Columba Martínez-Espinosa
email: columba.mar.es@gmail.com

Sabine Sauvage

Ahmad Al Bitar
email: ahmad.albitar@cesbio.cnes.fr

José Miguel Sánchez-Pérez
email: jose-miguel.sanchez-perez@cnrs.fr

* Affiliation

Columba Martinez Espinosa

C Martinez-Espinosa

J M Sanchez-Perez

A dynamic model for assessing soil denitrification in large-scale natural wetlands driven by Earth observations. Authors

Keywords: Nitrogen cycle, denitrification, wetlands, modelling, SMOS 1. Software and data availability

The Wetlands Soil Denitrification Model (WSDM) developed here for natural wetlands, is a physical based model that proposes: (i) the inclusion of soil moisture and temperature from satellite Earth Observations at diurnal temporal resolution, (ii) the distinction of soils under different wetland typologies (i.e., flooded forests, freshwater marshes, brackish wetlands, peatlands, and complex wetlands). Despite uncertainties involved, these two features are key to upscale (nitrification/denitrification dynamics) in natural wetlands at landscape, regional and global scale. In this study, the performance of WSDM was validated with soils of flooded forests and freshwater marshes in the central Amazonian floodplain. WSDM multiannual time series (2012)(2013)(2014)(2015)(2016)(2017)(2018)(2019) show that climate anomalies intensify denitrification events. Flooded forests were identified with the highest annual denitrification rates. Annual denitrification and N 2 O emissions estimated in this study are in line with previous studies.

Highlights

 A novel large-scale dynamic model for wetlands driven by satellite data is presented.

 Temperature and soil moisture are key to shape denitrification dynamics.

 Earth Observations data are advantageous when no field data is available.

Introduction

Large scale modelling of biogeochemical cycles to quantify GHG budgets has gained importance over the last century. The increasing rate of CO 2 , CH 4 and N 2 O in the atmosphere represent a major present and future driver of climate change and global warming [START_REF] Liu | A review of nitrogen enrichment effects on three biogenic GHGs: the CO2 sink may be largely offset by stimulated N2O and CH4 emission[END_REF][START_REF] Pasut | Spatiotemporal Assessment of GHG Emissions and Nutrient Sequestration Linked to Agronutrient Runoff in Global Wetlands[END_REF]. The intensive agricultural practices and the use of Nfertilizers has been identified as the main source of N 2 O emissions at the global scale. Current N 2 O emission coming from anthropogenic origin are doubling the natural emissions [START_REF] Tian | A comprehensive quantification of global nitrous oxide sources and sinks[END_REF][START_REF] Tian | The Global N2O Model Intercomparison Project[END_REF][START_REF] Tian | A review of indirect N2O emission factors from agricultural nitrogen leaching and runoff to update of the default IPCC values[END_REF].

In addition, fertilizers are also a major cause of eutrophication of rivers, groundwater and oceans [START_REF] Smith | Eutrophication of freshwater and coastal marine ecosystems -A global problem[END_REF]. Modelling of nitrogen transfer from terrestrial to aquatic ecosystems [START_REF] Boyer | Modeling denitrification in terrestrial and aquatic ecosystems at regional scales[END_REF] is used to quantify nutrient carrying capacity of aquatic ecosystems and resilience [START_REF] Asaeda | Modeling the effects of macrophyte growth and decomposition on the nutrient budget in Shallow Lakes[END_REF][START_REF] Green | Preindustrial and contemporary fluxes of nitrogen through rivers: a global assessment based on typology[END_REF].

Wetlands play a key role in the global nitrogen cycle [START_REF] Adame | Nitrogen removal by tropical floodplain wetlands through denitrification[END_REF][START_REF] Martínez-Espinosa | Denitrification in wetlands: A review towards a quantification at global scale[END_REF], and they have been identified as large scale denitrification hotspots [START_REF] Quin | Dissecting the ecosystem service of large-scale pollutant retention: The role of wetlands and other landscape features[END_REF][START_REF] Thorslund | Wetlands as large-scale nature-based solutions: Status and challenges for research, engineering and management[END_REF] as they carry out complete denitrification pathways reducing NO 3 to N 2 in four stages [START_REF] Canfield | The Evolution and Future of Earth's Nitrogen Cycle[END_REF]. Quantifying where, when and how much nitrification and denitrification occurs based only on large scale measurements remains a challenge.

The relative yield of the intermediates (N 2 O and NO) is a function of the soil moisture, nitrates availability and pH [START_REF] Chen | Soil Moisture and pH Control Relative Contributions of Fungi and Bacteria to N2O Production[END_REF].

Therefore, models have become essential tools to integrate current knowledge and available data. Denitrification and nitrification pathways are well understood and a number of different approaches have been used to develop N cycling models [START_REF] Adame | Nitrogen removal by tropical floodplain wetlands through denitrification[END_REF][START_REF] Bakken | Regulation of denitrification at the cellular level: a clue to the understanding of N2O emissions from soils[END_REF][START_REF] Burgin | Soil O2 controls denitrification rates and N2O yield in a riparian wetland[END_REF][START_REF] Groffman | Denitrification hysteresis during wetting and drying cycles in soil[END_REF][START_REF] Tiedje | Perspectives on measurement of denitrification in the field including recommended protocols for acetylene based methods[END_REF]), yet their large scale suitability is still limited.

Wetlands Soil Denitrification Model (hereafter WSDM) was specifically conceived for natural wetlands. It is established as a model with rate-controlling properties that allows denitrification quantification at a large scale, using satellite data. Despite all the uncertainties represented by satellite data, it provides data to produce temporal diurnal resolution multiyear time series and allows access to remote locations all over the globe.

Modelling the mechanisms responsible for terrestrial N 2 emission from natural sources is still limited. Assessment of global terrestrial N 2 O emissions from natural sources vary up to a factor 3 and range between 3.3 and 9.0 TgN.yr -1 [START_REF] Ciais | Carbon and other biogeochemical cycles[END_REF]. Wetlands N 2 O annual contribution estimation from 1981-2010 to the global budget was 0.97 ±0.7 TgN.yr -1 [START_REF] Tian | Global methane and nitrous oxide emissions from terrestrial ecosystems due to multiple environmental changes[END_REF]. Later studies reported that natural soils contribute with 5.6 TgN.yr -1 in a range of 4.9-6.5 TgN.yr -1 , and inland waters, estuaries and coastal zones contribute with 0.3 TgN.yr -1 (0.3-0.4 TgN.yr -1 ) [START_REF] Tian | A comprehensive quantification of global nitrous oxide sources and sinks[END_REF].

These annual estimates are insightful when comparing biomes, and to identify different wetlands entities and their contribution to the global nitrogen cycle. Yet, this assessment does not explain the role of different inland and coastal natural wetlands (i.e. freshwater marshes, flooded forest, peatlands, mangroves and saltmarshes). In addition, the annual assessment does not show evidence related to the hot moments (seasonality). Investigating inter-annual dynamics of denitrification and their relationship with flooding or drought events is relevant for future climate scenarios, WSDM aims to contribute to filling this gap, investigating denitrification exclusively in natural wetlands, and emphasizing the diversity of these ecosystems with spatialized information.

Materials and methods

WETLANDS SOILS DENITRIFICATION MODEL

The WSDM was developed specifically for natural wetlands ecosystems where complete denitrification occurs [START_REF] Adame | Nitrogen removal by tropical floodplain wetlands through denitrification[END_REF][START_REF] Guilhen | Denitrification, carbon and nitrogen emissions over the Amazonianwetlands[END_REF][START_REF] Martínez-Espinosa | Denitrification in wetlands: A review towards a quantification at global scale[END_REF]. The WSDM aims to model denitrification process in wetlands soils, influenced by three main physical parameters; soil moisture, temperature and nitrates availability. The WSDM model simplifies denitrification process by forcing available daily spatialized data to enable large scale application. A software was developed to harmonize the different databases and to carry out the simulations. The model is developed in Python programming language, using standard scientific computation and visualisation libraries with an open-source license.

The code is developed in functional approach with separate modules for pre-processing, simulation and post-processing. The WSDM model with a brief description is available at https://framagit.org/ahmad.albitar/WSDM/ MODEL IMPLEMENTATION WSDM needs two kind of spatialized data. On one hand, static datasets, define the wetland typology and extension, and the soil characteristics (i.e. texture, bulk density, organic carbon content). On the other hand, dynamic datasets (soil moisture and soil temperature) will lead the nitrification and denitrification processes. Before running the physical-model, a preprocessing phase is carried out. The user then has to define the Region of Interest (ROI), giving two latitude and two longitude points indicating ROI limits, followed by the starting and end date of the simulation period. Once these criteria are established, simulation is launched. Spatial harmonization of databases is the first step, to have n-dimensional data on a regular grid. Soil characteristics database -WISE30sec [START_REF] Batjes | World soil property estimates for broad-scale modelling (WISE30sec)[END_REF] was the reference grid, with a spatial resolution of 30 arcsec (1 km 2 ), well as Global Lakes and Wetlands Database (GLWD) (1 km 2 ) [START_REF] Lehner | Development and validation of a global database of lakes, reservoirs and wetlands[END_REF]. Brightness soil temperature and root zone soil moisture [START_REF] Ahmad | Algorithm Theoretical Basis Document (ATBD) for the SMOS Level 4 Root Zone Soil Moisture[END_REF][START_REF] Al Bitar | The global SMOS Level 3 daily soil moisture and brightness temperature maps[END_REF] have an original grid of 25km 2 , therefore a linear interpolation, using (https://scipy.org/) -scientific python library is compute in every simulation, before running the physical model.

The workflow summary WSDM processes (Figure 1). The physical model has different functions that are explained in the next subsections. The limitation of the approach is detailed in the discussion section. Nitrification will be active if soil moisture is low, if not, no nitrates will be created and no denitrification will be recorded, until that condition changes. Following the workflow, day i+1 soil moisture will define if nitrification or denitrification will occur. This loop will continue happening until the end of the defined simulation period.

NITRIFICATION

In WSDM, nitrification is the first process, as it calculates nitrate production. The first year of the model run is used for stabilization, as the nitrate budget is created at the beginning of the run, therefore a minimum of two years of input data is needed. Nitrification is linked to the stock and evolution of organic matter in the soil, both together define soil fertility.

Transported nitrates (i.e. agricultural sources) are not considered in this first version. Nitrates production (Eq. 1) is modelled as follows,

Eq. 1 where corresponds to the nitrates produced by nitrification in the local soil (mgN.kg - 1 .day -1 ), given by organic nitrogen in soil (mgN.kg -1 ) (Eq. 2),

Eq. 2 where is the organic carbon in soil (gC.kg -1 ), and C:N is the ratio of carbon and nitrogen. The destruction of humus by mineralization is variable depending on the soil and the bacteria activity expressed by coefficient, calculated from the quantity of stable humus in the soil. The rate of mineralization is always higher in warm, humid climates, and varies from 0.5 to 3%. To adjust the coefficient according to soil texture and temperature (Eq. 3), we used the model proposed for clay soils by [START_REF] Girard | Sols et environnement -2e édition -Cours, exercices et études de cas -Livre+compléments en ligne: Cours, exercices corrigés et études de cas[END_REF],

where C and [CaCO 3 ] correspond to the clay (%) and carbonate content (gC.kg -1 ) respectively, is the bulk density (kg.dm -3 ), and MAT (Mean Annual Temperature) (°C), the coefficients are conversion units factors. The third term represents soil saturation factor (Eq. 4), defined as follows,

If

Eq. 4

where , is the soil moisture of day (i) (m 3 .m -3 ), is the lowest soil moisture that allows the reaction to happen, and , (m 3 .m -3 ). A threshold indicating nitrification can only happen when aerobic conditions are guaranteed. will be used for denitrification (Eq.5), but conditioned inversely.

