DOI QR코드

DOI QR Code

Long-term forecasting reference evapotranspiration using statistically predicted temperature information

통계적 기온예측정보를 활용한 기준증발산량 장기예측

  • 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)
  • 김철겸 (한국건설기술연구원 수자원하천연구본부) ;
  • 이정우 (한국건설기술연구원 수자원하천연구본부) ;
  • 이정은 (한국건설기술연구원 수자원하천연구본부) ;
  • 김현준 (한국건설기술연구원 수자원하천연구본부)
  • Received : 2021.10.18
  • Accepted : 2021.10.28
  • Published : 2021.12.31

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.

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.

Keywords

Acknowledgement

본 연구는 한국건설기술연구원 주요사업 "가뭄대응 중소하천 물부족 위험도 평가 및 물 확보 기술 개발" 과제의 연구비 지원에 의해 수행되었습니다.

References

  1. Abd El-Wahed, M.H., and Abd El-Mageed, T.A. (2014). "Estimating reference evapotranspiration using modified Blaney-Criddle equation in arid region." Bothalia Journal, Vol. 44, No. 7, pp. 183-195.
  2. Ahmadi, S.H., and Fooladmand, H.R. (2008). "Spatially distributed monthly reference evapotranspiration derived from the calibration of Thornthwaite equation: A case study, South of Iran." Irrigation Science, Vol. 26, pp. 303-312. https://doi.org/10.1007/s00271-007-0094-8
  3. Allen, R.G., Pereira, L.S., Raes, D., and Smith, M. (1998). Crop evapotranspiration-guidelines for computing crop water requirements. FAO Irrigation and Drainage, Rome, Italy, pp. 23-56.
  4. Almorox, J., and Grieser, J. (2016). "Calibration of the Hargreaves Samani method for the calculation of reference evapotranspiration in different Koppen climate classes." Hydrology Research, Vol. 42, No. 2, pp. 521-531. https://doi.org/10.2166/nh.2015.091
  5. Alves, W.B., Rolim, G.S., and Aparecido, L.E.O. (2017). "Reference evapotranspiration forecasting by artificial neural networks." Journal of the Brazilian Association of Agricultural Engineering, Vol. 37, No. 6, pp. 1116-1125.
  6. Ashrafzadeh, A., Kisi, O., Aghelpour, P., Biazar, S.M., and Masouleh, M.A. (2020). "Comparative study of time series models, support vector machines, and GMDH in forecasting long-term evapotranspiration rates in northern Iran." Journal of Irrigation and Drainage Engineering, Vol. 146, No. 6. doi: 10.1061/(ASCE)IR.1943-4774.0001471
  7. Bayatvarkeshi, M., Zhang, B., Fasihi, R., Adnan, R.M., Kisi, O., and Yuan, X. (2020). "Investigation into the effects of climate change on reference evapotranspiration using the HadCM3 and LARS-WG." Water, Vol. 12, 666. doi: 10.3390/w12030666
  8. Chae, H.S., Kim, S.J., and Jung, K.S. (1999). "GRID-based daily evapotranspiration prediction model (GRIDET)." Journal of Korea Water Resources Association, Vol. 32, No. 6, pp. 721-730.
  9. Chang, X., Wang, S., Gao, Z., Luo, Y., and Chen, H. (2019). "Forecast of daily reference evapotranspiration using a modified daily Thornthwaite equation and temperature forecasts." Irrigation and Drainage, Vol. 68, pp. 297-317. https://doi.org/10.1002/ird.2309
  10. Chirico, G.B., Pelosi, A., De Michele, C., Bolognesi, S.F., and D'Urso, G. (2018). "Forecasting potential evapotranspiration by combining numerical weather predictions and visible and near-infrared satellite images: an application in southern Italy." The Journal of Agricultural Science, Vol. 156, No. 6, pp. 702-710. https://doi.org/10.1017/S0021859618000084
  11. Cristea, N.C., Kampf, S.K., and Burges, S.J. (2013). "Revised coefficients for Priestley-Taylor and Makkink-Hansen equations for estimating daily reference evapotranspiration." Journal of Hydrologic Engineering, ASCE, Vol. 18, No. 10, pp. 1289-1300. https://doi.org/10.1061/(ASCE)HE.1943-5584.0000679
  12. Dingman, S.L. (1994). Physical hydrology. Prentice-Hall, Inc., Englewood Cliffs, NJ, U.S.
  13. Droogers, P., and Allen, R.G. (2002). "Estimating reference evapotranspiration under inaccurate data conditions." Irrigation and Drainage Systems, Vol. 16, pp. 33-45. https://doi.org/10.1023/A:1015508322413
