Journal of Spatial Analysis

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Global changes in extremes of the climatic variables that have been observed in recent decades can only be accounted anthropogenic, as well as natural changes. Factors are considered, and under enhanced greenhouse gas forcing the frequency of some of these extreme events is likely to change (IPCC, 2007 Alexander et al., 2007). Folland et al. (2001) showed that in some regions both temperature and precipitation extremes have already shown amplified responses to changes in mean values. Extreme climatic events, such as heat waves, floods and droughts, can have strong impact on society and ecosystems and are thus important to study (Moberg and Jones, 2005). Climate change is characterized by variations of climatic variables both in mean and extremes values, as well as in the shape of their statistical distribution (Toreti and Desiato, 2008) and knowledge of climate extremes is important for everyday life and plays a critical role in the development and in the management of emergency situations. Studying climate change using climate extremes is rather complex, and can be tackled using a set of suitable indices describing the extremes of the climatic variables.    The Expert Team on climate change detection, monitoring and indices, sponsored by WMO (World Meteorological Organization) Commission for Climatology (CCL) and the Climate Variability and Predictability project (CLIVAR), an international research program started in 1995 in the framework of the World Climate Research Programme, has developed a set of indices (Peterson et al., 2001) that represents a common guideline for regional analysis of climate.    It is widely conceived that with the increase of temperature, the water cycling process will be accelerated, which will possibly result in the increase of precipitation amount and intensity. Wang et al. (2008), show that many outputs from Global Climate Models (GCMs) indicate the possibility of substantial increases in the frequency and magnitude of extreme daily precipitation.     eneral circulation models (GCMs) are three-dimensional mathematical models based on principles of fluid dynamics, thermodynamics and radiative heat transfer. These are easily capable of simulating or forecasting present-future values of various climatic parameters. Output of GCMs can be used to analyze Extreme climate. For this study high quality time series data of key climate variables (daily rainfall totals and Maximum and minimum temperature) of 27 Synoptic stations were used across Iran from a network of meteorological stations in the country. In order to get a downscaled time series using a weather generator (LARS-WG), the daily precipitation output of HadCM3 GCM, SRES A2 and A1B scenario for 2011-2040 are estimated.     The Nine selected precipitation indices of ETCCDMI[1] core climate indices are used to assess changes in precipitation extremes and monitor their trends in Iran in the standard-normal period 1961–1990 and future (2011-2030).    Due to the purpose of this study, at first changes in extreme precipitation indices in the standard-normal period is evaluated and its results show annual maximum 1-day precipitation increased in many regions in the East of Iran. Simple measure of daily rainfall intensity (SDII), annual maximum consecutive 5-day precipitation, annual count of days with daily precipitation greater than 10mm (R10mm), annual count of days when rainfall is equal to or greater than 20 mm (R20mm) have increased in the central areas, regions in the north , north east and southern parts of Iran. Similar results are obtained for the R25mm index.    The consecutive dry days (CDD) index has generally increased across the west areas, southwest, north, northwest and southeast of Iran and indices of consecutive wet days (CWD) decreased in these areas.    Trends of extreme precipitation indices simulated by HadCM3 SRES A2 showing increases RX1Day in North West expect west Azerbaijan Province, central, southwest, north east and coasts of Caspian Sea. Similar results are obtained for the R5mm index expects northeast. There are mixed changes in R10mm across Iran, increasing in west, southwest, coasts of Caspian Sea, Hormozgan and Ardebil provinces, East Azerbaijan, Zanjan and Qazvin  provinces. Similar results are obtained for the R20, 25 mm index in northeast, south of Caspian Sea, and some parts in western and central areas. Same as HadCM3 SRES A2 pattern there are mixed changes in R10mm across the region. Positive trends are seen in part of the Isfahan, Markazi, Kuhkilue , Lorestan, Ilam, Chaharmahaland Khozestan provinces and some part of Hormozgan and Kerman and some areas in north west. Similar results are obtained for the R20mm and R25mm index and in west of Yazd to north of Khozestan provinces have increased.    Consecutive wet days (CWD) have increased over most of the west of Iran, Khorasn Razavi and Southern Khorasn provinces, In contrast consecutive dry days (CDD) index has generally increased in many parts of the region.  
[1]. Expert Team on Climate Change Detection and Monitoring Indices

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Precipitation is one of the important aspects of the Earth’s climate that has both spatial and temporal variations. Understanding the behavior of this element and analyzing its spatial and temporal variation is importantwhich can lead to a comprehensive and detailed planning for water resource management and agriculture. Geostatistical techniques and spatial autocorrelation analysis are the most widely used techniques in the field of the spatial continuity. Spatial autocorrelation analysis is applied to help researchers understand the spatial patterns in the area.

