Trends In Series Research Articles

Interplay between Discrete Emotions and Preventive Behavior in Health Crises: Big Data Analysis of COVID-19

Understanding the interplay between discrete emotions and COVID-19 prevention behaviors will help healthcare professionals and providers to implement effective risk communication and effective risk decision making. This study analyzes data related to COVID-19 posted by the American public on Twitter and identifies three discrete negative emotions (anger, anxiety, and sadness) of the public from massive text data. Next, econometric analyses (i.e., the Granger causality test and impulse response functions) are performed to evaluate the interplay between discrete emotions and preventive behavior based on emotional time series and Google Shopping Trends time series, representing public preventive behavior. Based on the textual analysis of tweets from the United States, the following conclusions are drawn: Anger is a Granger cause of preventive behavior and has a slightly negative effect on the public's preventive behavior. Anxiety is a Granger cause of preventive behavior and has a positive effect on preventive behavior. Furthermore, preventive behavior is a Granger cause of anxiety and has a negative and lagging effect on anxiety. Exploring how discrete emotions, such as anger and anxiety, affect preventive behaviors will effectively demonstrate how discrete emotions play qualitatively different roles in promoting preventive behaviors. Moreover, understanding the impact of preventive behaviors on discrete emotions is useful for better risk communication.

Stabilitas Beta Indeks JII70 di Bursa Saham Indonesia Pada Pandemi Covid-19

COVID-19 pandemic attack all of Indonesia’s economic sectors, including stock market. One of the impacted were stocks that listed in JII70, which were index in Indonesia’s Stock Exchange that under the basis of sharia. To evaluate JII70 in handling pandemic, risk evaluation can be done, evaluation method that can be used is Rolling Beta. Beta show sensitivity of return that portfolio had against whole stock market performance. Rolling Beta itself is beta value that calculated in loop using Regression, in designated interval time, to make time series trend. Data were taken from JII70 and IHSG closing value, from May 2018–September 2022. Preliminary data analysis, using Augmented Dickey Fuller test show that JII70 and IHSG stationary before and during pandemic. Furthermore, beta analysis shows that JII70 has beta value at range 1,1–1,3 in 2018 before pandemic. However, after pandemic hit, JII70 beta value dropped, but can be maintained in 0,8–1,1. This showcase COVID-19 huge impact to Indonesia’s stock, including sharia stock, however, JII70 can rebound and maintain stable performance after pandemic going on.

Multiple Changepoint Analysis of COVID-19 Infection Progression and Related Deaths in the Small Island State of Malta

In December 2019, in the city of Wuhan (China), Severe Acute Respiratory Syndrome Coronavirus - 2 (SARS-CoV−2), a virus that causes what is known as Coronavirus Disease 2019 (better known as COVID-19), emerged. In a few months the virus spread around the world becoming a global pandemic that has shaken the world. On Malta (a nation consisting of an archipelago of islands of approximately 500000 people), which is the case study of this analysis, the first case was identified on 7/3/2020. In this paper, we shall fit a piecewise linear trend model to the log-scale of cumulative cases and deaths due to COVID-19 in Malta by implementing the SN-NOT changepoint model. This model combines the self-normalisation (SN) technique, which is used to test whether there is a single change-point in the linear trend of a time series, with the Narrowest Over Threshold algorithm (NOT) to achieve multiple change-point in the linear trend. Through analysis of news reports and other sources of information, estimated change-points are then compared to potential factors such as health restrictions, mass events, government policy and population behaviour that have affected these changes, in order to determine the efffect of these factors on the spread of the disease.

