Journal of Geographical Sciences ›› 2021, Vol. 31 ›› Issue (8): 1102-1122.doi: 10.1007/s11442-021-1887-z
• Special Issue: Ecohydrology • Previous Articles Next Articles
ZHANG Yuhang1(), YE Aizhong1,*(
), YOU Jinjun2, JING Xiangyang3
Received:
2020-08-26
Accepted:
2021-03-09
Online:
2021-08-25
Published:
2021-10-25
Contact:
YE Aizhong
E-mail:zhangyh19@mail.bnu.edu.cn;azye@bnu.edu.cn
About author:
Zhang Yuhang (1994-), specialized in hydrometeorological ensemble forecast. E-mail: zhangyh19@mail.bnu.edu.cn
Supported by:
ZHANG Yuhang, YE Aizhong, YOU Jinjun, JING Xiangyang. Quantification of human and climate contributions to multi-dimensional hydrological alterations: A case study in the Upper Minjiang River, China[J].Journal of Geographical Sciences, 2021, 31(8): 1102-1122.
Add to citation manager EndNote|Reference Manager|ProCite|BibTeX|RefWorks
Table 1
Formulas and description of the selected assessment criteria
Formulas | Description | Perfect/no skill |
---|---|---|
| Predictive skill of hydrological models and accuracy between simulations and observations | 1/≤0 |
| Linear correlation between simulations and observations | 1/≤0 |
| The percent difference between simulations and observations; model's performance with regard to its ability to maintain the water balance | 0/∞ |
| Association of simulations and observations | 0/∞ |
Table 2
Definition of IHA parameters and abbreviations used in the text (modified from Richter et al., 1996)
Groups and ID | IHA parameters | Abbreviations |
---|---|---|
G11 | Mean value of annual flow | Annual |
G12-G113 | Mean value of 12 months | Jan-Dec |
G21 | Annual 1-day minima | QN1D |
G22 | Annual 3-day minima | QN3D |
G23 | Annual 7-day minima | QN7D |
G24 | Annual 30-day minima | QN30D |
G25 | Annual 90-day minima | QN90D |
G26 | Annual 1-day maxima | QM1D |
G27 | Annual 3-day maxima | QM3D |
G28 | Annual 7-day maxima | QM7D |
G29 | Annual 30-day maxima | QM30D |
G210 | Annual 90-day maxima | QM90D |
G211 | Base flow index | Base flow |
G31 | Julian date of each annual 1-day maxima | Date max |
G32 | Julian date of each annual 1-day minima | Date min |
G41 | Number of low pulse | Lo pulse # |
G42 | Number of high pulse | Hi pulse # |
G43 | Mean duration of low pulse | Lo pulse L |
G44 | Mean duration of high pulse | Hi pulse L |
G51 | Rising rate | Rise rate |
G52 | Falling rate | Fall rate |
G53 | Number of hydrological reversals | Reversals |
Figure 6
Joint distribution of the change rate for each group of IHA parameters induced by climate change $(\Delta {{I}_{c}})$ versus those induced by human activities $(\Delta {{I}_{h}}).$ The horizontal axis in the figure represents the change rate of IHA parameters caused by climate, and the vertical axis represents the change rate of IHA parameters caused by human.
Table 4
IHA results, relative change and contributions calculated at the ZPP station. Bolded numbers indicate significant differences in the IHA indicators between two periods according to M-W U test at a significance level of 0.05. Mean (+) and mean (-) are the average values of increased and decreased parameters, respectively.
