Spatiotemporal changes and background atmospheric factors associated with forest fires in Turkiye.

In this study, spatiotemporal analysis of forest fires in Turkiye was undertaken, with a specific focus on the large-scale atmospheric systems responsible for causing these fires. For this purpose, long-term variations in forest fires were classified based on the occurrence types (i.e. natural/lightning, negligence/inattention, arson, accident, unknown). The role of large-scale atmospheric circulations causing natural originated forest fires was investigated using NCEP/NCAR Reanalysis sea level pressure, and surface wind products for the selected episodes. According to the main results, Mediterranean (MeR), Aegean (AR), and Marmara (MR) regions of Turkiye are highly susceptible to forest fires. Statistically significant number of forest fires in the MeR and MR regions are associated with global warming trend of the Eastern Mediterranean Basin. In monthly distribution, forest fires frequently occur in the MeR part of Turkiye during September, August, and June months, respectively, and heat waves are responsible for forest fires in 2021. As a consequence of the extending summer Asiatic monsoon to the inner parts of Turkiye and the location of Azores surface high over Balkan Peninsula result in atmospheric blocking and associated calm weather conditions in the MeR (e.g. Mugla and Antalya provinces). When this blocking continues for a long time, southerly winds on the back slopes of the Taurus Mountains create a foehn effect, calm weather conditions and lack of moisture in the soil of Antalya and Mugla settlements trigger the formation of forest fires.

Streamflow trends and flood frequency analysis: a regional study of the UK.

In recent years, the escalating effects of climate change on surface water bodies have underscored the critical importance of analyzing streamflow trends for effective water resource planning and management. This study conducts a comprehensive regional investigation into the streamflow rate trends of 18 rivers across the United Kingdom (UK). An enhanced Mann-Kendall (MK) test was employed to meticulously analyze both rainfall and streamflow trends on monthly and annual scales. Additionally, the Innovative Trend Analysis (ITA) method was applied to elucidate the variability of streamflow rates, providing a more nuanced understanding of hydrological changes in response to climatic shifts. MK test reveals statistically significant positive trends in streamflow rates, particularly for rivers in south-central Scotland and northern England. Specifically, in January, rivers such as the Tay at Ballathie, Tweed at Peebles, and Teviot at Ormiston showed Z-scores above 2. Annually, similar positive trends were observed, with the Tay at Ballathie (Z = 3.42) and Nith at Friars Carse (Z = 3.35) exhibiting the highest increases in streamflow rates. The ITA method showed no relevant trends for the lowest values of streamflow, except for the Thames at Kingston, while considerable variability was observed for the highest streamflow rates, with several rivers showing positive trends and, however, some England rivers, like Bure at Ingworth, Test at Broadlands, and Trent at Colwick, showing negative trends. From this perspective, a more in-depth analysis of the extreme streamflow trends was carried out. In particular, the flood frequency of the maximum annual streamflow was assessed, based on the fitting of the Generalized Extreme Value (GEV) distribution on the annual maxima. Increasing location parameter (µ) and return period trends were observed for several rivers across the UK. In particular, the Tay at Ballathie (Scotland) showed the most marked increase, with µ that ranged from about 730 m3/s to more than 900 m3/s. At the same time, slight decreasing trends were observed for the Trent River (µ from 378 m3/s to 341 m3/s). The critical comparison of the MK test, ITA, and GEV distribution fitting revealed both agreements and discrepancies among the methods. While the analyses generally aligned in detecting significant trends in streamflow rates, notable discrepancies were observed, particularly in rivers with negligible trends. These inconsistencies highlight the complexity of hydrological responses and the limitations of individual methods. Overall, the study provides a comprehensive view of how streamflow dynamics are evolving in UK rivers, highlighting regional variations in the impact of climate change. This understanding can improve water resource management strategies by integrating diverse analytical approaches.

GIS based geostatistical modelling and trends analysis of groundwater quality for suitable uses in Dhaka division.

