Next Article in Journal
Making Steppingstones out of Stumbling Blocks: A Bayesian Model Evidence Estimator with Application to Groundwater Transport Model Selection
Previous Article in Journal
Hydrogeochemical Characteristics and the Suitability of Groundwater in the Alluvial-Diluvial Plain of Southwest Shandong Province, China
Open AccessArticle

On Complex Network Construction of Rain Gauge Stations Considering Nonlinearity of Observed Daily Rainfall Data

1
Department of Civil Engineering, Inha University, Incheon 22201, Korea
2
Center for Hydrology and Ecology, Incheon 22201, Korea
*
Author to whom correspondence should be addressed.
Water 2019, 11(8), 1578; https://doi.org/10.3390/w11081578
Received: 28 June 2019 / Revised: 22 July 2019 / Accepted: 25 July 2019 / Published: 30 July 2019
(This article belongs to the Section Hydrology)
Rainfall data is frequently used as input and analysis data in the field of hydrology. To obtain adequate rainfall data, there should be a rain gauge network that can cover the relevant region. Therefore, it is necessary to analyze and evaluate the adequacy of rain gauge networks. Currently, a complex network analysis is frequently used in network analysis and in the hydrology field, Pearson correlation is used as strength of link in constructing networks. However, Pearson correlation is used for analyzing the linear relationship of data. Therefore, it is now suitable for nonlinear hydrological data (such as rainfall and runoff). Thus, a possible solution to this problem is to apply mutual information that can consider nonlinearity of data. The present study used a method of statistical analysis known as the Brock–Dechert–Scheinkman (BDS) statistics to test the nonlinearity of rainfall data from 55 Automated Synoptic Observing System (ASOS) rain gauge stations in South Korea. Analysis results indicated that all rain gauge stations showed nonlinearity in the data. Complex networks of these rain gauge stations were constructed by applying Pearson correlation and mutual information. Then, they were compared by computing their centrality values. Comparing the centrality rankings according to different thresholds for correlation showed that the network based on mutual information yielded consistent results in the rankings, whereas the network, which based on Pearson correlation exhibited much variability in the results. Thus, it was found that using mutual information is appropriate when constructing a complex network utilizing rainfall data with nonlinear characteristics. View Full-Text
Keywords: rainfall gauge; complex network; mutual information; centrality rainfall gauge; complex network; mutual information; centrality
Show Figures

Figure 1

MDPI and ACS Style

Kim, K.; Joo, H.; Han, D.; Kim, S.; Lee, T.; Kim, H.S. On Complex Network Construction of Rain Gauge Stations Considering Nonlinearity of Observed Daily Rainfall Data. Water 2019, 11, 1578.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop