模拟伊朗日降雨量变化的地质统计建模

M. Javari
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引用次数: 13

摘要

摘要在模拟不同环境条件下气候变化的空间模式时,降雨量可变性是面临的主要挑战之一,特别是在伊朗等干旱和半干旱气候的国家。通过地质统计建模,气候变化模拟有可能发展对空间变异性的理解,例如日降雨量。本文通过比较基于预测误差的地质统计学技术,对伊朗170个站点和39042个降雨点的日降雨量平均值进行了一些空间变异性模拟。为了模拟日降雨量平均值的空间变异性,使用1975–2014年的降雨数据系列来分析地质统计模型的准确性。使用四种统计误差评估指标,即平均绝对偏差预测误差、均方预测误差、都方根预测误差(RMSPE)和决定系数(R2),来评估和比较插值技术。根据其性能的顺序,选择了四面体普通克里格、指数核平滑、5阶多项式核平滑和四次核平滑作为模拟日降雨量变化的最佳空间模型。采用日降雨量平均值模型预测RMSPE在0.042和2.639之间变化。
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Geostatistical modeling to simulate daily rainfall variability in Iran
Abstract Rainfall variability is among the main challenges confronted when simulating the spatial patterns of climatic changes under different environmental conditions, particularly in countries with arid and semiarid climates such as Iran. Climate changes simulation, through geostatistical modeling, have made possible to develop the understanding of spatial variability, e.g. daily rainfall. This article presents some spatial variability simulations of average values of the daily rainfall for Iran from 170 stations and 39,042 rainfall points by comparing geostatistical techniques based on the prediction errors. For the spatial variability simulation of average values of the daily rainfall, rainfall data series of 1975–2014 was used to analyze the accuracy of geostatistical models. Four statistical error assessment measures, mean absolute deviation prediction errors, mean square prediction errors, root mean square prediction error (RMSPE), and coefficient of determination (R2), were used to assess and compare the interpolation techniques. Tetraspherical Ordinary Kriging, Exponential Kernel Smoothing, Order 5 Polynomial Kernel Smoothing, and Quartic Kernel Smoothing were selected as the best spatial models for simulating daily rainfall variability, in the order of their performance. The RMSPE varied between 0.042 and 2.639 were predicted by employed models for average values of the daily rainfall.
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Cogent Geoscience
Cogent Geoscience GEOSCIENCES, MULTIDISCIPLINARY-
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