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

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 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 can improve the performance of land surface models, leading to the potential for better flood, drought and climate projections.

COSMOS-UK: national soil moisture and hydrometeorology data for environmental science research

“…Furthermore, understanding soil moisture at identified landslip sites could help in the development of landslide early warning systems, for example using the Hollin Hill COSMOS-UK site in North Yorkshire (Bliss et al, 2020). At the site scale, soil moisture data from individual COSMOS-UK sites have proven valuable when paired with gas flux data provided by fieldscale methodologies such as eddy covariance (Cowan et al, 2018(Cowan et al, , 2020. Here the high temporal, spatially integrated soil moisture data can be used to better refine gap-filling methods, particularly for emissions of the powerful GHG nitrous oxide, which responds strongly to changes in soil aerobicity.…”

“…Here the high temporal, spatially integrated soil moisture data can be used to better refine gap-filling methods, particularly for emissions of the powerful GHG nitrous oxide, which responds strongly to changes in soil aerobicity. As all of the major GHGs (CO 2 , CH 4 , N 2 O), and many secondary GHGs and other sources of air pollution (CO, NO, NO 2 ) generated by soil microbial activity, are heavily influenced by soil moisture (Cowan et al, 2018;Davidson et al, 2000;Oertel et al, 2016;Van Den Pol-van Dasselaar et al, 1998), the COSMOS-UK network will provide the ability to better refine UK-scale emission inventories in the future as UK-scale soil moisture models are improved.…”

Retrieval of soil physical properties: field investigations, microwave remote sensing and data assimilation

“…Hence, the estimated soil hydraulic parameters are also based on a small-scale (cm), which may differ from the effective parameters at the field scale (~m). Cooper et al (2020) optimized the constants in the underlying PTFs to obtain soil hydraulic parameters representing the field scale by assimilating daily-averaged COSMOS-UK soil moisture data, and showed that the performances of LSMs in soil moisture simulations were improved with the optimized PTFs. In this study, in situ atmospheric forcing data are measured at the field scale while the assimilated SMAP data exhibit a large spatial resolution of 36 km.…”

“…Finally, SHPs & STPs can be retrieved either directly by bypassing PTFs (e.g., Gutmann et al 2010;Bandara et al 2014;Lee 2014;Dimitrov et al 2014) or indirectly by using PTFs (e.g., Han et al 2014;Santanello et al 2007;Yang et al 2016 mapping, and its accuracy can be evaluated with in situ and existing soil property datasets. The latter method guarantees soil physical consistency in landatmosphere processes and has been advised as a more suitable way to retrieve spatially aggregated SHPs & STPs (Cooper et al, 2020;Santanello et al, 2007;Soet & Stricker, 2003).…”

“…Spatiotemporally consistent soil moisture information can be obtained Vereecken et al, 2010), which use basic soil properties (i.e., soil texture and organic matter content) as input data. This consideration guarantees soil physical consistency in the land-atmosphere process (Cooper et al, 2020;Santanello et al, 2007;Soet & Stricker, 2003).…”