Science of the Total Environment 521–522 (2015) 346–358

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Science of the Total Environment journal homepage: www.elsevier.com/locate/scitotenv

Hydrological simulation of Po River (North Italy) discharge under climate change scenarios using the RCM COSMO-CLM R. Vezzoli a,⁎, P. Mercogliano a,b, S. Pecora c, A.L. Zollo a,b, C. Cacciamani d a

Regional Models and geo-Hydrological Impacts Division, Centro Euro-Mediterraneo sui Cambiamenti Climatici (CMCC-REMHI), via Maiorise snc, I-81043 Capua, (CE), Italy Meteo System & Instrumentation Laboratory, Italian Aerospace Research Center (CIRA), via Maiorise snc, I-81043 Capua, (CE), Italy ARPA Emilia-Romagna, Hydro-Meteo-Climate Service, Hydrology Area (ARPA SIMC), Via Garibaldi, 75, I-43121 Parma, (PR), Italy d ARPA Emilia-Romagna, Hydro-Meteo-Climate Service (ARPA SIMC), Viale Silvani, 6, I-40122 Bologna, (BO), Italy b c

H I G H L I G H T S • • • • •

Impacts on water availability of climate change on Po River basin Continuous climate and hydrological projections up to 2100 under RCP4.5 and RCP8.5 scenarios Bias correction of regional climate model outputs Anomalies in climate and discharge at Po River basin scale Changes in high and low flow frequency and magnitude under IPCC scenarios

a r t i c l e

i n f o

Article history: Received 7 January 2015 Received in revised form 10 March 2015 Accepted 22 March 2015 Available online xxxx Editor: Simon Pollard Keywords: Hydrological modelling Po River basin Water availability Climate projections Bias correction Regional climate model

a b s t r a c t The impacts of climate change on Po River discharges are investigated through a set of climate, hydrological, water-balance simulations continuous in space and time. Precipitation and 2 m mean temperature fields from climate projections under two different representative concentration pathways, RCP4.5 and RCP8.5, have been used to drive the hydrological model. Climate projections are obtained nesting the regional climate model COSMOCLM into the global climate model CMCC-CM. The bias in climate projections is corrected applying the distribution derived quantile mapping. The persistence of climate signal in precipitation and temperature after the bias correction is assessed in terms of climate anomaly for 2041–2070 and 2071–2100 periods versus 1982–2011. To account for the overall uncertainty of emission scenarios, climate models and bias correction, the hydrological/water balance simulations are carried out using both raw and bias corrected climate datasets. Results show that under both RCPs, either considering raw and bias corrected climate datasets, temperature is expected to increase on the whole Po River basin and in all the seasons; the most significant changes in precipitation and discharges occur in summer, when the reduction of precipitation leads to an increase in low flow duration and occurrence likelihood, and in autumn and winter where precipitation shows a positive variation increasing the high flows frequency. © 2015 Elsevier B.V. All rights reserved.

1. Introduction Changes in water availability, in space, time or quantity, are among the most relevant expected impacts of climate changes. According to climate projections, the Mediterranean area should experience a decrease of total precipitation, with the exception of the Alps in winter and an increase in the frequency of extreme precipitation events (Giorgi and Lionello, 2008; Coppola and Giorgi, 2010; IPCC, 2013) with a ⁎ Corresponding author. E-mail addresses: [email protected] (R. Vezzoli), [email protected] (P. Mercogliano), [email protected] (S. Pecora), [email protected] (A.L. Zollo), [email protected] (C. Cacciamani).

http://dx.doi.org/10.1016/j.scitotenv.2015.03.096 0048-9697/© 2015 Elsevier B.V. All rights reserved.

consequent increase of droughts and flood hazard (Blenkinsop and Fowler, 2007; IPCC, 2013). García-Ruiz et al. (2011) provide a review of climate change and land use impacts on water resources in European and Mediterranean areas evidencing that the combination of water scarcity due to climate change (e.g. low precipitation and high evaporation) and of increasing water demand related to economic and social development would amplify the current water stress. Analysis on climate change impacts on (Euro)Mediterranean rivers regime are consistent in prospecting a progressive reduction of average discharges and an earlier decline in high flows in spring, due to the alteration of snow accumulation/melting processes, and the intensification of low flows in summer (García-Ruiz et al., 2011). These considerations are supported by the findings of e.g., Lehner et al. (2006) or those of

