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Analysis of Spatial Autocorrelation Patterns of Heavy and Super-Heavy Rainfall in Iran


doi: 10.1007/s00376-017-6227-y

  • Rainfall is a highly variable climatic element, and rainfall-related changes occur in spatial and temporal dimensions within a regional climate. The purpose of this study is to investigate the spatial autocorrelation changes of Iran's heavy and super-heavy rainfall over the past 40 years. For this purpose, the daily rainfall data of 664 meteorological stations between 1971 and 2011 are used. To analyze the changes in rainfall within a decade, geostatistical techniques like spatial autocorrelation analysis of hot spots, based on the Getis-Ord Gi statistic, are employed. Furthermore, programming features in MATLAB, Surfer, and GIS are used. The results indicate that the Caspian coast, the northwest and west of the western foothills of the Zagros Mountains of Iran, the inner regions of Iran, and southern parts of Southeast and Northeast Iran, have the highest likelihood of heavy and super-heavy rainfall. The spatial pattern of heavy rainfall shows that, despite its oscillation in different periods, the maximum positive spatial autocorrelation pattern of heavy rainfall includes areas of the west, northwest and west coast of the Caspian Sea. On the other hand, a negative spatial autocorrelation pattern of heavy rainfall is observed in central Iran and parts of the east, particularly in Zabul. Finally, it is found that patterns of super-heavy rainfall are similar to those of heavy rainfall.
    摘要: 降水是一项非常灵活多变的气候影响因素, 而降水相关时空演变发生在区域气候尺度上. 本文旨在调研伊朗过去40年暴雨和特大暴雨空间自相关变化特征. 基于此研究目的, 使用了1971-2011年664个气象台站日降水资料. 为分析每个十年内的降水变化规律, 应用了基于Getis-Ord Gi统计识别(简称G指数)热点分析法的空间自相关分析地质统计学技术, 以及MATLAB科学计算语言, Surfer绘图软件和地理信息系统(GIS). 结果显示: 里海沿岸, 伊朗扎格罗斯山脉西侧山麓西北部和西部地区, 伊朗内陆区域, 以及伊朗东南和东北的南部地区, 均为暴雨和特大暴雨最高频发地带. 从暴雨空间分布可见, 尽管在不同时间段暴雨高频发区位置有所振荡, 暴雨空间自相关正极大值区域包括了里海西侧和西北侧海岸, 而负极大值区域位于伊朗中部和东部部分地区, 特别是扎布尔省. 进一步研究发现, 特大暴雨空间自相关格局分析结果, 与暴雨结果类似.
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  • Ahmedou O. C. A., H. Yasuda, K. Wang, and K. Hattori, 2008: Characteristics of precipitation in northern Mauritania and its links with sea surface temperature. Journal of Arid Environments,72, 2243-2250, doi: 10.1016/j.jaridenv.2008.07.011.http://europepmc.org/abstract/AGR/IND44114870
    Alexand er, L. V., Coauthors, 2006: Global observed changes in daily climate extremes of temperature and precipitation. J. Geophys. Res., 111,D05109, doi: 10.1029/2005JD006290.http://onlinelibrary.wiley.com/doi/10.1029/2005JD006290/full
    Alijani B., 1996: The Climate of Iran. Payam Noor University Press,221 pp.
    Alijani B., 2002: Identification of Tehran rainy weather types are calculated based on volubility. Geographical Research Quarterly, 114- 132. (In Persian)
    Alijani B., 2011: Spatial analysis of critical temperatures and daily precipitation in Iran. Journal of Geographical Sciences and Applied Research, 20, 9- 30. (In Persian)
    Alijani B., M. Doostkamyan, A. Bayat, Y. Balyani, and A. Javanmard, 2013: Analysis of precipitation in Iran. Proc. Second International Conf. Environmental Hazards, Khuarazmi University Press, 220- 231. (In Persian)
    Ashjaie Bashkand, M., 2000: Analysis of Synoptic Model of Heavy Precipitation in the North West of Iran, Faculty of Sciences. Tarbiat Modarres University, 120 pp. (In Persian)
    Azizi G., M. Niri, and S. Rostami Khalilian, 2009: Synoptic Analysis of heavy precipitation rainfall in the West Country Case Study: Period from 7 to 14 March, 2005. Physical Geography, 1- 13. (In Persian)
    Bozkurt D., O. L. Sen, 2011: Precipitation in the Anatolian Peninsula: sensitivity to increased SSTs in the surrounding seas.Climate Dyn.,36, 711-726, doi: 10.1007/s00382-009-0651-3.http://link.springer.com/10.1007/s00382-009-0651-3
    Bradford J. M., J. E. Ferris, and P. A. Remley, 1987: Interrill soil erosion processes: I. Effect of surface sealing on infiltration,runoff, and soil splash detachment. Soil Science Society of America Journal, 51, 1566-1571, doi: 10.2136/sssaj1987.03615995005100060029x.http://dl.sciencesocieties.org/publications/sssaj/abstracts/51/6/SS0510061566
    Chen C.-S., C.-Y. Lin, Y.-J. Chuang, and H.-C. Yeh, 2002: A study of afternoon heavy rainfall in Taiwan during the mei-yu season.Atmos. Res.,65, 129-149, doi: 10.1016/S0169-8095(02)00061-3.http://www.sciencedirect.com/science/article/pii/S0169809502000613
    Dimitrova T., R. Mitzeva, and A. Savtchenko, 2009: Environmental conditions responsible for the type of precipitation in summer convective storms over Bulgaria. Atmospheric Research,93, 30-38, doi: 10.1016/j.atmosres.2008.10.010.http://www.cabdirect.org/abstracts/20093188216.html
    Faraj Zadeh, M., S. Rajai Najaf Abadi, 2013: Analysis of synoptic conditions, heavy precipitation events in a flood (city Koohrang). Research Journal of Geography and Planning, 45, 143- 162. (In Persian)
    Griffith D. A., 1992: What is spatial autocorrelation? Reflections on the past 25 years of spatial statistics. Espace Gèographique, 21, 265- 280.http://www.researchgate.net/publication/272345234_What_is_Spatial_Autocorrelation_Reflections_on_the_Past_25_Years_of_Spatial_Statistics
    Groisman, P. Y., Coauthors, 1999: Changes in the probability of heavy precipitation: important indicators of climatic change. Climatic Change,42, 243-283, doi: 10.1023/ A:1005432803188.
    IPCC, 2014: Climate Change 2014-Impacts, Adaptation and Vulnerability: Part B: Regional Aspects. Cambridge University Press, Cambridge.
    Jansa A., A. Genoves, M. A. Picornell, J. Campins, R. Riosalido, and O. Carretero, 2001: Western Mediterranean cyclones and heavy rain. Part 2: Statistical approach. Meteorological Applications,8, 43-56, doi: 10.1017/S1350482701001049.http://onlinelibrary.wiley.com/doi/10.1017/S1350482701001049/full
    Kato T., K. Aranami, 2005: Formation Factors of 2004 Niigata-Fukushima and fukui heavy rainfalls and problems in the predictions using a cloud-resolving model. SOLA,1, 1-4, doi: 10.2151/sola.2005-001.
    Kaviani M. R., B. Alijani, 2001: Principles of Climatology. SAMT Press, 582 pp. (In Persian)
    Kawabata T., H. Seko, K. Saito, T. Kuroda, K. Tamiya, T. Tsuyuki, Y. Honda, and Y. Wakazuki, 2007. An assimilation and forecasting experiment of the nerima heavy rainfall with a cloud-resolving nonhydrostatic 4-Dimensional variational data assimilation system.J. Meteor. Soc. Japan.,85, 255-276, doi: 10.2151/jmsj.85.255.http://ci.nii.ac.jp/naid/110006318046
    Lana A., J. Campins, A. Genovès, and A. Jansa, 2007: Atmospheric patterns for heavy rain events in the Balearic Islands.Adv. Geosci.,12, 27-32, doi: 10.5194/adgeo-12-27-2007.http://www.oalib.com/paper/1368385
    Lashkari H., 2001: Synoptic patterns of heavy precipitation in south western Iran, geography. Tarbiat Modarres University, 163 pp. (In Persian)
    Lenderink G., E. Van Meijgaard, and F. Selten, 2009: Intense coastal rainfall in the Netherlands in response to high sea surface temperatures: analysis of the event of August 2006 from the perspective of a changing climate.Climate Dyn.,32, 19-33, doi: 10.1007/s00382-008-0366-x.http://www.springerlink.com/content/fulltext.pdf?id=doi:10.1007/s00382-008-0366-x
