A unified approach for process‐based hydrologic modeling: 2. Model implementation and case studies. Issue 4 (20th April 2015) - Clark, Martyn P.; Nijssen, Bart; Lundquist, Jessica D.; Kavetski, Dmitri; Rupp, David E.; Woods, Ross A.; Freer, Jim E.; Gutmann, Ethan D.; Wood, Andrew W.; Gochis, David J.; Rasmussen, Roy M.; Tarboton, David G.; et al.
Abstract: This work advances a unified approach to process‐based hydrologic modeling, which we term the "Structure for Unifying Multiple Modeling Alternatives (SUMMA)." The modeling framework, introduced in the companion paper, uses a general set of conservation equations with flexibility in the choice of process parameterizations (closure relationships) and spatial architecture. This second paper specifies the model equations and their spatial approximations, describes the hydrologic and biophysical process parameterizations currently supported within the framework, and illustrates how the framework can be used in conjunction with multivariate observations to identify model improvements and future research and data needs. The case studies illustrate the use of SUMMA to select among competing modeling approaches based on both observed data and theoretical considerations. Specific examples of preferable modeling approaches include the use of physiological methods to estimate stomatal resistance, careful specification of the shape of the within‐canopy and below‐canopy wind profile, explicitly accounting for dust concentrations within the snowpack, and explicitly representing distributed lateral flow processes. Results also demonstrate that changes in parameter values can make as much or more difference to the model predictions than changes in the process representation. This emphasizes that improvements in model fidelity require a sagacious choice of both process parameterizations and model parameters. In conclusion, we envisage that SUMMA can facilitate ongoing model development efforts, the diagnosis and correction of model structural errors, and improved characterization of model uncertainty. Key Points: Flexible model implementation enables evaluation of key modeling decisions Case studies illustrate capabilities to identify preferable modeling approaches Accelerates improvements in model fidelity & uncertainty characterization