Alain Dejean
email: alain.dejean@wanadoo.fr

Jérôme Orivel

Vivien Rossi

Olivier Roux

Jérémie Lauth

Pierre-Jean G Malé

Régis Céréghino

Céline Leroy

Predation Success by a Plant-Ant lndirectly Favours the Growth and Fitness of lts Host Myrmecophyte

Abstrad

Mutualisms, or interactions between species that lead to net fitness benefits for each species involved, are stable and ubiquitous in nature mostly due to "byproduct benefits" stem ming from the intrinsic traits of one partner that generate an indirect and positive outcome for the other. Here we verify if myrmecotrophy (where plants obtain nutrients from the refuse of their associated ants) can explain the stability of the tripartite association between the myrmecophyte Hirtel/a physophora, the ant Allomerus decemarticulatus and an Ascomycota fungus. The plant shelters and provides the ants with extrafloral nectar. The ants protect the plant from herbivores and integrate the fungus into the construction of a trap that they use to capture prey; they a Iso provide the fungus and their host plant with nutrients. Du ring a 9-month field study, we over-provisioned experimental ant colonies with insects, enhancing colony fitness (i.e., more winged females were produced). The rate of partial castration of the host plant, previously demonstrated, was not influenced by the experiment. Experimental plants showed higher 8 15 N values (confirming myrmecotrophy), plus enhanced vegetative growth (e.g., more leaves produced increased the possibility of lodging ants in leaf pouches) and fitness (i.e., more fruits produced and more flowers that matured into fruit). This study highlights the importance of myrmecotrophy on host plant fitness and the stability of ant-myrmecophyte mutualisms.

Introduction

Mutualisms, defmed as cooperative interactions between species where each partner derives a fitness benefit, are based on "invested benefits" corresponding ta an adaptation by each species ta obtain benefits from its partner with the retum exceeding the costs of the investment [1--4]. Such contextdependent outcomes vary according ta the interacting partners over space and rime, thus influencing the evolutionary fate of a mutualistic relationship and providing information on how and when mutualisms arise, persist, and vanish [START_REF] Sacbsjl | Pathways to mutualism breakdown[END_REF].

When hasts transmit symbionts by "vertical transmission" ta their offspring the mutualisms are evolutionarily stable; yet, most mutualistic interactions are transmitted "horizontally" as the partners disperse separately. ln the latter case, the reproduction of eacb partner might be subject ta a trade-off if the resources invested by one partner in its own reproduction are lost for the other partner. Such a trade-off is a major source of instability as the symbionts may evolve traits promoting a reduction in cast, engendering the emergence of "cheaters" obtaining benefits at minimal cast [START_REF] Leimar | Cooperation for direct fitness benefits[END_REF]. Over rime, they end up by completely sterilizing their hasts [7 ,8].

The subsequent instability would tum every mutualist into a parasite if not counterbalanced by specifie conditions such as partner selection and fidelity, spatial structure (i.e., limited dispersal), and, above ail, retaliation against exploiters [START_REF] Herre | The evolution of mutualisms: exploring the paths hetween conflict and cooperation[END_REF][START_REF] Wilkinson | Horizontally acquired mutualisms, an unsolved problem in ecology[END_REF][START_REF] West | Sanctions and mutualism stability: why do rhizobia fiX Nitrogen?[END_REF][START_REF] Bronsteinjl | Ecological dynamics ofmutualist/ antagonist communities[END_REF][11][START_REF] Sacbsjl | Pathways to mutualism breakdown[END_REF][START_REF] Szilâgyi | The evolution of intermediate castration virulence and ant coexistence in a spatially structured environment[END_REF]. Because mutualisms are stable and ubiquitous in nature, the intrinsic traits of one partner can generate an indirect and positive outcome for the other, resulting in "byproduct benefits". The resulting absence of cast (or very law cast) permits an equilibrium between the costs and benefits ta be easily established between the partners [START_REF] Connor | The benefits of mutualism-a conceptual framework[END_REF][START_REF] Sacbsjl | The evolution of cooperation[END_REF][16].

Ant relationships with myrmecophytes, or plants housing a limited number of so-called 'plant-ants' in domatia (i.e., hollow branches or thorns and leaf pouches), are interesting models for studying conflicts and breakdown within mutualisms. Also, myrmecophytes usually provide their guest ants with food, particularly extrafloral nectar (EFN) and/or food bodies (FBs), while, in retum, plant-ants protect them from herbivores, competitors and pathogens [START_REF] Rico-Gray | The ecology and evolution of ant-plant interactions[END_REF] through their intrinsic predatory, territorial and cleaning behaviours. Sa, these protections correspond ta byproduct benefits for the host myrmecophytes (see [START_REF] Thontham | Setting the trap: cleaning behaviour of Camponotus schmit<.i ants increases long-term capture efficiency oftheir pitcber plant host, .Nepentks hicaù:arata[END_REF]). The same is true when ants provide their host plants with nutrients (e.g. prey remains and faeces) that accumulate in the domatia. These nutrients are absorbed through the rhizomes, roots, protuberances, or the walls of the domatia. This phenomenon, called myrmecotrophy, has been noted for epiphytes and for sorne phanerophytes adapted to the nutrient-poor, lateritic soils of tropical rainforests [1 7, 19].

The size of plant-ant colonies can be limited by the availability of space and food. As the host myrmecophyte grows, the guest colonies have more nesting space thanks to a greater number of domatia and so can grow in turn, while the production of EFNs and FBs increases. Y et, certain plant-ant species are somewhat less dependent on their host myrmecophyte to provision them because they tend hemipterans to obtain honeydew and/ or are predators [START_REF] Rico-Gray | The ecology and evolution of ant-plant interactions[END_REF]. Mutualisms between myrmecophytes and plant-ants are transmitted horizontally, so that the energy invested by the myrmecophytes in producing flowers and fruits is not allocated to producing greater nesting space for the ant colonies. Reciprocally, winged ant sexuals are not involved in protecting the host plant foliage. This gives rise to a conflict ofinterest between the partners. By destroying flowers, the ants "sterilize" the plant and trigger the reallocation of host-plant resources from reproduction to vegetative growth. The sterilization is partial; otherwise, the ant species are considered parasites of the mutualism [13, 17 ,20--24].

We focused this study on Hirtella physophora (Chrysobalanaceae) that houses colonies of Allomerus decemarticulatus (Myrmicinae) in pouches situated at the base of the leaflamina (Fig. 1) and provides them with extrafloral nectar. Workers build galleries under the stems oftheir host-plants that serve as traps to capture insects ofup to 1800 times the weight of a worker (Fig. 1; [START_REF] Dejean | Arboreal ants build traps to capture prey[END_REF]). To build these galleries, the workers frrst eut plant trichomes along the stems, clearing a path; then, using uncut trichomes as pillars, they build the vault of the galleries by binding together the eut trichomes with the mycelium of a fungus that they manipulate [START_REF] Dejean | Arboreal ants build traps to capture prey[END_REF]. This third partner, an Ascomycota from the order Chaetothyriales, therefore serves a structural purpose. Allomerus decemarticulatus also supply their host tree with nutrients via the walls of the domatia and the fungus with wastes, whilst the fungus, in tum, also provides the host plant with nutrients [START_REF] Leroy | Trophic mediation by a fungus in an ant-plantmutualism[END_REF][START_REF] Ruiz-Gonzâlez | Specifie, non-nutritional association between an Ascomycete fungus and Allomerus plant-ants[END_REF]. Finally, the workers partially castrate their host plant by cutting and chewing both the sterile and fertile parts of the flower buds [START_REF] Malé | An ant symbiont directly and indirectly limits its host plant's reproductive success[END_REF][START_REF] Orive | Dynamics of the association between a long-lived understory myrmecophyte and its specifie associated ants[END_REF].

We hypothesized that when particularly successful in catching prey, mutualistic ant colonies increase their own fitness by producing more sexuals while also providing benefits to their host plant. Indeed, the more prey they capture, the more they provide the host plant with nutrients that then has greater growth (more leaves equals more housing available for guest ants) and fitness (more flowers and fruits produced in spi te of the partial castration).

Materials and Methods

Ethics Statement

This study was conducted according to relevant national and international guidelines. Sample collections necessary to scientific research were authorized by the French O.ffice National des Forêts ( ONF) provided that their impact upon the environment is considered negligible (details of the permit in [START_REF] Dejean | The eco! ogy and feeding habits of the arboreal trap-jawed ant Daceton ll17nigerum[END_REF]).

Study site and madel

This study was conducted between November 2007 and July 2008 on the top of a hill (05° 03.697' N; 52° 58.620' W -05° 03.638' N; 52° 58.612' W) situated in the pristine forest near the field research station at Petit Saut, Sinnamary, French Guiana. Hirtella plrysophora, an understory plant found in pristine Amazonian forests and strictly associated with A. decemarticulatus in the study area, has long-lived leaves that be ar a pair of leaf domatia at the base of each lamina; the flowers are segregated on racemous inflorescences and produce dark-purple drupes (Fig. 1 ). These trees have a much longer lifespan (up to ca. 350 years) than their associated ant colonies (ca. 20 years) [START_REF] Orive | Dynamics of the association between a long-lived understory myrmecophyte and its specifie associated ants[END_REF].

