Quantile Method Research Articles

Association between renal function impairment and body roundness index (BRI) in elderly hypertensive patients

Abstract Objective The purpose of this study was to explore the associations of renal function impairment and body roundness index (BRI) in elderly hypertensive patients. Methods This is a community-based observational and cross-sectional study.A total of 1482 elderly hypertensive patients from our center in 2021 were enrolled. Waist-to-hip ratio(WHR) , Body mass index(BMI) and BRI were calculated with validated formulas. According to tri quantile method of BRI level , the patients were divided into low , medium and high BRI groups. The incidence of renal function impairment in the three groups was compared to explore the associations of renal function impairment and BRI in elderly hypertensive patients. Result According to the estimated glomerular filtration rate (eGFR) level, the patients were divided into two groups: impaired renal function group and non impaired renal function group. 133 cases were included in the group with impaired renal function, and 1349 cases were included in the group without impaired renal function. The proportion of patients with impaired renal function was 8%, 497 were in the low BRI group, 491 in the medium BRI group, and 494 in the high BRI group. Comparing the incidence of renal function impairment in patients with different BRI levels, the results showed that the incidence of renal function impairment in patients with high BRI was higher than that in patients with low BRI (7.0%vs.11.5%,p=0.015). The results of binary logistic regression analysis showed that the risk of renal function impairment increased by 19% for each unit of BRI increase (95%CI 1.037-1.122,P=0.024). After adjusting for age, gender, blood lipid, smoking and drinking history, years of hypertension, diabetes, years of diabetes,and random blood glucose levels, the risk of renal function impairment in high BRI group was still significantly higher than low BRI group (OR=1.697,95%CI 1.031-2.794,P=0.038),shows that BRI is an independent risk factor for renal function impairment in elderly hypertension patients. The receiver operating characteristic curve (ROC) results show that the prediction ability of BRI in the whole population is better than that of BMI and waist circumference (WC), and the prediction ability of BRI in elderly female population is the best (AUC=0.569). Conclusion Increasing of BRI is correlated with the occurrence of renal function impairment in elderly hypertension patients, and has the best predictive value for renal function impairment in elderly women with hypertension.

Sex, Racial/Ethnic, and Regional Disparities in Pulmonary Embolism Mortality Trends in the USA, 1999-2020.

While the National Institutes of Health emphasize integrating sex as a biological variable into research, specific considerations of sex-related differences in pulmonary embolism (PE) mortality trends remain scarce. This study examines sex-based PE mortality trends across regional and demographic groups in the USA from 1999 to 2020. A retrospective analysis of National Center for Health Statistics mortality data from 1999 to 2020 was conducted. Using ICD-10 code I26, PE decedents were identified. Piecewise linear regression assessed sex-based temporal trends in PE mortality by age, race/ethnicity, and census region. Annual percentage changes and average annual percentage changes were derived using Weighted Bayesian Information Criteria. The 95% confidence intervals were estimated using the empirical quantile method. From 1999 to 2020, a total of 179,273 individuals died in the USA due to PE, resulting in an age-adjusted mortality rate of 2.5 per 100,000 persons (95% CI, 2.5-2.5). While men and women exhibited comparable rates in recent time segments and across most subcategories, a higher mortality trend among males compared to females was observed among non-Hispanic White and Hispanic individuals and residents of the Western US census region. These results remained robust even after excluding data from 2020, accounting for the potential impact of the COVID-19 pandemic. Our study highlights sex-based disparities in PE mortality trends in the USA from 1999 to 2020. Despite overall stable mortality rates, higher trends among males were evident in specific demographic groups and regions. These findings emphasize the importance of targeted interventions to mitigate PE-related mortality discrepancies across diverse populations.

