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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]

ABSTRACT: As the call for an international standard for milk from grassland-based production systems continues to grow, so too do the monitoring and evaluation policies surrounding this topic. Individual stipulations by countries and milk producers to market their milk under their own grass-fed labels include a compulsory number of grazing days per year (ranging from 120 d for certain labels to 180 d for others), a specified amount of herbage in the diet, or a prescribed dietary proportion of grassland-based forages (GBF) fed and produced on-farm. As these multifarious policy and label requirements are laborious and costly to monitor on-farm, fast economical proxies would be advantageous to verify the proportion of GBF consumed by the cows in the final product. With this in mind, we employed readily available mid-infrared spectral data (n = 1,132 spectra) from routine milk controls to develop binary classification models for 4 main feed groups from a primarily forage-based diet: total GBF (≥50% [n = 955], ≥75% [n = 599], ≥85% [n = 356]), pasture (≥20% [n = 451], ≥50% [n = 284], ≥70% [n = 152]), fresh herbage (pasture + fresh herbage indoor feeding; ≥20% [n = 517], ≥50% [n = 325], ≥70% [n = 182]), and whole plant corn (fresh + conserved; ≥10% [n = 646], ≥30% [n = 187]), with the latter as a negative control. We compared 4 machine learning methods to assess which statistical model performs best at discriminating these classes. Three of these models have not yet been tested for herd-level dietary proportion classification, and all 4 follow completely different approaches: least absolute shrinkage and selection operator (LASSO), partial least squares discriminant analysis (PLS-DA), random forest (RF), and support vector machines (SVM). Seasonality has been a missing element from previous dietary herbage proportion classification models. As grazing and fresh herbage indoor feeding are highly dependent on the season, we developed an indicator to incorporate seasonality in a consistent, unbiased manner into our models. We also tested 3 sets of covariates. The first set included only mid-infrared spectra derived data, the second included mid-infrared spectra derived data plus seasonality indices and the third included mid-infrared spectra derived data, seasonality indices and additional herd specific information (DIM, breed, and parity). Of the 4 machine learning algorithms tested for the binary classification of GBF proportion at herd level, LASSO and PLS-DA performed best according to evaluation metrics; however, the RF and SVM models were not far behind the best performing model evaluation metrics in each feed category. Our best performing model, the LASSO model containing seasonality indices and herd specific information, classified total GBF ≥50% with an accuracy of 78.6%, precision of 85.1%, sensitivity of 90.6%, specificity of 14.1%, and F1 score (harmonic mean of precision and sensitivity) of 87.7%; this was very similar to the PLS-DA model. Our results suggest that in general, LASSO and PLS-DA machine learning algorithms perform better for dietary GBF classification than RF or SVM algorithms.

This study examines the trend of meat imports from Romania using monthly data from January 2011 to May 2024. The analysis aims to project the values of meat imports until May 2025 by using various forecasting algorithms to understand import trends and seasonality. The study used the TempoOnline database for data analysis, and the forecast used two methods: a seasonally adjusted regression model and the Holt-Winters methodology. The regression model integrated seasonal indices to forecast future imports, while the Holt-Winters method, which adjusts for trends and seasonality with smoothing constants, provided a more advanced prediction. The forecast results indicate that meat imports have increased significantly, especially after 2022, influenced by factors such as the pandemic and geopolitical instability. The regression model (Model 1) predicted a decrease in imports for 2024 compared to 2023, while the Holt-Winters model (Model 2) projected a substantial increase. The Holt-Winters model showed higher accuracy with lower RMSE and MAPE values than the regression model. In conclusion, both forecasting models provided valuable information on the trends in Romanian meat imports. The Holt-Winters model proved more accurate, predicting higher future imports with more appropriate values. The findings suggest that meat imports from Romania will continue to grow, with significant fluctuations due to seasonal effects. We recommend the Holt-Winters approach for more accurate future planning due to its superior forecasting performance. [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]

