Index (SPI) and Standard Precipitation Evapotranspiration Index (SPEI) to assess the effects of meteorological drought characteristics over West Africa. The study area includes West Africa's Saharan, Sahelian, and Sudanese climate zones, and Copernicus Service provided the meteorological information (temperature and precipitation). The data was based on four monthly time scales (3, 6, 12, and 24) and covered 42 years (1979-2021). The data were analyzed and the characteristics of the drought were assessed using SPI and SPEI. According to the results, there was extreme drought between the years 1982 to 1985 and year 2021. However, 1982 was the year with the highest incidence of drought, across nearly all the stations. Additionally, it was found that location 4 experiences drought continuously from 1982 to 2021, whereas all other locations experienced absence of drought for a short period of time. Comparing the SPI and SPEI results, SPI produced results for the drought analysis that were comparable to those of SPEI for each of the monthly time scales for all locations. Majority of the locations will likely experience drought every seven to ten years, with location 4 possibly experiencing at least a mild drought every year. Finally, this study suggests that, in light of the time-scales analysis and drought classification for each location, SPI and SPEI represent the most suitable measures for the region's drought monitoring.

Spatial and Temporal Evolution Patterns of Droughts in China over the Past 61 Years Based on the Standardized Precipitation Evapotranspiration Index

Based on the data of 2254 daily meteorological stations in China from 1961 to 2021, this study calculated the standardized precipitation evapotranspiration index (SPEI) of the national multi-time scale by using the FAO Penman–Monteith model to quantify the changes in dry and wet conditions. The Mann–Kendall mutation test, wavelet analysis, and other methods were used to study the spatial pattern and temporal evolution of drought. The results showed: (1) In the past 61 years, there were obvious spatial and temporal differences in drought in China, and the interannual variation in drought severity in SPEI-1, SPEI-3, and SPEI-12 gradually decreased at a rate of 0.005/10a, 0.021/10a, and 0.092/10a, respectively. (2) The time point of dry and wet mutation was 1989 according to the MK mutagenicity test. (3) Wavelet analysis showed that the drought cycle on the annual scale and the seasonal scale was consistent, and the main period was about 30 years. (4) In the past 61 years, the drought intensity of different degrees in China has shown a weakening trend, and the drought intensity reached the highest value in 61 years in 1978, at 1836.42. In 2020, the drought intensity was the lowest, at 261.55. (5) The proportion of drought stations has shown a decreasing trend. The proportion of drought-free stations has fluctuated greatly, ranging from 42.12% to 89.25%, with 2020 being the highest. This study provides a scientific basis for further research on the causes and coping strategies of drought and is of great significance for strengthening China’s drought monitoring, early warning ,and adaptation capabilities.

The standardized precipitation evapotranspiration index based on cumulative effect attenuation

The drought index is an important indicator that reflects the degree of regional drought and plays an important role in drought disaster monitoring, prediction and evaluation. According to the process definition of the standard precipitation evapotranspiration index (SPEI), the influence of cumulative effect attenuation was considered and aggregated at different time scales k, the linear attenuation coefficient was defined, and attenuation parameters were used to adjust the degree of attenuation. A value closer to the current month was assigned a larger weight, and a value further from the current month was assigned a smaller weight. Thus, the cumulative water surplus or deficit was recalculated, and the standard precipitation evapotranspiration index based on the cumulative effect attenuation (CASPEI) was defined and applied to the drought analysis of Heilongjiang Province, which has the highest grain output in China. The comparative analysis showed that the CASPEI dataset was more consistent with the log-logistic distribution, and its sensitivity in identifying regional drought events was greater than that of the SPEI, which effectively corrected the original index's errors in identifying the drought levels of some stations and was more consistent with regional historical drought events. The temporal and spatial variation analysis revealed that drought occurrence is seasonal: summer is the season with the highest frequency of severe drought and extreme drought, and the area experiencing extreme drought is greater than that during other seasons. The areas with moderate drought in spring and autumn were relatively large. With the increase in time scale, the area of extreme drought decreased, and the area of severe drought increased. The area of moderate drought increased, while the area of light drought decreased. The CASPEI can accurately judge the extent to which drought occurs to respond to drought and effectively reduce or prevent large losses in people and the economy caused by drought.

