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High-rise buildings can cause high wind speed near ground level and on building balconies necessitating remedial action. In order to reduce wind discomfort on balconies, different measures can be taken. This paper presents the evaluation of a new second-skin facade concept to improve wind comfort at building balconies. The concept consists of a staggered semi-open second-skin facade in front of the balconies, which partly shields them from the wind. CFD simulations are performed for the building with and without second-skin facade concept. Validation is conducted using wind-tunnel measurements of surface pressure distribution on a building model with balconies. Next, the CFD results are combined with wind speed statistics and a comfort criterion to judge wind comfort on the balconies, according to the Dutch wind nuisance standard. The analysis shows that this concept is effective in providing a zone with pressure equalisation at the balconies. The related reduction in pressure gradients across the width of the facade strongly decreases the local wind speed. At many positions along the balconies this yields a wind comfort improvement of one or even two classes in the Dutch wind nuisance standard compared to the situation without implementation of this concept.

Designing the transition to low carbon economy is a very complex task that touches upon a wide variety of climate-energy-economic systems. We need to explore the various possible climate mitigation scenarios at different temporal and spatial scales. However, due to the diversity of the involved disciplines it is difficult to find one complete and unified modeling approach that works equally well in all those different domains. As a result we have to select 'appropriate' models, which represent only specific aspects of the scenarios and assemble them 'coherently'. In this research we have identified some challenges in integrating multidisciplinary models; and have developed a conceptual design for a multidisciplinary model integration framework that can harmonize the technical, semantic, and dataset aspects of interoperability.

Integrating multidisciplinary models requires linking models: that may operate at different temporal and spatial scales; developed using different methodologies, tools and techniques; different levels of complexity; calibrated for different ranges of inputs and outputs, etc. On the other hand, integration of models requires us to address technical, semantic, and dataset aspects of interoperability. So we need a genuine techniques that enable us to integrate various domain specific models for interdisciplinary study. In this research work, we investigated best practices of System Integration, Enterprise Application Integration, and Integration Design Patterns. We developed an architecture of a multidisciplinary model integration framework that brings these three aspects of integration together. Service-oriented-based platform independent architecture that enables to establish loosely coupled dependency among various models is presented.

Information on wind conditions in harbors and wind loads on ships is important for safe maneuvering and mooring of ships. The wind conditions in harbors are very complex and characterized by a multitude of internal atmospheric boundary layers due to the large variations in terrain roughness. This paper presents the development of data to support a software application for assessment of wind conditions in the Port of Rotterdam. An extensive measurement campaign using ultrasonic anemometers was performed in the harbor during a period of 6 months. These measurements were compared with the numerical simulations with Computational Fluid Dynamics (CFD) on a high-resolution grid. This comparison showed a good agreement for such a complex heterogeneous terrain. 89% of the simulated amplification factors (¿=Ump/Ump1) were within the range of the mean measured amplification factors ± two times the standard deviation. These data will be used in the near future to support a software application for the harbor authorities and tugboat pilots to allow improved safety, maneuverability and mooring of container ships.

