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Automated calibration of the EPA-SWMM model for a small suburban catchment using PEST: a case study

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Abstract

Rainfall-runoff models must be calibrated and validated before they can be used for urban stormwater management. Manual calibration is very difficult and time-consuming due to the large number of model parameters that must be estimated concurrently. Automatic calibration offers as a promising alternative, ideally supporting a user-independent and time-efficient approach to model parameters estimation. In this article, we test the use of a state-of-the-art standard package (PEST, Parameter ESTimation, http://www.pesthomepage.org/) for the automatic calibration of a rainfall-runoff EPA-SWMM (Storm Water Management Model) model developed for a small suburban catchment. Results reported in the paper demonstrate that the performance of automatically calibrated models still depends on a number of user-dependent choices (the level of catchment discretization, the selection of significant parameters, the optimization techniques adopted). Through a systematic analysis of the results, we try to identify the guidelines for the effective use of automatic calibration procedures based on modeling assumptions and target of the analysis.

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Authors and Affiliations

  1. Dipartimento Politecnico di Ingegneria e Architettura, Università di Udine, Via del Cotonificio 108, Udine, Italy

    Roberto Perin, Matteo Trigatti, Matteo Nicolini, Marina Campolo & Daniele Goi

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  1. Roberto Perin
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  2. Matteo Trigatti
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  3. Matteo Nicolini
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  4. Marina Campolo
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  5. Daniele Goi
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Correspondence to Roberto Perin.

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Appendix: PEST control parameter

Appendix: PEST control parameter

PHIRATSUF: fractional objective function sufficient for end of current iterations

PHIREDLAM: termination criterion for Marquardt lambda search

FACORIG: minimum fraction of original parameter value in evaluating relative change

PHIREDSWH: sets objective function change for introduction of central derivatives

PHIREDSTP: relative objective function reduction triggering termination

NPHISTP: number of successive iterations over which PHIREDSTP applies

NPHINORED: number of iterations since last drop in objective function to trigger termination

RELPARSTP: maximum relative parameter change triggering termination

NRELPAR: number of successive iteration over which RELPARSTP applies

ICOV: instruct PEST to record covariance matrix in matrix file

ICOR: instruct PEST to record correlation-coefficient matrix in matrix file

IEIG: instruct PEST to record eigenvectors in matrix file

DERINC: absolute or relative parameter increment

DERINCLB: absolute lower bound of relative parameter increment

DERINCMUL: derivative increment multiplier when undertaking central derivatives calculation

RLAMBDA1: initial Marquardt lambda

RLAMFAC: factor by which the Marquardt lambda is adjusted

NUMLAM: upper limit on the number of lambdas that PEST can test during any one optimization iteration

RELPARMAX: maximum relative change that a parameter is allowed to undergo between optimization iterations

FACPARMAX: maximum factor change that a parameter is allowed to undergo

NOPTMAX: maximum number of iterations that PEST is permitted to undertake on a particular parameter estimation run

INCTYP: method by which parameter increments are calculated

FORCEN: determines whether central derivatives calculation is done

DERMTHD: method of central derivatives calculation

PARCHGLIM: type of parameter change limit

PARTRANS: parameter transformation

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Perin, R., Trigatti, M., Nicolini, M. et al. Automated calibration of the EPA-SWMM model for a small suburban catchment using PEST: a case study. Environ Monit Assess 192, 374 (2020). https://doi.org/10.1007/s10661-020-08338-7

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