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

TLDR

The study presents a distributed physically based model that introduces novel approaches for representing surface‑subsurface processes and their interactions. The model employs a path‑based surface flow description with options for flow direction and channel geometry, treats lakes as special topographic depressions, uses threshold‑based boundary condition switching to partition fluxes, applies nested time stepping and mesh coarsening to balance surface and subsurface computations, and incorporates sequential data assimilation to update predictions with spatiotemporal observations. The model’s physical and numerical behavior was demonstrated across catchment scales from 0.0027 to 356 km², showing its ability to capture diverse hydrological processes and underscoring the importance of coupled surface‑subsurface flow representation.

Abstract

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 surface‐subsurface flow.