Analysis of the generalized extreme value of drought on the Korean Peninsula using inter-amount times<sup>†</sup>
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(2) 1024. Jihoon Lee · Taeyong Kwon · Sanghoo Yoon. ᄃ (2012)ᄋ ᆼ ᅳ ᅦᄉ ᅥ SPI, PDSI (palmer drought severity index)ᄋ ᅪ SPEI (standardized precipitation evapotranspiration index)ᄋ ᅴ ᄉ ᆼᄂ ᅥ ᆼᄋ ᅳ ᆯ ᄇ ᅳ ᅵᄀ ᅭᄒ ᅡᄋ ᆻᄀ ᅧ ᅩ, ᄉ ᆼᄀ ᅡ ᆫᄉ ᅪ ᆼ ᄇ ᅥ ᆫᄉ ᅮ ᆨᄋ ᅥ ᆯ ᄐ ᅳ ᆼᄒ ᅩ ᅢ SPIᄋ ᅪ SPEIᄋ ᅴ ᄉ ᆼᄂ ᅥ ᆼᄋ ᅳ ᅵ ᄋ ᅮᄉ ᅮᄒ ᆷᄋ ᅡ ᆯ ᄇ ᅳ ᆰ ᅡ ᆻᄃ ᅧ ᄒ ᅡ. ᄎ ᅬᄀ ᆫᄐ ᅳ ᆼᄀ ᅩ ᅨᄌ ᆨᄋ ᅥ ᅳᄅ ᅩᄌ ᆸᄀ ᅥ ᆫᄒ ᅳ ᅡᄋ ᅧᄀ ᅡᄆ ᆷᅳ ᅮ ᆯ ᄋᄑ ᆼᄀ ᅧ ᅡᄒ ᅡᄂ ᆫᄋ ᅳ ᆫᄀ ᅧ ᅮᄀ ᅡᄆ ᆭᄃ ᅡ ᅡ. Stagge ᄃ ᆼ (2016)ᄋ ᅳ ᅦᄉ ᅥ SPEIᄂ ᆫ GEV ᅳ (generalized extreme value) ᄇ ᆫᄑ ᅮ ᅩᅪ ᄋᄀ ᇀᄋ ᅡ ᅵᄁ ᅩᄅ ᅵᄋ ᆼᄋ ᅧ ᆨᄋ ᅧ ᅵᄃ ᅮᄁ ᅥᄋ ᆫᄒ ᅮ ᆨᄅ ᅪ ᆯᅮ ᅲ ᆫ ᄇᄑ ᅩᄆ ᅩᄒ ᆼᄋ ᅧ ᅦᄉ ᅥᄌ ᆨᄒ ᅥ ᆸᄒ ᅡ ᅡᄃ ᅡᄀ ᅩᄋ ᆯᄅ ᅡ ᅧᄌ ᅧᄋ ᆻ ᅵ ᅡ. Kim ᄃ ᄃ ᆼ (2011)ᄋ ᅳ ᆫᄌ ᅳ ᅩᄀ ᆫᄇ ᅥ ᅮ GEV ᄇ ᆫᄑ ᅮ ᅩᄅ ᆯᄋ ᅳ ᅵᄋ ᆼᄒ ᅭ ᅡᄋ ᅧᄇ ᆫᄃ ᅵ ᅩᄇ ᆫᄉ ᅮ ᆨᄒ ᅥ ᅡᄋ ᆻᄃ ᅧ ᅡ. Chikobvuᄋ ᅪ Chifurira (2015)ᄋ ᅦ ᅥᄀ ᄉ ᆼᅮ ᅡ ᄉᄅ ᆼᄋ ᅣ ᅴᄎ ᅬᄃ ᅢ·ᄎ ᅬᄉ ᅩᄇ ᆫᄑ ᅮ ᅩᄋ ᅴᄋ ᅵᄌ ᆼᄉ ᅮ ᆼᄋ ᅥ ᆯᄋ ᅳ ᅵᄋ ᆼᄒ ᅭ ᅡᄋ ᅧ GEV ᄇ ᆫᄑ ᅮ ᅩᄋ ᅦᄌ ᆨᄒ ᅥ ᆸᄒ ᅡ ᅡᄋ ᆻᄃ ᅧ ᅡ. ᄀ ᅡᄆ ᆷᄋ ᅮ ᅬᄋ ᅦᄃ ᅩ GEV ᄇ ᆫᄑ ᅮ ᅩᄂ ᆫ ᅳ ᅧᄅ ᄋ ᅥᄋ ᆫᄀ ᅧ ᅮᄋ ᅦᄉ ᅥᄋ ᅵᄋ ᆼᄃ ᅭ ᅬᄀ ᅩᄋ ᆻᄃ ᅵ ᅡ (Ryu ᄃ ᆼ, 2016; Ha ᄃ ᅳ ᆼ, 2020). ᅳ ᅪᄀ ᄀ ᅥᄋ ᅴᄀ ᅡᄆ ᆷᄋ ᅮ ᅦᄀ ᆫᄒ ᅪ ᆫᅧ ᅡ ᆫ ᄋᄀ ᅮᄃ ᆯᄋ ᅳ ᆫᄀ ᅳ ᅡᄆ ᆷᄌ ᅮ ᅵᄉ ᅮᄅ ᆯᄋ ᅳ ᅵᄋ ᆼᄒ ᅭ ᆫᄀ ᅡ ᅡᄆ ᆷᄀ ᅮ ᆫᄅ ᅪ ᅵᄋ ᅪᄌ ᆼᄅ ᅥ ᆼᄌ ᅣ ᆨᄀ ᅥ ᆫᄀ ᅪ ᅨᄇ ᆫᄉ ᅮ ᆨᄆ ᅥ ᆾᄌ ᅵ ᅢᄒ ᆫᄂ ᅧ ᆼᄅ ᅳ ᆨᄋ ᅧ ᅵᄋ ᅮᄉ ᅮᄒ ᆫ ᅡ ᅡᄆ ᄀ ᆷᅵ ᅮ ᄌᄉ ᅮᄅ ᆯᄎ ᅳ ᆽᄂ ᅡ ᆫᄋ ᅳ ᆫᄀ ᅧ ᅮᄀ ᅡᄋ ᅵᄅ ᅯᄌ ᅵᄀ ᅩᄋ ᆻᄋ ᅵ ᆷᅳ ᅳ ᆯ ᄋᄋ ᆯᄉ ᅡ ᅮᄋ ᆻᄃ ᅵ ᅡ. ᄒ ᅡᄌ ᅵᄆ ᆫᄆ ᅡ ᆭᄋ ᅡ ᆫᄀ ᅳ ᅡᄆ ᆷᄌ ᅮ ᅵᄉ ᅮᄃ ᆯᄋ ᅳ ᆫᄌ ᅳ ᅵᄉ ᆨᄌ ᅩ ᆨᄋ ᅥ ᆫᄀ ᅵ ᅡᄆ ᆷᄋ ᅮ ᅴᄋ ᅱᄒ ᆷ ᅥ ᆼᄋ ᅥ ᄉ ᆯᅵ ᅳ ᄉᄀ ᆫᅡ ᅡ ᆫ ᄃᄋ ᅱᄅ ᅩᄑ ᆼᄀ ᅧ ᅡᄒ ᅡᄂ ᆫᄃ ᅳ ᅦᄒ ᆫᄀ ᅡ ᅨᄌ ᆷᄋ ᅥ ᅵᄌ ᆫᄌ ᅩ ᅢᄒ ᆫᄃ ᅡ ᅡ. ᄄ ᅡᄅ ᅡᄉ ᅥᄇ ᆫᄋ ᅩ ᆫᄀ ᅧ ᅮᄋ ᅦᄉ ᅥᄂ ᆫᄐ ᅳ ᆨᄌ ᅳ ᆼᄒ ᅥ ᆫᄋ ᅡ ᆼᄋ ᅣ ᅴᄇ ᅵᄀ ᅡᄎ ᆯᄄ ᅡ ᅢᄁ ᅡᄌ ᅵᄋ ᅴ ᅵᄀ ᄉ ᆫᅵ ᅡ ᄋ inter-amount time (IAT)ᄅ ᆫ ᆯᄐ ᅳ ᆼᄒ ᅩ ᅢᄆ ᅮᄀ ᆼᄉ ᅡ ᅮᄋ ᆯᄉ ᅵ ᅮᄅ ᆯᄌ ᅳ ᆼᄅ ᅥ ᆼᄌ ᅣ ᆨᄋ ᅥ ᅳᄅ ᅩᄑ ᆼᄀ ᅧ ᅡᄒ ᆫᄃ ᅡ ᅡ. Ohᄋ ᅪ Yoon (2019)ᄋ ᅪ Schleissᄋ ᅪ Smith (2016) ᄋ ᅦᄉ ᅥᄌ ᅦᄋ ᆫᅡ ᅡ ᆫ ᄒ IATᄂ ᆫᄉ ᅳ ᅵᄀ ᆫᅡ ᅡ ᆼ ᄃᄂ ᅮᄌ ᆨᄀ ᅥ ᆼᄉ ᅡ ᅮᄅ ᆼᄋ ᅣ ᆯᄒ ᅳ ᆫᄀ ᅡ ᅨᄉ ᅮᄌ ᆫᄋ ᅮ ᅳᄅ ᅩᄂ ᅡᄂ ᅮᄋ ᅥᄆ ᆪᄋ ᅩ ᅵᄇ ᆫᄒ ᅧ ᅡᄂ ᆫ ᅳ ᅵᄌ ᄉ ᆷᄋ ᅥ ᆯᄀ ᅳ ᅨᄉ ᆫᄒ ᅡ ᅡᄂ ᆫᄇ ᅳ ᆼᄇ ᅡ ᆸᄋ ᅥ ᅵᄃ ᅡ. IATᄂ ᆫᄉ ᅳ ᅵᄌ ᆨᄌ ᅡ ᆷᄋ ᅥ ᅦᄄ ᅡᄅ ᅡᄃ ᅡᄅ ᆫᄌ ᅳ ᅡᄅ ᅭᄀ ᅡᄉ ᆼᅥ ᅢ ᆼ ᄉᄃ ᅬᄂ ᆫᄃ ᅳ ᆫᄌ ᅡ ᆷᄋ ᅥ ᅵᄋ ᆻᄋ ᅵ ᅥᄋ ᅵᄅ ᆯᄇ ᅳ ᅩᄋ ᆫᄒ ᅪ ᅡᄀ ᅩᄌ ᅡ ᆫᄋ ᅩ ᄇ ᆫᄀ ᅧ ᅮᄋ ᅦᄉ ᅥ M-IATᄅ ᆯᄌ ᅳ ᅦᄋ ᆫᅡ ᅡ ᆫ ᄒᄃ ᅡ. M-IATᄂ ᆫᄉ ᅳ ᅵᄀ ᆫᄃ ᅡ ᆫᄋ ᅡ ᅱᄋ ᅴᄀ ᆼᄉ ᅡ ᅮᄅ ᆼᄌ ᅣ ᅡᄅ ᅭᄋ ᅦᄉ ᅥᄒ ᆫᄀ ᅡ ᅨᄉ ᅮᄌ ᆫᄁ ᅮ ᅡᄌ ᅵᄎ ᅡᄂ ᆫᄃ ᅳ ᅦᄀ ᆯᄅ ᅥ ᅵᄂ ᆫ ᅳ ᅵᄀ ᄉ ᆫᄋ ᅡ ᆯᄀ ᅳ ᅨᄉ ᆫᄒ ᅡ ᅡᄂ ᆫᄇ ᅳ ᆼᄇ ᅡ ᆸᄋ ᅥ ᅵᄃ ᅡ. ᄋ ᅥᄄ ᆫᄇ ᅥ ᆼᄇ ᅡ ᆸᄋ ᅥ ᅵᄀ ᅡᄆ ᆷᄋ ᅮ ᅦᄆ ᆫᄀ ᅵ ᆷᅡ ᅡ ᆫ ᄒᄌ ᅵᄅ ᆯᄑ ᅳ ᆼᄀ ᅧ ᅡᄒ ᆫᄃ ᅡ ᅡ.. 2. 연구방법론 2.1. Inter-amount time ᅵᄉ ᄀ ᆼᅡ ᅡ ᆨ ᄒᄌ ᆨᄀ ᅥ ᅡᄆ ᆷᄋ ᅮ ᅴᄀ ᅵᄌ ᆫᄋ ᅮ ᆫᄂ ᅳ ᅮᄌ ᆨᄀ ᅥ ᆼᄉ ᅡ ᅮᄅ ᆼᄋ ᅣ ᅵ 0ᄋ ᆫᄋ ᅵ ᆯᄉ ᅵ ᅮ (ᄆ ᅮᄀ ᆼᄉ ᅡ ᅮᄋ ᆯᄉ ᅵ ᅮ)ᄀ ᅡᄌ ᅵᄉ ᆨᄃ ᅩ ᅬᄀ ᅥᄂ ᅡᄀ ᅵᄌ ᆫᄀ ᅮ ᆼᄉ ᅡ ᅮᄅ ᆼᄋ ᅣ ᆯᄂ ᅳ ᆷᄀ ᅥ ᅵᄌ ᅵᄆ ᆺ ᅩ ᄒᄂ ᅡ ᆫᅡ ᅳ ᆯ ᄂᄋ ᅵᄌ ᅵᄉ ᆨᄃ ᅩ ᅬᄂ ᆫᄀ ᅳ ᆺᄋ ᅥ ᆯᄋ ᅳ ᅴᄆ ᅵᄒ ᆫᄃ ᅡ ᅡ. Schleissᄋ ᅪ Smith (2016)ᄂ ᆫᄂ ᅳ ᅮᄌ ᆨᄀ ᅥ ᆼᄉ ᅡ ᅮᄅ ᆼᄋ ᅣ ᅵᄐ ᆨᄌ ᅳ ᆼᄎ ᅥ ᅵᄅ ᆯᄃ ᅳ ᅩᄃ ᆯᄒ ᅡ ᅡᄂ ᆫᄃ ᅳ ᅦᄀ ᆯ ᅥ ᅵᄂ ᄅ ᆫ ᅵ ᅳ ᄉᄀ ᆫᄋ ᅡ ᆯ IATᄅ ᅳ ᅩ ᄌ ᅦᄋ ᆫᄒ ᅡ ᅡᄋ ᆻᄃ ᅧ ᅡ. IATᄂ ᆫ ᄉ ᅳ ᅵᄀ ᆫᄌ ᅡ ᅡᄅ ᅭᄅ ᅩ 0ᄋ ᅵ ᄌ ᆫᄌ ᅩ ᅢᄒ ᅡᄌ ᅵ ᄋ ᆭᄋ ᅡ ᅡ ᄀ ᆼᄉ ᅡ ᅮᄅ ᆼᄇ ᅣ ᅩᄃ ᅡ ᄒ ᆨᄅ ᅪ ᆯᄇ ᅲ ᆫᄑ ᅮ ᅩᄋ ᅦ ᄇ ᅩᄃ ᅡ ᆨᄒ ᅥ ᄌ ᆸᅡ ᅡ ᄒᄃ ᅡ. ᄄ ᅩᄒ ᆫᄀ ᅡ ᅡᄆ ᆷᄋ ᅮ ᅵᄋ ᆷᄀ ᅵ ᅨᄀ ᆹᄋ ᅡ ᅵ 10mmᄅ ᅩᄌ ᅵᄌ ᆼᄃ ᅥ ᆫᄀ ᅬ ᆼᄋ ᅧ ᅮ 10mmᄇ ᅩᄃ ᅡᄂ ᆽᄋ ᅡ ᆫᄀ ᅳ ᆼᄉ ᅡ ᅮᄀ ᅡᄇ ᆯᄉ ᅡ ᆼᄒ ᅢ ᆫᄂ ᅡ ᆯᄋ ᅡ ᅵᄂ ᅮᄌ ᆨᄃ ᅥ ᅬᄋ ᅥ ᅨᄉ ᄀ ᆫᄃ ᅡ ᅬᄆ ᅳᄅ ᅩᄆ ᅮᄀ ᆼᄉ ᅡ ᅮᄋ ᅱᄒ ᆷᅥ ᅥ ᆼ ᄉᄋ ᆯᄌ ᅳ ᆼᄅ ᅥ ᆼᄌ ᅣ ᆨᄋ ᅥ ᅳᄅ ᅩᄑ ᆼᄀ ᅧ ᅡᄒ ᆯᄉ ᅡ ᅮᄋ ᆻᄃ ᅵ ᅡ. aᄋ ᅪ nᄋ ᅵᄀ ᆨᄀ ᅡ ᆨᄀ ᅡ ᅩᄌ ᆼᄃ ᅥ ᆫᄀ ᅬ ᆼᄉ ᅡ ᅮᄅ ᆼᄀ ᅣ ᅪᄌ ᅡᄋ ᆫᄉ ᅧ ᅮᄋ ᆯᄄ ᅵ ᅢ IATᄅ ᆯ τn (a)ᄅ ᅳ ᅡᄀ ᅩᄒ ᅡᄆ ᆫ τn (a)ᄂ ᅧ ᆫᄉ ᅳ ᆨ(2.1)ᄅ ᅵ ᅩᄌ ᆼᄋ ᅥ ᅴᄃ ᆫᄃ ᅬ ᅡ. τn (a) = tn (a) − tn−1 (a), ∀n ∈ N,. (2.1). ᄋᄀ ᅧ ᅵᅥ ᄉ tn (a)ᄂ ᆫ aᄋ ᅳ ᅦ nᄇ ᆫᄃ ᅥ ᅩᄃ ᆯᄒ ᅡ ᅡᄂ ᆫᄎ ᅳ ᅬᄉ ᅩᄋ ᅴᄉ ᅵᄀ ᆫᄋ ᅡ ᆯᄋ ᅳ ᅴᄆ ᅵᄒ ᆫᄃ ᅡ ᅡ. uᄂ ᆫᄉ ᅳ ᅵᄀ ᆫ, y(u)ᄂ ᅡ ᆫ uᄁ ᅳ ᅡᄌ ᅵᄋ ᅴᄂ ᅮᄌ ᆨᄀ ᅥ ᆼᄉ ᅡ ᅮᄅ ᆼᄋ ᅣ ᆯ ᅵ ᅢ IATᄂ ᄄ ᆫᄉ ᅳ ᆨ (2.2)ᄅ ᅵ ᅩᄑ ᅭᄒ ᆫᄒ ᅧ ᆯᄉ ᅡ ᅮᄋ ᆻᄃ ᅵ ᅡ. tn (a) = min{u : y(u) ≥ an}, ∀n ∈ N.. (2.2). IATᄂ ᆫᄀ ᅳ ᆼᄉ ᅡ ᅮᄅ ᆼ, ᄀ ᅣ ᆼᄉ ᅡ ᅮᄀ ᆼᄃ ᅡ ᅩ, ᄀ ᆼᄉ ᅡ ᅮᄇ ᆯᄉ ᅡ ᆼᄇ ᅢ ᆫᄃ ᅵ ᅩᄅ ᆯᄉ ᅳ ᅵᄀ ᆫᄋ ᅡ ᅴᄃ ᆫᄋ ᅡ ᅱᄅ ᅩᄌ ᅢᄑ ᅭᄒ ᆫᄒ ᅧ ᆫᄀ ᅡ ᆹᄋ ᅡ ᅳᄅ ᅩᄀ ᅵᄒ ᅮᄀ ᆨᄒ ᅳ ᆫᄉ ᅡ ᅡᄉ ᆼᄀ ᅡ ᅪᄀ ᆫᄅ ᅪ ᆫᄃ ᅧ ᆫᄐ ᅬ ᆼ ᅩ ᄀᄇ ᅨ ᆫᄉ ᅮ ᆨᄋ ᅥ ᅦᄌ ᅩᄀ ᆷᄃ ᅳ ᅥᄉ ᆸᄀ ᅱ ᅦᄌ ᆸᄀ ᅥ ᆫᄒ ᅳ ᆯᄉ ᅡ ᅮᄋ ᆻᄃ ᅵ ᅡ. Ohᄋ ᅪ Yoon (2019)ᄃ ᅩᄋ ᅵᄇ ᆼᄇ ᅡ ᆸᄋ ᅥ ᆯᄋ ᅳ ᅵᄋ ᆼᄒ ᅭ ᅡᄋ ᅧᄒ ᆫᄇ ᅡ ᆫᄃ ᅡ ᅩᄀ ᅡᄆ ᆷᄇ ᅮ ᆯᄉ ᅡ ᆼᄋ ᅢ ᅱ ᆷᄃ ᅥ ᄒ ᅩᄅ ᆯᄑ ᅳ ᆼᄀ ᅧ ᅡᄒ ᅡᄋ ᆻᄃ ᅧ ᅡ. ᄋ ᅵᄇ ᆼᄇ ᅡ ᆸᄋ ᅥ ᆫᄉ ᅳ ᅵᄌ ᆨᄉ ᅡ ᅵᄌ ᆷᄋ ᅥ ᆯᄋ ᅳ ᅥᄄ ᇂᄀ ᅥ ᅦᄉ ᆯᅥ ᅥ ᆼ ᄌᄒ ᅡᄂ ᅳᄂ ᅣᄋ ᅦᄄ ᅡᄅ ᅡ IATᄀ ᅡᄃ ᆯᄅ ᅡ ᅡᄌ ᆯᄉ ᅵ ᅮᄋ ᆻᄀ ᅵ ᅩ, n > 1ᄋ ᆫ ᅵ ᆼᄋ ᅧ ᄀ ᅮ τn (a)ᄀ ᅡᄍ ᆲᄀ ᅡ ᅦᄀ ᅨᄉ ᆫᄃ ᅡ ᅬᄂ ᆫᅮ ᅳ ᆫ ᄆᄌ ᅦᄀ ᅡᄋ ᆻᄃ ᅵ ᅡ. IATᄋ ᅴ ᄆ ᆫᄌ ᅮ ᅦᄌ ᆷᄋ ᅥ ᆯ ᄇ ᅳ ᅩᄋ ᆫᄒ ᅪ ᅡᄀ ᅵ ᄋ ᅱᄒ ᅢ ᄇ ᆫ ᄋ ᅩ ᆫᄀ ᅧ ᅮᄂ ᆫ M-IAT (modified inter-amount time)ᄅ ᅳ ᆯ ᄌ ᅳ ᅦᄉ ᅵᄒ ᆫᄃ ᅡ ᅡ. MIATᄂ ᆫ n = 1ᄋ ᅳ ᅳᄅ ᅩ ᄀ ᅩᄌ ᆼᄒ ᅥ ᅡᄀ ᅩ ᄀ ᅵᄌ ᆫᄉ ᅮ ᅵᄌ ᆷᄋ ᅥ ᅳᄅ ᅩᄇ ᅮᄐ ᅥ ᅪ ᄀᄀ ᅥᄉ ᅵᄀ ᆫ kᄁ ᅡ ᅡᄌ ᅵ ᄀ ᅩᄌ ᆼᄃ ᅥ ᆫ ᄀ ᅬ ᆼᄉ ᅡ ᅮᄅ ᆼ aᄀ ᅣ ᅡ ᄀ ᆯᄅ ᅥ ᅵᄂ ᆫ ᄉ ᅳ ᅵᄀ ᆫᄋ ᅡ ᅵᄃ ᅡ. zu ᄅ ᆯ uᄉ ᅳ ᅵᄌ ᆷᄋ ᅥ ᅦᄂ ᅢᄅ ᆫᄀ ᅵ ᆼᄉ ᅡ ᅮᄅ ᆼᄋ ᅣ ᅵᄅ ᅡᄀ ᅩᄌ ᆼᄋ ᅥ ᅴᄒ ᅡᄆ ᆫ, M-IATᄂ ᅧ ᆫᄃ ᅳ ᅡᄋ ᆷᄉ ᅳ ᆨᄋ ᅵ ᆯᄆ ᅳ ᆫᄌ ᅡ ᆨᄒ ᅩ ᆫᄃ ᅡ ᅡ. δ(a) = argmin. −k X u=0. zu ≥ a, k > 0.. (2.3).
(3) Analysis of the generalized extreme value of drought on the Korean Peninsula using inter-amount times1025. 히 ᅡ ᄌᄆ ᆫ M-IATᄂ ᅡ ᆫᄀ ᅳ ᆫᄎ ᅪ ᆨᄉ ᅳ ᅵᄌ ᆷᄋ ᅥ ᅳᄅ ᅩᄇ ᅮᄐ ᅥᄂ ᅮᄌ ᆨᄀ ᅥ ᆼᄉ ᅡ ᅮᄅ ᆼᄋ ᅣ ᅵᄀ ᅡᄃ ᆯᄄ ᅬ ᅢᄁ ᅡᄌ ᅵᄂ ᆫᄀ ᅳ ᆯᄎ ᅧ ᆨᄎ ᅳ ᅵᄅ ᅩᄎ ᅥᄅ ᅵᄃ ᆫᄃ ᅬ ᅡ. ᄋ ᆷᄋ ᅵ ᅴᄋ ᅴᆼ ᅡ ᄀᄉ ᅮᄅ ᆼ ᅣ ᅪᄀ ᄀ ᅩᄌ ᆼᄃ ᅥ ᆫᄀ ᅬ ᆼᄉ ᅡ ᅮᄅ ᆼᄀ ᅣ ᆹᄋ ᅡ ᆯ 10mmᄅ ᅳ ᅩᄌ ᅵᄌ ᆼᅢ ᅥ ᆻ ᄒᄋ ᆯᄄ ᅳ ᅢᄋ ᅴ IATᄋ ᅪ M-IATᄋ ᅴᄀ ᅨᄉ ᆫᄀ ᅡ ᆯᄀ ᅧ ᅪᄅ ᆯ Table 2.1ᄋ ᅳ ᆯᄐ ᅳ ᆼᄒ ᅩ ᅢᄂ ᅡᄐ ᅡᅢ ᄂ ᆻ ᅡ. ᄃ Table 2.1 The example of simulated data for IAT and M-IAT (a = 10) Date 2001-01-01 2001-01-01 2001-01-01 2001-01-01 2001-01-01 2001-01-01 2001-01-01 2001-01-01 2001-01-01 2001-01-01 2001-01-01 2001-01-01 2001-01-01 2001-01-01 2001-01-01. Hour 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15. Prec. 4.2 4.5 0.3 1.2 0.8 0.9 3.5 4.9 0.0 12.0 4.2 5.8 1.8 3.1 2.8. Cum. Prec. 4.2 8.7 9.0 10.2 11.0 11.9 15.4 20.3 20.3 32.3 36.5 42.3 44.1 47.2 50.0. N 0 0 0 1 1 1 1 2 2 3 3 4 4 4 5. τn (IAT) 5.11 5.11 2.87 2.87 2.80 2.80 2.80 4.40. δ(M-IAT) 3.95 4.76 5.55 5.73 3.88 4.88 0.83 1.48 2.00 2.57 2.88 3.40. 