    Lin Y.-L., S. Chiao, T.-A. Wang, M. L. Kaplan, and R. P. Weglarz, 2001: Some common ingredients for heavy orographic rainfall. Wea. Forecasting,16, 633-660, doi: 10.1175/1520-0434 (2001)016<0633:SCIFHO>2.0.CO;2.http://adsabs.harvard.edu/abs/2001WtFor..16..633L
    Masoudian S. A., 2009: Iran's rainfall area. Geography and Development, 13, 79- 91. (In Persian)
    Messager C., H. Gallèe, and O. Brasseur, 2004: Precipitation sensitivity to regional SST in a regional climate simulation during the West African monsoon for two dry years.Climate Dyn.,22, 249-266, doi: 10.1007/s00382-003-0381-x.http://link.springer.com/article/10.1007/s00382-003-0381-x
    Mofidi A., A. Zarrin, 2005: The synoptic study of low pressure systems of the Sudan in heavy rain falls in Iran. Geographical Research, 22, 113- 136. (In Persian)http://en.journals.sid.ir/ViewPaper.aspx?ID=49140
    Mohammadi B., S. A. Masoudian, 2010: Synoptic analysis of heavy precipitation in Iran Case Study November 1994. Geography and Development, 19, 47- 70. (In Persian)
    Montazeri M., 2009: Tempo-spatial analysis of extreme 24 hour precipitations in Iran. Journal of Geography and Environmental Planning, 20, 125- 140. (In Persian)
    Moradi H. R., 2002: synoptic analysis of the precipitation on southern coast of caspian sea in six cold months of years. Iranian Journal of Marine Sciences and Technology, 21, 61- 72. (In Persian)http://en.journals.sid.ir/ViewPaper.aspx?ID=55487
    Nouri H., H. Ghayour, A. Masoodian, M. Azadi, and A. Ildoromi, 2013: The effect of sea surface temperature and 2m air temperature on precipitation events in the southern coasts of Caspian Sea. Ecopersia, 1, 369- 383. (In Persian)http://ecopersia.modares.ac.ir/article_11070_0.html
    Nouri H., H. A. Ghayoor, S. A. Masoudian, and M. Azadi, 2012: Clouds analysis in heavy and super heavy precipitations in the southern coasts of Caspian Sea. Journal of Geography and Environmental Planning, 23, 1- 22. (In Persian)http://www.oalib.com/paper/2145328
    Omidvar K., M. Mahmodabadi, and F. Safarpoor, 2015: A synoptic analysis of heavy rains February 2011 in southern and central regions of Iran (with emphasis on Kerman Province). Geography and Planning, 9, 21- 39. (In Persian)
    Pastor F., J. A. Valiente, and M. J. Estrela, 2015: Sea surface temperature and torrential rains in the Valencia region: modelling the role of recharge areas. Natural Hazards and Earth System Science, 15, 1677- 1693.http://adsabs.harvard.edu/abs/2016EGUGA..18...85P
    Pielke, R. A. Jr., M. W. Downton, 2000: Precipitation and damaging floods: Trends in the United States,1932-97. J. Climate, 13, 3625-3637, doi: 10.1175/1520-0442(2000)013 <3625:PADFTI>2.0.CO;2.http://adsabs.harvard.edu/abs/2000JCli...13.3625P
    Qashqai G., 1996: Effect of Siberian high pressure winter precipitation on the southern coast of the Caspian Sea, Geography, Khouarazmi University, 107 pp. (In Persian)
    Römkens, M. J. M., K. Helming, S. N. Prasad, 2002: Soil erosion under different rainfall intensities, surface roughness, and soil water regimes. Catena, 46, 103- 123.http://www.sciencedirect.com/science/article/pii/S0341816201001618
    Rousta I., F. K. Akhlagh, M. Soltani, and S. M. Taheri, 2014: Assessment of blocking effects on rainfall in northwestern Iran. Proc. 12th International Conf. Meteorology,Climatology and Atmospheric Physics, M. Kanakidou et al., Eds., COMECAP2014, 1 ed., Crete University Press., Greece, 291 pp.
    Rousta I., M. Soltani, W. Zhou, and H. H. Cheung, 2016a: Analysis of extreme precipitation events over central Plateau of Iran. American Journal of Climate Change,5, 297-313, doi: 10.4236/ajcc.2016.53024.http://www.researchgate.net/publication/305801051_Analysis_of_Extreme_Precipitation_Events_over_Central_Plateau_of_Iran
    Rousta I., M. Doostkamian, E. Haghighi, and B. Mirzakhani, 2016b: Statistical-synoptic analysis of the atmosphere thickness pattern of iran's pervasive frosts. Climate,4(3): 41, doi: 10.3390/cli4030041http://www.researchgate.net/publication/307087213_Statistical-Synoptic_Analysis_of_the_Atmosphere_Thickness_Pattern_of_Iran's_Pervasive_Frosts
    Salehvand, I., H. Atai, M. Moemeni, M. Montazeri, 2014: The study of atmospheric movement patterns and its effect on heavy rainfalls in Iran: Case study (January 6th of 1998). Scientific Journal of Pure and Applied Sciences, 3, 906- 913. (in Persian)http://www.researchgate.net/publication/307692968_The_study_of_atmospheric_movement_patterns_and_its_effect_on_heavy_rainfalls_in_Iran_case_study_January_6th_of_1998
    Soltani M.; I. Rousta, S. M. Taheri, 2013: Using mann-kendall and time series techniques for statistical analysis of long-term precipitation in Gorgan weather station. World Applied Sciences Journal, 28, 902- 908. doi: 10.5829/idosi.wasj.2013.28. 07.946http://www.researchgate.net/publication/267450816_Using_Mann-Kendall_and_Time_Series_Techniques_for_Statistical_Analysis_of_Long-Term_Precipitation_in_Gorgan_Weather_Station
    Soltani M., I. Rousta, F. K. Akhlagh, S. M. Taheri, 2014b: Statistical synoptic analysis of summertime extreme precipitation events over Kerman province. 12th International Conference on Meteorology,Climatology and Atmospheric Physics, M. Kanakidou et al., Eds., COMECAP 2014, 1 ed., Crete University Press, Greece, 291 pp.
    Soltani, M., Coauthors, 2016: Assessment of climate variations in temperature and precipitation extreme events over Iran.Theor. Appl. Climatol.,126, 775-795, doi: 10.1007/ s00704-015-1609-5.http://link.springer.com/10.1007/s00704-015-1609-5
    Soltani M., M. Molanejad, F. Khoshakhlagh, A. R. SaadatAbadi, and F. Ranjbar, 2014a: Synoptic and thermodynamic characteristics of 30 March-2 April 2009 heavy rainfall event in Iran. Meteorology and Atmospheric Physics,126, 49-63, doi: 10.1007/s00703-014-0339-z.http://link.springer.com/article/10.1007/s00703-014-0339-z
    Sotodeh F., B. Alijani, 2015: The Relationship between Spatial Distribution of Heavy Precipitation and Pressure Patterns in Guilan Province. Journal of Spatial Analysis Environmental Hazarts, 2, 63- 73. (In Persian)
    Tabari H., H. Abghari, and P. H. Talaee, 2012: Temporal trends and spatial characteristics of drought and rainfall in arid and semiarid regions of Iran. Hydrological Processes, 26, 3351- 3361. (In Persian)http://onlinelibrary.wiley.com/doi/10.1002/hyp.8460/full
    Wischmeier W. H., D. D. Smith, 1958: Rainfall energy and its relationship to soil loss. EOS, Transactions American Geophysical Union, 39, 285-291, doi: 10.1029/ TR039i002p00285.http://onlinelibrary.wiley.com/doi/10.1029/TR039i002p00285/pdf
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Manuscript received: 13 September 2016
Manuscript revised: 11 April 2017
Manuscript accepted: 13 April 2017
通讯作者: 陈斌, bchen63@163.com
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Analysis of Spatial Autocorrelation Patterns of Heavy and Super-Heavy Rainfall in Iran

