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

Coss, S. P.; Durand, M. T.; Yi, Yuchan; Jia, Yuanyuan; Guo, Qi; Tuozzolo, S.; Shum, C. K.; Allen, George H.; Calmant, Stéphane; Pavelsky, T.

doi:10.5194/essd-12-137-2020

Cited by 28 publications

(20 citation statements)

References 49 publications

(65 reference statements)

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“…In recent decades, satellite radar altimetry has been successfully used to investigate the water level. Numerous studies have used altimetry observations to extract the water level for inland water bodies such as lakes and rivers [4], [10], [13], [19], [37]- [39]. It is important to extract accurate water levels using a retracking algorithm.…”

Section: Discussionmentioning

confidence: 99%

Improved Lake Level Estimation From Radar Altimeter Using an Automatic Multiscale-Based Peak Detection Retracker

Chen

Liao

Chao

2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Satellite radar altimetry is an important technique for monitoring the water levels of oceans and inland water bodies, especially in areas where in-situ data are sparse or nonexistent. This study presented an automatic multiscale-based peak detection retracker (AMPDR). The retracker can extract a robust threshold level for each track, then the stable lake level can be obtained from the multipeak waveforms using a shortest-path algorithm. Additionally, the retracker can be used for mountain lakes and for flat lakes, and is also suitable for many kinds of altimetry data, such as those of Cryosat-2, Sentinel-3, and Jason-2/3. To validate the lake levels derived by the AMPDR retracker, the in-situ gauge data of seven lakes in the Tibetan Plateau and two lakes in a flat area are used. Moreover, seven existing retrackers are compared to evaluate the performance of the proposed AMPDR retracker. The results suggest that AMPDR can efficiently process many complex multipeak waveforms, and the AMPDR has the lowest mean of all track standard deviations over all lakes. The root-mean-squared error (RMSE) of the lake level time series obtained using AMPDR is the lowest over several lakes: The mean RMSEs of all the lakes overpassed by Cryosat-2, Sentinel-3, and Jason-2/3 are 0.149, 0.139, and 0.181 m, respectively. The AMPDR retracker is easy to implement, computationally efficient, and can give a height estimate for even the most contaminated waveforms.

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Section: Discussionmentioning

confidence: 99%

Improved Lake Level Estimation From Radar Altimeter Using an Automatic Multiscale-Based Peak Detection Retracker

Chen

Liao

Chao

2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…The primary inputs to GRRATS1kd are measurements of river WSE at the intersection of radar altimeter ground tracks and rivers, or virtual stations (GRRATS, described by Coss et al (2020)), and river width measurements obtained using RivWidthCloud (see Text S1 in Supporting Information S1), a Landsat processing algorithm for measuring river width based on Google Earth Engine (Yang et al, 2019).…”

Section: Methods and Data Setsmentioning

confidence: 99%

Channel Water Storage Anomaly: A New Remotely Sensed Quantity for Global River Analysis

Coss

Durand

Shum

et al. 2023

Geophysical Research Letters

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River channels store large volumes of water globally, critically impacting ecological and biogeochemical processes. Despite the importance of river channel storage, there is not yet an observational constraint on this quantity. We introduce a 26‐year record of entirely remotely sensed volumetric channel water storage (CWS) change on 26 major world rivers. We find mainstem volumetric CWS climatology amplitude (CA) represents an appreciable amount of basin‐wide terrestrial water storage variability (median 2.78%, range 0.04%–12.54% across world rivers), despite mainstem rivers themselves represent an average of just 0.2% of basin area. We find that two global river routing schemes coupled with land surface models reasonably approximate CA (within ±50%) in only 11.5% (CaMa‐Flood) and 30.7% (HyMap) of rivers considered. These findings demonstrate volumetric CWS is a useful quantity for assessing global hydrological model performance, and for advancing understanding of spatial patterns in global hydrology.

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“…In addition, multimission data sets with time series are available for rivers, lakes, and reservoirs. (1) Hydroweb‐THEIA (Crétaux et al, 2011); (2) Global Reservoir and Lake Elevation Database (G‐REALM; Birkett et al, 2011); (3) Global Land Surface Altimetry Data (DLAS/ICESat GLA14; Zwally et al, 2014); (4) Database for Hydrological Time Series over Inland Waters (DAHITI; Schwatke et al, 2015); and (5) Global River Radar Altimetry Time Series (GRRATS https://podaac.jpl.nasa.gov/dataset/PRESWOT_HYDRO_GRRATS_L2_VIRTUAL_STATION_HEIGHTS_V2; Coss et al, 2019). Duan and Bastiaanssen (2013) describe the satellites used in some of these databases, the available data period, and the vertical reference system.…”

Section: Flood Frequency‐based Methodsmentioning

confidence: 99%

Lake Topography and Active Storage From Satellite Observations of Flood Frequency

Fassoni‐Andrade

Paiva

Fleischmann

2020

Water Resources Research

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Topography is critical information for water resources management in lakes, and remote sensing provides a unique opportunity to estimate topography in ungauged regions. We introduce here a new method that estimates nearshore topography of water bodies based on a flood frequency map and time series of water levels by assuming the equivalence between flood frequency and water level exceedance probability at a given area. Test cases are performed for two lakes and 12 hydropower reservoirs using the proposed Flood2Topo app. This new application generates the bottom level pixel by pixel and level‐area‐active storage relationship directly from the topography map, without the need to fit functions. Flood extent estimates from the Landsat based Global Surface Water (GSW) data set, the current state‐of‐the‐art, were used to run Flood2Topo, together with water levels from satellite altimetry and in situ gauges. Results show bottom level root mean square deviation (RMSD) values of 18.5 and 146 cm for Lake Poopó (Bolivia) and Lake Curuai (Amazon basin), respectively. For reservoir active storage, RMSD normalized values ranged from 2% to 11% for 11 reservoirs (average NRMSD of 6.4%). The method can be applied to any seasonally flooded area using any data set. Considering the surface water occurrence map from the GSW data set, the method is applicable to 35.8% (86%) of the global seasonally flooded areas delimited by flood frequencies between 0 and 95% (99%) over 35 years. The flood frequency‐based method is a promising tool for obtaining data for hydrodynamic simulations and monitoring of ungauged water bodies.

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

Cited by 28 publications

References 49 publications

Improved Lake Level Estimation From Radar Altimeter Using an Automatic Multiscale-Based Peak Detection Retracker

Improved Lake Level Estimation From Radar Altimeter Using an Automatic Multiscale-Based Peak Detection Retracker

Channel Water Storage Anomaly: A New Remotely Sensed Quantity for Global River Analysis

Lake Topography and Active Storage From Satellite Observations of Flood Frequency

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