[논문]통계적 기온예측정보를 활용한 기준증발산량 장기예측

김철겸; 이정우; 이정은; 김현준

doi:10.3741/jkwra.2021.54.12.1243

통계적 기온예측정보를 활용한 기준증발산량 장기예측
Long-term forecasting reference evapotranspiration using statistically predicted temperature information 원문보기

Journal of Korea Water Resources Association = 한국수자원학회논문집, v.54 no.12, 2021년, pp.1243 - 1254

김철겸 (한국건설기술연구원 수자원하천연구본부) , 이정우 (한국건설기술연구원 수자원하천연구본부) , 이정은 (한국건설기술연구원 수자원하천연구본부) , 김현준 (한국건설기술연구원 수자원하천연구본부)

초록
AI-Helper

수자원 운영이나 농업용수 관리 등을 위해서는 계절 또는 월 단위 이상의 장기간의 미래에 대한 증발산량의 정확한 예측이 중요하다. 본 연구에서는 한강권역을 대상으로 통계적으로 예측된 월 기온자료와, 기온자료를 기반으로 한 Hamon 증발산량 추정식을 활용하여 기준증발산량에 대한 장기전망(최대 12개월까지)을 수행하였다. 먼저 한강권역의 월 단위 기온예측정보를 시공간적으로 상세화하여 한강권역 내 15개 지점에 대한 일 단위 기온자료를 도출하였다. 지점별 상세화된 기온자료의 적합도를 분석한 결과, 월평균 최고기온에 대해서는 PBIAS는 1.3~6.9%, RSR은 0.22~0.27, NSE는 0.93~0.95, r은 0.97~0.98이었으며, 월평균 최저기온에 대해서는 PBIAS는 7.8~44.7%, RSR은 0.21~0.25, NSE는 0.94~0.96, r은 0.98~0.99로 대체로 관측값과 유사하게 상세화가 수행되었다. 상세화된 기온자료를 이용하여 Hamon 방법에 의한 기준증발산량을 산정하고 한강권역 전체에 대해 면적평균하여 관측값과 비교한 결과, PBIAS는 2.2~5.4%, RSR은 0.21~0.28, NSE는 0.92~0.96, r은 0.96~0.98로 매우 높은 적합도를 나타내었다. 통계적 모형의 특성상 과거와 전혀 다른 기온이 관측되는 경우의 예측성 저하, 시공간적 상세화 과정에서의 불확실성 등으로 인해 일부 기간에 대해서는 예측된 기준증발산량이 관측치와 다소 편차를 나타내기도 하지만 미래기간에 대한 예측결과라는 점을 고려할 때, 미래의 가용수자원에 대한 평가 및 수자원 관리를 위한 정보로 충분히 활용성이 있을 것이다.

Abstract ▼ AI-Helper

For water resources operation or agricultural water management, it is important to accurately predict evapotranspiration for a long-term future over a seasonal or monthly basis. In this study, reference evapotranspiration forecast (up to 12 months in advance) was performed using statistically predicted monthly temperatures and temperature-based Hamon method for the Han River basin. First, the daily maximum and minimum temperature data for 15 meterological stations in the basin were derived by spatial-temporal downscaling the monthly temperature forecasts. The results of goodness-of-fit test for the downscaled temperature data at each site showed that the percent bias (PBIAS) ranged from 1.3 to 6.9%, the ratio of the root mean square error to the standard deviation of the observations (RSR) ranged from 0.22 to 0.27, the Nash-Sutcliffe efficiency (NSE) ranged from 0.93 to 0.95, and the Pearson correlation coefficient (r) ranged from 0.97 to 0.98 for the monthly average daily maximum temperature. And for the monthly average daily minimum temperature, PBIAS was 7.8 to 44.7%, RSR was 0.21 to 0.25, NSE was 0.94 to 0.96, and r was 0.98 to 0.99. The difference by site was not large, and the downscaled results were similar to the observations. In the results of comparing the forecasted reference evapotranspiration calculated using the downscaled data with the observed values for the entire region, PBIAS was 2.2 to 5.4%, RSR was 0.21 to 0.28, NSE was 0.92 to 0.96, and r was 0.96 to 0.98, indicating a very high fit. Due to the characteristics of the statistical models and uncertainty in the downscaling process, the predicted reference evapotranspiration may slightly deviate from the observed value in some periods when temperatures completely different from the past are observed. However, considering that it is a forecast result for the future period, it will be sufficiently useful as information for the evaluation or operation of water resources in the future.

주제어

표/그림 (14)

그림 Fig. 1. Study area
그림 Fig. 2. Results of the goodness-of-fit test for monthly precipitation forecasts
그림 Fig. 3. Hit rate for the forecast range of monthly precipitation
표 Table 1. Monthly correction coefficient for each site
그림 Fig. 4. Tercile probability information for monthly forecasts issued in December 2019
그림 Fig. 5. Comparison between downscaled forecasts and observations for monthly average daily maximum temperature at Seoul (108)
그림 Fig. 6. Comparison between downscaled forecasts and observations for monthly average daily minimum temperature at Seoul (108)
그림 Fig. 7. Results of the goodness-of-fit test for monthly average daily maximum temperature forecasts at each site
그림 Fig. 8. Results of the goodness-of-fit test for monthly average daily minimum temperature forecasts at each site
그림 Fig. 9. Comparison of forecasted and observed monthly ETo
그림 Fig. 10. Result of the goodness-of-fit test for monthly ETo forecasts
그림 Fig. 11. Hit rate for the forecast range of monthly ETo
그림 Fig. 12. Comparison between forecasts and past observations of ETo for October 2021 to September 2022
그림 Fig. 13. Tercile forecast probability of ETo for October 2021 to September 2022

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