Multilevel calibration strategy for complex hydrologic simulation models

By L. Brazil. 1988

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Citations in the corpus

An evaluation of adaptive surrogate modeling based optimization with two benchmark problems (2014, background): “Three parameters RSERV, RIVA, and SIDE are fixed at pre-specified values according to Brazil (1988).”
A comprehensive evaluation of various sensitivity analysis methods: A case study with a hydrological model (2014, background): “Three other parameters RSERV, RIVA, and SIDE are fixed at pre-specified values according to Brazil (1988).”
Estimating epistemic and aleatory uncertainties during hydrologic modeling: An information theoretic approach (2013, methodology): “…this case study are HyMod (used earlier in the synthetic study), the Sacramento Soil Moisture Accounting (SAC-SMA) model used by the U.S. National Weather Service for flood forecasting [Burnash et al., 1973; Brazil, 1988], and the Xinanjiang model widely used in southern China [Zhao et al., 1980].”
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”
Estimating the uncertain mathematical structure of a water balance model via Bayesian data assimilation (2009, background): “This humid 1944 km(2) watershed, located near Collins in southern Mississippi, has been investigated intensively [e.g., Sorooshian et al., 1983; Brazil, 1988; Gupta et al., 1998].”
An integrated hydrologic Bayesian multimodel combination framework: Confronting input, parameter, and model structural uncertainty in hydrologic prediction (2006, methodology): “Schematic of the Sacramento soil moisture accounting (SAC-SMA) model [Brazil, 1988].”
Correcting the mathematical structure of a hydrological model via Bayesian data assimilation (2011, methodology): “Section 3 presents a case study in which the method is used to develop an improved basin‐scale daily time step model for the Leaf River catchment (Collins, Mississippi).”
A process‐based diagnostic approach to model evaluation: Application to the NWS distributed hydrologic model (2008, background): “A number of hybrid model evaluation strategies have sought, with varying degrees of success, to combine the important strengths of manual and automatic model evaluation [Brazil, 1988; Harlin, 1991; Zhang and Lindström, 1997; Boyle et al., 2000; Leavesley et al., 2003; Turcotte et al., 2003].”
Mathematical Modeling of Watershed Hydrology (2002, background): “Population-evolut based search strategies have been popular~Brazil and Krajewski 1987; Brazil 1988; Wang 1991; Duan et al. 1992, 1993; S rooshian et al. 1993!.”
Application of temporal streamflow descriptors in hydrologic model parameter estimation (2005, methodology): “The selection of these calibration and validation periods is consistent with previous parameter estimation studies that were conducted in the Leaf River [Brazil, 1988; Duan et al., 1993; Hogue et al., 2003], as further discussed in section 5.”