Assessing Climate Impacts on Hydropower Production: the Case of the Toce River Basin

climate

Article Assessing Climate Impacts on Hydropower Production: The Case of the Toce River Basin

Giovanni Ravazzani 1,*, Francesco Dalla Valle 2, Ludovic Gaudard 3,4, Thomas Mendlik 5, Andreas Gobiet 5,6 and Marco Mancini 1 1 Department of Civil and Environmental Engineering, Politecnico di Milano, Milan 20133, Italy; [email protected] 2 ENEL S.p.A.-Energy Management Division, Mestre 30172, Italy; [email protected] 3 Institute for Environmental Sciences, University of Geneva, Geneva 1205, Switzerland; [email protected] 4 Centre for Environmental Policy, Imperial College London, London SW7 1NA, UK 5 Wegener Center for Climate and Global Change (WEGC), University of Graz, Graz 8010, Austria; [email protected] (T.M.); [email protected] (A.G.) 6 Central Institute for Meteorology and Geodynamics (ZAMG), Graz 8053, Austria * Correspondence: [email protected]; Tel.: +39-02-2399-6231

Academic Editors: Daniele Bocchiola, Guglielmina Diolaiuti, Claudio Cassardo and Yang Zhang Received: 22 January 2016; Accepted: 11 March 2016; Published: 23 March 2016

Abstract: The aim of the presented study is to assess the impacts of climate change on hydropower production of the Toce Alpine river basin in Italy. For the meteorological forcing of future scenarios, time series were generated by applying a quantile-based error-correction approach to downscale simulations from two regional climate models to point scale. Beside a general temperature increase, climate models simulate an increase of mean annual precipitation distributed over spring, autumn and winter, and a significant decrease in summer. A model of the hydropower system was driven by discharge time series for future scenarios, simulated with a spatially distributed hydrological model, with the simulation goal of defining the reservoirs management rule that maximizes the economic value of the hydropower production. The assessment of hydropower production for future climate till 2050 respect to current climate (2001–2010) showed an increase of production in autumn, winter and spring, and a reduction in June and July. Significant change in the reservoir management policy is expected due to anticipation of the date when the maximum volume of stored water has to be reached and an increase of the reservoir drawdown during August and September to prepare storage capacity for autumn inflows.

Keywords: alpine basin; climate change; hydrological impact; hydropower production

1. Introduction According to the Fifth Assessment Report (AR5) of the United Nations Intergovernmental Panel on Climate Change (IPCC) [1], for average annual Northern Hemisphere temperatures, the period 1983–2012 was very likely the warmest 30-year period of the last 800 years. Changes in temperatures and precipitation patterns can have profound effects on river systems and cause important changes in the management of water, particularly on uses highly dependent on the hydrological regime, such as hydropower production [2–4]. In several European countries, this source of energy represents an important part of the electric mix. This technology also has the main advantage of being ﬂexible and of being able to store indirectly electricity at a low cost. These two characteristics will be even more necessary with the high penetration of intermittent sources of energy, e.g., wind and photovoltaic [5]. Hydropower also represents an important source of revenue for their owners and the collectivities in mountain regions through taxes and royalties [6].

Climate 2016, 4, 16; doi:10.3390/cli4020016 www.mdpi.com/journal/climate Climate 2016, 4, 16 2 of 15

The production of this power source depends on inflows and electricity price. Operators maximize the net value considering the constraints, i.e., reservoir volume and turbines capacity. Therefore, hydropower operators tend to increase production when the price is high, i.e., at midday and during winter and summer in Italy. Such management may cause conflicts with concurrent water uses. In the Alpine region, the rising temperatures have resulted in the loss of more than half of the glaciers volume since 1900. With a global temperature increased by 2–4 degrees, 50%–90% of the ice mass coming from mountain glaciers could disappear by the end of this century [7]. With earlier snow melting and rainfall variation, inter-annual run-off is changing towards less water during summer and more during the winter-season [8,9]. Depending on the watershed, the water quantity may increase initially due to the loss of ice stock. However, many case studies show a decrease in runoff in Central Europe [2]. The most common approach used to assess the hydrologic impact of global climate change involves climate models that simulate the climatic effects of increasing atmospheric concentrations of greenhouse gases and hydrological models to simulate the hydrological impacts of climate change [10–12]. The extreme complexity of processes involved in hydrology of mountainous areas and the great spatial variability of meteorological forcings and river basin characteristics require the use of physically based spatially distributed hydrological models to simulate the transformation of rainfall into runoff [13,14]. The present study aims at quantifying the climate change impacts on hydropower production of the Toce Alpine river basin, in Italy, where 18 plants (total installed capacity is about 470 MW) and 10 reservoirs with a total storage capacity of about 139 million m3 are analyzed. Compared to other case studies, only a few researches consider this catchment. Because the local characteristics are important when discussing the impact of climate change on hydrology, this study may increase the knowledge about the impact of global warming in the Alps. The study is performed in three steps: first, a distributed hydrological model driven by climate models is applied to simulate hourly riverflow time series for the period 2001–2050 in 36 representative sections of the hydropower system; second, a mathematical model of the hydropower system is set up to simulate reservoirs management under climate change condition; third, climate change impact is assessed by comparing simulation results of three different periods: period A, years from 2001 to 2010 (hereafter “reference period”), period B, years from 2011 to 2030, and period C, years from 2031 to 2050.

