Gilles Billen
email: gilles.billen@upmc.fr

Antsiva Ramarson

Vincent Thieu

Sylvain Théry

Marie Silvestre

Catherine Pasquier

Catherine Hénault

Josette Garnier

Nitrate retention at the river-watershed interface: a new conceptual modeling approach

Keywords: riparian retention, soil denitrification, nitrate, river systems

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Introduction

The Nitrogen Cascade [START_REF] Galloway | The nitrogen cascade[END_REF] designates the chain of transfers, transformations and effects initiated by the release into the environment of reactive nitrogen forms from agriculture and other anthropogenic activities [START_REF] Duretz | NitroScape: A model to integrate nitrogen transfers and transformations in rural landscapes[END_REF]. In areas with intensive industrial agriculture, nitrate contamination of ground-and surface water is widespread and a major threat for drinking water resources, aquatic biodiversity and coastal water ecosystem health [START_REF] Grizzetti | Nitrogen as a threat to European water quality[END_REF][START_REF] Billen | Nitrogen flows from European watersheds to coastal marine waters[END_REF].

Much debate exists regarding the level of nitrate concentration in surface river water that can be considered desirable. For drinking water, the maximum threshold set by the World Health Organization (WHO) (1970, reconfirmed in 2007) is 10 mgN/l, very close to level recommended by the European Drinking Water Directive (EC 98/83, 1998): 11.3 mgN/l (50 mgNO 3 /l). Half this value (5 mgN/l or 25-30 mgNO 3 /l) is often stated as a groundwater objective or vigilance level (AESN-SDAGE http://www.eau-seinenormandie.fr/mediatheque/flipbook_sdage/index.html). In surface water, a strong decrease in hydrophyte biodiversity is observed above a nitrate concentration of 2 mgN/l [START_REF] James | Nitrate availability and hydrophyte species richness in shallow lakes[END_REF]. This is also the level recommended by [START_REF] Camargo | Nitrate toxicity to aquatic animals: a review with new data for freshwater invertebrates[END_REF] for protecting the most sensitive freshwater species. As far as freshwater eutrophication risks are concerned, total N concentration levels above 1.5 mgN/l are considered to be a potential risk [START_REF] Grizzetti | Nitrogen as a threat to European water quality[END_REF]. For coastal marine eutrophication, because the problem is linked to excess nitrogen over silica with respect to the needs of diatom growth (Billen & Garnier, 2007), the maximum nitrate threshold should be related to the river silica concentration. As the latter varies within the range 3-7 mgSi/l under a temperate climate [START_REF] Garnier | The silicon cycle, human perturbations and impacts on aquatic system[END_REF], the corresponding maximum nitrate concentration must be within the range 1.2-2.8 mgN/l. Clearly, the water quality objectives for river water should be much lower than those stated for groundwater.

Nitrate concentrations in temperate European rivers are frequently 25-50% lower than in the groundwater feeding them [START_REF] Billen | Nitrogen transfer through the Seine drainage network: a budget based on the application of the RIVERSTRAHLER Model[END_REF][START_REF] Passy | A model reconstruction of riverine nutrient fluxes and eutrophication in the Belgian Coastal Zone since 1984[END_REF]. They also often show seasonal variations with lower values in summer low-water than in winter high-water periods.

This can in some cases be related to a larger contribution to total runoff of groundwater baseflow (if they have a lower nitrate concentration than surface runoff) in summer than in winter [START_REF] Thieu | Nutrient transfer in three contrasting NW European watersheds: The Seine, Somme, and Scheldt Rivers. A comparative application of the Seneque/Riverstrahler model[END_REF]. The seasonality of the river nitrate concentration has also been attributed to denitrification in riparian wetlands, more active at high summer temperatures.

The latter conclusion is supported by strong isotopic evidence, because the low nitrate concentration in summer low water is associated with the clear 15 N signature of a riparian denitrification process, and cannot be the result of in-stream denitrification by benthic processes [START_REF] Sebilo | Isotopic composition of nitratenitrogen as a marker of riparian and benthic denitrification at the scale of the whole Seine River system[END_REF]. Direct measurements of nitrate concentration along topographical gradients from the agricultural plateau down to river banks [START_REF] Cey | Role of the riparian zone in controlling the distribution and fate of agricultural nitrogen near a small stream in southern Ontario[END_REF][START_REF] Hill | Subsurface denitrification in a forest riparian zone: Interactions between hydrology and supplies of nitrate and organic carbon[END_REF][START_REF] Devito | Nitrate dynamics in relation to lithology and hydrologic flow path in a river riparian zone[END_REF]Ranalli & Maclady, 2010) clearly illustrate the role of riparian denitrification. In situ measurements of denitrification rates also show that riparian wetlands, together with areas where water tables remain shallow for extended periods [START_REF] Burt | Denitrification in riparian buffer zones: the role of floodplain hydrology[END_REF]Clement et al., 2002;[START_REF] Anderson | Assessing denitrification from seasonally saturated soils in an agricultural landscape: a farm scale mass-balance approach[END_REF]Oeler et al., 2008) are hotspots of denitrification in the landscape. Yet, modeling this process at the watershed scale remains a difficult task.

At the landscape scale, models like Nitroscape, TNT2 and CASIMOD'N [START_REF] Salmon-Monviola | Modelling spatial dynamics of cropping systems to assess agricultural practices at the catchment scale[END_REF][START_REF] Duretz | NitroScape: A model to integrate nitrogen transfers and transformations in rural landscapes[END_REF][START_REF] Moreau | CASIMOD'N : an agro-hydrological distributed model of catchment-scale nitrogen dynamics integrating farming system decisions[END_REF], based on a comprehensive description of all processes of water and nitrogen transfer and transformation, calculate at a metric grid scale all processes of the nitrogen cycle, including denitrification, plant uptake and storage in the soil organic matter pool. Such models allow one to estimate "landscape nitrogen retention" at a very fine spatial resolution. The application of such a fully spatially distributed modeling approach is limited, however, to landscapes smaller than a few dozen square kilometers.

At the other extreme, the NANI approach [START_REF] Howarth | Regional nitrogen budgets and riverine N & P fluxes for the drainages to the North Atlantic ocean: natural and human influences[END_REF], designed for application at the scale of large watersheds, reduces all processes related to the N cascade to a black box with inputs (fertilizer application, atmospheric deposition, symbiotic N 2 fixation, net imports of N in agricultural commodities) and output (river N flux at the watershed outlet). The balance defines a "N retention" term, typically 40-80% of the inputs, which can be empirically related to temperature and specific discharge [START_REF] Howarth | Regional nitrogen budgets and riverine N & P fluxes for the drainages to the North Atlantic ocean: natural and human influences[END_REF][START_REF] Howarth | The influence of climate on average nitrogen export from large watersheds in the northeastern United States[END_REF][START_REF] Billen | Anthropogenic nitrogen autotrophy and heterotrophy of the world's watersheds: Past, present, and future trends[END_REF][START_REF] Billen | Nutrient fluxes and water quality in the drainage network of the Scheldt basin over the last 50 years[END_REF]Billen et al, , 2006)), without, however, providing any clues on the nature of the processes involved. The term "retention," although commonly accepted in the literature, is misleading because only a fraction of the "missing" N is indeed stored in the landscape, the rest being denitrified to the atmospheric nonreactive N 2 pool, with, however, a fraction emitted as N 2 O, a powerful greenhouse gas [START_REF] Garnier | Curative vs. preventive management of nitrogen transfers in rural areas: Lessons from the case of the Orgeval watershed (Seine River basin, France)[END_REF].

Models such as Sparrow [START_REF] Alexander | Atmospheric nitrogen flux from the watersheds of major estuaries of the United States: An application of the SPARROW watershed model[END_REF]Smith et al., 2007), Green [START_REF] Grizzetti | A statistical approach to estimate nitrogen sectorial contribution to total load[END_REF][START_REF] Grizzetti | Assessment of Nitrogen and Phosphorus Environmental Pressure at European Scale[END_REF] and Nutting-N [START_REF] Dupas | Assessing N emissions in surface water at the national level: Comparison of country-wide vs. regionalized models[END_REF][START_REF] Anglade | Agriculture biologique et qualité des ressources en eau dans le bassin de la Seine. Caractérisation des pratiques et applications territorialisées[END_REF] are a little more explanatory. Among retention processes they distinguish between those occurring in the terrestrial part of the basin (and acting on diffuse sources of nitrogen) and those occurring in the river network (acting on both diffuse and point sources of nitrogen). Both retention types are parameterized according to hydrological and landscape characteristics of the watershed and calibrated on the basis of multiannual averages of observed nitrogen load at the outlet of the watersheds. These models generally show the predominance of terrestrial versus in-stream retention processes.

