One decade of multi-objective calibration approaches in hydrological modelling: a review

Efstratiadis, Andreas; Koutsoyiannis, Demetris

doi:10.1080/02626660903526292

Cited by 364 publications

(246 citation statements)

References 109 publications

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“…Automated calibration and other hybrid procedures have become increasingly common over the past decade (Madsen et al, 2002;Tolson and Shoemaker, 2007;Efstratiadis and Koutsoyiannis, 2010). The objective nature of automated parameter adjustment makes these approaches very appealing.…”

Section: Fully Automated and Intervention Type Calibration Proceduresmentioning

confidence: 99%

Multiple domain evaluation of watershed hydrology models

Kumarasamy

Belmont

2017

Preprint

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Abstract. Watershed scale models simulating hydrology and water quality have advanced rapidly in sophistication, process 5 representation, flexibility in model structure, and input data. Given the importance of these models to support decision-making for a wide range of environmental issues, the hydrology community is compelled to improve the metrics used to evaluate model performance. More targeted and comprehensive metrics will facilitate better and more efficient calibration and will help demonstrate that the model is useful for the intended purpose. Here we introduce a suite of new tools for model evaluation,packaged as an open-source Hydrologic Model Evaluation (HydroME) Toolbox. Specifically, we demonstrate the use of box 10 plots to illustrate the full distribution of common model performance metrics, such as R 2 , use of Euclidian distance, empirical Quantile-Quantile (Q-Q) plots and flow duration curves as simple metrics to identify and localize errors in model simulations.Further, we demonstrate the use of magnitude squared coherence to compare the frequency content between observed and modeled streamflow and wavelet coherence to localize frequency mismatches in time. We provide a rationale for a hierarchical selection of parameters to adjust during calibration and recommend that modelers progress from parameters with the most 15 uncertainty to the least uncertainty, namely starting with pure calibration parameters, followed by derived parameters, and finally measured parameters. We apply these techniques in the calibration and evaluation of models of two watersheds, the Le Sueur River Basin (2880 km 2 ) and Root River Basin (4300 km 2 ) in southern Minnesota, USA.

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Section: Fully Automated and Intervention Type Calibration Proceduresmentioning

confidence: 99%

Multiple domain evaluation of watershed hydrology models

Kumarasamy

Belmont

2017

Preprint

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“…Both topics are often stated as the main goals of model development e.g. Efstratiadis and Koutsoyiannis (2010) and Gupta and Nearing (2014).…”

Section: Does Model Incremental Breakdown Allow the Construction Of Imentioning

confidence: 99%

“…As single-objective calibration lowers the identifiability of model parameters and structural 10 elements (Efstratiadis and Koutsoyiannis, 2010) and often hide shortcomings of models (Ritter and Muñoz-Carpena, 2013), we pursued a multi-objective calibration procedure. Following the concept of Moriasi et al (2007), a model run was deemed behavioural, if the logarithmic Nash-Sutcliffe-Efficiency (logNSE) was >0.5, the percentage bias (PBIAS) was below/above ±25% and the ratio between root mean square error to the standard deviation of the measured data (RSR) was <0.7.…”

Section: Calibration and Validationmentioning

confidence: 99%

Incremental model breakdown to assess the multi-hypotheses problem

Jehn

Houska

Bestian

et al. 2017

Preprint

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Abstract.The ambiguous representation of hydrological processes have led to the formulation of the multiple hypotheses approach in hydrological modelling, which requires new ways of model construction. However, most recent studies focus 10 only on the comparison of predefined model structures or building a model step-by-step. This study tackles the problem the other way around: We start with one complex model structure, which includes all processes deemed to be important for the catchment. Next, we create 13 additional simplified models, where some of the processes from the starting structure are disabled. The performance of those models is evaluated using three objective functions (logarithmic Nash-Sutcliffe, percentage bias and the ratio between root mean square error to the standard deviation of the measured data). Through this 15 incremental breakdown, we identify the most important processes and detect the restraining ones. This procedure allows constructing a more streamlined, subsequent 15 th model with improved model performance, less uncertainty and higher model efficiency. We benchmark the original Model 1 with the final Model 15 and find that the incremental model breakdown leads to a structure with good model performance, fewer but more relevant processes and less model parameters.

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“…On the other hand, variational data assimilation is more akin to traditional calibration approaches (Efstratiadis and Koutsoyiannis, 2010). It seeks to find a single/deterministic initial state variable combination that minimizes the departures (or "variations") of the modelled values from the observations throughout an analysis window (Reichle et al, 2001).…”

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confidence: 99%

Hybridizing sequential and variational data assimilation for robust high-resolution hydrologic forecasting

Hernández¹,

Li²

2016

Preprint

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Abstract. There are two main frameworks for the estimation of initial states in geophysical models for real-time and forecasting applications: sequential data assimilation and variational data assimilation. However, modern high-resolution models offer challenges, both in terms of indeterminacy and computational requirements, which render most traditional methods insufficient. In this article we introduce a hybrid algorithm called OPTIMISTS which combines advantageous features from both of these data assimilation perspectives. These features are integrated with a multi-objective approach for 10 selecting ensemble members to create a probabilistic estimate of the state variables, which promotes the reduction of observational errors as well as the maintenance of the dynamic consistency of states. Additionally, we propose simplified computations as alternatives aimed at reducing memory and processor requirements. OPTIMISTS was tested on two models of real watersheds, one with over 1,000 variables and the second with over 30,000, on two distributed hydrologic modelling engines: VIC and the DHSVM. Our tests, consisting of assimilating streamflow observations, allowed determining which 15 features of the traditional approaches lead to more accurate forecasts while at the same time making an efficient use of the available computational resources. The results also demonstrated the benefits of the coupled probabilistic/multi-objective approach, which proved instrumental in reducing the harmful effects of overfitting-especially on the model with higher dimensionality.

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One decade of multi-objective calibration approaches in hydrological modelling: a review

Cited by 364 publications

References 109 publications

Multiple domain evaluation of watershed hydrology models

Multiple domain evaluation of watershed hydrology models

Incremental model breakdown to assess the multi-hypotheses problem

Hybridizing sequential and variational data assimilation for robust high-resolution hydrologic forecasting

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