Dear Colleagues,

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 of Uncertainty Quantification” (FrontUQ) organized by the GAMM Activity Group on Uncertainty Quantification will be held on 5-7 September 2018 in Pavia, Italy, and will be focused on Uncertainty Quantification in Subsurface Environments (see this website for first edition of the workshop, which was held in Munich, Germany, September 6-8, 2017).

Subsurface environments host natural resources which are critical to the needs of our society and to its development. The diversity of problems encountered in subsurface modeling naturally calls for multi-disciplinary research efforts, with contributions from a wide range of fields including, e.g., mathematics, hydrology, geology, physics and biogeochemistry. Complex mathematical models and numerical techniques are then often required to tackle the simulation of coupled processes which are ubiquitous in relevant applications. In this context, our knowledge of the structure and properties of subsurface porous media is of critical importance to parameterize these models, but yet is incomplete, the first and simple reason being that the subsurface itself is not easily accessible to our direct observation. Estimation of input parameters is then plagued by uncertainty and so are consequently target output variables. Designing effective numerical methods dealing with uncertainties becomes crucial in every phase of the workflow, from the solution of inverse problems for the identification of the flow and transport properties to the forward propagation of the uncertainties, the sensitivity analysis and the design of management policies.  This workshop gathers researchers with different backgrounds active in the area, presenting both advances in dedicated uncertainty quantification techniques and real-world test cases.

Ming

Grand Challenges:

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 reduction). For example, due to development of public-domain software (e.g., PEST, UCODE, and DREAM), uncertainty quantification using regression and Bayesian methods has become a common practice not only in academic but also in consulting industry and govern agencies. However, the hydrologic uncertainty community are still facing several grand challenges that have not been fully resolved, and new challenges emerge due to changing hydrology and environmental conditions. Below are three grand challenges that may be addressed in the coming decade:

(1)               Software development: Software for supporting decision-making and for communicating uncertainty with decision-makers and stakeholders is still needed. Given that there have been a number of software in the public domain, it appears to be necessary to launch an effort for community software development such as building libraries of uncertainty quantification, visualization, and communication. An effort is also needed to closely collaborate with software developers of physical models, so that uncertainty quantification can be built as a module of the modeling software for efficient and effective operations.

(2)               Information and knowledge extraction from data: While new technologies of data across multiple scales collection are always needed, it is of tantamount importance to develop methodologies that can extract information and knowledge from data. This includes identification of new and overlooked data needs (e.g., water management data such as water use), revisit of existing data (e.g., those collected by NASA or NOAA but have not been analyzed), and development of machine learning and deep learning methods suitable to hydrologic research. Machine learning is a hot topic in many research fields, and its value to hydrologic uncertainty quantification (especially on reducing model structure uncertainty) has not been intensively explored. A particular need

(3)               Computationally efficient algorithms: Uncertainty quantification nowadays mainly relies on Monte Carlo approaches, which is computationally expensive particularly for new models that are more complex than models several decades ago. Computationally efficient algorithms (e.g., parallel computing and surrogate modeling) will enable us to conduct more comprehensive and accurate uncertainty quantification. The effort of algorithm development requires close collaboration with scientists in other disciplinaries such as applied mathematics, statistics, and computational science.         

We feel that the research field of hydrologic uncertainty is in its transition stage in two sense. First, substantial progress has been made in the past but we need to finish the last mile. For example, we have developed many methods for uncertainty quantification, but need to work on efficient and effective communication of uncertainty to decision-makers and stakeholders. In addition, we are facing new challenges of developing more advanced methodologies to make a full use of existing data and emerging computational hardware and algorithms.

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 principles in watersheds and complex systems? How do we construct models that allow for direct learning form data, but also allow us to prescribe what we do know about the biogeophysics of ecohydrological systems?

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).

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 the meantime, to ask for your inputs. 

AGU is planning for a number of activities for this year’s AGU Centennial celebration. One of them is to identify the breakthroughs that have been made in the last contrary. WRR (and actually all AGU journals) will have a special issues for hydrologic “game changers”, e.g., paradigm-shift concepts, innovative sensor techniques, and computational algorithms and/or software. The identification of breakthrough is more like a review of academic history of hydrology. How were the breakthroughs initiated? How did they get there? Which paper or papers were they originally published? What have we learned during the breakthrough-making process? For our TC, it would be interesting that we identify the game changers for hydrologic uncertainty analysis. Please come up with one or two breakthroughs, and justify why you think that they are truly breakthroughs.

Another activity is to identify grand challenges for AGU communities. This links to the effort of Unresolved Problem in Hydrology (UPH) initiated recently by the International Association of Hydrological Science, and more information of UPH can be found at https://iahs.info/IAHS-UPH.do. It would be interesting to identify the uncertainty-related grand challenges and to also offer some solution from your own perspective. The list of grand challenges may be useful for organizing a Chapman Conference in next couple of years focusing on UQ. Again, please come up with one or two grand challenges and offer your insights for addressing the challenges.

We always focus on a narrow range of problems related to our research, and these AGU requests help us think something BIG. I personally view it as a great opportunity to reexamine our own research and the research of the UQ community, so that the TC can offer thoughtful and insightful guidelines to the UQ community.

Three last but not least notes:

(1)   The AGU nomination deadline is 3/15/2018. Please nominate our colleagues for the hydrology section awards.

(2)   AGU has started accepting session proposals, and the deadline is 4/18/2018.

(3)   The hydrology section is exploring the idea of TC-led sessions, i.e., a session proposed by each TC for promoting the TC theme research. Should you have ideas for TC-led sessions, please let me know.

Please feel free to comment on this post, and add your inputs to the identification of game changes and grand challenges.