  • 1. Department of Geography, Yazd University, Yazd 8915818411, Iran
  • 2. Department of Geography, Zanjan University, Zanjan 3879145371, Iran
  • 3. Department of Geography, Tabriz University, Tabriz 5166616471, Iran
  • 4. Department of Geography, Khwarizmi University, Tehran 1491115719, Iran

Abstract: Rainfall is a highly variable climatic element, and rainfall-related changes occur in spatial and temporal dimensions within a regional climate. The purpose of this study is to investigate the spatial autocorrelation changes of Iran's heavy and super-heavy rainfall over the past 40 years. For this purpose, the daily rainfall data of 664 meteorological stations between 1971 and 2011 are used. To analyze the changes in rainfall within a decade, geostatistical techniques like spatial autocorrelation analysis of hot spots, based on the Getis-Ord Gi statistic, are employed. Furthermore, programming features in MATLAB, Surfer, and GIS are used. The results indicate that the Caspian coast, the northwest and west of the western foothills of the Zagros Mountains of Iran, the inner regions of Iran, and southern parts of Southeast and Northeast Iran, have the highest likelihood of heavy and super-heavy rainfall. The spatial pattern of heavy rainfall shows that, despite its oscillation in different periods, the maximum positive spatial autocorrelation pattern of heavy rainfall includes areas of the west, northwest and west coast of the Caspian Sea. On the other hand, a negative spatial autocorrelation pattern of heavy rainfall is observed in central Iran and parts of the east, particularly in Zabul. Finally, it is found that patterns of super-heavy rainfall are similar to those of heavy rainfall.

