A conceptual precipitation-runoff modeling suite: Model selection, calibration and predictive uncertainty assessment

Developing an understanding of the dominant controls on runoff processes at the catchment scale is vital to the effectiveness of model predictions. This is especially true for watersheds that receive precipitation predominantly as snowfall. In these regions, the water responsible for generating a majority of the streamflow is stored in the snowpack and released during a rapid melt period. Recognizing the uncertainty that exists in the process of selecting one model structure over another, a conceptual precipitation-runoff modeling framework featuring a modular construction has been developed. The Simulation and Prediction Lab for Analysis of Snowmelt Hydrology (SPLASH), a companion graphical user interface, was developed to encompass all of the components of the modeling framework. SPLASH incorporates the tools necessary for implementing the framework, including model calibration, simulation, and uncertainty analysis, while also providing the tools necessary to perform model analysis including generating data files of model output and plotting tools for uncertainty analysis. The modular construction of the modeling approach allowed for the inclusion of a suite of competing component structures; namely, a soil moisture accounting and runoff routing component, a snowmelt accounting component, and a semi-distribution component (for the precipitation inputs). A total of thirty modular structures were investigated, representing the total number of possible combinations of the included components. The framework was applied to a test location, the Stringer Creek watershed, located in the Tenderfoot Creek Experimental Forest (TCEF) of Central Montana, USA. Stringer Creek watershed is heavily influenced by snowfall, accounting for more than 75% of the total yearly precipitation. The use of minimally parameterized 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. Because of the conceptual nature of the framework, some form of model calibration was necessary to obtain appropriate parameter approximations. In this case, a Bayesian inferential approach was adopted and implemented via the Delayed Rejection Adaptive Metropolis (DRAM) algorithm, a Markov chain Monte Carlo (MCMC) approach. The application of a Bayesian approach provides a statistical means of estimating uncertainty associated with the parameters, while also performing parameter estimation. This research focused on developing a greater understanding of the effect of model composition through identifying the benefit of each component on the model prediction. The modular structures were calibrated over a four year period and tested during a separate two year period. The benefits and limitations of this conceptual catchment modeling framework have been addressed through a focus on the three elements of model performance (fit to streamflow), uncertainty, and 'realism' (fit to snow water equivalents). Further consideration was also focused on the trade-off between model structural complexity and predictive performance. The application of the modeling framework indicated model performance improved with the incorporation of a probability distributed soil moisture accounting approach. Additionally, the importance of incorporating net radiation into the snowmelt accounting method greatly enhanced overall model performance (simulation of stream discharge), as well as the simulation of the internal dynamics of snow water equivalents. Beyond enabling the identification of model components that reproduce stream discharge well, the modeling framework also assisted in identifying specific traits of model components that led to the favorable simulations they generated.