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.

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.

Enhancing spatial resolution of GRACE-derived groundwater storage anomalies in Urmia catchment using machine learning downscaling methods.

The Urmia lake in north-west Iran has dried up to perilously low levels in the past two decades. In this study, we investigate the drivers behind the decline in lake water level with the help of in-situ and remote sensing data. We use total water storage (TWS) changes from the gravity recovery and climate experiment (GRACE) satellite mission. TWS from GRACE includes all the water storage compartments in a column and is the only remote sensing product that can help in estimating groundwater storage (GWS) changes. The coarse spatial (approx. 300 km) resolution of GRACE does not allow us to identify local changes that may have led to the Urmia lake disaster. In this study, we tackle the poor resolution of the GRACE data by employing three machine learning (ML) methods including random forest (RF), support vector regression (SVR) and multi-layer perceptron (MLP). The methods predict the groundwater storage anomaly (GWSA), derived from GRACE, as a function of hydro-climatic variables such as precipitation, evapotranspiration, land surface temperature (LST) and normalized difference vegetation index (NDVI) on a finer scale of 0.25° × 0.25°. We found that i) The RF model exhibited highest R (0.98), highest NSE (0.96) and lowest RMSE (18.36 mm) values. ii) The RF downscaled data indicated that the exploitation of groundwater resources in the aquifers is the main driver of groundwater storage and changes in the regional ecosystem, which has been corroborated by few other studies as well. The impact of precipitation and evapotranspiration on the GWSA was found to be rather weak, indicating that the anthropogenic derivers had the most significant impact on the GWSA changes. iii) We generally observed a significant negative trend in GWSA, having also significant positive correlations with the well data. However, over regions with dam construction significant negative correlations were found.

Exploring temperature dynamics in Madhya Pradesh: a spatial-temporal analysis.

Understanding the dynamics of temperature trends is vital for assessing the impacts of climate change on a regional scale. In this context, the present study focuses on Madhya Pradesh state in Central Indian region to explore the spatial-temporal distribution patterns of temperature changes from 1951 to 2021. Gridded temperature data obtained from the Indian Meteorological Department (IMD) in 1° × 1° across the state are utilised to analyse long-term trends and variations in temperature. The Mann-Kendall (MK) test and Sen's slope (SS) estimator were used to detect the trends, and Pettitt's test was utilised for change point detection. The analysis reveals significant warming trends in Madhya Pradesh during the study period during specific time frames. The temperature variables, such as the annual mean temperature (Tmean), maximum temperature (Tmax), and minimum temperature (Tmin), consistently increase, with the most pronounced warming observed during winter. The trend analysis reveals that the rate of warming has increased in the past few years, particularly since the 1990s. However, Pettitt's test points out significant changes in the temperature, with Tmean rising from 25.46 °C in 1951-2004 to 25.78 °C in 2005-2021 (+0.33 °C), Tmax shifting from 45.77 °C in 1951-2010 to 46.24 °C in 2011-2021 (+0.47°C), and Tmin increasing from 2.65 °C in 1951-1999 to 3.19 °C in 2000-2021 (+0.46 °C). These results, along with spatial-temporal distribution maps, shed important light on the alterations and variations in monthly Tmean, Tmax, and Tmin across the area, underlining the dynamic character of climate change and highlighting the demand for methods for adaptation and mitigation.

Fingerprinting of rainfall over semi-arid region, Western India, using MATLAB and GIS.