2. Materials and Methods

2.1. Study Area The Toce watershed is a typical glacial basin with steep hill slopes bounding a narrow valley located primarily in the north Piedmont region of Italy, and partially in Switzerland (10% of the total area), with a total drainage area of approximately 1800 km2 (Figure1). Its elevation ranges from 193 m above sea level (asl) at the outlet to approximately 4600 m asl at the Monte Rosa crest. The average elevation is 1641 m asl. A total of 14 major dams are located within the Toce watershed, with a total effective storage capacity of about 151 ˆ 106 m3 [15]. Available meteorological data include hourly temperature and precipitation observations provided by the Regional Agency for Environmental Protection (ARPA Piemonte) from 1 January 2000 to 31 December 2010 at the stations shown in Figure1. For the same period discharge observations were available at Candoglia (1534 km2 basin area). Other hydrometric stations exist along the Toce river and its tributaries, but their reliability is uncertain, so they were not used in this study. For the activity concerning the case study, i.e., the impact of climate change on hydropower production, hydroelectric system managed by the National Entity for Electricity (ENEL) has been taken into account, consisting of 18 plants (total installed capacity «470 MW) (Table1) and 10 reservoirs with a total storage capacity of about 139 ˆ 106 m3, that involve an area of 691.7 km2 within the Toce catchment (Figure1). Climate 2016, 4, 16 3 of 15

In the considered hydrographic catchment, irrigation is not present and drinking water uses are negligible, so management of the reservoirs can be simulated as completely oriented to hydropower production.Climate 2016 Furthermore,, 4, 16 no additional reservoirs are planned in this basin in the future. 3 of 15

18 #S N #S %U#S #S 16 W E #S %U%U 17 #S 15 #S 14 S %U$T 12 #S %U$T #S Reservoir 10 13 %U Power Plant %U%U #S %U #S Hydrometric Station 11 #S 8 $T Weather Station #S $T %U %U Hydropower area 9 6 $T Orta Lake 5%U%U $T River Toce 7 River Network $T 4#S %U 3 DEM ( m a.s.l.) #S%U #S 193 - 700 #S $T 700 - 1200 $T #S #S 1200 - 1700 2 $T #S 1700 - 2200 $T %U 2200 - 2700 #S #S #S %U $T 2700 - 3200 1 #S 3200 - 3700 3700 - 4200 $T $T 4200 - 4600 $T $T $T #S $T#S Candoglia

$T $T $T $T$T 0 10 20 Kilometers $T

Germany $T Austria

Liechtenstein Switzerland

Slovenia

France Italy Croatia

San Marino Monaco

FigureFigure 1. The 1. The Toce Toce watershed watershed extracted extracted from from thethe digitaldigital elevation elevation model model showing showing the thelocations locations of the of the automaticautomatic weather weather and and hydrometric hydrometric stations, stations, reservoirs,reservoirs, power power plants, plants, and and thethe area area included included in the in the analysis of power production. analysis of power production.

Table 1. Power plants show in Figure 1. Table 1. Power plants show in Figure1. Id Power Plant 1 Id PowerVilla Toce Plant 2 Calice 1 Villa Toce 3 Crevola Toce 2 Calice 4 Crevola Diveria 3 Crevola Toce 5 4Varzo Crevola 1 Diveria Diveria 6 5Varzo Varzo 1 1Cairasca Diveria 7 6 VarzoVarzo 1 Cairasca 2 6 7Crego Varzo 2 9 6Verampio Crego 10 9Goglio Verampio Agaro 11 Goglio Devero

Climate 2016, 4, 16 4 of 15

Table 1. Cont.

Id Power Plant 10 Goglio Agaro 11 Goglio Devero 12 Devero 13 Cadarese 14 Fondovalle 15 Ponte Vannino 16 Ponte Morasco 17 Ponte Toggia 18 Morasco

2.2. At-Site Bias-Corrected Climate-Scenario Forcings For the meteorological forcing of future scenarios, two different regional climate models (RCMs) were used, the REMO [16] and RegCM3 [17]. Both models cover Europe on a 25-km ˆ 25-km grid, cover the same simulation period (1951–2100), and are driven by the same global ocean-atmosphere-coupled model, ECHAM5 [18], using the observed greenhouse gas concentrations between 1951 and 2000 and IPCC’s (Intergovernmental Panel on Climate Change) greenhouse gas emission scenario A1B between 2001 and 2100. Both were produced within the EU FP6 Integrated Project ENSEMBLES on a daily basis. Hourly and three-hourly data were provided directly by the Max Planck Institute for Meteorology and the Abdus Salam International Center for Theoretical Physics. In comparison to the larger ensemble of regional simulations for Europe, the REMO and RegCM3 models represent moderate (below average) warming and near-average precipitation changes [19]. Recent research shows the influence of large dams on surrounding climate [20–22]. However, as the specific focus of this work is the assessment of the impact of climate change on hydropower production, and given that no more hydropower is to be developed in the Toce basin, the effect of dams on climate is not considered here. We used a quantile-based error-correction approach (quantile mapping) to downscale the RCM simulations to a point scale and to reduce its error characteristics [23–30]. In our study, the quantile mapping applied observational data from the gauging stations to climate data from the regional climate models on a daily basis. It adapted the modeled time series by adjusting the modeled to the observed empirical cumulative frequency distribution [31]. The method and its application to daily temperature and precipitation sums are discussed by [25,32], and its application to other meteorological variables such as relative humidity, global radiation, and wind speed is described and evaluated by [33]. All of the variables used for this study were error corrected for and downscaled to a station basis: the daily mean air temperature, precipitation sum, mean global radiation, mean relative humidity, and mean wind speed. A 31-day moving window of all of the years in the calibration period centered on the day to be corrected was used for constructing the empirical cumulative frequency distribution for a given meteorological forcing for that particular day of the year. This enabled an annual cycle-sensitive correction as well as a sufficiently large sample size. A point-wise implementation, which fits a separate statistical model for each observational station, was chosen to account for the regionally varying errors. Grid cell averages (3 ˆ 3) of the raw RCM data were used as predictors with respect to the effective resolution of the RCM, which is below the grid-resolution. The calibration period for the error correction ranged from 1 January 2000 to 31 December 2009. No error correction was performed for stations with less than 9 years of observational data (>10% missing data), because the climate variability could not be expected to be properly covered by only a few years of data. Quantile mapping assumes that the same statistical relations of the observed and modeled climate hold within the calibration period, as well as in future scenario periods. It must be kept in mind that even a calibration period of 9–10 years can be affected by decadal climate variability, which can degrade the results of the error correction applied to the future scenario period. Climate 2016, 4, 16 5 of 15

