VPSIRR (Vulnerability - Pressure - State - Impact - Risk And Response): An Approach To Determine The Condition Of Estuaries And To Assess Where Management Responses Are

Estuaries are highly variable in terms of type and geomorphic classification. The condition of these systems is often a reflection of activities taking place in their catchments and the susceptibility of these systems to each particular pressure. Effective management intervention can be achieved when there is an understanding of the current condition of the estuary or component of the estuary and of the pressures likely to affect them. If this can be linked to the susceptibility of the estuary to the pressure (risk), the management activity can be prioritised. A framework based on the Pressure-State-Impact-Response model, but which also includes the vulnerability of the system to each of the pressures has been developed. A key feature of this framework is that the links between indicators of pressure, state and impact are clearly identified ensuring that only indicators relevant to the local situation are selected. In addition, a risk assessment process has been developed. This approach is called a VPSIRR (Vulnerability - Pressure - State - Impact - Risk - Response)approach. Application of this method increases the likelihood of being able to identify the causes of any observed changes in condition, making it easier to identify appropriate management actions. It also enables information to be provided to the community in a user-friendly manner. We have developed a user friendly computer package which enables the risk that each estuary is under from various pressures to be assessed and linked to condition. The package enables the certainty about various data used to inform the process, to be reported. Importantly, the package enables indicator information to be updated as better information becomes available. It also enables new indicator information to be incorporated into the software should better knowledge become available. This component would only be made available to software administrators. The package produces a colour coded and numeric report card comprising of 5 colours or numbers which is designed to be easily understood and interpreted by users from a variety of backgrounds. The software can be used to inform managers of where to focus management investment, but can also be used to educate people about natural resource issues and the implications of different catchment and estuary based activities. Fact sheets imbedded within the software provide details about the various indicators. These include how to collect data and where necessary, how to analyse them in order to use the software. The fact sheets also provide information on management responses to a variety of issues

An agent-based framework for modelling social influence on travel behaviour

Recent travel forecasting models have focussed upon the fact that travel is derived from the activities in which people participate, such as work, school, shopping, sport, leisure, and social events. Non-discretionary activities such as work and school may be explained by the traveller's sociodemographic characteristics and generalised travel costs, as well as long-term decisions such as a decision to move to a particular town. Participation in social and leisure activities is determined by one's friends and the groups that one is a member of, i.e., their household, their workplace/school, sporting groups, voluntary organisations and clubs. These acquaintances form part of an individual's social network: a representation of the people one interacts with. This demonstrates a shift in the activity-travel modelling field from “where” to “what” and now towards “who with”. On top of this, our changing use of ICT is influencing our activity and travel patterns, as some activities can now be replaced by online activities, and online activities can lead to actual travel. Some researchers are already looking beyond households to the influence of social networks. However, we are not aware of any agent-based urban models considering activity-travel choice of individuals. Existing work is in the conceptual or early implementation phases. The aim of this project is to develop and validate a model combining social (“who with”) and spatial (“where”) networks for investigating and predicting social activities. In this paper, we describe the design of our model. Agent-based modelling is a good fit for our model. Our system consists of different people, their relationships and interactions with each other, and their activities in and possible movement around the environment. The topology is not homogeneous and clusters may form. We have used a combination of the metamodels found in mature agent-oriented software engineering methodologies to design our model, focussing on system goals, the environment, acquaintances, roles, and services. The design successfully caters for the description of the environment, the nature of activities, and the dynamics of individuals and their networks. The individuals in our model each have an agenda, and interact and negotiate with others to schedule social activities, in particular negotiating about the nature of the activity, participants, time, and location. Existing models do not capture the actual joint decision making process behind activity scheduling, and although some work on joint decisions has been undertaken, these models focus on outcomes of interactions within households and have not considered personal social networks at large. We use existing multi-issue negotiation theory to describe an interaction design, which is shown to satisfy a number of basic properties, such as termination, liveness, and safety. Due to the current interest in predicting social activities and the changing nature of social activities due to our use of ICT, this type of model is of increasing importance to planners who need to be able to predict social activities and travel. The model is currently being implemented in Java, and will be validated using an extensive dataset of people's activity participation and personal networks, collected in Eindhoven, Netherlands. Future work includes more empirical experimentation with the protocols and implementation of and experimentation with the entire model

Good Modelling Practice

Best-practice guidelines for Modelling have been developed by a numbers of organizations to promote better understanding of model development and application, facilitate tests of model quality and provide a framework for documenting and communicating modelling activities among modellers and decision makers. Good practice within a Data Mining paradigm is presented in Chapter 12

