Sciences in Cold and Arid Regions ›› 2017, Vol. 9 ›› Issue (5): 476–487.doi: 10.3724/SP.J.1226.2017.00476
• ARTICLES • 上一篇
Complex network analysis of climate change in the Tarim River Basin, Northwest China
ZuHan Liu1,3, JianHua Xu2,3, WeiHong Li4
- 1. Jiangxi Provincial Key Laboratory of Water Information Cooperative Sensing and Intelligent Processing, Nanchang Institute of Technology, Nanchang, Jiangxi 330099, China;
2. Key Laboratory of Geographic Information Science(Ministry of Education), School of Geographic Sciences, East China Normal University, Shanghai 200241, China;
3. Research Center for East-West Cooperation in China, East China Normal University, Shanghai 200241, China;
4. State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, Xinjiang 830011, China
Complex network analysis of climate change in the Tarim River Basin, Northwest China
ZuHan Liu1,3, JianHua Xu2,3, WeiHong Li4
- 1. Jiangxi Provincial Key Laboratory of Water Information Cooperative Sensing and Intelligent Processing, Nanchang Institute of Technology, Nanchang, Jiangxi 330099, China;
2. Key Laboratory of Geographic Information Science(Ministry of Education), School of Geographic Sciences, East China Normal University, Shanghai 200241, China;
3. Research Center for East-West Cooperation in China, East China Normal University, Shanghai 200241, China;
4. State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, Xinjiang 830011, China
摘要： The complex network theory provides an approach for understanding the complexity of climate change from a new perspective. In this study, we used the coarse graining process to convert the data series of daily mean temperature and daily precipitation from 1961 to 2011 into symbol sequences consisting of five characteristic symbols (i.e., R, r, e, d and D), and created the temperature fluctuation network (TFN) and precipitation fluctuation network (PFN) to discover the complex network characteristics of climate change in the Tarim River Basin of Northwest China. The results show that TFN and PEN both present characteristics of scale-free network and small-world network with short average path length and high clustering coefficient. The nodes with high degree in TFN are RRR, dRR and ReR while the nodes with high degree in PFN are rre, rrr, eee and err, which indicates that climate change modes represented by these nodes have large probability of occurrence. Symbol R and r are mostly included in the important nodes of TFN and PFN, which indicate that the fluctuating variation in temperature and precipitation in the Tarim River Basin mainly are rising over the past 50 years. The nodes RRR, DDD, ReR, RRd, DDd and Ree are the hub nodes in TFN, which undertake 19.71% betweenness centrality of the network. The nodes rre, rrr, eee and err are the hub nodes in PFN, which undertake 13.64% betweenness centrality of the network.
| Adamic LA, Huberman BA, Barabási AL, et al., 2000. Power-law distribution of the World Wide Web. Science, 287(5461): 2115. DOI: 10.1126/science.287.5461.2115a. Albert R, Jeong H, Barabási AL, 2000. Error and attack tolerance of complex networks. Nature, 406(6794): 378–382. DOI: 10.1038/35019019. Albert R, Barabási AL, 2002. Statistical mechanics of complex networks. Reviews of Modern Physics, 74(1): 47–97. DOI: 10.1103/RevModPhys.74.47. An F, Gao XY, Guan JH, et al., 2016. An evolution analysis of executive-based listed company relationships using complex networks. Physica A, 447: 276–285. DOI: 10.1016/j.physa.2015.12.050. Barabási AL, Albert R, 1999. Emergence of scaling in random networks. Science, 286(5439): 509–512. DOI: 10.1126/science.286.5439.509. Barrát A, Barthélemy M, Vespignani A, 2005. The effects of spatial constraints on the evolution of weighted complex networks. Journal of Statistical Mechanics: Theory and Experiment, 2005(5): P05003. DOI: 10.1088/1742-5468/2005/05/P05003. Boccaletti S, Latora V, Moreno Y, et al., 2006. Complex networks: Structure and dynamics. Physics Reports, 424(4–5): 175–308. DOI: 10.1016/j.physrep.2005.10.009. Brunsell NA, 2010. A multiscale information theory approach to assess spatial-temporal variability of daily precipitation. Journal