Preprints
https://doi.org/10.5194/gmd-2022-274
https://doi.org/10.5194/gmd-2022-274
Submitted as: model description paper
 | 
26 Jan 2023
Submitted as: model description paper |  | 26 Jan 2023
Status: this preprint was under review for the journal GMD. A final paper is not foreseen.

Randomized Block Nonparametric Temporal Disaggregation of Hydrological Variables RB-NPD (version1.0) – model development

Taesam Lee and Taha B. M. J. Ouarda

Abstract. Stochastically simulated data have been employed for hydrological variables in critical water-related risk management. The simulated data can be utilized to assess the existing flood protection structure and future mitigation frameworks. Disaggregation of the simulated annual data to a lower time scale is often required since water resource management and flood mitigation plans should be done in a fine scale such as a monthly or quarter-monthly. In the current study, the randomized random block length was proposed for the nonparametric disaggregation model since one of the major weakness points for the nonparametric disaggregation model is repetition of similar patterns in the disaggregated data. Furthermore, long-term dependence structure was also mainly focused to preserve since consistent high-flow results devastating damages to inundated area. The proposed model was compared with the existing parametric and nonparametric disaggregation models. The annual net basin supplies (NBS) of the Lake Champlain–Richelieu River (LCRR) Basin was employed to test the performance of the proposed model by reproducing the critical statistics of the 2011 flood in the LCRR Basin. The 2011 flood occurred and was sustained for a few months. The results show that the existing parametric and nonparametric models have limitations and shortcoming and do not provide sufficient temporal dependence. In contrast, the proposed random block-based nonparametric disaggregation (RB-NPD) model with further model enhancement by the genetic algorithm mixture illustrates that the proposed RB-NPD model can be a comparable alternative and that its enhancement is suitable for disaggregating the annual NBS data for the LCRR Basin.

This preprint has been withdrawn.

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Taesam Lee and Taha B. M. J. Ouarda

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on gmd-2022-274', Anonymous Referee #1, 30 Mar 2023
  • RC2: 'Comment on gmd-2022-274', Anonymous Referee #2, 04 Apr 2023

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on gmd-2022-274', Anonymous Referee #1, 30 Mar 2023
  • RC2: 'Comment on gmd-2022-274', Anonymous Referee #2, 04 Apr 2023
Taesam Lee and Taha B. M. J. Ouarda
Taesam Lee and Taha B. M. J. Ouarda

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This preprint has been withdrawn.

Short summary
The current study proposed random block based nonparametric disaggregation model so that the weakness point of the existing nonparametric disaggregation models can be resolved with preserving the long-term persistence. The proposed model illustrates superior performance for disaggregating the net basin supply of the LCRR basin in the Great Lakes, which experienced the worst flood in 2011.