Tracing Non-Point Source Pollution Signals with Accelerated Causal Inference

Effective management of water quality requires rigorous scientific understanding of the causal mechanisms driving changes over time, but reliably inferring and modeling these mechanisms is challenging due to the diffuse nature of non-point source pollution and the dynamic complexity of pollution and its downstream effects. Recently developed empirical methods based on nonlinear dynamics theory offer a reliable means of inferring causality by tracing downstream conditions (e.g., eutrophication) to upstream pollution sources, to guide targeted investments in restoration projects and water infrastructure upgrades. Studies published by the PI demonstrate the methods’ scientific validity and practical utility in systems whose flows are controlled by engineered structures, but the method is untested in naturally flowing rivers (unregulated flow). During Phase I, ECCO Scientific will evaluate the validity of the methodology in this setting, through a case study benchmarked against existing results of a conventional method (watershed modeling or stable isotope tracing). Further, existing software implementations lack a cohesive, computationally efficient, quality-controlled workflow conducive to performing causal analyses on a large scale. During Phase I, ECCO Scientific will evaluate the technical benefits of optimizing the code base for parallel execution on a graphics processing unit (GPU), by estimating the associated reduction in execution time.