جستجوی مقالات مرتبط با کلیدواژه « drought monitoring » در نشریات گروه « علوم انسانی »

Drought is a complicated natural phenomenon that occurs basically due to the lack of precipitation over a time period. And its occurrence usually results in great costs on various parts of the natural and the society. Among the various indices in climate drought monitoring, the SPI index (McKee et al., 1993) is the most well-known index, in terms of easy access to its data (rainfall); It is also possible to calculate it in any time window; Ability to calculate magnitude, frequency and continuity; The possibility of quick detection of soil moisture and the possibility of showing the spatial distribution of drought-dominated areas are widely used all over the world (Mishra and Desai, 2005). Standardized Precipitation Index (SPI) can be calculated for any location based on long-term recorded precipitation data. Calculating the SPI drought index in each of the time scales can be considered as one of the advantages of this index. The length of the rainfall data recording period as well as the nature of the probability distributions play an important role in calculating the SPI drought index and these factors are among its limitations.

Methods and materials:

In the present research, the uncertainty of SPI estimation in choosing the length of the statistical period and time scale was investigated using monthly rainfall based on the gamma distribution function at Bandar Abbas synoptic station in two periods of 31 and 64 years. Therefore, the rainfall for the time scales of 3, 6, 12, 24 and 48 months in the two mentioned time periods was calculated and then using the bootstrap method for each rainfall event in each time scale, 1000 random data were generated and the SPI confidence interval was about 95% confidence was calculated. The size of the confidence interval (the difference between the upper band and the lower band) was considered as uncertainty and the absolute ratio error (ARE) between the estimated and observed values was calculated.

ARE and uncertainty due to bootstrap estimation were estimated in two time periods and scales from 3 to 48. The greater the difference between the bands, the lower the certainty and the greater the uncertainty (Vergeni et al., 2015). Therefore, a longer period of time (large number of samplings) has less uncertainty, since the number of samples decreases as the time scale increases, so the uncertainty bandwidth increases. For example, in the 3 and 6-month scale in the 31-year period, the estimation error is 0.09, 0.11 and in the 66-year period is 0.03, 0.05, and the uncertainty has decreased from 0.051, 0.63 and 0.38, 0.41, respectively. In the 12-month scale, the error rate in the short and long term is 0.12 and 0.01, respectively, and the uncertainty is 0.81 and 0.55, respectively. In the 24-month scale, the error in the 31-year period is 0.31, which decreases to 0.1 in the long-term period of 66 years, and in 48 months, it decreases from 0.45 to 0.27, and in the same way, the uncertainty level in the long-term period is Scales 24 and 48 are reduced.Based on the results of two short (31 years) and long (66 years) samples, it showed that there was more uncertainty and error in the samples with a small number, and considering the overestimation or underestimation caused by the length of the short period in the diagnosis of drought classes Historical events have been misjudged and lead to inappropriate drought mitigation measures, as classifying a high-grade drought event (Class 9) to a lower grade (Class 8 or lower) can lead to misleading decision-making.

SPI was able to successfully detect historical droughts in the two investigated periods. The increase of records from 31 to 66 years led to the emergence of one of the most unique features of SPI, i.e. repeatability, reversibility and predictability. The confidence interval resulting from the difference between the upper and lower bands estimated by the bootstrap resampling method indicates uncertainty, the smaller the bandwidth or the result of the difference, the lower the uncertainty and the more reliable it is. In this research, the error caused by bootstrap estimation in 1000 sampling times was also estimated. Based on the obtained results, increasing the number of samples from 31 years to 66 years causes a decrease in uncertainty and error, and as a result, increasing the time scale due to the decrease in the number of samples due to data averaging causes an increase in uncertainty and estimation error. The studied area has It has had long dry and wetter periods in the past and has experienced numerous historical events. Therefore, it is necessary to pay attention to the possible judgments caused by wrong estimation and uncertainty in the observational data, and in this way, seek to reduce the possible damages caused by overestimating or underestimating drought.

