مدل‌سازی روابط فضایی بارش- ارتفاع در شمال شرق کشور با استفاده از مدل GWR
محورهای موضوعی :
مدیریت محیط زیست
مختار کرمی
1
(استادیار دانشکده جغرافیا و علوم محیطی، دانشگاه حکیم سبزواری، سبزوار، ایران)
الهام کدخدا
2
(کارشناس ارشد آب و هواشناسی، دانشکده جغرافیا و علوم محیطی، دانشگاه حکیم سبزواری، سبزوار، ایران)
تاریخ دریافت : 1396/04/17
تاریخ پذیرش : 1397/07/25
تاریخ انتشار : 1399/06/01
کلید واژه:
مدل‌سازی فضایی بارش- ارتفاع,
خودهمبستگی فضایی,
موران محلی,
مدل GWR,
ایران,
چکیده مقاله :
زمینه و هدف: بارش از متغیرترین فراسنج های اقلیمی است. این تغییرات هم در بعد مکان و هم در بعد زمان در قالب اقلیم منطقه رخ می‌دهد. این مطالعه باهدف مدل‌سازی روابط فضایی بارش فصلی شمال شرق کشور با دوره آماری مشترک ماهانه 30 ساله (2010-1980) انجام‌شد. روش: به‌منظور دست‌یابی به تغییرات فضایی بارش از روش‌های نوین آمار فضایی مانند خودهمبستگی فضایی، موران جهانی، شاخص پراکندگی آمار فضایی و مدل رگرسیون وزن‌دار جغرافیایی (GWR) از قابلیت‌های محیط بهره گرفته شد. نتایج: نتایج حاصل از این مطالعه نشان داد که تغییرات بارش در شمال شرق ایران دارای الگوی خوشه ای بالا یا همان مثبت می باشد. بطوریکه مقدار شاخص موران جهانی برای هر 4 فصل موردمطالعه و مجموع سالانه بالای 93/0 می‌باشد که بالاترین شاخص موران جهانی با مقدار 003219/1 مربوط به فصل تابستان بوده است. بحث و نتیجه گیری: نتایج حاصل از مدل GWR نشان داد که بارش در بخش‌های شمال منطقه موردمطالعه دارای خودهمبستگی فضایی مثبت و در بخش‌های جنوبی که غالباً مناطق کویری شمال شرق ایران تشکیل می‌دهند دارای خودهمبستگی فضایی منفی بوده است. همچنین نتایج آماره‌های پراکندگی، حاصل پیوستگی الگوی خوشه‌ای بارش در شمال شرق کشور بود. بر اساس شاخص فراوانی خوشه ها یا ICF فصل زمستان بزرگ‌ترین خوشه‌های بارشی با مقدار عددی 46/264 در شمال شرق کشور ایجاد می‌شود.
چکیده انگلیسی:
Background and Objective: Precipitation is one of the most variable climatic parameters. These changes occur both in terms of location and time in terms of the region's climate. This study was conducted to model the spatial relationships of seasonal rainfall in the northeast of the country with a joint monthly statistical period of 30 years (1980-2010). Method: In order to achieve spatial variation of rainfall, new methods of spatial statistics such as spatial autocorrelation, global Moran, spatial dispersion index and geographic weight regression model (GWR) were used in GIS software. Findings: The results of this study showed that rainfall changes in northeastern Iran have a high cluster pattern or positive. The Global Moran Index for each of the four seasons and the annual sum is above 0.93, the highest Global Moran index with the value of 0032191 is for the summer season. Discussion & Conclusion: The results of the GWR model showed that rainfall in the northern parts of the study area had positive spatial auto-correlation and in the southern parts, which are mostly desert areas had negative spatial auto-correlation. Also, the results of dispersion data were the result of cluster pattern of precipitation in the northeast of the country. Based on the frequency index of clusters or the ICF, the winter season is the largest cluster with a numerical value of 2646.26 in Northeast of the country.
منابع و مأخذ:
Chappell, A., Renzullo, L. J., Raupach, T. H., &Haylock, M. (2013). Evaluating geostatistical methods of blending satellite and gauge data to estimate near real-time daily rainfall for Australia. Journal of Hydrology, 493, 105-114.
