فصلنامه پژوهش های جغرافیای طبیعی
پیاپی 103 (بهار 1397)

Introduction1
The change climate and global warming is a problem in the world. The climate is changing and the global warming is happening. Increasing the green house gases compactness is the reason about this phenomenon (Dettinger et al, 2004, 2). According to IPPC report Average annual temperature of the earth has been raised between 0.3 to 0.6º because spreading the greenhouse gases and this report predicts this amount will increase between 1 to 3.5º till 2100(Masahbavani&Morid, 2005,41).
These are cusses drought, flood sharp, and snowmelt, storms and increasing air temperature in different regions. These phonemes are in the whole world but they have different together. Climate change is an important environmental challenge in recently years.
In the north Karun basin have altitude higher than 2500 m and snowfall. Therefore, it has supply water important role in Karun River. We will analysis the effect of minimum air temperature on snow cover and discharge changes in Karun River. In this basin, snowfall has more than rate of precipitation to whole basin.
According to studied in different regions in the world, rainfall annual has downward but heavy rains have upper ward trend. Average air temperature, maximum air temperature, minimum air temperature and evaporation have upper ward trend. In addition, flooding and flood huge have upper ward. However, these parameters have caused decreasing water resources.
That the temperature increasing, melting of polarized, the world’s seas level rise and change in are result of that. Among the negative affects about climate change are Non-uniformly distributed rainfall, increasing and continuity the droughts and finally on water resources in all over the world. North Karun basin has 2300km2 area. This basin is south west Iran. This basin is one of the basins important at supply water resources in southwest Iran. The volume water is resources almost 10billionm3 in North Karun basin.
We used to record by power ministry and meteorological organization in 1984-2014.These data are included air temperature minimum , ice daily, snow cover and discharge. In this research for changes assessment, simulation and forecast the minimum temperature has been used data as for 4 synoptic stations in the north Karun basin. Research period in stations divided to 2part:30 and 25 years. Therefore, the purpose of this research is evaluation changes of minimum temperature in the past and forecast that in the future and changes compare than observer period.
from CMIP5 were selected for future climate change projections over the HNZ under a very-low forcing scenarios (RCP2.6), a medium stabilization scenarios (RCP4.5) and a very high baseline emission scenarios(RCP8.5). CMIP5 data were interpolated to the spatial scale (0.4˚×0.4˚).there for downscaling by Matlab software (0.2˚ ×0.2˚). In the following we used for correction model in accordance with the equation:(Equ1) Also we used to of indexes Bias, RMSE and R for assessment models that their used in forecasting data in North Karun basin.One models of CMIP5 under, RCP4.5 and RCP8.5were utilized in this study.
The mention that for output CMIP5 and scenarios RCP4.5 and 8.5 is done compare, and finally the CMIP5 and MNA-44_ICHEC-EC, RCP4.5 scenario choice for simulation and forecast.
Eventually temperature changes were evaluated. In addition, we used to change snow cover of MODIS TERRA and Aqua satellite Image monthly for during 2000-2014.
-Mann- Kendall test one of the most important nonparametric tests is Mann- Kendall test. The following equation can be used: (Equ2) (Equ3) (Equ4) (Equ5) (Equ6) (Equ7) The climate change is causing water stress. The increasing air temperature, drought and decreasing water are signs climate change in the whole world. The results indicative to exist changes at minimum air temperature, snow cover and discharge in North Karun Basin. The results shown that minimum air temperature has upper ward at 95% and it is increasing between 0.1Cº to 4.4Cº in particular at cold season (November, December, January, February). In addition, Snow cover, ice daily and discharge have decreasing trend. Also, the results simulator by GCM shown that air temperature trend will perpetuate in this basin at future period. Therefore, the increasing air temperature minimum, decreasing snow cover and discharge are causing water stress in North Karun Basin and Karun River. The results show that air minimum temperature increased cause water resources decreasing and supply hydro-electric in the feature period

  Drought has always been a heavy financial burden on the country's economy. Therefore, being aware of the drought situation in high risk areas can reduce the damages, Drought management, reduce problems and increase the ability to manage natural resources and agricultural resources. Fortunately, the advent of new technologies to identify and zoning in high risk areas caused done extensive research in this field. Isfahan province is located in the Central Plains of Iran. It has the arm and dry weather. Unfortunately, decreasing rainfall, over exploitation of wells and misuse of water resources play an important role in the province's drought recently. Najaf Abad Plain is one of the Basins which is located in this province. Placement of a modern irrigation network, a sharp drop in groundwater levels and reduce of water discharge are reason of important this area. So this area was chosen in this study. Unfortunately, frequent droughts in parallel with drying up of Zayanderood River brought about undesirable effects on the local economy in the current years. The aim of this study is to help the researchers and decision makers for a proper management of water resources in this area by identifying areas susceptible to drought. To reach this goal, meteorological data and satellite images in a 25-year period were used. In this study, according to rainfall data from weather stations, three years of high rainfall (1995), low rainfall (2008) and normal year (2015) were selected and the amount of actual evapotranspiration was calculated using SEBAL algorithms on the ETM+ images and Penman-Monteith method on meteorological data. For this purpose, 36 images of landsat ETM+ were downloaded from the earth explorer site in these years and daily evapotranspiration was calculated. The period covered by each image found and the amount of Monthly reference evapotranspiration was calculated using Penman-Monteith and meteorological data. Monthly reference evapotranspiration were multiplied in the daily evapotranspiration values and monthly actual evapotranspiration was calculated. Annual evapotranspiration was obtained the monthly actual evapotranspiration values. Then, the rainfall zoning map was prepared by interpolation of rainfall data from weather stations. Fuzzy Method and weighted overlay are method for determination of susceptible to drought. Fuzzy method was used in this study because Fuzzy method shows better and clearly results. Finally, the sensitive areas were identified by overlaying fuzzy maps of rainfall and evapotranspiration in these three years. Comparing the results of Penman-Monteith and SEBAL algorithm showed that the root mean square error of these two methods is about 0.21 and 0.73 respectively. In other words, 73 percent of evapotranspiration in Penman-Monteith method justifies the changes resulting from the SEBAL method and so can be trusted regression equation. Because the determination coefficients of the regression are high so Changes dependent and independent variables show well. Studies show that 2008 is the most evapotranspiration and 1995 is the lowest evapotranspiration. The result shows that South and Southeast regions of Najaf Abad are more sensitive than other parts and they are higher risk for drought in 1995. Because in this part evapotranspiration is high and rainfall is low. Therefore, the drought sensitive areas have been identified. In 2008, the probability of drought in those regions has continued. The difference is that the number of pixels in 1995 showed less sensitive to drought than pixels in 2008. While at the same area in 2008 more pixels have been involved in drought and large scale mapping of sensitive areas. In addition to the central and western regions of Eastern have susceptible to drought also. This result matches with result of Standardized Precipitation Index (SPI). Because the Standardized Precipitation Index (SPI) shows that intense meteorological drought has occurred in this area. However, the zoning map sensitive area is represented the issue. The southern and southeastern areas out of drought situation and they have favorable conditions in 2015. This result shows that rainfall is better in this part of the basin. The results of this research help experts to identify areas susceptible to drought. We need proper planning to reduce the effects of drought in areas with high risk more than before based on the resulting map. It is recommended that the methods used for irrigation and cropping patterns according to region and the effects of drought reduced greatly.
Key Words:Drought, evapotranspiration, Rain fall, Remote sensing, GIS, Najaf Abad Plain.

