English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

In situ soil moisture sensors provide highly accurate but verylocal soil moisture measurements, while remotely sensed soil moisture isstrongly affected by vegetation and surface roughness. In contrast,cosmic-ray neutron sensors (CRNSs) allow highly accurate soil moistureestimation on the field scale which could be valuable to improve land surfacemodel predictions. In this study, the potential of a network of CRNSsinstalled in the 2354 km2 Rur catchment (Germany) for estimating soilhydraulic parameters and improving soil moisture states was tested. Datameasured by the CRNSs were assimilated with the local ensemble transformKalman filter in the Community Land Model version 4.5. Data of four, eight andnine CRNSs were assimilated for the years 2011 and 2012 (with and without soilhydraulic parameter estimation), followed by a verification year 2013 withoutdata assimilation. This was done using (i) a regional high-resolution soilmap, (ii) the FAO soil map and (iii) an erroneous, biased soil map as inputinformation for the simulations. For the regional soil map, soil moisturecharacterization was only improved in the assimilation period but not in theverification period. For the FAO soil map and the biased soil map, soilmoisture predictions improved strongly to a root mean square error of0.03 cm3 cm−3 for the assimilation period and 0.05 cm3 cm−3 forthe evaluation period. Improvements were limited by the measurement error ofCRNSs (0.03 cm3 cm−3). The positive results obtained with dataassimilation of nine CRNSs were confirmed by the jackknife experiments withfour and eight CRNSs used for assimilation. The results demonstrate thatassimilated data of a CRNS network can improve the characterization of soilmoisture content on the catchment scale by updating spatially distributedsoil hydraulic parameters of a land surface model

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