Cressie, N. (1993). Statistics for Spatial Data: Wiley Series in Probability and Statistics.
Haining, R. P. (2003). Spatial data analysis (pp. 67-72). Cambridge: Cambridge University Press.
Fortin, M. J., & Dale, M. R. T. (2005). Spatial analysis: a guide for ecologists. Cambridge University Press.
Cressie, N., &Wikle, C. K. (2011). Statistics for spatio-temporal data. John Wiley & Sons.
Brunsell, N. A. (2010). A multiscale information theory approach to assess spatial–temporal variability of daily precipitation. Journal of hydrology, 385(1), 165-172.
IPCC, (2013). Climate Change 2013: The Physical Science Basis, Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge.
Oki, T., Musiake, K., & Koike, T. (1991). Spatial rainfall distribution at a storm event in mountainous regions, estimated by orography and wind direction. Water resources research, 27(3), 359-369
Barry, R. G. (1992). Mountain climatology and past and potential future climatic changes in mountain regions: a review. Mountain Research and Development, 71-86.
Hofinger, S., Mayr, G. J., Dreiseitl, E., & Kuhn, M. (2000). Fine-scale observations of summertime precipitation in an intra-Alpine region. Meteorology and Atmospheric Physics, 72(2-4), 175-184.
Sturman, A., &Wanner, H. (2001). A comparative review of the weather and climate of the Southern Alps of New Zealand and the European Alps. Mountain Research and Development, 21(4), 359-369.
Sotillo, M. G., Ramis, C., Romero, R., Alonso Oroza, S., &Homar, V. (2003). Role of orography in the spatial distribution of precipitation over the Spanish Mediterranean zone. Climate Research, 23, 247-261.
Creutin, J. D., &Obled, C. (1982). Objective analyses and mapping techniques for rainfall fields: an objective comparison. Water resources research, 18(2), 413-431.
Duhan, D.& Pandey, A. (2013). Statistical analysis of long spatial and temporal trends of precipitation during 1901–2002 at Madhya Pradesh, India. Atmospheric Research, 122, 136-149.
Lebel, T., Bastin, G., Obled, C., &Creutin, J. D. (1987). On the accuracy of areal rainfall estimation: a case study. Water Resources Research, 23(11), 2123-2134.
Goovaerts, P. (2000). Geostatistical approaches for incorporating elevation into the spatial interpolation of rainfall. Journal of hydrology, 228(1), 113-129.
Ripley, B. D. (1977). Modelling spatial patterns. Journal of the Royal Statistical Society. Series B (Methodological), 172-212.
Williams,R., Nicks, A.., and Arnold, G., (1985). Simulatorfor water resources in rural basins. Journal of HydraulicEngineering, ASCE, 111 (6), 970–986.
Upton, G., &Fingleton, B. (1985). Spatial data analysis by example. Volume 1: Point pattern and quantitative data. John Wiley & Sons Ltd.
Diggle, P.J., (2003). Statistical Analysis of Spatial Point Patterns, second ed. Academic Press, London.GCOS, 2007. GCOS Upper-Air Network (GUAN): Justification, Requirements, Siting and Instrument Options. GCOS-112, WMOTD 1379.
Robeson, S. M., Li, A., & Huang, C. (2014). Point-pattern analysis on the sphere. Spatial Statistics,76-58
Jia, S., Zhu, W., Lű, A., & Yan, T. (2011). A statistical spatial downscaling algorithm of TRMM precipitation based on NDVI and DEM in the Qaidam Basin of China. Remote sensing of Environment, 115(12), 3069-3079.
Allard, D., &Soubeyrand, S. (2012). Skew-normality for climatic data and dispersal models for plant epidemiology: when application fields drive spatial statistics. Spatial Statistics, 1, 50-64.
David, F. N., & Moore, P. G. (1954). Notes on contagious distributions in plant populations. Annals of Botany, 18(1), 47-53
Green, R. H. (1966). Measurement of non-randomness in spatial distributions. Researches on Population Ecology, 8(1), 1-7.
Douglas, J. B. (1975). Clustering and aggregation. Sankhyā: The Indian Journal of Statistics, Series B, 398-417.