      The purpose of this study is to identify the heavy precipitation spatial patterns in Guilan Province. For this purpose, the 6- hourly sea level pressure of the network from  0 to 120 Easter longitude and 0 to 80 Northern latitude with 2.5×2.5 degrees spatial resolution were obtained from the National Center for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) for the period 1979-2010. The daily precipitation data of 21 stations were obtained from the Islamic Republic of Iran Meteorological Organization and Ministry of Energy.

      Guilan province is one of the most humid regions in the country. The heavy rain days were selected as days when more than 30 percent of the all stations had daily rain amount more the 95^th percentile. As a result, 321 days were selected as heavy and widespread rainy days. By using principal component analysis these 321 days were reduced to 9 factors. These factors then were subject to cluster analysis with Ward method and resulted in three surface pressure patterns of heavy rainy days. Within the resulted pressure patterns by using local geostatistical techniques we identified the heavy rain spots and their spatial orientation. These spatial methods include Kriging,  Geostatistical Analysis, and Anselin local Moran index.

According to the results of this research, the first pattern was characterized with a high pressure over northern part of the Black Sea causing the highest Variance of heavy rainfalls. The second pattern is identified as a low pressure on the Black Sea. But the third pattern showed a precipitation distribution with low variation caused by the Siberian high-pressure. The results of Spatial Statistics techniques indicated that heavy rains were clustered in all there patterns. The clusters of heavy rains were localized mostly over the coastal areas and some over the central regions. The clusters of the western high-pressure patterns penetrated somewhat inside the province, while clusters of the Siberian high pressures was located on the shoreline of the province. The precipitation of western migratory high-pressures was heavier than of the Siberian high-pressure. The results of the standard deviation ellipse showed that heavy rain clusters were oriented in the east-west direction and were nonhomogeneous. While the ones oriented in the south east direction were more homogeneous and clustered. Because of this arrangement, the entry of moisture from the Caspian Sea is relatively concentrated on the East or North East. Because of the concentration of heavy rains in the central areas of the coast, the risks of floods and soil erosion is very high in these areas. This study showed that contrary to the popular belief, the heavy rains of Guilan were produced by western systems and the role of the Siberian high pressure is less important and is limited only to the coastline.

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We can identify the flood not only considering circulation pattern in occurring day but also by studying circulation pattern a few days before fresh event. This subject has mutual approach. In one hand, it indicates  that circulation patterns which were before flood event have important role in determining the conditions and moisture content of studied area and playing the fundamental role in few coefficient of region because it determines the previous moisture. On the other hand, it indicates that we should tracking the rain-genesis synoptic systems from source to end place of their activity for studying floods and their meteorology factors which have created them. By this way, we can acquire more comprehensive recognition about the relationship between circulation pattern and floods. In the other words, the identification of synoptic patterns that have created the flood reveals not only the mechanism of their emergence but also is useful for prognosis and encountering with them. The extensive researches have been accomplished about Inundation in the world and Iran, but Iran haven’t much antiquity about synoptic researches. For foreign researches, we can name researchers such as Hireschboeck (1987), Kutiel et al(1996), Komusce and et al (1998), Krichak  and et al (2000), Rohli and et al (2001), Kahana (2002), Teruyuki Kato(2004), Ziv and et al (2005), Carlalima and et al (2009). The numerous researchers have studied the Inundation climatology in internal of country such as Bagheri (1373), Ghayour (1373), Kaviani and Hojatizadeh (1380), Moradi (1380), moradi (1383), Mofidy (1383), Masoodian (1384), Masoodian (1384), Hejazizadeh et al(1386), Parandeh Khozani and Lashkari (1389). In this research, we considered the heavy precipitation of Azar 1391 in southwestern of Iran that resulted in flood phenomenon in the cause and effect manner so that can do necessary prevention actions before occurring the flood for preventing the probable damages and optimal use of precipitations by forecasting the patterns that have created the flood.

