Changes in Temperature and Precipitation with the Analysis of Geomorphic Basin Chaos in Shiraz, Iran

Dehghan, Shahide; Kamaneh, Sayed Abdol Ali; Eslamian, Saeid; Gandomkar, Amir; Marani-Barzani, Maryam; Amoushahi-Khouzani, Mahboubeh; Singh, Vijay Pal; Ostad‐Ali‐Askari, Kaveh

doi:10.20431/2454-8693.0302004

Cited by 2 publications

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“…Rainfall data are important in various fields such as hydrology, water resources, environment, and ecology. These data are analyzed through the analysis of rainfall characteristics such as rainfall intensity, variability, statistical characteristics, and trends [1][2][3][4][5][6]. Moreover, they have been widely used as input data in runoff analysis, estimation of flood discharge and flood elevation, calculation of the vulnerability index, computation of the drought index, and so on [7][8][9][10].…”

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confidence: 99%

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

Kim

Joo²,

Han

et al. 2019

Water

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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.

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