Current water reservoir operating policies are facing growing water demands as well as the uncertainties associated with a changing climate. Two factors strongly limit the possibility of re–designing water reservoir regulation: institutional inertia and myopia. First, the legacies of historical agreements and regulatory constraints limit the rate that reservoir operations are innovated and creates policy inertia. Water institutions are highly unlikely to change their current practices in absence of a dramatic failure or water conflict. Yet, no guarantee exists that historical management policies will not fail in coming years, especially as water managers face growing water demands and increasingly uncertain hydrologic regimes. Second, in reference to institutional myopia, although it has long been recognized that water reservoir systems are generally framed in heterogeneous socio-economic contexts involving a myriad of conflicting, non-commensurable operating objectives, our broader understanding of the multi-objective consequences of current operating rules is severely limited.
This research 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 tradeoffs between alternative policies for balancing evolving 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. The proposed framework initially uses available streamflow observations to implicitly identify the current but unknown operating policy of Conowingo Dam. We assume that the dam operator is a rational agent seeking to maximize primary operational objectives (i.e., guaranteeing the public water supply and maximizing the hydropower revenue). The quality of the identified baseline policy is validated by its ability to replicate historical release dynamics. Starting from this baseline policy, we then combine evolutionary many-objective optimization with visual analytics to discover new operating policies that better balance the tradeoffs within the Lower Susquehanna. Our results confirm that the baseline operating policy, which only considers deterministic historical inflows, significantly overestimates the reliability of the reservoir’s competing demands. The proposed framework removes this bias by successfully identifying alternative reservoir policies that are more robust to hydroclimatic uncertainties while also better addressing the tradeoffs across the Conowingo Dam’s multi–sector services.