ارزیابی مقایسه‏‌ای کارایی روش‌های درون‌یابی در برآورد بارندگی سالانه و ماهانه حوزه‌آبخیز دریاچه نمک

نویسندگان
فریبا اسماعیلی 1
مهدی وفاخواه 2
1 دانشجوی کارشناسی ارشد گروه علوم و مهندسی آبخیزداری، دانشکده منابع طبیعی تربیت مدرس، نور، ایران
2 استاد گروه علوم و مهندسی آبخیزداری، دانشکده منابع طبیعی تربیت مدرس، مسئول نویسنده، ایران، نور
چکیده
یکی از مشکلات مهم مطالعات منابع آب، تخمین توزیع مکانی  بارندگی بر اساس مشاهدات نقطه‌ای می‌باشد. در پژوهش حاضر با استفاده از سیستم اطلاعات جغرافیایی، ارزیابی روش‌های درون‌یابی شامل روش‌های آمارکلاسیک (عکس فاصله‌ی وزنی، توابع پایه شعاعی، چند جمله‌ای عام، چند جمله‌ای محلی) و روش‌های زمین‌آماری (کریجینگ و کوکریجینگ) به‌منظور تخمین بارندگی ماهانه و سالانه در حوزه‌آبخیز دریاچه نمک و بررسی تغییرات مکانی آن‌ها در محدوده زمانی سال آبی 1367-1366 تا 1396-1395 انجام شد. در این پژوهش، به‌منظور مقایسه روش‌های درون‌یابی در تهیه نقشه هم‌باران حوزه‌آبخیز دریاچه نمک، از110 ایستگاه هواشناسی در داخل حوزه‌آبخیز مذکور با طول دوره آماری 30 ساله استفاده شد. نتایج اعتبارسنجی نشان داد به‌طور کلی در داده‌های سالانه با توجه به شاخص ریشه میانگین مربعات خطا (RMSE) روش‌های زمین‌آماری کوکریجینگ و کریجینگ به‌ترتیب در رتبه اول و دوم بهترین برآورد را از بارندگی به دست می‌دهند به‌طوری‌که در داده‌های سالانه روش کوکریجینگ با RMSE، 68/63 میلی‌متر بهترین برآورد را داشته است. هم‌چنین در داده‌های ماهانه روش‌های توابع‌ پایه شعاعی، کوکریجینگ و کریجینگ بهترین برآورد را از بارندگی داشتند. به‌طور کلی در اکثر موارد روش‌های زمین‌آماری در تخمین بارندگی ماهانه و سالانه حوزه‌آبخیز دریاچه نمک بیش‌تخمینی داشته‌اند.
کلیدواژه‌ها
بارندگی
تحلیل‌مکانی
زمین‌آمار
سامانه اطلاعات جغرافیایی
کریجینگ معمولی

عنوان مقاله English

A Comparative Performance Evaluation of Interpolation Methods for Estimating Annual and Monthly Rainfall in the Namak Lake Watershed, Iran

نویسندگان English

F. Esmaeili 1
M. Vafakhah 2
چکیده English

One of the important problems of water resources studies is estimating the spatial distribution of rainfall based on point rainfall observations. In the present study, interpolation methods including definitive mathematical methods including Inverse Distance Weighting(IDW)  and Radial Basis Functions (RBF), Global polynomials, and local polynomials, and Kriging and Cokriging geostatistical methods using Geographic Information System (GIS) to estimate monthly and annual rainfall in the Namak lake watershed were evaluated. Spatial distribution analysis was carried out in the period 1988-1999 to 2006-2007 water year. In this study, in order to compare interpolation methods in iso-rainfall of the Namak lake watershed, 110 meteorological stations into the mentioned watershed with the statistical period of 30 years were used. The results showed that overall in annual rainfall according to Root Mean Square (RMSE) index, Cokriging and Kriging geostatistical methods in rank one and two, respectively have the best rainfall estimation. Therefore, Cokriging method with RMSE of 63.68mm had the best estimation. RBF, Cokriging and Kriging methods had also in the best estimation for   monthly rainfall data. In general, geostatistical methods have been overestimated in monthly and annual rainfall estimation in the Namak lake watershed.

