Using Ensemble Data Assimilation to Estimate Transient Hydrologic Exchange Flow Under Highly Dynamic Flow Conditions

Chen, Kewei; Chen, Xingyuan; Song, Xuehang; Briggs, Martin A.; Jiang, Peishi; Shuai, Pin; Hammond, Glenn; Zhan, Hongbin; Zachara, John M.

doi:10.1029/2021WR030735

Title: Using Ensemble Data Assimilation to Estimate Transient Hydrologic Exchange Flow Under Highly Dynamic Flow Conditions

Journal Article · 27 April 2022 · Water Resources Research

DOI: https://doi.org/10.1029/2021WR030735 · OSTI ID:1870050

Chen, Kewei ^[1];

^[2];

^[3]; Jiang, Peishi ^[2];

^[2];

^[2]; Zhan, Hongbin ^[4];

^[2]

Pacific Northwest National Laboratory Richland WA USA, Southern University of Science and Technology Shenzhen China
Pacific Northwest National Laboratory Richland WA USA
Observing Systems Division Hydrologic Remote Sensing Branch US Geological Survey Storrs CT USA
Department of Geology and Geophysics Texas A&,M University College Station TX USA

Abstract Quantifying dynamic hydrologic exchange flows (HEFs) within river corridors that experience high‐frequency flow variations caused by dam regulations is important for understanding the biogeochemical processes at the river water and groundwater interfaces. Heat has been widely used as a tracer to infer steady‐state flow velocities through analytical solutions of heat transport defined by the diurnal temperature signals. Under sub‐daily dynamic flow conditions, however, such analytical solutions are not applicable due to the violation of their fundamental assumptions. In this study, we developed a data assimilation‐based approach to estimate the sub‐daily flux under highly dynamic flow conditions using multi‐depth temperature observations at a 5‐min resolution. If the hydraulic gradient is measured, Darcy's law was used to calculate the flux with permeability estimated from temperature responses below the riverbed. Otherwise, flux was estimated directly by assimilating multi‐depth temperature data at 1‐ or 2‐hr time intervals assuming one‐dimensional flow and heat transport governing equation. By comparing estimated fluxes with model‐generated synthetic truth, we demonstrated that both schemes have robust performance in estimating fluxes under highly dynamic flow conditions. This data assimilation‐based flux estimation method was able to capture the vertical sub‐daily fluxes using multi‐depth high‐resolution temperature data alone, even in the presence of multi‐dimensional flow. This approach has been successfully applied to real field temperature data collected at the Hanford site, which experiences highly dynamic HEFs. Our study shows the promise of adopting distributed 1‐D temperature monitoring to capture spatial and temporal exchange dynamics in river corridors at a watershed scale or beyond.

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Research Organization:: Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)

Sponsoring Organization:: USDOE; USDOE Office of Science (SC), Biological and Environmental Research (BER)

Grant/Contract Number:: AC02-05CH11231; AC05-76RL01830; AC06-76RL01830; SC0016412; SC0020339

OSTI ID:: 1870050

Report Number(s):: PNNL-SA-165451; e2021WR030735

Journal Information:: Water Resources Research, Journal Name: Water Resources Research Journal Issue: 5 Vol. 58; ISSN 0043-1397

Publisher:: American Geophysical Union (AGU)Copyright Statement

Country of Publication:: United States

Language:: English

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54 ENVIRONMENTAL SCIENCES
data assimilation
heat tracer
hydrologic exchange flow
hyporheic zone

Title: Using Ensemble Data Assimilation to Estimate Transient Hydrologic Exchange Flow Under Highly Dynamic Flow Conditions

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