This article analyzes the impact of the COVID-19 pandemic on the labor market in Armenia. It reveals that the pandemic has led to significant job losses in the country, and existing labor market policies were inadequate in addressing the challenges. Similar studies conducted in different countries also show negative effects on employment, with government assistance programs not effectively mitigating the impact. The study provides valuable insights into the consequences of the pandemic on employment and offers forecasting insights. It highlights the need for more effective measures and provides information for policymakers to develop strategies for employment recovery. [Extracted from the article]

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]

In the present time of changing climate, rainfall provides a vital information for effective agricultural and water resource planning and management. This study aimed at analyzing the rainfall characteristics in Kaduna state, Nigeria. Rainfall data of 20 years from 1999-2018 were obtained from the National Water Resource Institute Kaduna for the study. Walter's 1967 formula was used to calculate the rainfall Onset and Cessation dates while the Seasonality Index was calculated using the Walsh and Lawler's 1981 method. Results obtained revealed a decrease in trend for rainfall Onset and Cessation, Annual Rainfall amounts and also a negative trend in the Seasonality Index of the study area. While the Length of rainy season shows a positive trend for the reference period. The study suggests in-depth research in areas related to crop-climate relationship, so that variety of crops with optimum yield potentials will be cultivated in the area to feed the increasing population. This will help to avoid the risks of hunger and related food insecurity in the study area. [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]

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The number of tourist arrivals is one of the main points in assessing the success of the tourism sector. Because of that, the seasonal pattern of tourist arrivals is an important thing that must be observed in making policies in the field of tourism marketing. Therefore, identifying seasonal patterns will influence the strategy of allocating available resources, especially in markets that have great potential in producing quality tourists like Australia. By using the calculation of Seasonality Ratio, Seasonality Indicator, Gini Coefficients, and Seasonality Index, this study compares the seasonal patterns of Australian tourists arriving in Indonesia with other competing countries, namely Singapore and Thailand. This research has succeeded in identifying that the Seasonality Ratio and Seasonality Indicator values show the pattern of Australian tourists arrives in Indonesia has more fluctuations every month. Despite this, the seasonal level of Australian tourists to Indonesia is low the same as the two other countries. This is seen from the results of the Gini Coefficient calculation in the three countries are below 0.5, or the difference in tourist arrivals per month is not significant. The Seasonality Index indicator shows that January, April, and December are the peak season periods for Australian tourists arriving Singapore and Thailand. However, this is different in Indonesia which is in the off-season phase. Thus, Indonesia has opportunities to optimize Australian tourist arrives in the off-season with the right marketing policies.

The relevance.Motorgasoline, diesel fuel and other oil products differ in seasonality of consumption, a way of transportation, the territory of realization. The change in demand for petroleum products under the influence of these factors leads to a change in the range and volume of production of various fuels. In this regard, the actual task is to plan the production of petroleum products. It is proposed to solve the problem of predicting the production of petroleum products using autoregressive models taking into account the seasonality factor. The main aimof the research is todevelopand select mathematical models suitable for forecasting generation and consumption of petroleum products production. Objects: production of petroleum products. The models are based on the data of a single interdepartmental information and statistical system. Methods are based on the use of methods of mathematical and simulation modeling. Results. The authors have carried out the review of methods of modeling time series of production and consumption of fuel and energy resources.It compares various mathematical models for forecasting the production of motor gasoline through the example of the Volga federal district. The authors developed the models that differ in consideration of the seasonal components and the type of trend. The use of a multiplicative model containing a trend in the form of a linear, autoregressive, autoregressive-power model with the calculation of the seasonality index is proposed. The articles shows that the best results as per the criterion of the average relative error were obtained using the model with autoregressive-power trend. The efficiency of the obtained model is shown on the example of federal districts of the Russian Federation to assess the production of motor gasoline and diesel fuel. The research results were obtained using the Matlab software package. The authors made post-forecast production of fuels according to the proposed model with an average relative error not exceeding 11 %.