2.2. GEV 분포 ᆯᄅ ᅳ ᄇ ᆨᄎ ᅩ ᅬᄃ ᆺᄀ ᅢ ᆹᄆ ᅡ ᅩᄒ ᆼ (block maxima model)ᄋ ᅧ ᆫᄋ ᅳ ᆯᄌ ᅵ ᆼᄃ ᅥ ᆫᄋ ᅡ ᅱᄀ ᅵᄀ ᆫᄃ ᅡ ᆼᄋ ᅩ ᆫᄋ ᅡ ᅴᄀ ᆫᄎ ᅪ ᆨᄉ ᅳ ᅵᄀ ᆫᄋ ᅡ ᆯᄀ ᅳ ᇀᄋ ᅡ ᆫᄏ ᅳ ᅳᄀ ᅵᄋ ᅴᄉ ᅦᄇ ᅮᄀ ᅮ ᄀᄃ ᆫ ᅡ ᆯᅩ ᅳ ᄅ ᄂ ᅡᄂ ᆫ ᄒ ᅮ ᅮ ᄀ ᆨ ᄀ ᅡ ᅮᄀ ᆫᄋ ᅡ ᅴ ᄎ ᅬᄃ ᆺᄀ ᅢ ᆹᄀ ᅡ ᅪ ᄀ ᇀᄋ ᅡ ᆫ ᄀ ᅳ ᆨᄃ ᅳ ᆫᄎ ᅡ ᅵᄅ ᆯ ᄆ ᅳ ᅩᄒ ᆼᄒ ᅧ ᅪᄒ ᅡᄂ ᆫ ᄃ ᅳ ᅢᄑ ᅭᄌ ᆨᄋ ᅥ ᆫ ᄇ ᅵ ᆼᄇ ᅡ ᆸᄋ ᅥ ᅵᄃ ᅡ. Fisherᄋ ᅪ Tippet (1928)ᄋ ᅦ ᄄ ᅡᄅ ᅳᄆ ᆫ ᄀ ᅧ ᆨ ᄀ ᅡ ᅮᄀ ᆫᄋ ᅡ ᅴ ᄎ ᅬᄃ ᆺᄀ ᅢ ᆹᄃ ᅡ ᆯᄋ ᅳ ᆫ ᄑ ᅳ ᅭᄇ ᆫᄋ ᅩ ᅵ ᄌ ᆼᄀ ᅳ ᅡᄒ ᆯᄉ ᅡ ᅮᄅ ᆨ ᄌ ᅩ ᆷᄀ ᅥ ᆫᄌ ᅳ ᆨᄋ ᅥ ᅳᄅ ᅩ GEV ᄇ ᆫᄑ ᅮ ᅩᄅ ᆯ ᄄ ᅳ ᅡᄅ ᆫᄃ ᅳ ᅡ. ᄃ ᅡᄋ ᆷᄋ ᅳ ᆫ ᅳ GEV ᄇ ᆫᄑ ᅮ ᅩᄋ ᅴᄂ ᅮᄌ ᆨᄆ ᅥ ᆯᄃ ᅵ ᅩᄒ ᆷᄉ ᅡ ᅮᄋ ᅵᄃ ᅡ (Jenkinson, 1955). ξ(x − µ) x − µ − ξ1 )] }, X : 1 + > 0, (2.4) σ σ ᅧᄀ ᄋ ᅵᅥ ᄉ µ, σ, ξᄂ ᆫᄀ ᅳ ᆨᄀ ᅡ ᆨᄇ ᅡ ᆫᄑ ᅮ ᅩᄋ ᅴᄌ ᆼᄉ ᅮ ᆷᄋ ᅵ ᆯᄂ ᅳ ᅡᄐ ᅡᄂ ᅢᄂ ᆫᄋ ᅳ ᅱᄎ ᅵᄆ ᅩᄉ ᅮ (location parameter), ᄇ ᆫᄑ ᅮ ᅩᄋ ᅴᅡ ᆫ ᄉᄑ ᅩᄅ ᆯᄂ ᅳ ᅡᄐ ᅡᄂ ᅢ ᆫᄎ ᅳ ᄂ ᆨᄃ ᅥ ᅩᄆ ᅩᄉ ᅮ (scale parameter), ᄇ ᆫᄑ ᅮ ᅩᄋ ᅴᄁ ᅩᄅ ᅵᄒ ᆼᄐ ᅧ ᅢᄅ ᆯᄂ ᅳ ᅡᄐ ᅡᄂ ᅢᄂ ᆫᄒ ᅳ ᆼᄉ ᅧ ᆼᄆ ᅡ ᅩᄉ ᅮ (shape parameter)ᄋ ᅵᄃ ᅡ. GEV ᆫᄑ ᅮ ᄇ ᅩᄂ ᆫᄒ ᅳ ᆼᄉ ᅧ ᆼᄆ ᅡ ᅩᄉ ᅮᄋ ᅦᄄ ᅡᄅ ᅡᄇ ᆫᄑ ᅮ ᅩᄋ ᅴᄒ ᆼᄐ ᅧ ᅢᄀ ᅡᄀ ᆯᅥ ᅧ ᆼ ᄌᄃ ᅬᄂ ᆫᄃ ᅳ ᅦᄋ ᅵᄂ ᆫᄃ ᅳ ᅡᄋ ᆷᄀ ᅳ ᅪᄀ ᇀᄃ ᅡ ᅡ. G(x) = exp{−[1 + ξ(. exp{−(1 + ξ(x − µ) )− ξ1 }, 1 + ξ(x − µ)/σ > 0, σ F r´ echet(ξ > 0) : G(x) = 0, 1 + ξ(x − µ)/σ ≤ 0. Gumbel(ξ = 0) : G(x) = exp{−exp[−(. x−µ )]}, −∞ < x < ∞ σ. exp{(1 + ξ(x − µ) ) ξ1 }, 1 + ξ(x − µ)/σ > 0, σ W eibull(ξ < 0) : G(x) = 1, 1 + ξ(x − µ)/σ ≤ 0.. (2.5). (2.6). (2.7). GEV ᄇ ᆫᄑ ᅮ ᅩᄋ ᅴᄆ ᅩᄉ ᅮᄎ ᅮᄌ ᆼᄋ ᅥ ᆫᄎ ᅳ ᅬᄃ ᅢᄋ ᅮᄃ ᅩᄇ ᆸᄋ ᅥ ᅵᄂ ᅡ L-ᄌ ᆨᄅ ᅥ ᆯᄎ ᅲ ᅮᄌ ᆼᄇ ᅥ ᆸᄋ ᅥ ᆯᄋ ᅳ ᅵᄋ ᆼᄒ ᅭ ᆫᄃ ᅡ ᅡ. L-ᄌ ᆨᄅ ᅥ ᆯᄎ ᅲ ᅮᄌ ᆼᅥ ᅥ ᆸ ᄇᄋ ᆫᄌ ᅳ ᅡᄅ ᅭᄋ ᅴᄋ ᅵᄉ ᆼᄎ ᅡ ᅵ ᅦᄃ ᄋ ᆯᅵ ᅥ ᄆᄀ ᆫ ᆷᄒ ᅡ ᅡᄀ ᅩ, ᄑ ᅭᄇ ᆫᄋ ᅩ ᅴᄏ ᅳᄀ ᅵᄀ ᅡᄌ ᆨᄋ ᅡ ᅡᄎ ᅵᄋ ᅮᄎ ᆫᄇ ᅵ ᆫᄑ ᅮ ᅩᄋ ᅦᄃ ᅢᄒ ᆫᄆ ᅡ ᅩᄉ ᅮᄅ ᆯᄎ ᅳ ᅬᄃ ᅢᄋ ᅮᄃ ᅩᄇ ᆸᄇ ᅥ ᅩᄃ ᅡᄌ ᆯᄎ ᅡ ᅮᄌ ᆼᄒ ᅥ ᆫᄃ ᅡ ᅡ (Hosking, 1990). ᄄ ᅡᄅ ᅡᄉ ᅥᄇ ᆫᄋ ᅩ ᆫᄀ ᅧ ᅮᄂ ᆫᄑ ᅳ ᅭᄇ ᆫᄋ ᅩ ᅴᄉ ᅮᄀ ᅡᄌ ᆨᄋ ᅥ ᅳᄆ ᅳᄅ ᅩ L-ᄌ ᆨᄅ ᅥ ᆯᄎ ᅲ ᅮᄌ ᆼᅥ ᅥ ᆸ ᄇᄋ ᆯᄋ ᅳ ᅵᄋ ᆼᄒ ᅭ ᅡᄋ ᅧᄆ ᅩᄉ ᅮᄅ ᆯᄎ ᅳ ᅮᄌ ᆼᄒ ᅥ ᅡᄋ ᆻᄃ ᅧ ᅡ..
(4) 1026. Jihoon Lee · Taeyong Kwon · Sanghoo Yoon. ᄌᄒ ᅢ ᆫᄉ ᅧ ᅮᄌ ᆫ (Return Level)ᄋ ᅮ ᆫᄐ ᅳ ᆨᄌ ᅳ ᆼᄀ ᅥ ᅵᄀ ᆫᄋ ᅡ ᅦᄒ ᆫᅥ ᅡ ᆫ ᄇᄋ ᅵᄉ ᆼᄇ ᅡ ᆯᄉ ᅡ ᆼᄒ ᅢ ᆫᄉ ᅡ ᅡᄀ ᆫᄋ ᅥ ᅴᄌ ᆼᄃ ᅥ ᅩᄅ ᆯᄒ ᅳ ᆨᄅ ᅪ ᆯᄌ ᅲ ᆨᄋ ᅥ ᅳᄅ ᅩᄀ ᅨᄉ ᆫᅡ ᅡ ᆫ ᄒᄀ ᆺᄋ ᅥ ᅳᄅ ᅩ, ᆨᄃ ᅳ ᄀ ᆫᅥ ᅡ ᆨ ᄌᄋ ᆫᄉ ᅵ ᅡᄀ ᆫᄋ ᅥ ᅵᄋ ᆯᄋ ᅵ ᅥᄂ ᆻᄋ ᅡ ᆯᄄ ᅳ ᅢᄋ ᅵᄉ ᅡᄀ ᆫᄋ ᅥ ᅵᄋ ᆯᄆ ᅥ ᅡᄂ ᅡᄏ ᅳᄀ ᅦᄋ ᆯᄋ ᅵ ᅥᄂ ᆯᄌ ᅡ ᅵᄋ ᅦᄃ ᅢᄒ ᆫᄆ ᅡ ᆫᄌ ᅮ ᅦᄅ ᆯᄂ ᅳ ᅡᄐ ᅡᄂ ᆫᄃ ᅢ ᅡ (Yangᄀ ᅪ Yoon, 2017). ᄋ ᅧᄀ ᅵᄉ ᅥ rᄋ ᆯᄌ ᅳ ᅢᄒ ᆫᄀ ᅧ ᅵᄀ ᆫ (return period)ᄅ ᅡ ᅡᄒ ᆫᄃ ᅡ ᅡᄆ ᆫ GEV ᄇ ᅧ ᆫᄑ ᅮ ᅩᄋ ᅦᄀ ᅵᄇ ᆫᅡ ᅡ ᆫ ᄒᄌ ᅢᄒ ᆫᄉ ᅧ ᅮᄌ ᆫ xr ᄋ ᅮ ᆫᄃ ᅳ ᅡᄋ ᆷᄀ ᅳ ᅪᄀ ᇀ ᅡ ᅵᄎ ᄋ ᅮᄌ ᆼᄒ ᅥ ᆫᄃ ᅡ ᅡ. xr = µ ˆ−. ˆ σ ˆ 1 (1 − [−log(1 − )]−ξ , ξ ̸= 0, ˆ r ξ. (2.8). 1 )], ξ = 0. (2.9) r ᆫ ᄋ ᅩ ᄇ ᆫᄀ ᅧ ᅮᄋ ᅦᄉ ᅥ ᄉ ᅡᄋ ᆼᄃ ᅭ ᆫ ᄌ ᅬ ᅡᄅ ᅭᄅ ᅩᄇ ᅮᄐ ᅥ ᄋ ᆮᄋ ᅥ ᅥᄌ ᆫ ᄇ ᅵ ᆫᄑ ᅮ ᅩᄀ ᅡ GEV ᄇ ᆫᄑ ᅮ ᅩᄅ ᆯ ᄄ ᅳ ᅡᄅ ᅳᄂ ᆫᄌ ᅳ ᅵᄋ ᅦ ᄃ ᅢᄒ ᆫ ᄌ ᅡ ᆨᄒ ᅥ ᆸᅥ ᅡ ᆼ 서 ᆷ 거 ᆼ ᄌᄋ ᆯ ᄋ ᅳ ᅱᄒ ᅢ Cram´ er-von Mises test (CVM)ᄋ ᅪ Anderson-Darling test (AD)ᄅ ᆯᄋ ᅳ ᅵᄋ ᆼᄒ ᅭ ᅡᄋ ᆻᄃ ᅧ ᅡ. Andersonᄀ ᅪ Dariling (1952)ᄀ ᅡᄌ ᅦᄉ ᅵᄒ ᆫᅥ ᅡ ᆷ 거 ᆼ ᄌᄐ ᆼᄀ ᅩ ᅨᄅ ᆼᄋ ᅣ ᆫᄃ ᅳ ᅡᄋ ᆷᄀ ᅳ ᅪᄀ ᇀᄃ ᅡ ᅡ. xr = µ ˆ−σ ˆ ln[−log(1 −. Z. ∞. (Fn (x) − F (x))2 ψ(x)dF (x),. Wn = n. (2.10). −∞. ᄋᄀ ᅧ ᅵᅥ ᄉ ψ(x)ᄂ ᆫᄀ ᅳ ᅡᄌ ᆼᄎ ᅮ ᅵᄒ ᆷᄉ ᅡ ᅮᄋ ᅵᄀ ᅩ, F (x)ᄂ ᆫᄎ ᅳ ᅮᄌ ᆼᄃ ᅥ ᆫᄆ ᅬ ᅢᄀ ᅢᄇ ᆫᄉ ᅧ ᅮᄀ ᅡᄑ ᅩᄒ ᆷᄃ ᅡ ᆫᄂ ᅬ ᅮᄌ ᆨᄇ ᅥ ᆫᄑ ᅮ ᅩᄒ ᆷᄉ ᅡ ᅮ, Fn (x)ᄂ ᆫᄀ ᅳ ᆼᄒ ᅧ ᆷᄌ ᅥ ᆨᄇ ᅥ ᆫ ᅮ ᅩᄒ ᄑ ᆷᅮ ᅡ ᄉᄅ ᆯᄂ ᅳ ᅡᄐ ᅡᄂ ᆫᄃ ᅢ ᅡ. ψ(x) = [F (x)(1 − F (x))]−1 ᄋ ᆫᅧ ᅵ ᆼ ᄀᄋ ᅮ AD ᄀ ᆷᅥ ᅥ ᆼ ᄌᄐ ᆼᄀ ᅩ ᅨᄅ ᆼ A2 ᄋ ᅣ ᆫᄃ ᅳ ᅡᄋ ᆷᄀ ᅳ ᅪᄀ ᇀᄃ ᅡ ᅡ. A2 = n. Z. ∞. −∞. (Fn (x) − F (x))2 dF (x). F (x)(1 − F (x)). (2.11). CVM ᄀ ᆷᅥ ᅥ ᆼ ᄌᄐ ᆼᄀ ᅩ ᅨᄅ ᆼᄋ ᅣ ᆫᄉ ᅳ ᆨ (2.10)ᄋ ᅵ ᅦᄉ ᅥᄀ ᅡᄌ ᆼᄎ ᅮ ᅵᄒ ᆷᄉ ᅡ ᅮ ψ(x)ᄀ ᅡ 1ᄋ ᆫᅧ ᅵ ᆼ ᄀᄋ ᅮᄋ ᅵᄃ ᅡ. Z. ∞. (Fn (x) − F (x))2 dF (x).. Ω=n. (2.12). −∞. 