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Within a distributed hydrological modelling, the discretisation of the watershed is often performed using a regular rectangular grid, which is not appropriated for correctly representing the surface heterogeneities and the hydrological processes that take place at different spatial and temporal scales. In order to better take into account the surface and subsurface properties of the catchment, two discretisation levels have been implemented in the POWER (Planner Oriented Watershed model for Environmental Responses) hydrological model. First, the watershed is divided into a series of elementary independent sub-basins, which are called the Representative Elementary Watersheds (REWs), on the basis of a Digital Terrain Model analysis. The number of REWs is determined as a function of the Strahler (1957) order. Following the approach by Reggiani et al. (Adv. Water Res., 1998, 1999 and Wat. Res. Res., 2000), the conservation equations for mass, momentum and energy may be averaged within each REW. Hence, each REW is considered to be the basic unit for modelling the stream flow network and the regional aquifer. For the modelling of the unsaturated/saturated water flow, a subsequent discretisation is performed and each REW is subdivided into a series of irregular vertical columns called the Representative Elementary Columns (RECs). The RECs are determined by the analysis and the classification of a series of superimposed GIS layers such as land use, soil texture and infrastructure maps. They constitute irregular volumes, subdivided into a fixed number of soil layers with different textural and/or structural properties. Infiltration, soil evaporation, plant transpiration and interception, surface ponding and leaching are modelled at the REC scale using a modified, efficient and accurate solution of the Richard's equation proposed by Ross (Agronomy J., 2003, in press) The same routine is applied to calculate the lateral surface and subsurface fluxes between adjacent RECs and the vertical water content redistribution within a REC. The link between RECs and REWs is provided by a weighted average of the RECs fluxes over a REW.An application of these two levels of discretisation to real hydrological basins will be presented.; Dans un modèle hydrologique distribué, la discrétisation du bassin est souvent réalisée à l'aide d'une grille régulière, qui n'est pas appropriée pour représenter correctement les hétérogénéités de la surface et les processus hydrologiques qui ont lieu à des échelles d'espace et de temps différentes. Afin de mieux prendre en compte des propriétés de la surface et de la sub-surface dans un bassin, deux niveaux de discrétisation sont considérés dans le modèle hydrologique POWER ((Planner Oriented Watershed model for Environmental Responses). Tout d'abord, le bassin est divisé en une série de sous-bassins élémentaires, appelés les Representative Elementary Watersheds (REWs) à partir d'un modèle numérique de terrain. Le nombre de sous-bassins est déterminé par choix de l'ordre de Strahler (1957) retenu pour le découpage. Selon l'approche de Reggiani et al. (Adv. Water Res., 1998, 1999 and Wat. Res. Res., 2000), les équations de conservation de la masse, de la quantité de mouvement et de l'énergie peuvent être moyennées sur chaque REW. Dans POWER, chaque REW est, pour l'instant, considérée comme l'unité de base pour la modélisation des écoulements dans la rivière et dans la nappe. Pour la modélisation des flux dans la zone saturée-non saturée, un second niveau de discrétisation est réalisé à l'intérieur de chaque REW. Ce dernier est divisé en colonnes verticales, aux formes irrégulières, appelées les Representative Elementary Columns (RECs). Ces dernières sont déterminées par l'analyse et la classification d'une séries de couches de SIG superposées telles que l'occupation des sols, la texture du sol et l'infrastructure. Elles constituent des volumes irréguliers, subdivisés en un même nombre de couches aux propriétés texturale et/ou structurales qui peuvent être différentes. L'infiltration, l'évaporation du sol, la transpiration des plantes et l'interception, la saturation de la surface et les transferts de polluants sont modélisés à l'échelle du REC à partir d'une méthode numérique de l'équation de Richards rapide et précise (Ross, Agronomy Journal, 2003, in press). La même routine est utilisée pour calculer les écoulements latéraux et les écoulements de subsurface entre RECs voisins et la redistribution verticale à l'intérieur d'un même REC. Le lien entre les RECs et les REWs se fait par calcul de moyenne pondérée des flux sur l'ensemble des REWs. more...