Drought is a complex phenomenon caused by the breaking of water balance and it has always an impact on agricultural, ecological and socio-economic spheres. Although the drought indices deriving from remote sensing data have been used to monitor meteorological or agricultural drought, there are no indices that can suitably reflect the comprehensive information of drought from meteorological to agricultural aspects. In this study, the synthesized drought index (SDI) as a synthesized index from the vegetation condition index (VCI), temperature condition index (TCI) and precipitation condition index (PCI) were used for comprehensive drought monitoring in the Urmia Lake Basin (ULB) based on the Principal Component Analysis (PCA). For this purpose, MOD13A3, MOD11A2 and TRMM 3B43 data series were downloaded y for the period of 2001–2012. After initial processing, drought indicators were calculated using LST NDVI and TRMM data, and monthly drought severity maps were prepared. In order to validate SDI index, the Correlation relationship between SDI and SPI indices was obtained in the 3 month period during the growing season. As well as, SDI correlation relationships were investigated with wheat and barley crop yields. The results indicate that drought occurred in 2008 and 2001 in the ULB. The results of validation show that there is a correlation of 80% between the two SDI and SPI indicators. Also, the results of this study showed that the SDI index, as a comprehensive index of drought monitoring, reflects the effects of drought on agriculture.

The purpose of this study is to investigate drought using meteorological, hydrological, and remote sensing indicators in the Gharasou basin located in Ardabil province with an area of 495 square kilometers. Therefore, to calculate the SPI index was used monthly rainfall values related to 22 rainfall stations (1985-2015) and to calculate the SDI index, the monthly discharge values of 14 hydrometric stations (1985-2015). Then, SPI and SDI indices were compared with each other on a time scale of 1, 3, 6, 9, and 12 months, as well as seasonal and half a year. To study vegetation drought and calculate NDVI and VCI indices, were used MODIS images, with a spatial resolution of 500 meters for 15 years (2000-2015). The results of the meteorological drought time series in Gharasou showed that the drought occurred comprehensively in the years (2001-2000, 2006-2005, 2008-2007, and 2014-2013).  So that the most affected areas of drought were related to the northern and northeastern of the basin (Namin and Abarkooh). The results showed that meteorological drought has been more persistent in recent years and has been significantly correlated with hydrological drought with a delay of 1 to 3 months. NDVI and VCI indices showed that in identifying drought periods, these indices are consistent with meteorological drought, but are different correlation values. Finally, the comparison of NDVI and VCI indices with the SPI index showed that the VCI index with a value of r = 0.44 has the highest correlation with the SPI index. However, the correlation between NDVI and SPI indices is r = 0.38. The results of the VCI index showed that in drought conditions, only in the east of the basin and in the areas around Sabalan mountain, favorable vegetation conditions are observed, but in other areas of the basin, moderate and weak drought has occurred.

Due to declining rainfall in the last two decades, drought has become a major problem in the world, especially in arid and semi-arid regions such as Iran, so monitoring and managing it is important. Remote sensing and geographic information system (GIS) and remote sensing (RS) provide the ability to study various indicators to evaluate the types of droughts. So, in the present study, the drought of Iran using multi remote sensing indicators including precipitation condition index (PCI), temperature condition index (TCI), Vegetation Conditions Index (VCI), and the integrated under the heading the scaled drought condition Index (SDCI) during the statistical period 2000 to 2018 were evaluated. To evaluate the accuracy of the obtained results, these results were compared with the standardized precipitation-evapotranspiration index (SPEI). The results of this study showed that the three indices of PCI, VCI, and TCI are well matched. The results of the SDCI index indicated that severe droughts occurred in 2000, 2008, and 2017, which are consistent with SPEI index. It should be noted that minor differences between the two indicators (SDCI and SPEI) can be justified by the fact that the SPEI index is a climatic index that considers two parameters of temperature and precipitation for annual drought assessment, while the SDCI index in addition assessment to temperature and precipitation factors (‎meteorological drought), it also considers ‎agriculture drought and more comprehensively evaluates drought. Finally, it can be mentioned that based on the calculations performed, the SDCI has been more effective in assessing drought than other indicators used.

Drought is one of the natural hazards which can cause an irreparable damage in various sectors of agriculture, economics, and so on. In recent years, different regions of the world have experienced more severe drought (Mirzai et al., 2015). Also, drought is one of the most important natural disasters affecting agriculture and water resources which is abundant especially in arid and semi-arid regions (Shamsenya et al., 2008). Drought changes are well-suited for optimal management of water resources utilization (Alizadeh, 2017). Drought is also referred to as a climate phenomenon with a lack of humidity and rainfall relative to normal conditions. This phenomenon strongly affects all aspects of human activity (Zeinali & Safarian Zangir, 2017). Regarding the studies done inside and outside of the country, this study attempted to model and monitor the drought phenomenon in northwest of Iran using a new index.

