An improved methodology to estimate river stage and discharge using Jason-2 satellite data

Dubey, Amit Kumar; Gupta, Praveen Kumar; Dutta, Subashisa; Singh, R. P.

doi:10.1016/j.jhydrol.2015.08.009

Cited by 31 publications

(16 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For example, Lake Huangqihai, a shallow lake in Inner Mongolia, has a dramatic water area change in different seasons (Figure 9b1,b2), and the LSH profile may describe the lake bed profile when the lake dries up. It indicates that an advanced outlier removal method [37] or a dynamic water mask [38] is needed for shallow lakes, rather than adopting all footprints in a fixed lake boundary.…”

Section: Causes Of Non-horizontal Lsh Profile For the Icesat-2mentioning

confidence: 99%

Performance Assessment of ICESat-2 Laser Altimeter Data for Water-Level Measurement over Lakes and Reservoirs in China

2020

View full text Add to dashboard Cite

Although the Advanced Topographic Laser Altimeter System (ATLAS) onboard the Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) was primarily designed for glacier and sea-ice measurement, it can also be applied to monitor lake surface height (LSH). However, its performance in monitoring lakes/reservoirs has rarely been assessed. Here, we report an accuracy evaluation of the ICESat-2 laser altimetry data over 30 reservoirs in China using gauge data. To show its characteristics in large-scale lake monitoring, we also applied an advanced radar altimeter SARAL (Satellite for ARgos and ALtika) and the first laser altimeter ICESat (Ice, Cloud and land Elevation Satellite) to investigate all lakes and reservoirs (>10 km2) in China. We found that the ICESat-2 has a greatly improved altimetric capability, and the relative altimetric error was 0.06 m, while the relative altimetric error was 0.25 m for SARAL. Compared with SARAL and ICESat data, ICESat-2 data had the lowest measurement uncertainty (the standard deviation of along-track heights; 0.02 m vs. 0.17 m and 0.07 m), the greatest temporal frequency (3.43 vs. 1.35 and 1.48 times per year), and the second greatest lake coverage (636 vs. 814 and 311 lakes). The precise LSH profiles derived from the ICESat-2 data showed that most lakes (90% of 636 lakes) had a quasi-horizontal LSH profile (measurement uncertainty <0.05 m), and special methods are needed for mountainous lakes or shallow lakes to extract precise LSHs.

show abstract

Section: Causes Of Non-horizontal Lsh Profile For the Icesat-2mentioning

confidence: 99%

Performance Assessment of ICESat-2 Laser Altimeter Data for Water-Level Measurement over Lakes and Reservoirs in China

2020

View full text Add to dashboard Cite

show abstract

“…Recently, Dubey et al . [] proposed using the minimum value in the water height series. However, this method forces null discharge at the height selected for

Z_{0}

.…”

Section: Rating Curve and Stage‐discharge Relationshipmentioning

confidence: 99%

Stage‐discharge rating curves based on satellite altimetry and modeled discharge in the Amazon basin

Paris

Paiva

Silva

et al. 2016

Water Resources Research

134

106

View full text Add to dashboard Cite

In this study, rating curves (RCs) were determined by applying satellite altimetry to a poorly gauged basin. This study demonstrates the synergistic application of remote sensing and watershed modeling to capture the dynamics and quantity of flow in the Amazon River Basin, respectively. Three major advancements for estimating basin-scale patterns in river discharge are described. The first advancement is the preservation of the hydrological meanings of the parameters expressed by Manning's equation to obtain a data set containing the elevations of the river beds throughout the basin. The second advancement is the provision of parameter uncertainties and, therefore, the uncertainties in the rated discharge. The third advancement concerns estimating the discharge while considering backwater effects. We analyzed the Amazon Basin using nearly one thousand series that were obtained from ENVISAT and Jason-2 altimetry for more than 100 tributaries. Discharge values and related uncertainties were obtained from the rain-discharge MGB-IPH model. We used a global optimization algorithm based on the Monte Carlo Markov Chain and Bayesian framework to determine the rating curves. The data were randomly allocated into 80% calibration and 20% validation subsets. A comparison with the validation samples produced a Nash-Sutcliffe efficiency (E ns ) of 0.68. When the MGB discharge uncertainties were less than 5%, the E ns value increased to 0.81 (mean). A comparison with the in situ discharge resulted in an E ns value of 0.71 for the validation samples (and 0.77 for calibration). The E ns values at the mouths of the rivers that experienced backwater effects significantly improved when the mean monthly slope was included in the RC. Our RCs were not mission-dependent, and the E ns value was preserved when applying ENVISAT rating curves to Jason-2 altimetry at crossovers. The cease-to-flow parameter of our RCs provided a good proxy for determining river bed elevation. This proxy was validated against Acoustic Doppler current profiler (ADCP) cross sections with an accuracy of more than 90%. Altimetry measurements are routinely delivered within a few days, and this RC data set provides a simple and cost-effective tool for predicting discharge throughout the basin in nearly real time.

