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Iranian Water Researches Journal
Increase the accuracy of monthly and annual precipitation maps using covariates in Mazandaran province


 submission: 21/10/2019 | acception: 06/01/2020 | publication: 08/09/2020

DOI 

Authors
alireza yosefikebriya1

1-univercity of sari،salimatabay74@gmail.com



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Abstract

Interpolation methods are widely used in meteorological and hydrological studies. as well as the methods used to estimate missing meteorological data. Given the specific location and complex topography of Mazandaran province and the lack of high-altitude weather stations with long-term statistics and the random nature of rainfall data, determining the appropriate method of interpolating monthly and annual rainfall data in this province is necessary.In this research, four interpolation methods including Ordinary-Kriging, Co-Kriging, Inverse Distance Weighting and ۳D linear gradient were compared. In the variography analysis of rainfall data, five semivariogram models were fitted to the data. The methods were evaluated based on the root mean square error and mean bias error. The results of variography analysis showed that spherical and exponential models are the best theoretical semivariogram models. Comparison of investigated interpolation methods showed that the three-dimensional linear gradient method is the most appropriate interpolation method for monthly and annual rainfall data. So that, compared to other interpolation methods, it reduced the annual rainfall estimation error by more than ۱۰۰ mm. Also its bias error is close to zero. However, its accuracy is reduced in hot and dry months. Mapping the annual precipitation map with the selected method showed that the high rainfall region of province is located on the west coastal parts while the lowest rainfall occurs in the province's highlands. Also by moving from west to east and north to south the amount of annual precipitation is reduced. In addition, the results showed that the methods that use of altitude as covariate variable are more accurate than other methods.




Keywords

Interpolation  Three dimensional linear gradient  Geostatistic  covariate  



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