Naizin or Kervidy-Naizin and Kerbernez, Brittany, France 1975-

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Location and Scale[edit]

The Kervidy-Naizin catchment covers 5 km2 in central Brittany (latitude, 47.95; longitude, −2.8; WGS84). A second site, the Kerbernez site, is a complementary site. It is composed of a network of seven catchments with areas between 0.1 and 0.6 km in southwestern Brittany (latitude, 47.94; longitude, −4.1; WGS84). The two sites constitute the AgrHyS ERO (https://www6.inrae.fr/ore_agrhys_eng/) and they are headwater catchments and first- and second-order streams.

Dates[edit]

From the early 1970s to the present

The AgrHyS observatory of response times in agro-hydro systems, located in western France, is dedicated to the observation of agro-hydrosystems under a temperate climate characterized by shallow groundwater and intensive agriculture, mainly of annual crops receiving a high amount of mineral and organic inputs. AgrHyS is composed of instrumented catchments where environmental variables are monitored over the long term to study the processes controlling hydro-chemical fluxes in agro-ecosystems, with the objective of characterizing their response time to climatic and agricultural impacts. On the two sites research has been conducted since the early 1970s. In 2002, AgrHyS was declared by the French Ministry of Research as an Environmental Research Observatory (ERO). Gascuel-Odoux et al. (2018) analyzes the coevolution of research topics with observatories and methods used in Environmental Sciences based on the 50-yr history of the Kervidy Naizin site. Fovet et al (2020) highlights the synergies between the observations at the two sites based on the review of the missions and accomplishments of AgrHyS hydrological observatory since. These long-term observatories serve different missions and have several objectives: facilitate research, provide reference data sets, test methodologies, train new scientists, and educate the public. AgrHys is currently integrated in The French Network of Critical Zone Observatories created in 2017 (Gaillardet et al., 2018).

Climate[edit]

The climate is temperate and humid at both sites with a regional west–east gradient between sites. Average value calculated over 30 years are given below, for Kervidy-Naizain and Kerbernez, respectively. Rainfall, mm yr−1 837 ± 219 1114 ± 237 Penman potential evapotranspiration, mm yr−1 699 ± 58 680 ± 29 Temperature, °C Average 11.2 ± 0.6 12.0 ± 0.5   Maximal 32.6 ± 3.2 30.7 ± 2.6   Minimal −5.0 ± 2.0 −5.8 ± 5.4 Annual runoff, mm yr−1 325 ± 186 227 ± 115 to 448 ± 304

Geology[edit]

The geological bedrock consists of low-permeability crystalline rocks with a similar structure: above the impervious bedrock there is a fissured and fractured layer in which water is likely to percolate deeply. The fractured layer is mantled by a weathered layer where shallow groundwater can fluctuate. In the Kervidy-Naizin site, the bedrock is composed of upper Proterozoic schists, whereas in the Kerbernez site it is granite known as the Leucogranodiorite of Plomelin. The two major bedrock types of the Brittany region are thus represented by the two sites. The weathered zone is 1 to 30 m deep in the Kervidy-Naizin site, with hydraulic conductivity ranging from 4.10−6 to 2.10−5 m s−1 and 40 to 50% total porosity. In the Kerbernez site, the weathered layer is 20 to 40 m deep, with hydraulic conductivity between 2.10−6 and 5.10−4 m s−1 and 42 to 60% total porosity.

Topography and soils[edit]

Slopes are gentle at both sites (<5% on average). The soils are silty loams in Kervidy-Naizin between 0.5 and 1.5 m deep, and in Kerbernez there is mainly sandy loam that is 0.8 m deep on average. Soils are well-drained except in bottomlands close to the streams, where water excess leads to hydromorphic, poorly drained soils and Albeluvisols. Soil hydraulic conductivity at saturation ranges from 10−5 to 10−6 m s−1 in Kervidy-Naizin and from 10−5 to 10−4 ms−1 in Kerbernez. The superficial soil layers (0–40 cm in Kervidy-Naizin and 0–20 cm in Kerbernez) are rich in organic matter (2.5–6.5% in Kervidy-Naizin and 4.5–6% in Kerbernez).

