Sensitivity and uncertainty analysis to evaluate a new spatialized process-oriented model of water and pesticide transfers at the catchment scale

Intensive use of pesticides in agricultural catchments leads to a widespread contamination of rivers and groundwater. Pesticides applied on fields are transferred at surface and in subsurface to waterbodies. Such transfers are highly influenced by landscape elements that can accelerate or slow down and dissipate water and contaminant flows. The PESHMELBA model has been developed to simulate pesticide fate on small agricultural catchments and to represent the landscape elements in an explicit way. It is characterized by a process-oriented approach and an original spatial discretization that make it particularly suitable to simulate complex agricultural catchments. In the long run, we aim at setting up and comparing different landscape organization scenarios for decision-making support. However, before considering such operational use of PESHMELBA, it must be strongly evaluated and uncertainties must be quantified and reduced. In that respect, we performed uncertainty and sensitivity analysis of the model. As such evaluation had never been performed earlier on PESHMELBA, we first set a small virtual hillslope composed of plots, vegetative filter strips and river reaches. Even basic, this configuration led to a large set of parameters as the model is fully distributed and physically-based, implying, for example, horizontal and vertical heterogeneities of soil characteristics. Due to the large number of parameters, we firstly performed Morris-type screening to discard non-influential parameters with a limited number of model evaluations. Then, we sequentially performed a computer-intensive Sobol procedure on a reduced number of input factors. Preliminary results improved the understanding we have about the model functioning. Some guidelines about critical representation of processes or potential simplifications also arose. Above all, this study was the preliminary step of a data assimilation project. It allowed us to choose variables to be potentially assimilated and a suitable data assimilation method.