Application of stochastic parameter optimization to the Sacramento Soil Moisture Accounting model

By Jasper A. Vrugt, Hoshin V. Gupta, Stefan C. Dekker, Soroosh Sorooshian, Thorsten Wagener, Willem Bouten. Journal of Hydrology, 2006

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Hydrological models generally contain parameters that cannot be measured directly, but can only be meaningfully inferred by calibration against a historical record of input–output data. While considerable progress has been made in the development and application of automatic procedures for model calibration, such methods have received criticism for their lack of rigor in treating uncertainty in the parameter estimates. In this paper, we apply the recently developed Shuffled Complex Evolution Metropolis algorithm (SCEM-UA) to stochastic calibration of the parameters in the Sacramento Soil Moisture Accounting model (SAC-SMA) model using historical data from the Leaf River in Mississippi. The SCEM-UA algorithm is a Markov Chain Monte Carlo sampler that provides an estimate of the most likely parameter set and underlying posterior distribution within a single optimization run. In particular, we explore the relationship between the length and variability of the streamflow data and the Bayesian uncertainty associated with the SAC-SMA model parameters and compare SCEM-UA derived parameter values with those obtained using deterministic SCE-UA calibrations. Most significantly, for the Leaf River catchments under study our results demonstrate that most of the 13 SAC-SMA parameters are well identified by calibration to daily streamflow data suggesting that this data contains more information than has previously been reported in the literature. q 2006 Elsevier B.V. All rights reserved.

Subfields: Global and Planetary Change, Ocean Engineering, Water Science and Technology

View Paper: https://www.semanticscholar.org/paper/04081c7a27577519a016ee6bc8e260e68d419245

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Citations in the corpus

Development of a formal likelihood function for improved Bayesian inference of ephemeral catchments (2010, background): “…in hydrology has been under the assumption of uncorrelated, Gaussian errors, as evidenced by a plethora of studies [e.g., Ajami et al., 2007; Campbell et al., 1999; Duan et al., 2007; Hsu et al., 2009; Marshall et al., 2004; Samanta et al., 2008; Smith and Marshall, 2010; Vrugt et al., 2006].”
Treatment of uncertainty using ensemble methods: Comparison of sequential data assimilation and Bayesian model averaging (2007, methodology): “In a subsequent paper, we present a multiobjective optimization approach to calibration of forecast ensembles using BMA [Vrugt et al., 2006c].”
Toward Improved Predictions in Ungauged Basins: Exploiting the Power of Machine Learning (2019, background): “Moreover, Vrugt et al. (2006) argued that reliable streamflow predictions from lumped catchment models typically require at least 2 to 3 years of gauge data for calibration (even this is likely an underestimate of the amount of data necessary for reliable model calibration).”
The influence of parametric uncertainty on the relationships between HBV model parameters and climatic characteristics (2015, methodology): “The settings used for the calibration run are based on the recommendation of Vrugt et al. (2006) for complex problems (the number of complexes/ sequences is 5, population size is 250, maximum number of iterations is 50 000).”
Corruption of accuracy and efficiency of Markov chain Monte Carlo simulation by inaccurate numerical implementation of conceptual hydrologic models (2010, background): “It provides insights into the adequateness of the model, and indicates whether the data contain enough information to identify the model parameters [Vrugt et al., 2006].”
A formal likelihood function for parameter and predictive inference of hydrologic models with correlated, heteroscedastic, and non‐Gaussian errors (2010, methodology): “This is efficiently done using Monte Carlo Markov chain (MCMC) simulation [Bates and Campbell, 2001; Vrugt et al., 2003; Engeland et al., 2005; Vrugt et al., 2006; Kuczera and Parent, 1998; Smith and Marshall, 2008].”
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”
High‐dimensional posterior exploration of hydrologic models using multiple‐try DREAM(ZS) and high‐performance computing (2012, methodology): “In practice, it is advisable to use a longer record of calibration data [ Yapo et al. , 1996; Vrugt et al. , 2006], but deliberately we use only a 2 year record to result in a very Figure 3.”