Surface‐subsurface flow modeling with path‐based runoff routing, boundary condition‐based coupling, and assimilation of multisource observation data

Abstract

[1] A distributed physically based model incorporating novel approaches for the representation of surface-subsurface processes and interactions is presented. A path-based description of surface flow across the drainage basin is used, with several options for identifying flow directions, for separating channel cells from hillslope cells, and for representing stream channel hydraulic geometry. Lakes and other topographic depressions are identified and specially treated as part of the preprocessing procedures applied to the digital elevation data for the catchment. Threshold-based boundary condition switching is used to partition potential (atmospheric) fluxes into actual fluxes across the land surface and changes in surface storage, thus resolving the exchange fluxes, or coupling, between the surface and subsurface modules. Nested time stepping allows smaller steps to be taken for typically faster and explicitly solved surface runoff routing, while a mesh coarsening option allows larger grid elements to be used for typically slower and more compute-intensive subsurface flow. Sequential data assimilation schemes allow the model predictions to be updated with spatiotemporal observation data of surface and subsurface variables. These approaches are discussed in detail, and the physical and numerical behavior of the model is illustrated over catchment scales ranging from 0.0027 to 356 km 2 , addressing different hydrological processes and highlighting the importance of describing coupled surfacesubsurface flow.Citation: Camporese, M., C. Paniconi, M. Putti, and S. Orlandini (2010), Surface-subsurface flow modeling with path-based runoff routing, boundary condition-based coupling, and assimilation of multisource observation data, Water Resour.

Incipient subsurface heterogeneity and its effect on overland flow generation – insight from a modeling study of the first experiment at the Biosphere 2 Landscape Evolution Observatory

“…We use the CATHY (CATchment HYdrology) model (Camporese et al, 2010) to simulate the partitioning of rainfall between runoff and infiltration, the subsurface redistribution of soil moisture and groundwater, and the discharge through the LEO seepage face. The subsurface flow module in CATHY solves the 3-D Richards equation describing flow in variably saturated porous media (Paniconi and Putti, 1994), while the surface flow module solves the diffusion wave equation describing surface flow propagation over hillslopes and in stream channels identified using terrain topography and the hydraulic geometry concept (Orlandini and Rosso, 1998).…”

Improving the representation of hydrologic processes in Earth System Models

“…With this method, the water table simulated by the saturated zone is consistent with the soil moisture profile. This class of methods can dynamically describe return flow, regional groundwater circulation, groundwater discharge, and saturation excess runoff [see also Camporese et al, 2009;Camporese et al, 2010].…”

HESS Opinions: The complementary merits of competing modelling philosophies in hydrology

“…The red dots indicate the two endpoints along the resolutioncomplexity continuum. Models: 1: unit hydrograph (Sherman, 1932); 2: HBV (Bergström, 1992); 3: SUPERFLEX ; 4: FLEX-Topo 5: mhM (Samaniego et al, 2010); 6: mhM-topo (Nijzink et al, 2016a); 7: SWAT (Arnold et al, 1998); 8: NWS-Sacramento (Burnash, 1995); 9: GR4J (Perrin et al, 2003); 10: HYPE (Lindström et al, 2010); 11: VIC (Liang et al, 1994); 12: TOPMODEL (Beven and Kirkby, 1979); 13: CRHM; 14: TAC D (Uhlenbrook et al, 2004); 15: WASIM-ETH (Schulla and Kasper, 1998); 16: DHSVM (Wigmosta et al, 1994); 17: MIKE-SHE (Refsgaard and Storm, 1996); 18: PARFLOW ; 19: CATFLOW (Zehe et al, 2001); 20: HYDRUS-3D (Šimůnek et al, 2008); 21: CATHY (Camporese et al, 2010); 22: HydroGeoSphere (Jones et al, 2006); 23: PIHM (Qu and Duffy, 2007). and unambiguous terminology may be one of the reasons for many misunderstandings between the different model communities. We therefore think that a somewhat more rigorous model taxonomy needs to be the first step to clarify these misunderstandings and to pave the way for increased convergence of the individual modelling strategies.…”

“…Bottom-up approaches are typically accomplished using distributed and physically and continuum based models (e.g. Kollet and Maxwell, 2008;Kumar et al, 2009;Camporese et al, 2010;Kollet et al, 2010;Maxwell et al, 2015;Piras et al, 2014), but strictly speaking, any kind of prediction or virtual experiment is necessarily a bottom-up approach.…”