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.

Population growth and climate change will combine to pose substantial challenges for water management in the United States. Projections of water supply and demand over the 21st century show that in the absence of further adaptation efforts, serious water shortages are likely in some regions. Continued improvements in water use efficiency are likely but will be insufficient to avoid future shortages. Some adaptation measures that have been effective in the past, most importantly large additions to reservoir storage, have little promise. Other major adaptations commonly used in the past, especially instream flow removals and groundwater mining, can substantially lower shortages but have serious external costs. If those costs are to be avoided, transfers from irrigated agriculture probably will be needed and could be substantial.

[1] Turbulent fluxes of sensible and latent heat are important processes in the surface energy balance that drives snowmelt. Modeling these fluxes in a forested environment is complicated because of the canopy effects on the wind field. This paper presents and tests a turbulent flux model developed to represent these processes in an energy balance snowmelt model. The goal is to model these processes using the readily available inputs of canopy height and leaf area index in a way that minimizes the number of parameters, state variables, and assumptions about hard to quantify processes. Selected periods from 9 years of eddy-covariance (EC) measurements at Niwot Ridge, Colorado, were used to evaluate the effectiveness of this modeling approach. The model was able to reproduce the abovecanopy sensible and latent heat fluxes reasonably with the correlation higher for sensible heat than latent heat. The modeled values of the below-canopy latent heat fluxes also matched the EC-measured values. The model captured the nighttime below-canopy sensible heat flux quite well, but there were discrepancies in daytime sensible heat flux possibly due to mountain slope circulation not quantifiable in this kind of model. Despite the uncertainties in the below-canopy sensible heat fluxes, the results are encouraging and suggest that reasonable predictions of turbulent flux energy exchanges and subsequent vapor losses from snow in forested environments can be obtained with a parsimonious single-layer representation of the canopy. The model contributes an improved physically based capability for predicting the snow accumulation and melt in a forested environment.

[1] To better estimate the radiation energy within and beneath the forest canopy for energy balance snowmelt models, a two stream radiation transfer model that explicitly accounts for canopy scattering, absorption and reflection was developed. Upward and downward radiation streams represented by two differential equations using a single path assumption were solved analytically to approximate the radiation transmitted through or reflected by the canopy with multiple scattering. This approximation results in an exponential decrease of radiation intensity with canopy depth, similar to Beer's law for a deep canopy. The solution for a finite canopy is obtained by applying recursive superposition of this two stream single path deep canopy solution. This solution enhances capability for modeling energy balance processes of the snowpack in forested environments, which is important when quantifying the sensitivity of hydrologic response to input changes using physically based modeling. The radiation model was included in a distributed energy balance snowmelt model and results compared with observations made in three different vegetation classes (open, coniferous forest, deciduous forest) at a forest study area in the Rocky Mountains in Utah, USA. The model was able to capture the sensitivity of beneath canopy net radiation and snowmelt to vegetation class consistent with observations and achieve satisfactory predictions of snowmelt from forested areas from parsimonious practically available information. The model is simple enough to be applied in a spatially distributed way, but still relatively rigorously and explicitly represent variability in canopy properties in the simulation of snowmelt over a watershed.

Abstract:Recent improvements in the Utah Energy Balance (UEB) snowmelt model are focused on snow-vegetation-atmosphere interactions to understand how different types of vegetation affect snow processes in the mountains of Western USA. This work presents field work carried out in the Rocky Mountains of Northern Utah to evaluate new UEB model algorithms that represent the processes of canopy snow interception, sublimation, mass unloading and melt. Four years' continuous field observations showed generally smaller accumulations of snow beneath the forest canopies in comparison with open (sage and grass) areas, a difference that is attributed to interception and subsequent sublimation and redistribution of intercepted snow by wind, much of it into surrounding open areas. Accumulations beneath the denser forest (conifer) canopies were found to be less than the accumulation beneath the less dense forest (deciduous) canopies. The model was able to represent the accumulation of snow water equivalent in the open and beneath the deciduous forest quite well but without accounting for redistribution tended to overestimate the snow water equivalent beneath the conifer forest. Evidence of redistribution of the intercepted snow from the dense forest (i.e. conifer forest) to the adjacent area was inferred from observations. Including a simple representation of redistribution in the model gave satisfactory prediction of snow water equivalent beneath the coniferous forest. The simulated values of interception, sublimation and unloading were also compared with previous studies and found in agreement.