Over-provisioning the ants

We investigated the role of the ants in the nitrogen provisioning of their host plant by providing the colonies of experimental H. physoplwra with surplus prey twice a week for 9 months (colonies normally capture one to two prey items per week [START_REF] Dejean | Arboreal ants build traps to capture prey[END_REF]). Captured at a light trap situated in Petit Saut, the prey -large maths and grasshoppers-were eut into pieces and the thoraxes plus legs (ca. lg) were then provided to the ants by holding them close to the galleries where the workers immediately seized and dismantled them [START_REF] Dejean | Arboreal ants build traps to capture prey[END_REF]. The 41 control and 31 experimental H. physoplwra trees selected for the main experiment were of similar height, trunk diameter at their base and number ofleaves at the beginning of the experiment; height: 1.4±0.09 rn vs. 1.35±0.08 rn (t=0.80; tif= 70; p= 0.42), trunk diameter at the base of the trees: 1.58±0.13 cm vs.

1.72±0.15 cm (t=0.62; p=0.50), and number of leaves:

22. 7± 1.3 vs. 23.8± 1.9 (t= 0.50; p = 0.61). Because Hirtella trees are patchily distributed (severa! individuals within a 3-5 rn radius), we randomly allocated trees within each patch with respect to the treatment (giving us the fmal ratio of 41 control and 31 experimental trees). This keeps the majority of either experimental or control trees from being selected in the same zone since differences in the amount Twenty-four trees (height: 1-1.8 rn) that had lost their associated ant colonies before we began the experiment were taken into consideration for certain comparisons. Over the 9month study, only sorne queens began to found colonies on 11 trees, but no workers patrolled the foliage. Other trees from this hill were not selected because they were either too small to produce flowers (<0.9 rn), too tall (over 1.9 rn) to be easily paired, or were in the process of regenerating after being broken by a branch that fell from the canopy.

Each month we counted the number of leaves, flower buds, flowers and fruits on both control and experimental trees. We were therefore able to deduce the number of new leaves produced by each tree as well as its leve! of flower and fruit production.

Isotopie analysis

Nitrogen exists in two stable (non-radioactive) forms, 1 ~ and 15 N, and the isotopie nitrogen composition of animal tissue reflects the isotopie ratio of food eaten with a 15 N enrichment of 3-5%o at each trophic leve! [START_REF] Deniro | Influence of diet on the distribution of nitrogen isotopes in animais[END_REF]. Also, the o 15 N in plant tissue reflects the o 15 N value of the nitrogen source. Therefore, if ants supply their host myrmecophyte with nutrients, we reasoned that plants whose associated ants were experimentally provided with surplus prey must be richer in 15 N than those in the control treatment.

At the end of the experiment, a 4-cm 2 piece of a "young, welldeveloped leaf' (see "stage 3" leaf in [START_REF] Grangier | Indirect defense in a highly specifie ant-plant mutualism[END_REF] and in Fig. 2) was harvested from each host tree, freeze-dried and then ground into a homogeneous powder using a mixer mill. Stable isotope analyses on these plant samples were conducted at the Scottish Crop UK, using a Thermo-Finnigan Deltapluo Advantage gas isotoperatio mass spectrometer interfaced with a Costech Analytical ECS4010 elemental analyzer. The natural abundances of 15 N were calculated as follows:

where X is the element of interest and R..mp1e and R.tandarrl the molar ratios (i.e., 1 ~/ 1 ~ of the sample and the standard, respectivcly [START_REF] Deniro | Influence of diet on the distribution of nitrogen isotopes in animais[END_REF].

We conducted this study because we needed to be sure that the o 15 N of the experimental trees had truly increased compared to the control trees.

Biotic protection of trees

Foliar growth is slow in H. p/rysophora, and leaves can live for several years. While juvenile leaves benefit from the intense biotic protection pmvided by mutualistic ants, old leaves have their own efiicacious mechanical and chemical defences [START_REF] Grangier | Indirect defense in a highly specifie ant-plant mutualism[END_REF]. So, we compared the defoliation rates of the two other kinds of leaves ("stage 2" and "stage 3", respectively, according to Grangier et aL [START_REF] Grangier | Indirect defense in a highly specifie ant-plant mutualism[END_REF]; see Fig. 2) that were likely produced during the survey period: "expanding leaves" ("stage 2'') had fully developed domatia and an immature blade 8-to-15-cm-long (to be distinguished from "juvenile leaves" whose domatia were not fully developed), and "young, well-developed leaves" ("stage 3").

At the end of the survey 28 control trees, 25 experimental trees (among the 41 and 31, respectively, from the beginning) and the 24 trees that had naturally lost their ants before the survey period bore such leaves, enabling us to compare the levels of defoliation using the following scale: (1) intact leaves or Jess than 5% of the leafsurface destmyed; (2) between 5% and 25% of the leafsurface destroyed; (3) between 25% and 50% of the leaf surface destroyed; (4) between 50% and 75% of the leaf surface destroyed; and (5) more than 75% of the leaf surface destroyed.. We iUisigned the values 0, 1, 2, 3 and 4 for these levels of defoliation and we averaged these numbers OEde.folialitm values x 1ll.l1llber qf letzrMs -l) to obtain a rate of herbivory per tree.

Ant castes produced in control and experimental colonies

To preserve sorne H PlrYsoJlhora plants in the area at the end of the survey, we gathered colonies from only 23 control and 23 experimental trees so that sorne intact trees and colonies remained to re-çolonize the area. lndeed, to gather the ant colonies, we were obliged to eut off most of the host tree branches to collect bath the leaf ponches (so the entire leaf) and the galleries where numerous workers were hiding and quickly placed them into pwtic bags containing 0.5 L of96% ethanol. We placed a label identifYing the tree inside each bag, closed the bag, and then tagged the outside of the bag with the same code. The bags were then transported to the laboratory to quantify the size of the ant colony on each H physoplwra tree.

Ali of the colonies had a queen. ln the laboratory, we counted the number of males, male pupae, winged females, female pupae and larvae. For the production ofworkers, we could not consider the adult individuals as sorne of them likely emerged before the experimental period, so that we estimated the number of workers produced only from the number of pupae and larvae. We evaluated the number of worker larvae using the following formula:

Numher of worker larvae=(Total Nurnher of kJrvae) x (Number of worker pupae) x

(Total Nurnher of pupae)- 1 where Total Number qf pupae = 7llllk pupae + .firrulk pupae + worlrer pupae.

Statistical comparisons

The Student's t-test was used each timea comparison oftwo sets of data was necessary. The levels of defoliation between treatments were compared using the Kruskal-Wallis test followed by a Dunn's post-/wc test. The link between the treatment and the numbers of leaves, buds or fruits on each plant was modelled using a generalized linear model (GLM) with a log-Poisson link [START_REF] Agresti | Categorical data analysis[END_REF]. The signiflcance of the effect of the treatment WiUI assessed through Likelihood ratio tests. Using the same statistics, the link between thetreatment and the number of buds that developed into flowers was modelled for each plant to obtain the transition rates from bud to flower; the same WiUI done for the number of fl.owers that developed into fruits (R v. 2.14.2 software).

Results

The numbers of workers and males produced by the experimental and control colonies during the survey period were not signiflcantly different (means ± s.e.; workers: 167.60±13.24 vs. 174.40±19.36, t=0.289, rif =44, p=0.77; males: 5.09±1.17 vs. 4.61±1.12, t=0.294, 4f =44, p=0.77). However, over-provisioned colonies produced significantly more winged females than control colonies (3.17±0.97 vs. 1.00±0.34, t=2.24, 4f =44, p=0.03).

The very similar rates of defoliation for experimental (median, 25% and 75% percentiles: 1.00, 0.50, 1.33) and control trees (1.00, 0.62, 1.00) were much lower than for trees having lost their guest ant colony (3.33; 3.00; 3.41, Kruskal-Wallis test; H 2 , 77 =47.22, p<O.OOOl; Dunn's post-/wc test: rates of defoliation for experimental vs. control trees, not significant; experimental and control trees vs. trees having lost their guest ants,p<0.001). Amongthe 24 trees that had naturally ]ost their ants before the survey, six produced inflorescences and a total of 26 fl.ower buds. Defoliating insects destroyed 21 of these buds iUI weil iUI the five flowers produced.

Bath plant growth and reproductive investment were enhanced by over-pmvisioning the ant colonies iUI the experimental trees produced significantly more leaves (3.58±0.40 vs. 2.17±0.31, p<0.001), more flower buds (5.81±1.45 vs. 3.27±0.64, p<O.OOOl), more flowers (3.61±0.83 vs. 1.78±0.39, p<O.OOOl) and more fruits (1.90±0.50 vs. 0.49±0.19,p<0.0001) than control trees (Likelihood ratio test on the GLM-Poisson madel; R statistics). The percentage at which flower buds matured into flowers was not significantly different between experimental and control trees (64.3±5.5 % us. 49.9±7.3 %, p= 0.48 for the Likelihood ratio test on the GLM-Binomial madel). So, it is likely that the workers castrated their host trees regardless ofwhether the trees belonged to the experimental or the control group. Finally, the percentage of flowers that matured into fruit was significantly higher for experimental than for control trees (49.7±5.4 % vs. 23.3±6.0 %, p=0.02 for the Likelihood ratin test on the GLM-Binomial madel).