The resulting daily scenarios were further refined to a 3-hourly time series using the sub-daily data from the RCMs. For the air temperature, the differences between the 3-hourly RCM data and their daily-mean values were added to the corresponding corrected-daily values. For precipitation, the ratios of the 3-hourly RCM data and their daily sums were multiplied by the corrected daily sums. Similarly, for the globalClimate radiation,2016, 4, 16 wind speed, and relative humidity, the ratios of the 3-hourly RCM data5 andof 15 their daily mean values were multiplied by the corrected daily value. In the case of relative humidity values data and their daily mean values were multiplied by the corrected daily value. In the case of relative exceedinghumidity 100%, values all ofexceeding the values 100%, of all the of day the values were multipliedof the day were by a multiplied factor to shrinkby a factor the to daily shrin maximumk the valuedaily to 100%. maximum For an value accurate to 100%. assessment For an accura of thiste climate assessment time of series, this climate the reader time isseries referred, the toreader [9]. is referred to [9]. 2.3. The Spatially Distributed Hydrological Model 2.3. The Spatially Distributed Hydrological Model For rainfall-runoff transformation we employed the FEST-WB model (flash–Flood Event–based SpatiallyFor distributed rainfall-runoff rainfall–runoff transformation Transformation, we employed the including FEST-WB Water model Balance (flash–Flood [13,34 Event]) developed–based in FortranSpatially 90 programming distributed rainfall language–runoff on Transformation, top of MOSAICO including library Water [35 ].Balance FEST-WB [13,34]) computes developed the in main processesFortran of 90 the programming hydrological language cycle: evapotranspiration,on top of MOSAICO library infiltration, [35]. FEST surface-WB runoff,computes flow the routingmain in processes of the hydrological cycle: evapotranspiration, infiltration, surface runoff, flow routing in channels and reservoirs, subsurface flow, groundwater flux and interaction with the river flow, and channels and reservoirs, subsurface flow, groundwater flux and interaction with the river flow, and snow melt and accumulation (Figure2). The computation domain is discretized with a mesh of regular snow melt and accumulation (Figure 2). The computation domain is discretized with a mesh of regular squaresquare cells cells (200 (200 m ˆm 200× 200 m m here)here) in each each of of which which water water fluxes fluxes are calculated are calculated at hourly at time hourly step. time step.

Meteo Data

Spatial Interpolation

Snow Pack DEM

Soil Vegetation Glaciers

Least cost Least algorithm

Flow Paths

Parameters Soil Moisture Update

Hillslope and Channel Network Hillslope Channel Definition

Deep Surface Runon Drainage runoff

Groundwater Initial Route: Route: linear condition Muskingum- reservoir Cunge Reservoir routing

Outflow Hydrograph

FigureFigure 2. Scheme 2. Scheme of of the the primary primary features features of the FEST FEST-WB-WB distributed distributed hydrological hydrological model. model.

Flow routing through a reservoir is described using the third-order Runge-Kutta method [36]. FlowRelationship routing between through reservoir a reservoir water is level described and outflow using is the assigned third-order as a lookup Runge-Kutta table for methoda finite [36]. Relationshipnumber of between values. Intermediate reservoir water values level are found and outﬂow by linear is interpolation. assigned as R aeservoirs lookup tableare operated for a ﬁnite numberaccording of values. to a target Intermediate-level policy. values A target are level found was by assigned linear interpolation.for each reservoir Reservoirs and for each are day operated of accordingthe year. toa In target-level case the actual policy. simulated A target reservoir level was level assigned exceeds forthe eachtarget reservoir level, water and is forreleased each dayfrom of the the reservoir at the rate corresponding to the given level. In case the simulated reservoir level is lower than the target level, only the environmental flow is released from the reservoir. For further details about regulation policy, the reader can refer to [8].

Climate 2016, 4, 16 6 of 15 year. In case the actual simulated reservoir level exceeds the target level, water is released from the reservoir at the rate corresponding to the given level. In case the simulated reservoir level is lower than the target level, only the environmental flow is released from the reservoir. For further details about regulation policy, the reader can refer to [8]. Most of parameter maps were produced in fulfillment of the RAPHAEL (Runoff and Atmospheric Processes for flood HAzard forEcasting and controL) European Union research project, the objective of which was to improve flood forecasting in complex mountain watershed [37]. Further details about parameters used by the model can be found in [38]. For further details on the FEST-WB model and its applications, the reader can refer to [39–44]. The calibration of the snow module parameters was performed in a previous study described in [45]. No other parameters were calibrated as the first assigned values, which were based upon measured values, the reference literature, or an educated guess, provided satisfactory results in terms of the time series discharge simulation. The performance of the model was assessed by comparing the simulated and observed discharge at Candoglia hydrometric station in the period from 2001 to 2010. The year 2000 was treated as the period for the model initialization. The performance of the FEST-WB model was assessed through the goodness of fit indices, the root mean square error (RMSE) and the Nash and Sutcliffe efficiency (η), defined as follows:

0.5 n 2 i i Qsim ´ Qobs RMSE “ » i“1 fi (1) ř ` n ˘ — ffi — ffi — ffi – fl n i i 2 Qsim ´ Qobs η “ ´ i“1 1 řn ` ˘ (2) i 2 Qobs ´ Qobs i“1 ` ˘ i ř i where n is the total number of time steps, Qsim is the ith simulated discharge, Qobs is the ith observed discharge, and Qobs is the mean of the observed discharges.