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Forzieri et al. (2014): the frequency and severity of hydrological droughts will significantly increase in Southern and Southeastern Europe. At national or river basin scale, similar conclusions are presented, among others, by Seguí et al. (2010) for Rhone River in France and in North Italy by Gunawardhana and Kazama (2012) for Tagliamento River and Coppola et al. (2014) and Ravazzani et al. (2015) for Po River. Changes in runoff in eastern and southern Portugal have been discussed by Nunes et al. (2008) and Mourato et al. (2014), respectively; Kilsby et al. (2007) investigate the impacts of climate changes on water availability in Tejo and Guadiana Rivers basins at the Portugal/ Spain boundary and Estrela et al. (2012) and Raposo et al. (2013) discuss the expected variation in groundwater recharges in Spain. It is worth to note that, the picture sketched by those studies, based on different scenarios, climate and hydrological models and assessment methodologies, is quite robust. Most of the works dealing with floods frequency and severity on the Mediterranean area expect an increase in flood frequency, but the quantification of this increase is uncertain, in particular, for snow-dominated basins that may experience an increase in frequency of winter floods and a reduction of spring ones (Lehner et al., 2006; Dankers and Feyen, 2008). Narrowing the area of interest to Po River basin, Coppola et al. (2014) study the impact of climate change to the Upper basin, an area of about 25000 km2 including Piemonte and Valle d'Aosta regions, through an ensemble of 8 climate simulations from 1960 to 2050 under the A1B scenario forcing the CHyM distributed and physical based hydrological model. The results evidence that the spring peak is anticipated of one month due to earlier snow-melting and that discharges increase in winter and reduce in autumn, with a consequent extension of the hydrological dry period. Vezzoli et al. (2014) study the climate change impacts on the entire Po River basin under RCP4.5 scenario to 2040 applying the bias correction to the outputs of impact model and not to climate data, finding a reduction in discharges. Vezzoli et al. (submitted for publication) assess the performances of a climate/hydrological/water balance modelling chain to reproduce 20 years of Po River discharges at the Pontelagoscuro closure section, finding that, in the absence of the bias correction of the climate data, with respect to observations, the discharges are overestimated of about 20%, the spring peak is anticipated by one month, and summer droughts are shorter. The use of bias correct climate outputs to force the hydrological model results in a 12% underestimation of observed discharges, with floods and droughts timing correctly simulated. The investigation of Po River basin vulnerability to climate changes is of interest for its impacts on the society and economy (Bozzola and Swanson, 2014; Castellari et al., 2014) considering that about 40% of the annual Italian GDP and 35% of the Italian agricultural production come from this area inhabited by approximately 16 million people (Po River Basin Authority, 2006). In this work, the simulations presented in Vezzoli et al. (submitted for publication) are extended to 2100 under two IPCC scenarios, RCP4.5 and RCP8.5, to evaluate the water availability, for the XXI century, on the entire Po River basin. This objective is achieved through: (1) the generation of precipitation and temperature, with and without bias correction, to be used as input to an hydrological model; (2) the estimation of the climate anomaly and the check of the persistence of climate signal in the bias corrected variables for two different time slices: 2041–2070 and 2071–2100; (3) the analysis of changes in Po River discharges on the same periods as monthly anomalies and changes in extreme events. The outline of this work is as follows: Section 2 describes briefly the climate and hydrology of the Po River basin; Section 3 summarises a description of the climate and hydrological models used in this study and of the simulations scheme; in Section 4 the changes in precipitation and 2 m mean temperature fields over 2041–2070 and 2071–2100 periods with respect to the control one, 1982–2011, under RCP4.5 and RCP8.5 are presented and their impacts on Po River discharges are discussed; finally in Section 5 the main conclusions are drawn.

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2. Case study Po River basin is the widest one in Italy and covers an area of about 71,000 km2 in Italy, and about 3000 km2 in Switzerland and France, Fig. 1. The Po River is the longest in Italy, with a length of 652 km from its source in the Cottian Alps (at Pian del Re) to its mouth in the Adriatic Sea (north of Ravenna) and is also the largest one with an average discharge of 1540 m3/s. The maximum discharge recorded at the Pontelagoscuro cross section is the flood of 10,300 m3/s in 1951. Discharges are characterised by two maxima, in spring and autumn, and two minima, in winter and summer. Temperature drives the regime of Alpine tributaries; late spring and summer discharges are the results of snow and glacier melting processes with a maximum in summer and a minimum in winter; while precipitation drives the Apennines tributaries showing two maxima and two minima according to precipitation behaviour. The climate of Po River basin is strongly influenced by the orography; the Alps protect the Po Valley from cold winds from north Europe while the Apennines limit the mitigation action of the sea. The air temperature on Po River basin is strongly correlated to the altitude: the mountainous areas are characterised by an annual average temperature of about 5 °C, which increases to 10 °C at medium altitude either on Alps and Apennines. Po Valley is characterised by a higher average annual temperature, 10 °C to 15 °C, similar values are recorded also in Alpine Valley and close to the lakes. The average temperature, in proximity of the sea, is above 15 °C. The temperature variability across the year is more or less the same in any point of the basin: minimum temperature occurs in January, then temperature rises until July when the maximum is reached and it decreases from September to December which is characterised by values close to January. Winter season (December to February) results to be the coldest while summer (June to August) is the warmest; autumnal (September to November) temperatures are slightly higher than in spring (March to May) (Po River Basin Authority, 2006). Precipitation distribution on the basin is more complex than temperature; the alpine basins of Oglio, Adda and Ticino Rivers, effluents of the North Italy lakes, receive the maximum precipitation in summer and the minimum in winter, while precipitation, in the remaining areas of Po River basin, is characterised by two maxima, in spring and autumn, and two minima, in summer and winter (Po River Basin Authority, 2006). According to Po River Basin Authority (2006) the average annual areal precipitation on Po River basin is about 1200 mm at Pontelagoscuro closure section of which about 2/3 reaches the Adriatic sea as discharge and the remaining 1/3 is assumed to be lost for evaporation and/or vegetation interception processes. Climate data analysis shows an increase in annual maximum temperature with a trend of about 0.5 °C/decade since 1960, the temperature increases are particularly evident in summer where maximum temperature is higher than the reference climate (Tomozeiu et al., 2006; Tibaldi et al., 2010). Changes in precipitation, since 1980, are less evident than those in temperature: on average, precipitation events are more intense but less frequent; as results annual total precipitation is reduced of 20%. At seasonal scale, the highest reduction rate is found in spring and summer (up to 50%) while autumnal precipitation are almost unvaried; in winter, snowfalls reduces as well (Tibaldi et al., 2010). Ciccarelli et al. (2008) analyse time series, on the 1952–2002 period, of daily precipitation and minimum and maximum temperatures collected in Piemonte and in Valle d'Aosta regions finding a significant increase of about 1 °C on average temperatures, in particular, for maximum daily temperatures in winter and summer months, while for precipitation no significant trends are identified. In this work, we concentrate on the cross section of Pontelagoscuro, which is representative of the hydrological cycle on the whole Po River basin, but results in terms of climate and discharge anomalies achieved at intermediate closure sections are comparable to those presented here.

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Fig. 1. Po River basin, with indication of the main river network and of the Pontelagoscuro closure section. The inset reports the location of the basin in Italy and the grid points of the regional climate model used for the simulations.