Extreme drought along the tropic of cancer (Yunnan section) and its impact on vegetation

The frequent occurrence of extreme weather events is one of the future prospects of climate change, and how ecosystems respond to extreme drought is crucial for response to climate change. Taking the extreme drought event in the Tropic of Cancer (Yunnan section) during 2009–2010 as a case study, used the standardized precipitation evapotranspiration index to analyse the impact of extreme drought on enhanced vegetation index (EVI), leaf area index (LAI) and gross primary productivity (GPP), and to analyzed the post extreme drought vegetation recovery status. The results indicate the following: (1) Due to the cumulative effects of drought and vegetation phenology, vegetation growth in the months of March to May in 2010 was more severely affected. (2) Compared to EVI and LAI, GPP is more sensitive to drought and can accurately indicate areas where drought has impacted vegetation. (3) Following an extreme drought event, 70% of the vegetation can recover within 3 months, while 2.87–6.57% of the vegetation will remain unrecovered after 6 months. (4) Cropland and grassland show the strongest response, with longer recovery times, while woodland and shrubland exhibit weaker responses and shorter recovery times. This study provides a reference for the effects of extreme drought on vegetation.

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.

Response of Ecosystem Carbon–Water Fluxes to Extreme Drought in West Asia

Global warming has resulted in increases in the intensity, frequency, and duration of drought in most land areas at the regional and global scales. Nevertheless, comprehensive understanding of how water use efficiency (WUE), gross primary production (GPP), and actual evapotranspiration (AET)-induced water losses respond to exceptional drought and whether the responses are influenced by drought severity (DS) is still limited. Herein, we assess the fluctuation in the standardized precipitation evapotranspiration index (SPEI) over the Middle East from 1982 to 2017 to detect the drought events and further examine standardized anomalies of GPP, WUE, and AET responses to multiyear exceptional droughts, which are separated into five groups designed to characterize the severity of extreme drought. The intensification of the five drought events (based on its DS) increased the WUE, decreased the GPP and AET from D5 to D1, where both the positive and negative variance among the DS group was statistically significant. The results showed that the positive values of standardized WUE with the corresponding values of the negative GPP and AET were dominant (44.3% of the study area), where the AET values decreased more than the GPP, and the WUE fluctuation in this region is mostly controlled by physical processes, i.e., evaporation. Drought’s consequences on ecosystem carbon-water interactions ranged significantly among eco-system types due to the unique hydrothermal conditions of each biome. Our study indicates that forthcoming droughts, along with heightened climate variability, pose increased risks to semi-arid and sub-humid ecosystems, potentially leading to biome restructuring, starting with low-productivity, water-sensitive grasslands. Our assessment of WUE enhances understanding of water-carbon cycle linkages and aids in projecting ecosystem responses to climate change.

Drought Assessment of Yeşilırmak Basin Using Long-term Data

Drought is a prolonged period of inadequate rainfall, such as one season, one year or several years, on a statistical multi-year average for a region. Drought is a natural disaster effective on several socio-economic activities from agriculture to public health and leads to deterioration of the environment sustainability. The drought starts with meteorological drought, continues with agricultural and hydrological drought, and when it is in the socioeconomic dimension, the effects begin to be observed. Generally, drought studies are based on drought indices in the literature. This study applied long-term precipitation, temperature, and evaporation data from Samsun, Tokat, Merzifon, Çorum and Amasya meteorological stations from 1961 to 2022 to investigate the drought in the Yeşilırmak basin of Turkey. The present study applied Standardized Precipitation Index (SPI), and Effective Drought Index (EDI), China Z- Index (CZI) and Standardized Precipitation Evapotranspiration Index (SPEI) based on daily, monthly, seasonal, and annual time periods to evaluate drought. The Sen slope and Mann-Kendall test were employed for data analysis. The results revealed that the monthly drought indices for the study area were almost identical for the study area. Although dry and wet periods were observed.