A study on the backtesting and correlation of systemic risk in Chinese financial institutions: an empirical analysis based on MES and Delta CoVaR

Effectively measuring the risk level of financial institutions is the key to preventing and defusing systemic risks. This paper uses the unconditional coverage test method to backtest the systemic risk of 35 listed financial institutions in China, and uses the quantile coherence method to construct a quantile frequency domain correlation network to explore the frequency domain mechanism of systemic risk correlation of financial institutions. The study found that: (1) During the financial market crisis, the Delta CoVaR commonly used systemic risk measurement indicators such as MES, CoVaR, and failed to effectively measure the systemic risk of China's financial institutions. (2) Compared with the short-term correlation layer and the medium-term correlation layer, China's financial institutions have obvious extreme risk linkage effects in the long-term correlation layer.(3) In the short-term, medium-term and long-term correlation layers, small and medium-sized financial institutions such as Ningbo Bank, Industrial Securities and Dongfang Energy all have high correlation importance. Relevant departments should pay attention to preventing the risk of "too correlated to fail" of small and medium-sized financial institutions in my country.

The impact of urban morphology on land surface temperature across urban-rural gradients in the Pearl River Delta, China

Optimizing urban morphology effectively mitigates urban heat island effects. However, previous research on urban morphology and land surface temperature (LST) has often concentrated on entire cities or specific local areas, neglecting the heterogeneity within urban regions. This study investigates the impact of two-dimensional (2D) and three-dimensional (3D) urban morphology on LST based on urban-rural gradients. Firstly, we calculated nine 2D and 3D indicators to comprehensively depict the urban morphology. Then, we employed a multi-iterative quantile method based on nighttime light data to delineate three city subclasses: urban core, suburb, and rural area (USR). Finally, we quantified the impact of these indicators on LST thorough correlation analysis and a random forest (RF) model. Results indicate that, overall and post-USR classification, the sum of building area (SBA) is the primary factor influencing LST, contributing up to 31.6% in suburb. The main 3D factors affecting LST differ across subclasses: in rural area, it is the sum of building surface (SBS, 12.9%); in suburb, it is the spatial congestion degree (SCD, 9.7%); and in urban cores, it is the mean building height (MBH, 12.0%). Notably, the influence of 3D indicators increases from 68.1% in rural area to 72.3% in urban core. In urban core, MBH and standard deviation of height (SDH) are negatively correlated with LST. Case studies in Tianhe District (Guangzhou) and Futian District (Shenzhen) confirmed these indicators do have a certain effect on reducing the temperature. This study provides valuable insights for improving the urban thermal environment and promoting sustainable urban development.

What is the role of environmental stress on public health? Asymmetric evidence on carbon emissions, ecological footprint, and load capacity factor

The aim of this paper is to analyze the effects of carbon emission, ecological footprint, which takes into account the demand side of the environment, and load capacity factor, which takes into account both the supply and demand sides of the environment, on health expenditures with conventional and quantile methods. According to the conventional co-integration approach, there is no relationship between the environment and health expenditures. The other side, the findings obtained from the quantile co-integration method, which can give robust results in the presence of tailed distributions and possible endogeneity problems and consider the asymmetric structure in the data set, show the existence of a long-term relationship between the variables. According to the coefficient estimates, while carbon emission and ecological footprint increase health expenditures, the load capacity factor decreases.

Visualization and Analysis of Three-Way Data Using Accumulated Concept Graphs

This paper introduces the Accumulated Concept Graph (ACG), a visualization tool based on the quantile method designed to analyze three-way data, including distributional data. Such data often have complex structures that make it difficult to identify patterns using conventional visualization techniques. The ACG represents each object with one or more monotonic line graphs. As a result, the three-way data are visualized as a set of parallel monotonic line graphs that never intersect. This non-intersecting property allows for the easy identification of both macroscopic and microscopic patterns within the data. We demonstrate the usefulness of ACGs and principal component analysis in the analysis of real three-way datasets.

Association between fat-soluble vitamins and metabolic syndromes in US adults: a cross-section study from NHANES database

