比较各种预测建模方法,以估算空间异质性实地条件下的土壤侵蚀情况

IF 4.8 2区 环境科学与生态学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Environmental Modelling & Software Pub Date : 2024-07-14 DOI:10.1016/j.envsoft.2024.106145
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引用次数: 0

摘要

由于土壤水通量和作物生长的不确定性,在具有异质性田间条件的农业生态系统中,土壤侵蚀模型的准确性具有挑战性。在这项研究中,我们在 SIMPLACE 框架内将两种建模方法(Freebairn 和 Rose)与基于过程的作物和径流模型相结合来表示土壤侵蚀。它们的准确性与使用同一田块中 16 块侵蚀地(每块面积为 625 平方厘米)开发的统计模型进行了比较。不确定性分析表明,在这两个模型中,径流和坡角是预测沉积物产量的最关键要素,其次是 Freebairn 模型中的土壤可侵蚀性和 Rose 模型中的夹带效率。然而,由于地块大小的限制,没有研究坡长的影响。弗里贝恩模型预测泥沙产量的精度(均方根误差 = 0.69 吨/公顷-1 d-1)略高于罗斯模型(均方根误差 = 0.83 吨/公顷-1 d-1)。在参数值适当的情况下,这两个模型都能有效预测土壤流失。
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Comparison of predictive modeling approaches to estimate soil erosion under spatially heterogeneous field conditions

The accuracy of soil erosion models in agroecosystems with heterogeneous field conditions is challenging due to uncertainties from soil water fluxes and crop growth. In this study, we coupled two modeling methods (Freebairn and Rose) to represent soil erosion with a process-based crop and runoff models within the SIMPLACE framework. Their accuracy was compared to a statistical model developed using 16 erosion plots (each of 625 cm2) within the same field. Uncertainty analysis showed that runoff and slope angle were the most critical components for predicting sediment yield in both models, followed by soil erodibility in the Freebairn model and entrainment efficiency in the Rose model. However, due to plot size constraints, slope-length effects were not examined. The Freebairn model had a slightly higher accuracy (RMSE = 0.69 t ha−1 d−1) of sediment yield predictions than the Rose model (RMSE = 0.83 t ha−1 d−1). Both models are effective for predicting soil loss with appropriate parameter values.

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来源期刊
Environmental Modelling & Software
Environmental Modelling & Software 工程技术-工程:环境
CiteScore
9.30
自引率
8.20%
发文量
241
审稿时长
60 days
期刊介绍: Environmental Modelling & Software publishes contributions, in the form of research articles, reviews and short communications, on recent advances in environmental modelling and/or software. The aim is to improve our capacity to represent, understand, predict or manage the behaviour of environmental systems at all practical scales, and to communicate those improvements to a wide scientific and professional audience.
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