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A new proton-unbound isotope $^{204}$Ac was produced in the fusion-evaporation reaction Tm(40169Ca,5n)204Ac and identified via the recoil-α correlation at the gas-filled recoiled separators SHANS2 and SHANS. The excitation function for producing $^{204}$Ac was measured and the optimal beam energy was evaluated to be 212 MeV. Nineteen ER - α1 - α2 - α3 decay chains were observed for this isotope. Based on these chains, the α-particle energy and half-life of $^{204}$Ac were determined to be 7948(15) keV and 7.4−1.4+2.2 ms, respectively. Both are consistent with the theoretical prediction. The reduced α-decay width was deduced to be 49−15+10 keV, which indicates a favored α decay for $^{204}$Ac. In addition, more precise decay properties of Eα = 7883(15) keV and T1/2 = 7.7−1.6+2.7 ms were obtained for $^{205}$Ac.

The decay of $^{218}$Pa was investigated by means of α and γ spectroscopy. The nucleus was produced in the fusion reaction $^{40}$Ar+$^{182}$W and separated in flight by the gas-filled recoil separator SHANS (Spectrometer for Heavy Atoms and Nuclear Structure). The known α decays of $^{218}$Pa were measured with improved precision and newly assigned to be originating from the ground state, which clarified the ambiguities in their previous assignments. In addition, a new isomeric state in $^{218}$Pa decaying with α-particle energies of 9691(15) keV, 9595(21) keV and a half-life of 135−32+62 μs was identified. The spins and parities of the involved states were tentatively assigned on the basis of the systematic α-decay properties of the odd-proton N=127 isotones and shell model calculations.

Coupled socio-ecological systems (SES) are complex systems characterized by self-organization, non-linearities, interactions among heterogeneous elements within each subsystem, and feedbacks across scales and among subsystems. When such a system experiences a shock or a crisis, the consequences are difficult to predict. In this paper we first define what a shock or a crisis means for SES. Depending on where the system boundary is drawn, shocks can be seen as exogenous or endogenous. For example, human intervention in environmental systems could be seen as exogenous, but endogenous in a socio-environmental system. This difference in the origin and nature of shocks has certain consequences for coupled SES and for policies to ameliorate negative consequences of shocks. Having defined shocks, the paper then focuses on modelling challenges when studying shocks in coupled SES. If we are to explore, study and predict the responses of coupled SES to shocks, the models used need to be able to accommodate (exogenous) or produce (endogenous) a shock event. Various modelling choices need to be made. Specifically, the ‘sudden’ aspect of a shock suggests the time period over which an event claimed to be a shock occurred might be ‘quick’. What does that mean for a discrete event model? Turning to magnitude, what degree of change (in a variable or set of variables) is required for the event to be considered a shock? The ‘surprising’ nature of a shock means that none of the agents in the model should expect the shock to happen, but may need rules enabling them to generate behaviour in exceptional circumstances. This requires a certain design of the agents’ decision-making algorithms, their perception of a shock, memory of past events and formation of expectations, and the information available to them during the time the shock occurred.

Land developers play a key role in land-use and land cover change, as they directly make land development decisions and bridge the land and housing markets. Developers choose and purchase land from rural land owners, develop and subdivide land into parcel lots, build structures on lots, and sell houses to residential households. Developers determine the initial landscaping states of developed parcels, affecting the state and future trajectories of residential land cover, as well as land market activity. Despite their importance, developers are underrepresented in land use change models due to paucity of data and knowledge regarding their decision-making. Drawing on economic theories and empirical literature, we have developed a generalized model of land development decision-making within a broader agent-based model of land-use change via land markets. Developer’s strategies combine their specialty in developing of particular subdivision types, their perception of and attitude towards market uncertainty, and their learning and adaptation strategies based on the dynamics of the simulated land and housing markets. We present a new agent-based land market model that includes these elements. The model will be used to experiment with these different development decision-making methods and compare their impacts on model outputs, particularly on the quantity and spatial pattern of resultant land use changes. Coupling between the land market and a carbon sequestration model, developed for the larger SLUCE2 project, will allow us, in future work, to examine how different developer’s strategies will affect the carbon balance in residential landscapes