3. Modelling chain components and simulations setup Climate projections are the results of numerical simulations performed by climate models under different scenarios. Currently, RCP4.5 and RCP8.5 are the most commonly used scenarios among the four adopted by IPCC recently. The RCP4.5 is a stabilization scenario leading the radiative forcing at about 4.5 W/m2 in 2100, while RCP8.5 is a more extreme scenario, leading radiative forcing up to 8.5 W/m2 in 2100 compared to pre-industrial era (Meinshausen et al., 2011). Such scenarios are used to drive GCMs, which are characterised by a coarse horizontal resolution making their outputs not suitable as inputs for hydrological simulations. Thus, GCM outputs are dynamically downscaled by RCMs that provide climate variables at higher horizontal resolution comparable with the hydrological model one. However, RCM outputs are affected by a systematic bias caused by, e.g., uncertainty in the GCM/RCM parametrizations or assumptions, which has to be removed before performing quantitative evaluations on hydrological or other impacts (Teutschbein and Seibert, 2010). Each of the components of the modelling chain is a potential source of uncertainty that propagates within the modelling chain (Bosshard et al., 2013). In the last years, the uncertainty in climate change impacts has been widely discussed and GCMs are commonly indicated as the main source of uncertainty in hydrological applications, without neglecting the contribution of the other models (Chen et al., 2011; Bosshard et al., 2013) including bias correction, even if, it is aimed to reduce the error in climate fields (Chen et al., 2011; Ehret et al., 2012; Teutschbein and Seibert, 2012) and hydrological models, as shown in Jiang et al. (2007), Brigode et al. (2013) and Honti et al. (2014). The structure of the modelling chain used in this study and its main components are summarised in Fig. 2. The climate module is composed

of the following elements in cascade: scenarios (RCP4.5 and RCP8.5); global climate model (CMCC-CM; Scoccimarro et al. (2011)); regional climate model (COSMO-CLM; Rockel et al. (2008)) and distribution derived quantile mapping as bias correction method (QM, Piani et al. (2010)). The output variables of this module used to couple with the hydrological module are precipitation and 2 m mean temperature fields at daily timescale with a horizontal resolution of 0.0715° (about 8 km). The hydrological module included a spatially distributed and physically based hydrological model, TOPographic Kinematic Approximation and Integration (TOPKAPI, Liu and Todini (2002)) and, to account for the anthropogenic pressure on Po River basin (García-Ruiz et al., 2011), a water balance model, River Basin SIMulation (RIBASIM; Delft Hydraulics (2006)). 3.1. Climate model The GCM CMCC-CM is a coupled atmosphere-ocean general circulation model, which has been implemented and developed in the framework of the European project CIRCE (Gualdi et al., 2013): the atmospheric model component is ECHAM5 (Roeckner et al., 2003) with a T159 horizontal resolution (0.75°), while the global ocean component is OPA 8.2, in its ORCA2 global configuration, at a horizontal resolution of 2°. ORCA2 also includes the Louvain-La-Neuve (LIM) model for the dynamics and thermodynamics of sea-ice. A performance assessment of CMCC-CM in simulating the observed Sea Surface Temperature and precipitation is reported in Scoccimarro et al. (2011), while a comparison with other state-of-art GCMs available in the fifth phase of the Coupled Model Intercomparison Project (CMIP5) is presented in other different works (Elguindi et al., 2014). The performed analyses show that the capabilities of CMCC-CM are generally comparable with those

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Teutschbein and Seibert (2010, 2012); Lafon et al. (2013) report similar conclusions. For these reasons, in this study, distribution derived quantile mapping (hereinafter quantile mapping or QM) is applied for bias correction of COSMO-CLM outputs. The distribution derived quantile mapping correction is based on a transfer function that imposes the equality between the CDF (cumulative distribution function) of the observed and simulated variables (Piani et al., 2010); the bias corrected daily value of X(i), X∗(i), is estimated as

−1 X ðiÞ ¼ F obs;m F rcm;m ðX rcm ðiÞÞ

ð1Þ

where Frcm,m and Fobs,m are, respectively, for the m-th month, the CDF of daily simulated and observed variable X, i.e. precipitation or 2 m mean temperature, and i indicates the i-th element of the daily climate time series. On a monthly basis, the daily precipitation has been assumed to follow a Gamma distribution (Gutjahr and Heinemann, 2013; Teutschbein and Seibert, 2012) and the daily temperature a Gaussian distribution (Teutschbein and Seibert, 2012). For both observed and simulated precipitation and 2 m mean temperature, the parameters of distribution function have been estimated separately for each month and grid point. The distribution derived quantile mapping is suitable to correct future climate fields since it is based on continuous distribution functions. Under the assumption that the transformation expressed by Eq. (1) is stationary, thus the formulation to bias correct the future climate Xfut is


−1 X f ut ðiÞ ¼ F obs;m F rcm;m X f ut ðiÞ :

ð2Þ

The distribution validation of the derived quantile mapping on Po River basin and the comparison with other bias correction techniques on the same area are described in Vezzoli et al. (submitted for publication) and Zollo et al. (2015). Fig. 2. Structure of the climate/hydrological modelling chain.