In this synoptic study, we need to two database: one group is variables and atmospheric data consisting of geopotential height of 500 hpa level (in meter geopotential), zonal wind and meridional wind (in m/s) and special humidity (in gr/kg) during this times 00:00, 06:00, 12:00 and 18:00 Greenwich in 0-80° northern and 0-120° eastern with local resolution of 2.5*2.5 Arc that have been borrowed from database of (NCEP/NCAR) dependent to National Atmosphere and Oceanography Institute of USA, and other group is daily precipitation data of region rain gauge stations during 4-8^th Azar of 1391 (24^th November – 28^th November 2012). In continuation. By applying the environment- circulation approach, we took action to drawing circulation pattern maps of 500 hpa level, thickness of atmosphere patterns of 500-1000 hpa and moisture flux convergence function from 4-8^th Azar of 1391 (that for calendar, conform with 48 hours before beginning the showery precipitation until ending the storm activity) by using data which obtained from database of NCEP/NCAR and the synoptic conditions of above flood have been studied and interpreted in the region.

Flood is one of the most destructive natural hazards that have imposed and impose many damages to people during the history. Hence, the final aim of this research is to explain the key interactions between atmosphere and surface environment and in other words exploration of the relationship between circulation patterns leading to the flood generating precipitation in the southwestern of Iran for forecasting the time and intensity of showers occurrence that lead to flood. For this purpose, by applying environmental-circulation approach, the circulation patterns identified and studied which resulted in flood generating precipitation. The result of this research indicated that torrential precipitations in the region have formed the deep trough in days 4-8 of Azar on the east of Mediterranean and the studied region placed in the east half of this trough that is the location of atmosphere instability. At same time, thickness patterns, indicate the flux of cold air from northern Europe to lower latitudes and spreading the warm air of north of Africa to latitude 50° northern. As a result we expected the frontal discontinuity in the encountering place of these two air mass. Analysis of the moisture flux convergence patterns also indicated that torrential precipitations were the result of moisture flux from Mediterranean and Persian Gulf; and Red Sea and Arab Sea taken into account as reinforced sources.

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Understanding the changes in extreme precipitation over a region is very important for adaptation strategies to climate change. One of the most important topics in this field is detection and attribution of climate change. Over the past two decades, there has been an increasing interest for scientists, engineers and policy makers to study about the effects of external forcing to the climatic variables and associated natural resources and human systems and whether such effects have surpassed the influence of the climate’s natural internal variability. The definitions used in the 5th assessment report were taken from the IPCC guidance paper on detection and attribution, and were stated as follows: “Detection of change is defined as the process of demonstrating that climate or a system affected by climate has changed in some defined statistical sense without providing a reason for that change. An identified change is detected in observations if its likelihood of occurrence by chance due to internal variability alone is determined to be small.  Attribution is defined as the process of evaluating the relative contributions of multiple causal factors to a change or event with an assignment of statistical confidence”. Detection and attribution of human-induced climate change provide a formal tool to decipher the complex causes of climate change. In this study the optimal fingerprinting detection and attribution have been attempted to investigate the changes in the annual maximum of daily precipitation and the annual maximum of 5-day consecutive precipitation amount over the southwest of Iran.

This is achieved through the use of the Asian Precipitation—Highly Resolved Observational Data Integration Towards Evaluation of Water Resources Project(APHRODITE) dataset as observation, a climate model runs and the standard optimal fingerprint method. To evaluate the response of climate to external forcing and to estimate the internal variability of the climate system from pre-industrial runs, the Norwegian Climate Center’s Earth System Model- NorESM1-M was used. We used up scaling to remap both grid data of observations and simulations to a large pixel. This remapped pixel coverages the area of the southwest of Iran. The optimal finger printing method needs standardized values like probability index(PI) or anomalies as input data, since the magnitude of precipitation varied highly from one region to another. The General Extreme Value distribution (GEV) is used to convert time series of the Rx1day and Rx5day into corresponding time series of PI.  Then we calculated non-overlapping 5-year mean PI time series over the area study. In this research, we applied optimal fingerprinting method by using empirical orthogonal functions.  The implementation of optimal fingerprinting often involves projecting onto k leading EOFs in order to decrease the dimension of the data and improve the estimate of internal climate variability. A residual consistency test used to check if the estimated residuals in regression algorithm are consistent with the assumed internal climate variability. Indeed, as the covariance matrix of internal variability is assumed to be known in these statistical models, it is important to check whether the inferred residuals are consistent with it; such that they are a typical realization of such variability. If this test is passed, the overall statistical model can be considered suitable.

