فصلنامه پژوهش های جغرافیای طبیعی
پیاپی 96 (تابستان 1395)

In general, loess sediments are one of the most widespread forms of eolian sediments. During the past few decades, loess stratigraphy studies played key role in global climate changes. These sediments are usually yellowish in color and silt makes 70 to 90 percent of it volume. In Iran, loesses outcrop often in northeast of south Caspian Sea. The previous studies revealed that loess/paleosol sequences correspond to cold/ warm period of climate, especially during quaternary period. Therefore, loess deposits are the most important natural archive of climate changes and are ideal for reconstruction of paleoclimate and geomorphological change in Quaternary. The thick loess/paleosol sequences of northeast Iran provide unique opportunity to reconstruct the terrestrial paleoclimate changes recorded in these sediments.
A number of loesse-paleosol sequences contain a magnetic record of palaeoclimate through the Quaternary period. Anisotropy of magnetic susceptibility (AMS) was mentioned as a good tool to determine paleocurrent or paleodirection. AMS measurements were mostly used in the investigation of igneous, metamorphic and sedimentary rocks with an increasing number of applications in Quaternary loess and paleosol studies since the end of the1980s. The sediment magnetic properties depend on the magnetic content and characteristics of the source material and post-depositional weathering/soil formation processes. Loess in north of Iran is part of world loess belt and such as another loess sediments are evidence of paleoclimate change in continent.
In this study, Azadshar (Nowdeh Loess Section) was selected to reconstruct Late Quaternary climate change. The Nowdeh loess section with about 24 m thickness were sampled in 10 cm intervals for magnetometry analysis. For this aim, sampling location and method was determined after consecutive study area. This study used of reformed review of library references and lab practices combination. Magnetic susceptibility of all samples was measured in Environmental and Paleomagnteic laboratory based at Geological Survey of Iran, Tehran, Iran. All samples were placed in an 11 cm3 plastic cylinders to be used in magnetic measurement instruments.
Magnetic susceptibility was measured using AGICO company made Kappabridge model MFK1-A instrument. Magnetic susceptibility of all samples were measured. Base on this study results, samples with high frequency in magnetic susceptibility (increasing or decreasing) were selected for another studies of magnetic parameters.
The variation of magnetic susceptibility signal in the Nowdeh section suggests variation in climate conditions and mechanisms during the Late Quaternary. The magnetic susceptibility relationship with Loess/paleosol deposits resulted in low magnetic susceptibility values in cold and dry climate periods (Loess) and high magnetic susceptibility values in warm and humid climate periods (paleosoil). Therefore one can say that Loess and paleosol sequences studied here was formed at glacial and interglacial periods and under different climate condition. Results of this study showed that Magnetic Susceptibility, Natural remanent magnetization, Saturation isothermal remanent magnetization and HIRM in loess was less than paleosol. Instead amount of S_0.3 in loess layers was more than paleosol. Results of this study show that Nowdeh section has seen 8 periods of hot and humid climate (paleosol layers and similar paleosol layers with high magnetic susceptibility) in cold and dry periods (loess layers with low magnetic susceptibility) during past 150 ka.
This study was conducted to investigate and evaluate the capability of magnetite properties in reconstruction of Late Quaternary paleoclimate condition recorded in loess/paleosol deposits of Nowdeh section in Golestan province, northeast of Iran. Nowdeh loess/paleosol sequence is an indicator for periodic dry- cool (deposition of loess) and moist-warm (formation of paleosol) conditions. Formation of the studied loess and paleosols, have probably taken place in glacial and interglacial cycles with different climatic conditions, respectively. Nowdeh section magnetic properties completely is match with Neka sediment result that has obtained by mahdipour et al (2013). In 20 to 48 ka of past years show that in two sediment sections magnetic susceptibility was similar and wherever increased, hot and humid period integrated with paleosol layer formation. Also results of this research are accordance to with bear and storm (1995) in saturation of beryllium in Xifeng section and isotope δ18 of marine sediment.this is indicate that climate change event in two section are simultaneous.
Finally, should be say that magnetic properties because of depending of sensible mineral to climate change, is a very useful variable for climate change reconstruction.

The city population, in particular at the industrialized cities and centers of provinces, has increased dramatically in Iran during recent decades. Arak city is among of those industrialized cities as a centers of Markazi province which has experienced a fast increase in population. This changes in population numbers tend to increase in consuming water resources as well as increasing in energy resources demanding. This situation accompanied with global warming caused an increase in temperature values during recent decades.
