Agricultural Meteorology
A. Gholami; H. Mir Mousavi,; M. Jalali; K. Raispour
Abstract
Introduction
Clouds can be considered as one of the most complex and influential variables of the atmosphere system in forming of the climate structure of the earth. When the condensation process takes place at a higher altitude than the earth's surface, it creates clouds. Cloudiness represents ...
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Introduction
Clouds can be considered as one of the most complex and influential variables of the atmosphere system in forming of the climate structure of the earth. When the condensation process takes place at a higher altitude than the earth's surface, it creates clouds. Cloudiness represents the percentage of the atmosphere that is covered by clouds. Clouds, as one of the most complex variables of the climate system, besides changing the energy balance, are also effective in the spatial and temporal distribution of many climate variables. Clouds have a lot of temporal and spatial variability and can affect the climate through many complex relationships and affect the water cycle. The investigation of clouds holds great significance as they serve as the bridge between synoptic systems and the Earth's surface climatic conditions. Any alteration in cloud-related parameters can trigger a domino effect, influencing various other climatic variables. It's worth noting that Iran exhibits a lower average cloud cover of 26%, notably less than the global average of 50%. This places Iran in the category of countries with relatively minimal cloud cover.Hence, possessing insights into the atmospheric cloud cover conditions in Iran becomes imperative for early detection and management of hydroclimatic crises, particularly in the context of water scarcity and drought-related challenges.
Data and Methods
In the current research, the cloud data of 93 synoptic meteorological stations of Iran have been used in the daily time period during the statistical period of 1991-2021. The amount of cloudiness is an estimate of the nearest octa (eighth) and values 0 and 8 are completely clear and completely cloudy, respectively. In the present study, Kolmogorov-Smirnov, Anderson-Darling and Lilliefors test were used to determine the normality of the data at the 95% confidence level for annual, monthly and seasonal scales.
In the subsequent phase, we employed both parametric and nonparametric methods to discern trends within the cloudiness time series. The parametric approach involved a linear regression test based on the least squared error method, while the nonparametric method employed the Mann-Kendall test. These tests allowed us to identify data trends, accounting for both normal and non-normal distributions of cloudiness. Furthermore, we explored the interplay between cloud cover and spatial factors, namely latitude and longitude, employing Pearson's correlation coefficient. This analysis shed light on the relationships between these variables. Conclusively, we created a spatial distribution map depicting the extent of cloudiness across various stations. This mapping allowed us to dissect the temporal-spatial distribution of cloudiness, comprehend alterations in cloud cover, and investigate the contributing factors behind these changes.
Results and Discussion
The results of Normality Tests according to the Kolmogorov-Smirnov test showed that all the stations did not have a normal distribution however, during the other two tests, except Arak, Kashan, Sarakhs, Takab, Kahnuj, Ramhormoz and Ramsar, other stations had normal distribution. The tests to determine the trend based on the parametric linear regression test based on the least squares error method showed a decreasing trend in 44 stations and an increasing trend in 3 stations of Ardabil, Qom and Sarab. According to the non-parametric Mann-Kendall test, among the stations without normal distribution, Kahnuj, Ramhormoz and Sarakhs stations have a decreasing trend, and no special trend was observed in other stations. The relationship between the two factors of latitude and longitude with the cloudiness variable using the Pearson correlation coefficient indicates a negative relationship (-0.42) between the cloudiness variable and the longitude factor as the amount of cloudiness in Iran's atmosphere decreases with the increase of latitude. Hwoever, the relationship between cloudiness variable and latitude, a positive relationship (0.75) was obtained as the amount of cloudiness increases with the increase of latitude. The survey of the annual cloudiness map of the stations shows the highest amount of cloudiness is in the South, Southwest and East of Caspian Sea. The lowest amount of annual rainfall was in South and Southeast of Iran. The statistical analysis of annual cloudiness data in Iran showed that the amount of cloudiness in Iran is 27.5%. Examining the normal distribution of monthly and seasonal values indicates the non-normality of the data with the Kolmogorov-Smirnov test, but based on the Lilliefors and Anderson-Darling tests, the winter and spring seasons and the months of December, January, February, April and May had a normal distribution and the autumn and summer seasons and the months of June, July, August, September and October did not have normal distribution. Seasonal and monthly trend with linear regression method shows a decreasing trend in winter and spring seasons and cold months of the year. According to the Mann-Kendall method, there was a decreasing trend in the fall season and no significant trend was observed in the summer season.
