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Climate change has become an emerging topic, leading to widespread damage. However, when considering climate, attention is drawn to various scales, and urban microclimate has emerged as a trending subject due to its direct relevance to human living environments. Among the microclimatic factors, temperature and precipitation are utilized in order to identify trends. The identification of changes in precipitation and temperature from ground stations poses difficulties due to the lack of well-distributed stations; thus, satellite-based products are gaining popularity. The satellite products were validated against ground data, following which time-series and spatial analyses were conducted. The rainfall anomaly index, seasonality index, heat wave magnitude index, and mean temperature differ in the Colombo Metropolitan Area compared to the entire country. Each index is calculated decadal-wise to identify trends. By utilizing four climate indices, the analysis endeavors to investigate the microclimate identification in Colombo Metropolitan Area compared to its surrounding areas such as the Western Province and the entire country. This study aids local authorities in mitigating climate change by enhancing city resilience. These findings underscore the importance of understanding and addressing the impacts of climate change on temperature extremes to mitigate potential adverse effects on human activities and the environment. Understanding the specific reasons for spatial changes in rainfall anomalies often necessitates extensive climate modeling and data analysis. [ABSTRACT FROM AUTHOR]

Growing population and climate change have altered the hydro-climatic trend from past decades. This manuscript analyses the abrupt shift in these time series and their changing pattern using historical data sets. The Pettitt test and the Standard Normal Homogeneity Test were used to evaluate the time series' homogeneity. The Concentration Index, Precipitation Concentration Index and Seasonality Index were employed to analyse the spatial variability of daily, monthly and seasonal rainfall patterns over the Aghanashini River watershed. Furthermore, the temporal trend in the rainfall, streamflow, and temperature time series was investigated using Mann–Kendall (MK) and the graphical Innovative-Şen (IŞ) test. Clear evidence of climate change impact on the rainfall and streamflow pattern was recognized, as there is an upward shift in the maximum temperature time series and a downward shift in the rainfall and streamflow time series after 2001. The rainfall indices showed that the watershed has fewer percentage of rainy days and stronger rainfall seasonality, indicating a possible risk of flash floods in the downstream of the watershed. Additionally, the results of the MK and IŞ trend tests paralleled each other and provided support for the findings emphasized by rainfall indices. [ABSTRACT FROM AUTHOR]

We studied a mesotrophic swamp drained 25 years ago in the northern part of the Ob' River basin (56°23′710″ N, 84°34′043″ E). In peat soils (0–30 cm layer), the weighted average of peroxidase activity for the season (base level) constituted 14.4, 21.9, and 70 units (mL I/g abs. dry soil per 2 min) in the areas subjected to weak, moderate, and intensive drainage, respectively. Second-order parabola is the most adequate function of the main trend of development of seasonal fluctuations in peroxidase activity. Numerical values and signs of the parabolic trend parameters showed that the average peroxidase activity decreased from June to October weekly by 4.4, 7.6 and 15.2 units with weekly average acceleration by 0.31, 0.59 and 1.54 units in the mode of weak, moderate, and intensive drainage, respectively. The seasonal wave of peroxidase activity relative to the baseline level was characterized by a June increase in growth rates with a maximum in the layer of 0–10 cm. In July, there was a decrease in the growth rate according to the depth of reclamation: in the modes of weak and moderate drainage, the process embraced the entire soil profile in August; in case of intensive drainage, in October. The enzyme activity significantly and positively correlated with the soil volumetric water content and pH, negatively correlated with redox potential, and displayed multidirectional relationship with the soil temperature. Environmental conditions acted as mutually replacing parameters when assessing their contribution to the seasonal dynamics of peroxidase, creating the effect of interchangeability of environmental gradients. Canonical determination indices approximated the cumulative impact of the discussed set by 52–74%, depending on the depth of reclamation. Canonical weights showed that the hydrothermal regime was the main factor, regulating the seasonal activity of peroxidase: according to canonical correlations, temperature was the main factor under the conditions of weak drainage, moisture was the main factor under the conditions of intensive drainage, and both moisture and temperature were important factors under the conditions of moderate drainage. The peroxidase activity and the depth of humification in peat soils of different degrees of drainage were interrelated in 87% of cases. [ABSTRACT FROM AUTHOR]

