TR32-Database: Database of Transregio 32

[1675] - Catchment Tomography - An approach for spatial parameter estimation

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Citation
Baatz, D., Kurtz, W., Hendricks-Franssen, H., Vereecken, H., Kollet, S., 2017. Catchment Tomography - An approach for spatial parameter estimation. Advances in Water Resources, 107, 147 - 159. DOI: 10.1016/j.advwatres.2017.06.006.
Identification
Title(s):Main Title: Catchment Tomography - An approach for spatial parameter estimation
Description(s):Abstract: - A tomographic approach for estimating a distributed Manning’s roughness coefficient is proposed - Spatially distributed precipitation data, serving as transmitter, are essential. - A joint state-parameter update with the EnKF is applied assimilating stream water level observations - The distributed Manning’s coefficient is accurately estimated in a 2D synthetic experiment
Abstract: The use of distributed-physically based hydrological models is often hampered by the lack of information on key parameters and their spatial distribution and temporal dynamics. Typically, the estimation of parameter values is impeded by the lack of sufficient observations leading to mathematically underdetermined estimation problems and thus non-uniqueness. Catchment tomography (CT) presents a method to estimate spatially distributed model parameters by resolving the integrated signal of stream runoff in response to precipitation. Basically CT exploits the information content generated by a distributed precipitation signal both in time and space. In a moving transmitter-receiver concept, high resolution, radar based precipitation data are applied with a distributed surface runoff model. Synthetic stream water level observations, serving as receivers, are assimilated with an Ensemble Kalman Filter. With a joint state-parameter update the spatially distributed Manning's roughness coefficient, n, is estimated using the coupled Terrestrial Systems Modelling Platform and the Parallel Data Assimilation Framework (TerrSysMP-PDAF). The sequential data assimilation in combination with the distributed precipitation continuously integrates new information into the model, thus, increasingly constraining the parameter space. With this large amount of data included for the parameter estimation, CT reduces the problem of underdetermined model parameters. The initially biased Manning's coefficients spatially distributed in two and four fixed parameter zones are estimated with errors of less than 3% and 17%, respectively, with only 64 model realizations. It is shown that the distributed precipitation is of major importance for this approach.
Identifier(s):DOI: 10.1016/j.advwatres.2017.06.006
Responsible Party
Creator(s):Author: Dorina Baatz
Author: Wolfgang Kurtz
Author: Harrie-Jan Hendricks-Franssen
Author: Harry Vereecken
Author: Stefan Kollet
Publisher:Elsevier
Topic
TR32 Topic:Other
Related Sub-project(s):D7, C6
Subject(s):CRC/TR32 Keywords: Data Assimilation, ParFlow CLM, Overland Flow, Estimation, Precipitation, Radar, Hydrological Modelling, Surface Roughness, Streamflow, Parallel Computing
Topic Category:GeoScientificInformation
File Details
File Name:BaatzDorina_etal2017_AWR_CatchmentTomography.pdf
Data Type:Text
File Size:2314 kB (2.26 MB)
Date(s):Available: 2017-06-28
Mime Type:application/pdf
Data Format:PDF
Language:English
Status:Completed
Constraints
Download Permission:Free
General Access and Use Conditions:According to the TR32DB data policy agreement.
Access Limitations:According to the TR32DB data policy agreement.
Geographic
North:-no map data
East:-
South:-
West:-
Measurement Region:Germany
Measurement Location:--Germany--
Specific Informations - Publication
Status:Accepted
Review:PeerReview
Year:2017
Type:Article
Source:Advances in Water Resources
Volume:107
Number Of Pages:13
Page Range:147 - 159
Metadata Details
Metadata Creator:Dorina Walther
Metadata Created:2017-06-29
Metadata Last Updated:2017-06-29
Subproject:D7
Funding Phase:3
Metadata Language:English
Metadata Version:V42
Dataset Metrics
Page Visits:53
Metadata Downloads:0
Dataset Downloads:1
Dataset Activity
Features