Preprints
https://doi.org/10.5194/hess-2021-189
https://doi.org/10.5194/hess-2021-189

  23 Apr 2021

23 Apr 2021

Review status: a revised version of this preprint is currently under review for the journal HESS.

The value of satellite soil moisture and snow cover data for the transfer of hydrological model parameters to ungauged sites

Rui Tong1,2, Juraj Parajka1,2, Borbála Széles1,2, Isabella Pfeil1,3, Mariette Vreugdenhil3, Jürgen Komma2, Peter Valent2,4, and Günter Blöschl1,2 Rui Tong et al.
  • 1Centre for Water Resource Systems, TU Wien, Vienna 1040, Austria
  • 2Institute of Hydraulic Engineering and Water Resources Management, TU Wien, Vienna 1040, Austria
  • 3Department of Geodesy and Geoinformation, TU Wien, Vienna 1040, Austria
  • 4Department of Land and Water Resources Management, Slovak University of Technology in Bratislava, Bratislava 810 05, Slovakia

Abstract. The recent advances in remote sensing provide opportunities for more reliably estimating the parameters of conceptual hydrologic models. However, the question of whether and to what extent the use of satellite data in model calibration may assist in transferring model parameters to ungauged catchments has not been fully resolved. The aim of this study is to evaluate the efficiency of different methods for transferring model parameters obtained by multiple objective calibrations to ungauged sites and to assess the model performance in terms of runoff, soil moisture, and snow cover predictions relative to existing regionalization approaches. The model parameters are calibrated to daily runoff, satellite soil moisture (ASCAT), and snow cover (MODIS) data. The assessment is based on 213 catchments situated in different physiographic and climate zones of Austria. For the transfer of model parameters, eight methods (global and local variants of arithmetic mean, regression, spatial proximity, and similarity) are examined in two periods, i.e., the period in which the model is calibrated (2000–2010) and an independent validation period (2010–2014). The predictive accuracy is evaluated by leave-one-out cross-validation. The results show that the method by which the model is calibrated in the gauged catchment has a larger impact on runoff prediction accuracy in the ungauged catchments than the choice of the parameter transfer method. The best transfer methods are global and local similarity and the kriging approach. The performance of the transfer methods differs between lowland and alpine catchments. While the soil moisture and snow cover prediction efficiencies are higher in lowland catchments, the runoff prediction efficiency is higher in alpine catchments. A comparison of model transfer methods based on parameters calibrated to runoff, snow cover, and soil moisture with those based on parameters calibrated to runoff only indicates that the former outperforms the latter in terms of simulating soil moisture and snow cover. The performance of simulating runoff is similar, and the accuracy depends mainly on the weight given to the runoff objective in the multiple objective calibrations.

Rui Tong et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on hess-2021-189', Anonymous Referee #1, 12 May 2021
  • RC2: 'Comment on hess-2021-189', Luis Samaniego, 02 Jul 2021

Rui Tong et al.

Rui Tong et al.

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Short summary
The role and impact of using additional data (other than runoff) for the prediction of daily hydrographs in ungauged basins are not well understood. In this study, we assessed the model performance in terms of runoff, soil moisture, and snow cover predictions with the existing regionalization approaches. Results show the best transfer methods are the similarity and the kriging approach. The performance of the transfer methods differs between lowland and alpine catchments.