The SWAT [START_REF] Neitsch | SWAT Theoretical Documentation[END_REF] and Seneque-Riverstrahler [START_REF] Billen | Nitrogen transfer through the Seine drainage network: a budget based on the application of the RIVERSTRAHLER Model[END_REF][START_REF] Ruelland | SENEQUE: a multi-scaled GIS interface to the RIVERSTRAHLER model of the biogeochemical functioning of river systems[END_REF] N or NANI type. However, their weak point lies in the representation of riparian denitrification [START_REF] Grizzetti | The role of water nitrogen retention in integrated nutrient management: assessment in a large basin using different modelling approaches[END_REF]. Special modules have recently been developed for a better representation of alluvial denitrification by the SWAT model [START_REF] Sun | Improved simulation of river water and groundwater exchange in an alluvial plain using the SWAT model[END_REF]. In the Riverstrahler model, riparian retention was initially parameterized as a constant fraction of the nitrate flux transmitted from the watershed [START_REF] Billen | Nitrogen transfer through the Seine drainage network: a budget based on the application of the RIVERSTRAHLER Model[END_REF], possibly adjusted on the basis of a typology of river corridors [START_REF] Curie | Geomorphological methods to characterizes wetlands at the scale of the Seine watershed[END_REF].

In this paper, we describe a different vision of riparian denitrification based on a more mechanistic description of the processes involved in the interaction of surface and groundwater runoff with riparian wetland soils. We aimed at a better simulation of the spatial and temporal variations of nitrate concentrations in rivers than previous versions of the Riverstrahler model, for both small (100-1000 km²) and large (over 10,000 km²) watersheds.

Methods

The Riverstrahler model

The general principles and parameterization of the Riverstahler model have been fully described in the papers by [START_REF] Billen | Modelling phytoplankton development in whole drainage networks: The RIVERSTRAHLER model applied to the Seine river system[END_REF][START_REF] Billen | Nitrogen transfer through the Seine drainage network: a budget based on the application of the RIVERSTRAHLER Model[END_REF] and [START_REF] Garnier | Modeling transfer and retention of nutrients in the drainage network of the Danube River[END_REF][START_REF] Billen | Nutrient fluxes and water quality in the drainage network of the Scheldt basin over the last 50 years[END_REF]. Here we used the Seneque-Riverstrahler version with its GIS interface described by [START_REF] Ruelland | SENEQUE: a multi-scaled GIS interface to the RIVERSTRAHLER model of the biogeochemical functioning of river systems[END_REF] and extensively applied to the Seine, Somme and Scheldt watersheds (Thieu at al., 2009;[START_REF] Passy | A model reconstruction of riverine nutrient fluxes and eutrophication in the Belgian Coastal Zone since 1984[END_REF], as well as to several other watersheds with quite different climate and hydrological regimes and watershed sizes, such as the Lot (South of France) [START_REF] Garnier | How can water quality be improved when the urban waste water directive has been fulfilled? A case study of the Lot river (France)[END_REF], the Red River (China and Vietnam) [START_REF] Le | Modelling nutrient transfer in the sub-tropical Red River system (China and Vietnam): implementation of the Seneque/Riverstrahler model[END_REF], the Kalix and Lule Rivers (Sweden) [START_REF] Sferratore | Modelling nutrient fluxes from sub-arctic basins: comparison of pristine vs. dammed rivers[END_REF] and the Danube River [START_REF] Garnier | Modeling transfer and retention of nutrients in the drainage network of the Danube River[END_REF].

A complete description of the biogeochemical RIVE model, which constitutes the core of the Riverstrahler model, as well as a detailed description of the Seneque-Riverstrahler software, is available at www.fire.upmc.fr/rive Seneque calculates the discharge of each stream of the river network from the superficial and baseflow components of the runoff over its watershed, which have to be specified as an input to the model. They can be obtained either from observed daily discharge data at key stations in the river network using conventional recursive filters [START_REF] Arnold | Automated Base Flow Separation and Recession Analysis Techniques[END_REF][START_REF] Arnold | Continental scale simulation of the hydrologic balance[END_REF][START_REF] Eckardt | How to construct recursive digital filters for baseflow separation[END_REF]Eckardt, , 2008) ) or from the results of rainfall-discharge models if they are available on the watershed. In the Riverstrahler model, the absolute values and the relative importance of each of these components vary at each 10-day period, but the chemical composition of each component of the discharge (in particular for nitrate) is considered to remain constant. As explained below, the nitrate concentration in sub-root water flows is calculated from the long term N surplus, leaching and water runoff. The mass conservation between N soil balance and leaching is therefore met on the long run. At the scale of 10-day periods, it is assumed that the magnitude of the N soil pools act as an efficient buffer of nitrate concentration in runoff water in spite of the water flow variations. The N fluxes associated to the superficial and groundwater runoff define the diffuse sources from the watershed, which must be determined as an input to the model for each land use class considered.

For the superficial or hypodermic component of runoff, the nitrate concentration is that of the sub-root infiltration concentration. Depending on the scale at which the model is applied, the land-use classes can simply refer to divisions such as Corine Land Cover classes level 1 or 2 (Ministère de l'Environnement, de l'Energie et de la Mer, 2012), distinguishing urban area, forest and semi-natural areas, grassland and arable land, or they can distinguish many more classes of arable land with, e.g., different crop rotations. In both cases, the nitrate concentration in infiltrating sub-root water is calculated from the nitrogen soil balance (or surplus, kgN/ha/yr), taking into account inputs through fertilizers, manure, symbiotic fixation, atmospheric deposition, and export through harvest in each land use class considered. In the former case, the data are provided by regional agricultural statistics; in the latter case, detailed inquiry regarding the cultural practices and yield are used. The procedure for estimating leaching nitrate concentration from the N soil balance is explained below.

Regarding the nitrate concentration in baseflow, the same concentration as in sub-root fluxes is also considered in the present study, assuming that groundwater has reached its equilibrium with respect to this infiltration concentration. However, another value could be assigned to base-flow, when groundwater nitrate concentration differs from the sub-root one. This can happen for two reasons: either because of the legacy of a previous higher or lower contamination in recharge water in long residence time aquifers [START_REF] Thieu | Nutrient transfer in three contrasting NW European watersheds: The Seine, Somme, and Scheldt Rivers. A comparative application of the Seneque/Riverstrahler model[END_REF], or, in some cases, because of a process of denitrification occurring in deep groundwater due to anoxic conditions created by the presence of iron sulfides or residual organic matter [START_REF] Rivett | Nitrate attenuation in groundwater: A review of biogeochemical controlling processes[END_REF].

The model simulations in terms of nitrate concentration are compared with the observations available at some monitored stations in the river network. The root mean square error of the model simulation with respect to these observations, normalized to the range of the observations at all stations in the watershed, is used as an indicator of the model's goodness of fit.

Modeled watersheds and determination of leaching nitrate concentration

The new version of the Riverstrahler model has been applied in this paper to three different watersheds, with contrasting characteristics, to which the previous version of the Riverstrahler model has already been implemented and published (Table 1). The first one is the Haut Loir River, a 3590-km² tributary of the temperate oceanic Loire River system [START_REF] Moatar | La Loire fluviale et estuarienne :Un milieu en évolution[END_REF], located in the intensively cropped area of the central Paris basin (www.sage-loir.fr).

This area is subjected to high nitrate contamination of ground-and surface water due to intensive cereal cropping and rather low infiltration depth. The second application targets the Seine-Normandy watersheds (94 675 km²) as a whole. The characteristics of this basin, as well as previous applications of the model, are described in detail in Meybeck et al. (1998) and [START_REF] Billen | River basin nutrient delivery to the coastal sea: assessing its potential to sustain new production of non-siliceous algae[END_REF]. Finally we present application of the model to the Red River, a subtropical river system covering 141 120 km², with a quite different land use, climate and hydrological regime [START_REF] Le | Modelling nutrient transfer in the sub-tropical Red River system (China and Vietnam): implementation of the Seneque/Riverstrahler model[END_REF]2014). For these three applications, the procedure for estimating the nitrate leaching concentration from agricultural land differs significantly, because of the different data availability at these different scales. For the Haut Loir watershed, detailed knowledge of the crop distribution and succession is available from the analysis of the graphical land parcel registration declaration for farmers within the scope of the allocation of EU Common Agricultural Policy subsidies (https://www.data.gouv.fr/fr/datasets/registre-parcellaire-graphique-rpg-contours-des-ilotsculturaux-et-leur-groupe-de-cultures-majoritaire/), making it possible to propose a spatialized typology of crop rotations. The corresponding farming practices are also documented for the central Paris basin, based on the data collected and assembled by Puech et al. (2015) and [START_REF] Anglade | Relationships for estimating N2 fixation in legumes: incidence for N balance of legume-based cropping systems in Europe[END_REF]. From this, an average N soil balance can be calculated for each crop rotation type. At the scale of the Seine watershed, the N soil balance of crop-and grassland is calculated from regional agricultural statistics at the department (Nuts 3) level according to the procedure described in detail by [START_REF] Noë | How the structure of agro-food systems shapes nitrogen, phosphorus, and carbon fluxes: the Generalized Representation of Agro-Food System applied at the regional scale in France[END_REF]. For the Red River basin, a similar analysis was carried out at the scale of the three major sub-basins by Le et al. (2015), combining national and regional agricultural statistics.

