Development and Application of a Parsimonious Snow-Hydrologic Modeling Suite: Investigating the Link Between Model Complexity and Predictive Uncertainty

The simulation and modeling of snowmelt and hydrologic drivers is desirable for prediction of different hydrologic variables, most significantly streamflow at the catchment outlet. This is particularly true of mountainous regions where snowmelt drives major hydrologic events and water resource predictability. We have developed a suite of parsimonious models of first-order snow and hydrologic processes to investigate the link between overall model complexity (both snow and hydrologic elements) and predictive performance. The use of simper models is motivated by the desire to capture first-order processes, in line with a top-down modeling philosophy. Such models have the capability to be more efficient in modeling the system by having less uncertainty with similar predictive power when compared to more complex model structures. Constructed in a modular fashion, the modeling suite has the ability to assess the interaction between each snowmelt and hydrologic base structure coupling, as well as to separate error between each component. The modeling suite was applied to the Stringer Creek watershed of Tenderfoot Creek Experimental Forest (TCEF), located in central Montana, USA. Making use of meteorological data collected at one of the two NRCS SNOTEL stations within TCEF's borders and streamflow data from the USFS Rocky Mountain Research Station (TCEF's managing agency), we compare the performance of different model combinations using 6 years of available data. Implementation of a Markov chain Monte Carlo approach to parameter estimation and uncertainty estimation provides the ability to characterize errors in the models (including non-stationarities), explore complex parameter spaces and interdependence, and incorporate multiple sources of data for model conditioning. The necessity of such abilities becomes especially critical in the application of a top-down modeling approach, where conceptual models are used that often involve highly interdependent model parameters. Further, the flexibility and design of the coupled, modular framework allows for the separation of uncertainty with regard to both snow and hydrologic process components.