show abstract

“…Each mask was constructed using the width extent and upstream and downstream limits that were 2 km perpendicular to the crossing location. We extracted all altimeter returns with centroids falling within each mask for each pass from Jason-2 GDR version D (Dumont et al, 2009) and the Envisat GDR, version 2.1 or later (Soussi and Féménias, 2009), using corrections outlined in product documentation. We extracted ICE-1 retracked ranges from the GDR (Gommenginger et al, 2011;Wingham et al, 1986).…”

Section: Gdr Extractionmentioning

confidence: 99%

“…It thus requires further processing steps, known as "retracking". Using retracked river observations, inland radar altimetry can accurately measure changing river surface elevation (Koblinsky et al, 1993;Berry et al, 2005;Frappart et al, 2006;Alsdorf et al, 2007;Santos da Silva et al, 2010;Papa et al, 2010;Dubey et al, 2015;Tourian et al, 2016;Verron et al, 2018). While custom retrackers have been derived and tested in particular locations (Huang et al, 2018;Maillard et al, 2015;Sulistioadi et al, 2015) the ICE-1 retracker (Wingham et al, 1986) is arguably the best compromise between being consistently reliable and available for many altimeter missions (Biancamaria et al, 2017;Frappart et al, 2006;Santos da Silva et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

Global River Radar Altimetry Time Series (GRRATS): new river elevation earth science data records for the hydrologic community

Coss

Durand

et al. 2020

Earth Syst. Sci. Data

View full text Add to dashboard Cite

Abstract. The capabilities of radar altimetry to measure inland water bodies are well established, and several river altimetry datasets are available. Here we produced a globally distributed dataset, the Global River Radar Altimeter Time Series (GRRATS), using Envisat and Ocean Surface Topography Mission (OSTM)/Jason-2 radar altimeter data spanning the time period 2002–2016. We developed a method that runs unsupervised, without requiring parameterization at the measurement location, dubbed virtual station (VS) level, and applied it to all altimeter crossings of ocean-draining rivers with widths >900 m (>34 % of the global drainage area). We evaluated every VS, either quantitatively for VS locations where in situ gages are available or qualitatively using a grade system. We processed nearly 1.5 million altimeter measurements from 1478 VSs. After quality control, the final product contained 810 403 measurements distributed over 932 VSs located on 39 rivers. Available in situ data allowed quantitative evaluation of 389 VSs on 12 rivers. The median standard deviation of river elevation error is 0.93 m, Nash–Sutcliffe efficiency is 0.75, and correlation coefficient is 0.9. GRRATS is a consistent, well-documented dataset with a user-friendly data visualization portal, freely available for use by the global scientific community. Data are available at https://doi.org/10.5067/PSGRA-SA2V1 (Coss et al., 2016).

show abstract

An improved methodology to estimate river stage and discharge using Jason-2 satellite data

Cited by 31 publications

References 26 publications

Performance Assessment of ICESat-2 Laser Altimeter Data for Water-Level Measurement over Lakes and Reservoirs in China

Performance Assessment of ICESat-2 Laser Altimeter Data for Water-Level Measurement over Lakes and Reservoirs in China

Stage‐discharge rating curves based on satellite altimetry and modeled discharge in the Amazon basin

Global River Radar Altimetry Time Series (GRRATS): new river elevation earth science data records for the hydrologic community

Contact Info

Product

Resources

About