Abstract
Abstract. 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.
Highlights
Soil moisture is an important physical variable, significant in agriculture (Pinnington et al, 2018), flood events (Koster et al, 2010; Berghuijs et al, 2019), and processes related to weather and climate (Seneviratne et al, 2010)
Land surface models such as the Joint UK Land Environment Simulator (JULES) can be used to make predictions of soil moisture and generally rely on empirical pedotransfer functions (PTFs) to relate readily available or easy-to-measure soil characteristics such as soil texture to the soil hydraulics parameters required by the model (e.g. Van Looy et al, 2017)
The rest of the paper is organized as follows: in Sect. 2, we outline the JULES land surface model and the COSMOS-UK data used in this study; we describe the data assimilation experiment we have performed and introduce the metric we deployed to measure how well the model fits the observations
Summary
Soil moisture is an important physical variable, significant in agriculture (Pinnington et al, 2018), flood events (Koster et al, 2010; Berghuijs et al, 2019), and processes related to weather and climate (Seneviratne et al, 2010). Land surface models such as the Joint UK Land Environment Simulator (JULES) can be used to make predictions of soil moisture and generally rely on empirical pedotransfer functions (PTFs) to relate readily available or easy-to-measure soil characteristics such as soil texture to the soil hydraulics parameters required by the model (e.g. Van Looy et al, 2017). The recent development of novel in situ techniques for measuring soil moisture over field rather than point scale presents an opportunity to test whether land surface models, in conjunction with commonly used pedotransfer functions, are able to reproduce field-scale soil moisture observations
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