On the timescale of drought indices for monitoring streamflow drought considering catchment hydrological regimes

Abstract. There is a wide variety of drought indices, yet a consensus on suitable indices and temporal scales for monitoring streamflow drought remains elusive across diverse hydrological settings. Considering the growing interest in spatially distributed indices for ungauged areas, this study addresses the following questions: (i) What temporal scales of precipitation-based indices are most suitable to assess streamflow drought in catchments with different hydrological regimes? (ii) Do soil moisture indices outperform meteorological indices as proxies for streamflow drought? (iii) Are snow indices more effective than meteorological indices for assessing streamflow drought in snow-influenced catchments? To answer these questions, we examined 100 near-natural catchments in Chile with four hydrological regimes, using the standardised precipitation index (SPI), standardised precipitation evapotranspiration index (SPEI), empirical standardised soil moisture index (ESSMI), and standardised snow water equivalent index (SWEI), aggregated across various temporal scales. Cross-correlation and event coincidence analysis were applied between these indices and the standardised streamflow index at a temporal scale of 1 month (SSI-1), as representative of streamflow drought events. Our results underscore that there is not a single drought index and temporal scale best suited to characterise all streamflow droughts in Chile, and their suitability largely depends on catchment memory. Specifically, in snowmelt-driven catchments characterised by a slow streamflow response to precipitation, the SPI at accumulation periods of 12–24 months serves as the best proxy for characterising streamflow droughts, with median correlation and coincidence rates of approximately 0.70–0.75 and 0.58–0.75, respectively. In contrast, the SPI at a 3-month accumulation period is the best proxy over faster-response rainfall-driven catchments, with median coincidence rates of around 0.55. Despite soil moisture and snowpack being key variables that modulate the propagation of meteorological deficits into hydrological ones, meteorological indices are better proxies for streamflow drought. Finally, to exclude the influence of non-drought periods, we recommend using the event coincidence analysis, a method that helps assessing the suitability of meteorological, soil moisture, and/or snow drought indices as proxies for streamflow drought events.

Analysis of recent trends and spatiotemporal changes of droughts over Serbia using high-resolution gridded data

The purpose of this study is to analyze the spatiotemporal variations in drought in Serbia over recent decades using the standardized precipitation evapotranspiration index (SPEI). The analysis is performed for time series of 1-, 3-, 6-, and 12-month SPEI indices (SPEI-01, SPEI-03, SPEI-06 and SPEI-12, respectively) calculated using the E-OBS gridded dataset with a horizontal resolution of 0.1° for the period of 1950–2022. In general, it was found that negative SPEI trends prevailed in Serbia for all months and all analyzed SPEI timescales, indicating a more frequent appearance of droughts in recent decades. Moreover, for a large majority of month-timescale combinations, the area with a negative trend was larger than the area with a positive trend. For several reasons, we focused on the SPEI for August. First, the SPEI for this month shows one of the most pronounced negative trends, which is rather consistent over different timescales and consistent in terms of the area over which the trend was found to be significant. Statistically significant negative trends of SPEI-03, −06 and − 12 for August were found over 11, 17 and 38% of the country area, respectively. To better understand the trends and changes in the SPEI for August, focusing on extreme and severe drought events, an innovative trend analysis (ITA) was applied. It was found that the occurrence of both extreme and severe SPEI drought categories has doubled over the last thirty years in comparison with the previous thirty-year period. Additionally, more frequent transitions from neutral and wet categories to dry categories were observed through an application of the Markov chain. Second, the special focus on SPEI-03, −06 and − 12 for August was motivated by the fact that we found a significant correlation between these index values and the annual maize production in Serbia, indicating that the SPEI for August can be useful for the assessment of drought impact on agriculture. To better understand interannual variability and long-term trends, this paper also explores the relations between the SPEI over Serbia and large-scale circulation patterns, together with general global and continental warming trends. Our findings indicate that drought changes over recent decades may be driven by the interplay of long-term warming and large-scale circulation patterns. In the context of climate change, especially climate projections, Serbia is situated in a region for which climate projections of mean precipitation change demonstrate weak signals and often with conflicting signs of change within multimodel ensembles. From this point of view, our results can be seen as a contribution to a better understanding of changes in extremes, in our case droughts, that can be hidden by inconclusive changes in the mean values. Finally, due to the similar situation in terms of precipitation and drought trends and changes, our results may be relevant for a wider region, and not only for Serbia.

