Uncertainty

Engaging Communities to Advance Hydrologic Science (ECAHS)

(2019-2020) - Engaging Communities to Advance Hydrologic Science (ECAHS) Objectives: (a) to develop a participatory modeling framework that leverages established, high-level Bayesian techniques to explore the utility of data-based informative prior distributions in constraining hydrologic model performance and process fidelity; (b) to investigate the efficacy of community engagement and citizen science in hydrologic modeling through the use of hierarchical approaches within the Bayesian framework by assimilating multiple data sources (including qualitative, ‘soft’ information) into model forecasting; and c) to advance the understanding of hydrology as a topic of global importance through collaborative engagement across multiple project stakeholder groups (citizens, teachers, students) to transform the way hydrologic modeling is conducted.

A Bayesian Approach to Incorporate Imprecise Information on Hydraulic Knowledge in a River Reach and Assess Prediction Uncertainties in Streamflow Data

This paper presents a fully Bayesian model capable of incorporating imprecise knowledge on the hydraulic behavior of the river, when available, to estimate the uncertainties in the daily streamflow data.