Preserving the quality of groundwater has become Bangladesh's primary challenge in recent years. This study explores temporal trend variations in groundwater quality on a broader scale across 18 stations within the Dhaka division over 35 years. The data set encompasses an analysis of 15 distinct water quality parameters. Modified Mann-Kendal, Sens Slope and Mann-Kendal tests were performed to determine the trend's variation and slope. In addition, the spatial-temporal changes in the quality of groundwater are studied through Geographic Information System (GIS) mapping and Piper diagram was applied to identify the unique hydrochemical properties. This is the first study conducted on this area using various trends analysis and no in-depth study is available highlighting the trends analysis of groundwater quality on a larger magnitude. In contrast, the correlation matrix reveals a high association between Mg2+ and SO42-, Na+ and Cl- that affects salinity and overall hardness at the majority of sites. The Piper diagram also demonstrates that the groundwater in Madaripur Sadar has major salinity issues. The analysis reveals a distinctive dominance of bicarbonate (HCO3-) ions across all sampling stations, with (HCO3-) equivalent fractions consistently ranging from 0.70 to 0.99 which can cause a significant impact on groundwater uses. This extensive analysis of long-term groundwater quality trends in the Dhaka Division enables researchers to comprehend the overall transition of groundwater quality for hardness related complications in future. Moreover, it can be a baseline study considering the valuable implications and future steps for sustainable water resource management in this region.

Spatiotemporal variability and trends in rainfall and temperature extremes using ERA5 reanalysis and CORDEX-Africa model in Southern Ethiopia.

Rainfall and temperature are characterized by spatial and temporal variability in Ethiopia. However, less attention was given for the analysis of climate variability using advanced techniques and multiple sets of data. This study was conducted to examine spatiotemporal variability and trends in rainfall and temperature extremes in Ghibe III Dam watershed. Observational, ERA5, and regional simulation model data sets were used. The coefficient of variation (CV) and precipitation concentration index (PCI) were employed. The trends in rainfall and temperature extremes were examined using the modified Mann-Kendall test and the Sen Slope estimator in R-ClimDex in R 4.2.2 software. The warmest days exhibited temperature from 24.6°C to 40°C in Bele, 28.2°C to 35.43°C in Wolaita Sodo, 33.6 °C-44 °C in Areka, 31.64 °C-36.8 °C in Gesuba, and 29.19 °C-36.15 °C in Gena Bosa. The warmest nights showed temperature ranging from 14 °C to 18.74 °C in Bele and Gena Bosa, respectively. Annual warm days (TX90p) ranges from 11.34 to 57.1 days, with higher heating in the southern parts. The cool days (TX10p) range from 2.79 to 8.41, while the cool nights (TN10p) range from 0.04 to 8.26 days. The areal average temperature maximum and minimum range between 26.37 °C and 13.81 °C, respectively, with mean precipitation of 1446.92 mm.The rainfall extremes indices showed increasing and decreasing trends. The extreme temperature indices showed an overall warming trend. Based on ERA estimates, the rainfall in winter showed higher variability (CV = 72.4%-99.3 %) than the annual rainfall (CV = 33%-79.8 %). PCI showed a moderately (12 %) to very erratic (19.4 %) rainfall distribution. The climate model estimate showed high variability (CV = 20.65 %) in Climate Limited Area Modeling Community (CCLM) under representative concentration paths (RCP) 4.5 and 8.5 and extremely high variability (CV = 93.49 %) in the Regional Atmospheric Climate Model (RACMO) under RCP 4.5. Policymakers should design appropriate adaptation strategies applicable to farmers.

Temporal trend and driving effect of demographic transitions on embedded carbon emissions of Chinese households.

A population is regarded as the main non-economic driver of carbon emissions, causing the climatic crisis, especially in China experiencing a dramatic demographic transition. In contrast to aging, low fertility, the most remarkable feature of the Chinese population transition, has always been ignored when evaluating carbon emissions, due to the lack of long-run data. To narrow this gap, an integrated framework combining the continuous input-output tables from 1997 to 2018 with the Mann-Kendall test and vector auto-regression was presented to clarify the fluctuating trend of household embedded carbon emissions and the driving pattern of low fertility, aging, and urbanization. Our main findings showed that changes in household embedded carbon emissions have increased sharply in the last two decades. The growth of Chinese household embedded carbon emissions began to accelerate in 2001, which lagged 1 year behind the demographic indicators. Low fertility has a positive impact on households' embedded carbon emissions. More importantly, the impact of low fertility is more significant and far-reaching than that of aging. These suggest that aggressive policies for stimulating fertility and low-carbon lifestyles should be considered by policy makers.

