Understanding nitrogen transfer dynamics in a small agricultural catchment: Comparison of a distributed (TNT2) and a semi distributed (SWAT) modeling approaches

Ferrant, Sylvain; Oehler, Francois; Durand, Patrick; Ruiz, Laurent; Salmon-Monviola, Jordy; Justes, Éric; Dugast, Philippe; Probst, Anne; Probst, Jean‐Luc; Sanchez-Perez, José-Miguel

doi:10.1016/j.jhydrol.2011.05.026

Cited by 85 publications

(97 citation statements)

References 70 publications

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“…The Garonne watershed offers diversified topography and land cover, good data availability, good prior knowledge of the hydrological system [24] and some successful SWAT setups built around available ground-based climate data [25][26][27][28]. …”

Section: Study Sitementioning

confidence: 99%

Testing the SWAT Model with Gridded Weather Data of Different Spatial Resolutions

Grusson

Anctil

Sauvage

et al. 2017

Water

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This study explored the influence of the spatial resolution of a gridded weather dataset when inputted in the soil and water assessment tool (SWAT) over the Garonne River watershed. Several datasets are compared: ground-based weather stations, the 8-km SAFRAN product (Système d'Analyse Fournissant des Renseignements Adaptés à la Nivologie), the 0.5 • CFSR product (Climate Forecasting System Reanalysis) and several derived SAFRAN grids upscaled to 16, 32, 64 and 128 km. The SWAT model, calibrated on weather stations, was successively run with each gridded weather dataset. Performances with SAFRAN up to 64 or 128 km were poor, due to a contraction of the spatial variance of daily precipitation. Performances with 8-km SAFRAN are similar to that of the aggregated 16-and 32-km SAFRAN grids. The~30-km CFSR product was found to perform well at some sites, while in others, its performance was considerably inferior because of grid points where precipitation was overestimated. The same problem was found in the calibration, where data at some weather stations did not appear to be representative of the subwatershed in which they are used to compute hydrology. These results suggest that the difference in the representation of the climate was more influential than its spatial resolution, an analysis that was confirmed by similar performances obtained with the SWAT model calibrated on the 16-and 32-km SAFRAN grids. However, the better performances obtained from these two weather datasets than from the ground-based stations' dataset confirmed the advantage of using the SAFRAN product in SWAT modelling.

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Section: Study Sitementioning

confidence: 99%

Testing the SWAT Model with Gridded Weather Data of Different Spatial Resolutions

Grusson

Anctil

Sauvage

et al. 2017

Water

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“…Figure 2a represents the spatial SWHC defined in the model by both soil depth and retention porosity for two soil layers that can be explored by roots. It was set up for each soil type described by soil pits (Table 1, and in [26]). …”

Section: Spatial Water and Nitrogen Transfer Simulationmentioning

confidence: 99%

“…It was already evaluated in term of in-stream discharge and nitrogen fluxes at the outlet for the period 1985-2001 [26]. A lumped calibration of the hydrological parameters T 0 , m and drainage porosity was made for all the soil types on stream water discharge.…”

Section: Model Application In the Study Sitementioning

confidence: 99%

“…Most of these models are spatially distributed and require spatially explicit information: soil properties, land cover, crop type, sowing and harvesting dates, and fertilizer input. They simulate infiltration, runoff and evapotranspiration from crop [23][24][25] and nutrients transfer and transformation within the catchment [19,21,26]. Generally, the calibration and validation is performed using river discharge at the outlet of the watershed, groundwater and surface water storage fluctuations, and in-stream or groundwater nitrate concentrations.…”

Section: Introductionmentioning

confidence: 99%

“…Generally, the calibration and validation is performed using river discharge at the outlet of the watershed, groundwater and surface water storage fluctuations, and in-stream or groundwater nitrate concentrations. Crop productivity simulated by the crop model can be validated using basin averaged yields [26].…”