BACKGROUND: Water footprint assessment is essential for the evaluation of water scarcity that considers both direct and indirect water consumption along the supply chain. This paper presents the estimation of water footprint for locally grown fruits and vegetables in Australia. Water footprint was calculated based on the framework developed in the Water Footprint Assessment Manual for the crops which are the most practicable to grow in Australia. Nine different crops (apples, grapes, tomatoes, oranges, peaches/nectarines, cherries, potatoes, carrots/turnips and almonds) in the agricultural industry were selected and identified as the most water‐consumptive crop and least water‐consumptive crop. For each type of crop, the three main water footprint components (blue, green, and grey water) were calculated. RESULTS: It was found that almond had the highest water footprint (6671.96 m3 ton−1) and tomato had the lowest water footprint (212.24 m3 ton−1) in Australia. From the global comparison, it is revealed that total water footprint for Australian crops is much higher than the corresponding international average values, except for tomatoes, potatoes and almonds. Also, almonds had the highest water footprint among the nine crops investigated. CONCLUSION: The study provides an insight into future sustainable cropping patterns in Australia, which suggest that tomatoes, carrots/turnips, potatoes and apples should continue to be grown in Australia, whereas stone fruit (e.g., almonds) should no longer be grown because of its high water footprint. © 2020 Society of Chemical Industry [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]

Precipitation is the most important climatic factor, particularly in dry lands and its regime characteristics is affected by the change in climate. The precipitation regime is a result of physical, spatial and temporal interactions. The seasonality index (SI) specifies the distribution of monthly rainfall over the year and evaluates the seasonal variations of the rainfall regime. The present study was conducted to determine the precipitation regime and to detect the trend of precipitation SI at the central part of the Province of Ardabil. The rainfall data of 24 stations were collected for the 1989–2012 period. Descriptive statistics of monthly precipitation data were presented both graphically and statistically, and the spatial-temporal variations were estimated using the SI and coefficient of variation (CV). The trend of precipitation SI at different rain gauge stations was determined using the Mann-Kendall test. The results showed that the highest CV belonged to the Sad-Ghorichay and Lay Stations (65 and 63%), and the lowest was related to the Shamsir Khani Station (36%). Based on the precipitation SI of monthly data, two main regimes were determined: a short dry-season (SI= 0.2–0.3), and a wet-season having the SI value of 0.6. The results of the Mann-Kendall test showed that most stations had either negative or positive trends in the precipitation SI. The increasing trend in the study area was more than the decreasing trend; only in some stations was the trend statistically significant. Also, assessing the CV of precipitation data showed that the uniformity of monthly precipitation values had decreased during the study period. Increasing the CV and precipitation SI indicate a change in precipitation regime; consequently, an increase in the severity of possible droughts in the study area.

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]

Soil moisture is an essential variable for both the agricultural production and the functioning of natural ecosystems, as well as one of the main variables regulating the climate system. Like precipitation, soil moisture has a marked annual cycle that depends mainly on the behaviour of precipitation and evapotranspiration. In this study, we developed a methodology that allows describing and characterizing the annual cycle of soil moisture in Argentina. For this purpose, we estimated soil moisture at 121 sites of Argentina by means of a simple hydrological balance (the BHOA, an acronym for its name in Spanish: "Balance Hidrológico Operativo para el Agro") and analysed its intra‐annual variability by means of different indices. This methodology allowed recognizing five different annual behaviours, with a consistent geographical pattern throughout Argentina. The characterization was based mainly on the time of occurrence of the annual maximum and minimum values of soil moisture, and secondly by the annual soil moisture content and seasonality. The northwest and part of the centre of Argentina present the minimum soil moisture values in spring and the maximum ones in autumn, while the east and south present the minimum values in summer and the maximum ones in winter. In general, sites with higher soil water content were found to have lower seasonality and vice‐versa. [ABSTRACT FROM AUTHOR]