Eq. 3

DENITRIFICATION

Denitrification function on WSDM was structured as the NEMIS model (Eq.5) [START_REF] Hénault | NEMIS, a predictive model of denitrification on the field scale[END_REF], adapting the multi-component method [START_REF] Berner | Early diagenesis: a theoretical approach[END_REF] where diurnal denitrification (Eq.6) is given by the potential denitrification weighted by three main parameters (i.e. nitrate availability, soil moisture and temperature) adapted as follows,

Eq. 5

Where is the denitrification rate in day (i), is the potential denitrification rate in (mole N.dm -3 .d -1 ) calculated from the first order kinetic model [START_REF] Hunter | Kinetic modeling of microbially-driven redox chemistry of subsurface environments: coupling transport, microbial metabolism and geochemistry[END_REF]. This model (Eq. 5), was first adapted for the hyporheic zone by [START_REF] Peyrard | Longitudinal transformation of nitrogen and carbon in the hyporheic zone of an N-rich stream: A combined modelling and field study[END_REF]. Then modified and tested at larger scale, to calculate the dynamic potential denitrification of flooded areas using satellite data [START_REF] Guilhen | Denitrification, carbon and nitrogen emissions over the Amazonianwetlands[END_REF], and subsequently for daily denitrification quantification on floodplains areas in different watershed under tropical, temperate and arctic conditions [START_REF] Fabre | Daily denitrification rates in floodplains under contrasting pedo-climatic and anthropogenic contexts: modelling at the watershed scale[END_REF]. These advances were key evidence to adopt this denitrification model into the WSDM as follows,

Eq. 6

where is the potential denitrification rate in (mole N dm -3 .d -1 ), represent the stoichiometric proportion of nitrate consumed in denitrification compared to the organic matter used with = 5, is the bulk density (kg.dm -3 ), is the porosity (unitless), (d -1 ) is organic carbon mineralization constant define for each wetland typology (Table 2), (mole.kg -1 ) is the organic carbon available in the soil.

is different for each wetland typology, and regulated in function of three main parameters, these three unitless functions range from zero to optimal condition (one). First, represents nitrates soil availability (Eq. 7),

Eq. 7

where depends on the nitrates soil concentration (mgN.kg -1 ) and bacteria denitrification capacity, define on the , a half saturation constant for denitrification. This constant was calibrated for wetlands ecosystems in the present study.

HALF SATURATION CONSTANT

The half saturation constant is integrated in the denitrification model in a Michaelis Menten type function that limits the denitrification capacity, and is related to nitrates quantity [START_REF] Peyrard | Longitudinal transformation of nitrogen and carbon in the hyporheic zone of an N-rich stream: A combined modelling and field study[END_REF]. To adjust this value to wetlands ecosystems (Eq.9), the constant was calibrated experimentally in the present study (Appendix Experimental Methodology) using the emission results (Appendix Table A.3) as follows,

Eq. 9

where is a relation factor between (mgN.kg -1 ) potential denitrification, and (mgN.kg - 1) is the control denitrification of the same soil, with no addition of nutritive solution. This factor then is substituted as (Eq. 7) , which allows the to be calculated as follows,

Eq. 10 where (mgN.kg -1 ) is the half saturation denitrification constant cleared out from the saturation equation 7, using A, the unitless relation factor calculated above, as the known .

Then the mean value for all the samples was stablished as the wetlands ecosystems constant (Table 2). The second term represents soil saturation factor, explained above (Eq. 3), conditioned as follows,

If

where a threshold indicating denitrification trigger was set at , as denitrification can only happen when ≥ 70% of water in the soil pores [START_REF] Bateman | Contributions of nitrification and denitrification to N2O emissions from soils at different water-filled pore space[END_REF]. The third term defines temperature impact on denitrification (Eq. 11),

Eq. 11

where is the temperature of the given day and is the optimal temperature for denitrification established as 25°C in natural ecosystems [START_REF] Billen | Nitrate retention at the river-watershed interface: a new conceptual modeling approach[END_REF].

Denitrification obtained from these equations is volume based (mole N.dm -3 .d -1 ), in order to integrate the result to the WSDM, a conversion from volume to mass (Eq. 12) was applied as follows,

Eq. 12 where, is expressed in mgN.kg -1 .day -1 , is the nitrogen molar mass (g), 1000 is the conversion factor g to mg, is the bulk density (kg.dm -3 ), is the porosity (unitless).

Nitrates budget in the soil (Eq. 13), is calculated by integrating nitrification and denitrification processes defined as follows,

Eq. 13 where (mgN.kg -1 ) is the nitrates available in the day (i+1), given by (mgN.kg - 1 ), that represents the nitrates that are produced but not yet consumed, -(mgN.kg -1 ) is the nitrates produced by nitrification on a given day (i) and (mgN.kg -1 ), the nitrates consumed by denitrification on the same day (i) and , (day) to denote the time span. This function is activated at the end of each day, before starting the loop. WSDM continues the simulation until the end of the defined period.

Transformation of denitrification rates from mass to area (Eq. 14) was calculated as follows,

Eq. 14 where is the total denitrification (N 2 O-N+N 2 -N) estimation in kgN.ha -1 .day -1 , given by (mgN.kg -1 ) nitrates consumed, is the bulk density (kg.dm -3 ), refers to the active soil layer, here 30 cm was used as a constant depth of the active layer.

N 2 O-N 2 RATIO OUTGASSING
N 2 O emissions are highly variable in space and time, and there has not been a consensus on a methodology that responds to bottom-up or top-down scales. However, as scale increases, so does the agreement between estimates based on soil surface measurements (bottom-up approach) and estimates derived from changes in atmospheric concentration of N 2 O (topdown approach) [START_REF] Grosso | Estimating Agricultural Nitrous Oxide Emissions[END_REF].

The IPCC provides guidelines for estimating regional and global N 2 O emissions, which are calculated by multiplying N loading with the indirect emission factors (EF) (IPCC, 1996). The indirect N2O emission factor associated with N leaching and runoff (EF5: kg N2O-N per kg of -N) incorporates three components: (i) groundwater and surface drainage (EF5g); (ii) rivers (EF5r); (iii) and estuaries (EF5e). In the last IPPC report (2019), the EF5r and EF5e were adapted to 0.26 according to 91 data observations compiled by [START_REF] Tian | A review of indirect N2O emission factors from agricultural nitrogen leaching and runoff to update of the default IPCC values[END_REF], and a mean value of the EF5g for ground and surface drainage was 0.60. N 2 O emissions can be influence by several factors, yet to avoid adding up uncertainties we applied the constant ratios (Table 2) proposed for freshwater wetlands, for flooded soils and by the IPCC applied by [START_REF] Guilhen | Denitrification, carbon and nitrogen emissions over the Amazonianwetlands[END_REF] on the Amazon Basin.

The previously explained functions are the structure of the WSDM, the variables acronyms, units and description are summarised in Table 1, likewise for constants presented in Table 2. Soil saturation m 3 •m -3

Minimal soil saturation capacity m 3 •m -3

Maximal soil saturation capacity mole kg -1 Organic carbon content gC•kg -1 Organic carbon content mgN.kg -1 Nitrates concentration mgN.kg -1 Nitrates in soil mgN.kg -1 Nitrates denitrified °C Mean annual temperature -C: N ratio

WETLANDS SOILS DENITRIFICATION MODEL VALIDATION

The Amazon basin was selected as it is considered to have the optimal denitrification conditions, with a mean annual temperature of 26.6°C with little variations; average rainfall is 2100 mm per year [START_REF] Ribeiro | Local rainfall variability-a potential bias for bioecological studies in the Central Amazon[END_REF]. Amazonian floodplains are directly linked to the lateral flood pulses [START_REF] Junk | The flood pulse concept in river-floodplain systems[END_REF][START_REF] Keizer | A new look at the Flood Pulse Concept: The (ir)relevance of the moving littoral in temperate zone rivers[END_REF]. It has a very regular annual bimodal flood pulse that defines a zonation along the flooding gradient of the central amazon floodplains [START_REF] Kubitzki | The ecogeographical differentiation of Amazonian inundation forests[END_REF]. The Amazon river discharges roughly fifteen to twenty percent of the world's annual continental freshwater into the ocean [START_REF] Pekárová | Spatial and temporal runoff oscillation analysis of the main rivers of the world during the 19th-20th centuries[END_REF], and Amazonian floodplain forests cover an area of more than 97,000 km 2 [START_REF] Hamilton | Comparison of inundation patterns among major South American floodplains[END_REF].

Dimensions of this basin reflects the meaning of water movement and abundance in life shaping global biogeochemical cycles [START_REF] Vörösmarty | Continental scale models of water balance and fluvial transport: An application to South America[END_REF].

To calibrate and validate the WSDM, a field campaign in the central Amazonian [START_REF] Lehner | Development and validation of a global database of lakes, reservoirs and wetlands[END_REF].

The model validation was carried out in three steps: (a) static denitrification validation, comparing modelled potential denitrification and experimental denitrifier enzyme activity (hereafter DEA) results. DEA was measured using acetylene-inhibition technique (AIT) as described and applied in several studies [START_REF] Balderston | Blockage by acetylene of nitrous oxide reduction in Pseudomonas perfectomarinus[END_REF][START_REF] Koschorreck | Nitrogen dynamics in seasonally flooded soils in the Amazon floodplain[END_REF][START_REF] Tiedje | Anaerobic processes in soil[END_REF][START_REF] Tiedje | Perspectives on measurement of denitrification in the field including recommended protocols for acetylene based methods[END_REF]

Results

STATIC DENITRIFICATION VALIDATION

To illustrate the performance of the WSDM and the behaviour regarding the three main physical parameters, three soil samples results, each from three different effluents (Negro river -N1, Solimões river-S2, Madeira river-M2), are presented in the following figures.