Results obtained for response to anthropogenic and natural forcing combined forcing (ALL) for Rx1day and Rx5day show that scaling factors are significantly greater than zero and consistent with unit. These results indicate that the simulated ALL response is consistent with Rx1day observed changes. Also, it is found that the changes in observed extreme precipitation during 1951-2005 lie outside the range that is expected from natural internal variability of climate alone and greenhouse gasses alone, based on NorESM1-M climate model. Such changes are consistent with those expected from anthropogenic forcing alone. The detection results are sensitive to EOFs. We estimate the anthropogenic and natural forcing combined attributable change in PI over 1951–2005 to be 1.64% [0.18%, 3.1%, >90% confidence interval] for RX1day and 2.5% [1%,4%] for RX5day.

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Drought is one of the most important hazards that occur in all the earth especially in arid and semi-arid climates. Every year, about half of the earth’s surface experienced droughts and while drought is not a constant feature of any climate but occur more frequently in arid and semi-arid regions of the world. Although the occurrence of droughts cannot be prevented but by studying the nature and characteristics of droughts and also identify factors that affecting their occurrence useful information can be gained about drought and their destructive effects. The researches in recent years designed and proposed a lot of indices to study and analyze the droughts and today various characteristics such as intensity, duration, area and so on with these indices are studied. Many indices used by researches to analysis and identify properties of climatic droughts and dry periods. In these indices often the variables of precipitations, combination of precipitations and temperature, humidity or evaporation, crops yields and teleconnection climatic indices are used.

In this study using the CPEI index and 30 years (1980-2009) daily rainfall data in 40 synoptic stations overall Iran, to analysis and assess of Iran droughts suitable variables detected. Four seasons and annual period is considered in this study. To determine the appropriate variables in the design of suitable models and modeling of drought to assess and predict droughts Otun in 2005 proposed CPEI index as Conjunctive Precipitation Effectiveness Index. He selected 10 conjunctive precipitation variables as ORS(Onset of Rainy Season), CRS(Cessation of Rainy Season), LRS(Length of Rainy Season), TWD(The Total no of Wet Days), TDS(Total no of Dry Spell), TDW(Total no of Dry Days within a Wet Season), TDY(Total no of Dry Days within a Year), LDS(Length of the Dry Season), MDL(Maximum Dry Spell Length within a Wet Season), MAR(Mean Annual / Seasonal Rainfall Depth) and determined the relationships between variables in each synoptic stations and climatic regions. Since the units of measurement the rainfall variables are diverse, it is essential that the units be converted to a standard unit, in other words variables be standardized. The relationship between variables was determined by Pearson correlation coefficient. Finally, the right combination of precipitation variables for each station through the proposed formula Otun(2005) were determined. In the end, for each of the seasons and the annually period regionalization maps were prepared.

 All 40 synoptic stations were evaluated by Otun’s method (Aton, 2005). The results showed that 95 percent of stations in spring, 75 percent in fall, 57 percent in winter and 75 percent in annual period are compatible with used method. Thus, spring, fall and winter seasons and also annual period are compatible with above mentioned index. Among the used variables MAR, MDL, TDY and TDS which with respectively are as follows: total amount of precipitation in any period, the maximum duration of dry periods in a wet period, the total number of dry days in a wet period and the total number of dry period during wet period among the stations are more abundant. In annually period, in addition to the above mentioned variables, precipitation variable of LPS (length of dry period) also seen among some stations. Also, results showed that CPEI index can be used on most stations and climatic regions of Iran. It was also found that the spring compared the other seasons and annual period is more comparable on the base of CPEI index.   

  Otun in 2010 used the CPEI index in semi-arid region of Nigeria and has achieved good results. The results of our study show good agreement with Otun’s work. The use of this index in the study of meteorology, climatology, agriculture and many environmental projects can be beneficial because in many of these fields of study, precipitation and its characteristics have an important role. In general we can say that in regions where CPEI index does not show a high proportion or set of variables are not enough it is better to use other indices such as SPI and RAI. The results obtained in similar climate zones such as Nigeria has shown that CPEI index has very good ability to identify and explain the precipitation effectiveness variables which can be used in modeling of droughts and dry periods. There are many similarities between combination of precipitation variables that identified by CPEI index for Iran and other regions of the world. Similarities, especially with respect to MAR, MDL, TDY and TDS are abundant.

