Scholarlink

<p>Nitrogen loss in paddy fields has been widely recognized as a significant contributor to nonpoint source pollution. Predicting this process through modeling is crucial, yet model parameters always carry uncertainty. Clarifying the time-dependent importance of the model parameters can help to better know the process effect such as precipitation or chemical reaction on nitrogen loss. Therefore, to rank the parameter importance, a global sensitivity analysis (GSA) named Sobol method is applied to a nitrogen loss model in paddy fields based on the soil mixing layer theory via modifying HYDRUS-1D model. To reduce the computational cost, local sensitivity analysis (LSA) is applied to the prediction model firstly, and three important model parameters, including soil mixing layer depth (<i>d</i><sub><i>mix</i></sub>), soil detachability coefficient (<i>α</i>) and precipitation intensity (<i>p</i>), are selected. Then, the Sobol method is applied to the prediction model to analyze the sensitivities of these three parameters. It is novel but reasonable that the Sobol sensitivity indices (including the first, second and total order indices, FOI, SOI and TOI) of <i>d</i><sub><i>mix</i></sub>, <i>α</i> and <i>p</i> vary with time. The study results indicate that the importance of parameter varies with time during a rainfall. In surface runoff, <i>α</i> is most important at early times, while <i>p</i> becomes most important at later times for predicted urea and NO<sub>3</sub><sup>−</sup>-N concentrations. <i>α</i> is always the most sensitive parameter for the predicted NH<sub>4</sub><sup>+</sup>-N concentration in surface runoff. In soil, the GSA results are opposite for <i>α</i> and <i>p</i>. Generally, <i>d</i><sub><i>mix</i></sub> is less important than <i>α</i> and <i>p</i>, and the interaction between each two parameters reflected by the SOIs has limited importance. <i>d</i><sub><i>mix</i></sub> presented sensitivity in LSA but insensitivity in GSA. Sensitivity of <i>α</i> showed similar results in LSA (elasticity) and GSA (TOI and FOI), which decreases in surface runoff and increases in soil. All elasticities of <i>p</i> increase at first and decrease gradually later, while the GSA results of <i>p</i> vary oppositely in surface runoff and soil.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Local and global sensitivity analysis for a prediction model of nitrogen loss in Southern China’s paddy fields via HYDRUS-1D

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年7月10日

查看全文
  • Shuhuai Wang,
  • Juxiu Tong,
  • Chen Huang

摘要

Nitrogen loss in paddy fields has been widely recognized as a significant contributor to nonpoint source pollution. Predicting this process through modeling is crucial, yet model parameters always carry uncertainty. Clarifying the time-dependent importance of the model parameters can help to better know the process effect such as precipitation or chemical reaction on nitrogen loss. Therefore, to rank the parameter importance, a global sensitivity analysis (GSA) named Sobol method is applied to a nitrogen loss model in paddy fields based on the soil mixing layer theory via modifying HYDRUS-1D model. To reduce the computational cost, local sensitivity analysis (LSA) is applied to the prediction model firstly, and three important model parameters, including soil mixing layer depth (dmix), soil detachability coefficient (α) and precipitation intensity (p), are selected. Then, the Sobol method is applied to the prediction model to analyze the sensitivities of these three parameters. It is novel but reasonable that the Sobol sensitivity indices (including the first, second and total order indices, FOI, SOI and TOI) of dmix, α and p vary with time. The study results indicate that the importance of parameter varies with time during a rainfall. In surface runoff, α is most important at early times, while p becomes most important at later times for predicted urea and NO3−-N concentrations. α is always the most sensitive parameter for the predicted NH4+-N concentration in surface runoff. In soil, the GSA results are opposite for α and p. Generally, dmix is less important than α and p, and the interaction between each two parameters reflected by the SOIs has limited importance. dmix presented sensitivity in LSA but insensitivity in GSA. Sensitivity of α showed similar results in LSA (elasticity) and GSA (TOI and FOI), which decreases in surface runoff and increases in soil. All elasticities of p increase at first and decrease gradually later, while the GSA results of p vary oppositely in surface runoff and soil.