Water Budget Analysis within the Surrounding of Prominent Lakes and Reservoirs 
from Multi-Sensor Earth Observation Data and Hydrological Models: Case Studies 
of the Aral Sea and Lake Mead

Singh, Alka; Seitz, Florian; Eicker, Annette; Güntner, A.

doi:10.3390/rs8110953

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Water Budget Analysis within the Surrounding of Prominent Lakes and Reservoirs from Multi-Sensor Earth Observation Data and Hydrological Models: Case Studies of the Aral Sea and Lake Mead

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Singh, Alka
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Seitz, Florian
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Eicker, Annette
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/persons/resource/guentner

Güntner, A.
5.4 Hydrology, 5.0 Geoarchives, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum;

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Citation

Singh, A., Seitz, F., Eicker, A., Güntner, A. (2016): Water Budget Analysis within the Surrounding of Prominent Lakes and Reservoirs from Multi-Sensor Earth Observation Data and Hydrological Models: Case Studies of the Aral Sea and Lake Mead. - Remote Sensing, 8, 11, 953.
https://doi.org/10.3390/rs8110953

Cite as: https://gfzpublic.gfz-potsdam.de/pubman/item/item_1852888

Abstract

The hydrological budget of a region is determined based on the horizontal and vertical water fluxes acting in both inward and outward directions. These integrated water fluxes vary, altering the total water storage and consequently the gravitational force of the region. The time-dependent gravitational field can be observed through the Gravity Recovery and Climate Experiment (GRACE) gravimetric satellite mission, provided that the mass variation is above the sensitivity of GRACE. This study evaluates mass changes in prominent reservoir regions through three independent approaches viz. fluxes, storages, and gravity, by combining remote sensing products, in-situ data and hydrological model outputs using WaterGAP Global Hydrological Model (WGHM) and Global Land Data Assimilation System (GLDAS). The results show that the dynamics revealed by the GRACE signal can be better explored by a hybrid method, which combines remote sensing-based reservoir volume estimates with hydrological model outputs, than by exclusive model-based storage estimates. For the given arid/semi-arid regions, GLDAS based storage estimations perform better than WGHM.