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This work was performed within the framework of the following projects: (1) AQUACLEW, which is part of ERA4CS, an ERA-NET initiative by JPI Climate, and funded by FORMAS (SE) , DLR (DE) , BMWFW (AT) , IFD (DK) , MINECO (ES) , ANR (FR) with co-funding by the European Commission [Grant 690462] and (2) Flooding and erosion works in coastal areas of Andalusia under a climate change scenario, funded by the Ministry of Agriculture, Livestock, Fisheries and Sus-tainable Development of the Junta de Andalucia [Contrat No. CONTR 2018 66984] . Part of this study has been conducted using E.U. Copernicus Marine Service Information. Funding for open access charge: Universidad de Granada/CBUA., The assessment of the uncertainty about the evolution of complex processes usually requires different realizations consisting of multivariate temporal signals of environmental data. However, it is common to have only one observational set. MarineTools.temporal is an open-source Python package for the non-stationary parametric statistical analysis of vector random processes suitable for environmental and Earth modelling. It takes a single timeseries of observations and allows the simulation of many time series with the same probabilistic behavior. The software generalizes the use of piecewise and compound distributions with any number of arbitrary continuous distributions. The code contains, among others, multi-model negative log-likely functions, wrappednormal distributions, and generalized Fourier timeseries expansion. Its programming philosophy significantly improves the computing time and makes it compatible with future extensions of scipy.stats. We apply it to the analysis of freshwater river discharge, water currents, and the simulation of ensemble projections of sea waves, to show its capabilities., Swedish Research Council Formas, Helmholtz Association, German Aerospace Centre (DLR), BMWFW, Spanish Government, French National Research Agency (ANR), European Commission European Commission Joint Research Centre, Ministry of Agriculture, Livestock, Fisheries and Sus-tainable Development of the Junta de Andalucia 690462, CONTR 2018 66984

Complex environmental model outputs used to inform decisions often have systematic errors and are of inappropriate resolution, requiring downscaling and bias correction for local applications. Here we provide a new interpretation of dasymetric modelling (DM) as a spatial bias correction framework useful in environmental modelling. DM is based on areal interpolation where estimates of some variable at target zones are obtained from overlapping source zones using ancillary information. We explore DM by downscaling runoff output from a distributed hydrological model using two meta-models and describe the properties of the methodology in detail. Consistent with properties of linear scaling bias correction, results show that the methodology 1) reduces errors compared to the source data and meta-models, 2) improve the spatial structure of the estimates, and 3) improve the performance of the downscaled estimates, particularly where meta-models perform poorly. The framework is simple and useful in ensuring spatial coherence of downscaled products.

This paper presents the Agent-Based Modelling System of spatial distribution of species SDSim. SDSim is an agent-based modelling system designed to simulate spatial distribution of species and populations for conservation and management purposes. SDSim gives to modellers the ability to simulate movements and colonization patterns of species given locations under study and a set of eco-geographical variables in which species depends on., This work was supported by Centro-01-0145-FEDER- 000019 - C4 - Centro de Competências em Cloud Computing, cofinanced by the European Regional Development Fund (ERDF) through the Programa Operacional Regional do Centro (Centro 2020), in the scope of the Sistema de Apoio à Investigação Científica e Tecnológica - Programas Integrados de IC&DT. This work was also funded by FCT/MCTES through national funds and when applicable co-funded EU funds under the project UIDB/50008/2020.