At the end of the survey, while the percentage of nitrogen contained in the leaves ofboth experimental and control trees was not significantly different (mean ± SE: 1.46±0.03 % vs. 1.42±0.02 %; t=0.95; rif =70; p=0.35), the experimental trees had significandy higher ô 15 N values than control trees (2.45±0.18 o/oo vs. 1.59±0.11 %o; Welch-corrected t= 4.08, rif= 51, p<0.001; see Fig. 3).

Discussion

Here we show that colony fitness was enhanced through overprovisioning with prey as more winged females were produced by experimental colonies. Supplemental protein increased the production of males and gynes, but not of workers, in the Argentine ant Linepitlzema humile [START_REF] Aron | Role ofresource availability on sex, caste and reproductive allocation ratios in the Argentine ant Linepi.thema humile[END_REF], while the increased production of winged females was due to supplementary carbohydrates rather than proteins in A{ymzica brevispinosa [START_REF] Bono | Proximate and ultimate control of sex ratios in Mynnica brevi.rpinosa colonies[END_REF].

The experiment did not moclifY the worker patrolling activity because we noted non-significant differences in the: (1) number of workers produced per colony sheltered, (2) biotic protection (similar defoliation rate; having very law constitutive defences, the leaves at the chosen stages of development depend on ant protection [START_REF] Grangier | Indirect defense in a highly specifie ant-plant mutualism[END_REF]) and (3) castration rate between control and experimental trees.

On the plant side, experimental individuals had more 15 N than did control plants, confmning findings by Leroy et al. [START_REF] Leroy | Trophic mediation by a fungus in an ant-plantmutualism[END_REF] of myrmecotrophy in this mutualism. The novelty of this study is that "surplus" nutrients likely permitted the experimental trees to increase their investment in bath growth and reproduction as they produced significandy more leaves, flower buds, flowers and fruits than control trees. Furthermore, the experimental trees had a higher rate of flowers that matured into fruits compared to the control trees, showing that fruiting is likely limited by the quantity of available nitrogen.

Our results support the initial hypothesis that the more insects the ants capture, the more nutrients are available to the host plant for growth. This provides the ants with more housing, in tum providing accommodation for the additional winged females

Figure 1 .

 1 Figure 1. Hirtel/a physophora leaves, flowers and fruits. (a) Leaves bear leaf pouches (left arrow) at the base of their laminas. Allomerus decemarticulatus workers capture a green locust thanks to their trap: a gallery made using severed host plant trichomes and the mycelium of an Astomycota fungus that the ants manipulate to create a composite material pierced by numerous holes (from under which the workers ambush prey). A wasp is seen robbing a piece of the locust abdomen; the wasp was also captured in turn as was the red Reduviid (right arrow). (b) At the distal position of the branch, flowers are segregated on racemous inflorescences at different stages of maturation from flower buds to fully open flowers. (c) Development of young, green (i.e. unripe) drupes. (d) A dark purple (i.e. ripe) drupe. Scale bars represent 1 cm. doi:l 0.1371/journal.pone.0059405.g001

Figure 2 .

 2 Figure 2. Four developmental stages of Hirtella physphora leaves. (a) Juvenile leaves: less than 5 cm long, positioned vertically; the domatia are not fully developed. (b) Expanding leaves: have fully developed domatia, but the blade is still immature. (c) Young leaves: from 15 to 25 cm long; positioned horizontally, mature but still relatively tender, and light green in color. (d) Old leaves: older, nonsenescent, mature leaves of up to 30 cm in length, positioned horizontally, stiff, and dark green in color. Scale bars represent 5 cm. doi:1 0.1371/journal.pone.0059405.g002

  ~-~-~~.--~-----Number of flow ers or fruits

Figure 3 .

 3 Figure 3. Production of flowers (A) and fruits (B) versus li15N values (%o) for the leaves of Hirtella physophora. The resident Allomerus decemarticulatus colonies were over-provisioned (experimental trees; open circles; N = 31) or not (control trees; fi lied circles; N =41) (means ± se). doi:1 0.1371/journal.pone.0059405.g003 of 15 N in the soil between zones can occur with repercussions for the o 15 N of the plants.


Florence Habets
email: florence.habets@mines-paristech.fr

Julien Boé

Michel Déqué

Agnès Ducharne

Simon Gascoin

Ali Hachour

Eric Martin

Christian Pagé

Eric Sauquet

Laurent Terray

Dominique Thiéry

Ludovic Oudin

Pascal Viennot

Impact of climate change on the hydrogeology of two basins in Northern France

This study presents an analysis of climate change impacts on the water resources of two basins located in Northern France, by integrating four sources of uncertainty: climate modelling, hydrological modelling, downscaling methods, and emission scenarios. The analysis focused on the evolution of the water budget, the river discharges and piezometric heads. Seven hydrological models were used, from lumped rainfall-discharge to distributed hydrogeological models, and led to quite different estimates of the water balance components. One of the hydrological models, CLSM, was found to be unable to simulate the increased water stress and was thus considered as an outlier even though it gave fair results for the present day compared to observations. Although there were large differences in the results between the models, there was a marked tendency towards a decrease of the water resource in the rivers and aquifers (on average in 2050 about -14% and -2.5 m, respectively), associated with global warming and a reduction in annual precipitation (on average in 2050 +2.1 K and -3%, respectively). The uncertainty associated to climate models was shown to clearly dominate, while the three others were about the same order of magnitude and 3 to 4 times lower. In terms of impact, the results found in this work are rather different from those obtained in a previous study, even though two of the hydrological models and one of the climate models were used in both studies. This emphasizes the need for a survey of the climatic change impact on the water resource.

Introduction

Global warming is unequivocal (IPCC 2007;[START_REF] Meehl | Global climate projections, in Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the AR4 of the IPCC[END_REF], and is expected to lead to an increase in the use of water for cooling, energy production and irrigation, and to increased evapotranspiration [START_REF] Döll | Impact of climate change and variability on irrigation requirements: A global perspective[END_REF]Parry et al., 2007). This increased water use may be achieved by increased pumping from groundwater, rivers or dams, depending on the availability of the resource, which is also directly affected by climate change. In Europe, increased precipitation is likely to occur in winter in the northern areas, while southern Europe is expected to suffer a decrease in precipitation [START_REF] Arnell | Hydrology and water resources[END_REF][START_REF] Christensen | Regional Climate Projections[END_REF]. Northern

France is located on the border of these two zones and is therefore subject to considerable uncertainty, partly connected with the fact that the precipitation regime is linked to both the large scale circulation and the regional pattern of soil moisture, with a signal that varies depending on the season (Vautard and Yiou, 2010;Boé 2012). These variables have a large spread in the climate projections for Europe (Boé et al., 2009b), as confirmed by the analysis of the ENSEMBLE projections, which used several global and regional climate models over Europe and showed that the uncertainty was associated with both scales [START_REF] Déqué | The spread amongst ENSEMBLES regional scenarios: Regional climate models, driving general circulation models and interannual variability[END_REF].

Although it has a mid-latitude oceanic climate, Northern France can suffer from water resource issues, mainly because of the anthropogenic water demand. The high population density (above 200 inhabitants/km 2 ) particularly in the Paris area, together with intensive agriculture (for instance in the Beauce region) and a dense industrial fabric lead to high water demand for drinking, irrigation and cooling.

Such pressures affect water resources in terms of quality [START_REF] Meybeck | La Seine en son bassin. Fonctionnement écologique d'un système fluvial anthropisé[END_REF]Verjus 2008), and in terms of quantity since excessively low groundwater levels have led to restrictions on use in some areas for several years. In such conditions, the evolution of the climate is causing concern about the availability of water resources, especially in the context of regional development, which is tending to increase the population density.

To gain better insight into the impact of climate change on the water resources of the Seine and Somme basins of northern France the RExHySS project included several sources of uncertainty: it used two emissions scenarios, seven climate models, three downscaling methods and seven hydrological models ranging from simple rainfall-discharge models to more complex hydrogeological models. Few previous studies have addressed the uncertainty connected with the combination of emissions scenarios, climate models, downscaling methods and hydrological modelling (Wilby and Harris, 2006;[START_REF] Kay | Comparison of uncertainty sources for climate change impacts: flood frequency in England[END_REF][START_REF] Görgen | Assessment of Climate Change Impacts on Discharge in the Rhine River Basin: Results of the RheinBlick2050 Project[END_REF][START_REF] Chen | Overall uncertainty study of the hydrological impacts of climate change for a Canadian watershed[END_REF][START_REF] Chen | Comparison and evaluation of multiple GCMs, statistical downscaling and hydrological models in the study of climate change impacts on runoff[END_REF]; most studies have used either several downscaling methods but only a single hydrological model [START_REF] Dibike | Hydrologic impact of climate change in the Saguenay watershed: comparison of downscaling methods and hydrologic models[END_REF][START_REF] Khan | Uncertainty analysis of statistical downscaling methods[END_REF]Prudhomme and Davies;[START_REF] Kollet | Influence of soil heterogeneity on evapotranspiration under shallow water table conditions: transient, stochastic simulations[END_REF], Boé et al., 2009;;Quintana Seguì et al., 2010), or just one downscaling method with several hydrological models [START_REF] Ducharne | Long term prospective of the Seine river system: Confronting climatic and direct anthropogenic changes[END_REF][START_REF] Bae | Hydrologic uncertainties in climate change from IPCC AR4 GCM simulations of the Chungju Basin, Korea[END_REF]Teng et al., 2012).

