scholarly journals Global sensitivity analysis and calibration of parameters for a physically-based agro-hydrological model

2016 ◽  
Vol 83 ◽  
pp. 88-102 ◽  
Author(s):  
Xu Xu ◽  
Chen Sun ◽  
Guanhua Huang ◽  
Binayak P. Mohanty
Keyword(s):  
Sensitivity Analysis ◽  
Hydrological Model ◽  
Global Sensitivity Analysis ◽  
Global Sensitivity ◽  
Physically Based

scholarly journals Exploring the impact of forcing error characteristics on physically based snow simulations within a global sensitivity analysis framework

2014 ◽  
Vol 11 (12) ◽  
pp. 13745-13795 ◽  
Author(s):  
M. S. Raleigh ◽  
J. D. Lundquist ◽  
M. P. Clark
Keyword(s):  
Sensitivity Analysis ◽  
Global Sensitivity Analysis ◽  
Snow Water Equivalent ◽  
Model Parameters ◽  
Random Errors ◽  
Global Sensitivity ◽  
Error Characteristics ◽  
Error Distributions ◽  
Physically Based ◽  
Snow Water

Abstract. Physically based models provide insights into key hydrologic processes, but are associated with uncertainties due to deficiencies in forcing data, model parameters, and model structure. Forcing uncertainty is enhanced in snow-affected catchments, where weather stations are scarce and prone to measurement errors, and meteorological variables exhibit high variability. Hence, there is limited understanding of how forcing error characteristics affect simulations of cold region hydrology. Here we employ global sensitivity analysis to explore how different error types (i.e., bias, random errors), different error distributions, and different error magnitudes influence physically based simulations of four snow variables (snow water equivalent, ablation rates, snow disappearance, and sublimation). We use Sobol' global sensitivity analysis, which is typically used for model parameters, but adapted here for testing model sensitivity to co-existing errors in all forcings. We quantify the Utah Energy Balance model's sensitivity to forcing errors with 1 520 000 Monte Carlo simulations across four sites and four different scenarios. Model outputs were generally (1) more sensitive to forcing biases than random errors, (2) less sensitive to forcing error distributions, and (3) sensitive to different forcings depending on the relative magnitude of errors. For typical error magnitudes, precipitation bias was the most important factor for snow water equivalent, ablation rates, and snow disappearance timing, but other forcings had a significant impact depending on forcing error magnitudes. Additionally, the relative importance of forcing errors depended on the model output of interest. Sensitivity analysis can reveal which forcing error characteristics matter most for hydrologic modeling.

scholarly journals Exploring the impact of forcing error characteristics on physically based snow simulations within a global sensitivity analysis framework

2015 ◽  
Vol 19 (7) ◽  
pp. 3153-3179 ◽  
Author(s):  
M. S. Raleigh ◽  
J. D. Lundquist ◽  
M. P. Clark
Keyword(s):  
Sensitivity Analysis ◽  
Global Sensitivity Analysis ◽  
Snow Water Equivalent ◽  
Model Parameters ◽  
Random Errors ◽  
Global Sensitivity ◽  
Error Characteristics ◽  
Physically Based ◽  
Snow Water ◽  
The Impact

Abstract. Physically based models provide insights into key hydrologic processes but are associated with uncertainties due to deficiencies in forcing data, model parameters, and model structure. Forcing uncertainty is enhanced in snow-affected catchments, where weather stations are scarce and prone to measurement errors, and meteorological variables exhibit high variability. Hence, there is limited understanding of how forcing error characteristics affect simulations of cold region hydrology and which error characteristics are most important. Here we employ global sensitivity analysis to explore how (1) different error types (i.e., bias, random errors), (2) different error probability distributions, and (3) different error magnitudes influence physically based simulations of four snow variables (snow water equivalent, ablation rates, snow disappearance, and sublimation). We use the Sobol' global sensitivity analysis, which is typically used for model parameters but adapted here for testing model sensitivity to coexisting errors in all forcings. We quantify the Utah Energy Balance model's sensitivity to forcing errors with 1 840 000 Monte Carlo simulations across four sites and five different scenarios. Model outputs were (1) consistently more sensitive to forcing biases than random errors, (2) generally less sensitive to forcing error distributions, and (3) critically sensitive to different forcings depending on the relative magnitude of errors. For typical error magnitudes found in areas with drifting snow, precipitation bias was the most important factor for snow water equivalent, ablation rates, and snow disappearance timing, but other forcings had a more dominant impact when precipitation uncertainty was due solely to gauge undercatch. Additionally, the relative importance of forcing errors depended on the model output of interest. Sensitivity analysis can reveal which forcing error characteristics matter most for hydrologic modeling.

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