Inverting Regional Sensitivity Analysis to reveal sensitive model behaviors

We address the question of sensitivity analysis for model outputs of any dimension using Regional Sensitivity Analysis (RSA). Classical RSA computes sensitivity indices related to the impact of model inputs variations on the occurrence of a target region of the model output space. In this work, we invert this perspective by proposing to find, for a given target model input, the region whose occurrence is best explained by the variations of this input. When it exists, this region can be seen as a model behavior which is particularly sensitive to the variations of the model input under study. We name this method iRSA (for inverse RSA). iRSA is formalized as an optimization problem using region-based sensitivity indices and solved using dedicated numerical algorithms. Using analytical and numerical examples, including an environmental model producing time series, we show that iRSA can provide a new graphical and interpretable characterization of sensitivity for model outputs of various dimensions

Estimation of Soil Properties Using Observations and the Crop Model STICS. Interest of Global Sensitivity Analysis and Impact on the Prediction of Agro-Environmental Variables

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Examining wheat yield sensitivity to temperature and precipitation changes for a large ensemble of crop models using impact response surfaces

Impact response surfaces (IRSs) depict the response of an impact variable to changes in two explanatory variables as a plotted surface. Here, IRSs of spring and winter wheat yields were constructed from a 25-member ensemble of process-based crop simulation models. Twenty-one models were calibrated by different groups using a common set of calibration data, with calibrations applied independently to the same models in three cases. The sensitivity of modelled yield to changes in temperature and precipitation was tested by systematically modifying values of 1981-2010 baseline weather data to span the range of 19 changes projected for the late 21st century at three locations in Europe

Probabilistic assessment of adaptation options from an ensemble of crop models: a case study in the Mediterranean

Effective adaptation of agricultural systems to climate change has to: Consider local specificities; provide sound and practical information and deal with the uncertainty We present a methodology for assessing different aspects of adaptation. Our study case is adaptation of winter wheat in the Mediterranean

Applying adaptation response surfaces for managing wheat under perturbed climate and elevated CO2 in a Mediterranean environment

This study developed Adaptation Response Surfaces and applied them to a study case in North East Spain on winter crops adaptation, using rainfed winter wheat as reference crop.  Crop responses to perturbed temperature, precipitation and CO2 were simulated by an ensemble of crop models. A set of combined changes on cultivars (on vernalisation requirements and phenology) and management (on sowing date and irrigation) were considered as adaptation options and simulated by the crop model ensemble. The discussion focused on two main issues: 1) the recommended adaptation options for different soil types and perturbation levels, and 2) the need of applying our current knowledge (AOCK) when building a crop model ensemble. The study has been published Agricultural Systems (Available online 25 January 2017, https://doi.org/10.1016/j.agsy.2017.01.009), and the  text below consists on extracts from that paper

Wheat yield sensitivity to climate change across a European transect for a large ensemble of crop models

The sensitivity of a 26 member ensemble of process based wheat simulation models to perturbations in baseline temperature and precipitation was examined to construct impact response surfaces (IRS) of simulated yields

The chaos in calibrating crop models

Calibration, the estimation of model parameters based on fitting the model to experimental data, is among the first steps in many applications of system models and has an important impact on simulated values. Here we propose and illustrate a novel method of developing guidelines for calibration of system models. Our example is calibration of the phenology component of crop models. The approach is based on a multi-model study, where all teams are provided with the same data and asked to return simulations for the same conditions. All teams are asked to document in detail their calibration approach, including choices with respect to criteria for best parameters, choice of parameters to estimate and software. Based on an analysis of the advantages and disadvantages of the various choices, we propose calibration recommendations that cover a comprehensive list of decisions and that are based on actual practices.HighlightsWe propose a new approach to deriving calibration recommendations for system modelsApproach is based on analyzing calibration in multi-model simulation exercisesResulting recommendations are holistic and anchored in actual practiceWe apply the approach to calibration of crop models used to simulate phenologyRecommendations concern: objective function, parameters to estimate, software usedCompeting Interest StatementThe authors have declared no competing interest

Combining switching factors and filtering operators in GSA to analyze models with climatic inputs

This work is devoted to the analysis of models having functional inputs and is motivated by the intensive use of climatic variables in crop models. The main output of these models is the crop yield, which is estimated, among others, from daily-sampled climatic variables (Temperature, Rain, Radiation, Evapotranspiration). We want to test to what extent this fine temporal resolution is mandatory to generate accurate predictions and quantify how much a priori simplifications, such as lowering the temporal resolution, would affect the model results. This may lead to a better understanding of model behavior as well as to a simplification of the model and/or of the acquisition of its input variables.To this aim, we introduce the use of filtering operators into Global Sensitivity Analysis using switching factors [1]. Low pass filters are used to reduce the temporal resolution of climatic variables. GSA is required because we want to explore the impact of this input simplification in a global exploration of model inputs. Switching factors have been proposed [1], [2] in the context of spatially distributed inputs and further analyzed in [3]. They were initially introduced to assess the sensitivity to the presence of stochastic errors in spatial functional inputs. We use them here to test the sensitivity of a model to simplifications of the temporal structure of its climatic inputs