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AM Seminar: A Multistage Distributionally Robust Optimization Approach to Water Allocation under Climate Uncertainty

Speaker Name: 
Jangho Park
Speaker Title: 
Postdoctoral Researcher
Speaker Organization: 
Lawrence Berkeley National Laboratory
Start Time: 
Monday, November 23, 2020 - 4:00pm
End Time: 
Monday, November 23, 2020 - 5:00pm
Via Zoom Presentation
Assistant Professor Marcella Gomez


We investigate a Multistage Distributionally Robust Optimization (MDRO) approach to water allocation under climate uncertainty. The MDRO is formed by creating sets of conditional distributions (conditional ambiguity sets) on a finite scenario tree. The distributions in the conditional ambiguity sets remain close to a nominal conditional distribution according to a phi-divergence (e.g., Kullback-Leibler divergence, Hellinger distance, Burg entropy, etc.). In this talk, we discuss a decomposition algorithm to solve the resulting MDRO and apply the modeling and solution techniques to allocate water in a rapidly-developing area of Tucson, Arizona. Tucson, like many arid and semi-arid regions around the world, faces considerable uncertainty in its ability to provide water for its citizens in the future. The primary sources of uncertainty in the Tucson region include (1) unpredictable population growth, (2) the availability of water from the Colorado River, and (3) the effects of climate variability on water consumption. We integrate forecasts for all these sources of uncertainty into a single optimization model for robust and sustainable water allocation. This model is then used to analyze the value of constructing additional treatment facilities to reduce future water shortages. The results indicate that the MDRO approach can be very valuable for water managers by providing insights to minimize their risks and help them plan for the future.


Jangho Park is a postdoctoral researcher in the Center for Computational Sciences and Engineering in the Computational Research Division at the Lawrence Berkeley National Laboratory.  He received his Ph.D. in Operations Research from the Department of Integrated Systems Engineering at The Ohio State University. He also received his M.S. degree in Industrial Engineering from Pohang University of Science and Technology, Pohang, South Korea, and a B.S. degree in Industrial Engineering from Hanyang University, Seoul, South Korea. He is broadly interested in decision making under uncertainty, including models and methods for stochastic programming, distributionally robust optimization, and optimization for machine learning. He applies these models and methods for water resources management, such as water allocation and forecasting groundwater levels.

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