Please use this identifier to cite or link to this item: http://dx.doi.org/10.18419/opus-9945
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dc.contributor.authorZhao, Daixin-
dc.date.accessioned2018-08-01T12:34:57Z-
dc.date.available2018-08-01T12:34:57Z-
dc.date.issued2018de
dc.identifier.other508410967-
dc.identifier.urihttp://elib.uni-stuttgart.de/handle/11682/9962-
dc.identifier.urihttp://nbn-resolving.de/urn:nbn:de:bsz:93-opus-ds-99629de
dc.identifier.urihttp://dx.doi.org/10.18419/opus-9945-
dc.description.abstractInland surface water bodies, e.g. lakes and rivers, play vital roles in the nature and in the society. To understand the impact of the human activities and climate change on these vulnerable water resources, monitoring the water level variations with a finer spatial and temporal resolutions is a primary issue. On the other hand, the global available and free accessible in-situ gauge databases are unsatisfactory and insufficient. The spatial distribution of gauge stations is severely uneven and the data accuracy is highly dependent on processing method. Therefore, it is an essential requisite to have a constantly and reliable data stream. Over the past two decades, satellite altimetry has shown the capability to provide repeatable monitoring results for hydrologic cycle and inland water bodies. Several researches and studies are carried out with respect to the improvements on multi-mission data fusion, retracking methods, error estimation and outliers rejection. In this thesis, we take advantage of this state-of-art inland surface water level monitoring technique to generate the water level time series over Amazon River, Benue River and Tsimlyansk Reservoir. Initially, we investigate the measurement principle, corrections and retracking algorithms of radar altimeter throughly. Afterwards, the processing scheme for water level time series is divided into three steps: data selection, correction and result generation. In this thesis, we have chosen Jason-2 measurement data and the on-board Ice retracker. A validation has been performed between our results and the time series from other databases, e.g. DAHITI and Hydroweb. The comparisons showed a feasible and acceptable outcomes regarding to correlation coefficient and root-mean-square error (RMSE). The best result was given by Benue River case with 0:98 and 0:96 of correlation coefficient against DAHITI and Hydroweb, respectively. Also, the minimum RMSE difference, 17.1 cm, was achieved between our time series and the one from DAHITI. We also examined the potential error sources when encountering disagreements with others. Furthermore, possible solutions for error elimination and further improvements were also discussed in experiments and outlook.en
dc.language.isoende
dc.rightsinfo:eu-repo/semantics/openAccessde
dc.subject.ddc550de
dc.subject.ddc620de
dc.titleGenerating water level time series from satellite altimetry measurements for inland applicationsen
dc.typemasterThesisde
ubs.fakultaetLuft- und Raumfahrttechnik und Geodäsiede
ubs.institutGeodätisches Institutde
ubs.publikation.seitenxiv, 58de
ubs.publikation.typAbschlussarbeit (Master)de
Appears in Collections:06 Fakultät Luft- und Raumfahrttechnik und Geodäsie

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