摘要: 降水是一项非常灵活多变的气候影响因素, 而降水相关时空演变发生在区域气候尺度上. 本文旨在调研伊朗过去40年暴雨和特大暴雨空间自相关变化特征. 基于此研究目的, 使用了1971-2011年664个气象台站日降水资料. 为分析每个十年内的降水变化规律, 应用了基于Getis-Ord Gi统计识别(简称G指数)热点分析法的空间自相关分析地质统计学技术, 以及MATLAB科学计算语言, Surfer绘图软件和地理信息系统(GIS). 结果显示: 里海沿岸, 伊朗扎格罗斯山脉西侧山麓西北部和西部地区, 伊朗内陆区域, 以及伊朗东南和东北的南部地区, 均为暴雨和特大暴雨最高频发地带. 从暴雨空间分布可见, 尽管在不同时间段暴雨高频发区位置有所振荡, 暴雨空间自相关正极大值区域包括了里海西侧和西北侧海岸, 而负极大值区域位于伊朗中部和东部部分地区, 特别是扎布尔省. 进一步研究发现, 特大暴雨空间自相关格局分析结果, 与暴雨结果类似.

1. Introduction
  • Iran is an area with anomalous and irregular rainfall (Mohammadi and Masoudian, 2010; Tabari et al., 2012; Rousta et al., 2014, 2016a, 2016b; Sotodeh and Alijani, 2015; Soltani et al., 2013, 2016). Because of its specific topography, this country has favorable conditions for the possibility of heavy and super-heavy rainfall (Alexander et al., 2006). According to some scientists, most heavy rainfall occurs over mountainous areas (Ashjaie Bashkand, 2000; Lin et al., 2001; Chen et al., 2002; Kato and Aranami, 2005; Kawabata et al., 2007; Dimitrova et al., 2009). In addition, some scientists believe that the role of sea surface temperature is significant in heavy and super-heavy rains (Messager et al., 2004; Lana et al., 2007; Ahmedou et al., 2008; Lenderink et al., 2009; Bozkurt and Sen, 2011; Nouri et al., 2013; Pastor et al., 2015).

    However, changes in extreme climate have an important impact and are considered as one of the crucial issues in climate change (IPCC, 2014). For example, (Alexander et al., 2006) indicated that the precipitation index is higher now than it was in the 20th century. The occurrence of such a phenomenon is associated with flooding (Pielke and Downton, 2000), soil erosion (Wischmeier and Smith, 1958; Bradford et al., 1987; Römkens et al., 2002; Alijani, 2011), the destruction of water structures, and an increase in the volume of water resources, especially in arid areas (Faraj Zadeh and Rajai Najaf Abadi, 2013). In the climate literature, various definitions and different values have been proposed for "heavy rainfall" and "super-heavy rainfall". There are also considerable variations among studies conducted within Iran in terms of their definition of heavy and super-heavy rainfall. For example, based on local experience, a particular threshold is set for heavy and super-heavy rainfall (Groisman et al., 1999). Furthermore, some scientists have determined certain thresholds for heavy and super-heavy rainfall, e.g. 60 mm and 100 mm, respectively (Jansa et al., 2001). However, the threshold may vary regionally depending on the average annual rainfall. For example, in the northern part of Iran, with an annual average rainfall of 1800 mm, daily rainfall of 20 mm is regarded as normal. On the contrary, in the eastern part of the country, which has an annual average rainfall of 70 mm, the same daily amount of rainfall may lead to flooding and damage. Thus, in addition to soil type, the topographic features of an area are influential in determining the threshold of heavy and super-heavy rainfall.

    With regard to investigating and analyzing heavy rainfall, several studies have been carried out in Iran. For example: the impact of heavy rains and flooding caused by Sudanese low pressure systems in Iran (Mofidi and Zarrin, 2005); an analysis of clouds that produce heavy and super-heavy rainfall over the Caspian Sea (Nouri et al., 2012); an analysis of the synoptic conditions leading to floods resulting from heavy rainfall (Faraj Zadeh and Rajai Najaf Abadi, 2013); the synoptic and dynamic conditions of the highest rainfall in Khorasan (Salehvand et al., 2014; Soltani et al., 2014a); a thermodynamic analysis of heavy rainfall over the southern and central regions of Iran (Omidvar et al., 2015); and an analysis of the sea level pressure during pervasive heavy rainfall in Iran (Mohammadi and Masoudian, 2010; Soltani et al., 2014b). Critical rainfall at various temporal scales, particularly on a daily basis, causes large-scale destruction in human society and urban ecosystems. To achieve a calm and sustainable life and maintain balance in the environment, it is essential to accurately identify instances of heavy rainfall. However, no study has yet focused on heavy and super-heavy changes within different decades in Iran. Therefore, the aim of the present study is to investigate the spatial variations of heavy and super-heavy rainfall within at the decadal scale during a 40-year period. In this study, heavy rainfall refers to the amount of rain being equal to or greater than the 95th percentile, and with at least 50% of the area covered. Similarly, super-heavy rainfall refers to the amount of rain being equal to or greater than the 99th percentile and, again, with at least 50% of the area covered.

