جستجوی مقالات مرتبط با کلیدواژه « شاخص بارش استاندارد شده » در نشریات گروه « جغرافیا »

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 as a long-term stage of water scarcity is a challenging issue in water resources management and a very widespread natural disaster. Being aware of the drought situation can significantly reduce the risk of losses caused by this phenomenon through predicting and zoning the severity of the drought. One method of determining drought is the Standardized Precipitation Index (SPI), which was proposed by (McKay et al., 1993), for drought monitoring in the Colorado area. The SPI index is one of the appropriate indices to be used due to its advantages in the regional analysis of drought and the temporal relationship between events.

First, TWSI data were downloaded from GRACE-CSR satellite. The TWSI data obtained from GRACE satellite were received using coding in Google Earth engine in EXCEL format and were provided for the entire province of Khuzestan. Since the TSDI index provides a comprehensive picture of drought, TSDI values had to be calculated after reviewing the TWSA data from Google Earth Engine. To calculate this index, TSD and cumulative TSD values were calculated first. Then, the total water shortage was calculated cumulatively. In addition, the 15-year SPI index (2002-2006) was used to study drought in Khuzestan province in this study. To do this, from the stations that had better conditions in terms of data, 11 stations were selected and SPI-6-12-24 was obtained through DIP software for each of the selected stations on a monthly and annual basis.

Drought study of SPI-12-24 in Khuzestan province showed that the onset of drought in this province started in 2008 or 2009 and continued until 2013 or 2016. Among all the stations, 2009 and 2012 were the most severe years in terms of drought and in most of the stations in SPI-12-24 these two years were the driest years in the drought periods, and they were in drought conditions in all these stations during these two years. In terms of drought severity, Safi Abad, Omidiyeh, Ahvaz and Abadan stations were all ranked first to fourth with very severe drought. With regard to time, November and January in SPI-12 with frequency of 4 and May, July, August and September in SPI-24 were the most affected by standard precipitation drought. The TWSA values for Khuzestan province from 2002 to 2016 showed that according to this figure, the value of TWSA in this area found a negative trend from April 2008 to December 2016. in Khuzestan province from 2008 to 2016, three dry periods were observed, which are from April 2008 to January 2010, April 2010 to January 2014 and May 2014 to December 2016. The lowest TWSI values in each period were -11.27, -13.03, and -10.58 mm.

In this study, spatial-temporal changes of TSDI water deficit index in Khuzestan province were investigated. To do this, first the monthly index SPI-12-24 was calculated using the monthly rainfall values of 11 meteorological stations for the whole region in the period 2002 to 2016 using the DIP software. Then, to calculate the TSDI index, the data of total water storage anomalies obtained from GRACE-CSR satellite were used. Drought survey of SPI-12-24 in Khuzestan province showed that drought in this province started in 2008 or 2009 and continued until 2013 or 2016. Among all the stations, 2009 and 2012 were the most severe years in terms of drought, and in most stations in SPI-12-24 were the driest years in the drought periods. In terms of severe drought, Safiabad, Omidieh, Ahvaz and Abadan stations all ranked first to fourth with very severe drought. November and January in SPI-12 and August and September in SPI-24 were mostly affected by standard rainfall drought, with 2% of the area in normal condition, 27% in moderate drought condition, 68% in severe drought condition and 3% in a very severe drought situation, meaning that most of Khuzestan province was covered by severe and very severe drought. The study of water shortage in Khuzestan province showed that in Khuzestan province, August, January, and April were the most affected by water shortage and August with -6/89, the driest month in the whole statistical period was studied, which according to the classification The TSDI index is in a very strong category. In terms of seasonality in winter, due to the fact that the amount of groundwater was strengthened in this season, its amount changes sinusoidally and sometimes it was in a moderate position and sometimes in a very severe category. Among the seasons, autumn had the least changes compared to the other seasons and was located in the middle to upper class.

In this research, to determine the changes in the level of snow cover in relation to drought, satellite data has been used in order to determine the snow cover and meteorological station data has been applied to estimate the drought. In this regard, at the first stage, satellite data was collected from NASA's Web site from 1385 to 1395, and after geometric and atmospheric corrections based on the NDSI index and using of bands 4 and 6, was applied to determine the level of snow cover and  drought rate has been estimated based on SPI index. Studies of snow cover reveal that the level of snow cover has been reduced and this is proportional to the drought rate, and the prevailing trend in the level of snow cover and SPI is a downward trend. However, in spite of the prolonged and increased SPI index, the level of snow cover has been reduced or vice versa. The reason for this can be investigated through the type of precipitation during these years and it is possible that most type of the precipitation, during the years in which snow cover has been less, has been rainy type and in the drought years with increased snow cover, type of downfall has been more snowy.

