Standardized Index Research Articles

Characteristics of propagation from meteorological drought to ecological drought in China: Lag and cumulative effects

The increasing frequency and severity of droughts will seriously impact agricultural production and the ecological environment. Studying the drought lag (DL) and drought cumulative (DC) effects of propagation between meteorological drought to ecological drought can help to enhance the capacity of ecological drought monitoring. Therefore, in this study, the meteorological drought index (non-stationary standardized precipitation evapotranspiration index, NSPEI) and ecological drought index (vegetation condition index, VCI) were obtained using ERA5 reanalysis data and MODIS vegetation index data, respectively. The purpose of this study was to reveal the DL and DC effects of the propagation of meteorological drought to ecological drought and investigate the spatial heterogeneity of drought propagation in different underlying surfaces and climate zones. The results indicated that: 1) Northern China is more vulnerable to severe and extreme droughts, and 68% of the region has significantly slower ecological droughts. 2) The western temperate zone, highland climate zone, and subtropical zone have shorter DL times for subtropical drought and longer DL times for eastern temperate zones. 3) DC and DL effects have similar spatial distributions. However, the DC duration of grassland and bare ground is shorter in the northwest and south, and longer in grassland and farmland in northern China. 4) China overall is more affected by the DC effect, while the DL effect is more significant in southern temperate zone.

Design element extraction of plantar pressure imaging employing meta-learning-based graphic convolutional neural networks

Segmenting plantar pressure images intelligently can provide valuable insight for people with high blood pressure, making bespoke footwear requirements possible and resulting in more comfortable shoe designs. It is, however, difficult to extract design elements from a segmented image dataset. To address this challenge, we propose an ML-GNN model that segments plantar pressure images using metal-earning. The first part of the paper presents a method for extracting image features that reduce the complexity of the ML-GNN algorithm. To create the network structure, we propose optimization meta-based learning. Using a meta-learning-based graphic neural network, we enhance our mask-based CNN prediction model with VGG16 and CNN layers. We pre-processed the plantar pressure dataset using pressure-sensing data acquisition and compared the results. By defining standard image segmentation indices, we demonstrate the high effectiveness of our research. We have developed an ML-GNN model that improves the segmentation accuracy of plantar pressure images and can also be applied to other sensor image datasets. Through our shoe-last customization approach, we enable the shoe industry to manufacture shoes more efficiently, particularly for people with specific healthcare needs who require bespoke shoe designs. Our findings demonstrate the potential of intelligent image segmentation to advance the field of footwear design and improve the lives of people with specific health requirements.

Dryland hydroclimatic response to large tropical volcanic eruptions during the last millennium

Drylands are highly vulnerable to climate change due to their fragile ecosystems and limited ability to adapt. In contrast to the global drying after tropical volcanic eruptions shown previously, we demonstrate that large tropical volcanic eruptions can induce significant two-year hydroclimatic wetting over drylands by employing the last millennium simulations. During this wetting period, which extends from the first to the third boreal winter after the eruption, several hydroclimatic indicators, such as self-calibrating Palmer Drought Severity Index based on the Penman-Monteith equation for potential evapotranspiration (scPDSIpm), standard precipitation evapotranspiration index (SPEI), aridity index (AI), top-10cm soil moisture (SM10cm), and leaf area index (LAI), show significant positive anomalies over most drylands. The primary contribution to the wetting response is the potential evapotranspiration (PET) reduction resulting from dryland surface cooling and reduced solar radiation, as well as a weak contribution from increased precipitation. The latter is due to the wind convergence into drylands caused by slower tropical cooling compared to drylands. The wetting response of drylands to volcanic eruptions also demonstrates some benefits over the global hydrological slowdown resulting from stratospheric aerosol injection, which replicates the cooling effects of volcanic eruptions to address global warming.

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.

Improving the probabilistic drought prediction with soil moisture information under the ensemble streamflow prediction framework

Reliable drought prediction should be preceded to prevent damage from potential droughts. In this context, this study developed a hydrological drought prediction method, namely ensemble drought prediction (EDP) to reflect drought-related information under the ensemble streamflow prediction framework. After generating an ensemble of standardized runoff index by converting the ensemble of generated streamflow, the results were adopted as the prior distribution. Then, precipitation forecast and soil moisture were used to update the prior EDP. The EDP + A model included the precipitation forecast with the PDF-ratio method, and the observed soil moisture index was reflected in the former EDP and EDP + A via Bayes’ theorem, resulting in the EDP + S and EDP + AS models. Eight basins in Korea with more than 30 years of observation data were applied with the proposed methodology. As a result, the overall performance of the four EDP models yielded improved results than the climatological prediction. Moreover, reflecting soil moisture yielded improved evaluation metrics during short-term drought predictions, and in basins with larger drainage areas. Finally, the methodology presented in this study was more effective during periods with less intertemporal variabilities.

