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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 (RH , %) and mean wind speed (V , 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. [ABSTRACT FROM AUTHOR]

This study examines the changing rainfall patterns in the Vietnamese Mekong Delta (VMD) utilizing observational data spanning from 1978 to 2022. We employ the Mann–Kendall test, the sequential Mann–Kendall test, and innovative trend analysis to investigate trends in annual, wet, and dry season rainfall, as well as daily rainfall events. Our results show significant spatial variations. Ca Mau, a coastal province, consistently showed higher mean annual and seasonal rainfall compared to the further inland stations of Can Tho and Moc Hoa. Interestingly, Ca Mau experienced a notable decrease in annual rainfall. Conversely, Can Tho, showed an overall decrease in some months of the wet season and an increase in dry season rainfall. Furthermore, Moc Hoa showed an increase in the number of rainy days, especially during the dry season. Principal component analysis (PCA) further revealed strong correlations between annual rainfall and extreme weather events, particularly for Ca Mau, emphasizing the complex interplay of geographic and climatic factors within the region. Our findings offer insights for policymakers and planners, thus aiding the development of targeted interventions to manage water resources and prepare for changing climate conditions. [ABSTRACT FROM AUTHOR]

Estimation of baseflow is a complex hydrographic task. Baseflow techniques and coefficients vary from basin to basin, stream to stream, and year to year. In this study, meta-heuristic optimization is used to automatically identify baseflow. The Particle Swarm Optimization (PSO), a meta-heuristic optimization approach, is chosen. The constraint and cost functions were determined using the PSO algorithm, Lyne and Hollick techniques, and a computer application. Over the period 1980–2015, the data were collected at the Kale station in the Yesilirmak River basin to validate the study model. The results show that the hydrographs and baseflow dividing line were separated effectively. It has also been revealed that the PSO has a high speed as well as a high level of precision. In the research, in addition to the baseflow separation, the hydrograph, baseflow, and ratio of the baseflow to the streamflow at the station No. 1402 were assessed using the Mann–Kendall test and Innovative Trend Test (ITA), and as a result, their trends have been found. By the use of both of these methods, it has been shown that all parameters have an unfavorable trend. In addition, the research came to some other significant conclusions, such as the fact that the baseflow declines in tandem with the flow values and that the baseflow rates are low in years with high peak values of the hydrograph. [ABSTRACT FROM AUTHOR]

The main elements that define the climate of a place are air temperature and atmospheric precipitation. Through this study, we aim to highlight the significance and extent of the trends of the two climatic elements for the interval 1961- 2020 at the Cotnari meteorological station compared to those at the territorially neighboring stations Botoşani, Iaşi and Roman. For this purpose, we identified the basic thermo-pluviometric peculiarities at the Cotnari meteorological station for the period 1961-2020 and analyzed the evolution of the thermo-pluviometric parameters on interdecadal and interannual periods. We then applied the Mann-Kendall test combined with Sen's slope to estimate the trend over the reference period. Following these analyses, in Cotnari, taking as a reference the average annual temperature of 9.6°C and the average annual amount of precipitation of 543.8 mm, we found that after the year 2000, the trend of these two elements was positive, which shows an increase in air temperature and quantitative atmospheric precipitation. In total, the annual thermal averages increased statistically significantly by 0.33°C/decade. Annual precipitation increased during 1961–2020 by 2.2 mm/decade and showed modest statistical significance. [ABSTRACT FROM AUTHOR]

The article provides a long-term trend analysis of the Kesinga catchment daily gridded rainfall at a (0.25° × 0.25°) high spatial resolution from 1901 to 2020 (120 years). The trend in seasonal and annual rainfall was detected using rank-based nonparametric statistical tests, namely Spearman's rho and Mann-Kendall test, which are used for detecting monotonic trends in time series data at the 5% significant level, smoothing curve, Sen's slope test, and a plot of innovative trend analysis. The results showed that statistically significant trends had a pattern with both positive (increasing) and negative (decreasing) trends, with positive and negative trends evident in the winter and negative trends shown in the monsoon, PREMON, and annual seasons. The middle of the study area revealed the highest negative trend, and the lower Kesinga catchment showed the lowest negative annual rainfall trend. In the entire Kesinga catchment, the seasonal data and annual rainfall both showed statistically significant and non-significant patterns. Consistently, the MK and SR tests were both conducted at the validated significance level. In various contexts, the statistically significant massive trend that has occurred was negative (70%). If the current pattern continues in the future, there will be a scarcity of water and more strain on the control of water resources at the given grids in corresponding temporal scales. [ABSTRACT FROM AUTHOR]

The present study analyzes trends of extreme flows for the Krishna river at the Prakasam barrage, Vijayawada of Andhra Pradesh state, India. Annual maximum and minimum flows are considered using available data from the CWC (Central Water Commission) portal from 1965–1966 to 2019–2020. The trend analysis of extreme flows is carried out using the Mann-Kendall trend test, Sen's slope, and modified Mann-Kendall test, that is, bootstrapped Mann-Kendall trend test with optional bias-corrected pre-whitening. Also, R programming is applied for the trend analysis of extreme flows. Extreme annual flows are in decreasing trend. Annual maximum flow has significant decreasing trend with Sen's slope of 105.52 cumecs per year. Annual minimum flow has insignificant decreasing trend at a magnitude of 0.0002 cumecs per year. Trend results of the present study are useful for the trend analysis of extreme river flows. [ABSTRACT FROM AUTHOR]

As a climate-sensitive and fragile region, how the Tibetan Plateau (TP) responds to global warming would have significant implications for its ecological environment and sustainable socio-economic development. A crucial tool for assessing fine-scale climate impacts is the NEX-GDDP-CMIP6 dataset. This study utilized the NEX-GDDP-CMIP6 dataset to determine changes in extreme temperature and precipitation indices under different Shared Socioeconomic Pathways (SSP) relative to the baseline warming by 1.5 °C, 2 °C, and 3 °C. By using the Theil-Sen median trend and Mann-Kendall test methods, the research concluded that warming trends and increased humidity were on an upward trend. These changes led to amplified fluctuations in the frequency, intensity, and duration of multiple extreme indices. [ABSTRACT FROM AUTHOR]

As vegetation plays a critical role in terrestrial ecosystems, understanding its status and variation is vital for preserving the stability of an ecosystem. Central Asia serves as a representative example of an arid and semi-arid region characterized by sparse vegetation and poor soils, making its vegetation particularly fragile and sensitive. To investigate the vegetation condition in the region, this study examined the spatial and temporal characteristics of vegetation variation from 2001 to 2020, utilizing the normalized difference vegetation index (NDVI) as an indicator. Meanwhile, trend analysis, Mann–Kendall abrupt change point test, geodetector, and correlation analysis were used to quantitatively analyze the natural and anthropogenic drivers of these variations over the past two decades. The results suggest that vegetation coverage in Central Asia was relatively low, with an annual average NDVI of 0.16 over the past 20 years. Moreover, the spatial distribution of NDVI in Central Asia exhibited significant spatial heterogeneity, with vegetation coverage declining from north to south and from east to west. Furthermore, the NDVI exhibited a slightly increasing trend during the period of 2001 to 2020 with an increased rate of 0.00025/yr. However, we detected an abrupt change point in vegetation dynamics in Central Asia around 2010, which indicated a significant shift in vegetation variation in the region. Land-use type has a great influence on the spatial heterogeneity of NDVI in Central Asia, which can explain 46% of the vegetation distribution in this region. Moisture factors such as precipitation and soil water content followed with 35% and 32% contributions, respectively. Regarding the temporal variation of NDVI, it is mainly driven by the fluctuation in precipitation, with the degree of influence of precipitation on NDVI varying for different regions in various geographical conditions. This study offers a more comprehensive insight into the spatial and temporal dynamics of NDVI in Central Asia and indicates that precipitation plays a significant role in driving the spatial heterogeneity and temporal variation of NDVI. These findings are essential for predicting vegetation changes in arid regions under future environmental conditions and formulating effective strategies to prevent and alleviate vegetation degradation. [ABSTRACT FROM AUTHOR]

