Journal of Spatial Analysis

The latest report of the Intergovernmental Panel of Climate Change (IPCC) on climate and global warming Indicates that climate change and global warming in particular is one of the most important challenges of the world and drought, as a consequence of climate change around the world, has always influenced the many countries, including Iran. However, it seems that the climate changes, particularly in the West and Iran, especially among farmers and rural communities vulnerable to the effects of economic, social and environmental impacts that are more significant. In other words, Continuous droughts are faced villagers and farmers with various problems and challenges, In this regard, villagers Choose the local and specific strategies in the face of this creeping disaster that improve them adaptive capacity to drought. Nowadays, special emphasis is put on the notion of adaptive capacity instead of vulnerability. So the need to have research in rural levels obvious, especially in Iran where there has not yet been any deep and encompassing study on the concept of adaptive capacity in rural level. adaptive capacity to climate change is the ability of a system or an individual to adjust to climate change or climate variability so as to minimize the potential damages or cope with the consequences. Therefore, adaptive capacity is the ability to plan and use adaptation measures to moderate the effect of climate change. There is an increasing need to develop indicators of adaptive capacity to determine the robustness of response strategies over time and to understand better the underlying processes.

Adaptive capacities of villagers depend on certain factors or attributes such as their knowledge on and number of times they use a particular adaptation strategy. Other factors are the availability and accessibility of the adaptation strategy. Also, the number of consultations that a villagers makes on a particular adaptation strategy affect whether the villagers will be lowly or moderately or highly adaptive to drought.

Identifying the overall level of adaptive capacity to drought in rural areas, in order to Effective management is special importance, Because that by identifying and ranking of adaptive capacity in rural areas, adopt appropriate management strategies to reduce the damage caused by drought is possible.

Therefore, the purpose of this study is assessing the adaptive capacity to drought of between four villages in the central part of the city Rawansar in Kermanshah province. For this purpose five most effective and important index to measure the  adaptive capacity to drought as follows:  Knowledge, Use, Availability , Accessibility and Consultation, according to the literature, were selected. Then by using one sample T-test, the effectiveness of each of the above-mentioned indicators on the villagers adaptive capacity were reviewed and approved from the point of view Village contributors of the central city Rawansar (N = 48) who were selected by census method. In the next step, to determine the index weight, using the snowball technique and purpose sampling, 10 experts in jahad  agricultural  office in Rawansar city were selected and their comments were used. The results by TOPSIS technique based on these indicators, showed that rural areas of Hasan Abad and Zalu Ab  in the Rawansar city, had the greatest adaptive capacity to drought, While  rural areas of Dawlat Abad and Badr had fewer adaptive capacity to drought. The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is a multi-criteria decision analysis method, which  is based on the concept that the chosen alternative should have the shortest geometric distance from the positive ideal solution (PIS)and the longest geometric distance from the negative ideal solution (NIS).It is a method of compensatory aggregation that compares a set of alternatives by identifying weights for each criterion, normalising scores for each criterion and calculating the geometric distance between each alternative and the ideal alternative, which is the best score in each criterion.The findings of this study could have recommendations for rural planners to effective crisis management in order to reduce vulnerability and enhance resilience villagers to drought.

Due to the growth of industries and factories, deforestation and other environmental degradation as well as greenhouse gases have been increasing on the Earth's surface in recent decades. This increase disturbs the climate of the Earth and is called climate change. An Increase in greenhouse gases in the future could exacerbate the climate change phenomenon and have several negative consequences on different systems, including water resources, agriculture, environment, health and industry. On the other hand to evaluate the destructive effects of climate change on different systems, it is necessary to initially study the area affected by climate change phenomena. One of the most important effects of climate change on water resource is Drought.  On the other hand, one of the most serious consequences of climate change is how it will affect droughts and water resources.

Drought along with warmer temperature and less precipitation will threaten the water supplies for the crop irrigation, which will directly reduce the production of crops.The climate of the 21st century will very likely be quite different from the climate we observed in the past. The changes will continue to be large in the future period with increasing carbon dioxide emissions. Analyzing and quantifying the signal of climate change will be much in demand considering the above sectors, which are highly relating to the sustainability and human living.

In the past several decades, global climate models have been used to estimate future projections of precipitation [Intergovernmental Panel on Climate Change (IPCC), 2007], and have led to future estimation of drought, to quantify the impact of climate change and comparing the duration  and intensity of droughts under future climate conditions with current climate by using Atmospheric-Ocean General Circulation Models AOGCMs to predict future Precipitation. Global circulation models namely, coupled Atmosphere-Ocean Global Climate Models (AOGCMs) are current state of the art in climate change research. in This study aims at investigating the impact of climate change on droughts conditions in Iran using the Standard Precipitation Index (SPI).

The precipitation time series have been used for the estimation of Standardized Precipitation Index

(SPI) for three timescales, 3, 12 and 24 months, for the region. The outputs of HadCM3-A2 and A1B were applied for the assessment of climate change impact on droughts. One of the major problems in using the output of AOGCMs , is their low degree of resolution compared to the study area so to make them appropriate for use, downscaling methods are required. In this study we have used lars WG for downscaling monthly average of rainfall of AOGCM-HadCM3, and The HadCM3 outputs were downscaled statistically to the study area for a future period 2011-2040.then, was evaluated by the coefficient of determination (R2) between observed and downscaled data.  A method has been used for the estimation of annual cumulative drought severity-time scale-frequency curves. According to the rainfall results, in the 2011- 2040 period rainfall would decrease  to compared to baseline period in the study area.

The SPI time series were estimated (2011-2040) and compared with the respective time series of the historical period 1961-1990. Results revealed that there are decreases in the frequency of severe and mild droughts for the three examined SPI time series while there are increases in the duration of moderate droughts. This implies that droughts will be a concern in the future during the growing season (for the dominant crop) which should be considered in water resources management. specially in the west station of Iran.

Also, these frequency ratios were mapped by GIS on study area. Results showed that generally in the future periods, frequency of droughts ratio of three months drought time- scale will be increase in the North, North West and some parts of the south Alborz mountains and, The Ratio of long ( 24 months) drought for scenario A2 compare to the current climate shows increasing drought in the parts of the North khorasn, sistan and baluchestan and kerman provinces and parts of South West of Iran. scenario A1B shows increasing drought in the parts of the East of Mazandaran , Tehran , Horozgan and parts of Fars and Yazd  provinces.

Finally ,further  more analysis of drought, AWCDS-Timescale-Return Periods computed. These curves integrate the drought severity and frequency for various types of drought. The AWCDS time series were estimated

for basic period and 2011-2040 under scenarios A2 and A1B. The comparison indicated the three types of drought intensity increases for the three examined SPI time series in the South East of Iran.

Drought is the most important natural disaster, due to its widespread and comprehensive short and long term consequences. Several meteorological drought indices have been offered to determine the features. These indices are generally calculated based on one or more climatic elements. Due to ease of calculation and use of available precipitation data, SPI index usually was calculated for any desired time scale and it’s known as one of the most appropriate indices for drought analysis, especially analysis of location. In connection time changes, most studies were largely based on an analysis of trends and changes in environment but today special attention is to the variability and spatial autocorrelation. In this study we tried to analyze drought zones in the North West of Iran, using the approach spatial analysis functions of spatial statistics and detecting spatial autocorrelation relationship, due to repeated droughts in North West of Iran and the involvement of this area in the natural disaster.