In current research, in order to understand the nature of temperature changes in Arak, the temperature trends was analyzed for previous as well as future based on SDSM. Because, according to IPCC (2014: 563) it is vital to understand the nature of climate change in order to reduce its negative effects.
In current research, in order to understand the nature of temperature changes in Arak, the temperature trends was analyzed for previous as well as future based on SDSM. Because, according to IPCC (2014: 563) it is vital to understand the nature of climate change in order to reduce its negative effects.
In order to study temperature trends during recent decades in Arak, the temperature data selected based on having sufficient temporal records for carrying out the investigation and also sufficient accuracy that extend from 1961 until the end of the 2010 which is the most length of accessible temperature data record in Iran. The data of daily temperature is derived from Meteorological Organization of Iran. An initial check was carried out in order to test the quality of data. The NCEP/NCAR data and HadcM3 under scenario A2 and B2 also are used in current study in order to modeling and predicting the temperature values.
In order to discover whether the negative/positive trend are governed the data, the temperature data subjected to Mann-Kendal trend test. In order to fit a proper model on each character of Arak's temperature, linear and non-linear regression models were used. The best models are chosen based on conformation of ordinary statistics and indices.
All the results are performed by SPSS and MATLAB softwares and depicted in figures and shapes. Statistical downscaling model is used to simulate and predict the temperature of Arak station by using SDSM software.
Results and discustion : According to our study, the best fitted models on annual mean temperature, annual minimum temperature average, and annual maximum temperature average are cubic and quadratic models, while these models are fitted on absolute maximum temperature for spring and winter. There are no non-linear model which be fitted on minimal absolute temperature, due to the huge variability in this parameter. Based on correlation and partial correlation analyses which is used in current study, the explanatory variables for annual mean temperature are Sea Level Pressure (SLP), 500 hpa geopotential height (500hpa HGT). The explanatory variables for mean maximum temperature are Vorticity at 500 hpa, 500hpa HGT, relative humidity at 500 hpa, and mean temperature at 2m. Ultimately explanatory variables for mean minimal temperature are SLP, 500hpa HGT, relative humidity at 500 hpa and also mean temperature at height of 2 meters. After calibrating with using estimated models and abovementioned variables for period of 1961 to 2010 the data is evaluated and it became clear that the difference between simulated data with recorded data is very low. Then bused on two scenario A2 and B2 the temperature variables of Arak are predicted. Based on scenario A2 and B2 during 100 years there will be about 0.24 and 0.19 degree centigrade increase in annual mean temperature, while 0.25 and 0.2 degree centigrade the mean maximum temperature will increased. The mean minimum temperature will increased by 0.19 and 0.16 degree centigrade.
According to our finding, the Arak temperature trends are non-linear during the study period (1961 to 2010). Average of minimal temperature during summer shows increasing trend. Therefore energy and water demanding are increases in summer. Absolute values of maximum temperature of winter and summer recently have increased during last two decades. So that, the snow melts will have accrued very fast during winter and spring in future. The result of current research and several other researches which are performed in Iran and also in global scale are testify on temperature increasing of cities and also certify the IPCC reports on increasing trends at least during five recent decades and continues increase at least during next two decades. This temperature increasing trends also can influence other climate variables such as evaporation, rainfall, relative humidity and so on and accordingly can effect human activities such as consuming energy, and human environment such as air pollution. Accordingly the environmental management as well as environmental planning should take this reality in their account.

In spite of its short time period compared to the earth evolution history and formation, quaternary has had a significant effect on the final formation of the land form and vital resources. It is the final analysis of these levels and fully dependent sources to the earth genetic diseases and, above all, significant climate changes that have happened during this period; Gavkhouni basin morphological perspectives demonstrate several climate changes in quaternary. The changes whose effects on the lakes are a completely known phenomenon, indeed, the lakes can be viewed as an archive within which quaternary climate changes evidence has been maintained.
Gavkhouni basin has had a weather in past different from today’s weather, and so geomorphic forms and processes have experienced great evolutions during quaternary and have been different from today. Since the geomorphic evolution of Gavkhouni basin have been affected by the external processes, i.e., climate fluctuations of the Fourth Era, it is the resultant of these processes’ operations at the present by refer to which the past climate fluctuations in the area can be demonstrated.