BackgroundPrevious studies have shown significant associations between individual fat-soluble vitamins (FSVs) and metabolic syndromes (MetS). However, evidence on the multiple FSVs co-exposure and MetS odds is limited. Given that individuals are typically exposed to different levels of FSVs simultaneously, and FSVs can interact with each other. It’s necessary to explore the association between multiple FSVs co-exposure and MetS odds. This study aims to address this gap in general U.S. adults aged ≥ 20 years.MethodsWe conducted a cross-sectional study utilizing data from the National Health and Nutrition Examination Surveys (NHANESs) 2003–2006 and 2017–2018. Three FSV, including vitamin A (VA), vitamin E (VE), and vitamin D (VD), and MetS diagnosed according to the ATP III guidelines were selected as exposure and outcome, respectively. Multivariable-adjusted logistic model was used to explore the associations of individual FSV exposure with MetS odds and MetS components. Restricted cubic splines were performed to explore the dose–response relationships among them. The quantile g-computation method was adopted to explore the associations of multiple FSVs co-exposure with MetS odds and MetS components.ResultsThe presented study included a total of 13,975 individuals, with 2400 (17.17%) were diagnosed with MetS. After adjusting for various confounders, a positive linear pattern was observed for serum VA and VE and MetS associations. Serum VD was found to be negatively associated with MetS in a linear dose–response way. For each component of MetS, higher serum VA and VE were associated with higher triglyceride and high-density lipoprotein; higher serum VD was negatively associated with triglyceride, blood pressure, and fasting plasma glucose. MetS odds increased by 15% and 13%, respectively, in response to one quartile increase in FSVs co-exposure index (qgcomp) in the conditional model (OR = 1.15, 95%CI: 1.06, 1.24) and the marginal structural model (OR = 1.13, 95%CI: 1.06, 1.20). Besides, co-exposure to VA, VE, and VD was positively associated with triglyceride, high-density lipoprotein, and blood pressure levels.ConclusionFindings in the present study revealed that high serum VA and VE levels were associated with elevated MetS odds, while serum VD was inversely associated with MetS odds. FSVs co-exposure was positively associated with MetS odds.

Evolution characteristics and driving factors of potential non-point source pollution risks in a watershed affected by land use changes

Land use types, land development and utilization intensity within watersheds have changed based on intensifying human activities and climate change, thereby inducing spatiotemporal variations in non-point source pollution (NPS), significantly impacting soil and water quality. This study performed a case study on an ecological environment functional zone at the northern foot of Qinling Mountains, an area strongly affected by human activities and land use changes. It employed an improved potential non-point pollution index (PNPI) model to analyze potential non-point source pollution (PNPS) and associated risk evolution characteristics in watershed over the past 30 years. The results indicate that from 1990 to 2020, the dominant land use categories were forest and arable land, making up 95 % of the entire watershed area. Notably, urban residential land presented the most significant expansion rates and nearly doubled in area between 1990 and 2020, whereas shrubland, grassland, and unused land showed a decreasing trend. With the application of the quantile classification method, PNPS risk values were divided into five categories: very low, low, moderate, high, and very high. A polarized trend in risk was observed, with increases in areas influenced by human activities and rapid expansion of very high-risk regions. Concurrently, the pollution risk in the upstream water source area decreased. In recent years, accelerated urbanization has been the main driver causing expansion of high PNPS risk regions. This study explores the spatial and temporal evolution of PNPS risk in the Heihe Basin by using an improved PNPI model. The improved model is more accurate in calculations and provides a better understanding of the distribution of PNPS, which is an important reference for watershed management and water resource governance.

Analyzing Overnight Momentum Transmission: The Impact of Oil Price Volatility on Global Financial Markets

Fluctuations in oil prices substantially impact both the real economy and international financial markets. Despite extensive studies on oil market dynamics and overnight momentum, a comprehensive understanding of the link between oil price changes and energy market momentum, as well as their broader influence on global financial markets, remains elusive. This study delves into the intricate mechanics of overnight momentum transmission within financial markets, focusing on its origin in oil price fluctuations and its overarching impact on market dynamics. Employing the quantile VAR method, we analyze daily market data from 3 January 2014 to 17 January 2024. This study emphasizes the significance of overnight momentum on the transmission of volatility, particularly in the tails of the distribution, and highlights the necessity for efficient strategies to govern financial stability. The shale oil revolution, COVID-19, the Russia–Ukraine war, and the Israel–Hamas conflict have significantly impacted the interconnectivity of financial markets on a global scale. It is crucial for policymakers to give priority to the monitoring of the energy market to reduce risks and improve the resilience of the system.

