Assessment of the tracks of spatio-temporal precipitation, Iran

Document Type : Full length article

Authors

1 Assistant Professor of Earth Sciences, Shahid Beheshti University, Iran

2 Ph.D. Candidate in Urban Climatology, Earth Sciences, Shahid Beheshti University, Iran

Abstract

Introduction
Rainfall is a fundamental meteorological element that directly or indirectly affected human life. In many climatic studies, it is necessary to make reviews and analyses of suitable temporal and spatial resolution data in atmosphere based on detailed monitoring. There is also the need for resources and authoritative database. Today, with the emergence of the phenomenon of climate change and its consequences and the need to study all aspects of climate, these kinds of data especially in recent decades has been more needed. On the other hand, since spatial patterns of rainfall in short-term time scale was often too heterogeneous, it is essential to achieve a suitable method for estimating large regional rainfall in large areas.
 
Materials and Methods
To assess the temporal and spatial behavior of precipitation in Iran during last two decades, we used the Aphrodite pixel database. The base period of spatial data with spatial resolution of the Middle East and the period from 01/01/1988 to 31/12/2007 AD by pixel size value 25.0×25.0 arc were taken. To assess spatial autocorrelation data during last two decades we applied the spatial autocorrelation of Global Moran Method. In this study, we also used cluster analysis and outlier analysis (Anselin Local Moran's I) and also applied hot spot analysis (Getis-OrdGi*) to study the temporal and spatial changes in precipitation patterns.
 
Results and Discussion
The global Moran index for all four seasons of two periods of study is more than 0.75. Based on global Moran index, rainfall in the country in two decades of study indicates a cluster pattern of up to 95 and 99 percent confidence level. Due to the high value of Z and low value of Probability the hypothesis of no spatial autocorrelation between data during two periods is not verified. In most parts of the country (43.78%), there was not any type of patterns. Then, the lack of spatial autocorrelation during second period caused increase in the amount of area equal to 5.88 percent. The areas with no spatial autocorrelation in the summer reached maximum value, and it was in the first and second period equal to 90.88% and consequently 92.36%. In other seasons, spring and autumn, there were also the areas with no spatial autocorrelation pattern and is allocated relatively half of the country. The amounts of spatial autocorrelation for rainfall data in spring were negative during the first period and it decreased relative to the second period of 1.53 percent. This decrease is mostly found in Northern Khorasan and Central Zagras. There was a low-LL cluster at any of a two periods of study in the summer. The cluster of low (LL) in the autumn of the second period (25.30%) is compared with the first period (28.01 %) changed small value (2.71). However, it changed significantly in terms of location and the center of low precipitation patterns displaced toward east and southeast regions of Iran. The patterns of negative spatial autocorrelation in winter, autumn and spring show changes in spatial and temporal dimension. Then, most of the decline in the two decades of study is associated with the second period in winter season with a numerical reduction (6.62%) compared to the first period (31.30%). It is worth mentioning that most of the reduction is allocated to Zagros region, South East Iran and Northern Khorasan. In general it can be concluded that local factors and general atmospheric circulation systems in the first and second stages affected distribution of precipitation patterns in Iran.
 
 
Conclusion
The results showed that long-term rainfall patterns are formed over a period of interaction of local factors and elements of atmospheric circulation. The geographical feature of precipitation patterns are based on local factors, especially latitude and topography. However, we should not ignore the role of external factors in the formation of rainfall patterns because the external factors like general atmospheric circulation play a significant role in the precipitation regime and spatial and temporal changes of precipitation. If we pay attention to cluster rainfall map of Iran we can conclude that the cluster of high rainfall and low precipitation are not similar to each other due to the effect of the general atmospheric circulation patterns. In general, we can conclude that the precipitation patterns are affected and controlled by two main factors, which include: (1) local factors controlling the location (geographic feature of precipitation patterns) and (2) external factors controlling time (regime of precipitation patterns). Finally, this study could be a model for other studies of climatic parameters by a general comparison of precipitation patterns of pixel based data of Aphrodite compared with measured values in climatology and synoptic stations. In all the studies in general and in other fields of study for example ecology and environmental sciences in particular which require updated and accurate climatic data in terms of time and space, we can use Aphrodite data.