To calculate the nitrate concentration from the N soil balance, the following assumptions are considered: (i) Leaching is the major fate (70-80%) of the N soil balance (SoilNbal, kgN/ha/yr), except when catch crops are systematically sown before spring crops or in the case of permanent land cover [START_REF] Billen | The nitrogen cascade from agricultural soils to the sea: modelling N transfers at regional watershed and global scales[END_REF][START_REF] Anglade | Agriculture biologique et qualité des ressources en eau dans le bassin de la Seine. Caractérisation des pratiques et applications territorialisées[END_REF][START_REF] Anglade | Reconquérir la qualité de l'eau en régions de grande culture : agriculture biologique et reconnexion avec l'élevage[END_REF]; (2)

The nitrate leaching concentration corresponds to the "dilution" of the annual N leaching flux by the average annual infiltrating water depth (Infiltr, mm/y) and is assumed not to vary seasonally, because of the buffer capacity of soil N pools. The nitrate concentration in leaching water (NO 3 , mgN/L) is thus calculated for each land use class as:

NO 3 = ICA * SoilNbal / Infilr * 100 ( 1
)
where ICA is an indicator of vegetal soil coverage during high water periods [START_REF] Anglade | Reconquérir la qualité de l'eau en régions de grande culture : agriculture biologique et reconnexion avec l'élevage[END_REF].

The results for the three basins are shown in Figure 1.

Figure 1. Distribution of sub-root water nitrate concentrations calculated for the Haut Loir

River, the Seine River and the Red River, based on data on land cover and agricultural practices.

New model developments 1. Conceptual representation of riparian denitrification

The essence of the new representation of riparian retention is illustrated in Figure 2. Before they reach the river bed, superficial and phreatic water flows coming from the watershed, with their nitrate concentration determined by land use and agricultural practices, have to cross a more or less extended riparian area where biogeochemically active superficial soils, often rich in organic matter, are in contact with the river water table. These soils have a certain When a portion (tl) of the watershed area is equipped with tile drains, the concerned nitrate flow in superficial water (TD) is considered to by-pass the denitrification zone and is not subject to the above calculations. Equations ( 3) and (4) above become:

NO3Flw = ssr (1-tl) × NO3m + bfl × NO3m (3') IR = TD + MAX (NO3Flw -DenCap, thresh) (4')

Extent of active riparian wetlands and tile drainage

The soil topographic index (TI), defined by [START_REF] Beven | A physically based, variable contributing area model of basin hydrology[END_REF], is a useful indicator to estimate the position and extension of wetlands within large watersheds (Mérot et al., 1995;[START_REF] Curie | Geomorphological methods to characterizes wetlands at the scale of the Seine watershed[END_REF]. It is defined for any grid cell of a digital elevation model of the watershed as the natural logarithm of the ratio between the drainage area per unit contour length and the tangent of local slope: TI = ln [drainage area/ tg(slope)] TI expresses the propensity of water accumulation according to the topography of the watershed. [START_REF] Curie | Geomorphological methods to characterizes wetlands at the scale of the Seine watershed[END_REF] have shown that above a threshold of 12.5 (for a DEM of 50×50-m grid), the conditions for hydromorphic conditions are met.

For the whole of France, this approach has been adopted and compared to direct data on soil hydromorphy to provide a national map of potential wetlands 3a andb show the extension of the thus-defined active riparian wetlands for the whole Seine-Normandy watershed and for the Haut Loir basin. The same procedure was followed for the Red River basin, using the global SRTM 90-m digital elevation model [START_REF] Jarvis | Hole-filled SRTM for the globe Version 4[END_REF] and land use data from Chanan et al. ( 2014) (Fig. 3c).

From this information, the surface area (and volume, considering the 0.3-m active layer) of active wetlands can be calculated for each unit watershed in the basin under investigation (Table 1).

Potential denitrification rate

As explained above, the quantitative formulation of riparian retention requires assessing a value of the potential denitrification rate of wetland soils, under water saturation conditions, anoxia and a non-limiting concentration of nitrate. A literature survey was carried out for that purpose.

Measurement of soil denitrification potential

Many measurements of soil denitrification potential, often called denitrifying enzyme activity have been reported in literature. The original protocol proposed by Tidge (1982) consists of incubating the soil sample as a slurry with a de-oxygenated nitrate-rich solution, and measuring denitrification either by following the decrease of nitrate concentration in the solution or by measuring N 2 O production under inhibition of the last step of denitrification by acetylene. Many modifications of the protocol have been introduced, sometimes involving addition of labile organic matter substrates [START_REF] Pell | Potential denitrification activity assay in soil with or without chloramphenicol?[END_REF], which usually results in only slightly higher denitrification potential [START_REF] Clément | Seasonal dynamics of denitrification along topohydrosequences in three different riparian wetlands[END_REF][START_REF] Ullah | Denitrification potential of different land-use types in an agricultural watershed, lower Mississippi valley[END_REF][START_REF] Laverman | Nitrous oxide production kinetics during nitrate reduction in river sediments: the effect of nitrate and carbon[END_REF][START_REF] Wu | Effects of land cover and soil properties on denitrification potential in soils of two semi-arid grasslands in Inner Mongolia, China[END_REF]. Measurements (Table 2) show a very wide range of values, from less than 0.01 to more than 1 µgN/gsoil/h. Within a single group of sites with similar pedogenetic conditions, measurements often show an increase in denitrification potential with increasing soil organic C or fine particle content [START_REF] Dhondt | Temporal and spatial patterns of denitrification enzyme activity and nitrous oxide fluxes in three adjacent vegetated riparian buffer zones[END_REF][START_REF] Ullah | Denitrification potential of different land-use types in an agricultural watershed, lower Mississippi valley[END_REF], but this effect is not perfectly clear for all the measurements available in the literature (Fig. 5), confirming the conclusion of several authors that denitrification in soils cannot be predicted from a limited number of factors such as carbon content, texture, pH [START_REF] Knowles | Denitrification[END_REF][START_REF] Simek | Denitrification in arable soils in relation to their physico-chemical properties and fertilization practice[END_REF] but depends on a complex set of properties, namely those linked to their landscape position [START_REF] Florinsky | Topographiccontrolofsoil microbialactivity:acasestudyofdenitrifiers[END_REF][START_REF] Ullah | Denitrification potential of different land-use types in an agricultural watershed, lower Mississippi valley[END_REF]Oeler et al., 2008). Another difficulty lies in the fact that the denitrification potential of riparian soils, measured on homogeneous samples in slurry, is not fully expressed under natural conditions, namely because of the preferential water path through soil interstices or macroporosity [START_REF] Willems | Nitrate removal in riparian wetland soils: effects of flow rate, temperature, nitrate concentration and soil depth[END_REF][START_REF] Haag | Fate of nitrate fluxes and emissions in Central Europe: A critical review of concepts, data, and models for transport and retention[END_REF]. Therefore, [START_REF] Ambus | Control of denitrification enzyme activity in a streamside soil[END_REF] indicates that measurements on intact soil cores show values typically 10-20 times lower than those carried out on slurry, while [START_REF] Vilain | Nitrous oxide production from soil experiments: denitrification prevails over nitrification[END_REF] found the same order of magnitude for cores and slurries. Henault & Germon (2000), Clement et al. (2002) andOeler et al. (2007) also report a 2-to 3-order of magnitude difference between the potential and effective in situ value of denitrification.

We conclude from this brief literature survey that a priori defining a denitrification potential for riparian soils is very difficult, although a value of about 1 µgN/g/h, at 20°C appears to be a maximum under saturating nitrate concentration, i.e., a value of 400 mgN/m²/h, taking into account an apparent density of 1.4 g/cm 3 and an active soil depth of 0.3 m.

Effect of nitrate concentration and temperature

In their review paper on groundwater denitrification, [START_REF] Rivett | Nitrate attenuation in groundwater: A review of biogeochemical controlling processes[END_REF] estimate that denitrification obeys zero-order kinetics as long as the nitrate concentration remains above a threshold of 1 mgN/l. Half-saturation constants (Km) measured on slurries indeed show very low values (<0.1 mgN/l) [START_REF] Murray | Kinetics of Nitrate Utilization by Mixed Populations of Denitrifying Bacteria[END_REF][START_REF] Ambus | Control of denitrification enzyme activity in a streamside soil[END_REF], which is confirmed by the observation that the linear shape of nitrate decreases in slurry experiments. Although this is in contradiction with the hypothesis of the NEMIS model (Henault & Germon, 2000;[START_REF] Oehler | Modelling denitrification at the catchment scale Science of the Total[END_REF], which considers a Michaelis-Menten relationship between denitrification and nitrate concentration with a Km of 0.022 mgN/gsoil (roughly equivalent to about 80 mgN/L in the soil solution at field capacity) as measured from the comparison of denitrification rates in intact cores with and without added nitrate, we will consider that the denitrification rate is independent of the nitrate concentration in the water flow as long as it is above 0.5 mgN/L.

Denitrification is considered to stop below this threshold.

The denitrification rate is strongly temperature-dependent [START_REF] Stanford | Effect of Temperature on Denitrification Rate in Soils[END_REF][START_REF] Rivett | Nitrate attenuation in groundwater: A review of biogeochemical controlling processes[END_REF]. Empirical data gathered in Figure 6 to by-pass the riparian zone. In this calculation, the value of the denitrification potential is the single adjustable parameter, whose value is expected to be far below 400 mgN/m²/h. For the sake of simplicity, the same value of this parameter is considered over the entire watershed.