  land surface temperature plays a vital role in a wide range of scientific researches, inter alia, climatology, meteorology, hydrology, ecology, geology, medical sciences, design and optimization of transportation services, fire location and especially in calculating the real evaporation and transpiration. There are some determining factors which affect the land surface temperature, such as, the kind of surface elements, topography conditions, environmental conditions, climate condition and the amount of emitted energy from the sun. Recognition and analysis of the relation between the land surface temperature and various factors are so critical. The remote sensing method has a widespread application in preparing the land surface temperature images due to the extensive covering and continuous data. The purpose of this study was to investigate the effects of vegetation cover indices and topographic factors on land surface temperature and modeling the relationship between land surface temperature, topographic conditions and vegetation cover using Landsat 8 satellite imagery. In this study, sensor reflective bands OLI, Jimenez and Sobrino method were utilized to calculate the emissivity of the available phenomena in the area. By using TIRS land set 8 sensor thermal bands 10 and 11 and utilizing Split-window algorithm, the land surface temperature was calculated. Topography parameters, such as elevation, slope and slope aspect and area vegetation were extracted using digital elevation model and NDVI index, respectively. Then, the relation between the land surface temperature and topography factors in diverse conditions was investigated by statistical analysis, and then, the validity of relations was analyzed with a confidence level of 95%. For this purpose, ENVI 5.3, Arc GIS 10.4, ERDAS IMAGING 2014 software as well as SPSS statistical software were used. The obtained resultants indicate that the study area has a uniform vegetation cover in most areas and a high percentage of areas have the NDVI of 0.45-0.6. Nonetheless, due to the diversity in topographic and climatic conditions the area surface temperature is inhomogeneous and non-uniform. Consequently, there is no relation with a high correlation coefficient between the land surface temperature and vegetation in the area. However, there is a reverse linear relation between the land surface temperature and vegetation in the area. This relation gains a higher correlation coefficient in the form of linear relation compared to second order polynomial, Pearson, logarithmic etc. equations. Areas with southern and southeast slope have higher land surface average temperature compared to other directions during imaging due to their position which is in the direction of sun straight radiation. The temperature average is different in various slopes. Making the relation of temperature and elevation independent of slope parameters and slope aspect, give rise to an increase in the correlation coefficient between two parameters. The relation of the land surface temperature and elevation, regardless of slope and slope aspect for the studied area, is a reverse linear relation with the correlation coefficient of 0.54, whereas for the relation between the land surface temperature and elevation in the western slope direction and slope of 40-50 degree, there is a reverse linear relation with the correlation coefficient of 0.76. Moreover, in investigating the relation between the land surface temperature with topographic conditions, simultaneous consideration of both elevation and slope variables as independent variables for modeling the dependent variable of surface temperature lead to a strengthened relation. The addition of vegetation index parameter to relation independent parameters bring about a rise in relation’s correlation coefficient. For instance, relation’s correlation coefficient of the land surface temperature with elevation independent variables, slope, and vegetation in the western, northwest and southeast direction, are 0.84, 0.81 and 0.8, respectively. All the obtained relations are investigated in the confidence level of 95%, and validity of relations was confirmed by “t” statistic and resultant probe for relations’ coefficients. Results imply that by considering each elevation, slope, slope aspect and NDVI parameters independently for modeling the land surface temperature, adverse results would be obtained, and by simultaneous using of both topographic parameters and vegetation and also their combination, as dependent parameters, the land surface temperature can be precisely calculated. In addition, for accurate modeling of the land surface temperature all topographic, climatic and environmental conditions for the area should be taken into account. The thermal and reflective remote sensing technology are economical, fast and effective due to several positive aspects such as providing uniform topographic, vegetation data and environmental parameters. So, further researches and investigation are necessary.

