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Under the background of global warming, an analysis of the trend and variability of rainfall time series on various timescales is very important for understanding climate change on a regional scale. In this study, a trend analysis of rainfall time series on monthly, seasonal, and annual scales in Shanxi province, China, during the period 1957–2019...
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All agricultural activities are directly related to the climate. In recent years, temperature increases and changes in precipitation regimes adversely affect the Mediterranean Basin, including Türkiye. While increasing temperatures and irregular precipitation rise the need for irrigation in crop production in some regions, excessive and irregular r...
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... The fluctuation of these variables is crucial since the investigation of climate change is primarily focused on the likely changes in climatic variables like temperature and rainfall (Ekwueme and Agunwamba 2021). Precipitation and temperature are the most crucial climatic factors, which may have an impact on the intensity, duration, and frequency of extreme climate events (such as droughts and floods), the hydrological cycle and water resources, vegetation, agricultural production, water quality, and socioeconomic development (Eris et al. 2019;Singh et al. 2021;Esit et al. 2021;Aksoy and Cavus 2022;Gao et al. 2022). Heavy floods and prolonged droughts were caused by even little changes in the patterns of precipitation and temperature (Salman et al. 2019;Ita and Ogbemudia 2023). ...
The quality and consistency of historical temperature and precipitation records are extremely important to researchers who study water resources, hydrological processes, and climate change. In this regard, Homogeneity tests are helpful tools for managing the accuracy and consistency of the data. In this study, the homogeneity of long-term annual and seasonal precipitation and temperature records obtained from five meteorological stations located in Van, Turkey, is examined using Standard Normal Homogeneity (SNHT), Pettitt (PT), Buishand Range (BR), and Cumulative Deviation (CD) tests at a significance level of 0.05. Finally, change-points were determined for each station where the homogeneity was disturbed. As a result of the study, there is mostly homogeneity in the precipitation data, and the homogeneity in the temperature data is deteriorated. The results of this study constitute a source of information in terms of the reliability of the meteorological data series. As a result, the reliability of the data should be questioned in the hydrological studies to be carried out in the Van region and the data should be made reliable in the projects to be carried out.
... The fluctuation of these variables is crucial since the investigation of climate change is primarily focused on the likely changes in climatic variables like temperature and rainfall (Ekwueme and Agunwamba 2021). Precipitation and temperature are the most crucial climatic factors, which may have an impact on the intensity, duration, and frequency of extreme climate events (such as droughts and floods), the hydrological cycle and water resources, vegetation, agricultural production, water quality, and socioeconomic development (Eris et al. 2019;Singh et al. 2021;Esit et al. 2021;Aksoy and Cavus 2022;Gao et al. 2022). Heavy floods and prolonged droughts were caused by even little changes in the patterns of precipitation and temperature (Salman et al. 2019;Ita and Ogbemudia 2023). ...
The problem of detecting hydrometeorological trends is still a concern. In this study, monthly, seasonal, and annual temperature and precipitation variations in the Van Province of Türkiye are assessed using both traditional such as Mann–Kendall (MK) and Spearman’s Rho (SRHO) and graphical such as Innovative trend significance test (ITA-ST) and Innovative polygon trend analysis (IPTA) methods. In order to determine trend slope and change-point detection, Sen’s Slope and Sequential Mann–Kendall tests are also used, respectively. The findings show that the MK, SRHO, and ITA-ST approaches detect a decreasing trend for annual total precipitation at the Erciş and Başkale stations. Additionally, the MAM and SON seasons at Erciş and the SON season at Gevaş stations also show a noticeably decreasing trend. All stations have an increasing trend at a 95% significant level for annual mean temperatures. Except Gevaş station, all four seasons have an increasing trend for temperature series. Results from the IPTA show that for precipitation data, the transition from May to June exhibits the highest trend length. In addition, for temperature data, the minimum trend length and slope are captured in the transition from July to August. In general, ITA-ST and IPTA are superior to MK and SRHO at identifying trends and that the outcomes of these methods are significantly more suitable to visual inspection and linguistic interpretation. Different hydro-climatological variables can be analyzed more flexibly and thoroughly utilizing innovative methodologies.