GIS-based spatio-temporal analysis of rainfall trends under climate change in different agro-ecological zones of Wolaita zone, south Ethiopia.

Understanding the spatiotemporal dynamics of climatic conditions within a region is paramount for informed rural planning and decision-making processes, particularly in light of the prevailing challenges posed by climate change and variability. This study undertook an assessment of the spatial and temporal patterns of rainfall trends across various agro-ecological zones (AEZs) within Wolaita, utilizing data collected from ten strategically positioned rain gauge stations. The detection of trends and their magnitudes was facilitated through the application of the Mann-Kendall (MKs) test in conjunction with Sen's slope estimator. Spatial variability and temporal trends of rainfall were further analyzed utilizing ArcGIS10.8 environment and XLSTAT with R programming tools. The outcomes derived from ordinary kriging analyses unveiled notable disparities in the coefficient of variability (CV) for mean annual rainfall across distinct AEZs. Specifically, observations indicated that lowland regions exhibit relatively warmer climates and lower precipitation levels compared to their highland counterparts. Within the lowland AEZs, the majority of stations showcased statistically non-significant positive trends (p > 0.05) in annual rainfall, whereas approximately two-thirds of midland AEZ stations depicted statistically non-significant negative trends. Conversely, over half of the stations situated within highland AEZs displayed statistically non-significant positive trends in annual rainfall. During the rainy season, highland AEZs experienced higher precipitation levels, while the south-central midland areas received a moderate amount of rainfall. In contrast, the northeast and southeast lowland AEZs consistently received diminished rainfall across all seasons compared to other regions. This study underscores the necessity for the climate resilient development and implementation of spatiotemporally informed interventions through implementing region-specific adaptation strategies, such as water conservation measures and crop diversification, to mitigate the potential impact of changing rainfall patterns on agricultural productivity in Wolaita.

Spatiotemporal variability identification and analysis for non-stationary climatic trends for a tropical river basin of India.

The non-stationary behavior of climatic variables has been increasingly recognized as a challenge that disrupts the equilibrium of human-defined climate-based stationary processes, including hydrological and agricultural practices, and irrigation systems. This study aims to investigate long-term trends and non-stationarity in climatic variables across 23 stations of the Krishna River basin, India. Prominent trends in rainfall, temperature, and their extreme indices were identified using the Modified Mann-Kendall (MMK), Bootstrapped Mann-Kendall (BMK), and Sen's Slope Estimator tests, while the Innovative Trend Analysis (ITA) test uncovered hidden trends and potential shifts in climatic patterns. This study addresses a critical research gap by exploring both significant and hidden trends in climatic variables, providing a better understanding of future dynamics. Traditional methods like MMK and Sen's Slope were insufficient to reveal these hidden trends, but ITA offered a more comprehensive analysis. The findings revealed an increase in total annual rainfall for almost 50% of the basin, which aligns with rising maximum temperatures, suggesting enhanced evaporation rates and subsequent fluctuations in rainfall patterns. Seasonal analysis indicated a shift towards decreased rainfall during winter and pre-monsoon seasons, contrasted by increased precipitation during the monsoon and post-monsoon periods, highlighting a clear alteration in rainfall distribution. The Simple Daily Intensity Index (SDII) and other indices suggest intensified rainfall events despite a decrease in the number of rainy days, indicating fewer but more intense events. Temperature analysis showed an overall increase in maximum temperatures, with the Diurnal Temperature Range (DTR) significantly increasing across all stations, implying greater daily temperature variations and potential for intensified water cycles and extreme climatic events. Furthermore, the study simplifies these trends by classifying them into two attributes: intensity and frequency, aiding policymakers in site-specific management of water resources and planning for future climatic scenarios. The presence of non-stationarity in extreme rainfall was confirmed by the Augmented Dickey-Fuller (ADF), Phillips-Perron (PP), and Kwiatkowski-Phillips-Schmidt-Shin (KPSS) tests. These findings are significant as they conclude how climate change is altering hydrological patterns at each station. The study emphasizes the necessity for adaptive management strategies to mitigate the adverse impacts on agriculture, infrastructure, and human safety.