  More than half the world’s population lives in coastal region (Bird, 2008). The littoral is an unstable zone that comprises marine and terrestrial domains very sensitive to a variety of geological process; Waves and tides play a most important role in the coastal dynamics, and their mutual interaction accounts for redistribution of the sediment budget causing accumulation or erosion (Dabrio, 2000). The coastal ocean, where land, air and sea meet, is a region of very high physical energy and biological diversity that is heavily exploited by man (Murthy et al., 2002). Tidal flats are low-lying areas that are alternately covered by water and exposed to the air each day (Gore, 2010). Analysis of forms and coastal conditions provides a valuable key for coastal management. Many studies have been conducted on the impact of waves on beach (Bagnold, 1939; Ross, 1955; Hayashi and Hattori, 1958). In a related research on western Mokran coastal plain, Shayan et.al, (2014) found that from forming processes aspect and according to the coastline curves and rivers' hydrodynamics, coastal sand masses were located by coastal waves. The aim of this study is identifying the effects of river’s flows on sedimentological characteristics of shoreline in the western part of Mokran coastal plain, Iran.
The study area is located at 25o31’-27o09’N, 56o54’-59o19’E, in western of coastal plain of Mokran, south of Iran at the north of Oman Sea and east of Strait of Hormoz. In general the region under research could be assumed a dry land with very low rain and its remarkable nature is windy, sand storm, torrent shower, thunder-storm, higher humidity and hazy down(Akbarian et al., 2006). From geological aspect, this area affected by Mokran (Makoran) region general construction and mainly composed of shale, marl and sandstone layers. Research's' data and information include spatial distributions of tidal zones, velocities and directions of wind, fetch length, the morphologic and sedimentological characteristics including granulometeric and morphoscopic indicators. Geologic, topographic maps, satellite images, GPS, binocular microscope and computer's software including ArcGIS and Gradistat also were used as the research tools. For sediment measurement studies, 8 samples were taken from shoreline. Samples were analyzed using ASTM standard sieves and Gradistat software from granulometery aspect. Morphoscopic indicators were investigated by 40X binocular microscope. After that by using wind data, WRPlot View software and Molitor equations, wave characteristics were studied, the wave roses were plotted also. It can be concluded that the processes of marine sediment transportation, are in force across the range of this coastal zone. Waves and tides deliver sediment to the intertidal zone, so that wind is able to transport it landwards from the intertidal to the supratidal area (French, 2001).
Particle size windows (PSWs) are interpreted as reflecting different modes of sediment transport and deposition (Clarke et al., 2014). A beach can be composed of a wide variety of materials of many sizes and shapes (Dean and dalrymple, 2004). Based on sedimentological characteristics, tidal deposits are comprised of fine-grain to very fine sandy materials. In general these grains are angular to sub-rounded and their surface texture are bright. Samples don’t have clay hunks but have small fragments of marine shells.
Waves are crucial in stirring up sand in the nearshore; variations in direction of wave approach, combined with irregularities in sea floor topography results in refraction or bending of wave crests and this is turn initiates variations in energy levels received along a shoreline(kidd, 2001). On south coast, west and southwest waves are predominant from October till the end of June; In July, the percentage of southeast waves is considerably increased except of West Jask Cape. This situation continues till the end of September. So sediments which were brought by rivers, are drifted in different directions by waves along with coastline sharp curvatures. The present study results shows that coastal area sands were transported by the sea processes as a final stage. But morphometric indicators represent high impact of fluvial processes on coastal deposits. As conclusion, we can say that sources of the analyzed sediments is adjacent to mouth of the region rivers; the transportation ability of sea processes is less than performance need to eliminate fluvial effect on coastal deposits and sediments.

  Precipitation data have been widely used in many earth science applications ranging from crop yield estimates, tropical infectious diseases, drought and flood monitoring. However, in many tropical regions and parts of the mid-latitudes, rainfall estimates still remain a major challenge due to sparse rain gauges. To better develop applications for these regions, it is necessary to have rainfall data with adequate spatial and temporal resolutions. Precipitation data plays the key role in drought monitoring. Rain gauges are the mainly measuring methods for precipitation but they are concentrated in developed countries and are spare in developing countries and remote areas in the world. Researchers have shown that remote sensing techniques using space-borne sensors provide an excellent complement to continuous monitoring of rain events both spatially and temporally. Microwave and Visible/Infrared are the main forms of remote sensing technologies; both have varied advantages in terms of imaging accuracy and spatial-temporal resolutions. So, the fine spatial-temporal precipitation products need the coalescence of both. Tropical Precipitation Measuring Mission (TRMM) carrying sensors on precipitation provides the opportunity for fine spatial-temporal precipitation products. In this research for Central Iran, the precipitation data of TRMM satellite was evaluated and used to estimate the severity of a drought based on precipitation. Central Iran is located between 27N-37N latitudes and 48E-61E longitudes and has an area of about 837,184 km2. There are 50 synoptic stations within the area. The data set used includes monthly precipitation depth from both synoptic stations and TRMM data (3B43 V.7, in ASCII format). A five year (2001–2005) period were chosen for the analysis. The accuracy of precipitation data that are used from synoptic stations and TRMM satellite are provided by the source provider. Firstly, the evaluation of TRMM satellite data was measured using coefficient of determination (R2), mean error (ME), mean absolute error (MAE) and root mean square error (RMSE) in 95% confidence levels. Then, remote sensing data of TRMM are used to provide the required data of precipitation drought index in central Iran for mapping the spatial distribution of drought. Finally, accuracy of the PDI drought index based on satellite data carried out using the evaluation criteria compared with drought spatial distribution map of the PDI based on ground-based precipitation measurements data and soil moisture values of 50 synoptic stations. In this study for the first time, a comparison between monthly rainfall values estimated by satellite products and rain gauge observations was implemented over the Central Iran. The validation of TRMM 3B43 data were performed at monthly, season and annual scales. The average of monthly rainfall, seasonal and annual for all selected synoptic stations and TRMM data were compared during the period 2001-2005. TRMM data at all time steps except August, estimates the average of monthly rainfall more than observed data. The correlation coefficient between the average of monthly rainfall, seasonal and annual rain gauge and TRMM has shown that this ratio is variable between 0.45 to 0.94 for all time steps, the average of this ratio is equal to 0.76. Highest and lowest values of R2 at monthly time step obtained for April (0.92) and June (0.45) respectively. In this time step, lowest and Highest values of statistical error criteria obtained for June and January, respectively. The seasonal time step, the highest and lowest correlation is related to the spring and summer with determination coefficient (R2) of 0.94 and 0.64, respectively. In this time step, lowest and Highest values of statistical error criteria obtained for summer and winter, respectively. Generally, TRMM data performs best in summer, but worst in winter, which is likely to be associated with the effects of snow/ice-covered surfaces and shortcomings of precipitation retrieval algorithms. The correlation coefficient for the annual time step is equal to 0.83. The results of statistical criteria showed that TRMM rainfall data in all time steps overestimated for all months except of August. The lowest to the highest of statistical error criteria obtained for monthly, Seasonal and annually rainfall data, respectively. In the next step, spatial distribution of drought based on measured data from ground stations and TRMM data over the period 2001-2005 obtained from using Precipitation Drought Index (PDI) method in study area. The results of the statistical criteria of conformity assessment PDI spatial distribution map based on TRMM data with corresponding pixels spatial distribution map based on the synoptic stations precipitation data showed that the drought severity map based on TRMM data had a high precision and good conformity with ground data (R2=0.89, ME=0.08, MAE=0.14, RMSE=0.19). Also, the results of the evaluation criteria showed that PDI index in accordance with soil moisture values had the significant correlation (0.71) and the lowest estimation error (2.33). In this research, for estimation of drought severity index based on precipitation, the monthly precipitation data of TRMM satellite (3B43) was evaluated. The evaluation was measured using coefficient of determination (R2), mean error (ME), mean absolute error (MAE) and root mean square error (RMSE). This analysis has demonstrated that the TRMM rainfall products show very good agreement with gauge data over the selected area of Central Iran on monthly timescales and 0.25° space scales. In conclusion, it can be said that the satellite-based rainfall, e.g. TRMM data, have good potential for useful application to hydrological simulation and water balance calculations at monthly or seasonal time steps, which is a useful merit for regions where rain gauge observations are sparse or of bad quality. However, several shortcomings, such as the TRMM overestimates the rainfall in some years and areas and underestimates in other years and areas, and failed to detect the extreme rainfall, reduced the accuracy of stream flow simulation at short time steps and other applications including drought monitoring and flood forecasting. The above mentioned conclusions indicate that it is necessary to further develop algorithms of satellite-based rainfall estimation in terms of both the accuracy and spatiotemporal resolutions of rainfall estimates

