Reconciling at‐a‐Station and at‐Many‐Stations Hydraulic Geometry Through River‐Wide Geomorphology

Brinkerhoff, Craig; Gleason, C. J.; Ostendorf, David W.

doi:10.1029/2019gl084529

Cited by 26 publications

(38 citation statements)

References 26 publications

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“…Furthermore, the at‐many‐stations hydraulic geometry (AMHG) which is an extension to the classic AHG theory showed that the empirical parameters of AHG (valid at cross‐sections) are functionally related along a river (Gleason & Wang, 2015). The incorporation of AMHG could lead to further constraining the prior and posterior distributions of river discharge, as it is reconcilable with AHG (Brinkerhoff et al, 2019), making AMHG a promising approach for an algorithm such as SAD. Alternatively, other discharge estimation approaches that only depend on hydraulic geometry relations (e.g., Dingman & Sharma, 1997; López et al, 2007) Finally, the implementation of the SAD algorithm presented here is not applicable to an entire river network because it does not take into account any flows at junctions or from lateral tributaries.…”

Section: Discussionmentioning

confidence: 99%

Constraining the Assimilation of SWOT Observations With Hydraulic Geometry Relations

Andreadis

Brinkerhoff

Gleason

2020

Water Resources Research

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The Surface Water Ocean Topography (SWOT) satellite mission expected to launch in 2021 will offer a unique opportunity to map river discharge at an unprecedented spatial resolution globally from observations of water surface elevation, width, and slope. Because river discharge will not be directly observed from SWOT, a number of algorithms of varying complexity have been developed to estimate discharge from SWOT observables. Outstanding issues include the lack of accurate prior information and parameter equifinality. We developed a new data assimilation discharge algorithm that aimed to overcome these limitations by integrating a data-driven approach to estimate priors with a model informed by hydraulic geometry relations. A comprehensive simulated dataset of 18 rivers was used to evaluate the algorithm and four different configurations (rectangular channel, generic channel, and geomorphologically classified channel with and without regularization) to assess the impact of progressively adding hydraulic geometry constraints to the estimation problem. The algorithm with the full set of constraints outperformed the other configurations with median Nash-Sutcliffe coefficients of 0.77, compared with −0.46, 0.31 and 0.66, while other error metrics showed similar improvement. Results from this study show the promise of this hybrid data-driven approach to estimating river discharge from SWOT observations, although a number of enhancements need to be tested to improve the operational applicability of the algorithm.

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Section: Discussionmentioning

confidence: 99%

Constraining the Assimilation of SWOT Observations With Hydraulic Geometry Relations

Andreadis

Brinkerhoff

Gleason

2020

Water Resources Research

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“…1. Update BAM to reflect the latest geomorphic understanding of AMHG following Brinkerhoff et al (2019) and ingest geomorphic priors (section 3.1).…”

Section: Methodsmentioning

confidence: 99%

“…Our first task was to gather a comprehensive data set of measured river hydraulics to generate training data. We started with Brinkerhoff et al's (2019) data set. This data set merges USGS surface water measurements (NWIS; https://waterdata.usgs.gov/nwis/measurements) of channel discharge and geometry with the NHD (https://www.epa.gov/waterdata/nhdplus-national-hydrography-dataset-plus) and filters the data to include only those rivers that have at least six stations of 20 measurements each to derive that river's AMHG.…”

Section: Datamentioning

confidence: 99%

“…McFLIs are therefore defined by their flow laws. To date, all published McFLIs have used either Manning's equation (Andreadis et al, 2020; Bjerklie et al, 2018; Durand et al, 2014; Garambois & Monnier, 2015; Hagemann et al, 2017; Sichangi et al, 2018) or at‐many‐stations hydraulic geometry (AMHG; Feng et al, 2019; Gleason et al, 2014; Gleason & Wang, 2015; Hagemann et al, 2017) as a flow law, where AMHG reflects relationships between at‐a‐station hydraulic geometry (AHG) parameters along a river's course (Brinkerhoff et al, 2019; Gleason & Smith, 2014). Regardless of the geomorphology driving McFLI, all McFLIs suffer from equifinality issues, as multiple sets of flow law variables can solve the inversion in this ill‐posed estimation problem (Garambois & Monnier, 2015).…”

Section: Introductionmentioning

confidence: 99%

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Constraining Remote River Discharge Estimation Using Reach‐Scale Geomorphology