The present study investigates long-term changes in the rainfall regime over the Sabarmati River Basin, Western India, during 1981-2020 using computational and spatial analysis tools. Daily gridded rainfall data from India Meteorological Department (IMD) at 0.25 × 0.25 spatial resolution was employed to determine changes in rainfall at annual, monthly, and seasonal scales and analyze changes in rainfall characteristics using different thresholds for dry/ wet days and prolonged spells over Western India. Mann-Kendall test, Sen slope estimation, and linear regression analysis indicate that annual and monsoon rainfall over the basin has increased while the rest of the seasons have shown a declining trend. However, none of the trends obtained was found to be statistically significant. Spatial analysis of rainfall trends for each decade between 1980 and 2020 revealed that certain parts of the basin had experienced a significant declining trend during 1991-2000. Monthly rainfall analysis indicates the presence of a unimodal distribution of rainfall and a shift in rainfall towards later monsoon months (August and September). It is also inferred that days with moderate rainfall have decreased while low and extreme rainfall events have increased over the basin. It is evident from the study that the rainfall regime is highly erratic, and the study is important in understanding the changes in the rainfall regime during the last 40 years. The study has significant implications for water resource management, agricultural planning, and mitigation of water-related disasters.

Air pollution trends and exceedances: ozone and particulate matter outlook in Brazilian highly urbanized zones.

In Brazil, scarce air quality data hinders air pollutant chemical understanding and policy decisions regarding public health and environmental impacts. From this perspective, our study assessed the O3, PM2.5, and PM10 yearly and seasonal trends and also the WHO Air Quality Guidelines 2021 exceedance trends at 40 air quality stations located in four highly urbanized zones in Brazil (Belo Horizonte, São Paulo, Rio de Janeiro, and Espírito Santo) from early 1990s up to 2019. We applied the Mann-Kendall test aligned with Sen's Slope estimator to assess the trends and the Cox-Stuart test to verify the WHO AQG 2021 exceedances trends. Our findings pointed out that the current national legislation is outdated when compared to WHO AQG 2021 values, leading to multiple exceedances episodes. We also found out that 62% of São Paulo's stations presented O3 increasing trends, while in Rio de Janeiro 85.7% presented decreasing trends. The Cox-Stuart test pointed out that PM2.5 exceedance trends showcase positive values, and most of the significative values are located in São Paulo stations. Therefore, we endorse that the national legislation needs to be updated meanwhile the air monitoring network needs to expand its coverage.

Spatio-temporal drought assessment of the Subarnarekha River basin, India, using CHIRPS-derived hydrometeorological indices.

Precipitation studies have a crucial role in deciphering climate change and monitoring natural disasters such as droughts. Such studies lead to better assessment of rainfall amounts and spatial variabilities; and have a vital role in impact assessment, mitigation, and prediction of occurrence. Thus, this study has been undertaken in the Subarnarekha River basin using Climate Hazards group Infrared Precipitation with Stations (CHIRPS) dataset. Precipitation datasets helped in deriving hydrometeorological indices such as the Rainfall Anomaly Index (RAI) and Standardized Precipitation Index (SPI) for the identification of drought occurrences. The core objective was to infer spatio-temporal drought scenarios and their trend characterization covering four decades over the years 1981 to 2020. Quantitative drought assessment was done using run theory for identifying the Drought Duration (DD), Drought Severity (DS), Drought Intensity (DI), and Drought Frequency (DF). Mann-Kendall (MK) test was performed to understand the precipitation and drought trends at annual and seasonal scales. Eight severe drought events were identified in the Subarnarekha River basin for the past 40 years and the average DI value of 0.8 was recorded. MK test results for the precipitation showed a significant positive trend (95% confidence level) for pre-monsoon periods. However, for SPI, a significant positive trend was observed over the intervals of 3 (SPI3), 6 (SPI6), and 12 (SPI12) months respectively at an annual timescale, suggesting wetter conditions within the study area. Moreover, there had been insignificant negative trends for SPI1 and SPI3 during winter. It indicates that during the short-term SPI scale, i.e., 1 month (SPI1) and 3 months (SPI3), the instances of negative SPI values inferred were high, which point to the increasing incidences of meteorological drought possibly due to deficient soil moisture. Thus, the results indicated that the CHIRPS precipitation product-derived hydrometeorological indices could act as a valuable tool for assessing the past spatio-temporal drought conditions of the Subarnarekha River basin. This may further be helpful in planning for sustainable water resource management of such river basins.