Studies on historical patterns of climate variables and climate indices have attained significant importance because of the increasing frequency and severity of extreme events worldwide. While the recent events in the tropical state of Kerala (India) have drawn attention to the catastrophic impacts of extreme rainfall events leading to landslides and loss of human lives, a comprehensive and long-term spatiotemporal assessment of climate variables is still lacking. This study investigates the long-term trend analysis (119 years) of climate variables at 5% significance level over the state using gridded datasets of daily rainfall (0.25° × 0.25° spatial resolution) and temperature (1° × 1° spatial resolution) at annual and seasonal scales (south-west monsoon, north-east monsoon, winter and summer). Five trend analysis techniques including the Mann–Kendall test (MK), three modified Mann–Kendall tests and innovative trend analysis (ITA) test were performed in the study. It is evident from the trend analysis results that more than 83% of grid points were showing negative trends in annual and south-west monsoon season rainfall series (at a mean rate of 39.70 mm and 28.30 mm per decade respectively). All the trend analysis tests identified statistically significant increasing trends in mean and maximum temperature at annual and seasonal scales (0.10 to 0.20 °C/decade) for all grids. The K-means clustering algorithm delineated 59 grid points into five clusters for annual rainfall, illustrating a clear geographical pattern over the study area. There is a clear gradient in rainfall distribution and concentration inside the state at annual as well as seasonal scales. The majority of annual rainfall is concentrated in a few months of the year. That may lead the state vulnerable to water scarcity in non-rainy seasons. [ABSTRACT FROM AUTHOR]

This paper investigates the simultaneous use of in-situ hydrologic measurements in combination with two different artificial intelligent (AI) methods, namely, Adaptive Neuro Fuzzy Inference System (ANFIS) and Artificial Neural Network (ANN), for developing enhanced long-term streamflow forecasting models. To enhance the reliability of the proposed models' outputs, a sub-basin method using the regionalization approach is proposed. Furthermore, to accelerate the training process and achieve more accurate handling of seasonal changes, a parameter representing seasonal variations is introduced. The models are applied to the mountainous Talezang basin, southwestern Iran, for which there is a 14-year series of monthly in-situ data records and snow cover area data obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS). The results indicate that the use of the sub-basin approach significantly improves both AI methods' performances. Moreover, it is deduced that the use of seasonal information and satellite data has a great impact on the model performance and accuracy. Comparing the long-term flow forecasts of both models showed that the ANFIS model is superior to the ANN. [ABSTRACT FROM AUTHOR]

To solve the problems of saving thermal energy, instead of the currently used cumbersome methods or complex mathematical models, for example, in the form of partial differential equations, it is proposed to use simpler models of time series for the supply and production of thermal energy. The data of a specific heat supply organization on a city scale and the website of the Ministry of Energy of the Russian Federation were used as initial data. The proposed approach is based on a comparison of various mathematical models for predicting the production and supply of heat energy according to the criterion of adequacy (average relative error and coefficient of determination). The results of research carried out using the Matlab software package are presented. A substantiated comparison of the possibility of using different models is carried out. Models have been developed that take into account the seasonal component and higher accuracy. The possibility of using the following time series models is investigated: regression models with a seasonal component, triple exponential smoothing Holt–Winters, multiplicative seasonal autoregression model of the integrated moving average SARIMA (seasonal autoregression integrated moving average), and nonparametric model SSA (singular spectrum analysis). Multiplicative models containing a trend in the form of a linear or autoregression model with the calculation of the seasonality index, as well as additive models with a trend in the form of a linear or autoregression model with a seasonal component in the form of a Fourier series or sum of sines, were compared. In the Holt–Winters model, fminsearch, a function of the Matlab software package, was used to determine the tuning parameters. In the multiplicative SARIMA model, the orders of autoregression and moving average were fitted according to the autocorrelation and partial autocorrelation function. The nonparametric SSA model was used for comparison with the listed parametric models. An opportunity has been obtained to make reasonable forecasts of the production and supply of heat energy It is shown that the best results (average relative error 6.52%) were obtained using the SARIMA multiplicative model. [ABSTRACT FROM AUTHOR]