Autres auteurs : G. Bellocchi, J.-E. Bergez, M. Botta, E. Braudeau, S. Bregaglio, L. Carlini, E. Casellas, F. Celette, E. Ceotto, M.-H. Charron-Moirez, R. Confalonieri, M. Corbeels, L. Criscuolo, P. Cruz, A. di Guardo, D. Ditto, C. Dupraz, M. Duru, D. Fiorani, A. Gentile, F. Ewert, C. Gary, E. Habyarimana, C. Jouany, K. Kansou, R. Knapen, G. Lanza Filippi, P.A. Leffelaar, L. Manici, G. Martin, P. Martin, E. Meuter, N. Mugueta, R. Mulia, M. van Noordwijk, R. Oomen, A. Rosenmund, V. Rossi, F. Salinari, A. Serrano, A. Sorce, G. Vincent, J.-P. Theau, O. Thérond, M. Trevisan, P. Trevisiol, F.K. van Evert, D. Wallach, J. Wery et A. Zerourou; International audience; Although existing simulation tools can be used to study the impact of agricultural management on production activities in specific environments, they suffer from several limitations. They are largely specialized for specific production activities: arable crops/cropping systems, grassland, orchards, agro-forestry, livestock etc. Also, they often have a restricted ability to simulate system externalities which may have a negative environmental impact. Furthermore, the structure of such systems neither allows an easy plug-in of modules for other agricultural production activities, nor the use of alternative components for simulating processes. Finally, such systems are proprietary systems of either research groups or projects which inhibits further development by third parties. SEAMLESS aims to provide a tool to integrate analyses of impacts on the key aspects of sustainability and multi-functionality, particularly in Europe. This requires evaluating agricultural production and system externalities for the most important agricultural production systems. It also requires a simulation framework which can be extended and updated by research teams, which allows a manageable transfer of research results to operational tools, and which is transparent with respect to its contents and its functionality. The Agricultural Production and Externalities Simulator (APES) is a modular simulation system aimed at meeting these requirements, and targeted at estimating the biophysical behavior of agricultural production systems in response to the interaction of weather and agro-technical management. APES is a framework which uses components that offer simulation options for different processes of relevance to agricultural production systems. Models are described in the associated help files of components, and a shared ontology is built on the web. Components like these, which are designed to be inherently re-usable, that is not targeted specifically to a given modelling framework, also represent a way to share modelling knowledge with other projects and the scientific community in general. This chapter describes the current state of APES development and presents modelling options in the system, and its software architecture.

International audience

SEAMLESS (System for Environmental and Agricultural Modelling; Linking European Science and Society) integrated project, EU 6th Framework Programme, contract no. 010036-2; International audience; Policy makers and farmers have an interest in making ex-ante assessments of the outcomes of their choices in terms of policy and farm plan. This interest mainly concerns the assessment of socio-economic and environmental performance of farms as a result of innovations and policies. Mathematical models based on systems analysis are suited to explore and assess uncertain future states of systems. A Bio-Economic Farm Model (BEFM) is defined as a model that links formulations describing farmers’ resource management decisions to formulations that describe current and alternative production possibilities in terms of required inputs to achieve certain outputs and associated externalities. Currently many descriptions and applications of BEFMs are being published. A BEFM that is easy to transfer between locations or farm types is called generic, which we define as ‘being able to deal with different scales, locations, functionalities, and levels of detail. Although some model studies claim that their model is easily transferable to other regions and farm types, there is little evidence from literature supporting these claims. A generic bio-economic farm model should be able to adequately represent arable, livestock and perennial activities, current agricultural activities and future alternative activities, different objective functions, different resource and policy constraints, future policies and technological innovations as scenarios and should include good calibration procedures. In SEAMLESS the Farming Systems SIMulator (FSSIM) has been developed as a generic and transferable model that can easily be extended with new features and re-used across data-sets, farm types and locations. In this deliverable we (i) describe FSSIM and its design as an integrated generic bio-economic farm model, (ii) briefly describe each of the components of FSSIM and provide references to relevant deliverables and publications for more detail and (iii) evaluating FSSIM on criteria for generic models by describing applications of FSSIM.

Although the process of public and stakeholder participation continues to be intensivelyinvestigated and discussed in academic circles, the implementation of participatory methods inpractice remains problematic. This can be attributed to the lack of knowledge transfer on the onehand, and the general underestimation of participatory approaches in planning processes on theother.A possible solution - participatory processmanagement - is introduced in this article. Participatoryprocess management means that all participatory activities are embedded in the overall planningactivities of a project. The most significant criteria for a participatory process are identified as’objectives’, ’constraints’ and ’process’which together forma framework for combining generallyapplicablemethodswith local constraints and the objectives of a project. Themain elements of theparticipatory management framework introduced here are levels and classes of participation and ageneric process scheme includingmonitoring and evaluation of participatory processes. This workis based upon long-term experiences of consultants and scientists. However, the insights fromthe InterReg project TRUST are particularly valuable and confirm the hypotheses that differentwater management projects are comparable in terms of their participatory process performance.The participatory management framework is a step forward in closing the gap between scientificknowledge about participatory methods and their applicability in practice.