Keywords

Main Subjects

References

انتظاری، ع.؛ داداشی رودباری، ع. و اسدی، م. (1394). ارزیابی خودهمبستگی فضایی تغییرات زمانی- مکانی جزایر دمایی در خراسان رضوی، جغرافیاومخاطراتمحیطی، 4(16).
بارانی‌زاده، ا.؛ بهیار، م.ب.؛ جوانمرد، س. و عابدینی، ی.ع. (1390). صحت‌سنجی برآوردهای بارندگی الگوریتم ماهواره‏های PERSIANN با داده‏های بارش زمینی شبکه‏بندی‌شدة APHRODITE در ایران، مجموعهمقالاتاجلاسفیزیکایران، ص 2615 ـ 2618.
جامعی، م.، موسوی بایگی، م. و بنایان اول، م. (1393). اعتبارسنجی برآوردهای بارندگی روزانة شبکة APHRODITE و برآوردهای حاصل از درون‏یابی مکانی بارندگی در استان خوزستان، نشریة آب ‏و خاک (علوم و صنایع کشاورزی)، 28(3): 626 ـ 638.
عساکره، ح. (1387). کاربرد روش کریجینگ در میان‌یابی بارش، جغرافیاوتوسعه، ش 12.
عساکره، ح. و رزمی، ر. (1391). تحلیل تغییرات بارش سالانة شمال غرب ایران، جغرافیاوبرنامه‏ریزیمحیطی، 23(3): 147 ـ 162.
علی‏آبادی، ک. و داداشی رودباری، ع. (1394). بررسی تغییرات الگوهای خودهمبستگی فضایی دمای بیشینة ایران، مطالعاتجغرافیاییمناطقخشک، 6(21): 86 ـ 104.
علیجانی، ب. (1389). آب‌وهوایایران، چ 8، تهران: انتشارات دانشگاه پیام نور.
فلاح قالهری، غ.، اسدی، م. و داداشی رودباری، ع. (1394). تحلیل فضایی پراکنش رطوبت در ایران، پژوهش‏هایجغرافیایطبیعی، 47(4): 637 ـ 650.
کرمی، م. و داداشی رودباری، ع. (1393). ارزیابی الگوهای بارشی استان خراسان رضوی با استفاده از روش‏های نوین آمار فضایی، مجلة علمی‌- ترویجی سامانهوسطوحآبگیرباران، 4(3): 61 ـ 72.
محمدی، ح. و جاوری، م. (1385). تغییرات زمانی بارش ایران، محیط‌شناسی، 40: 87 ـ 100.
مسعودیان، ا. (1390). آب‏وهوایایران، مشهد: شریعة توس.
مسعودیان، ا.؛ کیخسروی کیانی، م. و رعیت‌پیشه، ف. (1393). معرفی و مقایسة پایگاه اسفزاری با پایگاه دادة CPCC، GPCP، و CMAP، فصل‌نامه تحقیقاتجغرافیایی، 29(1): 73 ـ 88.
میرموسوی، ح؛ مزیدی، ا. و خسروی، ی. (1389). تعیین بهترین روش زمین‏آمار جهت تخمین توزیع بارندگی با استفاده از GIS (مطالعة موردی: استان اصفهان)، فصل‌نامة فضایجغرافیایی، 10(30): 105 ـ 120.
نادی، م.؛ جامعی، م. و بذرافشان، م.ج. (1391). ارزیابی روش‏های مختلف درون‏یابی داده‏های بارندگی ماهانه و سالانه (مطالعة موردی: استان خوزستان)، مجلة پژوهش‏هایجغرافیاییطبیعی، 44(4): 117 ـ 130.
Adler, R.F.; Huffman, G.J.; Chang, A.; Ferraro, R.; Xie, P.; Janowiak, J.; Rudolf, B.; Schneider, U.; Curtis, S.; Bolvin, D.; Gruber, A.; Susskind, J. and Arkin, P. (2003). The Version 2 Global Precipitation Climatology Project (GPCP) Monthly Precipitation Analysis (1979-Present), J. Hydrometeor., 4: 1147-1167.
Aliabadi, K. and Dadashi Roudbari, A. (2016). Assessing changes patterns of spatial autocorrelation of maximum temperature of Iran, Arid Regions Geographic Studies, 6(21): 86-104 (In Persian).
Alijani, B. (2010). Weather Iran, Payam Noor University Press, Eighth Edition, Tehran.
Anselin, L. (1995). Local indicators of spatial association: LISA, Geogr Anal, 27(2): 93-115.
Anselin, L.; Syabri, I. and Kho, Y. (2009). GeoDa: an introduction to spatial data analysis, In Fischer MM, Getis A. (eds) Handbook of applied spatial analysis, Springer, Berlin, Heidelberg and New York, pp.73-89.
Asakereh, H. (2008). Potential of kriging method for precipitation interpolation, Geography and Development, N. 12 (In Persian).
Asakereh, H. and Razmi, R. (2012). Analysis of Annual Precipitation Changes in Northwest of Iran, Geography and Environmental Planning Journal, 47(3): 147-162 (In Persian).
Barani Zadeh, A.; Behyar, M.B.; Javanmard, S.A. and Abedini, Y. (2011). Validation of satellite estimates of rainfall PERSIANN algorithm networked with ground-based rainfall data APHRODITE in Iran, Iranian Physics Conference Proceedings, pp. 2615-2618 (In Persian).
Beven, K.J. (2001). Rainfall–runoff Modelling: The Primer, John Wiley and Sons Ltd., Chicheste.
