Many-objective reservoir policy identification and refinement to reduce policy inertia and myopia in water management

• Published in: Water resources research 2014-04, Vol.50 (4), p.3355-3377
• Main Authors: Giuliani, M., Herman, J. D., Castelletti, A., Reed, P.
• Format: Article
• Language: English
• Subjects: Decision trees; direct policy search; Environmental regulations; evolutionary algorithm; Freshwater; Hydroelectric power; Marine; multiobjective optimization; Nuclear energy; Optimization; reservoir operation; Reservoirs; River basin management; River basins; Rivers; visualization; Water bodies; Water management; Water supply
• ISSN: 0043-1397; 1944-7973
• DOI: 10.1002/2013WR014700

This study contributes a decision analytic framework to overcome policy inertia and myopia in complex river basin management contexts. The framework combines reservoir policy identification, many‐objective optimization under uncertainty, and visual analytics to characterize current operations and discover key trade‐offs between alternative policies for balancing competing demands and system uncertainties. The approach is demonstrated on the Conowingo Dam, located within the Lower Susquehanna River, USA. The Lower Susquehanna River is an interstate water body that has been subject to intensive water management efforts due to competing demands from urban water supply, atomic power plant cooling, hydropower production, and federally regulated environmental flows. We have identified a baseline operating policy for the Conowingo Dam that closely reproduces the dynamics of current releases and flows for the Lower Susquehanna and thus can be used to represent the preferences structure guiding current operations. Starting from this baseline policy, our proposed decision analytic framework then combines evolutionary many‐objective optimization with visual analytics to discover new operating policies that better balance the trade‐offs within the Lower Susquehanna. Our results confirm that the baseline operating policy, which only considers deterministic historical inflows, significantly overestimates the system's reliability in meeting the reservoir's competing demands. Our proposed framework removes this bias by successfully identifying alternative reservoir policies that are more robust to hydroclimatic uncertainties while also better addressing the trade‐offs across the Conowingo Dam's multisector services. Key Points We capture the historical reservoir operation via implicit policy identification The current policy is refined via many‐objective optimization under uncertainty Visual analytics helps DMs to overcome policy inertia and myopia