In this study, the study area is North West of Iran which includes the provinces of Ardebil, West Azerbaijan and East Azerbaijan. In this study, the 20-year average total monthly precipitation data (1995-2014) was used for 23 stations in the North West of Iran. In this study, to study SPI drought index, the annual precipitation data of considered stations were used. According to the statistical gaps in some studied meteorological stations, first considered statistics were completed. The correlation between the stations and linear regression model were used to reconstruct the statistical errors. Stations annual precipitation data for each month, were entered into Excel file for the under consideration separately and then these files were entered into Minitab software environment and the correlation between them was obtained to rebuild the statistical gaps. Using SPI values drought and wet period’s region were identified and zoning drought was done using ordinary kriging interpolation method with a variogram Gaussian model with the lowest RMS error. Using appropriate variogram, cells with dimensions of 5_×5km were extended to perform spatial analysis on the study area. With the establishment of spatial data in ARC GIS10.3 environment, Geostatistic Analyze redundant was used to Interpolation analysis Space and Global Moran's autocorrelation in GIS software and GeoDa was used to reveal the spatial relationships of variables.

The results showed that most studied stations are relatively well wet and this shows the accuracy of the results of the SPI index. Validation results of the various models revealed that Ordinary Kriging interpolation method with a variogram Gaussian model best explains the spatial distribution of drought in North West of Iran. So, using the above method the stations data interpolation related to SPI index in North West of Iran was done. The results showed that Moran index values for the analysis of results of standardized precipitation index (SPI) in all studied years, is more than 0.95. Since Moran’s obtained values are positive close to 1, it can be concluded that drought, in the North West of Iran during the statistical period has high spatial autocorrelation cluster pattern of 90, 95 and 99 percent. Results also showed that in all the years of study, Moran's global index is more than 0.95 percent. This type of distributed data suggests that spatial distribution patterns of drought in North West of Iran changes in multiple scales and distances from one distance to another and from scale to another and this result shows special space differences in different distances and scales in this region of the country. Results also showed that drought in North West of Iran in 2008 is composed of two parts: Moderate drought in parts of West and North West region (stations of Maku, Khoy, Salmas, Urmia, naghadeh, Mahabad and Piranshahr) and severe drought in the southeastern part of the study area (stations: Sarab, Khalkhal, Takab, Tabriz and Mianeh). So the pattern of cluster drought in the North West of Iran in 2008 is on the first and fourth quarter. The results of this index showed that drought and rain periods are similar in the studied stations. The results of the application of Moran's index about identifying spatial distribution of drought patterns showed that The values of the different years during the period,  have a positive a positive coefficient close to 1 (Moran's I> 0.959344) and this shows that the spatial distribution of drought is clustered. The results of the standard score Z values and the P-Value proved the clustering of spatial distribution of drought.

The results of the analysis of G public value, In order to ensure the existence of areas with clusters of high and low values showed that The stations of Maku, Khoy, Salmas, Urmia, naghadeh, Mahabad, Piranshahr and Parsabad follow the moderate drought pattern in the region and are significant at the 0.99 level. Jolfa station also has a mild drought of 0.95 percent confidence level and for Sardasht station is significant in 0.90 percent. High drought pattern in Sarab, Khalkhal, Takab, Tabriz and Mianeh stations was significant in 0.99 percent level and also for Ardabil, Sahand and Maragheh stations very high drought pattern was significant in 0.95 percent level and for Meshkinshahr and Ahar high drought pattern is significant in 0.90 percent. By detection of clusters of drought and rain in the North West of Iran using Moran’s spatial analysis technique and G general statistics a full recognition of the drought affected areas in this region can be obtained and take the necessary measures in its management 

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.

Hazard is potential source of harm or a situation to create a damage. So identification of zones exposed to hazards is necessary for planning or land use planning. But this situation becomes more critical when they appear at the population centers. So applying the principle of passive defense based on environmental capabilities is unarmed action that caused the reduction of human resources vulnerability, buildings, equipment, documents and arteries of the country against the crisis by natural factors such as drought, flood, earthquake, etc. Considering the possible occurrence of such risks in population centers, ready to deal with what is known unpleasant and undesirable consequences is necessary. On this basis and given the importance of population centers in Helle and Mond basins, in this study, the authors tried to analyze the Rain hazards of drought and flood.

