The Zoning of Comfortable Climate Using PET Index in Khorasan Razavi Province

Today accessibility to bioclimatology data is essential for application sciences including Tourism, Sport, medicine and et al. Climate-comforting conditions usually are expressed by indexes which a series of meteorological, human and environmental factors have been played a role in, and the possibility of comparison among different places is provided by. Since the 1960s, heat balance models of the human body have become more and more accepted in the assessment of thermal comfort. The basis for these models is the human energy balance equation. One of the first and still very popular heat balance models is the comfort equation defined by Fanger 1972). Comfortable climate condition generally states by indexes that involve the sets of meteorology, humanities and environmental elementary. Several thermal indices such as Predicted Mean Vote (PMV), Physiologically Equivalent Temperature (PET) and Standard Effective Temperature (SET) may be calculated for the assessment of human bioclimatics in a physiologically relevant manner as shown in several applications (Matzarakis et al., 1999; Blazejczyk, Matzarakis, 2007; etc). All indices have the known grades of thermal perception for human beings and physiological stress (Hoppe, 1999). PET is defined as a certain air temperature related to fixed standard indoor conditions at which the heat balance of the human body is maintained with core and skin temperature equal to those under the conditions being assessed. In this research, PET index has used for the mapping of Comfortable Climate inKhorasan Razavi province on monthly scale*.Materials and methods In the present study, Khorasan Razavi province has been mapping using PET index in monthly scale. For this purpose, first the daily scale of climate comfort index is calculated in the 14 number stations within the region. Then the results remove to ArcGis software for mapping. For extend result calculated to region study surface have been used Arc Gis software and karging interpolate method. The index used is physiologic equivalent temperature (PET). This index derived from body energy balance model. The Munich energy balance model for individuals” (MEMI) (Höppe 1993) is such a thermo physiological heat balance model. It is the basis for the calculation of the physiologically equivalent temperature (PET). In detail the MEMI model is based on the energy balance for the human body: M +W + R +C + ED + ERe + ESw + S = 0 The individual heat flows in Eq. 9. 1, are controlled by the following meteorological parameters (Verein Deutscher Ingenieure 1998; Höppe 1999):– Air temperature: C, ERe– Air humidity: ED, ERe, ESw– Wind velocity: C, ESw– Mean radiant temperature: RThermo-physiological parameters are required in addition:– Heat resistance of clothing (clo units)– Activity of humans (in Watt)The following assumptions are made for the indoor reference climate Frequency indicatorsFor each year, it has been determined the number of days with the threshold for the 1st, 5th, 10th, 90th, 95th and 99th percentiles based on the data for all years and identified the longest consecutive rain day streak for each year and station. Amount indicators All of the measures are used to check out the precipitation amount from each percentile.Extreme ratio The six indices, divided by the annual total precipitation; to find out the ratio of every percentiles precipitation in annual precipitation.All three categories indices have tested for long term changes (trend) based on Non- Parametric methods. The Non-Parametric trend analysis is used to analyze the trends of the frequency, amount and ratio of six indices of precipitation extreme for Zanjan station. The magnitude of a trend in a time series is estimated using a nonparametric approach hereinafter is: i j i j T T Z Z b median − − = Where b are estimate of the slope of a trend and i Z is the i-th observation in time of T. The slope determined is a robust estimate. For the non-parametric estimate of trend, the slopes b are computed between each possible pair of data points. Also Periodicities features in the time series of extreme events were analyzed by means of spectrum analyses.DiscussionPrecipitation amounts of extreme events are stable except in first and 95th percentile of precipitation that experienced a jump in 1980s as well as decreasing trends. There are same trends in precipitation ratio of extreme events and in days with extreme. There are about 2-3 year's cycles in first, 95th and 99th percentile in 99 confidence interval as well as 2, 37 year's cycles in first percentile.As a result of this study it could be concluded that the days with extreme events have reduced during recent decades, while the precipitation is stationary. So that the intensity of precipitation of Zanjan decreased during recent decades.Results and DiscussionClimate change could be investigated by frequency distribution centers (mean) studying as well as by tiles of frequency distributions (extremes). Because of tempo-spatial patterns dominant on climate change, the extreme values are determine by this patterns. So that the extreme values changes isnt the same in all time and places. Accordingly finding out climate change study needs to be in high resolutions in time and space. Finding out climatic extreme, different indices have been definite by scientific organization and experts. However in this investigation, we assembled daily precipitation records for Zanjan stations in Northwest of Iran over the period 1961-2006 and calculated 18 different annual indicators of extreme precipitation events that have been widely used in the professional literature. It has been used various unvaried statistical procedures to find significant trend in the occurrence of extreme events (extreme precipitation amount, frequency and ratio to total precipitation). There were two kinds of changes. The first is trends in precipitation extremes based on non-parametric technique. The second is cyclic behavior of precipitation. There is a decrease trends in total and ratio of first and ninety five percentileofprecipitation as well as a jump in 1980 to lower level. Also there are a 2-3 year cycles in all percentile indices. The frequency of first percentile also has reduced trends.