Conclusion
The purpose of this research was to investigate the temporal and spatial changes of cloudiness in Iran. The results showed a decreasing trend in 47 stations and an increasing trend in only 3 stations and no significant trend was observed in other stations. Also, in monthly and seasonal scales results indicated a decreasing trend in all stations in the cold months of the year and winter, spring and autumn seasons. Examining the relationship between the spatial factors of longitude and latitude with the cloudiness variable using Pearson's correlation coefficient also indicates a negative correlation with longitude and a positive correlation with latitude, and this indicates a large spatial difference in the amount of cloudiness in the country. In general, it can be said that spatial factors (longitude and latitude) were internal factors in the spatial changes of clouds and climatic systems such as Siberian high pressure, sub-tropical high pressure, westerlies system and moisture from the seas of Oman, India and the Persian Gulf and sometimes the Red Sea as external factors were in the temporal changes of clouds. So, cloudiness was a variable that was directly related to other climate variables. Thus, cloud cover was a variable that was directly related to other climatic variables, and its decrease or increase causes the values of elements such as temperature, precipitation, and humidity to change. Therefore, studying this important climate variable and investigating its changes is very important and especially in the discussions of droughts and water crises, it has a special place.
Mohammad Nazeri Tahroudi; Hossein Khozeymeh Nejad
Abstract
Introduction: Despite our scientific development and awareness of the consequences of regional and global climate change little attention has been paid to the effects of the changes in the Middle East and Central Asia yet. In the Middle East, climate change is a big challenge, especially if successive ...
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Introduction: Despite our scientific development and awareness of the consequences of regional and global climate change little attention has been paid to the effects of the changes in the Middle East and Central Asia yet. In the Middle East, climate change is a big challenge, especially if successive droughts and persistent increase as well as growing demand for water and water shortages attention, the challenge take on a large scale. Iran is a vast country with a different climate Rainfall distribution. Also precipitation is influenced by air mass entering the country from the neighboring countries, so rainfall in different regions of Iran is heavily influenced by the situation in neighboring countries. The aim of this study is evaluation of the trend of annual and monthly precipitations of the South-West of Asia with modified Mann-Kendall test by considering the effect of autocorrelation.
Materials and Methods: In this study monthly and annual precipitation data of 4152 rain gauge stations in Iran and its 15 neighboring in a period of 1970-2014 was used and been downscaled to evaluate the trend of monthly and annual precipitations. In this study the monthly and annual precipitations time series of Afghanistan, Azerbaijan, India, Iraq, Kuwait, Oman, Pakistan, Saudi Arabia, Syria, Tajikistan, Turkey, Turkmenistan, Qatar, Yemen and Iran were used. The purpose of the trend test is to specify the presence or absence of ascending or descending order in the data series. Since there are assumptions in the parametric methods such as the normality, stationary and independent variables and this assumption is often not valid for hydrological variables, the nonparametric Mann-Kendall method that is applicable to the hydrological and meteorological studies can be used.
Results and Discussion: The results of evaluating the trend of annual precipitation of study stations in the period of 1971-2014 using the Mann-Kendall modified by omitting the effect of autocorrelation indicated that all of the regions of Iran has decreasing trend in annual precipitations and there are significant decreasing trend in the western regions of Iran and western areas of Caspian sea, some central and eastern regions of Iran in five percentage significantly. The rest of the decreasing trend in annual rainfall amounts included in the country has experienced. In annual terms in countries, that border the study area is faced with an increasing trend in annual rainfall amounts so that the country at the center of the crisis (lack of rain) is located. The southern part of India, southwestern Saudi Arabia, the northern region of Turkmenistan and the eastern regions of Afghanistan and Pakistan with the increasing trend in annual rainfall amounts over the 1970-2014 statistical has faced. The trend of monthly rainfall amounts for the month of January (second month) showed that the amount of rainfall during the month trend of central and eastern regions of the study area is decreasing. In February (second month of the year) rainfall conditions in the study area as well as in the country in terms of changes time has improved and areas of Iran is faced with increasing precipitation. Changes decreasing the amount of monthly precipitation in March moved to the West study area and focus a significant decline in rainfall in the western regions of Iraq and Syria and Iran. However, in May (fifth month) most regions of Iran, Turkmenistan, northwestern Turkey and the West areas of India has been facing a decreasing trend in rainfall amounts. Other areas showed an increase in precipitation. In July (the seventh month), India (regions Northeast and East), Pakistan, Qatar, Saudi Arabia, the South East of United Arab Emirates has significant decreasing trend in rainfall amounts. Focus of decreasing monthly precipitation for the August moved to India and much of the country is included. Unlike other months of the study, in the eighth month (September) process to reduce the amount of monthly precipitation moved to south western parts of the study area (South West Asian countries) and Saudi Arabia in this month is central of decreasing.
