Water quality monitoring data, specifically pesticides, can represent best-available exposure profiles related to ecological risk assessment; however, there are challenges in synthesizing these data toward making strong conclusions about the nature of the potential range of risk.  Some of these challenges include: data collection frequency, monitoring period duration, interpreting exposure profiles from one location to another, and monitoring system scale.  To address these challenges, statistical approaches may be applied to characterize empirical trends.  Additionally, process-based numerical systems modeling approaches can offer a different perspective on synthesis and interpretation of monitoring data.  A comparison of a statistical and process-based numerical model was conducted to evaluate strengths and weaknesses of representing, synthesizing, and conclusions from monitoring data.  This comparison was developed from pesticide measurements of six intensively-monitored HUC12 headwater watersheds in the Midwest. SEAWAVE-Q is a regression model that incorporates a linear trend term, covariates accounting for seasonality, and a transformation of flow to represent a long-term pesticide trend at a specific monitoring location. PRZM/SWAT (pesticide root zone model/soil water assessment tool) is a spatially-distributed hydrologic and chemical transport numerical model that combines upland chemical and hydrologic processes from PRZM and stream flow and chemical transport processes from SWAT.  The predictive quality and limitations of these two models was assessed against observed, daily concentration measurements as well as hypothetical data collection frequency and timing (derived from sub-sampling the same data sets).  Results suggest that a process-based modeling approach, such as PRZM/SWAT, may be more advantageous when calibration data are available.

Interpreting water quality monitoring observations through modeling: PRZM/SWAT and SEAWAVE-Q

256th National Meeting (2018)

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