... Therefore, it became urgent to study and understand the impacts of meteorological changes on RH variability to better anticipate and mitigate these negative effects. Though several prior studies investigated the variability and trends of rainfall and temperature change in different countries [25,26], including Bangladesh [27][28][29][30], so far, very little attention has been paid on RH. In addition, no research has been found that investigates the influence of meteorological factors on RH dynamics. ...
The frequency and intensity of climate change and resulting impacts are more prevalent in South Asian countries, particularly in Bangladesh. Relative humidity (RH) is a crucial aspect of climate, and higher RH variability has far-reaching impacts on human health, agriculture, environment, and infrastructure. While temperature and rainfall have gained much research attention, RH studies have received scant attention in the research literature. This study investigated the trends and variability of RH levels in Bangladesh and the influence of other meteorological factors over the past 40 years. Variabilities in the meteorological factors were identified by calculating descriptive statistics. Innovative trend analysis (ITA) and Mann-Kendall test (MK-test) methods were utilized to assess monthly, seasonal, and annual trends. The magnitude of temperature, rainfall, and windspeed influences on RH variability were identified using Pearson's correlation, Spearman rank correlation, and Kendall correlation model. Variability analysis showed higher spatial variations in RH levels across the country, and RH skewed negatively in all stations. Results reveal that daily, monthly, seasonal, and annual trends of RH exhibited positive trends in all stations, with an increasing rate of 0.083–0.53% per year in summer, 0.43–0.68% per year in winter, and 0.58–0.31% per year in the rainy season. Both ITA and MK-test provided consistent results, indicating no discrepancies in trend results. All three models indicate that temperature, rainfall, and windspeed have weak to moderate positive influences on changing RH levels in Bangladesh. The study will contribute to decision-making to improve crop yields, health outcomes, and infrastructure efficiency.
... The fluctuation of these variables is crucial since the investigation of climate change is primarily focused on the likely changes in climatic variables like temperature and rainfall (Ekwueme and Agunwamba 2021). Precipitation and temperature are the most crucial climatic factors, which may have an impact on the intensity, duration, and frequency of extreme climate events (such as droughts and floods), the hydrological cycle and water resources, vegetation, agricultural production, water quality, and socioeconomic development (Eris et al. 2019;Singh et al. 2021;Esit et al. 2021;Aksoy and Cavus 2022;Gao et al. 2022). Heavy floods and prolonged droughts were caused by even little changes in the patterns of precipitation and temperature (Salman et al. 2019;Ita and Ogbemudia 2023). ...
Researchers studying water resources, hydrological processes, and climate change fields are of high importance to the accuracy and consistency of historical temperature and precipitation series. In this regard, Homogeneity tests are a helpful tool to manage the accuracy and consistency of the data. In this study, the homogeneity of seasonal and annual temperature and precipitation is examined using Standard Normal Homogeneity (SNHT), Pettitt (PT), Buishand Range (BR), and Cumulative Deviation (CD) tests at a significance level of 0.05. The four homogeneity tests are assessed using long-term annual and seasonal precipitation and temperature records obtained from five meteorological stations located in Van, Turkey. Based on the outcomes of different homogeneity tests, the precipitation and temperature series are divided into three categories: "useful," "doubtful," and "suspect." Finally, change point detection is investigated for each station. According to results, annual mean temperature rejects H0 hypothesis for all stations by four tests. While Ha alternative hypothesis is accepted for stations 17172 and 17852 and 17880 at annual total precipitation, stations 17784 and 17812 are observed as doubtful. There are some inconsistencies in the results of the statistical tests for the homogeneity of the precipitation and temperature time series, which may be related to the various sensitivity levels of the tests to breaks in the time series.
