How is a global sensitivity analysis of a catchment-scale, distributed pesticide transfer model performed? Application to the PESHMELBA model

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 could 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: a high number of input parameters and a limited size of sample due to computational cost and spatialized output. After a preliminary screening step, we calculated Sobol' indices obtained from polynomial chaos expansion, Hilbert–Schmidt independence criterion (HSIC) dependence measures and feature importance measures obtained from random forest surrogate model. Results from the different methods were compared regarding both the information they provide and their computational cost. 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 hydrodynamic behaviour. Conclusions about the advantages and disadvantages of each method may help modellers to conduct global sensitivity analysis on other such modular and distributed hydrological models as there has been a growing interest in these approaches in recent years.</p

Variational methods

International audienceThis contribution presents derivative-based methods for local sensitivity analysis, called Variational Sensitivity Analysis (VSA). If one defines an output called the response function, its sensitivity to inputs variations around a nominal value can be studied using derivative (gradient) information. The main issue of VSA is then to provide an efficient way of computing gradients. This contribution first presents the theoretical grounds of VSA: framework and problem statement, tangent and adjoint methods. Then it covers pratical means to compute derivatives, from naive to more sophisticated approaches, discussing their various 2 merits. Finally, applications of VSA are reviewed and some examples are presented, covering various applications fields: oceanography, glaciology, meteorology

Développement e méthodes de métamodelisation incluant des variables qualitatives pour évaluer un outil d'aide à la décision de dimensionnement de bande enherbée

