Optimal Alternative for Quantifying Reference Evapotranspiration in Northern Xinjiang
Abstract
:1. Introduction
2. Overview of the Study Area and Data
2.1. Study Area
2.2. Data Sources
3. Modelling Structure and Approach
3.1. FAO56 Penman–Monteith Model
3.2. Empirical Models
3.3. Random Forest-Based Reference Evapotranspiration (ET0) Model
3.4. Least Square Support Vector Regression
3.5. Bidirectional Long-Term and Short-Term Memory Network
3.6. Back Propagation Neural Network Optimized by Genetic Algorithm
3.7. Performance Evaluation of Models
4. Results and Analysis
4.1. Performance Appraisal of Seven Empirical Models (Temperature-Based and Mass Transfer-Based) for Estimating ET0
4.2. Components Comparison of the Four Algorithm Models for Estimating ET0
4.3. Evaluation of Optimal Reference Evapotranspiration Model under Different Time-Scale Conditions
5. Discussion
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Station | Lon (°E) | Lat (°S) | DEM (m) | T (°C) | RH (%) | U (m/s) | VPD (kPa) | Rainfall (mm) | ET0 (mm) | AI |
---|---|---|---|---|---|---|---|---|---|---|
Shawan | 85.37 | 44.2 | 522.2 | 7.94 (15.22) | 64.14 (18.35) | 1.17 (0.65) | 0.96 (0.90) | 253.96 (2.29) | 1086.02 (2.34) | 4.28 |
Wujiaqu | 87.32 | 44.12 | 440.5 | 7.10 (16.49) | 59.94 (19.41) | 1.31 (0.69) | 1.13 (1.07) | 169.39 (1.70) | 1155.58 (2.50) | 6.82 |
Fuyun | 89.31 | 46.59 | 807.5 | 4.53 (15.89) | 57.15 (19.05) | 1.34 (1.07) | 0.93 (0.89) | 230.12 (2.24) | 1028.77 (2.42) | 4.47 |
Hoboksar | 85.45 | 46.49 | 1322.1 | 4.45 (12.50) | 53.71 (15.78) | 1.89 (1.76) | 0.75 (0.64) | 174.92 (1.87) | 1038.12 (2.18) | 5.93 |
Qinghe | 90.23 | 46.4 | 1218.2 | 1.96 (15.63) | 57.81 (16.31) | 1.01 (0.68) | 0.75 (0.69) | 214.06 (2.19) | 909.92 (2.03) | 4.25 |
Karamay | 84.51 | 45.37 | 450.3 | 9.06 (16.00) | 50.28 (21.78) | 1.85 (1.18) | 1.25 (1.17) | 137.75 (1.56) | 1328.76 (2.89) | 9.65 |
Wusu | 84.4 | 44.26 | 478.7 | 8.92 (15.31) | 57.59 (20.12) | 1.25 (0.59) | 1.11 (1.06) | 214.14 (2.14) | 1106.45 (2.38) | 5.17 |
Hutubi | 86.51 | 44.1 | 575.1 | 8.15 (15.64) | 59.48 (20.86) | 1.59 (0.76) | 0.37 (1.04) | 210.05 (1.92) | 1192.17 (2.62) | 5.68 |
Meteorological Inputs | Equations | Proposed by |
---|---|---|
Based-temperature ET0 models | ||
Hargreaves and Allen [21] | ||
Berti [22] | ||
) | Dorji [23] | |
Mass transfer-based ET0 models | ||
Dalton [24] | ||
Meyer [25] | ||
WMO [26] | ||
Albrecht [27] |
Algorithm | T | Tmax | Tmin | Ra | Rs | RH | U2 | |||