2.4. The Model of the Hydropower System A network of nodes and arcs models the hydropower system (Figure3). Twenty-seven nodes represent intakes and reservoirs, and 64 arcs stand for rivers, channels, hydropower plants and water volume stored into the reservoirs. Reservoir capacity is considered constant along time as sediment deposits are periodically flushed in order to preserve hydropower production. Each node is characterized by an inflow time series. These describe the natural upstream discharge, which is generated by the hydrological model. The optimization of this complex system requires a high computational capacity. It has been opted to keep a hydraulic scheme as close to reality as possible but running the model at a two-hour time step. It keeps a reasonable computational time whilst obtaining adequate results. The runoff time series and price series were adjusted in consequence. The hydropower management simulation assumes that the operator maximizes the profit. For this, the energy prices must be integrated. We consider the “Prezzo Unico Nazionale” (PUN) from 1 January 2006 to 31 December 2010, which is the Italian electric energy market index registered by GME (Gestore dei Mercati Energetici, Energy Markets Operator). The computational limitation did not allow simulating the whole system with 5-year time series. To solve this problem, a “typical” year has been built on the basis of the historical data. Daily, weekly and yearly seasonality have been accounted. The simulations are based on this “reference” year for the whole period (2001–2050). Another concern is that climate change will affect air temperature, thus altering the energy demand [3,46]. Electricity consumption grows below 13 ˝C owing to the use of heating devices and Climate 2016, 4, 16 7 of 15 increases above 18.3 ˝C because of the need for cooling and air conditioning. In other words, the energy consumption in function of the air temperature follows a U-shape. To simulate this relationship between energy consumption and air temperature, one uses the Heating Degree Days, HDDt, and the Cooling Degree Days, CDDt. They are mathematically defined as [47]: Climate 2016, 4, 16 7 of 15 n HDDt pθ q “ max θ ´ θ , 0 (3) th n th e,k HDD k“1 max   ,0 t th ÿ ` th e, k  ˘ (3) nk 1

CDDt pθtcq “ n max θe,k ´ θtc, 0 (4) CDDt tc k“1 max  e, k  tc ,0 (4) ÿk 1 ` ˘ ˝ ˝ where θth and θtc are the threshold temperature for heating (13 C) and cooling (18.3 C), respectively, where θth and θtc are the threshold temperature for heating (13 °C) and cooling (18.3 °C), respectively, and θe,k is the daily mean external temperature with k denoting the day. and θe,k is the daily mean external temperature with k denoting the day.

Figure 3. The upper Toce hydropower system simulation model. Nodes represent intakes and Figure 3. The upper Toce hydropower system simulation model. Nodes represent intakes and reservoirs, arcs represent channel, power plant, river and water volumes stored in reservoirs. reservoirs, arcs represent channel, power plant, river and water volumes stored in reservoirs.

Climate change will alter these two variables. To quantify this effect, we first downscaled the Climate model data by using an Empirical Distribution Delta Method [3]. Based on the outcome, the future HDD and CDD for northern of Italy have been computed.

Climate 2016,, 4,, 16 88 ofof 1515

Finally, the relation between those two variables and electricity demand empirically defined. Climate change will alter these two variables. To quantify this effect, we first downscaled the We considered a log-log autoregressive model as detailed in [3]. It provides the electricity Climate model data by using an Empirical Distribution Delta Method [3]. Based on the outcome, the consumption depending on HDD, CDD; the situation occurring one hour before and an error term. future HDD and CDD for northern of Italy have been computed. Once calibrated by means of econometrics tools, the HDD and CDD for the future climate are injected Finally, the relation between those two variables and electricity demand empirically defined. into the model to test the impact of climate change on electricity demand. We considered a log-log autoregressive model as detailed in [3]. It provides the electricity consumption Once the model of the hydropower system has been set up, the simulation goal was the depending on HDD, CDD; the situation occurring one hour before and an error term. Once calibrated definition of the reservoir management rule that maximizes the economic value of the hydropower by means of econometrics tools, the HDD and CDD for the future climate are injected into the model production. Many methods have been developed to optimize the management of reservoirs by taking to test the impact of climate change on electricity demand. into account prices, technical constraints and installation schemes [48]. With the constant progression Once the model of the hydropower system has been set up, the simulation goal was the definition of computer capacity, computational methods are more common, especially for complex problems. of the reservoir management rule that maximizes the economic value of the hydropower production. In this study, the BPMPD solver was used [49]. Many methods have been developed to optimize the management of reservoirs by taking into account prices,3. Results technical and Discussion constraints and installation schemes [48]. With the constant progression of computer capacity, computational methods are more common, especially for complex problems. In this study, the3.1. BPMPDPerformance solver of the was FEST used-WB [49 Model]. in Reproducing Streamflow

3. ResultsThe FEST and-W DiscussionB model showed good performance in reproducing streamflow at Candoglia with RMSE = 26.7 m3/s and η = 0.81 (Figure 4b). 3.1. PerformanceIn Figure 4 ofa, the the FEST-WB mean flow Model duration in Reproducing curve at Streamﬂow Candoglia section as derived from observed dischargeThe FEST-WB is compared model to that showed derived good from performance simulated in discharge. reproducing Good streamﬂow agreement at Candogliais seen for withboth RMSEthe higher= 26.7 and m 3lower/s and discharges.η = 0.81 (Figure The error4b). in simulating cumulated discharge volume is 0.2%.

(a) 1000 (b) 3000 0

Observed 2500 10 Simulated

/s) 100 2000 20

3 /s) 3 observed discharge 1500 simulated discharge 30 precipitation

Discharge(m 1000 40

10 (mm/h) Precipitation Discharge(m

500 50

1 0 60 0 30 61 91 122 152 182 213 243 274 304 334 365 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 Days

Figure 4. (a(a) )Mean Mean flow ﬂow duration duration curve curve as as derived derived from from observed observed and and simulated simulated discharge discharge;; and and (b) (comparisonb) comparison between between the thesimulated simulated and and observed observed hourly hourly discharge discharge at Candoglia at Candoglia section. section.

