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

Abstract

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 km 2 . 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.

Multivariate sensitivity analysis for a large-scale climate impact and adaptation model

“…We note that it is practically infeasible to perform a fully spatiotemporal sensitivity analysis for the IAP2 model. We, therefore, follow the works of Kebede et al (2015) and Savall et al (2019), where we aggregated the output data into six spatial extents (see Figure 5 European regions to reduce computational expense. We subsampled 600 data points from each of the five scenarios (baseline, SSP1, SSP3, SSP4, SSP5) and three timeslices (2020s, 2050s, 2080s) giving 9,000 (5 × 3 × 600) samples per each output.…”

Global sensitivity analysis of the dynamics of a distributed hydrological model at the catchment scale

“…In environmental science modeling, the extensions were used in various areas. For instance, a sensitivity analysis on the spatialized maximum water depth of a coastal flooding risk model in Perrin et al (2021); both a temporal and a spatial sensitivity analysis of a model describing nitrogen transfers in Ferrer Savall et al (2019); and a comparison between various sensitivity indices on a spatio-temporal radionuclide atmospheric dispersion model in De Lozzo & Marrel (2017). A sensitivity analysis was also performed for spatially distributed PESHMELBA outputs in Rouzies et al (2023) by calculating site specific sensitivity indices, then aggregating them to blocks via the definition provided in Gamboa et al (2014), but without studying the sensitivity of the dynamics of PESHMELBA.…”

Cluster-based GSA: Global sensitivity analysis of models with temporal or spatial outputs using clustering

“…The geometrical analysis of the outputs variability along each basis functions along with the value of the associated sensitivity indices revealed in this case the impact of model inputs on up-down shift, left-right shift, symmetric kurtosis and tail-fattening components of the simulated curves. This approach was further extended by Lamboni et al [4,5] and Savall et al [6] applied it to a spatio-temporal model describing nitrogen transfers, transformations and losses at the landscape scale. A generic implementation using different bases such as eigenvectors, orthogonal polynomials, b-spline or principal component analysis can be found in Bidot et al [7].…”