Change and Climatic Linkage for Extreme Flows in Typical Catchments of Middle Tianshan Mountain, Northwest China
Abstract
:1. Introduction
2. Study Area and Data
3. Data and Methodology
3.1. Data Collection and Preprocessing
- (1)
- the time length t of the decreasing flank of the first event exceeds a time kp, t > kp;
- (2)
- the discharge drops down—in between the two events—to a fraction lower than f of the peak flow; where qmin, qmax, and qbase would be the minimum discharge, the maximum discharge and the base flow, respectively:
- (3)
- the discharge increment from to has a minimum height , . This procedure for peak flow selection has three parameters: kp, f and . It is based on the concept that a peak flow event can be considered largely independent from the next one, when the interevent discharge drops down to a low flow condition or almost to the baseflow level (see criterion (2)). Under this condition, the quick flow components attributed to the peak flow events are indeed nearly independent.
3.2. Methods
3.2.1. Trends Analysis and Change Point Detect
3.2.2. Frequency Analysis
3.2.3. Pearson Correlation Analysis
4. Results and Discussions
4.1. Trends and Alterations of the Extreme Flow Series
4.2. Extreme Event Analysis
4.3. Discussion of the Linkages between Extreme Flow Change and Climate Factors
5. Conclusions
Author Contributions
Acknowledgments
Conflicts of Interest
References
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Stations | Drainage Area (km2) | Time Interval | Longitude | Latitude | |
---|---|---|---|---|---|
Manasi River | KSWT | 5167.44 | 1958–2011 | E84.93 | N43.07 |
Kaidu River | BY | 6645.60 | 1978–2011 | E84.08 | N42.55 |
DSK | 18653.79 | 1958–2011 | E82.95 | N42.19 | |
Huangshui River | HSG | 4299.53 | 1958–1989 | E85.79 | N42.44 |
Qingshui River | KRGT | 1047.66 | 1958–1989 | E86.55 | N42.36 |
Rivers/Stations | AM | AMT | POT | POTN | |||||||||
p Value | Change Point | Sig. | p Value | Change Point | Sig. | p Value | Change Point | Sig. Level | p Value | Change Point | Sig. | ||
Manasi River | KSWT | 0.100 | R | 0.898 | R | 0.047 | 1986 | * | 0.149 | R | |||
Kaidu River | BY | 0.455 | R | 0.971 | R | 0.488 | R | 0.187 | R | ||||
DSK | 0.113 | R | 0.470 | R | 0.499 | R | 0.155 | R | |||||
Huangshui River | HSG | 1.000 | R | 0.282 | R | 0.318 | R | 1.000 | R | ||||
Qingshui River | KRGT | 0.298 | R | 0.948 | R | 0.059 | R | 0.622 | R | ||||
AM-SPR | AM-SPRT | AM-SUM | AM-SUMT | ||||||||||
p Value | Change Point | Sig. | p Value | Change Point | Sig. | p Value | Change Point | Sig. | p Value | Change Point | Sig. | ||
Manasi River | KSWT | 0.959 | R | 0.004 | 1978 | ** | 0.100 | R | 0.897 | R | |||
Kaidu River | BY | 0.620 | R | 0.595 | R | 0.282 | R | 0.541 | R | ||||
DSK | 0.338 | R | 0.441 | R | 0.123 | R | 0.075 | R | |||||
Huangshui River | HSG | 0.461 | R | 0.511 | R | 0.130 | R | 0.282 | R | ||||
Qingshui River | KRGT | 0.865 | R | 0.949 | R | 0.228 | R | 0.969 | R |
Rivers | Stations | Time Series | Parameters | Best Fit PDF | K-S Test | Correlation | ||
