Abstract The British Geological Survey (BGS) is developing integrated environmental models to address the grand challenges that face society. Here we describe the BGS vision for an Environmental Modelling Platform (BGS 2009) that will allow integrated models to be built, and describe case studies of emerging models in the United Kingdom. This Environmental Modelling Platform will be founded on the data and information that the BGS holds. This will have to be made as accessible and interoperable as possible to both the academic and stakeholder decision-making community. The geological models that have been built in an ad hoc way over the last 5–10 years will be encompassed in a National Geological Model that will be multi-scaled, beginning with onshore UK and eventually including the offshore continental shelf. The future will be characterized by the routine delivery of 3D model products from a multi-scaled and scalable 3D geological model of the UK that can be dynamically updated. The deployment of this model will generate further significant requirements across the Information and Knowledge Exchange spectrum, from applications development (database, GIS, web and mobile device), data management, information product development, to delivery to a growing number of publics and stakeholders.

Abstract Integrated environmental modelling (IEM) is a recent phenomenon that offers the opportunity to solve complex environmental problems. Whilst it has made great strides in recent years, there are still challenges to be met before IEM is universally accepted and used. This paper describes the current state of IEM and sets out a roadmap for achieving its full potential. A multidisciplinary, multi-agency approach will be required, the main goals of which are to: (1) raise awareness and build confidence in IEM; (2) ensure availability and accessibility of IEM techniques, tools and standards; (3) establish a minimum set of standards; (4) build the IEM skills base; (5) establish an underpinning research and development (R&D) programme; (6) co-ordinate and promote collaboration; and (7) foster IEM use by government, industry and the public. Once these goals have been achieved, then IEM can be deployed to help resolve currently intractable environmental issues, and the IEM methodology can be transferred to other fields.

Abstract The increasing complexity of numerical modelling systems in environmental sciences has led to the development of different supporting architectures. Integrated environmental modelling can be undertaken by building a ‘super model’ simulating many processes or by using a generic coupling framework to dynamically link distinct separate models during run-time. The application of systemic knowledge management to integrated environmental modelling indicates that we are at the onset of the norming stage, where gains will be made from consolidation in the range of standards and approaches that have proliferated in recent years. Consolidation is proposed in six topics: metadata for data and models; supporting information; Software-as-a-service; linking (or interface) technologies; diagnostic or reasoning tools; and the portrayal and understanding of integrated modelling. Consolidation in these topics will develop model fusion: the ability to link models, with easy access to information about the models, interface standards such as OpenMI and software tools to make integration easier. For this to happen, an open software architecture will be crucial, the use of open source software is likely to increase and a community must develop that values openness and the sharing of models and data as much as its publications and citation records.

Abstract This paper explains the background and development of the Environment Agency National Groundwater Modelling System (NGMS) to integrate recharge functionality with the existing groundwater modelling (Modflow functionality). The Environment Agency groundwater models were originally developed primarily as a tool for making high-level strategic decisions but their use for short-term extreme event scenarios, such as drought or flood, has been relatively limited. This functionality has been constrained by the format of the rainfall/recharge input datasets. Undertaking scenario runs based on change in climate and weather has only been possible by direct manual alteration of those input datasets, which is not always practical on a day-to-day basis. Full implementation of recharge models into NGMS changes this, allowing the recharge models to be run in the NGMS environment and output to be generated. The fusion aspect of the process involves that output being processed in such a way that it can be used by NGMS as input into a Modflow scenario run. This process is explained using the example of a recent drought scenario in the Wessex Basin groundwater model.

Abstract Data integration between different software is routinely needed in order to create suitable data formats or necessary data manipulation prior to importing the data. The procedures and workflows are not usually published. This paper presents the data integration between GSI3D (Geological Surveying and Investigation in 3 Dimensions) and groundwater flow modelling software GMS version 7.0 (Groundwater Modelling Systems) and FeFlow®. Geological models for two sites in Finland, an esker aquifer at Patamäki and a mine site in Luikonlahti, were constructed using dedicated 3D geological modelling software GSI3D. The data from the GSI3D model in Patamäki was exported as ASCII grid files directly to GMS in order to delineate hydrogeological features prior to groundwater flow modelling. The data from the GSI3D model in Luikonlahti was first manipulated in ArcGIS to make it amenable in FeFlow®. In both modelling locations, the detailed geological modelling greatly helped to discern different hydraulic conductivity zones that are based on different geological materials. This, in addition, eases the development of conceptual groundwater flow models, the model calibration process and potentially improves the simulation results.

