Evaluating model selection impacts on forecast uncertainty in the Upper Colorado River Basin: Implications for USGS modeling & monitoring

Model uncertainty analyses are critical to understanding equifinality, but opportunities remain to better understand how uncertainties in major parameter and calibration fields influence forecast uncertainty. In particular, snow accumulation and melt under the forest canopy is poorly represented in models, and soil parameters and soil moisture are poorly constrained due to a lack of fully spatially distributed observations and scaling uncertainties in soil hydraulic parameters. The proposed work will take new data-driven approaches to estimate uncertainty in subcanopy snow and soil moisture parameters, and use these new uncertainty estimates along with others in a Bayesian model uncertainty evaluation framework to determine the relative importance of a wide suite of model parameters, as well as snow and soil moisture uncertainties, to streamflow forecasting skill. The final deliverables of this research will be: (1) quantification of the key measurable sources of uncertainty in streamflow forecasts; and (2) a reproducible workflow of model parameter uncertainty estimation developed in consultation with USGS for model refinement and informing decisions concerning USGS observational networks.