Evaluation of rainfall kinetic energy and erosivity in northern Taiwan using kriging with climate characteristics
Autor: | Chien-Lin Huang, Cheng‐Chun Lee, J. C. Fan, Kuo-Wei Liao, Jia-Jun Guo |
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Rok vydání: | 2019 |
Předmět: |
Polynomial regression
Soil Science 04 agricultural and veterinary sciences 010501 environmental sciences Kinetic energy Atmospheric sciences 01 natural sciences Pollution Regression Universal Soil Loss Equation Kriging 040103 agronomy & agriculture 0401 agriculture forestry and fisheries Environmental science Zoning Variogram Agronomy and Crop Science Intensity (heat transfer) 0105 earth and related environmental sciences |
Zdroj: | Soil Use and Management. 35:630-642 |
ISSN: | 1475-2743 0266-0032 |
DOI: | 10.1111/sum.12519 |
Popis: | A localized rainfall kinetic energy (E) equation and an erosivity map were developed, and the suitability of the universal soil loss equation (USLE) for assessing the soil erosion of a non‐US region was investigated. After accurately measuring and gathering data regarding raindrop size using disdrometers in four northern Taiwan locations, this study investigated the drop size distribution under different conditions by categorizing the rainfall patterns to develop regression equations for estimating the unit volume‐specific kinetic energy (KEₘₘ) and the unit time‐specific kinetic energy (KEₜᵢₘₑ) of northern Taiwan. Climate zoning, which is not considered in currently used designs, was then implemented along with two‐stage cluster analysis to construct a rainfall erosivity (R) distribution map using the kriging model. The binary polynomial regression function of KEₜᵢₘₑ, which had the highest correlation (R² = 0.98), was suggested to estimate E in northern Taiwan. It was found that the pattern and intensity (I) of rainfall will slightly affect E. The climatic influence on the root mean square of the semivariogram was significant, which suggests that climate zoning can help estimate the rainfall erosivity (R). The outcomes were extended to estimate R in areas without rainfall stations. |
Databáze: | OpenAIRE |
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