This study is aimed at determining the temperature and moisture changes and transitions through tracing and revival of hot-waters and lake terraces and presenting a schematic image of the recovered lake of quaternary in Gavkhouni basin. In order to achieve this aim, statistics related to annual temperature and rainfall of 13 stations within the basin and around it were selected and, in the next stage, quaternary temperature was reconstructed using the Wright method based on the snow line, and its changes were plotted. Then, using the Peltier model which its two basic varieties are temperature and rainfall, the survey of morphoclimatic regions of Gavkhouni basin was measured both in Vurm and the current.
To estimate the current annual mean temperature and to provide isotherm map using the annual mean temperature and height of each station, a thermal gradient with correlation coefficient 0/92 was obtained and then by applying equation (1) in the height model of the basin isotherm lines map was provided and minimum, maximum, and the mean of the Gavkhouni lake basin were resulted. Then the locations of 153 glacial cirques within Gavkhouni Basin were determined using the curve form of topographical maps scale within a height range of 2500-3400 meters and the snow line of the basin measured based on Wright model was 2500 meters. By assuming the annual mean temperature at snow line as 0o and by exploiting the relation of temperature and height as well as given to the 5o reduction in past temperature compared to the present, the mean map of temperature during the cold period of the year was plotted and its minimum, maximum and mean were calculated. In the next stage, the nine-tuple regions were segregated using the Peltier graph, temperature parameters and annual rainfall and its result was plotted in the form of current morphoclimatic maps and the late quaternary phase.
Then, with regard to studies conducted on Zagros basin lakes and the resulted linear relation between the two variables, ice maker survey and lakes survey, with the correlation coefficient 0.70, it was demonstrated that there was a kind of coupling between the height and survey of the ice maker and the survey of the lake, so that more the height of the peaks more the survey of the ice maker and as the result, more the survey of the lake. Certainly, the lake survey and volume have decreased by the reduction of these variables at the present time, suggesting evidently the climate changes in the late quaternary phase compared to the present.
In order to investigate the weather fluctuations and environmental responses of Gavkhouni Basin Lake, we concentrated on the past temperature and rainfall reconstruction, measuring the depth and volume of the lake and the survey of the lake ice maker’s focuses in the past. Reconstruction of past temperature and rainfall and comparing it to the present indicates a 1/5 times reduction in rainfall and 5 degrees increase in the mean temperature of Gavkhouni Basin compared to the late phase of quaternary, a contradict which has had so many climate and geomorphic changes as the consequence. Peltier method-based morphoclimatic maps show that the vastest survey of the basin was related to semi-arid region with 48.45 percentage followed by Savan and Bouril regions with 35/28 and 14/95% percentage, respectively. At the present, the semi-arid region with almost a double increase up to 83.24% still has the most survey of the region. Dried region with 13/79% is placed in the second place. On the other hand, defining the limits of traced lake terraces through hot-waters represents the existence of a huge lake with a greater volume in the past, in other words, with regard to the correspondence of wet periods to glacial periods in the region, the basin extent has been augmented during the cold era by increasing the rainfall and consequently increasing the river debit, so that the volume of Gavkhouni lake water reached to 892km3 at the time, but during the warm era it reached to 21km3 because of the reduction of ice maker focuses in the region.

Natural sciences face to a great revolution, nowadays. Now, scientists think the world as a collection of complex systems which prediction of consequences of this complex systems is so difficult. In this situation the systems have rotational act so irregularly lead to regular and regularly lead to irregular. In the meantime, geomorphic landforms have special shapes, sizes and special aspects, and the spatial arrangement of these shapes to each other can be determined by many influence factors in their formation. Since the landforms behavior are nonlinear in nature, it can be analyzed using statistical methods which Fractal geometric is is one of them. The theory, attempts to use its equations to represent the complexity by mathematical way. The purpose of this study is fractal analysis of aeolian landforms of Ardestan Rig. For this purpose, we used Cartosat images of 2008 and 2011, and for fractal analysis, we divided typical aeolian landforms of study area into four categories ; longitudinal sand dunes, cross sand dunes, barkhans and planted sand dunes. To determine the fractal dimension, we used Box counting method. Results show that the geometric patterns of landforms have fractal characteristics so it can be analyzed for different years. The dimension of fractal as a main index, show that planted sand dunes that have a most extent, have a great dimension because it don’t changed during the study period, and indicating the stabilization of sand dunes. The maximum rate of change belongs to longitudinal and cross sand dunes that their extent is decreasing and this has been shown to reduce the fractal dimension and its implementation. In general, the result of fractal analysis matches with realities of aeolian landforms.