The first part of this article presents the observed climate data, and the hydrological models, downscaling methods and climate change projections used.

Then, the projected climate change in the two basins is presented, together with its impacts on hydrology in terms of water budget, river flows and aquifer levels. The uncertainties associated to the results are then discussed.

Materials and Methods

Baseline climate data

The baseline climate data were taken from the SAFRAN analysis [START_REF] Durand | A meteorological estimation of relevant parameters for snow models[END_REF]Quintana Seguì et al., 2008) which provided the solid and liquid precipitation, downward solar and atmospheric radiation, 2-m air temperature and humidity and 10-m wind speed, all at an hourly time step on an 8-km grid. Daily Potential Evapotranspiration (PET) was then computed using the Penman Monteith formula [START_REF] Monteith | Evaporation and environment[END_REF]. For the period 1971-2000, the mean annual precipitation is above 900 mm/year in the eastern part of the basin and can be lower than 650 mm/year in the western part (Figure 1). Precipitation is lower than potential evapotranspiration in 55% of the domain. Mean annual 2-m temperature varies by only 3 K throughout the domain, with warmer air in the urbanized areas and in the centre of the basin.

Hydrological models

To account for the uncertainty associated with the hydrological models, seven models already set up on the basins under study were used in the RExHySS project (Table 1). As the Seine and Somme basins are characterized by the presence of large aquifers that are estimated to account for 40% and 80%, respectively, of the discharge at the outlets (Rousset et al., 2004;[START_REF] Négrel | Strontium isotopes as tracers of groundwater-induced floods: the Somme case study (France)[END_REF], three of these models explicitly simulate multi-layer aquifer transfer (MARTHE, MODCOU and SIM), three take aquifer storage into account with a 1D vertical approach (CLSM, EROS and GARDENIA), and one does not consider the aquifer explicitly (GR4). Two of the hydrological models are based on a soil-vegetation-atmosphere transfer scheme (CLSM and SIM), and thus compute the diurnal evolution of the water and energy budgets instead of using daily PET. Four of the models were applied to both basins, while two models were set up for the Somme basin only, and one for the Seine basin only.

As the hydrological models were already set up in the basins, their calibration period, calibration method and initial conditions differed, preventing the model performance from being attributed to the model structure or calibration practice.

However, as the groundwater initial conditions may impact the simulations over several years, a common method was used for the three hydrogeological models: the initial piezometric heads were derived based on a steady simulation that used the mean annual recharge estimated from the first 10 years of the simulation. A brief analysis of the hydrological model results is presented in Table 1 for the main river gauges of the two basins shown in Figure 1. According to the observed discharge availability, different periods were used for the two gauges. All models obtained reasonable results, with a daily efficiency [START_REF] Nash | River flow forecasting through conceptual models[END_REF] above 0.67 and even above 0.8 in 68% of the cases. However, the bias could be rather large, up to 22% for MODCOU on the Seine basin. Online resource 1 gives additional insight into the monthly cycle. For the groundwater, an intercomparison study had already been performed on the chalk aquifer of the Somme basin with MARTHE, MODCOU and SIM [START_REF] Habets | Multi-model comparison of a major flood in the groundwater-fed basin of the Somme River (France)[END_REF]. On the 45 wells in common, MARTHE was shown to obtain best results, with a determination coefficient of 71%, while MODCOU and SIM obtained 68 and 67% respectively, the average biases being 0.88, -0.8 and -0.19 m.

Climate models and emission scenarios

Six time-slice climate projections made during CMIP3 and analysed in AR4 [START_REF] Meehl | Global climate projections, in Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the AR4 of the IPCC[END_REF] were used. These six projections are based on the A1B emissions scenario (IPCC 2000) and were selected on the basis of an analysis of their climate change impact projected over France and, more precisely, on the most marked change in terms of weather regime and precipitation. In addition to these six global climate models (GCMs), projections from the Arpege climate model [START_REF] Gibelin | Anthropogenic climate change over the Mediterranean region simulated by a global variable resolution model[END_REF] were used. This model was chosen because of its finer resolution over France (around 100 km) due to the use of a stretched grid.

Two of the Arpege projections used different emissions scenarios (A1B and A2) and were only available for the end of the century, and the last one was available continuously from 1950 to 2100. Such continuous projection is important for hydrological impact studies because it can be used to infer a methodology to initialize the long-term response of the aquifer piezometric head for the time-slice projection.

Downscaling methods

It is necessary to downscale climate projections to try to reduce their bias and to make them compatible with the finer spatial resolution of the hydrological models [START_REF] Maraun | Precipitation downscaling under climate change: Recent developments to bridge the gap between dynamical models and the end user[END_REF]. In RExHySS, three downscaling methods previously used over France were applied. Two of them, the conventional anomaly method (AN) and the quantile mapping (QM) are based on statistical comparison with local analysis, while the weather typing (WT) method is based on an analysis of large scale circulation. They used the SAFRAN present-day climate data so that the downscaled climate projections reached 8-km spatial resolution. The AN considers that the mean local future climate is shifted but that there is no evolution of the frequency distribution of the variables, which means, for instance, that there is no modification of the precipitation spells (the number of wet days is the same in the future as at present). In our case, the anomalies were computed at the monthly time scale, following the method of [START_REF] Caballero | Hydrological sensitivity of the Adour-Garonne river basin to climate change[END_REF], and considered as additive for the temperature and as multiplicative for the precipitation. The QM was more complex since not simply the mean of the variable but the whole probability density function was corrected, based on a seasonal approach. It was therefore usually applied on regional climate models [START_REF] Déqué | Frequency of precipitation and temperature extremes over France in an anthropogenic scenario: Model results and statistical correction according to observed values[END_REF]. In order to be able to consider the relationship between the atmospheric variables, the QM was applied to a matrix containing the set of variables and not independently on each time vector variable. The WT [START_REF] Boé | Statistical and dynamical downscaling of the Seine basin climate for hydro-meteorological studies[END_REF][START_REF] Kollet | Influence of soil heterogeneity on evapotranspiration under shallow water table conditions: transient, stochastic simulations[END_REF] was based on the analysis of the large-scale processes to provide a relationship between the large and regional scales. It considered that similar largescale patterns would lead to similar local-scale patterns. Thus, each day of a given season in the future was taken to have a proxy in the same season of the present-day analysis, and this proxy was used to provide the spatial pattern and the diurnal cycle of the given day, with special treatment for the temperature, which was assumed to be significantly warmer in the future.

The three methods allow good consistency to be obtained in the parameter (e.g. a rainy day has humid 2-m air). Only the anomaly method conserves the average impact projected by the GCM. The averaged impact from the other two methods can differ from that of the GCM due to their more sophisticated bias correction, which can modify both the spatial pattern and the range of the impacts.

Comparisons of these methods over France as a whole (Boé et al., 2009) and on the Mediterranean part of France (Quintana-Seguì et al., 2010 and[START_REF] Ducharne | Evolution potentielle du régime des crues de la Seine sous changement climatique[END_REF] have shown that, although the two most sophisticated methods, QM and WT, gave rather similar results compared to the simple AN one, they could lead to some important local differences on both average and extreme values (more precisely, mean summer temperature and driest summer precipitation).

WT was applied to all climate model runs, while QM and AN were applied only to the finer spatial resolution ARPEGE-V4 model runs. Thus, only the ARPEGE-V4 model runs with the A1B and A2 emissions scenarios were downscaled using the three methods (see Table 2). A detailed assessment of the downscaled climate projection for the present day is given in online resource 1.

Results

Projected climate change

Evolutions of the mean precipitation, PET and 2-m air temperature as provided by the 12 downscaled climate projections (DCPs hereafter) for the periods 2047-2067 (2050 hereafter) and 2082-2099 (2080 hereafter) as compared to the present day on average over the domain are presented Table 2. There is general agreement that the temperature will have increased by 1.7° to 2.7° in 2050, and 2.2° to 4.2° in 2080, and that the PET will have increased by 11 to 22% by the 2050s and by 15 to 32% by around 2080. There is less agreement on precipitation.

On an annual basis, the evolution of precipitation varies by +0.4% to -14% in 2050 and by +4% to -24% in 2080, with only two of the 12 projections predicting an increase in precipitation. There is not much difference between downscaling methods on an annual basis but seasonal differences are more marked (see online resource 2).