Investigating the causes of reduced dissolved oxygen concentrations in Kickapoo Creek, TX.

Excessive organic matter and nutrients can depress dissolved oxygen concentration (DOC) in streams. The aim of this study was to understand the depressed DOC of Kickapoo Creek in Texas, USA, which is an impaired stream; identify the possible sources causing the depressed DOC and elevated Escherichia coli levels; and identify possible remedial measures. Monthly grab water quality data was monitored for nine stations in the watershed. For three of the nine stations, 24-h DOC was also monitored for a comparison with the minimum and average DOC criteria. Correlational, graphical, spatial, and temporal analyses were carried out for DOC concentration with other water quality variables which have the potential to depress DOC in the stream. The correlational analyses show a weak to moderate correlation for DOC with nutrient and oxygen-demanding substance concentrations present in the stream. However, there are spatial and temporal trends in DOC data that can be attributed to the nutrient influx into the stream. A pattern of increasing nutrient concentrations from upstream to downstream partially explains the decreasing dissolved oxygen (DO) concentrations observed towards the lower reaches. Visual interpretations of riparian vegetation and sediment influx also support the spatial patterns in DO concentrations. The majority of the depressed DOC occurs in the summer months when streamflow is at a minimum. The depressed DOC during summer is partially explained by the increasing daily temperatures for the summer months as revealed by the trend analysis of the daily temperature data from 1981 to 2020 using the modified Mann-Kendall test, Pettitt test, and Sen's slope.

Sediment Assessment of the Pchelina Reservoir, Bulgaria.

The temporal dynamics of anthropogenic impacts on the Pchelina Reservoir is assessed based on chemical element analysis of three sediment cores at a depth of about 100-130 cm below the surface water. The 137Cs activity is measured to identify the layers corresponding to the 1986 Chernobyl accident. The obtained dating of sediment cores gives an average sedimentation rate of 0.44 cm/year in the Pchelina Reservoir. The elements' depth profiles (Ti, Mn, Fe, Zn, Cr, Ni, Cu, Mo, Sn, Sb, Pb, Co, Cd, Ce, Tl, Bi, Gd, La, Th and Unat) outline the Struma River as the main anthropogenic source for Pchelina Reservoir sediments. The principal component analysis reveals two groups of chemical elements connected with the anthropogenic impacts. The first group of chemical elements (Mn, Fe, Cr, Ni, Cu, Mo, Sn, Sb and Co) has increasing time trends in the Struma sediment core and no trend or decreasing ones at the Pchelina sampling core. The behavior of these elements is determined by the change of the profile of the industry in the Pernik town during the 1990s. The second group of elements (Zn, Pb, Cd, Bi and Unat) has increasing time trends in Struma and Pchelina sediment cores. The increased concentrations of these elements during the whole investigated period have led to moderate enrichments for Pb and Unat, and significant enrichments for Zn and Cd at the Pchelina sampling site. The moderately contaminated, according to the geoaccumulation indexes, Pchelina Reservoir surface sediment samples have low ecotoxicity.

Implications of changes in temperature and precipitation on the discharge of Brahmaputra River in the urban watershed of Guwahati, India.

