A –Streams of Thought– contribution by Sina Khatami (SK) Martyn is a Professor of Hydrology at the University of Saskatchewan, Associate Director of the University of Saskatchewan’s Centre for Hydrology and the Canmore Coldwater Laboratory, Editor-in-Chief of Water Resources Research, and
Water Resources Research: Tang, Y., Marshall, L., Sharma, A., Ajami, H., & Nott, D. J. (2019). Ecohydrologic error models for improved Bayesian inference in remotely sensed catchments. Water Resources Research. https://doi.org/10.1029/2019WR025055 Brunner, M. I., Hingray, B., Zappa, M., & Favre,
A –Streams of Thought– contribution by Sina Khatami. Asst/Prof. Grey Nearing is a hydrologist at the Department of Geological Sciences at The University of Alabama (UA). Prior to joining UA, he has worked as Project Scientist at the National Center for
Water Resources Research: Prieto, C., Le Vine, N., Kavetski, D., García, E., & Medina, R. (May 2019). Flow prediction in ungauged catchments using probabilistic random forests regionalization and new statistical adequacy tests. Water Resources Research, 55, 4364– 4392. https://doi.org/10.1029/2018WR023254 Zhao,
This Page is updated every month. Please visit the website regularly to see the latest publications about ‘Hydrologic Uncertainty’. Water Resources Research: Abbaszadeh, P., Moradkhani, H., & Daescu, D. N. (February 2019). The Quest for Model Uncertainty Quantification: A Hybrid
I would like to bring to your attention the UQ workshop that will be held this September in Italy. Below is the workshop description copied from the workshop website available at https://frontuq18.org/. The second edition of the outreach workshop “Frontiers
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
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
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
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