Vegetation / Land Use[edit]

Land use is dominated by intensive agriculture, with systems mixing cropping and indoor dairy and pig farming. In 2013, these catchments were mainly covered by maize, straw cereals, and grasslands. In Kervidy-Naizin these main land cover types correspond to 38, 30, and 15% of arable area, respectively, whereas in Kerbernez they vary between 10 and 70%, between 5 and 24%, and between 0 and 6% of arable area, respectively, depending on the catchment. In some Kerbernez catchments, the number of abandoned or converted fields (i.e., into a golf course) is increasing. To further interpret and model nutrient transfer over catchments, detailed information includes crop rotations (to control land cover maps and to fill potential gaps in them), fertilizer application, pesticide application, crop residues management, livestock size and types, animal feeding, and manure and dejection management. On the Kerbernez site, where each catchment is composed of a few fields only, the survey is focused on the two most instrumented catchments and is conducted every 4 yr. On the Kervidy-Naizin site, which includes 47 farms, three detailed surveys were conducted: in 1993 (Cheverry, 1998), in 2008 and in 2013, this last one specifically on dynamics of organic matter investigated the cropping systems (i.e., crop rotations and crop management practices at the field scale for each farm) (Viaud et al., 2018).

History[edit]

Hydrological settings The current observation protocol of AgrHyS consists of long-term records of meteorological, hydrological, and hydro-chemical variables and land cover maps. The longest time series started in 1993. Since that date, the Kerbernez and Kervidy-Naizin sites have been equipped with a weather station that records hourly rainfall, air and soil temperatures, air humidity, global radiation, and wind direction and speed, which allows the calculation of daily Penman evapotranspiration. Between 1995 and 1997, the outlets were equipped with U-notch or V-notch weirs for monitoring the stream water level. Most gauging stations are automated and use float-operated sensors that record stream level sub-hourly (from 1 to 10 min). Since 1999, the level sensors have been connected to a data logger. From 1997 to 2005, a network of piezometers was installed in the two sites to monitor shallow groundwater fluctuations and variations in its chemical composition. Currently, 31 piezometers are monitored on the two sites. The water table level is measured using automatic transducer sensors, sometimes coupled with temperature and electrical conductivity sensors that record water table data every 15 min in most piezometers (and manually in the others every month).

Various elemental concentrations are analyzed in the water depending on the station: major anions (Cl, NO2, NO3, SO4), major cations (Ca, Mg, K, Na), dissolved organic C (DOC) and dissolved inorganic C (sometimes coupled with Fe), total and soluble reactive phosphorus, and suspended solids. These concentrations are measured using grab sampling mostly. Various water quality parameters are also measured using sensors (electrical conductivity, temperature, turbidity, NO3 and DOC concentrations, and total reactive and total P concentrations). Grab samples have been conducted since 1993 in the stream to collect in situ standard physicochemical data. Monitoring has been intensified since 1999. Rain water composition has been investigated since 2010. The highest frequency of sampling in the stream (Kervidy-Naizin) is 1 d since 1999, and the highest frequency of groundwater sampling (Kerbernez piezometers) is 1 wk (between 2005 and 2012). Automatic samplers are used to sample storm flow events every 10 to 30 min over 10 h, which is consistent with the duration of such events in the catchments. Between 4 and 10 events are monitored every year. Since 2007, some complementary, nearly continuous (15–10 min) monitoring of physicochemical variables has been conducted using dedicated sensors: turbidity and concentrations of NO3, organic C, and P in stream water and temperature and electrical conductivity in stream and groundwater.

The evolution of land cover from one year to another is obtained by farm survey or in-field observations or is reconstructed from remote data by analyzing and classifying the information contained in images. The main annual land use in each field is recorded, at least in the case of cover crops. The land cover maps over the period 1993 to 2018 account for the modifications of the field limit over time. In addition to these data, additional observations are conducted for the duration of the project or even through several successive projects, leading to dedicated medium- to long-term data sets.

Soil[edit]