Climate change impact varies across different altitudinal ranges and demands local specific management strategies for water resource and farming system management. The present study analyses spacio-temporal climate parameters across different altitudes of Himachal Pradesh a hilly state of India. Analysis shows that annually, minimum temperature has significantly decreased by -0.09°C at altitude I (350 - 400 m) while maximum temperature has significantly increased by 0.05°C at altitudes I and II (1400-1500 m) and decreased significantly by -0.08°C at altitude III (2000- 2100 m). Higher regions Altitude – IV (2900-3000 m) received lowest rainfall (746.1 mm) with 30.2 % variation. Seasonal rainfall variability was higher in post monsoon (102 - 174%) and least in monsoon (21 - 57%). Annual rainfall at altitude I is strongly irregular (PCI 20.1 to 22.3), followed by altitude – IV (PCI 15-25); altitude – II irregular (PCI 15-20) and altitude – III moderate to irregular (PCI 12 -19) rainfall. Seasonal Index values for four altitudes fall between 0.91-0.96 revealed that rainfall is irregular and markedly seasonal with longer drier season. Higher wavelet powers in altitude - I and II after 2005 suggests frequency of extreme rainfall occurrence had increased.

Seasonal variation exists in disease incidence. The variation could occur across the different regions in a country. This paper argues that using national household data that are not adjusted for seasonal and regional variations in disease incidence may not be directly suitable for assessing socio-economic inequality in annual outpatient service utilisation, including for cross-country comparison. In fact, annual health service utilisation may be understated or overstated depending on the period of data collection. This may lead to miss-estimation of socio-economic inequality in health service utilisation depending, among other things, on how health service utilisation, across geographical areas, varies by socio-economic status. Using a nationally representative dataset from South Africa, the paper applies a seasonality index that is constructed from the District Health Information System, an administrative dataset, to annualise public outpatient health service visits. Using the concentration index, socio-economic inequality in health service visits, after accounting for seasonal variations, was compared with that when seasonal variations are ignored. It was found that, in some cases, socio-economic inequality in outpatient health service visits depends on the socio-economic distribution of the seasonality index. This may justify the need to account for seasonal and geographical variations. [ABSTRACT FROM AUTHOR]

Climate change impact varies across different altitudinal ranges and demands local specific management strategies for water resource and farming system management. The present study analyses spacio-temporal climate parameters across different altitudes of Himachal Pradesh a hilly state of India. Analysis shows that annually, minimum temperature has significantly decreased by -0.09°C at altitude I (350 - 400 m) while maximum temperature has significantly increased by 0.05°C at altitudes I and II (1400- 1500 m) and decreased significantly by -0.08°C at altitude III (2000- 2100 m). Higher regions Altitude -- IV (2900-3000 m) received lowest rainfall (746.1 mm) with 30.2 % variation. Seasonal rainfall variability was higher in post monsoon (102 - 174%) and least in monsoon (21 - 57%). Annual rainfall at altitude I is strongly irregular (PCI 20.1 to 22.3), followed by altitude -- IV (PCI 15-25); altitude -- II irregular (PCI 15-20) and altitude -- III moderate to irregular (PCI 12 -19) rainfall. Seasonal Index values for four altitudes fall between 0.91-0.96 revealed that rainfall is irregular and markedly seasonal with longer drier season. Higher wavelet powers in altitude - I and II after 2005 suggests frequency of extreme rainfall occurrence had increased. [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]

It is crucial to carry out a spatiotemporal evaluation of rainfall concentration in the Johor river basin (JRB) given that erratic rainfall can lead to floods. The study utilized the CHIRPS rainfall dataset from 1983 to 2018 to determine rainfall concentration using the Concentration Index (CI). To further define the rainfall regime, the Seasonality Index (SI) and Precipitation Concentration Index (PCI) were examined. The Mann-Kendall (MK) test was utilized to examine the trend at a 95% level of significance. The results showed the better accuracy of SI than PCI in classifying the rainfall regime in the JRB, defined as "equable but with a definite wetter season" type. The CI values ranged from 0.61 to 0.72 and were typically high all over the basin, with the northwestern and western regions having the highest values. At 27 grid points on the eastern border of the upper and middle parts of the basin, CI has significantly increased as a result of an upsurge in rainfall concentration brought on by climate change. More than 67% and up to 83% of the yearly rainfall for all grid locations was accounted for by the North East monsoon (NEM), with the 25% wettest days caused by high-intensity rainfall events. • Spatiotemporal assessment of CHIRP-based rainfall concentration in JRB. • SI better than PCI for basin-scale rainfall regime classification. • CI values is highest in north-western and western area (0.61–0.72). • Significant increase of CI is expected to cause more flood event in the future. [ABSTRACT FROM AUTHOR]

