A digital twin model for contaminant fate and transport in urban and natural drainage networks with online state estimation

Abstract

Increased pollutant loads caused by urbanization and climate change have led to widespread impairment of surface water systems. To better manage these threats, water managers are seeking digital twins that combine online models with sensor data to respond to water quality hazards in real-time. This study introduces Pipedream-WQ, a new model for contaminant transport in drainage networks that combines a novel implicit solver for the unsteady advection–reaction–diffusion (ARD) equation with an efficient data assimilation scheme based on Kalman Filtering. We show that this solver reliably reproduces analytical solutions to the ARD equation in steady conditions, and accurately captures unsteady contaminant transport behavior in a complex drainage network. Furthermore, we show that online sensor data assimilation enables better estimation of contaminant concentrations at ungauged locations compared to a model-only approach. This model will enable improved pollutant tracking and source identification, and active water quality management through real-time control of hydraulic infrastructure.