One of the major expected impact of possible climatic changes will certainly be to affect dramatically the hydrological cycle, and thus man's approach to water management. In our current stage of knowledge, the large-scale global climate models are probably the best available tools to provide estimates of the effects of raising greenhouse gases on rainfall and evaporation patterns through a continuous, three dimensional simulation of atmospheric, oceanic and cryospheric processes. However their spatial resolution (generally some thousands square kilometers) is generally not compatible with that of watershed hydrologic models. The main purpose of this study is to evaluate the impact of potential climate changes upon groundwater resources. A method which disaggregates outputs of large-scale models, and thus gives information directly usable by hydrologic models, is proposed. This method is illustrated with the development of a local weather generator to simulate a doubling of CO2 scenario on rainfall and evapotranspiration patterns. It is then used, through the coupling with a physically based model, to estimate the effects of climatic changes on groundwater recharge and soil moisture in the root zone., L'augmentation des gaz à effet serre influencera probablement le cycle hydrologique. Les changements climatiques auront donc des conséquences majeures quant à la gestion des ressources en eau. L'objectif de cet article est d'évaluer l'impact de ces changements sur les ressources en eau. Une approche de désagrégation spatiale de l'information fournie par les modèles de circulation générale (GCM) est développée. L'utilisation de cette approche est illustrée pour un scénario de doublement de CO2. Un générateur de climat local est ainsi développé et utilisé en conjonction avec un modèle physique spatialement distribué. Il est ainsi montré que sur le bassin versant de La côte St André localisé près de Grenoble, le doublement de CO2 aura des effets limités sur la pluviométrie, mais augmentera de manière notable la demande évaporative. Il est ainsi attendu de voir décroître les ressources en eau souterraines. Le maintien des cultures irriguées est aussi remis en cause. more...

Water use efficiency (WUE) is considered as a determinant of yield under stress and a component of crop drought resistance. Stomatal behavior regulates both transpiration rate and net assimilation and has been suggested to be crucial for improving crop WUE. In this work, a dynamic model was used to examine the impact of dynamic properties of stomata on WUE. The model includes sub-models of stomatal conductance dynamics, solute accumulation in the mesophyll, mesophyll water content, and water flow to the mesophyll. Using the instantaneous value of stomatal conductance, photosynthesis, and transpiration rate were simulated using a biochemical model and Penman-Monteith equation, respectively. The model was parameterized for a cucumber leaf and model outputs were evaluated using climatic data. Our simulations revealed that WUE was higher on a cloudy than a sunny day. Fast stomatal reaction to light decreased WUE during the period of increasing light (e.g., in the morning) by up to 10.2% and increased WUE during the period of decreasing light (afternoon) by up to 6.25%. Sensitivity of daily WUE to stomatal parameters and mesophyll conductance to CO2 was tested for sunny and cloudy days. Increasing mesophyll conductance to CO2 was more likely to increase WUE for all climatic conditions (up to 5.5% on the sunny day) than modifications of stomatal reaction speed to light and maximum stomatal conductance. more...

In the face of a changing climate, yield stability is becoming increasingly important for farmers and breeders. Long-term field experiments (LTEs) generate data sets that allow the quantification of stability for different agronomic treatments. However, there are no commonly accepted guidelines for assessing yield stability in LTEs. The large diversity of options impedes comparability of results and reduces confidence in conclusions. Here, we review and provide guidance for the most commonly encountered methodological issues when analysing yield stability in LTEs. The major points we recommend and discuss in individual sections are the following: researchers should (1) make data quality and methodological approaches in the analysis of yield stability from LTEs as transparent as possible; (2) test for and deal with outliers; (3) investigate and include, if present, potentially confounding factors in the statistical model; (4) explore the need for detrending of yield data; (5) account for temporal autocorrelation if necessary; (6) make explicit choice for the stability measures and consider the correlation between some of the measures; (7) consider and account for dependence of stability measures on the mean yield; (8) explore temporal trends of stability; and (9) report standard errors and statistical inference of stability measures where possible. For these issues, we discuss the pros and cons of the various methodological approaches and provide solutions and examples for illustration. We conclude to make ample use of linking up data sets, and to publish data, so that different approaches can be compared by other authors and, finally, consider the impacts of the choice of methods on the results when interpreting results of yield stability analyses. Consistent use of the suggested guidelines and recommendations may provide a basis for robust analyses of yield stability in LTEs and to subsequently design stable cropping systems that are better adapted to a changing climate., Agronomy for Sustainable Development, 41 (2), ISSN:1774-0746, ISSN:0249-5627, ISSN:1773-0155 more...