of Hydrology, 385(1–4): 165–172. DOI: 10.1016/j.jhydrol.2010.02.016. Chen YN, Takeuchi K, Xu CC, et al., 2006. Regional climate change and its effects on river runoff in the Tarim Basin, China. Hydrological Processes, 20(10): 2207–2216. DOI: 10.1002/hyp.6200. Deza JI, Masoller C, Barreiro M, 2014. Distinguishing the effects of internal and forced atmospheric variability in climate networks. Nonlinear Processes in Geophysics, 21(3): 617–631. DOI: 10.5194/npg-21-617-2014. Du HB, Wu ZF, Li M, et al., 2013. Characteristics of extreme daily minimum and maximum temperature over Northeast China, 1961-2009. Theoretical and Applied Climatology, 111(1–2): 161–171. DOI: 10.1007/s00704-012-0649-3. Faloutsos M, Faloutsos P, Faloutsos C, 1999. On power-law relationships of the internet topology. ACM Sigcomm Computer Communication Review, 29(4): 251–262. DOI: 10.1145/316194.316229. Fan QB, Wang YX, Zhu L, 2013. Complexity analysis of spatial-temporal precipitation system by PCA and SDLE. Applied Mathematical Modelling, 37(6): 4059–4066. DOI: 10.1016/j.apm.2012.09.009. Fan ZM, Yue TX, Chen CF, et al., 2011. Spatial change trends of temperature and precipitation in China. Journal of Geo-Information Science, 13(4): 526–533. DOI: 10.3724/SP.J.1047.2011.00526. Feldhoff JH, Lange S, Volkholz J, et al., 2015. Complex networks for climate model evaluation with application to statistical versus dynamical modeling of South American climate. Climate Dynamics, 44(5–6): 1567–1581. DOI: 10.1007/s00382-014-2182-9. Fraedrich K, 1986. Estimating the dimensions of weather and climate attractors. Journal of the Atmospheric Sciences, 43(5): 419–432. DOI: 10.1175/1520-0469(1986)043<0419:ETDOWA>2.0.CO;2. Hossain MM, Alam S, 2017. A complex network approach towards modeling and analysis of the Australian airport network. Journal of Air Transport Management, 60: 1–9. DOI: 10.1016/j.jairtraman.2016.12.008. Katz RW, Brown BG, 1992. Extreme events in a changing climate: Variability is more important than averages. Climatic Change, 21(3): 289–302. DOI: 10.1007/BF00139728. Li BF, Chen YN, Shi X, et al., 2013. Temperature and precipitation changes in different environments in the arid region of northwest China. Theoretical and Applied Climatology, 112(3–4): 589–596. DOI: 10.1007/s00704-012-0753-4. Li QH, Chen YN, Shen YJ, et al., 2011a. Spatial and temporal trends of climate change in Xinjiang, China. Journal of Geographical Sciences, 21(6): 1007–1018. DOI: 10.1007/s11442-011-0896-8. Li XM, Jiang FQ, Li LH, et al., 2011b. Spatial and temporal variability of precipitation concentration index, concentration degree and concentration period in Xinjiang, China. International Journal of Climatology, 31(11): 1679–1693. DOI: 10.1002/joc.2181. Liljeros F, Edling CR, Amaral LAN, et al., 2001. The web of human sexual contacts. Nature, 411(6840): 907–908. DOI: 10.1038/35082140. Liston GE, Pielke RA, 2000. A climate version of the regional atmospheric modeling system. Theoretical and Applied Climatology, 66(1–2): 29–47. DOI: 10.1007/s007040070031. Liu ML, Adam JC, Hamlet AF, 2013. Spatial-temporal variations of evapotranspiration and runoff/precipitation ratios responding to the changing climate in the Pacific Northwest during 1921-2006. Journal of Geophysical Research: Atmospheres, 118(2): 380–394. DOI: 10.1029/2012JD018400. Liu ZH, 2014. A Study of the Complex and Nonlinear Investigation of Climatological-Hydrological Process in the Tarim River Basin. Shanghai: East China Normal University, Doctoral thesis, pp. 287–291. Liu ZH, Wang LL, Yu X, et al., 2017. Multi-scale response of runoff to climate fluctuation in the headwater region of the Kaidu River in Xinjiang of China. Atmospheric Science Letters, 18(5): 230–236. DOI: 10.1002/asl.747. Liu ZH, Xu JH, Shi K, 2014a. Self-organized criticality of climate change. Theoretical and Applied Climatology, 115(3–4): 685–691. DOI: 10.1007/s00704-013-0929-6. Liu ZH, Xu JH, Chen ZS, et al., 2014b. Multifractal and long memory of humidity process in the Tarim River Basin. Stochastic Environmental Research and Risk Assessment, 28(6): 1383–1400. DOI: 10.1007/s00477-013-0832-9. Livada I, Charalambous G, Assimakopoulos MN, 