Optimization of sampling regimes to monitor runoff events

Abstract

Urban runoff threatens freshwater quality making it important to understand pollution sources and pathways. Measuring every pollutant in real-time is unfeasible, hence the need for in-situ grab sampling. Integrating real-time sensor data, rainfall forecasts and runoff models can optimize automated grab sampling beyond the current capabilities of autosamplers. This study evaluates two sampling strategies to optimize sampling times: (1) rainfall-volume based strategy which triggers sampling by accumulated rain volume (mm) or intensity (mm/h), (2) rainfall-runoff based strategy that triggers sampling by peak flow trigger (m³/s) estimated using a hydraulic model. Both strategies account for “no-event days”, the number of days with no runoff. Events of interest, here defined as first flush events resulting from heavy rains following consecutive days without rain, are identified. Different combinations of trigger parameters were tested, the sampling times from each strategy are evaluated based on whether the events of interest have been sampled, and the number of samples generated in those events. We highlight the potential of optimized sampling strategies in improving water quality monitoring. The ability to remotely trigger auto samplings, enables tailored measurements based on the specific data requirements or research questions.