Toward improved calibration of hydrologic models: Combining the strengths of manual and automatic methods

By D. Boyle, Hoshin V. Gupta, Soroosh Sorooshian. 2000

701 citations recorded by the source corpus.

Subfields: Environmental Engineering, Water Science and Technology

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Citations in the corpus

An evaluation of adaptive surrogate modeling based optimization with two benchmark problems (2014, methodology): “lable at ScienceDirect Contents lists avai Environmental Modelling & Software journal homepage: www.elsevier .com/locate/envsoft An evaluation of adaptive surrogate modeling based optimization with two benchmark problems Chen Wang, Qingyun Duan*, Wei Gong, Aizhong Ye, Zhenhua Di, Chiyuan Miao State ”
Ensemble evaluation of hydrological model hypotheses (2010, methodology): “In this study, time steps were aggregated over the three periods of the hydrograph suggested by Boyle et al. [2000]: periods driven by rain (performance measure Dkdriven), non‐driven high‐flow (“quick”) periods (performance measure Dkquick) and non‐driven low‐flow (“slow”) periods (performance measu”
Bayesian estimation of parameters in a regional hydrological model (2002, background): “The formulation of the likelihood function L(θ,ϕ|Y) controls the probability distribution of the parameters (Boyle et al., 2000).”
One decade of multi-objective calibration approaches in hydrological modelling: a review (2010, background): “In this respect, hybrid strategies taking advantage of the strengths of both manual and automatic calibration, can be most suitable approaches for such problems (Boyle et al., 2000).”
Comparison of Three Multiobjective Optimization Algorithms for Inverse Modeling of Vadose Zone Hydraulic Properties (2008, methodology): “We used the SCE-UA global optimization algorithm developed by Duan et al. (1992) to calculate the best attainable parameter values ui,opt for each of the objectives Fi. The SCE-UA algorithm has been reported to be consistent, effective, and effi cient for watershed model calibration (e.g., Kuczera, ”
Application of stochastic parameter optimization to the Sacramento Soil Moisture Accounting model (2006, methodology): “In keeping with previous studies (Boyle et al., 2000; Thiemann et al., 2001; Vrugt et al., 2003), the hydrological data used for model calibration and uncertainty assessment of the parameters of the SAC-SMA model consists of approximately 36 years (28 July, 1952–30 September, 1988) of streamflow…”
Comparison of different multi-objective calibration criteria using a conceptual rainfall-runoff model of flood events. (2007, background): “Attempts have already been made to include this knowledge objectively in the model calibration procedure (Boyle et al., 2000).”
Effective and efficient algorithm for multiobjective optimization of hydrologic models (2003, methodology): “…algorithm in hydrologic and hydrometeorologic calibration and evaluation studies [Gupta et al., 1998, 1999; Yapo et al., 1998; Bastidas et al., 1999; Boyle et al., 2000, 2001; Wagener et al., 2001; Xia et al., 2002; Leplastrier et al., 2002] have demonstrated the usefulness of the MOCOM algorithm.”
Sensitivity-guided reduction of parametric dimensionality for multi-objective calibration of watershed models (2009, neutral): “[4,5,13,14,18,24,31]).”
Use of an entropy‐based metric in multiobjective calibration to improve model performance (2014, background): “…during periods dominated by interflow or base flow; under the assumption that the catchment response is inherently different during periods driven by rainfall and periods without rainfall) [Boyle et al., 2000; Wagener et al., 2003; Choi and Beven, 2007; Reusser and Zehe, 2011; Zhang et al., 2011].”
Parameter uncertainty analysis for an operational hydrological model using residual-based and limits of acceptability approaches (2018, methodology): “In some studies an improved calibration of hydrologic models was obtained through independent calibration of sub-periods of a time series (e.g., Boyle et al., 2000; Samanta and Mackay, 2003).”
Convergence and uncertainty analyses in Monte-Carlo based sensitivity analysis (2011, neutral): “In the literature, the HYMOD model and the Leaf River data have been extensively investigated (e.g., Yapo et al., 1998; Boyle et al., 2000; Vrugt et al., 2002, 2003, 2005).”
The role of hydrograph indices in parameter estimation of rainfall–runoff models (2005, methodology): “Often, the use of a selected objective function yields model performances that reflect such selection and may fit specific aspects of the hydrograph at the expense of others (e.g., Boyle et al., 2000; Dunne, 1999).”
Comparing expert judgement and numerical criteria for hydrograph evaluation (2015, methodology): “Boyle et al. (2000) developed a calibration approach that combines manual and automatic methods.”