3.2. ProjectedIn Figure Changes4a, the in mean Hydrological ﬂow duration Regime curve at Candoglia section as derived from observed discharge is compared to that derived from simulated discharge. Good agreement is seen for both the Impacts of climate changes on hydrological regime are assessed by comparing the FEST-WB higher and lower discharges. The error in simulating cumulated discharge volume is 0.2%. results driven by the REMO and regCM3 for the period B (2011–2030) and period C (2031–2050) 3.2.respect Projected to reference Changes period in Hydrological (2001–2010). Regime In Table 2, mean temperature and annual precipitation simulated by the REMO and RegCM3 Impacts of climate changes on hydrological regime are assessed by comparing the FEST-WB during the three periods are reported. REMO and RegCM3 simulate increase of temperature equals results driven by the REMO and regCM3 for the period B (2011–2030) and period C (2031–2050) respect to 0.53 °C and 0.50 °C, respectively for period 2011–2030, and 1.23 °C and 1.04 °C for the period 2031– to reference period (2001–2010). 2050. REMO and RegCM3 simulate increase of mean annual precipitation equal to 15.24% and 33.1%, In Table2, mean temperature and annual precipitation simulated by the REMO and RegCM3 respectively for the period 2011–2030, and 17.58% and 40.11% for the period 2031–2050. On-going during the three periods are reported. REMO and RegCM3 simulate increase of temperature equals to analysis based on climate scenarios of the Fifth Assessment Report (AR5) from the Intergovernmental 0.53 ˝C and 0.50 ˝C, respectively for period 2011–2030, and 1.23 ˝C and 1.04 ˝C for the period 2031–2050. Panel on Climate Change confirms the increase of temperature for the river Toce area, while annual REMO and RegCM3 simulate increase of mean annual precipitation equal to 15.24% and 33.1%, precipitation is expected to increase or to remain unchanged according to the model considered. respectively for the period 2011–2030, and 17.58% and 40.11% for the period 2031–2050. On-going Table 2. Mean temperature (T), and mean annual precipitation (P) simulated by the REMO and RegCM3 climate models for periods 2001–2010, 2010–2030, and 2031–2050.

Climate 2016, 4, 16 9 of 15 analysis based on climate scenarios of the Fifth Assessment Report (AR5) from the Intergovernmental Panel on Climate Change conﬁrms the increase of temperature for the river Toce area, while annual precipitation is expected to increase or to remain unchanged according to the model considered.

Table 2. Mean temperature (T), and mean annual precipitation (P) simulated by the REMO and RegCM3 climate models for periods 2001–2010, 2010–2030, and 2031–2050.

Climate 2016, 4, 16 Period Model T (˝C) P (mm) 9 of 15 REMO 4.06 1399.25 Period2001–2010 Model T (°C) P (mm) REMORegCM3 4.094.06 1339.041399.25 2001–2010 RegCM3REMO 4.594.09 1612.501339.04 2011–2030 REMO 4.59 1612.50 2011–2030 RegCM3 4.59 1782.31 RegCM3 4.59 1782.31 REMOREMO 5.295.29 1645.261645.26 2031–22031–2050050 RegCM3RegCM3 5.135.13 1876.171876.17

MeanMean monthly monthly precipitation precipitation and and temperature temperature as as simulated simulated by by REMO REMO and and RegCM3 RegCM3 for reference for reference period,period, period period 2011–2030, 2011–2030, and and period period 2031–20502031–2050 are are shown shown in in Figure Figure 5. 5Both. Both REMO REMO and andRegCM3 RegCM3 models simulate a significant precipitation increase mostly concentrated in spring, autumn, and models simulate a significant precipitation increase mostly concentrated in spring, autumn, and winter. winter. In summer and November, the two climatic models predict a significant decrease of In summerprecipitation and November, that can reach the −35% two for climatic the RegCM3 models in predictNovember a significant of period 203 decrease1–2050. ofTemperature precipitation is that can reachgenerally´35% predicted for the to RegCM3 increase in more November significantly of period during 2031–2050. summer and Temperature late spring, iswhile generally a decrease predicted to increasein March more is expected. significantly during summer and late spring, while a decrease in March is expected.

a) 400 b) 400 2001-2010 2001-2010 2011-2030 2011-2030 300 2031-2050 300 2031-2050

200 200 Precipitation (mm) Precipitation 100 (mm) Precipitation 100

0 0 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 Month Month

c) 20 d) 20 C)

10 C) 10

0 0 Temperature ( Temperature 2001-2010 ( Temperature 2001-2010 2011-2030 2011-2030 2031-2050 2031-2050

-10 -10 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 Month Month

FigureFigure 5. Mean 5. Mean monthly monthly variables variables for for periods periods 2001–2010,2001–2010, 201 2011–2030,1–2030, and and 203 2031–2050:1–2050: (a) precipitation (a) precipitation simulated by REMO, (b) precipitation simulated by RegCM3, (c) temperature simulated by REMO, simulated by REMO, (b) precipitation simulated by RegCM3, (c) temperature simulated by REMO, and (d) temperature simulated by RegCM3. and (d) temperature simulated by RegCM3. In Figure 6, mean monthly snow water equivalent and discharge as computed for periods 2001– 2In010, Figure 2011–62,030, mean and monthly 2031–2050, snow by the water FEST equivalent-WB hydrological and discharge model driven as computed by the REMO for and periods 2001–2010,RegCM3 2011–2030, regional climate and 2031–2050, models are byshown. the FEST-WBThe seasonal hydrological shift observed model in precipitation driven by is the reflected REMO and RegCM3into monthly regional discharge. climatemodels Indeed, area significant shown. discharge The seasonal increase shift is observedexpected in in the precipitation winter period is and reflected into monthlyOctober, while discharge. a significant Indeed, decrease a significant is expected discharge in summer. increase An increase is expected of snow in water the winter equivalent period is and October,expected while in awinter significant, and early decrease spring is isexpected compensated in summer. in late spring An increaseby an accelerated of snow melt water rate. equivalent is expected in winter, and early spring is compensated in late spring by an accelerated melt rate.

Climate 2016, 4, 16 10 of 15 Climate 2016, 4, 16 10 of 15

a) 200 b) 200 Climate 2016, 4, 16 10 of 15

150 150 a) 200 b) 200 2001-2010 2001-2010 2011-2030 2011-2030 100 100 150 2031-2050 150 2031-2050

2001-2010 2001-2010 50 2011-2030 50 2011-2030

100 2031-2050 100 2031-2050

Snow water equivalent (mm) equivalent water Snow (mm) equivalent water Snow

0 0

50 1 2 3 4 5 6 7 8 9 10 11 12 50 1 2 3 4 5 6 7 8 9 10 11 12 Snow water equivalent (mm) equivalent water Snow Snow water equivalent (mm) equivalent water Snow Month Month c) 400 d) 400 0 2001-2010 0 2001-2010 1 2 3 4 5 6 2011-20307 8 9 10 11 12 1 2 3 4 5 6 2011-20307 8 9 10 11 12 300 Month2031-2050 300 Month2031-2050 c) 400 d) 400 2001-2010 2001-2010 2011-2030 2011-2030 200 200

