Assessing the detail needed to capture rainfall‐runoff dynamics with physics‐based hydrologic response simulation

Abstract

[1] Concept development simulation with distributed, physics-based models provides a quantitative approach for investigating runoff generation processes across environmental conditions. Disparities within data sets employed to design and parameterize boundary value problems used in heuristic simulation inevitably introduce various levels of bias. The objective was to evaluate the impact of boundary value problem complexity on process representation for different runoff generation mechanisms. The comprehensive physics-based hydrologic response model InHM has been employed to generate base case simulations for four well-characterized catchments. The C3 and CB catchments are located within steep, forested environments dominated by subsurface stormflow; the TW and R5 catchments are located in gently sloping rangeland environments dominated by Dunne and Horton overland flows. Observational details are well captured within all four of the base case simulations, but the characterization of soil depth, permeability, rainfall intensity, and evapotranspiration differs for each. These differences are investigated through the conversion of each base case into a reduced case scenario, all sharing the same level of complexity. Evaluation of how individual boundary value problem characteristics impact simulated runoff generation processes is facilitated by quantitative analysis of integrated and distributed responses at high spatial and temporal resolution. Generally, the base case reduction causes moderate changes in discharge and runoff patterns, with the dominant process remaining unchanged. Moderate differences between the base and reduced cases highlight the importance of detailed field observations for parameterizing and evaluating physics-based models. Overall, similarities between the base and reduced cases indicate that the simpler boundary value problems may be useful for concept development simulation to investigate fundamental controls on the spectrum of runoff generation mechanisms.Citation: Mirus, B. B., B. A. Ebel, C. S. Heppner, and K. Loague (2011), Assessing the detail needed to capture rainfall-runoff dynamics with physics-based hydrologic response simulation, Water Resour. Res., 47, W00H10,

Salt marsh ecohydrological zonation due to heterogeneous vegetation–groundwater–surface water interactions

“…The initial head of 3 m above mean sea level placed about 2 m of water on the marsh surface and so ensured complete flooding and saturation prior to the development of partially drained conditions. This approach to developing initial conditions is consistent with established groundwater modeling practice [e.g., Loague and VanderKwaak, 2002;Mirus et al, 2011]. This initial simulation period may be intuitively thought of in this case as if simulating a simplified (nonoscillatory), neap tide period immediately prior to testing subsequent model scenarios.…”

A model-based assessment of the potential use of compound-specific stable isotope analysis in river monitoring of diffuse pesticide pollution

“…To this end, we applied a distributed physically based model, which allowed for the simulation of hydrological processes in different flow domains in a detailed and spatially explicit way (Kampf and Burges, 2007) and the description of pesticide fluxes and concentrations (Thorsen et al, 1996). This study thus ties in with the virtual experiment approach in hillslope hydrology of Weiler and McDonnell (2004), Mirus et al (2011), and additionally considers reactive transport and isotope fractionation. We opted for a hillslope transect because hillslopes are a fundamental landscape element and form the basic hydrological unit of catchments.…”

“…The hillslope model therefore consists of three zones with different properties: the topsoil extends 0.3 m below the ground surface, the subsoil is located between 0.3 and 2 m below the ground surface, and the remaining part of the subsurface represents the bedrock. A similar layering was used for the numerical simulation of runoff generation mechanisms in different catchments by Mirus et al (2011). The saturated hydraulic conductivity for the three subsurface zones was set as follows: 1.0 m d −1 in the topsoil, 0.5 m d −1 in the subsoil, and 0.1 m d −1 in the bedrock (Table 1).…”

“…McDonnell, 2009, 2011;James et al, 2010;Mirus et al, 2011;Mirus and Loague, 2013), we did not include preferential flow in the simulations. We anticipate that vertical preferential flow leads to a faster transition of water through the soil, and thus a decrease in the extent of degradation in the soil layers.…”

“…Previous HGS simulations of the Borden site used uniform soil parameters and only considered the upper 4 m of the profile (Therrien et al, 2010), but we preferred to explicitly represent the bedrock. The bedrock parameters were therefore taken from simulations of the forested Coos Bay site in Oregon, USA (Mirus et al, 2011). They represent weathered bedrock and fractured sandstone.…”