Water vapor spreads in molecular, unstable and erratic phase and transfers by convection and advection process. This transfer happens from the earth and ocean's surfaces to the atmosphere by the hot air. This type of this transfer is considered as the main pattern. In the studies of the atmosphere, one of the prominent factors to be considered is the complexity of water vapor and its cycle.Water structure is different above the surface of the earth and the oceans. The study on water structure was first done in 1960, in which a great deal of information gathered by the meteoric satellites, and according to this fact the researches about this phenomenon were set off. One of the water structures is the atmospheric river, which is considered as a separate factor recently. The definition of the atmospheric river as the Tropospheric River was first introduced by Riginald Newell in 1992. Regarding to the fact that Middle East, especially Iran, is facing a sever trend of drought, it is necessary to provide programs and plans to prevent this phenomenon. More over, the vast and small water sources and the moisture entries have to be identified.
In this research the specific humidity maps were reanalyzed in three years time (2011, 12, 13), which were provided by NOAA administration. About 1000 maps from different surfaces were drawn in this period, and after the days with the occurrence of Atmospheric River were identified, jet stream maps were produced. For the jet stream maps two types of winds were used: UWND and VWND which are derived from the NOAA administration. The maps were designed by the GRADS program by the use of 300 hPa of the defined day. Afterwards, the relation between the atmospheric river and jet stream of 300 level of the defined day was studied. These relations with zonal wind were calculated in SPSS program by using the Pearson correlation coefficient.
The average occurrence of ARS phenomenon in Iran is 13 that enter the country from different directions. In 2012, the highest amount of atmospheric river was reported. Approximately, 27 percent of the rivers were from the West, and thesoutheast, southwest and south were 8, 46 and 19 percent effective. West ARs happen mostly in February, which 80 percent happens in the winter. Also the other 20 percent happens in the late autumn in December. The ARs are considered as 400hPa height, which 90 percent of them happen at this height level. East south AR's occurrence rate is the least with the low percentage of 8. Based on the seasons, these Atmospheric Rivers happen more in August and July. Their movement level is 600 hPa, which is in a lower level comparing the western ARs. West south ARs are considered as the main entries, due to the fact that 46 percent of the ARs are from this direction. These rivers are in lower levels, 600 and 700 hPa. About 60 percent of these rivers happen in the autumn, as the winter has the second rate. The other direction of the rivers is the southern part, which is considered the most unregulated AR and can happen during all the seasons except the winter. This direction is on the third stage according to the occurrence, and includes only 19 percent of the rivers. It enters Iran at the level of 700hPa, and is rarely reported by other levels.
The occurrence of Massive flood sin the southern regions on the one hand, and reduce the average of total rainfall in the country on the other hand, representing to being influencing Iran from the destructive and climate events and phenomena. For predicting flooding and accordance the types of economic activities related to water, it is necessary to be recognized their sources and the transport of moisture factors and type in different levels of the atmosphere. In this study, it has been studied Transport of water vapor through distinct phenomena on called synoptic atmospheric rivers (ARS). In the beginning of the research re-processed data of specific humidity, were taken for a period of three years (2011-2013)from the NOAA. Then, the maps were prepared by Software GRADS. The results show that about 12atmosphericrivers were observed during the study period on average annually and have been classified so-called western, southwestern, southern and southeastern Atmospheric Rivers. Studies show that River Winds have created the phenomena. Moisture in the river is about 6 times of Surroundings on the average. Atmospheric rivers feed atmospheric spring head along the way. Southern and southwestern Atmospheric Rivers have the most the amount of moisture. Rainfall maps also showed the rainfall of Southern Atmospheric Rivers have the amount and even lead to the Flood and Water logging passages in the southern cities. Pearson correlation coefficients showed that their relationship between atmospheric rivers with orbital indexes, respectively, are the southern, southwestern and western ARS which their Correlation values are 28, 53, 85 percentage.

Nowadays, the exploitation of the renewable energy sources such as wind plays a key role in human life. Although, Iran has a high potential for wind power generation, there is not an efficient energy planning yet. Environmental variables such as wind speed vary according to spatial points, so it seems reasonable to consider that there exists a spatial correlation between wind speed data at different locations. In geostatistics the spatial autocorrelation of data could be investigated by calculating the experimental semivariogram. The parameters of the fitted semivariogram model may be then used to better estimate the wind speed at unknown locations through kriging algorithms.