Index | obsbp | obsap | simap | obsnorm,bp | obsnorm,ap | simnorm,ap | | | | | |
---|---|---|---|---|---|---|---|---|---|---|---|
Annual | 452.1 | 418.6 | 456.4 | 0.478 | 0.352 | 0.494 | -12.6 | 1.6 | -14.2 | 10.1 | 89.9 |
January | 163.0 | 159.1 | 182.9 | 0.213 | 0.198 | 0.292 | -1.5 | 7.9 | -9.4 | 45.7 | 54.3 |
February | 143.2 | 140.5 | 153.3 | 0.235 | 0.221 | 0.284 | -1.4 | 4.9 | -6.3 | 43.8 | 56.3 |
March | 164.2 | 154.6 | 189.8 | 0.306 | 0.266 | 0.413 | -4.0 | 10.7 | -14.7 | 42.1 | 57.9 |
April | 246.7 | 257.4 | 397.6 | 0.185 | 0.205 | 0.464 | 2.0 | 27.9 | -25.9 | 51.9 | 48.1 |
May | 539.8 | 533.1 | 543.2 | 0.408 | 0.396 | 0.413 | -1.2 | 0.5 | -1.7 | 22.7 | 77.3 |
June | 684.4 | 774.5 | 613.6 | 0.393 | 0.517 | 0.295 | 12.4 | -9.8 | 22.2 | 30.6 | 69.4 |
July | 914.2 | 755.7 | 735.9 | 0.506 | 0.363 | 0.345 | -14.3 | -16.1 | 1.8 | 89.9 | 10.1 |
August | 689.3 | 602.1 | 716.3 | 0.425 | 0.325 | 0.456 | -10.0 | 3.1 | -13.1 | 19.1 | 80.9 |
September | 754.1 | 640.6 | 738.9 | 0.568 | 0.417 | 0.548 | -15.1 | -2.0 | -13.1 | 13.2 | 86.8 |
October | 592.6 | 524.5 | 603.1 | 0.461 | 0.350 | 0.478 | -11.1 | 1.7 | -12.8 | 11.7 | 88.3 |
November | 325.3 | 283.4 | 356.8 | 0.486 | 0.343 | 0.593 | -14.3 | 10.7 | -25.0 | 30.0 | 70.0 |
December | 209.2 | 197.6 | 245.7 | 0.410 | 0.344 | 0.618 | -6.6 | 20.8 | -27.4 | 43.2 | 56.8 |
QN1D | 136.9 | 112.6 | 138.7 | 0.596 | 0.400 | 0.610 | -19.6 | 1.4 | -21.0 | 6.3 | 93.8 |
QN3D | 137.7 | 115.1 | 140.0 | 0.584 | 0.404 | 0.602 | -18.0 | 1.8 | -19.8 | 8.3 | 91.7 |
QN7D | 138.9 | 118.6 | 142.5 | 0.566 | 0.409 | 0.593 | -15.7 | 2.7 | -18.4 | 12.8 | 87.2 |
QN30D | 142.2 | 132.2 | 150.8 | 0.375 | 0.302 | 0.439 | -7.3 | 6.4 | -13.7 | 31.8 | 68.2 |
QN90D | 158.3 | 152.2 | 180.2 | 0.346 | 0.312 | 0.470 | -3.4 | 12.4 | -15.8 | 44.0 | 56.0 |
QX1D | 2014.3 | 1868.7 | 1657.0 | 0.509 | 0.442 | 0.346 | -6.7 | -16.3 | 9.6 | 62.9 | 37.1 |
QX3D | 1711.8 | 1602.3 | 1538.0 | 0.450 | 0.393 | 0.359 | -5.7 | -9.1 | 3.4 | 72.8 | 27.2 |
QX7D | 1411.2 | 1338.8 | 1329.7 | 0.425 | 0.376 | 0.369 | -4.9 | -5.6 | 0.7 | 88.9 | 11.1 |
QX30D | 1091.5 | 1003.2 | 979.7 | 0.410 | 0.322 | 0.298 | -8.8 | -11.2 | 2.4 | 82.4 | 17.6 |
QX90D | 891.4 | 811.7 | 821.20 | 0.558 | 0.424 | 0.440 | -13.4 | -11.8 | -1.6 | 88.1 | 11.9 |
Base flow | 0.29 | 0.27 | 0.3 | 0.531 | 0.460 | 0.570 | -7.1 | 3.9 | -11.0 | 26.2 | 73.8 |
Date min | 56.7 | 72.5 | 56.5 | 0.077 | 0.124 | 0.076 | 4.7 | -0.1 | 4.8 | 2.0 | 98.0 |
Date max | 203.8 | 199.6 | 214.4 | 0.418 | 0.384 | 0.503 | -3.4 | 8.5 | -11.9 | 41.7 | 58.3 |
Lo pulse # | 2.1 | 2.6 | 1.9 | 0.302 | 0.369 | 0.276 | 6.7 | -2.6 | 9.3 | 21.8 | 78.2 |
Lo pulse L | 57.9 | 34.3 | 30.8 | 0.423 | 0.250 | 0.225 | -17.3 | -19.8 | 2.5 | 88.8 | 11.2 |
Hi pulse # | 11.3 | 12.0 | 10.2 | 0.431 | 0.473 | 0.367 | 4.2 | -6.4 | 10.6 | 37.6 | 62.4 |
Hi pulse L | 5.0 | 3.8 | 5.7 | 0.231 | 0.135 | 0.281 | -9.6 | 5.0 | -14.6 | 25.5 | 74.5 |
Rising rate | 36.6 | 23.3 | 26.9 | 0.714 | 0.381 | 0.472 | -33.3 | -24.2 | -9.1 | 72.7 | 27.3 |
Fall rate | -18.1 | -15.7 | -13.2 | 0.415 | 0.545 | 0.676 | 13.0 | 26.1 | -13.1 | 66.6 | 33.4 |