Vegetation and Evapotranspiration Responses to Increased Atmospheric Vapor Pressure Deficit across the Global Forest

A forest is vulnerable to drought and plays important roles in the regulation of carbon and water cycling in a terrestrial ecosystem. Atmospheric vapor pressure deficit (VPD) has been identified as an increasingly major factor in plant functioning and has been established as a main contributor to recent drought-induced plant mortality, independent of other drivers associated with climate change. However, most previous studies have focused on the effects of climate warming and CO2 enrichment on vegetation growth, without considering the effects of an increased VPD on vegetation growth and evapotranspiration (ET) in forest ecosystems. This could lead to a large uncertainty in estimating the variability in forest carbon sinks. Based on the long-term satellite data, we investigated the response of the leaf area index (LAI) and ET to the VPD via a partial correlation analysis in this study. We also examined the temporal variability in the partial coefficients within a ten-year moving window. The results showed that over 50% of the region displayed a negative partial correlation between the LAI, ET, and VPD, and those pixels were mainly concentrated in North America and the plains of Eastern Europe. Regions with a negative trend of partial correlation in both the LAI and ET are mostly located in the plains of Eastern Europe and the Siberian Plain of western Russia, while the positive trend is mainly in South America. The plains of Eastern Europe are becoming drier, which was proved by the interannual trend of the Standardized Precipitation Evapotranspiration Index (SPEI) and soil water content (SWC). Additionally, the LAI and ET in those areas exhibited a significant positive correlation with the SWC based on the moving window average. This study suggests that the role of the VPD on vegetation will become increasingly prominent in the context of future climate change for the forest ecosystem.

Unleashing the power of machine learning and remote sensing for robust seasonal drought monitoring: A stacking ensemble approach

Droughts cause significant economic losses in many regions around the world, highlighting a need to more accurately quantify implications of drought on production and water management. Remote sensing technologies and machine learning-based models offer promising solutions for timely and accurate regional drought monitoring, but the accuracy of such approaches is constrained by both the conceptual design and algorithms underpinning such approaches. We developed a machine learning stacking ensemble approach to overcome such limitations, drawing upon Precipitation Estimation from Remotely Sensed Information using Artificial Neural Network-Climate Data Record (PERSIANN-CDR), Moderate Resolution Imaging Spectroradiometer (MODIS) remote sensing products, and climate zoning data to estimate 3-month scale SPEI (Standardized Precipitation Evapotranspiration Index, SPEI-3) in 9 sub-regions of China. We compared 19 individual machine learning models and used the stacking approach to select the most robust meta-model for SPEI-3 prediction. We found that CatBoost Regressor (CBR), Extra Trees Regressor (ETR), Extreme Gradient Boosting (XGB), Light Gradient Boosting Machine (LGBM), and Random Forest (RF) were the top individual models for predicting drought, while using CBR as the stacked meta-model achieved the best performance. The R2 values for the stacking model with CBR as the meta-model were 0.9065 and 0.8218 in the eastern and western regions, respectively. We then employed the stacking model with CBR as the meta-model to generate seasonal drought maps based on SPEI-3 across different years and seasons. Our predicted SPEI-3 was compared with drought maps generated by the GPCC and ERA5 reanalysis datasets. We found a strong correlation between them with R2 exceeding 0.8 across multiple years and seasons, indicating our machine learning stacking ensemble approach had a good performance in monitoring seasonal drought conditions. The findings of our study establish a standardized protocol for predicting droughts in the agricultural sector, enabling its widespread application irrespective of variations in terrain and climate.

