TR32-Database: Database of Transregio 32

[1708] - Evaluation of a cosmic-ray neutron sensor network for improved land surface model prediction

All available metadata of the dataset are listed below. Some features are available, e.g. download of dataset or additional description file.

Features
Citation
Baatz, R., Hendricks-Franssen, H., Han, X., Tim, H., Bogena, H., Vereecken, H., 2017. Evaluation of a cosmic-ray neutron sensor network for improved land surface model prediction. Hydrology and Earth System Sciences, 21, 2509 - 2530. DOI: 10.5194/hess-21-2509-2017.
Identification
Title(s):Main Title: Evaluation of a cosmic-ray neutron sensor network for improved land surface model prediction
Description(s):Abstract: In situ soil moisture sensors provide highly accurate but very local soil moisture measurements, while remotely sensed soil moisture is strongly affected by vegetation and surface roughness. In contrast, cosmic-ray neutron sensors (CRNSs) allow highly accurate soil moisture estimation on the field scale which could be valuable to improve land surface model predictions. In this study, the potential of a network of CRNSs installed in the 2354 km2 Rur catchment (Germany) for estimating soil hydraulic parameters and improving soil moisture states was tested. Data measured by the CRNSs were assimilated with the local ensemble transform Kalman filter in the Community Land Model version 4.5. Data of four, eight and nine CRNSs were assimilated for the years 2011 and 2012 (with and without soil hydraulic parameter estimation), followed by a verification year 2013 without data assimilation. This was done using (i) a regional high-resolution soil map, (ii) the FAO soil map and (iii) an erroneous, biased soil map as input information for the simulations. For the regional soil map, soil moisture characterization was only improved in the assimilation period but not in the verification period. For the FAO soil map and the biased soil map, soil moisture predictions improved strongly to a root mean square error of 0.03 cm3 cm−3 for the assimilation period and 0.05 cm3 cm−3 for the evaluation period. Improvements were limited by the measurement error of CRNSs (0.03 cm3 cm−3). The positive results obtained with data assimilation of nine CRNSs were confirmed by the jackknife experiments with four and eight CRNSs used for assimilation. The results demonstrate that assimilated data of a CRNS network can improve the characterization of soil moisture content on the catchment scale by updating spatially distributed soil hydraulic parameters of a land surface model.
Identifier(s):DOI: 10.5194/hess-21-2509-2017
Citation Advice:Baatz, R., Hendricks Franssen, H.-J., Han, X., Hoar, T., Bogena, H. R., and Vereecken, H.: Evaluation of a cosmic-ray neutron sensor network for improved land surface model prediction, Hydrol. Earth Syst. Sci., 21, 2509-2530, https://doi.org/10.5194/hess-21-2509-2017, 2017.
Responsible Party
Creator(s):Author: Roland Baatz
Author: Harrie-Jan Hendricks-Franssen
Author: Xujun Han
Author: Hoar Tim
Author: Heye Bogena
Author: Harry Vereecken
Publisher:Copernicus
Topic
TR32 Topic:Soil
Related Sub-project(s):C6
Subject(s):CRC/TR32 Keywords: Data Assimilation, Land-Atmosphere Interaction, Soil Moisture
File Details
File Name:Baatz_etal_2017_HESS.pdf
Data Type:Text
File Size:5287 kB (5.163 MB)
Date(s):Available: 2017-05-16
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.
Licence:TR32DB Data policy agreement
Geographic
North:-no map data
East:-
South:-
West:-
Measurement Region:RurCatchment
Measurement Location:--RurCatchment--
Specific Informations - Publication
Status:Accepted
Review:PeerReview
Year:2017
Type:Article
Article Type:Journal
Source:Hydrology and Earth System Sciences
Volume:21
Page Range:2509 - 2530
Metadata Details
Metadata Creator:Wolfgang Kurtz
Metadata Created:2017-10-06
Metadata Last Updated:2017-10-06
Subproject:C6
Funding Phase:2
Metadata Language:English
Metadata Version:V42
Dataset Metrics
Page Visits:39
Metadata Downloads:0
Dataset Downloads:2
Dataset Activity
Features