Investigating the universality of the estimation models in multiple regions is important for improving the performance of the neural network structures and parameters. P and Rdc were calculated in the path analysis for all selected capital stations in the south (Tables 4 and 5, respectively). The hours of sunshine, N, which had the largest P, reaching 0.60–0.85, was selected as the core variable, and the relative humidity, RH, which was the only negative parameter and had the second largest absolute value among all parameters, was selected as the limited variable. Rdc, however, fluctuated between 0.53 and 0.81 when the single parameter N was selected as the input variable, so the error oscillation may be larger when applying the models on a larger scale. Rdc increased and maintained a range of 0.8–0.9 when RH was used as the second input variable, indicating that the double-parameter model was stable and highly credible when applied in the entire selected stations.
Path coefficients between each meteorological parameter and ET0 at the capital stations in southern China
P . | Tmean . | Tmax . | Tmin . | RH . | U2 . | N . |
---|---|---|---|---|---|---|
Guangzhou | 0.0088 | 0.0553 | 0.0660 | − 0.1504 | 0.0867 | 0.8216 |
Nanning | 0.0360 | 0.0686 | 0.0496 | − 0.0929 | 0.0972 | 0.8302 |
Kunming | 0.0387 | 0.0536 | 0.0610 | − 0.1715 | 0.0929 | 0.7940 |
Haikou | − 0.0111 | 0.1400 | 0.0507 | − 0.1435 | 0.0856 | 0.7986 |
Guiyang | 0.0032 | 0.0659 | 0.1038 | − 0.1827 | 0.1310 | 0.7423 |
Chongqing | 0.0547 | 0.0043 | 0.0831 | − 0.1620 | 0.1294 | 0.7435 |
Fuzhou | − 0.0376 | 0.1019 | 0.1047 | − 0.2326 | 0.1127 | 0.6880 |
Changsha | 0.0551 | 0.0282 | 0.0894 | − 0.2017 | 0.1070 | 0.6825 |
Hangzhou | 0.0355 | 0.0668 | 0.0983 | − 0.2310 | 0.0779 | 0.6673 |
Shanghai | − 0.0969 | 0.1715 | 0.1558 | − 0.2787 | 0.1077 | 0.6463 |
Nanchang | 0.0668 | 0.0649 | 0.0707 | − 0.1659 | 0.1060 | 0.6975 |
Wuhan | 0.1020 | 0.0231 | 0.0840 | − 0.1439 | 0.1282 | 0.7360 |
P . | Tmean . | Tmax . | Tmin . | RH . | U2 . | N . |
---|---|---|---|---|---|---|
Guangzhou | 0.0088 | 0.0553 | 0.0660 | − 0.1504 | 0.0867 | 0.8216 |
Nanning | 0.0360 | 0.0686 | 0.0496 | − 0.0929 | 0.0972 | 0.8302 |
Kunming | 0.0387 | 0.0536 | 0.0610 | − 0.1715 | 0.0929 | 0.7940 |
Haikou | − 0.0111 | 0.1400 | 0.0507 | − 0.1435 | 0.0856 | 0.7986 |
Guiyang | 0.0032 | 0.0659 | 0.1038 | − 0.1827 | 0.1310 | 0.7423 |
Chongqing | 0.0547 | 0.0043 | 0.0831 | − 0.1620 | 0.1294 | 0.7435 |
