Preprints
https://doi.org/10.5194/hess-2023-69
https://doi.org/10.5194/hess-2023-69
23 Mar 2023
 | 23 Mar 2023
Status: this preprint was under review for the journal HESS but the revision was not accepted.

Advancement of a blended hydrologic model for robust model performance

Robert Chlumsky, Juliane Mai, James R. Craig, and Bryan A. Tolson

Abstract. A blended model structure has emerged as an alternative to the traditional representation of model structure in a hydrologic model, in which multiple algorithmic choices are used to represent some hydrologic process within a model, and are combined within a single model run using a weighted average of process fluxes. This approach has been shown to improve overall model performance, as well as provide an efficient way to test multiple model structures. We propose that a blended model may also be at least a partial solution to the calls for a more robust Community Hydrologic Model, which can mitigate the need for developing new hydrologic models for each catchment and application.

We develop an updated version of the blended model configuration which defines the suite of all possible hydrologic process options in the blended model. Configuration development was guided by model performance for more than 30 different discrete model configurations across 12 MOPEX catchments. Improvements to the blended model include the introduction of blended potential melt and potential evapotranspiration as new process groups, inclusion of non-blended structural changes, and a revision of the process options within each existing group. This leads to a very high-performing model with a mean calibration Kling-Gupta Efficiency (KGE) score of 0.90 and mean validation KGE score of 0.80 across all 12 MOPEX catchments, a substantial improvement in model performance relative to the initial version of 0.06 and 0.07 in calibration and validation, respectively. We test for overfitting of models and find little statistical evidence that increasing the complexity of blended models reduces validation performance. We then select the preferred model configuration as version 2 of the blended model, and test it with 12 independent catchments, which shows a mean calibration and validation score of 0.89 and 0.76, respectively, and improvement over the original model (0.03 in mean calibration KGE score). Version 2 of the blended model is robust across a range of catchments without the need for adjusting its flexible model structure, and may be useful in future hydrology studies and applications alike.

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.
Robert Chlumsky, Juliane Mai, James R. Craig, and Bryan A. Tolson

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • AC1: 'Public repository link', Robert Chlumsky, 24 Apr 2023
  • RC1: 'Comment on hess-2023-69', Anonymous Referee #1, 02 May 2023
    • AC3: 'Reply on RC1', Robert Chlumsky, 03 Jul 2023
    • AC4: 'Reply on RC1', Robert Chlumsky, 03 Jul 2023
  • RC2: 'Comment on hess-2023-69', Janneke Remmers, 09 May 2023
    • AC2: 'Reply on RC2', Robert Chlumsky, 12 Jun 2023

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • AC1: 'Public repository link', Robert Chlumsky, 24 Apr 2023
  • RC1: 'Comment on hess-2023-69', Anonymous Referee #1, 02 May 2023
    • AC3: 'Reply on RC1', Robert Chlumsky, 03 Jul 2023
    • AC4: 'Reply on RC1', Robert Chlumsky, 03 Jul 2023
  • RC2: 'Comment on hess-2023-69', Janneke Remmers, 09 May 2023
    • AC2: 'Reply on RC2', Robert Chlumsky, 12 Jun 2023
Robert Chlumsky, Juliane Mai, James R. Craig, and Bryan A. Tolson
Robert Chlumsky, Juliane Mai, James R. Craig, and Bryan A. Tolson

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Short summary
A blended model allows multiple hydrologic processes to be represented in a single model, which allows for a model to achieve high performance without the need to modify its structure for different catchments. Here, we improve upon the initial blended version by testing more than 30 blended models in twelve catchments to improve the overall model performance. We validate our proposed, updated blended model version with independent catchments, and make this version available for open use.