Reversals | 107.8 | 129.5 | 87.6 | 0.289 | 0.463 | 0.126 | 17.4 | -16.3 | 33.7 | 32.6 | 67.4 |
Mean (+) | - | - | - | - | - | - | 8.6 | 8.3 | 9.2 | 41.4 | 58.6 |
Mean (-) | - | - | - | - | - | - | -10.2 | -10.8 | -14.3 |
Figure 7
Comparison of different climatic variables and discharge (a) and Pearson correlation coefficient between different IHA parameters (b). In (a), $\overline{{{P}_{bp}}}$ and $\overline{{{P}_{ap}}}$ are the mean precipitation in the baseline and altered periods, respectively; $\overline{{{T}_{bp}}}$ and $\overline{{{T}_{ap}}}$ are the mean temperature in the baseline and altered periods, respectively; $\overline{E{{T}_{bp}}}$ and $\overline{E{{T}_{ap}}} $are the mean evapotranspiration in the baseline and altered periods, respectively; and $\overline{{{Q}_{bp}}}$ and $\overline{{{Q}_{ap}}}$ are the mean discharge in the baseline and altered periods, respectively.
[1] |
Akbari S, Reddy M, 2019. Change detection and attribution of flow regime: A case study of Allegheny River catchment, PA (US). Science of The Total Environment, 662:192-204.
doi: 10.1016/j.scitotenv.2019.01.042 |
[2] |
Chen S, Zhang G, Yang S, 2003. Temporal and spatial changes of suspended sediment concentration and resuspension in the Yangtze River estuary. Journal of Geographical Sciences, 13(4):498-506.
doi: 10.1007/BF02837889 |
[3] |
Chen Y, Li W, Chen Y et al., 2004. Physiological response of natural plants to the change of groundwater level in the lower reaches of Tarim River, Xinjiang. Progress in Natural Science, 14(11):975-983.
doi: 10.1080/10020070412331344661 |
[4] |
Dey P, Mishra A, 2017. Separating the impacts of climate change and human activities on streamflow: A review of methodologies and critical assumptions. Journal of Hydrology, 548:278-290.
doi: 10.1016/j.jhydrol.2017.03.014 |
[5] |
Donat M, Lowry A, Alexander L et al., 2016. More extreme precipitation in the world's dry and wet regions. Nature Climate Change, 6(5):508-513.
doi: 10.1038/nclimate2941 |
[6] |
Du C, Ye A, Gan Y et al., 2017. Drainage network extraction from a high-resolution DEM using parallel programming in the NET framework. Journal of Hydrology, 555:506-517.
doi: 10.1016/j.jhydrol.2017.10.034 |
[7] |
Gao B, Yang D, Zhao T et al., 2002. Changes in the eco-flow metrics of the Upper Yangtze River from 1961 to 2008. Journal of Hydrology, 448/449:30-38.
doi: 10.1016/j.jhydrol.2012.03.045 |
[8] |
Graf W, 2006. Downstream hydrologic and geomorphic effects of large dams on American rivers. Geomorphology, 79(3):336-360.
doi: 10.1016/j.geomorph.2006.06.022 |
[9] |
Gupta H, Sorooshian S, Yapo P, 1999. Status of automatic calibration for hydrologic models: Comparison with multilevel expert calibration. Journal of Hydrologic Engineering, 4(2):135-143.
doi: 10.1061/(ASCE)1084-0699(1999)4:2(135) |
[10] |
Hou J, Ye A, You J et al., 2018. An estimate of human and natural contributions to changes in water resources in the upper reaches of the Min River. Science of The Total Environment, 635:901-912.