Adaptation to climate change might not always occur, with potentially catastrophic results. Success depends on coordinated actions at both governmental and individual levels (public and private adaptation). Even for a “wet” country like the Netherlands, climate change projections show that the frequency and severity of droughts are likely to increase. Freshwater is an important factor for agricultural production. A deficit causes damage to crop production and consequently to a loss of income. Adaptation is the key to decrease farmers’ vulnerability at the micro level and the sector’s vulnerability at the macro level. Individual adaptation decision-making is determined by the behavior of economic agents and social interaction among them. This can be best studied with agentbased modelling. Given the uncertainty about future weather conditions and the costs and effectiveness of adaptation strategies, a farmer in the model uses a cognitive process (or heuristic) to make adaptation decisions. In this process, he can rely on his experiences and on information from interactions within his social network. Interaction leads to the spread of information and knowledge that causes learning. Learning changes the conditions for individual adaptation decisionmaking. All these interactions cause emergent phenomena: the diffusion of adaptation strategies and a change of drought vulnerability of the agricultural sector. In this paper, we present a conceptual model and the first implementation of an agent-based model. The aim is to study the role of interaction in a farmer’s social network on adaptation decisions and on the diffusion of adaptation strategies and vulnerability of the agricultural sector. Micro-level survey data will be used to parameterize agents’ behavioral and interaction rules at a later stage. This knowledge is necessary for the successful design of public adaptation strategies, since governmental adaptation actions need to be fine-tuned to private adaptation behavior.

Sustainable water management in a changing environment full of uncertainty is a profound challenge. To deal with uncertainties, dynamic adaptive policies can be used. Such policies can change over time in response to how the future unfolds, to what we learn about the system, to changes in environment, and to changes in societal preferences. This paper presents a model driven approach that supports the development of dynamic adaptive policies, and illustrates the approach using a hypothetical case. The key idea of the approach is that the dynamic behavior of a fast and simple Integrated Assessment Meta Model (IAMM) is explored across a wide variety of uncertainties. Next, the performance of a set of candidate policy actions is assessed across these uncertainties. This provides insight into the sell-by date of the various actions, and thus when to modify or replace the action. From this, we deduce a logical sequencing of actions, constituting potential pathways. The performance of these pathways is in turn assessed, and iteratively improved. The hypothetical case is inspired by a river reach in the Rhine delta of the Netherlands. Like in the real world, this case is characterized by uncertainties about the future (e.g. climate change, socioeconomic developments, and natural variability), uncertainties about the system (e.g. chance on dike failure in relation to high water levels), and uncertainties about societal preferences (e.g. weight society gives to nature). Using a rule induction algorithm, we identify the vulnerabilities and opportunities presented by each pathway. We modify the pathways to address these opportunities and vulnerabilities through capitalizing and defensive actions. With the results it is possible to make an informed decision on a dynamic adaptive policy in a changing environment that is able to achieve the intended objectives despite the multitude of uncertainties present.

Water management is increasingly challenged by pressures from population growth, sea level rise and potential climate change. Moreover, these developments are highly uncertain and change over time. The challenge is to develop strategies that are either robust - insensitive to the future - or flexible enough to adapt to changing conditions. Adaptation is not only determined by what is known or anticipated at present, but also by what will be experienced and learned while the future unfolds, and by policy responses to social and water events. As a result, an adaptation pathway emerges. Exploring adaptation pathways into the future will provide indispensable support to decisionmaking in achieving sustainable water management. For a structured exploration of the various adaptation pathways, we are developing a computational model of the Rhine Delta in the Netherlands. With this model, we will simulate an evolvement of the system wherein pressures influence the water system state, and impacts on flood risk and water services such as agriculture and nature, which may then trigger a policy response. The result of this dynamic simulation is an adaptation pathway. The model should run fast enough to calculate many 100-year transient scenarios, representing the dominant processes and natural variability, and be able to implement various policy options. To enable such dynamic modelling we are developing an Integrated Assessment MetaModel (IAMM) using the technique of metamodelling. Metamodels are simple aggregated models that approximate the behaviour of complex and detailed models. The IAMM is simple in terms of process and spatial information, but complex in terms of time-series and policy response. The challenge is to capture enough detail and process information ensuring an adequate model behavior for policy analysis. We present the concept and describe for which situations - policy options and effects - the model can be used to support decisionmaking under uncertainty.

Accurate modeling of urban wind flow is important for the assessment of the natural ventilation of the outdoor environment and therefore for outdoor air quality. Different geometrical levels of analysis can be used, ranging from regular arrays of obstacles to real and complex case studies. In order to avoid the complexity of real case studies and to obtain more generic results, regular arrays of obstacles represent a suitable level to study the relation between urban morphology and outdoor ventilation. This paper presents the first results of a numerical study with Computational Fluid Dynamics (CFD) of isothermal wind flow in generic urban configurations. Nine urban configurations are defined with regular arrays of obstacles by increasing the plan area density (from 0.1 to 0.6) and the frontal area density (from 0.02 to 0.45). By this variation, the flow structure varies from isolated obstacle flow over wake interference flow to skimming flow. Among the nine configurations defined, four test cases are selected for a preliminary study. 3D steady Reynolds-Averaged Navier-Stokes (RANS) CFD simulations are performed for the four selected configurations and the simulations are compared with experimental wind-tunnel data. An overall fairly good agreement is found between the experimental and numerical results obtained with RANS simulations. The results allow establishing some relationships between morphological and fluid dynamics parameters of urban wind flow and outdoor ventilation. It is found that the upstream building density can cause a decrease of up to the 70% of the ventilation rate in the central street. Further work will focus on extending this parametric analysis and extrapolating it to real cities.

