Abstract
ABSTRACT A new approach to select a model for fitting to a seasonal daily average rainfall semi-variogram was developed in this study by interpolating rainfall using geostatistical methods. Using two deterministic algorithms (Thiessen polygon, THI and inverse distance weighting, IDW) and two geostatistical algorithms (ordinary kriging, ORK and universal kriging, UNK) based on a rain gauge network, a 20-year daily and annual rainfall time series was generated for the Zayandeh Rud Dam Basin (Isfahan, Iran). Seven models were fitted to the experimental semi-variogram. Evaluation criteria – root mean square error (RMSE) and correlation coefficient (R) – identified that the Gaussian model is the best fit to the experimental semi-variogram. The ORK and UNK algorithms performed better in generating data when network sizes of 27, 15 or 10 gauges were used, whereas THI produced better results at a network size of five gauges. The results show that increasing the number of gauges does not necessarily produce better estimates, and stochastic methods are more sensitive than deterministic algorithms to network size.
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