Analysing hydropower production in stressed river basins within the SEEA-W approach: the Jucar River case

Water is a central element for the existence of all who reside on our planet. From an environmental point of view, water represents the link between the atmosphere, the ocean, the surface of the Earth and the subsoil, between inanimate and living beings, and between nature and humans. From the point of view of society, water has been, is and will be a basic and irreplaceable component of socio-economic activities, agricultural and industrial production, energy generation, as a mean of transportation, among others.

In the coming years, as a consequence of climate change the reduction of water availability is expected, and this reduced water availability will have an impact on hydropower production, at least in Mediterranean areas (Pereira-Cardenal et al. 2014), requiring more efficient use of scarce water resources. The use of burning fossil fuels as a source of energy should be replaced, little by little, by renewable energy sources. As noted by Lehner et al. (2005) ‘electricity generation from hydropower makes a substantial contribution to meeting today's increasing world electricity demands’. In this sense, hydropower production represents a renewable source of energy, which returns the total amount of water used. Moreover, hydropower plants (HPP) can start and stop when energy is required in only a few seconds. Some HPP can concentrate their daily production in some hours in order to obtain the best price of energy. However, as an inconvenience, HPP need to build important infrastructures which are responsible for water losses from evaporation and flow alterations that affect the environment (Scherer & Pfister 2016). Interpreting these properties, we could define many advantages and disadvantages of hydropower (Dincer 2000).

Knowing the amount of water available for the different uses in a river basin is quite challenging, particularly in stressed river basins where water requirements may be even higher than water availability. In this sense, water accounts, defined as the integration of physical and economic information related to water consumption and use, are presented as a tool to achieve the objective of water efficiency (UNDS 2012):

• they have become a very powerful tool for improving water management;

• their methodology is based on a water balance approach (Molden & Sakthivadivel 1999);

• they allow policymakers to compare results between different territories.

One of the most widespread water accounting approaches is the system of economic and environmental accounts for water (SEEA-W), which has been developed by the United Nations Statistic Division in conjunction with the London Group on Environmental Accounting (UNDS 2012). Its main objective has been to provide a conceptual framework for organising economic and hydrological information.

Although the SEEA-W approach seems relatively simple, Dimova et al. (2014) noted that its application involves collecting a great amount of data from many stakeholders and actors. In most cases the information is not complete or cannot be used directly. The difficulty of monitoring all the variables within the water cycle or the mismatch between administrative and river basin boundaries represent its main handicap. Several authors have resorted to the use of simulation models in order to estimate the great amount of information required (Dimova et al. 2014; Pedro-Monzonís et al. 2016a, 2016b; Vicente et al. 2016). Over the last few years, much effort has been invested by the European Commission's DG Environment in applying the SEEA-W approach through several pilot projects (ASSET, DURERO, GuaSEEAW + , PAWA, PROTAGUS, SYWAG, and WAMCD).

In this work, we analyse the main hydropower features and its production within the SEEA-W. We pay special attention to the significance of this source of energy in stressed river basins such as the Jucar River Basin in Spain. In systems like this, the hydropower sector only turbines the water releases for other water uses located downstream. Moreover, HPP can turbine water in cyclical systems, pumping and turbining the same volume of water several times in one day, as in the case of energy storage stations. As results show, the inclusion of water abstracted by the energy sector can distort water balances, since the volume abstracted is often larger than the rest of the uses in the river basin.

Spain has a high hydroelectric tradition, developed over more than a century. Within renewable energy, hydropower is the most established and with the higher degree of maturity, thanks to the terrain orography and to the existence of a large number of dams. In Spain there is a total storage capacity of 55,000 hm³, of which 40% of this capacity corresponds to hydroelectric dams, one of the highest proportions in Europe and in the world. In 2015, the contribution of hydropower to the national electricity power accounted for 10% (IEA 2015). It was the fourth in production technology, behind nuclear (21%), wind (18%) and coal (20%).

The Jucar River Basin district is located in Spain, in the East of the Iberian Peninsula (Figure 1). It is composed of nine water exploitation systems.