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Identification and synoptic analysis of the highest precipitation linked to ARs in Iran
 
              Abstract
        Atmospheric rivers (ARs) are long-narrow, concentrated structures of water vapour flux associated with extreme rainfall and floods. Accordingly, the arid and semi-arid regions are more vulnerable to this phenomenon. Therefore, this study identifies and introduces the highest precipitation occurred during the presence of ARs from November to April (2007-2018). The study also attempted to demonstrate the importance of ARs in extreme precipitation, influenced areas and identifies the effective synoptic factors. To this end, integrated water vapour transport data were used to identify ARs, and documented thresholds applied. AR event dates were investigated by their daily precipitation, and eventually, ten of the highest precipitation events (equivalent to the 95^th percentile of maximum precipitation) associated with ARs were introduced and analyzed. The results showed that most ARs associated with extreme precipitation directly or indirectly originated from the southern warm seas. So the Red Sea, the Gulf of Aden and the Horn of Africa were the major source of ARs at the time of maximum IVT occurred. Synoptically, seven AR events formed from the low-pressure Sudanese system and three events from integration systems. The subtropical jet was the dominant dynamic of the upper troposphere, which helped to develop and constant of ARs. Moreover, the predominant structure of jets had a meridional tendency in Sudanese systems, while it was a zonal orientation in integration systems. The intense convective flows have caused extreme precipitation due to the dominance of strong upstream flow besides having the highest moisture flux. The station had the highest precipitation has been located in the eastern and northwestern region of the negative omega field or upstream flows.
 
        Keywords: Identification and synoptic analysis, highest precipitation, Ars, Iran.

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Explanation of the subject: The annual drought phenomenon, by affecting economic, social and environmental issues, leads to the vulnerability of urban and rural households and the instability of their livelihoods. Yazd is one of the provinces with drought. Consecutive droughts in the province necessitate integrated management and community adaptation in times of drought.
Method: Taking into account the length of the statistical period of 20 years and to obtain the results with a high level of confidence, the main data of the census documents that have been compiled for the development of cities and villages have been used. By analyzing the main components of several factors, it was selected as the main components. By calculating the standard precipitation index in the arid region, the driest year was determined and by calculating the weighted average of their correlation index with the main components of socio-economic and ecological environment based on appropriate statistical inference. At the end of the year, the effect of drought on different dimensions was presented by step-by-step linear regression, analysis and communication between them to adapt and resilience of individuals in society.
 According to the general results, one of the most important economic and dry economic losses is the annual income of the villagers, which can be due to the decrease in the area under cultivation and production of the main agricultural products. In the social sector, people with knowledge and awareness should increase their adaptive capacity to the occurrence of drought, in order to reduce the vulnerability of social issues to the phenomenon of drought. The results show that unemployment insurance has increased following the drought. The main reason for this is the unemployment of farmers affected by drought, so changing jobs along with temporary migration or the production of handicrafts, etc. can increase the relative income of households at the time of occurrence and prevent unemployment in these conditions. Increasing unemployment will cause other social harms such as poverty, declining health, increasing disease, and reducing judicial and social security. According to the results, one of the components that has established a high standard of rainfall during the drought year is the theft of livestock, which shows a decrease in the social security of the community. People in the study community increase their adaptability to the annual drought by increasing breeding work, such as rangeland improvement, rainfall collection, biological improvement, afforestation, and irrigation reform.

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The role of environmental and climatic environment on the transport and emission of carbon monoxide pollutants Iran in 2018
 
Introduction
Air pollution, as one of the most important environmental hazards in urban areas, is closely related to weather conditions. Today, pollution in metropolitan areas has become an important issue that requires the study and presentation of practical solutions to improve living conditions in this area. Therefore, understanding the relationship between synoptic systems and air pollutants helps a lot in how to solve environmental problems and future planning. Therefore, in this study, compression algorithms of carbon monoxide emission and transfer from domestic and foreign sources were analyzed. For this purpose, GEOS-5 / GMAO / NASA satellite images were used. The results showed that the highest amount of pollution from the seasonal point of view is related to the cold and early morning seasons and the lowest is related to the early afternoon and hot season of the year. And Khuzestan are densely populated carbon monoxide cores. Low pressures of the eastern Mediterranean play an important role in reducing pollutants in the southwest of the country and in the south of the country, under the influence of atmospheric currents from the topographic cut of Bandar Abbas, air streams polluted with carbon monoxide are able to penetrate into the interior to the southern half of Kerman. Increased by low pressure systems in Afghanistan and Pakistan. The Zagros Mountains also play an important role in preventing the entry of pollutants produced by western neighbors into Iran. In summer, Iran is polluted by carbon monoxide carriers by monsoon currents from central and southern Africa to Iran and has caused a lot of pollution.        
                                                       