Figure 3a. shows the soil saturation gradient, Figure 3b, the temperature gradient with 25°C as the optimal temperature and 4°C, as the activation temperature, and Figure 3c shows the soil nitrate pool. Complete modelled results for each sampling soil are compared to observed denitrification rates in laboratory-controlled conditions (Appendix Table A.2). The observations and model outputs have a good fit (p-value < 0.001) PBIAS = 4.15 where PBIAS is the percent bias between model and observation [START_REF] Yapo | Automatic calibration of conceptual rainfall-runoff models: sensitivity to calibration data[END_REF], DEA and model maximal denitrification have a Pearson coefficient of 0.68 with a p-value = 0.05. and total denitrification (N 2 +N 2 O) (kgN.ha -1 .day -1 ) are represented, while daily soil moisture factor dynamics are represented. The nitrate budget represents the nitrates available in the soil on a certain date, this budget is not produced in one day by nitrification, but is the accumulation of several days, therefore is represented as an area, that is consumed by denitrification events (orange lines). When soil moisture factor is zero (black line), there is no nitrification and therefore the nitrates budget stays steady.

N 2 O-N 2 RATIO VALIDATION

The modelled annual denitrifications from 2012-2019 calculated in each sample site are grouped by wetland typology. The resulted emissions after applying the three different R N2O from literature are compared with other studies in order to validate the WSDM total denitrification assessment (Table 3). Yearly WSDM denitrification results for each soil sample are presented in Appendix Table A.5. 5 Discussion

MODEL PERFORMANCE WITH RESPECT TO FIELD DATA

The eight soil samples analysed are heterogeneous with soil properties ranging from low organic carbon content (mineral soils) to rich organic soils. Even though the study area is small compared with the Amazon watershed dimension, the freshwater marshes and flooded forests, (the main freshwater wetland typologies of this basin), were sampled in each of the three main tributaries (i.e. Negro, Solimões and Madeira rivers). This study is not intended to better understand denitrification in the Amazon, but to use Amazon soils, as examples of freshwater wetlands (flooded forest and freshwater marshes) under conditions of low anthropogenic impact, with a bimodal hydrological cycle.

These samples were key to calibrate the WSDM and were intended to model complete denitrification rates on a large scale considering wetlands heterogeneity (i.e. soil characteristics and wetland typology) as well as physical dynamics (i.e. temperature and soil moisture).

WSDM is built up in a binary assumption, which does not allow nitrification to happen at the same time as denitrification in the same point. Fixing a soil moisture threshold. This may not be true when looking at the microscale, as the soil structure and the root zone has some oxygen available most of the time. During wet periods, the anoxic conditions trigger three different processes: denitrification, dissimilatory nitrate reduction (DNRA) and Anammox.

The WSDM, in its current configuration, does not simulate these two processes, and considers denitrification as the main path in wetlands, this assumption is endorsed by previous studies [START_REF] Adame | Nitrogen removal by tropical floodplain wetlands through denitrification[END_REF].

Due to the lack of in situ data of N 2 O emission in the selected area of interest, experiments were carried out in controlled conditions (optimal temperature and saturation) allowing bacterial activity to be isolated and quantify nitrate consumption exclusively. This experimentation was key for the calibration and validation of the WSDM. Simulated denitrification has a good fit (PBIAS: 4.15) compared with denitrification values observed in controlled laboratory conditions (i.e. optimal temperature, saturated soil and limited nitrates)

(Appendix Table A.2).
Flooded forest samples influenced by Negro river (N1, N3) have high organic carbon content, yet the recorded emissions are unlike. The difference lies in the concentration of nitrates in the soil. When there is nitrates surplus in soils the enzyme (nosZ) in charge of converting the N 2 O to N 2 (the last step of denitrification) is withdrawn [START_REF] Glass | Denitrification of high-nitrate, high-salinity wastewater[END_REF]. This withdrawal is frequently observed in agricultural fields, where fertilizers are added and the type of irrigation used is flooding [START_REF] Burgin | Soil O2 controls denitrification rates and N2O yield in a riparian wetland[END_REF][START_REF] Liu | A review of nitrogen enrichment effects on three biogenic GHGs: the CO2 sink may be largely offset by stimulated N2O and CH4 emission[END_REF].

Flooded forest soil sample (N1) influenced by the Negro river located downstream of the city of Manaus recorded a N 2 O emission rate of 0.32 + 0.1 gN.kg -1 .h -1 , which is two times higher than four of the other soil samples (N3, S1, M1 and M2). These four sites register a ratio that favours N 2 . On the other hand, flooded forest (N3), registered the highest N 2 emission rate (0.23 + 0.02 g N kg -1 h -1 ).

N1 and N3 samples are examples of high potential denitrification rates of Amazonian wetlands soils. Extensive fertilizer use in this watershed will have a negative effect in wetlands denitrification, as concentration of nitrates from exogenous sources will increase the budget of nitrates produced by nitrification, that in turn will increase denitrification event intensity (fold-4) and N 2 O emissions [START_REF] Weier | Denitrification and the Dinitrogen/Nitrous Oxide Ratio as Affected by Soil Water, Available Carbon, and Nitrate[END_REF][START_REF] Blackmer | Inhibitory effect of nitrate on reduction of N2O to N2 by soil microorganisms[END_REF][START_REF] Pärn | High greenhouse gas fluxes from peatlands under various disturbances in the Peruvian Amazon[END_REF]. A surplus of nitrates also affects ecosystem functionality, promotes eutrophication and biodiversity may be threatened [START_REF] Huang | Nitrogen and phosphorus losses and eutrophication potential associated with fertilizer application to cropland in China[END_REF][START_REF] Smith | Eutrophication of freshwater and coastal marine ecosystems -A global problem[END_REF].

Despite the importance of quantifying anthropogenic influence in natural wetlands, this version of the WSDM does not aim to calculate denitrification rates of anthropogenic input effects, but to identify physical parameters that shape the denitrification capacity of each wetland ecosystem, and assess these dynamics.

The major contribution of WSDM, is that simulated denitrification is influenced by wetland vegetation typology. The influence of the vegetation in wetlands has been reported previously by [START_REF] Alldred | Effects of wetland plants on denitrification rates: a meta-analysis[END_REF], they calculated that the presence of plants increased denitrification rates by 55% on average, but they also acknowledge the need to differentiate plant communities. The WSDM modelled these two factors, including a wetland typology that assumes a classification of wetlands ecosystems based on their vegetation structure (i.e. trees or herbs).

The link between vegetation and bacterial processes such as denitrification can be delicate.

The interaction occurs in the root areas, on a very local scale and depends on many physical and chemical aspects. Denitrification emission ratio depends on many local variables and it can exhibit drastic changes (i.e. pH, nitrate concentration). Acetylene-inhibition technique (AIT) has limitations regarding the N 2 O:N 2 emission ratio, and it has been shown to be inaccurate when recording low denitrification rates such as in groundwater. Nevertheless, AIT allows an estimation of total denitrification at high temporal resolution and on small spatial scales, with limited workload and costs involved [START_REF] Felber | Theoretical and practical limitations of the acetylene inhibition technique to determine total denitrification losses[END_REF]. A stable isotope approach might have been a preferable method to identify N 2 emissions. However, we had reservations concerning detection limits and how well the enriched solution would be distributed through the soil column.

Therefore, in the case of the WSDM the optimal compromise between accuracy and the available information was to link wetland typology to carbon decomposition rate (k OC ). This immediately implies the presence of bacterial community activity and integrates the different kinds of organic matter available. This correlation was validated with the denitrification rates observed in laboratory analysis, confirming that flooded forest samples had higher denitrification rates than freshwater marshes (both in optimal conditions).

COMPARISON WITH EARLIER MODELLING APPROACHES

The precedent denitrification model estimates a total denitrification (N 2 -N + N 2 O-N) rates at the Amazon floodplains from 38.8 to 142.5 kgN.ha -1 .yr -1 with a spatial grid of 25 km 2 [START_REF] Guilhen | Denitrification, carbon and nitrogen emissions over the Amazonianwetlands[END_REF]. The WSDM has a wider range (20.36 -315.90 kgN.ha -1 .yr -1 ), this difference is a consequence of model set up, the current version is sensitive to different organic carbon decomposition rates, soil moisture gradient, as well as nitrates limitation, calculated in 1 km 2 grid, constraints that were missing in the previous version.

The WSDM assumed that annual input flux of carbon is greater than losses (respiration; harvesting, export, burial). Consequently, carbon budget is set constant but not unlimited.

Other studies had modelled variable carbon input [START_REF] Fabre | Daily denitrification rates in floodplains under contrasting pedo-climatic and anthropogenic contexts: modelling at the watershed scale[END_REF][START_REF] Guilhen | Denitrification, carbon and nitrogen emissions over the Amazonianwetlands[END_REF][START_REF] Peyrard | Longitudinal transformation of nitrogen and carbon in the hyporheic zone of an N-rich stream: A combined modelling and field study[END_REF] showing that carbon is not a limiting factor but it can enhance the denitrification signal. This assumption is compensated by the mineralization rate of organic carbon (K OC ), which is specific for each kind of simplified wetland ecosystem typology.

The WSDM current version does not calculates N 2 O:N 2 ratio yet. The different N 2 O rate productions (Table 3), suggest that ratio (0.02) for freshwater wetlands proposed by [START_REF] Scheer | Estimating global terrestrial denitrification from measured N2O:(N2O+ N2) product ratios[END_REF] applied to the WSDM denitrification results, gives a N 2 O-N total contribution within the range of what has been reported in low impacted Amazonian soils by other studies.

This estimation should be taken as an indicator and could be improved by field measurements with other methodologies as 15 N isotopic tracers [START_REF] Bergsma | Measuring simultaneous fluxes from soil of N2O and N2 in the field using the 15N-gas "nonequilibrium" technique[END_REF]. Additionally, [START_REF] Pärn | High greenhouse gas fluxes from peatlands under various disturbances in the Peruvian Amazon[END_REF] WSDM, is complementary to both models, takes into account different wetlands ecosystems based on the vegetation structure, but with a simplified approach that is useful at large scale.

The results are given in terms of total denitrification, aiming to contribute with a particular vision of natural wetlands and their positive role in the global nitrogen cycle This natural wetland function via complete denitrification is often overlooked. The total denitrification account non-harmful emissions (N 2 ), which implies the subtraction of nitrate flux into aquatic ecosystems (i.e. rivers, lakes, coastal areas).

Conclusion

The WSDM main contribution is that it considers wetlands diversity, and calculates denitrification in a diurnal basis while remaining parsimonious, giving a dynamic spatialized assessment that identifies hot moments and hot spots. WSDM total denitrification can be used as an indicator tool for prioritizing conservation areas, or wetlands ecosystems that must be studied in depth. The WSDM was developed for a large-scale approach, as it does not require Between treatments, soil samples were left for 24 hours in aerated conditions before applying the next treatment. Once all the treatments were completed, the flasks were dried out in an oven at 100 °C for 24h. After that, the weight of each flask was measured. The dry samples were sieved and two grams of these homogenized samples were ignited at 500°C for 24h to obtain the organic matter content by weight difference. Pore space and bulk density were determined gravimetrically on volume samples assuming a measured particle density of 2.613 g cm -3 . Potential denitrification rates were calculated from the linear increase of the N 2 O content in the flask and expressed as µg N 2 O g -1 .h -1 . All data is expressed per gram of sediment on a dry basis. The total N 2 O production (Eq. 1) was calculated by the N 2 O gas (g) fraction and N 2 O liquid (l) fraction measured in ppm as follows,

Eq. 1 and then converted to µg N 2 O gas (Eq.2) and liquid (Eq. 3) as follows,

Eq. 2

Eq. 3 where is a density fraction is expressed in part per million of air (ppm) and refers to part per million in volume of solution (ppmv; 1 ppmv = 1 μL/L), water volume was calculated with the difference between the dry sample and wet sample weight.