A FMKL-GLD Quantile Method for Estimating Economic Growth in Nigeria in the Presence of Multicollinearity and Outlier

Nigeria’s economic growth posed to be a serious concern to policymakers, economics and scholars due to the challenges of economic characteristics, consequences and contradictions. Despite the effort made by the Central Bank of Nigeria in implementing several policies such as tightening of monetary policy rate and heavy borrowing for infrastructural development to stimulate economic growth in the past few years. The economic growth in Nigeria between 1999-2007 was 6.95 percent, between 2008-2010, it was stood 7.98 percent and it was 4.80 percent between 2011-2015 and from 2016 till day the economic growth stood at 0.81 percent. In this study, we estimate parameters to determine Nigerian economic growth in the presence of multicollinearity and outliers. We employed the FMKL-GLD quantile model to quarterly data from 1986 to 2021 obtained from the Central Bank of Nigeria. Exploratory data analysis (EDA) and diagnostic test carried out ascertained the presence of multicollinearity and outlier. However, it was discovered that in model that INDT, RINR, REXR and OPEN contributed positively to the economic growth to the turn of 0.15%, 0.24%, 0.06% and 1.76% while, it was revealed that EXDT contributed negatively to the economic growth in Nigeria thus, reduced economic growth by 0.02% and as such showed the tendency and potential macroeconomic variables under study as a veritable determinant of economic growth. The location, scale and space parameters , , , and for the fitted FMKL-GLD model were -0.0253, 34.1488 -0.3303, and -0.5758. Therefore, it can be concluded based on the location, scale and space parameters from FMKL-FGLD and GLD Q,Q plots as well as the estimated parameter of RGDP, FMKL-FGLD was the best model that described the economic situation in Nigeria by showing that economy was growing in retrogressive direction and the need for drastic effort and strong willed to curtail the situation. To achieve this, the adoption of economic openness to the development and the growth of economy in Nigeria is a veritable policy direction that must be strictly followed.

The Velocity of Money and Lessons for Monetary Policy in Nigeria: An Application of the Quantile ARDL Approach

AbstractThe paper investigates the determinants and stability of the income velocity of money while accounting for structural breaks in its approach from the period Q1 1981 to Q4 2023 in Nigeria. The paper extends previous works by accounting for four velocities of money functions having accounted for structural breaks in its approach. The study adopted the Bai and Perron breakpoint test, the Zivot and Andrews, and Perron and Vogelsang structural break unit root tests to determine the break dates and the variables’ stationary properties. In analyzing the main results, the study used the quantile ARDL method to establish the determinants of income velocity of money in Nigeria, while it used the CUSUM tests to establish the stability of money velocity in Nigeria. The study found that the determinants of the velocity of money are per capita income, the exchange rate, financial development, inflation, and the interest rate across the four models. Furthermore, the four velocities of money functions were unstable for Nigeria using the cumulative sum and cumulative sum of squares tests. Consequently, the study recommends effective monitoring and the provision of sound monetary policies to ensure a stable and predictable velocity of money. This can be achieved by understanding and focusing on these key determinants and crafting informed strategies to manage economic growth and stability. This holistic approach ensures that the benefits of economic policies are felt broadly across the economy, ultimately bolstering overall financial health and enhancing the velocity of money, which is crucial for sustained economic progress.

Estimated reduction in obesity prevalence and costs of a 20% and 30% ad valorem excise tax to sugar-sweetened beverages in Brazil: A modeling study.