Aridity is a climatic phenomenon characterized by shortage of water availability in a given time and space resulting in low moisture and reduced carrying capacity of ecosystems. It is represented by a numerical indicator known as Aridity Index (AI), a function of rainfall and temperature. Aridification is a slow and steady effect of climate change and assessing its spread and change is vital in context of global climatic variations. Aridity is predominantly significant for agrarian countries like India, where a slight rise in drylands area can have a significant impact on the economy and community sustenance. AI is an inclusive indicator of climatic conditions in most arid and semi-arid regions. It helps in identifying and interpreting large scale trend in temperature and precipitation; and thus, classifying region into different climatic classes. The present study assessed long-term AI based on precipitation and temperature data obtained from the India Meteorological Department at the resolution of 1 × 1 degree for years 1969–2017. AI is estimated as a ratio of mean precipitation to mean potential evapotranspiration, calculated using Thornthwaite method. The results highlight the trend of aridity over pan-India with Innovative Trend Analysis and Mann–Kendall test. The study concludes that there is a relatively slow, however steadily progressive drier conditions being established in most of the regions. A shift from 'Semi-arid' towards 'Arid' class appeared in central mainland. The north-eastern Himalaya showed decrease in humid conditions ('Humid' to 'Sub-humid'). The study implies that there is a rising aridity trend over the years due to changing climatic conditions. The shifts in aridity can have serious implications on agriculture, long-term water resource utilization and land use management plans. Our results have scope for future landscape management studies in drylands and better adaptation methods in arid regions. [ABSTRACT FROM AUTHOR]

The emission, transport, and deposition of desert dust are influenced by environmental factors evaluated mainly as a function of precipitation, air temperature, and wind. The present study estimates the desert dust balance and its relationship with environmental factors for the period 1981–2020 in the Baja California Sur region, Mexico. Monthly data on wind, air temperature, precipitation, dust emission and deposition from in situ measurements, reanalysis, satellite estimates and a numerical parameterization scheme are used. First, the in-situ time series were reconstructed with a non-linear principal component analysis based on an autoassociative neural network. We then evaluated the performance of the satellite estimates and the reanalysis data using the matching technique, after which we quantified the dust emission in three source zones. To estimate the state of the climate, we obtained the annual cycle of monthly climatologies, and yearly averages. The effect of climate on the dust budget is estimated by means of non-parametric associations between the variables of interest. Finally, we estimate climate trends using Pettitt, Modified Man-Kendall and Theil-Sen tests. The results show that the data sets used represent the mean and variability of in situ measurements. There are three seasons with transitional periods for precipitation and air temperature. The wind shows a longitudinal west–east gradient. Its maximum intensity is from April to September. Northern zone was dominated by highest emissions. Dry deposition mechanism was the most representative. Emissions and dry deposition are controlled by wind intensity and precipitation, while precipitation controls wet deposition. The Pettitt test showed that abrupt changes in the time series coincide with periods where positive sunspot anomalies converge with negative PDO and SOI anomalies. Finally, trend analyses identified a temporal broadening of the dry season, warmer winters, and summers with increases of ~ 0.02 °C, predominantly increasing trends in dust emissions and wind intensity, and a progressive decrease in dust deposition mechanisms. [ABSTRACT FROM AUTHOR]

This paper analyzed the variations of two air temperature indices, diurnal temperature range (DTR) and annual temperature range (ATR), calculated based on observations at the Zagreb Grič Observatory over a period of 133 years (1887–2019). In intense climate changes strongly manifested by the increased air temperature, these two climate indices were determined to significantly impact human health and the environment. This effect is especially evident in urban areas. The Zagreb Grič Observatory is located in the center of Zagreb and has not changed its location during the observed period. It has a long homogeneous series of climatological observations, enabling a detailed study of climate variation in the city, which is strongly influenced by various urbanization processes. In 133 years, both of the analyzed indicators showed a statistically insignificant downward trend. The Rescaled Adjusted Partial Sums (RAPS) method revealed statistically significant differences in DTR's time series between three sub-periods: 1887–1953, 1954–1989, and 1990–2019. The time series of ATR during 133 years behaved statistically differently in four sub-periods: 1887–1905; 1906–1926; 1927–1964; and 1965–2019. The analysis of monthly values of DTR showed that the DTR values are the highest in the warm part of the year, from May to August, when they are twice as high as those during the cold period from November to December. With an increase in precipitation, the DTR values decrease, while they increase as the mean annual temperature increases. [ABSTRACT FROM AUTHOR]

In recent years, with the rapid advancement of China's urbanization, the contradiction between urban development and the ecological environment has become increasingly prominent, and the urban ecological system now faces severe challenges. In this study, we proposed an ecological index-based approach to monitor and evaluate the ecological environment using a Google Earth Engine cloud-based platform and Landsat time series. Firstly, a long-term series of Landsat images was obtained to construct and calculate the remote sensing-based ecological index (RSEI). Then, the Theil–Sen median estimation and the Mann–Kendall test were used to evaluate the trend and significance of the RSEI time series and combined with the Hurst index to predict the future development trend of the ecological environment in the study area. Finally, the coefficient of variation method was used to determine the temporal stability of the ecological environment. Taking Zhoushan Archipelago, China, as the study area, we mapped the distribution of the ecological environment using a spatial resolution of 30 m and evaluated the ecological environment from 1985 to 2020. The results show that (1) from 1985 to 2020, the average RSEI in the Zhoushan Archipelago decreased from 0.7719 to 0.5817, increasing at a rate of −24.64%. (2) The changes in the areas of each level of ecological environmental quality show that the ecological environment in the Zhoushan Archipelago generally exhibited a decreasing trend. During the study period, the proportion of the areas with excellent ecological environmental quality decreased by 38.83%, while the proportion of areas with poor and relatively poor ecological environmental quality increased by 20.03%. (3) Based on the overall change trend, the degradation in the ecological environment in the Zhoushan Archipelago was greater than the improvement, with the degradation area accounting for 84.35% of the total area, the improvement area accounting for 12.61% of the total area, and the stable area accounting for 3.05% of the total area. (4) From the perspective of the sustainability of the changes, in 86.61% of the study area, the RSEI exhibited positive sustainability, indicating that the sustainability of the RSEI was relatively strong. (5) The coefficient of variation in the RSEI was concentrated in the range of 0–0.40, having an average value of 0.1627 and a standard deviation of 0.1467, indicating that the RSEI values in the Zhoushan Archipelago during the study period were concentrated, the interannual fluctuations of the data were small, and the time series was relatively stable. The results of this study provide theoretical methods and a decision-making basis for the dynamic monitoring and regional governance of the ecological environment in island areas. [ABSTRACT FROM AUTHOR]

