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

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 measurements has increased, but few studies have combined model-based with situ observations beyond point scale. Here we show that can markedly improve estimates Joint UK Land Environment Simulator (JULES) model using field-scale data assimilation techniques. Rather than directly updating towards observed values, optimize constants underlying pedotransfer functions, which relate texture to JULES physics parameters. this way, generate a single set newly calibrated functions based on number sites different textures. We demonstrate calibrating function way improves at 16 sites, leading potential for better flood, drought, climate projections.