Application to the Haut Loir watershed

The distribution of the nitrate concentration in sub-root water in the Haut Loir basin was calculated, as explained above, from the soil N balance of each crop rotation class in the basin considering an average infiltration water depth of 130 mm/year (Fig. 1). With this input, the model provides a satisfactory simulation of the winter high-water nitrate concentration level, which at that period is close to the average sub-root nitrate concentration. Observed seasonal variations of the river nitrate concentration are very pronounced in the northwest area of the basin; they have a much lower amplitude in the eastern part. The model captures these variations well for a value of the riparian denitrification potential at 20°C set within the range 0.75-1.5 mgN/m²/h. (Fig. 7), for which the RMSE of the simulation is the lowest (see Fig. 8).

The simulation by the model with the riparian denitrification potential set at zero indicates what would be the nitrate concentration in the absence of any riparian denitrification, with only in-stream benthic denitrification processes occurring. In the case of the Haut Loir basin, seasonal variations simulated without riparian retention are quite minor, indicating very low in-stream denitrification processes. The comparison of the model simulations with and without riparian denitrification thus allows calculating the average annual budget of nitrogen transfer and retention in the watershed (Table 3).

For the Haut Loir basin, riparian retention amounts to 55-85 kgN/km²/yr, i.e., 7-11% of the annual nitrate flux leached from the basin. The sensitivity of this estimation of riparian retention with respect to the value of the denitrification potential is illustrated in Figure 8.

Application to the whole Seine-Normandy watershed

The application of the model to the whole Seine-Normandy basin is based on a coarser definition of land use classes, derived from the Corine Land Cover 2012 GIS layer (Ministère de l'Environnement, de l'Energie et de la Mer, 2012), distinguishing between forest, urban areas, arable land and grassland. For the two latter land cover types, the N soil surpluses were calculated by [START_REF] Noë | How the structure of agro-food systems shapes nitrogen, phosphorus, and carbon fluxes: the Generalized Representation of Agro-Food System applied at the regional scale in France[END_REF] from agricultural statistics using the GRAFS methodology, at the scale of homogeneous agricultural areas, and converted into the nitrate concentration in the sub-root water taking into account the average infiltration depth of the corresponding regions (Fig. 2b).

Here again, the model run with zero riparian denitrification provides a good simulation of the winter level of the nitrate concentration everywhere in the watershed, even though this level varies widely, between 4 and 11 mgN/l, in the different tributaries (Fig. 9). The model captures the pronounced seasonal variations of nitrate concentrations well for a potential denitrification value at 20°C of about 0.75 mgN/m²/h (stRMSE= 0.15, Fig. 8b). This value of the parameter (very close to the one selected for the Haut Loir application) allows the model to catch the substantial differences observed between the seasonality of nitrate variations in the different parts of the basin, with much lower differences between winter and summer values in some downstream stations (Oise, Eure) than in upstream tributaries (Yonne, Marne, Seine, Loing). Based on these simulations, the average N transfer budget has been established for the Seine watershed at the entrance of the estuarine zone (station Poses) (Table 3). The sensitivity of the estimation of the annual riparian denitrification rate with respect to the value taken for the potential denitrification rate of riparian zones is illustrated in Figure 8b. Lower values of the potential denitrification in this sub-tropical basin would provide higher river nitrate concentrations than observed during summer high discharge periods. Owing to higher temperatures and a coincidence of high specific discharge with high temperature, which is characteristic of a tropical runoff regime, the calculated effect of wetland denitrification on river water nitrate is quite significant, because it reduces the concentration of infiltrated water by a factor of 3-5 throughout the year, and not only at low discharge periods. A wider extent of riparian areas, particularly in the delta region of the basin (Fig. 3), also plays a role in this important riparian denitrification. As a whole, riparian denitrification accounts for about half the total nitrogen leaching from watershed soils (Table 3).

Discussion and Conclusion

Groffman et al ( 2009) stressed the challenge to measure and model denitrification at regional scale arising from the fact that hotspots (small areas) and hot moments (brief periods) account for a high proportion of the total annual denitrification. This is indeed the challenge for modelling riparian denitrification at the watershed scale. The approach we present in this paper is based on the identification of potential wetlands (essentially defined from topographic attributes) as the major hotspots for denitrification in the landscape. The hot moments are those when high nitrate fluxes (most often associated with high water fluxes) from the watershed coincide with high denitrification capacity of the riparian soils (highly dependent on temperature). Our model thus offers an elegant way to calculate the cumulated effect of these seasonally and spatially highly variable processes at the watershed scale.

It simulates nitrate seasonal variations in the river network over a wide range of geographical scales, and with different strategies used to represent the diffuse agricultural sources of nitrate. In the three examples chosen in this paper, the model results offer a reasonable fit not only with the observed concentration levels, but also with their pattern of seasonal variations, which can vary widely between tributaries of a single watershed, namely owing to the distribution of riparian wetlands, the water runoff regime or the presence of tile drains in some areas.

Our model is based on the same principles as those expressed by [START_REF] Ranalli | The importance of the riparian zone and in-stream processes in nitrate attenuation in undisturbed and agricultural watersheds -A review of the scientific literature[END_REF] who highlighted the crucial role of the interaction of groundwater with soil labile organic matter, so that denitrification is very active in the upper riparian soil layers, but remains negligible in deep groundwater. These authors indicated that nitrate attenuation in groundwater traversing the riparian zone varies in its effectiveness according to the hydrogeologic properties of the riparian zone and its position with respect to groundwater flow systems. Such approaches do not consider the case of denitrification by riparian areas during periods of overbank flow.

Vidon [START_REF] Vidon | Landscape controls on nitrate removal in stream riparian zones[END_REF][START_REF] Vidon | A landscape-based approach to estimate riparian hydrological and nitrate removal functions[END_REF] proposed a conceptual model consisting of a typology of riparian zone based on topographic, soil, surficial geology, and vegetation maps to determine landscape attributes linked to nitrate removal efficiency. Our quantitative approach is much simpler operationally since it requires only the spatial distribution of active wetlands, obtained from topography and land cover GIS information, and the calibration of one single parameter related to potential denitrification rate of wetland soils.

In the two temperate basins, the Seine and Loir watersheds, a value within the range 0.75-1.5 mgN/m²/h is the best adjustment of the potential denitrification parameter to fit the observed data. In the subtropical Red River basin, higher values, within the range 4-5 mgN/m²/h are required, probably indicating much more active riparian soils under these conditions. Both values are compatible with those reported in the literature for potential denitrification measured on soil samples in the laboratory. Although the goodness of fit of these simulations could certainly be improved, in particular with a better definition of the nitrate leaching from cropland, these results show the high genericity of the modeling approach proposed in this paper, which is able to simulate the seasonal variations of nitrate submitted to riparian denitrification at different scales and under quite different climatic and hydrological conditions.

Furthermore, the modeling approach quantifies the role of riparian retention in the N cascade from watershed soils to the river outlet. According to the N budgets established from the model, riparian retention eliminates between 10 and 15% of the diffuse sources of nitrogen into the hydrosystem through soil nitrate leaching in the two temperate basins studied; in the tropical Red River, riparian retention amounts to 50% of diffuse N sources (Table 3). By comparison, [START_REF] Weller | Effects of riparian buffers on nitrate concentrations in watershed discharges: new models and management implications[END_REF] using statistical models linking cropland area, presence of riparian buffer and river nitrate concentration, estimated that riparian buffers currently remove 5 to 50 % of the nitrate inputs from cropland in the Chesapeake Bay watersheds (USA). In-stream retention represents 17, 23 and 22% of total N loading in the Haut Loir, Seine and Red River watersheds, respectively.

In our three example watersheds, the leaching rate of cropland areas was estimated as an average to 11, 32 and 46 kgN/ha/yr, in the Haut-Loir, the Seine and the Red River watersheds respectively (combining data from Table 1 and3). The denitrification rate estimated per ha of active riparian wetland in the 3 watersheds is respectively 23, 21 and 50 kgN/ha/yr. Although we are far from a five time more efficient rate per ha, this is enough however to attenuate 9-53% of the total nitrate load in wetlands occupying 3 to 20% of the total watershed area. In view of the very contrasted characteristics of the 3 basins studied here, these values can be reasonably considered as indicative of the range of riparian nitrate attenuation at regional scale.

Our study is focused on the processes occurring during the transit of groundwater from agricultural plateaus to the river, across riparian zones. Yet, the story is not finished once this step has been taken, and nitrates have reached surface water. Denitrification in the benthic zone of the drainage network itself, including its hydrological annexes such as ponds, lakes, surface water connected wetlands and surface transient storage zones, can play a further significant role in nitrogen removal [START_REF] Wollheim | Nitrate uptake dynamics of surface transient storage in stream channels and fluvial wetlands[END_REF]. This issue was previously may be more efficient per unit area than the most effective land-based nitrogen mitigation strategies including land retirement from agriculture. The possible side effects of enhanced N 2 O emissions could however be kept in mind when deciding between curative or preventive management strategies [START_REF] Garnier | Curative vs. preventive management of nitrogen transfers in rural areas: Lessons from the case of the Orgeval watershed (Seine River basin, France)[END_REF].

  models are both based on a spatially distributed representation of the river network, a seasonal temporal resolution and a mechanistic description of in-stream processes. Both models are fed by a 0D semi-distributed model of nitrogen transfer in the watershed's agricultural soils, based on the hypothesis of additivity of the contribution of each land-use class of the terrestrial watershed. They are therefore intermediate, in terms of spatial resolution and explanatory power, between landscape models and approaches of the Nutting-

  Fig 2) is assumed to determine the extent of the retention per unit watershed area (RR, gN/km²/h).