The study area,Helle and Mond basins, with about 21,274, 47653 km² area, respectively are located in the south of Iran. The Helle basin approximately is between 28° 20'N and 30° 10'N latitudes and between 50° E and 52° 20'E longitudes and Mond basin is between 27° 20' and 29° 55' latitudes and between 51° 15' and 30° 27'E longitudes.These basins are located in sides of a massive sources of moisture, Persian Gulf.

In this study, data from 23meteorological and synoptic stationsstations, during aperiod of20 years (1992-2011)in northern region of the Persian Gulf (Mond and helle basins)were used to calculate Standardized Precipitation Index (SPI). The data were collected by the Iranian Meteorological data website (http://www.weather.ir). The SPI is primarily a tool for defining and monitoring drought events. This index may be computed with different time steps (e.g. 1 month, 3months, 24 months). The SPI is defined for each of the above time scales as the difference between monthly precipitation (xi) and the mean value ( ), divided by the standard deviation. To assess flood risk zones, the flood, annual evapotranspiration, cities and populations centers layers were collected in Helle and Mond basins position. The annual precipitations and the SPI maps were drawn by Geostatistics, Kriging. It also the flood and annual evapotranspiration layers were weighted by Euclidian distance method, separately. Finally, all layers are weighted by AHP and fuzzy-linear methods (descending and ascending linear function) into vulnerable layers. The final map of vulnerable areas with flood and drought high risk was drawn based on the algorithm of linear-Fuzzy in a raster format.

According to the results, eastern, north eastern and south eastern part of Mond basin had high annual precipitation. Based on this result, it said that these parts of study area were known the least dangerous areas of vulnerability. The results also showed that with passing of the western regions and going to the center of the study area the annual rainfall have been added over the years. Kazeron, Chenar Shahijan, Firouz Abad, Borm plains and some parts of Khane Zenyan and Dash Arzhan are cities located in this regions. Low latitude, Proximity to the warm waters of the Persian Gulf, low annual precipitation and high temperature causing evaporation and inappropriate environmental conditions in Boushehr province and some coastal cities such as Genaveh, Deilam, Boushehr, Baghan, Lar and Khonj. Accordingly, west, north west, south and south west regions in Helle basin were located in extreme vulnerability zone with a loss of annual rainfall for drinking and agricultural production and poor nutrition underground aquifers.

Nowadays, the severity of the drought hazard has reached a point that has affected all the rural and urban areas surrounding it. Increasing the resilience of villages via livelihood solutions, is one of the best strategies for reducing the vulnerability of villages against natural hazards such as drought. The eastern side of the Lake Urmia consists of the six cities of Osku, Azarshahr, Bonab, Shabestar, Ajabshir and Malekan. Totally, there are 199 villages in this region, which are affected by the drought of the Lake, directly and indirectly and according to the statistics, the quantitative and qualitative reduction in agricultural and livestock productions and soil quality, the incidence of respiratory diseases and … have risen sharply compared to the past and a number of villages have been evacuated. Also because of the lack of a coherent strategy, which should be taken by the planners and authorities, the important measures to revitalize the Lake has not been taken yet and the dimensions of the threat are increasing day by day.

Current study investigate the factors affecting the resilience of rural settlements of the eastern side of the Lake Urmia against Drought. This is an applied and analytic-explanatory research. The data is collected by questionnaire from the villagers living in rural areas of the six cities, which are the statistical population of the research and the total number of the villages estimated 199 with 232295 persons.

The standardized Perception Index (SPI) is used to estimate the varying degrees of the villages in the eastern side of the Lake Urmia. In the next step, the possession index for each of the villages was calculated and the studied villages were classified based on it. On this basis and by considering the four status of drought and the three levels of possession, after sorting the villages on the basis of these two indexes, 43 villages were chosen from different regions of the eastern side of the Lake as the first level of analysis, using systematic random selection. Also, to classify the villages in the regard of possessing of the development facilities, the composite indicators called Morris pattern and 47 existing items are used, which are calculated in 9 different indexes. Finally, the obtained information were analyzed using SPSS and GIS software.

