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

Modelling complex systems such as agroecosystems often requires the quantification of a large number of input factors. Sensitivity analyses are useful to determine the appropriate spatial and temporal resolution of models and to reduce the number of factors to be measured or estimated accurately. Co...

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Bibliographic Details
Published in:Environmental modelling & software : with environment data news 2019-01, Vol.111, p.356-367
Main Authors: Ferrer Savall, Jordi, Franqueville, Damien, Barbillon, Pierre, Benhamou, Cyril, Durand, Patrick, Taupin, Marie-Luce, Monod, Hervé, Drouet, Jean-Louis
Format: Article
Language:English
Subjects:
Agricultural ecosystems
Agricultural sciences
Agronomy
Cluster analysis
Complex systems
Computer simulation
Ecology, environment
Ecosystems
Farm management
Intensive farming
Landscape
Landscape ecology
Landscape scale
Life Sciences
Mathematics
Nitrogen
Nitrogen cascade
Sensitivity analysis
Spatial analysis
Spatial distribution
Spatial model
Statistics
Temporal resolution
Transformations
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Summary:Modelling complex systems such as agroecosystems often requires the quantification of a large number of input factors. Sensitivity analyses are useful to determine the appropriate spatial and temporal resolution of models and to reduce the number of factors to be measured or estimated accurately. Comprehensive spatial and temporal 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 theoretical landscape that represented five years of intensive farm management and covering an area of 3 km2. Cluster analyses were applied to summarize the results of the sensitivity analysis on the ensemble of model outputs. The methodology we applied is useful to synthesize sensitivity analyses of models with multiple space-time input and output variables and could be ported to other models than NitroScape. •Novel analytical methods for dynamic and spatially distributed models were proposed.•Spatial resolution at which the model is run was found to impact most of outputs.•Visualization methods summarized spatially distributed output and temporal outputs.•Cluster analysis grouped together outputs that are sensitive to same sets of inputs.
ISSN:1364-8152
1873-6726
DOI:10.1016/j.envsoft.2018.09.010