2. Materials and methods
  • This study aims to investigate the frequency and variation of heavy and super-heavy rainfall at the decadal scale, for which the daily rainfall data over a 40-year period (1971 through 2011) are obtained from 664 synoptic and climatological stations in Iran. Figure 1 shows the annual average rainfall distribution of Iran, as well as the locations of all the meteorological stations. The spatial resolution of the data is 15× 15 km, which is imaged in the Lambert conical shape of the system. In this data the effect of elevation is considered by using the regression coefficient and variogram. Also, to accurately review the rainfall changes within particular decades in Iran, the statistical period is divided into four smaller periods: 1971-81, 1982-91, 1992-2001 and 2002-11. Since the aim is to examine periodic changes of heavyand super-heavy rainfall, we therefore use the decadal average for the calculation of anomalies. In this study, to select the best threshold for Iran's extreme rainfall in a period of 40 years, we choose the days with the following conditions: (i) the 95th percentile and 99th percentile are selected for heavy and super-heavy rainfall, respectively; (ii) rainfall covering at least 50% of the country's area and with spatial continuity; (iii) rainfall that persists for at least two days. Regarding the latter condition, extreme rainfall with a synoptic origin is separated from local rainfall based on environmental factors (e.g. height). First, by using the MatLab software, the total area of Iran based on the rainfall value for each pixel over the entire period, is divided into pixels. The Surfer and GIS software programs are also utilized to determine the spatial distribution of these thresholds. To gain a more accurate understanding of heavy and super-heavy rainfall in Iran, anomalies of heavy and super-heavy rainfall during different periods are analyzed. In this way, heavy and super-heavy rainfall departures from the normal climate for each period are compared with its predecessors using map algebra techniques, and the anomalies are calculated by determining their differences. For example, the amount of rainfall in the second period is subtracted from the same amount in the first period. If the frequency of pixels of heavy and super-heavy rainfall is higher than that of the previous period, it is considered as a positive anomaly.

    Then, to examine the spatial autocorrelation pattern of changes of heavy and super-heavy rainfall within a decade, hot spot analysis, using the Getis-Ord Gi statistic, is employed. Spatial autocorrelation refers to the dependencies that exist among observations that are attributable to the relative locations, or underlying two-dimensional ordering, of variable values in geographic space. In turn, these dependencies produce clustering of similar (positive spatial autocorrelation) or dissimilar (negative spatial autocorrelation) values, and hence induce some map pattern (Griffith, 1992). Analysis of hot spots calculates the Getis-Ord Gi statistic for all effects in the data, and Z-scores indicate in which section data are clustered in large or small quantities. In fact, this method considers every location in light of its neighboring locations. If a location has high values, it is interesting and important; however, it may not be a statistically significant hot spot by itself. For a location to be considered a hot spot and be statistically significant, both the location and its neighbors should contain high values. The local sum of a location and its neighbors is compared relatively to that of all the locations. When the local sum is significantly higher than the expected local sum, the Z-score will be obtained. In fact, this method considers every location in relation to its neighboring locations: \begin{eqnarray} \bar{x}_i&=&\dfrac{\sum_jx_j}{n-1} ;\ \ (1)\\ s^2&=&\dfrac{\sum_jx^2_j}{n-1}-[\bar{x}_i]^2 .\ \ (2) \end{eqnarray} Furthermore, Gi is calculated through the following formula: \begin{equation} {\rm Var}(G_i)=\dfrac{W_i(n-1-W_i)}{(n-1)^2(n-2)}\left[\dfrac{s_i}{\bar{x}_i}\right]^2 .\ \ (3) \end{equation} The values of Gi and G*i are calculated through this statistical procedure, Wi/(n-1), and is standardized through calculating the second root of its variance: \begin{equation} G^*_i(d)=\dfrac{\sum_jw_{i,j}(d)x_j-W_i\bar{x}_i}{s_i\{[(n-1)S_{1,i}-W_i^2]/(n-2)\}^{\frac{1}{2}}}, j\neq i , \ \ (4)\end{equation} where wi,j(d) is the weight of the matrix which represents the spatial structure of the data with ones for all links with points within the specified distance from point i; all other links are zero including the link of point i to itself. In our work, d=15 km. Gi considers only the values of its nearest neighbors within d and does not consider the value at the point i. If we also consider the weight of i (\(w_i,j\neq 0\)), the standardized G* is calculated through the following formula: \begin{equation} G_i^*(d)=\dfrac{\sum_jw_{i,j}(d)x_j-W_i^*\bar{x}}{s_i\{[(nS_{1,i}^*)-W_i^{*2}]/(n-2)\}^{\frac{1}{2}}}, j=i . \ \ (5)\end{equation} In Eqs. (4) and (5), Wi*=Wi+wi,j and S1,i=∑jwi,j2, where j≠ i, and S1,i*=∑jwi,j2, where j=i, and in Eqs. (1) to (5), \(\bar{x}-\) and s are the mean and variance of the model, respectively. Also in these equations, x is observed variable, n is the number of cells, i is the cell number, and j is the cell number of adjacent cell. The standardized values of G and G* are interpreted based on the table of Z-scores.

    Figure 1.  Annual average precipitation (mm) distribution in Iran during 1971-2011 (shading), and the locations of all meteorological stations (+).

3. Results and discussion
  • Figure 2 shows the spatial distribution of the frequency of heavy rainfall in Iran during the period from 1971 to 2011. As is evident from the map, the two realms with the maximum number of days of heavy rainfall events in Iran are the southern coast of the Caspian Sea and the western foothills of the Zagros Mountains. Some scientists believe that heavy rainfall over the Caspian Sea occurs because of a strong ridge over the Black Sea, eastern and central Europe, and the eastern Mediterranean, as well as a deep trough over the eastern Black Sea (Moradi, 2002).

    Figure 2.  Spatial distribution of the frequency of heavy rainfall in Iran during 1971-2011. The shading indicates the number of days on which heavy rainfall occurred during the study period, out of a total of 14 975 days.