[Departement_IRSTEA]Eaux [TR1_IRSTEA]ARCEAU [ADD1_IRSTEA]Hydrosystèmes et risques naturelsInternational audienceIn France and more generally over Europe, significant amounts of pollutants are measured in surface water, partly due to the use of pesticides by agriculture. In the European Water Framework Directive, Europe is advocating the development of best management practices to reduce pesticide transfers to the river network once they are applied in the watershed. This includes implementing vegetative filter strips (VFS), that ensure the interception and the mitigation of contaminant transfers arising from fields. VFS are now mandatory along rivers in many countries, due to their recognized effectiveness to limit pesticide and sediment transfer by surface runoff (Asmussen et al., 1977; Dosskey, 2001). However, the general effectiveness of these buffers to reduce runoff transport of pesticides highly depends on pedologic characteristics, climatic conditions, and cultural practices. It is thus necessary to use mechanistic models that represent processes occurring on a vegetative filter strip, such as VFSMOD (Vegetative Filer Strip Modeling, Muñoz-Carpena et al., 1999). These models are relevant tools to design properly the buffers accounting for local conditions, although they are rarely used in France, since they are considered too complex for operational use (Carluer et al., 2017). In France, in order to help decision-makers use physically-based modelling, Irstea developed the modeling toolkit BUVARD (BUffer strip for runoff Attenuation and pesticides Retention Design tool). It consists of several steps including analyzing the watershed and its characteristics (soil, climate, cultural practices), and running dynamical models, in particular the mechanistic model VFSMOD adapted to French conditions (Muñoz-Carpena et al., 2018, Lauvernet and Muñoz-Carpena, 2018). At the end this toolkit delivers the optimal VFS width considering the needed filter efficiency (for example, 70% of runoff reduction). However, this very complete method assumes that the user provides detailed field knowledge and data (type of soil of the contributive area and of the VFS, rainfall rate, water table depth, slope, etc.), which are not easily available in many practical applications. Moreover, the variety of tools, which rely on several interfaces or several programming languages, makes it relatively difficult to take over the design procedure. We get to situations where the tool is used, without any uncertainty quantification nor sensitivity analysis, although they should be performed together with the tool's simulations (Saltelli et al., 2008). The next step in seeking to increase the operational scope of the modeling toolkit was to use metamodeling techniques. By reducing the computational cost of the modeling toolkit, the metamodel of BUVARD will make it possible to apply the tool on new watersheds with far fewer input parameters (6 against 70), and to determine the output uncertainty and sensitivity to input parameters in other climatic and agronomic conditions at low cost. Metamodeling is still rarely used in the water quality domain, since processes related to pesticide transfer are highly nonlinear. The VFS sizing tool BUVARD and the physical processes it represents (water and pesticide transfer at surface/subsurface) includes high non-linearities, due to the dependence on qualitative inputs (or categorical variables). Indeed, two major inputs, the typeof soil of the VFS and the type of rainfall event, have been defined in BUVARD for operational purposes, as substitute to functional inputs (rainfall hyetograph) and to correlated inputs that are the hydrodynamics properties of the soil (saturated hydraulic conductivity, porosity, and van Genuchten parameters). Qualitative inputs generate discontinuities in the model's response that many methods are unable to deal with, removing the smoothness of the model's output that is generally a necessary condition to build a metamodel (Zang and Notz, 2015). In this study, we adapted kriging to mixed variables (qualitative and quantitative), by testing several covariance kernels for a mixture of qualitative and quantitative inputs. Their performances are compared to a linear model and to a generalized additive model (GAM) that have been often used in water quality metamodeling. The methods are validated with the physically-based simulations conducted on a full factorial test design. It will be shown that the adapted kriging is very efficient and weakly dependent on the sampling size of the experimental design. These metamodels will then be used to perform uncertainty quantification and global sensitivity analysis of the VFS efficiency in a French watershed in the Beaujolais vineyard region. Polynomial Chaos Expansion do not allow to account for qualitative variables to our knowledge, but they have the advantage of being very efficient on complex models, and to compute Sobol indices directly from polynomial chaos expansions (Le Gratiet et al., 2017). We will compare the analysis from the PCE on each group of modality to the one performed with the kriging with an adapted covariance kernel. The final aim of this study is to give the users a complete tool accounting for uncertainty and sensitivity of the model outputs to design their vegetative filter strips. Asmussen, L.E., White, A.W., Hauser, E.W., Sheridan, J.M., 1977. Reduction of 2,4-D Load in Surface Runoff Down a Grassed Waterway1. Journal of Environment Quality 6, 159. Carluer, N.; Lauvernet, C.; Noll, D., Muñoz-Carpena, R. Defining context-specific scenarios to design vegetated buffer zones that limit pesticide transfer via surface runoff Science of The Total Environment , 2017, 575, 701 - 712 Dosskey, M.G., Helmers, M.J., Eisenhauer, D.E., 2011. A design aid for sizing filter strips using buffer area ratio. Journal of Soil and Water Conservation 66, 29-39. Hastie, Tibshirani and Friedman, 2009. The Elements of Statistical Learning (2nd ed.). Springer-Verlag. Le Gratiet, L.; Marelli, S. & Sudret, B. Metamodel-Based Sensitivity Analysis: Polynomial Chaos Expansions and Gaussian Processes Handbook of Uncertainty Quantification, Springer International Publishing, 2017, 1289-1325 Lauvernet, C., Muñoz-Carpena, R. Shallow water table effects on water, sediment, and pesticide transport in vegetative filter strips -- Part 2: model coupling, application, factor importance, and uncertainty. Hydrology and Earth System Sciences, 2018, 22, 71-87 Muñoz-Carpena, R., J.E. Parsons, et J.W. Gilliam, 1999. Modeling hydrology and sediment transport in vegetative filter strips. Journal of Hydrology 214:111. Muñoz-Carpena, R.; Lauvernet, C., Carluer, N. Shallow water table effects on water, sediment, and pesticide transport in vegetative filter strips -- Part 1: nonuniform infiltration and soil water redistribution. Hydrology and Earth System Sciences, 2018, 22, 53-70 Rasmussen, C.E..and Williams, C.K.I. Gaussian Processes for Machine Learning. The MIT Press, 2006. ISBN 0-262-18253-X. Saltelli, A.; Ratto, M.; Andres, T.; Campolongo, F.; Cariboni, J.; Gatelli, D.; Saisana, M., Tarantola, S. Global Sensitivity Analysis: The Primer John Wiley & Sons, 2008 Zhang, Y., Notz, W. I., 2015. Computer experiments with qualitative and quantitative variables: A review and reexamination. Quality Engineering 27 (1), 2-13

