Abstract. SURFEX is a new externalized land and ocean surface platform that describes the surface fluxes and the evolution of four types of surfaces: nature, town, inland water and ocean. It is mostly based on pre-existing, well-validated scientific models that are continuously improved. The motivation for the building of SURFEX is to use strictly identical scientific models in a high range of applications in order to mutualise the research and development efforts. SURFEX can be run in offline mode (0-D or 2-D runs) or in coupled mode (from mesoscale models to numerical weather prediction and climate models). An assimilation mode is included for numerical weather prediction and monitoring. In addition to momentum, heat and water fluxes, SURFEX is able to simulate fluxes of carbon dioxide, chemical species, continental aerosols, sea salt and snow particles. The main principles of the organisation of the surface are described first. Then, a survey is made of the scientific module (including the coupling strategy). Finally, the main applications of the code are summarised. The validation work undertaken shows that replacing the pre-existing surface models by SURFEX in these applications is usually associated with improved skill, as the numerous scientific developments contained in this community code are used to good advantage.

The Model Parameter Estimation Experiment (MOPEX) is an international project aimed at developing enhanced techniques for the a priori estimation of parameters in hydrologic models and in land surface parameterization schemes of atmospheric models. The MOPEX science strategy involves three major steps: data preparation, a priori parameter estimation methodology development, and demonstration of parameter transferability. A comprehensive MOPEX database has been developed that contains historical hydrometeorological data and land surface characteristics data for many hydrologic basins in the United States (US) and in other countries. This database is being continuously expanded to include more basins in all parts of the world. A number of international MOPEX workshops have been convened to bring together interested hydrologists and land surface modelers from all over world to exchange knowledge and experience in developing a priori parameter estimation techniques. (Q. Duan). This paper describes the results from the second and third MOPEX workshops. The specific objective of these workshops is to examine the state of a priori parameter estimation techniques and how they can be potentially improved with observations from well-monitored hydrologic basins. Participants of the second and third MOPEX workshops were provided with data from 12 basins in the southeastern US and were asked to carry out a series of numerical experiments using a priori parameters as well as calibrated parameters developed for their respective hydrologic models. Different modeling groups carried out all the required experiments independently using eight different models, and the results from these models have been assembled for analysis in this paper. This paper presents an overview of the MOPEX experiment and its design. The main experimental results are analyzed. A key finding is that existing a priori parameter estimation procedures are problematic and need improvement. Significant improvement of these procedures may be achieved through model calibration of well-monitored hydrologic basins. This paper concludes with a discussion of the lessons learned, and points out further work and future strategy. q

International audienceSystème d'analyse fournissant des renseignements atmosphériques à la neige (SAFRAN) is a mesoscale atmospheric analysis system for surface variables. It produces an analysis at the hourly time step using ground data observations. One of SAFRAN's main features is that it is based on climatically homogeneous zones and is able to take vertical variations into account. Originally intended for mountainous areas, it was later extended to cover France. This paper focuses on the validation of the extended version. The principle of the analysis is described and its quality was tested for five parameters (air temperature, humidity, wind speed, rainfall, and incoming radiation), using Météo-France's observation network and data of some well-instrumented stations. Moreover, SAFRAN's rainfall was compared with another analysis, known as analyse utilisant le relief pour l'hydrométéorologie (Aurelhy). Last, two different versions of SAFRAN were compared for mountain conditions. Temperature and relative humidity were well reproduced, presenting no bias. Wind speed was also well reproduced; however, its bias was - 0.3 m s–1. The interpolation from the 6-h time step of the analysis to the 1h time step was one of the sources of error. The precipitation analysis was robust and not biased; its root-mean-square error was 2.4 mm day-1. This error was mainly due to the spatial heterogeneity of the precipitation within the geographical zones of analysis (1000 km2). The analysis of incoming solar radiation presented some biases, especially in coastal areas. The results of the comparison with some well-instrumented sites were encouraging. SAFRAN is being run operationally at Météo-France on a real-time basis for various applications

Abstract:Two downscaling methods designed for the study of the hydrological impact of climate change on the Seine basin in France are tested for present climate. First, a multivariate statistical downscaling (SD) methodology based on weather typing and conditional resampling is described. Then, a bias correction technique for dynamical downscaling based on quantile-quantile mapping is introduced. To evaluate the end-to-end SD methodology, the atmospheric forcing derived from the large-scale circulation (LSC) of the ERA40 reanalysis by SD is used to force a hydrological model. Simulated discharges reproduce historical values reasonably well. Next, the dynamical and statistical approaches are compared using the Météo-France ARPEGE general circulation model in a variable resolution configuration (resolution around 60 km over France). The ARPEGE simulation is downscaled using the two methodologies, and hydrological simulations are performed. Regarding downscaled temperature and precipitation, the statistical approach is more efficient in reproducing the temporal and spatial autocorrelation properties. The simulated river discharges from the two approaches are nevertheless very similar: the two methods reproduce well the seasonal cycle and the daily distribution of streamflows. Finally, the results of the study are discussed from a practical impact study perspective.