Observed time series trend analysis of climate variability and smallholder adoption of new agricultural technologies in west Shewa, Ethiopia

This study was aimed at analyzing time-series trends and variability analysis of observed rainfall and temperature records from 16 stations during the 1985–2015 period. Besides, it focused on the smallholders’ perceptions, including the analysis of their response strategies to both observed and perceived climate variability and its determinants. Both purposive and random sampling procedures were used. Mixed methods (quantitative and qualitative) of the research were used. The sample household survey method was used to collect primary data from smallholder farmers, while key informant interviews were used to gather qualitative data from the experts. A semi-structured questionnaire was used to collect quantitative data, whereas a discussion guide checklist was used to gather qualitative data. A total of 397 respondents and 24 key informants were used as sources of primary data. In addition, visits to the National Meteorology Agency of Ethiopia were made to collect recorded climate data. Descriptive statistics and econometric models were used to analyze quantitative data, whereas thematic categorization was used to analyze and report qualitative data. The results show that both the summer and annual rainfall have an increasing trend but are not statistically significant. With regards to variability, low to moderate levels of variability were recorded according to the seasons and annual rainfall. The result of the Mann-Kendall test shows that the mean minimum temperature was raised by 0.05 °C while the maximum temperature rose by 0.03 °C/30 years. The monthly maximum temperature also shows an increasing trend, with the lowest record during August (22.05 °C) and the highest in March (26.49 °C. Besides, smallholder farmers’ perceptions were in accordance with recorded station data. In response to both observed and perceived climate variability and its extremes, smallholder farmers in the study area were striving to adopt different adaptation strategies. Rain variability, frequency of extension contact, years of farming experience, access to institutions and infrastructure, wealth status, market access, and the number of active labor force within the household all hampered smallholder farmers' efforts to implement potential measures. In contrast, the high prices of chemicals and improved seed, access to credit services, land size, and limited access to accurate and timely early warning information all negatively and significantly influenced the adaptation decisions of smallholder farmers in the area. Therefore, the impacts of abnormal trends or shifts in observed rainfall and temperature patterns, along with socio-economic, technological, and behavioral factors, call for policymakers to set strategies that will enable smallholders to improve their livelihoods, ensure food security, and build resilience in the face of climatic extremes.

Environmental Conditions in Middle Eastern Megacities: A Comparative Spatiotemporal Analysis Using Remote Sensing Time Series

Rapid and timely evaluation and monitoring of the urban environment has gained significant importance in understanding the state of urban sustainability in metropolises. Multi-source remote sensing (RS) data are a valuable source for a comprehensive understanding of urban environmental changes in developing countries. However, in the Middle East, a region with several developing countries, limited study has been conducted to understand urban environmental changes. In this study, to evaluate the changes in the urban environment, 32 metropolises in the Middle East were studied between 2000 and 2019. For this purpose, a comprehensive environmental index (CEI) integrated with Google Earth Engine (GEE) platform for processing and analysis is introduced. The results show degraded environmental conditions in 19 metropolises based on a significant increasing trend in the time series of the CEI index. The highest increasing trend in the value of the CEI was observed in the cities of Makkah, Jeddah, Basra, Riyadh, and Sana’a. The results also show that the percentage of urban areas in all 32 cities that falls into the degraded class varies from 5% to 75% between 2005 and 2018. The results of CEI changes in megacities, such as Ajman, Tehran, Jeddah, Makkah, Riyadh, Karaj, and Sana’a show that these cities have increasingly suffered from the degradation of environmental conditions since 2001. According to the results, it is recommended to pay more attention to environmental issues regarding the future of urban development in these cities. The proposed approach in this study can be implemented for environmental assessment in other regions.