3. 연구자료 ᄆᄀ ᅮ ᆼᄉ ᅡ ᅮᄋ ᆯᄉ ᅵ ᅮᄋ ᅴᄌ ᆼᄅ ᅥ ᆼᄌ ᅣ ᆨᅧ ᅥ ᆼ ᄑᄀ ᅡᄂ ᆫᄀ ᅳ ᅵᄉ ᆼᄎ ᅡ ᆼᄋ ᅥ ᅦᄉ ᅥᄋ ᆫᄋ ᅮ ᆼᄒ ᅧ ᅡᄂ ᆫᄌ ᅳ ᆼᄀ ᅩ ᆫᄀ ᅪ ᅵᄉ ᆼᄀ ᅡ ᆫᄎ ᅪ ᆨᄌ ᅳ ᆼᄇ ᅡ ᅵ (automated synoptic observing system, ASOS)ᄋ ᅦᄉ ᅥᄉ ᅮᄌ ᆸᄃ ᅵ ᆫᄀ ᅬ ᆼᄉ ᅡ ᅮᄅ ᆼᄌ ᅣ ᅡᄅ ᅭᄅ ᆯᄋ ᅳ ᅵᄋ ᆼᄒ ᅭ ᅡᄋ ᆻᄃ ᅧ ᅡ. GEV ᄇ ᆫᄑ ᅮ ᅩᄂ ᆫᄃ ᅳ ᆫᄋ ᅡ ᅱᄀ ᅵᄀ ᆫᄃ ᅡ ᆼᄋ ᅩ ᆫᄎ ᅡ ᅬᄃ ᆺᄀ ᅢ ᆹᄋ ᅡ ᆯᄉ ᅳ ᅡ ᆼᄒ ᅭ ᄋ ᅡᄀ ᅵᄄ ᅢᄆ ᆫᄋ ᅮ ᅦᄃ ᆫᄀ ᅡ ᅵᄀ ᆫᄋ ᅡ ᅴᄌ ᅡᄅ ᅭᄅ ᅩᄂ ᆫᄀ ᅳ ᆨᄀ ᅢ ᆫᄌ ᅪ ᆨᄋ ᅥ ᆫᅧ ᅵ ᆼ ᄑᄀ ᅡᄋ ᅦᄌ ᆨᄒ ᅥ ᆸᄒ ᅡ ᅡᄌ ᅵᄋ ᆭᄃ ᅡ ᅡ. ᄀ ᅵᄉ ᆼᄎ ᅡ ᆼᄋ ᅥ ᅴᄀ ᅡᄆ ᆷᄑ ᅮ ᆫᅡ ᅡ ᆫ ᄃᄌ ᅵᄉ ᅮᄅ ᅩᄉ ᅡᄋ ᆼᄒ ᅭ ᅡᄀ ᅩ ᆻᄂ ᅵ ᄋ ᆫ Mckee ᄃ ᅳ ᆼ (1993)ᄋ ᅳ ᅴᄑ ᅭᄌ ᆫᄀ ᅮ ᆼᄉ ᅡ ᅮᄌ ᅵᄉ ᅮ (standardized precipitation index, SPI)ᄃ ᅩ 30ᄂ ᆫᄋ ᅧ ᅵᄉ ᆼᄋ ᅡ ᅴᄌ ᆼᄀ ᅡ ᅵᄀ ᆫ ᅡ ᅡᄅ ᄌ ᅭᄅ ᆯᄋ ᅳ ᅭᄀ ᅮᄒ ᅡᄆ ᅳᄅ ᅩ, ᄇ ᆫᄋ ᅩ ᆫᄀ ᅧ ᅮᄋ ᅦᄉ ᅥᄂ ᆫ 1990ᄂ ᅳ ᆫᄇ ᅧ ᅮᄐ ᅥ 2020ᄂ ᆫᄁ ᅧ ᅡᄌ ᅵᄎ ᆼ 31ᄂ ᅩ ᆫᄋ ᅧ ᅴᅡ ᆼ ᄌᄀ ᅵᄀ ᆫᄉ ᅡ ᅮᄌ ᆸᄃ ᅵ ᆫᄉ ᅬ ᅵᄀ ᆫᅡ ᅡ ᆫ ᄃᄋ ᅱᅡ ᆼ ᄀᄉ ᅮᄅ ᆼ ᅣ ᅡᄅ ᄌ ᅭᄅ ᆯᄋ ᅳ ᅵᄋ ᆼᄒ ᅭ ᅡᄋ ᆻᄃ ᅧ ᅡ. ᄋ ᆫᄀ ᅧ ᅮᄀ ᅵᄀ ᆫᄂ ᅡ ᅢᄋ ᆫᄋ ᅮ ᆼᄃ ᅧ ᆫ ASOSᄂ ᅬ ᆫᄎ ᅳ ᆼ 103ᄀ ᅩ ᅢᄉ ᅩᄋ ᅵᄆ ᅧ, 2020ᄂ ᆫᄌ ᅧ ᆫᄋ ᅥ ᅦᄀ ᆫᄎ ᅪ ᆨᄋ ᅳ ᅵᄌ ᆼᄃ ᅮ ᆫᄃ ᅡ ᆫ 6ᄀ ᅬ ᅢᄉ ᅩᄋ ᅪ 1990ᄂ ᆫᄋ ᅧ ᅵᄒ ᅮᄋ ᅦᄉ ᆯᄎ ᅥ ᅵᄃ ᆫ 29ᄀ ᅬ ᅢᄉ ᅩᄅ ᆯᄌ ᅳ ᅦᄋ ᅬᅡ ᆫ ᄒᄎ ᆼ 67ᄀ ᅩ ᅢᄉ ᅩᄅ ᆯᄉ ᅳ ᆫᅥ ᅥ ᆼ ᄌᄒ ᅡᄋ ᆻᄃ ᅧ ᅡ (Figure 3.1)..
(5) Analysis of the generalized extreme value of drought on the Korean Peninsula using inter-amount times1027. Figure 3.1 The location of weather stations. ᅮᄀ ᄆ ᆼᄉ ᅡ ᅮᄋ ᆯᄉ ᅵ ᅮᄂ ᆫᄌ ᅳ ᆼᄀ ᅡ ᅵᄀ ᆫᄋ ᅡ ᅦᄀ ᆯᄎ ᅥ ᅧᄂ ᅮᄌ ᆨᄀ ᅥ ᆼᄉ ᅡ ᅮᄅ ᆼᄋ ᅣ ᅵᄌ ᆨᅥ ᅥ ᆼ ᄌᄀ ᅵᄌ ᆫᄋ ᅮ ᅦᄆ ᅵᄎ ᅵᄌ ᅵᄆ ᆺᄒ ᅩ ᅡᄂ ᆫᄋ ᅳ ᆯᄉ ᅵ ᅮᄋ ᅵᄃ ᅡ. ᄀ ᅵᄉ ᆼᄎ ᅡ ᆼᄋ ᅥ ᅦᄉ ᅥᄂ ᆫᄀ ᅳ ᆼ ᅡ ᄉᄅ ᅮ ᆼᅵ ᅣ ᄋᄀ ᆨᄀ ᅡ ᆨ 0.0mm, 1.0mm, 5.0mm, 10.0mmᄅ ᅡ ᆯᄀ ᅳ ᅵᄌ ᆫᄋ ᅮ ᅳᄅ ᅩᄇ ᅵᄀ ᅡᄋ ᆫᄉ ᅧ ᆨᄒ ᅩ ᅢᄉ ᅥᄂ ᅡᄐ ᅡᄂ ᆫᄆ ᅡ ᅮᄀ ᆼᄉ ᅡ ᅮᄋ ᆯᄉ ᅵ ᅮᄅ ᆯᄌ ᅳ ᅦᄀ ᆼ ᅩ ᅡᄀ ᄒ ᅩ ᄋ ᆻᄃ ᅵ ᅡ. ᄋ ᆫᄀ ᅧ ᅮᄋ ᅴ ᄀ ᅡᄆ ᆷᄋ ᅮ ᅴ ᄌ ᆼᄅ ᅥ ᆼᄌ ᅣ ᆨ ᄑ ᅥ ᆼᄀ ᅧ ᅡᄅ ᆯ ᄋ ᅳ ᅱᅡ ᆫ ᄒ IAT ᄒ ᆫᄀ ᅡ ᅨᄉ ᅮᄌ ᆫᄋ ᅮ ᆫ 5mm, 10mmᄅ ᅳ ᅩ ᄉ ᆯᄌ ᅥ ᆼᄒ ᅥ ᅡᄋ ᆻᄃ ᅧ ᅡ. IATᄂ ᆫ ᅳ Schleissᄋ ᅪ Smith (2016)ᄋ ᅪ Oh ᄋ ᅪ Yoon (2019)ᄋ ᅴᅡ ᆼ ᄇᄇ ᆸᄋ ᅥ ᆯᄀ ᅳ ᅳᄃ ᅢᄅ ᅩᄌ ᆨᄋ ᅥ ᆼᄒ ᅭ ᅡᄋ ᆻᄀ ᅧ ᅩ, M-IATᄂ ᆫᅩ ᅳ ᆫ ᄇᄋ ᆫᄀ ᅧ ᅮᄋ ᅦᄉ ᅥᄌ ᅦ ᆫᅡ ᅡ ᄋ ᆫ ᄒᄇ ᆼᄇ ᅡ ᆸᄋ ᅥ ᅳᄅ ᅩᄀ ᅨᄉ ᆫᄒ ᅡ ᅡᄋ ᆻᄃ ᅧ ᅡ. M-IATᄂ ᆫᄒ ᅳ ᆫᄀ ᅡ ᅨᄉ ᅮᄌ ᆫᄁ ᅮ ᅡᄌ ᅵᄋ ᅴᄀ ᆹᄋ ᅡ ᅵᄀ ᆯᄎ ᅧ ᆨᄎ ᅳ ᅵᄅ ᅩᄎ ᅥᄅ ᅵᅬ ᄃᄆ ᅳᄅ ᅩ IATᄋ ᅪ M-IAT ᄆ ᅩᄃ ᅮ 1990ᄂ ᆫᄇ ᅧ ᅮᄐ ᅥ 2020ᄂ ᆫᄁ ᅧ ᅡᄌ ᅵᄋ ᅴᄌ ᅡᄅ ᅭᄅ ᆯᄋ ᅳ ᅵᄋ ᆼᄒ ᅭ ᅡᄋ ᅧᄀ ᅨᄉ ᆫᄒ ᅡ ᅮ 1990ᄂ ᆫᄌ ᅧ ᅡᄅ ᅭᄅ ᆯᄌ ᅳ ᅦᅬ ᄋᄉ ᅵᄏ ᆫᄎ ᅵ ᆼ 30ᄂ ᅩ ᆫᄌ ᅧ ᅡᄅ ᅭᄀ ᅡᄋ ᆫᄀ ᅧ ᅮᄋ ᅦᄋ ᅵ ᆼᄃ ᅭ ᄋ ᅬᅥ ᆻ ᄋᄃ ᅡ. ᆫᅥ ᅮ ᄇ ᆨ ᄉᄋ ᅦᄋ ᇁᄉ ᅡ ᅥ IATᄋ ᅪ M-IATᄋ ᅴᄎ ᅬᄃ ᆺᄀ ᅢ ᆹᄋ ᅡ ᅵ GEV ᄇ ᆫᄑ ᅮ ᅩᄅ ᆯᄄ ᅳ ᅡᄅ ᅳᄂ ᆫᄌ ᅳ ᅵᄐ ᆷᄉ ᅡ ᆨᄒ ᅢ ᅡᄀ ᅵᄋ ᅱᄒ ᅡᄋ ᅧᄒ ᅵᄉ ᅳᄐ ᅩᄀ ᅳᄅ ᆷᄋ ᅢ ᅴᄁ ᅩᄅ ᅵ ᆫᄑ ᅮ ᄇ ᅩᄅ ᆯᄒ ᅳ ᆨᄋ ᅪ ᆫᄒ ᅵ ᅡᄋ ᆻᄃ ᅧ ᅡ (Figure 3.2). IATᄂ ᆫ n > 1ᄋ ᅳ ᆯᄄ ᅵ ᅢ, τn (a)ᄀ ᅡᄍ ᆲᄀ ᅡ ᅦᄀ ᅨᄉ ᆫᄃ ᅡ ᅬᄂ ᆫᄃ ᅳ ᆫᄌ ᅡ ᆷᄄ ᅥ ᅢᄆ ᆫᄋ ᅮ ᅦ M-IATᄋ ᅴᄎ ᅬ ᆺᄀ ᅢ ᄃ ᆹᅵ ᅡ ᄋᄃ ᅥᄏ ᅳᄆ ᅳᄅ ᅩ M-IATᄋ ᅴᄁ ᅩᄅ ᅵᄇ ᆫᄑ ᅮ ᅩᄀ ᅡ IATᄋ ᅴᄁ ᅩᄅ ᅵᄇ ᆫᄑ ᅮ ᅩᄇ ᅩᄃ ᅡᄃ ᅥᄃ ᅮᄁ ᆸᄀ ᅥ ᅩᄇ ᆫᄑ ᅮ ᅩᄄ ᅩᄒ ᆫᄋ ᅡ ᅩᄅ ᆫᄍ ᅳ ᆨᄋ ᅩ ᅳᄅ ᅩᄃ ᅥᄎ ᅵᄋ ᅮ ᅧᄌ ᄎ ᅧᄋ ᆻᄂ ᅵ ᆫᄀ ᅳ ᆺᄋ ᅥ ᆯᄒ ᅳ ᆨᄋ ᅪ ᆫᄒ ᅵ ᆯᄉ ᅡ ᅮᄋ ᆻᄃ ᅵ ᅡ.. Ganghwa Figure 3.2 The distribution of IAT by tail domain using IAT, M-IAT.
