Calibration of rainfall‐runoff models: Application of global optimization to the Sacramento Soil Moisture Accounting Model

By Soroosh Sorooshian, Qingyun Duan, Vinod Kumar Gupta. 1993

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Citations in the corpus

An evaluation of adaptive surrogate modeling based optimization with two benchmark problems (2014, methodology): “Many case studies have demonstrated that SCE-UA algorithm is an effective and efficient method for model calibration (Sorooshian et al., 1993; Gan and Biftu, 1996; Hogue et al., 2000; Boyle et al., 2000; Moreno et al., 2012).”
Automatic calibration of a conceptual rainfall-runoff model using multiple objectives. (2000, methodology): “The SCE method has been widely applied for calibration of various conceptual rainfall–runoff models, including the Sacramento model (Sorooshian et al., 1993; Duan et al., 1994; Gan and Biftu, 1996; Yapo et al., 1996; Gan et al., 1997), the Tank model (Tanakamaru and Burges, 1996; Cooper et al.”
Multiobjective calibration with Pareto preference ordering: An application to rainfall‐runoff model calibration (2005, result): “…models has also been widely recognized over many years as demonstrated by the amount of work done in this area [Ibbitt and O’Donnell, 1971; Johnston and Pilgrim, 1976; Gupta and Sorooshian, 1985; Baffaut and Delleur, 1988; Sorooshian et al., 1993; Thyer et al., 1999; Madsen et al., 2002].”
Estimating epistemic and aleatory uncertainties during hydrologic modeling: An information theoretic approach (2013, methodology): “The basin has been widely used in many previous investigations of catchment modeling [Bulygina and Gupta, 2009, 2010, 2011; Sorooshian et al., 1993; Thiemann et al., 2001; Vrugt et al., 2003; Wagener et al., 2001].”
Hydrologic consistency as a basis for assessing complexity of monthly water balance models for the continental United States (2011, methodology): “For example, in the heteroscedastic maximum likelihood estimation approach (HMLE) [Sorooshian et al., 1993], the flow transformation parameter is estimated during calibration.”
Multi-model ensemble hydrologic prediction using Bayesian model averaging (2007, methodology): “These basins are chosen because they have been widely studied and the hydrologic data sets were carefully prepared by NWS Hydrology Laboratory [37,32].”
Application of temporal streamflow descriptors in hydrologic model parameter estimation (2005, background): “It has also received substantial attention from numerous research groups that use it to study modeling issues and the hydrologic responses of specific watersheds [Brazil, 1988; Sorooshian et al., 1993; Gan and Biftu, 1996; Koren et al., 2000; Carpenter et al., 2001].”
Application of stochastic parameter optimization to the Sacramento Soil Moisture Accounting model (2006, methodology): “Moreover, we provide a comparison between SCEM-UA derived values of the SAC-SMA model parameters and those obtained using classical deterministic SCE-UA calibrations ( Sorooshian et al., 1993 ).”
Estimating Uncertain Flow and Transport Parameters Using a Sequential Uncertainty Fitting Procedure (2004, methodology): “…that a complete single-criterion MACRO model using hourly discharge and EC data. optimization problem using the shuffle complex model as reported in Sorooshian et al. (1993) would require THEORYas many as 10 000 function calls, while 100 Pareto soluSimulation Program MACROtions will require…”
Improving the shuffled complex evolution scheme for optimization of complex nonlinear hydrological systems: Application to the calibration of the Sacramento soil‐moisture accounting model (2010, methodology): “[2] The capability of hydrologic models in predicting runoff has been improved through parameter calibration [Brazil, 1988; Sorooshian et al., 1993; Vrugt et al., 2006], which has motivated the development and improvement of a number of automatic calibration methodologies over the past several decad”