Application of Interpolation and Regression Methods in Spatial Estimation of Rainfall (Case Study: Kermanshah Province)

Document Type : Research Paper

Authors

1 Associate Professor of Climatology- Geography Department- Razi University of Kermanshah

2 M. Sc. in Climatology, Geography Department, Razi University of Kermanshah

Abstract

Estimation of rainfall in areas without stations is very important due to the lack of meteorological stations and significance of rainfall in various planning strategies. In this research, the daily rainfall data from 46 rain-gauge and synoptic stations in the Kermanshah province in a 20-year period has been used to estimate the seasonal and annual average rainfall in the region. To do this, univariate methods (deterministic and geostatistical) and multivariate methods (geostatistical and linear regression) have been used. The usual method for spatial estimation of rainfall in previous studies in Iran has been the use of one variable (usually altitude), and also default settings of the interpolation methods. However, in this study, firstly, in the multivariate methods, in addition to the altitude, other variables such as slope percent, latitude and longitude have been used as covariates (independent variables). Secondly, instead of using the default values of the models, various settings were performed on the 8 parameters in the deterministic methods, and up to 31 parameters in the geostatistical methods depending on the interpolation method used, and then the effect of each method was evaluated in the precipitation estimates considering the error of each method. For example, in the geostatistical methods, optimized semivariogram and covariogram were determined in each case according to the spatial structure of the variable, and then their characteristics were adjusted by taking into account the lowest errors in the estimation of precipitation. The results of cross-validation technique showed that the deterministic methods have more errors than the geostatistical methods in all cases. To estimate the average spring rainfall, linear multivariate regression method, and for average summer and autumn rainfalls, ordinary kriging method, and finally for average winter and annual rainfalls, ordinary cokriging method were selected as the best methods. Based on this, the average annual rainfall in the province was estimated about 479 mm (346 to 848 mm), with maximum seasonal rainfall of 212 mm in winter (equivalent to 44.3% of the annual rainfall).

