Simona Denaro

Hydrologic variability can cause large swings in hydropower generation, inducing significant volatility in power sales. Dry years often result in low revenues that can threaten a hydropower supplier’s ability to meet its fixed costs, leading to budget shortfalls, lower credit ratings, higher interest rates, and, ultimately, higher rates. This is particularly true for suppliers in hydropower-dominated regions, such as the Bonneville power administration (BPA). The BPA strategy for managing its hydrologic financial risk is multilayered, involving cash reserves, a line of credit, and tariff adjustments. Yet, compared to its long-term energy contracts and debt service, BPA’s risk assessment is conducted on a short-term basis, thereby neglecting medium- and long-term temporal dynamics impacting their financial risk. This paper focuses on (1) evaluating BPA’s hydrologic financial risk; and (2) testing the effectiveness of BPA’s existing risk management strategy. Results suggest that BPA’s financial risk will grow substantially over the next 20 years as its risk management tools become increasingly inadequate, providing cautionary lessons for organizations in similarly hydrodominated systems despite the current use of common risk management tools.

Worldwide, conflict over shared water resources is exacerbated by population growth, economic development, and climate change. In multipurpose water systems, stakeholders can face higher financial risks as a consequence of the higher costs or lower revenues arising from increased hydrological variability and recurrent extreme events. In this context, financial risk hedging tools able to bundle together the uncorrelated risks of wet and dry periods faced by different stakeholders may be an improved solution to both foster cooperation and manage the financial losses associated with extreme events. In this work, we explore the potential of risk diversification strategies, involving an index insurance contract solution, to simultaneously manage financial risk in a multipurpose water system prone to both drought and flood risk. Risk diversification can allow for reduced insurance premiums in situations in which the bundled risks are uncorrelated. Using an integrated operational and financial model, we characterize the financial risk associated with the water management policies and design a risk management strategy that bundles together the uncorrelated risk of drought and flood events. The approach is demonstrated using a case study on Lake Maggiore, a subalpine transboundary regulated lake whose management is controversial due to numerous and competing human activities. In particular, we focus on the ongoing conflict among the lake shore population, affected by flood risk, and the downstream farmers' districts, facing drought related losses. Results indicate that bundling uncorrelated risks from competing users is beneficial to both promoting insurance premium affordability and facilitating collaboration schemes at the catchment scale.

Climate change and anthropogenic pressures are expected to reduce global freshwater availability and to exacerbate water crises in the near futures. To manage water crisis, the most common path of the past century has been a “hard-path”, based on centralized structural actions, strongly affecting the environment. This work aims at developing novel adaptive water management control strategies, in a multi-stakeholders context, based on soft-path measures. We focus on the direct, data-driven use of exogenous information to improve the systems anticipation capability. Specifically, the value of snow data in informing water resource systems operation is explored using a model free approach. The underline idea is that information on the snow water equivalent (SWE) in the basin may be relevant to manage the reservoir releases. To this aim SWE estimates are retrieved from SAR COSMO-SkyMed X-band images. The approach is demonstrated on snow-rain fed river basin in the Italian Alps: the Lake Como watershed. Preliminary results show the relevance of snow information to the reservoir management as well as the potential for remote sensed products in data-driven optimization.

Water resource management (WRM) through dams or reservoirs is worldwide necessary to support key human‐related activities, ranging from hydropower production to water allocation and flood risk mitigation. Designing of reservoir operations aims primarily to fulfill the main purpose (or purposes) for which the structure has been built. However, it is well known that reservoirs strongly influence river geomorphic processes, causing sediment deficits downstream, altering water, and sediment fluxes, leading to riverbed incision and causing infrastructure instability and ecological degradation. We propose a framework that, by combining physically based modeling, surrogate modeling techniques, and multiobjective (MO) optimization, allows to include fluvial geomorphology into MO optimization whose main objectives are the maximization of hydropower revenue and the minimization of riverbed degradation. The case study is a run‐of‐the‐river power plant on the River Po (Italy). A 1‐D mobile‐bed hydro‐morphological model simulated the riverbed evolution over a 10 year horizon for alternatives operation rules of the power plant. The knowledge provided by such a physically based model is integrated into a MO optimization routine via surrogate modeling using the response surface methodology. Hence, this framework overcomes the high computational costs that so far hindered the integration of river geomorphology into WRM. We provided numerical proof that river morphologic processes and hydropower production are indeed in conflict but that the conflict may be mitigated with appropriate control strategies.

This work aims at integrating the understanding of the river geomorphic dynamic into the planning of reservoir operation rules. The case study is a...