Large-Eddy Simulation of pollutant dispersion from a stack on the roof of a low-rise building in downtown Montreal is performed. Two wind directions are considered, with different wind flow patterns and plume behaviours. The resulting mean concentration field is observed and analysed with the computed (mean) convective and turbulent mass fluxes. These two concepts allow gaining some insight into the dispersion process and analysing the deficiencies of less sophisticated turbulence models. When the emitting building is located downstream of a high-rise building (case SW), the turbulent mass flux is directed from the high to low levels of mean concentration and the gradient diffusion hypothesis often used with steady models is verified. However, when the influence of the surrounding buildings is smaller (case W), a counter-gradient mechanism is observed in the streamwise direction, confirming the results obtained on isolated buildings. The present study supports the use of generic, simplified cases to investigate environmental processes; the conclusions can subsequently be applied to real and more complex cases.

High-resolution CFD simulations and full-scale measurements have been performed to assess the dispersion of air pollutants (CO2) from the large semi-enclosed Amsterdam ArenA football stadium. The dispersion process is driven by natural ventilation by the urban wind flow and by buoyancy, and by the interaction between outdoor wind flow and indoor airflow which are only connected by the relatively small ventilation openings in the stadium facade. The CFD simulations are performed with the 3D Reynolds-averaged Navier-Stokes equations supplemented with the realizable k-e model to provide closure. The full-scale measurements include reference wind speed, wind direction, and outdoor and indoor air temperature, water vapor and indoor CO2 concentration. In particular, the focus is on CFD simulations and measurements for the few hours immediately after a concert, when the stadium roof remains closed and when indoor air temperature,water vapor and CO2 concentration have reached a maximum level due to the attendants. The removal of the sources/attendants allows an assessment of the natural ventilation rate using the concentration decay method. The CFD simulations compare favorably with the measurements in terms of mean wind velocity in the main ventilation openings and in terms of the CO2 concentration decay after the concerts. The validated CFD model will in the future be used for a detailed evaluation of indoor concentration gradients and the interaction between wind-induced and buoyancy-induced natural ventilation.

Knowledge of microscale wind conditions is important for maneuvering and mooring of ships and for optimizing the harbor design. The aim of this study is to translate the macroscale wind conditions measured at a near shore reference station to the local (microscale) wind conditions in the harbor docks. In the first part of the project, an extensive experimental campaign has been performed, which consisted of wind velocity measurements with 2D and 3D ultrasonic anemometers during a period of 6 months. These point measurements confirm the unique relation between the macroscale and microscale wind conditions during periods of strong winds. As the measurements only provide information at a number of discrete positions, the second part of the study consists of numerical simulations with Computational Fluid Dynamics (CFD) to map the wind environmental conditions over the entire study area. The measurements and simulations both show very large gradients in mean wind speed over the harbor area, with differences up to 100%. The numerical simulations are currently in progress and will be validated by comparison with the on-site measurements.

Total Maximum Daily Loads (TMDLs) identify the maximum amount of pollution that a water body can receive and still support its designated uses and allocates the maximum load to specific sources in the watershed. In the United States, The Clean Water Act requires public participation in the process of TMDL development. This requirement has been met through simple presentation of results at public meetings, strategic partnerships with key stakeholders, and/or to advisory committees in which stakeholders participate in critical decisions about TMDL definition and implementation. These decisions include model selection and assumptions, selection of water quality endpoints, load allocations, TMDL review, and implementation planning. In this article, we discuss the benefits and challenges of early and targeted engagement of stakeholders in TMDL development through a participatory modelling process based on our experience in Utah and Vermont.

In many cases model integration treats models as software components only, ignoring the fluid relationship between models and reality, the evolving nature of models and their constant modification and re-calibration. As a result, with integrated models we find increased complexity, where changes that used to impact only relatively contained models of subsystems, now propagate throughout the whole integrated system. This makes it harder to keep the overall complexity under control and, in a way, defeats the purpose of modularity, when efficiency is supposed to be gained from independent development of modules. Treating models only as software in solving the integration challenge may give birth to 'integronsters' - constructs that are perfectly valid as software products but ugly and useless as models. We argue that one possible remedy is to learn to use data as modules and integrate them into the models. Then the data that are available for module calibration can serve as an intermediate linkage tool, sitting between modules and providing a moduleindependent baseline dynamics, which is then incremented when scenarios are to be run. In this case it is not the model output that is directed into the next model input, but model output is presented as a variation around the baseline trajectory, and it is this variation that is then fed into the next module down the chain. The Chesapeake Bay Program suite of models is used to illustrate these problems and the possible solutions.