Application of the ReNuMa model in the Sha He river watershed: tools for watershed environmental management.
Application of the ReNuMa model in the Sha He river watershed: tools for watershed environmental management.
复制标题
DOI：
10.1016/j.jenvman.2013.03.030
复制
发表时间：
2013-07
影响因子：
8.7
通讯作者：
Jian Sha;Min Liu;Dong Wang;D. Swaney;Yuqiu Wang
中科院分区：
文献类型：
--
作者：
Jian Sha;Min Liu;Dong Wang;D. Swaney;Yuqiu Wang
模型和相关的分析方法是现代流域管理的重要工具。以沙河流域为例，研究了溶解氮源解析的定量模拟方法和相关工具，并对模型估算值的敏感性和不确定性进行了评价。区域养分管理模型（ReNuMa）被用来推断在SHR流域DN的主要来源。该模型是基于广义流域负荷函数（GWLF）和净人为营养输入（NANI）框架，修改，以提高地下水文和化粪池系统负荷的表征。水化学过程的SHR流域，包括径流，DN负荷通量，和相应的DN浓度响应，模拟以下校准对径流和DN通量的观测。采用蒙特卡罗分析进行不确定性分析，以改变模型参数，从而评估模型输出的相关变化。该模型准确地执行在流域尺度，并提供了每月径流量和营养负荷以及DN源分配的估计。模拟确定了农业土地利用的主要贡献和显着的月度变化。这些结果为基于科学的流域管理决策提供了有价值的支持，并表明ReNuMa在此类应用中的实用性。
Models and related analytical methods are critical tools for use in modern watershed management. A modeling approach for quantifying the source apportionment of dissolved nitrogen (DN) and associated tools for examining the sensitivity and uncertainty of the model estimates were assessed for the Sha He River (SHR) watershed in China. The Regional Nutrient Management model (ReNuMa) was used to infer the primary sources of DN in the SHR watershed. This model is based on the Generalized Watershed Loading Functions (GWLF) and the Net Anthropogenic Nutrient Input (NANI) framework, modified to improve the characterization of subsurface hydrology and septic system loads. Hydrochemical processes of the SHR watershed, including streamflow, DN load fluxes, and corresponding DN concentration responses, were simulated following calibrations against observations of streamflow and DN fluxes. Uncertainty analyses were conducted with a Monte Carlo analysis to vary model parameters for assessing the associated variations in model outputs. The model performed accurately at the watershed scale and provided estimates of monthly streamflows and nutrient loads as well as DN source apportionments. The simulations identified the dominant contribution of agricultural land use and significant monthly variations. These results provide valuable support for science-based watershed management decisions and indicate the utility of ReNuMa for such applications.