The urban watershed of Guwahati situated on the bank of the Brahmaputra River is one of the fastest growing cities of India. During the last two decades, water security concerns due to climatic variabilities have become a pronounced issue in the urban watershed of Guwahati. Thus, the study aims to calculate the long-term temporal trends of temperature, precipitation, extreme climate indices, and river discharge to assess the variations and patterns of hydro-climatic variations in the urban watershed of Guwahati from 1991 to 2019. Furthermore, the current study also tries to correlate these extreme climatic indices to river discharge to determine the degree of hydro-climatic variations. The Mann-Kendall statistical techniques and Sen's estimator were used to calculate the statistical significance, stability, and averaged magnitude of trends in the hydro-meteorological data. The result shows that the wetness indices, R20 and RX5Day, reported a decline in Guwahati's urban watershed from 1991 to 2019, resulting in a reduction in intensity and duration of heavy rainfalls while the dry spell (CDD) has been more distinct in the study area with a rise in the average temperature by 0.023 °C/year. Similarly, the most significant statistical trend was found in the monsoonal discharge of the Brahmaputra with a negative trend of - 204.16 m3/s/year. The results also show that fluctuations in rainfall patterns have a direct impact on the discharge of the Brahmaputra. These phenomena can affect the quantity of river water resulting in a severe impact on water security in the study area.

Ozone weekend effect in cities: Deep insights for urban air pollution control.

Studying weekend-weekday variation in ground-level ozone (O3) allows one to better understand O3 formation conditions, with a potential for developing effective strategies for O3 control. Reducing inappropriately the O3 precursors emissions can either produce no reduction or increase surface O3 concentrations. This paper analyzes the weekend-weekday differences of O3 at 300 rural and 808 urban background stations worldwide from 2005 to 2014, in order to investigate the O3 weekend effect over time and assess the effectiveness of the precursors emissions control policies for reducing O3 levels. Data were analyzed with the non-parametric Mann-Kendall test and Theil-Sen estimator. Rural sites typically did not experience a weekend-weekday effect. In all urban stations, the mean O3 concentration on the weekend was 12% higher than on weekdays. Between 2005 and 2014, the annual mean of daily O3 concentrations increased at 74% of urban sites worldwide (+ 0.41 ppb year-1) and decreased in the United Kingdom (- 0.18 ppb year-1). Over this time period, emissions of O3 precursors declined significantly. However, a greater decline in nitrogen oxides (NOx) emissions caused an increase in Volatile Organic Compounds (VOCs) to NOx ratios leading to O3 formation. In France, South Korea and the United Kingdom, most urban stations showed a significant upward trend (+ 1.15% per year) for O3 weekend effect. Conversely, in Canada, Germany, Japan, Italy and the United States, the O3 weekend effect showed a significant downward trend (- 0.26% per year). Further or inappropriate control of anthropogenic emissions in Canada, Southern Europe, Japan, South Korea and the United States might result in increased daily O3 levels in urban areas.

Assessing land use and landscape factors as determinants of water quality trends in Nyong River basin, Cameroon.

Changes in LULC and landscape factors impact water quality at spatial and temporal scales. In this study, we investigated the current status in water quality for sub-watersheds of the southern portion of the Nyong River basin of Cameroon from 1994 to 2014 using the WHO guideline. The trends in the water quality parameters were explored using Mann-Kendall test, and their relationship with changes in LULC and landscape factors were analysed using multiple linear regression and Pearson correlation. The current status in water quality did not exceed the WHO guideline limits for drinking water despite a 16% decrease in forest cover and 10% increase in agricultural areas during the period of record. The concentration and changes in water quality trends varied significantly among the sub-watersheds. The concentration of Ca2+, Mg2+, Na+, SiO2, K+, DOC, SPM and WT showed significantly increasing trends in the Nsimi small sub-watershed, while only Ca2+, Mg2+, Na+ and NO3- showed significantly increasing trends in the large sub-watersheds of Mbalmayo and Olama. A combination of one to five LULC and landscape factors, including changes in urban cover, young secondary forest, slope, elevation and population explained 10 to 70% of the changes in water quality trends at watershed scale. Although the interaction of LULC and landscape factors seems to have low impact on the water quality so far, maintaining greater than 70% forest cover and appropriate fallow farming system is invaluable to protecting water quality in the Nyong River basin in the Congo basin and in other forest-rich regions.

Trends and probabilistic stability index for evaluating groundwater quality: The case of quaternary alluvial and quartzite aquifer system of India.