  Thunderstorms are formed by overheating the earth's surface in air masses or in the weather fronts especially cold front (Kaviani and Alijani, 1991). These storms are part of climatic destructive Phenomenon that cause irreparable damage as hail, heavy rainfall and thunders to facilities, farms and houses every year, so it is necessary to study this phenomenon. Since climatic phenomena such as thunderstorms, as random phenomena, are not exactly predictable and can be gained useful information by monitoring them, that this is possible through the laws of probability. Markov chain is a common method for modeling random phenomena (Wilks, 2006). This model is one of the statistical methods to examine the temporal relationship between the various climatic phenomena and it is the most common method to determine the frequency of climatic phenomena sequences. In this method, the probability of occurrence of a climate state is projected at time t based on its condition at the time before (t-1) (Alizadeh, 2001). This model is widely used in various fields including atmospheric sciences. In recent decades, climate researchers have used this model in various fields such as rainfall, drought, thunderstorm, wind speed and solar radiation. Thunderstorms is known as one of the most important atmospheric phenomena, due to the obvious climatic function and imposition of natural and human catastrophic effects, and many scholars and researchers pays attention to it in the various branches of science. The aim of this study is to evaluate the occurrence probability and return period of thunderstorm using Markov chain model and its spatial analysis in the Northwest of Iran. In this study, to Spatial-temporal modeling of thunderstorm day's occurrence in the Northwest of Iran, were used data for 19 synoptic stations during the period of (2000-2015) related to specific phenomena and the thunderstorms codes (99-95) were extracted. Markov chain models are including stochastic processes that are widely used in discrete time series modeling. Time dependence of random variables expressed through autocorrelation coefficient or transition probability matrix (Ashgartousi et al., 2003). Transition probability matrix, is a square matrix, depending on its number of states that includes the number of possible n combinations of the transition probability from one state to another. The first order Markov chain, is the main form of the Markov chain which consists of a discrete time series which the behavior of the series in the next time step depends on present not on past time steps.
Result and To examine the sequence of thunderstorm days in the Northwest of Iran, at first the frequency of normal and Thunderstorm days were calculated. The results show that in April, the highest frequency of days with thunderstorms is in center, in May in northwestern and central areas and in June and July, can be seen in the northwest. The lowest frequency also can be seen in the southern parts of the study area. Frequency and transition probability matrix was calculated for time series of thunderstorm days, Then the fit of the two- state first order Markov chain model were studied using χ 2 test and the results showed that the states frequency follow a two-state Markov chain. In order to predict the thunderstorm period, at first, the frequency of n days’ period of thunderstorm days was projected in April, May, June and July for stations, the result show that by increasing the duration of period, the frequency of the dusty days is reduced.
The estimate of n day continuity of thunderstorm days is the abilities of the Markov chain model. In April the occurrence of two-days in the center and west of study area is more possible. In May, there is the highest frequency of thunderstorm days, the probability of two- days increased to 24%. In June, the average probability of two-days is 23%. The probability of three-days in April reduced an average of 6%. In May and June, the probability is about 10%. In these months, the probability of three-days has reached 15% in North and Northeast. In July, the probability of three-days has reduced to; While, in the western part is estimated up to 14%. Return period of one-day in all stations is on average 1.5. This means that every 1.5 days, a one-day will be in the Northwest Iran. For increasing duration of thunderstorm, the return period increases, it means that it takes a long time a five-thunderstorm day happens again. Spatial distribution of occurrence probability of thunderstorm days in the long-term (persistent probability) showed that in April, the lowest probability of thunderstorm is in the Northeast and the highest probability is located in the center. In May and June, the lowest probability is in the southern parts and the highest probability is in the North and Northwest. In July, the lowest probability is in the south and Northeast and the highest probability is located in the West and Northwest. In general, the probability of days with thunderstorm in the south and northeast is less than other areas and center, north and northwest have the highest probability. The present study is conducted aimed to evaluate the occurrence probability of thunderstorm using Markov chain model in the northwest of Iran. The results show that in all stations, frequency of normal days is more than days with thunderstorm. The highest frequency of days with thunderstorms in April is in center, in May in northwestern and central areas and in June and July is in the northwest. The estimate of 1-10 days continuity showed that by increasing the duration of period, frequency of dusty days reduce. The occurrence probability of two and three-days is low in the south and Northeast and high in center and Northwest. Return period of one-day on all stations and in all months is 1.5, but by increasing duration of dust, its return period increases exponentially. The spatial distribution of persistent probability matrix shows that the occurrence of thunderstorm in center, North and Northwest is more

