Department of Land, Air and Water Resources, University of California, Davis, 1 Shields Avenue, CA 95616, USA.
National Aeronautics and Space Administration, Ames Research Center, Moffett Field, CA 94035, USA.
Water Res. 2017 Sep 15;121:374-385. doi: 10.1016/j.watres.2017.05.032. Epub 2017 May 16.
Quantifying pesticide loading into the Sacramento-San Joaquin Delta of northern California is critical for water quality management in the region, and potentially useful for biological weed control planning. In this study, the Soil and Water Assessment Tool (SWAT) was applied to model streamflow, sediment, and pesticide diuron loading in the San Joaquin watershed, a major contributing area to the elevated pesticide levels in the downstream Delta. The Sequential Uncertainty Fitting version 2 (SUFI-2) algorithm was employed to perform calibration and uncertainty analysis. A combination of performance measures (PMs) and standardized performance evaluation criteria (PEC) was applied to evaluate model performance, while prediction uncertainty was quantified by 95% prediction uncertainty band (95PPU). Results showed that streamflow simulation was at least "satisfactory" at most stations, with more than 50% of the observed data bracketed by the 95PPU. Sediment simulation was rated as at least "satisfactory" based on two PMs, and diuron simulation was judged as "good" by all PMs. The 95PPU of sediment and diuron bracketed about 40% and 30% of the observed data, respectively. Significant correlations were observed between the diuron loads, and precipitation, streamflow, and the current and antecedent pesticide use. Results also showed that the majority (>70%) of agricultural diuron was transported during winter months, when direct exposure of biocontrol agents to diuron runoff is limited. However, exposure in the dry season could be a concern because diuron is relatively persistent in aquatic system. This study not only provides valuable information for the development of biological weed control plan in the Delta, but also serves as a foundation for the continued research on calibration, evaluation, and uncertainty analysis of spatially distributed, physically based hydrologic models.
量化加利福尼亚州北部萨克拉门托-圣华金三角洲的农药负荷对于该地区的水质管理至关重要,并且对于生物杂草控制规划可能有用。在本研究中,应用土壤和水评估工具 (SWAT) 来模拟圣华金流域的水流、泥沙和农药敌草隆负荷,该流域是下游三角洲农药水平升高的主要贡献区。采用序贯不确定性拟合版本 2 (SUFI-2) 算法进行校准和不确定性分析。应用性能指标 (PM) 和标准化性能评估标准 (PEC) 的组合来评估模型性能,同时通过 95%预测不确定性带 (95PPU) 来量化预测不确定性。结果表明,大多数站点的水流模拟至少为“满意”,超过 50%的观测数据在 95PPU 范围内。基于两个 PM,泥沙模拟被评为至少“满意”,而所有 PM 都判断敌草隆模拟为“良好”。泥沙和敌草隆的 95PPU 分别约为观测数据的 40%和 30%。敌草隆负荷与降水、水流以及当前和先前的农药使用之间观察到显著相关性。结果还表明,大部分(>70%)农业敌草隆在冬季运输,此时生物防治剂直接暴露于敌草隆径流的风险有限。然而,旱季的暴露可能是一个问题,因为敌草隆在水生系统中相对持久。本研究不仅为三角洲生物杂草控制计划的制定提供了有价值的信息,而且为空间分布、基于物理的水文模型的校准、评估和不确定性分析的持续研究奠定了基础。