Predicting ammonia nitrogen in surface water by a new attention-based deep learning hybrid model

Ammonia nitrogen (NH3–N) is closely related to the occurrence of cyanobacterial blooms and destruction of surface water ecosystems, and thus it is of great significance to develop predictive models for NH3–N. However, traditional models cannot fully consider the complex nonlinear relationship between NH3–N and various relative environmental parameters. The long short-term memory (LSTM) neural network can overcome this limitation. A new hybrid model BC-MODWT-DA-LSTM was proposed based on LSTM combining with the dual-stage attention (DA) mechanism and boundary corrected maximal overlap discrete wavelet transform (BC-MODWT) data decomposition method. By introducing attention mechanism, LSTM could selectively focus on the input data. BC-MODWT could decompose the input data into sublayers to determine the main swings and trends of the input feature series. The BC-MODWT-DA-LSTM hybrid model was superior to other studied models with lower average prediction errors. It could maintain NASH Sutcliffe efficiency coefficient (NSE) values above 0.900 under the lead time up to 7 days, and the area under the receiver operating characteristic (ROC) curve could reach 0.992. The hybrid model also had higher prediction accuracies at the peak spots, indicating that it was capable of early warning when sudden high NH3–N pollution occurred. The high forecasting accuracy of the suggested hybrid method proved that further improving LSTM model without introducing more complex topologies was a promising water quality prediction method.

Detrended fluctuation analysis based on best-fit polynomial

Detrended fluctuation analysis (DFA) can quantify long-range correlation (LRC) and fractal scaling behavior of signal. We compared the results of variant DFA methods by varying the order of the polynomial and found that the order of 6 was relatively better than the others when both the accuracy and computational cost were taken into account. An alternative DFA method is proposed to quantify the LRC exponent by using best-fit polynomial algorithm in each segment instead of the polynomial of the same order in all of segments. In this study, the best-fit polynomial algorithm with the maximum order of 6 is used to fit the local trend in each segment to detrend the trend of a time series, and then the revised DFA is used to quantify the LRC in the time series. A series of numerical studies demonstrate that the best-fit DFA performs better than regular DFA, especially for the time series with scaling exponent smaller than 0.5. This may be attributed to the improvement of the fitted trend at the end of each segment. The estimation results of variant DFA methods reach stable when the time series length is greater than 1,000.

CryptoNet: Using Auto-Regressive Multi-Layer Artificial Neural Networks to Predict Financial Time Series

When analyzing a financial asset, it is essential to study the trend of its time series. It is also necessary to examine its evolution and activity over time to statistically analyze its possible future behavior. Both retail and institutional investors base their trading strategies on these analyses. One of the most used techniques to study financial time series is to analyze its dynamic structure using auto-regressive models, simple moving average models (SMA), and mixed auto-regressive moving average models (ARMA). These techniques, unfortunately, do not always provide appreciable results both at a statistical level and as the Risk-Reward Ratio (RRR); above all, each system has its pros and cons. In this paper, we present CryptoNet; this system is based on the time series extraction exploiting the vast potential of artificial intelligence (AI) and machine learning (ML). Specifically, we focused on time series trends extraction by developing an artificial neural network, trained and tested on two famous crypto-currencies: Bitcoinand Ether. CryptoNet learning algorithm improved the classic linear regression model up to 31% of MAE (mean absolute error). Results from this work should encourage machine learning techniques in sectors classically reluctant to adopt non-standard approaches.

Energy Price Prediction Integrated with Singular Spectrum Analysis and Long Short-Term Memory Network against the Background of Carbon Neutrality