A new fuzzy method for investigating the effects of dam on aquifer: case study of Rudbal dam, south of Iran.

Dam construction has some qualitative and quantitative effects on groundwater resources. This effect may be in the form of an increasing groundwater table or a changing groundwater system. In this paper, the effect of the Rodbal dam construction on the Darab aquifer was investigated. For this paper water table levels from observation wells and precipitation data was used. The study aims to analyse the behaviour of the water table during precipitation by employing a combination of the standardisation process, Fuzzy Inference System (FIS), and Root Mean Square Error (RMSE) calculations. Fuzzy logic involves the fuzzification of input data, transforming precise values into fuzzy sets. The effectiveness of the FIS is highlighted, particularly in determining the number of membership functions for inputs. The performance of the results is assessed using indicators such as RMSE and Coefficient of Determination (R2). The FIS showed a high level of effectiveness in performance assessment, exhibiting a 70% similarity between the fuzzy and Mann-Kendall methods. Nonetheless, the Fuzzy Purpose Method corresponded more closely with the observed data, indicating a more accurate reflection of reality. The findings indicate that for P3, P7, and P8, the results from the Mann-Kendall method do not show a discernible trend. Conversely, the proposed Fuzzy method accounts for changes in the behaviour of these piezometers.

Measuring pain intensity through physical interaction in an experimental model of cold-induced pain: A method comparison study.

OBJECTIVES: Assessment of pain is challenging given its subjective nature. Standard pain assessment tools have limitations. We aimed to compare the verbal numeric rating scale (NRS) and Grasp, a novel handheld electronic device that reports pain by squeezing. METHODS: To compare Grasp and NRS, healthy adult volunteers were invited to undergo two subsequent standardised tests of cold-triggered pain using a cold pressor test (CPT) at a temperature of 3°C. Pain intensity was in a randomised manner reported by NRS (scale 0-10) or by squeezing Grasp (0-3 V) during the two CPTs. A third CPT was performed 1 to 14 days later where subjects reported pain by Grasp a second time in order to study the association of repeated Grasp measurements. Acceptable association was a priori considered as mean Kendall's τ-b coefficient (τ-b) ≥ 0.7. The subjects reported their experience of using Grasp in a purpose-made questionnaire. RESULTS: In total, 102 subjects were included, and 96 subjects (56 females) completed all three tests. The association of pain intensity reported by Grasp and NRS was moderate with a mean τ-b of 0.53 (95% confidence interval [CI] 0.47-0.58). The association between the repeated Grasp measurements was weak with a mean τ-b of 0.43 (95% CI 0.37-0.48). Most subjects reported that Grasp was intuitive and easy to use. CONCLUSIONS: Pain intensity reported by squeezing Grasp did not show acceptable association with pain intensity reported by NRS during CPTs. The association between pain intensity reported by Grasp during two CPTs on separate days was weak. Further improvements of the Grasp ball are needed before use in clinical settings.

Evaluating trends in groundwater quality of coastal alluvial aquifers of Eastern India for sustainable groundwater management.