The first soil maps were established in the early stages of the research sites (Lamandé, 2003; Walter, 1993) to provide an extensive description of the spatial organization of soils and connect them to their hydrological and geochemical functioning (Walter and Curmi, 1998). High-resolution soil maps (1:10,000 and 1:25,000) have been established on the basis of four criteria: soil parent material, soil depth, soil redoximorphic conditions, and soil types as defined in the French soil classification (Baize et al., 2002). The physicochemical properties of soils (texture, pH, nutrient content, organic matter, trace elements) have been measured over the catchment, and hydrodynamic properties of soil horizons (hydraulic conductivity, porosity) have been quantified for the main soil types. In more recent research projects, additional soil collection campaigns have been conducted in the Kervidy-Naizin site to quantify C, N, and P stocks in the soil compartment and to characterize the biochemical processes controlling C, N, and P cycling in soils. Detailed data on soil C, N, and P content; soil aggregation; and microbial communities (molecular microbial biomass estimated by soil DNA recovery, bacterial and fungal communities analyzed by 16S and 18S rRNA gene pyrosequencing) (Matos-Moreira et al., 2017) have been collected. Soil moisture sensors have been in use since 2010 in specific locations of the catchments and according to different strategies. At the Kerbernez site, 21 frequency domain reflectometry sensors are coupled with nested piezometers to monitor the groundwater recharge process. At the Kervidy-Naizin sites, time domain reflectometry sensors (often coupled with temperature sensors) are used either on soil profile (at −5, −20, and −50 cm deep) or in the upper layer of soil (−5 cm) to interpret measurements of gaseous fluxes (CO2, N2O) conducted in successive research projects. The time step of acquisition ranges from 15 to 30 min.

Recharge water was sampled using profiles of ceramic cups between −25 cm and −2.5 m on which suction was applied for water collection. At the Kervidy-Naizin site, the chemical composition of soil water has been investigated since 2011 focused on organic matter and P. Bottomland soils and wetland soils have been instrumented with zero-tension lysimeters and mini-piezometers to allow the sampling of free water in soils (Denis et al., 2017a, 2017b; Dupas et al., 2015c; Gu et al., 2017; Lambert et al., 2011). These samples are analyzed to determine their concentrations in anions, dissolved C (and Fe), and dissolved P, in addition to any other variables of interest.

Surface–Atmosphere Exchange Fluxes[edit]

A flux tower station was installed in 2015 over grazed grassland, recording surface/atmosphere H2O and CO2 fluxes continuously over a few years to capture their seasonal and interannual variability. During the day, CO2 fluxes are linked to H2O fluxes via photosynthesis and transpiration of the vegetation, which is controlled by bulk canopy stomatal conductance; during the night soil and plant respiration proceed. Fluxes are measured by eddy covariance, and a set of ancillary meteorological and soil variables is recorded on site. The measurement height is 2 m, corresponding to a flux footprint of the order of 1 to 3 ha depending on wind speed, atmospheric turbulence, and stability.

Analyzing Water Chemistry at Higher Temporal Resolution[edit]

Several technologies have been or are being tested regarding their ability to provide water concentrations at resolution >1 h. Sensors that estimate concentrations using indirect methods including in situ ionic specific probes and spectrophotometric probes were installed in 2010 to monitor NO3, Cl, and organic C concentrations. They have been tested in stream and piezometers, leading to satisfactory results for the spectrophotometer, which records NO3 and dissolved organic C every 15 min (Faucheux and Fovet, 2014). Ionic specific probes have been found more reliable for spatial campaigns than for continuous monitoring at a given point. The most recent generation of tools is based on river bank side analyzers, which pump water directly from the stream and determine concentrations by direct physicochemical methods equivalent to those used in the laboratory in real time, avoiding storage and transport issues. A P analyzer has recorded total and reactive P in the Kervidy-Naizin site since 2016 every 30 min (Jordan et al., 2007). The challenge to increase the temporal resolution of analysis for various chemical elements in water is also taken up with the development of a river laboratory prototype (Floury et al., 2017). A second prototype measures major ion concentrations (by ionic chromatography), dissolved silica (colorimetry), and organic C (acid mineralization and infrared CO2 measurement) approximately every 30 min in stream water at the outlet of the Kervidy-Naizin site.

Dedicated campaigns and experiments[edit]

Campaigns for Dynamic Mapping of Wetlands in Space and Time. According to their role in overland flow generation (Beven and Kirkby, 1979) and their role as hot spots for biogeochemical reactions (Sabater et al., 2003), wetlands have been the subject of several research projects. Wetlands result from the combination of climate, topography, and geomorphology zones in areas where the water table intercepts shallow soil horizons (Crave and Gascuel-Odoux, 1997). Such combinations are dynamic in space and time. Wetlands inventories have been made on the field by visual identification and by helicopter-borne radar (Gineste et al., 1998; Merot et al., 1994). Thanks to the inventory, a method for mapping those wetlands from a digital elevation model and using a climate-topographic index (Merot et al., 2003) has been developed.