Conclusion: The results of the annual trend of precipitation in Iran indicated that in an annual scale the North West of Iran is faced with the significant decrease trend in rainfall. The annual rainfall across eastern and northern part Iran also has significant decreasing trend and Central regions had a decreasing trend of precipitation in the period of studied. Iranian medium-scale review of the annual and monthly precipitation showed that the annual precipitation is reduced about 1.06 mm per year that the average amount of it’s in the study area (South-West of Asia) equal to the reduction of 0.33 mm per year which represents more than three times decreasing precipitation of Iran's regional in a year as South West Asia. Also the results of evaluating the slope of trend line in different months indicated that in December, March, January, the Iran’s precipitations is most decreasing as average of annual precipitation in studiing regions about 5, 3 and 5 times respectively
Keyvan Khalili; Mohammad Nazeri Tahrudi; Rasoul Mirabbasi Najaf Abadi; Farshad Ahmadi
Abstract
Introduction: Climate change in the current era is a very important environmental challenge. Our understanding of the impacts of human activities on the environment, especially those related to global warming caused by increased greenhouse gases indicates that, most probably, a number of hydro-climatic ...
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Introduction: Climate change in the current era is a very important environmental challenge. Our understanding of the impacts of human activities on the environment, especially those related to global warming caused by increased greenhouse gases indicates that, most probably, a number of hydro-climatic parameters are changing. Based on the scientific reports, the average temperature of the earth has increased about 0.6 degrees centigrade over the 20th century and it is expected that the amount of evaporation continues to rise. In this case, the atmosphere would be able to transport larger amounts of water vapor, influencing the amount of atmospheric precipitations (21). Low precipitation and its severe fluctuations in the daily, seasonal and annual time scales are the intrinsic characteristics of Iran’s climates. Based on the research background, it seems that no comprehensive study has been conducted on concentration of winter precipitation in Iran. The aim of this study is to calculate the concentration and Trend of precipitation of Iranian border stations over the last half-century.
Materials and Methods: Iran with an area of over16480000 square kilometers is situated in the northern hemisphere and southwest of Asia. Almost all parts of Iran have four seasons. In general, a year can be divided into two warm and cold seasons. In this study, 18 stations were selected among more than 200 synoptic stations existing in the country, for investigating the concentration and precipitation trend.
PCI Index The PCI index has been proposed as an index of precipitation concentration. The seasonal scales of this index are calculated as equation 1(18):
(1)
Where Pi is the amount of monthly precipitation in the ith month. Based on the proposed formula, the minimum value of theoretical PCI is 8.3, indicating absolute uniformity in the precipitation concentration (i.e. the same amount of precipitation occurs every month).
Trend analysis The aim of process test is to specify whether an ascending or a descending trend exists in data series. Since parametric tests have some assumptions including normality, stability, and independence of variables, where most of these assumptions do not apply to hydrologic variables, the nonparametric methods are more preferred in meteorological and hydrological studies.