3.2. Hydrological and water balance models of other state-of-art GCMs. The RCM adopted in this study is COSMOCLM, developed by the CLM Community and based on the meteorological weather forecast model COSMO-LM (Steppeler et al., 2003). The COSMO-CLM parametrization adopted is optimised for the Italian area (Bucchignani et al., 2013a,b). The RCM COSMO-CLM can be used for simulations with a horizontal resolution ranging between 1 and 50 km; furthermore, its non-hydrostatic formulation allows a better representation of convective phenomena and subgrid scale physical processes. COSMO-CLM is widely used to perform regional climate simulations over different areas of the world and in several projects, such as PRUDENCE (Christensen et al., 2007) or CORDEX (Giorgi et al., 2009), showing good performances in reproducing atmospherical variables of the specific area under study. The validation of the climate outputs of COSMO-CLM driven by CMCC-CM over the Po River basin with respect to observational gridded datasets evidences the presence of a bias into the climate variables of interest, e.g. an overestimation of winter precipitation and an average underestimation of the 2 m mean temperature were identified (Montesarchio et al., 2014; Vezzoli et al., submitted for publication). Under the strong assumption that biases between observations and model can be regarded as systematic and, therefore, almost independent of the control period, in literature, in the last years, several bias correction approaches have been proposed (Chen et al., 2013; Teutschbein and Seibert, 2010, 2012, 2013; Lafon et al., 2013; Guyennon et al., 2013; Zollo et al., 2014, 2015). For example, Zollo et al. (2015) compare the results of three different bias correction methods (linear-scaling, distribution derived quantile mapping and analogs) on precipitation outputs of COSMO-CLM driven by CMCC-CM over Po River basin; their results show that the distribution derived quantile mapping outperforms the other methods in terms of e.g. average and extreme values, spatial correlation and seasonal patterns,

Although wind velocity or solar radiation as well as other weather forcings, affect the extent of water and heat soil surface balances, precipitation and air temperature data are commonly assumed as main forcings for soil surface processes (Hagemann et al., 2014; Vrac et al., 2012). The physically based and distributed rainfall-runoff model TOPKAPI uses precipitation and 2 m mean temperature, together with the digital elevation model, and maps of soil type and land use to simulate the runoff through non-linear reservoir equations for soil drainage, overland flow and channel flow along the drainage network. A detailed description of TOPKAPI is given in Liu and Todini (2002). TOPKAPI runoff is used as input to the basin water balance model RIBASIM. This model accounts for water demand (e.g., the withdrawals related to agriculture, urban, industrial uses), reservoir presence, and other hydraulic components to distribute the available water along the river network. The river network is schematised as a series of nodes (e.g. reservoirs, dams, weirs, hydro-power stations, water users, inflows, bifurcations, intake structures, lakes, water withdraw or release point) connected by branches that transport the water between the nodes in function of its availability, demand and priority allocation rules. Natural and artificial reservoir releases are function of the period of the year and/or of the water demand downstream that varies across the year. In the performed simulations, the water withdrawals and releases are assumed to remain unvaried in the future to account for climate change impacts only. All the simulations are performed at daily timestep. The daily time resolution is a compromise among the capability of climate, hydrological and water balance models to reproduce the observed variability of precipitation, temperature and discharges at the scale of Po River basin, moreover, such choice is fully consistent with weather and hydrological datasets available for the model validation (Vezzoli et al., submitted for publication). Climate and hydrological simulation

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outputs will be referred to as RCPX.X when the raw CMCC-CM/COSMOCLM outputs are considered or as RCPX.X-QM if the bias corrected climate is used. The label CTRL and CTRL-QM are used to identify the simulation in the control period (1982–2011). Note that the historical run of CMCC-CM covers only the period 1971–2005: the extension to 2011 is obtained from the simulation under RCP4.5 scenario, which in the period 2006–2011 shows negligible differences from the one under RCP8.5 scenario. In the control period, the simulated average discharges, CTRL(-QM), are characterized by an average error of 29%(−8%). The validation and the estimate of the error introduced by the different components of the modelling chain are reported in Vezzoli et al. (submitted for publication). 4. Methods and results In this section, we present the results of the modelling chain for the 2041–2070 and 2071–2100 periods compared to the control period 1982–2011. The variables analysed are daily precipitation, 2 m mean temperature and Po River discharges at Pontelagoscuro providing an integral measure in space and time of Po River response to climate inputs. This section is structured as follows: Section 4.1 is dedicated to describe

the methods employed to evaluate the climate change and its impacts on Po River discharges, Sections 4.2 and 4.3 report the estimated anomalies for climate and hydrological variables for 2041–2070 and 2071– 2100 periods, respectively and, in Section 4.4, changes in extremely low and high discharges are analysed in terms of volume, frequency, and return period for high flows only. Comments to the results are given within each section. 4.1. Methods For both periods and scenarios, the anomalies of precipitation (in percentage) and 2 m mean temperature (in °C) are discussed as spatial distribution at seasonal scale and spatially averaged annual cycle. Seasonal anomaly maps of precipitation and 2 m mean temperature are of interest to identify those areas that are expected to be more affected by climate change, while spatially averaged annual cycle is aimed to identify shifts in water availability and detect the propagation of changes in precipitation and temperature to river discharge. Simulated discharges are analysed in terms of anomaly of the annual cycle (in percentage) and cumulative distribution function. Results are reported and discussed in Sections 4.2 and 4.3. In order to investigate the impacts

Precipitation RCP8.5 RCP4.5-QM

RCP8.5-QM

SON

JJA

MAM

DJF

RCP4.5

% (a)

(b) 2 meter mean temperature RCP8.5 RCP4.5-QM

RCP8.5-QM

SON

JJA

MAM

DJF

RCP4.5

(c)

°C

(d)

Fig. 3. Anomalies in (a,b) seasonal precipitation in % and (c,d) two meter mean temperature in °C over Po River basin, for the period 2041–2070. Left side (a,c) refers to raw CMCC-CM/ COSMO-CLM outputs, right side (b,d) to the bias corrected climate.