The N-N 2 O (Eq. 4), N-N 2 O+N 2 (Eq. 5) and N-N 2 O (Eq. 6) production ratio were calculated in dry sediment basis as follows,

Eq. 4

If C 2 H 2 added , Eq. 5

Eq. 6

where refers to the production rate of N 2 O (µg N g -1 h -1 ) when there is no inhibition with acetylene, and is the dry soil weight (g), when acetylene is applied then the N 2 O ratio recorded is which is the sum of (N 2 and N 2 O) and is also expressed in µg N g -1 h -1 , then refers only to the N 2 production ratio (µg N g -1 h -1 ), as subtraction of the two previous equations.

Results

Static validation

Figure 1 .

 1 Figure 1. Workflow illustration of the Wetlands Soil Denitrification Model processes flow (black arrows), the blue arrows represent the main two loops of the model. After setting up the model and defining the region of interest the simulation begins with nitrate budget generation in day i .Nitrification will be active if soil moisture is low, if not, no nitrates will be created and no denitrification will be recorded, until that condition changes. Following the workflow, day i+1 soil

  floodplain (from 14 th to 24th February 2020) was carried out to collect soil samples at eight sampling sites. The sampling points were distributed on the three main tributaries (Negro river, Solimões river and Madeira river) with different water quality. In each river, the two main wetland typologies (i.e. Freshwater marsh and Flooded forest) were sampled as shown in Figure2.

Figure 3 .

 3 Figure 3. Model responses to different limiting factors (a. soil moisture, b. temperature, c. nitrate concentration) of three sample sites (Madeira river (M2), Negro river (N1), and Solimões river (S2), compared to DEA laboratory mean observations (n=3).

4. 2

 2 DYNAMIC DENITRIFICATION VALIDATION Three sampling points (Negro river -N1, Solimões river -S2, Madeira river -M2) are shown as an example (Figure 4a-c). These time series show that each sampling points have different soil moisture dynamics. For instance, M2, the sampling point influenced by Madeira river (Figure 4.b) has an annual bimodal inundation dynamic, with one important denitrification event per year. When soil moisture is very low, nitrification becomes inactive and the nitrates soil budget of the soil stays constant. Low soil moisture events are present in 2012, 2015 and 2016 in the three wetland areas influenced by the three rivers. When the flooding of the plains begins, the nitrate budget is rapidly consumed. In the case of the Negro river -N1 andSolimões river -S2, soil saturation events are more frequent, so the nitrates budget is consumed faster and there are more denitrification events per year.

Figure 4 a

 4 Figure 4 a-c. Diurnal time series from 2011-2019, on the main y-axis, nitrate budget per day (kgN.ha -1 .day -1 )

  a watershed-based model. The main drivers of the model are soil moisture and temperature input data which have been identified as key for modelling the nitrate budget, and denitrification activation-deactivation dynamics at diurnal basis. Modelling these dynamics is a tool in wetlands areas where field data is not available yet. These results may be indicators of possible scenarios when the anomalies will repeat and/or intensify. In future, this model can be applied at different scales (continents or regions) as well as in different watersheds at different climatic regimes (i.e. boreal or temperate).Appendix Experimental methodologySoil samples of each site were collected, conserved at 4 °C, transported to Toulouse, France for further analysis. Sixty grams of each soil type were divided into three 125 ml glass flasks, having ~ 20 g per flask (n=3 each soil type). Biological activity was re-established in stored soils by incubating 48 hours at 25°C and 20 ml of deoxygenated water was added, having a saturated water-filled pore space. Each container was sealed with a screw-up lid in which a septum had been fitted for gas sampling. Weight of the glass flasks before, and after adding the soil sample and water were taken. The gas chromatograph was used in parallel with the G200 N 2 O device for quantifying N 2 O emission from soil activity in the control records as emissions were low. For the potential denitrification concentrations recorded were within the recording range of the real-time device (G200) (>1 ppm), therefore, it was used as the only reading device for the rest of the experiments. To achieve potential denitrification rates, soil cores were incubated during three hours at 25°C, under anaerobic condition, replacing the headspace with helium (He), 20 ml of acetylene (C 2 H 2 ) was added to each flask at the beginning of incubation. To identify the nitrate soil saturation linked to potential denitrification, five different treatments with progression of nutrient solutions (CH 3 COOH and KNO 3 ) were applied.Concentrations are shown in TableA.1 and were applied from 1 to 5. The measurement principle of total denitrification (the sum of N 2 O + N 2 fluxes), in AIT treated soil samples, assumes that every N 2 molecule produced from completed denitrification which would normally be emitted from the soil system, remains in the form of N 2 O, which is detected in our laboratory set-up by gas chromatography. A N 2 O content of the headspace was determined every half an hour within the following three hours. Soil characteristics (NO 3 content, Org Carbon content, bulk density, porosity) and the mean values (n=3) for each soil sample were done, are reported in TableA.2.

Table 1 .

 1 Variables for Wetlands Soil Denitrification Model.

	Variable	Units	Description
		-	Porosity
		kg•dm -3	Dry sediment density
		°C	Temperature
		m 3 •m -3

Table 2 .

 2 Constants for Wetlands Soil Denitrification Model.

  Sampling sites distributed in the central Amazonian floodplain, Negro river (N1, N2, N3), Solimões river (S1, S2, S3), Madeira river (M1, M2, M3). The sampling points are distributed in two main non-permanent wetland types: floodplain (light green) and flooded forest (dark green). Wetlands classification from

	Figure 2.		mgN.kg -1	Organic nitrogen
	Constant	Value	Units	Description	Reference
		25	°C	Optimal temperature	Billen et al., 2018
		30	cm	Soil active layer depth	Yang et al., 2019
		0.18	mgN.kg -1	Nitrate limitation constant This study
	Freshwater Marsh Flooded forest	0.062 0.016	day	Mineralization rate	Yin et al., 2019 Bridgham et al., 1998
	Freshwater wetlands R N2O	0.02			Scheer et al., 2020
	Flooded soils R N2O IPPC generic R N2O	0.082 0.1	-	Emission ratio	Schlesinger, 2009 Guilhen et al., 2020

  detailed methodology applied in this study is presented in Appendix -Experimental Methodology. (b) dynamic denitrification validation, nitrates soil budget and denitrification events were modelled using soil moisture and temperature diurnal satellite input data from (2011-2019), extracted at the eight sampling points WSDM results of the eight sample sites annual denitrification rates are presented on the Appendix Table A.4. (c) N 2 O-N 2 ratio validation, R N2O = 0.02 proposed for freshwater wetlands ecosystems by Scheer et al., (2020) was applied to yearly denitrification WSDM values for each soil sample (Appendix TableA.5). N 2 O range emission was calculated for freshwater wetlands and compared with other studies in order to validate the WSDM total denitrification assessment (Appendix TableA.2).

Table 3 .

 3 Annual N 2 O-N emissions with different emission ratio for freshwater wetlands and flooded soils

		Denitrification Rate
	Land cover, (R N2O )	(kgN 2 O-N ha -1 yr -1 )	Reference
		Mean value (range)
	Amazonian Forest	1.9	(Melillo et al., 2001)
	Palm swamp peat	0.5 to 2.6	(van Lent et al., 2015)
	Pantanal soil (day)	1.1-2.7	(Liengaard et al., 2014)
	High input cropping	2.3
	Low input cropping Shifting cultivation fallow Multistrata agroforestry	1.2 0.8 0.5	Peruvian Amazon (Palm et al., 2002)
	Peach palm plantation	0.8
	Forest fallow control	0.8
	Freshwater marshes, (0.02)	0.95 (0.4-1.8)
	Flooded forests, (0.02)	3.8 (2.3-6.3)
	Freshwater marshes, (0.082) Flooded forests, (0.082)	3.8 (1.7-7.4) 15.5 (9.3-25.9)	This study
	Freshwater marshes, (0.1)	4.6 (2.0-9.0)
	Flooded forests, (0.1)	18.9 (11.4-31.6)
	Land cover	Range (kgN 2 O-

N ha -1 yr -1 ) Reference

	Amazonian Forest	1.4 -24	(Davidson et al., 2001)
	Amazonian Forest	0.38-16.2	(Meurer et al., 2016)
	Freshwater marshes range Flooded forests range	0.4 -9.0 2.3 -31.6	This study

  reported by in situ measurements that the Peruvian palm peat swamp is a hot spot and that the emission ratio change depending on the season. They reported that March (the main peak) has low N 2 O emissions, and very high N 2 potential. On the other hand, in September the emission rate has a higher N 2 O production ratio. The same pattern was observed in the modelled time series, regarding the activation of denitrification, however the WSDM in the current version does not calculate seasonal N 2 O:N 2 ratios, but this evidence could serve for developing a N 2 O:N 2 ratio function.Relevant research has been done on this topic, on a large scale the Global N 2 O Model

	Intercomparison Project (Tian et al., 2018) presented a spatialized global-scale N 2 O estimate
	by making a comparative study of a dozen of hydrodynamic models that model the global
	nitrogen and carbon cycles. The resulted model, the Dynamic Land Ecosystem Model
	(DLEM), assesses the pre-industrial emissions (CO 2 , CH 4 and N 2 O) and the current trend, on
	a yearly, monthly and daily basis from 1901 to 2010 with a spatial resolution of 0.5° × 0.5°.
	This model exposes the anthropogenic impact, main pollution sources and the consequences
	towards the biosphere. This model also takes into account different ecosystems and divides
	wetlands into four categories (seasonal grass wetlands, seasonal forest wetlands, permanent
	grass wetlands and permanent forest wetlands).
	Likewise, DNDC (DeNitrification DeComposition) model is a powerful tool to evaluate N 2 O
	production, consumption and transport in agricultural soils, it was later modified and adapted
	to other ecosystems. Wetland-DNDC (Gilhespy et al., 2014) was adapted to calculate carbon
	emissions (CO 2 and CH

4 ). These two models have been improved by the scientific community for several years, and they are currently a very robust approach to local GHG emissions, and water dynamics. Both models have an empirical base that operate at watershed scale and demand copious input datasets, that may not be available for several tropical wetlands.

Table A . 1

 A1 Concentration of nitrates and carbon solution applied for DEA identification.

Table A .

 A 3. Soil samples properties and parameters used for c

	Table
	A.2.
	Deni
	trific
	ation
	rates
	from
	the
	Ama
	zon
	centr
	al
	flood
	plain
	wetla
	nds
	soil
	samp
	les
	(labo
	rator
	y-
	contr
	olled
	cond
	ition
	s)
	and
	mod
	elled
	with
	WS
	DM

Acknowledgements

The research presented in this paper is funded by CONACYT (The Mexican National Council for Science and Technology) in the frame of a PhD research grant (Scholar/Scholarship reference: 625261/ 471711 2017-2021) at Université Toulouse III -Paul Sabatier, Toulouse, France. TOSCA-SOLE project from French Space agency CNES for financial support.