In order to describe the behaviour of wind speed at a particular location, the data distribution should be first fitted by a suitable distribution function. There are different wind speed distribution models used to fit the wind speed distributions over a period of time. Among them, Weibull distribution function has been found to be the best all over the world because of its great flexibility and simplicity.
The aim of this study was to simulate the daily mean and maximum wind speed probability distribution by using Weibull distribution function and to investigate spatial variability of the wind speed data. This study was also aimed to interpolate and map the means of Weibull distribution functions of daily mean wind speed data observed at stations spread over Iran.
Study Area and Data Set
The study is based on a long term (20 years) wind data recorded in 104 synoptic stations spread over Iran. The wind data are recorded at 10m above the ground level (a.g.l.) and contain daily mean and maximum wind speed (m/s).
The Weibull Distribution Function
For each sites, the daily mean and maximum wind speed data were fitted by a two-parameter Weibull distribution, whose parameters (shape and scale) were determined through the maximum likelihood (ML) technique. The Weibull probability density function is defined as follows: where V is wind speed (m/s), 𝑐 is the scale parameter (m/s) and 𝑘 is shape parameter (dimensionless). The high and low 𝑘 values indicate the sharpness and the broadening of Weibull peak, respectively. The Weibull probability density function curve could be draw if the 𝑘 and 𝑐 values are obtained. This could be done through different ways, among which is maximum likelihood method as: where Vj is the wind speed for jth sample and n is the number of sample data. Equation (3) is an implicit equation and could be solved through an iteration method.
Interpolation Two interpolation methods including inverse distance weighing and ordinary kriging were used to estimate the theoretical mean values of the previously determined Weibull distributions of the wind speed data at unsampled locations.
Inverse Distance Weighing (IDW)
In absence of data spatial autocorrelation, IDW is usually used as an alternative method for spatial estimation of random field. IDW is a weighted averaging interpolator in which data is weighted according to their distance to the estimation point such that more distant points get less weight than closer points.
Ordinary Kriging (OK)
OK is the most popular kriging approach used in the spatial interpolation of the regionalized variables. It needs the parameters of the best fitted semivariogram model to incorporate spatial dependence of data into the estimation process. The semivariogram quantifies the dissimilarity between observations as the separation distance between them increases.
According to the obtained results, Semnan and Bandar-Abbass had the lowest and highest shape (k) factor of the fitted weibull distribution functions to the daily maximum wind speed data, respectively. For daily mean wind speed data, Nehbandan and Bandar-Abbass had the lowest and highest shape (k) factor of the fitted theoretical Weibull distributions, respectively. A high k value means less variation of the wind speed.
The annual duration of daily wind velocity of exceeding 4 m/s is also calculated for each site in order to obtain a first diagnostic sign of the most promising areas in terms of wind energy potential. According to the results, Cities of Rafsanjan, Zabol, Torbate Jam, Khodabandeh, Ardebil, Bijar and Kahnouj are of the most potential areas in terms of high wind speed.
The auto-correlation analysis showed that wind speed is moderately correlated in space with spatial structure model of spherical and a correlation distance of about 500 km (Figure 1 (a)). There was no apparent drift within the range of 500 km. The best semivariogram model was selected according to the cross validation results as well as highest correlation coefficient (r) and lowest residual sum of squares (RSS) functionally of GS software.
To predict the spatial distribution pattern of wind speed over Iran, Weibull mean wind speed data were interpolated over a point grid superimposed to the map of Iran by using IDW and OK. The cross validation results showed both methods performed similarly however the maps generated were visually different. Besides, unlike IDW, OK represented the map of estimation error which is useful in decision-making as it is provides a measure of uncertainty.
According to wind speed map generated by OK (Figure 1 (b)), eastern Iran (e.g. the cities of Zabol, Rafsanjan and Torbate Jam), as well as northwestern provinces (e.g. Ardebil) are the most promising areas for wind energy planning.
The spatial variability of wind speed and duration across Iran has been investigated. First, the frequency distribution of daily mean and maximum wind speed data during recent 20 years was simulated by using Weibull function. Then the mean values of the theoretical Weibull probability distribution functions are used to investigate the spatial variability and predict the spatial distribution pattern of wind speed across the country. According to the results, wind speed is moderately correlated in space with an influence range of about 500 km. The maps of wind speed at 10 m a.g.l. generated using IDW and OK encourage the utilization of wind energy on the eastern, and northwestern regions. Besides, additional measurements may be considered in areas of highest estimation uncertainty.

