In our daily lives, there is always uncertainty or lack of certainty due to lack of information and knowledge. When making decisions, we all want to quantify and reduce uncertainty.
This is also the case in Earth and environmental sciences because Earth and environmental systems are open and complex. So, when studying these systems, we need to quantify and reduce uncertainty to support science-informed decisions for many important issues such as energy safety and environmental sustainability. With information of uncertainty, risks of the decisions can also be estimated for policymaking.
The Department of Energy (DOE) Early Career Research Program Award allowed me to develop interdisciplinary approaches to quantify and reduce uncertainty in environmental studies.
I collaborated with colleagues in multiple research areas. We worked with applied mathematicians to develop new algorithms. These new, sparse grid algorithms now save substantial computational time when one needs to run a model many hundreds of thousands of times.
We also collaborated with computational statisticians to develop new Monte Carlo algorithms. They can efficiently search for optimum solutions in a high-dimension space, such as a space with tens of dimensions for environmental parameters. The algorithms also allow us to accurately estimate quantities of the high-dimension space needed for uncertainty quantification. One such algorithm was later incorporated into a public domain software. The software has since been widely used in environmental modeling.
To reduce uncertainty, we focused on identifying important system processes. This uses our limited time and resources to better understand what processes have the biggest impacts. We developed a new statistical index, called the process sensitivity index. Public domain software was developed for evaluating the index. This index is now used by our colleagues in ecology.
My research contributions have been recognized by both the geology and civil engineering communities. In 2012, I was elected as a Fellow of the Geological Society of America. In 2015, I received the Huber award. The Walter L. Huber Civil Engineering Research Prize from the American Society of Civil Engineers is awarded to researchers younger than 40 years old. My career achievements would not be possible without the DOE Early Career Research Program Award.
Ming Ye is a professor in the Department of Earth, Ocean, and Atmospheric Science and Department of Scientific Computing at the Florida State University.
The Early Career Research Program provides financial support that is foundational to early career investigators, enabling them to define and direct independent research in areas important to DOE missions. The development of outstanding scientists and research leaders is of paramount importance to the Department of Energy Office of Science. By investing in the next generation of researchers, the Office of Science champions lifelong careers in discovery science.
For more information, please go to the Early Career Research Program.
Title: Computational Bayesian Framework for Quantification and Reduction of Predictive Uncertainty in Groundwater Reactive Transport Modeling
Subsurface environmental systems, in which intricate biogeochemical processes interact across multiple spatial and temporal scales, are open and complex. Understanding and predicting system responses to natural forces and human activities is indispensable for environmental management and protection. However, predictions of the subsurface system are inherently uncertain, and uncertainty is one of the greatest obstacles in groundwater reactive transport modeling. The goals of this project are to (1) develop new computational and mathematical methods for quantification of predictive uncertainty and (2) use the developed methods as the basis to develop new methods of experimental design and data collection for reduction of predictive uncertainty. The proposed computational Bayesian framework is general and compatible with other widely used reactive transport models and numerical codes, so the advances can be easily applied to gain insights into subsurface biogeochemical processes that occur across a wide range of field sites and environmental conditions.
G Zhang, D Lu, M Ye, M Gunzburger, and C Webster, “An adaptive sparse-grid high-order stochastic collocation method for Bayesian inference in groundwater reactive transport modeling.” Water Resources Research 49, (2013). [DOI:10.1002/wrcr.20467]
H Dai, M Ye, AP Walker, and X Chen, “A new process sensitivity index to identify important system processes under process model and parametric uncertainty.” Water Resources Research 53, 3476 (2017). [DOI: 10.1002/2016WR019715]
AP Walker, M Ye, D Lu, MG De Kauwe, L Gu, BE Medlyn, A Rogers, and SP Serbin, “The Multi-Assumption Architecture and Testbed (MAAT v1.0): R code for generating ensembles with dynamics model structure and analysis of epistemic uncertainty from multiple sources.” Geoscientific Model Development 11, 3159 (2018). [DOI: 10.5194/gmd-11-3159-2018]
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