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위성기반 증발산 자료를 활용한 유역모델 성능 평가  

Lee, Sang-Cheol (서울시립대학교 환경공학부)
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Water for future / v.54, no.12, 2021 , pp. 30-35 More about this Journal
Keywords
Citations & Related Records
연도 인용수 순위
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1 Neitsch, S.., Arnold, J.., Kiniry, J.., Williams, J.., 2011. Soil & Water Assessment Tool Theoretical Documentation Version 2009, Texas Water Resources Institute. https://doi.org/10.1016/j.scitotenv.2015.11.063   DOI
2 Herman, M.R., Nejadhashemi, A.P., Abouali, M., Hernandez-Suarez, J.S., Daneshvar, F., Zhang, Z., Anderson, M.C., Sadeghi, A.M., Hain, C.R., Sharifi, A., 2018. Evaluating the role of evapotranspiration remote sensing data in improving hydrological modeling predictability. J. Hydrol. 556, 39-49. https://doi.org/10.1016/j.jhydrol.2017.11.009   DOI
3 Anderson, M.C., Norman, J.M., Mecikalski, J.R., Torn, R.D., Kustas, W.P., Basara, J.B., 2004. A multiscale remote sensing model for disaggregating regional fluxes to micrometeorological scales. J. Hydrometeorol. 5, 343-363. https://doi.org/10.1175/1525-7541(2004)005<0343:AMRSMF>2.0.CO;2   DOI
4 Anderson, M.C., Norman, J.M., Mecikalski, J.R., Otkin, J.A., Kustas, W.P., A climatological study of evapotranspiration and moisture stress across the continental United States based on thermal remote sensing: 1. Model formulation. J. Geophys. Res. Atmos. 2007, 112, D10117, doi:10.1029/2006JD007506.   DOI
5 Cha, Y., Shin, J., Kim, Y., 2020. Data-Driven Modeling of Freshwater Aquatic Systems: Status and Prospects. J. Korean Soc. Water Environ. 36(6), 611-620. https://doi.org/10.15681/KSWE.2020.36.6.611   DOI
6 Edwards, P.J., Williard, K.W.J., Schoonover, J.E., 2015. Fundamentals of Watershed Hydrology. J. Contemp. Water Res. Educ. 154, 3-20. https://doi.org/10.1111/j.1936-704x.2015.03185.x   DOI
7 Lee, S., Qi, J., Kim, H., McCarty, G.W., Moglen, G.E., Anderson, M., Zhang, X., Du, L., 2021. Utility of remotely sensed evapotranspiration products to assess an improved model structure. Sustain. 13, 2375. https://doi.org/10.3390/su13042375   DOI
8 Qi, J., Zhang, X., McCarty, G.W., Sadeghi, A.M., Cosh, M.H., Zeng, X., Gao, F., Daughtry, C.S.T., Huang, C., Lang, M.W., Arnold, J.G., 2018. Assessing the performance of a physically-based soil moisture module integrated within the Soil and Water Assessment Tool. Environ. Model. Softw. 109, 329-341. https://doi.org/10.1016/j.envsoft.2018.08.024   DOI
9 Anderson, M.C., Norman, J.M., Diak, G.R., Kustas, W.P., Mecikalski, J.R., A two-source time-integrated model for estimating surface fluxes using thermal infrared remote sensing. Remote Sens. Environ. 1997, 60, 195-216, doi:10.1016/S0034-4257(96)00215-5.   DOI
10 Yen, H., White, M.J., Ascough, J.C., Smith, D.R., Arnold, J.G., 2016. Augmenting Watershed Model Calibration with Incorporation of Ancillary Data Sources and Qualitative Soft Data Sources. J. Am. Water Resour. Assoc. 52, 788-798. https://doi.org/10.1111/1752-1688.12428   DOI
11 Becker, R., Koppa, A., Schulz, S., Usman, M., aus der Beek, T., Schuth, C., 2019. Spatially distributed model calibration of a highly managed hydrological system using remote sensing-derived ET data. J. Hydrol. 577, 123944. https://doi.org/10.1016/j.jhydrol.2019.123944   DOI
12 Gupta, H. V., Kling, H., Yilmaz, K.K., Martinez, G.F., 2009. Decomposition of the mean squared error and NSE performance criteria: Implications for improving hydrological modelling. J. Hydrol. 377, 80-91. https://doi.org/10.1016/j.jhydrol.2009.08.003   DOI