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http://dx.doi.org/10.3741/JKWRA.2021.54.12.1243

Long-term forecasting reference evapotranspiration using statistically predicted temperature information  

Kim, Chul-Gyum (Department of Hydro Science and Engineering Research, Korea Institute of Civil Engineering and Building Technology)
Lee, Jeongwoo (Department of Hydro Science and Engineering Research, Korea Institute of Civil Engineering and Building Technology)
Lee, Jeong Eun (Department of Hydro Science and Engineering Research, Korea Institute of Civil Engineering and Building Technology)
Kim, Hyeonjun (Department of Hydro Science and Engineering Research, Korea Institute of Civil Engineering and Building Technology)
Publication Information
Journal of Korea Water Resources Association / v.54, no.12, 2021 , pp. 1243-1254 More about this Journal
Abstract
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.
Keywords
Reference evapotranspiration; Hamon method; long-term forecasting; Statistical model;
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