Jonathan Quebbeman, PhD, researches applied water resource engineering systems, statistical hydrology, and data driven decision-making. His work includes precipitation analysis, synthetic weather generation, vegetation and micro-climate, transpiration, and runoff generation. Climate change is actively influencing all of these processes and our expectations of how water resource systems should be managed. Extreme hydrologic risks are changing for flood events, water supply expectations, and therefore impacting how systems should be managed.
From a system operations perspective, Dr. Quebbeman utilizes hydrologic responses linked to system operations and management in both real-time and planning frameworks. He applies optimization techniques such as linear programming, dynamic programming, and heuristic techniques including genetic algorithms and reinforcement learning algorithms to help inform planning and operational decisions.
Within the hydropower domain, Dr. Quebbeman brings together hydrologic forecasting tools for development of ensembles used within operational decision-support frameworks for flood mitigation, drought hedging, generation optimization, and resource value management (e.g., recreational planning, habitat assessment, and ancillary services). His many years in both the consulting industry and academic world bring together a holistic view of water resources systems and management with advanced techniques for utilizing large and complex information in engineering and decision-making processes.
Recent Project work:
- Department of Energy HydroWIRES Program: Power Grid-Environmental Tradeoffs with Oak Ridge National Lab
- World Bank India: Consultancy Services on Extended Hydrological Prediction/Multi-week Forecast
- World Bank India: Assam Integrated River Basin Management Project
- Texas Water Development Board: Water Supply (firm yield) Uncertainty Assessment