An application of the GLUE methodology for estimating the parameters of the INCA-N model

The conceptual and parameter uncertainty of the semi-distributed INCA-N (Integrated Nutrients in Catchments-Nitrogen) model was studied using the GLUE (Generalized Likelihood Uncertainty Estimation) methodology combined with quantitative experimental knowledge, the concept known as 'soft data'. Cumulative inorganic N leaching, annual plant N uptake and annual mineralization proved to be useful soft data to constrain the parameter space. The INCA-N model was able to simulate the seasonal and inter-annual variations in the stream-water nitrate concentrations, although the lowest concentrations during the growing season were not reproduced. This suggested that there were some retention processes or losses either in peatland/wetland areas or in the river which were not included in the INCA-N model. The results of the study suggested that soft data was a way to reduce parameter equifinality, and that the calibration and testing of distributed hydrological and nutrient leaching models should be based both on runoff and/or nutrient concentration data and the qualitative knowledge of experimentalist. (c) 2006 Elsevier B.V. All rights reserved.

Designing flexible procurement systems

The systems used for the procurement of buildings are organizational systems. They involve people in a series of strategic decisions, and a pattern of roles, responsibilities and relationships that combine to form the organizational structure of the project. To ensure effectiveness of the building team, this organizational structure needs to be contingent upon the environment within which the construction project takes place. In addition, a changing environment means that the organizational structure within a project needs to be responsive, and dynamic. These needs are often not satisfied in the construction industry, due to the lack of analytical tools with which to analyse the environment and to design appropriate temporary organizations. This paper presents two techniques. First is the technique of "Environmental Complexity Analysis", which identifies the key variables in the environment of the construction project. These are classified as Financial, Legal, Technological, Aesthetic and Policy. It is proposed that their identification will set the parameters within which the project has to be managed. This provides a basis for the project managers to define the relevant set of decision points that will be required for the project. The Environmental Complexity Analysis also identifies the project's requirements for control systems concerning Budget, Contractual, Functional, Quality and Time control. The process of environmental scanning needs to be done at regular points during the procurement process to ensure that the organizational structure is adaptive to the changing environment. The second technique introduced is the technique of "3R analysis", being a graphical technique for describing and modelling Roles, Responsibilities and Relationships. A list of steps is introduced that explains the procedure recommended for setting up a flexible organizational structure that is responsive to the environment of the project. This is by contrast with the current trend towards predetermined procurement paths that may not always be in the best interests of the client.

Robust dynamics of Amazon dieback to climate change with perturbed ecosystem model parameters

Climate change science is increasingly concerned with methods for managing and integrating sources of uncertainty from emission storylines, climate model projections, and ecosystem model parameterizations. In tropical ecosystems, regional climate projections and modeled ecosystem responses vary greatly, leading to a significant source of uncertainty in global biogeochemical accounting and possible future climate feedbacks. Here, we combine an ensemble of IPCC-AR4 climate change projections for the Amazon Basin (eight general circulation models) with alternative ecosystem parameter sets for the dynamic global vegetation model, LPJmL. We evaluate LPJmL simulations of carbon stocks and fluxes against flux tower and aboveground biomass datasets for individual sites and the entire basin. Variability in LPJmL model sensitivity to future climate change is primarily related to light and water limitations through biochemical and water-balance-related parameters. Temperature-dependent parameters related to plant respiration and photosynthesis appear to be less important than vegetation dynamics (and their parameters) for determining the magnitude of ecosystem response to climate change. Variance partitioning approaches reveal that relationships between uncertainty from ecosystem dynamics and climate projections are dependent on geographic location and the targeted ecosystem process. Parameter uncertainty from the LPJmL model does not affect the trajectory of ecosystem response for a given climate change scenario and the primary source of uncertainty for Amazon 'dieback' results from the uncertainty among climate projections. Our approach for describing uncertainty is applicable for informing and prioritizing policy options related to mitigation and adaptation where long-term investments are required.

Right ventromedial and dorsolateral prefrontal cortices mediate adaptive decisions under ambiguity by integrating choice utility and outcome evaluation.