doi: 10.1016/j.scitotenv.2018.04.163 |
[11] |
Jiang C, Zhang L, Tang Z et al., 2017. Multi-temporal scale changes of streamflow and sediment discharge in the headwaters of Yellow River and Yangtze River on the Tibetan Plateau, China. Ecological Engineering, 102:240-254.
doi: 10.1016/j.ecoleng.2017.01.029 |
[12] | Kendall M, Gibbons J, 1948. Rank Correlation Methods. 5th ed. London, UK: Edward Arnold, 320. |
[13] | Kundzewicz Z, 2008. Climate change impacts on the hydrological cycle. Ecohydrology & Hydrobiology, 8(2-4):195-203. |
[14] | Li M, 2014. Cumulative influence of cascade hydropower development on runoff in upper reaches of Min River[D]. Chengdu: Chengdu University of Technology. (in Chinese) |
[15] | Li M, Fu B, Wang Y et al., 2015. Characteristics and spatial patterns of hydropower development in the upper Min River basin. Resources and Environment in the Yangtze Basin, 24(1):74-80. (in Chinese) |
[16] |
Li Z, Li X, Xu Z, 2010. Impacts of water conservancy and soil conservation measures on annual runoff in the Chaohe River Basin during 1961-2005. Journal of Geographical Sciences, 20(6):947-960.
doi: 10.1007/s11442-010-0823-4 |
[17] |
Liang G, Ding S, 2004. Impacts of human activity and natural change on the wetland landscape pattern along the Yellow River in Henan Province. Journal of Geographical Sciences, 14(3):339-348.
doi: 10.1007/BF02837415 |
[18] | Liu X, Liu C, Luo Y et al., 2012. Dramatic decrease in streamflow from the headwater source in the central route of China's water diversion project: Climatic variation or human influence? Journal of Geophysical Research:Atmospheres, 117:D06113. |
[19] |
Liu X, Shen Y, Guo Y et al., 2015. Modelling demand/supply of water resources in the arid region of northwestern China during the late 1980s to 2010. Journal of Geographical Sciences, 25(5):573-591.
doi: 10.1007/s11442-015-1188-5 |
[20] |
Lu E, Zhao W, Zou X et al., 2017. Temporal-spatial monitoring of an extreme precipitation event: Determining simultaneously the time period it lasts and the geographic region it affects. Journal of Climate, 30(16):6123-6132.
doi: 10.1175/JCLI-D-17-0105.1 |
[21] |
Lu W, Lei H, Yang D et al., 2018. Quantifying the impacts of small dam construction on hydrological alterations in the Jiulong River basin of Southeast China. Journal of Hydrology, 567:382-392.
doi: 10.1016/j.jhydrol.2018.10.034 |
[22] |
Luca P, Messori G, Wilby R et al., 2019. Concurrent wet and dry hydrological extremes at the global scale. Earth System Dynamics, 11(1):251-266.
doi: 10.5194/esd-11-251-2020 |
[23] |
Ma F, Ye A, Gong W et al., 2014. An estimate of human and natural contributions to flood changes of the Huai River. Global and Planetary Change, 119(4):39-50.
doi: 10.1016/j.gloplacha.2014.05.003 |
[24] |
Ma H, Yang D, Tan S et al., 2010. Impact of climate variability and human activities on streamflow decrease in the Miyun Reservoir catchment. Journal of Hydrology, 389(3/4):317-324.
doi: 10.1016/j.jhydrol.2010.06.010 |
[25] |
Magilligan F, Nislow K, 2005. Changes in hydrologic regime by dams. Geomorphology, 71(1):61-78.
doi: 10.1016/j.geomorph.2004.08.017 |
[26] |
Mann H, 1945. Nonparametric test against trend. Econometrica, 13(3):245-259.
doi: 10.2307/1907187 |
[27] |
Mittal N, Bhave A, Mishra A et al., 2016. Impact of human intervention and climate change on natural flow regime. Water Resources Management, 30(2):685-699.
doi: 10.1007/s11269-015-1185-6 |
[28] |
Nachar N, 2008. The Mann-Whitney U: A test for assessing whether two independent samples come from the same distribution. Tutorials in Quantitative Methods for Psychology, 4(1):13-20.
doi: 10.20982/tqmp.04.1.p013 |
[29] |
Nakayama T, 2011. Simulation of the effect of irrigation on the hydrologic cycle in the highly cultivated Yellow River Basin. Agricultural and Forest Meteorology, 151(3):314-327.