Geospatial information plays an important role in environmental modelling, resource management, business operations, and government policy. However, very little or no commonality between formats of various geospatial data has led to difficulties in utilizing the available geospatial information. These disparate data sources must be aggregated before further extraction and analysis may be performed. The objective of this paper is to develop a framework called PlaniSphere, which aggregates various geospatial datasets, synthesizes raw data, and allows for third party customizations of the software. PlaniSphere uses NASA World Wind to access remote data and map servers using Web Map Service (WMS) as the underlying protocol that supports service-oriented architecture (SOA). The results show that PlaniSphere can aggregate and parses files that reside in local storage and conforms to the following formats: GeoTIFF, ESRI shape files, and KML. Spatial data retrieved using WMS from the Internet can create geospatial data sets (map data) from multiple sources, regardless of who the data providers are. The plug-in function of this framework can be expanded for wider uses, such as aggregating and fusing geospatial data from different data sources, by providing customizations to serve future uses, which the capacity of the commercial ESRI ArcGIS software is limited to add libraries and tools due to its closed-source architectures and proprietary data structures. Analysis and increasing availability of geo-referenced data may provide an effective way to manage spatial information by using large-scale storage, multidimensional data management, and Online Analytical Processing (OLAP) capabilities in one system.

Integrated environmental modelling (IEM) couples environmental models, geoprocessing algorithms, and data together to solve complex environmental problems. There are two prominent modelling frameworks for IEM: component-based and service-oriented frameworks. The former one has been widely employed in local computer environments, and has its advantage in having concrete and community-wide tools. The latter one can take advantage of Web technologies for integrating distributed environmental models. However, the complex interaction between environmental models such as time marching models is still a challenge for integrating models on the Web. This paper suggests to take the best of both frameworks. Models are shared on the Web as WebSocket services. A hybrid approach to leverage OpenMI and WebSocket for coupling components and services is proposed to provide a flexible IEM environment. The result illustrates the applicability of the approach and demonstrates the advantage of WebSocket over the traditional HTTP approach in model execution.

Environmental modelling is transitioning from the traditional paradigm that focuses on the model and its quantitative performance to a more holistic paradigm that recognises successful model-based outcomes are closely tied to undertaking modelling as a social process, not just as a technical procedure. This paper redefines evaluation as a multi-dimensional and multi-perspective concept, and proposes a more complete framework for identifying and measuring the effectiveness of modelling that serves the new paradigm. Under this framework, evaluation considers a broader set of success criteria, and emphasises the importance of contextual factors in determining the relevance and outcome of the criteria. These evaluation criteria are grouped into eight categories: project efficiency, model accessibility, credibility, saliency, legitimacy, satisfaction, application, and impact. Evaluation should be part of an iterative and adaptive process that attempts to improve model-based outcomes and foster pathways to better futures.

Dynamical earth and environmental systems models are typically computationally intensive and highly parameterized with many uncertain parameters. Together, these characteristics severely limit the applicability of Global Sensitivity Analysis (GSA) to high-dimensional models because very large numbers of model runs are typically required to achieve convergence and provide a robust assessment. Paradoxically, only 30 percent of GSA applications in the environmental modelling literature have investigated models with more than 20 parameters, suggesting that GSA is under-utilized on problems for which it should prove most useful. We develop a novel grouping strategy, based on bootstrap-based clustering, that enables efficient application of GSA to high-dimensional models. We also provide a new measure of robustness that assesses GSA stability and convergence. For two models, having 50 and 111 parameters, we show that grouping-enabled GSA provides results that are highly robust to sampling variability, while converging with a much smaller number of model runs.

International audience; The acceptance and usefulness of simulation models are often limited by the efficiency, transparency, reproducibility, and reliability of the modelling process. We address these issues by suggesting that modellers (1) "trace" the iterative modelling process by keeping a modelling notebook corresponding to the laboratory notebooks used by empirical researchers, (2) use a standardized notebook structure and terminology based on the existing TRACE documentation framework, and (3) use their notebooks to compile TRACE documents that supplement publications and reports. These practices have benefits for model developers, users, and stakeholders: improved and efficient model design, analysis, testing, and application; increased model acceptance and reuse; and replicability and reproducibility of the model and the simulation experiments. Using TRACE terminology and structure in modelling notebooks facilitates production of TRACE documents. We explain the rationale of TRACE, provide example TRACE documents, and suggest strategies for keeping "TRACE Modelling Notebooks."Highlights:• The quality and reproducibility of modelling would be increased by keeping modelling notebooks.• Simulation modellers have not yet developed a strong culture of keeping notebooks.• We propose a standard format for notebooks based on the TRACE document framework.• We make recommendations about the type of information to be included in the notebooks.• We describe existing tools to keep notebooks.