Results and Discussion: The PCI index was calculated using the monthly precipitation of the selected stations at seasonal and winter time scales over a 50-year period. This period was then divided into two 25-year sub-periods for the investigation of changes in average values of PCI (7). In the first 25-year span, the irregular precipitation distribution has been observed in the Bandarabbas station and its surroundings in winter season. In none of the studied stations, highly irregular precipitation occurred. The highest share of PCI was relatedto the precipitation average distribution class, and the northern, northwestern, and northeastern parts of the country have a uniform precipitation distribution. In winter, within the first 25-year period, the country had ideal conditions in terms of precipitation and its concentration in the mentioned regions. Within the second 25-year period, the intensity of irregular precipitation concentration decreased, as the regions that had confronted strong precipitation irregularities wereadded to regions with uniform concentration. At the seasonal scale and in winter, the country’s share of uniform distribution diminished in the second 25 years, and overall most parts of Iran have been covered by average precipitation distribution. The uniform precipitation distribution in recent years (second 25 years) has decreased in winter in such a way that no uniform distribution has been observed in the northeast of the country and uniform distribution belongedto the Caspian sea border strip, southern regions of west and east Azerbaijan stations (Urmia, Khoy and Tabriz stations) along with Kermanshah, Sanandaj, and Zanjan stations.
Trend analysis of the PCI In winter the Abadan, Ahwaz, Bandarabbas, Birjand, Kermanshah, Sanandaj, Urmia and Zahedan stations experienced an insignificant decreasing trend in PCI. At other stations, an insignificant increasing trend was observed in the PCI series. Overall, 9 out of 18 considered stations, witnessed increasing PCI trend implying increased irregularities in winter precipitation.
The results of Mann-Kendall trend test for precipitation Based on the results it can be observed that in winter Ahwaz, Gorgan, Khoramabad, Kermanshah, Ramsar, Rasht and Sanandaj experienced an insignificant decreasing trend in precipitation. In Khoy, Sanandaj, Tabriz, Urmia, Zahedan, and Zanjan stations, the decreasing precipitation trend in winter was significant. Overall, 12 out of 18 studied stations have been afflicted with a decreasing precipitation trend in winter.
Conclusion: Precipitation Concentration Index (PCI) is an index for determining the precipitation variations in a certain region and PCI analysis can reveal the accessibility to water in an environment. In this study, the PCI was used to analyze the precipitation concentration at two annual and seasonal time scales throughout the Iran (from 1961 to 2010). The PCI zoning results at the seasonal scale demonstrated that precipitation concentration had the same trend within the two 25-year sub-periods. These results also revealed a high PCI in provinces with low precipitation such as Zahedan. These stations, according to Oliver (18) classification, have irregular and sporadic precipitation duringwinter. Overall, the PCI analysis at the seasonal scale indicated that the regions covered by polar-continental, Europe-originated polar-continental and North Atlantic ocean-originated polar-continental have the best precipitation concentration throughout the country. The results of this index provided valuable information for water resources managers in regions with low-precipitation, consistent with research by Gozzini et al (7). The results of modified Mann-Kendall (MMK) test for PCI in Iran revealed a decreasing trend over the last 50 years. Based on the obtained results in winter, the Khoy, Sanandaj, Tabriz, Urmia, Zahedan, and Zanjan stations experienced a significant decreasing trend. The existence of an increasing trend in PCI albeit insignificant reveals changes in Iran's winter precipitations confirmed by Mann-Kendall test for precipitations in 18 studied stations. Overall, it can be concluded that the decreasing trend in Iran's winter precipitation has resulted in increasing PCI and thereby increased irregularities in winter precipitations, especially in winter season.
Majid Montaseri; Babak Amirataee; Keyvan Khalili
Abstract
Introduction: Droughts are natural extreme phenomena, which frequently occur around the world. This phenomenon can occur in any region, but its effects will be more severe in arid and semi-arid regions. Several studies have highlighted the increasing of droughts trend around the world. The majority of ...
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Introduction: Droughts are natural extreme phenomena, which frequently occur around the world. This phenomenon can occur in any region, but its effects will be more severe in arid and semi-arid regions. Several studies have highlighted the increasing of droughts trend around the world. The majority of studies in assessing the trend of time series are based on basic Mann-Kendall or Spearman's methods and no serious attention has been paid to the impact of autocorrelation coefficient on time series. However, limited numbers of studies have included the lag-1 autocorrelation coefficient and its impacts on the time series trend. The aim of this study was to investigate the trend of dry and wet periods in northwest of Iran using Mann-Kendall trend test with removing all significant autocorrelations coefficients based on SPI and RAI drought indices.
