Mehran Zand; Morteza Miri; Mohammadreza Kousari
Abstract
Climate change can lead to changes in the frequency, intensity, and duration of extreme climate events in different parts of the world. The purpose of this research is to investigate temperature and precipitation extremes in Lorestan Province. The data used in this study included precipitation and the ...
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Climate change can lead to changes in the frequency, intensity, and duration of extreme climate events in different parts of the world. The purpose of this research is to investigate temperature and precipitation extremes in Lorestan Province. The data used in this study included precipitation and the maximum and minimum daily temperature of nine synoptic stations in Lorestan Province during a 28-year (1990-2017) common period. The matrices of minimum and maximum temperatures and precipitation daily data for each station were prepared and used to compute the extreme climate indices (26 precipitation and temperature extreme indices based on the recommendation of CLIVAR \ CLL expert group) using the R programming software. The results of studying the trend of cold and hot extreme weather indicators during the period 1990-2017 in the province using the Mann-Kendall trend test showed that for all stations, the hot indices have increased and more cold indices have decreased. In different regions of the province, positive and negative trends of hot and cold indices with different intensities have occurred. The highest upward trend of the warm extreme indices has occurred for the hot night’s index. Among the cold indicators, the greatest decrease occurred for several frost days and cold days indices. The decreasing trend of ice days is significant for 45% of stations at 99% level and the decreasing trend of cold days for 77% of stations at different levels of 90, 95, and 99%. The results of the study of the frequency of occurrence and trend of precipitation extreme indicators in Lorestan province showed that the total rainfall of this province, like many regions of the country, has decreased. In contrast, the occurrence of maximum rainfall in addition to being significant in the province has an increasing trend during the period 2017-1990. These conditions can indicate an increase in the number of occurrences of heavy and short-term rainfall events and, shorten the period of the rainfall season in the region.
Morteza Miri; Mohammadreza Kousari; Mehran Zand
Abstract
One of the most common and effective problems in long-term climate studies is the presence of gaps in the time series of various climatic and hydrological data. Therefore, the present study evaluates the accuracy of methods for infilling missing data of daily, monthly and annual temperature time series ...
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One of the most common and effective problems in long-term climate studies is the presence of gaps in the time series of various climatic and hydrological data. Therefore, the present study evaluates the accuracy of methods for infilling missing data of daily, monthly and annual temperature time series in the arid regions of Iran. For this purpose, the observed daily minimum, average and maximum temperature data for the period 1987-2014 measured at 73 synoptic stations distributed all over arid regions of Iran were used. Methods of readjustment used include: Normal ratio method, linear regression, multivariate regression and Inverse Distance Weighting (IDW). In this study, the capability of each mentioned methods for infilling missing data of daily, monthly and annual precipitation time series in the arid regions of the Iran was investigated, while the proportion of missing data varies from 5 to 50% of total data. In order to compare and evaluate the accuracy of the four mentioned methods three statistical indicators, namely the correlation coefficient (R), the Root Mean Square Error (RMSE) and Nash coefficient were used. The results showed that in general, each of the methods mentioned had different functionalities at a special level of readjustment and time scale. On annual and monthly scales, linear regression and normal ratio methods are the most accurate method in readjustment temperature data in the arid region of Iran. The correlation value between the readjustment and observational data at different levels reaches more than 0.95 using these methods. On the daily scale, there is no significant difference between the accuracy of the methods used in the readjustment of temperature data, and almost all four of these methods have appropriate accuracy because in all methods the correlation between readjustment and observed data is more than 90%. However, multivariate regression methods with an average correlation of 0.99 showed the most accurate performance in readjustment daily data at different levels of readjustment. Generally, each method should be used in accordance with the conditions, and therefore it is recommended to develop a software package for infilling missing data.