Impact of climate change on the water budget

There were a total of 19 DCPs for the two periods and 11 hydrological modelling cases on the two basins, which makes a total of 209 possibilities. However, because of the simulation cost, not all the hydrological models used all the DCPs, and only 147 were effectively available (70%). The impacts of climate change estimated by these 147 simulations on total runoff are presented in Figure 2 and summarized in Table 3. The main result is a general agreement on a decrease of the total runoff for all the hydrological models and all the downscaled climate projections except the wettest ones (GM in 2080, andG1 in 2050). The average decrease is about 20% in 2050 and about 30% in 2080 (Figure 2). However, there is a large spread, which is associated with both the DCPs and the hydrological models. The standard deviation connected with the various DCPs for a given hydrological model ranges from 2 to 19%, and that connected with the various hydrological models for a given GCM ranges from 2 to 27% (Table 3). Most of the discrepancies on the hydrological models are related to only one of them, CLSM. When CLSM is excluded, the standard deviation is lower than 9% (Table 3). CLSM is the only hydrological model to project a median decrease of the total runoff larger than 30% in 2050 and larger than 50% in 2080. This is connected with an increase of the median actual evapotranspiration larger than 10% in the two basins in the future. Indeed, the soil moisture in CLSM is sustained by an upward groundwater flux that almost doubles in the future compared to present days although the ground water level decreases. This, associated to some weaknesses on the management of the soil water stress pointed out in an independent study, may lead to unrealistic results (see online resource 3 for a detailed analysis).

When the average impact of climate change on total runoff without the CLSM model is analysed, it clearly appears that the range associated with DCP (about 0 to -30 % in 2050, and about +4% to -40% for the DCPs using A1B emission in 2080) is larger than the range associated with hydrological modelling (-10% to -24% in 2050, and about -23% to -48% in 2080). The impact of the downscaling methods is also rather large, with an average impact on the Seine basin in 2080 varying from -32% to -50% (Figure 2 and Table 3). In the Somme basin, about 10% of the DCPs project almost no change on the annual cycle in both periods (slight increase in winter and slight decrease in summer), while in contrast, 10% project a decrease of approximately 30% in 2050 and above 50% in 2080 throughout the year. The monthly variations are more pronounced in the Seine basin: more than 25% and 10% of the DCPs project an increase of the riverflow from February to April in 2050 and 2080 respectively, while 25% of the simulations project a decrease of the summer discharge by about 30% and 40% in 2050 and 2080 respectively. For the 2080s, 75% of the simulations project a decrease of the discharge larger than the average decrease projected in the mid-century (Figure 3).

Impact on river flows

Impact of climate change on piezometric heads

The piezometric heads in the Seine and Somme basins were simulated by two and four hydrological models respectively. The number of wells simulated varied: 143 wells located on the free part of the aquifer layers for SIM and MODCOU in the Seine basin, and, for the chalk aquifer of the Somme, 57 wells for GARDENIA and MARTHE, and 50 wells for MODCOU and SIM.

The average evolutions of the piezometric head projected by all available DCPs are a diminution of the piezometric by 2.45 and 3.72 m in the Somme basin in 2050 and 2080, respectively, and by 3.88 and 6.05 m in the Seine basin (Figure 4, detailed results from the 1W continuous simulation are given in online resource 4). The two most contrasted DCPs are 2W and GS for the two basins, which is consistent with the results obtained on the total runoff. On the Somme basin, for which four hydrological models were available, for a given DCP (2W), the largest uncertainty associated with the hydrological model represents 5 m while, for a given hydrological model, the largest uncertainty reaches 8 m. When the results obtained with the same climate projection but with different downscaling methods are considered, the WT and QM methods reach a variation of 1, 3 and 5 m for MARTHE, SIM and MODCOU respectively, while the three downscaling methods lead to an uncertainty of 2 m for GARDENIA, which is the only model used for the three projections. GARDENIA presents the weakest evolution of the piezometric heads for a given DCP. This might be due to the fact that, in a lumped model, the piezometric heads are proportional to the level of a deep reservoir, and neither the evolution of the piezometric head gradient nor the relationship with the river is explicitly considered. The evolution of the piezometric head is more pronounced on the Seine basin than on the Somme basin, which might be due to the selection of the piezometric wells, with more wells located close to the river in the Somme basin. The amplitude of the aquifer decrease is similar to that obtained by Gordeniaux et al. (2011) in a Belgian basin, but larger than the amplitude obtained by [START_REF] Jackson | Modelling the effects of climate change and its uncertainty on UK Chalk groundwater resources from an ensemble of global climate model projections[END_REF] on a chalk aquifer in England. This could be explained by differences in the evolution of the precipitation.

Discussion

A detailed analysis was made of the uncertainty with an appropriate statistical method to manage the heterogeneous set of available impact projections (see online resource 5). Not surprisingly, the climate models appear to be the main sources of uncertainty, which is in agreement with the literature since about a decade (see, for instance, [START_REF] Arnell | The effect of climate change on hydrological regimes in Europe: A continental perspective[END_REF]Bergstrom et al., 2001;Nissjen et al., 2001). The three other sources of uncertainty, i.e., the ones associated with downscaling methods, emissions scenarios and the hydrological models are about the same range of value and about three times lower than the one associated to the climate modelling, at least when one focuses on the annual discharge, and when only physically sound results were kept. Indeed, it was possible to reduce the uncertainty on the hydrological modelling by considering one model as an outlier for physical reasons, highlighting a good fit with a single observed variable (here, the riverflows) is not sufficient to enable a model to be considered as a suitable tool for a projection study. Other sources of uncertainty were, however, neglected in this study. For instance, some physical feedbacks as the impact of the CO2 on plant transpiration or the modification of direct evaporation from groundwater are not explicitly taken into account by the models. Also, the land use was considered fixed, as was the anthropization of the water resources (no change in pumping pressure) although previous studies have shown their large impact on the water resource [START_REF] Holman | Climate change impacts on groundwater recharge-uncertainty, shortcomings, and the way forward? Hydrogeol[END_REF][START_REF] Candela | Impact assessment of combined climate and management scenarios on groundwater resources and associated wetland (Majorca, Spain)[END_REF]. Furthermore, the uncertainty associated with the natural variability was not explicitly addressed, although it had a large impact on the discharge of French rivers over the last century, and is recognized as being important in the early decades of climate projections [START_REF] Hawkins | The Potential to Narrow Uncertainty in Regional Climate Predictions[END_REF][START_REF] Kay | Comparison of uncertainty sources for climate change impacts: flood frequency in England[END_REF][START_REF] Terray | Quantifying 21 st -century France climate change and related uncertainties[END_REF]. Although this uncertainty analysis is not complete, it nevertheless appears that none of the four sources of uncertainty taken into account can be neglected.

The overall signal obtained in RexHySS is a clear decrease of the water resource of the Seine and Somme basins. This is consistent with the study by Boé et al. (2009), which shares some hydrological projections with RexHySS, but rather different from the one by [START_REF] Ducharne | Long term prospective of the Seine river system: Confronting climatic and direct anthropogenic changes[END_REF], for which a larger increase of winter flow was compensating the decrease of summer flow to lead to a quite stable annual discharge. Such results were obtained by employing two of the hydrological models used in this study (CLSM and MODCOU) and a former climate projection with the A2 emission scenario. This is the first time that two fairly recent climate change impact studies on a French basin have provided such contrasted results. For instance, the results of Boé et al. (2009) are quite consistent with those obtained in the Rhone and Garonne basins with earlier projections (Etchevers et al., 2001;[START_REF] Caballero | Hydrological sensitivity of the Adour-Garonne river basin to climate change[END_REF]. This is perhaps due to the fact that those two basins have a large snow component, for which global warming has quite a clear impact, whereas the Seine basin is hardly affected by snowfall and thus depends on the evolution of the rainfall, which is more uncertain. This reinforces the interest of knowing wether the use of CMIP5 climate projections would lead to similar results. CMIP5 GCMs also project a general tendency for an increase of winter precipitation and a decrease of summer precipitation in northern France [START_REF] Terray | Quantifying 21 st -century France climate change and related uncertainties[END_REF]. A first comparison of regional climate modelling performed in the framework of the CMIP5 Cordex experiment has shown a larger spread in winter precipitation. However, a full comparison is not yet possible because these projections have to be downscaled.

Although the present article does not focus on the extremes, the hydrological extremes projected by RExHySS have been analysed in a companion paper [START_REF] Ducharne | Evolution potentielle du régime des crues de la Seine sous changement climatique[END_REF], which reports that the risk of floods returning within 10year and 100-year periods is quite stable.

Conclusion

This article has attempted to present a full analysis of the impact of climate change on the hydrological component of two basins located in northern France, by including the analysis of surface water and groundwater. A set of 147 hydrological projections based on seven hydrological models, seven climate models, three downscaling methods, and two emissions scenarios was analysed.

There is a general agreement on a decrease of the river flow at the outlets of both basins by at least 14% by the 2050s and at least 22% by the 2080s. More than 90% of the hydrological projections predict a decrease of the summer flow at the outlets of the Seine and Somme basins (although this is less true for the Somme in the 2080s), and a decline of the piezometric heads for at least 90% of the wells.

