Unraveling uncertainties in hydrologic model calibration: Addressing the problem of compensatory parameters

2006 ◽  
Vol 33 (6) ◽  
Author(s):  
Martyn P. Clark ◽  
Jasper A. Vrugt
Keyword(s):  
Model Calibration ◽  
Hydrologic Model ◽  
Hydrologic Model Calibration

scholarly journals How effective and efficient are multiobjective evolutionary algorithms at hydrologic model calibration?

2006 ◽  
Vol 10 (2) ◽  
pp. 289-307 ◽  
Author(s):  
Y. Tang ◽  
P. Reed ◽  
T. Wagener
Keyword(s):  
Model Calibration ◽  
Standardized Test ◽  
A Priori ◽  
Relative Effectiveness ◽  
Hydrologic Model ◽  
Ease Of Use ◽  
Science Literature ◽  
Significant Trial ◽  
Computationally Intensive ◽  
Hydrologic Model Calibration

Abstract. This study provides a comprehensive assessment of state-of-the-art evolutionary multiobjective optimization (EMO) tools' relative effectiveness in calibrating hydrologic models. The relative computational efficiency, accuracy, and ease-of-use of the following EMO algorithms are tested: Epsilon Dominance Nondominated Sorted Genetic Algorithm-II (ε-NSGAII), the Multiobjective Shuffled Complex Evolution Metropolis algorithm (MOSCEM-UA), and the Strength Pareto Evolutionary Algorithm 2 (SPEA2). This study uses three test cases to compare the algorithms' performances: (1) a standardized test function suite from the computer science literature, (2) a benchmark hydrologic calibration test case for the Leaf River near Collins, Mississippi, and (3) a computationally intensive integrated surface-subsurface model application in the Shale Hills watershed in Pennsylvania. One challenge and contribution of this work is the development of a methodology for comprehensively comparing EMO algorithms that have different search operators and randomization techniques. Overall, SPEA2 attained competitive to superior results for most of the problems tested in this study. The primary strengths of the SPEA2 algorithm lie in its search reliability and its diversity preservation operator. The biggest challenge in maximizing the performance of SPEA2 lies in specifying an effective archive size without a priori knowledge of the Pareto set. In practice, this would require significant trial-and-error analysis, which is problematic for more complex, computationally intensive calibration applications. ε-NSGAII appears to be superior to MOSCEM-UA and competitive with SPEA2 for hydrologic model calibration. ε-NSGAII's primary strength lies in its ease-of-use due to its dynamic population sizing and archiving which lead to rapid convergence to very high quality solutions with minimal user input. MOSCEM-UA is best suited for hydrologic model calibration applications that have small parameter sets and small model evaluation times. In general, it would be expected that MOSCEM-UA's performance would be met or exceeded by either SPEA2 or ε-NSGAII.

Targeted hydrologic model calibration to improve prediction of ecologically-relevant flow metrics

2019 ◽  
Vol 573 ◽  
pp. 546-556 ◽  
Author(s):  
Sarah R. Parker ◽  
Stephen K. Adams ◽  
Roderick W. Lammers ◽  
Eric D. Stein ◽  
Brian P. Bledsoe
Keyword(s):  
Model Calibration ◽  
Hydrologic Model ◽  
Hydrologic Model Calibration

scholarly journals The Applicability of SWOT’s Non-Uniform Space–Time Sampling in Hydrologic Model Calibration

2020 ◽  
Vol 12 (19) ◽  
pp. 3241
Author(s):  
Cassandra Nickles ◽  
Edward Beighley ◽  
Dongmei Feng
Keyword(s):  
Surface Water ◽  
Model Calibration ◽  
Optimal Parameter ◽  
Uniform Space ◽  
Hydrologic Model ◽  
Space Time ◽  
Added Value ◽  
Model Parameters ◽  
Time Sampling ◽  
Hydrologic Model Calibration

The Surface Water and Ocean Topography (SWOT) satellite mission, expected to launch in 2022, will enable near global river discharge estimation from surface water extents and elevations. However, SWOT’s orbit specifications provide non-uniform space–time sampling. Previous studies have demonstrated that SWOT’s unique spatiotemporal sampling has a minimal impact on derived discharge frequency distributions, baseflow magnitudes, and annual discharge characteristics. In this study, we aim to extend the analysis of SWOT’s added value in the context of hydrologic model calibration. We calibrate a hydrologic model using previously derived synthetic SWOT discharges across 39 gauges in the Ohio River Basin. Three discharge timeseries are used for calibration: daily observations, SWOT temporally sampled, and SWOT temporally sampled including estimated uncertainty. Using 10,000 model iterations to explore predefined parameter ranges, each discharge timeseries results in similar optimal model parameters. We find that the annual mean and peak flow values at each gauge location from the optimal parameter sets derived from each discharge timeseries differ by less than 10% percent on average. Our findings suggest that hydrologic models calibrated using discharges derived from SWOT’s non-uniform space–time sampling are likely to achieve results similar to those based on calibrating with in situ daily observations.

scholarly journals How effective and efficient are multiobjective evolutionary algorithms at hydrologic model calibration?

2005 ◽  
Vol 2 (6) ◽  
pp. 2465-2520 ◽  
Author(s):  
Y. Tang ◽  
P. Reed ◽  
T. Wagener
Keyword(s):  
Model Calibration ◽  
Standardized Test ◽  
Relative Effectiveness ◽  
Hydrologic Model ◽  
Ease Of Use ◽  
Test Case ◽  
Science Literature ◽  
Shuffled Complex Evolution ◽  
Computationally Intensive ◽  
Hydrologic Model Calibration

Abstract. This study provides a comprehensive assessment of state-of-the-art evolutionary multiobjective optimization (EMO) tools' relative effectiveness in calibrating hydrologic models. The relative computational efficiency, accuracy, and ease-of-use of the following EMO algorithms are tested: Epsilon Dominance Nondominated Sorted Genetic Algorithm-II (ε-NSGAII), the Multiobjective Shuffled Complex Evolution Metropolis algorithm (MOSCEM-UA), and the Strength Pareto Evolutionary Algorithm 2 (SPEA2). This study uses three test cases to compare the algorithms' performances: (1) a standardized test function suite from the computer science literature, (2) a benchmark hydrologic calibration test case for the Leaf River near Collins, Mississippi, and (3) a computationally intensive integrated model application in the Shale Hills watershed in Pennsylvania. A challenge and contribution of this work is the development of a methodology for comprehensively comparing EMO algorithms that have different search operators and randomization techniques. Overall, SPEA2 is an excellent benchmark algorithm for multiobjective hydrologic model calibration. SPEA2 attained competitive to superior results for most of the problems tested in this study. ε-NSGAII appears to be superior to MOSCEM-UA and competitive with SPEA2 for hydrologic model calibration.

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