doi: 10.1016/j.agrformet.2010.11.006 |
[30] |
Nash J, Sutcliffe J, 1970. River flow forecasting through conceptual models: Part 1 A discussion of principles. Journal of Hydrology, 10(3):282-290.
doi: 10.1016/0022-1694(70)90255-6 |
[31] |
Räsänen T, Someth P, Lauri H et al., 2017. Observed river discharge changes due to hydropower operations in the Upper Mekong Basin. Journal of Hydrology, 545:28-41.
doi: 10.1016/j.jhydrol.2016.12.023 |
[32] |
Richter B, Baumgartner J, Powell J et al., 1996. A method for assessing hydrologic alteration within ecosystems. Conservation Biology, 10(4):1163-1174.
doi: 10.1046/j.1523-1739.1996.10041163.x |
[33] | Shepard D, 1984. Computer mapping:The SYMAP interpolation algorithm. In: Spatial Statistics and Models. Dordrecht: Springer,133-145. |
[34] | Shrestha S, Htut A, 2016. Land use and climate change impacts on the hydrology of the Bago River Basin, Myanmar. Environmental Modelling & Assessment, 21(6):819-833. |
[35] |
Sun Q, Miao C, Duan Q, 2015. Projected changes in temperature and precipitation in ten river basins over China in 21st century. International Journal of Climatology, 35(6):1125-1141.
doi: 10.1002/joc.2015.35.issue-6 |
[36] |
Talukdar S, Pal S, 2019. Effects of damming on the hydrological regime of Punarbhaba River basin wetlands. Ecological Engineering, 135:61-74.
doi: 10.1016/j.ecoleng.2019.05.014 |
[37] |
Wan Z, Chen X et al., 2020. Streamflow reconstruction and variation characteristic analysis of the Ganjiang River in China for the past 515 years. Sustainability, 12(3):1168.
doi: 10.3390/su12031168 |
[38] | Wang G, Xia J, Chen J, 2009. Quantification of effects of climate variations and human activities on runoff by a monthly water balance model: A case study of the Chaobai River basin in northern China. Water Resources Research, 45:W00A11. |
[39] | Wang G, Xia J, Tan G et al., 2002. A research on distributed time variant gain model: A case study on Chaohe River Basin. Progress in Geography, 21(6):573-582. (in Chinese) |
[40] |
Wang J, Dai Z, Mei X et al., 2018. Immediately downstream effects of Three Gorges Dam on channel sandbars morphodynamics between Yichang-Chenglingji Reach of the Changjiang River, China. Journal of Geographical Sciences, 28(5):629-646.
doi: 10.1007/s11442-018-1495-8 |
[41] |
Wang X, 2014. Advances in separating effects of climate variability and human activities on stream discharge: An overview. Advances in Water Resources, 71:209-218.
doi: 10.1016/j.advwatres.2014.06.007 |
[42] |
Wang X, Yang T, Wortmann M et al., 2017. Analysis of multi-dimensional hydrological alterations under climate change for four major river basins in different climate zones. Climatic Change, 141(3):483-498.
doi: 10.1007/s10584-016-1843-6 |
[43] |
Wei W, Shi P, Zhou J et al., 2013. Environmental suitability evaluation for human settlements in an arid inland river basin: A case study of the Shiyang River Basin. Journal of Geographical Sciences, 23(2):331-343.
doi: 10.1007/s11442-013-1013-y |
[44] |
Wu J, Miao C, Zhang X et al., 2017. Detecting the quantitative hydrological response to changes in climate and human activities. Science of The Total Environment, 586:328-337.
doi: 10.1016/j.scitotenv.2017.02.010 |
[45] |
Wu J, Miao C, Wang Y et al., 2016. Contribution analysis of the long-term changes in seasonal runoff on the Loess Plateau, China, using eight Budyko-based methods. Journal of Hydrology, 545:263-275.
doi: 10.1016/j.jhydrol.2016.12.050 |
[46] |
Wu X, Wang Z, Zhou X et al., 2016. Observed changes in precipitation extremes across 11 basins in China during 1961-2013. International Journal of Climatology, 36(8):2866-2885.
doi: 10.1002/joc.4524 |
[47] |
Xia J, 1991. Identification of a constrained nonlinear hydrological system described by volterra functional series. Water Resources Research, 27(9):2415-2420.