In the context of international carbon neutrality, energy prices are affected by several nonlinear and nonstationary factors, making it challenging for traditional forecasting models to predict energy prices effectively. The existing literature mainly uses linear models or a combination of multiple models to forecast energy prices. For the nonlinear relationship between variables and the mining of historical data information, the prediction strategy and accuracy of the existing literature need to be improved. Thus, this paper improves the prediction accuracy of energy prices by developing a “decomposition-reconstruction-integration” thinking strategy that affords medium- and short-term energy price prediction based on carbon constraint, eigenvalue transformation and deep learning neural networks. Considering 2011–2020 as the research period, the prices for traditional energy resources and polysilicon in clean photovoltaic energy raw materials are selected as representatives. Based on energy price decomposition using the Singular Spectrum Analysis (SSA) method, and combining it with Learning Vector Quantization (LVQ) cluster technology, the decomposed quantities are aggregated into price sequences with different characteristics. Additionally, the carbon intensity is considered the leading market’s overall constraint, which is input with the processed price data into a Long Short-Term Memory network (LSTM) model for training. Thus, the SSA-LSTM combined forecasting model is developed to predict the energy price under carbon neutrality. Four indices are employed to evaluate the prediction accuracy: Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and R-squared. The results highlight the following observations. (1) Using a sequence decomposition clustering strategy significantly improves the model’s prediction accuracy. This strategy enhances predicting the overall trend of the price series and the changes in different periods. For coal price, the RMSE value decreased from 0.135 to 0.098, the MAE value decreased from 0.087 to 0.054, the MAPE value decreased from 0.072 to 0.064, and the R-squared value increased from 0.643 to 0.725. Regarding the polysilicon price, the RMSE value decreased from 0.121 to 0.096, the MAE value decreased from 0.068 to 0.064, the MAPE value decreased from 0.069 to 0.048, and the R-squared value increased from 0.718 to 0.764. (2) The prediction effect is better in the case of carbon constraint. Considering “carbon emission intensity” as the overall constraint of the leading market, it can effectively explore the typical characteristics of energy price information. Four evaluation indicators show that the accuracy of the model prediction can be improved by more than 3%. (3) When the proposed SSA-LSTM model is used to predict both prices, the results show that the evaluation index of the prediction error remained at about 1%, while the model’s accuracy was high. This also proves that the proposed model can predict traditional energy prices and new energy sources such as solar energy.

The impact of economic sanctions and the Covis-19 pandemic on the efficiency of the Russian foreign exchange market

The article analyzes the effectiveness of the Russian currency market using the multi-fractal analysis of fluctuations without a trend (MF-DFA) of a time series composed of daily values of the US dollar (USD) to Russian ruble (RUB) (USD/RUB) exchange rate for the period time (01/01/2013 — 04/01/2022), during which events occurred that led to serious changes in the Russian foreign exchange market.
 Calculations showed that the time series made up of the daily values of the USD/RUB exchange rate showed the least efficiency after the introduction of the fiscal rule and the coronavirus pandemic, and the highest comparative efficiency after the introduction of the freely floating exchange rate regime.

Assessment of drought trends and variabilities over the agriculture-dominated Marathwada Region, India.

Drought is considered among the most perilous events with catastrophic consequences, particularly from the agro-economic point of view. These consequences are expected to exacerbate under the increasing meteorological aberrations due to changing climate, which necessitates investigating drought variabilities. This study presents a thorough spatiotemporal assessment of drought trends and variabilities over the agriculture-dominated Marathwada Region, Maharashtra, India. The precipitation data is extracted from the India Meteorological Department (IMD) gridded product, whereas actual evapotranspiration (ET) and Evaporative Stress Index (ESI) are obtained from Global Land Evaporation Amsterdam Model (GLEAM) datasets. Standardized Precipitation Index (SPI) is used to characterize drought occurrences at multiple time frames, whereas non-parametric tests, i.e., modified Mann-Kendall (MMK) and Sen's slope (SS) tests, are employed to detect trends. The results reveal the region to be prone to droughts, and SPI at a longer time frame (i.e., 12-monthly moving frame) can capture drought occurrences better than the shorter time frames, which can be attributed to the lesser randomness in the time series in the longer frame. A mix of positive/negative trends of SPI series are found for the monsoonal months; however, they are relatively more concentrated towards negative ZMMK. Hence, the Marathwada Region can be inferred to have exhibited a relatively increased tendency towards drought occurrences. The seasonal differences in mean values and trends of rainfall, ET, and ESI are discussed in detail. Since the Marathwada Region has a monsoon-dominated climate with high agricultural importance, the information reported in this study will help in devising water management strategies to minimize the repercussions of droughts.