Interactions between land use and cover and a basin hydrological regime are complex and dynamics. The identification of trends and changes in rainfall and flow regimes in a region is relevant in water resources planning and management so that environmental, economic and social adversities are avoided. Thus, this study aims to identify possible trends in the historical series of rainfall and fluviometric stations in Rio das Mortes basin, Minas Gerais, Brazil, as well as to relate the modifications in the flow rate behaviour due to changes in land use and cover in the basin between the years of 1985 and 2017. For that, hydrological data were used from the Ibituruna-MG river station and 12 rainfall stations and orbital images from Landsat-5 and Sentinel satellites. Flow rate and precipitation behavior were performed using the nonparametric Mann-Kendall test and the Pettitt's test, while the land use and cover classification was performed in an eCognition Developer® 64 environment. Significant trends were observed in the average of total annual rainfall series and decreasing trends in the average of monthly flow rate series of lowest runoff over the years 1925 to 2014. There were changes in land use and cover in the studied region, however, these were not significant enough to modify the hydrological regime of Rio das Mortes basin during the analysed period. [ABSTRACT FROM AUTHOR]

Climate change is a pressing issue that is affecting the lives and livelihoods of millions of people across the world. This study investigates the trend analysis and spatial-temporal variations of temperature and precipitation on a monthly, seasonal, and annual basis in Rajasthan state, India, over the past 40 years (1981-2020). The trend analysis of temperature and precipitation were analysed using the Mann-Kendall test at the confidence level of 95%. The magnitude (slope) was determined by using Theil-Sen's slope test. The results of the analysis revealed significant positive and negative trends of temperature and precipitation observed on a monthly, seasonal, and annual basis in all the 33 districts of Rajasthan state. The summer season experienced the maximum average temperature, while the winter season had the minimum. The study also found that the northern and western parts of Rajasthan experience "Mawat" rain during the winter due to cyclones happening in the Mediterranean Sea during that season. The annual average temperature and precipitation were observed to be maximum in the southern part and minimum in the northern and western parts of the state. The findings of this study provide valuable information for the future management of water resources and the likely impact of activities on the hydrologic cycle and natural resources in Rajasthan state. [ABSTRACT FROM AUTHOR]

Climate variability has significant implications for human health, necessitating a comprehensive understanding of the relationship between the two. The present study examines the trend analysis of climatic variables, and their possible impacts on health and identifies specific challenges faced by the people of Himachal Pradesh, India. The Mann-Kendall test, a non-parametric test, was used to examine temperature and rainfall trends. A survey was conducted to gauge public awareness regarding climate change and its health risks in the region. The study analysed weather patterns over 10 years, revealing fluctuating temperatures with the hottest years being 2012, 2015-17. Rainfall exhibited irregular patterns with less rainfall in 2012, 2014, 2017 and 2019. Trend analysis showed significant temperature trends in Dharamshala and Kangra. Weather-related incidents and fatalities peaked during 2012-14, while diseases, such as acute respiratory infections (ARI), asthma, cardiovascular diseases, diabetes, hypertension, and vector and water-borne diseases saw an increase over the decade. The survey results showed that the people of this region suffered mostly from respiratory problems, water-borne diseases and mental health issues. This study contributes to the existing knowledge by establishing a possible relationship between climate variability and the health of the people. Furthermore, it also provides a health database, facilitating the formulation of targeted interventions to address health-related challenges arising from the observed changes in weather patterns. [ABSTRACT FROM AUTHOR]

Mann–Kendall trend test and Sen's slope estimator method were used to find out significant changes in extreme climate indices for daily temperature and precipitation in Cherrapunji over the period from 1979 to 2020. In this study, 21 precipitation and temperature-based extreme climate indices were calculated using RClimDex v 1.9-3, among which six were derived from the number of days above nn mm rainfall (Rnn) according to India Meteorological Department (IMD) convention and the rest 15, in accordance with the Expert Team on Climate Change Detection and Indices (ETCCDI). It was observed that, among all the indices, consecutive dry days (CDD), summer days (SU25) and very light rainfall (VLR) days increased significantly with 0.54, 1.58 and 0.14 days/yr, respectively, while only consecutive wet days (CWD) decreased significantly with 0.36 days/yr. A slight negative trend was observed in the case of tropical nights (TR20) and among the other precipitation indices as well. Again, the indices associated with daily maximum temperature increased significantly with 0.06–0.07°C/yr. For the indices associated with daily minimum temperature, almost no change or a slight negative change was observed, except a significant positive trend in February and significant negative trend in November for monthly minimum of daily minimum temperature (TNN) only. The analysis revealed that some of the extreme climate indices which explain the climatic conditions of Cherrapunji have shown statistically significant change over the period of 42 years and if this trend continues, then Cherrapunji will be under threat when it comes to climate change. [ABSTRACT FROM AUTHOR]

The Mann–Kendall test for trend has gained a lot of attention in a range of disciplines, especially in the environmental sciences. One of the drawbacks of the Mann–Kendall test when applied to real data is that no distinction can be made between meaningful and non-meaningful differences in subsequent observations. We introduce the concept of partial ties, which allows inferences while accounting for (non)meaningful difference. We introduce the modified statistic that accounts for such a concept and derive its variance estimator. We also present analytical results for the behaviour of the test in a class of contiguous alternatives. Simulation results which illustrate the added value of the test are presented. We apply our extended version of the test to some real data concerning blood donation in Europe. [ABSTRACT FROM AUTHOR]

The most significant effects of climate change are related to temperature and precipitation. Changes in precipitation and temperature patterns affect water resource capacity, agricultural activities, global biodiversity and emergency management. In this study, using two statistical models (Mann-Kendall and Pettit’s test), climate data for the territory of Belgrade in the time interval 1961-2020 were processed. Trends were analyzed: average annual temperatures, average monthly temperatures, annual and monthly precipitation amounts. The aim of the research is to determine the statistical significance of the increasing/decreasing temperature and precipitation trends on a monthly and annual basis. Based on the obtained results, it was determined that the changing point of the annual temperature is in 1997, while a statistically significant increasing trend was observed in all months except November. The increasing trend recorded by the precipitation amount is 0.345 mm/year, but without statistical significance. The most significant results corresponding to July and October. [ABSTRACT FROM AUTHOR]

Hydrological conditions are strongly regulated by monsoon systems in the Mekong River Basin (MRB), while relevant studies investigating the intensity of the rainy season are still insufficient. This study employed the Mann-Kendall (M-K) test, Sen's slope estimator, and innovation trend analysis to detect the variation of summer discharge in the MRB. Wavelet analysis is used to investigate the correlation between discharge and two South Asian summer monsoon subsystems (SAMI1 and SAMI2). Results show that the summer discharge in the MRB generally shows significant downward trends during 1970–2016 with a Z value range of −3.59–−1.63, while the high discharge at Vientiane, Mukdahan, and Pakse increases after 1970. The mutation years of the summer discharge series are around 2010 for Chiang Sean and Vientiane, and in 2015 for Luang Prabang, which resulted from the newly built large dams, Xiaowan and Nuozhadu. The wavelet analysis shows that the SAMI1 can be used to predict the summer discharge at Chiang Sean at a ~8-year timescale, while the SAMI2 correlates with the summer discharge well at a 1–8-year scale, especially at Mukdahan and Kratie during 1980–2016. [ABSTRACT FROM AUTHOR]