---|---|---|---|---|---|---|---|---|
Manasi River | KSWT | AM | 231 | 66.43 | 0.29 | Frѐchet | 0.109 | 0.994 |
POT | 226 | 52.39 | 0.23 | Pareto | 0.207 | 0.997 | ||
Kaidu River | BY | AM | 73.9 | 46.95 | 0 | Gumbel | 0.258 | 0.885 |
POT | 110 | 32.75 | 0 | Exponential | 0.336 | 0.989 | ||
DSK | AM | 353 | 92.49 | 0.26 | Frѐchet | 0.148 | 0.991 | |
POT | 404 | 61.54 | 0.15 | Pareto | 0.223 | 0.998 | ||
Huangshui River | HSG | AM | 70.1 | 62.74 | 0 | Gumbel | 0.197 | 0.993 |
POT | 86.3 | 25.80 | 0.30 | Pareto | 0.104 | 0.988 | ||
Qingshui River | KRGT | AM | 46.2 | 23.88 | 0.52 | Gumbel | 0.196 | 0.986 |
POT | 28.1 | 14.44 | 0.51 | Pareto | 0.211 | 0.990 |
Stations | Number of Years | Observed | GEV Prediction | Error (%) | |
---|---|---|---|---|---|
5 year | KSWT | - | 330 | 360 | 9.1 |
BY | - | 137 | 136 | −0.7 | |
DSK | - | 478 | 488 | 2.1 | |
HSG | - | 170 | 169 | −0.5 | |
QSH | - | 85 | 90 | 5.9 | |
10 year | KSWT | - | 355 | 428 | 20.6 |
BY | - | 194 | 186 | −4.1 | |
DSK | - | 621 | 623 | 0.3 | |
HSG | - | 230 | 212 | −7.8 | |
QSH | - | 141 | 150 | 6.4 | |
20 year | KSWT | - | 430 | 511 | 18.8 |
BY | - | 243 | 270 | 11.1 | |
DSK | - | 652 | 735 | 12.7 | |
HSG | - | 238 | 241 | 1.3 | |
QSH | - | 162 | 206 | 27.2 | |
longest record | KSWT | 54 | 789 | 721 | −8.6 |
BY | 33 | 253 | 286 | 13.0 | |
DSK | 54 | 793 | 940 | 18.5 | |
HSG | 31 | 244 | 280 | 14.8 | |
QSH | 31 | 199 | 235 | 18.1 |
Stations | Number of Events | Observed | GDP Prediction | Error (%) | |
---|---|---|---|---|---|
5 year | KSWT | - | 421 | 423 | 0.5 |
BY | - | 197 | 196 | −0.5 | |
DSK | - | 597 | 600 | 0.5 | |
HSG | - | 171 | 172 | 0.6 | |
QSH | - | 139 | 138 | −0.7 | |
10 year | KSWT | - | 495 | 496 | 0.2 |
BY | - | 222 | 221 | −0.4 | |
DSK | - | 644 | 662 | 2.8 | |
HSG | - | 230 | 220 | −4.3 | |
QSH | - | 148 | 161 | 8.8 | |
20 year | KSWT | - | 750 | 600 | −14.0 |
BY | - | 244 | 240 | −1.6 | |
DSK | - | 702 | 796 | 13.4 | |
HSG | - | 239 | 265 | 10.9 | |
QSH | - | 165 | 198 | 20.0 | |
longest records | KSWT | 172 | 789 | 720 | −8.7 |
BY | 129 | 253 | 273 | 7.9 | |
DSK | 176 | 793 | 830 | 4.7 | |
HSG | 92 | 354 | 338 | −4.5 | |
QSH | 96 | 199 | 246 | 23.6 |
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Ma, Y.; Huang, Y.; Liu, T. Change and Climatic Linkage for Extreme Flows in Typical Catchments of Middle Tianshan Mountain, Northwest China. Water 2018, 10, 1061. https://doi.org/10.3390/w10081061
Ma Y, Huang Y, Liu T. Change and Climatic Linkage for Extreme Flows in Typical Catchments of Middle Tianshan Mountain, Northwest China. Water. 2018; 10(8):1061. https://doi.org/10.3390/w10081061
Chicago/Turabian StyleMa, Yonggang, Yue Huang, and Tie Liu. 2018. "Change and Climatic Linkage for Extreme Flows in Typical Catchments of Middle Tianshan Mountain, Northwest China" Water 10, no. 8: 1061. https://doi.org/10.3390/w10081061
APA StyleMa, Y., Huang, Y., & Liu, T. (2018). Change and Climatic Linkage for Extreme Flows in Typical Catchments of Middle Tianshan Mountain, Northwest China. Water, 10(8), 1061. https://doi.org/10.3390/w10081061