Satellite soil moisture data assimilation for improved operational continental water balance prediction

Abstract

Abstract. A simple and effective two-step data assimilation framework was developed to improve soil moisture representation in an operational large-scale water balance model. The first step is a Kalman-filter-type sequential state updating process that exploits temporal covariance statistics between modelled and satellite-derived soil moisture to produce analysed estimates. The second step is to use analysed surface moisture estimates to impart mass conservation constraints (mass redistribution) on related states and fluxes of the model using tangent linear modelling theory in a post-analysis adjustment after the state updating at each time step. In this study, we assimilate satellite soil moisture retrievals from both Soil Moisture Active Passive (SMAP) and Soil Moisture and Ocean Salinity (SMOS) missions simultaneously into the Australian Water Resources Assessment Landscape model (AWRA-L) using the proposed framework and evaluate its impact on the model's accuracy against in situ observations across water balance components. We show that the correlation between simulated surface soil moisture and in situ observation increases from 0.54 (open loop) to 0.77 (data assimilation). Furthermore, indirect verification of root-zone soil moisture using remotely sensed Enhanced Vegetation Index (EVI) time series across cropland areas results in significant improvements from 0.52 to 0.64 in correlation. The improvements gained from data assimilation can persist for more than 1 week in surface soil moisture estimates and 1 month in root-zone soil moisture estimates, thus demonstrating the efficacy of this data assimilation framework.

Spatio‐Temporal Model Variance‐Covariance Approach to Assimilating Streamflow Observations Into a Distributed Landscape Water Balance Model

“…The future work will be focused on the identifying and quantifying the potential of improving operational water balance forecasting with the assimilation of multiple water observations such as satellite soil moisture, surface water extent, vegetation condition, groundwater level, and total water storage to update all the state variable simultaneously. In the current operational running of AWRA for Australia implements a satellite soil moisture DA method to improve soil water balance components (Tian et al., 2021). The proposed streamflow DA will be applied together with the satellite soil moisture DA to further improve estimation of the whole water balance.…”

Regional Assessment of Soil Moisture Active Passive Enhanced L3 Soil Moisture Product and Its Application in Agriculture

“…The validation and assessment of satellite remote sensing soil moisture products are essential to ensure data accuracy before applying them. A large number of previous studies have conducted research on the accuracy of SMOS and SMAP data [21,22]. For example, Fernandez-Moran et al [23] conducted a validation of the SMOS-IC product, revealing that it exhibits high correlation and low unbiased root mean square error (ubRMSE) in the majority of pixels.…”

Development and preliminary validation of a land surface image assimilation system based on the Common Land Model

“…Ideally, a single-column assimilation system would also be able to reproduce the correct spatial-structure features of soil moisture anomalies if the assimilation can obtain the closest result to the true value at each column. Due to the non-uniform spatial distribution of precipitation, as well as the heterogeneous spatial distribution of soil properties, land cover types and topographic elevations, there are significant variations in the spatial distribution of soil moisture (Tian et al, 2021). The estimation of soil moisture by the land surface model is adversely impacted by the uncertainties in atmospheric forcing, model dynamics and parameterization, leading to significant spatial variations in the accuracy of simulated surface variables (Li, 2014;P.…”