Grey's Writing on Game Changers for Hydrologic Uncertainty Analysis. This is a great start. We may build a list, and then select for the top three or top ten.
First successful automatic calibration of a hydrology model:
Duan, Q., S. Sorooshian, and V. Gupta (1992), Effective and efficient global optimization for conceptual rainfall-runoff models, Water Resour. Res., 28(4), 1015–1031,
First use of real machine learning (ANNs) for hydrological prediction:
Hsu, K., H. V. Gupta, and S. Sorooshian (1995), Artificial Neural Network Modeling of the Rainfall-Runoff Process, Water Resour. Res., 31(10), 2517–2530, doi:10.1029/95WR01955.
First computer-based land surface model:
Charney, J.G., Halem, M., and Jastrow, R. (1969) Use of incomplete historical data to infer the present state of the atmosphere. Journal of Atmospheric Science, 26, 1160–1163.
Manabe, S., 1969. Climate and the ocean circulation. 1. The atmospheric circulation and the hydrology of the Earth’s surface. Mon. Weather Rev. 97(11), 739–774].
First global 1-km LSM or hydromet simulation:
Kumar, S. V., C. D. Peters-Lidard, Y. Tian, P. R. Houser, J. Geiger, S. Olden, L. Lighty, J. L. Eastman, B. Doty, P. Dirmeyer, J. Adams, K. Mitchell, E. F. Wood and J. Sheffield, 2006. Land Information System - An Interoperable Framework for High Resolution Land Surface Modeling. Environmental Modelling & Software, Vol. 21, 1402-1415.
Who had the first hydro DA paper? Jackson (1981) and Bernard (1981) apparently had the first direct insertion papers, but Milly had the first KF application
Jackson, T.J. et al. (1981) Soil moisture updating and microwave remote sensing for hydrological simulation. Hydrological Sciences B., 26(3), 305–319.
Bernard, R., Vauclin, M., and Vidal-Madjar, D. (1981) Possible use of active microwave remote sensing data for prediction of regional evaporation by numerical simulation of soil water movement in the unsaturated zone. Water Resources Research, 17(6), 1603–1610.
Milly, P.C.D. (1986) Integrated remote sensing modelling of soil moisture: sampling frequency, response time, and accuracy of estimates. Integrated Design of Hydrological Networks – Proceedings of the Budapest Symposium, 158, 201–211.
The call for physically-based models to be used in application
Milly, P. C., Betancourt, J., Falkenmark, M., Hirsch, R. M., Kundzewicz, Z. W., Lettenmaier, D. P., & Stouffer, R. J. (2008). Stationarity is dead: Whither water management?. Science, 319(5863), 573-574.
Our community’s start to uncertainty quantification
Beven, K., & Binley, A. (1992). The future of distributed models: model calibration and uncertainty prediction. Hydrological processes, 6(3), 279-298.
Information theory for hypothesis testing
Gong, W., Gupta, H. V., Yang, D., Sricharan, K., & Hero, A. O. (2013). Estimating epistemic and aleatory uncertainties during hydrologic modeling: An information theoretic approach. Water resources research, 49(4), 2253-2273.
First multiparameterization model
Clark, M. P., Slater, A. G., Rupp, D. E., Woods, R. A., Vrugt, J. A., Gupta, H. V., ... & Hay, L. E. (2008). Framework for Understanding Structural Errors (FUSE): A modular framework to diagnose differences between hydrological models. Water Resources Research, 44(12).