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Table 4

Performance of the probability distributions in modelling marginal water quality indexes for the three river streamflow scenarios of the three stations

StationScenarioWater quality indexDistributionParameter
K-S test
RMSE
ShapeScaleLocation
Jieshou S1 CODMn P-III 0.817 0.0531 2.374 0.105 0.189 0.038 
NH3-N GPA 22.345 0.192 −1.106 0.072 0.025 
S2 CODMn GEV 5.287 −0.367 8.162 0.066 0.150 0.027 
NH3-N P-III 0.888 0.068 −0.786 0.079 0.032 
S3 CODMn GEV 2.225 −0.255 5.377 0.052 0.148 0.015 
NH3-N P-III 0.672 0.144 −0.077 0.087 0.039 
Lutaizi S1 CODMn GEV 4.630 −0.535 1.407 0.086 0.177 0.033 
NH3-N GPA 1.158 −0.305 0.034 0.076 0.024 
S2 CODMn GLO 5.113 −0.205 1.093 0.064 0.173 0.023 
NH3-N GPA 1.656 0.159 −0.087 0.062 0.022 
S3 CODMn GLO 0.760 −0.227 4.530 0.079 0.156 0.028 
NH3-N P-III 0.576 0.888 0.018 0.069 0.029 
Bengbu S1 CODMn LN3 1.408 0.536 1.601 0.045 0.164 0.019 
NH3-N P-III 1.049 0.399 −0.112 0.065 0.022 
S2 CODMn GLO 0.708 −0.256 4.842 0.058 0.161 0.019 
NH3-N GPA 1.606 0.003 −0.162 0.11 0.042 
S3 CODMn GLO 0.612 −0.166 4.432 0.067 0.150 0.026 
NH3-N LN3 −1.034 1.169 −0.054 0.10 0.039 
StationScenarioWater quality indexDistributionParameter
K-S test
RMSE
ShapeScaleLocation
Jieshou S1 CODMn P-III 0.817 0.0531 2.374 0.105 0.189 0.038 
NH3-N GPA 22.345 0.192 −1.106 0.072 0.025 
S2 CODMn GEV 5.287 −0.367 8.162 0.066 0.150 0.027 
NH3-N P-III 0.888 0.068 −0.786 0.079 0.032 
S3 CODMn GEV 2.225 −0.255 5.377 0.052 0.148 0.015 
NH3-N P-III 0.672 0.144 −0.077 0.087 0.039 
Lutaizi S1 CODMn GEV 4.630 −0.535 1.407 0.086 0.177 0.033 
NH3-N GPA 1.158 −0.305 0.034 0.076 0.024 
S2 CODMn GLO 5.113 −0.205 1.093 0.064 0.173 0.023 
NH3-N GPA 1.656 0.159 −0.087 0.062 0.022 
S3 CODMn GLO 0.760 −0.227 4.530 0.079 0.156 0.028 
NH3-N P-III 0.576 0.888 0.018 0.069 0.029 
Bengbu S1 CODMn LN3 1.408 0.536 1.601 0.045 0.164 0.019 
NH3-N P-III 1.049 0.399 −0.112 0.065 0.022 
S2 CODMn GLO 0.708 −0.256 4.842 0.058 0.161 0.019 
NH3-N GPA 1.606 0.003 −0.162 0.11 0.042 
S3 CODMn GLO 0.612 −0.166 4.432 0.067 0.150 0.026 
NH3-N LN3 −1.034 1.169 −0.054 0.10 0.039 

Only the best-fit distributions for each scenario are presented.

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