Inter-annual water storage changes in the Aral Sea from multi-mission satellite altimetry, optical remote sensing, and GRACE satellite gravimetry

Singh, Alka; Seitz, Florian; Schwatke, C

doi:10.1016/j.rse.2012.01.001

Cited by 105 publications

(70 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Recently the GRACE satellite gravimetry has been used in combination with altimetry and satellite imagery data to study the variations of water volume in the very large inland water bodies, e.g. a study by [15]. These satellite altimetry databases are becoming attractive for operational applications in water resources management.…”

Section: H M Ebaid M Aziz Journal Of Geographic Information Systemmentioning

confidence: 99%

Integrating Radar Altimeters and Optical Imagery Data for Estimating Water Volume Variations in Lakes and Reservoirs (Case Study: Lake Nasser)

Ebaid¹,

Aziz²

2017

JGIS

View full text Add to dashboard Cite

Monitoring of variations in water for lakes and reservoirs is a requirement for meeting human needs and assessing ongoing climatic changes. However, regular gauging networks fail to provide the information needed for water volume data. The aim of this study is to evaluate an approach to estimate water volume variation for the southern part of Lake Nasser in Egypt without in-situ gauge measurements and bathymetry maps. Combination of both Hydroweb satellite altimetry and Landsat 8 satellite imagery data was used. As compared to in-situ water levels, satellite altimetry provided accurate water levels variations for Lake Nasser; the RMSE was 0.28 m, with excellent agreement (R 2 is 0.98). The lowest water level of altimetry database i.e. 174.57 m was used as a reference level for estimating water volumes variations for the study duration 8/2014-6/2015. All water altimetry levels were converted to differences of recorded water level above the lowest altimetry Level (∆WL). Series of Landsat 8 imagery data were selected to extract surface areas corresponding to radar altimetry water levels dates. Areas-∆WL relationship model was established as a polynomial function: A = f(∆WL), and therefore, the relationship of the water volume above the lowest water level for the study time (∆V) and ∆WL was obtained through the analytical integration of (Area-∆WL) model. Another approach (Heron method) was also applied for estimating water volume variations. Validation of these two approaches showed that estimated water volume variations above reference water level using both methods i.e. integration and Heron agreed well with in-situ measurements of volume variation deduced from recent bathymetry map and in-situ water levels (R 2 for both methods = . Another byproduct for these approaches was the modeling for a remote detecting water level. Once the F(L) relationship is set up for a given region, future Landsat images can be utilized to track water levels freely of radar altimetry. Finally it can be concluded that remote sensing resources (satellites radar altimeters and optical satellite images) that are openly accessible these days represent a great opportunity to remotely monitor reservoir water capacity and help in examining and observing hydrological and water driven procedures.

show abstract

Section: H M Ebaid M Aziz Journal Of Geographic Information Systemmentioning

confidence: 99%

Integrating Radar Altimeters and Optical Imagery Data for Estimating Water Volume Variations in Lakes and Reservoirs (Case Study: Lake Nasser)

Ebaid¹,

Aziz²

2017

JGIS

View full text Add to dashboard Cite

show abstract

“…Since then, many studies have illustrated certain case studies using satellite altimetry for lake surveys (Birkett et al 1999;Aladin et al 2005;Coe and Birkett 2005;Hwang et al 2005;Medina et al 2008;Lee et al 2011;Singh et al 2012;Kleinherenbrink et al 2015 and many others). The information obtained by this technique is the average water level above a reference surface for a set of lakes once they are overpassed by the satellite.…”

Section: Introductionmentioning

confidence: 99%

Lake Volume Monitoring from Space

Crétaux¹,

et al. 2016

View full text Add to dashboard Cite

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.

show abstract

“…The observed inconsistencies can be explained by the possibility of error in data acquisition [38,41] and its processing. In addition, for the large water bodies there is the possibility of a geoid anomaly.…”

Section: Discussionmentioning

confidence: 99%

“…They are mostly collected from publications [27,38], which are given without height reference information. Therefore we cross-validated the observations due to lack of possibility of validating with respective ground observations.…”

Section: Validation Of Water Heightmentioning

confidence: 99%

Remote Sensing of Storage Fluctuations of Poorly Gauged Reservoirs and State Space Model (SSM)-Based Estimation

2015

Self Cite

View full text Add to dashboard Cite

To reduce hydrological uncertainties in the regular monitoring of poorly gauged lakes and reservoirs, multi-dimensional remote sensing data have emerged as an excellent alternative. In this paper, we propose three methods to delineate the volume of such equipotential water bodies through a combination of altimetry (1D), Landsat (2D) and bathymetry (2D) data, namely an altimetry-bathymetry-volume method (ABV), a Landsat-bathymetry-volume method (LBV) and an altimetry-Landsat-volume-variation method (ALVV). The first two data products are further merged by a Kalman-filter-based state space model (SSM) to obtain a combined estimate (CSSME) time series and near future prediction. To validate our methods, we tested them on the well-measured Lake Mead and further applied them on the poorly gauged Aral Sea, which has inaccurate bathymetry and very limited ground observation data. We updated the lake bathymetry of the Aral Sea, which was more than half a century old. The resultant remote sensing products have a very good long-term agreement among each other. The Lake Mead volume estimations are very highly coherent with the ground observations for all cases (R 2 > 0.96 and NRMSE < 2.1%), except for the forecast (R 2 = 0.75 and NRMSE = 3.7%). Due to lack of in situ data for the Aral Sea, the estimated volumes are compared, and the entire Aral Sea LBV and ABV have R 2 = 0.91 and NRMSE = 5.5%, and the forecast compared to CSSME has R 2 = 0.60 and NRMSE = 2.4%.

show abstract

Inter-annual water storage changes in the Aral Sea from multi-mission satellite altimetry, optical remote sensing, and GRACE satellite gravimetry

Cited by 105 publications

References 34 publications

Integrating Radar Altimeters and Optical Imagery Data for Estimating Water Volume Variations in Lakes and Reservoirs (Case Study: Lake Nasser)

Integrating Radar Altimeters and Optical Imagery Data for Estimating Water Volume Variations in Lakes and Reservoirs (Case Study: Lake Nasser)

Lake Volume Monitoring from Space

Remote Sensing of Storage Fluctuations of Poorly Gauged Reservoirs and State Space Model (SSM)-Based Estimation

Contact Info

Product

Resources

About