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 for longer records. Notably, the expert model based on the most non-conforming expert opinion was more sensitive to data integration than a model based on harnessing the opinion of all experts, highlighting the importance of considering diverse experiential knowledge. We provide recommendations on leveraging limited datasets and models to guide efficient monitoring and management.

环境流（e-flow）决策依赖于流生态模型来预测不同流态下的生态结果。虽然传统上专家知识为这些模型提供了信息，但数据驱动方法的使用越来越多。我们研究了在澳大利亚维多利亚州电子流评估中通过专家启发开发的贝叶斯条件概率网络 (CPN) 的数据集成。使用基于监测数据的合成数据集，我们评估了不同数据特征对模型结果的影响。与基于专家的模型相比，纳入 10 年的数据对模型预测的影响最大，而记录时间越长，附加效应就越小。值得注意的是，基于最不一致的专家意见的专家模型比基于所有专家意见的模型对数据集成更敏感，凸显了考虑多样化经验知识的重要性。我们提供有关利用有限数据集和模型来指导有效监控和管理的建议。