Balancing observational data and experiential knowledge in environmental flows modeling

Abstract

Environmental flow (e-flow) decision making relies on flow-ecology models to predict ecological outcomes under different flow regimes. While expert knowledge has traditionally informed these models, there is increasing use of data-driven approaches. We investigated data integration for Bayesian conditional probability networks (CPNs) developed through expert elicitation in an e-flows assessment in Victoria, Australia. Using synthetic datasets based on monitoring data, we assessed the impact of varying data characteristics on model outcomes. Incorporating 10 years of data had the greatest influence on model predictions compared to the expert-based models, with diminishing additional effect fo..