8 References

fundamental scientific python library.  matplotlib (https://matplotlib.org/) -2D plotting library.  scipy (https://scipy.org/) -scientific python library  basemap (https://matplotlib.org/basemap) Program size: 9.6 MB / 4930 lines Availability This software is open-source and freely available since 2022 on Framagit https://framagit.org/ahmad.albitar/WSDM. An online documentation to help the user going through WSDM modules is provided https://framagit.org/ahmad.albitar/WSDM/-/blob/master/README.rst ftp.ifremer.fr user : ext-catds-cecos-ifremer/ password : catds2010 or ftp://ext-catds-cecos-ifremer:catds2010@ftp.ifremer.fr/

Dynamic validation results


Chunying Wang

Xinliang Wang

Gengchen Zhang

Feifei Zhang

Junfeng Li

Shuai Chen

Sabine Sauvage

José-Miguel Sánchez-Pérez

Yuping Han

Junguo Liu

Prediction of spatial and intra-annual variability of shallow groundwater nitrate in agricultural areas

Keywords: Groundwater nitrate, Effect factor, Nitrogen transformation, Random forest, Nitrogen and oxygen isotopes

à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Identification of critical effect factors for prediction of spatial and intra-annual variability of shallow groundwater nitrate in agricultural areas

Chunying Wang, Xinliang Wang, Gengchen Zhang, Feifei Zhang, Junfeng Li, -N variations stems from the ability to determine the most critical explanatory variables among the many factors. This question is challenging when using common traditional methods, such as correlation analysis, attribution analysis, cluster analysis, principal component analysis, and numerical and distributed hydrological models.

Overall, groundwater NO 3 --N concentrations are controlled by diverse factors that are linked through the complex interaction of multiple processes. These processes involve inorganic and organic N transformation and transport in soil and groundwater [START_REF] Zhang | Human health risk assessment of groundwater arsenic contamination in Jinghui irrigation district, China[END_REF]. Surface soil N leaching to groundwater and the groundwater environment are both affected by characteristics of the vadose zone. The leaching of different forms of N content from the surface soil, as the source of N in groundwater, indirectly affects groundwater -N pollution and uncover the mechanism that underlies its high spatial and temporal variability is important for future management of agricultural N input and water-quality protection in all developing and developed countries.

Materials and Methods

Site description

This study was conducted in an irrigated agricultural area (35°00′-35°30′ N, 113°31′-114°25′ E) located in the piedmont region of Taihang mountain, NCP, near the lower reaches of the Yellow River (Fig. 1). The study site covers about 1,500 km 2 and belongs to the temperate continental monsoon climate. Weather data from Xinxiang station, located near our study site, were obtained from the China Meteorological Data Service Center (http://data.cma.cn/). From 2010 to 2019, the average annual temperature was 15.6°C, with a maximum temperature of 40°C and a minimum temperature of -13.1°C.The average annual potential evaporation was 1,025 mm yr -1 , and the average annual rainfall was about 500 mm yr -1 . The rainfall mainly occurred from June to September. According to the aquifer data collected by field surveys, the average annual groundwater table depth ranged from 1.9 to 18 m, and the groundwater level ranged from 54.1 to 82.4 m (Fig. 1a-b). The particle size of the soil was analyzed using a Malvern laser particle size analyzer as reported in our previous study [START_REF] Wang | Estimating soil ammonium adsorption using pedotransfer functions in an irrigation district of the North China Plain[END_REF]. According to the international soil texture classification standard, the soil in the study area was classified into silt (8.3%), silty loam (61.1%), and sandy loam (30.6%) (Fig. 1c). The main crops were winter wheat and summer maize (44%), winter wheat and summer peanut (27%), and winter wheat and summer rice (11%) at the study site (Fig. 1d). Other land uses, including rural residential areas, urban areas, and water body areas, constitute the remaining 18% of the study site (Fig. 1d). Irrigation and fertilization play important roles in ensuring stable crop production and the N applied on the agricultural land surface was mainly derived from chemical fertilizers, manure, and crop residues. The crop residues of winter wheat and summer corn/summer peanuts were all returned to the fields. The large amount and temporal variability in N input causes the groundwater N content to show high spatiotemporal variations and increase continuously, which poses a serious burden on groundwater N pollution and its management.

Field monitoring, sampling and chemical analysis

Fourteen representative sampling sites were selected according to the geographical location, land use, soil types, and the groundwater table depth to conduct field monitoring and collect surface soil and groundwater samples (Fig. 1a and Table 1). The field monitoring and sample collection were conducted monthly from May 2017 to April 2019 at the 14 observation sites (Fig. 1a). The field monitoring involved the use of a multiparameter water quality probe (HORIBA, Ltd., Japan) to determine the basic physicochemical parameters of groundwater, including water temperature (GW-Temp), pH (GW-pH), total dissolved solids concentration (GW-TDS), oxidation-reduction potential (GW-ORP), and dissolved oxygen (GW-DO). At the same time, the depth of the groundwater table

(GW-Dep) was measured and groundwater water samples were collected using a bailer tube.

The data on irrigation were collected through a survey of the local farmers. Groundwater and soil samples were collected at a frequency of once a month during the two-year study period. The groundwater samples were collected from wells and filtered using a 0.45 μm filter. These groundwater samples were brought back to the laboratory, and 1 L of each sample was stored in a refrigerator at a temperature of 2°C until physiochemical analysis. At the same time, 100 ml groundwater samples were immediately frozen until isotope analysis. Moreover, soil samples were collected from farmland within 500 m of the wells where the groundwater samples were collected. The soil samples were gathered at the soil surface with a depth of 0-10 cm in the farmland. All the soil samples were randomly collected from six sites and mixed thoroughly to obtain a representative soil sample (500 g) at each sampling site. Meanwhile, additional soil samples were collected in aluminum boxes that were sealed and brought back to the laboratory. The mixed soil samples were dried naturally, crushed through a 2 mm sieve, and stored in a cool and dry place until Since August 2018, the influence of N leaching on groundwater has been weaker in the following eight-month dry period than in the wet period. Therefore, in this study the isotope analysis was performed on groundwater samples to identify nitrification and denitrification in groundwater from August 2018 to April 2019. The stable isotope ( 15 N and 18 O) abundance of groundwater NO 3 --N was determined using the denitrifying bacteria method.

A seed solution (of glycerol 500 μL + bacteria 500 μL) was shaken for 12-15 hrs, purged with N 2 for 3 hrs, and then added to the sample and placed in a shaking table at 100 rpm overnight, shaking and as determined by an IRMS-100 mass spectrometer. USG32, USG34, and USG35 were used as standard samples, and the results were corrected based on a two-point calibration method. The measured isotope values correspond to the international standard substances, expressed as follows: [START_REF] Amit | Shape quantization and recognition with randomized trees[END_REF] where is the isotope value of the corresponding sample, is the ratio of heavy and light isotopic abundance of elements in the sample, and R VSMOW is the ratio of heavy and light isotopic abundance of Vienna Standard Mean Ocean Water (VSMOW).

Random forest model

A random forest model was used to predict the intra-annual variations of groundwater NO 3 --N at the study site. The random forest model is a machine learning algorithm based on the combination of the bagging integrated learning theory and the random subspace algorithm, and it overcomes the drawbacks of overfitting and instability [START_REF] Breiman | Random Forests[END_REF].

The model constructs several regression trees (ntree) by setting nodes (mtry) on a random subset of the original training dataset, according to [START_REF] Amit | Shape quantization and recognition with randomized trees[END_REF]. The random forest model divides the data into training and test sets by setting a certain ratio (P), and the P ratio of 2:1 was chosen in this study. The random forest model was then calibrated using the training subset and validated using the test subset. A package of randomForest in Rstudio (version 4.2.2) was adopted to construct the random forest model.

The model accuracy was made to meet the research needs by adjusting the three parameters ntree, mtry, and P. Three error metrics, the root mean square error (RMSE), the coefficient of determination (R 2 ), and the mean absolute error (MAE), were selected to evaluate the random forest model's performance. The trained and validated random forest model was used to analyze the importance of groundwater NO 3 --N influencing factors. The importance of an effect factor is defined as the increase in the predicted mean squared error (MSE) after randomly permuting this factor [START_REF] Breiman | Random Forests[END_REF]. It reflects the contribution of each effect factor to a groundwater nitrate concentration. The normalized increased MSE, that is, the relative importance, for each effect factor was between 0% and 100%. The partial dependence shows the marginal effect of each explanatory variable for the response after considering the average effects of the other variables. The partial dependence plots of the important effect factors were used to analyze the relationship between a single independent variable and the groundwater NO 3 --N subject to the influence of the other independent variables.

Data analysis

The spatial distribution of the measured data was interpolated using inverse distance

Results and Discussion

Statistical analysis of groundwater nitrate concentration and its effect factors

Table 2 lists the variables analyzed in this study, that is, the precipitation, irrigation,

soil

Spatiotemporal variations of inorganic N species in groundwater

The temporal and spatial variations of groundwater inorganic N concentrations in the study area are shown in Figs. 23. There is a large difference in the temporal variations of groundwater inorganic N concentrations (Fig. 2a-c). The groundwater NO 3 -N was the lowest among them. Overall, all three forms of groundwater inorganic N in the second year were generally higher than in the first year (Fig. 2d-f). This increase in annual NO 3 --N indicates that the shallow groundwater system might not be able to rapidly recover from N pollution.

The spatial variations of inorganic N concentrations in groundwater at the 14 sampling sites are shown in Fig. 3 a-f. The GW-NO 3 --N concentrations were high and followed the order of SG3, SG4, SG6, SG7, and SG2, while they were lower at the other sites.

Groundwater NH 4 + -N concentrations were the highest at SG3, followed by SG2, SG7.

Groundwater NO 2 --N concentrations were the highest at SG6, followed by SG4, SG5, SG7.

Overall, all the forms of groundwater inorganic N concentrations varied spatially in the study area. Among them, the spatial variations of GW-NO 3 --N concentrations were highly variable in the study area. Site SG1 showed the lowest GW-Dep and low GW-NO 3 --N concentrations (Table 1). This is likely because the soil NO 3 --N source was lost by denitrification under intermittent ponding irrigation during the summer rice season. Thus, the NO 3 --N leaching was reduced at SG1. Compared with sites SG8-SG14, the sites SG2-SG4 and SG6-SG7 had lower GW-Dep and showed higher GW-NO 3 --N concentrations (Table 1). This could be because NO

Prediction of spatiotemporal variations of groundwater nitrate

In this study, the 19 effect factors in Table 2 were set as predictor variables, and GW-NO 3 --N was set as a response variable when applying random forest model. Fig. 5 shows the comparison between the measured and random forest model predicted

Relationship between effect factors and groundwater nitrate

The relationships between the observed mean values of each explanatory variable, i.e., each effect factor (Table 2) and GW-NO

Different forms of N in groundwater

The GW-NO However, it is notable that a positive correlation was identified when the SWC was above 0.30. This could be because higher SWC increases NO 3 --N leaching into the groundwater. No significant negative correlation (r = -0.56, p>0.05) was found between irrigation and GW-NO 3 --N (Fig. 8n). Meanwhile, a significant positive correlation (r = 0.92, p<0.01) was identified between precipitation and GW-NO 3 --N (Fig. 8g). Irrigation and precipitation influenced both soil water content and soil water percolation. Irrigation mainly results in the increase of SWC but might not increase N leaching loss because the higher SWC could enhance the denitrification rate, which reduces NO 3 --N content in root zone soil [START_REF] Sexstone | Temporal response of soil denitrification rates to rainfall and irrigation[END_REF]. However, precipitation is more likely to increase soil-soluble N leaching into the groundwater due to the increase of antecedent SWC by irrigation [START_REF] Razzaghi | Effect of nitrogen and water availability of three soil types on yield, radiation use efficiency and evapotranspiration in field-grown quinoa[END_REF]). Thus, precipitation exhibited higher importance than irrigation and SWC, as shown in Fig. 7.