materials and Method
The geographical location we study in this study is Iran. Iran is the 16th largest country in the world. Iran is located in the northern hemisphere, the eastern hemisphere in Asia and in the western part of the Iranian plateau and is one of the Middle Eastern countries. Meridian 5 44 passes east of the westernmost point of Iran and meridian 18 63 passes east of the easternmost point of Iran. 1648195 sq km is bordered by Armenia, Azerbaijan, and Turkmenistan to the north, Afghanistan and Pakistan to the east, Turkey and Iraq to the west, the Persian Gulf and the Sea of ​​Oman to the south. Iran is one-fifth the size of the United States and almost three times France. . Iran is a mountainous country. More than half of the country is covered by mountains and heights, and less than 1/4 of it is arable land. In general, Iran's heights can be divided into four mountain ranges: North, West, South and Central Mountains. East divided, which is therefore the twenty-third highest mountain in the world.                                        
This study is based on the method of environmental analysis to focus on circulation, so that based on the concentration of carbon monoxide in 2018, synoptic patterns of this phenomenon have been identified. Satellite imagery of surface carbon monoxide was then obtained from three GEOS-5 / GMAO / NASA organizations. Also for synoptic analysis, MSLP and WS satellite images were received and analyzed from GFS / NCEP / US National Weather Service organizations and also one of the sensors used for pollutant studies is MOPITT. The MOPITT sensor is a tool for measuring troposphere pollution that can detect atmospheric pollution. This sensor is the first satellite sensor designed for use in gas correlation spectroscopy and is part of NASA's Operational Program (ESE), which has been operating since 1999 and is installed on three satellites Terra, Aura, Aqua Depending on the type of mission in space, it acts as an orbiter. This sensor measures only two variables of methane and carbon monoxide in the atmosphere of the troposphere of the atmosphere, for which purpose 3 bands and 8 channels for measuring monoxide with a size of 62.4 microns (using 4 channels), 33.2 It uses microns (using 2 channels) and methane measuring 26.2 microns (using 2 channels). The MOPITT sensor is specifically designed to measure carbon monoxide. The geographical boundaries of the study area were also selected to include all atmospheric systems affecting the study area.     
                                                                                                                                    
Conclusion
The meteorological condition and the physical and dynamic properties of the atmosphere can play an important role in the level of air protection. The main factor that can cause the scattering and transmission of air forces is the use of the ground and the levels of reception of the atmosphere, and the synoptic systems as a service provider providing services for upward movement and distribution of air pollutants, as well as the definition of chalk. As a decision made in this field, Iran can use its images in this field in 2018 2018, MSLP, WS will provide you with GFS / NCEP / US National Weather Service. With great intensity you can go to Tehran and southwest to destroy yourself and access your officials. In the imagination carbon monoxide is possible and used in the southwest of the country. Now in your country and change the status of lists proposed by Coriolis, increase the high pressure of carbon monoxide in Mr. Tropical from the Middle East and Iran. This program allows you to modify your suggested lists. Carbon monoxide pollutants sent to a drawer in the international province of the country and available in Bandar Abbas, a road nest free from high mountains and as a corridor company you can get from this par of the air pollution as carbon monoxide through the air to this one Use the land up to the Kerman province.          
                                                               
Keywords: Carbon monoxide, Compression systems, Monson, Atmospheric pollution, Topography
 

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Comparison of the effectiveness of four artificial intelligence methods in predicting drought
Abstract
Problem statement:
Drought is a temporary disorder whose characteristics vary from region to region, therefore, it is not possible to define a complete and absolute definition of drought. Drought is one of the most important natural disasters that can occur in any climate regime. Since drought is unavoidable, it is important to know it in order to optimally manage water resources. Drought prediction can play an important role in managing this phenomenon. In other words, recognizing and predicting this phenomenon is one of the topics of interest for scientists who are interested in solving the problem of water shortage. More than 80% of Iran's area is covered by arid and semi-arid regions and lack of rain is a predominant phenomenon in this region. So far, several methods have been proposed to predict drought. Each method offers different results in specific conditions.  Therefore, identifying the best method for predicting drought in the climatic conditions of central Iran is essential.
 
