A framework for propagation of uncertainty contributed by parameterization, input data, model structure, and calibration/validation data in watershed modeling

• Published in: Environmental modelling & software : with environment data news Vol. 54; pp. 211 - 221
• Main Authors: Yen, Haw, Wang, Xiuying, Fontane, Darrell G., Harmel, R. Daren, Arabi, Mazdak
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 01.04.2014; Elsevier
• Subjects: Animal, plant and microbial ecology; Bayesian Model Averaging; Biological and medical sciences; Error propagation; Fundamental and applied biological sciences. Psychology; General aspects. Techniques; hydrologic data; hydrologic models; Indiana; Methods and techniques (sampling, tagging, trapping, modelling...); Model calibration; parameter uncertainty; prediction; SWAT model; Uncertainty analysis; watershed hydrology; watersheds; Indiana; SWAT model; Model calibration; Bayesian Model Averaging; Uncertainty analysis; Error propagation; Uncertainty; Propagation; Analysis; Watershed; Error; Parameterization; Modeling
• ISSN: 1364-8152; 1873-6726
• DOI: 10.1016/j.envsoft.2014.01.004

Failure to consider major sources of uncertainty may bias model predictions in simulating watershed behavior. A framework entitled the Integrated Parameter Estimation and Uncertainty Analysis Tool (IPEAT), was developed utilizing Bayesian inferences, an input error model and modified goodness-of-fit statistics to incorporate uncertainty in parameter, model structure, input data, and calibration/validation data in watershed modeling. Applications of the framework at the Eagle Creek Watershed in Indiana shows that watershed behavior was more realistically represented when the four uncertainty sources were considered jointly without having to embed watershed behavior constraints in auto-calibration. Accounting for the major sources of uncertainty associated with watershed modeling produces more realistic predictions, improves the quality of calibrated solutions, and consequently reduces predictive uncertainty. IPEAT is an innovative tool to investigate and explore the significance of uncertainty sources, which enhances watershed modeling by improved characterization and assessment of predictive uncertainty. •Presents framework incorporating major sources of watershed modeling uncertainty.•Designed to produce more realistic simulation of actual watershed processes.•Applications should increase stakeholder, practitioner confidence in model predictions.