No significant correlation (p>0.05) was observed between SSC and GW-NO 3 --N (Fig. 8o). However, it should be noted that when SSC is around 20%, the GW-NO 3 --N was obviously higher; that is, the GW-NO 3 --N decreased with SSC when the SSC was less than 60%. In contrast, the GW-NO 3 --N showed a slight increase when the SSC was higher than 60%. The SSC can influence SWC and the subsequent N leaching in soil.

Less sand content might be associated with higher antecedent SWC and more preferential flow [START_REF] Van Es | Effect of manure application timing, crop, and soil type on phosphorus leaching[END_REF][START_REF] Razzaghi | Effect of nitrogen and water availability of three soil types on yield, radiation use efficiency and evapotranspiration in field-grown quinoa[END_REF]. Thus, the N leaching might be increase with the decrease of SSC. On the other hand, the nitrification rate increased when the SSC was higher than 60% in sandy loam soil. Thus, the soil NO 3 --N content and leaching correspondingly increased. A significant positive correlation (r = 0.79, p <0.01) was identified between SOC and GW-NO 3 --N (Fig. 8f).

This could be due to the higher SOC, which usually associated with higher SON in soil and corresponds to more N leaching. Nevertheless, when SOC was below 15 g kg -1 , it had a minor impact on GW-NO 3 --N.

Groundwater environmental conditions

The DO and ORP are indicators of redox status in the groundwater environment.

In this study, DO usually ranged from 4 to 20 mg L -1 . It is generally believed that the appropriate DO concentration for denitrification is 2 mg L -1 [START_REF] Peng | Effects of DO on N 2 O emission during biological nitrogen removal using aerobic granular sludge via shortcut simultaneous nitrification and denitrification[END_REF].

Denitrification still exists when the DO concentration of groundwater is 2~6 mg L -1 , but the rate is reduced with higher DO [START_REF] Peng | Effects of DO on N 2 O emission during biological nitrogen removal using aerobic granular sludge via shortcut simultaneous nitrification and denitrification[END_REF]. The average monthly DO concentration in the study area from May 2017 to April 2019 was 11.78 mg L -1 , and there was no record of below 2 mg L -1 values in any of the samples. Instead, the DO values in 14.6% of the 336 samples were between about 2 and 6 mg L -1 , mainly in April and August-October. Overall, a significant positive correlation (r = 0.96, p<0.01) was identified between GW-DO and GW-NO 3 --N (Fig. 8m), which could be due to nitrification. Meanwhile a negative correlation was identified when DO was less than 10 mg L -1 (Fig. 8m). This could be due to stronger denitrification than nitrification. An increase of GW-NO 3 --N with ORP is observed when ORP is above 200 mV. This could be due to the positive correlation between ORP and actual nitrification [START_REF] Bohrerova | The relationship between redox potential and nitrification under different sequences of crop rotations[END_REF]. No significant correlation (p>0.05) was found between GW-ORP and GW-NO 3 --N (Fig. 8k). When ORP was less than -100 mV, it is notable that there was a negative correlation, while a positive correlation was identified when ORP was great than 100 mV. Nevertheless, when the ORP was between -100 mV and 100 mV, it had a minor impact on GW-NO 3 --N. According to the relationships between GW-NO 3 --N and DO and between GW-NO 3 --N and ORP based on the partial dependence plots of the random forest model (Fig. 8), the environment of groundwater can be classified as being under nitrate-reducing conditions if the DO is less than 10 mg L -1 and the ORP is less than -100 mV. On the other hand, the groundwater environment can be described as being and under nonreducing conditions if the DO is greater than 10 mg L -1 and the ORP is greater than 100 mV. This is in accordance with previous studies (Rivett et The DOC is considered as the source of electron donors for denitrification [START_REF] Thayalakumaran | Geochemical conditions in groundwater systems: Implications for the attenuation of agricultural nitrate[END_REF]. Overall, there was no significant correlation (r = -0.05, p>0.05) observed between GW-DOC and GW-NO 3 --N (Fig. 8p). A negative correlation was found when DOC was below 75 mg L -1 , while a positive correlation was found when DOC was above 75 mg L -1 . A concentration value of DOC > 1.0 mg L -1 can indicate the presence of electron donors significant enough to support denitrification [START_REF] Rivett | Nitrate attenuation in groundwater: A review of biogeochemical controlling processes[END_REF][START_REF] Thayalakumaran | Geochemical conditions in groundwater systems: Implications for the attenuation of agricultural nitrate[END_REF]. The DOC concentrations of the 336 samples observed in this study ranged from 0.42 to 280.26 mg L -1 , with more than 99% of samples exceeding 1.0 mg L -1 . The negative relationship between GW-NO 3 --N and DOC indicates the role of DOC as a carbon source in the reduction processes [START_REF] Rivas | Denitrification potential in the subsurface environment in the Manawatu River catchment, New Zealand: Indications from oxidation-reduction conditions, hydrogeological factors, and implications for nutrient management[END_REF]. The other electron donors, such as Fe 2+ , Mn 2+ , and S 2-, might also exist in groundwater and influence N transformation, but their roles are not dependent on temperature, DO, and ORP (Pang and Wang, 2021). A significant positive correlation (r = 0.94, p<0.01) was found between GW-TDS and GW-NO 3 --N (Fig. 8j). The GW-TDS was usually higher than 0.5 g L -1 ; however, when it was lower than this value, there was a negative correlation between them. The other electron donors, rather than DOC, might have played critical roles when TDS was below 0.50 g L -1 , causing the negative correlation between GW-NO 3 --N and TDS. However, when it was higher than 0.5 g L -1 , a positive correlation was found between NO 3 --N and TDS. This could be because the growth of denitrification bacteria is reduced with the increase of salinity. A previous study reported that denitrification was more sensitive to salt compared to nitrification [START_REF] Dinçer | Salt inhibition of nitrification and denitrification in saline wastewater[END_REF]. Besides, it could also be because the groundwater TDS was increased by a large amount of NO 3 --N leaching.

The groundwater temperature controls microbial activity. Overall, a positive correlation (r = 0.62, p<0.01) was found between GW-Temp and GW-NO 3 --N (Fig. 8e), which could be due to higher N leaching to groundwater in the summer period.

When the GW-Temp was below 20℃, a negative correlation was found between GW-Temp and GW-NO 3 --N. This could be because the increased temperature enhances the growth and activity of denitrifiers, thus, reducing NO 3 --N in groundwater [START_REF] Singh | Microorganisms and climate change: terrestrial feedbacks and mitigation options[END_REF]. The groundwater temperature also impacts other environmental factors, such as DO and DOC availability [START_REF] Thayalakumaran | Geochemical conditions in groundwater systems: Implications for the attenuation of agricultural nitrate[END_REF][START_REF] Nikolenko | Isotopic composition of nitrogen species in groundwater under agricultural areas: A review[END_REF]. The DOC can be stimulated, and the DO can be depleted by increased groundwater temperature [START_REF] Guo | Long-term nitrogen & phosphorus additions reduce soil microbial respiration but increase its temperature sensitivity in a Tibetan alpine meadow[END_REF]. The suitable temperature for the nitrification rate is 10°C-35°C, with the optimum temperature ranging between 25°C-30°C [START_REF] Hayatsu | Autotrophic nitrification in acid tea soils[END_REF]. In contrast, the suitable temperature for denitrification is 20°C-43°C [START_REF] Strous | Key physiology of anaerobic ammonium oxidation[END_REF]. In general, higher temperatures result in stronger denitrification and nitrification activity in the groundwater environment with temperatures less than 25°C. The optimum temperature for denitrification is relatively higher than that for nitrification [START_REF] Nikolenko | Isotopic composition of nitrogen species in groundwater under agricultural areas: A review[END_REF].

Although the relative importance of groundwater environmental factors (i.e., temperature, DO, ORP, DOC, TDS, and pH) to groundwater NO 3 --N were less than 5.0% (Fig. 7), the partial dependence plots generated by the random forest model suggested that they play important roles in influencing nitrification and/or denitrification in groundwater (Fig. 8). The groundwater NO 3 --N, 15 N-NO 3 -and 18 O-NO 3 -exhibited tremendous changes in the summer periods in this study, probably due to simultaneous leaching and denitrification (Figs. 234). The nitrification rate also increases with an increase in temperature. In November, January, and February, when the temperature is low, the DO and ORP increase, and less DOC is consumed, indicating a more oxidizing condition. During these months, denitrifying bacteria growth might be limited by the low temperature [START_REF] Nikolenko | Isotopic composition of nitrogen species in groundwater under agricultural areas: A review[END_REF].

Thus, the denitrification is low, and nitrification might superimpose on denitrification during these low-temperature periods.

Identification of key N transformation processes in groundwater using isotopes

The simultaneous increase of δ 15 N-NO 3 -and δ 18 O-NO 3 -with a slope of 0. [START_REF] Vidon | Denitrification and patterns of electron donors and acceptors in eight riparian zones with contrasting hydrogeology[END_REF]. As for SG9, there was a river nearby where surface water could recharge the groundwater. The mean DO concentration was about 14.6 mg L -1 at SG9, while it ranged from 10.2 to 12.8 mg L -1 at the other sites. The relatively higher DO at SG9 than the other sites could support the reasoning that surface water existed recharge to the groundwater. The denitrification occurred underneath the riverbed, and the denitrified NO 3 --N continuously flowed to the groundwater at SG9. In addition, the DOC was lower at SG9 (41.9 mg L -1 ) than at the neighboring sites of SG11 (45.8 mg L -1 ) and SG 12 (45.2 mg L -1 ) because DOC was consumed by denitrification underneath the riverbed. Therefore, the isotopic composition and proportion of NO 3 --N sources at SG9 differed from that of the other sites. The sites SG6 and SG4 near L3 in Fig. 10 lay along the groundwater water-level contour line. The value of δ 15 N-NO 3 -at SG6 was higher than that at SG4. The groundwater table depth at SG6 was slightly higher than that of SG4. In addition, the concentration of DOC in the groundwater of SG6 was higher than that at SG4. Therefore, the stronger denitrification at SG6 could be attributed to the sufficient carbon sources in the groundwater to support denitrification. Furthermore, the deeper vadose zone thickness could provide enough time for denitrification before entering the groundwater.

