Computing under-ice discharge: A proof-of-concept using hydroacoustics and the Probability Concept

Fulton, John W.; Henneberg, Mark F.; Mills, Taylor J.; Kohn, Michael S.; Epstein, Brian; Hittle, Elizabeth; Damschen, William C.; Laveau, Christopher D.; Lambrecht, J.M.; Farmer, William

doi:10.1016/j.jhydrol.2018.04.073

Cited by 6 publications

(5 citation statements)

References 20 publications

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“…Values of φ, which is constant at any cross-section and used to transform u max to u mean , ranged from 0.571 to 0.658, which is consistent with previously published work (Chiu and Hsu, 2006;Fulton et al, 2020bFulton et al, , 2018Moramarco et al, 2017) where values ranging from 0.522 to > 1 have been reported. Table 4 shows the velocity distributions at the y-axis for each cross-section of interest, which was established through repeat current-meter wading measurements.…”

Section: Lspiv Pc Model Validationsupporting

confidence: 89%

Unpiloted Aerial Vehicle (UAV) image velocimetry for validation of two-dimensional hydraulic model simulations

Masafu

Williams

Shi

et al. 2022

Journal of Hydrology

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Section: Lspiv Pc Model Validationsupporting

confidence: 89%

Unpiloted Aerial Vehicle (UAV) image velocimetry for validation of two-dimensional hydraulic model simulations

Masafu

Williams

Shi

et al. 2022

Journal of Hydrology

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“…Below-ice discharge is difficult to measure, and therefore determining flow conditions, including whether flows have dried or completely frozen, can be challenging (Melcher & Walker, 1992). If flow cannot be safely or accurately measured, flows are typically estimated (Fulton et al, 2018;Melcher & Walker, 1992;Hamilton & Moore, 2012;Sauer & Turnipseed, 2010), and especially in very cold regions, long periods of inferred zero flow may be reported after the river profile has been observed to completely freeze (Figure 3a). A zero-flow reading is likely due to disruption of equipment by ice or because of fully frozen, no-flow conditions or when downstream ice-jams cause upstream backwater effects (Beltaos & Prowse, 2009;Spencer, 1910).…”

Section: Frozen Surface Watermentioning

confidence: 99%

“…Detection of ice presence is typically based on direct observation, including drone use, or indirectly detected based on temperature readings. The USGS typically flags ice-affected flow measurements using the USGS Quality Code of "Ice" and rates the accuracy classification as "poor" (Fulton et al, 2018). Ice break-up is generally easier to identify than ice freeze-up due to a characteristic spike in the hydrograph (Figure 3a); post hoc corrections of iceaffected flow conditions can be useful.…”

Section: Frozen Surface Watermentioning

confidence: 99%

“…However, there has not been a focus on quantifying the accuracy of zero-flow readings associated with ice over large regions. Uncertainty in flow estimates typically increases during ice conditions, but can be reduced by established approaches (e.g., Fulton et al, 2018;Melcher & Walker, 1992;Sauer & Turnipseed, 2010), including measuring temperature profiles from the ice surface to the channel bed or direct measurement of sub-ice flows, if present.…”

Section: Frozen Surface Watermentioning

confidence: 99%

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Zero or not? Causes and consequences of zero‐flow stream gage readings

et al. 2020

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Streamflow observations can be used to understand, predict, and contextualize hydrologic, ecological, and biogeochemical processes and conditions in streams. Stream gages are point measurements along rivers where streamflow is measured, and are often used to infer upstream watershed-scale processes.When stream gages read zero, this may indicate that the stream has dried at this location; however, zero-flow readings can also be caused by a wide range of other factors. Our ability to identify whether or not a zero-flow gage reading indicates a dry fluvial system has far reaching environmental implications. Incorrect identification and interpretation by the data user can lead to inaccurate hydrologic, ecological, and/or biogeochemical predictions from models and analyses. Here, we describe several causes of zero-flow gage readings: frozen surface water, flow reversals, instrument error, and natural or human-driven upstream source losses or bypass flow. For these examples, we discuss the implications of zero-flow interpretations. We also highlight additional methods for determining flow presence, including direct observations, statistical methods, and hydrologic models, which can be applied to interpret causes of zero-flow gage readings and implications for reach-and watershedscale dynamics. Such efforts are necessary to improve our ability to understand and predict surface flow activation, cessation, and connectivity across river networks. Developing this integrated understanding of the wide range of possible meanings of zero-flows will only attain greater importance in a more variable and changing hydrologic climate.

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“…However, under-ice discharge must also be considered when estimating the fresh water flux into open-sea, but the measurement water flow for ice-affected sites are generally qualified as poor. To overcome this obstacle, one possible field approach is to moor an ADCP at the sea bottom and river bed to analyze the impacts of ice cover on near-bed flow characteristics in rivers (Fulton et al, 2018;Lotsari et al, 2022) and estimate vertical and temporal variability in total suspended particulate matter (Ha et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Field survey and analysis of water flux and salinity gradients considering the effects of sea ice coverage and rubber dam: a case study of the Liao River Estuary, China

Guo

Yang

et al. 2023

Front. Mar. Sci.

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Predicting net river fluxes is important to promote good water quality, maritime transport, and water exchange in estuaries. However, few studies have observed and evaluated net water fluxes to estuaries under complex conditions. This study used advanced survey techniques to obtain high-frequency monitoring data of cross-sectional current velocity, water level, and salinity in the Liao River Estuary (LRE) from 2017 to 2020. The net water flux into the sea was computed based on field data and the impacts of the rubber dam and sea ice cover on water flux and salinity processes were analyzed in the study region. In the Liao River Station (LRS), the fluctuations of water level and discharge were not obvious in winter due to the sea ice cover. There were significant seasonal and inter-annual changes in water fluxes due to variability in river discharge and tidal oscillations. The results also showed that the net water flux into the sea from the LRS was positive in wet season, and greater during ebb tides than flood tides. The net water fluxes in the normal and dry seasons were mostly negative due to the influence of tides, indicating that the annual runoff from the Liao River fluctuated greatly throughout the year. The water flux in the LRS was more suitable for representing water flux into the sea than the Liujianfang Hydrometric Station (LHS) in the LRE. The impacts of the rubber dam and Panshan Sluice on water fluxes to the sea were both significant. Lower salinity in the study area coincided mostly with height water fluxes to the sea and periods when the rubber dam was raised. This study results provide us new insights to measure the water flux into sea under the condition of ice cover in the tidal reach of estuary and the method can be used for water flux observation for other estuaries.

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Computing under-ice discharge: A proof-of-concept using hydroacoustics and the Probability Concept

Cited by 6 publications

References 20 publications

Unpiloted Aerial Vehicle (UAV) image velocimetry for validation of two-dimensional hydraulic model simulations

Unpiloted Aerial Vehicle (UAV) image velocimetry for validation of two-dimensional hydraulic model simulations

Zero or not? Causes and consequences of zero‐flow stream gage readings

Field survey and analysis of water flux and salinity gradients considering the effects of sea ice coverage and rubber dam: a case study of the Liao River Estuary, China

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