Masoumeh Moghbel
Abstract
Soil depth temperature is one of the most effective factors on agricultural products. However, the it has highest missing data in synoptic weather stations. Hence, this research aims to evaluate soil depth temperature changes and determine the accuracy and applicability of ECMWF re-analysis data in estimation ...
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Soil depth temperature is one of the most effective factors on agricultural products. However, the it has highest missing data in synoptic weather stations. Hence, this research aims to evaluate soil depth temperature changes and determine the accuracy and applicability of ECMWF re-analysis data in estimation of SDT. To do so, soil depth temperature data for different depths (5,10, 20, 30, 50 and 100 cm) of Mehrabad, Geophysic, Shomal-e-Tehran and Doushan Tapeh stations were extracted in hourly time intervals (03, 09, 15 UTC) from Iran’s Meteorology Organization. Then, trend analysis was carried out by Mann-Kendall test. Also, gridded re-analysis SDT data of ERA5 were extracted from ECMWF from 1997 to 2018 statistical period. Re-analysis data were convert from Netcdf format to text using GIS. Then, their accuracy was analyzed by ME, MAE and RMSE tests. The climatic trend of soil depth temperature presents the general increase trend in all studied stations during the 1997-2018. Furthermore, the results showed close correlations between observational and re-analysis data at different depths. Re-analysis data could mainly reproduce the temporal-spatial distributions of soil depth temperature in study area. The correlation coefficient between observational and re-analysis data was 0.97 and 0.95 for first and second studied depths of the soil, respectively. It indicates a significant linear relationship between observational data and ERA5 re-analysis data in hourly time intervals. However, the ERA5 overestimates the SDT data in comparison with observational data
Mahsa Mirdashtvan; Ali Najafinejad; Amir Sadoddin
Abstract
Trend and stationarity analyses of hydrological variables are useful tools for understanding climate change and may provide useful information about likely changes in the future. As non-stationary of time series can occur due to various reasons such as trend existence in data; therefor, in current study ...
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Trend and stationarity analyses of hydrological variables are useful tools for understanding climate change and may provide useful information about likely changes in the future. As non-stationary of time series can occur due to various reasons such as trend existence in data; therefor, in current study the non-parametric Mann-Kendall trend test was used to detect the trend of the data. A corrector approach, namely “TFPW” was utilized to modify the effects of serial dependence of the time series on the trend detection results. The stationarity of time series was tested by unit root and stationarity tests to evaluate the relationship between trend and stationarity of the time series. The results showed that the surface flows of all of the studied rivers have a decreasing trend; although the significant trends changed to insignificant ones after applying TFPW approach. The results of the stationarity tests showed non-stationary time series for all of the sites after removing the serial dependence of the series, which is a sign of the lack of trend existence in the time series; however, Latian station (Lar river) reveals non-stationarity after applying TFPW which may be originated from the existence of abrupt changes in the series. The findings of current study can help the planners and policy-makers and water resources managers to cope with climate change in the future.
Shirin Mohammadkhan
Abstract
The phenomenon of dust storm causes a number of damages such as aggravation of heart or lung disease, air and land traffic. Occurrence of dust storm has been growing inrecent years and has created many problems in some cities of Iran. Dust storms of Iran arise either from internal or external sources. ...
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The phenomenon of dust storm causes a number of damages such as aggravation of heart or lung disease, air and land traffic. Occurrence of dust storm has been growing inrecent years and has created many problems in some cities of Iran. Dust storms of Iran arise either from internal or external sources. In this paper, Climatology of dust storms in Iran is compiled based on observational data of 112 meteorological stations from 1985 to 2005. Results show that the total number of dust stormy days varies from 11 to 3833. Accordingly, we have identified five types of cities; 1- less than 492 days; 2- from 588 to 1153 days; 3- from 1243 to 1757 4 days; from 2007 to 2239 5 days; 5- more than 3832 days. Afterwards, we examined each of them separately. The first group is fixed. Cities of this group are located mostly in North, East and Center of Iran. The second group contains three parts:First, an ascending trend to 1992 and then a descending part to 1998 and again,an ascending part to 2005. Cities of second group are located in the southern pasrt of the country. The third group contains four parts: First, a down trend to 1990 and then an ascending part to 1993 and again, a descending part to 1998 and finally, a branch of the ascending to 2005. Cities of the third group are located almost in the southwest. The cities of the fourth group are located in Sistan&Baloochestan province and are affected by the120-day wind. The occurrence of dust storm in the fourth group is ascending. Finally, using GIS and interpolation systems, we have plotted dust storm zone classification map of Iran from 1998 to 2005.
Maryam Azarakhshi; Jalil Farzadmehr; Mahdi Eslah; Hossein Sahabi
Abstract
Climate change is defined as long term and irreversible changes in the climatic behaviorof a region. Many studies have been conducted in different regions of the world on climatechange. The results of these studies show considerable changes in climatic factors especiallyin precipitation and temperature. ...
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Climate change is defined as long term and irreversible changes in the climatic behaviorof a region. Many studies have been conducted in different regions of the world on climatechange. The results of these studies show considerable changes in climatic factors especiallyin precipitation and temperature. In this research, the trend of changes in annual and seasonalrainfall and temperature in 24 synoptic stations over a 50-year data period (1956-2005) wasinvestigated in Iran. The Mann- Kendall test and linear regression technique were used to analyzethe trend of changes in climatic factors. The results showed both increasing and decreasingtrends in annual rainfall at various regions of Iran. Annual rainfall in northern slopes of Alborzand western slopes of the Zagros Mountain as well as in eastern and southeast parts of Iran hada decreasing trend while in the central of Iran the trend of changes was increasing. In southernregion of Iran the rainfall had an increasing trend. The results also showed that temperature inmost of the studied stations over the considered period was increasing. The highest and lowestchanges in temperature were seen in the mean temperature of summer and winter, respectively.Ahwaz and Khorramabad stations had a decreasing trend and over all seasons would go towardmore cooling. The temperature trend in Oromiye was decreasing in autumn and summer whileShahrekord and Bandar abbas had the same trend in summer and winter.
Mehdi Teimouri; Ali Fathzadeh
Abstract
The discharge data used for hydrological modeling should be the long-term suitable random data without trend and jump which is followed a specific statistical distribution. In this study, the above mentioned conditions were evaluated for 31 years period (1974-2004) of annual mean discharge data of 10 ...
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The discharge data used for hydrological modeling should be the long-term suitable random data without trend and jump which is followed a specific statistical distribution. In this study, the above mentioned conditions were evaluated for 31 years period (1974-2004) of annual mean discharge data of 10 gauging stations of West Azarbaijan province. For this purpose, the non-parametric Spearman correlation coefficient as well as Mann-Kendall method, non-parametric Run-test, non-parametric without distribution test of CUSUM and Kolmogorov–Smirnov test were used to trend, jump, stochastic and distribution analysis of data, respectively. The results showed that data of all stations were stochastic with no jump and trend (except Pol-e-Bahramloo gauging station). Also, data of most of the stations followed the gamma probability distribution function
