From static rules to adaptive policies: developing a forecast-informed reservoir operation for balancing irrigation and ecosystem needs, a case study of Olivo reservoir, Sicily, Italy

Abstract                                                                                                                      

Climate change and anthropogenic activities are threatening the spatiotemporal variabilities of water resources (Samimi et al., 2022; Swain et al., 2020). Particularly, arid and semi-arid regions like the Mediterranean are highly vulnerable to hydroclimatic variabilities and drought-related risks. In this regard, reservoirs play a vital role in moderating hydrologic variabilities and help to buffer water demand deficits (Giuliani et al., 2021). However, many reservoirs are still managed with static rule-based operations, which do not have the flexibility to account for evolving hydrometeorological information, such as inflow forecasts, nor do they readily adapt to changes in climate regimes or water use priorities Tu et al., 2003). In this study, a risk-aware stochastic model predictive control (SMPC) (Castelletti et al., 2023) was proposed for adaptive reservoir operation under uncertain future conditions for the Olivo reservoir located within the Imera Meridionale River basin (IMRB), Sicily, Italy. The proposed SMPC accounts for extreme deficit risk through conditional value-at-risk (CVaR). The framework aims to evaluate the value of using seasonal streamflow forecasts for multi-objective reservoir management within the SMPC framework by comparing four operating strategies: i) baseline standard operating policy (SOP) without forecast, ii) Deterministic model predictive control (MPC) with perfect forecast (pseudo-observed streamflow as forecast), iii) Deterministic MPC using climatological (monthly means from pseudo-observations) as forecast, and iv) SMPC driven by ensemble seasonal streamflow forecast. The results indicated that the ensemble-based SMPC provides significantly better performance over the climatological forecast, demonstrating the positive value of using ensemble forecasts. The perfect forecast-driven MPC provides the upper bound of achievable performance and is used to penalize the forecast. Conversely, the climatological forecast-driven MPC and SOP have shown lower performance in response to hydro climatological extremes, which reflects the averaging effect of the climatological forecast and the blindness of SOP about the future. Overall, the findings may support water managers in risk-aware proactive management of the reservoir stems in the IMRB.

 

Keywords,

SMPC, Forecast Value, FIRO, Conditional Value-at-Risk, Drought, SOP, IMRB

 

References.

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Giuliani, M., Lamontagne, J.R., Reed, P.M., Castelletti, A., 2021. A State-of-the-Art Review of Optimal Reservoir Control for Managing Conflicting Demands in a Changing World. Water Resour Res. https://doi.org/10.1029/2021WR029927

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