  14. Fooladmand, H.R., and Haghighat, M. (2007). "Spatial and temporal calibration of Hargreaves equation for calculating monthly ETo based on Penman-Monteith method." Irrigation and Drainage, Vol. 56, pp. 439-449. https://doi.org/10.1002/ird.305
  15. Gurski, B.C., Jerszurki, D., and Souza, J.L.M. (2018). "Alternative methods of reference evapotranspiration for Brazilian climate types." Revista Brasileira de Meteorologia, Vol. 33, No. 3, pp. 567-578. https://doi.org/10.1590/0102-7786333015
  16. Hamon, W.R. (1960). Estimating potential evapotranspiration. Master thesis, Massachusetts Institute of Technology, Cambridge, MA, U.S.
  17. Hamon, W.R. (1963). "Computation of direct runoff amounts from storm rainfall." International Association of Scientific Hydrology, Vol. 63, pp. 52-62.
  18. Kim, C.-G., Lee, J., Lee, J.E., and Kim, H. (2020a), "Evaluation of improvement effect on the spatial-temporal correction of several reference evapotranspiration methods." Journal of Korea Water Resources Association, Vol. 53, No. 9, pp. 701-715. https://doi.org/10.3741/JKWRA.2020.53.9.701
  19. Kim, C.-G., Lee, J., Lee, J.E., Kim, N.W., and Kim, H. (2020b). "Monthly precipitation forecasting in the Han River basin, South Korea, using large-scale teleconnections and multiple regression models." Water, Vol. 12, No. 6, 1590. doi: 10.3390/w12061590
  20. Kim, C.-G., Lee, J., Lee, J.E., Kim, N.W., and Kim, H. (2021), "Monthly temperature forecasting using large-scale climate teleconnections and multiple regression models." Journal of Korea Water Resources Association, Vol. 54, No. 9, pp. 731-745. https://doi.org/10.3741/JKWRA.2021.54.9.731
  21. Kim, M., Kim, W., and Jeong, Y. (2019). Development of APCC seasonal prediction downscaling method using a weather generator-Based on probabilistic seasonal forecast. Technical Report, 2018-11, APEC Climate Center.
  22. Lang, D., Zheng, J., Shi, Jiaqi, Liao, F., Ma, X., Wang, W., Chen, X., and Zhang, M. (2017). "A comparative study of potential evapotranspiration estimation by eight methods with FAO PenmanMonteith method in southwestern China." Water, Vol. 9, No. 10, 734. doi: 10.3390/w9100734
  23. Lee, K.-H., and Park, J.-H. (2008). "Calibration of the Hargreaves equation for the reference evapotranspiration estimation on a nation-wide scale." Journal of the Korean Society of Civil Engineers, KSCE, Vol. 28, No. 6B, pp. 675-681.
  24. Lee, K.-H., Cho, H.-Y., and Oh, N.-S. (2008). "Calibration and validation of the Hargreaves equation for the reference evapotranspiration estimation in Gyeonggi bay watershed." Journal of Korea Water Resources Association, KWRA, Vol. 41, No. 4, pp. 413-422. https://doi.org/10.3741/JKWRA.2008.41.4.413
  25. Liu, Z., Lu, J., Huang, J., Chen, X., and Zhang, L. (2021). "Projection of reference crop evapotranspiration under future climate change in Poyang Lake watershed, China." Journal of Hydrologic Engineering, Vol. 26, No. 1, 05020042. doi: 10.1061/(ASCE)HE.1943-5584.0002020
  26. Lu, J., Sun, G., McNulty, S.G., and Amatya, D.M. (2005). "A comparison of six potential evapotranspiration methods for regional use in the southwestern United States." Journal of the American Water Resources Association, AWRA, Vol. 41, pp. 621-633. https://doi.org/10.1111/j.1752-1688.2005.tb03759.x
  27. Lu, X., Fan, J., Wu, L., and Dong, J. (2020). "Forecasting multistep ahead monthly reference evapotranspiration using hybrid extreme gradient boosting with Grey Wolf Optimization algorithm." Computer Modeling in Engineering & Sciences, Vol. 125, No. 2, pp. 699-723. https://doi.org/10.32604/cmes.2020.011004
  28. McEvoy, D.J., Huntington, J.L., Mejia, J.F., and Hobbins, M.T. (2015). "Improved seasonal drought forecasts using reference evapotranspiration anomalies." Geophysical Research Letters, Vol. 43, pp. 377-385. https://doi.org/10.1002/2015GL067009
  29. Nash, J.E., and Sutcliffe, J.V. (1970) "River flow forecasting through conceptual model. part 1-A discussion of principles." Journal of Hydrology, Vol. 10, pp. 282-290. https://doi.org/10.1016/0022-1694(70)90255-6