This paper will propose a general approach for the analysis and measurement of seasonality in tourism, based on an analysis of the pattern of seasonal swing, as a preliminary step for the assessment of seasonal amplitude. The seasonality of tourism demand across European countries will be analyzed and clusters of countries identified, which are based on a similarity of their seasonal pattern. After discussing the limitations of the most frequently used indices employed in the tourism literature, a new index for measuring seasonality in tourism will be suggested in order to measure seasonal amplitude. The latter takes into account the ordinal and cyclical structures of seasonal variations. The results demonstrate a strong connection between seasonal patterns and the spatial distribution throughout European countries, which may orient future policy actions for dealing with seasonality on a European level. [ABSTRACT FROM AUTHOR]

This paper examines rainfall variability in Nigeria using monthly rainfall data from 1901 to 2012. This is necessary for planning and management of agriculture and water resource systems. Data for the study were retrieved from the portal archives of World Bank Climate Change Knowledge. Descriptive statistics, trend analysis, and seasonality index were employed in the study. Fluctuations across the months and years were respectively examined through MAKESENS® statistics and Seasonality Index. The result revealed minimum and maximum amounts of rainfall in January and August respectively. The Z and Q statistics indicate an upward trend and positive slope respectively but the null hypothesis of no trend is accepted only in November, December and January due to very low amount of rainfall. The highest maximum and minimum amount of rainfall occurred in July and August while the lowest maximum and minimum both took place in January but the years in which these sets of maximums and minimums happened each followed a zigzag pattern which connotes fluctuations in the amount of rainfall. The maximum, minimum and average seasonality indexes are 0.924, 0.706 and 0.779 respectively, which indicate seasonal and markedly seasonal with a long dry season. There is the need to harvest, store and treat excess water during the wet months for use in dry months. The study recommends that the ministry of water resources should adopt best practices to provide water for Nigerians during the long dry months. [ABSTRACT FROM AUTHOR]

Abstract: Kerala, a maritime state of India is bestowed with abundant rainfall which is about three times the national average. This study is conducted to have a better understanding of rainfall variability and trend at regional level for this state during the last 100 years. It is found that the rainfall variation in northern and southern regions of Kerala is large and the deviation is on different timescales. There is a shifting of rainfall mean and variability during the seasons. The trend analysis on rainfall data over the last 100 years reveals that there is a significant (99%) decreasing trend in most of the regions of Kerala especially in the month of January, July and November. The annual and seasonal trends of rainfall in most regions of Kerala are also found to be decreasing significantly. This decreasing trend may be related to global anomalies as a result of anthropogenic green house gas (GHG) emissions due to increased fossil fuel use, land-use change due to urbanisation and deforestation, proliferation in transportation associated atmospheric pollutants. We have also conducted a study of the seasonality index (SI) and found that only one district in the northern region (Kasaragod) has seasonality index of more than 1 and that the distribution of monthly rainfall in this district is mostly attributed to 1 or 2 months. In rest of the districts, the rainfall is markedly seasonal. The trend in SI reveals that the rainfall distribution in these districts has become asymmetric with changes in rainfall distribution. [Copyright &y& Elsevier]

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]