(6) 1028. Jihoon Lee · Taeyong Kwon · Sanghoo Yoon. 4. 연구 결과 4.1. 한계수준에 따른 GEV분포 ᆫᄋ ᅩ ᄇ ᆫᄀ ᅧ ᅮᄋ ᅦᄉ ᅥᄂ ᆫᄒ ᅳ ᆫᄇ ᅡ ᆫᄃ ᅡ ᅩᄋ ᅴᄆ ᅮᄀ ᆼᄉ ᅡ ᅮᄋ ᆯᄉ ᅵ ᅮᄋ ᅴᄌ ᅢᄒ ᆫᄀ ᅧ ᅵᄀ ᆫᄋ ᅡ ᆯᄀ ᅳ ᅮᄒ ᅡᄀ ᅵᄋ ᅱᄒ ᅢᄉ ᅵᄀ ᆫᅡ ᅡ ᆫ ᄃᄋ ᅱᄀ ᆼᄉ ᅡ ᅮᄅ ᆼᄋ ᅣ ᆯ IATᄋ ᅳ ᅪ M-IATᄅ ᅩ ᄀᄉ ᅨ ᆫᅡ ᅡ ᄒᄀ ᅩ, ᄋ ᆫᄀ ᅧ ᆫᄎ ᅡ ᅬᄃ ᅢᄀ ᆹᄋ ᅡ ᆯᄎ ᅳ ᅮᄎ ᆯᄒ ᅮ ᅡᄋ ᅧ GEV ᄇ ᆫᄑ ᅮ ᅩᄋ ᅦᄌ ᆨᄒ ᅥ ᆸᄉ ᅡ ᅵᄏ ᆻᄃ ᅧ ᅡ. GEV ᄇ ᆫᄑ ᅮ ᅩᄋ ᅴᄆ ᅩᄉ ᅮᄂ ᆫᄉ ᅳ ᅩᄑ ᅭᄇ ᆫᄋ ᅩ ᅦᄌ ᆨᄒ ᅥ ᆸᄒ ᅡ ᆫ Lᅡ ᆨᄅ ᅥ ᄌ ᆯᅮ ᅲ ᄎᄌ ᆼᅥ ᅥ ᆸ ᄇᄋ ᅳᄅ ᅩᄎ ᅮᄌ ᆼᄒ ᅥ ᅡᄋ ᆻᄀ ᅧ ᅩᄇ ᆺᄉ ᅮ ᅳᄐ ᅳᄅ ᆸᄋ ᅢ ᆯᄋ ᅳ ᅵᄋ ᆼᄒ ᅭ ᅡᄋ ᅧ 95% ᄉ ᆫᄅ ᅵ ᅬᄀ ᅮᄀ ᆫᄋ ᅡ ᆯᄀ ᅳ ᅮᄒ ᅡᄋ ᆻᄃ ᅧ ᅡ. ᆼᄉ ᅡ ᄀ ᅮᄅ ᆼᄋ ᅣ ᆯ 5mmᄅ ᅳ ᅩᄉ ᆯᄌ ᅥ ᆼᄒ ᅥ ᅡᄋ ᅧ IATᄋ ᅪ M-IATᄅ ᅩᄌ ᅵᄋ ᆨᅧ ᅧ ᆯ ᄇ GEVᄇ ᆫᄑ ᅮ ᅩᄋ ᅴᄆ ᅩᄉ ᅮᄅ ᆯᄎ ᅳ ᅮᄌ ᆼᄒ ᅥ ᆫᅧ ᅡ ᆯ ᄀᄀ ᅪᄂ ᆫ Table 4.1ᄀ ᅳ ᅪ Table 4.2ᄋ ᅵᄃ ᅡ. ᄌ ᆨᄒ ᅥ ᆸᅥ ᅡ ᆼ ᄉᄀ ᆷᅥ ᅥ ᆼ ᄌᄀ ᆯᄀ ᅧ ᅪᄆ ᅩᄃ ᆫᄌ ᅳ ᅵᄋ ᆨᄋ ᅧ ᅦᄉ ᅥ GEV ᄇ ᆫᄑ ᅮ ᅩᄀ ᅡᄌ ᆨᄒ ᅥ ᆸᄒ ᅡ ᅡᄋ ᅧᄋ ᅵᄅ ᆯᄌ ᅳ ᆨᄋ ᅥ ᆼᄒ ᅭ ᅡᄋ ᆻᄃ ᅧ ᅡ. ᄒ ᆼᄉ ᅧ ᆼᄆ ᅡ ᅩᄉ ᅮᄋ ᅦ ᅡᄅ ᄄ ᅡᄁ ᅩᄅ ᅵᄇ ᆫᄑ ᅮ ᅩᄀ ᅡᄀ ᆯᅥ ᅧ ᆼ ᄌᄃ ᅬᄆ ᅳᄅ ᅩ ξᄋ ᅦᄃ ᅢᄒ ᅢᄀ ᅡᄉ ᆯᅥ ᅥ ᆷ ᄀᄌ ᆼᄒ ᅥ ᅡᄆ ᆫ IATᄋ ᅧ ᅦᄉ ᅥᄋ ᆫᄃ ᅡ ᆼ, ᄉ ᅩ ᅥᄀ ᅱᄑ ᅩ M-IATᄋ ᅦᄉ ᅥᄋ ᆯᄅ ᅮ ᆼᄃ ᅳ ᅩ, ᄉ ᆼᄉ ᅥ ᆫ, ᅡ ᅥᄀ ᄉ ᅱᅩ ᄑ, ᄋ ᆼᄑ ᅣ ᆼᄋ ᅧ ᅦᄉ ᅥ Weibull ᄇ ᆫᄑ ᅮ ᅩ (ξ < 0)ᄋ ᆯᄄ ᅳ ᅡᄅ ᅳᄀ ᅩᄋ ᅵᄅ ᆯᄌ ᅳ ᅦᅬ 아 ᆫ ᄒᄆ ᅩᄃ ᆫᄌ ᅳ ᅵᄋ ᆨᄋ ᅧ ᅦᄉ ᅥ Gumbel ᄇ ᆫᄑ ᅮ ᅩ (ξ = 0)ᄅ ᆯ ᅳ ᅡᄅ ᄄ ᆫᅡ ᅳ ᄃ. IATᄋ ᅪ M-IAT ᄀ ᆨᄀ ᅡ ᆨᄋ ᅡ ᅴᄇ ᆼᄇ ᅡ ᆸᄋ ᅥ ᅦᄄ ᅡᄅ ᅡ GEV ᄇ ᆫᄑ ᅮ ᅩᄋ ᅴᄆ ᅩᄉ ᅮᄀ ᆼᄒ ᅧ ᆼᄋ ᅣ ᆯᄉ ᅳ ᆯᄑ ᅡ ᅧᄇ ᅩᄆ ᆫᄋ ᅧ ᅱᄎ ᅵᄆ ᅩᄉ ᅮ (ˆ µ)ᄂ ᆫᄌ ᅳ ᆫᄎ ᅥ ᅦ ᅵᄋ ᄌ ᆨᅦ ᅧ ᄋᄉ ᅥ M-IATᄋ ᅴᄀ ᆹᄋ ᅡ ᅵ IATᄋ ᅴᄀ ᆹᄇ ᅡ ᅩᄃ ᅡᄏ ᅳᄃ ᅡ. IATᄂ ᆫᄉ ᅳ ᅵᄌ ᆨᄌ ᅡ ᆷᄋ ᅥ ᅳᄅ ᅩᄇ ᅮᄐ ᅥᄒ ᆫᄀ ᅡ ᅨᄉ ᅮᄌ ᆫᄋ ᅮ ᅦᄉ ᅥᄆ ᆪᄋ ᅩ ᅵᄇ ᆫᄒ ᅧ ᅡᄂ ᆫᄀ ᅳ ᆹᄋ ᅡ ᆯ ᅳ ᅨᄉ ᄀ ᆫᅡ ᅡ ᄒᄋ ᅧᄂ ᅡᄆ ᅥᄌ ᅵᄀ ᅡᄀ ᅨᄉ ᆨᄂ ᅩ ᆷᄋ ᅡ ᅡᄋ ᆻᄀ ᅵ ᅩ, M-IATᄂ ᆫᄒ ᅳ ᆫᄀ ᅡ ᅨᄉ ᅮᄌ ᆫᄁ ᅮ ᅡᄌ ᅵᄎ ᅡᄂ ᆫᄃ ᅳ ᅦᄀ ᆯᄅ ᅥ ᅵᄂ ᆫᄉ ᅳ ᅵᄀ ᆫᄋ ᅡ ᆯᄀ ᅳ ᅨᄉ ᆫᄒ ᅡ ᅡᄋ ᅧᄂ ᅡᄆ ᅥᄌ ᅵᄀ ᅡ ᆹᄃ ᅥ ᄋ ᅡ. ᄄ ᅡᄅ ᅡᄉ ᅥᄀ ᅮᄀ ᆫᄇ ᅡ ᆯᄎ ᅧ ᅬᄃ ᆺᄀ ᅢ ᆹᄋ ᅡ ᆫ M-IATᄋ ᅳ ᅦᄉ ᅥᄃ ᅥᄏ ᅳᄀ ᅦᄆ ᆫᄃ ᅡ ᆯᄋ ᅳ ᅥᄌ ᅵᄆ ᅧᄀ ᅳᄀ ᆯᄀ ᅧ ᅪ IATᄋ ᅦᄉ ᅥᅪ ᄀᄉ ᅩᄎ ᅮᄌ ᆼᄃ ᅥ ᆫᄃ ᅬ ᅡ.. Station Seoul Incheon Daejeon Daegu Ulsan Gwangju Busan Suwon Wonju Cheongju Cheonan Pohang Andong Jeonju Yeosu Taebaek Ganghwa . . . Jeju. Table 4.1 The result of parameter estimation to GEV distribution IAT at 5mm µ ˆ σ ˆ ξˆ Est. 723.7 808.7 619.3 836.7 744.0 595.0 724.7 708.7 684.4 641.0 680.2 761.0 890.2 574.9 810.3 803.5 869.8 . . . 571.1. 95% CI (640.4, 831.8) (706.9, 918.0) (558.3, 694.5) (737.7, 956.9) (655.0, 841.9) (546.9, 655.6) (650.8, 807.9) (634.5, 786.0) (597.2, 779.8) (560.6, 721.9) (629.6, 748.8) (675.2, 860.3) (800.2, 992.0) (504.3, 656.8) (721.4, 913.4) (731.4, 873.8) (770.1, 977.1) . . . (524.5, 626.6). Est. 248.5 279.6 162.3 285.3 247.6 135.4 203.6 189.5 227.8 205.4 147.6 246.3 247.7 186.5 242.8 178.6 258.0 . . . 131.1. 95% CI (170.0, 320.3) (196.9, 368.6) (107.3, 224.5) (195.9, 374.4) (175.9, 320.0) (95.9, 176.7) (138.1, 273.9) (129.2, 249.1) (165.2, 297.1) (141.9, 276.6) (105.5, 199.3) (170.7, 327.5) (177.1, 319.3) (132.5, 244.3) (172.8, 327.7) (131.5, 234.1) (184.8, 336.3) . . . (90.2, 170.7). Est. -0.030 -0.127 0.178 -0.029 -0.048 0.035 0.093 -0.009 -0.166 0.080 0.172 0.090 -0.243 -0.120 0.053 -0.251 -0.183 . . . -0.032. 95% CI (-0.309, 0.213) (-0.443, 0.117) (-0.165, 0.476) (-0.299, 0.212) (-0.345, 0.193) (-0.265, 0.289) (-0.248, 0.350) (-0.293, 0.242) (-0.448, 0.068) (-0.230, 0.344) (-0.189, 0.439) (-0.272, 0.344) (-0.535, -0.015)* (-0.409, 0.113) (-0.238, 0.291) (-0.496, 0.016) (-0.475, 0.081) . . . (-0.343, 0.220). Ω. A2. 0.017 0.032 0.024 0.029 0.043 0.023 0.041 0.039 0.017 0.041 0.039 0.020 0.054 0.041 0.018 0.035 0.048 . . . 0.044. 0.129 0.217 0.156 0.182 0.285 0.205 0.277 0.234 0.140 0.238 0.319 0.204 0.340 0.276 0.150 0.243 0.266 . . . 0.300.
