Hyporheic hydraulic geometry: Conceptualizing relationships among hyporheic exchange, storage, and water age

Abstract

Hyporheic exchange is now widely acknowledged as a key driver of ecosystem processes in many streams. Yet stream ecologists have been slow to adopt nuanced hydrologic frameworks developed and applied by engineers and hydrologists to describe the relationship between water storage, water age, and water balance in finite hydrosystems such as hyporheic zones. Here, in the context of hyporheic hydrology, we summarize a well-established mathematical framework useful for describing hyporheic hydrology, while also applying the framework heuristically to visualize the relationships between water age, rates of hyporheic exchange, and water volume within hyporheic zones. Building on this heuristic application, we discuss how improved accuracy in the conceptualization of hyporheic exchange can yield a deeper understanding of the role of the hyporheic zone in stream ecosystems. Although the equations presented here have been well-described for decades, our aim is to make the mathematical basis as accessible as possible and to encourage broader understanding among aquatic ecologists of the implications of tailed age distributions commonly observed in water discharged from and stored within hyporheic zones. Our quantitative description of “hyporheic hydraulic geometry,” associated visualizations, and discussion offer a nuanced and realistic understanding of hyporheic hydrology to aid in considering hyporheic exchange in the context of river and stream ecosystem science and management.

Combined Surface-Subsurface Stream Restoration Structures Can Optimize Hyporheic Attenuation of Stream Water Contaminants

“…While the model was not calibrated to the flume studies because sufficient data for calibration of the surface and subsurface models were not collected during those field experiments, we used the experiment described by Posselt as a basis to construct our simulations. Thus, our simulations are heuristic, intended to provide a conceptual yet realistic representation of surface and hyporheic flow around the restoration structures without requiring calibration nor validation, as in refs , − . The numerical model represented a longitudinal section along the flume centerline (Figure ).…”

Thermal insulation versus capacitance: A simulation experiment comparing effects of shade and hyporheic exchange on daily and seasonal stream temperature cycles

“…To compare the effects of shade and hyporheic exchange on stream channel temperature, we conducted a simulation experiment where we systematically varied shade density and hyporheic exchange rates within a physically‐based stream temperature model while holding other parameters and driving variables constant (Figure 1). The stream temperature model, ‘TempTool’, combined well‐established equations that represent heat exchange between channel water and the atmosphere (Evans et al, 1998; Webb & Zhang, 1997) with a novel approach to simulating advective heat transport through the hyporheic zone by incorporating estimates of gross bidirectional hyporheic exchange and associated hyporheic exit‐age distributions (Poole et al, 2022) using foundational equations from chemical engineering research (Butt, 1999; Coker, 2001; Danckwerts, 1953).…”

“…Instead, increasing the thermal conductivity associated with a channel‐streambed conduction equation provides a coarse mechanism for representing thermal effects of systems with high hyporheic exchange (Webb & Zhang, 1997). In our heat‐energy model we use a novel representation of hyporheic heat exchange based on methods presented by Poole et al (2022), which simulates advection by storing and releasing water and associated heat from the hyporheic zone based on a power‐law exit‐age distribution (also known as ‘residence time distribution’). This method allows us to simulate the dynamic and interdependent nature of channel and hyporheic temperatures (Faulkner et al, 2020; Munz et al, 2017), and to accurately represent the effects of high gross hyporheic exchange rates on channel temperatures.…”

Exploring Temporal Dynamics of River Discharge using Univariate Long Short-Term Memory (LSTM) Recurrent Neural Network at East Branch of Delaware River

“…LSTM RNN is suitable for various water related variables with time series e.g., river flow, groundwater table, precipitation, etc. [32,64,[69][70][71][72].…”

“…Consequently, fluctuation of the river flow at the area has increased through the years. Fish and Fluvial ecology are one of those sectors which are getting significant impact due to these rapidly changing dynamics of river discharge [32,33]. Therefore, a robust data-driven predictive approach utilizing only the previous observed discharge data could contribute substantially to the nearby community in addressing the issues with less computational efforts.…”