Assimilation of SMOS‐derived soil moisture in a fully integrated hydrological and soil‐vegetation‐atmosphere transfer model in Western Denmark

Abstract

Real surface soil moisture retrieved from the Soil Moisture and Ocean Salinity (SMOS) satellite is downscaled and assimilated in a fully integrated hydrological and soil-vegetation-atmosphere transfer (MIKE SHE SW-ET) model using a bias aware ensemble transform Kalman filter (Bias-ETKF). Satellite-derived soil moisture assimilation in a catchment scale model is typically restricted by two challenges: (1) passive microwave is too coarse for direct assimilation and (2) the data tend to be biased. The solution proposed in this study is to disaggregate the SMOS bias using a higher resolution land cover classification map that was derived from Landsat thermal images. Using known correlations between SMOS bias and vegetation type, the assimilation filter is adapted to calculate biases online, using an initial bias estimate. Real SMOS-derived soil moisture is assimilated in a precalibrated catchment model in Denmark. The objective is to determine if any additional gains can be achieved by SMOS surface soil moisture assimilation beyond the optimized model. A series of assimilation experiments were designed to (1) determine how effectively soil moisture corrections propagate downward in the soil column, (2) compare the efficacy of in situ versus SMOS assimilation, and (3) determine how soil moisture assimilation affects fluxes and discharge in the catchment. We find that assimilation of SMOS improved R 2 soil moisture correlations in the upper 5 cm compared to a network of 30 in situ sensors for most land cover classes. Assimilation also brought modest gains in R 2 at 25 cm depth but slightly degraded the correlation at 50 cm depth. Assimilation overcorrected discharge peaks.

Application of Remote Sensing Data to Constrain Operational Rainfall-Driven Flood Forecasting: A Review

“…Beside a couple of tests using synthetic RS-SM data [104,185,196], ASCAT [102,103,182,184,186,191,192,197], AMSR-E [103,180,182,192] and SMOS [102,103,182,183,198] soil moisture products were widely implemented during this period. Simultaneously assimilating multi-source RS-SM products has also been tested recently [102,103,182,192].…”

Multivariate hydrological data assimilation of soil moisture and groundwater head

“…For example, Sakov and Bertino (2010) use the Gaspari and Cohn 1-D taper function to compare different localization methods. Ridler et al (2014a) use a 2-D squared exponential covariance function as a taper function to localize the soil moisture updating. In this study, due to the difference in variable type and variable dimension, the taper function is chosen to be case specific based on the 2-D squared exponential covariance function.…”

“…The parameter perturbations are based on the uncertainty information of 13 parameters listed in Table 2, of which the first 7 from the model calibration and the remaining 6 from the unsaturated zone are empirically defined from literature values. The unsaturated zone uncertainty is introduced by perturbing the van Genuchten n for the dominant soil type at all three depths with a standard deviation of 0.05 (Ridler et al, 2014a). Overall, we try to keep the ensemble spread relatively large and model responses physically realistic.…”

Sensitivity of Soil Moisture Analyses to Contrasting Background and Observation Error Scenarios

“…The last decade has seen a large number of studies using data assimilation methods to constrain soil moisture with available in situ and remotely sensed observations [7][8][9][10]. An important component of these systems is their ability to incorporate not only the information from independent observations, but also its associated uncertainty.…”