This study proposed a novel groundwater-quality stability index (GQSI), which considers probabilistic estimate of reliability and resilience based on multi-year dataset. The developed index is validated and optimized adopting optimum index factor approach. The vulnerabilities of different groundwater quality parameters are also computed to provide an insight about the deviations of their concentrations from the safe drinking water limits. The application of the developed stability index is demonstrated through a case study in quaternary alluvial and quartzite aquifer system of India. In addition, trends in the groundwater quality parameters are identified by using variance-corrected Mann-Kendall test, and trends are quantified by using Sen's slope estimation test. Box-whisker plots revealed that EC and TDS mostly exceed their maximum permissible limits prescribed for drinking water in the southern and southwest hard-rock formations. Whereas, most parameters do not cross their maximum desirable limits in the central and northern alluvial formations. Increasing trends of potassium and bicarbonate, and decreasing trends of carbonate, calcium, sulfate, and fluoride are found prominent. The GQSI values indicated high stability of groundwater quality under older alluvium geology and low stability under the gneiss and mica-schist. Results of the GQSI are found in agreement with that of groundwater-quality index (GQI) at 84% sites, which proved adequacy of the developed GQSI. Also, three classes ('low'/'poor', 'moderate', and 'high'/'good') of both the GQSI and GQI showed a good coherence at 83, 78, and 87% sites. However, GQSI is more advantageous than GQI due to former's statistical framework, consistency and comparability over different areas. Three optimum index factors, i.e., TDS, pH and nitrate, are found to have the maximum impact on overall groundwater quality with their largest variations. Results of the optimum groundwater-quality stability index (OGQSI) and GQSI closely matched with each other, and a significant linear relationship (R2 = 0.70) existed between them. Therefore, OGQSI is a cost-effective approach for adequate monitoring and satisfactory evaluation of the groundwater quality in low-income nations.

Spatiotemporal trends in ground-level ozone concentrations and metrics in France over the time period 1999-2012.

The hourly ozone (O3) data from 332 background monitoring stations, spread in France, were analyzed over the period 1999-2012 and short-term trends were calculated. In the current climate change context, the calculation of human health- and vegetation-relevant metrics, and of associated trends, provides a consistent method to establish proper and effective policies to reduce the adverse O3 effects. The generation of optimal O3 maps, for risk and exposure assessment, is challenging. To overcome this issue, starting from a set of stations, a hybrid regression-interpolation approach was proposed. Annual surface O3 metrics, O3 human health metrics (number of exceedances of daily maximum 8-h values greater than 60 ppb and SOMO35) and O3 vegetation impact metrics (AOT40 for vegetation and forests) were investigated at individual sites. Citizens are more exposed to high O3 levels in rural areas than people living in the cities. The annual mean concentrations decreased by -0.12ppbyear(-1) at rural stations, and the significant reduction at 67% of stations, particularly during the warm season, in the number of episodic high O3 concentrations (e.g. 98th percentile, -0.19ppbyear(-1)) can be associated with the substantial reductions in NOx and VOCs emissions in the EU-28 countries since the early 1990s Inversely, the O3 background level is rising at 76% of urban sites (+0.14ppbyear(-1)), particularly during the cold period. This rise can be attributed to increases in imported O3 by long-range transport and to a low O3 titration by NO due to the reduction in local NOx emissions. The decrease in health-related and vegetation-relevant O3 metrics, at almost all stations, is driven by decreases in regional photochemical O3 formation and in peak O3 concentrations. The short-term trends highlight that the threat to population and vegetation declined between 1999 and 2012 in France, demonstrating the success of European control strategies over the last 20 years. However, for all exposure metrics, the issue of non-attainment of the target value for O3 persists in comparison with the objectives of air quality directives. The region at highest O3 risk is the South-eastern France. This study contains new information on the i) spatial distribution of surface O3 concentration, ii) exceedances and iii) trends to define more suitable standards for human health and environmental protection in France.

