Robust Statistical Processing of Long-Time Data Series to Estimate Soil Water Content

IF 2.8 3区 地球科学 Q2 GEOSCIENCES, MULTIDISCIPLINARY
Mirko Anello, Marco Bittelli, Massimiliano Bordoni, Fabrizio Laurini, Claudia Meisina, Marco Riani, Roberto Valentino
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引用次数: 0

Abstract

Abstract The research presented in this paper aims at providing a statistical model that is capable of estimating soil water content based on weather data. The model was tested using a long-time series of field experimental data from continuous monitoring at a test site in Oltrepò Pavese (northern Italy). An innovative statistical function was developed in order to predict the evolution of soil–water content from precipitation and air temperature. The data were analysed in a framework of robust statistics by using a combination of robust parametric and non-parametric models. Specifically, a statistical model, which includes the typical seasonal trend of field data, has been set up. The proposed model showed that relevant features present in the field of experimental data can be obtained and correctly described for predictive purposes.

Abstract Image

长期数据序列的鲁棒统计处理估算土壤含水量
摘要:本文旨在建立一种基于气象数据估算土壤含水量的统计模型。利用在Oltrepò Pavese(意大利北部)一个试验点连续监测的一系列长期现场实验数据对该模型进行了测试。为了预测降水和气温对土壤含水量的影响,提出了一种新颖的统计函数。数据通过使用鲁棒参数和非参数模型的组合在鲁棒统计的框架中进行分析。具体而言,建立了一个包含典型野外数据季节趋势的统计模型。所提出的模型表明,可以获得实验数据领域的相关特征,并正确描述其用于预测目的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Mathematical Geosciences
Mathematical Geosciences 地学-地球科学综合
CiteScore
5.30
自引率
15.40%
发文量
50
审稿时长
>12 weeks
期刊介绍: Mathematical Geosciences (formerly Mathematical Geology) publishes original, high-quality, interdisciplinary papers in geomathematics focusing on quantitative methods and studies of the Earth, its natural resources and the environment. This international publication is the official journal of the IAMG. Mathematical Geosciences is an essential reference for researchers and practitioners of geomathematics who develop and apply quantitative models to earth science and geo-engineering problems.
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