참고문헌 (8)

1. Clark, M.P., Vogel, R.M., Lamontagne, J., Mizukami, N., Knoben, W., Tang, G., Gharari, S., Freer, J.E., Whitfield, P.H., Shook, K., and Papalexiou, S.M. (2021). The abuse of popular performance metrics in hydrologic modeling, AGU Fall Meeting 2021. 

2. Di Baldassarre, G., Wei, Y., Savelli, E., Barendrecht, M., Khatami, S., Xu, L, van Oel, P.R., O'Keefe, J., Shanono, N.J., and Famiglietti, J.S. (2021). Hydrology and society: Phenomena emerging from the interactions and feedbacks between human and water systems, AGU Fall Meeting 2021. 

3. Elkurdy, M., Sampson, A.K., Howard, J., Zimmerman, J., Klausmeyer, K., and Read, L. (2021). Predicting Ungauged, Human-Altered Streamflow with Theory-Guided Machine Learning, AGU Fall Meeting 2021. 

4. Gan Y. and Zhang, Y. (2021). Impacts of Forcing and Model Uncertainties on Snowpack Estimation of the National Water Model in the Upper Colorado River Basin, AGU Fall Meeting 2021. 

5. Ma, Y., Mononey, K.M., Cifelli, R., and Chandrasekar, V. (2021). Evaluating the Impact of Urbanization on Flood Risk during Heavy Precipitation Events in Ellicott City, Maryland, 2022 AMS Annual Meeting. 

6. Nijssen, B., Harrell, J., Cheng, Y., Arnold, J.R., and Frans, C. (2021). The Need to Account for Water Resources Management in Climate Change Impact and Adaptation Studies, AGU Fall Meeting 2021. 

7. Qualls, L.M., Frame, J.M., Saeedimoghaddam, M., Nearing, G.S., and Johnson, M.R. (2021). Dynamic Attributes in Deep Learning Rainfall-Runoff Models to Address Non-Stationarity, AGU Fall Meeting 2021. 

8. Yao, S., Chen, H., Han, L., and Chandrasekar, V. (2021). Hurricane-Net: An Adaptive Deep Learning Framework for Rainfall Prediction of Landfalling Hurricanes, 2022 AMS Annual Meeting. 

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