Développement e méthodes de métamodelisation incluant des variables qualitatives pour évaluer un outil d'aide à la décision de dimensionnement de bande enherbée

International audienceIn France and more generally over Europe, significant amounts of pollutants are measured in surface water, partly due to the use of pesticides by agriculture. In the European Water Framework Directive, Europe is advocating the development of best management practices to reduce pesticide transfers to the river network once they are applied in the watershed. This includes implementing vegetative filter strips (VFS), that ensure the interception and the mitigation of contaminant transfers arising from fields. VFS are now mandatory along rivers in many countries, due to their recognized effectiveness to limit pesticide and sediment transfer by surface runoff (Asmussen et al., 1977; Dosskey, 2001). However, the general effectiveness of these buffers to reduce runoff transport of pesticides highly depends on pedologic characteristics, climatic conditions, and cultural practices. It is thus necessary to use mechanistic models that represent processes occurring on a vegetative filter strip, such as VFSMOD (Vegetative Filer Strip Modeling, Muñoz-Carpena et al., 1999). These models are relevant tools to design properly the buffers accounting for local conditions, although they are rarely used in France, since they are considered too complex for operational use (Carluer et al., 2017). In France, in order to help decision-makers use physically-based modelling, Irstea developed the modeling toolkit BUVARD (BUffer strip for runoff Attenuation and pesticides Retention Design tool). It consists of several steps including analyzing the watershed and its characteristics (soil, climate, cultural practices), and running dynamical models, in particular the mechanistic model VFSMOD adapted to French conditions (Muñoz-Carpena et al., 2018, Lauvernet and Muñoz-Carpena, 2018). At the end this toolkit delivers the optimal VFS width considering the needed filter efficiency (for example, 70% of runoff reduction). However, this very complete method assumes that the user provides detailed field knowledge and data (type of soil of the contributive area and of the VFS, rainfall rate, water table depth, slope, etc.), which are not easily available in many practical applications. Moreover, the variety of tools, which rely on several interfaces or several programming languages, makes it relatively difficult to take over the design procedure. We get to situations where the tool is used, without any uncertainty quantification nor sensitivity analysis, although they should be performed together with the tool's simulations (Saltelli et al., 2008). The next step in seeking to increase the operational scope of the modeling toolkit was to use metamodeling techniques. By reducing the computational cost of the modeling toolkit, the metamodel of BUVARD will make it possible to apply the tool on new watersheds with far fewer input parameters (6 against 70), and to determine the output uncertainty and sensitivity to input parameters in other climatic and agronomic conditions at low cost. Metamodeling is still rarely used in the water quality domain, since processes related to pesticide transfer are highly nonlinear. The VFS sizing tool BUVARD and the physical processes it represents (water and pesticide transfer at surface/subsurface) includes high non-linearities, due to the dependence on qualitative inputs (or categorical variables). Indeed, two major inputs, the typeof soil of the VFS and the type of rainfall event, have been defined in BUVARD for operational purposes, as substitute to functional inputs (rainfall hyetograph) and to correlated inputs that are the hydrodynamics properties of the soil (saturated hydraulic conductivity, porosity, and van Genuchten parameters). Qualitative inputs generate discontinuities in the model's response that many methods are unable to deal with, removing the smoothness of the model's output that is generally a necessary condition to build a metamodel (Zang and Notz, 2015). In this study, we adapted kriging to mixed variables (qualitative and quantitative), by testing several covariance kernels for a mixture of qualitative and quantitative inputs. Their performances are compared to a linear model and to a generalized additive model (GAM) that have been often used in water quality metamodeling. The methods are validated with the physically-based simulations conducted on a full factorial test design. It will be shown that the adapted kriging is very efficient and weakly dependent on the sampling size of the experimental design. These metamodels will then be used to perform uncertainty quantification and global sensitivity analysis of the VFS efficiency in a French watershed in the Beaujolais vineyard region. Polynomial Chaos Expansion do not allow to account for qualitative variables to our knowledge, but they have the advantage of being very efficient on complex models, and to compute Sobol indices directly from polynomial chaos expansions (Le Gratiet et al., 2017). We will compare the analysis from the PCE on each group of modality to the one performed with the kriging with an adapted covariance kernel. The final aim of this study is to give the users a complete tool accounting for uncertainty and sensitivity of the model outputs to design their vegetative filter strips. Asmussen, L.E., White, A.W., Hauser, E.W., Sheridan, J.M., 1977. Reduction of 2,4-D Load in Surface Runoff Down a Grassed Waterway1. Journal of Environment Quality 6, 159. Carluer, N.; Lauvernet, C.; Noll, D., Muñoz-Carpena, R. Defining context-specific scenarios to design vegetated buffer zones that limit pesticide transfer via surface runoff Science of The Total Environment , 2017, 575, 701 - 712 Dosskey, M.G., Helmers, M.J., Eisenhauer, D.E., 2011. A design aid for sizing filter strips using buffer area ratio. Journal of Soil and Water Conservation 66, 29-39. Hastie, Tibshirani and Friedman, 2009. The Elements of Statistical Learning (2nd ed.). Springer-Verlag. Le Gratiet, L.; Marelli, S. & Sudret, B. Metamodel-Based Sensitivity Analysis: Polynomial Chaos Expansions and Gaussian Processes Handbook of Uncertainty Quantification, Springer International Publishing, 2017, 1289-1325 Lauvernet, C., Muñoz-Carpena, R. Shallow water table effects on water, sediment, and pesticide transport in vegetative filter strips -- Part 2: model coupling, application, factor importance, and uncertainty. Hydrology and Earth System Sciences, 2018, 22, 71-87 Muñoz-Carpena, R., J.E. Parsons, et J.W. Gilliam, 1999. Modeling hydrology and sediment transport in vegetative filter strips. Journal of Hydrology 214:111. Muñoz-Carpena, R.; Lauvernet, C., Carluer, N. Shallow water table effects on water, sediment, and pesticide transport in vegetative filter strips -- Part 1: nonuniform infiltration and soil water redistribution. Hydrology and Earth System Sciences, 2018, 22, 53-70 Rasmussen, C.E..and Williams, C.K.I. Gaussian Processes for Machine Learning. The MIT Press, 2006. ISBN 0-262-18253-X. Saltelli, A.; Ratto, M.; Andres, T.; Campolongo, F.; Cariboni, J.; Gatelli, D.; Saisana, M., Tarantola, S. Global Sensitivity Analysis: The Primer John Wiley & Sons, 2008 Zhang, Y., Notz, W. I., 2015. Computer experiments with qualitative and quantitative variables: A review and reexamination. Quality Engineering 27 (1), 2-13