Material and methods:
In this research, in order to introduce a suitable method for predicting drought for the next month, four methods of artificial intelligence including Deeplearning (using the Alexnet network, one of the convoluted networks), K nearest neighbor algorithm (KNN), multi-class Support vector machines (SVM-MultiClass) and decision tree have been used. Monthly rainfall data from 11 syntactic stations of Yazd province during the 29-year statistical period (1988 to 2017) were used as experimental data. Standardized precipitation index (SPI) was calculated to indicate drought status in terms of severity and duration on 1, 3, 6, 9, 12 and 24 month time scales. Precipitation data was used as neural network input and SPI classification as network output and 80 percent of the data was used for training and 20 percent for testing the networks.
In this study, the Recurrence Plot method was used to interpret the time series to convert these series into images and RG and B pages were created. To convert rainfall data into photos at 1, 3, 6, 9, 12 and 24 month time scales, photo layers were created using a standardized rainfall formula, and by merging these three output layers into colored photos and black and white photos were obtained. Using three pages created in MATLAB software and merging them, the output was in the form of a photo, which was placed as the input of the Alexnet network. Combination of Recurrence Plot to create images and deep learning network for classification of drought data has been used for the first time in this research. To evaluate the effectiveness of the classification strategy, standard criteria of accuracy, micro-F1 and macro-F1 were used.
 
Results Description and interpretation:
 The results showed that all networks were able to predict drought. However, on short time scales such as 3 and 9 months, the accuracy assessment criteria for some classes are zero and the methods of learning from these classes are practically ignored due to their lack of data. But on a larger time scale, this issue has been addressed and the data of those classes are well categorized. Deep learning network with image input could not predict well in the short term due to lack of data, but in the long term due to increased data has improved its performance and had the best performance. The SVM method at different time scales has shown unreliable and variable behaviors that can not be said to be a suitable method for predicting drought at different time scales. Decision Tree and KNN methods have been able to predict drought better in the short term than in the long term. The two methods have been closely related. .Based on the Deeplearning network macro-f1 evaluation criterion, the one-month time scale with 22.71% was the most inefficient method and the Decision Tree with 64.65% was the most efficient method, But with the increase in time scale, the Deeplearning network improved its performance, so that at the 24-month time scale with 65.35%, the best performance for the Deeplearning network followed by the SVM-MultiClass network with 57.40%. For future research, it is suggested that Decision Tree and KNN methods be used to predict short-term drought. In this study, with increasing the time scale and increasing the data used, these two methods have lost their effectiveness compared to the short term.
 
key words: Drought, Standardized Precipitation Index, Artificial Intelligence, Deep Learning, Alexent, Recarence Plot
 

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Introduction
Geographical situation of Iran is a place for interacting many physical and human processes which lead to specific precipitation climatology in the country. The month to month variation of precipitation is one of  the features which the precipitation climatology may reflect due to tempo - spatial characteristics. In fact, monthly distribution of precipitation is one of precipitation normal features building up the climate structure. In order to recognize this fundamental characteristic three following questions have been raised:
1) Have the month to month distribution of precipitation changed over recent four decades?
2) How is the pattern of relationship of month to month distribution of precipitation and spatio - topographical variables?
3) Is it possible to find a spatial pattern for decadal changes of precipitation of month to month distribution?

Data and Methods
In order to find a responses for the abovementioned questions the distribution of month to month precipitation and its decadal changes was considered by adopting coefficients of variations (CV) for 46 years (1970-2016)  and using the third version of Asfazari dataset. The relationship of precipitation data and spatio-topographical variables calculated based on regression techniques. Moreover, the spatial pattern considered by using cluster analysis.  The CV calculated as follow:

