Preprints
https://doi.org/10.5194/hess-2021-156
https://doi.org/10.5194/hess-2021-156

  23 Apr 2021

23 Apr 2021

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

On the selection of precipitation products for the regionalisation of hydrological model parameters

Oscar M. Baez-Villanueva1,2, Mauricio Zambrano-Bigiarini3,4, Pablo A. Mendoza5,6, Ian McNamara1, Hylke E. Beck7, Joschka Thurner1, Alexandra Nauditt1, Lars Ribbe1, and Nguyen Xuan Thinh2 Oscar M. Baez-Villanueva et al.
  • 1Institute for Technology and Resources Management in the Tropics and Subtropics (ITT), TH Köln, Cologne, Germany
  • 2Faculty of Spatial Planning, TU Dortmund University, Dortmund, Germany
  • 3Department of Civil Engineering, Universidad de la Frontera, Temuco, Chile
  • 4Center for Climate and Resilience Research, Universidad de Chile, Santiago, Chile
  • 5Department of Civil Engineering, Universidad de Chile, Santiago, Chile
  • 6Advanced Mining Technology Center (AMTC), Universidad de Chile, Santiago, Chile
  • 7GloH2O, Almere, the Netherlands

Abstract. Over the past years, novel parameter regionalisation techniques have been developed to predict streamflow in data-scarce regions. In this paper, we examined how the choice of gridded daily precipitation (P) products affects individual catchment calibration and verification, as well as the relative performance of three well-known regionalisation techniques (spatial proximity, feature similarity, and parameter regression) over 100 near-natural catchments with diverse hydrological regimes across Chile. We configured and calibrated a conceptual semi-distributed HBV-like hydrological model for each catchment, using four P products (ERA5, MSWEPv2.8, RF-MEPv2, and CR2MET), and two objective functions. The three regionalisation techniques were applied and evaluated for each combination of P product and objective function, using a leave-one-out cross-validation procedure. Despite differences in the spatio-temporal distribution of P quantities, all P products provided good performance during calibration (median KGE's > 0.77), two independent verification periods (median KGE's > 0.70 and 0.61, for near normal and dry conditions, respectively), and regionalisation results (with median KGE's for the best method ranging from 0.56 to 0.63). Our results suggest that model calibration is able to compensate, to some extent, differences between forcing datasets, and that the spatial resolution of P products does not substantially affect the regionalisation performance. Overall, feature similarity provided the best results, followed closely by spatial proximity, while parameter regression performed the worst, thus reinforcing the importance of transferring complete parameter sets to ungauged catchments. Our results suggest that: i) merging P products and ground-based measurements does not necessarily translate into an improved hydrological modelling performance; ii) a P product that provides the best individual model performance during calibration and verification does not necessarily provide the best performance in terms of parameter regionalisation; and iii) the hydrological regime affects the performance of regionalisation methods, with rain-dominated catchments with a snow component performing the best over Chile for spatial proximity and feature similarity.

Oscar M. Baez-Villanueva et al.

Status: open (until 18 Jun 2021)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on hess-2021-156', Juraj Parajka, 02 May 2021 reply

Oscar M. Baez-Villanueva et al.

Oscar M. Baez-Villanueva et al.

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Short summary
Most rivers worldwide are ungauged, which hinders the sustainable management of water resources. Regionalisation methods use information from gauged rivers to estimate streamflow over ungauged ones. Through hydrological modelling, we assessed how the selection of precipitation product affects the performance of three regionalisation methods. We found that a precipitation product that provides the best results in hydrological modelling does not necessarily perform the best for regionalisation.