Soil moisture downscaling across climate regions and its emergent properties

[1] Land surface models of water and energy fluxes can benefit from the characterization of soil moisture variability provided by robust downscaling algorithms over a wide range of climatic settings. In this study, we present the application of a multifractal-based statistical downscaling scheme using 800 m aircraft-derived soil moisture data collected during three field campaigns in contrasting climatic regimes. The disaggregation scheme was tested in a previous work using data of the Southern Great Plains experiment in 1997 (SGP97) in a temperate region in Oklahoma. Here, we explore its capability on different climates by using data from two other campaigns: Soil Moisture Experiment in 2002 (SMEX02), in an agricultural region with subhumid climate in Iowa, and Soil Moisture Experiment in 2004 (SMEX04), conducted in two semiarid areas in Arizona and Sonora (Mexico). We first demonstrate the presence of multifractality in soil moisture fields over the scale range from 0.8 km (aircraft footprint) to 25.6 km (satellite footprint) over most wetness conditions. Next, we identify an empirical regional calibration relation linking model parameters with the spatial mean soil moisture and coarse-scale predictors that account for topography, soil texture, and land cover in each site. The downscaling model shows good performance in a broad range of conditions, except for a few cases where specific physiographic features introduce relevant spatial nonhomogeneity in the soil moisture field. The calibrated downscaling model is then applied to study the relation between spatial variability and mean soil moisture across the different climate settings. In such a way, we explain the diverse shapes presented in previous studies and suggest possible physical explanations for intraregional and interregional differences.