Studying the dynamics of thermokarst lakes in the West Siberian Arctic based on the analysis of time series of satellite measurements

Subject of research: article is the time series of changes in the area of thermokarst lakes in the Arctic under the conditions of modern climatic changes.
 Purpose of research: to analyze the time series of satellite measurements of lake areas for studying the dynamics of lakes in the West Siberian Arctic.
 Methods and objects of research: we present the results of formation and analysis of time series of lake areas intended for studying the dynamics of thermokarst lakes in the Arctic zone of Western Siberia over a 36-year period. The time series were obtained based on the results of remote measurements of the lake areas on the territory of 627 thousand km2 using 688 Landsat satellite images, sampled in the period 1985-2021. The study area was divided into three ecoregions to analyze the territorial features of the vast Arctic zone of Western Siberia. The analysis of the time series showed that the change in the average lake area in the territory of the Arctic zone of Western Siberia exhibits a marked negative linear trend.
 Main results of research: based on the comparison of the trends in the time series of lake areas in the territories of the three ecoregions of the West Siberian Arctic, it was found that the thermokarst lakes of Yamal exhibit the highest rate of change in the areas compared to the other Arctic ecoregions.

Prior Knowledge-Based Causal Inference Algorithms and Their Applications for China COVID-19 Analysis

Causal inference has become an important research direction in the field of computing. Traditional methods have mainly used Bayesian networks to discover the causal effects between variables. These methods have limitations, namely, on the one hand, the computing cost is expensive if one wants to achieve accurate results, i.e., exponential growth along with the number of variables. On the other hand, the accuracy is not good enough if one tries to reduce the computing cost. In this study, we use prior knowledge iteration or time series trend fitting between causal variables to resolve the limitations and discover bidirectional causal edges between the variables. Subsequently, we obtain real causal graphs, thus establishing a more accurate causal model for the evaluation and calculation of causal effects. We present two new algorithms, namely, the PC+ algorithm and the DCM algorithm. The PC+ algorithm is used to address the problem of the traditional PC algorithm, which needs to enumerate all Markov equivalence classes at a high computational cost or with immediate output of non-directional causal edges. In the PC+ algorithm, the causal tendency among some variables was analyzed via partial exhaustive analysis. By fixing the relatively certain causality as prior knowledge, a causal graph of higher accuracy is the final output at a low running cost. The DCM algorithm uses the d-separation strategy to improve the traditional CCM algorithm, which can only handle the pairwise fitting of variables, and thus identify the indirect causality as the direct one. By using the d-separation strategy, our DCM algorithm achieves higher accuracy while following the basic criteria of Bayesian networks. In this study, we evaluate the proposed algorithms based on the COVID-19 pandemic with experimental and theoretical analysis. The experimental results show that our improved algorithms are effective and efficient. Compared to the exponential cost of the PC algorithm, the time complexity of the PC+ algorithm is reduced to a linear level. Moreover, the accuracies of the PC+ algorithm and DCM algorithm are improved to different degrees; specifically, the accuracy of the PC+ algorithm reaches 91%, much higher than the 33% of the PC algorithm.

A Bayesian-based classification framework for financial time series trend prediction.

Financial time series have been extensively studied within the past decades; however, the advent of machine learning and deep neural networks opened new horizons to apply supercomputing techniques to extract more insights from the underlying patterns of price data. This paper presents a tri-state labeling approach to classify the underlying patterns in price data into up, down and no-action classes. The introduction of a no-action state in our novel approach alleviates the burden of denoising the dataset as a preprocessing task. The performance of our labeling algorithm is experimented with using machine learning and deep learning models. The framework is augmented by applying the Bayesian optimization technique for the selection of the best tuning values of the hyperparameters. The price trend prediction module generates the required trading signals. The results show that the average annualized Sharpe ratio as the trading performance metric is about 2.823, indicating the framework produces excellent cumulative returns.

Protactinium and Actinium Monohydrides: A Theoretical Study on Their Spectroscopic and Thermodynamic Properties.