Materials and Methods: Study area has a region of 334,000 square kilometers, with wet, arid and semiarid climate, located in the northwest of Iran. The rainfall data were collected from 39 synoptic stations with average rainfall of 146 mm as the minimum of Gom station, and the highest annual rainfall of 1687 mm, in the Bandaranzali station. In this study, Standardized Precipitation Index (SPI) and Rainfall Anomaly Index (RAI) were used for trend analysis of dry and wet periods. SPI was developed by McKee et al. in 1993 to determine and monitor droughts. This index is able to determine the wet and dry situations for a specific time scale for each location using rainfall data. RAI index was developed by Van Rooy in 1965 to calculate the deviation of rainfall from the normal amount of rainfall and it evaluates monthly or annual rainfall on a linear scale resulting from a data series. Then, correlation coefficients of time series of these drought indices with different lags were determined for check the dependence or independence of the SPI and RAI values. Finally, based on dependence or independence of the time series values, trend analysis of wet and dry periods was conducted in different stations using one of the basic or modified Mann-Kendall tests. Also, the magnitude of the trends was derived from the Theil- Sen’s slope estimator.
Results and Discussion: Time series of SPI and RAI drought indices for a given annual rainfall as an example for three stations of Marivan, Gom and Maku show that during 1991 to 1994 and from 2002 to 2007 are in wet period and during 1987 to 1990 and 1998 to 2001 are in the dry period. It is clearly show that, dry and wet periods in RAI index are more severe than SPI. Comparison the correlation between Lag-1 autocorrelation coefficients values of SPI and RAI time series and Lag-1 autocorrelation coefficients of annual rainfall data indicate that these correlations are high and about 0.97 and 0.99, respectively. This difference is due to the different classification of SPI and RAI drought indices. The results of trend analysis indicate a decreasing trend in most of stations. Also, Mann-Kendall statistic has been declining while eliminating the effect of all significant correlation coefficients of dry and wet periods. This result in both SPI and RAI indices are similar and have a high correlation with R = 0.99. According to results, west of the study area have a significant decreasing (negative) trend. The spatial distribution of dry and wet periods showed that the difference between Mann-Kendall statistics of SPI and RAI indices is minimal. Also, The results show that, the slope of the trend line based on the SPI and RAI drought indices is negative in most of stations and correlation between these two indices in determining the slope of the trend line is high. But, this correlation compared with the trend statistics of SPI and RAI time series is less.
Conclusions: In this study, first the time series of SPI and RAI time series based on annual precipitation and common quantitative classification of mentioned two drought indices were determined. Then, trends of dry and wet periods of selected stations in northwest of Iran were evaluated based on these indices using the Mann-Kendall trend test with removing all significant autocorrelation coefficients. The results from this study indicate that using Mann-Kendall test with removing all significant autocorrelation coefficients effects are essential in assessing trend in time series. Although, according to various studies available in the literature, SPI is known as more accurate than RAI in drought mitigation, but according the results of this study, can solely be used both RAI and SPI index for trend detection.
M. Moravej; K. Khalili; J. Behmanesh
Abstract
Introduction: Studying the hydrological cycle, especially in large scales such as water catchments, is difficult and complicated despite the fact that the numbers of hydrological components are limited. This complexity rises from complex interactions between hydrological components and environment. Recognition, ...
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Introduction: Studying the hydrological cycle, especially in large scales such as water catchments, is difficult and complicated despite the fact that the numbers of hydrological components are limited. This complexity rises from complex interactions between hydrological components and environment. Recognition, determination and modeling of all interactive processes are needed to address this issue, but it's not feasible for dealing with practical engineering problems. So, it is more convenient to consider hydrological components as stochastic phenomenon, and use stochastic models for modeling them. Stochastic simulation of time series models related to water resources, particularly hydrologic time series, have been widely used in recent decades in order to solve issues pertaining planning and management of water resource systems. In this study time series models fitted to the precipitation, evaporation and stream flow series separately and the relationships between stream flow and precipitation processes are investigated. In fact, the three mentioned processes should be modeled in parallel to each other in order to acquire a comprehensive vision of hydrological conditions in the region. Moreover, the relationship between the hydrologic processes has been mostly studied with respect to their trends. It is desirable to investigate the relationship between trends of hydrological processes and climate change, while the relationship of the models has not been taken into consideration. The main objective of this study is to investigate the relationship between hydrological processes and their effects on each other and the selected models.
