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Volume 2, Issue 1 (4-2015)
Air pollution has become one of the main problems of cities. Among the sources of air pollution, vehicular traffic plays an important role. Planning for efficient management and control of the air pollution caused by vehicular traffic requires accurate information on spatio-temporal dispersion of the pollutions. This research studies 3D spatio-temporal dispersion of NOx pollution caused by vehicular traffic at Valieasr-Fatemi intersection resides in Tehran, Iran. It is selected for being crowded and having the required meteorological and pollution data sensed by the Air Quality Control Corp. of Tehran Municipality.
This study uses GRAL that is a local micro-scale air dispersion model defined based on Euleran-Lagrangian dispersion models. It investigates the level of spatio-temporal autocorrelation generated by GRAL simulations at both 2D and 3D modes and discusses how it adapts with the reality.
Adopting the GRAL air pollution dispersion model, streets are defined as the linear source of pollution of NOx caused by vehicular traffic. The traffic rate is estimated based on street areas and directions, the designed average traffic velocity, traffic volume and car passage counting at the intersection. The 3D geometry of the buildings is also added to the model. All the required data that were available for winter of 2007 are gathered and introduced into the model.
The model is executed at 9 heights vary from 1.7 m to 52.5 m. These heights are defined covering a range from an average human level height to average building height and above. These levels are considered both separately in 2D mode and integrated into a 3D mode. The formation of NOx clusters is investigated analyzing their autocorrelation using Moran Index at global and local scale.
The calculated Moran-I at global scale at each 9 levels of heights, varies from 0.7 to 0.9 that depicts the validity of the GRAL model adopted to simulate the expected autocorrelation of pollution density affected by spatial issues. The Moran-I increases at higher levels as less air turbulence happens. However the result show that the turbulence increases temporarily at about 10m to 15m which are the average building heights. At local scale, the Moran-I/Anselin shows that HH clusters dominate at lower levels, around streets central areas that are farther from the buildings, and around the intersections. At higher levels, esp. higher than buildings average height, the LL clusters dominate. However the HH clusters formed around intersections, while are shrank, are still visible at high levels. The turbulence caused by building fronts and their down wash effect is also shown in the result as no definite cluster is formed near the buildings front and back.
The autocorrelation analysis is also carried for an integrated 3D model consists of all the 9 levels of heights. Considering the weight matrix for a 20m 2D neighborhood and 1m/s dispersion of the pollution vertically, the global calculated Moran-I equals 0.229 which shows existence of a spatio-temporal autocorrelation of the results generated by GRAL. At local scale the results show that the HH clusters have higher temporal dispersion rate than LL clusters.
Volume 2, Issue 1 (4-2015)
Precipitation is one of the important aspects of the Earth’s climate that has both spatial and temporal variations. Understanding the behavior of this element and analyzing its spatial and temporal variation is importantwhich can lead to a comprehensive and detailed planning for water resource management and agriculture. Geostatistical techniques and spatial autocorrelation analysis are the most widely used techniques in the field of the spatial continuity. Spatial autocorrelation analysis is applied to help researchers understand the spatial patterns in the area.
The purpose of this study is to identify the heavy precipitation spatial patterns in Guilan Province. For this purpose, the 6- hourly sea level pressure of the network from 0 to 120 Easter longitude and 0 to 80 Northern latitude with 2.5×2.5 degrees spatial resolution were obtained from the National Center for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) for the period 1979-2010. The daily precipitation data of 21 stations were obtained from the Islamic Republic of Iran Meteorological Organization and Ministry of Energy.
Guilan province is one of the most humid regions in the country. The heavy rain days were selected as days when more than 30 percent of the all stations had daily rain amount more the 95th percentile. As a result, 321 days were selected as heavy and widespread rainy days. By using principal component analysis these 321 days were reduced to 9 factors. These factors then were subject to cluster analysis with Ward method and resulted in three surface pressure patterns of heavy rainy days. Within the resulted pressure patterns by using local geostatistical techniques we identified the heavy rain spots and their spatial orientation. These spatial methods include Kriging, Geostatistical Analysis, and Anselin local Moran index.