Spectroscopic and thermodynamics properties including bond dissociation energies (BDEs), adiabatic electron affinities (AEAs), and ionization energies (IEs) have been predicted for AcH and PaH using the Feller-Peterson-Dixon composite approach. Comparisons with previous studies on ThH and UH were performed to identify possible trends in the actinide series. Multireference CASPT2 calculations were used to predict the spin-orbit effects and obtain potential energy curves for the low-lying Ω states around the equilibrium distance as well as the vertical detachment energies (VDEs) from AcH- and PaH- to excited states of the neutral species. The calculated AEA for AnH (An = Ac, Th, Pa, U) showed that the AEA increases from AcH (0.425 eV) to ThH (0.820 eV) and decreases to PaH (0.781 eV) and to UH (0.457 eV), whereas the IE values are 5.887 eV (AcH), 6.181 eV (ThH), 6.204 eV (PaH), and 6.182 eV (UH). The ground state of AcH, AcH-, PaH, and PaH- are predicted to be1Σ+0,2Π3/2, 3H4, and 4I9/2, respectively. The BDEs for AcH and PaH are 276.4 and 237.2 kJ/mol, and those for AcH- and PaH- are 242.8 and 239.8 kJ/mol, respectively. The natural bond analysis shows a significant ionic character, An+H-, in the bonding of the neutral hydrides.

The day-to-day temperature variability method as a tool for urban heat island analysis: A case of Zagreb-Grič Observatory (1887–2018)

The paper applies the relatively new day-to-day (DTD) method to analyse the day-to-day variability of minimum, mean and maximum daily air temperatures measured at the Zagreb Grič Observatory in the period from 1878 to 2018. The observatory is located in the centre of this rapidly developed city. The DTD values were calculated and compared for the whole year and for the warm period of the year (June 15th to September 15th). Neither positive nor negative trends in the time series of mean daily, DTDmean, and minimum daily, DTDTmin, day-to-day temperatures variabilities were found. There was a statistically significant difference in the behaviour of the series in the sub-period 1887–1958 and in the sub-period 1959–2018, when the maximum daily temperatures series, DTDTmax, and the difference between the maximum and minimum daily temperatures, ΔDTD, were considered. The DTD variabilities appeared in the annual series and those for the warm period of the year. Statistically lower inter-day variabilities were observed over the last 60 years. Variabilities in the warm period of the year, DTDTmin, are significantly lower than for the year as a whole. The variabilities in annual differences, ΔDTD, are significantly lower than those in the warm period of the year.

Temporal Variations and Futuristic Projections of Temperature and Precipitation: A study of Vijayawada city

Abstract: Examining the temporal dynamics of meteorological variables in the context of changing climate, particularly in Vijayawada, where agriculture is predominant. Therefore, the study uses trend analysis of rainfall and temperature change in Vijayawada using precipitation data obtained from Precipitation and temperature data. Data have been analysed using the coefficient of variation and standard deviation. Furthermore, the Mann-Kendall test was used to detect the time series trend. The result revealed variability of rainfall and temperature. The month-wise analysis of the temperature and precipitation trends established different unique patterns. This is due to the fact that the sensitivity of the world to climate change varies temporally and spatially. The scope of the study lies in developing the analysis to study different extreme events such as cloudbursts, heat waves, cold waves, etc. Furthermore, the spatial variations of the study can be used to establish the climate sensitivity with temporal factors.