Material and Method: In the current study, the four sub-basins of Lake Urmia Basin namely Zolachay (A), Nazloochay (B), Shahrchay (C) and Barandoozchay (D) were considered. Precipitation, evaporation and stream flow time series were modeled by linear time series. Fundamental assumptions of time series analysis namely normalization and stationarity were considered. Skewness test applied to evaluate normalization of evaporation, precipitation and stream flow time series and logarithmic transformation function executed for in order to improve normalization. Stationarity of studied time series were evaluated by well-known powerful ADF and KPSS stationarity tests. Time series model's order was determined using modified AICC test and the portmanteau goodness of fit test was used to evaluate the adequacy of developed linear time series models. Man-Kendall trend analysis was also conducted for the precipitation amount, the number of rainy days, the maximum precipitation with 24 hours duration, the evaporation and stream flow in monthly and annual time scales.
Results and Discussion: Inferring to the physical base of ARMA models provided by Salas et al (1998), the precipitation has been considered independently and stochastically. If this assumption is not true in a given basin, it is expected that the MA component of stream flow discharge model be eliminated or washed out. This case occurred in basins A, B and C. In these basins, the behavior of precipitation and evaporation was autoregressive. It was observed that the stream flow discharge behavior also follows autoregressive models that had greater lags than precipitation and evaporation lags. This result proved that the precipitation, evaporation, and stream flow processes in the basin were regular processes. In basin D, the behavior of precipitation was stochastic and followed the MA model, which was related to the stochastic processes. In this basin, the stochastic behavior of precipitation affected the stream flow behavior, and it was observed that the stochastic term of MA also appeared in the stream flow. Thus, this leads to decrease the memory of stream flow discharge. The fact that the MA component in the stream flow discharge was greater than the MA component in precipitation indicated that during the process of producing stream flow discharge from precipitation, the stochastic factors performed an important role.
Conclusion: A comprehensive investigation on hydrological time series models of precipitation, evaporation and stream flow were investigated in this study. The framework of the study consists of trend analysis using Mann-Kendall test and time series. Trend analysis results indicate the significant changes of water resources in the studied area. It means that sustainable development in studied area is greatly threatened. The results of parallel modeling of precipitation, evaporation and stream flow time series showed that the behavior of stream flow models are greatly affected by precipitation models. In other words, this study evaluate the physical concept of ARMA models in real-world monthly time scale for three main hydrologic cycle components and suggest that considering parallel hydrological time series modeling could increase the accuracy to select a model for simulation and prediction of stream flow time series. In addition, it suggested that there is a relation between climate pattern and hydrological time series models.
Keywords: ARMA models, Stationarity, Trend analysis, Water cycle components
A.R. Araghi; M. Mousavi Baygi; majid hashemi nia
Abstract
Introduction: Studying long-term trend changes of meteorological parameters is one of the routine methods in atmospheric studies, especially in the climate change subject. Among the meteorological parameters, temperature is always considered as one of the most atmospheric elements and studying it in ...
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Introduction: Studying long-term trend changes of meteorological parameters is one of the routine methods in atmospheric studies, especially in the climate change subject. Among the meteorological parameters, temperature is always considered as one of the most atmospheric elements and studying it in order to gain a better understanding of the climate change phenomenon, has been effective. In addition to identifying trends, extraction of oscillatory patterns in the atmospheric phenomena and parameters occurrence can be an applicable and reliable method to explore the complex relations between atmospheric-oceanic cycles and short term or long term consequences of meteorological parameters.