In recent years, electrical fires caused by arc faults have been increasing, seriously affecting the safety of people's lives and property. Considering the complex arc fault characteristics of actual low-voltage users, the non-intrusive arc fault detection and localization method is studied. First, the characteristics of arc current waveforms are analyzed, and event detection based on the Mann–Kendall Test is performed for the difference between the current waveforms of two adjacent cycles, rather than using the current waveforms directly. Then, the current waveforms of the two segments are calculated via subtraction to obtain the current waveform of the electric appliances causing the event. A current feature parameter database of the normal and arc currents is constructed via harmonic analysis, and a multi-appliance current decomposition model considering the sparse operation characteristics of appliances is established; thus, the arc localization problem is transformed into an optimization problem. Finally, a genetic algorithm is used to optimize the differential current decomposition results, and then, locate the arc fault. A household arc fault simulation experiment is carried out for the common electric appliances of actual low-voltage users. The experimental results show that the proposed non-intrusive arc fault detection and localization method is effective. [ABSTRACT FROM AUTHOR]

Given the increasing impact of extreme rainfall and flooding on human life, studying and predicting changes in atmospheric water vapor (AWV) becomes particularly important. This paper analyzes the moderate-resolution imaging spectroradiometer (MODIS) data of the East Asian region from January 2003 to February 2023. The AWV data are examined in the time and frequency domain using methods such as empirical orthogonal function (EOF), Mann–Kendall (MK) analysis, and others. Additionally, four prediction models are applied to forecast the monthly average AWV data for the next year. The accuracy of these models is evaluated using metrics such as mean square error (MSE), mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R2). The findings reveal several key insights: (1) The East Asian region exhibits highly variable seasonal variability in AWV, with identified mutation points after the MK test. (2) Spatial analysis shows high AWV data in the southern coastal areas of China, Thailand, Myanmar, Nansha Islands, and other regions during winter, while the Qinghai-Tibet Plateau region experiences low AWV during summer. (3) The first mode obtained through EOF decomposition contributes over 60% of the variance. Analysis of this mode reveals an increasing trend in AWV data for regions such as the Indian peninsula, Mongolia, and central and northeastern China over the past nine years. Conversely, the Bay of Bengal, Spratly Islands, eastern coast, and certain areas display a decreasing trend. (4) Employing the ensemble empirical mode decomposition (EEMD), the study identifies AWV data as a non-stationary series with an overall decreasing trend from 2003 to 2022. The filtered AWV series undergoes fast Fourier transform (FFT), uncovering periodicities of 2.6 years, 5 years, and 19 years. (5) Among the four forecasting models compared, the seasonal autoregressive integrated moving average model (SARIMA) demonstrates superior performance with the smallest MSE of 0.00782, MAE of 0.06977, RMSE of 0.08843, and the largest R2 value of 0.98454. These results clearly indicate that the SARIMA model provides the best fit. Therefore, the SARIMA forecasting model can be effectively utilized for forecasting AWV data, offering valuable insights for studying weather variability. [ABSTRACT FROM AUTHOR]

Non‐stationarity in heavy rainfall time series is often apparent in the form of trends because of long‐term climate changes. We have built non‐stationary (NS) models for annual maximum daily (AMP1) and 2‐day precipitation (AMP2) data observed between 1984 and 2020 years by 71 stations and between 1960 and 2020 by eight stations over Thailand. The generalized extreme value (GEV) models are used. Totally, 16 time‐dependent functions of the location and scale parameters of the GEV model are considered. On each station, a model is selected by using Bayesian and Akaike information criteria among these candidates. The return levels corresponding to some years are calculated and predicted for the future. The stations with the highest return levels are Trad, Samui, and Narathiwat, for both AMP1 and AMP2 data. We found some evidence of increasing (decreasing) trends in maximum precipitation for 22 (10) stations in Thailand, based on NS GEV models. [ABSTRACT FROM AUTHOR]

Rainfall characteristics are changing due to several reasons and change/trend detection is required. Literature survey reveals many relevant studies whose outcomes are divergent, possibly because different data series and different methodologies have been applied. This paper presents a critical appraisal of past studies and methodologies for trend analysis. Results of trend analysis of Indian rainfall data are presented. Data for all of India and for five homogenous regions (North-West, Central North-East, North-East, West Central, and Peninsular India) for 1871-2016 were used. The Pettitt change point test, regression, Mann-Kendall (MK), and Wavelet Decomposition were used to study different aspects of changes. Results of the change point test showed that most rainfall series had change points around 1957-65, possibly due to large-scale land use, cultivation, irrigation, and industrial changes in this period. Generally, rainfall for most homogenous regions and sub-divisions show falling trends; some are statistically significant. Series was also decomposed by the wavelet method. Approximate and detailed components of some decomposed series showed a significant declining trend. This work has focused on the magnitude of rainfalls; trends in rainfall intensities are also important. It is necessary to establish denser observation networks to collect short-term data and analyze. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to study hourly time series of temperature and humidity from six meteorological stations in Oman from 2009 to 2018. Since our data contains missing values due to device failures, we have compared several methods of imputation and found that regression with lagging is most reasonable. To extract the annual oscillations and daily periodicities from hourly time series we apply Singular Spectrum Analysis. By applying the Mann–Kendall test, Spearman's rho test and the ITM test to the annual oscillations and daily periodicities monotonic trends in temperature and humidity over the ten year period have not been confirmed. [ABSTRACT FROM AUTHOR]

Anthropogenic climate change poses a significant threat to both natural and social systems worldwide. In this study, we aim to identify regions most impacted by climate change using the National Centers for Environmental Prediction and the National Center for Atmospheric Research (NCEP-NCAR) reanalysis of near-surface daily air temperature data spanning 73 years (1948–2020). We develop a novel climate network framework to identify "hot spots", regions that exhibit significant impact or impacted characteristics. Specifically, we use the node degree, a fundamental feature of the network, to measure the influence of each region and analyze its trend over time using the Mann–Kendall test. Our findings reveal that the majority of land areas experiencing increasing degrees are more closely connected to other regions, while the ocean shows the opposite trend due to weakened oceanic circulations. In particular, the degree in the central Pacific Ocean's El Niño region is significantly reduced. Notably, we identify three "hot spots" in East Asia, South America, and North Africa, respectively, with intensive increasing network degree fields. Additionally, we find that the hot spot in East Asia is teleconnected to remote regions, such as the South Pacific, Siberia, and North America, with stronger teleconnections in recent years. This provides a new perspective for assessing the planetary impacts of anthropogenic global warming. By using a novel climate network framework, our study highlights regions that are most vulnerable to the effects of climate change and emphasizes the importance of understanding network structures to assess the global impacts of anthropogenic climate change. [ABSTRACT FROM AUTHOR]

Since climate change is a major challenge that affects the environment, especially the hydrological flow, this research has used the Soil and Water Assessment Tool (SWAT) to study the climate change effects on the runoff in Alvand mountain basin in Iran. The SWAT model calibration was done based on the SUFI-2 algorithm and the validation results for a 20-year prediction period (2020–2040) showed that the model had high accuracy in simulating the runoffs. CMIP5 models were used to predict the basin's temperature and rainfall (the best scenario was Miroc5) and the results showed that the basin's future climate conditions increase in temperature and decrease in rainfall were appropriate. According to Miroc5 (RCP8.5), the annual runoff in the mentioned period would decrease by 8.36% compared to the past. In a seasonal scale, the largest runoff reduction would be 75% in summer (under the Miroc5 (RCP4.5)), and in a monthly scale, it would be 79% in April increasing dramatically to reach 335% in September (under Miroc5 (RCP8.5)). The trend of the future runoff was investigated using the Mann–Kendall model and the Gresson estimation method. Results showed that there would be no significant trend in the mentioned period and the trend of the future drought intensity would be increasing under all studied scenarios. [ABSTRACT FROM AUTHOR]

