Downscaling Precipitation in the Data-Scarce Inland River Basin of Northwest China Based on Earth System Data Products
Abstract
:1. Introduction
2. Study Area and Materials
2.1. Description of the Study Area
2.2. Data Source
3. Methods
3.1. The BGD-Based Polynomial Regression
3.2. Specific Steps of the Downscaling Model
3.3. The Evaluation of Downscaling Simulation
3.4. Mann-Kendall Test
4. Results
4.1. Downscaling Precipitation
4.2. Accuracy Test of Downscaling Results
4.2.1. Comparing the Downscaling Results of Ordinary Polynomial Regression and BGD-Based Polynomial Regression
4.2.2. The Accuracy Test for Downscaling Precipitation
4.2.3. Comparing the Spatial Distribution of Downscaling Results and TMPA
4.3. The Temporal and Spatial Variation of Precipitation
5. Discussion
6. Conclusions
Supplementary Materials
Author Contributions
Funding
Conflicts of Interest
References
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Meteorological Stations | Learning Rates | R | R2 | RMSE | MAE | NMSE |
---|---|---|---|---|---|---|
Tazhong | 0.001 | 0.6674 * | 0.4454 | 4.4290 | 3.7167 | 0.5121 |
0.01 | 0.7475 * | 0.5587 | 4.1845 | 3.7584 | 0.4572 | |
0.1 | 0.7925 * | 0.6280 | 3.7869 | 3.4501 | 0.3744 | |
1 | 0.8648 * | 0.7470 | 2.9803 | 2.5185 | 0.2319 | |
1.2 | 0.8279 * | 0.6854 | 3.4823 | 3.1418 | 0.3166 | |
Tieganlik | 0.001 | 0.7246 | 0.5251 | 5.0187 | 4.1417 | 0.4349 |
0.01 | 0.7529 | 0.5668 | 4.3454 | 3.6417 | 0.4210 | |
0.1 | 0.7917 | 0.6268 | 3.6246 | 3.1250 | 0.3625 | |
1 | 0.9224 * | 0.8012 | 2.5699 | 1.8721 | 0.1822 | |
1.2 | 0.8735 | 0.7630 | 3.4869 | 3.0667 | 0.3355 | |
Yarkant | 0.001 | 0.7590 * | 0.5760 | 4.1774 | 3.2417 | 0.4140 |
0.01 | 0.7988 * | 0.6381 | 4.1333 | 3.1583 | 0.3519 | |
0.1 | 0.8604 * | 0.7402 | 3.5451 | 2.7583 | 0.2360 | |
1 | 0.9326 * | 0.8651 | 1.9327 | 1.4685 | 0.1236 | |
1.2 | 0.9218 * | 0.8497 | 2.8875 | 2.4417 | 0.2760 | |
Hotan | 0.001 | 0.4001 * | 0.1600 | 6.6599 | 4.5917 | 0.9540 |
0.01 | 0.6284 * | 0.3949 | 5.3728 | 3.7250 | 0.6209 | |
0.1 | 0.7317 * | 0.5354 | 4.6860 | 2.8667 | 0.4723 | |
1 | 0.8938 * | 0.7819 | 3.0488 | 2.2269 | 0.1999 | |
1.2 | 0.8168 * | 0.6672 | 3.8255 | 3.0083 | 0.3148 |
Meteorological Stations | Category | R | R2 | RMSE | MAE | NMSE |
---|---|---|---|---|---|---|
Kumux | TMPA | 0.7162 * | 0.5129 | 3.8102 | 2.9245 | 0.4647 |
PR | 0.7192 * | 0.5173 | 3.9872 | 3.2548 | 0.5089 | |
BGD | 0.9763 * | 0.9531 | 1.2257 | 0.9911 | 0.0481 | |
Bayanbulak | TMPA | 0.9394 * | 0.8334 | 9.7502 | 7.0894 | 0.1527 |
PR | 0.8791 * | 0.7727 | 21.7066 | 15.6610 | 0.7568 | |
BGD | 0.9722 * | 0.9264 | 6.4804 | 4.7365 | 0.0675 | |
Korla | TMPA | 0.8456 * | 0.6406 | 1.4138 | 1.0941 | 0.3294 |