Decreasing of precipitation and Increasing of temperature, leads to extreme climate events such as drought which drastically impact on agricultural. Knowledge about the timing, severity and extent of drought can aid planning and decision-making. Drought indices derived from in-situ meteorological data have coarse spatial and temporal resolutions. Thus, obtaining a real-time drought condition over a large area is difficult. Therefore, drought indices which is derived from remote-sensing data, has been widely used for drought monitoring..In this study, Vegetation Drought Index (VDI) was evaluated in Sistan & Balouchestan Province To do this, Terra Moderate Resolution Imaging Spectroradiometer MODIS (MOD02HKM and MOD11A1 ), Standard Precipitation Index (SPI) and monthly precipitation data GLDAS from 2000 to 2018 were utilized to evaluate VDI.Accuracy of the Drought spatial distribution maps based on Pearson correlation coefficient was used data. Results indicate high significant correlation rate in the study area. Thus VDI, has the potential to monitor agricultural drought in the case of study and the drought indices based on remote sensing data could well use in drought early warning systems.

In recent years, the impact of climate change and drought forecasting on water resources planning and management has received much attention. In the present study, probable climate change on Malayer basin temperature and precipitation over the period 2014-2014 was investigated and monthly, seasonal and annual forecasts for the near future (2030-2011) under three scenarios A2, B1 and A1B using HadCM3 general circulation model The LARS-WG model was used for performing and exponential micro-scale.
Data and

ARIMA multiplication time series and AIC and SBC criteria and Pert-Manto test in predicting precipitation and SPI and SDI indices have been used to predict drought for the period (1397-1418) of Merville, Pihan and Wasjeh hydrometric stations.The results show an increase in precipitation and temperature in all three monthly, seasonal and annual scales in the coming period, and Shows that the largest meteorological drought for the base period in 1998-1999 is -1/96 and In the coming year 1418-1418 there was adecrease of -2/4. Surveys show that moderate and severe droughts will increase in the coming statistical period at the Mervil, Peyhan and Vasge stations.

Drought occurrence reduces discharge and hydrological drought. The results show that due to variability of precipitation and mean air temperature, the trend of drought changes is not the same in different months. Therefore, the duration, severity and frequency of droughts vary from month to year.

Investigation of correlation (r) and mean error (MSE) values ​​between observed and calculated values ​​of discharge and precipitation at the stations under study indicate the high capability of ARIMA model in simulating monthly discharge. And it can be used in other parts of the country.

Drought is one of the most important natural disasters in the country, with devastating environmental and economic effects. Most drought studies have focused on drought severity, and other drought characteristics such as magnitude, duration, frequency and extent have not been studied.In this research, the capability of meteorological indices and remote sensing data are combined and used to study all drought characteristics such as severity, duration, magnitude and extent of drought in inland and coastal areas. For this purpose, the SPI index was calculated using TRMM satellite precipitation products and the results were compared with synoptic stations. The results showed that the correlation coefficient between the SPI calculated from the image and the station data was 0.94. Also, the correlation coefficient was more than 0.83 in all of the stations in the study area and the total MAE was 0.24. Then, using these data, the characteristics of intensity, duration, magnitude and frequency of drought in the study area were calculated. The results showed that it is possible to obtain the drought extent characteristic using satellite imagery that cannot be calculated by other methods. This shows that satellite imagery has been able to eliminate the lack of spatial details of the station data and provide a comprehensive view of the drought situation at the area.

Spatial distribution of drought indices and most importantly, its modeling accuracy, are considered an important topic to obtain reliable results in the spatial analysis of these indicators. Management’s approaches against drought are not only requires quantity information about drought but also knowing the likelihood and risk of occurrence of this phenomenon can help in managing the appropriate measures. In this study, the rainfall data of 27 climatic stations in Boushehr province for period of 19 years (1995-2013) were used for calculation the SPI index. According to the initial assessment, the 1999-2000 was known as the most arid year and the spatially variability of SPI index was evaluated only in this year. In this research, several geostatistical methods including Ordinary kriging, Inverse Distance Weighted and PSS were assessed for the derivation of maps of drought indices at 27 climatic stations in the Boushehr province of Iran. The results indicated that IDW method with the power of two is more appropriate for spatial analysis of SPI index and Ordinary Kriging method is more appropriate for spatial analysis of EDI index. Several geostatistical methods including Ordinary kriging, Inverse Distance Weighted (IDW) and TPSS were used to assess the spatial analysis of SPI index, mapping the seasonal and annual drought index. The results indicated that IDW method with the power of two is more appropriate for spatial analysis of SPI index.