Precipitation is a particularly important part of the Earth's hydrological cycle and, therefore, is a necessary variable for maintaining natural balance. This study investigated past, present, and future changes in precipitation in the Marmara region, and examined the effects of global warming on this variable. The study period was from 1960 to 2020, and the climate data of 15 synoptic stations in the Marmara region were used for this purpose. To achieve the objectives of the study, linear and 6th order polynomial regression, ombrothermic and hythergraph diagrams, geostatistical models, Mann-Kendall test, Pearson correlation, standard Z-scores, and multi-layer perceptron artificial neural network models (MLP-ANN) were used to model and predict precipitation. The results of the linear regression analysis showed that of the 15 stations, 6 stations had an increasing trend, 6 stations had a trendless pattern, and 3 stations had a decreasing trend. In terms of periodic analysis, the main downward trend started in 1964 and continued until 1992, while the main periodic upward trend started in 1992 and continued until 2016. The synoptic stations in the Marmara region showed a lack of precipitation over six to seven months of the year, and the precipitation changes in the region were stronger than the temperature changes. In addition, the highest precipitation was observed on the southeast coast of the Black Sea, and the lowest precipitation was observed in the eastern parts of the region. Moreover, except for the Bilecik and Kocaeli stations, the changes in the long-term trend of precipitation at the other stations were significant. Among the 15 stations, only the Kocaeli and Sarıyer stations showed a positive correlation with global temperature during the annual period. In addition, the developed ANN model was accurate in simulating and predicting precipitation and showed an upward trend over the next seven years. [ABSTRACT FROM AUTHOR]

The study was conducted to analyze spatial and temporal variations of lightning activity over Sri Lanka and the surrounding coastal belt region bounded by 5.75–10.00 N and 79.50–89.00 E. Flash data collected by the Lightning Imaging Sensor (LIS) on NASA's Tropical Rainfall Measuring Mission (TRMM) satellite from 1998 to 2014 and the Lightning Imaging Sensor placed on the International Space Station (ISS) from 2018 to 2021 were used for the study. The Mann-Kendall test and Sen's slope estimator were applied to annual and seasonal lightning data from 1998 to 2014 to identify the trends in the TRMM dataset. A positive slope of 0.23 was obtained for annual flash densities, while a slope of 0.956 was obtained for First Inter-Monsoon (FIM) seasonal data. Considering the ISS data, the annual variation of lightning activity in 2020 displays the lowest flash density, whereas the highest is represented in 2019 with a value of 10.48 flashes km−2 year−1. The highest mean flash density is observed in Colombo in 2019 at a value of 34.85 flashes km−2 year−1. Overall, April displayed the highest annual flash distribution from 2018 to 2021, whereas the second peak was mostly viewed around September and November. All districts have displayed a significant amount of lightning during April for the period 2018 to 2021. FIM displayed the highest lightning distribution over the country. When considering the seasonal variation, districts belonging to the wet zone and intermediate zone displayed most flashes during the FIM. [ABSTRACT FROM AUTHOR]

Andit tid watershed is part of Blue Nile basin located in the central highlands of Ethiopia. The lack of data and information at watershed level resulted in different conclusions from trend studies of river flow, sediment yield and crop productivity at a basin scale. There is an opportunity to improve water and land if it can be underpinned by a better scientific understanding of trends of flow, sediment yield and crop production at the basin level. This research is carried out using descriptive statistics, Mann-Kendall (MK) and Pettit's test to determine the potential trends of river flow, sediment yield and crop productivity using Andit tid watershed case. The result showed that there was high variability of interannual river flow with CV >30%. The Pettitt test showed a significant abrupt change in monthly (March, July, August, September and October) and seasonal (summer and winter) river flow. The Pettitt test result of sediment yield and crop production showed no change. MK test showed a significant (P < 0.05) decreasing trend in March, August, September and October river flow. The other MK values showed no significant trends for all parameters. Researchers should consider representative watershed-based information and data for the analysis and interpretation of large basins. [ABSTRACT FROM AUTHOR]

Recently, extreme climatic events have been a major concern across the world. Assessments on various climates and geographic regions are needed to understand the uncertainties in extreme climate events' responses to global warming. Developing countries particularly Ethiopia is highly affected by such extreme climate events due to its low adaptive capacity. The aim of this study was to investigate the trends of daily extreme temperatures indices in Ethiopia. The changes and trends in climate extremes of temperature were evaluated based on 14 indices of extreme temperatures over Ethiopia. The Mann-Kendall (MK) trend test is used to investigate the trends in the selected datasets. In the present study, three zones, which represent 34 stations, were selected based of extreme climate indices. The results of 14 extreme temperature indices showed that warm days (TX90p) and nights (TN90p) increased by 2.65 (P < 0.05) and 2.04 (P < 0.05) days/decade, respectively. The annual maximum and minimum temperatures (TXam and TNam) as well as the diurnal temperature range (DTR) were 2.68/decade, 2.04/decade and 0.62/decade, respectively. 73.5% and 26.5% of the stations show an increasing trend for (TXx and TNx), respectively. The extreme hot temperature indices (TXx, TXn, TX90, TN90 and SU) and cold temperature indices (ID, TNn, TXn, TX10P, TN10P and FD) exhibited contrasting variations over the stations. As far as spatial patterns concerned, most of the stations show significant increasing trends in northern and southeastern parts of Ethiopia. Thus, changes in climate extremes will likely increase the risk of severe extreme events and the society to become more vulnerable to the climate shocks. Generally, the findings of this study could help decision and policymakers to take immediate measures to tackle the climate change impacts. [ABSTRACT FROM AUTHOR]

In this study, trend analyses of six climatic variables (mean, minimum, and maximum temperature, relative humidity, wind speed, and precipitation) for 1966-2020 are conducted for the Southeastern Anatolia Region, which is the main focus of the integrated development project in Turkey (Turkish acronym GAP). The trends for seasonal and annual periods are determined using the Mann-Kendall (MK) test, and Sen's slope method and regression analyses are used to find the trends' slopes. Moreover, Innovative Trend Analysis (ITA) is also used to find the time series changes for low, medium, and high values. As a result of the analyses, the mean, minimum, and maximum temperatures in the GAP region show increasing trends according to both methods. Significant trends are obtained at a limited number of stations for the precipitation, relative humidity, and wind speed with the MK test, while consistent decreasing trends are found at most stations with the ITA method. [ABSTRACT FROM AUTHOR]

Exploring the long-term spatiotemporal pattern of vegetation changes and its response to natural factors and human activities is vital for making informed decisions regarding ecosystem protection. The spatial–temporal distribution characteristics of vegetation dynamics and its driving factors in Shaanxi Province from 2000 to 2019 were analysed using the normalized difference vegetation index (NDVI). The explanatory power of solar radiation was the highest (63.45%), followed by relative humidity (59.07%), land use (56.73%), precipitation (52.17%), temperature (51.28%), and vegetation type (50.95%). In terms of climatic factors, the influences of solar radiation and relative humidity on vegetation changes were stronger than those of precipitation and temperature. The comprehensive impact of the pairwise factors was higher than those of the independent factors. Our results contribute to a better understanding of the complex mechanisms of vegetation changes and provide scientific recommendations for the prevention remediation of vegetation degradation in fragile ecosystems. [ABSTRACT FROM AUTHOR]