Dietary quality and dietary greenhouse gas emissions in the USA: a comparison of the planetary health diet index, healthy eating index-2015, and dietary approaches to stop hypertension

BackgroundThe Planetary Health Diet Index (PHDI) measures adherence to the dietary pattern presented by the EAT-Lancet Commission, which aligns health and sustainability targets. There is a need to understand how PHDI scores correlate with dietary greenhouse gas emissions (GHGE) and how this differs from the carbon footprints of scores on established dietary recommendations. The objectives of this study were to compare how the PHDI, Healthy Eating Index-2015 (HEI-2015) and Dietary Approaches to Stop Hypertension (DASH) relate to (a) dietary GHGE and (b) to examine the influence of PHDI food components on dietary GHGE.MethodsWe used life cycle assessment data from the Database of Food Recall Impacts on the Environment for Nutrition and Dietary Studies to calculate the mean dietary GHGE of 8,128 adult participants in the 2015–2016 and 2017–2018 cycles of the National Health and Nutrition Examination Survey (NHANES). Poisson regression was used to estimate the association of (a) quintiles of diet score and (b) standardized dietary index Z-scores with dietary GHGE for PHDI, HEI-2015, and DASH scores. In secondary analyses, we used Poisson regression to assess the influence of individual PHDI component scores on dietary GHGE.ResultsWe found that higher dietary quality on all three indices was correlated with lower dietary GHGE. The magnitude of the dietary quality-dietary GHGE relationship was larger for PHDI [-0.4, 95% CI (-0.5, -0.3) kg CO2 equivalents per one standard deviation change] and for DASH [-0.5, (-0.4, -0.6) kg CO2-equivalents] than for HEI-2015 [-0.2, (-0.2, -0.3) kg CO2-equivalents]. When examining PHDI component scores, we found that diet-related GHGE were driven largely by red and processed meat intake.ConclusionsImproved dietary quality has the potential to lower the emissions impacts of US diets. Future efforts to promote healthy, sustainable diets could apply the recommendations of the established DASH guidelines as well as the new guidance provided by the PHDI to increase their environmental benefits.

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.

Assessment of the Global Relationship of Different Types of Droughts in Model Simulations Under High Anthropogenic Emissions

AbstractThis study provides a global analysis of the relationship between drought metrics obtained from several climatic, hydrologic and ecological variables in a climate change framework using CMIP6 model data. A comprehensive analysis of the evolution of drought severity on a global scale is carried out for the historical experiment (1850–2014) and for future simulations under a high emissions scenario (SSP5‐8.5). This study focuses on comparing trends in the magnitude and duration of drought events according to different standardized indices over the world land‐surface area. The spatial and temporal relationship between the different drought indices on a global scale was also evaluated. Overall, there is a fairly large consensus among models and drought metrics in pointing to drought increase in southern North America, Central America, the Amazon region, the Mediterranean, southern Africa and southern Australia. Our results show important spatial differences in drought projections, which are highly dependent on the drought metric employed. While a strong relationship between climatic indices was evident, climatic and ecological drought metrics showed less dependency over both space and time. Importantly, our study demonstrates uncertainties in future projections of drought trends and their interannual variability related to the relationship among indices, stressing the importance of coherent climatic, hydrological and plant physiological patterns when analyzing CMIP6 model simulations of droughts under a warming climate scenario.

IJCM_388A: Sleep disturbances and associated factors among undergraduate medical students in a tertiary health care center in central India: A cross sectional study

Background: Within the rigorous environment of medical education, the well-being of students is a critical concern. Sleep disturbances, a prevalent yet understudied issue, may pose significant challenges to the academics and mental health of medical students. Objective: To assess the sleep disturbances and associated factors among undergraduate medical students in a tertiary health care center. Methodology: A cross-sectional study was conducted among 256 undergraduate medical students in a tertiary health care center from September to November 2023. Study was conducted using self-administered questionnaire consisting of sociodemographic profile and standardized tool Pittsburgh Sleep Quality Index. Descriptive and analytical statistics were used. Results: Out of 256 students,161(62.9%) were suffering from sleep disturbances.72% of them had less than 1 hour time gap between screen time and their regular sleep. There was a significant association between sleep disturbances and repeated worrying about lots of tests, illness during past month, felt isolated and feeling lost something in life. Conclusion: This study showed that majority of undergraduate medical students suffer from sleep disturbances. It becomes evident that fostering a supportive environment and promoting healthy sleep practices are indispensable for the overall wellbeing and success of medical students.

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.

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.

Modelling The Volatility of Frankfurt Stock Exchange (DAX) Returns Using hybrid Models