Due to its effect on weather and its propensity to cause catastrophic incidents, climate change has garnered considerable global attention. Depending on the area, the effects of climate change may vary. Rainfall is among the most significant meteorological factors associated with climate change. In Malaysia, changes in rainfall distribution pattern have led to many floods and droughts events which lead to La Nina and El Nino where Johor is one of the states in southern part that usually affected. Thus, rainfall trend analysis is important to identify changes in rainfall pattern as it gives an initial overview for future analysis. This research aims to evaluate historical rainfall data of Johor between 1991 and 2020. Normality and homogeneity tests were used to ensure the quality of data followed by Mann-Kendall and Sen's slope analysis to determine rainfall trend as the rainfall data is not normally distributed (p > 0.05). Standardized precipitation anomaly, coefficient of variation, precipitation concentration index and rainfall anomaly index were used to identify rainfall variability and intensity while standard precipitation index was used to evaluate drought severity. The lowest annual rainfall recorded was 1725.07 mm in 2016 and the highest was 2993.19 mm in 2007. Annual rainfall and seasonal rainfall showed a declining trend although it is not statistically significant (p > 0.05). Results reveal that Johor experienced extreme wet and dry years, leading to drought and flood incidents. Major floods arose in 2006, 2007, 2008, 2010 and 2011 while driest years occurred in 1997, 1998 and 2016 which led to El Nino phenomenon. March and April were identified as the driest months among all. Thus, the findings from this study would assist researchers and decision-makers in the development of applicable adaptation and mitigation strategies to reduce climate change impact. It is recommended that more data analysis from more stations should be done in the future research study to obtain a clearer view and more comprehensive results.
Assessment of spatiotemporal dynamics of meteorological variables and their forecast is essential in the context of climate change. Such analysis can help suggest possible solutions for ora and fauna in protected areas and adaptation strategies to make forests and communities more resilient. The present study attempts to analyze climate variability, trend and forecast of temperature and rainfall in the Valmiki Tiger Reserve, India. We utilized rainfall and temperature gridded data obtained from the Indian Meteorological Department during 1981-2020. The Mann-Kendall test and Sen's slope estimator were employed to examine the time series trend and magnitude of change at the annual, monthly and seasonal levels. Random forest machine learning algorithm was used to estimate seasonal prediction and forecasting of rainfall and temperature trend for the next ten years (2021-2030). The predictive capacity of the model was evaluated by statistical performance assessors of coecient of correlation, mean absolute error, mean absolute percentage error and root mean squared error. The ndings revealed a signicant decreasing trend in rainfall and an increasing trend in temperature. However, a declining trend for maximum temperature has been observed for winter and post-monsoon seasons. The results of seasonal forecasting exhibited a considerable decrease in rainfall and temperature across the Reserve during all the seasons. However, the temperature will increase during the summer season. The random forest machine learning algorithm has shown its eectiveness in forecasting the temperature and rainfall variables. The ndings suggest that these approaches may be used at various spatial scales in dierent geographical locations.
Rainfall variability and water scarcity are the major issues in the semi-arid Marathwada region of Maharashtra. Analysis of seasonal and annual rainfall distribution and trends over the Marathwada is the main objective of the research. Therefore, district-wise monthly rainfall data of 8 districts for 121 years (1901-2021) in the Marathwada was employed, while coefficient of variation (CV), Mann-Kendall (MK), Sen's slope estimator (SSE), innovative trend analysis (ITA), inverse distance weighted (IDW), and rainfall departure from the mean methods were used for analyses. The results showed that monsoon (22.7%) and annual (21.3%) rainfall variability is less than that of post-monsoon (59.9%), winter (110%), and pre-monsoon (67%) seasons. The MK and ITA result specified no significant trends (positive and negative) in monsoon, post-monsoon, and annual rainfall over the region. During the winter (Z = − 1.65) and pre-monsoon (Z = − 1.67) seasons, a significant decreasing trend in rainfall was observed for the Marathwada. However, negative changes (− 25.54% and − 30.81%) in the magnitude of winter and pre-monsoon rainfall were significant in the region. Annual rainfall 55% of the time was below the long-term annual average rainfall (776 mm) of Marathwada. The region experienced three extremely wet years (1916, 1983, and 2019) and two dry years (1920 and 1972). Although seasonal and annual rainfall variations, the monsoon and annual rainfall have not shown significant positive or negative trends over the Marathwada region during 1901-2021. Since Marathwada is vulnerable to water scarcity, this study could help policymakers associated with water resources and agriculture management.
