Data-model comparison of temporal variability in long-term time series of large-scale soil moisture

Soil moisture is at the heart of many processes connected to water cycle, climate, ecosystem, and societal conditions. This paper investigates the ability of a relatively simple analytical soil moisture model to reproduce temporal variability dynamics in long-term data series for (i) remotely sensed large-scale water storage change in 25 large catchments around the world and (ii) measured soil water content and groundwater level in individual stations within 10 smaller catchments across the United States. The model-data comparison for large-scale water storage change (i) shows good model ability to reproduce the observed temporal variability around long-term average conditions in most of the large study catchments. Also, the model comparison with locally measured data for soil water content and groundwater level in the smaller U.S. catchments (ii) shows good representation of relative seasonal and longer-term fluctuations and their timings and frequencies. Overall, the model results tend to underestimate rather than exaggerate the range of temporal soil moisture fluctuations and storage changes. The model synthesis of large-scale hydroclimatic data is based on fundamental catchment-scale water balance and is as such useful for identifying flux imbalance biases in the hydroclimatic data series that are used as model inputs.