کلیدواژه‌ها English

Rainfall
Spatial analysis
Geostatistics
Geographic information system
Ordinary Kriging
  1. Adhikary, S. K., Muttil, N., and Yilmaz, A. G. 2017. Cokriging for enhanced spatial interpolation of rainfall in two Australian catchments. Hydrological Processes, 31(12): 2143-2161.
  2. Asadzadeh, F., Malahat, E., and Shakiba, S.  2018. Prediction of the spatial distribution pattern of precipitation using geostatistical methods in Urmia region, Journal of New Findings in Applied Geology, 11(22): 87-95. 
  3. Bargaoui, Z. K., and Chebbi, A. 2009. Comparison of two kriging interpolation methods applied to spatiotemporal rainfall. Journal of Hydrology, 365(1-2): 56-73.
  4. Basistha, A., Arya, D. S., and Goel, N. K. 2008. Spatial distribution of rainfall in Indian Himalayas–a case study of Uttarakhand region: Water Resources Management, 22, 1325-1346.
  5. Bayat, B., Zahraie, B., Taghavi, F., and Nasseri, M. 2012. Evaluating the efficiency of spatial geostatistical methods for identifying the spatial patterns of precipitation: a case study of Namak lake watershed, Iranian Journal of Geophysics, 5(4): 89 (In Persian).
  6. Ceron, W. L., Andreoli, R. V., Kayano, M. T., Canchala, T., Carvajal-Escobar, Y., and Souza, R. A. 2021. Comparison of spatial interpolation methods for annual and seasonal rainfall in two hotspots of biodiversity in South America. Anais da Academia Brasileira de Ciências, 93.
  7. Cho, H. L., and Jeong, J. C. 2006. Application of spatial interpolation to rainfall data. Spatial Information Research, 14(1): 29-41.
  8. Correa, C. C. G., Teodoro, P. E., Cunha, E. D., Oliveira-Júnior, J. D., Gois, G., Ribeiro, L. P., and Torres, F. E. 2014. Spatial interpolation of annual rainfall in the State Mato Grosso do Sul (Brazil) using different transitive theoretical mathematical models. International Journal of Innovative Research in Science, Engineering and Technology, 3(10): 16618-16625.
  9. Coulibaly, M., and Becker, S. 2007. Spatial interpolation of annual precipitation in South Africa-Comparison and evaluation of methods. Water International, 32(3): 494-502.
  10. Da Silva, A. S. A., Stosic, B., Menezes, R. S. C., and Singh, V. P. 2019. Comparison of interpolation methods for spatial distribution of monthly precipitation in the state of Pernambuco, Brazil. Journal of Hydrologic Engineering, 24(3): 04018068.
  11. Daechini, F., Vafakhah, M., and Zabihi Silabi, M. 2021. Performance Evaluation of Geostatistics Methods for Rainfall Estimation in Haraz Watershed, Journal of Extension and Development of Watershed Management, 8(31): 27-41 (In Persian).
  12. Fathizad, H., Karimi, H., and Tazeh, M. 2015. Study on different geostatistical algorithms for annual rainfall zoning in Ilam province, Journal of Applied Researches in Geographical Sciences, 14(35): 139-154.
  13. Frazier, A. G., Giambelluca, T. W., Diaz, H. F., and Needham, H. L. 2016. Comparison of geostatistical approaches to spatially interpolate month‐year rainfall for the Hawaiian Islands, International Journal of Climatology. 36(3): 1459-1470.
  14. Gundogdu, I. B. 2017. Usage of multivariate geostatistics in interpolation processes for meteorological precipitation maps, Theoretical and applied climatology. 127: 81-86.
  15. Hao, W., and Chang, X. 2013. Comparison of spatial interpolation methods for precipitation in Ningxia, China. International Journal of Science and Research, India, 2(8): 181-184.
  16. Keblouti, M., Ouerdachi, L., and Boutaghane, H. 2012 Spatial interpolation of annual precipitation in Annaba-Algeria-comparison and evaluation of methods. Energy Procedia, 18, 468-475.
  17. Ly, S., Charles, C., and Degre, A. 2011. Geostatistical interpolation of daily rainfall at catchment scale: the use of several variogram models in the Ourthe and Ambleve catchments, Belgium, Hydrology and Earth System Sciences. 15(7): 2259-2274.
