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Fardin Saberi Louyeh, Bohlol Alijani, Shahriar Khaledi,
Volume 6, Issue 1 (5-2019)
Volume 6, Issue 1 (5-2019)
Abstract
. Caspian Sea south coast future climate change estimations through regional climate model
many physical of the procedures related to climate change are not perceived thoroughly. Scientific knowledge used to show those procedures completely, and to analyses forecasts is so complex, since most current studies about climate physical model have been done through semi experimental and random models and most of the current analysis techniques are still going through early stages. One of the important aspects of this study is modeling physical procedures of sea level rise geographical pattern, which is used practically for SLR threat evaluation of special geographical location, meaning Caspian basin. Since Caspian basin is a closed sea, it is heavily influenced by climate change and meanwhile is changing due to physical level and environmental change. It is necessary to define Caspian coast climate change possibility with specific focus on climatology and meteorology fine data, also to define the scale of sea level fluctuations for the sake of exact planning in different fields. This study aims at presenting a new dynamic method, via using an integrated model system named SIMCLIM, which can clarify SLR satellite changes well.
According to scientific examination existing in this study, based on scatter scenario 4.5 RCP and 8.5 RCP for the following years, until 2100, temperature and precipitation change proposal have been presented. On one hand, Caspian coastal climate change analysis and estimation were based on climate patterns and water flows in the form of regional climate statistical model in order to simulate and forecast, on the other hand surveying chronological changes of Caspian sea coast slope with satellite height measurement was done to measure sea surface height fluctuations The present study has used SIMCLIM model for the first time in order to clarify Caspian sea level changes, elements, and effective climate reasons, all simultaneously in one project. The project base is according to coastal systems and procedures. Coast line shore change simulations are based in Bruun law.
In future the frequency and intensity of extreme events temperature and precipitation will increase. Extreme events illustrate changes in extreme temperature and precipitation measures, in comparison with the base period of 1981-2010 which convey precipitation sum or the temperature beyond 95 percentile of base period. Temperature and precipitation coefficient of variation for the whole Caspian basin is positive and it varies from 25 to 88 percent. A disordered pattern is dominating south basin of the sea. Sea level changes, considering vertical earth movements, which is 2 mm in a year, resulted from subsidence of Caspian pit seabed have been obtained for both scenarios. In general, annual sea level average while ignoring seasonal changes, is increasing consistently and it was calculated 1.22 cm each year according to high estimation procedure in scenario 8.5 RCP and it was 0.93 cm based on scenario 4.5 RCP. Predicted results were compared with real results of base20-year period from 1995-2015. Base period results in three levels of sensitivity of low, mid, high shows 8.4, 10.1, and 11.8 cm rise; after comparing them with model forecast results, meaningful coordination at the level of 95 percent was found out. In both scenarios, all over the Caspian shoreline water advance and destruction will exist. In the worst case scenario of 8.5 RCP of 2030, current coast will decrease about 23 meters and in 2060 it will be about 53 and in 2100, there will be 117 meters advance towards land.
Precipitation and temperature percent for 2030, 2060, 2100 will change increasingly. Spatial variability and annul coefficient of variation are various in different regions. North, western north, eastern north and east will include the least temperature fluctuations, and the highest percent of precipitation with the highest coefficient of variation all convey chronological period precipitation distribution with disordered accumulation and more local difference in this region in comparison with other regions. Then Caucasus mountainous region will have the highest increase in precipitation with a suitable scatteredness, during a year. The southern part of Caspian Sea will be with the highest increase in temperature and the least amount of increase in precipitation in percent. High coefficient of variation in this area illustrates abnormal and disordered pattern on the threshold of precipitation for both scenarios.
fluctuations in sea level based on subsidence of Caspian pit seabed was calculated.In general, average annual sea level is increasing which will be 1.22 cm, per year for scenario RCP 8.5 and 0.93 cm for scenario 4.5. Due to incapability of world community in decreasing releasing greenhouse gases, it is expected scenario that 8.5 RCP to come to reality.
Caspian Sea shoreline is influenced by water advance and destruction. The difference between two scenarios in 2060 will be 3 meters and in 2100 will be 12 meters. Instinctually, such advances in coasts with less depth and less slope will be more. This study suggests that coastal changes are inevitable. However, this region inhabitant owns no systems or no systems have not yet developed to aid them be able to adopt with the climate changes.
Keywords: Sea level rise, South Caspian basin, Extreme event, Coefficient of variations, shoreline.
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.
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