In this study, drought modeling in northwest of Iran was carried out using climatic data of rainfall, temperature, sunshine, relative humidity, and wind speed monthly (6 and 12 months scale) for the period of 32 years (1987-2018) in five provinces of Ardebil, East Azarbaijan, West Azarbaijan, Zanjan, and Kordestan in 21 stations using a new index modeling called TIBI architecture, fuzzyized from four indicators (SET, SPI, SEB, MCZI) Valid in World Meteorological Organization. The position of the studied areas are shown in Fig. 1 and the coordinates of the stations are presented in Table (1).

Monitoring of drought fluctuations based on four integrated indicators in T.I.B.I
In order to investigate the effect of drought fluctuations in drought conditions of stations, it is possible to find changes in the parameters (SET, SPI, SEB, & MCZI) within the TIBI index. Considering the large number of stations studied, for the sake of better understanding, only the drought series chart of Tabriz station was presented on two 6-and 12-month scale. (In the above-mentioned Station diagram, the Flash line Red shows a 6-month-old drought margin with a value of 0.44 and more, and a 12-month scale with a value of 0.98 and more). The analysis of these forms shows that at the 6 and 12-month scale of Tabriz station, the amount of evapotranspiration was similar to drought conditions, which decreased from March 1993 to July 1998, while after this month an increase was observed. The impact of rainfall on a 6-month scale is weaker than the 12-month scale. From April 1996 to December 2004, it has grown steadily, and then followed the same pattern. The indicators (SET, SPI, SEB, & MCZI) affect the TIBI index and show some trends indicating that the new TIBI fuzzy index is well reflected in the four indicators. Its drought classes scale was presented in Table 5. The T.I.B.I index on a 12-month scale shows a sharper shape compared to a six-month scale.

In recent years, drought is one of the most important damaging issues in different sectors such as agriculture, economics, etc. In different parts of Iran including Northwest of Iran, Researchers have done a lot of research on drought monitoring. They presented different models but did not adequately cover the subject. The purpose of this study was to model and investigate drought in northwest of Iran during the 6 and 12-month scale. At stations, the intensity of the 12-month scale and the frequency of droughts are more than 6 months. Drought persistence is more than 12 months old. Drought had a lower continuity in the short-run time scale and was influenced by the temperature parameter. The severity of drought in the long periods of time was less responsive to rainfall variations. The trend of drought in the northwest of Iran increased and the temperature trend was mildly increasing. The most frequent occurrence of drought occurred at the 6th and 12th month scale in Orumiyeh station, while the lowest in both the 6 and 12-month scale in Sanandaj station. The percentage of drought frequency in Jolfa, Maku, and Uromieh stations was 12 months higher than the 6-month scale.