Implications and Limitations

Groundwater nitrate pollution risk and attenuation in agricultural areas

The spatiotemporal variations of groundwater NO

Implications of groundwater nitrate prediction and limitations of this study

The performance of the random forest model demonstrates that it can be well Therefore, it is necessary to measure the N transformation rates using the 15 N tracing method, and analyze microbial communities based on in situ experiments and laboratory simulation of the groundwater environment to precisely understand the fate of N in groundwater in future studies.

Conclusions

Nitrate as the primary source of inorganic N pollutants in shallow groundwater, showed highly variable spatiotemporal patterns in an irrigated agricultural area, the NCP. Monthly soil physiochemical data, groundwater quality data, and groundwater

3 --

 3 Chen, Sabine Sauvage, José-Miguel Sánchez-Pérez, Yuping Han, Junguo Liu To cite this version: Chunying Wang, Xinliang Wang, Gengchen Zhang, Feifei Zhang, Junfeng Li, et al.. Identification of critical effect factors for prediction of spatial and intra-annual variability of shallow groundwater nitrate in agricultural areas. Science of the Total Environment, 2023, 891, pp.164342. 10.1016/j.scitotenv.2023.164342. hal-04278549 1. Introduction Groundwater is currently facing the severe challenges of depletion and deteriorating quality due to natural and anthropogenic factors around the world (Liu et al., 2016; Famiglietti and Ferguson, 2021). Among the many groundwater problems, nitrogen (N) pollution has become a global issue due to the use of N fertilizers and manures and elevated atmospheric deposition (Vystavna et al., 2017). Groundwater N pollution reduces N use efficiency and threatens the safety of the water supply in China (Gan et al., 2022; Gao et al., 2022). The nitrate nitrogen (NO 3 --N) contamination of groundwater is a severe problem threatening the environment and human health, especially in intensively irrigated agricultural areas. Accurately predicting the variations of shallow groundwater NO 3 --N concentrations at high spatiotemporal resolution is very difficult due to the complex effect factors and processes (Hinkle and Tesoriero, 2014; Biddau et al., 2019; He et al., 2022). Therefore, exploration of the spatiotemporal patterns and any associated effect factors and processes is urgently needed to prevent widespread water-quality issues around the world. Climate variables, soil texture, land use, and human activities are commonly investigated and recognized as the main factors affecting the inter-annual variations of groundwater NO N pollution on the large or macroscopic scale (Pennino et al., 2020; El Amri et al., 2022; He et al., 2022). Nonetheless, comprehensive identification of the effect factors and evaluation of their impact on dynamics of groundwater NO 3 --N has seldomly been done at a high spatial and monthly temporal resolution, especially in irrigated agricultural areas. Climate variables and agricultural management activities, such as precipitation, irrigation, and fertilizer input, cause the high level of variability in the different forms of N content in soil and subsequent leaching to groundwater. Vadose zone thickness (i.e., the depth of the groundwater table) contributes significantly to the variations of groundwater NO 3 --N concentrations by affecting the amounts of different forms of N leaching and groundwater environmental conditions (Li et al., 2021; Weitzman et al., 2022). Meanwhile, groundwater environmental parameters influence the different forms of N content by impacting transformation processes. Environmental factors related to N transformation in groundwater include temperature, dissolved oxygen (DO), oxidation-reduction potential (ORP), and dissolved organic carbon (DOC). The difficulty in understanding groundwater NO 3 -

NO 3 -- 3 -- 3 -- 3 --

 3333 N dynamics through N transformation in groundwater. The N transformation in groundwater is affected by the groundwater environment, and it increases the importance and difficulty of identifying the roles of N transformation in groundwater. In terms of the N in groundwater, the widely reported mineralization, nitrification, and denitrification processes in groundwater can modify different forms of N species balance (Liu et al., 2022; Shen et al., 2023). For instance, the mineralization and nitrification processes in groundwater can produce NO 3 --N, while denitrification can reduce NO 3 --N. These processes in groundwater play a key influence in the spatiotemporal variations of NO 3 --N dynamics in addition to N leaching in soil. Therefore, identifying the main N transformation processes in groundwater and evaluating their importance will further elucidate the mechanisms of groundwater NO 3 --N pollution in agricultural areas. The complex factors and processes influencing groundwater NO 3 --N concentrations lead to great difficulty in making predictions at high spatial and temporal resolution. For instance, physically based numerical and distributed hydrological models (e.g., HYDRUS, AgriFlux, and SWAT [Soil & Water Assessment Tool]) need to be coupled with groundwater flow models, such as MODFLOW-MT3D and the model of Lasserre et al. (1999) linked to a GIS (geographic information system), to predict groundwater NO 3 --N dynamics (Wang et al., 2016; El Amri et al., 2022). The performance of these models basically depends on an adequate understanding of hydrological behaviors and biogeochemical processes and the availability of detailed data on the properties of the vadose zone and groundwater system. These data are usually difficult to measure and collect, resulting in unsatisfactory model performance (Coppola et al., 2005). Machine learning methods, for example, artificial neural networks, multiple logistic regressions, generalized additive models, generalized linear models, support vector regressions, and random forest models, have been widely used to predict the N status in agricultural and natural waste waters (Chlingaryan et al., 2018; Bagherzadeh et al., 2021; He et al., 2022). They were gradually accepted by researchers due to their advantages, such as their high generalization ability and low cost, but most of them have the problem of overfitting (Castrillo and García, 2020). Among these machine learning methods, the random forest model has the advantages of strong resistance to overfitting, no feature selection, and automatic data filling. It has been proven to accurately predict inter-annual groundwater NO N concentrations on a large scale (Band et al., 2020; He et al., 2022). However, the random forest model has not yet been applied to predict intra-annual variations of shallow groundwater NO N in irrigated agricultural areas.The North China Plain (NCP) is the main crop production area in China, and groundwater resources in the NCP are generally dealing with the issue of N pollution. This study was conducted in a semiarid heavily irrigated agricultural area located in the NCP, that is characterized by a shallow groundwater table depth and, thus, vulnerable to groundwater pollution. Monthly meteorological data were obtained from the Xinxiang weather station, which is located near the study site. Two years' worth of monthly soil physiochemical data and groundwater quality parameters were measured in the field and laboratory. The objectives of this study were to: (1) reveal the spatial and intra-annual variations of inorganic N species and isotopic signature of NO 3 --N in groundwater,[START_REF] Awais | Assessing nitrate contamination risks in groundwater: A machine learning approach[END_REF] construct a random forest model to predict spatiotemporal variations of groundwater NO N using a simplified approach to the model, and (3) identify the main effect factors and processes, and evaluate their relationships with groundwater NO 3 --N based on the constructed random forest model and isotope approaches. The ability to prediction the levels of groundwater NO 3 -

Fig. 1 .

 1 Fig. 1. Location of the study area in the North China Plain and the sampling sites, and maps of (a) the depth of the groundwater table, (b) the groundwater level, (c) the soil type and (d) land use. The land use map was obtained from the Geospatial Data Cloud (http://www.gscloud.cn/search)

  physiochemical analysis. The soil samples stored in the aluminum boxes were further used to analyze the soil water content (SWC) through the oven-drying method. The parameters measured in the laboratory include groundwater dissolved organic carbon (GW-DOC), groundwater dissolved organic N (GW-DON), groundwater ammonium (GW-, soil organic carbon (SOC), soil-soluble organic N (S-SON), soil nitrate (S-NO 3 --N), soil nitrite (S-NO 2 --N), and soil ammonium (S-NH 4 + -N). All these chemical parameters of soil and groundwater were determined by the colorimetric method using a spectrophotometer (Thermo Fisher Scientific, Inc, USA) according to the procedures reported by Hood-Nowotny et al. (2010) and Wang et al. (2021).

  weighting in ArcGIS to analyze the spatial distribution characteristics of different N species in groundwater from the study site. The Spearman coefficient (r), paired t-tests, and a one-way analysis of variance (ANOVA) were used to calculate the correlation coefficient and test the significance using SPSS software (version 25.0, IBM Corporation, Chicago, Illinois). Denitrification was identified based on NO 3 --N isotopes and their relationships, that is, the enrichment of δ 15 N-NO 3 -and δ 18 O-NO 3 -with a slope of 0.5-1.0 (Δδ 18 O/δ 15 N), and a decrease of NO 3 --N concentrations. An increase of NO 3 --N concentrations and decrease of δ 15 N-NO 3 -and δ 18 O-NO 3 -were analyzed to identify nitrification.

3 -

 3 sand content (SSC), SWC, SOC, S-SON, S-NO GW-DON, GW-DOC, GW-Dep, GW-Temp, GW-TDS, GW-pH, GW-DO, and GW-ORP. The 19 predictor variables in Table2were considered as effect factors that could potentially influence groundwater GW-NO 3 --N. The maximum, minimum and mean values; standard deviation (SD); coefficient of variation (CV); and quartile of these variabiles are shown in Table2. Different forms of N content from surface soil were considered instead of N input due to the complex sources and transformation processes of N input. The statistical characteristics of N input were not analyzed. As shown in Table2, all the CV values, which can reflect the size of dispersion of the measured data, ranged from 0.08 to 2.06, and most of the factors had small CVs and low variability. However, the CV values of groundwater inorganic N were all greater than 1, indicating that spatial and temporal patterns of groundwater inorganic N concentrations were highly variable in the study area. The SD and CV of groundwater NO 3 was the highest among the three forms of inorganic N.

  organic N content (S-SON), soil nitrate content (S-NO 3 --N), soil nitrite content (S-NO 2 --N), soil ammonium content (S-NH 4 + -N), groundwater pH (GW-pH), groundwater temperature (GW-Temp), groundwater dissolved oxygen content (GW-DO), groundwater oxidation-reduction potential (GW-ORP), groundwater dissolved organic carbon concentration (GW-DOC), groundwater total dissolved solids (GW-TDS), groundwater dissolved organic N concentration (GW-DON), groundwater ammonium concentration (GW-NH 4 + -N), groundwater nitrite concentration (GW-NO 2 --N), groundwater nitrate concentration (GW-NO 3 --N), Limit of detection (LOD).

  ranged from 0.09 to 132.16 mg L -1 . The groundwater NO 3 --N concentrations were higher from May to August and usually decreased from September to April of the following year. In this study, the peaks of the groundwater NO 3 --N concentrations were caused by high level of NO 3 --N leaching after fertilization, irrigation, and heavy rain in the rainy season (May-August). This result is consistent with the previous findings of Biddau et al. (2019), who reported the high levels of NO 3 --N concentrations (up to 162 mg L -1 ) in shallow groundwater also occur when NO 3 --N leaching is high, particularly during fertilization and irrigation periods. The groundwater NH 4 + -N concentrations ranged from 0.04 to 22.8 mg L -1 . They showed a fluctuating increasing trend from May 2017 to May 2018, and the values were relatively high and stable from September 2018 to April 2019. The concentrations of groundwater NO 2 --N ranged from 0.01 to 0.52 mg L -1 , and they were higher from May to October than in other months for both 2017 and 2018. Comparing the three inorganic forms of N, the concentrations of groundwater NO 3 --N were the highest, while NO 2 -

3 --Fig. 3

 33 Fig. 2 Temporal variations of concentrations of (a) monthly nitrate, (b) monthly ammonium, (c) monthly nitrite, (d) annual nitrate, (e) annual ammonium, and (f) annual nitrite in groundwater. The different letters above the error bars in (d), (e), and (f) indicate significant difference (p<0.05) between the different years.