Groundwater is a precious natural element which ensures global water, food, and environmental security in the twenty-first century. Systematic monitoring, sustainable utilization, preservation and remediation are critical aspects of efficient groundwater resource management. This study deals with the analysis of spatial variability and trend in groundwater chemistry as well as identification of possible contamination sources in a coastal alluvial basin of eastern India. Pre-monsoon season data of 14 groundwater-quality variables measured in 'leaky confined' and 'confined' aquifers were analyzed for ten years (2012-2021). Mann-Kendall (M-K) test with the Sen's Slope Estimator, Spearman Rank Order Correlation (SROC) and Innovative Trend Analysis (ITA) tests were employed to assess decadal (2012-2021) trends. The analysis of the results indicated that the 'critical' water-quality parameters exceeding the acceptable limits for drinking are TDS, EC, TH, pH, Mg2+, Na+, K+, Fe2+, HCO3-, Cl- and NO3-. Weak negative correlations between rainfall and groundwater elevation for both the aquifers reveal poor rainfall recharge into the aquifers. Therefore, a reduction in groundwater abstraction and augmentation of groundwater recharge is recommended. Trend analysis results indicated that the concentrations of TH, Mg2+ and Fe2+ exhibit significant increasing trends in the 'leaky confined aquifer'. In contrast, significant rising trends in TH, Mg2+, Na+, Fe2+, HCO3- and NO3- concentrations are identified in the 'confined aquifer'. Further, the SROC test could not detect the trends in groundwater quality in most blocks and for many parameters. On the other hand, the ITA test revealed significant trends in most of the parameters of the two aquifers in almost all the blocks. Trend magnitudes of the groundwater-quality parameters based on the Sen's Slope Estimator and the ITA test vary from -63.7 to 58.65 mg/L/year for TDS, -14 to 39.07 mg/L/year for TH, -1.49 to 4.83 mg/L/year for Mg2+, -7.14 to 22.96 mg/L/year for Na+, -0.32 to 0.44 mg/L/year for Fe2+, -8.33 to 20.75 mg/L/year for HCO3-, -26.52 to 31.01 mg/L/year for Cl- and 1.29 to 3.76 mg/L/year for NO3- over the study area. The results of M-K and ITA tests were found in agreement in all the blocks for both the aquifers. Groundwater contamination in both the aquifers can be attributed to weathering, geogenic processes, mineral dissolution, seawater intrusion, poor recharge pattern and injudicious anthropogenic activities. It is strongly recommended that concerned authorities urgently formulate efficient strategies for managing groundwater quality in the 'leaky confined' and 'confined' aquifers which serve as vital sources of drinking and irrigation water supplies in the study area.

Analysis of factors affecting evapotranspiration zoning.

Reference evapotranspiration (ETo) has a significant role in water resource planning and management as well as analysis of crop production and other agricultural tasks. Methods for estimating ETo may require diurnal/monthly assessments to perceive the consequences of climatic changes on local regions. The spatial and temporal patterns of ETo were analyzed in the current work using data from 340 weather stations in Iran. The entropy theory was used to assess the uncertainty of the utilized variables and the modified Kendall test was applied for temporal trend analysis. The interpolation (e.g., kriging) and ordinary least squares (OLS) methods were used for spatio-temporal ETo classification/modeling. The spatial analysis demonstrated that the OLS method with a good fit measure (R2 = 0.985) successfully simulated the spatial relationships of ETo with climatic parameters. After examining error indices, the cokriging method with an exponential variogram was introduced as the best method of seasonal and annual ETo classification in Iran. Spatially and temporally calculated ETo patterns using modified Hargreaves (MHGR) and MODIS methods closely resembled the standard FAO Penman-Monteith (FPM-56) method, all indicating a gradual increase in ETo. MHGR and MODIS methods serve as suitable alternatives for estimating ETo in various climatic regions of Iran, provided data availability.

Assessing the utility of shellfish sanitation monitoring data for long-term estuarine water quality analysis.

Regular testing of coastal waters for fecal coliform bacteria by shellfish sanitation programs could provide data to fill large gaps in existing coastal water quality monitoring, but research is needed to understand the opportunities and limitations of using these data for inference of long-term trends. In this study, we analyzed spatiotemporal trends from multidecadal fecal coliform concentration observations collected by a shellfish sanitation program, and assessed the feasibility of using these monitoring data to infer long-term water quality dynamics. We evaluated trends in fecal coliform concentrations for a 20-year period (1999-2021) using data collected from spatially fixed sampling sites (n = 466) in North Carolina (USA). Findings indicated that shellfish sanitation data can be used for long-term water quality inference under relatively stationary management conditions, and that salinity trends can be used to investigate management-driven bias in fecal coliform observations collected in a particular area.

Multifactor and multidimensional data quality analysis of judge scoring in diving competition.