Long‐term precipitation monitoring plays a vital role in water resource management and disaster prevention and mitigation. This study assesses spatial and temporal trends in seasonal and annual precipitation in Pakistan between 1960 and 2016 at an interannual scale. The Mann–Kendall (MK) test, Sen's slope (SS) estimator, and Sequential Mann–Kendall (SQMK) test were employed to assess trends. Cluster analysis and L‐moment approach were used to identify the homogenous precipitation regions. In general, increasing precipitation trends between 1960 and 2016 were evident. Results indicated increasing precipitation in winter, autumn, summer and annual scale at the rates of 0.20, 2.18, 5.16, and 10.89 mm·decade−1, respectively. In spring, the precipitation trend shows a decreasing trend at −0.67 mm·decade−1. Moreover, a significant decreasing trend occurred in winter in southern Pakistan. The overall increasing trends were more noticeable between 1960 and 1988, compared to the declining precipitation during 1989–2016. SQMK analysis indicates a clear downward trend in most regions during 1989–2016, except in autumn. Annual precipitation has increased topographically except at 500 m and 1,500 m during 1960–2016 with a significant increase of 1.37 mm·decade−1 at elevation <250 m. Results indicate a negative correlation in SS test value with seasonal and annual precipitation with elevation and a positive correlation in winter. The seasonal and annual precipitation trends exhibit increasing and decreasing trends before and after 1990, respectively, in most subregions. The notable finding based on the outcomes of this study is that the whole country observed an increasing trend during 1960–1988, followed by a decreasing trend in during 1989–2016. This decreasing tendency is particularly pronounced between 1985 and 1995, except in autumn. Agriculture production is largely reliant on precipitation in many regions. So, a detailed study of the influence of monsoon trends and large‐scale climatic variability controls over Pakistan is vital for improved water resource management in the context of global warming and rising human activity. The results will help policy makers while establishing and updating water‐related initiatives and regulations. [ABSTRACT FROM AUTHOR]

There is high confidence that climate change has increased the probability of concurrent temperature‐precipitation extremes, changed their spatial‐temporal variations and affected the relationships between drivers of such natural hazards. However, the extent of such changes has been less investigated in Australia. Daily data spanning the period 1889‐2019 (131 years) were extracted from SILO gridded dataset at 700 grid cells (1◦ × 1◦) across Australia to calculate annual and seasonal mean daily maximum temperature (MMT) and total precipitation (TPR). A nonparametric multivariate copula framework was adopted to estimate the return period of compound hot‐dry (CHD) events based on an 'And' hazard scenario (hotter than a threshold 'And' drier than a threshold). CHD extremes were defined as years with joint return periods of longer than 25 years calculated over the period 1889‐2019. Mann‐Kendall nonparametric tests were used to analyse trends in MMT and TPR as well as in the frequency of univariate and CHD extremes. Results showed a general cooling‐wetting trend over 1889‐1989. Significant increasing trends were detected over 1990‐2019 in the frequency and severity of hot extremes across the country while trends in dry extremes were mostly insignificant (and decreasing). A significant increase in the association between temperature and precipitation was identified at various temporal scales. While the frequency of CHD extremes was mostly stable over 1889‐1989, it significantly increased between 1990 and 2019 at 44% of studied grid cells, mostly located in the north, south‐east and southwest. [ABSTRACT FROM AUTHOR]

This study examines the variations in groundwater levels from the perspectives of the dynamic layers soil moisture (SM), normalized difference vegetation index (VI), temperature (TE), and rainfall (RA), along with static layers lithology and geomorphology. Using a Bayesian Ensemble Algorithm, the trend changes are examined at 385 sites in Kerala for the years 1996 to 2016 and for the months January, April, August, and November. An inference in terms of area under the probability curve for positive, zero, and negative trend was used to deduce the changes. Positive or negative changes were noticed at 19, 32, 26, and 18 locations, in that order. These well sites will be the subject of additional dynamic and static layer investigation. According to the study, additional similar trends were seen in SM during January and April, in TE during August, and in TE and VI during November. According to the monthly order, the matching percentages were 63.2%, 59.4%, 76.9%, and 66.7%. An innovative index named SMVITERA that uses dynamic layers has been created using the aforementioned variables. The average proportion of groundwater levels that follow index trends is greater. The findings of the study can assist agronomists, hydrologists, environmentalists, and industrialists in decision making for groundwater resources. [ABSTRACT FROM AUTHOR]

Climate change is increasingly occurring not only in Slovakia and Europe, but worldwide. One of the consequences is frequent droughts alternating with extreme rainfall. Drought, especially in the spring months, causes water shortages in the soil and limited crop growth. Extreme rainfall causes frequent floods and destroys crops and property. The aim of this work was to statistically evaluate precipitation during January–December for the period 1921–2020 in the most fertile part of Slovakia – the Danube Lowland. The results show a statistically significant (p <0.10) decrease in precipitation in April and an increase in September, with the annual total precipitation from 535 to 600 mm (except for extremes in some years). The work provides a statistical analysis of changes and rainfall distributions over 100 years, which can help identify and address drought problems. [ABSTRACT FROM AUTHOR]

Analysis of rainfall distribution and its changing pattern plays a vital role in managing water resources in a region. This work examined the spatio-temporal variability and trend of rainfall on yearly and season-wise scales during 1971–2013 in the Wainganga river basin situated in middle India. The Mann–Kendall (MK) test was implemented for identifying the temporal variation in rainfall trends. The magnitude of this changing trend was estimated by applying Sen's slope (SS) method. A paired sample t-test was also employed to appraise the statistical significance of changes in rainfall data. The results of MK and SS tests show both upward and downward trends. The results show positive and negative trends in the south-eastern and northwestern parts of the study area for annual rainfall. The monsoon rainfall also shows very close proximity with annual rainfall data trends. The t-test states that the observed changes in precipitation are statistically significant. After trend and pattern analysis, state-space models (SSMs) were used to predict rainfall for future scenarios. Four SSMs (single, double, and triple exponential, autoregressive integrated moving average (ARIMA)) were employed for rainfall prediction. The analysis shows that the ARIMA is best for rainfall prediction with architecture of (0,0,0) (2,1,0) and can be used in Wainganga River basin. [ABSTRACT FROM AUTHOR]

The analysis of the meteorological indices provides valuable information about the evolution of climate during past and may predict its reflection in the future. Analysis of trends in meteorological variables is challenging as they are of non-stationary nature and may include both stochastic and noise components. The aim of this research is to integrate the signal processing technique Discrete Wavelet Transform (DWT) with different versions of Mann-Kendall (MK) trend tests for investigating the dominant climate systems characterizing the long-term time-series of the two most important meteorological parameters: mean air temperature (oC) and total rainfall (mm) for the different stations lying in the upper Indus River basin of Pakistan. The observed time series were categorized into monthly, seasonal, seasonally-based, and annual time series for investigating trends. The analysis showed that winters have experienced significant warming over the study area except for stations Nowshera and Parachinar while summers are experiencing decreasing temperature trends. Rainfall trends were dominated by positive trends. The results from the DWT and MK tests (at 5% significance level) on the different data types revealed that for higher-resolution data (monthly); the high-frequency intra-annual periodicities affecting air-temperature trends were dominated by 8-monthly fluctuations, and for the lower-resolution seasonal and annual data the interannual periodicities ranged from 2-4 years. For total rainfall data: 2-8 monthly; 2-4 yearly; and 2-8 yearly periodic modes were found dominant and characterizing the monthly, seasonally-based, and annual trends, respectively. It was found that DWT effectively extracted the two-dimensional time-amplitude-frequency information from the time series that was hidden in the observed raw data i.e. the time-frequency information manifested in the shape of time periodicities of intra-annual to inter-annual events. [ABSTRACT FROM AUTHOR]