Physically, the Jucar River Basin district is described as an interior mountainous zone, with spots at high altitude and a coastal zone composed of plains. The average precipitation is 510 mm/year, presenting a high spatial variability, and the average temperature is 13.6°C. Average natural resources reach 2,929 hm³/year, which represents the top limit of the renewable resources of the basin. The total population depending on the Jucar River Basin district represents a water demand of 502 hm³/year and the water demand for irrigated agriculture reaches 2,560 hm³/year. The supply to urban areas comes mainly from wells and springs, although the main metropolitan areas, such as Albacete, Sagunto and Valencia, use surface/dammed water.

As is shown in Figure 2, comparing the total streamflows with the water demand for urban and agrarian use during an average hydrological year demonstrates that consumptive uses and water resources are not synchronized in time. Water demands are concentrated in harvest months; however, natural resources are slightly higher during the winter and go down in summer. It is noteworthy that natural resources are not reduced dramatically in summer months due to the strong interaction between surface water and groundwater. Thus, joint management of surface and groundwater is carried out by the river basin authority in charge of water management (Pedro-Monzonís et al. 2016b).

Using the values of total water demands (3,062 hm³/year) and total natural resources (2,929 hm³/year) in the Jucar River Basin District for the 1980/81–2012/13 period, a first indicator of the water stress in the district is deduced by the ratio between both values, resulting in a value of 104%, reflecting the high degree of exploitation suffered by this system.

Turning to the hydropower sector in Jucar River Basin District, there are many HPPs, but most of them are small, being the reason why we will focus only on the most important HPPs, which are located in the Jucar Water Exploitation System. Only three of them are available to drive the production, and only one is able to store energy. These four HPPs are the largest in terms of power installed. Figure 3 represents a longitudinal view of the Jucar River where these HPPs are located.

The pumped system (Cortes-La Muela) is one of the largest in Europe and it is an example of the ancient hydroelectric tradition in Spain. It consists of two functionally different elements, which are the Salto de Cortes II and the Pumped Storage Hydropower (PSH) Salto de la Muela. Iberdrola hydroelectric plant, which is composed of Salto de Cortes II and PSH Salto de la Muela, is located in Cortes de Pallás (Valencia) and is the largest pumping station in Europe. It is located on the right bank of the Jucar River and thanks to the four reversible groups installed it takes advantage of the height difference of 500 m existing between the reservoir of La Muela and impoundment of Cortes de Pallas. The plant is capable of producing 1,625 GWh and meets the annual demand of about 400,000 households (El Periódico de la Energía 2016).

SEEA-W is an information system that feeds knowledge into decision-making processes, assisting policy makers in taking informed decisions on allocating water resources efficiently; improving water efficiency; understanding the impacts of water management on all users; getting the most value for money from investment in infrastructure; linking water availability and use, providing a standardized information system which harmonizes information from different sources, it is accepted by the stakeholders and it is used for the derivation of indicators; and getting stakeholders involved in decision-making (European Commission 2016).

The SEEA-W framework considers the flows between the environment and the economy. In relation to the environment, it comprises surface water, groundwater and soil water; and in relation to the economy, it includes the abstractions, imports, exports and returns of the most relevant economic agents. It comprises five categories of accounts: (1) Physical supply and use tables and emission accounts; (2) Hybrid and economic accounts; (3) Assets accounts; (4) Quality accounts; and (5) Valuation (economic) of water resources. This research has focused mainly on physical supply and use tables. As noted before, these tables involve collecting a vast amount of data. Two approaches can be used. The first one implies the use of official statistical data from different databases of diverse administrations. This approach requires a great availability of data and it may present information gaps and problems with closing balances. The second approach includes the use of simulation models in order to obtain the information required.