A shape function approach for predicting deteriorated heat transfer to supercritical pressure fluids on account of a thermal entry length phenomenon

The paper addresses the problem of predicting deteriorated heat transfer to supercritical pressure fluids, trying a novel approach whose main features are obtained by consideration of the experimental trends of a meaningful series of experimental data. This approach takes into consideration the modifications that the axial velocity profile undergoes at the entrance of a bare tube, when a supercritical pressure fluid is heated at the wall and decreases its density owing to thermal expansion, giving rise to buoyancy effects. Combining the observation of measured wall temperature trends with their successful predictions by CFD RANS models, the effects giving rise to flow laminarization are firstly interpreted as a very peculiar form of the entry length problem applicable to supercritical pressure fluids. Then, their quantitative prediction is addressed by the use of an iterative procedure based on shape functions tailored on the observed trends of the Nusselt number and of the consequent wall temperature trends. In this frame, the results of a recently developed fluid-to-fluid similarity theory for heat transfer at supercritical pressures provide indication about the relevant dimensionless numbers to be involved in determining the shape functions. Being based at the moment on a series of water experimental data collected at 25 MPa for vertical upward flow, the methodology may suggest a way for predicting heat transfer deterioration in the proposed supercritical water nuclear reactor conditions, providing an additional means to tackle this critical problem by a different approach that may result useful as a practical means to bound expected wall temperature trends.

Trend Analysis of Hydrometeorological Data of Gilgel Gibe Catchment, Ethiopia

Trend analysis of hydrometeorological data is vital for proper water resources planning and management. This paper examines the trends of the hydrometeorological data in Gilgel Gibe catchment and whether the trends are significant. Daily rainfall, temperature, and streamflow data of the stations in/around (nearby) the catchment (7 stations for rainfall, 4 stations for temperatures, and 6 stations for streamflow) for a period longer than 25 years were collected and then analyzed to detect the variability and the changes in trend. Prior to conducting trend tests, the missed data were filled, and their inconsistencies were also adjusted. The nonparametric Mann-Kendall test along with Sen’s slope technique was employed to detect monotonic trends in the data series. The results showed that, on average, the rainfall exhibits an insignificant increasing tendency. It was also observed that there is, in general, an increasing trend in temperature (both maximum and minimum) in the study area. The analysis of the stream flows indicated that only one station (Bulbul Nr. Serbo) showed a positive slope at a 5% significance level. Two stations (Aweitu Nr. Babu and Gibe Nr. Seka) showed a slightly increasing trend, whereas the remaining 3 stations (Gibe Nr. Assendabo, Aweitu at Jimma, and Kitto Nr. Jimma) indicated an insignificant decreasing trend. The streamflow of the catchment generally shows a tiny decreasing tendency (0.007% per year) at its outlet. However, the results in general specified statistically insignificant trend changes of the hydrometrological data of the study catchment.

Analysis of long‐term trends and variations in extreme high air temperatures in May over Turkey and a record‐breaking heatwave event of May 2020

AbstractThis paper investigated the trends and variability in warmest daily maximum (TXx) and warmest daily minimum (TNx) air temperatures, and heatwave characteristics in Turkey in May during 1950–2020. It also analysed the climatological and synoptic meteorological evolutions of heatwave (HW) observed in May 2020. Trend analysis revealed significant increasing trends in TXx and TNx series and heatwave characteristics in May since 1950. Since the mid‐1980s, a continuously increasing trend in TXx and TNx series, and the number, frequency, and magnitude of the heatwaves were observed that accelerated with the mid‐1990s. The most severe heatwave in Turkey since 1950 in May was observed in 2020. The record‐breaking daily maximum (daily minimum) air temperatures were noted at 32 (23) of 96 stations in May 2020, mainly over Turkey's western and southern parts. Record‐high maximum (minimum) air temperature was 43.2°C (31.1°C) reached for the first time in May since 1950. The generalized extreme value model applied to the May TXx and TNx series revealed that the daily air temperatures in May 2020 exceeded 100 years' return periods at many stations of Turkey. This heatwave was linked to a regionalized strong anticyclonic blocking anomaly circulation and other associated atmospheric anomalies in May 2020. This also resulted in the centring of an unprecedented subsiding, calm, and stable warm air mass over Turkey. Adiabatic warming and drying developed very likely under subsidence control because of relatively weak circulation conditions. Such circumstances resulted in extremely hotter conditions in May 2020 compared to the long‐term averages.