Drought is a serious danger with very extensive impacts on the soil, economy, and the threat to the livelihood and health of local communities. This disaster as an unpleasant climatic phenomenon that directly affects the communities through restrictions on access to water resources, causes high economic, social and environmental costs. Meteorological drought indicators are calculated directly from meteorological events such as precipitation, and in the absence of these data, drought monitoring will not be useful. Due to the fact that meteorological drought indicators are only valid for a single location and do not have the required spatial resolution and are also dependent on weather station information, and these stations are often distributed distantly, the reliability of these indicators has been questioned. Given the characteristics of satellite data such as spatial and temporal resolution, extensive coverage of studied areas, and direct investigation of vegetation status by satellite indices, many studies have been carried out for drought modeling using this technology and these indicators. Over the past four decades, far-reaching drought monitoring tools have been widely developed and drought monitoring models are widely proposed, which are generally based on vegetation indices, surface temperature, humidity and reflection in the visible and infrared regions. These include leaf water content index, vegetation cover index and temperature - drought - vegetation index. Therefore, remote sensing techniques can be a useful tool in drought monitoring. The purpose of this study is to monitor drought and vegetation health in the city of Kermanshah using LANDSAT satellite imagery. For this purpose, first, by examining the rain-gauging and synoptic data of existing stations and using the standard precipitation index model, the driest year and one wet year were selected as the sample. In this study, two years of 2015 and 2016 were selected as the dry and wet years and then, the vegetation cover of the region was compared with the Landsat images. To use these images, it is first necessary to make sure that there is no geometric error. For this purpose, the road vector layer was used, which was revealed that the images have geometric errors. Images with less than a half-pixel error were corrected geometrically using 21 and 24 auxiliary points. The adaptation of the vector layers with the roads existing in the image indicated the accuracy of the correction. At the next stage, the driest year and one wet year were selected as samples by examining the rain-gauging and synoptic data of the existing stations and by using the standardized model of rainfall index. At the next stage, the Temperature Condition Indices and Vegetation Health Index (VHI) were compared in two wet and drought periods were studied in order to determine the differences of these indices during a dry year and a year with high precipitation. For this purpose, each of the aforementioned indices was built using the LANDSAT-8 imagery, and the stages of building these indices were subsequently presented.  The required pre-processing and processing as well as the geometric and radiometric corrections were first performed on the satellite images. Then, temperature condition indices, vegetation status index and vegetation health index were prepared for drought monitoring. Considering that, the meteorological drought indices are only valid for a single location and lack the required spatial resolution and are also dependent on the information of the meteorological stations, and these stations are often distributed far apart from each other, the reliability of these indexes has been questioned. Satellite data characteristics like high spatial and temporal resolution, extensive coverage of the study areas, and direct survey of the vegetation status by the satellite indexes have led to a large number of studies on drought modeling using this technology, and the confirmation of the use of these indices. The aim of this study is to determine the moisture, heat and health of the vegetation using the LANDST images. Thus, the results of the study in the next stage indicated that the LANDSAT images and the built indices have the required capabilities to monitor drought. The results of this research can be a proper option for decision-makers to effectively supervise, examine and resolve the drought conditions and double the necessity of profile definition. Supplementary studies are suggested for spatial drought monitoring by satellite imagery through ground measurements of the quantitative changes in the coverage and temperature of the earth’s surface. There are limitations in the use of NDVI and satellite thermal bands. These include weather and cloud conditions that should be considered. Using maps obtained from the drought monitoring and evaluating indices can help improve drought management programs and play a significant role in reducing the effects of drought. Using vegetation health status index, it was determined that the vegetation status has had a lot of changes during drought compared to the wet period, hydrological drought has had a major share in the destruction of vegetation and drying of the lakes and, consequently, the abandonment of agricultural lands and the lack of access to alternative water resources, as well as the lack of groundwater resources or the lack of alternative surface water resources have intensified, and it seems that, this part of Iran will face numerous problems if the drought continues in the coming years and the appropriate methods are not used to deal with it. Also, given that the water resources of the region are going to decrease in the coming decades, the necessity of using comprehensive water management methods in all sectors, including the reserve, transfer and distribution sectors seems very essential and inevitable. Finally, it is expected that the trend of destruction of vegetation decreases in the future by applying proper management practices, sustainable water distribution, regional negotiations, methodical agriculture as well as the establishment of optimal hydrological conditions.