The long time series and consistent "urban core-suburban-rural" (USR) structure in a city region is essential to understanding urban–suburban–rural interaction and urbanization pathways. It is always considered to be a single land use type (e.g., impervious area) in remote sensing research. The long-term (1992–present) nighttime light (NTL) data of the Defense Meteorological Satellite Program-Operational Linescan System (DMSP-OLS) and the Suomi National Polar-Orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) provide the potential for retrieving time series of USR structure. In this study, we propose an improved approach to mapping the USR structure of the three subcategories based on a heuristic algorithm of Mann–Kendall mutation detection on the NTL quantile curve. First, a minor adjustment of VIIRS NTL is applied for matching the value ranges of DMSP NTL data and keeping the advantage of VIIRS to generate a long-term NTL dataset. Second, the heuristic algorithm of Mann–Kendall mutation detection is processed to find two optimal thresholds in the NTL quantile curve, which is used for USR extraction. Finally, a temporal consistency check is used to post-process the initial USR area for obtaining a more consistent and reliable USR sequence. To evaluate the performance of the proposed method, we retrieved the USR structures of 19 typical cities in China from 1992 to 2020 based on NTL datasets. The evaluations of spatiotemporal consistency compared with the validation data indicate that the USR retrieval results show good agreement with the land use map derived from Landsat images and the time series product from MODIS. The average overall accuracy (OA) of overall urban extent is higher than 0.95 and the average kappa coefficient (KC) reaches 0.6. Moreover, we investigated the urban dynamics and USR interactions of 19 cities from 1992 to 2020. Overall, this study proposes an improved approach for long-term USR mapping from NTL images at a regional scale and it will provide a valuable method for urbanization dynamics analysis. [ABSTRACT FROM AUTHOR]

The intensity of solar energy that is received by a particular location is affected by most meteorological conditions including, the solar irradiance received by the location, precipitation, extreme heat as a result of the surface or ambient temperature, etc. We obtain the monthly global solar irradiation and ambient temperature for the three (3) eco-climatic zones in the south of Nigeria (17 locations) for 12 years (2005 - 2016) from the Photovoltaic Geographical Information System (PVGIS) Satellite. The goal of this study is to understand how regional meteorological conditions affect radiation and temperature reception. Monthly and annual trends were plotted and compared for both variables in each region to show the similarity or dichotomy in their trends. The Mann-Kendall (M-K) trend test has been adopted to reveal the changes in the variations on an annual basis, and results showed that the trend were not significant for both variables. Box plots have been used to give a better description of the data, and compared to show similarities and differences. Finally, we adopted the Gaussian (normal) distribution to show, understand and compare the data distribution. Linear regression plots for each zone shows that the relationship between the solar irradiation and temperature is high. Results show that the climate and vegetation of a region contributes majorly to the variation of radiation and temperature. Inhomogeneity of data or results for locations in the same zones may be attributed to local meteorological conditions. The results obtained here will prove vital in decision making relating to the adoption of solar energy technologies in the region. Results show that the climate and vegetation of a region contributes majorly to the variation of radiation and temperature. Inhomogeneity of data or results for locations in the same zones may be attributed to local meteorological conditions. [ABSTRACT FROM AUTHOR]

Climate change and human activities have been heavily affecting oceanic and inland waters, and it is critical to have a comprehensive understanding of the aquatic optical properties of lakes. Since many key watercolor parameters of Qinghai Lake are not yet available, this paper aims to study the spatial and temporal variations of the water clarity (i.e., Secchi-disk depth, Z SD ) and suspended particulate matter concentration ( C SPM ) in Qinghai Lake from 2001 to 2020 using MODIS images. First, the four atmospheric correction models, including the NIR–SWIR, MUMM, POLYMER, and C2RCC were tested. The NIR–SWIR with decent accuracy in all bands was chosen for the experiment. Then, four existing models for Z SD and six models for C SPM were evaluated. Two semi-analytical models proposed by Lee (2015) and Jiang (2021) were selected for Z SD ( R 2 = 0.74) and C SPM ( R 2 = 0.73), respectively. Finally, the distribution and variation of the Z SD and C SPM were derived over the past 20 years. Overall, the water of Qinghai Lake is quite clear: the monthly mean Z SD is 5.34 ± 1.33 m, and C SPM is 2.05 ± 1.22 mg/L. Further analytical results reveal that the Z SD and C SPM are highly correlated, and the relationship can be formulated with Z SD = 8.072 e − 0.212 C SPM ( R 2 = 0.65). Moreover, turbid water mainly exists along the edge of Qinghai Lake, especially on the northwestern and northeastern shores. The variation in the lakeshore exhibits some irregularity, while the main area of the lake experiences mild water quality deterioration. Statistically, 81.67% of the total area is dominated by constantly increased C SPM , and the area with decreased C SPM occupies 4.56%. There has been distinct seasonal water quality deterioration in the non-frozen period (from May to October). The water quality broadly deteriorated from 2001 to 2008. The year 2008 witnessed a sudden distinct improvement, and after that, the water quality experienced an extremely inconspicuous degradation. This study can fill the gap regarding the long-time monitoring of water clarity and total suspended matter in Qinghai Lake and is expected to provide a scientific reference for the protection and management of the lake. [ABSTRACT FROM AUTHOR]

Since historical times, the climate of the earth has been changing. In terms of diversity and population, India is a vast country. Any significant change in its climatic conditions can have an impact on the country's entire economic and cultural structure. Temperature is the most important variable when focussing on climate change study. This study aims to recognise the changing trend of temperature in India. For this purpose, temperature data from 1901 to 2020 has been analysed. To find out the variability in temperature, various statistical tools were applied. The Mann-Kendall method has been used along with Sen's slope estimator for determining the trend and slope magnitude. ARIMA modelling is applied to the data series to forecast the temperature trend. The study has shown that variability in temperature is increasing in recent years and the temperature trend is increasing in all the months, especially in the cold months of November, December and January. The average annual temperature of India in the year has increased significantly in the past 120 years. The trend analysis of temperature is tested significantly statistically at a 95% confidence level. [ABSTRACT FROM AUTHOR]

The current study focuses on the altering historical rainfall data analysis and its variability in Kolkata (Kolkata Municipal Corporation), a metropolitan city in India. The research area experiences detrimental urban floods (pluvial floods) at near-annual regularity during the monsoon, and during the pre-monsoon seasons it commonly experiences water shortage problems. Analysing trends and temporal variability of rainfall over 120 years from 1901 to 2020 is the main objective of this study. The original Mann-Kendall (M-K) test has been applied to the rainfall dataset in conjunction with Sen's Slope Estimator using Python 3.10, after the Durbin-Watson (DW) statistic initially suggested that there is no serial correlation effect. The M-K test, with a Kendall's tau of 0.17058 (significant at a 5% level), shows an upward trend in annual rainfall between 1901 and 2020. The Sen's slope, which measures the rate of change annually, has a value of 2.48152. Regression analysis and other dispersion measures are also used in this study to investigate the monthly rainfall trend and its variability. The phase-wise (30-year) analysis of annual rainfall variability reveals a considerable variation over 120 years. While fitting the linear regression line month by month over the entire period, mostly negative trends were found in the pre-monsoon and positive trends in the monsoon and post-monsoon seasons. The findings of this analysis could be useful to urban planners for water supply and management in the study area. The primary concern of planners for effectively managing rainwater and the accompanying issues should be the growing variability of annual precipitation. [ABSTRACT FROM AUTHOR]

