Journal cover Journal topic
Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
Journal topic

Journal metrics

IF value: 5.153
IF5.153
IF 5-year value: 5.460
IF 5-year
5.460
CiteScore value: 7.8
CiteScore
7.8
SNIP value: 1.623
SNIP1.623
IPP value: 4.91
IPP4.91
SJR value: 2.092
SJR2.092
Scimago H <br class='widget-line-break'>index value: 123
Scimago H
index
123
h5-index value: 65
h5-index65
Preprints
https://doi.org/10.5194/hess-2020-485
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-2020-485
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  12 Oct 2020

12 Oct 2020

Review status
This preprint is currently under review for the journal HESS.

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

Siyuan Tian1, Luigi J. Renzullo1, Robert C. Pipunic2, Julien Lerat2, Wendy Sharples2, and Chantal Donnelly2 Siyuan Tian et al.
  • 1Fenner School of Environment & Society, Australian National University, Canberra, 2601, Australia
  • 2Bureau of Meteorology, Melbourne, 3000, Australia

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 the 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 in a post-analysis adjustment after the state updating at each time step. In this study, we apply the data assimilation framework to the Australian Water Resources Assessment Landscape model (AWRA-L) 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 vegetation time series across cropland areas results in significant improvements of 0.11 correlation units. The improvements gained from data assimilation can persist for more than one week in surface soil moisture estimates and one month in root-zone soil moisture estimates, thus demonstrating the efficacy of this data assimilation framework.

Siyuan Tian et al.

Interactive discussion

Status: open (until 07 Dec 2020)
Status: open (until 07 Dec 2020)
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
[Subscribe to comment alert] Printer-friendly Version - Printer-friendly version Supplement - Supplement

Siyuan Tian et al.

Siyuan Tian et al.

Viewed

Total article views: 293 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
231 59 3 293 2 2
  • HTML: 231
  • PDF: 59
  • XML: 3
  • Total: 293
  • BibTeX: 2
  • EndNote: 2
Views and downloads (calculated since 12 Oct 2020)
Cumulative views and downloads (calculated since 12 Oct 2020)

Viewed (geographical distribution)

Total article views: 264 (including HTML, PDF, and XML) Thereof 256 with geography defined and 8 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Saved

No saved metrics found.

Discussed

No discussed metrics found.
Latest update: 26 Oct 2020
Publications Copernicus
Download
Short summary
Accurate continental water storage predictions is valuable for water management practitioners and policy makers in support of water resources assessment and agriculture planning. This study improves the accuracy and spatial representation of water storage predictions of an operational water balance modelling system through data assimilation. The integration of satellite soil moisture products can provide persistent constraints in model predictions for several weeks.  
Accurate continental water storage predictions is valuable for water management practitioners...
Citation