Regarding to the research findings at the eastern side of the Lake and on the basis of Standardized Precipitation Index (SPI), about 78% of this area has been experiencing drought. Also, the status of the overall indicators of household's livelihood capital on the basis of the Normal Scale from 0 to 10 is 3.34, which shows the unfavorable status of this index. The results of the study in the field of the level of civil and institutional development showed that on the basis of the Normal scale from 0 to 10, civil development is 4.86 and institutional development is 3.69. Lastly, the research findings for the three levels of the sustainable development of the livelihood shows that the livelihood diversification is 3.61, in depth agriculture 3.24 and migration strategy is 3.02. The analysis of the results of the sustainable livelihood shows that the decrease of drought of the villages increases the diversity of the livelihood of the villagers. According to the results obtained, the mean of the resilience index of the investigated households on the basis of 0 to 10 equals to 4.86, which is close to the average level. The classified distribution of the resilience level and the focus of the more than of 56% of the households with average level of resilience confirms this situation. 30.26% of the households has low resilience and 15.64% has high resilience in the face of existing conditions. Upon this basis, the highest amount of the resilience equals to 5.38, which exists in the villages with severe drought conditions and by decrease of the drought, the resilience of household’s decreases. Finally it can be said that the villages with a long history of vulnerability from drought and also having more intense droughts, has a higher resilience level in dealing with the situation.

According to the results, the highest amount of vulnerability exists in the villages with low experience in dealing with the long-term conditions of drought, which their economic and social structures are not prepared to deal with the conditions. While the average amount of the livelihood capitals and the resilience of the studied statistical population do not show an appropriate conditions, but totally, the results and relationships of the studied variables conforms the role of possessing all dimensions of livelihood capital on taking appropriate approach to dealing with the conditions of drought in the Lake Urmia. In the field of taking the approaches of diversifying the livelihood resources of the villagers, there are several scientific and examined solutions, such as considering the education and awareness as a definite reality, also the knowledge and skills of the villagers in the fields of modifying the crop patterns, water saving strategies, the use of efficient products and making use of the other high-income jobs must be increased.

In the field of educational solutions, besides providing modern knowledge and international successful experiences, it must be possible to make use of the indigenous knowledge and experiences of the villagers.

Drought is a concept that is generally understood on a basic level, but is difficult to quantify. Palmer defined a drought as a meteorological phenomenon that is characterized by ‘‘prolonged and abnormal moisture deficiency. A drought can alternatively be broadly defined as a temporary, recurring reduction in the precipitation in an area.

Aridity and drought are not synonymous. Aridity is a measure of long-term average climatic conditions. Both humid and arid regions experience droughts. However, the inter-year variation in precipitation is greater in arid regions and there is a greater probability of below average precipitation in any particular year. Arid regions are thus more prone to droughts and may experience more severe impacts from droughts.

In this research was used temperature and precipitation monthly data of Urmia, Tabriz, saghez, Maragheh, and Mahabad station in statistically period 1985-2014. Run test was used to study the homogeneity of data. Randomness and homogeneity of data was approved.at a confidence level of %95. SEPI Index and ANFIS model was used for determining and forecasting drought in Urmia lake basin. SEPI index is more complete than SPI. Results of SEPI were used in ANFIS model.

Fuzzy index SEPI[1]: Standardized precipitation index and evapotranspiration (SEPI) to address some of the disadvantages of SPI index is provided. Evapotranspiration and precipitation index SPI index and SEI standardized integration is achieved. The index is the result of drought monitoring phase of architectural models using fuzzy logic in a fuzzy inference system is designed. How to design this model and determine SEPI is described below.

Fuzzy architecture drought monitoring: for derivatization indices SPI and SEI using Fuzzy Inference System, Due to the structure of fuzzy models were considered.