In this study, temperature characteristics and heat load in the city of Dubrovnik are investigated by using temperature data observed at the local meteorological station in Dubrovnik for the period 1961--2019, satellite data collected by LANDSAT5 satellite for the period 2001-2010, and climate indices data obtained from simulations of an urban climate model (MUKLIMO_3) for the period 2001-2010. Trends in daily mean, maximum, minimum, and seasonal temperatures were analysed by using Sen's slope and the Mann-Kendall test. Results reveal rising trends for all of the studied temperature-related elements. However, it is demonstrated that temperature increase is greatest for the summer season with the highest rise for daily maximum temperatures. The same approach was applied to examine trends of climate indices (summer days and tropical nights), which indicates an increase in the number of both summer days and tropical nights. Results of satellite data of average summer land surface temperatures for the period 2001-2010 indicate that urbanised surfaces and bare rock areas heat up more than natural surfaces with vegetation. Climate indices (summer and hot days, warm evenings, and tropical nights) simulated by the urban climate model MUKLIMO&lowbar;3 also reveal that, on average, in the city of Dubrovnik urbanised surfaces heat up more than natural surfaces with vegetation and that nocturnal heat load is reduced in lower-density builtup areas. [ABSTRACT FROM AUTHOR]

Climate change has a substantial influence on a range of meteorological factors and the global hydrological cycle. This paper presents an analysis of the annual rainfall (P), temperature (including maximum temperature: T max , minimum temperature: T min , and average temperature: T avg), stream flow (Q) data from 6 gauging sites, and groundwater level (GL) data from 7 groundwater stations in and around the Barak River Basin in India from 1986 to 2020. To identify patterns in the time series, the simple linear regression (SLR) approach, the Mann-Kendall (MK) test, the Hamid Mann-Kendall (HMK) test and the Spearman's Rho (SRO) tests have been employed. Pettit test has been used to identify the change points in the time series. The results indicated an increase in the T max and the T min across the study area, while no clear patterns were found in the T avg time series. Except for the 'Hailakhandi' station, the annual total rainfall series showed an increasing trend. T max and T min showed a directional change point between 1999 and 2005, and between 2001 and 2007, respectively, according to change point analysis. Except for two stations, overall declining trends in the annual stream flow were found. Moreover, a total of five out of seven groundwater stations indicated a rise in groundwater levels. The findings presented here offer valuable insights into the current condition of the Barak basin and emphasize the importance of implementing sustainable water management practices. • Significant impact of climate change on hydro-meteorological parameters in the study area. • A noteworthy increase in both maximum and minimum temperatures. • Overall declining trends in annual stream flow across most stations in the basin. • Groundwater stations demonstrate rising groundwater levels. [ABSTRACT FROM AUTHOR]

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. • Through rainfall analysis design storm,evaluated and further used as an input data for HEC-HMS hydrological modelling. • Annual and monthly Trend analysis of 40 years rainfall from 8 rain gauge stations is studied using statistical tests, and graphical technique. • Using IDF method with Gumbel's frequency distribution, Isohyetal maps, IDF curves and tables were prepared. • No statistically significant trend was found and results from graphical and Statistical Trend Analysis methods are consistent. • The results obtained are site specific, so more reliable, hence can be used with greater confidence for deriving design storm. [ABSTRACT FROM AUTHOR]

Long-term assessment of basic meteorological field variability is an important factor that influences the Indian summer monsoon and consequently affects the socio-economic aspects of India. In this study, the spatial and temporal variation of meteorological parameters during summer monsoon season using NCEP/NCAR reanalysis datasets for the period of 70 years (1948–2017) has been analyzed in climatology, early-late phase and multidecadal epochs over India and its regions. Statistical techniques such as the standardized anomaly index of surface temperature, rainfall and zonal and meridional wind (at 850 and 200 hPa) and temporal analysis of Mann–Kendall trend test over six selected regions, viz., North India (NI), Central India (CI), Southern India (SI), Arabian Sea (AS), Bay of Bengal (BoB) and Equatorial Indian Ocean (EIO) reveal higher variability during summer monsoon season from 1948 to 2017. The significant spatial changes in the value of standard deviation and coefficient of variation confirm the early-late phase and multidecadal modulation of the seasonal variability of selected climatic parameters. The results indicate that the escalation in the surface temperature multidecadal variability and trend has dominating characteristics over NI, CI and SI regions at an alarming range (0.5–1.0°C). The major hotspots of increasing early-late phase and multidecadal variability and average precipitation have been found over BoB, EIO and SI (~1–3.5 mm/day). The decreasing changes in the mean rainfall pattern and associated variability is strongly linked with increasing surface warming and significant reduction in the strength of surface zonal wind over BoB, IO, SI and CI region which cause the weakening of important atmospheric circulations such as the role of Somali jet and strong low-level jet (LLJ) during Indian summer monsoon season. Also, the meridional wind at the surface and upper level has shown significant enhancement over AS and EIO. The recent decadal anomaly (2008–2017) is really a matter of concern as precipitation and wind circulation anomaly at 850 and 200 hPa have shown decreasing trends over all the regions. In recent years, the variation in meteorological parameters and distribution are asymmetrical during summer monsoon season in changing climate. [ABSTRACT FROM AUTHOR]

In this study, the trends in river discharge were investigated on different time scales for the period 1951/52–2009/10 over the northern part of the Korean peninsula based on the reliable flow datasets measured at the stations with near-natural states, and no or less anthropogenic activities. The annual mean discharge decreased by 5–11%/decade and its abrupt years appeared around 1980. On the seasonal scale, the trends in the rainy season were the most definitely observed with the decreasing rate of 6–12%/decade and could be seen to begin earlier than the annual trends. Unlike the annual and seasonal trends, the monthly trends exhibited very fluctuant variations depending on the individual months and locations. Overall, considering the detected significant trends, there is strong evidence that the river discharge remarkably decreased with the enhanced variability and the decreasing trends were a predominant part of the changes in the river discharge in the study period. With regard to the hydrological regions, the result suggested that the river discharge in the coastal hydrological regions influenced by the oceanic climate could be more sensitive to climate change than in the northern inland area with the continental climate. [ABSTRACT FROM AUTHOR]

Climate change is viewed as the main obstacle to agricultural development in developing countries. The high dependence on agriculture and allied sectors makes many countries vulnerable to the climate change phenomenon. There is a gap in macro and micro-level understanding of climate change. Thoughtful farmers' perceptions and impacts of climate change on farming are fundamental for developing various mitigation and adaptation strategies. Therefore, the main aim of the present study was to understand the pattern of climate variability, farmers' perceptions about climate change, and farmers' adaptation strategies based on their socio-cultural background in the villages of Goa, on the west coast of India. The results reveal that about 62% of the sampled farmers have experienced climate change in terms of meteorological indicators such as increased average temperature, decreased total rainfall, delayed onset of monsoon, and an increase in the length of the summer season. The temperature trend analysis (0.009 °C/year) validated farmers' perceptions, while the perception of rainfall differed (−1.49 mm/year). Farmers are convinced that climate change has affected their farming (declining crop and livestock productivity, water depletion, and other related farm operations). They strive to adapt to climate change through crop diversification, an integrated crop-livestock system, contingency crop planning, and the adaptation of new crops and varieties. This study could be helpful for policymakers to establish a climate-resilient agriculture system by ensuring timely availability of farm inputs, accurate weather forecasting, and encouraging insurance products for crop and livestock enterprises, which will help farmers cope with the changing climate to enhance their income and economic wellbeing. Further, adaption of integrated farming, agroforestry, and indigenous technical knowledge is imperative to combat the ill effects of climate change. [ABSTRACT FROM AUTHOR]