Droughts are long-term phenomena that affect vast areas, causing significant economic damages andlosses in human lives. Droughts are the most costly natural disaster in the world, and affect more people than any other natural disaster. Therefore, it is important to develop early warning systems to mitigate the effects of drought. The easiest way to monitor drought is to use drought indices that calculate drought severity, duration and actual range for each drought type. Several drought indices have been developed based on different variables and parametersto assess drought types. Soil moisture is a significant hydrological variable related to flood and drought and plays an important role in the process of converting precipitation into runoff andstorage of groundwater. Due to the difficulty, cost and time required for the field measurements of soil moisture, this parameter has not been widely used in drought indexes. Recent developments of global databases, based on satellite estimates, as well as rapid progress in hardware and software for modeling complex processes governing the water balance at the ground surface, have led to many efforts to deploy this new tool to reduce the limitations in this field. In this research, a new drought index based on soil moisture, derived from the land surface models of Global Land Data Assimilation System (GLDAS-SMDI) has been provided to monitor the evolution of drought severity.Thisindex is based on the fact that soil moisture is a determinant factor in most of complex environmental processes and has an important role in the occurrence of drought. The central Iran is located between 27N-37N latitudes and 48E-61E longitudes with an area of about 837,184 km2. There are 50 synoptic stations within the area. In the present study, soil moisture derived from Global Land Data Assimilation System using the GLDAS-SMDI index was used to prepare the spatial distribution map of drought in central Iran over the period of 2001-2004. The accuracy of the GLDAS-SMDI index based on satellite data was carried out using the evaluation criteria of R and RMSE compared with drought spatial distribution map derived from the SPI index based on monthly precipitationdata of 50 synoptic stations. In this study, the drought spatial distribution index of Soil Moisture based on the Global Land Data Assimilation System (GLDAS-SMDI) and SPI was obtained based on the monthly precipitation data from 50 synoptic stations over the period of 2001-2004. The results of the statistical criteria of the moisture drought spatial distribution mapcompatibility assessment based on GLDAS data with corresponding pixels on the drought spatial distribution map based on the precipitation data of thesynoptic stations showed that the drought severity map has had a high precision and good conformity with the land data (R=0.65, RMSE=0.22) based on GLDAS data.The highest correlation coefficient (0.74) was in 2004 and the lowest (0.45) in 2003.
The lowest and the highest mean errors in 2004 and 2001were 0.19 and 0.26, respectively,.The highest droughtseverity based on the GLDAS-SMDI index occurred in the Central Iran region at Iranshahr, Kahnuj, Bam, Baft and Birjandstationsduring the studied period. Droughts are hydro-meteorological anomalies characterized by prolonged shortage in regional water supply and can cause temporary difficulties (even failures) in water reservoirs. Today, most of the severe droughts are breaking out in terms of frequency, magnitude and duration due to constantly increasing water consumption, causing serious social, economic and environmental problems worldwide. Therefore, in order to deal with frequent droughts, great efforts have been made to estimate a more accurate assessment for better decision-making in order to prevent and mitigate drought losses. The most successful efforts among these methods might be the development and the use of various objective indices. In this research, the monthlymoisture data of the Global Land Data Assimilation System was evaluated to estimate the drought severity index based on soil moisture. The evaluation was performed using the coefficient of determination (R2) and Root Mean Square Error (RMSE). This analysis has demonstrated that the GLDAS products have very good compatibility with the land data over the selected area of Central Iran on monthly timescales and a 0.25° spatial scale. As a result, it can be said that the GLDAS data has a good potential for useful application of hydrological simulation and the calculation of water balance sheet, in the regions with low observations and low quality station. Therefore, it can be concluded that the soil moisture output of Global Land Data Assimilation System can be used for rapid and low cost estimation of drought severity based on soil moisture, which is a major factor in many complex environmental processes and has an important role in the occurrence ofdrought. In order to increase the spatial accuracy of drought intensity maps, it is recommended that the satellite data be combined with the values ​​of ground stations.

Drought is a climate phenomenon with a lack of moisture and rainfall compared to normal conditions. This phenomenon strongly affects all aspects of human activity. In this Article, were discussed the drought situation in Urmia Lake using MATLAB software and system capabilities combined indicators SEPI on two-time scales 6 and 12 months. Therefore was used climate data of temperature and precipitation for 5 synoptic stations basin of Lake Urmia (Tabriz, Maragheh, Urmia, Saghez, and Mahabad). Results indicated that SEPI index show features of SPI and SEPI index. As well as it enters temperatures as a powerful factor in the drought severity in order to the investigation of the drought conditions; So SEPI drought index is better than the SPI for the study of drought. Drought studies on SEPI index indicated that drought trend in Urmia Lake Basin is increasing. The temperature has an increasing trend with more intensity. The longest duration of drought in the basin of Lake Urmia is in Urmia station on a scale of 6 months from May 2005 to November 2006 for 19 months. The highest percentage of drought observed in Urmia and the lowest percentage in Mahabad stations.

Drought is a natural and climatic phenomenon that occurs in wide areas across the world every year and this occurrence becomes an inevitable issue. This phenomenon is impaired in ecosystem. Arid rangeland ecosystems are a significant part of our land that is brittle systems that climatic changes are useful to simply unfounded destruction. Therefore, drought identifying and monitoring valid descriptive statistics indicators is the first step toward managing this phenomenon. Drought monitoring by using traditional systems is difficult. Remote sensing technology, coupled with geographic information systems have shown their ability in data performance in time of drought. This study was conducted at rangelands of Yazd Province to monitor of drought. In this study has been trying to research this index NDVI, EVI, NMDI, LST and TCI bands of MODIS images extracted from the data and precipitation data from climatology stations to use the range. Regarding the comparison between drought indices and indicators for climate satellite and satellite set performance indicators, to evaluate the climatic index SPI closest climatology stations to the monthly precipitation statistics for the period 2000-2008 types of rangeland in the study were used. To extract the parameters of satellite imagery resolution bands of MODIS images of 500 meters, the eight-day harvest interval for the period 2000 to 2012 and during the months of February through September, before and after it was prepared. Due to the different periods of precipitation data and satellite imagery, Joint Range Index Comparison between 2000-2007 was considered. Data from other years were used to verify the accuracy of the results. Indices at intervals of 3, 6, 9, 12, 18 and 24 months showed that the volatility of short-term drought intervals is greater than long-term intervals. But it has a bit of persistence. So in short periods of drought month intervals is greater than the other. Regarding the relationship between drought and precipitation, precipitation and meteorological drought index results showed that drought index ranged from three to nine months with precipitation amounts of one percentage correlations are significant. In other words, changes in the amount of monthly precipitation index SPI is effective in short-term timeframe. Statistical comparison between results of calculation of satellite indicators and meteorological drought index showed that SPI index in short term with heat index and NMDI index has the highest correlation in one percent level.