SPI index[2]: Standardized Precipitation Index is an indicator widely used in Drought Monitoring. This index is one of the few indicators drought monitoring and could even say the only indicator that the time scale is considered. Depending on the time scale to determine the effect of different sources of agricultural drought, hydrological and so determined. Time scale can be determined from one month to several years. SPI index is used to calculate the only element rainy climate. Monthly precipitation amounts for each station in the desired time scale is calculated.

SEI index[3]: Since the index SPI Single Entry, rain, The SPI index values under the influence of changes in temperature and evapotranspiration parameter that is powerful factor in the drought, it will not be. So to enter the effect of temperature and evapotranspiration in SPI, SEI (evapotranspiration index Standard) To calculate this index, before any measures should reference evapotranspiration for the period to be estimated.

define the rules for combining indicators SPI and SEI: Different classes index SPI and SEI rules or the same combination of conditional statements in the form if, as a class of SEPI index in the lead, is defined. This rule only a combination of different modes SPI and SEI indices that lead to SEPI index shows. In this regard, the rules can be combined to fit different for successive written and stored in the knowledge base. Since the output of the resultant composition, indices SPI and SEI are involved in determining the status of SEPI, Weight each of the indicators with regard to the effect of precipitation and temperature parameters on the severity of the drought was considered As a result, SPI indices and weights 0.667  and 0.333, respectively SEI were included in the calculations.

According to the results, according to the research, education Anfis model with 75 percent of the data series is well done SEPI and much has been done to ensure education is nearly 100 percent. So that the graphic maximum of 0.26 percent error in saghez station on a scale of 6 months and the lowest average error of 0.10 percent in Urmia station is on a scale of 6 months. In modeling, validation data, the average error modeling is naturally higher than the average training error. Most average forecast error saghez on a scale of 6 months at the station 0.34 percent and 0.10 percent, the lowest on a scale of Urmia station is 6 months. But the coding maximum of 0.65 percent error in saghez station on a scale of 6 months and the lowest average error of 0.32 percent in Tabriz station is on a scale of 6 months. SEPI index in the time scale of 6 and 12 months is used for investigate the characteristics  of adaptive neuro-fuzzy inference system in order to drought and drought forecasting model. According to the findings in this study, the frequency of drought in the stations of Urmia and Saghez and Maragheh on a scale of 6 months is more than the scale of 12 months in the basin of Lake Urmia but in Tabriz and Mahabad Stations situation is the vice versa. The drought in Urmia Lake basin is increasing trend but temperature has increasing trend with more intensity. The highest and lowest percentage of drought was seen in Urmia and Mahabad station respectively. The results of the forecasting of index by ANFIS model showed that the most training error is in Tabriz station (0.51) and the lowest training error is in Maragheh station (0.36) in a scale of 12 months in coding. In validation data modeling the average of modeling error is higher than the average training error naturally. According to the definition of drought SEPI was presented based on amounts of 0.73 or higher or mild drought to higher floors as dry conditions arise The scale of 6 months in Urmia station with 13.14 percent to 10.89 percent saghez station, Tabriz stations with 5.58 percent, with a 5.1% Mahabad station and Maragheh with the amount of 4.82 percent, the drought has occurred. The time scale of 12 months in Tabriz station by 9%, saghez station with 7.26 percent, with 6.11 percent of Urmia station, Maragheh with 5.5% and the amount of Mahabad stations with a 3.44 percent, from months of study in the series, drought has occurred.

Results of SPEI are:

1. Drought trend is increasing in urmia lake basin. Temperature has increasing trend extremely.
2. The highest percentage of drought is in Urmia station and its lowest is in Mahabad station.
3. Percent of frequency of drought in Urmia station, Saghez and Maragheh on a scale of 6 months is more than to 12 months, but the scale of Tabriz and Mahabad stations with the photos. Stations Tabriz and Mahabad is in the opposite situation.

Results of ANFIS Model are:

In study area and in ANFIS model whatever forecasting coming years is shorter; confidence of forecasting will be more.

Due to the errors amount obtained in model validation, in study area forecasting of drought by ANFIS model was done with confidence 94%.