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

Abstract

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 km 2 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 3 cm −3 for the assimilation period and 0.05 cm 3 cm −3 for the evaluation period. Improvements were limited by the measurement error of CRNSs (0.03 cm 3 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.

Status and Perspectives on the Cosmic‐Ray Neutron Method for Soil Moisture Estimation and Other Environmental Science Applications

“…In a synthetic study, soil hydraulic parameters were updated by assimilation of CRNs, providing an improved soil moisture characterization (Han et al, 2016). Baatz et al (2017) successfully assimilated neutron intensity time series from a network of CRN detectors in the CLM model, which also led to improved soil moisture characterization.…”

Response to reviewer 4 -R Baatz

“…The scale of these products are orders of magnitude larger than those of traditional in situ sensors, which creates an issue because of the well-documented small-scale variability in soil moisture Western and Blöschl, 1999). Some researchers have overcome this issue by establishing soil moisture monitoring networks (Bogena et al, 2010;Smith et al, 2012), but the extent of sensor networks is still relatively small (< 1 km 2 ).…”

Author response to Reviewer #2

“…The main advantages of the method are its capabilities to capture different components of the water cycle in air, soil, and vegetation non-invasively (e.g. Baroni and Oswald, 2015) and to provide a representative spatial average of the environmental water content. Therefore, the method is a promising candidate to support hydrogeophysical and climate research in complex terrain (e.g.…”

“…For exam-ple, Chiba et al (1975) and Oh et al (2013) revealed clear discrepancies between high-energy neutron monitors which were related to device-specific configurations. Intercalibration may be employed to normalize such differences from unit to unit and also to account for any residual instrumental configuration inconsistencies (Bachelet et al, 1965;Krüger et al, 2008).…”