Journal of Geographical Sciences

, Volume 29, Issue 9, pp 1441–1461 | Cite as

Analyses of geographical observations in the Heihe River Basin: Perspectives from complexity theory

  • Jianbo GaoEmail author
  • Peng Fang
  • Lihua Yuan


Since 2005, dozens of geographical observational stations have been established in the Heihe River Basin (HRB), and by now a large amount of meteorological, hydrological, and ecological observations as well as data pertaining to water resources, soil and vegetation have been collected. To adequately analyze these available data and data to be further collected in future, we present a perspective from complexity theory. The concrete materials covered include a presentation of adaptive multiscale filter, which can readily determine arbitrary trends, maximally reduce noise, and reliably perform fractal and multifractal analysis, and a presentation of scale-dependent Lyapunov exponent (SDLE), which can reliably distinguish deterministic chaos from random processes, determine the error doubling time for prediction, and obtain the defining parameters of the process examined. The adaptive filter is illustrated by applying it to obtain the global warming trend and the Atlantic multidecadal oscillation from sea surface temperature data, and by applying it to some variables collected at the HRB to determine diurnal cycle and fractal properties. The SDLE is illustrated to determine intermittent chaos from river flow data.


Heihe River basin geographical observation complexity theory adaptive multiscale filter fractal analysis scale-dependent Lyapunov exponent 


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Copyright information

© Science Press 2019

Authors and Affiliations

  1. 1.State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical ScienceBeijing Normal UniversityBeijingChina
  2. 2.Institute of Complexity Science and Big Data TechnologyGuangxi UniversityNanningChina
  3. 3.Wuhan National Laboratory for Optoelectronics, Key Laboratory of Information Storage System, Engineering Research Center of Data Storage Systems and Technology, Ministry of Education of China, School of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhanChina
  4. 4.Center for GeoData and Analysis, Faculty of Geographical ScienceBeijing Normal UniversityBeijingChina

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