Materials and Methods: In this paper, monthly average temperature time series in Mashhad synoptic station in 55 years period (from 1956 to 2010) in monthly, seasonal, annual and seasons separately (winter, spring, summer and autumn) have been analyzed. Discrete wavelet transform and Mann-Kendall trend test were the main methods for performing this research. Wavelet transform is a powerful method in signal processing and it is an advanced version of short time Fourier transforms. Moreover, it has many improvements and more capabilities compared with Fourier transform. In the first step, temperature time series in various time scales (which was mentioned above) have been decomposed via discrete wavelet transforms into approximation (A) and detail (D) components. For the second step, Mann-Kendall trend test was applied to the various combinations of these decomposed components. For detecting the most dominant periodic component for each of the time scales datasets, results of Mann-Kendall test for the original time series and the decomposed components were compared to each other. The nearest value indicated the most dominant periodicity based on the D component’s level. To detect the similarity between results of the Mann-Kendall test, relative error method was employed. Additionally, it must be noted that before applying Mann-Kendall test, time series has to be assessed for its autocorrelation status. If there are seasonality patterns in the studied time series or lag-1 autocorrelation coefficient of data is significant, then some modified versions of the Mann-Kendall test have to be employed.
Results and Discussion: Results of this study showed that the temperature trend at every time scaled dataset (monthly, seasonal, annual and seasons separately) is positive and significant. Autocorrelation coefficients indicated that only seasonal time series and winter datasets did not have significant ACFs. On the other hand, monthly and seasonal datasets had seasonality pattern. Based on these results, Hirsch and Slack’s modified version of Mann-Kendall test was employed for monthly and seasonal time series and for the winter temperature data, the original version of the Mann-Kendall test was applied. For the remaining time series, the Hamed and Rao’s modified version of the Mann-Kendall trend test was employed. Dominant periodicities in monthly, seasonal and annual, confirmed the oscillatory behavior of each other. However, in the seasons, it seems that periodic patterns with the same temperature ranges are more similar. On the other hand, due to the greater similarity between the results of the Mann-Kendall test in the warmer seasons and the data with monthly, seasonal and annual time scale, it seems that yearly warm period has more noticeable impacts on the positive and significant trend of temperature in the study area. It must be noted that in any of the studied time series, results of the Mann-Kendall test for detail (D) component was not significant and after adding approximation (A) component, Mann-Kendall statistics turned to a significant value. This happens because the long term variations or trends appear in approximation components in most of the time series.
Conclusion: In this study, a powerful signal processing method called wavelet transform was employed to detect the most dominant periodic components in temperature time series in various time scales, in Mashhad synoptic station. Results showed that using frequency-time analysis methods has more benefits compared with the use of only classic statistical methods, since one can explore any time series with more accuracy. Because most of the meteorological variables have periodic structures, it seems that using advanced signal processing methods like wavelet for analysis of these variables can have many advantages compared with linear-based methods. It can be suggested for future studies to use and employ signal processing methods for exploring the large scaled phenomena (e.g. ENSO, NAO, etc.) and discovering the relationship between these phenomena and climate change in recent decades.
Keywords: Discrete wavelet transforms, Mann-Kendall test, Oscillatory pattern, Trend
F. Ahmadi; F. Radmanesh
Abstract
The temperature is one of the essential elements in formation of climate and its changes can alter the climate of each region, Therefore study of temperature changes at different spatial and temporal scales is devoted a large part of research to climatology. The mean temperature changes of the northern ...
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The temperature is one of the essential elements in formation of climate and its changes can alter the climate of each region, Therefore study of temperature changes at different spatial and temporal scales is devoted a large part of research to climatology. The mean temperature changes of the northern half area of Iran (18 Synoptic stations) in monthly or annual scales (1961-2010) are tested with using non-parametric Mann-Kendall test and elimination of all auto-correlation coefficients. To determine the slope of temperature gradient, the Sen’s slope estimation method was used. The results showed that 61% of the stations have experienced a significant increase in annual scale, in expect of Urmia, Zanjan, Qazvin and Gorgan stations. Arak is also a significant decrease, Torbate Heydarie and Saghez have experienced non-significant negative trend in annual scale. In monthly scale, number of months with increasing trend was greater than decreasing trend. April, September and October have significant increasing trend in most stations. December has lowest changing in compare with others. In conclusion, the studied temperature area in past half century 1.15 C is increased
B. Ghahraman
Abstract
Fractional Gaussian noise (fGn) is an important and widely used self-similar process, which is mainly parametrized by its Hurst exponent (H) to specify its long-term persistence (LTP). Many researchers have proposed methods for estimating the Hurst exponent of fGn. But there is only a few researches ...