Abstract Global economic losses related to natural hazards are large and increasing, peaking at US$380 billion in 2011 driven by earthquakes in Japan and New Zealand and flooding in Thailand. Catastrophe models are stochastic event-set based computer models, first created 25 years ago, that are now vital to risk assessment within the insurance and reinsurance industry. They estimate likely losses from extreme events, whether natural or man-made. Most catastrophe models limit the level of user interaction, stereotyped as ‘black boxes’. In this paper we investigate how model fusion techniques could be used to develop ‘plug and play’ catastrophe models and discuss the impact of open access modelling on the insurance industry and other stakeholders (e.g. local government).

Abstract In managing fish populations, especially at-risk species, realistic mathematical models are needed to help predict population response to potential management actions in the context of environmental conditions and changing climate while effectively incorporating the stochastic nature of real world conditions. We provide a key component of such a model for the endangered pallid sturgeon ( Scaphirhynchus albus ) in the form of an individual-based bioenergetics model influenced not only by temperature but also by flow. This component is based on modification of a known individual-based bioenergetics model through incorporation of: the observed ontogenetic shift in pallid sturgeon diet from marcroinvertebrates to fish; the energetic costs of swimming under flowing-water conditions; and stochasticity. We provide an assessment of how differences in environmental conditions could potentially alter pallid sturgeon growth estimates, using observed temperature and velocity from channelized portions of the Lower Missouri River mainstem. We do this using separate relationships between the proportion of maximum consumption and fork length and swimming cost standard error estimates for fish captured above and below the Kansas River in the Lower Missouri River. Critical to our matching observed growth in the field with predicted growth based on observed environmental conditions was a two-step shift in diet from macroinvertebrates to fish.

Abstract We present a hierarchical series of spatially decreasing and temporally increasing models to evaluate the uncertainty in the atmosphere – ocean global climate model (AOGCM) and the regional climate model (RCM) relative to the uncertainty in the somatic growth of the endangered pallid sturgeon ( Scaphirhynchus albus ). For effects on fish populations of riverine ecosystems, climate output simulated by coarse-resolution AOGCMs and RCMs must be downscaled to basins to river hydrology to population response. One needs to transfer the information from these climate simulations down to the individual scale in a way that minimizes extrapolation and can account for spatio-temporal variability in the intervening stages. The goal is a framework to determine whether, given uncertainties in the climate models and the biological response, meaningful inference can still be made. The non-linear downscaling of climate information to the river scale requires that one realistically account for spatial and temporal variability across scale. Our downscaling procedure includes the use of fixed/calibrated hydrological flow and temperature models coupled with a stochastically parameterized sturgeon bioenergetics model. We show that, although there is a large amount of uncertainty associated with both the climate model output and the fish growth process, one can establish significant differences in fish growth distributions between models, and between future and current climates for a given model.

Abstract Geological maps can be seen as a type of model and can be implemented in digital systems as geological spatial databases. In this context, geological map fusion can be implemented at different levels: harmonization of the conceptual data model describing the map objects; the use of shared concepts to describe properties in the model to give semantic harmonization; and ensuring geometric consistency. GeoSciML has been developed as an interchange language for geosciences information, derived from a common conceptual data model, along with common vocabularies of concepts to populate the object properties. GeoSciML and the vocabularies were used in the OneGeology-Europe project where a 1:1 million scale geological map of Europe was delivered using disseminated web services from 20 different data providers. The lessons learnt from the OneGeology-Europe project informed the development of the INSPIRE Geology Data Specification. The INSPIRE data specification is used to define what information must be made available through web services under the INSPIRE legislation, so has to be kept simple. The INSPIRE data model can be extended with GeoSciML and will provide a basis for geological map fusion.