Characteristics and determining factors of spring-summer consecutive drought variations in Northwest China

Northwest China (NWC, defined by its administrative division) is an arid and semi-arid region where continuous drought occurs in spring and summer that severely affects crop growth and yields. However, the characteristics of spring-summer consecutive drought (SSCD) in NWC remain unknown. Based on daily observation data from 118 stations in NWC for the period 1961–2017, we investigated the variations in SSCD, as defined by the standardized precipitation evapotranspiration index (SPEI), and their determining factors. The SSCD intensity in NWC underwent a two-stage change rather than a sustained non-significant decrease during 1961–2017. Specifically, this intensity significantly decreased before 1993 (−0.19 decade−1; p < 0.05) and increased thereafter (0.21 decade−1; p < 0.05). The variations in SSCD in NWC were primarily determined by the variations in potential evapotranspiration (ET0). The decrease and increase in ET0 (−19.52 mm decade−1 and 10.77 mm decade−1, respectively; p < 0.05) dominated those of SSCD in NWC during 1961–1993 and 1994–2017, respectively, with respective explanatory variances of 99.6% and 93.2%. Furthermore, the decrease in wind speed (−0.14 m s−1 decade−1; p < 0.05) dominated that in ET0 before 1993, whereas the decrease in relative humidity (−1.47% decade−1; p < 0.05) and the weak recovery of wind speed (−0.06 m s−1 decade−1; p < 0.05) jointly dominated the increase in ET0 after 1993. Along with the increased SSCD after 1993, significant changes also occurred in the atmospheric circulation. An intensified anticyclonic anomaly occurred to the west of NWC, which provided a favorable circulation background for the increase in SSCD in NWC after the early 1990s. These findings provide a basis for further understanding drought and sustainable agricultural development in NWC under climate change.

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.

Characteristics of Dry and Wet Changes and Future Trends in the Tarim River Basin Based on the Standardized Precipitation Evapotranspiration Index

Global changes in drought and wetness and their future trends in arid regions have recently become a major focus of research attention. The Tarim River Basin (TRB) in Xinjiang, China, is among the most climate-sensitive regions in the world. This study uses data from the past 60 years (1962–2021) to analyze the spatial and temporal features of drought and wetness conditions in the TRB, calculating the Standardized Precipitation Evapotranspiration Index (SPEI). Trend detection for SPEI is performed using the BEAST mutation test, identification of drought events using the theory of operations, and spatial and temporal analyses of dry and wet changes using Empirical Orthogonal Function (EOF) decomposition. Additionally, the CMIP6 dataset is used to estimate future changes. The study results indicate the following: (1) From 1962 to 1998, the TRB exhibited a “warm and wet” trend that suddenly shifted from “wet-to-dry” in 1998 and subsequently transitioned to a pronounced “warm and dry” trend. (2) After the “wet-to-dry” shift, the frequency of drought events noticeably increased. The northern section of the basin witnessed more frequent drought events, albeit with lower severity, while the southern part had fewer occurrences but with higher severity. The spatial distribution of drought event frequency and severity is inconsistent. (3) The EOF decomposition results for SPEI-variable fields at 1-, 3-, and 6-month time scales show that the cumulative variance contribution rate of the first three principal spatial modal feature vectors exceeds 70%. The spatial distribution of the modes includes a consistent pattern across the entire basin, a north–south opposite pattern, and an east–west opposite pattern. (4) The future trend of drought in the TRB is expected to intensify, manifesting a spatial pattern characterized by dryness in the middle of the basin and wetness around the periphery. These research findings can provide support for decisions addressing regional drought risks.

Agricultural drought risk assessment in Southern Plateau and Hills using multi threshold run theory

Agricultural drought in this study is characterized and assessed using the Standardized Soil Moisture Index (SSMI). Employing run theory with three thresholds, SSMI identifies grid-wise drought events, quantifying them in terms of peak, frequency, duration and intensity. To evaluate SSMI's performance in assessing drought risk, a comparison is made with the Standardized Precipitation Evapotranspiration Index (SPEI). Spatially, frequency aligns with duration for both indices, indicating regions with longer drought events also experience more frequent occurrences. Meteorological droughts in SPH from 1991 to 2020 are found to be more intense, frequent, longer-lasting, but less severe. Spatially heterogeneous drought events are identified by both indices, with SSMI proving a reliable indicator of drought risk, similar to SPEI. Archimedean copula functions reveal high joint occurrences of various drought characteristics, suggesting significant correlations. The results show that the joint occurrences of duration and peak, intensity and frequency, and duration and peak are all high. This insight is crucial for enhancing drought risk management and developing more effective warning systems.

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.