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

By Andreas Efstratiadis, Demetris Koutsoyiannis. 2010

476 citations recorded by the source corpus.

Subfields: Environmental Engineering, Ocean Engineering, Water Science and Technology

View Paper: https://www.semanticscholar.org/paper/739c44dd89a4e93eccc2fb5e64dbdd5acd19c878

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Environmental Engineering

1. S. Pool
2. Jan Seibert
3. M. Vis
4. Maarten S. Krol
5. Caitline A. Barber
6. Martijn J. Booij
7. Richard M. Vogel
8. Thomas V. Schuler

In Ocean Engineering

1. Martijn J. Booij
2. Maarten S. Krol
3. Caitline A. Barber
4. Richard M. Vogel
5. Thomas V. Schuler
6. John F. Burkhart
7. J. Lamontagne
8. Misgana K. Muleta

In Water Science and Technology

1. Richard M. Vogel
2. Caitline A. Barber
3. J. Lamontagne
4. S. Pool
5. Jan Seibert
6. Martijn J. Booij
7. M. Vis
8. Maarten S. Krol

Citations in the corpus

• Evaluating model performance: towards a non-parametric variant of the Kling-Gupta efficiency (2018, background): “Multi-objective functions were originally composed mostly of purely statistical metrics, such as the root mean squared error of low, high or peak flows (see review of Efstratiadis and Koutsoyiannis 2010).”
• Balance between calibration objectives in a conceptual hydrological model (2010, background): “in the case of reservoir operation), the calibration problem should be considered in a multi-objective framework (Yapo et al , 1998 ; Madsen, 2000 ; see Efstratiadis & Koutsoyiannis, 2010 , for an excellent review of this topic).”
• Parameter uncertainty analysis for an operational hydrological model using residual-based and limits of acceptability approaches (2018, methodology): “The GLUE methodology has been widely used in various disciplines (Beven, 2009; Efstratiadis and Koutsoyiannis, 2010) primarily due to its conceptual simplicity and ease of implementation.”
• Model Performance Sensitivity to Objective Function during Automated Calibrations (2012, background): “, RMS and MNSD) to improve accuracy of simulating both low flows as well as peak flows (Madsen 2000; Efstratiadis and Koutsoyiannis 2010).”
• Improved Estimators of Model Performance Efficiency for Skewed Hydrologic Data (2020, methodology): “(2007) and Efstratiadis and Koutsoyiannis (2010). Koppa et al. (2019) review studies, which document improvements resulting from the multiobjective calibration of rainfall‐runoff models. Although multiple measures of goodness of fit are generally recommended and applied in practice, we only concentr”