Abstract Integrated Environmental Modelling (IEM) is an invaluable tool for understanding the complex, dynamic ecosystems that house our natural resources and control our environments. Human behaviour affects the ways in which the science of IEM is assembled and used for meaningful societal applications. In particular, human biases and heuristics reflect adaptation and experiential learning to issues with frequent, sharply distinguished, feedbacks. Unfortunately, human behaviour is not adapted to the more diffusely experienced problems that IEM typically seeks to address. Twelve biases are identified that affect IEM (and science in general). These biases are supported by personal observations and by the findings of behavioural scientists. A process for critical analysis is proposed that addresses some human challenges of IEM and solicits explicit description of (1) represented processes and information, (2) unrepresented processes and information, and (3) accounting for, and cognizance of, potential human biases. Several other suggestions are also made that generally complement maintaining attitudes of watchful humility, open-mindedness, honesty and transparent accountability. These suggestions include (1) creating a new area of study in the behavioural biogeosciences, (2) using structured processes for engaging the modelling and stakeholder communities in IEM, and (3) using ‘red teams’ to increase resilience of IEM constructs and use.

Abstract Socio-hydrology brings an interest in human values, markets, social organizations and public policy to the traditional emphasis of water science on climate, hydrology, toxicology and ecology. It also conveys a decision focus in the form of decision support tools, stakeholder engagement and new knowledge about the science–policy interface. This paper demonstrates how policy decisions and human behaviour can be better integrated into climate and hydrological models to improve their usefulness for decision support. Examples from SW USA and western Canada highlight uncertainties, vulnerabilities and critical tradeoffs facing water decision makers in the face of rapidly changing environmental and societal conditions. Irreducible uncertainties in downscaled climate and hydrological models limit the usefulness of climate-driven, predict-and-plan methods of water resource planning and management. Thus, it is argued that such methods should be replaced by approaches that use exploratory modelling, scenario planning and risk assessment in which the emphasis is on managing uncertainty rather than on reducing it. Model fusion supports all of these processes in integrating human and biophysical aspects of water systems, allowing policy impacts to be quantified and clarified, and fostering public engagement with water resource modelling.

Abstract Engineering is currently expanding its conceptual boundaries by accepting the challenge of interdisciplinarity, while often adopting social and biological concepts in developing tools (e.g. evolutionary optimization or interactive autonomous agents) or even world views (e.g. co-evolution, resilience, adaptation). The emerging socio-technical knowledge domain is still very much restricted by partial knowledge associated with the lack of long-term transdisciplinary research effort and the unavailability of robust, integrated tools able to cover both the technical and the socio-economic domains and to act as ‘thinking platforms’ for long-term scenario planning and strategic decision making under (high-order) uncertainties. Here we present an example of a toolkit that attempts to bridge this gap focusing on urban water (UW) systems and their management. The toolkit consists of three tools: the UW Optioneering Tool (UWOT); the UW Agent Based Modelling Platform (UWABM); and the UW System Dynamic Environment (UWSDE). The tools are briefly presented and discussed, focusing on interactions and data flows between them and their typical results are illustrated through a case study example. A further tool (a Cellular Automata Based Urban Growth Model) is currently under development and an early coupling with the other tools is also discussed. It is argued that this type of extended model fusion, beyond what has traditionally been thought of as ‘integrated modelling’ in the engineering domain is a new frontier in the understanding of environmental systems and presents a promising, emerging field in modelling interactions between our societies and cities, and our environment.

Abstract Natural-resource managers and stakeholders face difficult challenges when managing interactions between natural and societal systems. Potential changes in climate could alter interactions between environmental and societal systems and adversely affect the availability of water resources in many coastal communities. The availability of freshwater in coastal streams can be threatened by saltwater intrusion. Even though the collective interests and computer skills of the community of managers, scientists and other stakeholders are quite varied, there is an overarching need for equal access by all to the scientific knowledge needed to make the best possible decisions. This paper describes a decision support system, PRISM-2, developed to evaluate salinity intrusion due to potential climate change along the South Carolina coast in southeastern USA. The decision support system is disseminated as a spreadsheet application and integrates the output of global circulation models, watershed models and salinity intrusion models with real-time databases for simulation, graphical user interfaces, and streaming displays of results. The results from PRISM-2 showed that a 31-cm and 62-cm increase in sea level reduced the daily availability of freshwater supply to a coastal municipal intake by 4% and 12% of the time, respectively. Future climate change projections by a global circulation model showed a seasonal change in salinity intrusion events from the summer to the fall for the majority of events.