Keywords

References

1-     حسنی­­پاک، علی­اصغر. 1392.زمین­آمار (ژئواستاتیستیک)،چاپ چهارم، تهران،انتشارات دانشگاه تهران.
2-   ذبیحی، علیرضا، کریمسلیمانی، مرتضی شعبانی و صادقآبروش. 1390.بررسیتوزیعمکانیبارشسالانهبااستفادهازروش­هایزمین­آماری (مطالعۀموردی:استانقم)، پژوهش­هایجغرافیایطبیعی، دورۀ 43، شمارۀ 78، صص 102-112.
3-     رندو، جی. ام.1371. اصول زمین­آماری، ترجمۀعلی­اصغرخدایاری، چاپ اول، تهران، انتشارات جهاد دانشگاهی دانشکدۀ فنی دانشگاه تهران.
4-   عزیزی، قاسم،حسنعلیفرجیسبکبار رحیم­علیعباسپور و طاهرصفرراد.(1389. مدلتغییراتمکانیبارشدرزاگرسمیانی، پژوهش­هایجغرافیایطبیعی، شمارۀ 72، صص 35-51.
5-   عیوضی، معصومه و ابوالفضلمساعدی. 1391. بررسی الگوی گسترش مکانی بارش در سطح استان گلستان با استفاده از مدل­های قطعی و زمین­آماری، نشریۀ آب­وخاک(علوم و صنایع کشاورزی)، جلد 26، شمارۀ 1، صص53-64.
6-      قهرودی­تالی، منیژه و ام­السلمه بابایی­فینی. 1391.درآمدی بر سیستم­های اطلاعات جغرافیایی، چاپ سوم، تهران،انتشارات دانشگاه پیام نور.
7-   صفرراد،طاهر،حسنعلیفرجیسبکبار،قاسمعزیزی ورحیم­علیعباسپور. 1392. تحلیلمکانی تغییراتبارشدرزاگرسمیانیازطریقروش­هایزمینآمار (1995-2004)، جغرافیاوتوسعه،شماره 31، صص 149-164.
8-   مظفری، غلام­علی،سیدحسینمیرموسوی و یونسخسروی. 1391.ارزیابی روش­های زمین­آمار و رگرسیون خطی در تعیین توزیع مکانی بارش (مورد: استان بوشهر)،جغرافیا و توسعه، دورۀ 10، شمارۀ 27، صص63-76.
9-   مهرشاهی، داریوش و یونس خسروی. 1389. ارزیابیروش­هایمیان­یابیکریجینگورگرسیون خطیبرپایةمدل ارتفاعیرقومیجهتتعیینتوزیعمکانیبارشسالانه (مطالعةموردی:استاناصفهان)، مدرسعلومانسانی (برنامه­ریزیوآمایشفضا)، دورۀ 14، شمارۀ 4، صص 233-250.
10-   Abo-Monasar, A.,andAl-Zahrani, M.A. 2014, Estimation of Rainfall Distribution for the Southwestern Region of Saudi Arabia, Hydrological Sciences Journal, 59(2): 420-431.
11-   Bajat, B., M. Pejović, J. Luković, P. Manojlović, V. Ducić & S. Mustafić, 2013. Mapping Average Annual Precipitation in Serbia (1961-1990) by Using Regression Kriging, Theoretical and Applied Climatology, 112(1-2):1-13.
12-   Bostan, P.A., Heuvelink, G.B.M. andAkyurek,S.Z. 2012.Comparison of Regression and Kriging Techniques for Mapping the Average Annual Precipitation of Turkey, International Journal of Applied Earth Observation and Geoinformation, vol.19, Pp. 115-126.
13-   Chen, D., OU,T.,Gong,L.,Xu,C.Y.,Li,W.,Ho, C.H.,andQian,W. 2010.Spatial Interpolation of Daily Precipitation in China: 1951-2005, Advances In Atmospheric Sciences, 27(6): 1221-1232.
14-   Coulibaly, M. andBecker,S. 2007.Spatial Interpolation of Annual Precipitation in SouthAfrica- Comparison and Evaluation of Methods, Water International, 32 (3): 494-502.
15-   Dingman, S.L., Seely-Reynolds, D.M. andReynolds,R.C. 1988.Application of Kriging to Estimating Mean Anual Precipitation in a Region of Orographic Influence, Journal of American Water Resources Association, 24 (2): 329-339.
16-   Goovaerts, P. 2000.Geostatistical Approaches for Incorporating Elevation into the Spatial Interpolation of Rainfall, Journal of Hydrology, 228 (1-2): 113-129.
17-   Hevesi, J.A., Istok, J.D. andFlint,A.L. 1992.Precipitation Estimation in Mountainous Terrain Using Multivariate Geostatistics, Part Ӏ: Structural Analysis, Journal of Applied Meteorology, 31:661-676.
18-   Kastelec, D. andKošmelj,K. 2002.Spatial Interpolation of Mean Yearly Precipitation Using Universal Kriging, Developments in Statistics, 17: 149-162.
19-   Ly, S., Charles, C. and A. Degr´E. 2010.Spatial Interpolation of Daily Rainfall at Catchment Scale: A Case Study of the Ourthe and Ambleve Catchments, Belgium, Hydrology and Earth System Sciences Discussions, 7: 7383-7416.
20-   Martines-cob, A. 1995.Estimation of Mean Annual Precipitation as Affected by Elevation Using Multivariate Geostatistics,Water Resources Management,9(2):139-159.
21-   Maris, F.P., Kitikidou,K.,Angelidis, P.andPotouridis,S.2013.Kriging Interpolation Method for Estimation of Continuous Spatial Distribution of Precipitation in Cyprus, British Journal of Applied Science and Technology, 3 (4): 1286-1300.
22-   Mir´as-Avalos J.M., Paz-Gonz´alez,A.,Vidal-V´azquez, E.,&Sande-Fouz,P. 2007.Mapping Monthly Rainfall Data in Galicia (NW Spain) Using Inverse Distances and Geostatistical Methods, Advances in Geosciences, 10: 51-57.
23-   Moral, F.J. 2010.Comparison of Different Geostatistical Approaches to Map Climate Variables: Application to Precipitation, International Journal of Climatology,30 (4): 620-631.
24-   Subyani, A.M.andAl-Dakheel,A.M. 2009.Multivariate Geostatistical Methods of Mean Annual and Seasonal Rainfall in Southwest Saudi Arabia,Arabian Journal of Geosciences, 2(1):19-27.
25-   Taesombat,W.andSriwongsitanon,N. 2009.Areal Rainfall Estimation Using Spatial Interpolation Techniques, ScienceAsia,35: 268-275.
26-   Yavuz, H. andErdoğan,S. 2012.Spatial Analysis of Monthly and Annual Precipitation Trends in Turkey, Water Resources Management, 26 (3):609-621.
Geographical Planning of Space
Volume 5, Issue 16 - Serial Number 16
September 2015
Pages 182-197

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