    However, in the territory of the southern shores of the Caspian Sea, days of rain have fallen from west to east, so the results of Nouri et al. (2012, 2013) and (Alijani et al., 2013) confirm the findings above. The first hotspot of the maximum number of days of heavy rains over the coastal shores of the Caspian Sea is located in Gilan and with a smaller number of days, is observed in Mazandaran (Sari). In this region towards the northeast, the frequency of heavy rainfall events has decreased (Fig. 2). The Caspian Sea coast located along a path that affect by humid westerly winds in the east valley, and then the low height area has penetrated between the Hezar Masjed and Binalud mountains to Mashhad. For this reason, there is a gradual trend of decreasing rainfall eastwards from the Caspian region. But in south and west sides of the Alborz mountain this decreasing is abrupt due to the Alborz range (Alijani, 1996). The second domain of the frequency of heavy rainfall, the western Zagros Mountains, is a dual-core maximum of rainfall occurring around the cities of Kermanshah and Shahrekord (Fig. 2). Such rainfall can occur in this region and is the result of a strengthened and intensified monsoon low pressure center and the Sudanese Red Sea converging into a dynamic and thermodynamic system (Azizi et al., 2009; Lashkari, 2001).

    Of course, as the map clearly shows, the frequency of heavy precipitation events in west parts of the Zagros is smaller than those of the Caspian Sea. The Zagros mountains play a significant role in the abundance of rains in the territory. The largest bulk of the rainfall occurs in the entry of west winds to Iran and happens in the windward slopes of mountain barriers. However, mountains cannot be the only reason for increasing rainfall in the west of Iran. One should look for other influential factors. One of these factors can be the direction of arrival of Mediterranean cyclone and west winds (Alijani, 1995). There is also an area on the map that corresponds to the lowest frequency of heavy precipitation; this territory is located in the inner regions of Iran. Many researchers believe that the cause is being far from water sources (Alijani, 2002; Montazeri, 2009). Figure 3 indicates spatial distribution of the frequency of heavy precipitation in Iran during the study period. Compared to heavy precipitation, super heavy precipitation shows a lower frequency of occurrence during the period of study. In this map, in two realms, the maximum frequency of occurrence of heavy precipitation corresponds with heavy precipitation (Fig 3). The hotspot of heavy precipitation is observed in the territory of the southern coast of the Caspian Sea in Gilan, with its intensity being measurably reduced as one moves toward east and south (Fig 3). The map indicates a sharp decrease in the frequency of super heavy precipitation events in the North West compared to the frequency of heavy rainfall (specifically, along the eastern slopes of the Zagros Mountains and the southern slopes of the Alborz Mountains in the inner regions of Northwest Iran), to a rate of less than 200 days.

    To gain a more accurate understanding, heavy and super-heavy rainfall is analyzed in different periods (1961-71, 1972-81, 1982-91, 1992-2001 and 2002-11) (see Fig. 4). The spatial distribution of heavy and super-heavy rainfall in the first period (1971-81) suggests that the frequency of these phenomena is mainly observed over the southern shores of the Caspian Sea, along the Zagros Mountains from the northwest to the southeast, and in Northwest, West and Northeast Iran. Figure 4 shows that the concentrations of heavy and super-heavy rainfall, or the hotspots of these rainfall types, are mainly located along the coastal shores of the Caspian Sea, especially in Gilan Province (heavy and super-heavy rainfall respectively have a frequency to 781 days and 416 days). The frequency of heavy and super-heavy rainfall reduces towards the east and south. This feature can be explained by the wind convection, which is one of the main factors causing autumn showers in Gilan (Kaviani and Alijani, 2001). However, in a vast area of the country——especially in the inner part, southern part, and southeastern part——the frequency of rainfall has dropped to its lowest level (12 days for heavy rainfall and 6 days for super-heavy rainfall) (Fig. 4). Some researchers (Masoudian, 2009) claim that the lack of rainfall in the inner regions of Iran, including the deserts of central and eastern Iran, can be attributed to the dominance of subtropical high pressure on hot days of the year and the location of this area (i.e. being far from the rainfall of the Zagros Mountains). The influence on this area of the rainfall over the Zagros Mountains is reduced because of the long distance between them. It can also be seen that, in the areas of the country where there is a high frequency of heavy rain, the maximum frequency for super-heavy rain occurs too. In fact, the frequency of occurrence of super-heavy rainfall areas follows that of heavy rainfall events. The interdependence of heavy and super-heavy rainfall is clearly apparent in the maps. The highest frequency of such rainfall is seen over the coastal shores of the Caspian Sea. Of particular importance in this regard is Gilan, which has experienced 416 days of super-heavy rain and 781 days of heavy rain (Fig. 4).

    Figure 3.  Spatial distribution of the frequency of super-heavy rainfall in Iran during 1971-2011. The shading indicates the number of days on which super-heavy rainfall occurred during the study period, out of a total of 14 975 days.

    Figure 4.  Spatial distribution of the frequency of heavy and super-heavy rainfall in Iran during 1971-2011. The shading indicates the number of days on which heavy and super-heavy rainfall occurred during the study period, out of a total of 14 975 days.

    Figure 5.  Spatial distribution of heavy and super-heavy rainfall anomalies over different periods.