Fuzhou | − 0.0376 | 0.1019 | 0.1047 | − 0.2326 | 0.1127 | 0.6880 |
Changsha | 0.0551 | 0.0282 | 0.0894 | − 0.2017 | 0.1070 | 0.6825 |
Hangzhou | 0.0355 | 0.0668 | 0.0983 | − 0.2310 | 0.0779 | 0.6673 |
Shanghai | − 0.0969 | 0.1715 | 0.1558 | − 0.2787 | 0.1077 | 0.6463 |
Nanchang | 0.0668 | 0.0649 | 0.0707 | − 0.1659 | 0.1060 | 0.6975 |
Wuhan | 0.1020 | 0.0231 | 0.0840 | − 0.1439 | 0.1282 | 0.7360 |
Decision contribution rates (Rdc) between each meteorological parameter and ET0 at the capital stations in southern China
Rdc . | Tmean . | Tmax . | Tmin . | RH . | U2 . | N . | N+RH . |
---|---|---|---|---|---|---|---|
Guangzhou | 0.0068 | 0.0437 | 0.0312 | 0.1058 | 0.0015 | 0.7985 | 0.9044 |
Nanning | 0.0275 | 0.0567 | 0.0154 | 0.0683 | 0.0080 | 0.8136 | 0.8820 |
Kunming | 0.0262 | 0.0420 | 0.0082 | 0.1186 | 0.0303 | 0.7599 | 0.8785 |
Haikou | − 0.0082 | 0.1037 | 0.0194 | 0.1010 | 0.0044 | 0.7703 | 0.8713 |
Guiyang | 0.0024 | 0.0529 | 0.0367 | 0.1456 | 0.0373 | 0.7112 | 0.8567 |
Chongqing | 0.0449 | 0.0037 | 0.0486 | 0.1381 | 0.0387 | 0.7109 | 0.8490 |
Fuzhou | − 0.0296 | 0.0839 | 0.0587 | 0.1867 | 0.0302 | 0.6515 | 0.8382 |
Changsha | 0.0463 | 0.0238 | 0.0596 | 0.1763 | 0.0276 | 0.6530 | 0.8293 |
Hangzhou | 0.0273 | 0.0547 | 0.0520 | 0.1919 | 0.0193 | 0.6339 | 0.8258 |
Shanghai | − 0.0646 | 0.1232 | 0.0818 | 0.2148 | 0.0265 | 0.5978 | 0.8125 |
Nanchang | 0.0543 | 0.0540 | 0.0454 | 0.1398 | 0.0201 | 0.6707 | 0.8105 |
Wuhan | 0.0773 | 0.0188 | 0.0453 | 0.1071 | 0.0313 | 0.7023 | 0.8094 |
Rdc . | Tmean . | Tmax . | Tmin . | RH . | U2 . | N . | N+RH . |
---|---|---|---|---|---|---|---|
Guangzhou | 0.0068 | 0.0437 | 0.0312 | 0.1058 | 0.0015 | 0.7985 | 0.9044 |
Nanning | 0.0275 | 0.0567 | 0.0154 | 0.0683 | 0.0080 | 0.8136 | 0.8820 |
Kunming | 0.0262 | 0.0420 | 0.0082 | 0.1186 | 0.0303 | 0.7599 | 0.8785 |
Haikou | − 0.0082 | 0.1037 | 0.0194 | 0.1010 | 0.0044 | 0.7703 | 0.8713 |
Guiyang | 0.0024 | 0.0529 | 0.0367 | 0.1456 | 0.0373 | 0.7112 | 0.8567 |
Chongqing | 0.0449 | 0.0037 | 0.0486 | 0.1381 | 0.0387 | 0.7109 | 0.8490 |
Fuzhou | − 0.0296 | 0.0839 | 0.0587 | 0.1867 | 0.0302 | 0.6515 | 0.8382 |
Changsha | 0.0463 | 0.0238 | 0.0596 | 0.1763 | 0.0276 | 0.6530 | 0.8293 |
Hangzhou | 0.0273 | 0.0547 | 0.0520 | 0.1919 | 0.0193 | 0.6339 | 0.8258 |
Shanghai | − 0.0646 | 0.1232 | 0.0818 | 0.2148 | 0.0265 | 0.5978 | 0.8125 |
Nanchang | 0.0543 | 0.0540 | 0.0454 | 0.1398 | 0.0201 | 0.6707 | 0.8105 |
Wuhan | 0.0773 | 0.0188 | 0.0453 | 0.1071 | 0.0313 | 0.7023 | 0.8094 |