---|---|---|---|---|---|---|---|---|---|---|
RF1 | LS-SVR1 | Bi-LSTM1 | GA-BP1 | | | | | |||
RF2 | LS-SVR2 | Bi-LSTM2 | GA-BP2 | | | | | | ||
RF3 | LS-SVR3 | Bi-LSTM3 | GA-BP3 | | | | | | ||
RF4 | LS-SVR4 | Bi-LSTM4 | GA-BP4 | | | | | | | |
RF5 | LS-SVR5 | Bi-LSTM5 | GA-BP5 | | | | | | | |
RF6 | LS-SVR6 | Bi-LSTM6 | GA-BP6 | | | | | | | |
RF7 | LS-SVR7 | Bi-LSTM7 | GA-BP7 | | | | | |
Models | Training Period (2000–2014) | |||||
---|---|---|---|---|---|---|
RMSE (mm·d−1) | MAE (mm·d−1) | MBE | R2 | GPI | Rank | |
RF1 | 0.2664 | 0.1672 | −0.0006 | 0.9891 | 0.4857 | 13 |
RF2 | 0.2391 | 0.1493 | 0.0012 | 0.9912 | 0.7515 | 7 |
RF3 | 0.2370 | 0.1487 | 0.0005 | 0.9914 | 0.7162 | 8 |
RF4 | 0.2244 | 0.1385 | 0.0008 | 0.9923 | 0.8095 | 5 |
RF5 | 0.1349 | 0.0873 | 0.0008 | 0.9972 | 1.2466 | 2 |
RF6 | 0.1154 | 0.0075 | −0.0004 | 0.9980 | 1.4516 | 1 |
RF7 | 0.1620 | 0.1032 | 0.0003 | 0.9960 | 1.0855 | 3 |
LS-SVR1 | 0.5755 | 0.3659 | 0.0000 | 0.9491 | −1.5234 | 26 |
LS-SVR2 | 0.5099 | 0.3267 | 0.0000 | 0.9601 | −1.0476 | 21 |
LS-SVR3 | 0.5078 | 0.3246 | 0.0000 | 0.9604 | −1.0304 | 20 |
LS-SVR4 | 0.4654 | 0.2975 | 0.0000 | 0.9667 | −0.7329 | 18 |
LS-SVR5 | 0.2522 | 0.1665 | 0.0000 | 0.9902 | 0.5774 | 11 |
LS-SVR6 | 0.2105 | 0.1447 | 0.0000 | 0.9932 | 0.7894 | 6 |
LS-SVR7 | 0.3346 | 0.2154 | 0.0000 | 0.9828 | 0.1099 | 14 |
BiLSTM1 | 0.5461 | 0.3541 | −0.0006 | 0.9542 | −1.3624 | 25 |
BiLSTM2 | 0.5007 | 0.3226 | 0.0002 | 0.9615 | −0.9742 | 19 |
BiLSTM3 | 0.5285 | 0.3373 | −0.0004 | 0.9571 | −1.2048 | 23 |
BiLSTM4 | 0.4678 | 0.2972 | 0.0002 | 0.9664 | −0.7319 | 17 |
BiLSTM5 | 0.2648 | 0.1785 | 0.0000 | 0.9892 | 0.4959 | 12 |
BiLSTM6 | 0.2342 | 0.1587 | −0.0006 | 0.9916 | 0.6282 | 10 |
BiLSTM7 | 0.3518 | 0.2310 | 0.0000 | 0.9810 | −0.0084 | 16 |
GA-BP1 | 0.5658 | 0.3591 | −0.0060 | 0.9508 | −1.8223 | 28 |
GA-BP2 | 0.5041 | 0.3295 | −0.0006 | 0.9610 | −1.0606 | 22 |
GA-BP3 | 0.5071 | 0.3289 | −0.0047 | 0.9605 | −1.3323 | 24 |
GA-BP4 | 0.4761 | 0.3109 | −0.0139 | 0.9652 | −1.6939 | 27 |
GA-BP5 | 0.2508 | 0.1692 | 0.0021 | 0.9903 | 0.7063 | 9 |
GA-BP6 | 0.2145 | 0.1504 | 0.0011 | 0.9929 | 0.8261 | 4 |
GA-BP7 | 0.3256 | 0.2129 | −0.0014 | 0.9837 | 0.0659 | 15 |
Models | Testing Period (2015–2020) | |||||
---|---|---|---|---|---|---|
RMSE (mm·d−1) | MAE (mm·d−1) | MBE | R2 | GPI | Rank | |
RF1 | 0.7805 | 0.5168 | −0.3149 | 0.9229 | −1.6354 | 27 |
RF2 | 0.7161 | 0.4633 | −0.2893 | 0.9351 | −1.1373 | 18 |
RF3 | 0.7159 | 0.4631 | −0.2903 | 0.9351 | −1.1386 | 19 |