Zarrineh Roud River based on different variables, such as channel planform, geological controls, bed material and anthropogenic effects, was divided into five different reaches: Reach (1) from start point to Shahindejh industrial town; reach (2) from industrial town to Norouzlu Dam; reach (3) from this dam to Miandoab city; reach (4) from Miandoab to Chelik village; reach (5) from Chelik village to Urmia Lake. Reach (1) is type C3, which according to the slope and bed material divided into two sub-reach: sub-reach C3b and sub-reach C3. Type C3 streams have a good recovery potential, moderate sediment supply, moderate streambank erosion potential and very high vegetation controlling. In this reach, the lateral dynamic of channel is low due to the geological variable control and low erodibility of bank material. Also, the bed incision is largely limited due to the presence of coarse material and bed armoring. Therefore, this reach is relatively stable. From start point of reach (2) appears significant changes in the geomorphological characteristics of the Zarrineh Roud River. Floodplain width significantly developed and geological controls reduced. In this reach, Zarrineh Roud River become specific example of gravel bed rivers that extends to the downstream of Norouzlu Dam. At the upstream of reach (2), the river transformed into a type D4 stream. According to the field studies, high erodibility of bank materials along with poor control of vegetation are the main reasons to create the type D in the some parts of this reach. Other parts of reach (2) are very specific example of type C stream that according to the slope and bed material, belong to the type C4. This reach, shows a complete characteristics of type C4 streams, so that, has a high sediment supply, very high streambank erosion potential and very high vegetation controlling. In fact, vegetation cover combined with the erodibility of the streambanks determines the degree of lateral adjustment and stability of this reach. In this reach, most meanders are active. Lateral migration of meanders accompanied by the mass failure in elevated clay banks. Reach (3), from downstream of Norouzlu dam to Miandoab city, in most sections, are converted to type G and type F streams. In fact, in this reach, a conversion has taken place from type C (C4 in upstream and C5 in downstream) to type G and type F due to very extensive anthropogenic interference. In this reach, sand and gravel mining is widely and uncontrolled. River bed entrenched up to several meters (confined) and often with steep and vertical banks isolated from floodplain. Type G5, type F4 and type F5 streams have Very poor recovery potential, very high sediment supply, very high streambank erosion potential and high vegetation controlling. This reach in terms of natural lateral dynamic is inactive. Based on the delineative criteria of Rosgen model, reach (4) have characteristics of the type C5b streams. In this reach, flood prone areas are reduced due to the human interference. Flood prone areas mainly accordance with scroll bars which developed in the convex sides of meanders. This reach due to the anthropogenic disturbances does not have typical characteristic of type C streams and is better be considered as reach that converting from type C to type F. Reach (5) be converted to a type E that according to substrate material and slope, divided into two sub-reaches: sub-reach type E5b in the upstream and sub-reach type E6b in the downstream. In this reach, floodplain is very broad and developed. Creating a developed meandering pattern is related to the low stream power and cohesive bank material. This reach is relatively stable.
Efficiency of Rosgen model in the upstream reaches of Norouzlu Dam and final reach is relatively good and almost all sections are compatible with a type of Rosgen streams. In these reaches, river form and pattern is largely indicates the processes governing river channel and river morphology controlled by variations of stream power and variability of bank and riparian conditions. In the both reaches (3) and (4), due to high control of anthropogenic factors, explanation ability of the Rosgen model limited and incompatibilities observed in determining the type of Rosgen streams. These two reaches are critical reaches along the Zarrineh Roud River. Rosgen (1997) proposed four priority in its geomorphological approach to restoration of incised rivers that prioritized as follows: Convert G and/or F stream types to C or E at previous elevation w/floodplain; Convert F and/or G stream types to C or E; reestablishment of floodplain at existing level or higher but not at original level; Convert to a new stream type without an active floodplain but containing a flood prone area. Convert G to B stream type or F to Bc; Stabilize channel in place. Considering that rehabilitation and restoration of type G and type F are difficult, it is better to with apply restrictions in relation to sand mining avoided of the conversion the reach (4) (downstream Miandoab) to type G or type F. For type F and type G reaches, due to the high population density in the region and increase the probability of flood event, the second priority, Convert F and/or G stream types to C or E and reestablishment of floodplain at existing level or higher but not at original level is recommended

