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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Volume 21, issue 5
Hydrol. Earth Syst. Sci., 21, 2509-2530, 2017
https://doi.org/10.5194/hess-21-2509-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
Hydrol. Earth Syst. Sci., 21, 2509-2530, 2017
https://doi.org/10.5194/hess-21-2509-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 16 May 2017

Research article | 16 May 2017

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

Roland Baatz1,2, Harrie-Jan Hendricks Franssen1,2, Xujun Han1,2, Tim Hoar3, Heye Reemt Bogena1, and Harry Vereecken1,2 Roland Baatz et al.
  • 1Agrosphere (IBG-3), Forschungszentrum Jülich GmbH, 52425 Jülich, Germany
  • 2HPSC-TerrSys, 52425 Jülich, Germany
  • 3NCAR Data Assimilation Research Section, Boulder, CO, USA

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 2354km2 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 0.03cm3cm−3 for the assimilation period and 0.05cm3cm−3 for the evaluation period. Improvements were limited by the measurement error of CRNSs (0.03cm3cm−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.

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Soil moisture is a major variable that affects regional climate, weather and hydrologic processes on the Earth's surface. In this study, real-world data of a network of cosmic-ray sensors were assimilated into a regional land surface model to improve model states and soil hydraulic parameters. The results show the potential of these networks for improving model states and parameters. It is suggested to widen the number of observed variables and to increase the number of estimated parameters.
Soil moisture is a major variable that affects regional climate, weather and hydrologic...
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