Abstract
This work focuses on developing methods to better manage significant imbalances between water supply and demand during droughts. A service-driven approach (Model as a Service, or MaaS) is used to couple river modeling services with optimization services for determining optimal water allocation strategies under daily drought scenarios. It demonstrates the promise of coupling simulation-optimization model services to improve real-time water management in a service driven framework, which should be beneficial to many other water resource applications. The approach is implemented using the DataWolf workflow tool and AzureML Cloud machine learning services and applied to an April 2015 drought event in the Upper Guadalupe River Basin, Texas. Weather and water demand uncertainty are considered through scenario-based optimization. The optimization objective is to minimize the daily total curtailment hours across all groups of permit holders. The scenario analysis shows that the current permit grouping system has a significant impact on the optimal water allocation strategy. The scenarios also demonstrate that noncompliance of junior water users is predicted to have a much greater effect on the river system than noncompliance of senior water users. The resulting framework can be deployed for water allocation in any area by updating water user information, water allocation policy constraints, and river data that can be obtained from publicly available sources.
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Data Availability Statement
Some or all data, models, or code used during the study were provided by a third party. Direct requests for these materials may be made to the provider as indicated in the Acknowledgements. The data used in this paper were provided by Kathy Alexander, Cindy Hooper, and others from the Texas Commission on Environmental Quality (TCEQ) and are not publicly available. The code generated during this study can be accessed at https://github.com/tzhao2017/service-driven-modeling. The workflow tool that was used to implement the model services, DataWolf, is open-source and is available at https://datawolf.ncsa.illinois.edu/. The machine learning model development and deployment service applied in this work, Microsoft AzureML, can be accessed through https://azure.microsoft.com/en-us/services/machine-learning/.
Acknowledgments
The authors are grateful to Kathy Alexander, Cindy Hooper, and others from the Texas Commission on Environmental Quality (TCEQ) for providing research data and assistance with formulating the problem. The authors also acknowledge Microsoft Research for funding support, and the Microsoft Azure data science team for technical support.
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Published In
Journal of Water Resources Planning and Management
Volume 147 • Issue 11 • November 2021
Copyright
© 2021 American Society of Civil Engineers.
History
Received: Sep 9, 2020
Accepted: Jun 18, 2021
Published online: Sep 1, 2021
Published in print: Nov 1, 2021
Discussion open until: Feb 1, 2022
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