Hydrological Appraisal of Climate Change Impacts on the Water Resources of the Xijiang Basin, South China
Abstract
:1. Introduction
2. Data and Methodology
2.1. Methodology
2.2. Study Area and Hydrological Data
2.3. Climate Change Scenarios and Datasets
3. Hydrological Modelling and Parametrisation
3.1. Lumped Xinanjiang Model (XAJ)
3.2. Distributed Liuxihe Model (LXH)
3.3. Model Calibration and Validation
4. Results and Discussion
4.1. Evaluation of Rainfall Products from the Climate Model
4.2. Future Rainfall–Runoff Simulations
5. Summary and Concluding Comments
- (1)
- Both the distributed LXH model and lumped XAJ models can reproduce almost equally well the historical runoff data series. The simulated peak flows were more accurate in the LXH model, while the base flow was better simulated by the XAJ model.
- (2)
- The cumulative catchment daily rainfall produced from the climate model matched the rain gauge measurements very well, but they tended to produce more small and medium peak flows and to underestimate the high peak flows during the hydrological model simulations.
- (3)
- Using RCP4.5 and RCP8.5 precipitation data as input to the tested models, marginal variations were found in the median flow simulation in the same hydrological model, which may imply that the difference of the impact from the two climate scenarios on the monthly streamflow simulation in this study was small.
- (4)
- Under future climate conditions, the distributed LXH model produced more streamflow than the lumped XAJ model during January to August, but more streamflow was modelled in the XAJ model than in the LXH model from September to December.
- (5)
- The RCP4.5 climate data could result in the smallest and the highest annual streamflow in the XAJ and LXH models respectively before 2050, but the RCP8.5 rainfall data could produce the smallest annual streamflow in the XAJ and the highest annual streamflow in the LXH model from 2050 to 2099.
- (6)
- The flood frequency analysis suggests that the floods under climate change in the future will be more severe than the historical floods, especially under the RCP8.5 scenario in the LXH model simulations.
Acknowledgments
Author Contributions
Conflicts of Interest
References
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Criterions | NS | Cor | RMSE | PF | MSLE | |||||
---|---|---|---|---|---|---|---|---|---|---|
LXH | XAJ | LXH | XAJ | LXH | XAJ | LXH | XAJ | LXH | XAJ | |
Calibration | 0.80 | 0.89 | 0.92 | 0.95 | 2898 | 2072 | –1.9% | –3.1% | 0.52 | 0.11 |
Validation 1 | 0.81 | 0.83 | 0.92 | 0.91 | 2513 | 2365 | –5.3% | –8.6% | 0.56 | 0.08 |
Validation 2 | 0.60 | 0.72 | 0.92 | 0.93 | 3298 | 2721 | 15% | 18.1% | 0.66 | 0.17 |
Jan. | Feb. | Mar. | Apr. | May | Jun. | Jul. | Aug. | Sep. | Oct. | Nov. | Dec. | |
---|---|---|---|---|---|---|---|---|---|---|---|---|
RCP4.5 | ||||||||||||
Min | 429 | 1042 | 645 | 3410 | 3144 | 3955 | 4249 | 4764 | 2711 | 339 | 243 | 268 |
Q1 | 1610 | 3750 | 3782 | 6536 | 6604 | 7232 | 10,512 | 10,712 | 5483 | 1415 | 583 | 609 |
Median | 2792 | 4917 | 5470 | 8545 | 7826 | 9702 | 12,850 | 12,692 | 7599 | 2282 | 991 | 1199 |
Q3 | 4065 | 6557 | 8807 | 10,619 | 9280 | 12,536 | 15,967 | 16,267 | 9896 | 4317 | 2032 | 1857 |
Max | 6630 | 10,550 | 14,131 | 16,013 | 11,810 | 19,743 | 23,287 | 23,715 | 15,834 | 8285 | 3721 | 3662 |
RCP8.5 | ||||||||||||
Min | 208 | 518 | 1118 | 1934 | 3225 | 3741 | 4982 | 6077 | 1518 | 430 | 255 | 211 |
Q1 | 1357 | 2831 | 3668 | 5910 | 6740 | 6544 | 10,607 | 10,680 | 4882 | 1487 | 603 | 688 |
Median | 2437 | 4193 | 5475 | 7903 | 7870 | 9147 | 13,916 | 13,132 | 7658 | 2405 | 931 | 1156 |
Q3 | 3705 | 6467 | 8215 | 9824 | 10,050 | 11,623 | 16,507 | 15,731 | 10,835 | 3593 | 1638 | 1681 |
Max | 6714 | 11,071 | 12,783 | 14,577 | 13,793 | 19,055 | 25,257 | 21,240 | 17,868 | 6734 | 2884 | 3130 |
Jan. | Feb. | Mar. | Apr. | May | Jun. | Jul. | Aug. | Sep. | Oct. | Nov. | Dec. | |
---|---|---|---|---|---|---|---|---|---|---|---|---|
RCP4.5 | ||||||||||||
Min | 452 | 1085 | 956 | 2168 | 1312 | 2709 | 2458 | 3146 | 2179 | 1384 | 707 | 534 |
Q1 | 1237 | 2172 | 2927 | 4550 | 4776 | 5342 | 7451 | 7990 | 5557 | 2561 | 1408 | 1126 |
Median | 1977 | 3274 | 4230 | 6127 | 6297 | 6827 | 9375 | 10,323 | 7109 | 3663 | 1976 | 1451 |
Q3 | 2748 | 4588 | 6596 | 8445 | 7630 | 9266 | 12,114 | 13,425 | 9556 | 4698 | 2536 | 2005 |
Max | 4838 | 8050 | 10,908 | 13,647 | 11,315 | 12,305 | 18,193 | 19,545 | 13,801 | 7712 | 4103 | 3324 |
RCP8.5 | ||||||||||||
Min | 356 | 496 | 819 | 1817 | 2540 | 2161 | 3036 | 4650 | 1989 | 1024 | 723 | 517 |
Q1 | 1175 | 1938 | 2546 | 4357 | 5014 | 5005 | 7455 | 8893 | 5398 | 2732 | 1525 | 1106 |
Median | 1706 | 2690 | 4209 | 6190 | 6216 | 6940 | 10,281 | 10,514 | 7437 | 3620 | 1810 | 1422 |
Q3 | 2672 | 4286 | 6138 | 7593 | 7722 | 8957 | 12,449 | 13,145 | 9762 | 4476 | 2312 | 1804 |
Max | 4816 | 7264 | 10,868 | 11,241 | 10,965 | 14,122 | 19,467 | 19,285 | 16,082 | 6960 | 3231 | 2809 |
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Zhu, D.; Das, S.; Ren, Q. Hydrological Appraisal of Climate Change Impacts on the Water Resources of the Xijiang Basin, South China. Water 2017, 9, 793. https://doi.org/10.3390/w9100793
Zhu D, Das S, Ren Q. Hydrological Appraisal of Climate Change Impacts on the Water Resources of the Xijiang Basin, South China. Water. 2017; 9(10):793. https://doi.org/10.3390/w9100793
Chicago/Turabian StyleZhu, Dehua, Samiran Das, and Qiwei Ren. 2017. "Hydrological Appraisal of Climate Change Impacts on the Water Resources of the Xijiang Basin, South China" Water 9, no. 10: 793. https://doi.org/10.3390/w9100793