• Energy Research

Summary This article describes the development and calibration of a mass and energy balance snowmelt point model for the south-facing slope of the Sierra Nevada Mountains (Spain) and brings attention to snow processes taking place in a Mediterranean site, especially to high evaporation rates. This mountain range has peaks rising to 3500 m, and is located at latitude 37 ∘ N , surrounded by a mild Mediterranean climate. Energy balance over the snowpack is subjected to very changeable weather conditions such as episodic strong low humidity winds, clear skies with very low nocturnal temperatures, intense solar radiation and sudden increases in temperatures. We found the behavior of the snow to be extremely variable throughout the year, especially in regards to melting cycles and evaporation. Simulations, based on snow water equivalent measurements, show that during 2004–2005, 42% of the total snow precipitation evaporated. Only 124 mm of the 300 mm of snowfall collected at the monitoring point actually melted. However, evaporation can range from 80% in December to 20% during the spring months and from 20% to 40% between years. The variability of the balance of energy fluxes acting on the snow cover in this basin means that all fluxes will be important at some point during the accumulation process. This is an excellent context for testing an energy balance model because of the widely diverse situations occurring at this location during the same time period. In order to calibrate the model, it was necessary to remove the stability-correction factors for non-adiabatic temperature gradients of the turbulent transfer, which are important for evaporation. The sensible-heat transfer coefficient in windless conditions is raised to 6 W/m 2 K . An empirical relationship for atmospheric emissivity calculation is derived from the observed relative humidity, as the well-known Brutsaert’s formula clearly underestimated this value under cloudy skies.

The challenge to guarantee energy supply short and medium-term is essential for today’s energy sector in Europe. The high dependence on the weather of the renewable resources is currently moving the facilities management forward to the assessment and planning of the energy production. In this context, this work is focused on the use of forecasting climate data to foresee operation feasibility in small hydropower plants. With this aim, a technological climate service, SHYMAT (Small Hydropower Management Assessment Tool), is conceived as an on-line application targeted at end-users which brings automatically updated information of historical, real time and forecast data as well as local data of the specific hydropower plants. SHYMAT provides forecast of river streamflow and displays it in a user friendly web interface. Moreover this climate service shows information about the water volume available to be turbined (taking into account the minimum environmental flow), the energy production and the number of days of operability of the facility expected according to the forecast provided. This new climate service has been co-designed in tied connection with end users, perfectly suiting their needs and has been developed to be easily applied in other systems in order to satisfy local energy demands.

Snow constitutes a key component of the water cycle, which is directly affected by changes in climate. Mountainous regions, especially those located in semiarid environments, are highly vulnerable to shifts from snowfall to rainfall. This study evaluates the influence of future climate scenarios on the snowfall regime in the Sierra Nevada Mountains, an Alpine/Mediterranean climate region in southern Spain. Precipitation and temperature projections from two future climate scenarios representative concentration pathway (RCP) 4.5 and RCP 8.5, Fifth Assessment Report of the Intergovernmental Panel for Climate Change (AR5 IPCC)) were used to estimate the projected evolution of the snowfall regime on both annual and decadal scales during the period of 2006–2100. Specific snowfall descriptors of torrentiality are also analyzed. A general decrease of the annual snowfall was estimated, with a significant trend that ranged from 0.21 to 0.55 (mm·year−1)·year−1. These changes are dependent on the scenario and region in the study area. However, the major impact of future climate scenarios on the snowfall regime relates to an increased torrentiality of snowfall occurrence, with a decreased trend of the annual number of snowfall days (RCP 4.5: −0.068 (days·year−1)·year−1 and RCP 8.5: −0.111 (days·year−1)·year−1) and an increased trend in the annual mean snowfall intensity (RCP 4.5: 0.006 (mm·days−1)·year−1 and RCP8.5: 0.01 (mm·days−1)·year−1)) under both scenarios. This enhanced torrentiality is heterogeneously distributed, with the most semiarid region, which is currently the one least influenced by snow, being the region most affected within the study area.

The operation feasibility of small hydropower plants in mountainous sites is subjected to the run-of-river flow which is also depending on a high variability in precipitation and snow cover. Moreover, the management of this kind of systems has to be performed with some particular operation conditions of the plant (e.g. turbine minimum and maximum discharge) but also some environmental flow requirements. In this context, a technological climate service is conceived in tight connection with end users, perfectly answering the needs of the management of small hydropower systems in a pilot area, and providing forecast of river streamflow together with other operation data. This paper presents an overview of the service but also a set of lessons learnt related to features, requirements and considerations to bear in mind from the point of view of climate services developers. In addition, the outcomes give insight into how this kind of services could change the traditional management (normally based on the past experience), providing a probability range of future river flow based on future weather scenarios according to the range of future weather possibilities. This highlights the utility of the co-generation process to implement climate services for water and energy fields but also that seasonal climate forecast could improve the business as usual of this kind of facilities.

The highly temporal variability of the hydrological response in Mediterranean areas affects the operation of hydropower systems, especially in run-of-river (RoR) plants located in mountainous areas. Here, the water flow regime strongly determines failure, defined as no operating days due to inflows below the minimum operating flow. A Bayesian dynamics stochastic model was developed with statistical modeling of both rainfall as the forcing agent and water inflows to the plants as the dependent variable using two approaches—parametric adjustments and non-parametric methods. Failure frequency analysis and its related operationality, along with their uncertainty associated with different time scales, were performed through 250 Monte Carlo stochastic replications of a 20-year period of daily rainfall. Finally, a scenario analysis was performed, including the effects of 3 and 30 days of water storage in a plant loading chamber to minimize the plant’s dependence on the river’s flow. The approach was applied to a mini-hydropower RoR plant in Poqueira (Southern Spain), located in a semi-arid Mediterranean alpine area. The results reveal that the influence of snow had greater operationality in the spring months when snowmelt was outstanding, with a 25% probability of having fewer than 2 days of failure in May and April, as opposed to 12 days in the winter months. Moreover, the effect of water storage was greater between June and November, when rainfall events are scarce, and snowmelt has almost finished with operationality levels of 0.04–0.74 for 15 days of failure without storage, which increased to 0.1–0.87 with 3 days of storage. The methodology proposed constitutes a simple and useful tool to assess uncertainty in the operationality of RoR plants in Mediterranean mountainous areas where rainfall constitutes the main source of uncertainty in river flows.

Run of river (RoR) hydropower systems, despite being one of the most cost-effective and environmentally benign energy technologies, have the disadvantage that production is not constant because it is subject to a high variability in precipitation and snow cover. In addition, the management of RoR plants has to comply with some particular operating conditions, but also with some environmental flow requirements. This work presents the assessment of the main inputs included in a climate service, historical local data and the seasonal forecast of water inflow to RoR plants, which are used to predict the operability and the expected energy production. The analysis is presented through the application in a pilot RoR system located in the south of Spain, in a semi-arid Mediterranean area impacted by snow, where seasonal forecasting is especially challenging. The results show the high interannual variability of the operation in this kind of facilities. The outcomes indicate that seasonal climate forecast information would improve the prediction of observed river streamflow by 7.4% in reliability and 3.2% in sharpness compared to the current operational forecast based on historical data. The climate forecasts thus provide valuable information for the exploitation of available water resources, which generates a significant value for the operation of the plant and the energy production market.