Geophysical Campaigns and Experimental Determination of Hydrodynamic Properties.[edit]

To characterize the hydrodynamic properties of the weathered rock, slug tests and pumping tests were performed in some of the piezometers to provide estimates of hydraulic conductivity (Martin et al., 2006; Molenat and Gascuel-Odoux, 2002; Molénat et al., 2005; Pauwels, 1994; Vouillamoz, 2003).In the Kerbernez site, these tests were combined with geophysical surveys (electrical imaging, electromagnetic and magnetic resonance sounding) and indicate that the thickness of the weathered granite increases from upslope toward downslope areas of the catchments in the form of a deep graben structure (Legchenko et al., 2004). Subsequently, an experimental determination of weathered material was performed using the Wind method (Rouxel et al., 2012), showing that the retention curve of weathered granite is different from soil retention curves and cannot be easily estimated using pedotransfer function approaches.

Tracer Experiments and Use of Geochemical and Isotopic Tools.[edit]

Hydrological and geochemical deconvolution can use various tracers to identify contributive flowpaths and biogeochemical reaction processes or to estimate water and element residence times in catchment compartments such as soil, vadose zone, or groundwater. Various tracers have been used for different objectives. In the Kervidy-Naizin site, a lot of projects were dedicated to storm flow generation and associated exports of dissolved organic matter (DOM). Storm flow deconvolutions based on water isotopes (18O) showed the small portion of recent water in storm hydrographs (Merot et al., 1995), pointing to the importance of old water from soil and groundwater in the genesis of storm flow. Then, several tracer approaches were used and compared to identify and quantify the contributions of different compartments to DOC export during storm events and thereby also to improve our understanding of subsurface flows. The 13C of C (Lambert et al., 2011, 2013, 2014), the fluorescence signature, and the molecular composition of DOM (Denis et al., 2017a; Jeanneau et al., 2014) confirmed the major contribution of riparian wetlands to storm DOC and emphasized a secondary contribution of downslope areas, which decreased along successive storms. The fluorescence signature also emphasized the potential direct contribution of animal manure to the stream DOM during intense spring storm events. In the Kerbernez sites, tracer experiments have been conducted to gain a better understanding in groundwater recharge. The first attempt to use 2H in tracer campaigns at the scale of shallow piezometers was not conclusive, most likely because of excessive transfer times. Tracer experiments with Br and 2H helped to estimate the water velocity in soils and showed the bimodal properties of the velocities. Enzymatic activity was also used in combination with solute concentrations in laboratory experiments to characterize the biogeochemically reactive transfer in the soil and the weathered zone, showing that heterotrophic denitrification was the dominant process (Legout et al., 2005, 2007). Groundwater age was determined using atmospheric tracer chlorofluorocarbons and SF6, highlighting the distinction between the weathered zone (where apparent ages ranged from 12 to 25 yr) and the weathered-fissured and fractured parts (where apparent age increased with depth and was >25 yr) (Ayraud et al., 2008; Molénat et al., 2013). In both catchments, shallow groundwater feeds the streams; therefore, different experimental tools have been explored to quantify the subsurface fluxes and their temporal or spatial variations. Preliminary tests on radon were started in 2015, and studies on temperature using distributed temperature sensing by optic fiber were started in 2016. Atmospheric Emissions and Deposition. Several campaigns have been conducted since 2007 to assess various fluxes, such as CO2, N2O, and NH3, at the landscape scale. For example, Buysse et al. (2016) measured soil CO2 efflux with closed dynamic respiration chambers over a 1-yr period (36 weekly to biweekly measurement dates) at 22 sites across the Kervidy-Naizin catchment. They found that water regime, land-use, and crop rotation significantly affect soil CO2 emissions, with lower emissions observed in poorly drained soils either due to lower respiration or to limited CO2 transport in saturated soils. A network of passive and low-cost sensors for atmospheric ammonia (NH3), which integrates concentrations over monthly periods, was also established across the catchment (Tang et al., 2001). These low-resolution atmospheric NH3 data were complemented by mobile NH3 plume measurement campaigns, downwind of the main agricultural NH3 sources (animal housing), using fast-response (1 s) quantum cascade laser technology. These datasets were used to improve landscape-scale estimates of total NH3 emissions as well as local dry deposition, which contribute significantly to the total N load of agroecosystems in areas of intensive animal farming (Bell, 2017).