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Fractional Gaussian noise (fGn) is an important and widely used self-similar process, which is mainly parametrized by its Hurst exponent (H) to specify its long-term persistence (LTP). Many researchers have proposed methods for estimating the Hurst exponent of fGn. But there is only a few researches that has compared different methods for different time series covering different length of records. In this paper, we have compared the performance of 7 different methods covering rescaled range (R/S), 3 different approaches of aggregated standard deviation method (ASD[0], ASD[rec], ASD[opt]), variance method (VAR), and 2 approaches of autocorrelation method ([1] and [2]). Seven different time series including Mashhad annual temperature (127 and 66 years), yearly minimal water levels at the Nile River (660 years), two global phenomena of North Atlantic Oscillation (NAO) (62 years) and two Pacific Decadal Oscillation (PDO) series (112 and 331 years), and concentration of atmospheric CO2 measured at Mauna Loa, Hawaii (55 years) were considered. The results showed that NAO and CO2 series do not have LTP (H
F. Fathian; S. Morid; S. Arshad
Abstract
The drawdown trend of the water level in Urmia Lake poses a serious problem for northwestern Iran that will have a negative impact on the agriculture and industry. This research investigated the possible causes of this adversity by estimating trends in the time series of hydro-climatic variables of the ...
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The drawdown trend of the water level in Urmia Lake poses a serious problem for northwestern Iran that will have a negative impact on the agriculture and industry. This research investigated the possible causes of this adversity by estimating trends in the time series of hydro-climatic variables of the basin as well as tracking changes in the land use of the study area, using satellite images. Four non-parametric statistical tests, the Mann-Kendall, Theil-Sen, Spearman Rho and Sen's T test, were applied to estimate the trends in the annual time series of streamflow, precipitation and temperature at 18 stations throughout the case study. Furthermore, by using the LANDSAT satellite images of 1976, 1989, 2002 and 2011, land use classification was determined using maximum likelihood, minimum distance and mahalanobis distance methods. The results showed significant increasing temperature trend throughout the region and an area-specific precipitation trend. The trend tests also confirmed a general decreasing trend in region streamflows that was more pronounced in the downstream stations. The results showed that the classification by the maximum likelihood method wass associated with minimum error. The results of processing the images showed that the irrigated crops, horticultural and dry lands have increased during last 35 years by 412, 485 and 672 percent, respectively. But, the pasture area is decreased by 34 percent. Finally, correlation between streamflow changes with simultaneous changes in climatic variables and land use showed it is significant in case of temperature and land use; and insignificant in case of precipitation. However, the determination coefficient of land use is higher than temperature.
H. Asakereh; R. Khoshraftar; F. Sotudeh
Abstract
Rainfall and debit of rivers are two tempo-spatial non-linear and changeable factors. One way to study and analysis these parameters is investigate appearance and latent oscillations. Spectral Analysis is a useful technique to reveal these oscillations in time series. In this paper it has been attempted ...
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Rainfall and debit of rivers are two tempo-spatial non-linear and changeable factors. One way to study and analysis these parameters is investigate appearance and latent oscillations. Spectral Analysis is a useful technique to reveal these oscillations in time series. In this paper it has been attempted to detect cycles in rainfall and debit time series at Mashinkhaneh station in Talesh (Garakanrood) catchment’s during Mehr 1354 to Shahrivar 1386 in the three time scales (annual, seasonal and monthly). Accordingly, the discharge and precipitation data at Mashinkhaneh station in Talesh (Garakanrood) catchment from Mehr 1354 to Shahrivar 1386 have been used. The results of applying the spectral analysis procedures to discharge and rainfall time series in each three category of time scales, suggested the absence of significant non-sinusoidal (trend) in the 95% confidence level. However, significantly sinusoidal cycles various in the two time series were extracted. The 2-4 year cycle, and 4-5.3 years have the most occurrences in the both time series. In the annual scale, 6.4 years cycle, 2-5.3 years, 7.7 years seasonal and 2-4, 4- 5.3, 6.4, 8, 10.7 and 16 year in the monthly scale cycles has been extracted. Studies carried out by many researchers indicate that the mentioned cycles are in relation with oscillation periods of ENSO, NAO and QBO in other parts of the world.
