Work

Assessment of small watershed stormwater sampling methods

Year: 2026

Type: article

Abstract: ABSTRACT Measurements of contaminants in runoff are essential for stormwater management. Sampling accuracy is difficult to assess due to the cost and labor of field studies and the large variation in runoff behavior between sites, rain events, and pollutants. Stormwater sampling requires autosamplers to be programmed based on weather forecasts to accurately sample events, and forecasting errors will affect results in ways that have not been thoroughly examined. We developed an easily interpretable idealized model for runoff pollution and applied the model to sampling methods for a variety of typical conditions observed in small watersheds with moderate to high runoff potential. We examined forecast errors and found a large impact on sampler accuracy, in some situations leading to biases in concentration estimates of greater than 20% along with substantial uncertainty. We found time-paced samplers to generally underestimate concentrations. We analyzed sampler robustness to pollutant and watershed parameters and found that runoff volume had a large effect on sampler accuracy. We developed a novel sampler timing approach that outperformed conventional strategies for overforecast events, and a novel cost-free change to sample compositing that can improve accuracy for underforecast events.

Source: Water Science & Technology

DOI: 10.2166/wst.2026.335

Authors: Daniel Furuta, Jacques C. Finlay, Jiayu Li, Bruce Wilson

Institutions: University of Minnesota, University of Miami

Language: English

Topic: Urban Stormwater Management Solutions

Subfield: Environmental Engineering

Field: Environmental Science

Domain: Physical Sciences

SDG: Clean water and sanitation

Open Access status: diamond

Funder: Water Resources Center, University of Minnesota