Identifying the phenological stages and the heating and chilling accumulation of fruit trees on the basis of the climatic conditions is of great importance in discovering the potentialities and identifying the compatible species. Most deciduous trees need chilling and heating accumulation in order for complete growth and development. In fact, chilling accumulation is necessary to overcome the dormancy or sleep period and heating accumulation is necessary for flowering and the change of phenological stages. The study of the phenological behavior of fruit trees is important as an impact of environmental conditions. Weather and climate constitute a key determinant in successful production of deciduous fruits. Identifying the phenological stages and climatic condition requirements for fruit trees can help promoters and gardeners to choose the best and most suitable varieties. This research as statistical-field is an applied research. In the field study part, in order to identify the time of the occurrence of phenological stages and the temperature thresholds, a series of daily and weekly visits and writing field notes were regularly conducted in the growing season of the crab apple tree. For this purpose, a commercial fertile garden with an appropriate cultivated land area of crab apple tree was selected. The garden complex is located adjacent to Karaj Meteorological Station, Karaj-Mahdasht Road, Karaj County, Alborz Province, Iran. In the process of conducting field observations, with the assistance of gardening and horticulture experts, from a set of one hectare of apple trees, four Kohanz crab apple trees were identified in different parts of the garden as an Iranian early-season variety. The phenological stages and temperature conditions were recorded on the basis of principal and secondary codes of the BBCH scale with daily and weekly visits. In the process of field visit, the phenological conditions of the trees were examined and compared, and ultimately, the final date and temperature threshold of the stage were recorded.
The required statistical data for the hourly and daily climatic parameters from 1985 to 2014 were obtained from the Iranian Meteorological Organization as well as Alborz Province Meteorological Department. The chilling requirement was determined through such models as the Chilling Hours (CH), UTAH and dynamic (CP or Chill Portions) models, and the heating requirement was identified through the effective and active growing degree day and growing degree hour models on the basis of Anderson and Richardson’s models. The Mann-Kendall's nonparametric method was also used to determine the process of temperature changes. In the early-season apples, the end and beginning of the sleep or dormancy period occur in late February and mid-October. The longest phenological stage is the fruit development stage, starting from the end of flowering in the first ten days of April till the end of June and early July. In the Karaj climate, early-season apples begin to sprout in late February and are completely asleep in late October. Its winter chilling accumulation was obtained as 1027 chill hours based on the CH model, 1771 chilling units based on the UTAH Model, and 76 chilling portions based on the dynamic (CP) model. Based on Anderson’s model, 7203 growing degree hours (GDH) and based on Richardson’s model, 12086 growing degree hours (GDH) are required for heating accumulation from the end of dormancy to full flowering. During the whole growth period for the seven main phenological stages, 2223 and 3026 effective and active growing degree days are required. The increase in the hourly temperature, especially the minimum temperatures, is obvious during dormancy at the end of the cold season at most hours of the day. The temperature has a significant upward trend in February and March. The climatic conditions of Karaj has the necessary chilling and heating accumulation for cultivation of the early-season varieties of crab apple. Considering the rising hourly temperature, the early emergence of flowering in the middle of the winter in the future and the risk of frost are not unexpected. The output of chilling accumulation determination models on the basis of long-term hourly temperature data from Karaj Station showed that there is sufficient chilling accumulation for the early-season varieties of the crab apple tree in Karaj climate. The highest chilling accumulation occurs during the dormancy period in December, January and February. The chilling accumulation results can be used as a model for other deciduous trees. The climatic potential of the region provides chilling accumulation for early-season varieties, but it is limited for late-season varieties with high requirements. In terms of heating accumulation, there is no specific limitation for the apple trees.
The results of hourly temperature variations in the cold season of the year, i.e. the dormancy period of the fruit trees in the studied area indicated that the air temperature has an increasing trend at the end of the winter season, especially in February and March. This increasing trend is more visible during the night and morning hours when the minimum temperatures occur. This significant increase in the hourly temperature of the cold season of the year and the dormancy period of the fruit trees, on the one hand, will reduce the chilling accumulation of the apple trees and other similar trees, and on the other hand, by accelerating the early emergence of germination and flowering phenological stages at the end of the cold season will bring about a serious risk of late-frost and cold for the early-season apple trees as well as other fruit trees. Hence, it is important to take necessary decisions for dealing with the frost and cold climate crisis.
The results of this study indicated that the temperature conditions constitute the most important climatic necessity for the fruit trees and that fruit trees are highly responsive to any temperature changes. The use of hourly and daily temperature data is of great importance in measuring the chilling and heating potentials in order to select the compatible species with the climatic conditions of each region for preventing the loss of capital and resources

Isfahan province is one of the driest provinces in central part of the country center where two-thirds of its water requirement is supplied by groundwater resources. In recent years with population growth and the increase of water demand in different sectors of agriculture, industry, drinking, and health, groundwater resources have faced a lot of pressure. So, Understanding the behavior of the groundwater body and its long term trends are essential for making any management decision in areas. Mann–Kendall statistical test has been commonly used in many researches to assess the significance of trends in hydro-meteorological time series. As well as, geostatistical analysis has been theoretically defined and applied by many researchers, they have emphasized that geostatistics is a management and decision support system tool for analyzing spatial and temporal variations of groundwater level fluctuations. Geostatistical approaches can provide more helpful, reliable and efficient tools to increase the number of measurement points at unsampled locations, and variogram analyses for examining structural relationship of data (Rakhmatullaev et al. 2011).

In this study, the temporal and spatial variations of groundwater level for Isfahan-Borkhar, Najafabad and Chadegan plains with a 25 years period (1990-2015) investigated. Monthly water level data have been collected from 29, 40 and 8 observation wells in Isfahan-Borkhar, Najafabad and Chadegan plains, respectively during the study period. Precipitation data from synoptic, and climatology stations.were used in this research.
The trend analyses were done at monthly, seasonally and annual scales using Mann-Kendall Test and Sen's slope estimator nonparametric methods by Matlab 7.11 software. The Mann-Kendall test is a non-parametric method used for trend analysis of time series data. In this method, the presence of a statistically significant trend evaluated using Z value (equation 1).
(1)
A positive value of Z indicates an increasing trend and negative value indicates decreasing trend.
In this research, null hypothesis (H0) was that “there is no trend in time series of groundwater levels” is rejected if the absolute value of Z is greater than ±1.96 and ±2.58 in 5% and 1% level of significance, respectively. If value of Z is out of this range, null hypothesis is accepted and there isn’t any trend in time series (Yin et al. 2015). The groundwater level hydrograph was drawn also for each plain during 25 years (January 1990 to October 2015) in Excel software.
Prior to the geostatistical estimation, its require a model that compute a semivariogram value for any possible sampling interval. The experimental semivariograms were fitted with various theoretical models like spherical, exponential, Gaussian, linear and power in GS+ software. The underground water level zoning maps was prepared by Ordinary Kriging method for every year of the study period. Spatial zoning maps were prepared and ultimately, iso-falling map was provided at the beginning and end of the study in Arc GIS 10 package.