However, the results present a large spread, with a magnitude of the signal on river flow and piezometric head of the same order of magnitude as the standard deviation. About 10% of the hydrological projections estimated a chance of increased river flow in winter in the Seine and thoughout the year in the Somme, while 10% projected a decrease of more than 40% the river discharge at the outlets. The uncertainty on the evolution of the piezometric head seems even larger, since the range of variation can reach 8 m on average over the simulated piezometers.

The spread is mainly due to the uncertainty associated with the climate projections, especially because of two wetter downscaled climate projections that give an opposite signal on the evolution of the piezometric head and discharge.

However, it has been shown that the other three sources of uncertainty taken into account in this study, i.e. the emission scenario, the downscaling method and the hydrological model, are also important, and that other sources of uncertainty as physical feedbacks and natural climate variability were not taken into account.

Therefore, there is a need to make a survey of the impact of climate change in such basins. A systematic approach will be helped by the availability of climate services, such as the DRIAS project over France (http://www.drias-climat.fr). 2 : Downscaled climate projection used ant their impacts on the Precipitation (P), Potential evapotranspiration (PET) and 2-m temperature (T) averaged over the Seine and Somme basins for the 2050s and the 2080s as projected by the downscaled present day GCM climate. AN: anomaly method, QM: quantile mapping, WT: weather typing. A grey column means that the projection is not available for the period. The acronyms beginning with a number refer to the emissions scenarios (1 for A1B, 2 for A2), while the letter stands for the downscaling method. The other acronyms stand for projections using A1B emission scenario and WT.

Evaluation of the downscaled climate modelling in present day

The comparison of downscaled present day GCM simulations (hereafter DPD) with present day analyses or observations helps to understand some properties of the downscaling methods and characterize some discrepancies associated with the deficiency of these methods or with the uncertainty associated on the natural variability. A DPD is not an atmospheric analysis and is not intented to reproduce the day-to-day meteorology. However, over a 30-year period, the main climate characteristics can be captured by the DPD. This is why in this part the assessment is performed on the mean monthly cycle. By construction, the anomaly method only provides data for the future, so only the Quantile Mapping and Weather Typing present-day projections are assessed, by comparison with the baseline analysis, and when used by the hydrological models.

Comparison with the present day atmospheric analysis.

Table 4 presents a comparison of the downscaled present-day climate modelling with the SAFRAN analysis over the 30-year period of 1971-2000 for the precipitation, potential evapotranspiration and temperature averaged over the domain. The DPD temperature and PET are quite close to the SAFRAN analysis on average, with biases lower than 0.35 K and 3%, respectively, and a mean monthly correlation close to 100%. Focusing on the precipitation, the mean bias is lower than 1% for six DPDs out of 10, but reaches 4% for two DPDs (GM and G1). The correlation computed on the mean monthly cycle reaches 80% for three DPDs, but is lower than 40% for two DPDs (G0 and G1). The two downscaling methods applied to the same ARPEGE-V4 simulation (2Q and 2W) show rather contrasted results with biases of +0.86% and -1.57%, and correlations of 54 and 84% respectively.

Analysis of the hydrological modelling forced by present-day downscaled GCM climate

The use of the DPDs instead of the baseline forcing analysis by the hydrological models led to some differences in the simulations of the river discharge. Figure 5 2 : Downscaled climate projection used ant their impacts on the Precipitation (P), Potential evapotranspiration (PET) and 2-m temperature (T) averaged over the Seine and Somme basins for the 2050s and the 2080s as projected by the downscaled present day GCM climate. AN: anomaly method, QM: quantile mapping, WT: weather typing. A grey column means that the projection is not available for the period. The acronyms beginning with a number refer to the emissions scenarios (1 for A1B, 2 for A2), while the letter stands for the downscaling method. The other acronyms stand for projections using A1B emission scenario and WT.

present the results for the Seine at Poses compared with the simulations performed with SAFRAN. For GR4, MODCOU and SIM, the DPDs lead to an average -13% negative bias on the Seine river flow with a standard deviation of 10%, while they lead to an average positive bias of 5% ±10 for EROS. CLSM

shows the strongest sensitivity to the DPDs, with an average positive bias of 8% and a standard deviation of 20%. This large standard deviation is mainly due to the use of the Arpege-V4 simulation downscaled with the Quantile Mapping approach (1Q and 2Q). The reasons for such behaviour are not clear. Although

CLSM uses not the daily potential evapotranspiration but the downscaled hourly values of the eight atmospheric variables, this is also the case for SIM, which does not show the same behaviour. Arpege-V4 was also downscaled using the WT method (1W and 2W). The impact of these two downscaling methods was rather large since, on average over the five hydrological models, the mean annual river flow increased by 13% for QM and decreased by 14% for WT. There was more discrepancy in the estimates of the river flow by the hydrological models when they used the forcing of the two downscaling methods applied to the same climate model than when they used the same downscaling method applied to various GCMs. However, the number of cases was too small for a general conclusion to be reached.

From this analysis, it can be said that i) in northern France, the QM and WT downscaling methods applied to present day simulations of climate models lead to atmospheric forcings that are suitable for use by hydrological models to infer annual river flow close to the one simulated using baseline meteorological forcing and also close to observations ii) The hydrological models are obviously sensitive to these DPDs, but they do not react in the same way. The discrepancy is considerable even for similar hydrological model types (lumped, distributed, with or without explicit representation of groundwater) and iii) the downscaling methods can lead to notable differences in the DPDs, which are probably linked to the natural variability of the climate model as shown by [START_REF] Terray | Quantifying 21 st -century France climate change and related uncertainties[END_REF] pointed out by previous studies, the uncertainty on the soil moisture or on evaporation in the present day leads to larger uncertainty in the future (Bergstrom et al., 2001[START_REF] Douville | Sensitivity of the hydrological cycle to increasing amounts of greenhouse gases and aerosols[END_REF][START_REF] Boé | Uncertainties in summer evapotranspiration changes over Europe and implications for regional climate change[END_REF], Severinatne et al., 2010).

In the 2050s and 2080s, the increase in atmospheric water demand leads to an increase in actual evapotranspiration in spring, which is associated with a faster decrease of the soil water storage. This increases the water stress, which, in turn, leads to a decrease of the actual evapotranspiration in summer time. This is true for all models except CLSM, for which actual evapotranspiration is sustained by a larger depletion of the soil moisture (about twice the present day value), with dramatic consequences on the river flow. Indeed, CLSM is able to mobilize a larger soil water resource, partly due to an upward flux from the groundwater which almost doubles from present day to future day (0.2 mm/d to about 0.36 mm/d on average over the whole basin). Upward water flux from groundwater to surface can be effective when the saturated zone is shallow [START_REF] Miguez-Macho | Incorporating water table dynamics in climate modeling: 2. Formulation, validation, and soil moisture simulation[END_REF][START_REF] Kollet | Influence of soil heterogeneity on evapotranspiration under shallow water table conditions: transient, stochastic simulations[END_REF]). However, this is not the case for most of the Seine groundwater bodies. Moreover, according to hydrogeological simulations, such fraction is projected to decrease. For instance, for MODCOU about 18% of the Seine basin has an aquifer lying within 5 m of depth in present day, and about 14% in the 2050s. Thus, the results of CLSM are probably unrealistic, and a previous independent study has pointed out that the model shows some weaknesses in managing soil water stress.

Therefore, it is very likely that the spread induced by this hydrological model can be classified as a "bad source of uncertainty", i.e. closer to a wrong simulation of the physical processes than to a real source of uncertainty.

Impact of climate change on the piezometric head of the continuous climate projection

Figure 9 presents the evolution of the piezometric heads, on average and for the quantiles 10, 25, 75 and 90, as projected using the 1W continuous simulation relative to the mean computed over the period 1971-2000. The piezometric head shows a decreasing trend, which is more marked for the Seine basin (about 6 m on average for MODCOU to 7 m for SIM at the end of the century) than for the Somme basin (about 2 m on average for GARDENIA to 4 m in SIM). However, there is a large difference in the well responses: in both basins, about 10% of the wells show almost no variation (these wells are low in the valley, close to the river). In the Somme basin, a 5-m decrease of the piezometric head is predicted on 25% of the wells by all models except GARDENIA (for which this threshold is reached by more than 10% of the wells) and a 10-m decrease is predicted for 10% of the wells in MOCOU and SIM at the end of the century (these wells being located on the plateaux). In the Seine basin, a 10-m decrease is predicted for 10% of the wells by MODCOU and for more than 25% of the wells as early as midcentury by SIM. As SIM and MODCOU share the same modelling of the groundwater transfer, this difference is due purely to the estimation of the water budget since the actual evapotranspiration is projected to undergo a larger decrease in MODCOU than in SIM, thus limiting the decrease of the aquifer recharge in MODCOU compared to SIM. and the GCM (V(G), and evolution of the annual discharge expressed in m 3 /s.

Figure 3

 3 Figure 3 presents the monthly anomalies of the Seine and Somme river flows in 2050 and 2080 compared to the present day climate. The sets of DCPs and hydrological models vary for each basin and each period as does the total number of simulations (see caption). The mean monthly anomalies in the Somme basin are around -20% in 2050 and -30% in 2080, while in the Seine basin, the decrease is larger in summer (-30% in 2050, -40% in 2080) than in winter (0% in 2050 and -15% in 2080).