PR | 0.7669 * | 0.5789 | 1.5303 | 1.1367 | 0.3860 | |
BGD | 0.9871 * | 0.9483 | 0.5363 | 0.4694 | 0.0474 | |
Kalpin | TMPA | 0.9415 * | 0.8860 | 3.3912 | 2.3654 | 0.1045 |
PR | 0.7991 * | 0.6385 | 8.1513 | 6.0004 | 0.6035 | |
BGD | 0.9659 * | 0.9239 | 2.7704 | 2.1234 | 0.0697 | |
Alaer | TMPA | 0.7262 * | 0.5273 | 3.9437 | 2.1817 | 0.5797 |
PR | 0.7168 * | 0.5139 | 3.8866 | 2.1003 | 0.5630 | |
BGD | 0.9350 * | 0.8365 | 2.0055 | 1.1281 | 0.1499 | |
Tazhong | TMPA | 0.7505 * | 0.5632 | 1.4524 | 0.6817 | 0.4094 |
PR | 0.8056 * | 0.6469 | 1.5500 | 1.2753 | 0.4663 | |
BGD | 0.9722 * | 0.9403 | 0.5311 | 0.2698 | 0.0547 | |
Tieganlik | TMPA | 0.9542 * | 0.8339 | 0.3235 | 0.2131 | 0.1522 |
PR | 0.8016 * | 0.6425 | 0.6717 | 0.6016 | 0.6563 | |
BGD | 0.9765 * | 0.9425 | 0.1904 | 0.1471 | 0.0527 | |
Yarkant | TMPA | 0.9682 * | 0.9000 | 1.6390 | 1.3038 | 0.0917 |
PR | 0.7154 * | 0.5118 | 3.7256 | 3.0942 | 0.4737 | |
BGD | 0.9715 * | 0.9438 | 1.6639 | 1.2537 | 0.0945 | |
Pishan | TMPA | 0.9789 * | 0.8115 | 3.0822 | 2.2317 | 0.1773 |
PR | 0.8000 * | 0.6399 | 4.5850 | 3.5451 | 0.3924 | |
BGD | 0.9864 * | 0.8066 | 3.0425 | 1.9604 | 0.1728 | |
Hotan | TMPA | 0.8630 * | 0.7316 | 1.5303 | 1.0287 | 0.2460 |
PR | 0.7550 * | 0.5700 | 2.0259 | 1.6649 | 0.4311 | |
BGD | 0.9156 * | 0.8217 | 1.2473 | 0.8306 | 0.1634 |
Meteorological Stations | R | R2 | RMSE | MAE | NMSE |
---|---|---|---|---|---|
Kumux | 0.8842 * | 0.7243 | 3.8320 | 2.8080 | 0.2744 |
Bayanbulak | 0.9506 * | 0.8366 | 12.0196 | 8.1375 | 0.1626 |
Korla | 0.9592 * | 0.9147 | 2.9264 | 2.0685 | 0.0849 |
Kalpin | 0.9088 * | 0.8184 | 7.0488 | 4.7426 | 0.1807 |
Alaer | 0.9035 * | 0.7855 | 3.6482 | 2.4421 | 0.2135 |
Tazhong | 0.8761 * | 07421 | 2.8438 | 1.6513 | 0.2566 |
Tieganlik | 0.9477 * | 0.8957 | 2.2782 | 1.3692 | 0.1038 |
Yarkant | 0.8958 * | 0.7649 | 5.0749 | 2.7532 | 0.2339 |
Pishan | 0.8660 * | 0.7352 | 5.9348 | 3.6784 | 0.2635 |
Hotan | 0.8878 * | 0.7754 | 3.4006 | 2.3494 | 0.2235 |
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Zuo, J.; Xu, J.; Chen, Y.; Wang, C. Downscaling Precipitation in the Data-Scarce Inland River Basin of Northwest China Based on Earth System Data Products. Atmosphere 2019, 10, 613. https://doi.org/10.3390/atmos10100613
Zuo J, Xu J, Chen Y, Wang C. Downscaling Precipitation in the Data-Scarce Inland River Basin of Northwest China Based on Earth System Data Products. Atmosphere. 2019; 10(10):613. https://doi.org/10.3390/atmos10100613
Chicago/Turabian StyleZuo, Jingping, Jianhua Xu, Yaning Chen, and Chong Wang. 2019. "Downscaling Precipitation in the Data-Scarce Inland River Basin of Northwest China Based on Earth System Data Products" Atmosphere 10, no. 10: 613. https://doi.org/10.3390/atmos10100613