Upscaling Hillslope‐Scale Subsurface Flow to Inform Catchment‐Scale Recession Behavior

Abstract

Hydrologic models are vital tools for understanding the complex interaction of water fluxes throughout a basin. In a hydrologic model, each hydrologic process should ideally be characterized with the objective of generating a cohesive understanding of the sources, sinks, fluxes, and stores of water within the basin. In this work, we focus on the characterization of subsurface flow in hydrologic models. The fundamental mass balance algorithms of subsurface flow depend strongly on the heterogeneity of the subsurface, and yet, integrated at the basin scale, the subsurface has been successfully characterized as a lumped reservoir that releases water according to an abstracted, prescribed relationship, such as a power law relationship (Tallaksen, 1995). This simplification of complex, heterogeneous subsurface physics into a convenient abstracted lumped reservoir deserves investigation: Can the physics of subsurface flow be used to inform the simplified subsurface flow description in a hydrological model with minimal computational cost? In contrast to the robust (but computationally expensive) practice of coupling hydrologic and hydrogeologic algorithms, this work is motivated to close the gap between subsurface physics and semi-distributed hydrological models by other means. That is, we conceptualize this problem as an upscaling problem, in which careful consideration of the aggregate behavior of finely resolved hillslope physics can be used to inform the basin-scale power law representation of hillslope drainage. Here, we use the term "upscaling" to refer to the translation of the hillslope-scale physics of subsurface flow to an aggregate basin-scale

Upland Hillslope Groundwater Subsidy Affects Low‐Flow Storage–Discharge Relationship

“…On one hand, the hydraulic of groundwater discharge from the representative shallow riparian fast‐moving storage unit only under certain circumstances could be presented by a linear reservoir (Wittenberg, 1999). On the other hand, the hydraulic properties of upland hillslopes could be variable among hillslopes and the use of one linear reservoir could neglect the (potentially large) variability of transmission characteristics among hillslopes (Ranjram & Craig, 2022). Such a simplified treatment, however, allowed us to explore our overarching hypothesis, setting the stage for more realistic theoretical analyses in future works (e.g., the use of two nonlinear reservoirs or multiple (more than two) linear reservoirs).…”

Statistical evaluation of a long‐memory process using the generalized entropic value‐at‐risk

“…The hourly data of the streamflow discharge in mountainous rivers have a long memory (Yoshioka, 2021; Yoshioka et al, 2023). The long memory of a streamflow discharge has been explained as the presence of watershed runoff processes with multiple time scales, which emerge as a superposition of exponential correlations with different relaxation rates (Mudelsee, 2007; Ranjram & Craig, 2022). Therefore, the stochastic process model of a streamflow environment should be considered as a long‐memory process subject to ambiguity.…”