Rainfall prediction at regional and global scales is mostly as principle component of hydro-meteorological studies in un-gauged regions. Ground-based measurements of precipitation are available with high accuracy in synoptic stations. Spatial distribution of operational stations is now as one of the biggest problems in the developing countries such as Iran, which the spatial distribution of stations are not enough. In recent decades, remote sensing data have been widely used by many researchers in the world for drought monitoring and management of water resources. The satellites data can be utilized as compensation for temporal and spatial distribution of rainfall. The satellite-based rainfall estimates provided by the Tropical Rainfall Measuring Mission (TRMM) satellite at global scale, are now available freely as only data source at regions without in-situ measurements. Most regions of Iran have arid and semi-arid climates. The evaluation and calibration of TRMM data in different regions of Iran at daily and monthly time scales is very important before those data are used by researchers, experts, climate scientist, hydrologist, etc. Therefore, a comprehensive evaluation and calibration of the TRMM 3B43 and 3B42 dataset at 87 synoptic stations in Iran including six climatic zones, is the main objective of present research.
This research was carried out in Iran. It is located between 44˚14’ to 63˚20 E longitude and 25˚03’ to 39˚47 N latitude, with an area of more than 1.6 million Km2. Alijani et al. (2008) classified Iran’s climate according to climatological parameters to six separate climatic classes: desert, semi desert, mountainous, semi- mountainous, coastal wet and coastal desert. This study aims to evaluate the accuracy of the Tropical Rainfall Measuring Mission (TRMM) satellite and its calibration on the daily, monthly, seasonal and annual scales at the synoptic stations located in climate zones of Iran. The daily TRMM-3B42 and monthly TRMM-3B43 collection data were downloaded from the NASA website and processed. After early processing, a comparative analysis was carried out for satellite data and observed rainfall data at 87 synoptic stations during a 12-year data period of 2009-1998. The Desert, semi desert, mountain, semi-mountain, coastal desert and coastal wet climate zones are included 22, 19, 19, 12, 8 and 7 stations, respectively. We utilized different error measures (R, ME, MAE and RMSE), and agreement indices (POD, FAR, CSI and TSS) for satellite data evaluation. Since there were noticeable errors, regional mean data were calibrated in the daily and monthly scales and finally two correction coefficients were introduced based on regression analysis.
Day-to-day rainfall comparisons showed that the TRMM rainfall estimates are very similar to the observed data values, even if a general overestimation in the satellite products must be highlighted. We found out a high similarity between two sources of rainfall data at 87 synoptic stations in most of climatic zones. Furthermore, The TRMM showed the highest error at Ramsar, Bandar Anzali, Rasht and Babolsar stations, and the lowest errors at Zahedan, Bam and Esfahan stations. In other words, the TRMM revealed the highest error in coastal wet zone and the lowest error in desert zone. The False Alarm ratio (FAR) indicator has the lowest amount in coastal wet zone that shows TRMM applicability to predict rainfall amount at these stations. The highest correlation coefficients at 0.01 significance level on monthly and daily scales, were 0.86 and 0.998 in the semi mountainous zone, respectively, while the lowest values as 0.49 and 0.78 were in the humid zone, respectively. After applying the calibration coefficients, The RMSE values were significantly reduced at monthly scale. This indicates that the calibrated TRMM data is mostly similar with observed rainfall data at different time scales and climatic zones.
In recent years, the accurate measurement of precipitation and its spatial and temporal distribution frequently at un-gauged regions have been addressed in the world. At present, the estimation of rainfall by the TRMM satellite is only data source, which is available freely at global scale. The main purpose of present study is to evaluate the TRMM rainfall data and to provide the correction coefficients in desert, semi-desert, mountainous, semi-mountainous, coastal wet and coastal desert climatic zones, on daily and monthly scale. The main advantage of this work is to apply various statistical error criteria and newly introduced agreement indicators, to evaluate TRMM data. The results reveal that the TRMM overestimates rainfall on daily and monthly scales at 68% of stations. In general, The TRMM could detect most of rainy days in the climate zone and Iran during 1998-2009 period. The calibrated data were very similar with the measured values. Therefore, our research findings showed that the calibration process could improve rainfall estimates at most of climatic zones, significantly.