doi: 10.1029/91WR01364 |
[48] | Xia J, Wang G, Lv A et al., 2003. A research on distributed time variant gain modelling. Acta Geographica Sinica, 58(5):789-796. (in Chinese) |
[49] | Xia J, Wang G, Tan G et al., 2005. Development of distributed time-variant gain model for nonlinear hydrological systems. Science in China Series D:Earth Sciences, 48(6):713-723. |
[50] |
Xin Z, Li Y, Zhang L et al., 2019. Quantifying the relative contribution of climate and human impacts on seasonal streamflow. Journal of Hydrology, 574:936-945.
doi: 10.1016/j.jhydrol.2019.04.095 |
[51] |
Xu C, Wang J, Li Q, 2018: A new method for temperature spatial interpolation based on sparse historical stations. Journal of Climate, 31:1757-1770.
doi: 10.1175/JCLI-D-17-0150.1 |
[52] |
Yang S, Milliman J, Li P et al., 2011. 50,000 dams later: Erosion of the Yangtze River and its delta. Global and Planetary Change, 75(1/2):14-20.
doi: 10.1016/j.gloplacha.2010.09.006 |
[53] | Yang T, Cui T, Xu C et al., 2017. Development of a new IHA method for impact assessment of climate change on flow regime. Global & Planetary Change, 156(9):68-79. |
[54] |
Yang T, Zhang Q, Chen Y et al., 2008. A spatial assessment of hydrologic alteration caused by dam construction in the middle and lower Yellow River, China. Hydrological Processes, 22(18):3829-3843.
doi: 10.1002/hyp.v22:18 |
[55] |
Yang Z, Yan Y, Liu Q, 2012. Assessment of the flow regime alterations in the Lower Yellow River, China. Ecological Informatics, 10(7):56-64.
doi: 10.1016/j.ecoinf.2011.10.002 |
[56] |
Ye A, Duan Q, Chu W et al., 2014. The impact of the south-north water transfer project (CTP)'s central route on groundwater table in the Hai River Basin, North China. Hydrological Processes, 28(23):5755-5768.
doi: 10.1002/hyp.10081 |
[57] |
Ye A, Duan Q, Schaake J et al., 2015. Post-processing of ensemble low flow forecasts. Hydrological Processes, 29:2438-2453.
doi: 10.1002/hyp.10374 |
[58] | Ye A, Duan Q, Zeng H et al., 2010. A distributed time-variant gain hydrological model based on remote sensing. Journal of Resources and Ecology, 1(3):222-230. |
[59] |
Ye A, Duan Q, Zhan C et al., 2013. Improving kinematic wave routing scheme in Community Land Model. Hydrology Research, 44(5):886-903.
doi: 10.2166/nh.2012.145 |
[60] | Ye A, Xia J, Wang G, 2006. Dynamic network-based distributed kinematic wave affluent model. Yellow River, 28(2):26-29. (in Chinese) |
[61] |
Zhai H, Cui B, Hu B et al., 2010. Prediction of river ecological integrity after cascade hydropower dam construction on the mainstream of rivers in Longitudinal Range-Gorge Region (LRGR), China. Ecological Engineering, 36(4):361-372.
doi: 10.1016/j.ecoleng.2009.10.002 |
[62] | Zhang M, Wei X, Sun P et al., 2012. The effect of forest harvesting and climatic variability on runoff in a large watershed: The case study in the Upper Min River of Yangtze River Basin. Journal of Hydrology, 464:1-11. |
[63] |
Zhao G, Tian P, Mu X et al., 2014. Quantifying the impact of climate variability and human activities on streamflow in the middle reaches of the Yellow River Basin, China. Journal of Hydrology, 519:387-398.
doi: 10.1016/j.jhydrol.2014.07.014 |
[64] | Zhao L, Peng Q, Li C et al., 2014. Analysis of eco-hydrological alteration of upper Yangtze mainstream sections in the nature reserves for rare and endemic fishes. Journal of Hydroelectric Engineering, 33(3):106-111. (in Chinese) |
[65] |
Zhao Q, Liu S, Deng L et al., 2012. The effects of dam construction and precipitation variability on hydrologic alteration in the Lancang River Basin of Southwest China. Stochastic Environmental Research and Risk Assessment, 26(7):993-1011.
doi: 10.1007/s00477-012-0583-z |
[66] |
Zhou B, Wen, Q, Xu Y et al., 2014. Projected changes in temperature and precipitation extremes in China by the CMIP5 multi-model ensembles. Journal of Climate, 27(17):6591-6611.