GW-NO 3 --Fig. 5 2

 352 Fig. 5 Comparison between the observed and random forest model predicted groundwater nitrate concentrations in groundwater for the (a) training and (b) test datasetsThe spatial and temporal distribution of the observed and random forest model

Fig. 6

 6 Fig. 6 Comparison between (a) observed and (b) the random forest model predicted spatiotemporal variation of nitrate in groundwater

Fig. 7

 7 Fig. 7 Relative importance of potential effect factors to groundwater nitrate concentrations

3 -- 2 -

 32 N at the 14 sites are shown in Fig. S1 in the supporting information. Significant correlations were found between GW-NO r=0.56, p<0.05), and between GW-ORP and GW-NO 3 --N (r=0.57, p<0.05). However, the interactions of all the effect factors resulted in nonsignificant correlations between other factors and GW-NO 3 --N, and led to difficulty in reflecting their impacts on groundwater NO 3 --N concentrations. Therefore, the partial dependence plots based on the random forest model were used to show the complex nonlinear relationships between GW-NO 3 --N and each effect factor, along with the frequency distribution (Fig. 8a-s).

2 - 2 --N showed the highest impacts on groundwater NO 3 -- 3 --N as an electron acceptor rather than NO 2 -- 3 -- 2 - 3 --N production by nitrification and enhanced denitrification removal of NO 3 --

 223323233 Fig. 8 Partial dependence plots of constructed random forest model. Blue lines show the partial dependence function, and red lines show the x-axis factor frequency

3 --

 3 al., 2008; Jahangir et al., 2017; Thayalakumaran et al., 2008). Groundwater DO and ORP are shown to be pH dependent. Thus, the nitrification and denitrification rates could vary with changes in pH (Bohrerova et al., 2004). The GW-pH showed a significantly negative correlation with GW-NO 3 --N (r = -0.28, p<0.05) (Fig. 8q). The groundwater NO 3 --N decreased with an increasing trend of pH from 6.0 to 7.0, which could be attributed to denitrification. Meanwhile groundwater NO 3 --N increased with pH from 7.0 to 8.0, which could be attributed to nitrification. The results of this study are consistent with previous studies, which reported that the highest denitrification removal of NO 3 --N was at a pH of 6.0-7.5 and the most adapted pH range for nitrifying bacteria is 7.0 to 9.0 (Ghafari et al., 2009; Hu et al., 2018; Bergamasco et al., 2019). A drastic increase of NO N is observed when pH was above 8.0. This could be attributed to soil water percolation to shallow groundwater following fertilization and irrigation during the rainy season (Jendia et al., 2020).

5 - 1 . 0 (δ 18 OFig. 9 .

 510189 Fig. 9. Relationship between δ 15 N-NO 3 -and δ 18 O-NO 3 -in groundwater (the * indicates the range of nitrified δ 18 O-NO 3 -within the dotted line, the 1:2 broken line indicates strong denitrification, and the 1:1 broken line indicates nitrification and denitrification) Nitrification will simultaneously increase NO 3 --N concentrations and reduce the abundance of δ 15 N-NO 3 -due to the isotope fractionation. The δ 15 N-NO 3 -and δ 18 O-NO 3 -vary along the 1:1 line and, with an R 2 closer to 1, indicates that the

Fig. 10 . 3 --N concentrations at the 14 sampling

 10314 Fig. 10. (a) Locations of sampling sites along the groundwater flow path lines (L1 and L2) or the groundwater-level contour line (L3) and the relationship between monthly average (from September 2018-April 2019) groundwater δ 15 N-NO 3 -and NO 3 --N concentrations at the 14 sampling sites along (b) L1, (c) L2, and (d) L3 (SG11*: data with negative δ 15 N-NO 3 -abundance in September 2019, January 2019, and April 2019 were excluded from SG11)

3 -- 3 --

 33 level can be transported to a lower groundwater water-level region. In addition, the groundwater is undergoing nitrification along the groundwater-level gradient. Thus, slow DON mineralization and nitrification might continuously increase NO 3 --N along the groundwater flow path, threatening groundwater quality for a long time. Therefore, the soil DON and NH 4 + -N leaching and the subsequent transformation in groundwater should not be overlooked when evaluating groundwater N pollution and making nonpoint source control policies. In our research, the groundwater flow could export the N out of the study area, thus, contributing to the temporal decrease of NO 3 --N in the local groundwater while also posing as a groundwater pollution threat to neighboring areas. The rapid decrease of NO 3 --N during May-August indicated that the leached NO N was consumed immediately by denitrification. The temporal increases of NO N were observed from September 2017 to January 2018 and from January 2019 to March 2019. In addition, the increase of NO 3 --N occurred along the groundwater flow during September 2018-April 2019. These results suggested that the NO 3 --N accumulation might occur under low groundwater temperature conditions. This is mainly due to the denitrification rate decreasing at lower temperatures in groundwater (Singh et al., 2010). Therefore, sufficient DOC must be provided for the efficient removal of groundwater NO 3 --N when the groundwater temperature is suitable or optimum for denitrification, particularly in late spring, summer, and early autumn. In the future, quantifying nitrification and denitrification rates at different time scales (e.g., daily, monthly, seasonally, or annually) is necessary to put forward N attenuation measures. Agronomists recommend the application of organic fertilizer and crop residue return with reduced chemical fertilizer in China and around the world to increase soil quality (Zhao et al., 2016). Management of organic fertilizers and crop residue is as crucial as chemical fertilizer application for reducing the risk of SON accumulation and subsequent groundwater and surface water pollution while providing enough DOC for denitrification. Furthermore, scientists and policy makers should pay attention to the leaching risk of stocked N and evaluate its transport delay time to protect groundwater quality in agricultural areas.

3 -

 3 applied for predicting spatial and intra-annual variations of groundwater nitrate concentrations. The importance analysis for the influencing factors likewise provides strong evidence for determining the main influencing factors and processes of NO 3 --N in groundwater. In the NCP irrigation area, the effect of N transformation in groundwater is as important as soil N leaching on the variations of NO 3 --N in shallow groundwater. The findings of this study can provide technical support for the rapid prediction and evaluation of N pollution in shallow groundwater through readily available effect factors at higher spatial and temporal resolution. The simplified approach of the random forest model can be applied in irrigation regions around the world to predict the spatial and temporal variations of NO adsorption/desorption processes dominate DON and NH 4 + -N transport in the soil-groundwater systems (Vandenbruwane et al., 2007; Wang et al., 2021). Therefore, other factors related to adsorption/desorption processes are required to predict spatiotemporal variations of DON and NH 4 + -N concentrations in groundwater using machine learning models (Wang et al., 2016; Wang et al., 2021). The N leaching and transformation in groundwater, especially denitrification and nitrification, must be considered in other numerical and/or distributed groundwater flow models to accurately predict NO 3 --N dynamics. The NO 3 --N in groundwater tends to be the most sensitive to NO 2 --N, indicating that biotic factors play critical roles in controlling the N transformation (Yang et al., 2012). These biochemical processes cause the difficulty and challenge of building and verifying groundwater flow and solute transport models. It is essential to study microbial communities and growth response to changes in N and C substrates and environmental factors to gain better insights into N transformation processes and rates (Nikolenko et al., 2018). It should be noted that except for the commonly considered nitrification and denitrification processes identified in this study, other N transformation processes in shallow groundwater are also possible. For instance, NO 2 --N accumulation in denitrification can provide the substrate for anammox (Smith et al., 2015), DNRA (Jahangir et al., 2017), and shortcut nitrification-denitrification (Hu et al., 2018).

NO 3 --

 3 N isotopic composition data were measured and analyzed from May 2017 to April 2019 in the study area. This research focused on predicting spatial and intra-annual variations of shallow groundwater NO 3 --N and evaluating their main effect factors and processes. The random forest model, as a simplified approach compared to numerical and distributed hydrological models, performed well in predicting the spatial and intra-annual variations of NO 3 --N in groundwater with R 2 value of 0.93 and 0.92, RMSE value of 4.94 and 3.87, and MAE values of 2.10 and 2.89 for the training and test datasets, respectively. The application of the random forest model may have important implications for groundwater NO 3 --N prediction in other regions worldwide. Evaluation of the importance of the effect factors emphasized the roles of N transformation, including denitrification and nitrification,

Table 1 .

 1 Soil texture, crops, depth of groundwater table, and groundwater level at sampling sites

	Sampling site	Soil texture	Crop rotation	Groundwater table depth (m)	Groundwater level (m)
	SG1	Silt	Winter wheat-summer rice	2	82.4
	SG2	Silty loam	Winter wheat-summer corn	3.9	65.5
	SG3	Silty loam	Winter wheat-summer corn	4.9	65.9
	SG4	Silty loam	Winter wheat-summer corn	6	69.3
	SG5	Silty loam	Winter wheat-summer corn	9.4	74.5
	SG6	Silty loam	Winter wheat-summer corn	9.9	64.5
	SG7	Silty loam	Winter wheat-summer corn	10.5	63.6
	SG8	Silty loam	Winter wheat-summer corn	10.7	71.5
	SG9	Sandy loam Winter wheat-summer peanuts	12.2	60.1
	SG10	Silty loam	Winter wheat-summer corn	12.3	60.8
	SG11	Sandy loam Winter wheat-summer peanuts	12.9	58.8
	SG12	Sandy loam Winter wheat-summer peanuts	13.3	55.2
	SG13	Silty loam	Winter wheat-summer corn	14.8	54.1
	SG14	Silty loam	Winter wheat-summer corn	18	60.1

Table 2

 2

							Quartiles
	Variables	Max	Min	Mean	SD	CV
						25th	50th	75th

Summary statistics of the variables analyzed in this study (May 2017-April 2019) Note: standard deviation (SD), coefficient of variation (CV), depth of groundwater table (GW-Dep), soil sand content (SSC), soil water content (SWC), soil organic carbon content (SOC), soil soluble

Acknowledgment: This research was funded by the National Natural Science Foundation of China (No. 51609084). The authors sincerely thank the anonymous reviewers for their insightful comments.

Different forms of N in surface soil and vadose zone characteristics

The spatiotemporal variations of GW-NO 3 --N depended on different forms of N content in the soil and leaching. In terms of N forms in the soil, there are positive correlations bwtween SON and GW-NO

Declaration of Competing Interest:

The authors declare no conflict of interest.

Data availability:

The data analyzed and presented in this study are openly available.