Drought is one of the devastating hazards of human history. It can occur in all times and any places. Drought is a regional event that its occurrence in many world zones cannot be avoided. It is more important than other natural misfortunes due to loss of life and property and social factors. This event is more display in arid zones such as Iran for the sake of regional characteristics like small precipitation, intensity in oscillations of rainfall in different years, inappropriate distribution of the rainfall in a period of years and the great fluctuations inplace and time of the rainfall. For this reason this research has studied the severity and spatial organization of these droughts over the country. Important parameters that influences drought are temperature and precipitation. In this research, in order to make a spatial analysis of the long duration droughts in Iran, the precipitation data were used. For this reason the monthly precipitation of 70 long period stations during 1976- 2005 were obtained from the Meteorological Organization of Iran. Researchers all over the world offer different methods for study of the droughts. One of these is Standardized Precipitation Index (SPI). This method was developed by McKee, Doesken, and Kleist (1993) to analyze the impact of precipitation deficit on groundwater, reservoir storage, soil moisture, snowpack, and stream flow in different time scales for defining and monitoring the phenomenon. These time scales reflect the impact of drought on the availability of different water resources. Soil moisture conditions respond to precipitation anomalies in a relatively short time scale. Groundwater, stream flow, and reservoir storage reflect the longer-period precipitation anomalies. For these reasons, McKee et al. (1993) originally calculated the SPI for 3, 6, 12, 24, and 48 months time scales. The SPI computes the temporal frequency and anomalies of droughts. It can also be used to determine periods of anomalously wet events. The SPI is recognized as a better quality model compared to other models. The advantage of this index is that allows the analyzer to determine the number of occurrences of droughts in different stages of time. In the other words, it is, because of the non distance of this index that we could use of it to compare information of different zones and make carefully anextent of drought maps. The other advantage of this index is that it is measuring the drought function of probable density so that you can perform more analysis about that. The study has used the SPI method for a period of12 and 24 months during the 1976-2005. The spatial distribution of the drought was interpolatedvia ordinary kringingin the ArcGIS environment. In this study SPI time series were calculated in time scales of 12 and 24 months. Then, the frequency and spatial distribution of droughts were prepared and analyzed using the Geostatistics methods (Ordinary Kringing). These results have showed that the intensive 12- months period droughts were frequent in the east, southwest, central, and western parts of the country (for example Birjand Station 8%, Ramhormoze Station 6% Ilam Station 5% and Sirjan Station 5%), while the southeast and northern parts experienced moderate and weak droughts. But the 24- months droughts were intensive in the eastern parts of the country(for example, Tabass Station 6%,Ramhormoze Station 6%,Iranshahr Station 6%, and Sirjan Station 5%). This means that the longer droughts are common in the east, where they affect the underground water resources. Drought is one of the devastating hazards inhuman histories. The occurrence of the phenomenon is possible in any time and places. Iran as an arid country with fragile climate is prone to frequent droughts. For this reason this research has studied the severity and spatial organization of these droughts over the country. The study has used SPI method with the 12 and 24 months scales during the 1976-2005 periods. The spatial distribution of the drought was performed via Ordinary Kringingin the Arc GIS software. The results have indicated that the 12-month period droughts were intensive in the east, southwest, central, and western parts of the country while the southeast and northern parts experienced moderate and weak droughts. But, the 24- months scale droughts were intensive on the eastern parts of the country. This indicates that the longer droughts are common in the east, where they affect the underground water resources.

Drought is one of the most important natural hazards that works in a slow and creeping way. This phenomenon is due to climatic processes, and its severity and frequency depend to a certain extent on the geographical position of the site, which happens every few years as a result of falling precipitation. In order to study and assess the droughts and wet years of Khoy station, the precipitation data of 32 years (1976-2007) were analyzed using standard distribution index and SPSS and Excel software, and it was found that the most severe drought in Khoy station has been in 1995 with an SPI coefficient of more than -1.6, and in 2001 with an SPI coefficient of more than -1.4, while its highest rate of wet year has happened in 1982 with an SPI coefficient of more than 2.8.
Of noteworthy points about the continuity and sequence of droughts is the occurrence of severe droughts during the years 1995-2001 with varying degrees in this station. In order to predict the climatic condition of the Khoy station in the long run, the Markov chain model was used, which suggested that the probability of droughts was 43% and the probability of occurrence of wet years was 42.7%. In other cases, meaning 14.3% of the time, the normal condition of precipitation is prevailing. Similarly, the likelihood of droughts in the long run in winter, spring and autumn was estimated 41.8, 30.5 and 57.3 per cent, respectively

Drought is a recurrent feature in different climates, occurrence of drought do not limited to specific regions on times it should be possible in the region with high – rain in each seasons of the year. One of the most important stages of drought monitoring is to identify the indices to analysis severity – duration and frequency of drought events. This paper evaluates the Standardized Precipitation Index (SPI) on 3, 6, 12, 24 and 48- month scales using monthly precipitation totals for 10 stations in south-west basins of Iran from January 1960 to December 2000. Severity-Duration curves, as well as return periods were extracted for each station. Severity maps showed that in the east and the south of the region, drought intensity is more than other areas. The results show that when SPI index have negative sequences, drought will occur and when SPI values are -1 or less, drought occurrence is severe. These time scales explain the impact of drought to availability of water resources. Thus the reaction of soil moisture to precipitation occurs in short time while the response of underground water and stream flows are a long term processes.