  18. Marofi, S. Golmohammadi, G., Mohammadi, K., Zare Abyaneh, H. 2010. Evaluation of Geostatisical Methods for Estimating Spatial Distribution of Annual Rainfall Using GIS Media in Hamedan Province, Iran, 19(2): 147 (In Persian).
  19. Matkan, A., Shakiba, A., and Yazdani, A. 2007. Evaluation of different interpolation methods on dailyrainfall estimation, Case study: Fars Province, Territory, 4(13): 54-68 (In Persian).
  20. Mehdizadeh, M., Mahdian, M., and Hajjam, S. 2006. Application of geostatistical methods for climatic classification (A case study, Ourmieh lake basin). Journal of the Earth and Space Physics, 32(1): 103-116.
  21. Mir Mousavi, S.H., Mazidi, A., and Khosravi, Y. 2010. The Determination of Optimum Geo-statistics Method for Estimating Precipitation Distribution Using GIS (Case Study of Esfahan Province) Geographic Space, 10(30): 105.
  22. Moral,F. J. 2010. Comparison of different geostatistical approaches to map climate variables: Application to precipitation, Int. J. Climatol., 30(4): 620–631
  23. Nabipour, Y., and Vatakhah, M. 2016. Comparison of Different Geostatistical Methods for Estimating Rainfall in Hajighoshan Watershed. Journal of Range and Watershed Managment, 69(2): 487-502 (In Persian).
  24. nadi, M., Jamei, M., Bazrafshan, J., and Janatrostami, S. 2012. Evaluation of Different Methods for Interpolation of Mean Monthly and Annual Precipitation Data (Case Study: Khuzestan Province). Physical Geography Research Quarterly, 44(4): 117-130.
  25. Nikolopoulos, E. I., Borga, M., Creutin, J. D., and Marra, F. 2015. Estimation of debris flow triggering rainfall: Influence of rain gauge density and interpolation methods. Geomorphology, 243, 40-50.
  26. Pellicone G, Caloiero T, Modica G Guagliardi I. 2018. Application of several spatial interpolation techniques to monthly rainfall data in the Calabria region (southern Italy). Int J Climatol 38: 3651-3666.
  27. Peng, S. H. I., & Xiao-fang, R. U. I. 2005. Comparison and improvement of spatial rainfall interpolation methods [J]. Journal of Hehai University (Natural Sciences): 4(001).
  28. Rahimi Bandarabadi, S., and Mahdian, M. H. 2005. Study of spatial distribution methods of daily and monthly rainfall in the Caspian Sea basin, Journal of Research and Construction, 69: 72-63.
  29. Raoof, M., and Mirzaei, S. 2019. Determination of the Most Appropriate Geostatistical Method of Spatial Interpolation for Maximum Daily Rainfalls in Atashgah Basin in the Ardabil Province, 29(3): 13-25.
  30. Rata, M., Douaoui, A., Larid, M., and Douaik, A. 2020. Comparison of geostatistical interpolation methods to map annual rainfall in the Chéliff watershed, Algeria. Theoretical and Applied Climatology, 141, 1009-1024.
  31. Sobhanei, B., sareisaraf, B., azadei, M., and hoseinei, A. 2014. Modeling Rainfall in the West and Southwest of Caspian Sea in GIS Environment, with using Spatial Interpolation Methods Dr. Behrooz Sobhani Assistant Professor of Climatology University of Mohaghegh Ardabili Dr. Behrooz Sari Sarraf Professor of Climatology University of Tabriz Mohammad Azadi Mobaraki M. Sc of Climatology University of Mohaghegh Ardabili Seyyed Asad Hosseini Ph.D Student of Climatology University of Mohaghegh Ardabili. Geography and Development Iranian Journal, 11(30): 23-34 (In Persian).
  32. Wentz,E. A., Peuquet,D. J. and Anderson,S. 2010.An ensemble approach to space-time interpolation, Int. J. Geogr. Inf. Sci., 24(9): 1309–1325.
  33. Zandkarimi, A., and Mokhtari, D. 2018. Analysis of the accuracy of the various interpolation methods in estimating the rainfall distribution in order to select the most optimal algorithm (Case Study: Kurdistan Province): Physical Geography Research Quarterly, 50(104): 323-338 (In Persian).

Zoraghein H.R., Alesheikh A.A., Alimohammadi A., Vahidnia M.H. 2012. A New Approach for Interpolation of Precipitation Data by Soft Indicator Kriging and Genetic Algorithm, Iranian Journal of Remote Sencing & GIS, 3(3): 15 (In Persian).