In recent decades, mismanagement, human activities and climatic conditions has been showed a new view of Iran's ecosystems, which called desertification. Life cycle assessment (LCA) is a method to construct the environemental profile of production systems. That was developed by industrial instruments, but in recent years it applied by agricultural production process as well. Today, it is acknowledged that land use should be assessed by LCA, but there is still no consensus on the parameters for assessment. In order to assess such land use impact, it is first necessary to define the variables in the LCI. Once the inventory data is gathered, the LCI results have to be characterised in the impact assessment phase. The main framwork of LCA is based on the "from cradle to grave" where we are able for evaluating environmental impacts truely from start point to the end. In this way we can use the theory of LCA to assess desertification indicators and estimation of ecosystem resistance to this phenomenon. So In this research was applied an LCA approch for estimating ecosystem susceptibility to desertification.
This research concentrated on role of LCA to distinguish ecosystem susceptibility to desertification phenomenon. In this way, in the first the land units were considered Ecoregions, the region with similar ecological and climataic characterestics, and six ecoregions has been identified. Then based on Delphi methodology, six main factor were detemined including aridity, landuse, wind erosion, soil erodibility, salinity and vegetation density.To calculate aridity, FAO/UNEP aridity index (P/ETP) was used. The land use map was developed by ETM imagery data and distinguished six classes including; desert, bare lands, cultivated lands, settlements, rangelands and forest. A report of critical center of wind erosion prepared by KR organzation of Natural Resources and watershed management was pplied for wind erosion. Soil erodibility was calculated based on the Sepehr et al, 2014. Salinity and vegetation indices were calculated by spectural ratio of imagery data. To assess susceptibility degree a characterestic factor (CF) for each ecoregion has been calculated. One of the main contributions of this study is the establishment of desertification impact CFs for the ecoregion. The divisions between these areas are based on climatic and vegetative cover factors, both aspects having a major influence on soil desertification risk. So after calculating CF for each ecoregion total characterestic factor was developed by geometric mean of each CF. Ultimately the susceptibility degree to the desertification was evaluated and mapped.
The results indicated the high preference aridity and wind erosion at Khorasan Razavi province which is in relation to the climatic conditions and land use changes in recent years. The greatest desertification risk is found in the moderate arid desert ecoregion, with a CF of 2.21. The susceptible ecoregions mainly covered more than 70% of the KR areas. In this case the desertification impact of the activity should not be integrated in LCA studies. This can be used to identify those cases without desertification impact. The LCIADesertification value is also zero when CFi or any other variable is zero. A value of zero for CFi means that the activity being studied is in an ecoregion with no desertification risk. The LCIDesertification value of the activity being assessed is determined by the addition of the individual values given to each of the sex variables, according to a scale of values. This paper provides CFs for including desertification impact in LCA studies, and the variables suggested allow the comparison of the benefits and threats posed by different human activities.
In this research, an LCA methodology was developed for assessing ecosystem susceptibility to desertification phenomenon. Main biophysical variables including aridity, wind erosion, landuse, erodibility, salinity and vegetation density belonging to the driving force, state and pressure frameworks were selected. The desertification impact evaluation of any human activity in a LCA should include these common, basic four variables. the purpose of this research is investigating desertification susceptibility degree of ecoregions at Khorasan Razavi as vulnerable province to land degradation and desertification in Iran. In this study was applied Life Cycle Assessment (LCA) framework to assess susceptibility. In the first, an ecoregions map was provided by adjusted De-Marton climate index. Six main indicators including aridity, land use, wind erosion, soil erodibility, salinity, and vegetation cover were determined by Delphi methodology. The preference degree of each indicator was calculated using Entropy algorithm. Ultimately was estimated characterization factor (CF) for each ecoregion. The layer integrating was done using geometric mean and desertification susceptibility map was prepared. The results showed that ecoregion of moderate arid desert is most susceptible to desertification.

Daily weather information is currently available for about 40000 stations across the world. But, distribution of these stations is relatively uneven in some parts of the world. Moreover, there are often large amounts of missing values (Schuol and Abbaspour, 2007:301). Using generated data can help to fill missing or even to correct erroneously measured data (Fodor et al., 2010: 91). LARS-WG is a stochastic weather generator which can simulate weather data under both current and future climate conditions at a single site (Semenov and Barrow, 2002: 3).
