Substantial progress has been made in the last several decades for quantification and communication of hydrologic uncertainty (uncertainty quantification here is in a broad sense, including parameter estimation, sensitivity analysis, uncertainty propagation, and experimental data and data-worth analysis for uncertainty
Grey’s view on grand challenge
As for a ‘grand challenge’, I see the biggest open challenge as about how to build models that learn. That is, how do we leverage the power of machine learning and modern inference techniques for learning multi-scale physical and emergent
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
Meeting details and general update
I attended a tele-conference today (3/12/2018) organized by Jeffrey McDonnell, the President of the AGU Hydrology Section, for the Section’s Technical Committee (TC) chairs. There are a number of items that I would like to share with you and, at
Uncertainty about Uncertainty
I like to point to Keith Beven’s (1987) conference paper, titled ‘Towards a new paradigm in hydrology’, as a place for new hydrologists to start to develop an understanding of uncertainty in the hydrological sciences[1]. In that paper Keith argued