RCP4.5

RCP8.5

RCP4.5−QM

RCP8.5−QM

50

25

25

0

0

−25

−25

−50

−50

J F M A M J

J A S O N D

J F M A M J

Anomaly T2m (°C)

(a)

J A S O N D

(b)

10

10 RCP4.5

RCP8.5

RCP4.5−QM

RCP8.5−QM

8

8

6

6

4

4

2

2

0 J F M A M J

Anomaly P (%)

50

351

J A S O N D

J F M A M J

(c)

Anomaly T2m (°C)

Anomaly P (%)

R. Vezzoli et al. / Science of the Total Environment 521–522 (2015) 346–358

0 J A S O N D

(d)

Fig. 4. Anomaly of raw (a,c) and bias corrected (b,d) average monthly areal precipitation (a,b), 2 m mean temperature (c,d) over Po River basin under RCP4.5 (grey dashes line) and RCP8.5 (grey continuous line) scenarios, for the projection period 2041–2070 with respect to 1982–2011.

RCP4.5

J F M A M J

RCP8.5

J A S O N D

RCP4.5−QM

J F M A M J

(a)

RCP8.5−QM 75 50 25 0 25 −50 J A S O N D

(b)

F(Q)

1

1

0.8

0.8

0.6

0.6

0.4

CTRL RCP4.5 RCP8.5

0.2 0 100

Anomaly Q (%)

75 50 25 0 −25 −50

potentially exposed the infrastructures interacting with rivers. However, the adoption of a bias correction approach allows a better quantitative assessment of Po River simulated discharges (Vezzoli et al., submitted for publication), only these ones are used for computing climate change impacts on low and high flows. The choice of Q300

1000

10000 3

Q (m /s) (c)

100

1000

0.4 CTRL−QM RCP4.5−QM 0.2 RCP8.5−QM 0 10000

F(Q)

Anomaly Q (%)

of climate change on Po River water availability, in Section 4.4, the volume and the duration of future low and high flows are estimated with respect to Q300 and Q7 thresholds respectively, together with changes in the distribution of annual maximum discharges to estimate the variation of the hazard induced by climate changes to which will be

Q (m3/s) (d)

Fig. 5. Anomaly (in %) in monthly average discharge (a,b) and cumulative distribution function (c,d) of Po River discharges at Pontelagoscuro under RCP4.5 (dashed grey) and RCP8.5 (continuous grey) scenarios, for the period 2041–2070 with respect to 1982–2011 (in black in panels (c,d)). Discharges simulated using raw RCM outputs are in panels (a,c) and those obtained from bias corrected climate are in panels (b,d).

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Table 1 Anomalies, in percentage, of some significant percentiles of RCP4.5, RCP8.5, RCP4.5-QM, and RCP8.5-QM discharges at Pontelagoscuro for the period 2041–2070 with respect to reference period 1982–2011. Percentile

RCP4.5

RCP8.5

RCP4.5-QM

RCP8.5-QM

Q (F = 0.05) Q (F = 0.10) Q (F = 0.25) Q (F = 0.50) Q (F = 0.75) Q (F = 0.90) Q (F = 0.95)

−5.4 −10.9 −8.5 −8.4 −9.8 −8.2 −7.3

−10.8 −10.8 −8.5 −12.8 −14.8 −13.9 −8.2

−26.2 −25.7 −21.0 −12.0 −5.8 −2.2 2.0

−37.5 −34.7 −21.1 −13.2 −9.9 −8.1 −5.6

(discharge that is exceeded, on average, for 300 days a year) as low flow threshold is justified by its use as drought alarm threshold in Po River droughts management guidelines (AA. VV., 2011; De Michele et al., 2013). Under the assumption that the water demand is constant in the future, the average low flow volume is estimated as difference between the monthly discharge and the threshold itself. For high flows,

the threshold considered is the Q7 (i.e, the discharge that is exceeded, on average, for 7 days a year or with an exceedance probability of 0.02, Yilmaz et al. (2008)). In this case, we estimate the volume above the Q7 threshold, the average occurrence in each month. Both Q300 and Q7 thresholds are estimated on the basis of the CTRL-QM discharge dataset. Since the design of the infrastructures interacting with rivers is based on statistical analysis of annual maxima flows, we performed a peak over threshold analysis of the Q N Q7 datasets to provide an indication on how climate change would modify this project parameter. In this case, the peak over threshold analysis is preferred to the annual maxima analysis since it is based on larger samples making the results more robust. The peak over threshold samples is assumed to be distributed as a generalised Pareto 8

−1=k > < 1− 1 þ k q−μ σ  F ðqÞ ¼
q−μ > : 1−exp − σ

if k≠0 if k ¼ 0

ð3Þ

where k ≥ 0, μ ≤ q b + ∞ and σ are, respectively, the shape, location and

Precipitation RCP8.5

RCP4.5-QM

RCP8.5-QM

SON

JJA

MAM

DJF

RCP4.5

(a)

%

(b)

2 meter mean temperature RCP8.5 RCP4.5-QM

RCP8.5-QM

SON

JJA

MAM

DJF

RCP4.5

(c)

°C

(d)

Fig. 6. Anomalies in (a,b) seasonal precipitation in % and (c,d) two meter mean temperature in °C over Po River basin, for the period 2071–2100. Left side (a,c) refers to raw CMCC-CM/ COSMO-CLM outputs, right side (b,d) to the bias corrected climate.

