The Relevance of GLAS/ICESat Elevation Data for the Monitoring of River Networks

Baghdadi, Nicolas; Lemarquand, Nicolas; Abdallah, Hani; Bailly, Jean‐Stéphane

doi:10.3390/rs3040708

Cited by 53 publications

(33 citation statements)

References 19 publications

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“…Although the satellite's repeat cycle and longevity were was not optimal for hydrology, spatial coverage was very high and had the potential to measure water level over a large set of lakes, as shown over the Tibetan Plateau where more than 100 lakes have been studied (Zhang et al 2013;Phan et al 2011;Kropáček et al 2012;Song et al 2013;Wang et al 2013). It was also used to monitor river water level variations (Baghdadi et al 2011;Jarihani et al 2013). Furthermore, it ensured accuracy of up to a few centimeters due to the signal's very small footprint, and data provided by the National Snow and Ice Data Center (NSIDC) needed no additional corrections.…”

Section: Past Present and Future Satellite Altimetrymentioning

confidence: 99%

Lake Volume Monitoring from Space

Crétaux¹,

et al. 2016

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Lakes are integrators of environmental change occurring at both the regional and global scale. They present a wide range of behavior on a variety of timescales (cyclic and secular) depending on their morphology and climate conditions. Lakes play a crucial role in retaining and stocking water, and because of the significant global environmental changes occurring at several anthropocentric levels, the necessity to monitor all morphodynamic characteristics [e.g., water level, surface (water contour) and volume] has increased substantially. Satellite altimetry and imagery are now widely used together to calculate lake and reservoir water storage changes worldwide. However, strategies and algorithms to calculate these characteristics are not straightforward, and specific approaches need to be developed. We present a review of some of these methodologies by using lakes over the Tibetan Plateau to illustrate some critical aspects and issues (technical and scientific) linked to the observation of climate change impact on surface waters from remote sensing data. Many authors have measured water variation using the limited remote sensing measurements available over short time periods, even though the time series are probably too short to directly link these results with climate change. Indeed, there are many processes and factors, like the influence of lake morphology, that are beyond observation and are still uncertain. The time response for lakes to reach a new state of equilibrium is a key aspect that is often neglected in current literature. Observations over a long period of time, including maintaining a constellation of comprehensive and complementary satellite missions with service continuity over decades, are therefore necessary especially when the ground gauge network is too limited. In addition, the design of future satellite missions with new instrumental concepts (e.g., SAR, SARin, Ka band altimetry, Ka interferometry) will also be suitable for complete monitoring of continental waters.

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Section: Past Present and Future Satellite Altimetrymentioning

confidence: 99%

Lake Volume Monitoring from Space

Crétaux¹,

et al. 2016

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“…It is already well recognized [30] that GLAS data have the potential for monitoring the level of inland water bodies. In this study, the method developed by Zhang et al [11] was used to extract the footprints of Lake Qinghai.…”

Section: Icesat Data Processingmentioning

confidence: 99%

“…ICESat/GLAS level 2 altimetry product (GLA14) provides surface elevations for land, including rivers and lakes, plus laser footprint centroid geolocation, range measurements, geoid and many other parameters. Although the main mission of the GLAS instrument is to detect elevation changes in the polar ice caps, ICESat-GLA14 elevation data over water surface has been widely examined in various parts of the world and have shown accuracy of better than 10 cm [27][28][29][30][31][32][33][34][35]. All ICESat/GLAS14 release-33 data covering Lake Qinghai during [2003][2004][2005][2006][2007][2008][2009] were downloaded from NASA's National Snow and Ice Data Center (NSIDC) and processed.…”

Section: Icesat/glasmentioning

confidence: 99%

Monitoring the Fluctuation of Lake Qinghai Using Multi-Source Remote Sensing Data

