Research Assistant Professor
Global Water Futures
Hydrology Research Group
Dept. Civil and Environmental Engineering
University of Waterloo
Email:juliane.mai[at]uwaterloo.ca
Phone:+1 (519) 888-4567 ext. 30016
Office:PHY-3016
Address:200 University Ave West
Waterloo, ON N2L 3G1
ORCID:ORCID iD iconorcid.org/0000-0002-1132-2342

News

Session at AGU Fall Meeting 2017 in New Orleans

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Aug 2017

Bryan Tolson, Amin Haghnegahdar, Kimberly C. Brumble, and I proposed a session for AGU's Fall Meeting 2017 in New Orleans. Since this a merge of several sessions there are actually much more people who were initially involved in proposing this topic as a session. I want to name them here to appreciate their efforts: Grey S. Nearing, Mary C. Hill, Hoshin V. Gupta, Saman Razavi, Jasper Vrugt, James Craig, Matthias Cuntz, Dmitri Kavetski, Rafael Rosolem.
The topic of the session is "Diagnostics, Sensitivity, and Uncertainty Analysis of Earth and Environmental Models". Environmental models are key monitoring and prediction tools used in science and engineering. Complex models not only have uncertain inputs and structure, which propagate into uncertain model outputs, but also continually increase in computation demand. Sensitivity and Uncertainty Analyses (SA/UA) are hence used to understand model structure, characterize uncertainties, and reduce the computational load of such models. This session invites contributions on both theory and application of SA/UA methods, that may include but are not limited to parameter estimation, input data uncertainty quantification, algorithmic improvements, dimensionality reductions, approximation techniques, surrogate models, or surrogate assisted methods. We further welcome contributions focusing on aspects of the model-data interface, which plays an important role in uncertainty quantification.

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Successful proposal in Compute Canada's Research Platforms and Portals (RPP) Competition

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May 2017

We succeeded in a call for proposals in the annual Compute Canada's Research Platforms and Portals (RPP) Competition. Bryan Tolson (University of Waterloo) is the Principal investigator in this project. We proposed the development of the new Canadian Surface Prediction Archive (CaSPAr) platform. CaSPAr is an accessible rolling archive of 10 of Meteorological Service of Canada's numerical weather prediction products. The 500TB platform will allow users to extract specific time periods, regions of interest and variables of interest in an easy to access NetCDF format. CaSPAr and community contributed post-processing scripts and tools are being developed such that the users, for example, can interpolate the data due to their needs or auto-generate model forcing files. Compute Candada guaranteed us 135 core-years computing resources on Compute Canada's new cluster Graham and 264 TB storage for the first year of CaSPAr development. Kurt C. Kornelsen is the core developer of the frontend, I am the main developer of CaSPAr's backend. The whole project is supported by Paulin DL Coulibaly (McMaster University, Hamilton), Francois Anctil (Laval University, Quebec City), Vincent Fortin (Environment and Climate Change Canada), Michael Leahy (Esri Canada) and Brent Hall (Esri Canada).

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Associated Editor for Water Resources Research

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Apr 2017

I was very proud when the new Editor-in-Chief of Water Resources Research Martyn Clark asked me if I would be interested to become an Associated Editor. It is my pleasure to work together with Martyn and the other people serving in the editorial board. I'm looking forward to the new experiences and to have a look behind the scenes of a journal.

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Published article: "The impact of standard and hard-coded parameters on the hydrologic fluxes in the Noah-MP land surface model"

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Sep 2016

Land surface models incorporate a large number of process descriptions, containing a multitude of parameters. These parameters are typically read from tabulated input files. Some of these parameters might be fixed numbers in the computer code though, which hinder model agility during calibration. Here we identified 139 hard-coded parameters in the model code of the Noah land surface model with multiple process options (Noah-MP). We performed a Sobol’ global sensitivity analysis of Noah-MP for a specific set of process options, which includes 42 out of the 71 standard parameters and 75 out of the 139 hard-coded parameters. The sensitivities of the hydrologic output fluxes latent heat and total runoff as well as their component fluxes were evaluated at 12 catchments within the United States with very different hydrometeorological regimes. Noah-MP’s hydrologic output fluxes are sensitive to two thirds of its applicable standard parameters (i.e., Sobol’ indexes above 1%). The most sensitive parameter is, however, a hard-coded value in the formulation of soil surface resistance for direct evaporation, which proved to be oversensitive in other land surface models as well. Surface runoff is sensitive to almost all hard-coded parameters of the snow processes and the meteorological inputs. These parameter sensitivities diminish in total runoff. Assessing these parameters in model calibration would require detailed snow observations or the calculation of hydrologic signatures of the runoff data. Latent heat and total runoff exhibit very similar sensitivities because of their tight coupling via the water balance. A calibration of Noah-MP against either of these fluxes should therefore give comparable results. Moreover, these fluxes are sensitive to both plant and soil parameters. Calibrating, for example, only soil parameters hence limit the ability to derive realistic model parameters. It is thus recommended to include the most sensitive hard-coded model parameters that were exposed in this study when calibrating Noah-MP.

Cuntz, M., Mai, J., Samaniego, L., Clark, M. P., Wulfmeyer, V., Branch, O., et al. (2016). The impact of standard and hard-coded parameters on the hydrologic fluxes in the Noah-MP land surface model. Journal of Geophysical Research: Atmospheres, 1–25. http://doi.org/10.1002/(ISSN)2169-8996

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General Annual Meeting: "Floodnet"

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Sep 2016

As one of my first events after starting with a new PostDoc beginning of September 2016 at the University of Waterloo, I attended the General Annual Meeting of the Floodnet project. Floodnet is a interdisciplinary project where not only academic researcher are involved but also end-users and governmental representatives. The aim of this initiative is to improve the effective flood forecasting in Canada. Sounds interesting! I am happy to be part of this innovative and dynamic team. My contribution to this project is to improve the data assimilation of a hydrologic model including data and parameter uncertainty and the subsequent forecasting based on ensemble meteorological (i.e. precipitation and temperature) forecasts.

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