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Ny =N 1−F ðqÞ

ð4Þ

where Ny = 30 is the number of years considered, N is the sample size and it is a function of the simulation considered. 4.2. Projections to 2041–2070

Anomaly P (%)

In terms of seasonal anomalies in precipitation, in winter, RCP4.5 and RCP8.5 projections show opposite behaviours: the first one sees an average reduction of 3.8%, mostly localised in the western Po Valley in Piemonte region and on the Apennines, the latter an increase of 7.3% mostly in the eastern Po Valley in Lombardia, Fig. 3(a). Winter bias corrected precipitation shows similar patterns with slightly different variations: − 4.4% (RCP4.5-QM) and 12% (RCP8.5-QM), Fig. 3(b). The two scenarios, either for raw and bias corrected, agree in the sign of December (positive) and February (negative) anomalies, while in January, RCP4.5 projects a decrease and RCP8.5 an increase, Fig. 4(a,b). In spring, both scenarios simulate a reduction in precipitation, mostly localised on Apennines and Lombardia region i.e − 7.9%(− 8.0%) for RCP4.5(-QM) and − 9.2%(− 10%) for RCP8.5(-QM), Fig. 3(a,b) with most of the reduction about 17% for RCP4.5(-QM) and 20% for RCP8.5(-QM), occurring in May, Fig. 4(a,b). In summer, the precipitation is about 1/3 less than in the control period under both scenarios either for raw and bias corrected precipitations for RCP4.5(-QM) the reduction is almost constant across the season, while RCP8.5(-QM) show the highest reduction in July, Fig. 4(a,b). In terms of spatial distribution, the Po Valley is characterised by the maximum anomalies while Alps are characterised by lower changes, Fig. 3(a,b). In autumn, RCP4.5(-QM) project more precipitation, on average 18%(13%), Fig. 3(a,b), than the control period, in all months, especially on the eastern part of the basin and along the main river channel, instead, under RCP8.5(-QM) negligible variations 0.9%(−1.6%) are expected, with September and November

50

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T¼

anomalies that compensate each other, Fig. 4(a,b). All temperature anomalies are positive with slightly higher values for RCP8.5(-QM) than RCP4.5(-QM). The anomalies range between 1.7 °C(2.4 °C) in spring and 3.1 °C(3.7 °C) in summer for RCP4.5(RCP8.5), and between 1.6 °C(2.4 °C) in winter and 2.7 °C(3.1 °C) in summer for RCP4.5QM(RCP8.5-QM). In particular, the Po Valley is expected to warm more than Alps, Fig. 3(c,d). The annual cycle of temperature shows the maximum anomaly in August: 3.3 °C(2.7 °C) and 4.1 °C(3.3 °C) for RCP4.5(-QM) and RCP8.5(-QM), respectively and a minimum in February of 0.8 °C either for RCP4.5 and RCP4.5-QM and of 1.8 °C(2.0 °C) in April for RCP8.5(-QM) projections, Fig. 4(c,d). Fig. 5 summarises the changes in Po River discharge at Pontelagoscuro in terms of monthly anomaly (a,b) and CDF (c,d) considering both scenarios and raw (a,c)/bias corrected (b,d) climate data. Simulations show a general agreement on the shape of anomalies while more differences are present in the anomaly magnitude. In particular, from October to September, RCP4.5(-QM) simulation shows higher discharges with respect to RCP8.5(-QM) one, while between January and March the behaviour is opposite. This is, in agreement with the precipitation anomalies, higher in autumn for RCP4.5(-QM) and in winter for RCP8.5(-QM). Between March(April) and September, a discharge reduction is projected with a maximum of 40% occurring in August. These results are in line with Coppola et al. (2014) and Ravazzani et al. (2015) projections at 2050 of Upper Po River discharges. The comparison between the CDFs on the control period and those projected shows RCP4.5 and RCP8.5 discharges' tendency to be lower than those in the control period, Fig. 5(c), but variations are limited (less than 15%), Table 1, while for RCP4.5-QM and RCP8.5QM discharges the modification of CDFs is evident, in particular in the lowest discharges, e.g. a reduction equal to 26.2% (37.5%) is estimated for Q(F = 0.05) in RCP4.5-QM (RCP8.5-QM) simulation, while maximum discharges are almost unchanged, e.g. Q(F = 0.95). The similarity between RCP4.5(-QM) and RCP8.5(-QM) variables is reasonable considering that the effects of different scenarios are more evident in the second half of the XXI century (Schneider et al., 2013).

Anomaly P (%)

scale parameter of the distribution. The return period T, in year, is related to F(q) through

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(d)

Fig. 7. Anomaly of raw (a,c) and bias corrected (b,d) average monthly areal precipitation (a,b), 2 m mean temperature (c,d) over Po River basin under RCP4.5 (grey dashes line) and RCP8.5 (grey continuous line) scenarios, for the projection period 2071–2100 with respect to 1982–2011.

R. Vezzoli et al. / Science of the Total Environment 521–522 (2015) 346–358

Anomaly Q (%)

Figs. 6 and 7 report the spatial and temporal anomalies, respectively. RCP4.5(-QM) winter precipitation is characterised by a positive anomaly, 11%(15%), in particular over Po Valley, whereas negligible differences are found on Alps. On the same months, RCP8.5(-QM) expects an increase of about 38%(50%) over the whole Po River basin in particular on Alps, Fig. 6(a,b). In both RCP4.5 and RCP4.5-QM simulations, the lowest anomalies, about 5% for both cases, occur in December while the highest are found in January, 20% and 28% respectively; for RCP8.5(-QM), the most significant changes, more than 40%(50%) occur in December and January while February anomaly is limited to 20%(33%), Fig. 7(a,b). In spring, both scenarios show a reduction of precipitation on the eastern part of the basin, more marked under RCP8.5(-QM), while on the western Alps, RCP4.5(-QM) expects a light increase and RPC8.5(-QM) a reduction. At the basin scale, the winter precipitation is expected to decrease by about − 1.4%(− 0.3%) for RCP4.5(-QM) and −14%(−3%) for RCP8.5(-QM), Fig. 6(a,b). In winter, for both scenarios, the precipitation will be increasing in March and decreasing in May, while for April the sign of the anomaly is positive for RCP4.5(-QM) and negative for RCP8.5(-QM), Fig. 7(a,b). In summer, RCP4.5(-QM) precipitation reduction is increasing from June to August and, on average, it is comparable with the 2041–2070 period, but with no more evident dependency on altitude. The RCP8.5(-QM) precipitation reduces by about 57%(60%), which is almost the double that of the 2041–2070 period; with the highest reduction rate, about 67%(68%), in August, Figs. 6(a,b) and 7(a,b). As last, in autumn, RCP4.5(-QM) precipitation increases of about 5.3%(2.0%) and changes are localised in Piemonte and along the Po River main channel (RCP4.5 only); similarly to 2041–2070 period, RCP8.5(-QM) precipitation reduces on Apennines and partially on Alps while it is unvaried in the Po Valley, the overall reduction is less than 5%(10%), Fig. 6(a,b). According to both scenarios, September precipitation is less than in the control period, about 10% for RCP4.5(-QM) and 40% for RCP8.5(-QM), while in November, positive anomalies, about 20% for RCP4.5(-QM)

