Using Ensemble Data Assimilation to Estimate Transient Hydrologic Exchange Flow Under Highly Dynamic Flow Conditions
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.
Pacific Northwest National Laboratory[DE-AC06-76RL01830];U.S. Department of Energy[DESC0016412];
|WOS Accession No|
|EI Accession Number|
Groundwater ; Laws and legislation ; Rivers ; Stream flow
|ESI Classification Code|
Waterways:407.2 ; Groundwater:444.2 ; Fluid Flow, General:631.1 ; Heat Transfer:641.2 ; Social Sciences:971
|ESI Research Field|
Cited Times [WOS]:4
|Document Type||Journal Article|
|Department||Southern University of Science and Technology|
1.Pacific Northwest National Laboratory,Richland,United States
2.Southern University of Science and Technology,Shenzhen,China
3.Observing Systems Division,Hydrologic Remote Sensing Branch,US Geological Survey,Storrs,United States
4.Department of Geology and Geophysics,Texas A&M University,College Station,United States
|First Author Affilication||Southern University of Science and Technology|
Chen，Kewei,Chen，Xingyuan,Song，Xuehang,et al. Using Ensemble Data Assimilation to Estimate Transient Hydrologic Exchange Flow Under Highly Dynamic Flow Conditions[J]. WATER RESOURCES RESEARCH,2022,58(5).
Chen，Kewei.,Chen，Xingyuan.,Song，Xuehang.,Briggs，Martin A..,Jiang，Peishi.,...&Zachara，John M..(2022).Using Ensemble Data Assimilation to Estimate Transient Hydrologic Exchange Flow Under Highly Dynamic Flow Conditions.WATER RESOURCES RESEARCH,58(5).
Chen，Kewei,et al."Using Ensemble Data Assimilation to Estimate Transient Hydrologic Exchange Flow Under Highly Dynamic Flow Conditions".WATER RESOURCES RESEARCH 58.5(2022).
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