Abstract. For approximately 20 years, there has been a concerted effort, by several different research groups, to simulate observed rainfall-runoff events from the well-known R-5 catchment, located near Chickasha, Oklahoma. These prior simulation efforts, with relatively simple models of Horton-type overland flow, have not been entirely successful, as the streamflow generation process for the R-5 catchment, as now recognized, may not be totally dominated by the Horton mechanism. In the effort reported here, a new fully coupled comprehensive physics-based hydrologic-response model, the Integrated Hydrology Model (InHM), is tested for two R-5 rainfall-runoff events. The InHM simulations in this study clearly show, in a hypothesis-testing mode, that both the Horton and Dunne overland flow mechanisms can be important streamflow generation processes for R-5 events. The InHM simulations reported here also suggest that accurate accounting of soil water storage can be as important as exhaustive characterization of spatial variations in near-surface permeability.

Distributed hydrologic models capable of simulating fully-coupled surface water and groundwater flow are increasingly used to examine problems in the hydrologic sciences. Several techniques are currently available to couple the surface and subsurface; the two most frequently employed approaches are first-order exchange coefficients (a.k.a., the surface conductance method) and enforced continuity of pressure and flux at the surface-subsurface boundary condition. The effort reported here examines the parameter sensitivity of simulated hydrologic response for the first-order exchange coefficients at a well-characterized field site using the fully coupled Integrated Hydrology Model (InHM ). This investigation demonstrates that the first-order exchange coefficients can be selected such that the simulated hydrologic response is insensitive to the parameter choice, while simulation time is considerably reduced. Alternatively, the ability to choose a firstorder exchange coefficient that intentionally decouples the surface and subsurface facilitates concept-development simulations to examine real-world situations where the surface-subsurface exchange is impaired. While the parameters comprising the first-order exchange coefficient cannot be directly estimated or measured, the insensitivity of the simulated flow system to these parameters (when chosen appropriately) combined with the ability to mimic actual physical processes suggests that the first-order exchange coefficient approach can be consistent with a physics-based framework.Enforcing simultaneous continuity of pressure between the surface and subsurface, typically accomplished implicitly using iterative boundary condition matching, is an alternative to the first-order exchange coefficient approach, predating it by decades

The comprehensive physics-based hydrologic-response model InHM was used to simulate 3D variably-saturated flow and solute transport for three controlled sprinkling experiments at the Coos Bay 1 (CB1) experimental catchment in the Oregon Coast Range. The InHM-simulated hydrologic-response was evaluated against observed discharge, pressure head, total head, soil-water content, and deuterium concentration records. Runoff generation, tensiometric/piezometric response in the soil, pore-water pressure generation, and solute (tracer) transport were all simulated well, based on statistical and graphical model performance evaluation. The InHM simulations reported herein indicate that the 3D geometry and hydraulic characteristics of the layered geologic interfaces at CB1 can control the development of saturation and pore-water pressures at the soil-saprolite interface. The weathered bedrock piezometric response and runoff contribution were not simulated well with InHM in this study, most likely as a result of the uncertainty in the weathered bedrock layer geometry and fractured-rock hydraulic properties that preclude accurate fracture flow representation. Sensitivity analyses for the CB1 boundary-value problem indicate that: (i) hysteretic unsaturated flow in the CB1 soil is important for accurate hydrologicresponse simulation, (ii) using an impermeable boundary condition to represent layered geologic interfaces leads to large errors in simulated magnitudes of runoff generation and pore-water pressure development, and (iii) field-based retention curve measurements can dramatically improve variably-saturated hydrologic-response simulation at sites with steep soil-water retention curves. The near-surface CB1 simulations reported herein demonstrate that physics-based models like InHM are useful for characterizing detailed spatio-temporal hydrologic-response, developing processbased concepts, and identifying information shortfalls for the next generation of field experiments. The field-based observations and hydrologic-response simulations from CB1 highlight the challenges in characterizing/simulating fractured bedrock flow at small catchments, which has important consequences for hydrologic response and landslide initiation.

The aim of a model is, of course, precisely not to reproduce reality in all its complexity. It is rather to capture in a vivid, often formal, way what is essential to understanding some aspect of its structure or behavior. . .. We select, for inclusion in our model, those features of reality that we consider to be essential to our purpose . . . the ultimate criteria, being based on intentions and purposes as they must be, are finally determined by the individual, that is, human, modeler. Joseph Weizenbaum (1976)There are Lots (and Lots) of Rainfall-Runoff Models Clarke (1973) generalizes a mathematical rainfall-runoff model bywhere p t are the input variables, q t are the output variables, a n are the system parameters, x t is the residual error, and f is the functional form of the model. Encoded in this relationship is a fundamental distinction between model elements (i.e. variables change with time, parameters remain constant). The functional form of the relationship can be conceptual or empirical; the input and output variables, as well as the system parameters and residual error, can be either stochastic or deterministic. A model is stochastic if any of the variables are described by a probability distribution; it is deterministic if all the variables are (viewed as) free from random variations. Models are conceptual if their functional form is derived from consideration of physical processes, and empirical if not. Clarke (1973) categorizes mathematical models as stochastic-conceptual, stochastic-empirical, deterministicconceptual, or deterministic-empirical. In the last half-century there have been hundreds (if not thousands) of hydrologic-response models, each with their own attributes and shortcomings, developed by researchers, students, and consultants, covering the entire spectrum of Clarke's classification. It is important to recognize that not all of these models were created equally. The relatively simple empirical models, whose system parameters cannot (most often) be measured in the field, perform successfully only within a calibrated range. Many conceptual models have been developed around a single process (e.g. the Horton mechanism). A typical example of the misuse of the single-process