Introduction: In sports competitions, judge scoring data serve as an objective measure of an athlete's performance level. However, research has indicated the unreliability of objective measurements. Controversy often arises regarding the quality of judge scoring data, undermining fairness and justice in sports competitions. Method: This paper proposes a method utilizing the Kendall covariance coefficient and the Kendall correlation coefficient for the thorough evaluation of judging data quality in diving events. The analysis is structured around four key elements: overall competition, individual divers, specific rounds, and distinct diving techniques. Each element is analyzed across three dimensions: the collective data quality from the judging panel, interjudge data quality comparisons, and the alignment of individual judges' scores with the final tallied scores. Results: Two case studies serve to illustrate the application of this method. The Kendall covariance coefficient is employed to assess the data quality from the judges as a unified entity, whereas the Kendall correlation coefficient is utilized to evaluate the data quality from individual judges. Results show that the data quality of the judge group's scoring is high, while the data quality of the judge group's scoring for the 6th diver, the 5th round, Dive No. 5152B, Judge 5 and 6 in the Competition 1, and the 1st diver, the 3rd round, Dive No. 6245D, Judge 4 in the Competition 2 is inconsistent with the others. Discussion: This approach uncovers disparities in data quality attributed to the judges' panel across each diver, each round, and the various diving maneuvers. However, the Kendall correlation coefficient may not be suitable for evaluating data quality when both the data differences and the sample size are small.

Rainfall analysis and deriving design storm for hydrological modelling of Koyna dam catchment area in Maharashtra state of India.

Rainfall is a key hydro meteorological variable. Climate change is disrupting the hydrological cycle and altering the usual cycle of rainfall, which frequently results in long-lasting storms with significant rainfall. A first step in hydrologic design of project is to determine the design storm or rainfall events to be used. For deriving design storm, researchers concluded that instead of using generalized readily available curves or maps, it is better to estimate design storm based on site specific historical rainfall data. The objective of the study is to analyze the rainfall data in the koyna watershed area in order to evaluate the design storm, which will be further used as an input data for HEC-HMS event based hydrological modelling of flood peak attenuation of design storm flow at koyna dam during extreme rainfall event. In this study, 40 years (1982-2021) of rainfall data from 8 rain gauge stations in Koyna Dam Catchment area is used initially for performing trend analysis through statistical and graphical techniques and then for Isopluvial analysis. The Sen's slope test and the Mann-Kendall test are the statistical techniques employed, and Innovative Trend Analysis is the graphical technique used. IDF approach is used for deriving design storm, and using Gumbel's frequency distribution method Isohyetal maps, IDF tables and curves are prepared for 2,10,25,50,75 and 100 year return periods and 6,12,24,48 and 96 h durations. Results obtained from statistical and graphical trend analysis of annual rainfall series are consistent. No statistically significant trend in annual rainfall series is observed, however there is rising and falling trend was observed in annual as well as monthly rainfall series. From the results of design storm study, the design storm hyetograph of 10 years return period and 96 h duration is selected, which gives the rainfall intensity of 10.88 mm/h for the koyna catchment. There are various dams nearby koyna catchment, The Isohyet maps, IDF curves and table output available from this study can be more reliably used during planning and design of hydraulic structure for other areas near by koyna catchment.

Assessing groundwater quality dynamics in Madhya Pradesh: Chemical contaminants and their temporal patterns.

Groundwater is essential for maintaining ecosystem health and overall well-being as a pivotal resource for plants and animals. The increasing public consciousness of the deterioration of groundwater quality has emphasized the significance of undertaking extended evaluations of groundwater water quality, particularly in regions undergoing substantial hydrological alterations. This study primarily aims to investigate the spatio-temporal variations in groundwater quality and evaluate its suitability for potable purposes in the region of Madhya Pradesh. The study combines the Mann-Kendall (MK) test and Sen's Slope (SS) to analyze the changes in groundwater quality of all 51 districts of Madhya Pradesh, India, utilizing 12 water quality indices using MATLAB. Data was sourced from the Central Ground Water Board (CGWB) in India from the year 2001-2021. The data was then tested for homogeneity at all 1154 sampling stations using the software XLSTAT. Piper plot clustering characterized the state's groundwater as bicarbonate-calcium-magnesium (HCO3--Ca2+-Mg2+) type. The study found that the groundwater in the area is heavily impacted by high levels of nitrate and hardness, which is caused by an increase in multivalent cations. The water was classified as ranging from hard to extremely hard, and approximately 25.49% of the state's groundwater has nitrate levels that exceed the acceptable limits. The MK test showed a significant increasing correlation in trends for parameters such as nitrate, sulfate, fluoride, chloride, bicarbonate, total hardness, and electrical conductivity. It also showed a significant decreasing correlation for calcium, magnesium, potassium, and sodium. These results were observed at a confidence level of 95%. The analysis of trends has shown that human-related factors have a considerable effect on the characteristics of groundwater quality. It is therefore recommended that such human-related factors be taken into consideration when developing policies for managing groundwater resources. Consequently, these policies should emphasize the strict enforcement of rules and standards that limit the overuse of fertilizers, ensure the appropriate disposal of municipal solid and liquid wastes, and regulate industrial pollutants.

