Coherent Doppler lidar measurements are of increasing interest for the wind energy industry. Wind measurements are fundamental inputs for the evaluation of potential energy yield and performance of wind farms. Three‐dimensional scanning Doppler lidar may provide a new basis for wind farm site selection, design and optimization. In this paper, the authors discuss Doppler lidar measurements obtained for a wind energy development. The possibility of using lidar measurements to more fully characterize the wind field is discussed, specifically terrain effects, spatial variation of winds, power density and the effect of shear at different layers within the rotor swept area. Vector retrieval methods have been applied to the lidar data, and results are presented on an elevated terrain‐following surface at hub height. The vector retrieval estimates are compared with tower measurements, after interpolation to the appropriate level. Doppler lidar data are used to estimate the spatial power density at hub height (for the period of the deployment). An example wind farm layout is presented for demonstration purposes based purely on lidar measurement, even though the lidar data acquisition period cannot be considered climatological. The strength of this approach is the ability to directly measure spatial variations of the wind field over the wind farm. Also, because Doppler lidar can measure winds at different vertical levels, an approach for estimating wind power density over the rotor swept area (rather than only the hub height) is explored. Finally, advanced vector retrieval algorithms have been applied to better characterize local wind variations and shear. Copyright © 2012 John Wiley & Sons, Ltd.

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Résumé La méthode SHYREG est une approche développée pour la connaissance régionale de l'aléa pluvial (SHYREG pluie) et hydrologique (SHYREG débit) en tout point du territoire français. Elle est basée sur le couplage d'un générateur stochastique de pluie horaire et d'un modèle hydrologique. Cet article présente les résultats de la mise en oeuvre de la méthode sur 1605 bassins versants répartis sur la France métropolitaine. Sur les fréquences courantes (c.à.d. périodes de retour inférieures à 10 ans), la méthode restitue correctement les quantiles de débit de crue ajustés à une loi statistique sur les observations (loi GEV, selon le critère de NashSutcliffe). Plusieurs critères sont utilisés pour valider l'extrapolation des débits à des fréquences extrêmes: (a) en la confrontant à de longues chroniques de débits observés, (b) en analysant dans le modèle hydrologique la saturation du réservoir de production synonyme de comportement asymptotique avec les pluies, et (c) en étudiant la stabilité de la méthode à travers les critères statistiques.Mots clefs SHYREG ; aléa hydrologique ; générateur stochastique ; pluie-débit ; quantile de débit ; validation ; fréquenceThe SHYREG flow method-application to 1605 basins in metropolitan France Abstract The SHYREG method is a flood frequency analysis method that can be applied to any location in the French metropolitan territory for flood risk management. It is based on an hourly stochastic rainfall generator coupled with a simplified distributed rainfall-runoff model. This paper presents the validation of flood frequency estimates made using SHYREG for a wide range of 1605 French catchments. For current return periods (i.e. of up to 10 years), the SHYREG-estimated flood frequency values are consistent with estimates from the generalized extreme value (GEV) distribution based on the Nash-Sutcliffe criterion. For extreme return periods, validation of flood frequency estimates is based on: (a) consistent peak and daily discharges estimated from a long observed flow record; (b) reasonable modelled saturation of the production storage for extreme events; and (c) studying the robustness of the SHYREG method by means of statistical criteria.

La méthode SHYREG a été développée pour la connaissance régionale des quantiles de débits de crue (débit de pointe et lames d'eau maximales écoulées sur les durées de 1 h à 72 h) pour les périodes de retour de 2 à 100 ans suivant une approche spatialisée. Elle associe un simulateur de pluies horaires et une modélisation simple pluie-débit, mis en oeuvre à une résolution kilométrique. Les quantiles de débits se déduisent directement des distributions de fréquence empiriques des valeurs maximales extraites des très longues chroniques de débit simulées. On obtient alors une base de quantiles de crues que l'on peut agréger à l'échelle de n'importe quel bassin versant, moyennant une règle d'abattement avec la surface. La régionalisation de la méthode a été réalisée sur la France métropolitaine, à l'exclusion de la Corse, en exploitant les données hydrométriques de 1 359 stations de jaugeage et des caractéristiques hydro-climatiques et hydrogéologiques spatialisées permettant de décrire la variabilité du paramètre saisonnier du modèle. Au final, cette régionalisation permet la connaissance des quantiles de débits de crue en tout bassin versant de la France métropolitaine avec une bonne restitution des quantiles de débit de pointe et journalier, pour les périodes de retour comprises entre 2 et 10 ans : un critère de Nash minimum de 80 % est obtenu sur les quantiles de débit de pointe pseudo-spécifique et de débit journalier spécifique des bassins versants non utilisés pour la régionalisation.The SHYREG method was developed for regional flood frequency analysis to estimate peak flow and flood discharges for various durations (1 h to 72 h) and return periods (2 to 100 years), according to a spatialized approach. For each 1-km2 pixel, the method combines an hourly rainfall model with a simple rainfall-runoff model. The discharge flood frequency estimates are deduced directly from the empirical frequency distributions for the maximum values, which are extracted from very long simulated discharge time series. This gives a database of 1-km2 gridded flood quantiles that can be aggregated for any catchment by using an areal averaging method. The method was regionalized for metropolitan France, excluding Corsica, using flow data from 1,359 gauging stations and regional hydroclimatic and hydrogeological characteristics to describe the variability of the rainfall-runoff model parameter. Such regionalization provides flood discharge quantiles for any catchment in metropolitan France for various durations and return periods. Regarding the method performance, accurate estimates of flood quantiles were produced for peak discharge and mean daily discharge for return periods of two to 10 years for gauged basins in dependent validation and cross-validation. A minimum NASH criterion of 80% is obtained for peak flow and mean daily discharge for the catchments not used in the regionalization process

Abstract. Calibration of a conceptual distributed model is challenging due to a number of reasons, which include fundamental (model adequacy and identifiability) and algorithmic (e.g., local search vs. global search) issues. The aim of the presented study is to investigate the potential of the variational approach for calibrating a simple continuous hydrological model (GRD; Génie Rural distributed involved in several flash flood modeling applications. This model is defined on a rectangular 1 km2 resolution grid, with three parameters being associated with each cell. The Gardon d'Anduze watershed (543 km2) is chosen as the study benchmark. For this watershed, the discharge observations at five gauging stations, gridded rainfall and potential-evapotranspiration estimates are continuously available for the 2007–2018 period at an hourly time step. In the variational approach one looks for the optimal solution by minimizing the standard quadratic cost function, which penalizes the misfit between the observed and predicted values, under some additional a priori constraints. The cost function gradient is efficiently computed using the adjoint model. In numerical experiments, the benefits of using the distributed against the uniform calibration are measured in terms of the model predictive performance, in temporal, spatial and spatiotemporal validation, both globally and for particular flood events. Overall, distributed calibration shows encouraging results, providing better model predictions and relevant spatial distribution of some parameters. The numerical stability analysis has been performed to understand the impact of different factors on the calibration quality. This analysis indicates the possible directions for future developments, which may include considering a non-Gaussian likelihood and upgrading the model structure.