Section: Introductionmentioning

confidence: 99%

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Extracting Soil Water Holding Capacity Parameters of a Distributed Agro-Hydrological Model from High Resolution Optical Satellite Observations Series

et al. 2016

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Sentinel-2 (S2) earth observation satellite mission, launched in 2015, is foreseen to promote within-field decisions in Precision Agriculture (PA) for both: (1) optimizing crop production; and (2) regulating environmental impacts. In this second scope, a set of Leaf Area Index (LAI) derived from S2 type time-series (2006-2010, using Formosat-2 satellite) is used to spatially constrain the within-field crop growth and the related nitrogen contamination of surface water simulated at a small experimental catchment scale with the distributed agro-hydrological model Topography Nitrogen Transfer and Transformation (TNT2). The Soil Water Holding Capacity (SWHC), represented by two parameters, soil depth and retention porosity, is used to fit the yearly maximum of LAI (LAX) at each pixel of the satellite image. Possible combinations of soil parameters, defining 154 realistic SWHC found on the study site are used to force spatially homogeneous SWHC. LAX simulated at the pixel level for the 154 SWHC, for each of the five years of the study period, are recorded and hereafter referred to as synthetic LAX. Optimal SWHC year_I,pixel_j , corresponding to minimal difference between observed and synthetic LAX year_I,pixel_j , is selected for each pixel, independent of the value at neighboring pixels. Each re-estimated soil maps are used to re-simulate LAX year_I . Results show that simulated and synthetic LAX year_I,allpixels obtained from SWHC year_I,allpixels are close and accurately fit the observed LAX year_I,allpixels (RMSE = 0.05 m 2 /m 2 to 0. SWHC can be derived from remote sensing series for one year. Unique SWHC solutions for each pixel that limit the LAX error for the five years to less than 0.2 m 2 /m 2 are found for only 10% of the pixels. Selection of unique soil parameters using multi-year LAX and neighborhood solution is expected to deliver more robust soil parameters solutions and need to be assessed further. The use of optical remote sensing series is then a promising calibration step to represent crop growth within crop field at catchment level. Nevertheless, this study discusses the model and data improvements that are needed to get realistic spatial representation of agro-hydrological processes simulated within catchments.

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Nitrogen mitigation scenarios to reduce coastal eutrophication

Malik

Oehler²,

Sgro

et al. 2022

Agrosystems Geosci & Env

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Macroalgal blooms in coastal areas, which pose unique risks to the environment, citizens, stakeholders, and the economy, mainly are due to nitrate pollution in rivers. The Saint‐Brieuc Bay has some of the largest algal blooms in Brittany (western France). It is fed by three catchments—Gouessant (420 km2), Gouet (256 km2), and Anse d'Yffiniac (130 km2)—characterized by an oceanic climate and mixed‐farming systems with high livestock density. The main objective of this study was to assess the ability of nitrogen (N) mitigation scenarios to reduce N emissions to coastal water using the spatially distributed agrohydrological model TNT2. Once TNT2 was calibrated and validated for the catchments, scenarios were simulated for them from 2008 through 2035: (a) reference (REF) scenario, (b) agricultural management practices (AMP) scenario (i.e., changes to mineral and organic N fertilization, catch crops, and crop rotations), (c) increasing percentages of agricultural land conversion (ALC) into unmanaged grassland from downhill to uphill, and (d) scenarios that combined AMP and ALC. Results showed that the AMP scenario could reduce N load in the bay by 31% vs. the REF scenario. The ALC scenario was much more effective when located downhill, with >50% of maximum effectiveness achieved with 10% of agricultural land converted. These results varied strongly among catchments. Because reducing coastal eutrophication requires a drastic decrease in N loads, we recommend combining AMP and ALC scenarios to achieve good environmental status. The study illustrates the TNT2 model's ability to simulate complex scenarios and guide mitigation policy.

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Understanding nitrogen transfer dynamics in a small agricultural catchment: Comparison of a distributed (TNT2) and a semi distributed (SWAT) modeling approaches

Cited by 85 publications

References 70 publications

Testing the SWAT Model with Gridded Weather Data of Different Spatial Resolutions

Testing the SWAT Model with Gridded Weather Data of Different Spatial Resolutions

Extracting Soil Water Holding Capacity Parameters of a Distributed Agro-Hydrological Model from High Resolution Optical Satellite Observations Series

Nitrogen mitigation scenarios to reduce coastal eutrophication

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