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首页> 美国卫生研究院文献>Springer Open Choice >Modeling the Impacts of Spatial Heterogeneity in the Castor Watershed on Runoff Sediment and Phosphorus Loss Using SWAT: I. Impacts of Spatial Variability of Soil Properties
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Modeling the Impacts of Spatial Heterogeneity in the Castor Watershed on Runoff Sediment and Phosphorus Loss Using SWAT: I. Impacts of Spatial Variability of Soil Properties

机译：利用SWAT建模蓖麻流域空间异质性对径流沉积物和磷流失的影响：I.土壤特性空间变异性的影响

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Spatial accuracy of hydrologic modeling inputs influences the output from hydrologic models. A pertinent question is to know the optimal level of soil sampling or how many soil samples are needed for model input, in order to improve model predictions. In this study, measured soil properties were clustered into five different configurations as inputs to the Soil and Water Assessment Tool (SWAT) simulation of the Castor River watershed (11-km2 area) in southern Quebec, Canada. SWAT is a process-based model that predicts the impacts of climate and land use management on water yield, sediment, and nutrient fluxes. SWAT requires geographical information system inputs such as the digital elevation model as well as soil and land use maps. Mean values of soil properties are used in soil polygons (soil series); thus, the spatial variability of these properties is neglected. The primary objective of this study was to quantify the impacts of spatial variability of soil properties on the prediction of runoff, sediment, and total phosphorus using SWAT. The spatial clustering of the measured soil properties was undertaken using the regionalized with dynamically constrained agglomerative clustering and partitioning method. Measured soil data were clustered into 5, 10, 15, 20, and 24 heterogeneous regions. Soil data from the Castor watershed which have been used in previous studies was also set up and termed “Reference”. Overall, there was no significant difference in runoff simulation across the five configurations including the reference. This may be attributable to SWAT's use of the soil conservation service curve number method in flow simulation. Therefore having high spatial resolution inputs for soil data may not necessarily improve predictions when they are used in hydrologic modeling.
机译：水文模型输入的空间精度会影响水文模型的输出。一个相关的问题是知道最佳的土壤采样水平或模型输入需要多少土壤采样，以改善模型预测。在这项研究中，将测量的土壤特性分为五种不同的配置，作为加拿大魁北克南部卡斯特河流域（11 km 2 地区）的水土评估工具（SWAT）模拟的输入。 SWAT是一种基于过程的模型，可预测气候和土地利用管理对水产量，沉积物和养分通量的影响。特警队需要地理信息系统输入，例如数字高程模型以及土壤和土地使用图。在土壤多边形（土壤系列）中使用土壤特性的平均值。因此，这些特性的空间变异性被忽略了。这项研究的主要目的是量化土壤性质的空间变异性对利用SWAT预测径流，沉积物和总磷的影响。使用动态约束的聚类聚类和分区方法进行区域化，对测得的土壤性质进行空间聚类。测得的土壤数据分为5、10、15、20和24个异质区域。还建立了先前研究中使用的蓖麻流域的土壤数据，并将其称为“参考”。总体而言，包括参考在内的五种配置之间的径流模拟没有显着差异。这可能是由于SWAT在流量模拟中使用了土壤保护服务曲线编号方法。因此，在土壤数据中使用高空间分辨率输入数据时，不一定会改善预测结果。

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