Classification of Hydrological Relevant Parameters by Soil Hydraulic Behaviour
Classification of Hydrological Relevant Parameters by Soil Hydraulic Behaviour
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DOI：
10.3390/geosciences9050206
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发表时间：
2019-05
期刊：
影响因子：
2.7
通讯作者：
P. Kreye;M. Gelleszun;M. Somasundaram;G. Meon
中科院分区：
文献类型：
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作者：
P. Kreye;M. Gelleszun;M. Somasundaram;G. Meon
水文中观尺度或宏观尺度的土壤水分模拟需要描述土壤物理特性的参数。在这些尺度上，关于土壤特性的信息大多仅在非常粗糙的空间分辨率上可用，具有基于纹理的土壤表征，难以选择代表性的土壤水力参数。我们通过引入一个新的土壤分类系统，这是基于土壤水力学行为，以真实地再现土壤水的相互作用中尺度水文模型的参数估计。土壤水通量的时间序列进行了模拟的基础上，一百万个不同的参数化，然后利用相似性分析，同时应用k-均值聚类。由此产生的类显示出不同的模式相比，美国农业部（USDA）纹理为基础的类。代表性的时间序列的水通量代表的新类进行了比较的时间序列的USDA纹理分类。新的类别显示出明显更低的不确定性。与新系统相比，USDA系统的类内时间序列带宽高出几个数量级。同一类内的模拟水通量时间序列的相似性评价也明显优于新系统。
Soil water simulations on hydrological meso- or macroscale require parameters that describe the physical characteristics of the soil. At these scales, information regarding soil properties is mostly only available on very coarse spatial resolutions with texture based soil characterisations, where it is difficult to select representative soil hydraulic parameters. We improved the parameter estimation by introducing a new soil classification system, which is based on soil hydraulic behaviour in order to realistically reproduce the soil water interaction within meso-scaled hydrological models. The time series of soil water flux were simulated based on one million different parameterisations, which were then utilised for similarity analyses while applying the k-means clustering. The resulting classes show a different pattern when compared to the United States Department of Agriculture (USDA) texture based classes. Representative time series of water flux representative of the new classes were compared to time series of the USDA texture classification. The new classes show remarkably lower uncertainties. The bandwidth of the time series within a class is orders of magnitudes higher for the USDA system when compared to the new system. The evaluation of similarity of the simulated water flux time series within one and the same class were also clearly better for the new system.