  Parry ML, Canziani OF, Palutikof JP, van der Linden PJ, Hanson CE (2007) Contribution of Working Group II to the AR4 of the IPCC Change Eds. Cambridge University Press, Cambridge, UK, 976 pp Perrin C, Michel C, Andréassian V (2003) Improvement of a parsimonious model for streamflow simulation. J Hydrol 279(1-4): 275-289 Prudhomme C and Davies H (2009) Assessing uncertainties in climate change impact analyses on the river flow regimes in the UK. Part 2: future climate. Climatic Change 93 197-222 Quintana-Segui P, et al., (2008). The SAFRAN atmospheric analysis: Description and validation. J Appl Meteorol Clim doi: http://dx.doi.org/10.1175/2007JAMC1636.1. Quintana-Seguì P, Ribes A, Martin E, Habets F, Boé J (2010) Comparison of three downscaling methods in simulating the impact of climate change on the hydrology of Mediterranean basins. J Hydrol, 383 (1-2) 111-124 Quintana-Seguì P, Habets F, Martin E (2011). Comparison of past and future Mediterranean high and low extremes of precipitation and river flow projected using different statistical downscaling methods. Nat Hazards Earth Syst Sci 11 (5) 1411-1432 Rousset F, et al., (2004) Hydrometeorological modeling of the Seine basin using the SAFRAN-ISBA-MODCOU system. J Geophys Res A 109(D14) Terray L, Boé J (2013) Quantifying 21 st -century France climate change and related uncertainties. C R Geoscience 345 136-149. Teng J, Vaze J, Chiew FHS, Wang B, Perraud JM (2012) Estimating the Relative Uncertainties Sourced from GCMs and Hydrological Models in Modeling Climate Change Impact on Runoff. J Hydrometeor, 13 122-139. Thiéry D (1990) Logiciel MARTHE. Modélisation d'Aquifère par un maillage Rectangulaire en régime Transitoire pour le calcul hydrodynamique des écoulements, version 4.3. BRGM report, 4S/EAU R32210 1990 Thiéry D (2003) Logiciel GARDENIA version 6.0 -Guide d'utilisation. BRGM report, RP-52832-FR, 102 pp., 42 Thiéry D, Moutzopoulos C (1995) Un modèle hydrologique spatialisé pour la simulation de très grands bassins : le modèle EROS formé de grappes de modèles globaux élémentaires. Le Barbé et E. Servat (Ed.) ORSTOM Editions, pp. 285-295. Vautard R, Yiou P (2009) Control of recent European surface climate change by atmospheric flow. Geophys Res Lett, 36, L22702 Verjus P (2008) Elaboration du SDAGE du bassin Seine Normandie relatif à la gestion de la rareté de la ressource en eau. Houille Blanche DOI: 10.1051/lhb:2008026 Wilby R L, Harris I (2006) A framework for assessing uncertainties in climate change impacts: Low-flow scenarios for the River Thames, UK. Water Resour Res 42 W02419

Figure 1

 1 Figure 1 Spatial distribution of the mean annual precipitation (left), ratio of the precipitation to potential evapotranspiration (centre) and 2-m temperature (right) on the domain, as provided by the SAFRAN analysis from 1971 to 2000.

Figure 2

 2 Figure 2 Evolution of the runoff (%) for mid-century (top) and end-of-century (bottom), for each hydrological model (x-axis) for the Seine basin (circles) and the Somme basin (squares), for each downscaled climate projection. The acronyms of the DCPs are given and, for each model, the boxes indicate the average impact +/-the standard deviation.

Figure 3 :

 3 Figure 3 : Monthly evolution of the Seine (left) and Somme (right) river flows for the mid-century (top) and end-of-century (bottom) relative to the simulated present day climate. The envelope represents the minimal and maximal values for each month, the continuous blue thin lines the 10 and 90 quantiles, the dotted lines the 25 and 75 quantiles, and the black line the average. The average in mid-century is plotted at the bottom with pink diamonds. The number of simulations varies on each plot: 36 and 37 for the Seine basin at mid-century and end-of-century, and 26 and 39 respectively for the Somme.

Figure 4 Florence

 4 Figure 4 Evolution of the piezometric head (m) on average for each of the simulated wells as projected by the hydrological models for each DCP for the Seine basin (filled squares) and the Somme basin (circles). The acronyms of the DCPs are given, and the boxes indicate the average impact +/-the standard deviation. The upper panel presents the results for the mid-century, and the bottom one the end-of-century.

Figure 5

 5 Figure 5 Monthly river flows of the Seine at Poses (m 3 /s) observed (thick red line) and simulated by the hydrological models using the SAFRAN analysis (thick green line) over the 1971-2001 period, and simulated by the hydrological models forced by the downscaled present-day GCM climate for the 1970-2000 (symbol lines), downscaled with either QM (blue lines) or WT (black lines).

Figure 6

 6 Figure6Seasonal and annual evolutions of the precipitation (%, top), potential evapotranspiration (%, middle) and 2-m air temperature (K, bottom) as projected by the downscaled climate projections for around 2050(2047-2065 versus 1971-2000, left), and 2080 (2082-2099 versus 1971-2000, right). The symbols correspond to the DCPs, those downscaled with WT in black, with QM in blue, and with AN in red.

Figure 7

 7 Figure 7 Evolution of the actual evapotranspiration (%) for mid-century (top) and end-of-century (bottom), for each hydrological model (x-axis) for the Seine basin (empty circles) and the Somme basin (filled squares), for each downscaled climate projection. The acronyms of the DCPs are given and, for each hydrological model, the box represents the average impact +/-the standard deviation.

Figure 8 Florence

 8 Figure 8 Monthly actual evapotranspiration (top), evolution of the soil water storage (middle) and total runoff (bottom) estimated by four hydrological models on the Seine basin, on average on present day (black), mid-century (blue) and at end of century (red). The unit for each plot is mm/d.

Figure 9

 9 Figure 9 Annual evolution of the piezometric heads (m) projected on the Somme and Seine basins by the hydrological models for the 1W continuous DCP, in comparison with the average 1971-2000 period. The average over the simulated wells is plotted in thick black lines, the quantiles 10 and 90 are plotted in red, and the quantiles 25 and 75 in green.

  and

	Table
			emissions	downscaling	2050	2080
	Acronym GCM	scenarios	method
					P PET T P PET T
			A2	AN		-	29.9 4.2
	2A	ARPEGE-V4				15.3
			A2	QM		-	31.5 3.8
	2Q	ARPEGE-V4				23.7
			A2	WT		-	30.5 3.6
	2W	ARPEGE-V4				22.2
			A1B	AN		-	22.6 3.3
	1A	ARPEGE-V4				11.8
			A1B	QM		-	25.3 2.9
	1Q	ARPEGE-V4				18.0
			A1B	WT	-	19.6 2.4 -	25.9 3.1
	1W	ARPEGE-V4-CONT			14.0	15.9
			A1B	WT	-4.3 21.9 2.7 -	32.2 3.8
	G0	GFDL_CM2_0				14.2
	G1	GFDL_CM2_1	A1B	WT	-4.7 15.7 1.7 -9.5 26.2 3.0
	EC	ECHAM5/MPI-OM A1B	WT	0.4 13.1 2.2 -6.8 26.0 3.5
	MR	MRI-CGCM2.3.2	A1B	WT	-4.5 11.3 1.9 -6.3 18.9 2.6
	GM	GISS-MODEL-ER A1B	WT	-0.8 11.8 1.7 3.8 15.8 2.2
	CC	CCCMA_GCGM3_1A1B	WT	-5.8 18.0 2.5 -3.2 23.9 3.2

  by analysing the various member of the CMIP5 climate models over France. Table 5 Difference (%) between the Seine river flows at Poses computed by the hydrological model forced by the downscaled present-day GCM climate for the period 1970-2000 and the flows computed by the model forced by the SAFRAN analysis over the 1971-2001 period (the reference value is provided in the SAFRAN line, in m 3 /s). Last two columns: average and standard deviation associated with each downscaled present-day GCM climate. Last two lines: average and standard deviation associated with each hydrological model.