Long-term trends in tourism climate index scores for 40 stations across Iran: the role of climate change and influence on tourism sustainability.

Tourism is a rapidly growing international sector and relies intrinsically on an amenable climate to attract visitors. Climate change is likely to influence the locations preferred by tourists and the time of year of peak travel. This study investigates the effect of climate change on the Tourism Climate Index (TCI) for Iran. The paper first calculates the monthly TCI for 40 cities across Iran for each year from 1961 to 2010. Changes in the TCI over the study period for each of the cities are then explored. Increases in TCI are observed for at least one station in each month, whilst for some months no decreases occurred. For October, the maximum of 45% of stations demonstrated significant changes in TCI, whilst for December only 10% of stations demonstrated change. The stations Kashan, Orumiyeh, Shahrekord, Tabriz, Torbat-e-Heidarieh and Zahedan experienced significant increases in TCI for over 6 months. The beginning of the change in TCI is calculated to have occurred from 1970 to 1980 for all stations. Given the economic dependence on oil exports, the development of sustainable tourism in Iran is of importance. This critically requires the identification of locations most suitable for tourism, now and in the future, to guide strategic investment.

Analysis of temporal and spatial trends of hydro-climatic variables in the Wei River Basin.

The Wei River is the largest tributary of the Yellow River in China. The relationship between runoff and precipitation in the Wei River Basin has been changed due to the changing climate and increasingly intensified human activities. In this paper, we determine abrupt changes in hydro-climatic variables and identify the main driving factors for the changes in the Wei River Basin. The nature of the changes is analysed based on data collected at twenty-one weather stations and five hydrological stations in the period of 1960-2010. The sequential Mann-Kendall test analysis is used to capture temporal trends and abrupt changes in the five sub-catchments of the Wei River Basin. A non-parametric trend test at the basin scale for annual data shows a decreasing trend of precipitation and runoff over the past fifty-one years. The temperature exhibits an increase trend in the entire period. The potential evaporation was calculated based on the Penman-Monteith equation, presenting an increasing trend of evaporation since 1990. The stations with a significant decreasing trend in annual runoff mainly are located in the west of the Wei River primarily interfered by human activities. Regression analysis indicates that human activity was possibly the main cause of the decline of runoff after 1970.

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.

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.

Influence of short- and long-term persistence on identification of rainfall temporal trends using different versions of the Mann-Kendall test in Mizoram, Northeast India.

Investigating the temporal dynamics of rainfall in a changing climate, especially in rainfed agriculture regions, is crucial for analyzing climate-induced changes and offering adaptation options. Since Mizoram experiences unfavorable impacts of rain nearly every year, the region rainfall has been altering over the years, and vital climatic activity is becoming uncontrollable. The current study is primarily concerned with the changing trend of rainfall over Mizoram, which includes both short-term persistence (STP) and long-term persistence (LTP) of rainfall in seasonal and annual time series of rainfall overseeing for the period of 25 years of daily average rainfall from 1996 to 2020 collected collectively from the seven stations over the study area of Mizoram. Four different Mann-Kendall method iterations were used to analyze rainfall trends: the original or conventional method (without autocorrelation) (MnKn1), removing lag-1 autocorrelation (trend-free pre-whitening), considering multiple lag autocorrelation (more than lag-1 autocorrelation) (MnKn3), and Hurst coefficient or LTP (MnKn4). In the analysis, the study found that during monsoon, station Lawngtlai (LT) observed the highest rainfall having a Z value of 1.986, increased by 0.466 cm/year, while station Serchhip (SC) observed the lowest rainfall having Z value of -2.282, decreased by -0.163 cm/year. After applying modified MnKn4, we observed LTP of rainfall in winter at station Lawngtlai (LT) with an increasing trend and other stations observing STP in almost all seasons either increasing or decreasing trend. Therefore, possible climate change adaptation measures should be made to optimize rainfall use for various applications for the states of Mizoram.

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.