2008. Spatial and temporal study of precipitation characteristics over Greece. Theoretical and Applied Climatology, 93(1–2): 45–55. DOI: 10.1007/s00704-007-0331-2. Lorenz EN, 1991. Dimension of weather and climate attractors. Nature, 353(6341): 241–244. DOI: 10.1038/353241a0. Lymperopoulos IN, Ioannou GD, 2016. Understanding and modeling the complex dynamics of the online social networks: a scalable conceptual approach. Evolving Systems, 7(3): 207–232. DOI: 10.1007/s12530-016-9145-9. Martínez MD, Lana X, Burgueño A, et al., 2007. Spatial and temporal daily rainfall regime in Catalonia (NE Spain) derived from four precipitation indices, years 1950-2000. International Journal of Climatology, 27(1): 123–138. DOI: 10.1002/joc.1369. Maslov S, Sneppen K, 2002. Specificity and stability in topology of protein networks. Science, 296(5569): 910–913. DOI: 10.1126/science.1065103. McLuhan M, 1964. Understanding Media: The Extensions of Man. New York: McGraw Hill. Meng X, Shen EH, Chen F, et al., 2000. Coarse graining in complexity analysis of EEG: I. Over coarse graining and a comparison among three kinds of complexities. Acta Biophysica Sinica, 16(4): 701–706. DOI: 10.3321/j.issn:1000-6737.2000.04.007. Millán H, Kalauzi A, Llerena G, et al., 2009. Meteorological complexity in the Amazonian area of Ecuador: An approach based on dynamical system theory. Ecological Complexity, 6(3): 278–285. DOI: 10.1016/j.ecocom.2009.05.004. Montoya JM, Pimm SL, Solé RV, 2006. Ecological networks and their fragility. Nature, 442(7100): 259–264. DOI: 10.1038/nature04927. Newman MEJ, 2001. Clustering and preferential attachment in growing networks. Physical Review E, 64(2): 025102. DOI: 10.1103/PhysRevE.64.025102. Newman MEJ, 2010. Networks: An Introduction. Oxford, UK: Oxford University Press. Nie Q, Xu JH, Li Z, et al., 2012. Spatial-temporal variations of vegetation cover in Yellow River Basin of China during 1998-2008. Sciences in Cold and Arid Regions, 4(3): 211–221. DOI: 10.3724/SP.J.1226.2012.00211. Palmer TN, 1999. A nonlinear dynamical perspective on climate prediction. Journal of Climate, 12(2): 575–591. DOI: 10.1175/1520-0442(1999)012<0575:ANDPOC>2.0.CO;2. Palmer TN, 2000. Predicting uncertainty in forecasts of weather and climate. Reports on Progress in Physics, 63(2): 71–116. DOI: 10.1088/0034-4885/63/2/201. Pasten D, Abe S, Muñoz V, et al., 2010. Scale-free and small-world properties of earthquake network in Chile. arXiv Preprint arXiv: 1005.5548. Ray A, 2004. Symbolic dynamic analysis of complex systems for anomaly detection. Signal Processing, 84(7): 1115–1130. DOI: 10.1016/j.sigpro.2004.03.011. Rendón de la Torre S, Kalda J, Kitt R, et al., 2016. On the topologic structure of economic complex networks: Empirical evidence from large scale payment network of Estonia. Chaos, Solitons & Fractals, 90: 18–27. DOI: 10.1016/j.chaos.2016.01.018. Rezvanian A, Meybodi MR, 2016. Stochastic graph as a model for social networks. Computers in Human Behavior, 64: 621–640. DOI: 10.1016/j.chb.2016.07.032. Rial JA, Pielke RA, Beniston M, et al., 2004. Nonlinearities, feedbacks and critical thresholds within the Earth's climate system. Climatic Change, 65(1–2): 11–38. DOI: 10.1023/B:CLIM.0000037493.89489.3f. Rodriguez-Iturbe I, De Power FB, Sharifi MB, et al., 1989. Chaos in rainfall. Water Resources Research, 25(7): 1667–1675. DOI: 10.1029/WR025i007p01667. Saha M, Mitra P, 2015. Climate network based index discovery for prediction of Indian monsoon. In: Kryszkiewicz M, Bandyopadhyay S, Rybinski H, et al. (eds.). Pattern Recognition and Machine Intelligence. Cham: Springer, pp. 554–564. DOI: 10.1007/978-3-319-19941-2_53. Steinhaeuser K, Ganguly AR, Chawla NV, 2009. Complex networks as a tool of choice for improving the science of climate extremes and reducing uncertainty in their projections. In: AGU Fall Meeting Abstracts. Washington D.C. American Geophysical Union. Steinhaeuser K, Chawla NV, Ganguly AR, 2011. Complex networks as a unified framework for descriptive analysis and predictive modeling in climate science. Statistical Analysis and Data Mining, 4(5): 497–511. DOI: 10.1002/sam.10100. Sun LN, Liu ZH, Wang JY, et al., 2016. The