The methodology employed has been published in Pedro-Monzonís et al. (2016b). Here we present a brief summary (see Figure 4). The approach is represented as a modelling chain comprised of three steps. The first step lies in the hydrological model, which allows us to obtain the state variables and flows within the water cycle in natural regime. Secondly, this information is employed in the Decision Support System (DSS) in order to simulate the allocation of water resources, enabling us to obtain river flows, water supplies or the volumes of water stores in reservoirs. Thirdly, once we know the water allocated for the different uses in the river basin we are able to organise all the data and results in order to construct the tables defined according to the SEEA-W approach, representing the third stage. At this stage, an economic valuation of water services costs is proposed by using results achieved during the assessment of cost recovery of water services, as required by WFD (EP 2000). This economic component is not taken into consideration in this application.

To guide this work we have used PATRICAL hydrological model (Pérez-Martín et al. 2014), AQUATOOL DSS (Andreu et al. 1996) and the acquisition tool AQUACCOUNTS (Pedro-Monzonís et al. 2016b).

The PATRICAL model (Pérez-Martín et al. 2014) is a conceptual, large-scale, monthly and spatially distributed water balance model which includes water quality. It divides the river basin into two vertical layers: a lower zone (working as a semi-distributed model) and an upper zone (working as a distributed model). Its main inputs are monthly air temperature and precipitation. Currently, PATRICAL is used in the Jucar RBD for the River Basin Management Plan, in the analysis of nitrate concentration in groundwater bodies in Spain (Pérez-Martín et al. 2012) and also in the study of climate change impacts on water resources (Estrela et al. 2012).

AQUATOOL (Andreu et al. 1996) is a user-friendly DSS composed of several modules that enables different analysis in water resources systems. One of them is the SIMGES module (Andreu et al. 1996) which can simulate the water resources system, on a monthly time scale. It can consider different water use priorities, the aquifers, the returns to the groundwater and surface environment, the consideration of environmental flows, the infiltration losses and evaporation from reservoirs, the energy production, as well as the definition of operating rules.

AQUACCOUNTS (Pedro-Monzonís et al. 2016b) enables the linking of central variables of the rainfall-runoff model such as actual evapotranspiration, precipitation, infiltration, surface runoff and river-aquifer interaction; with the results of the water allocation model, such as water allocations, reserves, evaporation in reservoirs, among others.

The models include: 28 artificial reservoirs, 18 lakes, 210 user groups and 116 groundwater bodies, identifying the detail and complexity of the system. The greatest amount of data required was provided by Jucar River Basin Authority. In the case of surface water bodies, they include all the rivers, reservoirs and lakes defined by the Centre for Public Works Studies and Experimentation (CEDEX 2005). On the other hand, there are many methodological decisions, such as the reliability criteria, the length of the time period used in the simulation among others which are recommended by the Spanish Guideline of Water Planning (MAGRAMA 2008) with normative status in Spain guaranteeing consistency of the results. The main features of the models built with PATRICAL and AQUATOOL DSS for the case study are accessible in Pérez-Martín et al. (2014) and Pedro-Monzonís et al. (2016b) respectively.

In relation to hydropower modelling, it is included in the water balance model through the incorporation of the data related to the maximum turbinable flow, along with its operating rules that reflect in which conditions hydropower users can exploit the system's water resources. In our case study, HPPs cannot release water from reservoirs; they only can exploit the circulating flow that is released for other purposes. Moreover, among the data required to model HPPs, there is the minimum previously established flow that cannot be turbined and it is supposed to flow through the river in order to minimize the environmental impacts of hydropower. The water balance model also utilizes gross head data and the plant efficiency data to calculate energy production results. These results can then be used in the economic balance step together with an estimated energy price in order to obtain economic outcomes.

The physical use table and the physical supply table within the SEEA-W approach are presented below. The reference period used for the determination of these tables is the average year 1980/81–2011/12. The economic activities are classified according to the International Standard Industrial Classification of All Economic Activities (ISIC) (UN 2008), distinguishing the following groups:

• (a)

  ISIC divisions 1–3, which include agriculture, forestry and fishing;

• (b)

  ISIC divisions 5–33 and 41–43, which include mining and quarrying, manufacturing, and construction;

• (c)

  ISIC division 35: electricity, gas, steam and air-conditioning supply;

• (d)

  ISIC division 36: water collection, treatment and supply;

• (e)

  ISIC division 37: sewerage;

• (f)

  ISIC divisions 38, 39 and 45–99, which correspond to the service industries.