75 50 25 0 −25 −50

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and 11% for RCP8.5(-QM) are found, Fig. 7(a,b). Temperatures are expected to increase in all months, and RCP8.5(-QM) projects higher values than RCP4.5-QM, Figs. 6 (c,d) and 7(c,d). RCP4.5 temperature anomaly will range between 2.3 °C in winter and spring and 3.5 °C in summer and, for RCP8.5, between 4.1 °C in spring and 7 °C in summer, Fig. 6(c). For bias corrected temperature, the range of variability is similar to raw data, between 2.0 °C(3.9 °C) in winter and 3.0 °C(6.0 °C) in summer for RCP4.5-QM(RCP8.5-QM), Fig. 6(d). The shape of the monthly temperature anomaly, Fig. 7(c,d), is quite similar for both scenarios but, also for this variable, the magnitude of the change is quite different: between 2.7 °C and 4.2 °C for RCP4.5 and between 3.6 °C and 5.2 °C for RCP8.5 scenario. The bias corrected temperatures maintain the climate signal but with a different variability: 1.6 °C (February) to 3.4 °C (August) for RCP4.5-QM and 3.7 °C (February) to 6.5 °C (August) for RCP8.5-QM. The increase in summer temperature may explain the reduction of autumnal precipitation with respect to 2041–2070 period. The high summer temperatures enhance the exchange of water from soil to atmosphere, since the amount of water vapour that the air can hold is a function of air temperature, thus the atmosphere becomes richer of water and the soil poorer. At the end of summer, the soil water content is very low and this limits the water exchanges to atmosphere, as consequence the autumnal precipitation reduces (Allan et al., 2014). Note that, among the autumnal months, only in September, in all the simulations except RCP4.5(-QM) for the period 2041–2070, a negative anomaly is returned. The same mechanism may explain the increase of winter precipitation, by the end of the autumn, the soil water content is recovered and water exchanges with the atmosphere may occur, since temperatures are higher the atmosphere can hold more water that returns to soil as precipitation. Fig. 8 summarises the projected changes in Po River discharge at Pontelagoscuro for the 2071–2100 period in terms of monthly anomalies (a,b) and CDFs (c,d) considering both emission scenarios and raw (a,c)/bias corrected (b,d) climate data. Simulations show a general agreement on the shape of anomalies: more differences are found in the magnitude of variation. In particular, from April to November, RCP4.5(-QM) simulations show

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Fig. 8. Anomaly (in %) in monthly average discharge (a,b) and cumulative distribution function (c,d) of Po River discharges at Pontelagoscuro under RCP4.5 (dashed grey) and RCP8.5 (continuous grey) scenarios, for the period 2071–2100 with respect to 1982–2011 (in black in panels (c,d)). Discharges simulated using raw RCM outputs are in panels (a,c) and those obtained from bias corrected climate are in panels (b,d).

R. Vezzoli et al. / Science of the Total Environment 521–522 (2015) 346–358

4.4.1. Low flows Under the assumption that the total water demand will not change in the future, Fig. 9 depicts for each month, for CTRL-QM, RCP4.5-QM and RCP8.5-QM discharges, (a) the average number of days in which the discharge is below the Q300 threshold and (b) the monthly average deficit. It is evident that, low flows are concentrated between July and September and their duration is expected to increase. In the 2041– 2070 period, according to RCP4.5-QM(RCP8.5-QM) simulation low flow duration changes from 16 to 27(28) days in July, from 17 to 27(28) in August and from 7 to 18(16) in September. The extension of hydrological drought period is reported also in Coppola et al. (2014) on the 2021–2050 period. According to 2071–2100 projections the low flow occurrence changes to 28(30) days in July; 30(30) days in August and 23(28) days in September. In July–September period, the low flow duration is similar under the two scenarios with differences between 2041–2070 and 2071–2100 periods; considering the average deficit, RCP8.5-QM projection shows the most severe deficit in July and August, about 70% higher than RCP4.5-QM one (both in 2041– 2070 and 2071–2100 periods) and 50% higher than RCP8.5-QM in the 2041-2070 period, and, in addition, for the water scarcity is prolonged to September, Fig. 9(b).

Table 2 Anomalies, in percentage, of some significant percentiles of RCP4.5, RCP8.5, RCP4.5-QM, and RCP8.5-QM discharges at Pontelagoscuro for the period 2071–2100 with respect to reference period 1982–2011. Percentile

RCP4.5

RCP8.5

RCP4.5-QM

RCP8.5-QM

Q(F = 0.05) Q(F = 0.10) Q(F = 0.25) Q(F = 0.50) Q(F = 0.75) Q(F = 0.90) Q(F = 0.95)

