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Entropy 2014, 16(8), 4626-4647; doi:10.3390/e16084626

Spatiotemporal Scaling Effect on Rainfall Network Design Using Entropy

1
The Experimental Forest, College of Bio-Resources and Agriculture, National Taiwan University, No.12 Chien-Shan Rd. Sec.1 Jushan Township, Nantou 55750, Taiwan
2
Department of Natural Resources, Chinese Culture University, Taipei 11114, Taiwan
3
Department of Civil Engineering, National Taipei University of Technology, Taipei 10608, Taiwan
*
Author to whom correspondence should be addressed.
Received: 28 January 2014 / Revised: 26 May 2014 / Accepted: 7 August 2014 / Published: 18 August 2014
(This article belongs to the Special Issue Entropy in Hydrology)
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Abstract

Because of high variation in mountainous areas, rainfall data at different spatiotemporal scales may yield potential uncertainty for network design. However, few studies focus on the scaling effect on both the spatial and the temporal scale. By calculating the maximum joint entropy of hourly typhoon events, monthly, six dry and wet months and annual rainfall between 1992 and 2012 for 1-, 3-, and 5-km grids, the relocated candidate rain gauges in the National Taiwan University Experimental Forest of Central Taiwan are prioritized. The results show: (1) the network exhibits different locations for first prioritized candidate rain gauges for different spatiotemporal scales; (2) the effect of spatial scales is insignificant compared to temporal scales; and (3) a smaller number and a lower percentage of required stations (PRS) reach stable joint entropy for a long duration at finer spatial scale. Prioritized candidate rain gauges provide key reference points for adjusting the network to capture more accurate information and minimize redundancy. View Full-Text
Keywords: scaling effect; rainfall network design; entropy scaling effect; rainfall network design; entropy
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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Wei, C.; Yeh, H.-C.; Chen, Y.-C. Spatiotemporal Scaling Effect on Rainfall Network Design Using Entropy. Entropy 2014, 16, 4626-4647.

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