Daniel Scherer

Daniel Scherer
Technische Universität München | TUM · Deutsches Geodätisches Forschungsinstitut

Master of Engineering

About

4
Publications
795
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47
Citations

Publications

Publications (4)
Article
Full-text available
Remote sensing data are essential for monitoring the Earth's surface waters, especially since the amount of publicly available in-situ data is declining. Satellite altimetry provides valuable information on the water levels and variations of lakes, reservoirs and rivers. In combination with satellite imagery, the derived time series allow the monit...
Article
Full-text available
Despite increasing interest in monitoring the global water cycle, the availability of in situ gauging and discharge time series is decreasing. However, this lack of ground data can partly be compensated for by using remote sensing techniques to observe river stages and discharge. In this paper, a new approach for estimating discharge by combining w...
Conference Paper
In this paper, a new approach for estimating the discharge of large rivers based on long-term remote sensing data and using the Manning equation is presented. The key idea is to observe the river’s cross-sectional geometry from the combination of satellite altimetry and water masks extracted from optical remote sensing imagery. The water surface he...
Article
Full-text available
In this study, a new approach for the automated extraction of high-resolution time-variable water surfaces is presented. For that purpose, optical images from Landsat and Sentinel-2 are used between January 1984 and June 2018. The first part of this new approach is the extraction of land-water masks by combining five water indexes and using an auto...

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Projects

Project (1)
Project
The primary goals are: (1) develop a multi-observation ensemble-based calibration and data assimilation (C/DA) methodology to combine observational data of model output variables (time series of gauge-based streamflow, GRACE/GRACE-FO total water storage variations, remotely-sensed extent and level of surface water bodies, snow cover, glacier mass change and streamflow) with hydrological models in an optimal manner. (2) exploit this methodology with the global hydrological model WaterGAP to provide an improved quantitative assessment of freshwater fluxes and storages including their uncertainties in response to climate and anthropogenic forcing. To reach these goals, nine partners work together, collaborating with two Mercator fellows. The Goethe University Frankfurt and the University of Bonn coordinate the project.