Figure 2 .

 2 Figure 2. Processes involved in nitrate transfer from the watershed to surface water as represented in the new module of the Riverstrahler model.

Figure 3 .Figure 4 .

 34 Figure 3. Riparian wetland zones in the Seine-Normandy, Haut Loir and in Red River watersheds. Riparian wetlands covered with forest or grassland are represented in green: these are considered as active retention zones. In red are wetlands occupied by cropland, considered as non retentive for nitrate flows from the watershed.

Figure 5 .

 5 Figure 5. Potential denitrification rate (slurry method) in the top layer of agricultural or riparian soils, as a function of their organic C content, measured with (open symbols) or without (black symbols) addition of labile organic substrate.

Figure 6 .

 6 Figure 6. Temperature dependency of the potential denitrification rate (measured in slurry) in different soil samples. The function fntemp(T) is defined as the ratio between the rate measured at temperature T with respect to the rate at 20°C. (a) Plateau luvisol in Brie[START_REF] Benoit | Nitrous oxide production from nitrification and denitrification in agricultural soils: determination of temperature relationships in batch experiments Processes in[END_REF]; (b) alluvial meadow soil in Brie (this study); (c) riparian soil in Brie (this study).

  The concepts developed above for taking into account riparian retention of diffuse sources of nitrogen from the basin have been implemented as a new version of the Riverstrahler model, called Seneque 3.7. For each elementary watershed (the drainage area of a segment of river between two confluences, or from the spring to the first confluence), at each 10-day period, the flux of nitrate accompanying surface and base flow runoff (calculated based on the average sub-root concentration of each land use class), is compared with the potential denitrification rate of the riparian wetland area of the same elementary watershed given the water temperature at that moment of the seasonal cycle, and considering an adjustable parameter representing the riparian soil potential denitrification (in mgN/m²/h). Only nitrate in excess over this denitrification potential reaches the surface water. If a certain fraction of the elementary watershed is tile-drained, this fraction of the superficial water flow is considered

Figure 7 .

 7 Figure 7. Simulation by the Seneque 3.7 model of the seasonal variations of the nitrate concentration at different stations of the Haut Loir river network, for different values of the potential denitrification rate of active riparian areas (from 0 to 3.5 mgN/m²/h )

Figure 8 .

 8 Figure 8. Sensitivity of the annual riparian retention rate over the whole watershed estimates with respect to the value of denitrification potential for the Haut Loir (a), the Seine (b) and the Red river (c) watersheds. The normalized root mean square error of the simulations with respect to the observed nitrate concentration is also plotted (dotted line): its minimum indicates the most likely value of the denitrification potential.

Figure 9 .

 9 Figure 9. Seasonal and interannual variations of nitrate concentration at several stations of the Seine River network simulated by the Riverstrahler model, for four values of the potential riparian denitrification rate (0, 0.75, 1.5 and 3.5 mgN/m²/h).

  addressed with the Riverstrahler model by[START_REF] Passy | Restoration of ponds in rural landscapes: modelling the effect on nitrate contamination of surface water (the Seine watershed, France)[END_REF] who simulated a 25% reduction of the annual nitrogen flows for a scenarios of pond restoration in rural landscapes at a density of 5% of the agricultural area. More recently,[START_REF] Czuba | Contextualizing wetlands within a river network to assess nitrate removal and inform watershed management[END_REF] and[START_REF] Hansen | Contribution of wetlands to nitrate removal at the watershed scale[END_REF] have shown how the collective biogeochemical functioning of the network of reaches and connected hydrological annexes, with shifts between nitrate, organic carbon and residence time limitation of nitrification, could lead to extremely efficient nitrate removal at the watershed scale. All these results converge to conclude that wetlands (open water or riparian)

Table 1 .

 1 General characteristics of the Seine, Haut Loir and Red River watersheds

	Haut Loir	Seine-	Red River
		Normandy

  (INRA Infosol -AgroCampus Ouest, 2010).(Berthier et al., 2013; http://geowww.agrocampus-ouest.fr/web/?p=1538) By combining this GIS layer with the Corine Land Cover map (Ministère de l'Environnement, de l'Energie et de la Mer, 2012), we defined wetlands covered with either grassland or forest as active riparian areas, thus excluding croplands from retentive riparian areas. Figure

Table 2 .

 2 Potential denitrification rate measured at 20°C in the top layer of agricultural or riparian soils in slurry, with or without addition of labile organic substrate.

	Soil	Location	%Corg denit pot	Method		Reference
					Detection	Org
				µgN/g/h	method	substr
			30-34
	Riparian strip, morainic landscape	DK	(totC)	1.3-6.1	C2H2 block	with	Ambus (1993)
	Riparian soils	USA, lower					Ullah & Faulkner (2006)
	forested wetlands Mississippi	3.3	1.18 C2H2 block	without
	depressional wetlands		1.7	0.77 C2H2 block	without
	constructed wetlands		1.2	0.82 C2H2 block	without
	veg ditches		1.2	0.66 C2H2 block	without
	unveg ditches		1.6	0.5 C2H2 block	without
	ag-low		1.4	0.4 C2H2 block	without
	ag-high		0.6	0.16 C2H2 block	without
	Agricultural Lanton soil	USA	4.3	0.09 C2H2 block	with	Murray et al. (1989)
	Agricultural Maury soil	USA	1.9	0.01 C2H2 block	with
	Sandy soil (Melby)	SW	1.9	0.29 C2H2 block	with	Pell et al. (1996)
	Silty-clay soil (Lanna)	SW	2.2	0.52 C2H2 block	with
	Heavy clay soil (Ekhaga)	SW	4.1	3.0 C2H2 block	with
	Agricultural soils	France					Hénault et al. (2001)
	Villamblain Beauce	1.4	0.21 C2H2 block	without
	Arrou		1.1	0.31 C2H2 block	without
	La Saussay		1.2	0.11 C2H2 block	without
	eutric leptosol Bourgogne	3.09	1 C2H2 block	without
	gleyic luvisol		0.86	0.15 C2H2 block	without
	rendzic leptosol		1.67	2 C2H2 block	without
	cultivated calcaric fluvisol		3.34	2 C2H2 block	without
	cultivated gleyic cambisol A		1.52	0.4 C2H2 block	without
	cultivated gleyic cambisol B		1.99	0.5 C2H2 block	without
	grassland calcaric fluvisol		5.18	5 C2H2 block	without
	grassland gleyic cambisol A		2.38	1.2 C2H2 block	without
	grassland gleyic cambisol B		3.03	1.2 C2H2 block	without
	Slope transect Morand River	CH		0.61 C2H2 block	with	Cosandey et al. (2003)
				0.085 C2H2 block	with
	Loamy riparian soils	B					Dhondt et al. (2004)
	mixed vegetation, III 0-30 cm		4.5	0.170 C2H2 block	without
	mixed vegetation, II 0-30 cm		6.4	0.200 C2H2 block	without
	mixed vegetation, I 0-30 cm		2.5	0.125 C2H2 block	without
	forest, III 0-30 cm		2.2	0.175 C2H2 block	without
	forest, II 0-30 cm		2.9	0.25 C2H2 block	without
	forest, I 0-30 cm		3.4	0.175 C2H2 block	without
	grassland, III 0-30 cm		5.8	0.188 C2H2 block	without
	grassland, II 0-30 cm		5.15	0.196 C2H2 block	without
	grassland, I 0-30 cm		3.2	0.167 C2H2 block	without
	Clayey riparian soils river Han	China					Liu et al. (2016)
	agricultural riparian soils		2.3	0.0028 C2H2 block	with
	forested riparian soils		1.7	0.00066 C2H2 block	with
	Restored forest wetlands	NC, USA					Sutton-Grier (2010)
	Charlotte		7.4	0.19 C2H2 block	with
	Duke		11	0.54 C2H2 block	with
	Riparian and slope soils	Brittany, F					Oeler et al. (2009)

Table 3 .

 3 Budget of nitrogen transfer and retention in the three watersheds over the 2009-2011 period, as estimated from the simulation with Seneque 3.7 of the nitrate concentration with and without riparian denitrification.

	Haut Loir	Seine	Red River
	(at St Jean)	(at Poses)	(at Hanoi)

Acknowledgements

The study was conducted within the framework of several scientific projects, Escapade and HydroGES supported by the National Agency for Research (ANR) and the Agency for the Environment and Mastery of Energy (ADEME), respectively. The PIREN-Seine programme and the Fédération Ile-de-France de Recherche pour l'Environnement (FIRE) are also acknowledged for their support.

