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.

中文翻译：

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伊朗 Zayandeh Rud 盆地降雨量插值的地统计和确定性方法

摘要 在本研究中，通过使用地质统计学方法对降雨进行插值，开发了一种新的方法来选择拟合季节性日平均降雨半变异函数的模型。使用两种确定性算法（泰森多边形，THI 和反距离加权，IDW）和两种基于雨量计网络的地统计算法（普通克里金法，ORK 和通用克里金法，UNK），生成了 20 年的日和年降雨量时间序列Zayandeh Rud Dam 盆地（伊朗伊斯法罕）。七个模型拟合实验半变异函数。评估标准——均方根误差 (RMSE) 和相关系数 (R)——确定高斯模型最适合实验半变异函数。当使用 27、15 或 10 个规格的网络大小时，ORK 和 UNK 算法在生成数据方面表现更好，而 THI 在五个规格的网络尺寸下产生了更好的结果。结果表明，增加量规的数量并不一定会产生更好的估计，并且随机方法比确定性算法对网络规模更敏感。