The consumption of sugar-sweetened beverages (SSBs) is associated with obesity, metabolic diseases, and incremental healthcare costs. Given their health consequences, the World Health Organization (WHO) recommended that countries implement taxes on SSB. Over the last 10 years, obesity prevalence has almost doubled in Brazil, yet, in 2016, the Brazilian government cut the existing federal SSB taxes to their current 4%. Since 2022, a bill to impose a 20% tax on SSB has been under discussion in the Brazilian Senate. To simulate the potential impact of increasing taxes on SSB in Brazil, we aimed to estimate the price-elasticity of SSB and the potential impact of a new 20% or 30% excise SSB tax on consumption, obesity prevalence, and cost savings. Using household purchases data from the Brazilian Household Budget Survey (POF) from 2017/2018, we estimated constant elasticity regressions. We used a log-log specification by income level for all beverage categories: (1) sugar-sweetened beverages; (2) alcoholic beverages; (3) unsweetened beverages; and (4) low-calorie or artificially sweetened beverages. We estimated the adult nationwide baseline intake for each beverage category using 24-h dietary recall data collected in 2017/2018. Taking group one as the taxed beverages, we applied the price and cross-price elasticities to the baseline intake data, we obtained changes in caloric intake. The caloric reduction was introduced into an individual dynamic model to estimate changes in weight and obesity prevalence. No benefits on cost savings were modeled during the first 3 years of intervention to account for the time lag in obesity cases to reduce costs. We multiplied the reduction in obesity cases during 7 years by the obesity costs per capita to predict the costs savings attributable to the sweetened beverage tax. SSB price elasticities were higher among the lowest tertile of income (-1.24) than in the highest income tertile (-1.13), and cross-price elasticities suggest SSB were weakly substituted by milk, water, and 100% fruit juices. We estimated a caloric change of -17.3 kcal/day/person under a 20% excise tax and -25.9 kcal/day/person under a 30% tax. Ten years after implementation, a 20% tax is expected to reduce obesity prevalence by 6.7%; 9.1% for a 30% tax. These reductions translate into a -2.8 million and -3.8 million obesity cases for a 20% and 30% tax, respectively, and a reduction of $US 13.3 billion and $US 17.9 billion in obesity costs over 10 years for a 20% and 30% tax, respectively. Study limitations include using a quantile distribution method to adjust self-reported baseline weight and height, which could be insufficient to correct for reporting bias; also, weight, height, and physical activity were assumed to be steady over time. Adding a 20% to 30% excise tax on top of Brazil's current federal tax could help to reduce the consumption of ultra-processed beverages, empty calories, and body weight while avoiding large health-related costs. Given the recent cuts to SSB taxes in Brazil, a program to revise and implement excise taxes could prove beneficial for the Brazilian population.

Combined deep-learning optimization predictive models for determining carbon dioxide solubility in ionic liquids

This study explores the development of predictive models for carbon dioxide (CO2) solubility in ionic liquids based on a compiled dataset of 10,116 experimentally measured data points involving four input variables: pressure (P), temperature (T), cation type, and anion type. The deep-learning (DL) predictive models evaluated are standalone and hybrid versions of convolutional neural network (CNN) and long short-term memory (LSTM) algorithms with cuckoo optimization algorithm (COA) and gradient-based optimization (GBO). The laboratory-measured data was separated into training and test categories, and each category was normalized separately to improve the performance of the deep learning algorithms. The Mahalanobis distance-based quantile method was utilized to identify any outliers in the training data. Once identified, the outlier data points were eliminated from the training dataset. The control parameters of the deep learning algorithms were optimized using COA to enhance their efficiency, and the algorithms were hybridized with optimization algorithms to further improve their performance. The resulting models were analyzed to assess their accuracy, degree of overfitting, and the importance of input features. The study found that using 80% of the data for training and 20% for testing results in more accurate and generalizable models. Using the outlier detection method on the training data led to 307 data points being eliminated as outliers. Developing CO2-solubility predictive model showed that, the CNNCOA model had the lowest RMSE and highest R2 among the developed models, indicating high generalizability for data unseen by the trained model. The analysis revealed that using optimization algorithms increased the CO2-solubility prediction performance of DL algorithms and reduced overfitting. T and cation type were the most and least important input features, respectively. Simultaneous changes in cation and anion type on CO2-solubility predictions displayed no systematic pattern. For increases in T, CO2 solubility typically decreased, whereas for increases in P CO2 solubility always increased but at variable rates. The results of this study can be used to develop accurate and generalizable CO2-solubility predictive models for various applications.

Investigation of the flood event under global climate change with different analysis methods for both historical and future periods

ABSTRACT Global climate change is a phenomenon resulting from the complex interaction of human influences and natural factors. These changes lead to imbalances in climate systems as human activities such as greenhouse gas emissions increase atmospheric gas concentrations. This situation affects the frequency and intensity of climate events worldwide, with floods being one of them. Floods manifest as water inundations due to factors such as changes in rainfall patterns, rising temperatures, erosion, and sea level rise. These floods cause significant damage to sensitive areas such as residential areas, agricultural lands, river valleys, and coastal regions, adversely impacting people's lives, economies, and environments. Therefore, flood risk has been investigated in decision-making processes in the Diyarbakır province using the Analytical Hierarchy Process (AHP) method and future disintegration of global climate model data. HadGEM-ES, GFDL-ESM2M, and MPI-ESM-MR models with RCP4.5 and RCP8.5 scenarios were used. Model data were disintegrated using the equivalent quantile matching method. The study reveals flood risk findings in the HadGEM-ES model while no flood risk was found in the GFDL-ESM2M and MPI-ESM-MR models. In the AHP method, flood risk analysis was conducted based on historical data across Diyarbakır and interpreted alongside future rainfall data.

