Journal of Geographical Sciences ›› 2019, Vol. 29 ›› Issue (9): 14411461.doi: 10.1007/s1144201916706
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GAO Jianbo^{1,}^{2}, FANG Peng^{2,}^{3}, YUAN Lihua^{1,}^{4}
Received:
20181030
Accepted:
20190320
Online:
20190925
Published:
20191211
About author:
Gao Jianbo, Professor, specialized in complexity theory. Email: jbgao.pmb@aliyun.com
Supported by:
GAO Jianbo, FANG Peng, YUAN Lihua. Analyses of geographical observations in the Heihe River Basin: Perspectives from complexity theory[J].Journal of Geographical Sciences, 2019, 29(9): 14411461.
Figure 1
Adaptive algorithm used to capture the trend signals for the global annual sea surface temperature (SST): (a) original data and trends determined by global linear trend and AFA with two window sizes; (b) the residuals related to the three trends. The residuals designated as the blue and the red curves had been shifted upward and downward by 0.5, respectively."
Figure 2
Adaptive algorithm used to capture the trend signals for Atlantic Multidecadal Oscillation (AMO): the detrended North Atlantic sea surface temperature anomalies data (grey) and the blue multideccadal signal are obtained from the NOAA°s website, http://www.cdc.noaa.gov/data/climateindices/List, the red and green signals are obtained by the adaptive algorithm. Clearly, the red curve is better than the blue one in tracing out the variations in the original signal, while the green curve is the best in only capturing the multidecadal oscillation."
Figure 9
Detecting chaos in the Colorado River flow data: (a): error growth curves; (b): SDLE curves. The blue (solid) and red (dashed) curves are for the original and denoised data, respectively. Here, embedding parameters are m=6, L=3, and different curves are based on a few different shells described by Eq. (16). Except at the initial stage in the error growth curves, they collapse on each other."
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