RF4 | 0.7003 | 0.4457 | −0.2979 | 0.9379 | −1.0424 | 17 |
RF5 | 0.3492 | 0.2276 | −0.0440 | 0.9846 | 1.4763 | 9 |
RF6 | 0.3201 | 0.2043 | −0.0720 | 0.9870 | 1.5575 | 7 |
RF7 | 0.4156 | 0.2790 | −0.0675 | 0.9781 | 1.0678 | 11 |
LS-SVR1 | 0.7522 | 0.4963 | −0.3260 | 0.9284 | −1.4778 | 24 |
LS-SVR2 | 0.7146 | 0.4646 | −0.3314 | 0.9353 | −1.2383 | 20 |
LS-SVR3 | 0.6593 | 0.4293 | −0.2770 | 0.9450 | −0.7710 | 14 |
LS-SVR4 | 0.6366 | 0.4101 | −0.2882 | 0.9487 | −0.6528 | 13 |
LS-SVR5 | 0.2621 | 0.1724 | −0.0062 | 0.9913 | 1.9727 | 4 |
LS-SVR6 | 0.2380 | 0.1604 | −0.0425 | 0.9928 | 1.9809 | 2 |
LS-SVR7 | 0.3548 | 0.2371 | −0.0455 | 0.9841 | 1.4300 | 10 |
BiLSTM1 | 0.7811 | 0.5374 | −0.3776 | 0.9227 | −1.8483 | 28 |
BiLSTM2 | 0.7400 | 0.4925 | −0.3797 | 0.9307 | −1.5453 | 26 |
BiLSTM3 | 0.7189 | 0.4799 | −0.3198 | 0.9346 | −1.2692 | 22 |
BiLSTM4 | 0.7011 | 0.4647 | −0.3570 | 0.9378 | −1.2427 | 21 |
BiLSTM5 | 0.2787 | 0.1831 | 0.0245 | 0.9902 | 1.9735 | 3 |
BiLSTM6 | 0.3033 | 0.2022 | −0.0877 | 0.9884 | 1.5742 | 6 |
BiLSTM7 | 0.4287 | 0.2990 | −0.0887 | 0.9767 | 0.9184 | 12 |
GA-BP1 | 0.7572 | 0.5009 | −0.3312 | 0.9274 | −1.5261 | 25 |
GA-BP2 | 0.7330 | 0.4832 | −0.3578 | 0.9320 | −1.4350 | 23 |
GA-BP3 | 0.6673 | 0.4405 | −0.2923 | 0.9436 | −0.8724 | 16 |
GA-BP4 | 0.6571 | 0.4314 | −0.3058 | 0.9453 | −0.8388 | 15 |
GA-BP5 | 0.2542 | 0.1706 | 0.0039 | 0.9918 | 2.0245 | 1 |
GA-BP6 | 0.2434 | 0.1684 | −0.0480 | 0.9925 | 1.9312 | 5 |
GA-BP7 | 0.3407 | 0.2273 | −0.0331 | 0.9853 | 1.5301 | 8 |
Optimal Model | T | Tmax | Tmin | Ra | Rs | RH | U2 |
---|---|---|---|---|---|---|---|
LS-SVR1 | | | | | |||
RF2 | | | | | | ||
LS-SVR3 | | | | | | ||
LS-SVR4 | | | | | | | |
GA-BP5 | | | | | | | |
LS-SVR6 | | | | | | | |
GA-BP7 | | | | | |
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Jiao, P.; Hu, S.-J. Optimal Alternative for Quantifying Reference Evapotranspiration in Northern Xinjiang. Water 2022, 14, 1. https://doi.org/10.3390/w14010001
Jiao P, Hu S-J. Optimal Alternative for Quantifying Reference Evapotranspiration in Northern Xinjiang. Water. 2022; 14(1):1. https://doi.org/10.3390/w14010001
Chicago/Turabian StyleJiao, Ping, and Shun-Jun Hu. 2022. "Optimal Alternative for Quantifying Reference Evapotranspiration in Northern Xinjiang" Water 14, no. 1: 1. https://doi.org/10.3390/w14010001
APA StyleJiao, P., & Hu, S.-J. (2022). Optimal Alternative for Quantifying Reference Evapotranspiration in Northern Xinjiang. Water, 14(1), 1. https://doi.org/10.3390/w14010001