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Sensitivity analysis of spatio-temporal models describing nitrogen transfers, transformations and losses at the landscape scale

  • Savall, Jordi Ferrer
  • Franqueville, Damien
  • Barbillon, Pierre
  • Benhamou, Cyril
  • Durand, Patrick
  • Taupin, Marie-Luce
  • Monod, Hervé
  • Drouet, Jean-Louis
Publication Date
Jan 01, 2019
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Modelling complex systems such as agroecosystems often requires the quantification of a large number of input factors. Sensitivity analyses are useful to fix the appropriate spatial and temporal resolution of models and to reduce the number of factors to be measured or estimated accurately. Comprehensive spatial and dynamic sensitivity analyses were applied to the NitroScape model, a deterministic spatially distributed model describing nitrogen transfers and transformations in rural landscapes. Simulations were led on a virtual landscape that represented five years of intensive farm management and covering an area of 3 $km^2$. Cluster analyses were applied to summarize the results of the sensitivity analysis on the ensemble of model outcomes. The 29 studied output variables were split into five different clusters that grouped outcomes with similar response to input factors. Among the 11 studied factors, model outcomes were mainly sensitive to the inputs characterizing the type of fertilization and the hydrological features of soils. The total amount of nitrogen in the catchment discharge was the type of nitrogen used in fertilization, while nitrogen concentration in catchment discharge was mainly driven by soil porosity and lateral water transmissivity. The vertical resolution of the model had a significant impact on the ammonium surface content and on the nitrate groundwater concentration, while the model horizontal resolution had a significant impact on the dynamic and spatial distributions of model outcomes, but it did not significantly affect nitrogen variables when they were spatially- and temporally-aggregated. The methodology we applied should prove useful to synthesise sensitivity analyses of models with multiple space-time input and output variables.

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