Assessing the operational value of short-term forecast information under climate change

<p>Climate change is expected to increase the variability of hydrological regimes, generating more recurrent and intense floods and droughts. This trend will very likely diminish the resilience of reservoir systems in supplying water, controlling floods, and generating energy. While forecast information has proven valuable for improving water systems operations under stationary hydroclimatic conditions, little is known about its potential value in more variable regimes and its capacity in mitigating the increased risks. In this work, we propose a framework to quantify the future operational value of forecast information under different climate change projections. Specifically, a stochastic model replicating observed forecast error is calibrated over a hindcast dataset from the Subseasonal to Seasonal (S2S) prediction project and used to generate synthetic forecasts for future hydrologic scenarios. Then, a policy search routine is used to design optimal operating policies informed by the forecast information. The forecast operational value is quantified by comparing the performance of these policies against a baseline solution not informed by any forecast and an upper bound solution which uses perfect knowledge of the future. This experiment is performed on a case study of Folsom Reservoir, California. Results indicate that the use of forecasts can improve future operations both in terms of water supply and flood control. We assess the forecast value in two distinct forms: the absolute value, which is the total gain generated by the use of forecast information and aligns with the provider point of view, and the relative value, which measures the gain with respect to the no-forecast case and relates to the reservoir operator perspective. The absolute value of forecasts is projected to increase for all selected scenarios. Conversely, projected relative forecast value depends on the nature of the climate scenario, increasing in wet scenarios while decreasing in dry scenarios. This experiment suggests that risks associated with increasing precipitation variability on seasonal to interannual timescales can be at least partially mitigated by the use of short-term forecasts. Future work will consider the potential for the forecast error structure to change over time as a result of climate change and improved weather models.</p>

Fuzzy reliability-based optimization of a hydropower reservoir

Reservoir operation modeling and optimization are inevitable components of water resources planning and management. Determination of reservoir operating policy is a multi-stage decision-making problem characterized by uncertainty. Uncertainty in inflows and power demands lead to varying degrees of the working of a reservoir from one period to another. This transition, being ambiguous in nature, can be addressed in a fuzzy framework. The different working states of the reservoir are described as fuzzy states. Based on the degree of success in meeting the power demand and randomness associated with inflows, hydropower production is considered as a random fuzzy event. This paper examines the scope of profust reliability theory, a theory used in the reliability analysis of manufactured systems, in the performance optimization of a hydropower reservoir system. The operating policy derived from a profust reliability-based optimization model is compared with a simulation model. The model is then used to derive the optimal operation policy for a hypothetical reservoir fed by normally distributed inflow, for a period of five years. The results show that the model is useful in deriving optimal operating policies with improved reliabilities in hydropower production.