Evaluation of streamflow as a covariate in models for predicting daily pesticide concentrations

IF 2.6 4区 环境科学与生态学 Q3 ENGINEERING, ENVIRONMENTAL Journal of The American Water Resources Association Pub Date : 2023-06-12 DOI:10.1111/1752-1688.13138
Paul L. Mosquin, Jeremy Aldworth, Wenlin Chen, Shanique Grant
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Abstract

Several models have been developed with streamflow as a covariate for predicting daily pesticide concentrations in surface water systems. Among these models, the SEAWAVE-QEX model has been proposed by the United States Environmental Protection Agency for regulatory assessments. In this paper, the model was modified to include alternative transformations of streamflow data, and to include no streamflow covariates. The predictive performance of the modified models was evaluated and compared with the original SEAWAVE-QEX model using a high frequency sampling dataset that includes 9 sites with 10 years of data from the Atrazine Ecological Monitoring Program. Streamflow transformations evaluated included those in the SEAWAVE-QEX model (short-term and mid-term flow anomalies), reduced models with only short-term flow anomaly or without any flow covariates, normalized Box-Cox transformation of flow, and combinations of normalized Box-Cox and flow anomalies. Loglinear interpolation was also evaluated. The normalized Box-Cox transformation provided best predictive performance and significantly better predictive performance than that of the SEAWAVE-QEX model for a target quantity of regulatory interest, such as the maximum 1-day rolling average (similarly for the maximum 60-day rolling average, but not significantly so). The no-flow covariate model was only slightly worse than Box-Cox. Significant differences in predictive performance of the SEAWAVE-QEX model were detected across sites.

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在预测每日农药浓度的模型中评估作为协变量的河水流量
目前已开发出多个以溪流作为协变量的模型,用于预测地表水系统中农药的日浓度。在这些模型中,SEAWAVE-QEX 模型已被美国环境保护局提议用于监管评估。在本文中,对该模型进行了修改,以包括对溪流数据的其他转换,并不包括溪流协变量。使用高频采样数据集对修改后模型的预测性能进行了评估,并与原始的 SEAWAVE-QEX 模型进行了比较,高频采样数据集包括阿特拉津生态监测计划中 9 个站点的 10 年数据。评估的流量变换包括 SEAWAVE-QEX 模型(短期和中期流量异常)、仅有短期流量异常或无任何流量协变量的简化模型、归一化 Box-Cox 流量变换以及归一化 Box-Cox 和流量异常的组合。此外,还对对数插值法进行了评估。归一化 Box-Cox 变量的预测性能最好,对于监管关注的目标量(如最大 1 天滚动平均值),其预测性能明显优于 SEAWAVE-QEX 模型(对于最大 60 天滚动平均值也是如此,但不明显)。无流量协变量模型仅略逊于 Box-Cox 模型。在不同地点,SEAWAVE-QEX 模型的预测性能存在显著差异。
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来源期刊
Journal of The American Water Resources Association
Journal of The American Water Resources Association 环境科学-地球科学综合
CiteScore
4.10
自引率
12.50%
发文量
100
审稿时长
3 months
期刊介绍: JAWRA seeks to be the preeminent scholarly publication on multidisciplinary water resources issues. JAWRA papers present ideas derived from multiple disciplines woven together to give insight into a critical water issue, or are based primarily upon a single discipline with important applications to other disciplines. Papers often cover the topics of recent AWRA conferences such as riparian ecology, geographic information systems, adaptive management, and water policy. JAWRA authors present work within their disciplinary fields to a broader audience. Our Associate Editors and reviewers reflect this diversity to ensure a knowledgeable and fair review of a broad range of topics. We particularly encourage submissions of papers which impart a ''take home message'' our readers can use.
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