Hydrological and biogeochemical Knowledge Gained[edit]

Over the years, the research conducted on the two sites has contributed to new insights into the hydrological sciences. In the early stages (1990–2000), novel findings highlighted the role of landscape geomorphology on water flowpaths and their dynamics. This was achieved by assessing the role of bottomlands on surface flows (Merot and Bruneau, 1993) and then refining the delineation of variable contributive area concepts (Crave and Gascuel-Odoux, 1997; Merot et al., 1994, 1995), studying the effect of hillslope geomorphology (Beaujouan et al., 2001) and then the role of hedgerow network (Benhamou et al., 2013; Merot, 1999; Viaud et al., 2005) on water and dissolved N fluxes. Kervidy-Naizin also hosted several pioneer studies of hydrograph separations (Durand and Torres 1996; Merot et al., 1995; Morel et al., 2009) that were continued later using DOM as a tracer of water pathways (Denis et al., 2017a, 2017b; Jeanneau et al., 2014; Lambert et al., 2011). Another key contribution highlights shallow groundwater seasonal and its interannual fluctuations as a major driver of hydrological and chemical fluxes in such crystalline systems by controlling fluxes and storages (Martin et al., 2006; Molenat and Gascuel-Odoux, 2001; Molenat et al., 2008; Ruiz et al., 2002a, 2002b) as well as the connectivity (Dupas et al., 2015a, 2015b; Humbert et al. submitted, Fovet et al., 2018; Gu, 2017) and the onset of specific biogeochemical processes (e.g., reductive dissolution of soil Fe oxides that induce nitrate reduction or P release) or location of biogeochemically reactive hot spots in the landscape (Grybos et al., 2009; Lambert et al., 2014; Legout et al., 2007; Oehler et al., 2009). This key role of shallow groundwater is of major importance because it reveals high response times of surface water quality to changes in agricultural inputs in such agro-ecosystems (Ayraud et al., 2008; Fovet et al., 2015b; Molenat and Gascuel-Odoux, 2002, Molénat et al., 2013;). Indeed, transit times of water and solutes have been shown to be very variable and much higher than expected considering that, in such systems with a hard rock aquifer and oceanic climate, subsurface pathways are rather short and storage capacity is low compared with annual drainage.

These contributions also enhance the biogeochemical sciences with original characterization of fractured schist reactivity (Pauwels, 1994) and unique characterization of groundwater age in shallower parts of a weathered aquifer (Ayraud et al., 2008; de Montéty et al., 2018). Due to their specificity of human activities with intensive agriculture, including farming systems, these catchments were also a unique opportunity to investigate the effect of local anthropization on weathering and acidification processes (Pierson-Wickmann et al., 2009a, 2009b), which have been mostly studied in pristine areas. These sites have also supported considerable work on the biogeochemistry of wetland soils, including OM, Fe, rare Earth elements, and P speciation, and combined controls exerted by soil characteristics, hydroclimate variability, and topography on the occurrence and intensity of biogeochemical reactions in those soils (Davranche et al., 2011, 2013, 2015; Gu et al., 2017, 2018; Grybos et al., 2007, 2009; Pourret et al., 2007, 2010). Finally, research conducted on these catchments led to major contributions in highlighting and quantifying the role of wetland soils on the export of DOM and P in headwater lowland catchments on impervious bedrock (Gu et al., 2017; Humbert et al., 2015; Lambert et al., 2013; Morel et al., 2009).

After a decade of observations, these catchments started to offer a unique opportunity to conduct long-term analyses, in particular for investigating the effect of seasonal, interannual, and pluriannual variability of climatic features and farming practices on water and nutrient fluxes. Original data treatments methods and approaches to unravel such effects have been tested and developed on this unique data set (Aubert et al., 2013b,c, 2014; Dupas et al., 2015b). Detailed analyses of how seasonality is structured over the climatic interannual variability and how it structures the catchment behavior regarding chemical elements was only possible thanks to daily long records at daily frequency (Humbert et al., 2015) combined with storm records at sub-hourly frequency (Dupas et al., 2015a, 2015b; Vongvixay et al., 2018). Modeling studies are also strongly enriched with such long-term and multiparameter and multicompartment data sets (Fovet et al., 2015a; Salmon-Monviola et al., 2013) because they offer a unique case for testing how well the model is constrained and how realistic it behaves regarding multiple criteria (Fovet et al., 2015a; Hrachowitz et al., 2014).