Abstract Society requires rapid, most-probable predictions for specific and/or multifaceted questions related to environmental and geological science. In principle, models that encapsulate disciplinary knowledge are useful tools for making predictions and testing theory, but academic rewards favour disciplinary specialism and a proliferation of often insufficiently tested models. Decision makers have to assess the quality and robustness of predictions for complex environmental issues, and may prefer a model that performs accurately in a case study to a more parsimonious and generalizable model. Predictive ecosystem models tend to grow, as more processes are considered, even when a simpler model may be more appropriate and give results that are easier to interpret within a policy-relevant timeframe. Model fusion provides a practical way to combine knowledge from different disciplines, but can accelerate model growth. How then can we facilitate the evolution of useful predictive models? Coherent design is essential. When combining models it is often necessary to resolve overlapping scope, so tools need to allow for the disaggregation of model implementations as well as their fusion. Modelling software and integration frameworks can help resolve technical constraints, but to make models useful and used it is essential to involve stakeholders in their design and interpretation.

Abstract The ‘Big Data’ paradigm will revolutionize understanding of the natural environment. New technologies are revolutionizing our ability to measure, model, understand and make robust, evidence-based predictions at increasingly spatial and temporal resolutions. Realising this potential will require reengineering of environmental sciences in the observation infrastructure, in data management and processing, and in the culture of environmental sciences. Collectively these will deliver vibrant, integrated research communities. Manipulating such enormous data streams requires a new data infrastructure underpinned by four technologies. Pervasive environmental sensor networks will continuously measure suites of environmental parameters and transmit these wirelessly to scientists, regulators and modellers in real time. Integrated environmental modelling will process data, streamed from sensor networks, using components synthesizing natural systems developed by domain experts, each of which will be linked at runtime to other expert developed components. Semantic interoperability will facilitate cross-disciplinary working, as has already happened within the biosciences so that data items can be exchanged with unambiguous, shared meaning. Cloud computing will revolutionize data processing allowing scalable computing close to observations on an as-needed basis. Leveraging the full potential of these technologies requires a major culture change in the environmental sciences where national and continental scale observatories of sensors networks become basic scientific tools.

Abstract There are increasing demands in assessing the impacts of change on environmental systems to couple different model components together in a cascade, the outputs from one component providing the inputs to another with or without feedbacks in the coupling. Each model component will necessarily involve some uncertainty in its specification and simulations that can be conditioned using some observational data. Taking account of this uncertainty should result in more robust decision making and may change the nature of the decision made. The difficulty in environmental decision making is in making proper estimates of uncertainties when so many of the sources of uncertainty result from lack of knowledge (epistemic uncertainties) rather than uncertainty that can be treated as random variability (aleatory uncertainty). This is particularly the case for problems that involve cascades of model components. Examples are the use of UKCP09 climate scenarios in impact studies, flood risk assessment involving models of runoff generation and their impact on hydraulic models of flood plains, and integrated catchment management involving upstream to downstream surface and subsurface routing of water quality variables. The uncertainties are such that, even for relatively simple problems, they can result in wide ranges of potential outputs. This poses the questions that will be considered in this paper: how to take account of knowledge uncertainties in cascades of model components; and how to constrain the potential uncertainties for use in making decisions. In particular we highlight the difficulties of defining statistical likelihood functions that properly reflect the non-stationary uncertainty characteristics expected of epistemic sources of uncertainty.

Abstract The discipline of Integrated Environmental Modelling (IEM) has developed in order to solve complex environmental problems, for example understanding the impacts of climate change on the physical environment. IEM provides methods to fuse or link models together, this in turn requires facilities to make models discoverable and also to make the outputs of modelling easily visualized. The vision and challenges for IEM going forward are summarized by leading proponents. Several case studies describe the application of model fusion to a range of real-world problems including integrating groundwater and recharge models within the UK Environment Agency, and the development of ‘catastrophe’ models to predict better the impact of natural hazards. Communicating modelling results to end users who are often not specialist modellers is also an emerging area of research addressed within the volume. Also included are papers that highlight current developments of the technology platforms underpinning model fusion.