Using data assimilation to optimize pedotransfer functions using field-scale in situ soil moisture observations

Cooper, Elizabeth ORCID:; Blyth, Eleanor ORCID:; Cooper, Hollie ORCID:; Ellis, Rich; Pinnington, Ewan ORCID:; Dadson, Simon J.. 2021 Using data assimilation to optimize pedotransfer functions using field-scale in situ soil moisture observations. Hydrology and Earth System Sciences, 25 (5). 2445-2458.

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Soil moisture predictions from land surface models are important in hydrological, ecological, and meteorological applications. In recent years, the availability of wide-area soil moisture measurements has increased, but few studies have combined model-based soil moisture predictions with in situ observations beyond the point scale. Here we show that we can markedly improve soil moisture estimates from the Joint UK Land Environment Simulator (JULES) land surface model using field-scale observations and data assimilation techniques. Rather than directly updating soil moisture estimates towards observed values, we optimize constants in the underlying pedotransfer functions, which relate soil texture to JULES soil physics parameters. In this way, we generate a single set of newly calibrated pedotransfer functions based on observations from a number of UK sites with different soil textures. We demonstrate that calibrating a pedotransfer function in this way improves the soil moisture predictions of a land surface model at 16 UK sites, leading to the potential for better flood, drought, and climate projections.

Item Type: Publication - Article
Digital Object Identifier (DOI):
UKCEH and CEH Sections/Science Areas: Hydro-climate Risks (Science Area 2017-)
ISSN: 1027-5606
Additional Information. Not used in RCUK Gateway to Research.: Open Access paper - full text available via Official URL link.
NORA Subject Terms: Hydrology
Date made live: 11 Sep 2020 09:10 +0 (UTC)

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