ISIC division 35 includes mainly the hydropower sector and the production of nuclear energy in Cofrentes nuclear power plant. It is noteworthy to highlight that although this division includes other industries linked with the energy sector, such as gas, steam and air-conditioning supply, in the case study, their use of water is irrelevant. Focusing on ISIC division 35, Tables 1 and 2 show that total abstraction comes from surface water resources and that 99.5% of these abstractions return to the environment. The rest of the abstractions (0.5%) are consumed during the evaporation of hydropower reservoirs (12 hm³) and for the cooling system of the nuclear power plant (20 hm³). These turbined flows (6,622 hm³) correspond to 1,131 MW of energy production.

Figure 5 focuses on the influence of the different water uses of the case study. The horizontal axis represents the water uses. In this sense, we distinguish between industries and households. In relation to the legend, as presented in Tables 1 and 2, total abstractions comprise the abstractions from inland water resources (surface, groundwater and soil water), the collection from precipitation and the abstractions from the sea. Total returns represent the returns into the environment, distinguishing surface, groundwater, soil water and other sources (sea water). The use of water received from other economic units includes reused water and transfers from other territories.

As the figure shows, the major use is allocated for agrarian requirements (7,772 hm³/year) and energy production (6,622 hm³/year) followed at some distance by households (376 hm³/year) and service industries (114 hm³/year). On the other hand, according to the use of water received from other economic units, water transferred from other territories in conjunction with reused water plays an important role in the district, representing 81 and 115 hm³/year respectively. Reused water is mainly used in agrarian and industrial supplies. In relation to the returns into the environment, the highest returns come from hydropower production, which are practically equal to its abstractions.

If we focus on hydropower production, these data could be difficult to understand, because the same volume of water goes through many hydropower installations. This fact has been highlighted by Dimova et al. (2014), as HPP in Vit River Basin are situated in cascade, thus the same volume of water is accounted several times. As we observe in the scheme of the hydropower production in Jucar River Basin presented in Figure 3, water flows through three HPPs with reservoirs upstream and downstream; and flows up and down in the pumping station, daily. This fact also may be considered as a weakness of the model, due to the models run at monthly scale and they do not reflect the issue of water moving through the pumped storage facility daily or even hourly.

In connection with hydropower abstractions, the values presented in the tables represent the result of a water allocation model, which might overestimate the hydropower production (which is 1,131 MW for the average year) as it considers that HPPs are operating 24 hours a day. Moreover, the values of hydropower abstractions mislead the main uses of water in the district. As remarked by (Pedro-Monzonís et al. 2016b) this fact is noteworthy given that in stressed river basins the volume of water abstracted for hydropower generation depends on the water resources management and, in the case study, water resources are mainly managed for urban and agrarian uses. Similarly, agriculture abstractions are also masked due to rainfed agriculture, which traditionally has not been considered in water resources planning and management.

This situation is further complicated when we try to build the hybrid tables, since there is a relevant difference depending on the type of HPP considered. In this sense, we distinguish between three types of stations.

• 1.

  Run-of-river stations: they extract the potential energy from water while water flows throw the river.

• 2.

  Reservoir stations: they need one reservoir in order to regulate water on an hourly scale allowing the station to concentrate the production in the best hours within the day.

• 3.

  Pumped storage stations: they consist of an upper and a lower reservoir with turbines, which can generate or store electricity by changing the turbines work between pumping and turbining.

Considering that fact, the economic value of hydropower production is proportional to the volume used and with different proportionality factors depending on the installation considered. The proposed classification of HPP, although it does not completely solve the problem, improves its description in the following aspects: the first type (run-of-river stations) represents an energy production derived directly from the passage of water managed for other purposes, therefore it is a net profit whose only cost is the environmental affection located in the river section affected; the second one (reservoir stations) does cause water losses in the system and therefore it can compete with other uses of water in the system; and thirdly, pumped storage stations move (theoretically) the same volume of water between two reservoirs, so that the volume of water resource used should be considered null, so in this case the proposal is to consider 0 the net water flowing as pure pumping (this decision has already been taken into account in the tables presented). As a result, run-of-river stations use the average of daily prices, reservoir stations consider approximately the maximum daily price and finally, the energy storage (pumping stations) needs to buy energy at a low price to pump it with a higher price. These energy prices are the result of market strategies (Monteiro et al. 2014).

