Deriving River Discharge Using Remotely Sensed Water Surface Characteristics and Satellite Altimetry in the Mississippi River Basin

Gehring, Jaclyn; Duvvuri, Bhavya; Beighley, R. Edward

doi:10.3390/rs14153541

Cited by 8 publications

(4 citation statements)

References 40 publications

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“…This is an important limitation in water level monitoring and flow estimation, especially since the latter requires continuous and regular data. A recent study [32] explored the estimation of river discharge in the Mississippi River Basin using observed water surface characteristics from satellites and satellite altimetry data. This approach employed the optimized Manning equation for parameters such as surface roughness and channel bathymetry, combining altimetric observations from JASON-2/3 and Sentinel-3A/B satellites with features from the SWOT River Database (SWORD).…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Automatic River Flow Estimation Using RADARSAT Imagery

Ziadi,

Chokmani,

Chaabani

et al. 2024

Remote Sensing

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Estimating river flow is a key parameter for effective water resource management, flood risk prevention, and hydroelectric facilities planning. Yet, traditional gauging methods are not reliable under very high flows or extreme events. Hydrometric network stations are often sparse, and their spatial distribution is not optimal. Therefore, many river sections cannot be monitored using traditional flow measurements and observations. In the last few decades, satellite sensors have been considered as complementary observation sources to traditional water level and flow measurements. This kind of approach has provided a way to maintain and expand the hydrometric observation network. Remote sensing data can be used to estimate flow from rating curves that relate instantaneous flow (Q) to channel cross-section geometry (effective width or depth of the water surface). Yet, remote sensing has limitations, notably its dependence on rating curves. Due to their empirical nature, rating curves are limited to specific river sections (reaches) and cannot be applied to other watercourses. Recently, deep-learning techniques have been successfully applied to hydrology. The primary goal of this study is to develop a deep-learning approach for estimating river flow in the Boreal Shield ecozone of Eastern Canada using RADARSAT-1 and -2 imagery and convolutional neural networks (CNN). Data from 39 hydrographic sites in this region were used in modeling. A new CNN architecture was developed to provide a straightforward estimation of the instantaneous river flow rate. Our results yielded a coefficient of determination (R2) and a Nash–Sutcliffe value of 0.91 and a root mean square error of 33 m3/s. Notably, the model performs exceptionally well for rivers wider than 40 m, reflecting its capability to adapt to varied hydrological contexts. These results underscore the potential of integrating advanced satellite imagery with deep learning to enhance hydrological monitoring across vast and remote areas.

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Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Automatic River Flow Estimation Using RADARSAT Imagery

Ziadi,

Chokmani,

Chaabani

et al. 2024

Remote Sensing

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“…Optical sensors measure river extent or width and its variations, thanks to their frequent (some of them daily) observations and large spatial coverage [46,47]. Despite these advancements, challenges in this approach persist, including the roughness estimation [48] and the balance between the spatial and temporal resolution-a high spatial resolution entails fewer observations. Additionally, ground data remain necessary to establish rating curves that link satellite observations with in situ discharge data [49].…”

Section: Introductionmentioning

confidence: 99%

Monitoring Discharge in Vegetated Floodplains: A Case Study of the Piave River

Herrera Gómez,

Ravazzani,

Mancini

et al. 2023

Water

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The accurate assessment of discharge in vegetated floodplains during floods is a persistent challenge in river engineering due to the difficulty of acquiring hydraulic data, the variability in vegetation roughness, and the limitations of on-site vegetation characterization. This study introduces a novel approach that combines the continuous slope-area method with LiDAR-derived vegetation data and water depths measured with piezoresistive sensors to evaluate floodplain discharges while considering variations in roughness coefficients induced by arboreal vegetation. We apply this approach to a specific reach of the Piave River in Italy using data collected during the December 2020 flood event. The study demonstrates the capability of the employed measurement system to record extreme floods and emphasizes the importance of including vegetation roughness variations in floodplain discharge calculations. The proposed approach has the potential to be applied in similar scenarios, providing valuable insights for floodplain discharge estimation in vegetated areas.

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“…However, in situ measurements are in decline mainly as a result of the cost and effort of maintaining monitoring infrastructure (Gleason and Durand, 2020;Samboko et al, 2020;Gehring et al, 2022), leaving few regions with concentrated in-stream networks and some major rivers ungauged (Pavelsky et al, 2014;Frasson et al, 2021) Monitoring river discharge improves understanding of stream ecosystem health, water quality, potential for flooding, and water supply. Such measurements are critical for water resource management, plus the monitoring and modeling of changing hydrologic conditions due to anthropogenic climate forcing (Gleason and Durand, 2020;Gehring et al, 2022). Thus, there is a need for remote sensing as a means to augment the limited availability of in situ streamflow measurements.…”

Section: Introductionmentioning

confidence: 99%

Assessing the potential for the Surface Water and Ocean Topography (SWOT) mission for constituent flux estimations

Gehring,

Beighley,

Stubbins

2023

Front. Earth Sci.

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The recently launched Surface Water and Ocean Topography (SWOT) satellite will simultaneously measure river surface water widths, elevations, and slopes. These novel observations combined with assumptions for unobserved bathymetry and roughness enable the derivation of river discharge. Derived discharge data will not be available until the fall of 2023, despite the satellite having completed approximately 6 months of observations for validation and calibration and transitioning into the nominal orbit phase. SWOT has an irregular flyover frequency, ranging from roughly 1 to 10 times per 21 days. Here, we present how best to use SWOT data when it becomes live, including consideration of how best to accommodate or utilize the irregular flyover frequency of SWOT as it intersects with river reaches. We investigate the predicted capabilities of SWOT for several major rivers using synthetic/theoretical SWOT time series data and evaluate how the characteristics of river discharge dynamics and SWOT sampling frequency impact discharge estimates. This analysis indicates the irregular frequency of SWOT best captures the hydrology of larger, more stable, rivers but presents challenges in smaller, flashier rivers, particularly when sampling frequency decreases (i.e., falls to once per 21 days). Further, the use of SWOT discharge for quantifying constituent fluxes is considered. We provide recommendations concerning how to best use SWOT data for applications related to hydrology and biogeochemistry, including how to design studies to accommodate its irregular orbit cycle.

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Deriving River Discharge Using Remotely Sensed Water Surface Characteristics and Satellite Altimetry in the Mississippi River Basin

Cited by 8 publications

References 40 publications

Deep Learning-Based Automatic River Flow Estimation Using RADARSAT Imagery

Deep Learning-Based Automatic River Flow Estimation Using RADARSAT Imagery

Monitoring Discharge in Vegetated Floodplains: A Case Study of the Piave River

Assessing the potential for the Surface Water and Ocean Topography (SWOT) mission for constituent flux estimations

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