  30. Oh, N.S., Lee, K.H., and Ko, Y.C. (2002). "Capability of evapotranspiration estimation with short field data." Journal of the Korean Society of Civil Engineers, Vol. 22, No. 6B, pp. 795-801.
  31. Park, J., Cho, J., Lee, E.-J., and Jung, I. (2017). "Evaluation of reference evapotranspiration in South Korea according to CMIP5 GCMs and estimation methods." Journal of the Korean Society of Rural Planning, Vol. 23, No. 4, pp. 153-168. https://doi.org/10.7851/Ksrp.2017.23.4.153
  32. Peng, L., Li, Y., and Feng, H. (2017). "The best alternative for estimating reference crop evapotranspiration in different subregions of mainland China." Scientific Reports, Vol. 7, No. 1, 5458, doi: 10.1038/s41598-017-05660-y
  33. Rajabi, A., and Babakhani, Z. (2018). "The study of potential evapotranspiration in future periods due to climate change in west of Iran." International Journal of Climate Change Strategies and Management, Vol. 10, No. 1, pp. 161-177. https://doi.org/10.1108/ijccsm-01-2017-0008
  34. Tegos, A., Malamos, N., Efstratiadis, A. Tsoukalas, I., Karanasios, A., and Koutsoyiannis, D. (2017). "Parametric modelling of potential evapotranspiration: A global survey." Water, Vol. 9, No. 10, 795. doi: 10.3390/w9100795
  35. Tian, D., and Martinez, C.J. (2012). "Forecasting reference evapotranspiration using retrospective forecast analogs in the southeastern United States." Journal of Hydrometeorology, Vol. 13, No. 6, pp. 1874-1892. https://doi.org/10.1175/JHM-D-12-037.1
  36. Tian, D., Martinez, C.J., and Graham, W.D. (2014). "Seasonal prediction of regional reference evapotranspiration based on climate forecast system version 2." Journal of Hydrometeorology, Vol. 15, No. 3, pp. 1166-1188. https://doi.org/10.1175/JHM-D-13-087.1
  37. Valipour, M. (2015a). "Evaluation of radiation methods to study potential evapotranspiration of 31 provinces." Meteorological and Atmospheric Physics, Vol. 127, pp. 289-303. https://doi.org/10.1007/s00703-014-0351-3
  38. Valipour, M. (2015b). "Temperature analysis of reference evapotranspiration models." Meteorological Applications, Vol. 22, pp. 385-394. https://doi.org/10.1002/met.1465
  39. Xu, C.-Y., and Singh, V.P. (2002). "Cross comparison of empirical equations for calculating potential evapotranspiration with data from Switzerland." Water Resources Management, Vol. 16, pp. 197-219. https://doi.org/10.1023/A:1020282515975
  40. Xu, Y., Wu, Y., and Xu, G. (2019). "Variation of reference evapotranspiration and its teleconnection with multiple large-scale climate oscillations in the Yangtze River Delta, China." International Journal of Climatology, Vol. 39, No. 5, pp. 2630-2645. https://doi.org/10.1002/joc.5977
  41. Xystrakis, F., and Matzarakis, A. (2011). "Evaluation of 13 empirical reference potential evapotranspiration equations on the island of Crete in southern Greece." Journal of Irrigation and Drainage Engineering, ASCE, Vol. 137, No. 4, pp. 211-222. https://doi.org/10.1061/(ASCE)IR.1943-4774.0000283
  42. Yan, X., and Mohammadian, A. (2020). "Forecasting daily reference evapotranspiration for Canada using the Penman-Monteith model and statistically downscaled global climate model projections." Alexandria Engineering Journal, Vol. 59, pp. 883-891. https://doi.org/10.1016/j.aej.2020.03.020
  43. Zhao, T., Wang, Q.J., Schepen, A., and Griffiths, M. (2019). "Ensemble forecasting of monthly and seasonal reference crop evapotranspiration based on global climate model outputs." Agricultural and Forest Meteorology, Vol. 264, pp. 114-124. https://doi.org/10.1016/j.agrformet.2018.10.001
  44. Zhao, Z., Wang, H., Wang, C., Li, W., Chen, H., and Deng, C. (2020). "Changes in reference evapotranspiration over Northwest China from 1957 to 2018: variation characteristics, cause analysis and relationships with atmospheric circulation." Agricultural Water Management, Vol. 231, doi: 10.1016/j.agwat.2019.105958