Publication Details

Category Text Publication
Reference Category Journals
DOI 10.5194/hess-25-1617-2021
Licence creative commons licence
Title (Primary) Improving soil moisture prediction of a high-resolution land surface model by parameterising pedotransfer functions through assimilation of SMAP satellite data
Author Pinnington, E.; Amezcua, J.; Cooper, E.; Dadson, S.; Ellis, R.; Peng, J. ORCID logo ; Robinson, E.; Morrison, R.; Osborne, S.; Quaife, T.
Source Titel Hydrology and Earth System Sciences
Year 2021
Department RS
Volume 25
Issue 3
Page From 1617
Page To 1641
Language englisch
Topic T5 Future Landscapes
Data and Software links https://doi.org/10.5281/zenodo.2654853
Supplements https://hess.copernicus.org/articles/25/1617/2021/hess-25-1617-2021-supplement.pdf
Abstract Pedotransfer functions are used to relate gridded databases of soil texture information to the soil hydraulic and thermal parameters of land surface models. The parameters within these pedotransfer functions are uncertain and calibrated through analyses of point soil samples. How these calibrations relate to the soil parameters at the spatial scale of modern land surface models is unclear because gridded databases of soil texture represent an area average. We present a novel approach for calibrating such pedotransfer functions to improve land surface model soil moisture prediction by using observations from the Soil Moisture Active Passive (SMAP) satellite mission within a data assimilation framework. Unlike traditional calibration procedures, data assimilation always takes into account the relative uncertainties given to both model and observed estimates to find a maximum likelihood estimate. After performing the calibration procedure, we find improved estimates of soil moisture and heat flux for the Joint UK Land Environment Simulator (JULES) land surface model (run at a 1 km resolution) when compared to estimates from a cosmic-ray soil moisture monitoring network (COSMOS-UK) and three flux tower sites. The spatial resolution of the COSMOS probes is much more representative of the 1 km model grid than traditional point-based soil moisture sensors. For 11 cosmic-ray neutron soil moisture probes located across the modelled domain, we find an average 22 % reduction in root mean squared error, a 16 % reduction in unbiased root mean squared error and a 16 % increase in correlation after using data assimilation techniques to retrieve new pedotransfer function parameters.
Persistent UFZ Identifier https://www.ufz.de/index.php?en=20939&ufzPublicationIdentifier=24546
Pinnington, E., Amezcua, J., Cooper, E., Dadson, S., Ellis, R., Peng, J., Robinson, E., Morrison, R., Osborne, S., Quaife, T. (2021):
Improving soil moisture prediction of a high-resolution land surface model by parameterising pedotransfer functions through assimilation of SMAP satellite data
Hydrol. Earth Syst. Sci. 25 (3), 1617 - 1641 10.5194/hess-25-1617-2021