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-2020-359
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-2020-359
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  06 Aug 2020

06 Aug 2020

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

Using data assimilation to optimize pedotransfer functions using large-scale in-situ soil moisture observations

Elizabeth Cooper1, Eleanor Blyth1, Hollie Cooper1, Rich Ellis1, Ewan Pinnington3, and Simon J. Dadson1,2 Elizabeth Cooper et al.
  • 1UK Centre for Ecology and Hydrology, Wallingford, UK
  • 2School of Geography and the Environment, South Parks Road, Oxford OX1 3QY
  • 3National Center for Earth Observation, Department of Meteorology, University of Reading, Reading, UK

Abstract. Soil moisture predictions from land surface models are important in hydrological, ecological and meteorological applications. In recent years the availability of wide-area soil-moisture measurements has increased, but few studies have combined model-based soil moisture predictions with in-situ observations beyond the point scale. Here we show that we can markedly improve soil moisture estimates from the JULES land surface model using field scale observations and data assimilation techniques. Rather than directly updating soil moisture estimates towards observed values, we optimize constants in the underlying pedotransfer functions, which relate soil texture to JULES soil physics parameters. In this way we generate a single set of newly calibrated pedotransfer functions based on observations from a number of UK sites with different soil textures. We demonstrate that calibrating a pedotransfer function in this way can improve the performance of land surface models, leading to the potential for better flood, drought and climate projections.

Elizabeth Cooper et al.

Interactive discussion

Status: open (until 01 Oct 2020)
Status: open (until 01 Oct 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

Elizabeth Cooper et al.

Elizabeth Cooper et al.

Viewed

Total article views: 350 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
263 84 3 350 3 3
  • HTML: 263
  • PDF: 84
  • XML: 3
  • Total: 350
  • BibTeX: 3
  • EndNote: 3
Views and downloads (calculated since 06 Aug 2020)
Cumulative views and downloads (calculated since 06 Aug 2020)

Viewed (geographical distribution)

Total article views: 237 (including HTML, PDF, and XML) Thereof 237 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Saved

No saved metrics found.

Discussed

No discussed metrics found.
Latest update: 28 Sep 2020
Publications Copernicus
Download
Short summary
Soil moisture estimates from land surface models are important for forecasting floods, droughts, weather and climate trends. We show that by combining model estimates of soil moisture with measurements from large-scale, ground based sensors we can improve the performance of the land surface model in predicting soil moisture values.
Soil moisture estimates from land surface models are important for forecasting floods, droughts,...
Citation