The Spatial distribution of trend by the Mann-Kendal test for annual groundwater level data indicated a general trend in all plains where most piezometers groundwater level is negative. the piezometer wells with positive trend were so low and the most positive trend is related to Isfahan plain. In Chadegan plain there wasn’t any positive trend in piezometers level.
The investigation of trend line slopes revealed that in average, the groundwater level of Isfahan-Borkhar, Najafabad and Chadegan plains has been decreased about 0.468, 1.12, 0.638 m y-1, respectively. The general trend of the groundwater level hydrograph shows that the level and storage of water continuously decreased in the last years.
Gaussian variogram model was the best for spatial structure of these data. Generally, the spatial pattern of zoning Groundwater maps indicated that direction of groundwater flow is northwest to southeast in all study plains. As well as, iso-falling map showed that groundwater level has dropped in most parts of the plains during the study period, Which was in agreement with this research (Rahmati et al. 2014, Azareh et al, 2014, De Brito Neto et al. 2016). The cuase of this agreement could be vulnerability of arid region ecosystems into human interference and other stressful factors.

Due to the lack of rainfall trend in the most of the stations across study area, the major cause of reduced groundwater could be related to human factors. overexploitation, growth of population, and increasing number of wells are the most important factors in decreasing of groundwater table. So the decision to management groundwater in the study area is required. it is necessary to immediately practice water conservation methods and water harvesting systems in the study area to prevent the causes of more damages to the available water resource

  All studies in the field of climate change impact assessment needs climate data with different spatial and temporal scales. The lack of temperature and precipitation data with high spatial resolution is a major limitation to analyzing future climate change. In addition, the output of the models has error that needs to be corrected; otherwise they will make a significant bias for assessing effect of climate change. Therefore, identifying the best regional climate model for downscale the global climate models is essential to better understanding of climate conditions in the local and regional scale. In the last few years, using various regional climate models in order to produce a multi-member set of the downscaled data in the CMIP5 project by World Climate Research Program (WCRP) in action with Coordinated Regional climate Downscaling Experiment (CORDEX) with the aim of producing regional climate change forecasts, was established as an input to researches on the impacts of climate change and ways for adaptation to it. The main objective of this research is accuracy evaluation of different model outputs of the CORDEX project with different domain and resolution in Iran. In the CORDEX project there are two domain that covering Iran. These two domains are North Africa-Middle East (CORDEX-MNA) including latitude of 7° S to 45° N and longitude 27° W to 76° E and South Asia (CORDEX-WAS) that includes latitude 13° S to 44° N and longitude is 27° E to 107° E (Figure 1). To do this research, first daily output of precipitation, maximum and minimum temperatures in the period of 1990-2005 for three regional climate models with a special resolution of 0.22 ° and 0.44 ° that performed by three international meteorology institutes, available at ESGF web site (Table 1). Daily observation data that recorded in 304 synoptic stations in Iran for the three variables were collected from Iran Meteorology Organization and transferred to a matrix with 3044×5844 dimensions. Then, several scripts were written in the MATLAB software for extract the model data in the domain of Iran and compare output model and observational data with two conditions. The first condition is in the output models resolution of 0.44° (spatiotemporal matrix with dimensions of 5844×740), the observation station should have a distance of less than 25 km, and the next condition is in the resolution of 0.22 ° (spatiotemporal matrix with dimensions of 5844×3218) should have a distance of less than 12 km. The difference between observation data and its corresponding estimated cell were investigated with statistical method such as Mean Error (ME), Pearson Correlation Coefficient, Root Mean Square Error (RMSE) and Standard Deviation (SD). Also, we were used Box-Whisker plots and Taylor Diagram to find the best regional climate model.
Result and The precipitation accuracy of regional climate models output presented by different meteorological institutes (Table 1) was evaluated with observational data in two domain, CORDEX-MENA and CORDEX-WAS, in Iran (Fig. 4). The calculation of the outputs mean error of different models showed that none of the models have a suitable estimation of precipitation values in research domain. The HadRM3P model shows the lowest RMSE calculated with comparing to observational data for the maximum temperature across Iran except the central parts. However, for the minimum temperature RegCM4.1 model shows the lowest difference with compare to observation data in most parts of the research domain. For annual precipitation using the Box-Whisker plot, which compares the correlation coefficients between the observed data and the corresponding cells in the northern and southern half of Iran, in general, none of the models have an accurate estimate of precipitation in Iran (Fig. 8a). This plot for different models showed that the outputs of the HadRM3P and RegCM4.1 models, respectively, for maximum and minimum temperatures in most cells, have more than 0.8, correlation coefficient (Fig. 8b and c). The correlation of rainfall data shows that most models in the central and mountainous regions of Iran do not have high correlation coefficient with observational data. Spatial distribution of correlation between maximum temperature model outputs and observational data in Iran shows that the two HadRM3P and RCA4-WAS0.44 models have a strong correlation coefficient, respectively and changes in the correlation coefficient in the HadRM3P model are low in both the northern half and the southern half of Iran. The RegCM4.1 model had the stronger correlation in the northern half in compare to the southern parts of Iran. Also, the mean difference of estimated model output with observation data of this variable in the whole of Iran is less than 1°C and this model is the most appropriate model among the available models for minimum temperature in Iran

