A catchment management optimization approach to mitigate rainfall induced pesticide contamination in water supply systems

Abstract

Surface water resources are under increasing strain from non-point pollution such as pesticides. Simulation based spatial land use optimization can be used to prioritize mitigation interventions within catchments. However, currently available methods rely on simulations at relatively coarse temporal scales which may not be appropriate when considering implications for water supply/abstraction systems. This paper develops an event-based distributed simulation and genetic algorithm optimization methodology which is compatible with fully distributed models accounting for high resolution, sub daily acute rainfall driven impacts. This is enabled by the use of a novel methodology to efficiently sample rainfall events for optimization purposes. Within the case study catchment, the methodology is used to identify targeted interventions of up to 5% of the catchment area to reduce the duration that the target pesticide remains above the threshold target for UK/EU drinking water. The proposed intervention is then evaluated based on historical rainfall records and compared against alternate intervention strategies, reducing the duration above threshold by 7.7% and significantly outperforming alternate random and clustered intervention strategies. The outputs demonstrate the complexities of the interactions between water quality dynamics, objective function and target thresholds at this temporal scale. It is anticipated the proposed approach may be utilized by catchment managers to effectively target catchment interventions to reduce pesticide contamination risks to water resource systems.