		CLSM	Emissions EROS GR4	DM2050 2010P MODCOU SIM	PET		T Standard
	Acronym GCM Downscaled		scenarios				Average	deviation
	GCM							Bias	Cor Bias	Cor Bias Cor
	2A							%	%
	2A 2Q	ARPEGE-V4 46.5	5.3	A2	5	AN 6	Y	2.7	13.1		18.7
	2W	-3.1	-17		-15.7	-15.4		-19.6	-14.2		6.4
	2Q	ARPEGE-V4		A2		QM	Y	0.86 0.54-2.71 1.0 0.17 1.0
	1A
	2W 1Q	ARPEGE-V4 46.5	5.3	A2	5	WT 6	Y	-1.57 0.842.71 0.990.27 1.0 2.7 13.1 18.7
	1A 1W	ARPEGE-V4 -3.6	11.0	A1B	-17.3	AN -17	Y	-19.5	-9.3		13.0
	1Q G0	ARPEGE-V4 8.3	11.9	A1B	-15.9	QM -16.1	Y	0.86 0.54-2.71 1.0 0.17 1.0 -13.6 -5.1 14.0
	1W G1	ARPEGE-V4-CONT A1B -6.1 0.6	-25.9	WT Y -25.6	Y	-1.28 0.772.78 0.990.26 1.0 -24.6 -16.3 12.6
	G0 EC	GFDL_CM2_0 0.7	10.8	A1B	-15.9	WT Y -17.7	Y	-0.13 0.392.01 0.990.29 0.99 -16.8 -7.8 12.9
	G1 MR	GFDL_CM2_1 2	14	A1B	-13.8	WT Y -12.5	Y	-5.17 0.341.07 0.990.14 1.0 -14.3 -4.9 12.6
	EC GM	ECHAM5/MPI-OM A1B -7.5	-22.4	WT Y -22.9	Y	-0.97 0.811.71 0.990.20 1.0 -23 -19.0 7.6
	MR CC	MRI-CGCM2.3.2 -0.4	A1B	-17.1	WT Y -18.7	Y	-0.66 0.741.62 0.990.34 1.0 -19.2 -13.9 9.0
	GM Average	GISS-MODEL-ER A1B 8.3 5.2	-13.4	WT Y -13.4	Y	-4.43 0.842.52 0.990.28 1.0 -14.5
	Standard
	CC deviation	CCCMA_GCGM3_1A1B 20.6 10.0	10.3	WT Y 10.9	Y	-0.45 0.672.20 0.990.18 1.0 9.7
	SAFRAN	488.8	518.6	566.9	615.1		2.18 547.2	2.14		283.19
	(m3/s)	SAFRAN						mm/d	mm/d		K
	Table 4 Precipitation (P), Potential evapotranspiration (PET) and 2-m temperature (T)
	averaged over the Seine and Somme basins for 1971-2000 for the SAFRAN analysis and
	the downscaled present day GCM climate. Cor stands for correlation ( GM SAFRAN GCM SAFRAN cor σ σ σ , = , with σ the covariance) which was computed on the mean monthly
	cycle. DM: downscaling method, AN: anomaly method, QM: quantile mapping, WT:
	weather typing. Y in the 2050s and 2080s columns indicates the availability of the climate
	projection for the period.

Table 6

 6 Uncertainty analysis of the impact of climate change on the discharge at the outlets of the Seine and Somme basins around 2050 and 2080 for the hydrological projection using the A1B climate model downscaled by the WT method: number of missing values reconstructed by the statistical method, variance (%) associated with the uncertainty of the hydrological model (V(H))

	Type	Seine at Poses Qobs=538m 3 /s	Somme	at	Abbeville:
				Qobs=34.9 m 3 /s
		All	All but CLSM All	All but CLSM
			2050
	Missing values 3 (out of 35)	2 (out of 28)	15 (out of 42) 9 (out of 35)
	V(H)	59	27	88	29
	V(G)	47	77	13	77
	Evolution	-99+/-68	-76+/-54	-11+/-10	-7+/-4
			2080
	Missing Values 7 (out of 35)	6 (out of 28)	19 (out of 42) 13 (out of 35)
	V(H)	51	43	84	21
	V(G)	51	58	16	82
	Evolution	-157+/-102	-133 +/-90	-12+/-12	-8+/-5

Acknowledgements

The RExHySS project (Influence du changement climatique sur la ressource en eau et les extrêmes hydrologiques dans les bassins de la Seine et de la Somme) was supported by the research programme "Gestion et Impact du Changement Climatique" of the French Ministère de l'Ecologie et du Développement Durable.

Table 3 Anomaly on the total runoff in percent estimated for the two basins and the two periods in %. For each downscaled climate projection and hydrological model, the average anomaly +/-the standard deviation computed on the available simulations are given (but the average is not computed from the same number of members). For the DCP, the statistics obtained by all hydrological models except CLSM are also provided (id means identical, no change).

Projected climate change

Figure 6 presents the evolution of the seasonal and annual precipitation, PET and 2-m air temperature as projected by the 12 downscaled climate projections for 2050 and 2080. There is a trend towards decreasing precipitation in summer and autumn, but no clear signal in winter and spring. The difference between downscaling methods is exacerbated on the summer temperature for the ARPEGE-V4 A2 projection (Figure 6), for which the AN and QM provide an average increase of 6° in 2080, while WT only gives a 4° increase. Another difference that might have an important impact on the hydrology is the evolution of the precipitation in winter. While AN and QM lead to weak decreases (-1.5% and -6% respectively), WT leads to a larger decrease (-16%).

Online resource 3

Impact of climate change on the hydrogeology of two basins in Northern France

Florence Habets 1,2 ,Julien Boé 3 ,Michel Déqué 4 ,Agnès Ducharne 1 ,Simon Gascoin 5 ,Ali Hachour 2 ,Eric Martin 4 ,Christian Pagé 5 ,Eric Sauquet 6 ,Laurent Terray 3

Impact of climate change on the water balance

Figure 7 presents the evolution of the mean actual evaporation for each period as simulated by each hydrological model forced by the DCPs. In the 2050's, the median evolution of the actual evaporation is close to zero for each hydrological model except MODCOU (-5%) and CLSM (above +10%). In the 2080's, all the hydrological models estimate a median decrease of the actual evaporation by 5 to 15% except CLSM which estimates an increase by more than 10%. CLSM thus appears as an outlier model here.

To investigate the source of such dispersion, a thorough analysis was performed.

Monthly evolutions of the actual evapotranspiration, variation of soil moisture and river flow at the outlet of the Seine basin (Poses) according to four hydrological models for the present day, 2050 and 2080 are plotted in Figure 8. In the present day (black lines), although the mean total runoff is similar for all the hydrological models, the other two variables show more discrepancies: the maximum value of the monthly evapotranspiration ranges from 2 to 3 mm/day, and the annual amplitude of the monthly soil water storage ranges from 1 to 4 mm/day. This is because the hydrological models only use the river discharge as a control variable, and do not (or cannot) assess the other terms of the water budget. But, as already

Uncertainty analysis

In order to understand its origin, an analysis of the uncertainty was made by investigating the variance associated with the various sources, i.e. the climate modelling, the hydrological modelling, the emission scenario, and the downscaling methods. To do this, we focused on a variable encompassing the characteristics of the basins: the evolution of the discharge at the outlets. To perform such analysis it is necessary to use a homogeneous set of impact projections, and we used the method of [START_REF] Kendall | design and analysis, and time series[END_REF] described in [START_REF] Déqué | An intercomparison of regional climate simulations for Europe: assessing uncertainties in model projections[END_REF] to reconstruct the missing values. To avoid using a sparse matrix, as only one climate model was downscaled by several methods and using two different emission scenarios the analysis was made in three parts: first, an analysis of the results using the seven climate projections based on the A1B emission scenario and downscaled by the WT method, which allowed us to study the uncertainty associated with the hydrological and climate modelling in the two periods of interest (Table 6), then an analysis of the results using a given climate model (ARPEGE-V4 in 2080), but with two different emission scenarios and three downscaling methods (Table 7) and finally, an analysis of the results in 2080 with all climate models, hydrological models, downscaling methods and emissions scenarios (Table 3). In this way, the two first matrices used to compute the variance were almost complete, and the statistical method applied to fill the matrix had a smaller impact. For instance, in Table 6, there are only two components: the hydrological models and the climate models (noted with indices i and j respectively). The total variance in the case of Table 6 is given by:

where X(..) is the average of the evolution for each hydrological model and each climate model, X(i.) is the average of the evolution of the hydrological model i for each climate model, nb_hydro is the number of hydrological models, and nb_proj is the number of downscaled climate projections.

This method estimates missing values, using the approximation:

In such cases, the variance associated with the hydrological model is expressed as ( )

and the sum V(H) +V(G) thus exceeds 100%.

The evolution of the discharge computed with the A1B projections with the full matrix (without missing values) shows a rather large decrease of the river discharge (-14 and -18 % in 2050, -24 and -22% in 2080 for the Seine and Somme basins respectively) but with an uncertainty, expressed by the standard deviation, that is about as large (Table 6). The discharge decrease is more pronounced when the same estimation is made using the ARPEGE-V4 projections with different emission scenarios and downscaling methods (-34 and -40% in 2080 for the Seine and Somme basins, Table 7) but the standard deviation is reduced, which reinforces the signal of a marked decrease in the discharge.

Without considering the results of CLSM, Table 6 shows that the main source of uncertainty is clearly the climate models for all basins and periods, although it is not strongly dominant for the Seine in 2080. Table 7 shows that the uncertainties associated with the downscaling methods and the emission scenarios are of the same order as that associated with the hydrological models, and are even higher in the Somme basin. The consideration of the four sources of uncertainty in 2080, although involving the use of a sparse matrix, provides results that are in between the two previous estimations (Table 3). The variances obtained confirm that the uncertainty on the climate models dominates, with a total variance about three times above the other ones, the three other sources of uncertainty being of the same order of magnitude, at least as regards the impact on the evolution of the annual discharge.

It can be seen in the tables that quite different conclusions would be reached if the CLSM projections were taken into account, as the impact of the uncertainty associated with hydrological modelling would then be greater.