DOI: 10.5194/hess-21-2509-2017
Scopus记录号: 2-s2.0-85019495109
论文题名: Evaluation of a cosmic-ray neutron sensor network for improved land surface model prediction
作者: Baatz R ; , Franssen H ; -J ; H ; , Han X ; , Hoar T ; , Reemt Bogena H ; , Vereecken H
刊名: Hydrology and Earth System Sciences
ISSN: 10275606
出版年: 2017
卷: 21, 期: 5 起始页码: 2509
结束页码: 2530
语种: 英语
Scopus关键词: Catchments
; Cosmic rays
; Cosmology
; Forecasting
; Mean square error
; Moisture
; Moisture control
; Moisture determination
; Moisture meters
; Neutrons
; Parameter estimation
; Runoff
; Sensor networks
; Soil moisture
; Soils
; Surface measurement
; Surface roughness
; Vegetation
; Community land models
; Land surface modeling
; Remotely sensed soil moisture
; Root mean square errors
; Soil hydraulic parameters
; Soil moisture estimation
; Soil moisture measurement
; Soil moisture predictions
; Soil surveys
; catchment
; cosmic ray
; data assimilation
; estimation method
; experimental study
; hydraulic conductivity
; Kalman filter
; land surface
; prediction
; remote sensing
; sensor
; soil moisture
; Germany
英文摘要: 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 2354g 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 0.03g cm3g cmg-3 for the assimilation period and 0.05g cm3g cmg-3 for the evaluation period. Improvements were limited by the measurement error of CRNSs (0.03g cm3g cmg-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. © 2017 Author(s). Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/79172
Appears in Collections: 气候变化事实与影响
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作者单位: Agrosphere (IBG-3), Forschungszentrum Jülich GmbH, Jülich, Germany; HPSC-TerrSys, Jülich, Germany; NCAR Data Assimilation Research Section, Boulder, CO, United States
Recommended Citation:
Baatz R,, Franssen H,-J,et al. Evaluation of a cosmic-ray neutron sensor network for improved land surface model prediction[J]. Hydrology and Earth System Sciences,2017-01-01,21(5)