Iran as one of the countrieslocated in arid and semi-arid regions of the world, has been in drought danger. Shortage information about long-term weather conditions in many regions of the country, is one of the most important problems in drought monitoring. In this article, spectral vegetation indices (SVIs) have been employed in order to drought modeling and its forecast. To this end, SPI drought indicator (standardized precipitation index) used to represent period of drought and its intensity. Some broad band spectral vegetation indices including Normalized Difference Vegetation Index (NDVI), Temperature Condition Index (TCI) and Vegetation Condition Index (VCI) were extracted by using NOAA-AVHRR satellite imagery. These indices entered to SVM classifier model to gain the SPI index as its result. After comparing the results, TCI was diagnosed as the best index to predict drought condition via 3 months SPI (trimester SPI).

Drought defined as a climatic phenomenon with the humidity and rainfall shortage as compared with normal conditions. This phenomenon affects on all of aspects of human activities‌, severely. while, studies associated with this phenomenon on the basis of appropriate methods are very low. In the present study, effort has been made to analyze the drought state in Ardabil province by means of software system capabilities in MATLAB and SEPI index in two temporal scales of 6 and 12 months. The climatic data of synoptically stations of Ardabil were used, Parsabad and khalkhal countian in Ardabil province. Results of study show that SEPI index reflects the features of two SPI and SEI indices well. And also enters temperature in the studying of drought conditions as one of the effective parameters in changing of drought intensity. There‌fore, the investigation into drought with SEPI index is better than SPI index. Studies associated to drought on the basis of SEPI index shows that drought process is increasing in Ardabil province. Temperature also has an increasing flow with higher intensity. Longest temporal continuation of drought in province, has occurred in Parsabad station in temporal scale of 12 months, from June‌, 1998 to November, 1999 in 18 month period. The greatest percentage of drought occurrence is in Parsabad station and the minimum of that is observed in Khalkhal station.

Drought is the most complicated natural hazards. This phenomenon has different characteristics which are calculated by different indices, where as these indices are depondant on climatic variables and meteorological stations, therefore their ability for recording is not continuse. On this basis, for estimating the amount of meteorological variables in non-sampled locations and preparing continuse maps, spatial interpolation methods are used. In this paper, fristly by using Standard Precipitation Index(SPI),the drought severity is calculated, and then for assessment of different interpolation methods, different types of spatial interpolation methods including Kriging, Cokriging, Inverse Distance Weighting (IDW), Local Polynomial, Completely Regularzide Spline, Spline with Tension and Thin Plate Spline were used. Also for comparing interpolation methods, the cross-validation technique and error criterions including MSE,MBA, RMSE and %G were used.The results indicate that Kriging Ordinery method is the best method for interpolation of drought severity and SPI index, because it has the least errors in most of months and in 9 months of the year, G% value is calculated as positive. In fact, this method has the least deviation. The most errors of interpolation methods is relevant to Thin Plate Spline method, indeed each of the three evaluation criteria shows greater value of error than the other methods.

Drought as a natural hazard is formed and occurred from many years ago, in various countries especially across vast areas of warm and dry. Deleterious effects due to the occurrence of natural hazards has appeared slowly and caused decline and loss of agricultural products, food, economic instability and social and environmental crises are even. Undoubtedly, the first step to coping with drought and its consequences is to have accurate understanding of this phenomenon and its effects on various dimensions, so the required steps to solve this issue can be taken. The method of this research is analytic-descriptive, its nature is from type of applied research and the data collection method is library research. The study population comprised 35 villages of Abarkouh city which by using the classified sampling method eight villages were selected for study. For data analysis, descriptive statistics (including frequency, mean and standard deviation) and inferential statistics (including factor analysis) is used and also to study the drought monitoring, the valid standard of precipitation index (SPI) is applied. The results based on SPI indicate that Abarkuh city has been in drought situation during this decade. In addition, factor analysis discoveries show that the Continues Droughts has brought irreparable economic damage to this city.