doi: 10.1175/JCLI-D-13-00761.1 |
[1] | Ilan STAVI, Eli ZAADY, Alexander GUSAROV, Hezi YIZHAQ. Dead shrub patches as ecosystem engineers in degraded drylands [J]. Journal of Geographical Sciences, 2021, 31(8): 1187-1204. |
[2] | LI Yu, HAN Qin, HAO Lu, ZHANG Xinzhong, CHEN Dawei, ZHANG Yuxin, XU Lingmei, YE Wangting, PENG Simin, LI Yichan, FENG Zhuowen, LIU Hebin. Paleoclimatic proxies from global closed basins and the possible beginning of Anthropocene [J]. Journal of Geographical Sciences, 2021, 31(6): 765-784. |
[3] | HUANG Chang, ZHANG Shiqiang, DONG Linyao, WANG Zucheng, LI Linyi, CUI Luming. Spatial and temporal variabilities of rainstorms over China under climate change [J]. Journal of Geographical Sciences, 2021, 31(4): 479-496. |
[4] | ZHANG Chi, WU Shaohong, LENG Guoyong. Possible NPP changes and risky ecosystem region identification in China during the 21st century based on BCC-CSM2 [J]. Journal of Geographical Sciences, 2020, 30(8): 1219-1232. |
[5] | LIU Haimeng, FANG Chuanglin, FANG Kai. Coupled Human and Natural Cube: A novel framework for analyzing the multiple interactions between humans and nature [J]. Journal of Geographical Sciences, 2020, 30(3): 355-377. |
[6] | LIU Juan, YAO Xiaojun, LIU Shiyin, GUO Wanqin, XU Junli. Glacial changes in the Gangdisê Mountains from 1970 to 2016 [J]. Journal of Geographical Sciences, 2020, 30(1): 131-144. |
[7] | BA Wulong, DU Pengfei, LIU Tie, BAO Anming, CHEN Xi, LIU Jiao, QIN Chengxin. Impacts of climate change and agricultural activities on water quality in the Lower Kaidu River Basin, China [J]. Journal of Geographical Sciences, 2020, 30(1): 164-176. |
[8] | FAN Zemeng, BAI Ruyu, YUE Tianxiang. Scenarios of land cover in Eurasia under climate change [J]. Journal of Geographical Sciences, 2020, 30(1): 3-17. |
[9] | CHEN Qihui, CHEN Hua, ZHANG Jun, HOU Yukun, SHEN Mingxi, CHEN Jie, XU Chongyu. Impacts of climate change and LULC change on runoff in the Jinsha River Basin [J]. Journal of Geographical Sciences, 2020, 30(1): 85-102. |
[10] | Martha Elizabeth APPLE, Macy Kara RICKETTS, Alice Caroline MARTIN. Plant functional traits and microbes vary with position on striped periglacial patterned ground at Glacier National Park, Montana [J]. Journal of Geographical Sciences, 2019, 29(7): 1127-1141. |
[11] | Yuan ZHANG, Shuying ZANG, Li SUN, Binghe YAN, Tianpeng YANG, Wenjia YAN, E Michael MEADOWS, Cuizhen WANG, Jiaguo QI. Characterizing the changing environment of cropland in the Songnen Plain, Northeast China, from 1990 to 2015 [J]. Journal of Geographical Sciences, 2019, 29(5): 658-674. |
[12] | Yujie LIU, Ya QIN, Quansheng GE. Spatiotemporal differentiation of changes in maize phenology in China from 1981 to 2010 [J]. Journal of Geographical Sciences, 2019, 29(3): 351-362. |
[13] | ZHONG Linsheng, YU Hu, ZENG Yuxi. Impact of climate change on Tibet tourism based on tourism climate index [J]. Journal of Geographical Sciences, 2019, 29(12): 2085-2100. |
[14] | GAO Jiangbo, JIAO Kewei, WU Shaohong. Investigating the spatially heterogeneous relationships between climate factors and NDVI in China during 1982 to 2013 [J]. Journal of Geographical Sciences, 2019, 29(10): 1597-1609. |
[15] | Danyang MA, Haoyu DENG, Yunhe YIN, Shaohong WU, Du ZHENG. Sensitivity of arid/humid patterns in China to future climate change under a high-emissions scenario [J]. Journal of Geographical Sciences, 2019, 29(1): 29-48. |
|