Interdisciplinary approaches[edit]

A major feature of these catchments are their role in support of interdisciplinary approaches. They are unique in their ability to make researchers from various disciplines work together on both scientific and technical or methodological issues. Since the beginning, they have been designed with an interdisciplinary approach combining the monitoring of hydro-meteorological variables and of water quality parameters with soil characterization (Le Bissonnais et al., 2002; Matos-Moreira et al., 2017; Walter, 1993) and agricultural systems description and understanding. They have helped to realize that understanding the relations between agriculture and environment despite nonstationary conditions (climate variability) and buffering effects due to residence times and biogeochemical transformations requires integrative approaches that involve hydrology, biogeochemistry, soil science, geophysics, bioclimatology, agricultural science, and ecology. The long-term dimension of the observatory is crucial here because building such integrative science needs time and has been possible because of shared field work and shared perceptual models between researchers from various disciplines on those sites. Therefore, these catchments are a major lever for building up and accumulating knowledge on agro-ecosystems, to understand their complexity, their huge range of spatial and temporal scales at which the key controlling processes operate, and the strong unsteadiness of its forcing variables (i.e., climate and human activities).

Links and review papers[edit]

Kervidy-Naizin and Kerbernez

The Catchment data

French catchment network, included Kervidy and Kerbernez

Dupas R. et al., 2023. A French hydrologist’s research for sustainable agriculture. Journal of Hydrology, 128907

Fovet, O. et al., 2018. AgrHyS: An Observatory of Response Times in Agro-Hydro Systems. Vadose Zone Journal, 17(1). DOI:10.2136/vzj2018.04.0066

Gaillardet, J. et al., 2018. OZCAR: The French Network of Critical Zone Observatories. Vadose Zone Journal, 17(1). DOI:10.2136/vzj2018.04.0067

Gascuel-Odoux, C., Fovet, O., Gruau, G., Ruiz, L., Merot, P., 2018. Evolution of scientific questions over 50 years in the Kervidy-Naizin catchment: from catchment hydrology to integrated studies of biogeochemical cycles and agroecosystems in a rural landscape. 2018. DOI:10.18172/cig.3383

References[edit]

Aquilina, L. et al., 2015. Impact of climate changes during the last 5 million years on groundwater in basement aquifers. Scientific Reports, 5. DOI:10.1038/srep14132

Aubert, A., Merot, P., Gascuel, C., 2015. Temporal patterns of water quality: decadal high-frequency data-driven analysis from an hydrological observatory under agricultural land-use. La Houille Blanche - Revue internationale de l'eau(6): 5-11. DOI:10.1051/lhb/20150062

Aubert, A.H., 2013. Analyse des motifs temporels d’une chronique décennale haute-fréquence de qualité de l’eau dans un observatoire agro-hydrologique. Thèse de doctorat Thesis, Agrocampus Ouest, 159 p. pp.

Aubert, A.H. et al., 2013a. Solute transport dynamics in small, shallow groundwater-dominated agricultural catchments: insights from a high-frequency, multisolute 10 yr-long monitoring study. Hydrology and Earth System Sciences, 17(4): 1379-1391. DOI:10.5194/hess-17-1379-2013

Aubert, A.H., Gascuel-Odoux, C., Merot, P., 2013b. Annual hysteresis of water quality: A method to analyse the effect of intra- and inter-annual climatic conditions. Journal of Hydrology, 478: 29-39. DOI:10.1016/j.jhydrol.2012.11.027

Aubert, A.H. et al., 2014. Fractal Water Quality Fluctuations Spanning the Periodic Table in an Intensively Farmed Watershed. Environmental Science & Technology, 48(2): 930-937. DOI:10.1021/es403723r

Aubert, A.H. et al., 2013c. Clustering flood events from water quality time series using Latent Dirichlet Allocation model. Water Resour. Res., 49(12): 8187-8199. DOI:10.1002/2013wr014086

Ayraud, V. et al., 2008. Compartmentalization of physical and chemical properties in hard-rock aquifers deduced from chemical and groundwater age analyses. Applied Geochemistry, 23(9): 2686-2707. DOI:10.1016/j.apgeochem.2008.06.001

Basset-Mens, C., Anibar, L., Durand, P., van der Werf, H.M.G., 2006. Spatialised fate factors for nitrate in catchments: Modelling approach and implication for LCA results. Science of the Total Environment, 367(1): 367-382. DOI:http://dx.doi.org/10.1016/j.scitotenv.2005.12.026

