Constraining Remote River Discharge Estimation Using Reach‐Scale Geomorphology

Abstract

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.

Spatial Distributions of At‐Many‐Stations Hydraulic Geometry for Mountain Rivers Originated From the Qinghai‐Tibet Plateau

“…and decile thresholding discharge estimation method developed by Mengen et al (2020) contribute to a significant improvement in discharge estimation. However, estimation accuracy of the aforementioned method using only satellite-observed cross-sectional width data is not always guaranteed if no previous estimates were made (Hagemann et al, 2017;Brinkerhoff et al, 2020).…”

“…Feng et al (2019) affirmed the possibility of estimating discharge independent of groundbased measurements with BAM. Recently, geo-BAM method proposed byBrinkerhoff et al (2020)…”

Discharge Estimation Using Integrated Satellite Data and Hybrid Model in the Midstream Yangtze River

“…Assimilation of future pre-SWOT data clearly corrected hydrological models on a global and continental scale, by reducing discharge errors from~30% to~24% [43]. The remote sensing-enabled hydrological simulation has been conducted across many basins [44,45]. Furthermore, revolutionary advances in machine learning methods have demonstrated great potential in streamflow prediction.…”

Modeling Daily Floods in the Lancang‐Mekong River Basin Using an Improved Hydrological‐Hydrodynamic Model

“…Further, the current hydrodynamic module has not considered the regional parameterization which could also be important for flood performance. Studies have indicated that the use of identical parameters for the entire basin is problematic (e.g., Mateo et al., 2014; Yamazaki et al., 2011), which can be attributed to the unique geomorphology and hydraulics of the basin and its sub‐basins (Brinkerhoff et al., 2020). Accurate flood simulations thus require regional parameterization.…”