300 2031-2050 300 2031-2050

Discharge (mm) Discharge (mm) Discharge 100 100

200 200 Discharge (mm) Discharge Discharge (mm) Discharge 0 0 100 1 2 3 4 5 6 7 8 9 10 11 12 100 1 2 3 4 5 6 7 8 9 10 11 12 Month Month Figure 6. Mean0 monthly variables computed for periods 2000 1–2010, 2011–2030, and 2031–2050: (a) Figure 6. Mean monthly1 2 variables3 4 5 6 computed7 8 9 10 11 for12 periods 2001–2010,1 2 3 2011–2030,4 5 6 7 and8 9 2031–2050:10 11 12 (a) snow snow water equivalent drivenMonth by REMO, (b) snow water equivalent driven Monthby RegCM3, (c) discharge water equivalent driven by REMO, (b) snow water equivalent driven by RegCM3, (c) discharge driven driven by REMO, and (d) discharge driven by RegCM3. by REMO,Figure and6. Mean (d) discharge monthly variables driven bycomputed RegCM3. for periods 2001–2010, 2011–2030, and 2031–2050: (a) 3.3. Projectedsnow water Changes equivalent in Hydropower driven by REMO, Production (b) snow water equivalent driven by RegCM3, (c) discharge 3.3. Projecteddriven Changes by REMO, in Hydropowerand (d) discharge Production driven by RegCM3. In order to assess climate change impacts on hydropower production, the following quantities wereIn3.3. order Projected compared: to assessChanges total climate hydropowerin Hydropower change production, Production impacts hydropower on hydropower product production,ion from reservoir the following power plants, quantities hydropower production from run of river power plants, and water volume stored in reservoirs. were compared:In order totalto assess hydropower climate change production, impacts hydropoweron hydropower production production, from the following reservoir quantities power plants, Figure 7 presents a comparison between the monthly total hydropower system production hydropowerwere compared: production total hydropower from run of production, river power hydropower plants, and product waterion volume from reservoir stored in power reservoirs. plants, evaluated by the model for the three periods employing time series calculated by FEST-WB forced Figurehydropower7 presents production a comparison from run of betweenriver power the plants, monthly and water total volume hydropower stored in system reservoirs. production by REMO and RegCM3 climate datasets. Both climatic scenarios highlight a relevant increase in Figure 7 presents a comparison between the monthly total hydropower system production evaluatedhydropower by the production: model for +11% the threefor REMO periods dataset employing and +19% timefor RegCM3 series calculateddataset. Production by FEST-WB increase forced evaluated by the model for the three periods employing time series calculated by FEST-WB forced by REMOis expected and in RegCM3 autumn, climatewinter and datasets. spring; in Both summer, climatic especially scenarios in June highlight and July, simulation a relevant outputs increase in by REMO and RegCM3 climate datasets. Both climatic scenarios highlight a relevant increase in hydropowershow a decrease production: up to − +11%11% (REMO for REMO dataset, dataset June and2031 +19%–2050). for RegCM3 dataset. Production increase hydropower production: +11% for REMO dataset and +19% for RegCM3 dataset. Production increase is expected in autumn, winter and spring; in summer, especially in June and July, simulation outputs is expecteda) in autumn, winter and spring; in summer, especially in June and July, simulation outputs show a decrease up to ´11% (REMO dataset, June 2031–2050).b) show250 a decrease up to −11% (REMO dataset,1500 June 2031–2502050). 1500

2001-20102001-2010 2001-20102001-2010 a) 2011-2030 2011-20302011-2030 200 2011-2030 200b) 2031-2050 250 2031-20502031-2050 1500 250 2031-2050 1500 1000 1000 2001-20102001-2010 2001-20102001-2010 150 150 2011-2030 2011-20302011-2030 200 2011-2030 200 2031-20502031-2050 2031-20502031-2050 100 1000 100 1000 150 500 150 500

50 50

Annual Energy production (GWh) production Energy Annual (GWh) production Energy Annual Monthly Energy production (GWh) production Energy Monthly Monthly Energy production (GWh) production Energy Monthly 100 100 500 500

0 0 0 0

1 2 3 4 5 6 7 8 9 1 2 3 4 5 6 7 8 9

11 12 10 11 12

50 1 2 3 4 5 6 7 8 9 1010 11 12 50 1 2 3 4 5 6 7 8 9 10 11 12

Annual Energy production (GWh) production Energy Annual (GWh) production Energy Annual

Year Year Monthly Energy production (GWh) production Energy Monthly Monthly Energy production (GWh) production Energy Monthly MonthMonth MonthMonth

Figure0 7. Average monthly and annual total0 hydropower 0 production evaluated by the hydropower0

1 2 3 4 5 6 7 8 9 1 2 3 4 5 6 7 8 9

11 12 10 11 12

1 2 3 4 5 6 7 8 9 1010 11 12 1 2 3 4 5 6 7 8 9 10 11 12 Year simulation modelMonth usingMonth the dischargeYear dataset built starting from (a) MonthREMOMonth and (b) RegCM3 climatic scenarios. FigureFigure 7. Average 7. Average monthly monthly and and annual annual total total hydropower hydropower production production evaluated evaluated by bythe the hydropower hydropower simulation model using the discharge dataset built starting from (a) REMO and (b) RegCM3 simulation model using the discharge dataset built starting from (a) REMO and (b) RegCM3 climatic scenarios. climatic scenarios.