Analyzing temperature, humidity, and precipitation trends in six regions of Thailand using innovative trend analysis.

The change of temperature and weather parameters is a major concern affecting sustainable development and impacting various sectors, such as agriculture, tourism, and industry. Changing weather patterns and their impact on water resources are important climatic factors that society is facing. In Thailand, climatological features such as ambient temperature, relative humidity, and precipitation play a substantial role in affecting extreme weather events, which cause damage to the economy, agriculture, tourism, and livelihood of people. To investigate recent serious changes in annual trends of temperature, relative humidity, and precipitation in Thailand, this study used the Mann-Kendall (MK) test and innovative trend analysis (ITA) methods. The MK test showed that all six regions had an upward trend in temperature and humidity index (humidex, how hot the weather feels to the average person), while relative humidity and precipitation showed both upward and downward trends across different regions. The ITA method further confirmed the upward trend in temperature and humidex and showed that most data points fell above the 1:1 line. However, the upward trend in most variables was not significant at the 5% level. The southern and eastern regions showed a significant upward trend in relative humidity and humidex at a 5% level of significance according to the MK test. The output of this study can help in the understanding of weather variations and predict future situations and can be used for adaptation strategies.

Grid-based climate variability analysis of Addis Ababa, Ethiopia.

Climate change is an intricate global environmental concern. However, its impact is more pervasive in developing nations such as Ethiopia. Hence, this manuscript examines temperature variability and the magnitude of change over 38 years in the specific case of Addis Ababa, Ethiopia. Gridded meteorological data consisting of minimum and maximum temperatures on a monthly time scale ranging from 1981 to 2018 was obtained from the National Meteorological Agency of Ethiopia. The coefficient of variation (CV) and standardized anomaly index (SAI) were used to examine the rate and extent of temperature anomalies. Geostatistical models, particularly ordinary kriging, are presented as a means of spatially interpolating temperature data. Modified Mann-Kendall test (MMK), Sen's Slope (SS) estimator, principal component analysis (PCA), and T-test were employed to determine the monthly, annual, and seasonal trends using Geospatial technologies, "R" programming, and statistical software. The findings revealed substantial spatial and temporal variation in Addis Ababa's annual and seasonal maximum and minimum temperatures. The long-term mean annual maximum and minimum temperatures were 25.8 °C and 12.6 °C, respectively. The monthly, annual, and seasonal temperatures accrued significantly except in the months of January and September. It is noteworthy that the decadal maximum temperature has risen by 2.7 °C, while minimum temperatures have displayed comparatively minor fluctuations. Moreover, the findings also exhibited that the average maximum and minimum temperatures increased by 1.88 °C and 1.72 °C, correspondingly and the highest temperature occurred during the spring (Belg) season. The first two PCAs (Annual and Kiremt Tmax) account for 90% of the temperature variation. In conclusion, the findings underscore the pressing need for the implementation of climate adaptation strategies and policy measures, which can strengthen the city's resilience to imminent climate change-induced hazards. The mounting temperature presents substantial challenges across various sectors within the city, emphasizing the urgency of preemptive actions to mitigate potential repercussions.

Trend analysis of MODIS NDVI time series and its relationship to temperature and precipitation in Northeastern of Iran.

