Preprints
https://doi.org/10.5194/hess-2021-49
https://doi.org/10.5194/hess-2021-49

  03 Mar 2021

03 Mar 2021

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

Identifying Sensitivities in Flood Frequency Analyses using a Stochastic Hydrologic Modeling System

Andrew J. Newman1, Amanda G. Stone2, Manabendra Saharia1,a, Kathleen D. Holman2, Nans Addor3, and Martyn P. Clark1,b Andrew J. Newman et al.
  • 1Research Applications Laboratory, National Center for Atmospheric Research, Boulder CO, 80503, USA
  • 2Technical Service Center, Bureau of Reclamation, Lakewood CO, 80215, USA
  • 3Geography, College of Life and Environmental Sciences, University of Exeter, Exeter, EX4 4RJ, UK
  • anow at: Department of Civil Engineering, Indian Institute of Technology, New Delhi 110016, India
  • bnow at: University of Saskatchewan Coldwater Lab, Canmore, Alberta T1W 3G1, Canada, and Centre for Hydrology, University of Saskatchewan, Saskatoon, Saskatchewan S7N 1K2, Canada

Abstract. This study assesses sources of variance in stochastic hydrologic modelling to support flood frequency analyses. The major components of the modelling chain, including model structure, model parameter estimation, initial conditions, and precipitation inputs were examined across return periods from 2 to 100,000 years at two watersheds representing different hydro-climates across the western United States. Ten hydrologic model structures were configured, calibrated and run within the Framework for Understanding Structural Errors (FUSE) modular modelling framework for each of the two watersheds. Model parameters and initial conditions were derived from long-term calibrated simulations using a 100-member historical meteorology ensemble. A stochastic event-based hydrologic modelling workflow was developed using the calibrated models; millions of flood event simulations were performed at each basin. The analysis of variance method was then used to quantify the relative contributions of model structure, model parameters, initial conditions, and precipitation inputs to flood magnitudes for different return periods. The attribution of the variance of flood frequencies to each component of a stochastic hydrological modelling framework, including several hydrological model structures, is a novel contribution to the flood modelling literature. Results demonstrate that different components of the modelling chain have different sensitivities for different return periods. Precipitation inputs contribute most to the variance of rare events, while initial conditions are most influential for the more frequent events. However, the hydrological model structure and structure-parameter interactions together play an equally important role in specific cases, depending on the basin characteristics and type of flood metric of interest. This study highlights the importance of critically assessing model underpinnings, understanding flood generation processes, and selecting appropriate hydrological models that are consistent with our understanding of flood generation processes.

Andrew J. Newman et al.

Status: open (until 28 Apr 2021)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on hess-2021-49', Anonymous Referee #1, 16 Mar 2021 reply
  • RC2: 'Comment on hess-2021-49', Daniel Wright, 06 Apr 2021 reply
  • RC3: 'Comment on hess-2021-49', Anonymous Referee #3, 06 Apr 2021 reply

Andrew J. Newman et al.

Model code and software

Framework for Understanding Structural Error Model Source Code Martyn P. Clark, Brian Henn, Nans Addor, and Andrew J. Newman https://github.com/anewman89/fuse

Andrew J. Newman et al.

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Short summary
This study assesses methods that estimate flood return periods to identify when we would obtain a large flood return estimate change if the method or input data was changed (sensitivities). We include examination of multiple flood generating models, which is a novel addition to the flood estimation literature. We highlight the need to select appropriate flood models for the study watershed. These results will help operational water agencies develop more robust risk assessments.