Improving the shuffled complex evolution scheme for optimization of complex nonlinear hydrological systems: Application to the calibration of the Sacramento soil‐moisture accounting model

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Bridging the gap between GLUE and formal statistical approaches: approximate Bayesian computation (2013, background): “…and multiple different regions of attraction has stimulated the development of population based search algorithms that use multiple different points concurrently to locate the global optimum (Wang, 1991; Duan et al., 1992; Yapo et al., 1998; Seibert, 2000; Khu and Mad-sen, 2005; Chu et al., 2010).”
Improvement of hydrological model calibration by selecting multiple parameter ranges (2016, unknown): “…has been intensively employed to calibrate parameters in the field of hydrology, the most advanced algorithm inevitably falls into local solution because of the strong non-linear problem of a hydrological model and parameter correlation (Chu et al., 2010;Jiang et al., 2010;Jiang et al., 2015).”
High‐dimensional posterior exploration of hydrologic models using multiple‐try DREAM(ZS) and high‐performance computing (2012, methodology): “We therefore exclude SCE-UA from our analysis, and instead consider the SP-UCI method of Chu et al. [2010].”