Acknowledgements

The study was conducted within the framework of several scientific projects, Escapade and HydroGES supported by the National Agency for Research (ANR) and the Agency for the Environment and Mastery of Energy (ADEME), respectively. The PIREN-Seine programme and the Fédération Ile-de-France de Recherche pour l'Environnement (FIRE) are also acknowledged for their support.

Application to the Red River system

For the Red River basin, an even coarser approach was used to define the spatial distribution sub-root concentration, as described by Le et al. (2005) (Fig. 1c). Using these input data, together with the wetland distribution calculated from the DEM (Fig. 3c), provides a reasonable agreement of the general level of calculated nitrate concentrations with observed values at the outlet of the major branches of the Red River system (Fig. 10), for a denitrification potential value close to 4 mgN/m²/h, significantly higher than the value used for the other two watersheds. The Red River system is characterized by its tropical hydrological regime, with high discharge during the summer period and much higher specific runoff values than in the previous two temperate watersheds. The nitrate concentration remains at rather low levels in all tributaries of the Red River and show only limited seasonal variations compared with temperate rivers. [START_REF] Le | Modelling nutrient transfer in the sub-tropical Red River system (China and Vietnam): implementation of the Seneque/Riverstrahler model[END_REF]. Water discharge variations at the Hanoi station are also shown.


D Perna

M A Barucci

M Fulchignoni

M Popescu

I Belskaya

S Fornasier

A Doressoundiram

C Lantz

F Merlin

 Scott

Keil, K E R D Goldstein

in "Asteroids IV", eds F P E Michel

Demeo

A spectroscopic survey of the small near-Earth asteroid population: peculiar taxonomic distribution and phase reddening

Keywords: techniques, spectroscopicminor planets, asteroids, generalnear-Earth asteroidstaxonomyphase reddening

We present the results of the first-ever visible spectroscopic survey fully dedicated to the small (absolute magnitude H20) near-Earth asteroid (NEA) population. Observations have been performed at the New Technology Telescope (NTT) of the European Southern Observatory (ESO), during a 30-night Guaranteed Time Observations programme, in the framework of the European Commission financed NEOShield-2 project. The visible spectra of 147 objects have been obtained and taxonomically classified. They show a peculiar taxonomic distribution, with respect to larger NEAs. In particular, olivine-rich A-types and organic-rich D-types are more abundant than what could be expected by extrapolating the taxonomic distribution of larger NEAs. Such results have implications for the investigation of the first phases of solar system history, including the delivery of prebiotic material on the early Earth. Having been obtained over a large range of solar phase angles, our data allowed us to evidence peculiar phase reddening behaviours for asteroids belonging to different taxonomic types. Low-albedo asteroids display no or limited phase reddening, compared to moderate-and high-albedo objects. This result suggests a promising novel way to distinguish primitive asteroids in the X-complex. In agreement with previous laboratory experiments, olivine-rich surfaces are the most affected by phase reddening.

Introduction

The investigation of near-Earth asteroids (NEAs) can provide crucial information on the formation and early evolution of the solar system, including topics like the delivery of water and organic-rich material to the early Earth, and the emergence of life (e.g., Marty et al., 2016). The reserve in water and rare metals held by NEAs is also getting the attention from government agencies and private companies, as asteroid mining could expand the Earth's resource bases in the near future (e.g., Sanchez & McInnes, 2013). Furthermore, NEAs represent a well-founded threat to human beings and life in general: the PHAs ("potentially hazardous asteroids") could in principle collide with the Earth within the next century and cause extensive damage (e.g., Perna et al., 2013Perna et al., , 2016)).

The discovery rate of NEAs is constantly increasing and rapidly approaching the 2000 objects/year barrier. Current discoveries mainly concern "small" (tens/hundreds of metres in diameter) NEAs close approaching the Earth. The investigation of such small-sized NEAs is particularly important to constrain the asteroidal contribution to the delivery of prebiotic material (water and organic molecules) to our planet (e.g., Pierazzo & Chyba, 1999;Morbidelli et al., 2000;Saladino et al., 2013;O'Brien et al., 2014). Also in terms of the current impact risk, the small objects deserve our particular attention, as they have the highest statistical likelihood of impact, and can still produce a catastrophe at a regional/national scale (e.g., Perna et al., 2015a). More in general, the proximity of NEAs allows us to study asteroids about two to three orders of magnitude smaller than those observable in the main belt (i.e., down to metre-sized objects), hence to open new frontiers in asteroid science. Indeed, recent results already evidenced that small asteroids behave differently than the larger bodies in terms of rotational properties (Statler et al., 2013) and regolith generation (Delbo et al., 2014).

However, such small asteroids become bright enough to be physically characterized from Earth only for very limited time spans, coinciding with their close approaches with our planet, whereupon they could be unobservable for years or even for decades. Rapid-response physical observations of such bodies are hence necessary in order to not leave the characterization rate behind the discovery rate. In particular, visible and near-infrared photometry and reflectance spectroscopy of asteroids allow their taxonomic classification and provide clues about their surface composition, mineralogy and scattering properties. Such techniques can be "calibrated" via laboratory measurements on minerals and meteorites, as well as on the first returned samples from asteroids that we are getting in these years (e.g., DeMeo et al., 2015;Reddy et al., 2015). Currently, the fraction of NEAs with assigned taxonomic class (with respect to the known population in the same size range) drops from ~1/3 for km-sized bodies to ~1/10 for objects in the 0.3-1 km range, to ~1/100 for NEAs smaller than 300 m.

To soften this deficiency, in the framework of the NEOShield-2 project2 , we performed the first-ever spectroscopic survey fully dedicated to the "small" NEAs, through a 30-night Guaranteed Time Observations programme at the 3.6-m New Technology Telescope of the European Southern Observatory (La Silla, Chile). The observational circumstances, as well as the data reduction and analysis are described in Section 2. In Section 3 we present our results, while Section 4 contains a discussion and our conclusions.

Methods

For our astronomical observations, the EFOSC2 spectrograph was used with the Grism #1 diffraction element. This configuration covers the spectral interval 0.40-0.92 μm with a resolution R~500 (~1.4 nm/pixel). Data were collected during twenty-four different nights spanning two years (April 2015 to March 2017), while six out of the thirty allocated nights were lost due to bad weather conditions. During each observing run, we gave preference to the smallest NEA observable (many of our targets were newly discovered bodies in proximity of their close approaches with the Earth), and limited our observations to objects with absolute magnitude equal or fainter than H=20 (corresponding to a maximum diameter of about 300 m assuming a value of 0.20 for the visual albedo). On each observing night, we also observed several solar analogue stars. All of the spectra have been acquired through the 2 arcsec slit, oriented along the parallactic angle to minimize the effects of atmospheric differential refraction.

The data reduction followed standard procedures (e.g., Perna et al., 2015b): bias and background subtraction, flat-field correction, one-dimensional spectra extraction, atmospheric extinction correction. We used the Octave and IRAF software packages. Wavelength calibration was obtained using He-Ar lamps emission lines. The reflectance of the asteroids (normalized at 0.55 µm) was obtained by dividing their spectra by those of solar analogue stars observed close in time and in airmass to the scientific frames.

We taxonomically classified each object by performing curve matching with the visible part of the 25 template spectra defined by the Bus-DeMeo scheme (DeMeo et al., 2009), using the M4AST online tool (Popescu et al., 2012).

Overall, in this work we report the observations of 147 "small" NEAs. For all of them, Table 1 lists the asteroid number/designation, the absolute magnitude H, the observational circumstances (date and UT starting time, exposure time, airmass, and phase angle), as well as the solar analogue star used to obtain the reflectance spectrum (and the airmass at which the star was observed). The table also includes the spectral slopes and the associated errors computed using the Octave's polyfit routine (no further statistical/systematic potential sources of errors are considered in our analysis) in the 0.44-0.65 μm range (this wavelength interval was selected to roughly correspond to the B-R photometric colour, and to avoid both the silicate band and the lower signal-to-noise region longward of 0.7 µm), as well as the result of the taxonomic classification, the considered albedo (cf. Table notes) and the computed equivalent diameter of each object. For NEA 2011 AM24 we found a quite odd spectral shape, not fitting any of the taxonomic types: while we tentatively classify this object as a potential D-type, we discard it from our following analysis. The distribution of the equivalent diameters of our targets is shown in 3 Results

Taxonomic distribution

The taxonomic distribution of the observed NEAs is shown in Fig. 3. We report separate statistics for objects in the size bins 0-100 m and 100-300 m. Taking into account the uncertainties due to the availability of visible spectra only, to increase the significance of our analysis we group together objects belonging to the S-, C-and X-complexes. Moreover, we put in the X-complex the only target we classified as a T-type (with the objective of keeping well separated the primitive D-types from other featureless spectra in the X-complex). The distribution we found from our observations is dominated by the S-complex at all sizes, in agreement with previous results for larger NEAs. Such dominance is the consequence of both observational biases (albedo and phase angle effects) and preferential transport mechanisms from the inner asteroid main belt where S-type asteroids are dominant (e.g., Binzel et al., 2004). A proper debiasing of our observational results is beyond the scope of this paper, as it would be very much complicated by the still low statistics available for the small NEAs. We stress however that due to our constraint in the target selection concerning the absolute magnitude (only objects with H ≥ 20 were considered), all of the larger objects (> 300 m) in our sample are basically low-albedo ones. Conversely, one should remind of a bias against the discovery/observation of small low-albedo objects, which makes them very probably underrepresented in the 100-300 m and (especially) in the 0-100 m size ranges. For comparison, Fig. 4 shows the distribution of taxonomic types in the literature, retrieved from the European Asteroid Research Node (EARN)3 database of NEA physical properties. Taxonomic information for a total of 733 objects is reported, for different size bins (0-100 m, 100-300 m, 300-1000 m, >1 km). We use the same grouping criteria as in Fig. 3.