Lloyd, M. (1967). Mean crowding. The Journal of Animal Ecology, 1-30.
Morisita, M. (1959). Measuring of the dispersion of individuals and analysis of the distributional patterns. Mem. Fac. Sci. Kyushu Univ. Ser. E, 2(21), 5-235.
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.
Illian, J., Penttinen, A., Stoyan, H., and Stoyan, D. (2008). Statistical Analysis and Modelling of Spatial Point Patterns. John Wiley and Sons, Chichester.
Getis A, Ord JK (1992), the analysis of spatial association by use of distance statistics.Geogr Anal 24(3):189-206.
Levine N (1996) spatial statistics and GIS: software tools to quantify spatial patterns. JAmPlannAssoc 62(3):381-391.
Mitchell A (2005) The ESRI guide to GIS analysis, volume 2: spatial measurements and statistics. ESRI, Redlands [CA].
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.
Griffith, D., (1987), spatial Autocorrelation: A Primer. Resource Publication in Geography, Association of American geographers.
Fotheringham, A. S., Charlton, M. E., &Brunsdon, C. (2001). "Spatial variations in school performance: a local analysis using geographically weighted regression". Geographical and Environmental Modelling, 5(1), 43-66.
Mennis, J. (2006). "Mapping the results of geographically weighted regression", The Cartographic Journal, 43(2), 171-179.
Charlton, M., Fotheringham, S., &Brunsdon, C. (2009). "Geographically weighted regression". White paper. National Centre for Geocomputation. National University of Ireland Maynooth.
Hurvich, C. M., Simonoff, J. S., & Tsai, C. L. (1998). "Smoothing parameter selection in nonparametric regression using an improved Akaike information criterion". Journal of the Royal Statistical Society: Series B (Statistical Methodology), 60(2), 271-293.
Scott, L. M., &Janikas, M. V. (2010). "Spatial statistics in ArcGIS. In Handbook of applied spatial analysis", (pp. 27-41). Springer Berlin Heidelberg.
_||_
Chappell, A., Renzullo, L. J., Raupach, T. H., &Haylock, M. (2013). Evaluating geostatistical methods of blending satellite and gauge data to estimate near real-time daily rainfall for Australia. Journal of Hydrology, 493, 105-114.
Cressie, N. (1993). Statistics for Spatial Data: Wiley Series in Probability and Statistics.
Haining, R. P. (2003). Spatial data analysis (pp. 67-72). Cambridge: Cambridge University Press.
Fortin, M. J., & Dale, M. R. T. (2005). Spatial analysis: a guide for ecologists. Cambridge University Press.
Cressie, N., &Wikle, C. K. (2011). Statistics for spatio-temporal data. John Wiley & Sons.
Brunsell, N. A. (2010). A multiscale information theory approach to assess spatial–temporal variability of daily precipitation. Journal of hydrology, 385(1), 165-172.
IPCC, (2013). Climate Change 2013: The Physical Science Basis, Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge.
Oki, T., Musiake, K., & Koike, T. (1991). Spatial rainfall distribution at a storm event in mountainous regions, estimated by orography and wind direction. Water resources research, 27(3), 359-369
Barry, R. G. (1992). Mountain climatology and past and potential future climatic changes in mountain regions: a review. Mountain Research and Development, 71-86.
Hofinger, S., Mayr, G. J., Dreiseitl, E., & Kuhn, M. (2000). Fine-scale observations of summertime precipitation in an intra-Alpine region. Meteorology and Atmospheric Physics, 72(2-4), 175-184.
Sturman, A., &Wanner, H. (2001). A comparative review of the weather and climate of the Southern Alps of New Zealand and the European Alps. Mountain Research and Development, 21(4), 359-369.
Sotillo, M. G., Ramis, C., Romero, R., Alonso Oroza, S., &Homar, V. (2003). Role of orography in the spatial distribution of precipitation over the Spanish Mediterranean zone. Climate Research, 23, 247-261.
Creutin, J. D., &Obled, C. (1982). Objective analyses and mapping techniques for rainfall fields: an objective comparison. Water resources research, 18(2), 413-431.
