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.
Highlights
The most crucial information required for planning, constructing, and operating hydraulic structures is rainfall data
The proposed model can be used to design an optimal rainfall network that provides the majority of rainfall information as the existing rainfall network
We demonstrated that the candidate rainfall network is able to reduce the number of rain gauge stations, while accurately reflecting the location of precipitation
Summary
The most crucial information required for planning, constructing, and operating hydraulic structures is rainfall data. The objective of a rainfall network is to design hydraulic structures efficiently and economically, according to the researched rainfall data [1]. Planning a suitable and optimal rain gauge station network is a challenging task. Research shows that even when two rain gauge stations are in close proximity to each other (5 km), the correlation coefficient of their precipitation time sequences may be lower than 0.5 [2]. This low correlation complicates the design and modulation of a rainfall network
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