Evaluation of a cosmic-ray neutron sensor network for improved land surface model prediction
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 s...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2017-05-01
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| Series: | Hydrology and Earth System Sciences |
| Online Access: | http://www.hydrol-earth-syst-sci.net/21/2509/2017/hess-21-2509-2017.pdf |
| Summary: | 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 km<sup>2</sup> 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 cm<sup>3</sup> cm<sup>−3</sup> for the assimilation period and 0.05 cm<sup>3</sup> cm<sup>−3</sup> for
the evaluation period. Improvements were limited by the measurement error of
CRNSs (0.03 cm<sup>3</sup> cm<sup>−3</sup>). 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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| ISSN: | 1027-5606 1607-7938 |