Improving Soil Moisture Prediction of a High–Resolution Land Surface Model by Parameterising Pedotransfer Functions through Assimilation of SMAP Satellite Data

Abstract

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 for the JULES land surface model (run at a 1 km resolution) when compared to estimates from a cosmic-ray soil moisture monitoring network (COSMOS-UK). The spatial resolution of these 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.

Hydrological impact of widespread afforestation in Great Britain using a large ensemble of modelled scenarios

“…Even though model performance is not the same as achieved with calibrated conceptual or empirical models, it allows us to determine the effects of vegetation changes on the hydrological cycle. JULES' ability to faithfully represent hydrological land-surface processes in Great Britain has been evaluated in several studies 58,79,80 and the plant functional type parameters it uses at global scales 81,82 . To validate the ability of our configuration of JULES to represent soil moisture and potential evapotranspiration rates, we compare the model output with observations from twelve COSMOS-UK sites within our catchments covering grasslands, croplands, coniferous and broadleaf woodland 83 (Supplementary Fig.…”

A regional coupled approach to water cycle prediction during winter 2013/14 in the United Kingdom

“…A number of these enhancements are being currently delivered and coordinated through the Hydro-JULES programme (https://hydrojules.org/). Hydro-JULES research is also deriving improved land surface parameters through a data assimilation framework using the COSMOS observations (Cooper et al, 2020;Pinnington et al, 2020).…”

Continental scale downscaling of AWRA-L analysed soil moisture using random forest regression

“…The depth of AWRA-L upper layer is different from the general depth of the surface SM (5-cm). Pinnington et al (2021) found the assimilation results of LSM are usually consistent while running with either a 10-cm top layer or a 5-cm top layer. The comparison between AWRA-L upper layer and other independent surface SM can be considered fair.…”