here ،،  are ith raw's and jth column's CV, standard deviation, and monthly mean, respectively.
CV and its relationships with spatio-topographical variables were calculated in two temporal scale, for whole the under investigation period (1970-2016) and in decadal period for four decades (1977-1986, 1987-1996, 1997-2006, 2007-2016).
Discussion
 The results of current study proved that the month to month different in precipitation amounts have had spatial variations, whilst the temporal trends is not statistically significant. In addition, the minimum, maximum, and consequently, the range of values also the averages have not experienced significantly changes. However, the region experiencing the same values of precipitation illustrated oscillatory behavior. Accordingly, the decadal variations have happened in different areas. Although the there have been statistically significant relationships between monthly CV and spatio - topographical factors, the correlations were low. Based on cluster analysis, we found 5 regions according to CV and its anomalies in compares with normal CV for all under investigation period. These regions generally follow the latitudes from 32 N toward northern latitudes, whilst the region in the south of 32 N generally follow the longitude patterns.
Results
Precipitation is known a chiastic and complicated climate element. One of chiastic behaviors which precipitation shows in its different time - scale behavior is its month to month distribution among a given year. In current research the decadal variation of  above-mentioned behavior among recent four decades and the variation of its relationships and the spatio - topographical features , as parts of climate structure of the country, have investigated in details. 
Our finding illustrated that the month to month different in precipitation amounts have had tempo - spatial variations, whilst the temporal long - term trends is not statistically significant. Moreover, the values of minimum, maximum, and consequently, the range of month to month CV also the decadal averages have not experienced significantly changes over four under study decades. However, the region experiencing the same values of precipitation depicted oscillatory behavior. consequently, the decadal variations have happened in different areas. Although there have been statistically significant relationships between monthly CV and spatio - topographical variables, the correlations were not considerably high. Based on cluster analysis technique, we found 5 regions according to CV and its anomalies in compares to normal CV for all under study decades. These regions generally follow the latitudes from 32 N toward northern latitudes, whilst the region in the south of 32 N generally follow the longitude patterns.

KeyWords: Iran precipitation, Month to month changes in precipitation, Inter annual variation of precipitation, Precipitation anomaly, Spatial analysis of precipitation

 

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Vulnerability assessment of Miangaran wetland ecosystem

To support the proper management of ecosystems, vulnerability analysis of ecosystems is very important. Vulnerability analysis of ecosystems provides information about weaknesses and capacity of the studied ecosystem for recovery after damage. Considering the degradation status of Miangaran wetland, vulnerability evaluation of this wetland is one of the most important management methods in the region. For this purpose, in this study, after identifying and evaluating the threatening factors of Miangaran wetland, these factors were scored using evaluation matrices. Then, the interaction between these values ​​and threatening factors was examined and the vulnerability of wetland values ​​was obtained by multiplying the scores of all studied factors. Finally, management solutions were presented to deal with the most important threatening factors. According to the results, the most vulnerability is to the hydrological and ecological values ​​of the wetland. The highest effects of threats on the ecological value are also on the birds of Miangaran wetland. The results of the evaluation of Miangaran Wetland show that this wetland has a high potential for ecosystem functions of the wetland. These functions have been neglected in the planning and managing of wetlands at the local, regional and national levels. As a result, ecosystem-based management is suggested as the best management approach. The management in these areas should take action to prevent the vulnerability of Miangaran wetland. Also, the vulnerability evaluation method used in this study can provide a good understanding of the relationship between wetland functions and the resulting services for the management of the ecosystem of Miangaran Wetland.
Key words: Miangaran wetland, ecosystem management, vulnerability assessment
 

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The statistical and spatial analysis of extreme rainfall is considered as one of the components of the management tool to prevent or control the risks caused by this phenomenon. The purpose of this research is to statistically investigate and spatially analyze the extreme precipitations in the Kashan Plain.The extreme rainfall of Kashan synoptic station were statistically analyzed in the period of 1971-2022 AD and the water year of 1350-1351 to 1401-1400 for a total of 18618 days.Then six cases of widespread extreme rainfall were selected and analyzed with the rainfall data of 13 synoptic stations and 11 rain gauge stations using geostatistics and spatial analysis methods.The extreme rainfall zonation maps of Kashan plain were prepared using by variogram models and kriging method.The results showed that the frequency of heavy and super heavy rains in winter and very heavy rains in spring is more than other seasons.The very high correlation of annual rainfall with the total and frequency of extreme rainfall shows that the volume of annual rainfall is more affected by the concentration of rainfall in short periods of a few days than by the distribution of rainfall throughout the year.Therefore, it was found that extreme precipitation plays an important role in the total precipitation and surface runoff, and as a result, the water balance of the region.The zoning maps showed that the rainfall of April 8, 2020, which is concentrated on the western belt and the heights of the basin, causes the erosion of the heights and causes floods in the foothills and low-lying areas of the plain. Also, rains such as the rains of March 8, 2019, which are most concentrated in the central areas, have a high potential to cause flooding.