Brinkerhoff

Gleason

Feng

et al. 2020

Water Resources Research

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Recent advances in remote sensing and the upcoming launch of the joint NASA/CNES/CSA/ UKSA Surface Water and Ocean Topography (SWOT) satellite point toward improved river discharge estimates in ungauged basins. Existing discharge methods rely on "prior river knowledge" to infer parameters not directly measured from space. Here, we show that discharge estimation is improved by classifying and parameterizing rivers based on their unique geomorphology and hydraulics. Using over 370,000 in situ hydraulic observations as training data, we test unsupervised learning and an "expert" method to assign these hydraulics and geomorphology to rivers via remote sensing. This intervention, along with updates to model physics, constitutes a new method we term "geoBAM," an update of the Bayesian At-many-stations hydraulic geometry-Manning's (BAM) algorithm. We tested geoBAM on Landsat imagery over more than 7,500 rivers (108 are gauged) in Canada's Mackenzie River basin and on simulated hydraulic data for 19 rivers that mimic SWOT observations without measurement error. geoBAM yielded considerable improvement over BAM, improving the median Nash-Sutcliffe efficiency (NSE) for the Mackenzie River from −0.05 to 0.26 and from 0.16 to 0.46 for the SWOT rivers. Further, NSE improved by at least 0.10 in 78/108 gauged Mackenzie rivers and 8/19 SWOT rivers. We attribute geoBAM improvement to parameterizing rivers by type rather than globally, but prediction accuracy worsens if parameters are misassigned. This method is easily mapped to rivers at the global scale and paves the way for improving future discharge estimates, especially when coupled with hydrologic models.

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“…that drainage area threshold. This procedure follows general hydraulic correlations between channel size, slope, total discharge, and n (Brinkerhoff et al, 2019). Hillslope flow is modelled as an explicit kinematic wave for non-channelized flow (Li et al 1975), which requires a surface roughness coefficient (i.e., hillslope friction), and we limit hillslope friction to between 0.05 (non-dimensional; a hillslope with friction equivalent to a rough channel) and 25 (a hillslope with extreme friction to approximate slow interflow through weathering crust).…”

Section: Model Calibrationmentioning

confidence: 99%

Hourly surface meltwater routing for a Greenlandic supraglacial catchment across hillslopes and through a dense topological channel network

Gleason¹,

Yang²,

Feng³

et al. 2020

Preprint

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Abstract. Recent work has identified complex perennial supraglacial stream/river networks in areas of the Greenland Ice Sheet (GrIS) ablation zone. Current surface mass balance (SMB) models appear to overestimate meltwater runoff in these networks compared to in-channel measurements of supraglacial discharge. Here, we constrain SMB models using the Hillslope River Routing Model (HRR), a spatially explicit flow routing model used in terrestrial hydrology, in a 63 km2 supraglacial river catchment in southwest Greenland. HRR conserves water mass and momentum and explicitly accounts for hillslope routing, and we produce hourly flows for nearly 10,000 channels given inputs of an ice surface DEM, a remotely sensed supraglacial channel network, SMB-modelled runoff, and an in situ discharge dataset used for calibration. Model calibration yields a Nash Sutcliffe Efficiency as high as 0.92 and physically realistic parameters. We confirm earlier assertions that SMB runoff exceeds the conserved mass of water routed to match measured flows in this catchment (by 12–59 %) and that large channels do not dewater overnight despite a diurnal shutdown of SMB runoff production. We further test hillslope routing and network density controls on channel discharge and conclude that explicitly including hillslope flow and routing runoff through a realistically fine channel network produces the most accurate results. Modelling complex surface water processes is thus both possible and necessary to accurately simulate the timing and magnitude of supraglacial channel flows, and we highlight a need for additional in situ discharge datasets to better calibrate and apply this method elsewhere on the ice sheet.

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Reconciling at‐a‐Station and at‐Many‐Stations Hydraulic Geometry Through River‐Wide Geomorphology

Cited by 26 publications

References 26 publications

Constraining the Assimilation of SWOT Observations With Hydraulic Geometry Relations

Constraining the Assimilation of SWOT Observations With Hydraulic Geometry Relations

Constraining Remote River Discharge Estimation Using Reach‐Scale Geomorphology

Hourly surface meltwater routing for a Greenlandic supraglacial catchment across hillslopes and through a dense topological channel network

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