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 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.
Highlights
Land surface models are important tools for translating meteorological forecasts and reanalyses into real-world impacts by providing schemes for how energy, water and other matter will interact with the Earth’s surface, outputting relevant diagnostics and variables and understanding the role of variability in the terrestrial hydrological cycle in the Earth system
One of the parameters with a strong change is φa, which is decreased compared to the prior; this parameter controls the absolute magnitude of the saturated soil moisture
We have presented novel methods for calibrating pedotransfer functions used to create the soil parameter ancillaries of a land surface model by using satellite data from the NASA Soil Moisture Active Passive (SMAP) mission
Summary
Land surface models are important tools for translating meteorological forecasts and reanalyses into real-world impacts by providing schemes for how energy, water and other matter will interact with the Earth’s surface, outputting relevant diagnostics and variables and understanding the role of variability in the terrestrial hydrological cycle in the Earth system. As the spatial resolution of available meteorological information has become increasingly fine (Clark et al, 2016), it is necessary to ensure land surface models can utilise this information at its native resolution in order to provide outputs that are as accurate as possible for local populations. Our focus is on soil moisture, which plays an essential role in agriculture (Asfaw et al, 2018), weather and climate prediction (Hauser et al, 2017) and land surface energy partitioning (Beljaars et al, 1996; Bateni and Entekhabi, 2012). Pinnington et al.: Parameterising pedotransfer functions through assimilation of SMAP
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