Effets d'une nappe peu profonde sur le transport d'eau, sédiments et pesticides dans une bande enherbée: part B. Couplage du modèle, application, analyse de sensibilité et d'incertitude

International audienceVegetative filter strips are often used for protecting surface waters from pollution transferred by surface runoff in agricultural watersheds. In Europe, they are often prescribed along the stream banks, --where a seasonal shallow water table (WT) could decrease the buffer zone efficiency. In spite of this potentially important effect, there are no systematic experimental or theoretical studies on the effect of this soil boundary condition on the VFS efficiency. In the companion paper (Munoz-Carpena et al., 2018), we developed a physically based numerical algorithm (SWINGO) that allows the representation of soil infiltration with a shallow water table. Here we present the dynamic coupling of SWINGO with VFSMOD, an overland flow and transport mathematical model to study the WT influence on VFS efficiency in terms of reductions of overland flow, sediment, and pesticide transport. This new version of VFSMOD was applied to two contrasted benchmark field studies in France (sandy-loam soil in a Mediterranean semicontinental climate, and silty clay in a temperate oceanic climate), --where limited testing of the model with field data on one of the sites showed promising results. The application showed that for the conditions of the studies, VFS efficiency decreases markedly when the water table is 0 to 1.5 m from the surface. In order to evaluate the relative importance of WT among other input factors controlling VFS efficiency, global sensitivity and uncertainty analysis (GSA) was applied on the benchmark studies. The most important factors found for VFS overland flow reduction were saturated hydraulic conductivity and WT depth, added to sediment characteristics and VFS dimensions for sediment and pesticide reductions. The relative importance of WT varied as a function of soil type (most important at the silty-clay soil) and hydraulic loading (rainfall + incoming runoff) at each site. The presence of WT introduced more complex responses dominated by strong interactions in the modeled system response, reducing the typical predominance of saturated hydraulic conductivity on infiltration under deep water table conditions. This study demonstrates that when present, the WT should be considered as a key hydrologic factor in buffer design and evaluation as a water quality mitigation practice