    Estimations of heavy rainfall in the second period (1982-91) indicate a decreased frequency of rainfall occurrence in the north and northwest, west, east and southeast parts of the country, compared with 1971-81 decade. This phenomenon can be clearly observed in the anomaly map of the second decade (Fig. 4). In particular, the frequency of maximum rainfall in the provinces of Gilan and Mazandaran has reduced. Moreover, as the map shows, the number of rainy days has increased for southern coastal areas and some internal and northeastern areas of Iran. In southern and southeastern regions, the occurrence of heavy rainfall has increased from 12 days to 56 days. Super-heavy rainfall also has a similar pattern during this period; but, the bodies that covered by this two types of rainfall has reduced. Furthermore, during this period, for parts of Zahedan, Yazd, and the central desert of Iran, the number of days with heavy rain has reduced from 12 to 6 and the number of super-heavy rainfall days has decreased from 6 to 2 days. The hotspots for heavy (773 days) and super-heavy (411 days) rainfall in Gilan and Mazandaran are limited to Rasht, Anzali, and Sari. In general, the anomaly map illustrates that the proportion of days with heavy rain has reduced in 46.9% of the country's area and risen in 51.6%. The increase has reached a maximum of 153 days of heavy rainfall. Interestingly, this increase is seen in the southern part of the country and its hotspot is located around the province of Fars. At the same time, the core focus of the negative anomaly, with a maximum of -297 days, is in the northern part of the country. With respect to super-heavy rainfall, 37.5% of the country has experienced a negative anomaly of -174 days, with the hotspot being located in the northern regions. In contrast, 57.5% of the country has experienced a positive anomaly of 110 days, with the hotspot being located in southern areas, especially Fars (Fig. 5). This decline in northern parts and increase in southern parts may be attributable to the way precipitation systems enter the country.

    As the anomaly map of the third decade (1992-2001) shows (Fig. 5), the frequency of heavy and super-heavy rainfall has considerably increased in comparison with the second period (1982-91). The maximum frequency of heavy rainfall (944 days) and super-heavy rainfall (599 days) is concentrated in Gilan (around Anzali). The important point is that, compared with the previous decade, most of the southern parts of the country has experienced a reduction in the frequency of rainy days. In this decade, about 56.2% of the total area of the country, including the southern slopes of the Alborz Mountains, western slopes of the Zagros Mountains, and vast areas of inner parts and southern parts of Iran, has experienced a frequency of less than 100 days of heavy rainfall. However, 92.8% of the area of the country has experienced less than 100 days of super-heavy rainfall. As is evident from the maps, the areas that have had over 100 days of heavy and super-heavy rainfall can be found mainly in northern coastal areas (next to the Caspian Sea), in the Zagros heights, and in the western and northwestern parts of the country.

    (Qashqai, 1996) suggested that the frequency of heavy and super-heavy rainfall in coastal areas of the Caspian Sea, especially in Gilan, can be attributed to migratory anticyclones. He suggested that the Siberian high can cause heavy rainfall over the region, only if a core pressure of 1035 hPa is shaped over the Caspian Sea and, at the 500 hPa level, a deep trough is placed on the area (Qashqai, 1996). Moreover, according to previous research, the frequency of such rainfall over the Zagros heights can be explained based on the fact that westerly winds enter the country from this area and are blocked by the mountains——a phenomenon that causes rainfall in this region. Interestingly, with respect to the frequency of heavy rainfall, negative anomalies decrease in comparison with the previous decade. This kind of anomaly covers only 36.8% of the country's area (Table 1). However, with respect to the frequency of super-heavy rainfall, 45.2% of Iran has experienced a negative anomaly. Along the southern coasts of the country, both heavy and super-heavy rainfall show an inverse pattern in comparison to the previous period. Positive anomalies cover 61.5% of the area of the country for heavy rainfall, and 48.5% for super-heavy rainfall. This kind of anomaly mainly occurs in the northern, northwestern and western parts of the country.

    The spatial distribution of the frequency of heavy and super-heavy rainfall in the period 2002-11 indicates that, similar to the previous decade, the frequency along the southern coast of the Caspian Sea, in the northwest, and over the Zagros Mountains, is higher than in other parts of the country. However, compared to the previous three decades, this decade experiences a smaller scope, with 55.3% and 90.7% of the area of the country having heavy and super-heavy rainfall, respectively, with a frequency of occurrence of less than 100 days (Table 2). This means that only 9.3% of the area of the country has experienced super-heavy rainfall for over 100 days and only 44.7% for more than 100 days. Additionally, the anomaly map indicates that the negative anomaly for heavy rainfall can be mainly found in the northern parts, with its hotspot (136 days) located in Gilan, and in the west, northwest, northeast, and southeast of the country. It is thus distributed across the country and covers an area of 50.1% (Fig. 5). Positive anomalies, on the other hand, cover 47.5% of the country's area and can be observed in southern coastal regions, inner parts of the country, in the east of the country, and in the heights of the southern Zagros Mountains. Positive anomalies can also be observed as sporadic cells in northwestern and western parts of Iran. With respect to super-heavy rainfall, negative and positive anomalies can be observed in 41.9% and 51.7% area of the country, respectively (Fig. 5). Like heavy rainfall, negative anomalies can be seen in coastal areas in the north, as well as parts of the northwest and southwest. Positive anomalies, on the other hand, can be detected over the Zagros Mountains, coastal areas of the south, and some parts of the northwest and west. Its hotspot (136 days of heavy rainfall and 159 days of super-heavy rainfall) is located near Fars.

    To investigate the changes in heavy and super-heavy rainfall within the four decades, the spatial autocorrelation (hotspot index) is employed. The results are presented in Figs. 6 and 7. The Gi statistic, which is calculated for each region, is a type of Z-score. With respect to positive and statistically significant Z-scores, the larger the Z-score, the higher the clustering of values, and hence the formation of hotspots (in other words, having a positive spatial autocorrelation). With respect to negative and statistically significant Z-scores, the lower the Z-score, the greater the clustering of lower values (hence, negative autocorrelation), which indicates "coldspots". Figures 8 and 9 display the results of the spatial analysis of hotspots for heavy and super-heavy rainfall during the four periods.

    Figure 6.  Distribution of the spatial autocorrelation of heavy rainfall in Iran during 1971-2011.

    Figure 5 indicates that, in all the periods, 99% of the coastal areas of the Caspian Sea and the Zagros Mountains have experienced heavy rainfall, hence forming high cluster patterns or positive spatial autocorrelation. A less strong hotspot can be observed in 95% of the areas around this focal point (Fig. 6). Therefore, based on the hotspot model, the probability of heavy rainfall in these areas is very high. Negative spatial autocorrelation at the level of 99% and 95% for all the periods can be mainly observed in southeastern and central parts of Iran. It is thus concluded that these areas experience less in terms of heavy rainfall.

