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Showing 4 results for Mann-Kendal
Dr Hamid Ghorbani, Dr Abbas Ali Vali, Mr Hadi Zarepour,
Volume 6, Issue 2 (9-2019)
Volume 6, Issue 2 (9-2019)
Abstract
Drought is one of the most complex and unknown natural phenomena that causes a periodic water crisis in the affected areas. Increasing water demand on the one hand and the experience of droughts in the province in recent years have led to the water crisis. Knowing the drought is one of the requirements for water crisis management. The purpose of this study was to analyze the trend of the SPI drought index in Isfahan province using nonparametric Sen’s slope test, Pettitt’s change point test and Man-Kendall test. From the monthly climatic data of 10 synoptic stations with a length of 27 years (1990-2017) for time series The results of applying Mann–Kendall and Sen’s slope tests based on SPI Index for 9, 12, 18, 24 and 48 month time periods, shows drought trend is significantly increasing for all stations out of Ardestan, Esfahan and Shahreza stations. In Ardestan station, the drought trend is significantly decreasing for 9, 12, 18, 24 and 48 month time periods and in Isahan station, the drought trend is significantly decreasing for only 48 month time period, and in Shahreza statition, the drought trend is significantly increasingonly for only 18 month time period.
Despite all stations, the drought trend for one month time period, is significantly increasing just for Naein station.
In addition, applying Mann–Kendall test on monthly rainfall for all station shows downward but not significant trend.
Finally, applying Pettitt’s change point test based on SPI Index for 9, 12, 18, 24 and 48 month time periods indicates the existence of a significant change point. For same periods we observe no change point for the monthly rainfall in all stations.
In summation, considering the SPI drought index, about 59% of all stations show significant downward trend bases on Mann-Kendall test and 60% of all stations show significant slope based on Sen's slope test and 75% of all stations show significant change point based on Pettitt's test. In general, for drought analysis using different time periods for the SPI index, in a short time period. (such as 6 months) drought is more frequent but shorter, and as the period increases the duration of drought also increases but frequency decreases. All together, we are facing a water crisis in Isfahan province and we must manage water demand very urgently.
Roya Poorkarim, Hossein Asakereh, Abdollah Faraji, Mahmood Khosravi,
Volume 9, Issue 4 (3-2023)
Volume 9, Issue 4 (3-2023)
Abstract
In the present study, the data of the ECMWF for a period of 1979 to 2018 was adopted to analyze the long term changes (trends) of the number of cyclones centers of the Mediterranean Sea.There are many methods (e.g. parametric and non- parametric) for examining changes and trends in a given dataset. The linear regression method is of parametric category and the most common nonparametric method is Mann-Kendall test. By fitting the Mann-kendall model and the linear regression model, the frequency of the cyclone centers of the Mediterranean basin was evaluated in seasonal and annual time scales. Analyzing the trend of changes of the number of cyclone centers on a seasonal scale showed that the five-day duration have had a significant trend in spring, autumn and summer. Whilest on an annual scale, there was no significant trend in any of the duration. By fitting the regression model on seasonal and annual scale, one- and two-day duration have a positive regression line slop.
Seddigheh Farhood, Asadollah Khoorani, Abbas Eftekharian,
Volume 10, Issue 2 (9-2023)
Volume 10, Issue 2 (9-2023)
Abstract
Introduction
In recent years, research on climate change has increased due to its economic and social importance and the damages of increasing extreme events. In most studies related to climate change, detecting potential trends in the long-term average of climate variables have been proposed, while studying the spatio-temporal variability of extreme events is also important. Expert Team on Climate Change Detection and Indices (ETCCDI) has proposed several climate indices for daily temperature and precipitation data in order to determine climate variability and changes based on R package.
Various methods have been presented to investigate changes and trends in precipitation and temperature time series, which are divided into two statistical categories, parametric and non-parametric. The most common non-parametric method is the Mann-Kendall trend test. One of the main issues of this research is the estimation of each index value in different return periods. The return period is the reverse of probability, and it is the number of years between the occurrence of two similar events (Kamri and Nouri, 2015). Accordingly, choosing the best probability distribution function is of particular importance in meteorology and hydrology.
Despite of the enormous previous studies, there is no comprehensive research on the estimation of extreme indices values for different return periods. Accordingly, this study focuses on two main goals: First, the changes in temperature and rainfall intensity are analyzed by analyzing the findings obtained from extreme climate indices (15 indices) and then (second) estimating the values of the indicators for three different return periods (50, 200 and 500 years).