While the size distributions of our targets and of NEAs in the EARN database partly overlap, we can in first approximation consider them as representing "smaller" and "larger" asteroids, respectively. To compare statistically these two datasets in terms of taxonomic distribution, we used the following approach: (i) we randomly selected 146 objects from the EARN sample (i.e. the same number of NEAs characterized in our survey); (ii) we checked the taxonomic distribution of such random subsample; (iii) we repeated 10000 times steps (i) and (ii), and recorded the obtained mean and standard deviation for the population of each taxon.

The results of such analysis are reported in Table 2. The most striking difference emerging when comparing our sample (i.e., "smaller" NEAs) with data reported in the literature (i.e., "larger" NEAs) concerns the overabundance of A-and D-types within the "small" NEA population, with deviations of about 5.6 and 8.9 with respect to the expected population of these taxa if the taxonomic distribution of NEAs in the EARN database could be assumed as the reference distribution. Being based on visible data only, our findings about A-types have to reckon with some uncertainties due to possible misassignments with respect to other taxa presenting the 1-µm silicate band (we incidentally stress that we find the S-complex asteroids underrepresented in our sample, with respect to larger NEAs, with a deviation of -3.5). However, the results seem statistically very robust, also in consideration of the overall good quality of our spectra. The abundance of D-types (4 of which with diameter < 100 m) among our targets looks even more glittering, considering the low albedo of these objects that should disadvantage their detection at the smallest size ranges. Noteworthy, we also outline a relatively high abundance in our sample (+2.5) of featureless X-type NEAs, part of which could be of carbonaceous, primitive nature (see Fornasier et al., 2011, for a review on X-type asteroids). The populations of the rest of the taxa do not differ by more than 2 with respect to the expected values based on the literature data. The possible implications of an overabundance of organic-rich D-types and olivine-rich A-types among the small NEA population are further discussed in Sec. 4.

Phase reddening

Phase reddening (i.e., the increasing of the spectral slope with increasing solar phase angle  at the moment of the observation) has been evidenced to affect many bodies of the solar system, from the Moon to Mercury, from Uranus' moons to asteroids (e.g., Schröder et al., 2014, and references therein).

Ideally, to study the phase reddening one should observe each object at different phase angles, and obtain particular coefficients for each asteroid. Of course this would be very much time-demanding if a large population of bodies has to be investigated. However, our observations of NEAs have been performed over a wide range of solar phase angles (~2-94 degrees), allowing us to constrain the "average" phase reddening effect on the visible spectral slope (0.44-0.65 μm; cf. Table 1) for the different taxonomic types. We warn however the reader of the limitations of our approach, as in this way we cannot separate the phase reddening due to the observing geometry from other factors affecting the spectral properties of each asteroid, such as space weathering, particle size effects, etcetera.

The average values of the reddening coefficient γ (e.g., Luu & Jewitt, 1990), defined as the linear fit of the spectral slope vs. phase angle dependence, are given in Table 3 for the most populated compositional groups in our sample (together with the number of objects in each taxon and the measured range of phase angles; again, we used the Octave's polyfit routine).

The phase reddening seems to strongly affect moderate-to-high albedo spectral types. The largest phase reddening is seen for olivine-rich A-types (and, to a less extent, Q-types).

Asteroids belonging to the S-complex barely show phase reddening, with a large data scatter most probably due to a lower level of composition homogeneity within this taxon (Fig. 5).

Conversely to silicate-rich asteroids, NEAs in the C-complex do not show phase reddening (in particular, disregarding the only point at  > 50 degrees one would obtain γ=-0.0100.013). The visible spectral slopes of the D-types also seem to not increase with the phase angle (disregarding the two reddest D-types, whose spectral slopes are considerably higher than others reported for this class, one would obtain γ=0.0530.027). In both cases, the data scatter is rather large. The X-complex also shows large dispersions in spectral slopes (probably because of the coexistence in this taxon of objects of either carbonaceous, silicaceous, enstatitic, or metallic nature), although on average a reddening trend could be present (Fig. 6).

Discussion and conclusions

Despite of its intrinsic limitations (visible data only; still low statistics), our visible spectroscopic survey of the "small" (H20) near-Earth asteroids reveals some peculiar characteristics of the distribution of taxonomic types within such population, compared to larger bodies.

First of all, we note the relative abundance of olivine-rich A-types (5-10%). Such bodies are very rare among larger NEAs and in the asteroid main belt (where objects of just a few hundred metres in size are not observable). Here we remind the reader of the long-lasting "missing olivine problem" (e.g., Scott et al., 2015, and references therein): decay of 26 Al in the early solar system produced a differentiation of the largest accreting planetesimals. Later mutual collisions shattered these early-formed differentiated bodies to produce a much bigger number of smaller asteroids. Several tens of differentiated parent bodies are needed to account for the range of iron meteorite types (coming from the cores of such differentiated bodies) in our collections. However, a differentiated body of chondritic composition should be dominated by the olivine-rich mantle material (~60-80% in mass; Toplis et al., 2013). The apparent shortage of olivine-rich asteroids is a still poorly understood problem in solar system science. At least a dozen of alternative or complementary solutions have been proposed to this missing olivine problem. E.g., Consolmagno et al. (2015) suggested that early differentiated planetesimals may have had non-chondritic bulk compositions, hence not crystallising significant olivine in their mantles, while Elkins-Tanton et al. ( 2014) proposed that formation of significant olivine was prevented by high viscosity and rapid heat loss in planetesimals. We refer the reader to, e.g., Greenwood et al. (2015), and references therein, for a recent summary about the wide range of possible explanations for such underrepresentation of olivine-rich material that have been proposed. Here we stress that our results may support the "battered to bits" scenario (Burbine et al., 1996): mantle fragments from disrupted differentiated parent bodies could have been shattered to dimensions below a few hundreds of metres, below the limit of detectability of previous spectroscopic surveys. Noteworthy, our findings about the relative abundance of A-types among small asteroids may also strengthen the hypothesis of an exogenous origin for the olivine detected as isolated outcrops on the surface of Vesta (Turrini et al., 2016, and references therein).

In our survey we also found an unexpectedly high number of D-type asteroids, believed to be the most primitive in the solar system. Their very low albedo and featureless red spectra suggest a high abundance of organics and volatiles, which may hold clues regarding the planetary processes that preceded life on Earth (e.g., Hiroi et al., 2001). Indeed, isotopic ratio measurements seem to exclude the hypothesis of a significant contribution of comets to the water of the Earth's oceans and atmosphere, and primitive, carbonaceous asteroids could have played the main role in this regard (Altwegg et al., 2015;Marty et al., 2016). However, D-type asteroids were considered to be very rare in the NEA population. Our results could conversely indicate that they are quite abundant among the small-sized bodies, probably as a consequence of the high fragility of these carbonaceous asteroids, which favors the fragmentation of larger bodies (e.g., Granvik et al., 2016). The robustness of this result is outlined by the fact that the very dark D-types should be more difficult to discover/observe at the smallest size ranges. Noteworthy, DeMeo et al. ( 2014) found a high abundance of Dtypes among the smallest (< 15 km) asteroids in the inner main belt, from which the NEA population is mostly replenished via well-known dynamical paths. Our results hence imply that the asteroidal contribution to the delivery of the prebiotic material to the primitive Earth could be even much more important than foreseen prior of our observations.

Being based on the observation of numerous different targets over a wide range of phase angles, our work also allows us to constrain the spectral phase reddening of asteroids belonging to different compositional/taxonomic groups. On average, we found that lowalbedo asteroids (C-complex and D-types) show no/limited phase reddening (confirming and reinforcing the preliminary findings by Lantz et al. 2018), incidentally suggesting a brand new way to discriminate primitive objects within the X-complex, whenever measurements are available spanning a wide range of phase angles. We note however that a quite strong phase reddening (0.1040.003 %/100 nm/deg) has been measured from observations of the nucleus of comet 67P/Churyumov-Gerasimenko by the OSIRIS instrument onboard the ROSETTA mission (Fornasier et al., 2015). The strongest phase reddening is seen for olivine-rich surfaces (A-and Q-types). This is in agreement with previous laboratory results showing that olivine-rich ordinary chondrites are the most affected by phase reddening, though the reason of such behaviour is still unclear (Sanchez et al., 2012). For all of the taxonomic groups, the dependence of the spectral slope by the phase angle is monotonous. We stress that the wavelength dependence of the brightness phase function has always been found monotonous for astronomical observations, with only a few exceptions: those of the martian surface by the Viking 1 lander evidenced a change from reddening to blueing with increasing phase angle, producing an "arch-shaped" phase dependence of the observed spectral slope (Guinness, 1981); moreover, a not-monotonous behaviour is displayed by some observations of the lunar soil (e.g., Shkuratov et al., 2011). Based on laboratory experiments and numerical simulations, Schröder et al. ( 2014) found a monotonous phase reddening for a microscopically rough regolith, and a non-monotonous arch-shaped behaviour for smooth surfaces. Grynko & Shkuratov (2008), using numerical modelling based on the geometric optics approximation, evidenced a monotonous phase dependence of the spectral slope in the case of single-particle scattering, and non-monotonous behaviour for multiple-component scattering. These authors also found that monotonous phase blueing can happen for particles larger than about 250 μm. Overall, our results hence suggest the presence of a microscopically rough regolith on the surfaces of the observed NEAs, and a similar surface texture for objects belonging to the same compositional group. We remind however that in our approach we averaged out other factorse.g., peculiar space weathering and particle sizepotentially affecting the spectral response of the surfaces of the individual asteroids in each taxon. More dedicated studies of the phase reddening (i.e., with a complete phase coverage for each object) are still needed.

