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Constraints of the two calibrations on parameter space were further explored from the standpoint of parameter correlation. Ideally, model parameters are assumed to be mutually independent. However, parameter correlation is usually found in hydrological modeling and can be a source of modeling uncertainty (Blasone & Vrugt 2008). Therefore, examining parameter correlation can help to explore the reason for changes in simulation uncertainty when using soil parameter values estimated from pedotransfer functions. Correlation matrices of CAL_10 and CAL_19 are shown in Tables 7 and 8, respectively. Patterns of parameter correlation for the two cases are similar, i.e., most correlations were weak and only a very few were significant at the 0.01 confidence level. A similar weak correlation pattern was detected by Yang et al. (2008). They concluded that this phenomenon arises from the inherent assumptions in GLUE, which tend to flatten the true response surface by removing sharp peaks and valleys. In our study, the parameter correlation patterns from CAL_10 and CAL_19 were not much different. Combining this fact with the analysis results concerning posterior parameter distribution, it is implied that the effects of incorporating soil parameter values derived from pedotransfer functions on other calibrated parameters are minor. Therefore, the differences in parameter space mainly stem from soil hydraulic parameters. More specifically, the reduction in simulation uncertainty most likely originated from the more reasonable description of soil hydraulic characteristics by the estimates obtained from pedotransfer functions than those obtained from calibration against streamflow data. This reveals the necessity of implementing the proposed method to SWAT hydrological modeling in data-sparse basins that lack field survey data of soil hydraulic characteristics.

Table 7

Correlation matrix of posterior parameter distribution obtained from CAL_19 (bold text indicants significant at 0.01 level)

 CN2ALPHA_BFGW_DELAYGWQMNGW_REVAPESCOCH_N2CH_K2ALPHA_BNKSFTMP
CN2          
ALPHA_BF 0.004         
GW_DELAY 0.001 −0.000        
GWQMN 0.020 −0.020 0.012       
GW_REVAP −0.009 0.018 0.015 0.015      
ESCO −0.016 −0.030 −0.016 0.018 0.006     
CH_N2 0.133 0.010 0.005 0.005 −0.014 −0.002    
CH_K2 0.092 0.027 0.008 −0.001 0.009 0.043 0.339   
ALPHA_BNK 0.103 0.014 0.002 −0.014 0.004 0.021 0.127 0.328  
SFTMP −0.024 0.010 −0.013 −0.004 0.015 0.013 −0.020 0.060 0.02 
 CN2ALPHA_BFGW_DELAYGWQMNGW_REVAPESCOCH_N2CH_K2ALPHA_BNKSFTMP
CN2          
ALPHA_BF 0.004         
GW_DELAY 0.001 −0.000        
GWQMN 0.020 −0.020 0.012       
GW_REVAP −0.009 0.018 0.015 0.015      
ESCO −0.016 −0.030 −0.016 0.018 0.006     
CH_N2 0.133 0.010 0.005 0.005 −0.014 −0.002    
CH_K2 0.092 0.027 0.008 −0.001 0.009 0.043 0.339   
ALPHA_BNK 0.103 0.014 0.002 −0.014 0.004 0.021 0.127 0.328  
SFTMP −0.024 0.010 −0.013 −0.004 0.015 0.013 −0.020 0.060 0.02 
Table 8

Correlation matrix of the posterior parameter distribution obtained from CAL_10 (bold text indicants significant at 0.01 level)

 CN2ALPHA_BFGW_DELAYGWQMNGW_REVAPESCOCH_N2CH_K2ALPHA_BNKSFTMP
CN2          
ALPHA_BF −0.021         
GW_DELAY 0.000 −0.011        
GWQMN −0.007 0.013 0.005       
GW_REVAP 0.006 0.014 0.008 −0.010      
ESCO −0.130 0.013 0.017 −0.017 0.007     
CH_N2 0.213 0.003 0.004 −0.018 −0.009 0.021    
CH_K2 0.182 0.002 −0.008 −0.007 0.008 0.006 0.174   
ALPHA_BNK 0.101 0.007 0.034 0.003 0.014 −0.023 0.121 0.288  
SFTMP 0.002 0.003 −0.004 0.008 0.014 0.07 −0.009 −0.004 −0.016 
 CN2ALPHA_BFGW_DELAYGWQMNGW_REVAPESCOCH_N2CH_K2ALPHA_BNKSFTMP
CN2          
ALPHA_BF −0.021         
GW_DELAY 0.000 −0.011        
GWQMN −0.007 0.013 0.005       
GW_REVAP 0.006 0.014 0.008 −0.010      
ESCO −0.130 0.013 0.017 −0.017 0.007     
CH_N2 0.213 0.003 0.004 −0.018 −0.009 0.021    
CH_K2 0.182 0.002 −0.008 −0.007 0.008 0.006 0.174   
ALPHA_BNK 0.101 0.007 0.034 0.003 0.014 −0.023 0.121 0.288  
SFTMP 0.002 0.003 −0.004 0.008 0.014 0.07 −0.009 −0.004 −0.016 

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