Garp3 is a domain independent, multi-platform, qualitative modelling and simulation environment. It allows modellers to articulate and refine their conceptual domain knowledge and analyse this knowledge through simulation. Garp3 has been successfully applied in Ecology and Sustainable Development (SD) and is freely available via (http://www.garp3.org).

An integrated modelling framework is an extension of a modelling framework, which supports multiple modelling domains and paradigms. The number of integrated modelling frameworks is considerably more limited, especially if we restrain to the intersection of social, economic and environmental modelling. We list some of the most notable initiatives in the various fields. In this document we will express the requirements imposed by the SEAMLESS project vision on the SEAMFRAME modelling framework and we will critically assess these requirements against a suitable set of existing modelling frameworks. As a result, we shall be able to understand what to pick up and what to drop from these previous experiences in order to improve the trade-off between re-use and innovation, and maximise our users’ satisfaction.

Given the growing complexity of water-resources management there will be an increasing need for integrated tools to support policy analysis, communication, and research. A key aspect of the design is the combination of process models from different scientific disciplines in an integrated system. In general these models differ in sensitivity and accuracy, while non-linear and qualitative models can be present. The current practice is that the preferences of the designers of a decision-support system, and practical considerations such as data availability guide the selection of models and data. Due to a lack of clear scientific guidelines the design becomes an ad-hoc process, depending on the case study at hand, while selected models can be overly complex or too coarse for their purpose. Ideally, the design should allow for the ranking of selected management measures according to the objectives set by end users, without being more complex than necessary. De Kok and Wind [2003] refer to this approach as appropriate modeling. A good case example is the ongoing pilot project aiming at the design of a decision-support system for the Elbe river basin. Four functions are accounted for: navigability, floodplain ecology, flooding safety, and water quality. This paper concerns the response of floodplain biotope types to river engineering works and changes in the flooding frequency of the floodplains. The HBV-D conceptual rainfall-runoff model is used to simulate the impact of climate and land use change on the discharge statistics. The question was raised how well this rainfall-runoff model should be calibrated as compared to the observed discharge data. Sensitivity analyses indicate that a value of R2 = 0.87 should be sufficient.

Decision support systems (DSSs) are increasingly being used in water management for the evaluation of impacts of policy measures under different scenarios. The exact impacts generally are unknown and surrounded with considerable uncertainties. These uncertainties stem from natural randomness, uncertainty in data, models and parameters, and uncertainty about measures and scenarios. It may therefore be difficult to make a selection of measures relevant for a particular water management problem. In order to support policy makers to make a strategic selection between different measures in a DSS while taking uncertainty into account, a methodology for the ranking of measures has been developed. The methodology has been applied to a pilot DSS for flood control in the Red River basin in Vietnam and China. The decision variable is the total flood damage and possible flood reducing measures are dike heightening, reforestation and the construction of a retention basin. For illustrative purposes, only parameter uncertainty is taken into account. The methodology consists of a Monte Carlo uncertainty analysis employing Latin Hypercube Sampling and a ranking procedure based on the significance of the difference between output distributions for different measures. The significance is determined with the Student test for Gaussian distributions and with the non-parametric Wilcoxon test for non-Gaussian distributions. The results show Gaussian distributions for the flood damage in all situations. The mean flood damage in the base situation is about 2.2 billion US$ for the year 1996 with a standard deviation due to parameter uncertainty of about 1 billion US$. Selected applications of the measures reforestation, dike heightening and the construction of a retention basin reduce the flood damage with about 5, 55 and 300 million US$ respectively. The construction of a retention basin significantly reduces flood damage in the Red River basin, while dike heightening and reforestation reduce flood damage, but not significantly.