Journal of Geographical Sciences ›› 2022, Vol. 32 ›› Issue (10): 1998-2012.doi: 10.1007/s11442-022-2033-2
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WANG Xiaohong(), LIU Xianfeng*(
), SUN Gaopeng
Received:
2021-12-24
Accepted:
2022-03-14
Online:
2022-10-25
Published:
2022-12-25
Contact:
LIU Xianfeng
E-mail:sherryhale@163.com;liuxianfeng7987@163.com
About author:
Wang Xiaohong (1998-), Master Candidate, specialized in remote sensing of ecological environment. E-mail: sherryhale@163.com
Supported by:
WANG Xiaohong, LIU Xianfeng, SUN Gaopeng. Increasing probability of concurrent drought between the water intake and receiving regions of the Hanjiang to Weihe River Water Diversion Project, China[J].Journal of Geographical Sciences, 2022, 32(10): 1998-2012.
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Figure 2
Probability distribution of 12-month SPI in the water intake (a) and receiving (c) regions during 1969- 2018. Probability density curves of 12-month SPI in the water intake (b) and receiving (d) regions in 1969-1990 (P1) and 1991-2018 (P2). The intersection of the black dashed lines represents the threshold of drought. The shaded portions and percentages represent the probability of meteorological drought (when SPI < -1).
Table A1
Brief description of the subprojects of the Hanjiang to Weihe River Water Diversion Project
Name of the Subproject | Location | Water storage (106 m3) | Maximum height (m) | Designed discharge (m³/s) | Function |
---|---|---|---|---|---|
Huangjinxia Water Control Project | Upper Hanjiang, 62 km from Yang County | 229 | 68 | 70 | Water supply, power generation and improvement of navigation conditions |
Sanhekou Water Control Project | 2 km from the junction of Jiaoxi River, Pu River and Wenshui River | 710 | 145 | 50 | Regulating and storing water |
Qinling water conveyance tunnel | Connect the Sanhekou Water Control Project and the Huangchi River in Zhouzhi County | / | / | 70 | Transport water |
Table A2
Basic information of meteorological stations
Name | ID | Longitude | Latitude | Elevation (m) |
---|---|---|---|---|
Changwu | 53929 | 35°12'N | 107°48'E | 1206.5 |
Tongchuan | 53947 | 35°05'N | 109°04'E | 978.9 |
Baoji | 57016 | 34°21'N | 107°08'E | 612.4 |
Fengxiang | 57025 | 34°31'N | 107°23'E | 781.1 |
Yaoxian | 57037 | 34°56'N | 108°59'E | 710.0 |
Huashan | 57046 | 34°29'N | 110°05'E | 2064.9 |
Lueyang | 57106 | 33°19'N | 106°09'E | 794.2 |
Hanzhong | 57127 | 33°04'N | 107°02'E | 509.5 |
Foping | 57134 | 33°31'N | 107°59'E | 827.2 |
Shangzhou | 57143 | 33°52'N | 109°58'E | 742.2 |
Zhenan | 57144 | 33°26'N | 109°09'E | 693.7 |
Shiquan | 57232 | 33°03'N | 108°16'E | 484.9 |
Wugong | 57234 | 34°15'N | 108°13'E | 447.8 |
Ankang | 57245 | 32°43'N | 109°02'E | 290.8 |
Table A3
CMIP6 models and corresponding variables used in this study along with Pearson correlation coefficient with site observations at a significance level of p < 0.01. Only one ensemble member (r1i1p1f1) was used for each model. All the GCM outputs were interpolated into 0.5° × 0.5° for further analysis
Model abbreviation | Institute ID | Horizontal resolution (°lon × °lat) | Ensembles variables | Study area | ||
---|---|---|---|---|---|---|
Water intake | Water receiving | |||||
BCC-CSM2-MR | BCC | 1.125, 1.125 (gn) | r1i1p1f1 | pr | 0.5938 | 0.5388 |
BCC-CSM2-MR | BCC | 1.125, 1.125 (gn) | r1i1p1f1 | ta | 0.9732 | 0.9738 |
CNRM-CM6-1 | CNRM-CERFACS | 1.406, 1.406 (gr) | r1i1p1f1 | pr | 0.4633 | 0.4126 |
FGOALS-f3-L | LASG-CESS | 1.25, 1 (gr) | r1i1p1f1 | pr | 0.5778 | 0.5135 |
GFDL-ESM4 | NOAA GFDL | 1.25, 1 (gr1) | r1i1p1f1 | pr | 0.5447 | 0.5310 |
MIROC6 | MIROC | 1.406, 1.406 (gn) | r1i1p1f1 | pr | 0.5403 | 0.4820 |
MRI-ESM2-0 | MRI | 1.125, 1.125 (gn) | r1i1p1f1 | pr | 0.4598 | 0.4101 |
MPI-ESM1-2-LR | MPI-M | 1.875, 1.875 (gn) | r1i1p1f1 | ta | 0.9739 | 0.9735 |
Table A5
Goodness-of-fit values of the five probability distributions of SPI in both areas in 1969-2018
Region | Margin | p value |
---|---|---|
Water intake | Normal | 0.9837 |
Logistic | 0.9057 | |
Generalized extreme value | 0.9675 | |
t location-scale | 0.9762 | |
Extreme value | 0.3369 | |
Water receiving | Normal | 0.9246 |
Logistic | 0.9627 | |
Generalized extreme value | 0.8944 | |
t location-scale | 0.9389 | |
Extreme value | 0.3159 |
Table A6
The correlation coefficient of SPI in the water intake and receiving regions
Data source | Period | Pearson (rn) | Spearman (ρn) | Kendall (τn) |
---|---|---|---|---|
Observation | 1969-2018 | 0.8154 | 0.7565 | 0.5902 |
Simulation under SSP 2-4.5 | 1969-2050 | 0.8016 | 0.7634 | 0.5748 |
Simulation under SSP 5-8.5 | 1969-2050 | 0.8295 | 0.8231 | 0.6387 |
Table A7
Goodness-of-fit values of Copula functions between the optimal marginal distributions in the two regions
Period | 1969-2018 | 1969-1990 | 1991-2018 | ||||
---|---|---|---|---|---|---|---|
criterion | AIC | BIC | AIC | BIC | AIC | BIC | |
Copulas | t | -2291.16 | -2271.69 | -1887.2 | -1867.7 | -2071.8 | -2052.3 |
Gaussian | -2297.85 | -2278.38 | -1887.8 | -1868.4 | -2074.4 | -2055.0 | |
Clayton | -2240.16 | -2220.69 | -1885.5 | -1866.0 | -2045.9 | -2026.4 | |
Gumbel | -2387.53 | -2368.06 | -1942.7 | -1923.3 | -2129.4 | -2109.9 | |
Frank | -2398.88 | -2379.41 | -1974.7 | -1955.2 | -2172.2 | -2152.7 |
Figure A2
Variations in precipitation variability in the water intake versus water receiving basins in 1991-2018 (a), 2019-2050 (b) relative to the multi-year average in 1970-1990. The steps for calculating precipitation variability are as follows: 1) 12-month SPI series in the two regions in 1969-2050 are calculated based on monthly precipitation under the SSP 2-4.5 and 5-8.5 scenarios, respectively; 2) calculate the standard deviation of 12-month SPI in each year from 1970-2050, which represents the precipitation variability in the corresponding year. The precipitation variability in 1969 is ignored because 12-month SPI in 1969 has only one value.
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