Journal Article

FZJ-2017-04442

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Evaluation of a cosmic-ray neutron sensor network for improved land surface model predictio

Baatz, R. (Corresponding author)FZJ* ; Hendricks-Franssen, H.-J.FZJ* ; Han, X. ; Hoar, T. ; Bogena, H.FZJ* ; Vereecken, H.FZJ*

2017
EGU Katlenburg-Lindau

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Please use a persistent id in citations: http://hdl.handle.net/2128/14833  doi:10.5194/hess-21-2509-2017

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 (CRNS) allow highly accurate soil moisture estimation at the field scale which could be valuable to improve land surface model predictions. In this study, the potential of a network of CRNS installed in the 2354 km2 Rur catchment (Germany) for estimating soil hydraulic parameters and improving soil moisture states was tested. Data measured by the CRNS were assimilated with the local ensemble transform Kalman filter in the Community Land Model v. 4.5. Data of four, eight and nine CRNS 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/cm3 for the assimilation period and 0.05 cm3/cm3 for the evaluation period. Improvements were limited by the measurement error of CRNS (0.03 cm3/cm3). The positive results obtained with data assimilation of nine CRNS were confirmed by the jackknife experiments with four and eight CRNS used for assimilation. The results demonstrate that assimilated data of a CRNS network can improve the characterization of soil moisture content at the catchment scale by updating spatially distributed soil hydraulic parameters of a land surface model.

Keyword(s): Earth, Environment and Cultural Heritage (1st)

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Contributing Institute(s):

1. Agrosphäre (IBG-3)

Research Program(s):

1. 255 - Terrestrial Systems: From Observation to Prediction (POF3-255) (POF3-255)

Appears in the scientific report 2017

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Database coverage:
; ; ; Current Contents - Physical, Chemical and Earth Sciences ; DOAJ Seal ; IF < 5 ; JCR ; SCOPUS ; Science Citation Index Expanded ; Thomson Reuters Master Journal List ; Web of Science Core Collection

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