Métamodélisation basée sur du krigeage avec variables qualitatives pour dimensionner des bandes enherbées dans un petit bassin versant

International audienc

Analyse des structures temporelles de pluies pour la définition de hyetogrammes en entrée de la chaine de dimensionnement des bandes tampons végétalisées BUVARD

[Departement_IRSTEA]Eaux [TR1_IRSTEA]ARCEAUCe rapport décrit la méthode utilisée pour déterminer des hyétogrammes de pluie pour fournir en entrée à la chaîne d'outils de modélisation BUVARD de dimensionnement de bande enherbée. L'objectif de la méthode est de différencier les structures temporelles des événements pluvieux selon leur durée, la saison et la localisation géographique, en se basant sur des données et non plus sur des formes arbitraires d’événement de pluie. Ce travail s’appuie sur l’analyse des observations à pas de temps fin réalisées par quatre postes pluviométriques jugés représentatifs de quatre grandes zones climatiques sur la France métropolitaine + Corse

BUVARD Online : dimensionner les bandes tampons enherbées afin de limiter les transferts de pesticides par ruissellement

National audienceLes bandes tampons végétalisées constituent une solution intéressante pour limiter les transferts de pesticides par ruissellement et réduire la pollution diffuse de l'eau. Pour être pleinement efficaces, elles doivent être adaptées au contexte dans lequel elles sont implantées. BUVARD Online est un outil disponible gratuitement en ligne (https://Buvard.irstea.fr), qui permet de dimensionner des bandes enherbées en tenant compte des conditions locales sur la France métropolitaine

Guide d'utilisation de l'outil BUVARD pour le dimensionnement des bandes tampons végétalisées destinées à limiter les transferts de pesticides par ruissellement

The BUVARD tool (Attenuation and Pesticide Retention Design tool) has been developed to allow the design of grass strips designed to mitigate the transfer of pesticides by runoff between farm plots and aquatic environments. It is based on a modeling chain developed by Irstea in recent years. This guide constitutes the instructions for use of the BUVARD tool in full version. It is essential for the proper handling of the tool, especially during the first use. The first two sections are devoted to presenting the dimensioning process, the associated reasoning and the tools mobilized to do so (sections 1 and 2). The following sections describe how to use the tool itself: the architecture and operation of the program (section 3); the parameterization (section 4). Finally, a part devoted to the interpretation of the results (section 5) is proposed to help the user to determine an optimal sizing among several scenarios, according to the desired objective. In some cases, it may guide the reflection to other more favorable locationsL'outil BUVARD (BUffer strip runoff Attenuation and pesticide Retention Design tool) a été développé pour permettre le dimensionnement de bandes enherbées destinées à atténuer les transferts de pesticides par ruissellement entre les parcelles agricoles et les milieux aquatiques. Il s'appuie sur une chaine de modélisation développée par Irstea au cours des dernières années Le présent guide constitue la notice d'utilisation de l'outil BUVARD en version complète. Il est indispensable à la bonne manipulation de l'outil, notamment lors des premières utilisations. Les deux premières parties sont consacrées à présenter la démarche de dimensionnement, le raisonnement associé ainsi que les outils mobilisés pour ce faire (sections 1‎1 et 2‎2). Les parties suivantes présentent les modalités d'utilisation de l'outil proprement dites : l'architecture et le fonctionnement du programme (section 3‎3) ; le paramétrage (section 4‎4).Enfin, une partie consacrée à l'interprétation des résultats (section 5‎5) est proposée pour aider l'utilisateur à déterminer un dimensionnement optimal parmi plusieurs scénarios, selon l'objectif recherché, voire orienter la réflexion vers d'autres choix d'implantation plus favorables