Beaujouan, V., Durand, P., Ruiz, L., Aurousseau, P., Cotteret, G., 2002. A hydrological model dedicated to topography-based simulation of nitrogen transfer and transformation: rationale and application to the geomorphology-denitrification relationship. Hydrological Processes, 16(2): 493-507. DOI:10.1002/hyp.327

Benettin, P., Fovet, O., Li, L., 2020. Nitrate removal and young stream water fractions at the catchment scale. Hydrological Processes, 34(12): 2725-2738. DOI:10.1002/hyp.13781

Benhamou, C., Salmon-Monviola, J., Durand, P., Grimaldi, C., Merot, P., 2013. Modeling the interaction between fields and a surrounding hedgerow network and its impact on water and nitrogen flows of a small watershed. Agricultural Water Management, 121(0): 62-72. DOI:http://dx.doi.org/10.1016/j.agwat.2013.01.004

Bhaduri, B., Sekhar, M., Fovet, O., Ruiz, L., 2022. Estimating solute travel times from time series of nitrate concentration in groundwater: Application to a small agricultural catchment in Brittany, France. Journal of Hydrology, 613: 128390. DOI:https://doi.org/10.1016/j.jhydrol.2022.128390

Birgand, F. et al., 2010. Uncertainties in assessing annual nitrate loads and concentration indicators: Part 1. Impact of sampling frequency and load estimation algorithms. Transactions of the Asabe, 53(2): 437-446.

Birgand, F. et al., 2009. Une approche quantitative du rôle de la fréquence d'échantillonnage sur les incertitudes associées aux calculs des flux et des concentrations moyennes en nitrate en Bretagne. . Ingénieries, 59(60): 23-37.

Birgand, F., Faucheux, C., Gruau, G., Moatar, F., Meybeck, M., 2011. Uncertainties in assessing annual nitrate loads and concentration indicators: Part 2. Deriving sampling frequency charts in Brittany, France. Transactions of the Asabe, 54(1): 93-104.

Bougon, N. et al., 2012. Influence of depth and time on diversity of free-living microbial community in the variably saturated zone of a granitic aquifer. FEMS Microbiology Ecology, 80(1): 98-113. DOI:10.1111/j.1574-6941.2011.01273.x

Braud, I. et al., 2020. Building the information system of the French Critical Zone Observatories network: Theia/OZCAR-IS. Hydrological Sciences Journal: 1-19. DOI:10.1080/02626667.2020.1764568

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Bruneau, P., Gascuelodoux, C., Robin, P., Merot, P., Beven, K., 1995. Sensitivity to space and time resolution of a hydrological model using digital elevation data. Hydrological Processes, 9(1): 69-81. DOI:10.1002/hyp.3360090107

Buysse, P., Flechard, C.R., Hamon, Y., Viaud, V., 2016. Impacts of water regime and land-use on soil CO2 efflux in a small temperate agricultural catchment. Biogeochemistry, 130(3): 267-288. DOI:10.1007/s10533-016-0256-y

Carluer, N., Marsily, G.D., 2004. Assessment and modelling of the influence of man-made networks on the hydrology of a small watershed: implications for fast flow components, water quality and landscape management. Journal of Hydrology, 285(1): 76-95. DOI:https://doi.org/10.1016/j.jhydrol.2003.08.008

Casal, L. et al., 2019a. Reduction of stream nitrate concentrations by land management in contrasted landscapes. Nutrient Cycling in Agroecosystems, 114(1): 1-17. DOI:10.1007/s10705-019-09985-0

Casal, L. et al., 2019b. Optimal location of set-aside areas to reduce nitrogen pollution: a modelling study. The Journal of Agricultural Science, 156(9): 1090-1102. DOI:10.1017/S0021859618001144

Caubel, V., Grimaldi, C., Merot, P., Grimaldi, M., 2003. Influence of a hedge surrounding bottomland on seasonal soil-water movement. Hydrological Processes, 17(9): 1811-1821. DOI:10.1002/hyp.1214

Chaplot, V., Walter, C., 2003. Subsurface topography to enhance the prediction of the spatial distribution of soil wetness. Hydrological Processes, 17(13): 2567-2580. DOI:10.1002/hyp.1273

Chaplot, V., Walter, C., Curmi, P., 2003. Testing Quantitative Soil-Landscape Models for Predicting the Soil Hydromorphic Index At A Regional Scale. Soil Science, 168(6): 445-454.

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