(7) Analysis of the generalized extreme value of drought on the Korean Peninsula using inter-amount times1029. Station Seoul Incheon Daejeon Daegu Ulsan Gwangju Busan Suwon Wonju Cheongju Cheonan Pohang Andong Jeonju Yeosu Taebaek Ganghwa . . . Jeju. Table 4.2 The result of parameter estimation to GEV distribution M-IAT at 5mm µ ˆ σ ˆ ξˆ Est. 873.7 980.1 780.8 1027.0 917.1 713.5 920.9 852.4 823.8 804.8 808.8 932.1 1028.3 705.0 985.6 910.4 869.8 . . . 664.5. 95% CI (780.7, 967.1) (889.2, 1086.4) (704.4, 863.3) (932.6, 1141.3) (828.2, 1013.5) (651.6, 781.4) (818.1, 1039.1) (783.5, 930.2) (725.3, 924.5) (712.3, 898.7) (739.8, 885.5) (850.3, 1034.8) (946.8, 1131.5) (631.6, 785.1) (893.5, 1080.0) (816.1, 1010.6) (770.1, 977.1) . . . (611.6, 722.9). Est. 260.2 255.9 192.0 277.3 247.7 179.1 286.6 185.2 258.0 233.5 196.9 231.5 234.0 207.2 241.6 234.6 258.0 . . . 142.6. 95% CI (187.9, 329.2) (185.4, 331.9) (134.8, 253.6) (204.1, 366.6) (169.9, 343.8) (129.7, 230.6) (199.0, 383.3) (129.9, 249.4) (183.7, 326.6) (162.3, 310.5) (140.3, 254.6) (158.9, 321.9) (160.0, 318.2) (141.7, 271.0) (167.0, 310.1) (166.8, 300.9) (184.8, 336.3) . . . (105.0, 185.7). Est. -0.124 -0.062 0.005 -0.034 0.136 -0.194 0.002 0.091 -0.186 -0.082 -0.008 0.142 0.048 -0.037 -0.099 -0.141 -0.183 . . . -0.090. 95% (-0.393, (-0.341, (-0.285, (-0.318, (-0.194, (-0.481, (-0.322, (-0.230, (-0.457, (-0.381, (-0.294, (-0.189, (-0.258, (-0.334, (-0.407, (-0.465, (-0.475, . . . (-0.373,. CI 0.123) 0.191) 0.259) 0.207) 0.403) 0.054) 0.256) 0.333) 0.058) 0.149) 0.245) 0.380) 0.294) 0.222) 0.140) 0.105) 0.081). 0.122). Ω. A2. 0.041 0.035 0.038 0.029 0.022 0.035 0.034 0.047 0.037 0.043 0.026 0.020 0.025 0.042 0.027 0.048 0.048 . . . 0.039. 0.253 0.227 0.200 0.187 0.145 0.252 0.219 0.379 0.230 0.281 0.201 0.150 0.221 0.321 0.216 0.309 0.266 . . . 0.266. ᄀᄉ ᆼ ᅡ ᅮᄅ ᆼᄋ ᅣ ᆯ 10mmᄅ ᅳ ᅩ ᄉ ᆯᅥ ᅥ ᆼ ᄌᄒ ᅡᄋ ᅧ IATᄋ ᅪ M-IATᄅ ᅩ ᄌ ᅵᄋ ᆨᅧ ᅧ ᆯ ᄇ GEV ᄇ ᆫᄑ ᅮ ᅩᄋ ᅴ ᄆ ᅩᄉ ᅮᄅ ᆯ ᄎ ᅳ ᅮᄌ ᆼᄒ ᅥ ᆫ ᄀ ᅡ ᆯᄀ ᅧ ᅪᄂ ᆫ Table ᅳ 4.3ᄀ ᅪ Table 4.4ᄋ ᅵᄃ ᅡ. ᄌ ᆨᄒ ᅥ ᆸᅥ ᅡ ᆼ 서 ᆷ 거 ᆼ 져 ᆯ ᄀᄀ ᅪᄆ ᅩᄃ ᆫᄌ ᅳ ᅵᄋ ᆨᄋ ᅧ ᅦᄉ ᅥ GEV ᄇ ᆫᄑ ᅮ ᅩᄀ ᅡᄌ ᆨᄒ ᅥ ᆸᄒ ᅡ ᅡᄋ ᅧᄋ ᅵᄅ ᆯᄌ ᅳ ᆨᄋ ᅥ ᆼᄒ ᅭ ᅡᄋ ᆻᄃ ᅧ ᅡ. IATᄋ ᅦ ᅥᄌ ᄉ ᅦᅮ ᄌ, ᄀ ᆼᄒ ᅡ ᅪ, ᄀ ᅥᄎ ᆼ, M-IATᄋ ᅡ ᅦᄉ ᅥᄐ ᅢᄇ ᆨᄋ ᅢ ᅦᄉ ᅥ Weibull ᄇ ᆫᄑ ᅮ ᅩ (ξ < 0)ᄋ ᆯᄄ ᅳ ᅡᄅ ᅳᄀ ᅩᄋ ᅵᄅ ᆯᄌ ᅳ ᅦᅬ ᄋᄒ ᆫᄆ ᅡ ᅩᄃ ᆫᄌ ᅳ ᅵᄋ ᆨᄋ ᅧ ᅦᄉ ᅥ Gumbel ᄇ ᆫᄑ ᅮ ᅩ (ξ = 0)ᄅ ᆯᄄ ᅳ ᅡᄅ ᆫᄃ ᅳ ᅡ. GEV ᄇ ᆫᄑ ᅮ ᅩᄋ ᅴᄆ ᅩᄉ ᅮᄀ ᆼᄒ ᅧ ᆼᄋ ᅣ ᆯᄉ ᅳ ᆯᄑ ᅡ ᅧᄇ ᅩᄆ ᆫᄒ ᅧ ᆫᄀ ᅡ ᅨᄉ ᅮᄌ ᆫ 5mmᄋ ᅮ ᆯᄄ ᅵ ᅢᄋ ᅪᄋ ᅲᄉ ᅡᄒ ᅡᄀ ᅦ ᅱᄎ ᄋ ᅵᄆ ᅩᄉ ᅮ (ˆ µ)ᄂ ᆫᄌ ᅳ ᆫᄎ ᅥ ᅦᄌ ᅵᄋ ᆨᄋ ᅧ ᅦᄉ ᅥ M-IATᄋ ᅴᄀ ᆹᄋ ᅡ ᅵ IATᄋ ᅴᄀ ᆹᄇ ᅡ ᅩᄃ ᅡᄏ ᅳᄃ ᅡ.. Station Seoul Incheon Daejeon Daegu Ulsan Gwangju Busan Suwon Wonju Cheongju Cheonan Pohang Andong Jeonju Yeosu Taebaek Ganghwa . . . Jeju. Table 4.3 The result of parameter estimation to GEV distribution IAT at 10mm µ ˆ σ ˆ ξˆ Est. 867.5 932.9 831.9 995.8 901.1 736.3 865.7 883.5 798.9 786.7 854.0 858.5 1099.1 713.4 922.8 913.5 869.8 . . . 685.4. 95% CI (781.6, 979.1) (820.0, 1049.9) (746.2, 934.7) (863.7, 1128.7) (799.1, 1001.0) (668.9, 813.5) (759.9, 985.0) (790.2, 986.9) (687.8, 902.2) (705.4, 885.3) (789.3, 931.7) (789.2, 957.3) (962.5, 1253.8) (628.8, 802.0) (822.8, 1019.8) (836.7, 1011.5) (770.1, 977.1) . . . (626.7, 752.0). Est. 255.1 295.1 250.5 310.4 271.3 184.5 271.8 239.3 269.4 230.6 190.0 202.2 358.8 223.7 253.0 226.6 258.0 . . . 158.4. 95% CI (182.0, 344.5) (213.4, 388.2) (172.9, 322.1) (215.7, 405.3) (195.1, 351.0) (127.0, 242.0) (193.1, 366.1) (165.6, 309.1) (194.1, 340.3) (159.1, 302.7) (133.7, 245.6) (141.6, 294.1) (261.9, 465.4) (158.7, 292.7) (177.5, 341.9) (155.8, 299.6) (184.8, 336.3) . . . (114.4, 206.2). Est. 0.066 -0.049 -0.169 0.032 -0.143 -0.197 0.111 -0.037 -0.197 0.057 -0.075 0.198 -0.182 -0.192 -0.064 -0.007 -0.183 . . . -0.325. 95% CI (-0.284, 0.307) (-0.331, 0.194) (-0.446, 0.084) (-0.300, 0.281) (-0.431, 0.117) (-0.493, 0.028) (-0.267, 0.361) (-0.331, 0.201) (-0.469, 0.041) (-0.259, 0.318) (-0.385, 0.150) (-0.121, 0.462) (-0.495, 0.061) (-0.462, 0.031) (-0.354, 0.193) (-0.316, 0.251) (-0.475, 0.081) . . . (-0.609, -0.072)*. Ω. A2. 0.056 0.028 0.017 0.042 0.035 0.078 0.033 0.056 0.019 0.028 0.024 0.028 0.047 0.029 0.029 0.026 0.048 . . . 0.070. 0.367 0.177 0.147 0.318 0.215 0.493 0.241 0.341 0.135 0.201 0.210 0.189 0.320 0.195 0.204 0.209 0.266 . . . 0.477.