Data and methods
In this research, the daily data of maximum, minimum and total annual precipitation of 49 synoptic stations for 1991-2020 were used to analyze 15 extreme indices of precipitation and temperature. Namely, FD, Number of frost days: Annual count of days when TN (daily minimum temperature) < 0oC; SU, Number of summer days: Annual count of days when TX (daily maximum temperature) > 25oC, ID, Number of icing days: Annual count of days when TX (daily maximum temperature) < 0oC; TXx, Monthly maximum value of daily maximum temperature; TNx, Monthly maximum value of daily minimum temperature; TXn, Monthly minimum value of daily maximum temperature; TNn, Monthly minimum value of daily minimum temperature; DTR, Daily temperature range: Monthly mean difference between TX and TN; Rx1day, Monthly maximum 1-day precipitation; Rx5day, Monthly maximum consecutive 5-day precipitation; SDII Simple precipitation intensity index; R10mm Annual count of days when PRCP≥ 10mm; R20mm Annual count of days when PRCP≥ 20mm; CDD. Maximum length of dry spell, maximum number of consecutive days with RR < 1mm; CWD. Maximum length of wet spell, maximum number of consecutive days with RR ≥ 1mm. Finally, the trends of indices were estimated using the non-parametric Mann-Kendall test and the values of these indicators were estimated for 50, 200 and 500 years return periods.
In order to calculate values of each indicator for a given return period, the annual time series and its probability of occurrence are estimated and the most appropriate statistical distribution function that can be fitted on the data is selected from among twelve functions. In this estimation, EASY-FIT (a hydrology software), which supports a higher range of distribution functions, is used. The intended significance level for 500, 200 and 50 years return periods were 0.998, 0.995 and 0.98, respectively. The functions used in this research include: Lognormal (3P), Lognormal, Normal, Log-Pearson 3, Gamma (3P), Gumbel, Pearson 5 (3P), Log-Gamma, Inv. Gaussian, Pearson 6 (4P), Pearson 6, Gamma. Kolmogorov–Smirnov test is used to assess the goodness of fit of the estimation from three return periods.
Results
The results indicate that while the trend of precipitation indices except for the Maximum length of dry spell (CDD) is decreasing, the trend of temperature indices was increasing, except for two indices of the days with daily maximum and minimum temperatures below zero degrees. From a spatial perspective, hot indices in the northwestern regions, cold indices in the southern half of the country shows an increasing trend, and the Caspian Sea, Oman Sea, Persian Gulf coastal regions, and the Zagros foothills are the most affected areas as a result of the increasing trends. Also, the index values were estimated for 50, 200 and 500 years return periods. As a result of the investigations, for temperature indices the north-west of the country has the highest values by different return periods. The increase in the values of R10, R20, RX1day and RX5day indices in the different return periods was more in the Zagros and Alborz mountain ranges, and the CWD, CDD and SDII indices have the highest values in the Caspian Sea and Persian Gulf Coastal areas.
In recent years, research on climate change has increased due to its economic and social importance and the damages of increasing extreme events. In most studies related to climate change, detecting potential trends in the long-term average of climate variables have been proposed, while studying the spatio-temporal variability of extreme events is also important. Expert Team on Climate Change Detection and Indices (ETCCDI) has proposed several climate indices for daily temperature and precipitation data in order to determine climate variability and changes based on R package.
Various methods have been presented to investigate changes and trends in precipitation and temperature time series, which are divided into two statistical categories, parametric and non-parametric. The most common non-parametric method is the Mann-Kendall trend test. One of the main issues of this research is the estimation of each index value in different return periods. The return period is the reverse of probability, and it is the number of years between the occurrence of two similar events (Kamri and Nouri, 2015). Accordingly, choosing the best probability distribution function is of particular importance in meteorology and hydrology.
Despite of the enormous previous studies, there is no comprehensive research on the estimation of extreme indices values for different return periods. Accordingly, this study focuses on two main goals: First, the changes in temperature and rainfall intensity are analyzed by analyzing the findings obtained from extreme climate indices (15 indices) and then (second) estimating the values of the indicators for three different return periods (50, 200 and 500 years).
