Assimilation of ASCAT near-surface soil moisture into the SIM hydrological model over France

Abstract

Abstract. This study examines whether the assimilation of remotely sensed near-surface soil moisture observations might benefit an operational hydrological model, specifically Météo-France's SAFRAN-ISBA-MODCOU (SIM) model. Soil moisture data derived from ASCAT backscatter observations are assimilated into SIM using a Simplified Extended Kalman Filter (SEKF) over 3.5 years. The benefit of the assimilation is tested by comparison to a delayed cut-off version of SIM, in which the land surface is forced with more accurate atmospheric analyses, due to the availability of additional atmospheric observations after the near-real time data cut-off. However, comparing the near-real time and delayed cut-off SIM models revealed that the main difference between them is a dry bias in the near-real time precipitation forcing, which resulted in a dry bias in the rootzone soil moisture and associated surface moisture flux forecasts. While assimilating the ASCAT data did reduce the root-zone soil moisture dry bias (by nearly 50 %), this was more likely due to a bias within the SEKF, than due to the assimilation having accurately responded to the precipitation errors. Several improvements to the assimilation are identified to address this, and a bias-aware strategy is suggested for explicitly correcting the model bias. However, in this experiment the moisture added by the SEKF was quickly lost from the model surface due to the enhanced surface fluxes (particularly drainage) induced by the wetter soil moisture states. Consequently, by the end of each winter, during which frozen conditions prevent the ASCAT data from being assimilated, the model land surface had returned to its original (dry-biased) climate. This highlights that it would be more effective to address the precipitation bias directly, than to correct it by constraining the model soil moisture through data assimilation.

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

“…The choice of the data assimilation algorithm turned to be more consistent in this period. The majority of studies chose the ensemble Kalman filter (EnKF) [102][103][104]180,182,183,185,191,196,198], while just a few used nudging [186], hard updating [192] and EKF [197]. There were also few particle filter (PF) applications.…”

“…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].…”

Improving soil moisture and runoff simulations over Europe using a high-resolution data-assimilation modeling framework

“…Recent studies showed that assimilation of soil moisture data into hydrologic modeling 20 could improve water balance predictions such as evaporation and runoff (e.g. Crow et al, 2017;Mohanty et al, 2013;Brocca et al, 2012;Matgen et al, 2012;Draper et al, 2011;Pauwels et al, 2002;2001). However, many of these studies mainly focused on improved predictions at watershed scales using in situ observations.…”

Depth scaling of soil moisture content from surface to profile: multistation testing of observation operators

“…However, these instruments generally measure soil moisture at relatively shallow layers. For example, microwave remote sensing methods (either active or passive) retrieve near-surface soil moisture that only reaches several centimetres beneath the soil surface (Draper et al, 2011). Therefore, it is necessary to link surface soil moisture to profile soil moisture via robust depth-scaling approaches.…”

“…Data assimilation methods refer to techniques which incorporate surface soil moisture measurements (e.g. remote sensing products) into physically-based hydrologic models to obtain an analysis that best represents profile soil moisture and a number of data assimilation algorithms have been developed (Evensen, 1994;Walker et al, 2002;Heathman et al, 2003;Reichle et al, 2007;Draper et al, 2011;Dumedah et al, 2015). However, its application may be constrained by the required model parameters (soil properties, vegetation features and atmospheric forcing), which are difficult to obtain at larger scales, as well as by uncertainties related to the physical description of soil hydrological processes (Albergel et al, 2008;Hu and Si, 2014).…”