Sensitivity analysis (SA) as a ‘formal’ and ‘standard’ component of scientific development and policy support is relatively young. Many researchers and practitioners from a wide range of disciplines have contributed to SA over the last three decades, and the SAMO (sensitivity analysis of model output) conferences, since 1995, have been the primary driver of breeding a community culture in this heterogeneous population. Now, SA is evolving into a mature and independent field of science, indeed a discipline with emerging applications extending well into new areas such as data science and machine learning. At this growth stage, the present editorial leads a special issue consisting of one Position Paper on “The future of sensitivity analysis” and 11 research papers on “Sensitivity analysis for environmental modelling” published in Environmental Modelling & Software in 2020–21. publishedVersion

International audience; There is growing interest in the application of the latest trends in computing and data science methods to improve environmental science. However we found the penetration of best practice from computing domains such as software engineering and cloud computing into supporting every day environmental science to be poor. We take from this work a real need to re-evaluate the complexity of software tools and bring these to the right level of abstraction for environmental scientists to be able to leverage the latest developments in computing. In the Models in the Cloud project, we look at the role of model driven engineering, software frameworks and cloud computing in achieving this abstraction. As a case study we deployed a complex weather model to the cloud and developed a collaborative notebook interface for orchestrating the deployment and analysis of results. We navigate relatively poor support for complex high performance computing in the cloud to develop abstractions from complexity in cloud deployment and model configuration. We found great potential in cloud computing to transform science by enabling models to leverage elastic, flexible computing infrastructure and support new ways to deliver collaborative and open science.

Bayesian Networks (BNs) are an increasingly popular method for modelling environmental systems. The discretization of continuous variables is often required to use BNs. There are three main methods of discretization; manual, unsupervised, and supervised. Here, we compare and demonstrate each approach with a BN that predicts coastal erosion. Results reveal that supervised discretization methods produced BNs of the highest average predictive skill (73.8%), followed by manual discretization (69.0%) and unsupervised discretization (64.8%). However, each method has specific advantages that may make them more suitable for particular applications. Manual methods can produce physical meaningful BNs, which is favorable in environmental modelling. Supervised methods can autonomously and optimally discretize variables and may be preferred when predictive skill is a modelling priority. Unsupervised methods are computationally simple and versatile. The optimal discretization scheme should consider both the performance and practicality of the scheme.

Taking the path perspective helps to understand and improve the practice of environmental modelling and decision making. A path is the sequence of steps taken in a modelling project. The problem solving team faces several forks where alternative choices can be made. These choices determine the path, together with the impact of uncertainties and exogenous effects. This paper discusses phenomena that influence the problem solvers' choices at the forks. Situations are described where it can be desirable to re-direct the path or backtrack on it. Phenomena are identified that can cause the modelling project to get stuck on a poor path. The concept of a path draws attention to the interplay of behavioral phenomena and the sequential nature of modelling processes. This helps understand the overall effect of the behavioral phenomena. A path checklist is developed to help practitioners detect forks and reflect on the path of the modelling project. The steps taken in a modelling project create a path the path can change the results.Biases and other phenomena influence modeler choices at decision forks along the path.Backtracking steps can be necessary to avoid getting stuck on a poor path.Understanding the risks of path dependence in environmental modelling is important.A path checklist helps modelers detect forks and cope with path dependence.