(8) 1030. Station Seoul Incheon Daejeon Daegu Ulsan Gwangju Busan Suwon Wonju Cheongju Cheonan Pohang Andong Jeonju Yeosu Taebaek Ganghwa . . . Jeju. Jihoon Lee · Taeyong Kwon · Sanghoo Yoon. Table 4.4 The result of parameter estimation to GEV distribution M-IAT at 10mm µ ˆ σ ˆ ξˆ Est. 1081.3 1212.8 981.1 1215.5 1027.9 849.9 1066.8 1033.1 1000.8 993.3 1006.3 1037.7 1271.2 903.7 1147.0 1101.4 869.8 . . . 779.7. 95% CI (978.1, 1201.6) (1112.1, 1313.7) (895.2, 1067.6) (1073.7, 1381.4) (966.8, 1115.6) (776.7, 923.9) (953.4, 1199.3) (947.8, 1146.5) (902.8, 1104.8) (914.6, 1086.7) (943.9, 1081.2) (958.8, 1135.4) (1147.4, 1418.3) (808.1, 998.3) (1034.3, 1280.8) (999.8, 1210.4) (770.1, 977.1) . . . (727.2, 838.4). Est. 270.7 259.0 218.5 372.9 201.5 194.9 295.5 233.7 254.4 225.9 178.7 218.9 364.4 257.2 308.6 285.9 258.0 . . . 152.3. 95% CI (190.1, 359.7) (187.9, 334.4) (155.7, 291.8) (260.5, 483.5) (136.3, 277.4) (140.8, 260.0) (210.6, 390.4) (160.5, 318.2) (183.8, 343.0) (161.4, 295.3) (124.4, 248.4) (145.3, 321.4) (264.1, 461.8) (184.9, 323.2) (215.4, 404.1) (209.9, 364.3) (184.8, 336.3) . . . (109.1, 197.4). Est. 0.051 -0.201 0.013 0.047 0.194 -0.098 -0.014 0.067 -0.023 0.062 0.215 0.213 -0.127 -0.089 -0.168 -0.250 -0.183 . . . -0.245. 95% CI (-0.241, 0.298) (-0.484, 0.017) (-0.318, 0.250) (-0.259, 0.320) (-0.114, 0.443) (-0.379, 0.155) (-0.309, 0.209) (-0.248, 0.325) (-0.355, 0.205) (-0.216, 0.324) (-0.120, 0.475) (-0.151, 0.490) (-0.417, 0.099) (-0.361, 0.142) (-0.466, 0.099) (-0.538, -0.013)* (-0.475, 0.081) . . . (-0.555, 0.020). Ω. A2. 0.049 0.066 0.050 0.089 0.062 0.033 0.012 0.057 0.066 0.029 0.034 0.054 0.068 0.026 0.040 0.037 0.048 . . . 0.086. 0.311 0.363 0.346 0.547 0.380 0.267 0.099 0.366 0.329 0.237 0.237 0.322 0.412 0.188 0.252 0.216 0.266 . . . 0.510. 4.2. 재현수준에 따른 한반도 등고선 그림 ᆼᄉ ᅡ ᄀ ᅮᄅ ᆼᄀ ᅣ ᅪ IATᄋ ᅪ M-IATᄋ ᅦᄄ ᅡᄅ ᅡᄎ ᅮᄌ ᆼᄃ ᅥ ᆫᄆ ᅬ ᅩᄉ ᅮᄃ ᆯᅳ ᅳ ᆯ ᄋᄉ ᅡᄋ ᆼᄒ ᅭ ᅡᄋ ᅧᄌ ᅢᄒ ᆫᄀ ᅧ ᅵᄀ ᆫ 25ᄂ ᅡ ᆫ, 50ᄂ ᅧ ᆫ, 100ᄂ ᅧ ᆫᄋ ᅧ ᅴᄌ ᅢᄒ ᆫᄉ ᅧ ᅮᄌ ᆫ ᅮ ᄋᄀ ᆯ ᅳ ᅨᄉ ᆫᄒ ᅡ ᅡᄆ ᆫᄒ ᅧ ᆫᄇ ᅡ ᆫᄃ ᅡ ᅩᄆ ᅮᄀ ᆼᄉ ᅡ ᅮᄋ ᅱᄒ ᆷᄉ ᅥ ᆼᄋ ᅥ ᆯᄋ ᅳ ᆯᄉ ᅡ ᅮᄋ ᆻᄃ ᅵ ᅡ. ᄌ ᅢᄒ ᆫᄉ ᅧ ᅮᄌ ᆫᄋ ᅮ ᆫᄉ ᅳ ᆨ (2.8)ᄋ ᅵ ᆯᄋ ᅳ ᅵᄋ ᆼᄒ ᅭ ᅡᄋ ᆻᄃ ᅧ ᅡ. Gumbel ᄇ ᆫᄑ ᅮ ᅩᄅ ᆯ ᅳ ᅡᄅ ᄄ ᅳᄂ ᆫᄌ ᅳ ᅵᄋ ᅧᄇ ᅮᄋ ᅦᄄ ᅡᄅ ᅡᄌ ᅢᄒ ᆫᄉ ᅧ ᅮᄌ ᆫᄀ ᅮ ᆹᄋ ᅡ ᅦᄎ ᅡᄋ ᅵᄀ ᅡᄇ ᆯᄉ ᅡ ᆼᄒ ᅢ ᅡᄋ ᅧ ξ ̸= 0ᄋ ᆯᄀ ᅳ ᅵᄇ ᆫᄋ ᅡ ᅳᄅ ᅩᄌ ᅢᄒ ᆫᄉ ᅧ ᅮᄌ ᆫᅳ ᅮ ᆯ ᄋᄀ ᅨᄉ ᆫᄒ ᅡ ᅡᄋ ᅧᄉ ᅵᄀ ᆨᄒ ᅡ ᅪᄒ ᅡ ᆫ Figure 4.1ᄀ ᅧ ᄆ ᅪ Figure 4.2ᄋ ᅵᄃ ᅡ. ᆼᄉ ᅡ ᄀ ᅮᄅ ᆼᄋ ᅣ ᆯ 5mm ᄀ ᅳ ᅵᄌ ᆫᄋ ᅮ ᅳᄅ ᅩᄒ ᆫᄇ ᅡ ᆫᄃ ᅡ ᅩᄆ ᅮᄀ ᆼᄉ ᅡ ᅮᄌ ᅢᄒ ᆫᄉ ᅧ ᅮᄌ ᆫᅳ ᅮ ᆯ ᄋᄀ ᅮᄒ ᅡᄆ ᆫ IATᄋ ᅧ ᅦᄉ ᅥᄂ ᆫᄃ ᅳ ᆼᄒ ᅩ ᅢᄋ ᆫᄂ ᅡ ᅢᄅ ᆨᄌ ᅲ ᅵᄋ ᆨᄋ ᅧ ᆫᄀ ᅵ ᆼᄋ ᅡ ᆫᄃ ᅯ ᅩᄒ ᆼ ᅩ ᆫ, ᄀ ᅥ ᄎ ᆼᄉ ᅧ ᆼᄇ ᅡ ᆨᄃ ᅮ ᅩᄋ ᅴᄉ ᆼ, ᄃ ᅥ ᅢᄀ ᅮᄀ ᆼᄋ ᅪ ᆨᄉ ᅧ ᅵ, ᄀ ᆼᄉ ᅧ ᆼᄂ ᅡ ᆷᄃ ᅡ ᅩᄒ ᆸᅥ ᅡ ᆫ ᄎᄋ ᆯᄃ ᅵ ᅢᄋ ᅦᄉ ᅥᄌ ᅢᄒ ᆫᄉ ᅧ ᅮᄌ ᆫᄋ ᅮ ᅵᄂ ᇁᄋ ᅩ ᆻᄃ ᅡ ᅡ. M-IATᄋ ᅦᄉ ᅥᄂ ᆫᄀ ᅳ ᆼᄉ ᅧ ᆼᄇ ᅡ ᆨᄃ ᅮ ᅩ ᆼᅥ ᅧ ᄋ ᆨ ᄃ, ᅴ ᄋᄉ ᆼ, ᄋ ᅥ ᆼᄎ ᅧ ᆫ, ᄀ ᅥ ᆼᄉ ᅧ ᆼᄂ ᅡ ᆷᄃ ᅡ ᅩᄒ ᆸᅥ ᅡ ᆫ ᄎᄋ ᆯᄃ ᅵ ᅢᄋ ᅦᄉ ᅥᄌ ᅢᄒ ᆫᄉ ᅧ ᅮᄌ ᆫᄋ ᅮ ᅵᄂ ᇁᄋ ᅩ ᆻᄃ ᅡ ᅡ. ᄃ ᅢᄇ ᅮᄇ ᆫᄌ ᅮ ᅵᄋ ᆨᄋ ᅧ ᅦᄉ ᅥ M-IATᄅ ᆯᄋ ᅳ ᅵᄋ ᆼᄒ ᅭ ᆫᄌ ᅡ ᅢᄒ ᆫ ᅧ ᅮᄌ ᄉ ᆫᅵ ᅮ ᄋᄃ ᅥᄏ ᅳᄀ ᅦᄂ ᅡᄐ ᅡᄂ ᆻᄃ ᅡ ᅡ. ᄋ ᅵᄂ ᆫ M-IATᄋ ᅳ ᅴᄋ ᅱᄎ ᅵᄆ ᅩᄉ ᅮ (ˆ µ)ᄀ ᅡ IATᄇ ᅩᄃ ᅡᄏ ᅳᄀ ᅵᄄ ᅢᄆ ᆫᄋ ᅮ ᅵᄃ ᅡ. ᄀ ᆫᄉ ᅪ ᆷᄌ ᅵ ᅵᄋ ᆨᄋ ᅧ ᆫᄀ ᅳ ᆼᄉ ᅧ ᆼᄇ ᅡ ᆨ ᅮ ᅩᄋ ᄃ ᅴᄉ ᆼ, ᄋ ᅥ ᆼᅥ ᅧ ᆨ ᄃ, ᄋ ᆼᅥ ᅧ ᆫ ᄎ, ᄋ ᆯᄉ ᅮ ᆫᄀ ᅡ ᆼᄋ ᅪ ᆨᄉ ᅧ ᅵᄌ ᅵᄋ ᆨᄋ ᅧ ᅵ ᆯᄃ ᅢᄅ ᅩᄀ ᆼᄉ ᅡ ᅮᄇ ᆫᄃ ᅵ ᅩᄂ ᆫᄂ ᅳ ᇁᄌ ᅩ ᅵᄆ ᆫᄋ ᅡ ᆫᄑ ᅧ ᆼᄀ ᅧ ᆫᄀ ᅲ ᆼᄉ ᅡ ᅮᄅ ᆼᄀ ᅣ ᅪᄋ ᅧᄅ ᆷᄎ ᅳ ᆯᄀ ᅥ ᆼᄉ ᅡ ᅮᄅ ᆼᄋ ᅣ ᅵᄂ ᆽ ᅡ ᆫᄌ ᅳ ᄋ ᅵᅧ ᆨ ᄋᄋ ᅵᄃ ᅡ. ᄋ ᅵᄌ ᅵᄋ ᆨᄋ ᅧ ᆫᄂ ᅳ ᅮᄌ ᆨᄀ ᅥ ᆼᄉ ᅡ ᅮᄅ ᆼᄋ ᅣ ᅵᄌ ᆨᅥ ᅥ ᆼ ᄌᄀ ᅵᄌ ᆫᄋ ᅮ ᅦᄆ ᆺᄆ ᅩ ᅵᄎ ᅵᄂ ᆫᄆ ᅳ ᅮᄀ ᆼᄉ ᅡ ᅮᄋ ᆯᄉ ᅵ ᅮᄀ ᅡᄀ ᆯᄋ ᅵ ᅥᄌ ᆯᄀ ᅵ ᅡᄂ ᆼᄉ ᅳ ᆼᄋ ᅥ ᅵᄆ ᆭᄃ ᅡ ᅡ. ᆼᄉ ᅡ ᄀ ᅮᄅ ᆼᄋ ᅣ ᆯ 10mm ᄀ ᅳ ᅵᄌ ᆫᄋ ᅮ ᅳᄅ ᅩᄒ ᆫᄇ ᅡ ᆫᄃ ᅡ ᅩᄆ ᅮᄀ ᆼᄉ ᅡ ᅮᄌ ᅢᄒ ᆫᄉ ᅧ ᅮᄌ ᆫᅳ ᅮ ᆯ ᄋᄀ ᅮᄒ ᅡᄆ ᆫ IATᄋ ᅧ ᅦᄉ ᅥᄂ ᆫᄀ ᅳ ᆼᄋ ᅡ ᆫᄃ ᅯ ᅩᄒ ᆼᄎ ᅩ ᆫ, ᄀ ᅥ ᆼᄉ ᅧ ᆼᄇ ᅡ ᆨᄃ ᅮ ᅩᄋ ᅴᄉ ᆼ, ᅥ ᆼᅥ ᅧ ᄋ ᆫ ᄎ, ᄃ ᅢᄀ ᅮᄀ ᆼᄋ ᅪ ᆨᄉ ᅧ ᅵᅪ ᄋᄀ ᆼᄉ ᅧ ᆼᄂ ᅡ ᆷᄃ ᅡ ᅩᄒ ᆸᅥ ᅡ ᆫ ᄎ, ᄉ ᆫᄎ ᅡ ᆼᄋ ᅥ ᆯᄃ ᅵ ᅢᄋ ᅦᄉ ᅥᄌ ᅢᄒ ᆫᄉ ᅧ ᅮᄌ ᆫᄋ ᅮ ᅵᄂ ᇁᄋ ᅩ ᆻᄃ ᅡ ᅡ. M-IATᄋ ᅦᄉ ᅥᄂ ᆫᄀ ᅳ ᆼᄋ ᅡ ᆫᄃ ᅯ ᅩᄒ ᆼᄎ ᅩ ᆫ, ᄀ ᅥ ᆼ ᅧ ᆼᄇ ᅡ ᄉ ᆨᅩ ᅮ ᄃᄋ ᅴᄉ ᆼ, ᄋ ᅥ ᆼᅥ ᅧ ᆨ ᄃ, ᄃ ᅢᄀ ᅮᄀ ᆼᄋ ᅪ ᆨᄉ ᅧ ᅵᅪ ᄋᄀ ᆼᄉ ᅧ ᆼᄂ ᅡ ᆷᄃ ᅡ ᅩᄒ ᆸᅥ ᅡ ᆫ ᄎᄋ ᆯᄃ ᅵ ᅢᄋ ᅦᄉ ᅥᄌ ᅢᄒ ᆫᄉ ᅧ ᅮᄌ ᆫᄋ ᅮ ᅵᄂ ᇁᄋ ᅩ ᆻᄃ ᅡ ᅡ. IATᄋ ᅪ M-IATᄋ ᅴᄎ ᅡᄋ ᅵᄂ ᆫ ᅳ ᆫᄀ ᅡ ᄒ ᅨᅮ ᄉᄌ ᆫᅳ ᅮ ᆯ ᄋ 5mmᄋ ᆯᄄ ᅵ ᅢᄋ ᅪᄀ ᇀᄋ ᅡ ᅵᄃ ᅢᄇ ᅮᄇ ᆫᄋ ᅮ ᅴᄌ ᅵᄋ ᆨᄋ ᅧ ᅦᄉ ᅥ M-IATᄅ ᆯᄋ ᅳ ᅵᄋ ᆼᄒ ᅭ ᆫᄌ ᅡ ᅢᄒ ᆫᄉ ᅧ ᅮᄌ ᆫᄋ ᅮ ᅵᄂ ᇁᄋ ᅩ ᆻᄃ ᅡ ᅡ. ᄀ ᆼᄀ ᅧ ᅵᄃ ᅩᄀ ᆼᄒ ᅡ ᅪ, ᆼᄋ ᅡ ᄀ ᆫᅩ ᅯ ᄃᄀ ᆼᄅ ᅡ ᆼ, ᄀ ᅳ ᆼᄉ ᅧ ᆼᄇ ᅡ ᆨᄃ ᅮ ᅩᄋ ᆼᅥ ᅧ ᆫ ᄎ, ᄃ ᅢᄀ ᅮᄀ ᆼᄋ ᅪ ᆨᄉ ᅧ ᅵ, ᄀ ᆼᄉ ᅧ ᆼᄂ ᅡ ᆷᄃ ᅡ ᅩᄉ ᆫᄎ ᅡ ᆼᄌ ᅥ ᅵᄋ ᆨᄋ ᅧ ᆯᄃ ᅵ ᅢᄋ ᅦᄉ ᅥᄌ ᅢᄒ ᆫᄉ ᅧ ᅮᄌ ᆫᄋ ᅮ ᅴᄎ ᅡᄋ ᅵᄀ ᅡᄏ ᅳᄀ ᅦᄂ ᅡᄐ ᅡᄂ ᆻ ᅡ ᅡ. ᅵ ᄃ ᄋᄌ ᅵᄋ ᆨᄋ ᅧ ᆫᄋ ᅳ ᅧᄅ ᆷᄀ ᅳ ᆼᄉ ᅡ ᅮᄅ ᆼᄋ ᅣ ᆫᄆ ᅳ ᆭᄌ ᅡ ᅵᄆ ᆫᄋ ᅡ ᆫᅧ ᅧ ᆼ ᄑᄀ ᆫᄀ ᅲ ᆼᄉ ᅡ ᅮᄅ ᆼᄀ ᅣ ᅪᄀ ᅧᄋ ᆯᄀ ᅮ ᆼᄉ ᅡ ᅮᄅ ᆼᄋ ᅣ ᅵᄉ ᆼᄃ ᅡ ᅢᄌ ᆨᄋ ᅥ ᅳᄅ ᅩᄌ ᆨᄋ ᅥ ᆫᄌ ᅳ ᅵᄋ ᆨᄋ ᅧ ᅵᄃ ᅡ. ᄀ ᆼᄉ ᅡ ᅮ ᆼᄋ ᅣ ᄅ ᆫᄌ ᅳ ᆨᄌ ᅥ ᅵᄆ ᆫᅡ ᅡ ᆼ ᄀᄉ ᅮᄇ ᆫᄃ ᅵ ᅩᄀ ᅡᄌ ᆽᄋ ᅡ ᆫᄌ ᅳ ᅵᄋ ᆨᄀ ᅧ ᅪᄀ ᅧᄋ ᆯᄎ ᅮ ᆯᄀ ᅥ ᆼᄉ ᅡ ᅮᄅ ᆼᄋ ᅣ ᅵᄌ ᆨᄋ ᅥ ᆫᄌ ᅳ ᅵᄋ ᆨᄋ ᅧ ᅦᄉ ᅥ IATᄋ ᅪ M-IATᄀ ᅡᄎ ᅡᄋ ᅵᄀ ᅡᄌ ᅮᄅ ᅩᄇ ᆯ ᅡ ᆼᄒ ᅢ ᄉ ᅡᄀ ᅩᄋ ᆻᄃ ᅵ ᅡ..
(9) Analysis of the generalized extreme value of drought on the Korean Peninsula using inter-amount times1031. 25year. 50year. 100year. IAT. M-IAT. M-IAT - IAT. Figure 4.1 The contour plots at 5mm threshold (return level : 25, 50, 100 year).
(10) 1032. Jihoon Lee · Taeyong Kwon · Sanghoo Yoon. 25year. 50year. 100year. IAT. M-IAT. M-IAT - IAT. Figure 4.2 The contour plots at 10mm threshold (return level : 25, 50, 100 year). 5. 결론 ᅮᄀ ᄆ ᆼᄉ ᅡ ᅮᄋ ᆯᄉ ᅵ ᅮᄀ ᅡᄌ ᅵᄉ ᆨᄃ ᅩ ᆯᄋ ᅬ ᅱᄒ ᆷᄉ ᅥ ᆼᄋ ᅥ ᆯᄌ ᅳ ᆼᄅ ᅥ ᆼᄌ ᅣ ᆨᄋ ᅥ ᅳᄅ ᅩᄑ ᆼᄀ ᅧ ᅡᄒ ᅡᄀ ᅵᄋ ᅱᄒ ᅢᄂ ᅮᄌ ᆨᄀ ᅥ ᆼᄉ ᅡ ᅮᄅ ᆼᄋ ᅣ ᅵᄒ ᆫᄀ ᅡ ᅨᄉ ᅮᄌ ᆫᄁ ᅮ ᅡᄌ ᅵᄃ ᅩᄃ ᆯᄒ ᅡ ᅡᄂ ᆫᄃ ᅳ ᅦ ᄀᄅ ᆯ ᅥ ᅵᄂ ᆫᄉ ᅳ ᅵᄀ ᆫᄋ ᅡ ᆯᄀ ᅳ ᅨᄉ ᆫᄒ ᅡ ᅡᄂ ᆫᄇ ᅳ ᆼᄇ ᅡ ᆸᄋ ᅥ ᆫ IATᄀ ᅵ ᅡᄌ ᅦᄉ ᅵᅬ ᄃᄋ ᆻᄃ ᅥ ᅡ. ᄇ ᆫᄋ ᅩ ᆫᄀ ᅧ ᅮᄋ ᅦᄉ ᅥᄂ ᆫᄉ ᅳ ᅵᄌ ᆨᄉ ᅡ ᅵᄌ ᆷᄋ ᅥ ᅦᄄ ᅡᄅ ᅡᄃ ᅡᄅ ᅳᄀ ᅦᄀ ᅨᄉ ᆫᄃ ᅡ ᅬᄂ ᆫ ᅳ IATᄋ ᅴᄆ ᆫᄌ ᅮ ᅦᄌ ᆷᄋ ᅥ ᆯᄇ ᅳ ᅩᄋ ᆫᄒ ᅪ ᆫ M-IATᄅ ᅡ ᆯᄌ ᅳ ᅦᄋ ᆫᄒ ᅡ ᅡᄋ ᆻᄃ ᅧ ᅡ. ᄋ ᆫᄀ ᅧ ᅮᄅ ᆯᄋ ᅳ ᅱᄒ ᅢ 1991ᄂ ᆫᄇ ᅧ ᅮᄐ ᅥ 2020ᄂ ᆫᄁ ᅧ ᅡᄌ ᅵ 30ᄂ ᆫᄀ ᅧ ᆫᄎ ᅡ ᆼ 67ᄀ ᅩ ᅢᄋ ᅴ ASOSᄌ ᅡᄅ ᅭᄅ ᆯᄋ ᅳ ᅵᄋ ᆼᄒ ᅭ ᅡᄋ ᆻᄃ ᅧ ᅡ..