According to the results of this research, the first pattern was characterized with a high pressure over northern part of the Black Sea causing the highest Variance of heavy rainfalls. The second pattern is identified as a low pressure on the Black Sea. But the third pattern showed a precipitation distribution with low variation caused by the Siberian high-pressure. The results of Spatial Statistics techniques indicated that heavy rains were clustered in all there patterns. The clusters of heavy rains were localized mostly over the coastal areas and some over the central regions. The clusters of the western high-pressure patterns penetrated somewhat inside the province, while clusters of the Siberian high pressures was located on the shoreline of the province. The precipitation of western migratory high-pressures was heavier than of the Siberian high-pressure. The results of the standard deviation ellipse showed that heavy rain clusters were oriented in the east-west direction and were nonhomogeneous. While the ones oriented in the south east direction were more homogeneous and clustered. Because of this arrangement, the entry of moisture from the Caspian Sea is relatively concentrated on the East or North East. Because of the concentration of heavy rains in the central areas of the coast, the risks of floods and soil erosion is very high in these areas. This study showed that contrary to the popular belief, the heavy rains of Guilan were produced by western systems and the role of the Siberian high pressure is less important and is limited only to the coastline.
Volume 3, Issue 3 (10-2016)
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
Volume 9, Issue 4 (3-2023)
Explaining the spatial patterns of drought intensities in Iran
Abstract
Recognition of spatial patterns of drought plays an important role in monitoring, predicting, confronting, reducing vulnerability, and increasing adaptation to this hazard. This study aims to identify the spatial distribution and analyze the spatial patterns of annual, seasonal, and monthly drought intensities in Iran. For this purpose, the European center Medium-Range Weather Forecast (ECMWF) data for the period 1979-2021 and the ZSI index were used to extract the drought intensities. To achieve the research goal and explain the spatial pattern of the frequency of drought intensities (Extreme, severe, moderate, and weak), spatial statistical methods such as global Moran’s I, Anselin local Moran’s Index, and hot spots were used. The results of the global Moran’s I showed that with increasing intensity, the spatial distribution of drought events has become clustered. The spatial distribution of the local Moran’s Index and hot spots also confirms this. Very clear contrast was observed in the local clusters of high (low) occurrence as well as hot (cold) spots of severe (Extreme) yearly droughts in the south, southeast, and east. In autumn, weak to Extreme droughts show a southeast-northwest pattern. But in spring and winter, the spatial pattern of drought is very strong as opposed to severe and moderate drought. Despite the relatively high variability of maximum positive spatial Autocorrelation of severe and Extreme monthly droughts, their spatial pattern is almost similar. The spatial clusters of severe and very severe droughts in the northwest, northeast, and especially on the Caspian coast, are a serious warning for the management of water resources, especially for precipitation-based activities, such as agriculture.
Introduction
Drought or lack of precipitation over some time is the most widespread natural hazard on the earth compared to its long-term average. This risk negatively affects various sectors such as hydropower generation, health, industry, tourism, agriculture, livestock, environment, and economy. To reduce these negative or destructive effects, it must be determined how often drought occurs during the period and in which areas it is most severe. Doing so requires determining the characteristics of the drought. These characteristics include area, intensity, duration, and frequency of drought. Discovering the geographical focus, recognizing the pattern governing the frequency of occurrence and temporal-spatial distribution as well as changes in the dynamics of this hazard facilitate an important role in drought monitoring, early warning, forecasting, and dealing with these potential hazards; this information can be used to create a drought plan by providing analysts and decision-makers with ideas about drought, helping to reduce the negative and vulnerable effects and ultimately make it easier to protect or replace for greater adaptation. Many researchers have been led by these approaches to the use of statistical analysis. Numerous studies have been conducted in the study of climatic phenomena such as drought with space statistics techniques in various regions, including China, India, South Korea, and even Iran. Part of the domestic research on spatial patterns of drought is without the use of spatial statistics and a limited number of others who have used these analyzes have only studied the overall intensity of drought and have not studied the spatial patterns of different drought intensities. The main purpose of this study is to identify the distribution and spatial patterns of drought intensities in Iran using spatial analysis functions of spatial statistics based on the frequency of drought intensities (Extreme, severe, moderate, and weak) with yearly, seasonal and monthly multi-scale approach. Therefore, this study will answer the questions: a) What is the spatial distribution of drought intensity data in Iran? And b) What is the variability of spatial patterns of Iranian droughts at different time scales?