The Iowa gambling task (IGT) is one of the most influential behavioral paradigms in reward-related decision making and has been, most notably, associated with ventromedial prefrontal cortex function. However, performance in the IGT relies on a complex set of cognitive subprocesses, in particular integrating information about the outcome of choices into a continuously updated decision strategy under ambiguous conditions. The complexity of the task has made it difficult for neuroimaging studies to disentangle the underlying neurocognitive processes. In this study, we used functional magnetic resonance imaging in combination with a novel adaptation of the task, which allowed us to examine separately activation associated with the moment of decision or the evaluation of decision outcomes. Importantly, using whole-brain regression analyses with individual performance, in combination with the choice/outcome history of individual subjects, we aimed to identify the neural overlap between areas that are involved in the evaluation of outcomes and in the progressive discrimination of the relative value of available choice options, thus mapping the two fundamental cognitive processes that lead to adaptive decision making. We show that activation in right ventromedial and dorsolateral prefrontal cortex was predictive of adaptive performance, in both discriminating disadvantageous from advantageous decisions and confirming negative decision outcomes. We propose that these two prefrontal areas mediate shifting away from disadvantageous choices through their sensitivity to accumulating negative outcomes. These findings provide functional evidence of the underlying processes by which these prefrontal subregions drive adaptive choice in the task, namely through contingency-sensitive outcome evaluation.

Mediating between hearer's and speaker's views in the generation of adaptive explanations

In this article, we examine the case of a system that cooperates with a “direct” user to plan an activity that some “indirect” user, not interacting with the system, should perform. The specific application we consider is the prescription of drugs. In this case, the direct user is the prescriber and the indirect user is the person who is responsible for performing the therapy. Relevant characteristics of the two users are represented in two user models. Explanation strategies are represented in planning operators whose preconditions encode the cognitive state of the indirect user; this allows tailoring the message to the indirect user's characteristics. Expansion of optional subgoals and selection among candidate operators is made by applying decision criteria represented as metarules, that negotiate between direct and indirect users' views also taking into account the context where explanation is provided. After the message has been generated, the direct user may ask to add or remove some items, or change the message style. The system defends the indirect user's needs as far as possible by mentioning the rationale behind the generated message. If needed, the plan is repaired and the direct user model is revised accordingly, so that the system learns progressively to generate messages suited to the preferences of people with whom it interacts.

An adaptive finite element water column model using the Mellor-Yamada level 2.5 turbulence closure scheme

A one-dimensional water column model using the Mellor and Yamada level 2.5 parameterization of vertical turbulent fluxes is presented. The model equations are discretized with a mixed finite element scheme. Details of the finite element discrete equations are given and adaptive mesh refinement strategies are presented. The refinement criterion is an "a posteriori" error estimator based on stratification, shear and distance to surface. The model performances are assessed by studying the stress driven penetration of a turbulent layer into a stratified fluid. This example illustrates the ability of the presented model to follow some internal structures of the flow and paves the way for truly generalized vertical coordinates. (c) 2005 Elsevier Ltd. All rights reserved.

High-resolution, unstructured meshes for hydrodynamic models of the Great Barrier Reef, Australia

Accuracy and mesh generation are key issues for the high-resolution hydrodynamic modelling of the whole Great Barrier Reef. Our objective is to generate suitable unstructured grids that can resolve topological and dynamical features like tidal jets and recirculation eddies in the wake of islands. A new strategy is suggested to refine the mesh in areas of interest taking into account the bathymetric field and an approximated distance to islands and reefs. Such a distance is obtained by solving an elliptic differential operator, with specific boundary conditions. Meshes produced illustrate both the validity and the efficiency of the adaptive strategy. Selection of refinement and geometrical parameters is discussed. (c) 2006 Elsevier Ltd. All rights reserved.

Adjoint goal-based error norms for adaptive mesh ocean modelling

Flow in the world's oceans occurs at a wide range of spatial scales, from a fraction of a metre up to many thousands of kilometers. In particular, regions of intense flow are often highly localised, for example, western boundary currents, equatorial jets, overflows and convective plumes. Conventional numerical ocean models generally use static meshes. The use of dynamically-adaptive meshes has many potential advantages but needs to be guided by an error measure reflecting the underlying physics. A method of defining an error measure to guide an adaptive meshing algorithm for unstructured tetrahedral finite elements, utilizing an adjoint or goal-based method, is described here. This method is based upon a functional, encompassing important features of the flow structure. The sensitivity of this functional, with respect to the solution variables, is used as the basis from which an error measure is derived. This error measure acts to predict those areas of the domain where resolution should be changed. A barotropic wind driven gyre problem is used to demonstrate the capabilities of the method. The overall objective of this work is to develop robust error measures for use in an oceanographic context which will ensure areas of fine mesh resolution are used only where and when they are required. (c) 2006 Elsevier Ltd. All rights reserved.