Application of Hourly Rainfall Data to Estimate the Rainfall Erosivity Index- A Case Study of Kaohsiung and Pingtung Area

碩士 === 國立屏東科技大學 === 水土保持系所 === 104 === The 30-min rainfall erosivity index (R30) is commonly used in the Universal Soil Loss Equation for predicting soil loss from agricultural hillslopes. R30 values are calculated from breakpoint rainfall information obtained from continuous recording rain gauge ch...

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Bibliographic Details
Main Authors: Hsu, I-Ping, 徐一平
Other Authors: Lee, Ming-Hsi
Format: Others
Language:zh-TW
Published: 2016
Online Access:http://ndltd.ncl.edu.tw/handle/h5vd46
Description
Summary:碩士 === 國立屏東科技大學 === 水土保持系所 === 104 === The 30-min rainfall erosivity index (R30) is commonly used in the Universal Soil Loss Equation for predicting soil loss from agricultural hillslopes. R30 values are calculated from breakpoint rainfall information obtained from continuous recording rain gauge charts; however, in many places in Taiwan and other parts of the world, detailed chart-recorded rain gauge data relative to storm intensities are not readily available, whereas hourly rainfall is readily available. A simple method for estimating the rainfall erosivity index R30 by using the value of R60 calculated from the rainfall kinetic energy (E60) and maximum intensity (I60max) measured at multiple rainfall stations was established. This study involved calculating R30 and R60 by using 10- and 60-min data obtained from 51 rainfall stations in Southern Taiwan. The results show that the kinetic energy values derived using the two sets of data are related as E10 = 1.04E60 (r2= 0.99). In addition, the maximum rainfall intensity values of 30- and 60-min intervals are related as I30max = 1.56I60 max (r2= 0.90). The R30j associated with a rainfall event and R60j associated with a rainfall event are related as R30j = 1.31R60j (r2= 0.95). Finally, the annual average R30y and annual average R60y are related as R30y = 1.44R60y (r2= 0.93). The 60-min rainfall data can be successfully mused to estimate rainfall erosivity where no finer time resolution data are available and there was a marked improvement in predictions between the 60-min data and the 30-min data. The method is expected to be of significant benefit in future studies concerning the effects of climate change.