Material &Method
ERA5 monthly precipitation data for a period of 43 years from 1979 to 2021 were used for this study. an array of dimensions of 78×59×504 of data were formed in MATLAB software in which 78×59 is the number of nodes with a spatial resolution of 0.25 degrees and 504 represents the month. After creating the database, the ZSI index was used to calculate the severity of drought in annual, seasonal, and monthly comparisons. Finally, to achieve the research goal and explain the spatial pattern governing the frequency of drought intensities (Extreme, severe, moderate, and weak), spatial statistical methods such as global Moran’s I, Anselin local Moran I and hot spots was used.
Discussion of Results
Due to its ecological conditions, geographical location, and location in an arid and semi-arid region of the world, Iran is among the most vulnerable countries due to natural hazards, including drought. It has experienced many severe droughts in the last century. The occurrence of drought and its effects is one of the major challenges of water resources management in this century. The results of the Global Moran’s Index for all three annual, seasonal, and monthly scales showed a highly clustered pattern of drought events in the country. Spatial clustering of the occurrence of severe and Extreme yearly droughts in the eastern, southeastern, and southern regions is also an interesting result. These conditions are due to low precipitation and high spatial variation coefficient in these areas. This contrast of spatial clusters of drought intensities indicates the relationship between drought and temporal-spatial anomalies of precipitation so that with increasing precipitation, spatial variability of precipitation decreases, and consequently spatial homogeneity of precipitation increases. severe and moderate-intensity spots in the south-southeast in autumn and spring can be affected by fluctuations in the beginning and end of the monsoon season in South Asia due to the high variability of atmospheric circulation at the beginning and end of precipitation in these areas. Some studies have also shown the relationship between precipitation in these areas and the monsoon behavior of South Asia. Extreme drought events in winter and spring have had a positive spatial correlation pattern in the southwest, west, and northwest. However, precipitation at this time of year is concentrated in these areas. Warm clusters or concentrations of very severe drought events in the northern strip of the country, especially in the Caspian region, can be due to the high variability of precipitation at the beginning of the annual precipitation season (late summer and early autumn). Observations of these conditions in the northern strip indicate that an event with a high frequency of severe droughts, even in rainy areas, should not be unexpected. Spatial clusters of Extreme, severe, moderate, and weak drought every month using both local Moran and hot spots statistics show the fact that in Iran, the most severe droughts have occurred in the western, northwestern, and coastal areas of the Caspian Sea. However, the absence of severe droughts or spatial clusters has been the occurrence of low drought in the southeast and to some extent in the south. On a yearly scale, the south, southeast, and east have played a significant role in the spatial cluster of severe and extreme droughts. So that these areas of the country have had positive spatial solidarity. However, in these areas, negative spatial correlation prevailed in the autumn for severe drought. This may indicate an anomaly and a tendency to concentrate more precipitation in Iran, as well as many changes in seasonal and local precipitation regimes. According to the research results, a high incidence of severe and extreme drought on all three scales (monthly, seasonal and annual) even in the wettest climate of the country (northern Iran, especially the southern shores of the Caspian Sea) shows that High-intensity droughts can occur in all parts of the country, regardless of the weather conditions.
Keywords: Natural hazards, spatial patterns, Moran statistics, spatial autocorrelation, hot spots
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