Vegetation plays a crucial role in providing organic matter and regulating energy exchange on the Earth's surface. This study investigates the changes in vegetation cover, temperature, and precipitation in northeastern Iran during 2001-2020. MODIS-NDVI time series data and climatic data from 11 synoptic stations were utilized. The nonparametric Mann-Kendall method was employed to detect trends in vegetation cover and climatic variables. Additionally, the correlation between climatic parameters and vegetation was examined. Trend analysis revealed significant increases/decreases in vegetation cover in 32%/26% of the region, respectively. The increasing trend in vegetation cover was predominantly observed in highlands, suggesting that a warmer climate has enhanced the living conditions for plants in these regions. The vegetation trend map indicates an expansion of vegetation cover in the northern and central parts of Iran during the past 20 years, whereas the southern and eastern portions experienced declines. The relationship between vegetation and elevation revealed that vegetation increased above 1,850 m and decreased below 850 m. Trend analysis showed no significant trend in precipitation data since the beginning of the twenty-first century, but an increasing trend in temperature was observed in 82% of the region's area, excluding the western strip. Correlation coefficients between temperature, precipitation, and vegetation indicated that declining temperatures are the limiting factors for vegetation in the highlands, while in lowland areas, the decrease in precipitation significantly diminishes vegetation growth.

Spatiotemporal variability in human thermal comfort perception in open-air spaces: application to the state of Minas Gerais, Brazil.

The objective of this study was to propose bioclimatic zoning to classify human thermal comfort and discomfort in the state of Minas Gerais, Brazil; both historical and future scenarios are considered. Thus, historical series (1961 to 2017) of the effective temperature index as a function of the wind (ETW) were obtained as a function of the monthly average values of the minimum, mean, and maximum dry-bulb air temperatures (tdb,min, tdb,mean, and tdb,max, respectively), in addition to the mean relative humidity ([Formula: see text], %) and mean wind speed ([Formula: see text], m s -1). The data were obtained from 34 weather stations and subjected to trend analysis by using the nonparametric Mann-Kendall test, thus enabling the simulation of future scenarios (for 2028 and 2038). Then, to define the thermal ranges of the bioclimatic zoning, maps of ETWmin, ETWmean, and ETWmax were created from geostatistical analysis. Overall, the results show warming trends for the upcoming years in Minas Gerais municipalities. All climatic seasons showed an increase in the frequency of new classifications in the upper adjacent classes, which indicates climate warming. Therefore, when considering future scenarios for the autumn and winter seasons, attention should be given to changes in predicted thermal sensation, especially in the Central Minas Gerais, Belo Horizonte Metropolitan, South/Southwest Minas, Campo das Vertentes, and Zona da Mata.

Diagnosis of Covid-19 from CT slices using Whale Optimization Algorithm, Support Vector Machine and Multi-Layer Perceptron.

BACKGROUND: The coronavirus disease 2019 is a serious and highly contagious disease caused by infection with a newly discovered virus, named severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). OBJECTIVE: A Computer Aided Diagnosis (CAD) system to assist physicians to diagnose Covid-19 from chest Computed Tomography (CT) slices is modelled and experimented. METHODS: The lung tissues are segmented using Otsu's thresholding method. The Covid-19 lesions have been annotated as the Regions of Interest (ROIs), which is followed by texture and shape extraction. The obtained features are stored as feature vectors and split into 80:20 train and test sets. To choose the optimal features, Whale Optimization Algorithm (WOA) with Support Vector Machine (SVM) classifier's accuracy is employed. A Multi-Layer Perceptron (MLP) classifier is trained to perform classification with the selected features. RESULTS: Comparative experimentations of the proposed system with existing eight benchmark Machine Learning classifiers using real-time dataset demonstrates that the proposed system with 88.94% accuracy outperforms the benchmark classifier's results. Statistical analysis namely, Friedman test, Mann Whitney U test and Kendall's Rank Correlation Coefficient Test has been performed which indicates that the proposed method has a significant impact on the novel dataset considered. CONCLUSION: The MLP classifier's accuracy without feature selection yielded 80.40%, whereas with feature selection using WOA, it yielded 88.94%.