Climate 2016, 4, 16 11 of 15 Climate 2016, 4, 16 11 of 15 Climate 2016, 4, 16 11 of 15 By considering only the ﬁve power plants whose inﬂow may be regulated by seasonal reservoirs By considering only the five power plants whose inflow may be regulated by seasonal reservoirs (Morasco, Ponte Morasco, Ponte Toggia, Ponte Vannino, Devero and Goglio Agaro) (Table1), the (Morasco, Ponte Morasco, Ponte Toggia, Ponte Vannino, Devero and Goglio Agaro) (Table 1), the expected production increase over the reference period is even higher, reaching a percentage of +18% expected production increase over the reference period is even higher, reaching a percentage of +18% for REMO dataset and +28% for RegCM3 dataset (Figure8). Spring months, March, April and May, for REMO dataset and +28% for RegCM3 dataset (Figure 8). Spring months, March, April and May, show the greatest increase of power production, with an additional peak production in August. show the greatest increase of power production, with an additional peak production in August. a) a) b) 50 300 50 300 50 300 50 300 2001-20102001-2010 2001-2010 2001-20102001-2010 2001-2010 2001-2010 2011-20302011-2030 250 2001-2010 2011-2030 250 40 2011-20302011-2030 250 40 2011-2030 2011-2030 250 40 2031-20502031-2050 40 2011-2030 2031-2050 2031-20502031-2050 2031-2050 2031-2050 200 2031-2050 200 200 200 30 30 30 30 150 150 150 150 20 20 20 20 100 100 100 100 10 10

10 50 10 50

Annual Energy production (GWh) production Energy Annual (GWh) production Energy Annual Monthly Energy production (GWh) production Energy Monthly

Monthly Energy production (GWh) production Energy Monthly 50 50

Annual Energy production (GWh) production Energy Annual (GWh) production Energy Annual

Monthly Energy production (GWh) production Energy Monthly (GWh) production Energy Monthly

0 0 0 0

1 2 3 4 5 6 7 8 9 1 2 3 4 5 6 7 8 9

11 12 10 11 12

1 2 3 4 5 6 7 8 9 1010 11 12 1 2 3 4 5 6 7 8 9 10 11 12

1 2 3 4 5 6 7 8 9 1 2 3 4 5 6 7 8 9

11 12 10 11 12

1 2 3 4 5 6 7 8 9 1010 11 12 1 2 3 4 5 6 7 8 9 10 11 12 Year

MonthMonth Year MonthMonth Year MonthMonth Year MonthMonth Figure 8. 8.Average Average monthly monthly and annualand annual reservoir reservoir hydropower hydro productionpower production evaluated byevaluated the hydropower by the hydropowersimulation model simulation using model the discharge using the dataset discharge built dataset starting built from starting (a) REMO from (a and) REMO (b) RegCM3 and (b) RegCM3climatic scenarios. climatic scenarios.

By considering production of the four run run of of river river plants plants (Crevola (Crevola Toce, Toce, Crevola Crevola Diveria, Diveria, Calice, Calice, and VillaVilla Toce) Toce) (Table (Table1), the 1), production the production increases increases over the over reference the reference reach on reach a yearly on basis a yearly a percentage basis a percentageof +9% for of REMO +9% for dataset REMO (2011–2030)dataset (2011 and–2030) +14% and for+14% REG-CM3 for REG-CM3 dataset dataset (2031–2050) (2031–2050) (Figure (Figure9). All9). All simulation simulation outputs outputs highlight highlight a a hydropower hydropower production production decrease decrease duringduring thethe summer months, June, July and August. It It is is interesting interesting to notice notice the the strong strong increase of production during the autumn months gained in case the REG-CM3REG-CM3 dataset is considered ((≈« 550%–60%).0%–60%).

a) a) b) 80 400 80 400 80 400 80 400 2001-2010 2001-2010 2001-2010 2001-2010 2001-2010 2001-20102001-2010 2011-2030 2011-2030 2001-20102011-2030 2011-2030 2011-2030 2011-20302011-2030 60 2031-2050 2031-2050 300 60 2011-20302031-2050 300 60 2031-2050 2031-2050 300 60 2031-20502031-2050 300 2031-2050

40 200 40 200 40 200 40 200

20 100 20 100

Annual Energy production (GWh) production Energy Annual (GWh) production Energy Annual

Monthly Energy production (GWh) production Energy Monthly (GWh) production Energy Monthly

Annual Energy production (GWh) production Energy Annual (GWh) production Energy Annual

Monthly Energy production (GWh) production Energy Monthly (GWh) production Energy Monthly

0 0 0 0

2 3 4 5 6 7 8 9 1 2 3 4 5 6 7 8 9

0 1 0 0 0

11 12 10 11 12

1 2 3 4 5 6 7 8 9 1010 11 12 1 2 3 4 5 6 7 8 9 10 11 12

1 2 3 4 5 6 7 8 9 1 2 3 4 5 6 7 8 9

11 12 10 11 12

1 2 3 4 5 6 7 8 9 1010 11 12 1 2 3 4 5 6 7 8 9 10 11 12 Year

MonthMonth Year MonthMonth Year MonthMonth Year MonthMonth Figure 9. Average monthly and annual run of river hydropower production evaluated by the Figure 9. AverageAverage m monthlyonthly and and annual annual run run of rivriverer hydropower production evaluated by the hydropower simulation model using the discharge dataset built starting from (a) REMO and (b) hydropower simulation simulation model model using using the the discharge discharge dataset dataset built built starting starting from from (a) (REMOa) REMO and and(b) RegCM3 climatic scenarios. (RegCM3b) RegCM3 climatic climatic scenarios. scenarios.

Under the hypothesis of invariance of energy market prices, the expected hydrological regime Under the hypothesis of invariance of energy market prices,prices, the expected hydrological regime modifications induce a change in the reservoirs management policy (Figure 10). According to results, mmodiﬁcationsodifications induce a change in the reservoirs management policy ( (FigureFigure 10).). According According to to results, results, in fact, optimal regulation found for both the 2011–2030 and 2031–2050 periods, anticipates the date when the maximum stored volume is reached, that moves from August to July. A drawdown of

Climate 2016, 4, 16 12 of 15

inClimate fact, 2016 optimal, 4, 16 regulation found for both the 2011–2030 and 2031–2050 periods, anticipates the12 date of 15 when the maximum stored volume is reached, that moves from August to July. A drawdown of stored volumestored volume is expected is expected in subsequent in subsequent two months, two months, August andAugust September, and September, to prepare to empty prepare storage empty capacitystorage capacity for autumn for inﬂows,autumn whichinflows, are which subjected are subjected to a signiﬁcant to a significant increase. increase.

a) 100% b) 100%

80% 80%

60% 60%

40% 2001-2010 40% 2001-2010 2011-2030 2011-2030 2031-2050 2031-2050

20% 20% Stored Volume / Storage Capacity (%) CapacityStorage/ Volume Stored 0% (%) CapacityStorage/ Volume Stored 0% 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 Month Month Figure 10. Average monthly stored water volume evaluated by the hydropower simulation model Figure 10. Average monthly stored water volume evaluated by the hydropower simulation model using the discharge dataset built starting from (a) REMO and (b) RegCM3 climatic scenarios. using the discharge dataset built starting from (a) REMO and (b) RegCM3 climatic scenarios.