    Figure 7.  Distribution of the spatial autocorrelation of super-heavy rainfall in Iran during 1971-2011.

    Figure 8.  Distribution of the spatial autocorrelation of Iran's heavy rainfall in different periods.

    Comparative analysis of the autocorrelation patterns of heavy rainfall in periods of heavy rainfall spatial displacement indicates that, although the oscillation is small, considerable changes can be seen in terms of the level of significance. For example, in the first period, 13.4% of the country's area has a pattern of negative spatial autocorrelation at the 95% level, while in the second period, 3.7% of the total area of the country has a low cluster model at the 95% level (Table 3). Overall, it can be said that, on average, 21% of the area of Iran has experienced a heavy rainfall pattern in low clusters at the 95% and 99% levels. Thus, 13% of the country's area has had a positive spatial autocorrelation (high clustering pattern). In general, in almost all the periods, around 62% of the country's area does not follow any specific pattern when it comes to heavy rainfall (Fig. 4). The annual pattern of heavy rainfall is similar to that on the decadal scale.

    Figure 7 shows the spatial distribution pattern of super-heavy rainfall. Similar to the heavy rainfall pattern, the super-heavy rainfall pattern is a highly clustered one (positive spatial autocorrelation) along the Caspian coast and over the Zagros Mountains. The only difference is that, in comparison with heavy rainfall, a lower area is affected by super-heavy rainfall. For example, in all the periods, approximately 10.5% of the area of the country experiences super-heavy rainfall (positive spatial autocorrelation). This shows a 2% decline compared to heavy rainfall (Table 3).

    In contrast to the patterns of heavy rainfall, negative spatial autocorrelation patterns of super-heavy rainfall are significant only at the 95% level. They mainly include central and southeastern parts of the country, particularly in Zabul (Fig. 8). Therefore, this part of the country has a very low probability of heavy rainfall occurrence. In the first and third periods, respectively, 7.7% and 1.6% of the area of the country has heavy rain with a low cluster pattern (negative spatial autocorrelation). Similar to heavy rainfall, super-heavy rainfall does not follow any specific pattern in almost 75% of the country's area (Fig. 9). In the first and third periods, negative spatial autocorrelation patterns show a significant decline. In the other two periods, however, they show insignificant fluctuations. As a result, heavy rainfall events form strong clusters only in parts of the west, northwest, and shores of the Caspian Sea, while they form weak clusters in central parts and the east of the country.

    Figure 9.  Distribution of the spatial autocorrelation of Iran's super-heavy rainfall in different periods.

    Figure 10.  Gi regression lines fitted to heavy and super-heavy rainfall in different periods.

    For investigating the relationship between two variables, the first logical step is to plot the data as points in a coordinate system, i.e. as a scatterplot. One of the aims of this model is exploring the relationship between different variables and the way they are influenced by each other. Generally, changes in one variable may lead to changes in the dependent variable. If the nature of the relationship between independent and dependent variables is clear, one can infer a model of the way the independent variables influence dependent ones. Furthermore, the dependent variables can be predicted based on the independent variable. To make accurate predictions, one can use the line of best fit of the regression analysis. The line of best fit indicates an equation for calculating one variable based on another. This line offers the means to calculate changes in a dependent variable based on one or more independent variables. In other words, the line is drawn along the greatest change observed in the plot. Figure 10 shows scatterplots and the lines of best fit for the relationship between heavy and super-heavy rainfall. Based on the Gi statistic, a significant direct relationship can be observed between heavy and super-heavy rainfall during all periods with a 95% confidence interval. This suggests that heavy and super-heavy rainfall events are interdependent, so an increase in heavy rainfall leads to a rise in super-heavy rainfall, and vice versa (Fig. 10).

4. Conclusions
  • The maps and frequency of occurrence of heavy and super-heavy rainfall in the decades studied in this work suggest that three general territories in Iran can be identified for the occurrence of these rainfall types:

    (1) Along the coast of the Caspian Sea, which is often a hotspot for both types of rainfall.

    (2) In the northwest and west along the western foothills of the Zagros Mountains.

    (3) Central as well as southeastern and northeastern parts of the country (the lowest frequency of the three territories).

    Also, the anomaly maps indicate that, when the occurrence of heavy and super-heavy rainfall has yielded positive anomalies in coastal areas of the Caspian Sea, the same phenomena have resulted in negative anomalies in the southern part of the country (along southern coasts) and vice versa. The important point is that, when it comes to the frequency of occurrence of heavy and super-heavy rainfall, changes throughout the decades have been more significant than the frequency of minimum rainfall. Changes that have occurred in the frequency of heavy and super-heavy rainfall (e.g. the 250-day reduction in heavy rainfall and 156-day reduction in super-heavy rainfall in the second period compared to the first; the 192-day increase in heavy rainfall and 79-day increase in super-heavy rainfall in the third decade compared to the second; and the 57-day reduction in heavy rainfall and 53-day reduction in super-heavy rainfall in the fourth decade compared to the third) are important phenomena. The results of analyzing the spatial pattern of hotspots indicate that, although the maximum occurrence of heavy rainfall has experienced spatial and temporal oscillations, it has mainly concentrated in the west and northwest of the country, as well as coastal areas of the Caspian Sea. On the contrary, central and eastern parts of Iran, especially Zabul, lack frequent heavy rainfall. In other words, their heavy rainfall patterns follow a negative spatial autocorrelation. The pattern of super-heavy rainfall is similar to that of heavy rainfall, with the only difference that it encompasses a smaller area of the country. Spatial changes in super-heavy rainfall show a considerable reduction in recent years. In contrast, heavy rainfall has had a smaller number of changes. Thus, according to the spatial index of hotspots, heavy rainfall has a cluster pattern in northern and western parts of the country (positive spatial autocorrelation). However, there is no dominant pattern in the majority of the country (around 70%).

Reference

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