Ensemble predictions of hydro-biogeochemical fluxes at the landscape scale

Model predictions of biogeochemical fluxes on the landscape scale are highly uncertain, both with respect to stochastic (parameter) and structural uncertainty. The idea of our ensemble modelling approach is to reduce the predictive uncertainty by covering part of the parameter and model structural uncertainty. In this study 4 different models (LASCAM, a modified INCA model, SWAT and HBV-N-D) designed to simulate hydrological fluxes as well as mobilization and transport of one or several nitrogen species are applied over the meso-scaled River Fyris catchment in Mid-Eastern Sweden. Hydrological calibration against 5 years of recorded discharge at two stations gives highly variable results from Nash-Sutcliffe Efficiency (NSE) values above 0.80 to values around 0.50. SWAT and HBV-N-D gives alternatively the best simulation result at each station respectively. Alteration of nitrogen parameters following Monte-Carlo or Latin-Hypercube stratified sampling schemes is realized in order to cover the parameter uncertainty of predictions for 3 nitrogen species: nitrate (NO3), ammonium (NH4) and total nitrogen (Tot-N) in terms of exported loads. For each model and each nitrogen species, predictions are ranked in two different ways regarding the performance indicated by two different objective functions: the coefficient of determination R2 and the Nash-Sutcliffe Efficiency (NSE). Model ensembles were compiled in various ways. A total of 396 Single Model Ensembles (SME) are generated using an increasing number of model members. Finally, 78 Multi-Model Ensembles (MME) are combined by using the best SME for each model, nitrogen species and station. The evolution of the two aforementioned objective functions is used as performance descriptor of the ensemble procedure. In each studied case, there is always at least one compilation scheme which outperforms any of its members. The best SME are multiple-linear regression models with R2 selected members, increasing the best NSE values from negativity up to very high ones (0.83). The uncertainty bounds of the SME are almost always smaller than the one introduced by the whole set of selected single model runs still including most of measurements and even more (half of the cases) than the bounds of the selected single runs set. In the same way, there is always at least one MME combination scheme which outperforms all the SME, but the increase in model performance is pronounced than the difference between single model runs and SME. The best MME are the ones with the most members and both R2 and NSE values are reaching 0.89 in the best case. Uncertainty areas described by MME are alternatively increased or reduced compared to the bounds delineated by their members. No general trend is deduced for the studied cases.</p

Comparison of soil moisture in GLDAS model simulations and satellite observations over the Murray Darling Basin

Soil moisture is a key hydrometeorological variable that can be derived from both modeling simulations and satellite observations. This study compares Global Land Data Assimilation System (GLDAS) output over the Murray Darling Basin against retrievals from a newly developed remote sensing product using the AMSR-E sensor onboard NASA's Aqua satellite. GLDAS is comprised of a number of land surface models, two of which include the Community Land Model (CLM) and NOAH land surface scheme, which provide a temporally and spatially consistent characterization of the hydrological cycle. GLDAS derived estimates are 3-hourly products with 0.25-degree spatial resolution, while satellite based observations offer twice-daily instantaneous retrievals at similar spatial scales. The models represent different soil moisture averaging depths (roughly 2, 5, and 10 cm in CLM and 10 cm in NOAH) and retrievals from AMSR-E C-band approximate the soil moisture in the top 1.5 cm layer. The spatial distribution and coherence of soil moisture are investigated seasonally and under both wetting and drying conditions. From the spatial aspect, AMSR-E observations and GLDAS simulations show similar seasonal patterns, while simulated soil moisture is slightly higher during summer and autumn over the north-eastern Murray Darling Basin (MDB). This may be explained by the positive biases of GLDAS forcing precipitation data. From the temporal perspective, the best match between AMSR-E soil moisture and model simulations is found over the regions with strong precipitation in warm months, e.g. north-eastern MDB. Over the regions with high precipitation during cool months, AMSR-E soil moisture is systematically higher than model simulations. For the regions with extremely low annual rainfall, the peak values in soil moisture between AMSR-E and model simulations match very well, while low values of soil moisture display the greatest differences. Generally, the agreements between AMSR-E observations and GLDAS simulations vary under different wetting and drying conditions. Both of them can represent the 'true' soil moisture to some extent. How to best blend soil moisture products derived from these two different techniques, in addition to data assimilation approaches, will be explored in future research

Characterising performance of environmental models

In order to use environmental models effectively for management and decision-making, it is vital to establish an appropriate level of confidence in their performance. This paper reviews techniques available across various fields for characterising the performance of environmental models with focus on numerical, graphical and qualitative methods. General classes of direct value comparison, coupling real and modelled values, preserving data patterns, indirect metrics based on parameter values, and data transformations are discussed. In practice environmental modelling requires the use and implementation of workflows that combine several methods, tailored to the model purpose and dependent upon the data and information available. A five-step procedure for performance evaluation of models is suggested, with the key elements including: (i) (re)assessment of the model's aim, scale and scope; (ii) characterisation of the data for calibration and testing; (iii) visual and other analysis to detect under- or non-modelled behaviour and to gain an overview of overall performance; (iv) selection of basic performance criteria; and (v) consideration of more advanced methods to handle problems such as systematic divergence between modelled and observed values