(11) Analysis of the generalized extreme value of drought on the Korean Peninsula using inter-amount times1033. ᄇᄉ ᆫ ᅮ ᆨᅧ ᅥ ᆯ ᄀᄀ ᅪᅡ ᆼ ᄀᄋ ᆫᄃ ᅯ ᅩᄒ ᆼᄎ ᅩ ᆫᄋ ᅥ ᆯᄃ ᅵ ᅢᅪ ᄋᄀ ᆼᄉ ᅧ ᆼᄃ ᅡ ᅩᄌ ᅵᄋ ᆨᄋ ᅧ ᆫᄃ ᅳ ᅡᄅ ᆫᄌ ᅳ ᅵᄋ ᆨᄋ ᅧ ᅦᄇ ᅵᄒ ᅢᄀ ᅡᄆ ᆷᄋ ᅮ ᅴᄋ ᅱᄒ ᆷᄉ ᅥ ᆼᄋ ᅥ ᅵᄉ ᆼᄃ ᅡ ᅢᄌ ᆨᄋ ᅥ ᅳᄅ ᅩᄂ ᇁᄋ ᅩ ᆻᄃ ᅡ ᆫ ᅥ ᆫᄆ ᅡ ᄇ ᆫᅦ ᅧ ᄋᄎ ᆼᄎ ᅮ ᆼᄂ ᅥ ᆷᄃ ᅡ ᅩᄋ ᆯᄇ ᅵ ᅮᄌ ᅵᄋ ᆨᄀ ᅧ ᅪᄌ ᅦᄌ ᅮᄃ ᅩ, ᄌ ᆫᄅ ᅥ ᅡᄃ ᅩᄌ ᅵᄋ ᆨᄋ ᅧ ᆫᄀ ᅳ ᅡᄆ ᆷᄋ ᅮ ᅴᄋ ᅱᄒ ᆷᅥ ᅥ ᆼ ᄉᄋ ᅵᄉ ᆼᄃ ᅡ ᅢᄌ ᆨᄋ ᅥ ᅳᄅ ᅩᄂ ᆽᄋ ᅡ ᆻᄃ ᅡ ᅡ. ᄄ ᅩᄒ ᆫ IATᄇ ᅡ ᅩ ᅡ M-IATᄋ ᄃ ᅦᄉ ᅥᄆ ᅮᄀ ᆼᄉ ᅡ ᅮᄋ ᆯᄉ ᅵ ᅮᄋ ᅴᄋ ᅱᄒ ᆷᅥ ᅥ ᆼ ᄉᄋ ᅵᄃ ᅥᄏ ᅳᄀ ᅦᄂ ᅡᄐ ᅡᄂ ᆻᄂ ᅡ ᆫᄃ ᅳ ᅦ, ᄌ ᅮᄅ ᅩᄀ ᆼᄉ ᅡ ᅮᄅ ᆼᄋ ᅣ ᆫᄌ ᅳ ᆨᄌ ᅥ ᅵᄆ ᆫᅡ ᅡ ᆼ ᄀᄉ ᅮᄇ ᆫᄃ ᅵ ᅩᄀ ᅡᄌ ᆽᄋ ᅡ ᆫᄌ ᅳ ᅵ ᆨᄀ ᅧ ᄋ ᅪᅧ ᄀᄋ ᆯᄎ ᅮ ᆯᄀ ᅥ ᆼᄉ ᅡ ᅮᄅ ᆼᄋ ᅣ ᅵᄌ ᆨᄋ ᅥ ᆫᄌ ᅳ ᅵᄋ ᆨᄋ ᅧ ᅦᄉ ᅥ IATᄇ ᅩᄃ ᅡ M-IATᄋ ᅦᄉ ᅥᄎ ᅡᄋ ᅵᄀ ᅡᄏ ᅳᄀ ᅦᄂ ᅡᄐ ᅡᄂ ᆻᄃ ᅡ ᅡ. ᆫᄋ ᅩ ᄇ ᆫᄀ ᅧ ᅮᄋ ᅦᄉ ᅥᄂ ᆫ IATᄅ ᅳ ᆯᄇ ᅳ ᅩᄋ ᆫᄒ ᅪ ᆫ M-IATᄅ ᅡ ᆯᄌ ᅳ ᅦᄋ ᆫᄒ ᅡ ᅡᄀ ᅩ, ᄃ ᅮᄇ ᆼᄇ ᅡ ᆸᄋ ᅥ ᅴᄎ ᅡᄋ ᅵᄅ ᆯᄇ ᅳ ᅵᄀ ᅭᄒ ᅡᄋ ᅧ M-IATᄀ ᅡᄀ ᅡᄆ ᆷᄋ ᅮ ᅦᄆ ᆫᄀ ᅵ ᆷ ᅡ ᆫᄌ ᅡ ᄒ ᅵᄋ ᆨᄋ ᅧ ᆯᄃ ᅳ ᅥᄌ ᆯᅡ ᅡ ᆽ ᄎᄋ ᆷᄋ ᅳ ᆯᄒ ᅳ ᆨᄋ ᅪ ᆫᄒ ᅵ ᅡᄋ ᆻᄃ ᅧ ᅡ. ᄒ ᆼᄒ ᅣ ᅮᄋ ᆫᄀ ᅧ ᅮᄋ ᅦᄉ ᅥᄂ ᆫ M-IATᄅ ᅳ ᆯᄋ ᅳ ᅵᄋ ᆼᄒ ᅭ ᆫᄌ ᅡ ᆸᄌ ᅵ ᆼᄒ ᅮ ᅩᄋ ᅮᄋ ᅱᄒ ᆷᅥ ᅥ ᆼ 셔 ᆼ ᄑᄀ ᅡᄅ ᆯᄃ ᅳ ᅡᄅ ᆯ ᅮ ᆯᄋ ᅵ ᄑ ᅭᄀ ᅡᄋ ᆻᄃ ᅵ ᅡ. ᄌ ᆸᄌ ᅵ ᆼᄒ ᅮ ᅩᄋ ᅮᄂ ᆫᄉ ᅳ ᅩᄋ ᅲᄋ ᆨᄋ ᅧ ᅦᄍ ᆲᄋ ᅡ ᆫᄉ ᅳ ᅵᄀ ᆫᄆ ᅡ ᆭᄋ ᅡ ᆫᄋ ᅳ ᆼᄋ ᅣ ᅴᄇ ᅵᄀ ᅡᄂ ᅢᄅ ᅧᄇ ᆯᄉ ᅡ ᆼᄒ ᅢ ᅡᄆ ᅳᄅ ᅩᄀ ᆼᄉ ᅡ ᅮᄅ ᆼᄀ ᅣ ᅵᄌ ᆫᅳ ᅮ ᆯ 오 ᇁ ᄂᄒ ᅧᄋ ᆫ ᅧ ᅬᄉ ᄎ ᅩ M-IATᄋ ᆯᄀ ᅳ ᅮᄒ ᆫᄃ ᅡ ᅡᄆ ᆫᄌ ᅧ ᆸᄌ ᅵ ᆼᄒ ᅮ ᅩᄋ ᅮᄋ ᅴᄋ ᅱᄒ ᆷᅥ ᅥ ᆼ ᄉᄃ ᅩᄌ ᆼᄅ ᅥ ᆼᄌ ᅣ ᆨᄋ ᅥ ᅳᄅ ᅩᄌ ᅦᄉ ᅵᄒ ᆯᄉ ᅡ ᅮᄋ ᆻᄃ ᅵ ᅡ.. References Arshad, M., Rasool, M. T. and Ahmad, M. I. (2003). Anderson Darling and modified Anderson Darling tests for generalized Pareto distribution. Pakistan Journal of Applied Sciences, 3, 85-88. Chikobvu, D. and Chifurira, R. (2015). Modelling of extreme minimum rainfall using generalised extreme value distribution for Zimbabwe. South African Journal of Science, 111, 01-08. Fisher, R. A. and Tippett, L. H. C. (1928). Limiting forms of the frequency distribution of the largest or smallest member of a sample. Mathematical Proceedings of the Cambridge Philosophical Society, 24, 180-190. Ha, I., Jang, D., Rhee, K., Lee J., Lee, S., Ko, N. and Kim, J. (2020). Prediction of earthquake magnitude for return period using generalized extreme value distribution: Korea, Japan, China and Taiwan. Journal of the Korean Data & Information Science Society, 31, 97-108. Hosking, J. R. M. (1990). L-moments: Analysis and estimation of distributions using linear combinations of order statistics. Journal of the Royal Statistical Society B , 52, 105-124. Huntington, T. G. (2006). Evidence for intensification of the global water cycle: review and synthesis. Journal of Hydrology, 319, 83-95. Jenkinson, A. F. (1955). The frequency distribution of the annual maximum (or minimum) values of meteorological elements. Quarterly Journal of the Royal Meteorological Society, 81, 158-171. Kim, B., Lee, J.,, Kim, H. and Lee, J. (2011). Non-stationary frequency analysis with climate variability using conditional generalized extreme value distribution. Journal of Wetlands Researh, 13, 499-514. Mckee, T. B., Doesken, N. J. and Kleist, J. (1993). The relationship of drought frequency and duration to time scales. 8th Conference on Applied Climatology, Aneheim, California. Milly, P. C. D., Wetherald, R. T., Dunne, K. A. and Delworth, T. L. (2002). Increasing risk of great floods in a changing climate. Nature, 415, 514-517. Oh, H. and Yoon, S. (2019). Generalized extreme value distribution for a drought based on inter-amount time. Journal of the Korean Data & Information Science Society, 30, 563-571. Ryu, S., Eom, E., Kwon, T. and Yoon, S. (2016). The estimation of CO concentration in Daegu-Gyeongbuk area using GEV distribution. Journal of the Korean Data and Information Science Society, 27, 10011012. Schleiss, M. and Smith, J. A. (2016). Two simple metrics for quantifying rainfall intermittency: The burstiness and memory of interamount times. Journal of Hydrometeorology, 17, 421?436. Seager, R., Naik, N. and Vecchi, G. A. (2010). Thermodynamic and dynamic mechanisms for large-scale changes in the hydrological cycle in response to global warming. Journal of Climate, 23, 4651-4668. Stagge, J. H., Tallaksen, L. M., Gudmundsson, L., Van Loon, A. F. and Stahl, K. (2016). Response to comment on ‘candidate distributions for climatological drought indices (SPI and SPEI)’. International Journal of Climatology, 36, 2132-2138. Tsakiris, G. and Vangelis, H. J. E. W. (2005). Establishing a drought index incorporating evapotranspiration. European Water , 9, 3-11. Vicente-Serrano, S. M., Beguer´ia, S., Lorenzo-Lacruz, J., Camarero, J. J., L´ opez-Moreno, J. I, AzorinMolina, C., Revuelto. J., Mor´ an-Tejeda. E. and Sanchez-Lorenzo, A. (2012). Performance of drought indices for ecological, agricultural, and hydrological applications. Earth Interactions, 16, 1-27. Yang, M. and Yoon, S. (2017). Evaluation of the impact of typhoon on daily maximum precipitation. Journal of the Korean Data and Information Science Society, 28, 1415-1425..
(12) Journal of the Korean Data & Information Science Society 2021, 32(5), 1023–1034. http://dx.doi.org/10.7465/jkdi.2021.32.5.1023 ᆫᄀ ᅡ ᄒ ᆨᄃ ᅮ ᅦᄋ ᅵᄐ ᅥᄌ ᆼᄇ ᅥ ᅩᄀ ᅪᄒ ᆨᄒ ᅡ ᅬᄌ ᅵ. Analysis of the generalized extreme value of drought on †. the Korean Peninsula using inter-amount times Jihoon Lee1 · Taeyong Kwon2 · Sanghoo Yoon3 13. Division of mathematics and Big data science, Daegu University 2 Department of Statistics, Daegu University. Received 19 August 2021, revised 15 September 2021, accepted 23 September 2021. Abstract The risk of drought is increasing due to global warming, causing enormous damage to mankind. To prevent this, the risk of drought is assessed according to various standards. Among them, the number of days without precipitation is continuing or days that do not exceed the standard precipitation is called the meterological criterion. Inter-amount time (IAT) is an expression of the meteorological drought criteria in hours. This can be used to stochastically evaluate the number of days without precipitation, but there is a disadvantage that values change with the start point, and to compensate for this, we present modified inter-amount time (M-IAT) in this study. To compare the two models, 67 ASOS data were used for generalized extreme models from 1990 to 2020, and through return level, found areas where IAT and M-IAT difference and other areas and areas of constant risk days without precipitation. Most of them were larger in M-IAT than IAT, mainly there was a big difference in areas with less precipitation but high precipitation frequency and areas with less precipitation in winter. The riskness of number of days without precipitation was higher in Gangwon Hongcheon and Gyeongsang than in other regions. Keywords: generalized extreme value distribution, inter-amount time, modified interamount time, return level.. †. This research was supported by the Daegu University, 2020. Undergraduate student, Division of Mathematics and Big Data Science, Daegu University, Gyeongsan 38453, Republic of Korea. 2 Doctor’s course, Department of Statistics, Daegu University, Gyeongbuk 38453, Republic of Korea. 3 Corresponding author: Assistant professor, Division of mathematics and Big data science, Daegu University, Gyeongbuk 38453, Korea. E-mail: [email protected] 1.
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