Parameter estimation in distributed hydrological catchment modelling using automatic calibration with multiple objectives

By Henrik Madsen. 2003

535 citations recorded by the source corpus.

Subfields: Environmental Engineering, Global and Planetary Change, Water Science and Technology

View Paper: https://www.semanticscholar.org/paper/9fab9be38d01350344e6eccbdeacf518f5242a12

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In Environmental Engineering

In Global and Planetary Change

In Water Science and Technology

Citations in the corpus

A process‐based diagnostic approach to model evaluation: Application to the NWS distributed hydrologic model (2008, background): “An increasing body of literature suggests that ‘‘a priori’’ parameters derived in this way may need to be refined through an adjustment process to ensure proper consistency between the model input-state-output behavior and the available data [Gupta et al., 1998; Madsen, 2003; Reed et al., 2004].”
Model identification for hydrological forecasting under uncertainty (2005, background): “In addition, we of course need a multi-objective approach when we have multiple output variables (e.g. Hooper et al. 1988; Madsen 2003; Madsen et al. 2003).”
Sensitivity analysis and parameter estimation for distributed hydrological modeling: potential of variational methods (2009, unknown): “For parameter estimation, scalar multipliers are used in order to adjust spatially distributed parameters featuring a variability which is ﬁxed using prior information (Refsgaard, 1997; Madsen, 2003).”
Which objective function to calibrate rainfall–runoff models for low-flow index simulations? (2017, background): “…in the theoretical framework of multi-objective optimization such as a Pareto front (Khu and Madsen 2005, Fenicia et al. 2007), or more simply by deriving aggregated (combined) objective functions (Madsen 2003, Merz and Blöschl 2004, Oudin et al. 2006, Booij and Krol 2010, Nicolle et al. 2014).”
One decade of multi-objective calibration approaches in hydrological modelling: a review (2010, methodology): “We note that, while most of early studies focused on lumped rainfall–runoff models, there is a growing number of recent studies on semi-distributed and distributed schemes, usually involving a small portion of the total model parameters (Madsen, 2003; Ajami et al., 2004; Muleta & Nicklow, 2005; Vrug”
Balance between calibration objectives in a conceptual hydrological model (2010, methodology): “In general, a multi-objective function aggregates a number of single-objective functions using weights; see, for example, Lindström (1997) and Oudin et al. (2006) for the case of two singleobjective functions, and Madsen (2003) and Schoups et al. (2005) for a general formulation.”
Modelling nonlinear trend for developing non‐stationary rainfall intensity–duration–frequency curve (2017, methodology): “For multi-modal objective functions, global search methods have been developed that are especially designed for locating the global optimum and not being trapped in local optima (Madsen, 2003).”
Multiobjective calibration with Pareto preference ordering: An application to rainfall‐runoff model calibration (2005, methodology): “…been applied to a number of hydrologic model applications, including the Sacramento soil moisture accounting model [Yapo et al., 1998], the MIKE11/NAM model [Madsen, 2000], the USEPA-SWMM model [Liong et al., 2001], the Xinanjiang model [Cheng et al., 2002], and the MIKE SHE model [Madsen, 2003].”
Comparison of Three Multiobjective Optimization Algorithms for Inverse Modeling of Vadose Zone Hydraulic Properties (2008, methodology): “Multiobjective optimization does not result in a single unique set of parameters but consists of a Pareto set of solutions (Gupta et al., 1998; Madsen, 2003; Vrugt et al., 2003a).”
Generalized likelihood uncertainty estimation (GLUE) using adaptive Markov Chain Monte Carlo sampling (2008, background): “…al., 1991; van Straten and Keesman, 1991; Vrugt et al., 2001), multiple criteria (Gupta et al., 1998; Yapo et al., 1998; Boyle et al., 2000, Madsen, 2000; Madsen, 2003; Vrugt et al., 2003b) and sequential data assimilation methods (Madsen et al., 2003; Vrugt et al., 2005; Moradkhani et al., 2005).”
Decomposition of the mean squared error and NSE performance criteria: Implications for improving hydrological modelling (2009, methodology): “…the latter approach exist in the literature where NSE or MSE appear in an overall objective function (e.g. Lindström, 1997; Bergström et al., 2002; Madsen, 2003; van Griensven and Bauwens, 2003; Parajka et al., 2005; Young, 2006; Rode et al., 2007; Marce et al., 2008; Wang et al., 2009; Safari et…”
Comparison of different multi-objective calibration criteria using a conceptual rainfall-runoff model of flood events. (2007, background): “These limits are chosen according to physical and mathematical constraints, information about physical characteristics of the system, and from modelling experiences (Kuczera, 1997; Madsen, 2000, 2003; Madsen et al., 2002).”
Evaluating model performance: towards a non-parametric variant of the Kling-Gupta efficiency (2018, methodology): “These results are consistent with the observation that more robust hydrograph simulations are achieved with multiobjective model calibration (e.g. Lindström 1997, Gupta et al. 1998, Boyle et al. 2000, Madsen 2003, Efstratiadis and Koutsoyiannis 2010).”