Validation of Grid APHRODIT Daily Precipitation Estimates and Estimates derived from spatial interpolation of Precipitation in the Khuzestan province

Document Type : Research Article

Authors

• M. Jamei
• M. Mousavi Baygi
• M. Bannayan Awal

Ferdowsi University of Mashhad

10.22067/jsw.v0i0.22014

Abstract

Available accurate and reliable precipitation data are so important in water resources management and planning. In this study,to determine the best method of regional precipitation estimate in Khuzestan province, estimated daily precipitation data from the best interpolation method and APHRODIT Daily Grid Precipitation data during the 2000-2007 years were compared with 44 meteorological stations. Four interpolation methods i.e. Inverse Distance Weighted, Ordinary Kriging, Cokriging, and Regression Kriging were assessed to determine the most appropriate interpolation method for daily precipitation.For the variography analysis in Kriging models, five variogram models including spherical, exponential, linear, linear to sill and Gaussian fitted on the precipitation data. Near neighbor method was used to compare APHRODIT Daily Precipitation data with station recorded data. Cross validation technique was employed to evaluate the interpolation methods and the most appropriate method was determined based on Root Mean Square Error,Mean Bias Error, Mean Absolute Error indices and regression analysis. The result of error evaluation of interpolation methods showed that regression Kriging method has the highest accurate to interpolation of daily precipitation data in Khuzestan province. Therefore, regression-based interpolation methods which using covariates would be improved precipitation evaluate accurate in the area. Comparison of error indices and regression analysis of regression Kriging interpolation method and estimate of APHRODITE show that on most days the accurately estimate of regression Kriging is higher than the APHRODITE. Therefore to understanding of spatial distribution and estimate of daily precipitation data in Khuzestan Province, Regression Kriging interpolation method is more accurate than available APHRODITE data

Keywords

• Daily Precipitation
• Regression Kriging
• APHRODITE
• Khuzestan

20.1001.1.20084757.1393.28.3.18.8

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References

1- بارانی زاده الف.، بهیار م.ب.، جوانمرد س. و عابدینی ی.ع. 1390. صحت سنجی برآوردهای بارندگی الگوریتم ماهوارهای PERSIANN با داده های بارش زمینی شبکه بندی شده APHRODIT در ایران. مجموعه مقالات کنفرانس فیزیک ایران. 2618-2615.

2- حسنی پاک ع.الف.1386. زمین آمار(ژئواستاتیستیک)، انتشارات دانشگاه تهران.

3- رضیئی ط.، عزیزی ق.، محمدی ح. و خوش اخلاق ف. 1389. الگوهای روزانه گردش جو زمستانه تراز 500 هکتوپاسکال بر روی ایران و خاورمیانه. مجله پژوهش های جغرافیای طبیعی74 : 34-17.

4- مدنی ح.1373. مبانی زمین آمار. دانشگاه صنعتی امیرکبیر- واحد تفرش.

5- میرموسوی ح.، مزیدی الف. و خسروی ی. 1389. تعیین بهترین روش زمین آمار جهت تخمین توزیع بارندگی با استفاده از GIS (مطالعه موردی: استان اصفهان).،‌ فصلنامه فضای جغرافیایی 10(30) :120-105.

6- نادی م.، جامعی م. و بذرافشان م.ج .1391. ارزیابی روش های مختلف درون یابی داده های بارندگی ماهانه و سالانه(مطالعه موردی: استان خوزستان).مجله پژوهش های جغرافیای طبیعی 44(4) :130-117.

7- Beven K.J. 2001. Rainfall–runoff Modelling: The Primer. John Wiley and Sons Ltd., Chicheste.

8- 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.

9- Di Piazza A., Lo Conti F., Noto 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.

10- Francisco J.M. 2010. Comparison of different geostatistical approaches to map climate variables: application to precipitation. International Jornal of Climatology, 30: 620-631.

11- Kamiguchi K., Arakawa O., Kitoh A., Yatagai A., Hamada A. and Yasutomi N. 2010. Development of APHRO_JP, the first Japanese high-resolution daily precipitation product for more than 100 years. Hydrological Research Letters, 4:60–64.

12- Odeh I.O.A., McBratney A.B. and Chittleborough D.J. 1995. Further results on prediction of soil properties from train attributes: heterotopic Cokriging and regression-kriging. Geoderma 673: 215–226.

13- Rajeevan M. and Bhate J. 2009. A high resolution daily gridded rainfall dataset (1971–2005) for meso-scale meteorological studies. Curr. Sci, 96:558-562.

14- Raziei T., Mofidi A., Santos J. and Bordi I. 2011.Spatial patterns and regimes of daily precipitation in Iran in relation to large-scale atmospheric circulation. International Journal of Climatology.International Journal of Climatology, 32(8), 1226–1237.

15- 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.

16- Verworn A. and Haberlandt U. 2011. Spatial interpolation of hourly rainfall – effect of additional information, variogram inference and storm properties. Hydrology and Earth System Sciences, 15:569–584.

17- Vu M.T., Raghavan S.V. and Liong S.Y. 2012. SWAT use of gridded observations for simulating runoff – a Vietnam river basin study Hydrology and Earth System Sciences, 16: 2801–2811.

18- Wagner P.D., Fiener P., Wilken F., Kumar Sh. and Schneider K. 2012. Comparison and evaluation of spatial interpolation schemes for daily rainfall in data scarce regions.Journal of Hydrology, 464–465 (2012): 388–400.

19- Wilks D.S. 2006. Statistical Methods in the Atmospheric Sciences, Second Edition, Academic Press is an imprint of Elsevier, Cornell University, USA.

20- 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.

21- 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. Bulltion of American Meteorological Society, 9: 1401–1415