On the other hand, there are some aspects of the SEEA-W tables that should be improved. One of them is the time scale of the analysis because from a hydropower point of view the approach should be designed for shorter time scales, not monthly. The weaknesses in hydropower representation are added to the ones previously noticed in environmental requirements (Pellicer-Martínez & Martínez-Paz 2016), being a critical issue in water stressed river basins. The consideration of environmental water stress and/or the damages on ecosystems caused by hydropower could be another turn of the screw towards the improvement of SEEA-W approach, as other water accounting approaches do, such as water footprint (Scherer & Pfister 2016). It is worth noting that the approach used in this research to obtain SEEA-W tables implies the use of DSSs, which are very powerful tools that enable improvement and extend the information presented by these tables. As noticed in previous works (Pedro-Monzonís et al. 2016a), improving water accounting approaches in order to include environmental needs represents a clear requirement.

The main target of this paper was to analyse hydropower generation in stressed river basins within the System of Environmental-Economic Accounting for Water approach. With this paper the authors have tried to demonstrate that filling the SEEA-W tables needs a significant degree of knowledge about the environment and the economy and the flows between them.

As we have seen, the SEEA-W approach exemplifies a powerful device for describing the water cycle, and improving transparency in water planning and management decisions (Dimova et al. 2014; Pedro-Monzonís et al. 2016a; Vicente et al. 2016). Note that in water planning the most appropriate use of water among several proposed alternatives must be selected. If we are able to simulate each alternative separately and obtain the corresponding SEEAW tables, it will be possible to understand at a glance conclusions about the advantages and costs of each option, favoring the correct decision-making.

In general, the most widely used approach for building SEEA-W tables is the use of hydrological and water management models, which are able to generate the huge amount of required data regarding the different components of the water cycle that cannot be obtained by monitoring and to include the numerous water management alternatives in a river basin.

Regarding hydropower production, there are some key issues that should be better considered. Firstly, the inclusion of water abstracted by the energy sector can distort water balances, since the volume abstracted is often larger than the rest of the uses in the river basin. This aspect is also remarkable when we analyse the abstraction of soil water for rainfed agriculture. Both values mask the level of stress suffered by the system, since reused water and transfers from other territories are unnoticed. Secondly, the volume of water used for energy production is accounted several times due to HPP being placed each one next to the other. Thirdly, depending on the type of hydropower station (run-of-river, reservoir or pumped storage station) the management of the system may be affected by the production of hydroelectricity. The fourth question is the time scale of the analysis, since it has been proved that the inclusion of hydropower production requires the use of daily or even hourly models. Finally, regarding the water scarcity conditions of the case study, it is required to emphasise the competition of hydropower production, not only with the rest of the users of the system, but also with environmental requirements, which are not considered in SEEA-W approach. This information is useful for decision makers to introduce these methodological decisions to guarantee consistency and comparability of the results.

The authors thank the anonymous reviewers for their valuable comments, suggestions and positive feedback. All remaining errors, however, are solely the responsibility of the authors. We would also like to express our gratitude to the Jucar River Basin Authority – Confederación Hidrográfica del Júcar (Spanish Ministry of Agriculture, Food and Environment) for providing data to develop this study. The authors wish to thank the Spanish Ministry of Economy and Competitiveness for its financial support through the NUTEGES project (CGL2012-34978) and ERAS project (CTM2016-77804-P). We also value the support provided by the European Community's Seventh Framework Program in financing the projects ENHANCE (FP7-ENV-2012, 308438), AGUAMOD (Interreg V-B Sudoe 2016), SWICCA (ECMRWF-Copernicus-FA 2015/C3S_441-LOT1/SMHI) and IMPREX (H2020-WATER-2014-2015, 641811).