Duhan, D.& Pandey, A. (2013). Statistical analysis of long spatial and temporal trends of precipitation during 1901–2002 at Madhya Pradesh, India. Atmospheric Research, 122, 136-149.
Lebel, T., Bastin, G., Obled, C., &Creutin, J. D. (1987). On the accuracy of areal rainfall estimation: a case study. Water Resources Research, 23(11), 2123-2134.
Goovaerts, P. (2000). Geostatistical approaches for incorporating elevation into the spatial interpolation of rainfall. Journal of hydrology, 228(1), 113-129.
Ripley, B. D. (1977). Modelling spatial patterns. Journal of the Royal Statistical Society. Series B (Methodological), 172-212.
Williams,R., Nicks, A.., and Arnold, G., (1985). Simulatorfor water resources in rural basins. Journal of HydraulicEngineering, ASCE, 111 (6), 970–986.
Upton, G., &Fingleton, B. (1985). Spatial data analysis by example. Volume 1: Point pattern and quantitative data. John Wiley & Sons Ltd.
Diggle, P.J., (2003). Statistical Analysis of Spatial Point Patterns, second ed. Academic Press, London.GCOS, 2007. GCOS Upper-Air Network (GUAN): Justification, Requirements, Siting and Instrument Options. GCOS-112, WMOTD 1379.
Robeson, S. M., Li, A., & Huang, C. (2014). Point-pattern analysis on the sphere. Spatial Statistics,76-58
Jia, S., Zhu, W., Lű, A., & Yan, T. (2011). A statistical spatial downscaling algorithm of TRMM precipitation based on NDVI and DEM in the Qaidam Basin of China. Remote sensing of Environment, 115(12), 3069-3079.
Allard, D., &Soubeyrand, S. (2012). Skew-normality for climatic data and dispersal models for plant epidemiology: when application fields drive spatial statistics. Spatial Statistics, 1, 50-64.
David, F. N., & Moore, P. G. (1954). Notes on contagious distributions in plant populations. Annals of Botany, 18(1), 47-53
Green, R. H. (1966). Measurement of non-randomness in spatial distributions. Researches on Population Ecology, 8(1), 1-7.
Douglas, J. B. (1975). Clustering and aggregation. Sankhyā: The Indian Journal of Statistics, Series B, 398-417.
Lloyd, M. (1967). Mean crowding. The Journal of Animal Ecology, 1-30.
Morisita, M. (1959). Measuring of the dispersion of individuals and analysis of the distributional patterns. Mem. Fac. Sci. Kyushu Univ. Ser. E, 2(21), 5-235.
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.
Illian, J., Penttinen, A., Stoyan, H., and Stoyan, D. (2008). Statistical Analysis and Modelling of Spatial Point Patterns. John Wiley and Sons, Chichester.
Getis A, Ord JK (1992), the analysis of spatial association by use of distance statistics.Geogr Anal 24(3):189-206.
Levine N (1996) spatial statistics and GIS: software tools to quantify spatial patterns. JAmPlannAssoc 62(3):381-391.
Mitchell A (2005) The ESRI guide to GIS analysis, volume 2: spatial measurements and statistics. ESRI, Redlands [CA].
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.
Griffith, D., (1987), spatial Autocorrelation: A Primer. Resource Publication in Geography, Association of American geographers.
Fotheringham, A. S., Charlton, M. E., &Brunsdon, C. (2001). "Spatial variations in school performance: a local analysis using geographically weighted regression". Geographical and Environmental Modelling, 5(1), 43-66.
Mennis, J. (2006). "Mapping the results of geographically weighted regression", The Cartographic Journal, 43(2), 171-179.
Charlton, M., Fotheringham, S., &Brunsdon, C. (2009). "Geographically weighted regression". White paper. National Centre for Geocomputation. National University of Ireland Maynooth.
Hurvich, C. M., Simonoff, J. S., & Tsai, C. L. (1998). "Smoothing parameter selection in nonparametric regression using an improved Akaike information criterion". Journal of the Royal Statistical Society: Series B (Statistical Methodology), 60(2), 271-293.
Scott, L. M., &Janikas, M. V. (2010). "Spatial statistics in ArcGIS. In Handbook of applied spatial analysis", (pp. 27-41). Springer Berlin Heidelberg.
