Equifinality and automatic calibration: What is the impact of hypothesizing an optimal parameter set on modelled hydrological processes?

Foulon, Étienne; Rousseau, Alain N.

doi:10.1080/07011784.2018.1430620

Cited by 18 publications

(6 citation statements)

References 57 publications

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“…Through the Monte Carlo behavioural calibration, it was possible to obtain a set of parameters that ensure the highest performance in terms of NSE. For the investigated basins the maximum NSE during the calibration period (NSE = 0.72 in both cases) was achieved for the following parameter sets: However, as often reported in hydrologic literature, equifinality conditions are present (Beven and Freer 2001;Foulon and Rousseau 2018), implying that similar model performance (e.g. in terms of NSE) can be achieved by using different sets of parameters.…”

Section: Model Calibration and Reference Set Of Parametersmentioning

confidence: 51%

EHSMu: a New Ecohydrological Streamflow Model to Estimate Runoff in Urban Areas

Cristiano

Deidda

Viola

2020

Water Resour Manage

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A conceptual lumped ecohydrological streamflow model (EHSMu) is presented as a promising tool to simulate runoff in urban catchments. The model, based on the interaction between a soil bucket and two linear reservoirs, enables also evapotranspiration and aquifer recharge to be estimated. Notwithstanding its minimalism, EHSMu describes interactions among soil moisture dynamics, hydrological fluxes and ecological processes. The model was calibrated and validated within two densely urbanized sub-basins in Charlotte (US). A Monte Carlo procedure is used to investigate the efficiency of random sets of 8 model parameters. Results show the high model performance (NSE = 0.72). The influence of land use change is evaluated, by varying the imperviousness and crop coefficients. Synthetic experiments show that increasing urbanization triggers a linear decrease in evapotranspiration and aquifer recharge, while it increases the fast runoff. An opposite response is achieved by installing vegetation with higher potential evapotranspiration, which would contribute to the actual evapotranspiration making up 50–55% of the total water balance.

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Section: Model Calibration and Reference Set Of Parametersmentioning

confidence: 51%

EHSMu: a New Ecohydrological Streamflow Model to Estimate Runoff in Urban Areas

Cristiano

Deidda

Viola

2020

Water Resour Manage

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“…In model validation, the obtained single parameter set is validated with the deterministic forcing, generating a deterministic flow simulation. Calibration approach 2Calibration approach 2 uses the same deterministic forcing and objective function as approach 1 but identifies numerous behavioural parameter sets by the DDS‐Approximation of Uncertainty (DDS‐AU) algorithm (Tolson & Shoemaker, 2008). DDS‐AU is composed of multiple independent optimization trials of the DDS algorithm and is used in uncertainty‐based hydrologic modelling applications (Duethmann et al, 2013; Foulon & Rousseau, 2018; Huang et al, 2014; Qi et al, 2016; Samaniego et al, 2013). The behavioural threshold is identified using the optimal criteria‐aggregation‐based method proposed by Shafii et al (2015).…”

Section: Experimental Designmentioning

confidence: 99%

Leveraging ensemble meteorological forcing data to improve parameter estimation of hydrologic models

Liu

Tolson

Newman

et al. 2021

Hydrological Processes

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As continental to global scale high‐resolution meteorological datasets continue to be developed, there are sufficient meteorological datasets available now for modellers to construct a historical forcing ensemble. The forcing ensemble can be a collection of multiple deterministic meteorological datasets or come from an ensemble meteorological dataset. In hydrological model calibration, the forcing ensemble can be used to represent forcing data uncertainty. This study examines the potential of using the forcing ensemble to identify more robust parameters through model calibration. Specifically, we compare an ensemble forcing‐based calibration with two deterministic forcing‐based calibrations and investigate their flow simulation and parameter estimation properties and the ability to resist poor‐quality forcings. The comparison experiment is conducted with a six‐parameter hydrological model for 30 synthetic studies and 20 real data studies to provide a better assessment of the average performance of the deterministic and ensemble forcing‐based calibrations. Results show that the ensemble forcing‐based calibration generates parameter estimates that are less biased and have higher frequency of covering the true parameter values than the deterministic forcing‐based calibration does. Using a forcing ensemble in model calibration reduces the risk of inaccurate flow simulation caused by poor‐quality meteorological inputs, and improves the reliability and overall simulation skill of ensemble simulation results. The poor‐quality meteorological inputs can be effectively filtered out via our ensemble forcing‐based calibration methodology and thus discarded in any post‐calibration model applications. The proposed ensemble forcing‐based calibration method can be considered as a more generalized framework to include parameter and forcing uncertainties in model calibration.

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“…Several studies have illustrated the importance of choosing suitable objective functions (Efstratiadis & Koutsoyiannis, 2010;Her & Seong, 2018). Commonly, model performance evaluation metrics are used as objective functions (Brighenti et al, 2019;Dung et al, 2010;Foulon & Rousseau, 2018;Jiang et al, 2020;Ong et al, 2017). In this study, the Nash-Sutcliffe Efficiency (NSE) (Nash & Sutcliffe, 1970), which was commonly used in previous studies, was chosen as the objective function for each gauging station.…”

Section: Formulation Of Objective Functionmentioning

confidence: 99%

Improved method for identifying Manning’s roughness coefficients in plain looped river network area

Yang

et al. 2020

Engineering Applications of Computational Fluid Mechanics

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Equifinality and automatic calibration: What is the impact of hypothesizing an optimal parameter set on modelled hydrological processes?

Cited by 18 publications

References 57 publications

EHSMu: a New Ecohydrological Streamflow Model to Estimate Runoff in Urban Areas

EHSMu: a New Ecohydrological Streamflow Model to Estimate Runoff in Urban Areas

Leveraging ensemble meteorological forcing data to improve parameter estimation of hydrologic models

Improved method for identifying Manning’s roughness coefficients in plain looped river network area

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