Dynamic effects of fiscal consolidations on income inequality in Sub-Saharan African countries

ABSTRACT This study explores the short-to-medium-term effects of fiscal consolidation on income inequality in 40 Sub-Saharan African countries from 1990 to 2021 by applying the quantile local projection method at the median. The study finds that periods of fiscal consolidation reduce inequality in the short run, with spending-driven consolidation having a positive effect on income parity, while tax-driven consolidations have an insignificant impact. The study concludes that the distributional effects of fiscal consolidation in SSA countries are “progressive,” implying that well-designed and targeted fiscal measures contribute to more equitable income distribution.

Wildlife habitat suitability analysis and mapping the former dhidhessa wildlife sanctuary using GIS-based analytical hierarchal process and weighted linear combination methods

Management of wildlife populations and the creation of conservation programs depend on the evaluation of wildlife habitats. Habitat suitability mapping is a technique typically used to map appropriate environmental factors and assess species existence in different areas. This study aims to map wildlife habitat suitability sites in Former Dhidhessa wildlife sanctuary, Ethiopia, using GIS-based Analytical Hierarchal Process and Weighted Linear Combination Methods. This study used both primary and secondary data sources. Datasets used to collect data include Digital Elevation Model (DEM), Landsat 9 (OLI/TIRS) and population data. Beside, large mammalian species occurrence data obtained from field survey was used. To map wildlife habitat suitability sites in Former Dhidhessa wildlife sanctuary, environmental factors such as proximity of road network, distance to surface water, land use land cover types, slope, population density and topography were used with the integration of species occurrence data recorded from the study area. These environmental factors scaled to common ranges, and assigned appropriate weights. The quantile classification method was utilized to classify suitability index into five zones (unsuitable, less suitable, moderately suitable, suitable, and highly suitable) to produce the map. Accordingly, the model revealed that 18.9 % of the study area is highly suitable, 19.5 % is suitable, 19.9 % is moderately suitable, 19.5 % is less suitable, and 22.2 % is unsuitable for wildlife. About 58.3 % of the study area is currently identified as suitable for wildlife whereas 41.7 % is unsuitable. This showed that the former Dhidhessa wildlife sanctuary is still having large suitable habitats that can support wide ranges of wildlife. Hence, based on the developed preliminary habitat suitability indices and maps, the federal and local governments shall reevaluate the status of former Dhidhessa wildlife sanctuary and develop future conservation and management plans to enhance the conservation of wildlife and their habitats in the area.

Dynamic quantile connectedness between oil and stock markets: The impact of the interest rate

This paper explores the heterogeneous and dynamic connectedness between the oil and stock markets of emerging economies under various market conditions by introducing a novel quantile regression TVP-VAR network method. Moreover, a semiparametric model is used to analyze the impact of interest rates on the connectedness. The results show that (1) the total connectedness between the oil and stock markets of emerging economies in bull and bear markets is significantly larger than under normal market conditions. Moreover, the total connectedness is time-varying and crisis-sensitive in all market scenarios. (2) The total connectedness has asymmetric characteristics in bull and bear markets. The net information spillover from oil markets to stock markets of emerging economies shows heterogeneity under different market backgrounds. (3) The impact of interest rates on the total connectedness exhibits a “U-shaped” curve pattern for all market statuses. This study can serve as a reference for regulators aiming to formulate monetary policies for different market environments, especially extreme markets, and for investors aiming to adjust their investment strategies and optimize their investment portfolios according to market conditions.