Meteorological drought in India arises due to significant deficiency of rainfall for abnormal periods over an area. The large spatial and temporal variability of Indian summer monsoon rainfall (ISMR) over the Eastern Gangetic Plain (EGP) of India triggers meteorological drought (further leading to agricultural and hydrological drought), with widespread effects on both agricultural production and water resources over the area. To assess meteorological drought over agro-climatic zones of the states of Uttar Pradesh (UP), Bihar and West Bengal (WB) in the EGP, high-resolution gridded rainfall data (1961–2013) at resolution of 0.25° × 0.25° of India Meteorological Department (IMD) and u, v wind at 850 hPa at resolution of 0.25 × 0.25° of ERA-40 (1961–2002) of the European Centre for Medium-Range Weather Forecasts (ECMWF) is considered. Over the agro-climatic zones, the seasonality index (SI) of summer monsoon rainfall, spatial and temporal distribution of the 4-month Standardized Precipitation Index (SPI-4), frequency and probability of drought occurrence is estimated. The severe drought-prone zones are found to be over agro-climatic zones 6, 8 and 10 of UP; 1, 2 and 3B of Bihar with more than 50% probability of drought occurrence. At a 95% confidence level, a significant decrease in rainfall (for the period 1961–2013) is found over these zones. Over the EGP, a low-level easterly wind at 850 hPa in July is shifted towards foothills of the Himalaya, while in August it is weakened during drought conditions. This low-level easterly wind may be responsible for less moisture incursion over the Gangetic Plain from the Bay of Bengal, and may be the probable cause of less rainfall over the EGP, leading to meteorological drought. [ABSTRACT FROM AUTHOR]

Previous observational analyses have shown a declining rainfall trend over Israel, mostly statistically insignificant. The current study, for the period 1975–2020, undermines these findings, and the alarming future projections, and elaborates other ingredients of the rain regime. No trend is found for the annual rainfall, reflecting a balance between a negative trend in the number of rainy days and a positive trend in the daily rainfall intensity, both on the order of 2.0%/decade. In the mid-winter, the rainfall and the daily intensity increased, while both declined in the autumn and spring, implying a contraction of the rainy season. The time span between accumulation of 10% and 90% of the annual rainfall, being 112 days on the average, shortened by 7 days during the study period. This is also expressed by an increase of the Seasonality Index, indicating that the regional climate is shifting from “markedly seasonal with a long dry season” to “most rain in ≤3 months.” The intra-seasonal course of the rainfall trend corresponds to that of the occurrence and intensity of the Cyprus Lows and the Mediterranean Oscillation. The contraction of the rainy season and the increase in the daily intensity have far-reaching environmental impacts in this vulnerable region. [ABSTRACT FROM AUTHOR]

Knowledge of mean rainfall and its variability of smaller spatial scale are important for the planners in various sectors including water and agriculture. In the present work, long rainfall data series (1901-2006) of districts of Maharashtra in monthly and seasonal scales are constructed and then mean rainfall and coefficient of variability are analyzed to get the spatial pattern and variability. Significant long term changes in monthly rainfall in the district scale are identified by trend analysis of rainfall time series. The seasonality index which is the measure of distribution of precipitation throughout the seasonal cycle is used to classify the different rainfall regime. Also long term changes of the seasonality index are identified by the trend analysis. The state Maharashtra which is to the northwest of peninsular India is highly influenced by the southwest monsoon and the state is facing water scarcity almost every year. This study will help to find out possible reason for the increasing water scarcity in Maharashtra. [ABSTRACT FROM AUTHOR]

The North Maharashtra region comprises three districts, namely Jalgaon, Dhule, and Nandurbar. The region comprises 25 talukas, which are mostly covered by agricultural fields. Seasonality and trend analysis of rainfall over North Maharashtra region from 1901 to 2016 is conducted in the present work. The data were analyzed on the basis of season, i.e. winter, pre-monsoon, monsoon, and post monsoon. For trend detection, the Sen’s slope estimator and the Mann-Kendall test are used. The largest negative rainfall trends are found in the talukas Nandurbar and Jamner. In Akkalkuwa, the strongest positive trend is found. The rainfall trends in the recent years are discussed in the present work. [ABSTRACT FROM AUTHOR]