Improving soil moisture prediction of a high-resolution land surface model by parameterising pedotransfer functions through assimilation of SMAP satellite data

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...

Full description

Bibliographic Details
Main Authors: Pinnington, E, Amezcua, J, Cooper, E, Dadson, S, Ellis, R, Peng, J, Robinson, E, Morrison, R, Osborne, S, Quaife, T
Format: Journal article
Language:English
Published: Copernicus Publications 2021
_version_ 1797109374804557824
author Pinnington, E
Amezcua, J
Cooper, E
Dadson, S
Ellis, R
Peng, J
Robinson, E
Morrison, R
Osborne, S
Quaife, T
author_facet Pinnington, E
Amezcua, J
Cooper, E
Dadson, S
Ellis, R
Peng, J
Robinson, E
Morrison, R
Osborne, S
Quaife, T
author_sort Pinnington, E
collection OXFORD
description 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.
first_indexed 2024-03-07T07:41:00Z
format Journal article
id oxford-uuid:77425b11-3a2d-42a4-a6f9-a1463744129e
institution University of Oxford
language English
last_indexed 2024-03-07T07:41:00Z
publishDate 2021
publisher Copernicus Publications
record_format dspace
spelling oxford-uuid:77425b11-3a2d-42a4-a6f9-a1463744129e2023-04-26T10:43:51ZImproving soil moisture prediction of a high-resolution land surface model by parameterising pedotransfer functions through assimilation of SMAP satellite dataJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:77425b11-3a2d-42a4-a6f9-a1463744129eEnglishSymplectic ElementsCopernicus Publications2021Pinnington, EAmezcua, JCooper, EDadson, SEllis, RPeng, JRobinson, EMorrison, ROsborne, SQuaife, TPedotransfer 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.
spellingShingle Pinnington, E
Amezcua, J
Cooper, E
Dadson, S
Ellis, R
Peng, J
Robinson, E
Morrison, R
Osborne, S
Quaife, T
Improving soil moisture prediction of a high-resolution land surface model by parameterising pedotransfer functions through assimilation of SMAP satellite data
title Improving soil moisture prediction of a high-resolution land surface model by parameterising pedotransfer functions through assimilation of SMAP satellite data
title_full Improving soil moisture prediction of a high-resolution land surface model by parameterising pedotransfer functions through assimilation of SMAP satellite data
title_fullStr Improving soil moisture prediction of a high-resolution land surface model by parameterising pedotransfer functions through assimilation of SMAP satellite data
title_full_unstemmed Improving soil moisture prediction of a high-resolution land surface model by parameterising pedotransfer functions through assimilation of SMAP satellite data
title_short Improving soil moisture prediction of a high-resolution land surface model by parameterising pedotransfer functions through assimilation of SMAP satellite data
title_sort improving soil moisture prediction of a high resolution land surface model by parameterising pedotransfer functions through assimilation of smap satellite data
work_keys_str_mv AT pinningtone improvingsoilmoisturepredictionofahighresolutionlandsurfacemodelbyparameterisingpedotransferfunctionsthroughassimilationofsmapsatellitedata
AT amezcuaj improvingsoilmoisturepredictionofahighresolutionlandsurfacemodelbyparameterisingpedotransferfunctionsthroughassimilationofsmapsatellitedata
AT coopere improvingsoilmoisturepredictionofahighresolutionlandsurfacemodelbyparameterisingpedotransferfunctionsthroughassimilationofsmapsatellitedata
AT dadsons improvingsoilmoisturepredictionofahighresolutionlandsurfacemodelbyparameterisingpedotransferfunctionsthroughassimilationofsmapsatellitedata
AT ellisr improvingsoilmoisturepredictionofahighresolutionlandsurfacemodelbyparameterisingpedotransferfunctionsthroughassimilationofsmapsatellitedata
AT pengj improvingsoilmoisturepredictionofahighresolutionlandsurfacemodelbyparameterisingpedotransferfunctionsthroughassimilationofsmapsatellitedata
AT robinsone improvingsoilmoisturepredictionofahighresolutionlandsurfacemodelbyparameterisingpedotransferfunctionsthroughassimilationofsmapsatellitedata
AT morrisonr improvingsoilmoisturepredictionofahighresolutionlandsurfacemodelbyparameterisingpedotransferfunctionsthroughassimilationofsmapsatellitedata
AT osbornes improvingsoilmoisturepredictionofahighresolutionlandsurfacemodelbyparameterisingpedotransferfunctionsthroughassimilationofsmapsatellitedata
AT quaifet improvingsoilmoisturepredictionofahighresolutionlandsurfacemodelbyparameterisingpedotransferfunctionsthroughassimilationofsmapsatellitedata