Chen, M.; Xie, P.; Janowiak, J.E. and Arkin, P.A. (2002). Global Land Precipitation: A 50-yr Monthly Analysis Based on Gauge Observations, J. of Hydrometeorology, 3: 249-266.
Coulibaly, M. and Becker, S. (2007). Spatial Interpolation of Annual Precipitation in South Africa -Comparison and Evaluation of Methods, Journal of International Water Resources Association, 32(3): 494-502.
Di Piazza, A.; Lo Conti, F.; Notol , L.V.; Viola, F. and Loggia, G.La. (2011). Comparative analysis of different techniques for spatial interpolation of rainfall data to create a serially complete monthly time series of precipitation for Sicily, Italy, International Journal of Applied Earth Observation and Geoinformation, 13: 396-408.
Diggle, P.J. (2003). Statistical Analysis of Spatial Point Patterns, Arnold, London, Second edition.
Entezari, A.; DadashiRoudbari, A. and Asadi, M. (2015). Assessment of spatial autocorrelation of spatial-temporal alteration of temperature heat islands in Khorasan Razavi province, Geography and environmental hazards, 4(4).
Fallah Ghalhari, GH.; Asadi, M. and Dadashi Roudbari, A. (2016). Spatial Analysis of Humidity Propagation over Iran, Physical Geography Research Quarterly, 47(4): 637-650.
Getis A. and Aldstadt, J. (2004). Constructing the spatial weights matrix using a local statistic, Geogr Anal, 36(2):90-104.
Getis A. and Ord, J.K. (1992). The analysis of spatial association by use of distance statistics, Geogr Anal, 24(3):189-206.
Griffith, D. (1987). Spatial Autocorrelation: A Primer, Resource Publication in Geography, Association of American geographers.
Huffman, G.J.; Adler, R.F.; Arkin, P.; Chang, A.; Ferraro, R.; Gruber, A. and Schneider, U. (1997). The global precipitation climatology project (GPCP) combined precipitation dataset, Bulletin of the American Meteorological Society, 78(1): 5-20.
Huffman, G.J.; Adler, R.F.; Bolvin, David T. and Guojun, Gu. (2009) Improving the global precipitation record: GPCP Version 2.1, Geophysical Research Letters, 36:17.
Illian, J.; Penttinen, A.; Stoyan, H. and Stoyan, D. (2008). Statistical Analysis and Modelling of Spatial Point Patterns, John Wiley and Sons, Chichester.
Jacquez, G.M. and Greiling, D.A. (2003). Local clustering in breast, lung and colorectal cancer in Long Island, New York. Int J Health Geographics, 2: 3.
Jamei, M.; Mousavi Baygim, M. and Bannayan Awal, M. (2014). Validation of Grid APHRODIT Daily Precipitation Estimates and Estimates Derived from Spatial Interpolation of Precipitation in the Khuzestan Province, Journal of Water and Soil, 28(3): 626-638 (In Persian).
Javanmard, S.; Jamali, J.; Yatagai, A. and  Mahdavi, E. (2011). Spatial and Temporal Analysis of Precipitation over Iran Using Gridded Precipitation Data of APHRODITE.
Karami, M. and DadashiRodbari, A. (2014). Evaluation of Rainfall Patterns Using Modern Spatial Statistical Methods in the Khorasan Razavi Province, Journal of Rainwater Catchment Systems, 4(3):61-72.
Levine, N. (1996). Spatial statistics and GIS: Software tools to quantify spatial patterns, JAm Plann Assoc, 62(3): 381-391.
Masoodian, A. (2011). Weather of Iran, Mashhad Birch Sharia Publishing, Printing 1, Mashhad (In Persian).
Masoodian, A.; Keikhosravi Kayani, M. and Rayat Pisheh, F. (2014). Asfazari introduced with databases and database comparison GPCC, GPCP and CMAP, Geographical Research Quarterly, 29(1): 73-88 (In Persian).
Mir Mousavi, H; Mazidi, A. and Khosravi, y. (2010). Geostatistics to determine the best method for estimating the distribution of rainfall using GIS (Case Study: Isfahan Province), Quarterly geographical space, 10(30): 120-105 (In Persian).
