메뉴 건너뛰기
.. 내서재 .. 알림
소속 기관/학교 인증
인증하면 논문, 학술자료 등을  무료로 열람할 수 있어요.
한국대학교, 누리자동차, 시립도서관 등 나의 기관을 확인해보세요
(국내 대학 90% 이상 구독 중)
로그인 회원가입 고객센터 ENG
주제분류

추천
검색

논문 기본 정보

자료유형
학술저널
저자정보
김시내 (서울대학교) 전상민 (서울대학교) 이현지 (서울대학교) 황순호 (서울대학교) 최순군 (국립농업과학원) 강문성 (서울대학교)
저널정보
한국농공학회 한국농공학회논문집 한국농공학회논문집 제62권 제4호
발행연도
2020.1
수록면
33 - 43 (11page)

이용수

표지
📌
연구주제
📖
연구배경
🔬
연구방법
🏆
연구결과
AI에게 요청하기
추천
검색

초록· 키워드

오류제보하기
In order to reduce damage from farmland inundation caused by recent climate change, it is necessary to predict the risk of farmland inundationaccurately. Inundation modeling should be performed by considering multiple time distributions of possible rainfalls, as digital forecasts of KoreaMeteorological Administration is provided on a six-hour basis. As building multiple inputs and creating inundation models take a lot of time, it isnecessary to shorten the forecast time by building a data base (DB) of farmland inundation probability. Therefore, the objective of this study is toestablish a DB of farmland inundation probability in accordance with forecasted rainfalls. In this study, historical data of the digital forecasts wascollected and used for time division. Inundation modeling was performed 100 times for each rainfall event. Time disaggregation of forecasted rainfallwas performed by applying the Multiplicative Random Cascade (MRC) model, which uses consistency of fractal characteristics to six-hour rainfall data. To analyze the inundation of farmland, the river level was simulated using the Hydrologic Engineering Center - River Analysis System (HEC-RAS). The level of farmland was calculated by applying a simulation technique based on the water balance equation. The inundation probability was calculatedby extracting the number of inundation occurrences out of the total number of simulations, and the results were stored in the DB of farmland inundationprobability. The results of this study can be used to quickly predict the risk of farmland inundation, and to prepare measures to reduce damage frominundation.

목차

등록된 정보가 없습니다.

참고문헌 (24)

참고문헌 신청

함께 읽어보면 좋을 논문

논문 유사도에 따라 DBpia 가 추천하는 논문입니다. 함께 보면 좋을 연관 논문을 확인해보세요!

이 논문의 저자 정보

최근 본 자료

전체보기

댓글(0)

0