Twenty-one land surface schemes (LSSs) performed simulations forced by 18 yr of observed meteorological data from a grassland catchment at Valdai, Russia, as part of the Project for the Intercomparison of Land-Surface Parameterization Schemes (PILPS) Phase 2(d). In this paper the authors examine the simulation of snow. In comparison with observations, the models are able to capture the broad features of the snow regime on both an intra-and interannual basis. However, weaknesses in the simulations exist, and early season ablation events are a significant source of model scatter. Over the 18-yr simulation, systematic differences between the models' snow simulations are evident and reveal specific aspects of snow model parameterization and design as being responsible. Vapor exchange at the snow surface varies widely among the models, ranging from a large net loss to a small net source for the snow season. Snow albedo, fractional snow cover, and their interplay have a large effect on energy available for ablation, with differences among models most evident at low snow depths. The incorporation of the snowpack within an LSS structure affects the method by which snow accesses, as well as utilizes, available energy for ablation. The sensitivity of some models to longwave radiation, the dominant winter radiative flux, is partly due to a stability-induced feedback and the differing abilities of models to exchange turbulent energy with the atmosphere. Results presented in this paper suggest where weaknesses in macroscale snow modeling lie and where both theoretical and observational work should be focused to address these weaknesses.

The mapped rivers and streams of the contiguous United States are available in a geographic information system (GIS) dataset called National Hydrography Dataset Plus (NHDPlus). This hydrographic dataset has about 3 million river and water body reaches along with information on how they are connected into networks. The U.S. Geological Survey (USGS) National Water Information System (NWIS) provides streamflow observations at about 20 thousand gauges located on the NHDPlus river network. A river network model called Routing Application for Parallel Computation of Discharge (RAPID) is developed for the NHDPlus river network whose lateral inflow to the river network is calculated by a land surface model. A matrix-based version of the Muskingum method is developed herein, which RAPID uses to calculate flow and volume of water in all reaches of a river network with many thousands of reaches, including at ungauged locations. Gauges situated across river basins (not only at basin outlets) are used to automatically optimize the Muskingum parameters and to assess river flow computations, hence allowing the diagnosis of runoff computations provided by land surface models. RAPID is applied to the Guadalupe and San Antonio River basins in Texas, where flow wave celerities are estimated at multiple locations using 15-min data and can be reproduced reasonably with RAPID. This river model can be adapted for parallel computing and although the matrix method initially adds a large overhead, river flow results can be obtained faster than with the traditional Muskingum method when using a few processing cores, as demonstrated in a synthetic study using the upper Mississippi River basin.

[1] The hydrometeorological model SIM consists of a meteorological analysis system (SAFRAN), a land surface model (ISBA), and a hydrogeological model (MODCOU). It generates atmospheric forcing at an hourly time step, and it computes water and surface energy budgets, the river flow at more than 900 river-gauging stations, and the level of several aquifers. SIM was extended over all of France in order to have a homogeneous nationwide monitoring of the water resources: it can therefore be used to forecast flood risk and to monitor drought risk over the entire nation. The hydrometeorological model was applied over a 10-year period from 1995 to 2005. In this paper the databases used by the SIM model are presented; then the 10-year simulation is assessed by using the observations of daily streamflow, piezometric head, and snow depth. This assessment shows that SIM is able to reproduce the spatial and temporal variabilities of the water fluxes. The efficiency is above 0.55 (reasonable results) for 66% of the simulated river gauges, and above 0.65 (rather good results) for 36% of them. However, the SIM system produces worse results during the driest years, which is more likely due to the fact that only few aquifers are simulated explicitly. The annual evolution of the snow depth is well reproduced, with a square correlation coefficient around 0.9 over the large altitude range in the domain. The streamflow observations were used to estimate the overall error of the simulated latent heat flux, which was estimated to be less than 4%.

The Rhône-Aggregation (Rhône-AGG) Land Surface Scheme (LSS) intercomparison project is an initiative within the Global Energy and Water Cycle Experiment (GEWEX)/Global Land-Atmosphere System Study (GLASS) panel of the World Climate Research Programme (WCRP). It is a intermediate step leading up to the next phase of the Global Soil Wetness Project (GSWP) (Phase 2), for which there will be a broader investigation of the aggregation between global scales (GSWP-1) and the river scale. This project makes use of the Rhône modeling system, which was developed in recent years by the French research community in order to study the continental water cycle on a regional scale. The main goals of this study are to investigate how 15 LSSs simulate the water balance for several annual cycles compared to data from a dense observation network consisting of daily discharge from over 145 gauges and daily snow depth from 24 sites, and to examine the impact of changing the spatial scale on the simulations. The overall evapotranspiration, runoff, and monthly change in water storage are similarly simulated by the LSSs, however, the differing partitioning among the fluxes results in very different river discharges and soil moisture equilibrium states. Subgrid runoff is especially important for discharge at the daily timescale and for smallerscale basins. Also, models using an explicit treatment of the snowpack compared better with the observations than simpler composite schemes. Results from a series of scaling experiments are examined for which the spatial resolution of the computational grid is decreased to be consistent with large-scale atmospheric models. The impact of upscaling on the domainaveraged hydrological components is similar among most LSSs, with increased evaporation of water intercepted by the canopy and a decrease in surface runoff representing the most consistent inter-LSS responses. A significant finding is that the snow water equivalent is greatly reduced by upscaling in all LSSs but one that explicitly accounts for subgrid-scale orography effects on the atmospheric forcing.