Parameter uncertainty analysis for an operational hydrological model using residual based and limits of acceptability approaches

Teweldebrhan, Aynom Tesfay; Burkhart, John F.; Schuler, Thomas

doi:10.5194/hess-2018-158

Cited by 6 publications

(11 citation statements)

References 39 publications

(51 reference statements)

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“…the MLMs was generally consistent with the result obtained in previous study focused on parameter uncertainty analysis using the GLUE methodology (Teweldebrhan et al, 2018).…”

supporting

confidence: 90%

“…The procedure followed in GLUE pLoA for relaxing the original formulation is detailed in Teweldebrhan et al (2018). For completeness, we include a summary of the steps herein:…”

Section: Parameter Identification Using the Time-relaxed Limits Of Acmentioning

confidence: 99%

“…The modelling framework (shyft) and the PT_GS_K model in particular were described in previous studies (e.g. Burkhart et al, 2016;Teweldebrhan et al, 2018)…”

Section: Model Performance Measuresmentioning

confidence: 99%

“…These approaches include relaxing the limits (e.g. Blazkova and Beven, 2009;Liu et al, 2009), using different model realizations for different periods of a hydrological year (e.g., Choi and Beven, 2007) and using a time relaxed approach with the degree of relaxation constrained by an additional efficiency criterion (Teweldebrhan et al, 2018). The time relaxed GLUE LoA approach (hereafter referred as GLUE pLoA) was based on the empirical relationship between model efficiency and uncertainty in response to the percentage of model predictions that fall 15 within the observation error bounds (pLoA).…”

Section: Introductionmentioning

confidence: 99%

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Coupled machine learning and the limits of acceptability approach applied in parameter identification for a distributed hydrological model

Teweldebrhan¹,

Burkhart²,

Schuler³

et al. 2019

Preprint

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Abstract. Monte Carlo (MC) methods have been widely used in uncertainty analysis and parameter identification for hydrological models. The main challenge with these approaches is, however, the prohibitive number of model runs required to get an adequate sample size which may take from days to months especially when the simulations are run in distributed mode. In the past, emulators have been used to minimize the computational burden of the MC simulation through direct estimation of the residual based response surfaces. Here, we apply emulators of MC simulation in parameter identification for a distributed conceptual hydrological model using two likelihood measures, i.e. the absolute bias of model predictions (Score) and another based on the time relaxed limits of acceptability concept (pLoA). Three machine learning models (MLMs) were built using model parameter sets and response surfaces with limited number of model realizations (4000). The developed MLMs were applied to predict pLoA and Score for a large set of model parameters (95 000). The behavioural parameter sets were identified using a time relaxed limits of acceptability approach based on the predicted pLoA values; and applied to estimate the quantile streamflow predictions weighted by their respective Score. The three MLMs were able to adequately mimic the response surfaces directly estimated from MC simulations; and the models identified using the coupled ML emulators and the limits of acceptability approach have performed very well in reproducing the median streamflow prediction both during the calibration and validation periods.

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“…the MLMs was generally consistent with the result obtained in previous study focused on parameter uncertainty analysis using the GLUE methodology (Teweldebrhan et al, 2018).…”

supporting

confidence: 90%

“…The procedure followed in GLUE pLoA for relaxing the original formulation is detailed in Teweldebrhan et al (2018). For completeness, we include a summary of the steps herein:…”

Section: Parameter Identification Using the Time-relaxed Limits Of Acmentioning

confidence: 99%

“…The modelling framework (shyft) and the PT_GS_K model in particular were described in previous studies (e.g. Burkhart et al, 2016;Teweldebrhan et al, 2018)…”

Section: Model Performance Measuresmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Coupled machine learning and the limits of acceptability approach applied in parameter identification for a distributed hydrological model

Teweldebrhan¹,

Burkhart²,

Schuler³

et al. 2019

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Numerous sensitivity analysis methods are available, such as the Morris method (Morris, 1991) and Sobol analysis (Sobol, 1993). The Morris method provides similar results to Sobol analysis with a reduced computational burden (Rebolho et al, 2018;Teweldebrhan et al, 2018;Yang et al, 2018).…”

Section: Parameter Sensitivity Analysis Methodsmentioning

confidence: 85%

A time-varying parameter estimation approach using split-sample calibration based on dynamic programming

Zhang¹,

Liu²

2019

Preprint

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Abstract. Although the parameters of hydrological models are usually regarded as constant, temporal variations can occur in a changing environment. Thus, effectively estimating time-varying parameters becomes a significant challenge. Following a survey of existing estimation methodologies, this paper describes a new method that combines (1) the basic concept of split-sample calibration (SSC), whereby parameters are assumed to be stable for one sub-period, and (2) the parameter continuity assumption, i.e., the differences between parameters in consecutive time steps are small. Dynamic programming is then used to determine the optimal parameter trajectory by considering two objective functions: maximization of simulation accuracy and maximization of parameter continuity. The efficiency of the proposed method is evaluated by two synthetic experiments, one with a simple two-parameter monthly model and the second using a more complex 15-parameter daily model. The results show that the proposed method is superior to SSC alone, and outperforms the ensemble Kalman filter if the proper sub-period length is used. An application to the Wuding River basin indicates that the soil water capacity parameter varies before and after 1972, which can be interpreted according to land use and land cover changes. Further application to the Xun River basin shows that parameters are generally stationary on an annual scale, but exhibit significant changes over seasonal scales. These results demonstrate that the proposed method is an effective tool for identifying time-varying parameters in a changing environment.

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On (in)validating environmental models. 2. Implementation of a Turing‐like test to modelling hydrological processes

Beven

Lane

Page

et al. 2022

Hydrological Processes

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Part 1 of this study discussed the concept of using a form of Turing‐like test for model evaluation, together with eight principles for implementing such an approach. In this part, the framing of fitness‐for‐purpose as a Turing‐like test is discussed, together with an example application of trying to assess whether a rainfall‐runoff model might be an adequate representation of the discharge response in a catchment for predicting future natural flood management scenarios. It is shown that the variation between event runoff coefficients in the record can be used to create some limits of acceptability that implicitly take some account of the epistemic uncertainties arising from lack of knowledge about errors in rainfall and discharge observations. In the case study it is demonstrated that the model used cannot be validated in this way across all the range of observed discharges, but that behavioural models can be found for the peak flows that are the subject of interest in the application. Thinking in terms of the Turing‐like test focusses attention on the critical observations needed to test whether streamflow is being produced in the right way so that a model is considered as fit‐for‐purpose in predicting the impacts of future change scenarios. As is the case for uncertainty estimation in general, it is argued that the assumptions made in setting behavioural limits of acceptability should be stated explicitly to leave an audit trail in any application that can be reviewed by users of the model outputs.

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Parameter uncertainty analysis for an operational hydrological model using residual based and limits of acceptability approaches

Cited by 6 publications

References 39 publications

Coupled machine learning and the limits of acceptability approach applied in parameter identification for a distributed hydrological model

Coupled machine learning and the limits of acceptability approach applied in parameter identification for a distributed hydrological model

A time-varying parameter estimation approach using split-sample calibration based on dynamic programming

On (in)validating environmental models. 2. Implementation of a Turing‐like test to modelling hydrological processes

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