−8.9 −12.7 −11.6 −6.5 −2.8 −3.4 2.0

−35.3 −38.0 −28.3 −13.4 −11.3 −4.3 0.01

−25.8 −25.5 −24.5 −11.2 −0.5 4.7 8.2

−50.6 −52.3 −47.3 −21.8 −6.9 3.0 7.8

higher discharges than RCP8.5(-QM), while between December and March the behaviour is opposite in agreement with the precipitation anomaly. Between March and October, the discharges reduce of 11%(14%) under RCP4.5(-QM) and of 27%(29%) under RCP8.5(-QM). It is worth to note that, the amplitude of precipitation and discharge anomalies is comparable for RCP4.5(-QM) while under RCP8.5 scenario, the winter(spring) discharge anomaly is higher(lower) than the precipitation one, this may be justified by the temperature increase that reduces the solid fraction of the precipitation, increasing the winter runoff (less snow is accumulated) and reducing the spring snowmelting (García-Ruiz et al., 2011). The comparison with the control CDF shows more marked differences between RCP4.5(-QM) and RCP8.5(-QM) than in 2041–2070. In particular, Po River discharges are expected to reduce significantly under RCP8.5(-QM) scenario, e.g., the lowest discharges, Q(F = 0.05), will reduce by 35%(50%), Table 2. The highest discharges exhibit an almost null variation under both RCP4.5 and RCP8.5 scenarios, while considering RCP4.5(-QM) and RCP8.5(-QM) simulation the change is about 8%, Table 2.

4.4.2. High flows In the control period, high flows occur mostly in autumn and spring (flood seasons) and the volume associated with autumnal events is higher than the spring ones. According to the simulations performed, in the future, discharges exceed Q7 more often from November to June and less often in September and October, Fig. 10(a). In autumn, RCP4.5-QM shows higher exceedance probability than RCP8.5-QM, in winter the behaviour is opposite, this is coherent with the precipitation anomaly described in Sections 4.2 and 4.3. In terms of volume associated with the threshold exceedance, Fig. 10(b), in winter it is comparable to the control period, with the exception of RCP4.5-QM on 2041–2070 in December; in spring, projections at 2071–2100 are both characterised by a significant increase in the volume, while at 2041– 2070 only April is quite different from the control period; in summer almost no exceedance occur and volume associated are negligible; in September and October, both projections show less frequent and lower volumes than the control period and RCP4.5-QM dominates

4.4. Climate change impacts on Po River extreme discharges In order to illustrate the impacts of climate change on Po River water availability, we briefly focus on changes in extreme discharges as provided by RCP4.5-QM and RCP8.5-QM simulations. J

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Fig. 10. (a) Average number of days within each month with Q N Q7 for the CTRL-QM, RCP4.5-QM and RCP8.5-QM simulations. (b) Monthly average volume associated with Q N Q7. The Q7 threshold is estimated on the basis of CTRL-QM simulation.

RCP8.5-QM projection but in November both projections are more severe than the control period. With reference to the Q7 threshold adopted to study changes in high flow volume and frequency, we estimate the changes in discharges for the following return periods: 10, 20, 50 and 100 years, Table 3. The evaluation confirms the expected increase in the exceedance frequency of the threshold. RCP4.5-QM simulation projects an increase of discharges with T b 100 years and the 100-year statistics is almost unvaried at 2041–2070, and a generalised reduction at 2071–2100. RCP8.5-QM simulation projects, in the 2041-2070 period, an increase of the maximum discharges with increasing return period, while, in the 2071-2100 period, maximum discharges are about 10% higher than in the control period, indipendently on the return period considered. 5. Conclusions Results of climate projections show that precipitation seasonality on Po River basin will be more marked than the present one. Precipitation is expected to decrease in spring and summer and increase in fall (RCP4.5 only) and winter, while temperatures are increasing in all seasons. The reduction of precipitation in spring/summer affects discharges, while winter precipitation and temperature changes modify the snow accumulation/melting processes and, consequently, the discharges (however this aspect has not been quantified yet). In terms of discharges, the simulations performed show an average decrease, under both RCPs considered and either for raw and bias corrected projections. The magnitude of this reduction is a function either of the scenario and the projection period considered. In particular, for the 2041–

Table 3 Variation, in percentage, of annual maximum discharges for assigned return period T, in year, with respect to the control period. Return period

10 20 50 100

RCP4.5-QM

RCP8.5-QM

2041–2070

2071–2100

2041–2070

2071–2100

12 8 2 −3

3 −2 −8 −13

−5 2 15 30

12 11 11 10

2070 period, November to April discharges are comparable with those in the control period, while in the rest of the year discharges are lower, leading to longer low flows periods. According to 2071–2100 projections, discharge reduction from May to November persists and it is more severe than in 2041–2070; in the remaining part of the year, discharges are expected to increase up to 60%, and in winter minima are higher. The main difference between hydrological simulations driven by raw and bias corrected climate concerns the magnitude of the anomaly and not the shape of the signal itself. The exceedance analysis on low flows shows that, in summer, Po River these events are expected to become more common and the water deficit associated with them is not negligible. In terms of high flow, on average, the threshold would be exceeded more frequently and the associated volumes are higher, especially in November. The results of the peak over threshold analysis performed evidences that the Q7 threshold is exceeded more frequently in the future. Under both scenarios, for assigned return periods, the 2041– 2070 is characterized by higher maximum discharge than the control period, while results are more uncertain for the 2071–2100. Although, the findings shown here are affected by the overall uncertainty of the modelling chain itself (Teutschbein et al., 2011; Bosshard et al., 2013), it seems clear that, in the absence of any adaptation strategy in water management and demand, Po River basin is expected to experience a general reduction in terms of water availability, in particular in summer. Acknowledgments The research leading to these results has received funding from the Italian Ministry of Education, University and Research and the Italian Ministry of Environment, Land and Sea under the GEMINA and Next Data projects. The authors would like to thank Dr A. Navarra and Dr. S. Tibaldi for their comments and suggestions. Renata Vezzoli thanks the personnel of ARPA SIMC Emilia Romagna and CMCC-REHMI Capua division for the continuous assistance and support. References AA. VV., 2011. Le magre del Po. Conoscerle per prevederle, cooperare per prevenirle. http://www.cimafoundation.org/wp-content/uploads/doc/magre.pdf, accessed 04 December 2014, Fondazione CIMA, Parma (Italy) (in Italian).

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