There is another watershed modeling problem, which weather stations are often outside of/or at a long distance from the watersheds. So, recorded data may not meaningfully indicate the weather taking place over a watershed. Therefore, some researchers have developed radar data to supply precipitation inputs in watershed modeling (Fuka et al., 2013: 1). But, these data are only available in small parts of the world. So, considering additional methods to generate weather conditions over watersheds is necessary. Using reanalysis dataset (CFSR) is one option (Fuka et al., 2013: 1).
Dile and Srinivasan (2013) investigated CFSR climate data in the Lake Tana basin in the Nile basin. The results showed simulations with CFSR and conventional weather gave trivial differences in the water balance components in all except one watershed. In the four zones, both weather simulations indicated similar annual crop yields. Nevertheless the conventional weather simulation results were better than the CFSR weather simulation, but they can be applied as important option for regions where no weather stations exist such as remote subbasin of the Upper Nile basin. Soltani and Hoogenboom (2003) evaluated the weather generators WGEN and SIMMETEO for 5 Iranian locations. The results showed that WGEN was successful to generate maximum and minimum temperatures and SIMMETEO was acceptable to reproduce minimum temperature and solar radiation.
The objective of current study was assessment of accuracy of weather generator LARS-WG and CFSR data in simulation of climate parameters of Chaharmahal and Bakhtiari province.
The study was conducted in Chaharmahal and Bakhtiary province. This province, with an area of 16532 km2, is located between to 31° 09' to 32° 48' north latitude and 49° 28' to 51° 25' East longitude and provides more than 10% of the water resources of Iran.
1- LARS-WG model
LARS-WG model applies complex statistical distributions for simulation of meteorological variables. The basis of this model to simulate dry and wet periods is daily precipitation and radiation series semi-empirical distribution. The temperature is estimated by Fourier series. The output of this model includes minimum temperature, maximum temperature, precipitation and solar radiation (Babaeian et al., 2007: 62).
2- Required data for LARS-WG model
Required data for LARS-WG model includes daily maximum temperature, minimum temperature, precipitation and solar radiation (sunshine hours). These data were provided for four selected synoptic weather stations (Shahrekord, Koohrang, Boroojen and Lordegan).
3- CFSR data
Reanalysis is a systematic approach to produce data sets for climate monitoring. Reanalysis data are created through a fixed data assimilation design and models which use all available observations every 6 hours over the period being analyzed. CFSR data has a global horizontal resolution of 38 km. The CFSR adjacent stations were determined for the four mentioned stations.
Daily weather data of each station during 1991-2010 was implemented in the LARS-WG model. For assessment of both data, the comparison of statistical indices such as RMSE, MBE, MAE and R2 was used in daily, monthly, annual and decade scales.
The results showed that there is no correlation between the output of LARS-WG model and observed daily precipitation data in each of the four stations. The values of these coefficients for minimum and maximum temperatures increased in all stations. In general, due to high values of RMSE and MAE, this model was not successful in simulation of daily climate parameters. Performance of the model to simulate monthly and annual scale was better than daily. Ability of LARS-WG model in simulation of long-term period (decade) was satisfactory. Also, results indicated that monthly and annual climate parameters by CFSR data have been predicted by a more effective performance. Because statistical indices of CFSR data are lower than LARS-WG. These data underestimated the precipitation in Shahrekord station. RMSE and MAE values of monthly precipitation are 20.49 and 11.19 respectively In Shahrekord station, for CFSR data. These values for annual precipitation are 92.88 and 72.51. For LARS-WG model in monthly scale, RMSE and MAE values are 41.45and 24/75 and these values in annual scale are 164.75 and 123.43.
In recent years, it is necessary to get accurate and long-term meteorological data due to climate events and scarcity of meteorological stations across the country. So, it is a reasonable solution to use weather generators. The objective of current study was assessment of accuracy of weather generator LARS-WG and CFSR data in simulation of climate parameters of Chaharmahal and Bakhtiari province. In general, the results showed the ability of LARS-WG model in simulation of long-term period (decade) data. So, values of statistical indicators are not satisfying in short-time periods. Statistical indices of CFSR data are lower than LARS-WG in simulation of short-time period (monthly and annual). They are highly correlated with the observations and they can simulate climate parameters in short- time. Therefore, considering the purposes of any specific research, both LARS-WG model and CFSR dataset can be used. Moreover, CFSR data can be applied as valuable option for regions where there are no weather stations.