Data and methods
In this research, the daily data of maximum, minimum and total annual precipitation of 49 synoptic stations for 1991-2020 were used to analyze 15 extreme indices of precipitation and temperature. Namely, FD, Number of frost days: Annual count of days when TN (daily minimum temperature) < 0oC; SU, Number of summer days: Annual count of days when TX (daily maximum temperature) > 25oC, ID, Number of icing days: Annual count of days when TX (daily maximum temperature) < 0oC; TXx, Monthly maximum value of daily maximum temperature; TNx, Monthly maximum value of daily minimum temperature; TXn, Monthly minimum value of daily maximum temperature; TNn, Monthly minimum value of daily minimum temperature; DTR, Daily temperature range: Monthly mean difference between TX and TN; Rx1day, Monthly maximum 1-day precipitation; Rx5day, Monthly maximum consecutive 5-day precipitation; SDII Simple precipitation intensity index; R10mm Annual count of days when PRCP≥ 10mm; R20mm Annual count of days when PRCP≥ 20mm; CDD. Maximum length of dry spell, maximum number of consecutive days with RR < 1mm; CWD. Maximum length of wet spell, maximum number of consecutive days with RR ≥ 1mm. Finally, the trends of indices were estimated using the non-parametric Mann-Kendall test and the values of these indicators were estimated for 50, 200 and 500 years return periods.
In order to calculate values of each indicator for a given return period, the annual time series and its probability of occurrence are estimated and the most appropriate statistical distribution function that can be fitted on the data is selected from among twelve functions. In this estimation, EASY-FIT (a hydrology software), which supports a higher range of distribution functions, is used. The intended significance level for 500, 200 and 50 years return periods were 0.998, 0.995 and 0.98, respectively. The functions used in this research include: Lognormal (3P), Lognormal, Normal, Log-Pearson 3, Gamma (3P), Gumbel, Pearson 5 (3P), Log-Gamma, Inv. Gaussian, Pearson 6 (4P), Pearson 6, Gamma. Kolmogorov–Smirnov test is used to assess the goodness of fit of the estimation from three return periods.
Results
The results indicate that while the trend of precipitation indices except for the Maximum length of dry spell (CDD) is decreasing, the trend of temperature indices was increasing, except for two indices of the days with daily maximum and minimum temperatures below zero degrees. From a spatial perspective, hot indices in the northwestern regions, cold indices in the southern half of the country shows an increasing trend, and the Caspian Sea, Oman Sea, Persian Gulf coastal regions, and the Zagros foothills are the most affected areas as a result of the increasing trends. Also, the index values were estimated for 50, 200 and 500 years return periods. As a result of the investigations, for temperature indices the north-west of the country has the highest values by different return periods. The increase in the values of R10, R20, RX1day and RX5day indices in the different return periods was more in the Zagros and Alborz mountain ranges, and the CWD, CDD and SDII indices have the highest values in the Caspian Sea and Persian Gulf Coastal areas.
Dr Saeed Jahanbakhsh Asl, Dr Yagob Dinpashoh, Phd Student Asma Azadeh Garebagh,
Volume 11, Issue 2 (8-2024)
Volume 11, Issue 2 (8-2024)
Abstract
Evapotranspiration is one of the main elements of hydrologic cycle. Accurate determination of reference crop potential evapotranspiration (ET0) is crucial in efficient use of water in irrigation practices. ET0 can be measured directly by lysimeters or estimated indirectly by many different empirical methods. Direct measurement is cumbersome, needs for more time and costly. Therefore, many investigators used empirical methods instead of direct measurements to estimate ET0. Nowadays, the FAO-56 Penman Monteith (PMF56) method is known a bench mark for comparing the other empirical methods. For example, in the works of Zare Abyaneh et al. (2016), Biazar et al. (2019), Dinpashoh et al. (2021) and Dinpashoh et al. (2011) PMF56 method was used to estimate ET0 and comparing the outputs of other empirical methods. Many researchers analyzed trends in ET0 time series in different sites around the Earth. Among them it can be referred to the works of Sabziparvar et al. (2008), Babamiri & Dinpashoh (2015), Dinpashoh et al. (2021), Dinpashoh (2026) and Tabari et al. (2013). ET0 can be affected by many different climatic factors such as maximum air temperature (Tmax), minimum air temperature (Tmin), relative humidity (RH), wind speed, and actual sunshine hours. Factor analysis (FA) is a multivariate method that reduces data dimensionality. In general, climatic variables have high correlation with each other. On the other hand, these variables affect ET0. The FA can be used to reduce data dimensionality in which correlated variables converted to few uncorrelated factors.
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