In this study we examine three seasonality indices for their potential in regionalizing low flows. The indices are seasonality histograms (SH5) that represent the monthly distribution of low flows, a cyclic seasonality index (SI) that represents the average timing of low flows within a year, and the seasonality ratio (SR), which is the ratio of summer and winter low flows. The rationale of examining these indices is the recognition that summer and winter low flows are subject to important differences in the underlying hydrological processes. We analyse specific low flow discharges q95, i.e. the specific discharge that is exceeded on 95% of all days at a particular site. Data from 325 subcatchments in Austria, ranging in catchment area from 7 to 963 km2, are used in the analysis. In a first step, three seasonality indices are compared. Their spatial patterns can be interpreted well on hydrological grounds. In a second step, the indices are used to classify the catchments into two, three, and eight regions based on different combinations of the indices. In a third step, the value of the seasonality indices for low flow regionalization is examined by comparing the cross- validation performance of multiple regressions between low flows and catchment characteristics. The regressions make use of the three seasonality-based classifications. The results indicate that grouping the study area into two regions and three regions and separate regressions in each region gives the best performance. A global regression model yields the lowest performance and a global regression model that uses different calibration coefficients in each of the eight regions only performs slightly better. This suggests that separate regression models in each of the regions are to be preferred over a global model in order to represent differences in the way catchment characteristics are related to low flows. [ABSTRACT FROM AUTHOR]

Abstract: Four catchment grouping methods are compared in terms of their performance in predicting specific low flow discharges q 95, i.e. the specific discharge that is exceeded on 95% of all days. The grouping methods are the residual pattern approach, weighted cluster analysis, regression trees and an approach based on seasonality regions. For each group, a regression model between catchment characteristics and q 95 is fitted independently. Data from 325 sub-catchments in Austria ranging in catchment area from 7 to 963km2 are used in the analysis. The performance of the methods is assessed by leave-one-out cross-validation of the regressionestimates, which emulates the case of ungauged catchments. Results indicate that the grouping based on seasonality regions performs best and explains 70% of the spatial variance of q 95. The favourable performance of this grouping method is likely related to the striking differences in seasonal low flow processes in the study domain. Winter low flows are associated with the retention of solid precipitation in the seasonal snow pack while summer low flows are related to the relatively large moisture deficits in the lowland catchments during summer. The regression tree grouping performs second best (explained variance of 64%) and the performance of the residual pattern approach is similar (explained variance of 63%). The weighted cluster analysis only explains 59% of the spatial variance of q 95, which is only a minor improvement over the global regression model, i.e. without using any grouping (explained variance of 57%). An analysis of the sample characteristics of all methods suggests that, again, the grouping method based on the seasonality regions has the most favourable characteristics although all methods tend to underestimate specific low flow discharges in the very wet catchments. [Copyright &y& Elsevier]

The study of rainfall seasonality in different geographical areas in Greece using an individual seasonality index was the purpose of the present work. The correlation of the seasonality index (—SI, sum of the absolute deviation of mean monthly rainfall from the overall monthly mean divided by the mean annual rainfall) with the mean individual seasonality index (SI, average of— SIi for each year i) was initially determined. A regression analysis of SI with geographical latitude resulted in a statistically significant inverse linear correlation. The time series trend of the SI1 was examined to verify that no significant change in rainfall seasonality occurred. [ABSTRACT FROM AUTHOR]

A reasonable flood season delineation can effectively implement staged reservoir scheduling and improve water resource efficiency. Therefore, this study is aimed at analyzing the flood period segmentation and optimizing the staged flood limit water levels (FLWLs) for a multi-purpose reservoir, the Longtan Reservoir, China. The rainfall seasonality index ( S I P ) and the runoff seasonality index ( S I R ) are used to evaluate the feasibility and rationality of the flood period staging. The fractal method is then used to segment the flood season. Finally, the design flood is carried out to optimize the staged FLWLs. The results show that the S I is an effective indicator for judging the feasibility and verifying the rationality of flood segmentation. The flood period can be segmented into the pre-flood season (12 April–29 May), the main flood season (30 May–3 September), and the post-flood season (4 September–9 November). The FLWLs in the main flood and the post-flood season can be raised by 2.05 m and 3.45 m, and the effective reservoir capacity is increased by 5.810 billion m3 and 6.337 billion m3, according to the results of the flood season division. [ABSTRACT FROM AUTHOR]