  Among the natural disasters, flood causes heavy losses on the agriculture, fishery, housing and infrastructures. It also strongly affects the economic and social activities. the use of modern science, especially remote sensing and GIS techniques helps the planners to assess the risk map of natural hazards such as floods for a region in the least possible time. Multi-Criteria Decision Analysis (MCDA) provides methods and techniques required to analyze complex decision problems which often includes non-comparable data and metrics. Nekarood watershed due to its special properties is Prone to serious and subversive floods. the aim of this study is to assess the risk map of Nekarood Basin caused by floods using multi-criteria decision and based on intuitive reasoning. According to the opinion of experts the two sets of effective criteria on flood risk and vulnerability has been used for the study area. Hydro-climatologic, topographic, land cover and geological factors as effective criteria on flood risk and population density, yields and distance from the road network factors as effective criteria on flood risk were determined for study area. Primary data used in this study included digital elevation model, 8 Satellite LANDSAT 8 images for different months of 1395 to 1393 years to provide vegetation maps, land use map, Geological map, isorain shapefile, road network shapefile, waterways network shapefile, demographic statistics and information relating to agriculture lands and gardens. to dispel uncertainty in the experts opinions about the importance of different criteria on flood risk, Intuitive reasoning theory and Dempster-Shafer model was has been used and final weight of each criterion is obtained. Then, by using the multi-criteria decision method the flood risk map has been made. using the mean and standard deviation, risk map was normalized and the study area In terms of risk was classified to five classes; very low, low, medium, high and very high. Finally, the state of sub-basins and land uses in terms of risk was assessed, compared and analyzed. The results show that topographic factors and population density have the highest degree of importance on risk and vulnerability Caused by floods respectively. to prepare flood risk map for the study area, the number 0.701 was considered for weight of risk factors and 0.299 for vulnerability weight factors. The results of the different risk classes study show that downstream sub-basin of Neka watershed contains the most area of high and very high risk classes and upstream of Neka include the lowest area of high and very high risk classes. Generally; 87 percent of high and very high-risk areas are in downstream sub-basin of Neka. also for dense forest, thin forest and rangeland the area ratio of low and very low risk classes, is greater than area ratio of high and very high risk classes. But for made and agriculture land uses area ratio of high and very high risk classes are more than area ratio of low and very low risk calsses. Results indicate that 59% of the made land use of Nekarood watershed Located in high and very high Risk classes. Flood risk for Neka city in very high and 67% of it located in very high risk class while 90% of Neka city is located in high and very high classes. Nekarood river enters the city of Neka with a considerable amount of water discharge after passing a very long way, approximately 130 km. this route has mounainous topography, steep slopes and high altitude. this amount of water passes Neka city through a single channel and finally empties into the sea. Since this channel has limitted capacity ,during flood time, water spreads into the city and consequently dangerous flood occurs. The results indicate that the use of new technologies such as remote sensing and GIS for mapping natural disasters such as flood Risk is very efficient and useful. In the meantime using multi-criteria decision based on intuitive reasoning is very useful to assess flood Risk of Nekarood watershaed. The use of this model in the study according to consider many criteria, well-calculated the degree of hazard, vulnerability and finally risk for the study area. Neka city due to their particular position has very high potential for flood risk and vulnerability. The results indicate the necessity of the managers and planners attention in various agencies related to floods in the study area

  Heavy rainfalls in small basins can lead to devastating flash flood, and causing a number of fatalities and tremendous damages. Thus, forecasting of heavy rainfall is an important step in development of a flood warning system. Various models were used for rainfall forecasting such as artificial neural network (Moustris et al. 2011), time series models (Sapmson et al, 2013), wavelet theory (Partal and Kişi, 2007) and regression tree model (Fallahi et al, 2011). In recent decades, the Numerical Weather Prediction (NWP) models were widely applied for weather prediction. Numerical weather predictions (NWPs) usually have uncertainties in initial conditions and model structures. In recent decades, Ensemble Prediction Systems (EPS) have been increasingly used to capture the uncertainties in NWPs. Several operational centers, including the European Centre for Medium-Range Weather Forecasts (ECMWF), the National Centers for Environmental Prediction (NCEP), the Japan Meteorological Agency (JMA), and the United Kingdom Meteorological Office (UKMO) offer valuable operational numerical predictions at a global scale (Hsiao et al, 2013).
The purpose of the present study is the comparison of the ECMWF, UKMO, and NCEP global ensemble prediction systems for forecasting of heavy rainfalls in Kan watershed, Tehran, Iran. In this paper, the performance of the global ensemble prediction models are evaluated for heavy rainfall forecasting in Kan Basin, Tehran, Iran. This research was conducted for 8 heavy rainfalls (flood producer) occurred in the study area using two different precipitation thresholds including 5 and 10 mm. For this purpose, the UKMO, NCEP and ECMWF ensemble predictions which are archived in the TIGGE database were used. Other forecast centers were not used in this study for various reasons, such as the unavailability ensemble forecasts of some centers in 0000 UTC. It is worth noting that interpolated predictions on 0.125 degree resolution were used in this study.
Then, the heavy rainfalls predicted by UKMO, NCEP and ECMWF were compared to the observed rainfall. Three criteria including the accuracy, reliability and sharpness were applied to assess the predictive efficiency of ensemble forecasters. The Brier Score, reliability diagram and average width of 50% and 90% prediction intervals were respectively used to assess the three mentioned criteria.
The Brier score is widely applied in meteorology to assess the probability and ensemble forecasts. This score is presented as following equation:† BS= 1/N ∑_(i=1)^N▒〖(P_i-O_i)〗^2 (1)
In the above equation, P_i is the forecast probability of the event, O_i is the observational value equal to 1 or 0 depending on whether the event occurred or not and N is the number of forecast-observation pairs. A minimum Brier score is equal to zero for a perfect forecaster.
Reliability diagrams is a graph of the observed frequency plotted against the forecast probability of the event. For perfect reliability, the forecast probability and the frequency of event is equal, thus the closer the reliability curve to the diameter is the higher the reliability.
Sharpness is a feature of the forecasts that refers to the concentration of the predictive distributions.
The more concentrated the predictive distributions are, the sharper the forecasts and thus the better the predictive model. The results showed that for 5 and 10 mm rainfall thresholds, UKMO’s ensembles were the least efficient, reliable and sharp. Thus UKMO’s ensembles are not suitable for heavy rainfall forecasting in the study area. It was also observed that for 5 mm rainfall threshold, there was not a significant difference between the accuracy and reliability of NCEP and ECMWF ensembles but with increasing the level of threshold to 10 mm, NCEP’s ensembles had higher efficiency and were more reliable. In terms of sharpness, NCEP’s ensembles was also the most sharp, followed by ECMWF and UKMO. Since the higher threshold is necessary for heavy rainfall prediction, so the 10 mm rainfall threshold was used in assessment of the predictions by the criteria. Comparing of the results based on three mentioned criteria showed that NCEP’s ensembles had the best performance compared to the other predictions. Thus, it is recommended to study the NCEP’s ensembles for prediction of heavy and flood producer rainfalls in mountainous watersheds like Kan Basin