evolving concept of air pollution: a small-world network or scale-free network? Atmospheric Science Letters, 17(5): 308–314. DOI: 10.1002/asl.659. Tirabassi G, Masoller C, 2016. Unravelling the community structure of the climate system by using lags and symbolic time-series analysis. Scientific Reports, 6: 29804. DOI: 10.1038/srep29804. Tsonis AA, Elsner JB, 1988. The weather attractor over very short timescales. Nature, 333(6173): 545–547. DOI: 10.1038/333545a0. Vázquez A, Pastor-Satorras R, Vespignani A, 2002. Large-scale topological and dynamical properties of the Internet. Physical Review E, 65(6): 066130. DOI: 10.1103/PhysRevE.65.066130. Wang MG, Tian LX, 2016. From time series to complex networks: The phase space coarse graining. Physica A, 461: 456–468. DOI: 10.1016/j.physa.2016.06.028. Watts DJ, Strogatz SH, 1998. Collective dynamics of ‘small-world’ networks. Nature, 393(6684): 440–442. DOI: 10.1038/30918. Wiedermann M, Radebach A, Donges JF, et al., 2016. A climate network-based index to discriminate different types of El Niño and La Niña. Geophysical Research Letters, 43(13): 7176–7185. DOI: 10.1002/2016GL069119. Xu JH, Chen YN, Li WH, et al., 2009. Wavelet analysis and nonparametric test for climate change in Tarim River Basin of Xinjiang during 1959-2006. Chinese Geographical Science, 19(4): 306–313. DOI: 10.1007/s11769-009-0306-7. Xu JH, Li WH, Ji MH, et al., 2010. A comprehensive approach to characterization of the nonlinearity of runoff in the headwaters of the Tarim River, western China. Hydrological Processes, 24(2): 136–146. DOI: 10.1002/hyp.7484. Xu JH, Chen YN, Li WH, et al., 2011a. An integrated statistical approach to identify the nonlinear trend of runoff in the Hotan River and its relation with climatic factors. Stochastic Environmental Research and Risk Assessment, 25(2): 223–233. DOI: 10.1007/s00477-010-0433-9. Xu JH, Chen YN, Lu F, et al. 2011b. The nonlinear trend of runoff and its response to climate change in the Aksu River, western China. International Journal of Climatology, 31(5): 687–695. DOI: 10.1002/joc.2110. Xu JH, Chen YN, Li WH, et al., 2013a. The nonlinear hydro-climatic process in the Yarkand River, northwestern China. Stochastic Environmental Research and Risk Assessment, 27(2): 389–399. DOI: 10.1007/s00477-012-0606-9. Xu JH, Chen YN, Li WH, et al., 2013b. Understanding the complexity of temperature dynamics in Xinjiang, China, from multitemporal scale and spatial perspectives. The Scientific World Journal, 2013: 259248. DOI: 10.1155/2013/259248. Xu JH, Chen YN, Li WH, et al., 2013c. Combining BPANN and wavelet analysis to simulate hydro-climatic processes-a case study of the Kaidu River, North-west China. Frontiers of Earth Science, 7(2): 227–237. DOI: 10.1007/s11707-013-0354-2. Xu JH, Chen YN, Li WH, et al., 2014a. Integrating wavelet analysis and BPANN to simulate the annual runoff with regional climate change: a case study of Yarkand River, northwest China. Water Resources Management, 28(9): 2523–2537. DOI: 10.1007/s11269-014-0625-z. Xu JH, Li WH, Hong YL, et al., 2014b. A quantitative assessment on groundwater salinization in the Tarim River lower reaches, Northwest China. Sciences in Cold and Arid Regions, 6(1): 44–51. DOI: 10.3724/SP.J.1226.2014.00044. Xu JH, Chen YN, Li WH, et al., 2016a. Understanding temporal and spatial complexity of precipitation distribution in Xinjiang, China. Theoretical and Applied Climatology, 123(1–2): 321–333. DOI: 10.1007/s00704-014-1364-z. Xu JH, Chen YN, Bai L, et al., 2016b. A hybrid model to simulate the annual runoff of the Kaidu River in northwest China. Hydrology and Earth System Sciences, 20(4): 1447–1457. DOI: 10.5194/hess-20-1447-2016. Zeng X, Pielke RA, Eykholt R, 1992. Estimating the fractal dimension and the predictability of the atmosphere. Journal of the Atmospheric Sciences, 49(8): 649–659. DOI: 10.1175/1520-0469(1992)049<0649:ETFDAT>2.0.CO;2. Zhou L, Gong ZQ, Zhi R, et al., 2008. An approach to research the topology of Chinese temperature sequence based on complex network. Acta Physica Sinica, 57(11): 7380–7389. DOI: 10.3321/j.issn:1000-3290.2008.11.111. |
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