Plug-in bandwidth selection rules for the kernel quantile estimator

The large sample theory of the kernel quantile estimator is extended by separately treating the cases where the underlying density has critical points. Our attempts of improving the quality of quantile estimation resulted in proposing the beta distribution’s bandwidth selection method that is quite successful in the case of a normal distribution. The performance of the beta reference rule is compared to that of two other plug-in type bandwidth selectors. Based on the theoretical and numerical results, we provide certain recommendations regarding using different type of estimators in practice. In particular, the beta reference rule and truncated normal rule are recommended for estimating the quantiles of the distributions close to normal. The sample quantile estimator worked well in all simulation settings and is thus recommended as a default quantile estimation method in a non-normal case.

Flash droughts in a hotspot region: Spatiotemporal patterns, possible climatic drivings and ecological impacts

Northeast China, recognized as a global flash drought hotspot and a region of nationally important commercial grains and ecological fragility, is highly susceptible to the profound impacts of droughts on both food security and ecological safety. However, the regional-scale characteristics, possible causes and impacts of flash droughts across Northeast China are rarely investigated. Soil moisture data from 2000 to 2022 were utilized to identify the onset, frequency and duration of flash droughts using the quantile method. The spatial trajectories of flash droughts were determined based on the patch-scale centroid transfers. Further, the possible drivings and ecological impacts of flash droughts were analyzed using datasets of climatic variables and gross primary productivity. We found that flash drought coverage with short onset (1- pentad onset) was generally larger than that with long onset (2-, 3- and 4-pentad onset). The affected area of flash droughts generally decreased, while the onset speed increased over the past two decades. Flash droughts frequently occurred the intersection of western Jilin Province, southern Inner Mongolia Autonomous Region, and northern Liaoning Province but with short duration. Compared with 2001–2011, the start, middle and end centroids of flash droughts in 2012–2021 showed more spatial dynamic changes, mainly shifting towards the northeast-southwest and southeast-northwest directions. The onset of flash drought due to potential evapotranspiration, temperature, precipitation deficit, and vapor pressure anomalies account for an average of 33%, 28%, 22%, and 16%, respectively. However, the contributions of dominant meteorological factors and their combinations varied remarkably in different sub-regions. Long onset flash droughts exhibited larger impact on GPP than short onset flash droughts. This study highlights that due to climate change, the affected area of flash droughts in hotspot regions decreased, but the onset speed and spatial dynamics increased, and followed by a more severe ecological impact of short-onset flash drought. Therefore, it is imperative to incorporate the increasing impacts of flash droughts, which is critical to ensuring regional food security and ecological safety in the Northeast China.

Solar prominence diagnostics and their associated estimated errors from 1D NLTE Mg II h&k modelling

Aims. We present further development of the rolling root mean square (rRMS) algorithm. These improvements consist of an increase in computational speed and an estimation of the uncertainty on the recovered diagnostics. This improved algorithm is named the cross root mean square (xRMS) algorithm. Methods. We used the quantile method to recover the statistics of the line profiles in order to study the evolution of the prominence observed by IRIS on 1 October 2019. We then introduced the improvements to rRMS. These improvements greatly increased the computational speed, and this increase in speed allowed us to use a large model grid. Thus, we utilised a grid of 23 940 models to recover the thermodynamic diagnostics. We used the “good” (but not “best”) fitting models to recover an estimate of the uncertainty on the recovered diagnostics. Results. The maximum line-of-sight (LOS) velocities were found to be 70 km s−1. The line widths were mostly 0.4 Å, with the asymmetries of most pixels around zero. The central temperature of the prominence was found to range from 10 kK to 20 kK, with uncertainties of approximately ±5 to ±15 kK. The central pressure was around 0.2 dyn cm−2, with uncertainties of ±0.2 to ±0.3 dyn cm−2. The ionisation degree ranged from 1 to 1000, with uncertainties mostly in the range ±10 to ±100. The electron density was mostly 1010 cm−3, with uncertainties of mostly ±109. Conclusions. The new xRMS algorithm finds an estimation of the errors of the recovered thermodynamic properties. To our knowledge, this is the first attempt at systematically determining the errors from forward modelling. The large range of errors found may hint at the degeneracies present when using a single ion and/or species from forward modelling. In the future, co-aligned observations of more than one ion and/or species should be used to attempt to constrain this problem.