Pesticide transfers and fate are highly influenced by the presence of discontinuities such as grass strips, slopes, hedgerows or roads that can accelerate or slow down and dissipate water and contaminant fluxes. That is why those landscape elements must be integrated into watershed management plans. It implies taking them into account when modeling water and contaminant fluxes at the small catchment scale. However, if the influence of landscape elements has already been widely explored at field scale, models generally do not reach the catchment scale. The project PESHMELBA aims at developing a new modeling tool of water and contaminants circulation and fate at the scale of small catchments in order to optimize landscape organization. The model explicitly takes into account spatial organization of landscape by representing existing elements, their locations and shapes. The final aim of this modeling tool is to efficiently test and rank different development scenarios in order to assess the influence of agricultural practices, land uses and landscape management strategies on water quality. In PESHMELBA, dominating processes ruling water and contaminants circulation and dissipation for each element type are mainly represented by existing and validated models. New components have also been developed when no suitable model was found in the literature. All these models present different levels of conceptualization and are used as modeling units ensuring a modular structure. Then, the different units are gathered and connected in the OpenPALM coupler (Fouilloux and Piacentini, 1999) in order to implement the spatial and temporal couplings. This innovative approach leads to a spatialized model of the whole catchment. Applications cases are tested with an increasing complexity, from a case with two plots to the hillslope scale with several plots, ditches and rivers. They show that PESHMELBA is a promising tool to compare scenarios considering water and pesticide fate in different complex landscapes.

Abstract. Pesticide transfers in agricultural catchments are responsible for diffuse but major risks to water quality. Spatialized pesticide transfer models are useful tools to assess the impact of the structure of the landscape on water quality. Before considering using these tools in operational contexts, quantifying their uncertainties is a preliminary necessary step. In this study, we explored how global sensitivity analysis can be applied to the recent PESHMELBA pesticide transfer model to quantify uncertainties on transfer simulations. We set up a virtual catchment based on a real one and we compared different approaches for sensitivity analysis that could handle the specificities of the model: high number of input parameters, limited size of sample due to computational cost and spatialized output. We compared Sobol' indices obtained from Polynomial Chaos Expansion, HSIC dependence measures and feature importance measures obtained from Random Forest surrogate model. Results showed the consistency of the different methods and they highlighted the relevance of Sobol' indices to capture interactions between parameters. Sensitivity indices were first computed for each landscape element (site sensitivity indices). Second, we proposed to aggregate them at the hillslope and the catchment scale in order to get a summary of the model sensitivity and a valuable insight into the model hydrodynamical behaviour. The methodology proposed in this paper may be extended to other modular and distributed hydrological models as there has been a growing interest in these methods in recent years.

<p align="justify"><span lang="en-US">In small agricultural catchments over Europe, intensive use of pesticides leads to widespread contamination of rivers and groundwater, largely due to hydraulic transfers of these reactive solutes from plots to rivers. These transfers must be better understood and described in the watershed in order to be able to propose best management practices adapted to the catchment and to reduce its contamination. The physically based model CATHY simulates interactions between surface and subsurface hydrology and reactive solute transport. However, the high sensitivity of pesticide transfers to spatially heterogeneous soil properties induces uncertainty that should be quantified and reduced. In situ data on pesticides in a catchment are usually rare and not continuous in time and space. Likewise, satellite imagery can provide spatial observations of hydrologic variables but not generally of pesticide fluxes and concentrations, and at limited scale and time frequency. The objective of this work is to combine these 3 types of information (model, in situ data, images) and their associated errors with data assimilation methods, in order to reduce pesticide and hydrological variable uncertainties. The sensitivity to spatial density and temporal frequency of the data will be evaluated, as well as the coupled data assimilation efficiency, i.e., the effect of assimilating hydrological data on pesticide-related variables. The methods will be developed using a Python package, and compared/evaluated on twin experiments using virtual data that are however generated over a real vineyard catchment, in Beaujolais, France, in order to ensure realism of the experiments, data, and associated errors.</span></p>

<p>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 subsurface to waterbodies, resulting from the interaction of various physical processes. They are also 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 a modular structure that couples different models.</p><p>In the long run, we aim at setting up and comparing different landscape organization scenarios for decision-making support. However, before considering such operationnal use of PESHMELBA, uncertainties must be quantified and reduced. Additionally, the model is physically-based, fully-spatialized which leads to a large set of parameters that must be carefully estimated. To tackle both objectives, we set up a data assimilation framework based on satellite images and <em>in situ</em> data and we evaluate the potential of Ensemble Smoother for joint variable-parameter assimilation. Assimilating surface moisture images allows for direct correction of variables and parameters on the top part of the soil. However, due to the PESHMELBA structure based on a dynamic parallel code coupler (OpenPALM), the impact of such correction on other compartments and other physical processes has to be finely assessed.</p><p>In this preliminary study, a fairly simple virtual hillslope inspired from a realistic catchment is set up and data assimilation is performed on twin experiments, <em>i.e.</em>, using virtual surface moisture images. The potential of such technique for improving the global performances of the model is scrutinized and the sensitivity to the assimilation framework (ensemble size, frequency of observations, errors, etc.) is assessed. Valuable information on the coupling functioning are obtained allowing for anticipating performances in a real case. Identified limitations of surface moisture assimilation also give precious indications about existing gaps and pave the way for multi-source data assimilation.</p><p> </p>