Zhu

Jia

2014

Remote Sensing

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Abstract:The knowledge of water storage variations in ungauged lakes is of fundamental importance to understanding the water balance on the Tibetan Plateau. In this paper, a simple framework was presented to monitor the fluctuation of inland water bodies by the combination of satellite altimetry measurements and optical satellite imagery without any in situ measurements. The fluctuation of water level, surface area, and water storage variations in Lake Qinghai were estimated to demonstrate this framework. Water levels retrieved from ICESat (Ice, Cloud, and and Elevation Satellite) elevation data and lake surface area derived from MODIS (Moderate Resolution Imaging Spectroradiometer) product were fitted by linear regression during the period from 2003 to 2009 when the overpass time for both of them was coincident. Based on this relationship, the time series of water levels from 1999 to 2002 were extended by using the water surface area extracted from Landsat TM/ETM+ images as inputs, and finally the variations of water volume in Lake Qinghai were estimated from 1999 to 2009. The overall errors of water levels retrieved by the simple method in our work were comparable with other globally available test results with r = 0.93, MAE = 0.07 m, and RMSE = 0.09 m. The annual average rate of increase was 0.11 m/yr, which was very close to the results obtained from in situ measurements. High accuracy was obtained in the estimation of surface areas. The MAE and RMSE were only 6 km 2 , and 8 km 2 , respectively, which were even lower than the MAE and RMAE of surface area extracted from Landsat TM images. The estimated water volume variations effectively captured the trend of annual OPEN ACCESSRemote Sens. 2014, 6 10458 variation of Lake Qinghai. Good agreement was achieved between the estimated and measured water volume variations with MAE = 0.4 billion m 3 , and RMSE = 0.5 billion m 3 , which only account for 0.7% of the total water volume of Lake Qinghai. This study demonstrates that it is feasible to monitor comprehensively the fluctuation of large water bodies based entirely on remote sensing data.

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“…For example, errors in topographic forcing data (Jarihani et al, 2015), flood extent (Jarihani et al, 2014;Ticehurst et al, 2014) and water elevation time series (Baghdadi et al, 2011;Birkett and Beckley, 2010;Hall et al, 2012;Jarihani et al, 2013) could possibly add low to medium uncertainty (related to the error budget) within hydrodynamic model results (Table 7).…”

Section: Uncertainty and Limitationsmentioning

confidence: 99%

“…(iv) quantification of flood levels (Baghdadi et al, 2011;Birkett and Beckley, 2010;Jarihani et al, 2013;Troitskaya et al, 2012;Zhang et al, 2011);…”

Section: Introductionmentioning

confidence: 99%

Where does all the water go? Partitioning water transmission losses in a data-sparse, multi-channel and low-gradient dryland river system using modelling and remote sensing

Jarihani

Larsen

Callow

et al. 2015

Journal of Hydrology

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Where does all the water go? Partitioning water transmission losses in a data-sparse, multi-channel and low-gradient dryland river system using modelling and remote sensing, Journal of Hydrology (2015), doi: http://dx.doi.org/10.1016/j.jhydrol. 2015.08.030 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. 1Where does all the water go? Partitioning water transmission losses in a data-sparse, multi-channel and low-gradient dryland river system using modelling and remote sensing and April 2012 along a 180 km reach of the Diamantina River in the Lake Eyre Basin, Australia.Transmission losses were found to be high, on average 46% of total inflow within 180 km reach segment or 7 GL/km (range: 4 to 10 GL/km). However, in 180 km reach, transmission losses vary non-linearly with flood discharge, with smaller flows resulting in higher losses (up to 68%), which diminish in higher flows (down to 24%) and in general there is a minor increase in losses with distance downstream. Partitioning these total losses into the major components shows that actual evaporation was the most significant component (21.6% of total inflow), followed by infiltration (13.2%) and terminal water storage (11.2%). Lateral inflow can be up to 200% of upstream inflow (mean = 86%) and is therefore a critical parameter in the water balance and 3 transmission loss calculations. This study shows that it is possible to constrain the water balance using hydrodynamic models in dryland river systems using remote sensing and simple field measurements to address the otherwise scarce availability of data. The results of this study also enable a better understanding of the water resources available for ecosystems in these unique multi-channel and large floodplain rivers. The combined modelling / remote sensing approach of this study can be applied elsewhere in the world to better understand the water balances and water transmission losses, important drivers of ecohydrological processes in dryland environments.

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The Relevance of GLAS/ICESat Elevation Data for the Monitoring of River Networks

Cited by 53 publications

References 19 publications

Lake Volume Monitoring from Space

Lake Volume Monitoring from Space

Monitoring the Fluctuation of Lake Qinghai Using Multi-Source Remote Sensing Data

Where does all the water go? Partitioning water transmission losses in a data-sparse, multi-channel and low-gradient dryland river system using modelling and remote sensing

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