초록
지속가능한 수자원 관리 시스템을 위한 수문 예측은 안정적인 장단기 용수 공급에 있어 중요한 과제이며, 이를 위해는 다양한 기후 정보를 이용한 시스템의 평가가 우선되어야 한다. 본 연구에서는 미국 플로리다 템파 지역의 연간 월 강우와 하천 유량 예측을 위해 본 시험지역에 운용되고 있는 유량 모의 시스템 (flow modeling system, FMS)을 소개하고, 관측된 강우 자료를 '최적 예측 강우 시나리오 (the best rainfall forecast)'로 가정하여 FMS의 기후 예측 정보에 대한 활용성을 평가하였다. 연구 결과, 기본적으로 FMS에 의해 예측된 월 강우량 앙상블의 중앙값이 관측 강우량을 잘 재현하는 것으로 나타났다. 강우 예측 모델 입력자료로 사용되는 초기 월 강우량은 2개월까지의 예측에 간섭하며 이 후 예측치는 동일한 범주로 수렴하여 관측자료로 부터 추정된 통계치에 의존하는 것으로 나타났다. 이는 예측 모델이 최대 2개월간의 예측 효용성을 가짐을 의미한다. 월 강우량 앙상블을 이용하여 예측된 하천 유량 앙상블은 4-6개월까지의 예측 효용성을 보였다. 예측된 강우량 대신 실제 관측 월강우 시계열 자료를 유량 예측을 위한 강우 입력자료로 적용한 결과, 예측된 유량의 범주가 현저히 감소하였으며 예측의 불확실성이 감소하는 것으로 나타났다. 본 연구 결과는 시험 지역에 대한 신뢰도 높은 강우 예측 자료의 확보가 기존의 수문 예측 시스템 개선에 기여할수 있다는 것을 보여준다.
This paper introduced the flow forecast modeling system that a water management agency in west central Florida, Tampa Bay Water has been operated to forecast monthly rainfall and streamflow in the Tampa Bay region, Florida. We evaluated current 1-year monthly rainfall forecasts and flow forecasts and actual observations to investigate the benefits of incorporating rainfall forecasts into monthly flow forecast. Results for rainfall forecasts showed that the observed annual cycle of monthly rainfall was accurately reproduced by the $50^{th}$ percentile of forecasts. While observed monthly rainfall was within the $25^{th}$ and $75^{th}$ percentile of forecasts for most months, several outliers were found during the dry months especially in the dry year of 2007. The flow forecast results for the three streamflow stations (HRD, MB, and BS) indicated that while the 90 % confidence interval mostly covers the observed monthly streamflow, the $50^{th}$ percentile forecast generally overestimated observed streamflow. Especially for HRD station, observed streamflow was reproduced within $5^{th}$ and $25^{th}$ percentile of forecasts while monthly rainfall observations closely followed the $50^{th}$ percentile of rainfall forecasts. This was due to the historical variability at the station was significantly high and it resulted in a wide range of forecasts. Additionally, it was found that the forecasts for each station tend to converge after several months as the influence of the initial condition diminished. The forecast period to converge to simulation bounds was estimated by comparing the forecast results for 2006 and 2007. We found that initial conditions have influence on forecasts during the first 4-6 months, indicating that FMS forecasts should be updated at least every 4-6 months. That is, knowledge of initial condition (i.e., monthly flow observation in the last-recent month) provided no foreknowledge of the flows after 4-6 months of simulation. Based on the experimental flow forecasts using the observed rainfall data, we found that the 90 % confidence interval band for flow predictions was significantly reduced for all stations. This result evidently shows that accurate short-term rainfall forecasts could reduce the range of streamflow forecasts and improve forecast skill compared to employing the stochastic rainfall forecasts. We expect that the framework employed in this study using available observations could be used to investigate the applicability of existing hydrological and water management modeling system for use of stateof-the-art climate forecasts.