Mitchell, A. (2005). The ESRI guide to GIS analysis, Vol. 2: Spatial measurements and statistics, ESRI, Redlands [CA].
Mohammadi, H. and Jhaveri, M. (2006). Changes in rainfall Iran, Ecology, 40: 87-100.
Nadi, M.; Jamei, M. and Bazrafshan, M.J. (2012). Various interpolation methods for assessing monthly and annual rainfall data (Case study: Khuzestan Province), Ntural geographic Journal, 44 (4): 117-130 (In Persian).
Nasrabadi, E.; Masoodian, S.A. and Asakereh, H. (2013). Comparison of Gridded Precipitation Time Series Data in APHRODITE and Asfazari Databases within Iran’s Territory, Atmospheric and Climate Sciences, 3: 235.
Ord, J.K. and Getis, A. (1995). Local spatial autocorrelation statistics: distributional issues and anapplication, Geogr Anal, 27(4): 287-306.
Rogerson, P.A. (2006). Statistics Methods for Geographers: Students Guide, SAGE Publications, Los Angeles, California.
Rudolf, B.; Beck, C.; Grieser, J. and Schneider, U. (2005). Global precipitation analysis products of the GPCC, Climate Monitoring-Tornadoklimatologie-Aktuelle Ergebnisse des Klimamonitorings, 163-170.
Shrivastava, R.; Dash, S.K.; Hegde, M.N.; Pradeepkumar, K.S. and Sharma, D.N. (2014). Validation of the TRMM Multi Satellite Rainfall Product 3B42 and estimation of scavenging coefficients for <sup> 131</sup> I and< sup> 137</sup> Cs using TRMM 3B42 rainfall data, Journal of environmental radioactivity, 138: 132-136.
Torres, M.P.J. and Jacquin, A.P. (2011). Geostatistical interpolation of precipitation data over an Andean catchment in Central Chile, Geophysical Research Abstracts, 13: EGU2011-3829-1.
Waagepetersenand, R. and Schweder, T. (2006). Likelihood-based inference for clustered line transect data, Journal of Agricultural, Biological, and Environ- mental Statistics, 11: 264-279.
Wheeler, D. (2007). A comparison of spatial clustering and cluster detection techniques for childhood leukemia incidence in Ohio, 1996-2003, Int J. Health Geographics, 6(1): 13.
Wheeler, D. and Paéz A. (2009). Geographically Weighted Regression. In Fischer MM, Getis A. (eds) Handbook of applied spatial analysis, Springer, Berlin, Heidelberg and New York, pp. 461-486.
Xie, H.; Ringler, C.; Zhu, T. and Waqas, A. (2013). Droughts in Pakistan: a spatiotemporal variability analysis using the Standardized Precipitation Index, Water International, 38(5): 620-631.
Yatagai, A.; Kamiguchi, K.; Arakawa, O.; Hamada, A.; Yasutomi, N. and Kitoh, A. (2012). APHRODITE: Constructing a long-term daily gridded precipitation dataset for Asia based on a dense network of rain gauges, Bulletin of the American Meteorological Society, 93(9): 1401-1415.
Yatagai, A.; Krishnamurti, T.N.; Kumar, V.; Mishra, A.K. and Simon, A. (2014). Use of APHRODITE Rain Gauge–Based Precipitation and TRMM 3B43 Products for Improving Asian Monsoon Seasonal Precipitation Forecasts by the Superensemble Method, Journal of Climate, 27(3): 1062-1069.
Zhang, C.; Luo, L.; Xu, W. and Ledwith, V. (2008). Use of local Moran’s I and GIS to identify pollution hotspots of Pb in urban soils of Galway, Ireland, Sci Total Environ, 398(1-3): 212-221.
Zhang, X. and Srinivasan, R. (2009). GIS-Based Spatial Precipitation Estimation: A Comparison of Geostatistical Approaches, Journal of the American Water Resources Association, 45(4): 894-906.
Physical Geography Research
Volume 48, Issue 3
October 2016
Pages 465-484

Files

History

  • Receive Date: 21 January 2016
  • Revise Date: 02 April 2016
  • Accept Date: 30 April 2016

Share

How to cite

Statistics