As a concluding remark, we want to emphasize the very important role that the investigation of the "small" near-Earth asteroids can represent for solar system science. The results presented in this paper already provide some clues on different topics related to the formation and early evolution of planetesimals. We should however keep in mind that these results are based on a relatively limited quantity of available data. Future, larger-scale surveys for the systematic physical characterization of newly-discovered NEAs down to the metre-sized (possibly extending to near-infrared wavelengths, to minimize the risk of taxonomic misassignments) will be crucial to further test and extend our findings.

 Shkuratov, Y., Kaydash, V., Korokhin, V., et al. 2011, P&SS, 59, 1326 Statler T. S., Cotto-Figueroa, D., Riethmiller, D. A., & Sweeney, K. M. 2013, Icarus, 225, 141  Toplis, M. J., Mizzon, H., Monnereau, M., et al. 2013, M&PS, 48, 2300 Trilling, D. E., Mueller, M., Hora, J. L., et al. 2010, AJ, 140, 770  Turrini, D., Svetsov, V., Consolmagno, G., Sirono, S., & Pirani, S. 2016, Icarus, 280, 328

Fig. 1 .

 1 The spectra of 129 asteroids are presented in Fig. 2, while the remaining spectra of 18 objects classified as belonging to the D or A taxonomic types are presented and discussed in Perna et al. (2017; regarding D-type asteroid 1993 HA), Barucci et al. (2018; regarding further 9 D-types) and Popescu et al. (2018; regarding A-types).

Figure 1 :

 1 Figure 1: Distribution of the equivalent diameters of the observed NEAs.

Figure 2 :

 2 Figure 2: Obtained reflectance spectra (normalized at 0.55 µm) for 129 NEAs (spectra of further 18 targets are presented in parallel papers, cf. text in Section 2).

Figure 3 :

 3 Figure 3: Taxonomic type distribution of the observed NEAs (left: absolute numbers; right: relative percentages). The distributions for objects smaller than 100 m and for objects in the 100-300 m size bin are shown separately.

Figure 4 :

 4 Figure 4: Taxonomic type distribution of NEAs in the EARN database (left: absolute numbers; right: relative percentages). The distributions for objects with size in the ranges <100 m, 100-300 m, 300-1000 m and >1 km, are shown separately.

Fig. 5 :

 5 Fig. 5: Spectral slope (0.44-0.65 µm) vs. phase angle for A-, S-and Q-types (blue dots, red diamonds and green squares, respectively). Linear fits are also reported with continuous lines for the three compositional groups (cf. Table3).

Fig. 6 :

 6 Fig. 6: Spectral slope (0.44-0.65 µm) vs. phase angle for D-, X-and C-types (blue down-triangles, red uptriangles and green dots, respectively). Linear fits are also reported with continuous lines for the three compositional groups (cf.Table 3).

Table 1 : Observational circumstances and data analysis results. Object H (mag) Date/UTstart texp (s) Airmass Solar analogue (airmass) Phase angle (deg) Slope (%/10 3 Å) Taxon Albedo Equiv. diam. (m)

 1

			2015-6-8			SA102-1081
	Asclepius	20.7	6:02	31200	1.097	(1.149)	18.8	6.1630.740	Cgh	0.065	378
			2015-11-5			HD11123
	HA	20.1	6:41	2450	1.06	(1.021)	41.1	12.3650.338	D	0.140 (1)	339
			2015-11-6			HD9729
	WN10	20.2	8:15	2450	1.091	(1.089)	57.3	11.6490.267	Sq	0.243	246
			2015-7-19			SA112-1333
	YJ11	20.6	2:19	11200	1.109	(1.366)	17.7	16.6421.019	S	0.211	219
			2016-8-29			HD202282
	NN4	20	0:49	21200	1.187	(1.029)	44.5	3.1510.476	X	0.047	613
			2015-6-9			Hip106102
	EW	20.7	9:20	2600	1.082	(1.051)	44	5.1670.526	X	0.340 (2)	165
			2015-4-14			SA112-1333
	HY7	20.5	9:41	2600	1.176	(1.260)	34.6	8.4801.057	Sq	0.243	214
			2015-7-20			SA110-361
	JV19	20.7	5:40	1900	1.369	(1.176)	39.4	3.3560.578	C	0.050	431
			2015-6-8			SA107-998
	RQ17	22.5	7:44	2600	1.288	(1.265)	50.7	40.8510.477	A	0.191	96
			2017-1-3			SA102-1081
	AV31	20.6	7:54	21000	1.222	(1.143)	33.8	5.8990.398	Xk	0.095	327
			2016-8-30			HD202282
	FQ6	20.8	4:45	31200	1.084	(1.038)	27.1	13.9030.236	S	0.211	200
			2016-11-29			HD33792
	VN	20.5	8:07	1800	1.145	(1.191)	94.1	14.5600.688	V	0.362	175
			2016-4-1			HD171207
	GG2	22.8	6:07	11100	1.05	(1.030)	12.2	13.8451.567	Sq	0.243	74
			2016-4-1			HD106649
	EZ2	20.2	4:48	2700	1.224	(1.039)	8.1	13.9790.336	S	0.400 (2)	192
			2015-7-19			SA115-271
	XP88	20.7	8:10	2900	1.191	(1.290)	15.4	7.0620.303	Xc	0.129	268
			2016-4-1			SA107-998
	TZ3	20.4	6:25	2600	1.343	(1.358)	25.6	4.2500.285	C	0.050	494
			2015-11-6			SA112-1333
	RV17	20.4	1:40	21100	1.244	(1.260)	84.2	15.7370.480	Sv	0.309	199

equivalent diameter of each object is computed by taking into account the mean albedo of its assigned taxon (as from Mainzer et al., 2011a), except when an albedo measurement is available in the literature (reported values from: (1) Mueller et al., 2011, (2) Trilling et al., 2010, (3) Mainzer et al., 2014, (4) Mainzer et al., 2011b).

Table 2 : Statistical comparison between taxonomic distributions of NEAs in our sample and in the literature.

 2

	Taxon	A	B	C	D	K	L	O	Q	R	S	V	X
	Our	5.48	0.68	8.90	6.85	0.68	2.74	0.68	11.64	2.05	39.73	2.74	17.81
	sample (%)	(8/146)	(1/146)	(13/146)	(10/146)	(1/146)	(4/146)	(1/146)	(17/146)	(3/146)	(58/146)	(4/146)	(26/146)
	146-unit	0.47	2.45	8.38	1.38	1.49	1.09	0.54	14.23	0.68	53.94	4.10	11.25
	random
	samples	0.56	1.27	2.26	0.97	0.99	0.87	0.61	2.86	0.67	4.11	1.62	2.61
	from EARN
	database
	(%) [1]
	Deviation	+8.9	-1.4	+0.2	+5.6	-0.8	+1.9	+0.2	-0.9	+2.0	-3.5	-0.8	+2.5
	()

Table 3 : Reddening coefficient γ for different spectral types.

 3

	Taxon	Phase angle	Number of	γ
		range (deg)	objects	(%/100 nm/deg)
				[0.44-0.65 μm]
	A	20-51	8	0.492±0.072
	Q	10-80	17	0.074±0.018
	S	4-92	58	0.013±0.009
	X	5-56	26	0.074±0.021
	D	6-64	10	-0.024±0.027
	C	2-69	13	-0.032±0.030

Table 3 ).

 3

Table 3 ).

 3

http://www.neoshield.eu/

http://earn.dlr.de; retrieved on 13 August 2017.

Acknowledgements

This work is based on observations collected at the European Organisation for Astronomical Research in the Southern Hemisphere under ESO programme 095.C-0087. We acknowledge financial support from the NEOShield-2 project, funded by the European Union's Horizon 2020 research and innovation programme (contract No. PROTEC-2-2014-640351). DP has received further funding from the Horizon 2020 programme also under the Marie Skłodowska-Curie grant agreement n. 664931. This work was also supported by the Programme National de Planétologie (PNP) of CNRS/INSU, co-funded by CNES.