The expected impact of climate change on demand is not significant. The annual consumption The expected impact of climate change on demand is not signiﬁcant. The annual consumption will will grow by 1% by 2100. Concerning the seasonality, summer demand will increase while the winter grow by 1% by 2100. Concerning the seasonality, summer demand will increase while the winter one one will decline. However, the simulations do not consider the impact of global warming on the will decline. However, the simulations do not consider the impact of global warming on the consumer consumer behaviors. For instance, more cooling systems could be installed in a warmer climate. To behaviors. For instance, more cooling systems could be installed in a warmer climate. To tackle this tackle this issue, social research should be carried out which is beyond the scope of this paper. This issue, social research should be carried out which is beyond the scope of this paper. This analysis, analysis, however, proves that the invariance of electricity price is consistent only if we consider the however, proves that the invariance of electricity price is consistent only if we consider the direct direct impact of climate change. impact of climate change. As energy prices are affected by socio-economic variables, long-term projection are hard. The As energy prices are affected by socio-economic variables, long-term projection are hard. uncertainty sharply grows with time. This is a topical issue and various methods are developed to The uncertainty sharply grows with time. This is a topical issue and various methods are developed to tackle this issue. For instance, [50] provide a sensitivity analysis to some price parameters for an tackle this issue. For instance, [50] provide a sensitivity analysis to some price parameters for an alpine alpine hydropower plant. Reference [51] makes an analysis of uncertainty for future hydropower hydropower plant. Reference [51] makes an analysis of uncertainty for future hydropower revenue revenue brought by climate change and electricity prices. Some research looks at decision making brought by climate change and electricity prices. Some research looks at decision making under deep under deep uncertainty and argues for increasing robustness rather than relying on scenarios [52]. uncertainty and argues for increasing robustness rather than relying on scenarios [52]. However, it is However, it is not the aim of this paper to provide a review on this topic. not the aim of this paper to provide a review on this topic.

4.4. Conclusions ThisThis studystudy investigatesinvestigates thethe impactimpact ofof climateclimate changechange onon hydropowerhydropower productionproduction ofof ToceToce alpinealpine riverriver basin.basin. ForFor this,this, riverflowriverflow isis simulatedsimulated byby thethe FEST-WBFEST-WB hydrologicalhydrological modelmodel drivendriven byby weatherweather datasetsdatasets generatedgenerated byby thethe REMOREMO andand RegCM3RegCM3 climatic climatic models. models. BothBoth REMO REMO and and RegCM3 RegCM3 models models simulate simulate a significant a significant precipitation precipitati increaseon mostly increase concentrated mostly inconcentrated spring, autumn, in spring, and winter. autumn, In summer and winter. and November, In summer the and two November climatic models, the two predict climatic a significant models decreasepredict a ofsignificant precipitation, decrease and of seasonalprecipitation shift, and observed seasonal in shift precipitation observed in is precipitation reflected into is monthlyreflected discharge.into monthly Indeed, discharge. significant Indeed discharge, significant increase discharge is expected increase throughout is expected the throughout year, except the during year, summerexcept during when asummer decrease when is expected. a decrease is expected. BothBoth groups groups of simulations of simulations based based on inflows on inflows generated generated by the FEST-WB by the model FEST- usingWB modelprecipitation using andprecipitation temperature and datatemperature gained fromdata REMOgained from and REG-CM3REMO and meteorological REG-CM3 met modelseorological show models a relevant show increasea relevant of increase hydropower of hydropower production production comparing comparing the reference the period reference (2001–2010) period (200 to the1–2010) following to the following decades. The production increase is distributed in autumn, winter and spring, while, in June and July, simulation results show a reduction of hydropower production. Under the hypothesis of invariance of energy market prices, the modifications of the hydrological regime expected in 2011–2050 induce a significant change in the reservoir management policy. More specifically, we expect to anticipate the date when the maximum volume of stored water

Climate 2016, 4, 16 13 of 15 decades. The production increase is distributed in autumn, winter and spring, while, in June and July, simulation results show a reduction of hydropower production. Under the hypothesis of invariance of energy market prices, the modifications of the hydrological regime expected in 2011–2050 induce a significant change in the reservoir management policy. More specifically, we expect to anticipate the date when the maximum volume of stored water is reached, which moves from August to July, and a drawdown of stored volume in August and September, to prepare empty storage capacity for autumn inflows. Results of this analysis depend on shift in temperature and precipitation patterns and amount expected for the Toce river case study and cannot be generalized to the whole Alpine region. Similar analysis performed on adjacent basins show the opposite, consisting of a decrease of discharge [3] or an increase in the middle term followed by a decrease in the long term [53]. A future work will assess significance and uncertainty of climate change impacts on the Toce river basin.

Acknowledgments: This work was supported by ACQWA EU/FP7 project (grant number 212250) “Assessing Climate impacts on the Quantity and quality of WAter” (link: http://www.acqwa.ch). The authors are grateful to ARPA Regione Piemonte (Italy) (link: http://www.arpa.piemonte.it) for providing meteorological and hydrological observations. The three anonymous reviewers are deeply acknowledged for their efforts to improve the quality and contents of this manuscript. Author Contributions: Thomas Mendlik and Andreas Gobiet provided the at-site bias-corrected climate-scenario forcings for the investigated area; Giovanni Ravazzani and Marco Mancini performed hydrological simulation; and Francesco Dalla Valle and Ludovic Gaudard performed modeling of the hydropower system. All authors contributed to writing the paper. Conﬂicts of Interest: The authors declare no conﬂict of interest.

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