HS5.3.2

Highly varying hydro-climatological conditions, multi-party decision-making contexts, and the dynamic interconnection between water and other critical infrastructures create a wealth of challenges and opportunities for water resources planning and management. For example, reservoir operators must account for a number of time-varying drivers, such as the downstream users’ demands, short- and long-term water availability, electricity prices, and the share of power supplied by wind and solar technologies. In this context, adaptive and robust management solutions are paramount to the reliability and resilience of water resources systems. To this purpose, emerging work is focusing on the development of models and algorithms that adapt short-term decisions to newly available information, often issued in the form of weather or streamflow forecasts, or extracted from observational data collected via pervasive sensor networks, remote sensing, cyberinfrastructure, or crowdsourcing.
In this session, we solicit novel contributions related to improved multi-sectoral forecasts (e.g., water availability and demand, energy and crop prices), novel data analytics and machine learning tools for processing observational data, and real-time control solutions taking advantage of this new information. Examples include: 1) approaches for incorporating additional information within control problems; 2) methods for characterizing the effect of forecast uncertainty on the decision-making process; 3) integration of information with users’ preferences, behavioral uncertainty, and institutional setting; 4) studies on the scalability and robustness of optimal control algorithms. We welcome real-world examples on the successful application of these methods into decision-making practice.

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Convener: Matteo Giuliani | Co-conveners: Stefano Galelli, Julianne Quinn, Amaury Tilmant
Orals
| Tue, 09 Apr, 08:30–10:15
 
Room 2.31
Posters
| Attendance Tue, 09 Apr, 10:45–12:30
 
Hall A
Highly varying hydro-climatological conditions, multi-party decision-making contexts, and the dynamic interconnection between water and other critical infrastructures create a wealth of challenges and opportunities for water resources planning and management. For example, reservoir operators must account for a number of time-varying drivers, such as the downstream users’ demands, short- and long-term water availability, electricity prices, and the share of power supplied by wind and solar technologies. In this context, adaptive and robust management solutions are paramount to the reliability and resilience of water resources systems. To this purpose, emerging work is focusing on the development of models and algorithms that adapt short-term decisions to newly available information, often issued in the form of weather or streamflow forecasts, or extracted from observational data collected via pervasive sensor networks, remote sensing, cyberinfrastructure, or crowdsourcing.
In this session, we solicit novel contributions related to improved multi-sectoral forecasts (e.g., water availability and demand, energy and crop prices), novel data analytics and machine learning tools for processing observational data, and real-time control solutions taking advantage of this new information. Examples include: 1) approaches for incorporating additional information within control problems; 2) methods for characterizing the effect of forecast uncertainty on the decision-making process; 3) integration of information with users’ preferences, behavioral uncertainty, and institutional setting; 4) studies on the scalability and robustness of optimal control algorithms. We welcome real-world examples on the successful application of these methods into decision-making practice.