한국농림기상학회지, 제 14권 제4호(2012) (pISSN 1229-5671, eISSN 2288-1859)
Korean Journal of Agricultural and Forest Meteorology, Vol. 14, No. 4, (2012), pp. 143~154
DOI: 10.5532/KJAFM.2012.14.4.143
ⓒ Author(s) 2014. CC Attribution 3.0 License.


ENSO 패턴에 대한 MM5 강수 모의 결과의 유역단위 성능 평가:
플로리다 템파 지역을 중심으로

황세운(1,2), 호세 헤르난데즈(3)
(1)플로리다 대학교, 농공학과, (2)플로리다 대학교, 수자원 연구소,
(3)루이지애나 해양에너지 관리국

(2012년 06월 01일 접수; 2012년 10월 05일 수정; 2012년 10월 15일 수락)

Combining Bias-correction on Regional Climate Simulations and
ENSO Signal for Water Management: Case Study for
Tampa Bay, Florida, U.S.

Syewoon Hwang(1,2), Jose Hernandez(3)
(1)Department of Agricultural and Biological engineering, University of Florida, Gainesville, Florida
(2)Water institute, University of Florida, Gainesville, Florida
(3)Bureau of Ocean Energy Management, New Orleans, Louisiana

(Received June 01, 2012; Revised October 05, 2012; Accepted October 15, 2012)

ABSTRACT
As demand of water resources and attentions to changes in climate (e.g., due to ENSO) increase, long/short term prediction of precipitation is getting necessary in water planning. This research evaluated the ability of MM5 to predict precipitation in the Tampa Bay region over 23 year period from 1986 to 2008. Additionally MM5 results were statistically bias-corrected using observation data at 33 stations over the study area using CDF-mapping approach and evaluated comparing to raw results for each ENSO phase (i.e., El Niño and La Niña). The bias-corrected model results accurately reproduced the monthly mean point precipitation values. Areal average daily/monthly precipitation predictions estimated using block-kriging algorithm showed fairly high accuracy with mean error of daily precipitation, 0.8 mm and mean error of monthly precipitation, 7.1 mm. The results evaluated according to ENSO phase showed that the accuracy in model output varies with the seasons and ENSO phases. Reasons for low predictions skills and alternatives for simulation improvement are discussed. A comprehensive evaluation including sensitivity to physics schemes, boundary conditions reanalysis products and updating land use maps is suggested to enhance model performance. We believe that the outcome of this research guides to a better implementation of regional climate modeling tools in water management at regional/seasonal scale.

Keyword: MM5, Precipitation simulations, Statistically bias-corrected, Block-kriging, ENSO, Tampa Bay

MAIN

적요

수자원의 수요 증가와 ENSO (El Niño/La Niña Southern Oscillation) 등의 기후변화 현상으로 인한 수자원 공급의 불안정 요소가 제기됨에 따라, 수자원관리 계획 수립 시 장/단기강우 모의의 중요성이 강조되고 있다. 본 연구에서는 미국 플로리다 템파 지역의 두 개 유역을 대상으로 1986년부터 2008년까지의 MM5 지역기후모델을 이용한 강우모의 결과를 시험지역의 33개 관측자료와 CDF-mapping 기법을 이용하여 통계적으로 보정하였으며 그 결과를 바탕으로 ENSO패턴에 따른 모델의 성능을 평가하였다. 보정된 MM5일 강우 모의결과는 대체적으로 각 관측소의 월 평균 강우량 (ME: 1.0mm)을 잘 모의하는 것으로 나타났다. 블락-크리깅 기법을 이용하여 추정된 유역 평균 일/월 강우량 또한 관측치를 잘 재현하였다(일 강우 ME:0.8mm, 월 강우 ME: 7.1mm). 한편, ONI (Oceanic Niño index)를 이용하여 구분한 ENSO 패턴에 따른 강우 모의치를 분석한 결과, 월별 엘리뇨/라니냐 해에 대한 유역 단위의 강우량 모의 성능이 상이한 것으로 나타났다. 이 원인으로 한정된 모수화 적용 및 모델 경계자료 오차 등을 제시하고 이에 대한 보정 방법 개선 등의 추가 연구의 필요성을 지적하였다. 본 연구는 ENSO 패턴을 고려한 월별 기후모델 결과를 활용함에 있어 유의점을 제시하였기에, 우기와 건기에 대한 수자원 관리를 위한 적용 등에 유용하게 활용될 것으로 기대된다.

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