Besides benefits associated to increased water availability for irrigation, reuse projects of urban water can also provide positive environmental impacts, as they contribute to improve water quality of the receiving bodies by diverting wastewater from their outlet. This represents a typical win-win situation where significant synergies can be achieved between urban and agricultural sector, and the environment. These favorable conditions, however, do not necessarily imply that water reuse is either feasible from an economic perspective nor that the underlying supply chain is going to be triggered, if certain conditions are not met. Cost-Benefit Analysis (CBA) is considered a sound, theoretically well-grounded tool to analyze the financial and economical sustainability of an investment. The paper presents the CBA of an existing reuse scheme in Puglia, in southern Italy, reclaiming wastewater for irrigation from a coastal area with growing recreational, beach-related activities. Supported by operational data, official statistics and sector documents, the CBA reveals that in almost all scenarios the existence of environmental benefits must be invoked in order to consider the project economically sustainable. Coherent screening of the different impacts, isolating the ones that are applicable to the specific case-study, shows that these benefits are mainly non-use benefits related to the aesthetic enjoyment of clean water in the reclaimed stretch of coastline where wastewater discharge may no longer take place or take place in a way that significantly reduce seawater pollution.

Pumped hydro storage (PHS) is one of the more suitable energy storage technologies to provide bulk storage of intermittent renewable energy sources (RES) such as wind. Since the main limiting factors to the expansion of this mature technology are environmental and financial concerns, the use of an existing reservoir can help mitigate both types of impacts. In addition, the high number of reservoirs for municipal and irrigation supply in many areas of the world makes the idea of using PHS as a relatively diffuse, open-market, technology for RES management attractive. These arguments in favor of PHS must, however, be convincing for investors and regulators from an economic standpoint. To this end, this paper presents a methodological tool to screen the feasibility of a PHS facility around an existing reservoir based on the principles of cost–benefit analysis, calibrated with data from Sicily, Italy. Each potential plant is characterized by two locational and two plant-specific parameters. Costs and benefits are assessed through a simulation model of the storage–release process on an hourly basis. Costs include both investment, and operation and maintenance expenditures, while the benefits considered include the opportunity cost of the current energy mix substituted by the stored energy, and the avoided CO₂ emissions. The evaluation exercise is carried out parametrically, i.e., looking at a large number of combinations of the four parameters, in order to explore a wide range of possible plant configurations and to identify optimal ones under different locational conditions. A sensitivity analysis performed on models’ parameters points out the sensitivity of results to benefit, rather than cost-related, input parameters, such as the efficiency of the generating and pumping system and the opportunity cost of energy.

After shaping, in the companion paper, an uncertainty environment where to embed investment appraisal, in this paper we intend to demonstrate how robustness criteria can be used to investigate the behaviour of different investment alternatives in this environment, in a cost-benefit analysis framework. The performance indicator is the NPV (Net Present Value) that also accounts for the life-cycle costs of the alternatives. As an example, three supply-enhancing investments to increase water resources availability, in a part of the multi-purpose water supply system of the Southern Apennine River Basin District (Italy), are evaluated. The effect of each of three identified alternatives is assessed through a simulation model of the system, described in the companion paper, that allows the evaluation of the benefits as the difference between scarcity costs with and without the investment in each of the 486 scenarios considered. Robustness is evaluated with reference to various metrics such as maxi-max, maxi-min, regret-related metrics, and satisficing metrics. The application shows that valuable insights can be gained by systematically comparing the alternatives according to the different robustness criteria: for instance, the investment with the seemingly worst NPV in an evaluation framework ignoring uncertainty, due to its high investment and operation costs, proves to be the second-best option when uncertainty surrounding the supply-demand balance is considered, as it turns out to be the best-performing option in a set of critical scenarios that have an overall non-negligible occurrence probability.

Sustainable water resource management in semi-arid Mediterranean regions is critically dependent on reliable spring discharge forecasting and on quantifying the long-term impacts of climate change on groundwater resources. This study develops and evaluates a suite of non-autoregressive machine learning architectures, MLP, CNN, LSTM, and a hybrid CNN–LSTM, for predicting the monthly discharge of major springs supplying Palermo, Italy. Unlike autoregressive approaches, which are susceptible to error accumulation in multi-step projections and cannot be applied under projected climate conditions, the proposed models rely exclusively on exogenous meteorological inputs, making them inherently suited for operational forecasting and architecturally compatible with long-term climate impact assessment. The CNN–LSTM architecture achieves the best performance among all non-autoregressive configurations, approaching the idealized autoregressive benchmark while avoiding its operational limitations. In the operational phase, the calibrated CNN–LSTM model is driven by bias-corrected ECMWF SEAS5 seasonal forecasts, maintaining useful predictive performance up to six months ahead for RIS, SCI, and the aggregated system (SYS), with more limited skill for GAB at longer lead times. Diebold–Mariano tests confirm that this advantage is statistically significant for two of the three springs, whereas for the third, whose dynamics are dominated by internal aquifer memory, no non-autoregressive architecture prevails. This framework not only enables retrospective analysis of spring discharge behavior but is also, thanks to its non-autoregressive design, structurally suited for future coupling with climate projections, offering water authorities an effective solution for proactive and sustainable management of this crucial Mediterranean water resource.