Recently, the interest of researchers in the use of hybrid models in the process of analyzing model time series with fluctuations and forecasting fluctuations in financial time series has increased significantly. Hybrid ARMA-GARCH models were created for medium- and long-term forecasts of time series of financial market index prices: ARMA models are used to analyze their linear component, which is a combination of autoregressive models and moving average models, and GARCH models are used to analyze the nonlinear component. which are generalized autoregressive models that depend on the nonconstancy of variance models. Hybrid ARMA-GARCH models eliminate the weaknesses and gaps that exist in each group of models (ARMA and GARCH) separately, which increases their forecasting accuracy and reliability, so they have already been successfully applied to model and forecast daily stock returns for three standard indices in the USA. The purpose of this article is to investigate which of the hybrid ARMA-GARCH models is optimal for forecasting the return of the DAX index, which is the most important stock index of the German securities market. It is the German equivalent of the American Dow Jones Index, has been calculated since 1988 by Deutsche Börse AG and reflects the total return on capital of the largest stock companies listed on the Frankfurt Stock Exchange (currently 40; by 2021 – 30): calculated as a weighted average of capitalization of the value of Free Float share prices on the Xetra electronic exchange, and also takes into account dividends on shares, assuming that the dividend is reinvested in the share on which it was accrued. The database of this study consisted of the daily closing prices of the DAX index presented on the official website of the Frankfurt Stock Exchange during the period from 01.01.2018 to 09.30.2023 (altogether about 1,500 observations), the stability of the time series was assessed using Expanded Dickey Fuller Liquidity (ADF). The article proposes 7 hybrid models, from which the one that is best suited for modeling the volatility of the DAX index is selected. It is an ARMA (2,3)-EGARCH (1,1) model because it captures volatility and leverage effects on DAX returns and its expected returns more than other models. The selection of the best alternative from the developed array of hybrid models was carried out according to the following criteria: AIC (Akaike Information Criterion), BIC (Bayesian Information Criterion), H-QIC (Hannan-Quinn Information Criterion).

FACTORS IMPACTING FINANCIAL LITERACY OF FARMERS IN KALABURAGI, KARNATAKA

The empirical study was conducted in 2023 in Kalaburagi, Karnataka. This reseacrh evaluated farmers’ ‘’financial literacy’’ levels and their impact on financial management. The financial literacy of farmers was assessed through a standardized knowledge index. Financial literacy was measured according to the OECD Toolkit (Organization for Economic Co-operation and Development). In the study area, 29 percent of farmers were high financial literate, 32 percent were moderate, and 39 percent were low financial literate. The financial literacy of farmers needed to improve. It was noted that was significant, implying 67 percent of the variation. Moreover, the significant F ratio (18.52) indicated that thefarmers’ financial literacy was impacted by age, education, farm income, and bank accounts. Farmer’s financial literacy significantly affected crop insurance, investment, KCC, mobile banking, crop loans, and other agriculture loans. To reach the farmers, better coordination should be with financial literacy centers and banks.

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.

수원국의 ESG 요인이 공여국의 ODA 재원 배분에 미친 영향

Purpose: This study aims to empirically analyze how ESG factors in recipient countries influence the donor’s ODA allocation.
 Originality: It analyses donors' ODA allocation behavior from an ESG perspective. It also explains the environmental factors of recipient countries, which have been increasingly emphasized since the UN SDGs agenda.
 Methodology: To analyze the impact of recipient countries' ESG factors on the donor’s ODA allocation, a standardized ESG index for each country is constructed. There are various unobservable factors which likely to influence the ODA allocation, hence time-varying and time-invariant fixed effects are incorporated. Meanwhile, the country-specific ESG indices are constructed using quantitative techniques.
 Result: ESG and SDG indicators demonstrate a statistically significant positive correlation. An analysis of the impact of ESG factors on the donor’s ODA allocation reveals that social factors played a significant role during 2005-2013. From 2014 to 2020, both social and environmental factors were statistically significant, while governance factors did not attain statistical significance.
 Conclusion and Implication: When examining the correlation between ESG and SDG indicators, a statistically significant positive correlation is observed. This suggests that ESG factors can act as a building block towards the achievement of SDGs in recipient countries. The ESG factors played varying roles over time. From 2005 to 2013, social factors were significant, while from 2014 to 2020, both social and environmental factors showed statistical significance. It can be explained by the paradigm shift in development cooperation. However, governance factors did not achieve statistical significance in any estimation methods, implying that, considerations of governance factors might have been relatively insufficient.

Air and noise pollution analyses near oil and gas fields in the Mahakam Delta, Kalimantan, Indonesia

The Mahakam Delta is a strategic industrial area in East Kalimantan, Indonesia, in which oil and gas industries reduce ambient air quality and generate excessive noise. This research aimed to analyze the ambient air and noise pollution attributed to oil and gas production activities in the delta. Relevant parameters at five sampling points (UA-02 to UA-06) were measured and further analyzed in the laboratory, including total suspended particulate (TSP), SO2, Pb CO, NO2, O3, temperature, humidity, wind direction, wind speed, and noise. Indonesia’s air pollutant standard index (locally abbreviated as ISPU) with four parameters (CO, NO2, SO2, and O3) was used to determine the ambient air quality, and provisions written in the Decree of the Minister of Environment No. 50 Kep-48/MENLH/11/1996 were consulted for the noise quality assessment. Results showed ISPU values in the range of 0–50 at the five sampling points, suggesting good ambient air quality and compliance with Government Regulation No. 22 of 2021. In addition, it was revealed that the noise parameter was lower than the upper threshold set in the Ministerial Decree, namely 65 dB. Nevertheless, monitoring air and noise quality at the main pollutant sources should be regulated through policies and implemented to protect the public from exposure to potential pollutants.

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.