An increase in volumes of data and a shift towards live data enabled a stronger focus on resource-intensive tasks which run continuously over long periods. A Grid has potential to offer the required resources for these tasks, while considering a fair and balanced allocation of resources among multiple client agents. Taking this into account, a Grid might be unwilling to allocate its resources for long time, leading to task interruptions. This problem becomes even more serious if an interruption of one task may lead to the interruption of dependent tasks. Here, we discuss a new strategy for resource re-allocation which is utilized by a client with the aim to prevent too long interruptions by re-allocating resources between its own tasks. Those re-allocations are suggested by a client agent, but only a Grid can re-allocate resources if agreed. Our strategy was tested under the different Grid settings, accounting for the adjusted coefficients, and demonstrated noticeable improvements in client utilities as compared to when it is not considered. Our experiment was also extended to tests with environmental modelling and realistic Grid resource simulation, grounded in real-life Grid studies. These tests have also shown a useful application of our strategy.

We wish to introduce QWET, a new version of the free and open-source QGIS plugin for the aquatic ecosystem model WET. QWET is as a graphical user interface for the application, evaluation and experimentation of WET. Several new features have been incorporated since its predecessor and, here, we demonstrate elements of the new plugin by applying it to Danish Lake Ravn. Among others, we compare model simulations against observations and describe how the scenario platform now supports scheduling of state-variable manipulation, which allows users to explore lake or reservoir restoration interventions such as biomanipulation and oxygenation. With QWET, we seek to aid practitioners who do not possess the sufficient technical expertise to operate a state-of-the art complex model system, such as WET, and thereby hope to facilitate a wider use and adaptation of aquatic ecosystem models.

Despite the growing acknowledgment of reproducibility crisis in computational science, there is still a lack of clarity around what exactly constitutes a reproducible or replicable study in many computational fields, including environmental modelling. To this end, we put forth a taxonomy that defines an environmental modelling study as being either 1) repeatable, 2) runnable, 3) reproducible, or 4) replicable. We introduce these terms with illustrative examples from hydrology using a hydrologic modelling framework along with cyberinfrastructure aimed at fostering reproducibility. Using this taxonomy as a guide, we argue that containerization is an important but lacking component needed to achieve the goal of computational reproducibility in hydrology and environmental modelling. Examples from hydrology are provided to demonstrate how new tools, including a user-friendly tool for containerization of computational analyses called Sciunit, can lower the barrier to reproducibility and replicability in the environmental modelling community.

The pathway of a modelling project is commonly described as an adaptively adjusted chain of steps at which various decisions are made. Communication and documentation about these decisions are crucial to enabling reflection and adapting the pathway to changing circumstances, such that well-informed planning is required. Project decision making, however, often remains a black box; it is rare to find reporting of dead ends, alternative decisions, and changes in decisions during the project. This paper uses an integrated environmental management case study in Iran to demonstrate the importance of reflective documentation and communication within the pathway. We show how a pathway diagram, incorporating some 14 symbols depicting steps, decision forks, options (selected and alternatives), actions, communication and documentation, can illustrate the role of communication within the project and identify lessons learnt. We also encourage further work on application of agile project management and social science techniques to improve modelling practices.

There is an increasing need for environmental management advice that is wide-scoped, covering various interlinked policies, and realistic about the uncertainties related to the possible management actions. To achieve this, efficient decision support integrates the results of pre-existing models. Many environmental models are deterministic, but the uncertainty of their outcomes needs to be estimated when they are utilized for decision support. We review various methods that have been or could be applied to evaluate the uncertainty related to deterministic models' outputs. We cover expert judgement, model emulation, sensitivity analysis, temporal and spatial variability in the model outputs, the use of multiple models, and statistical approaches, and evaluate when these methods are appropriate and what must be taken into account when utilizing them. The best way to evaluate the uncertainty depends on the definitions of the source models and the amount and quality of information available to the modeller. We review different types of uncertainty present in environmental modelling.We review methods to evaluate uncertainty related to model results.Best way to evaluate uncertainty depends on the models and available information.

In this paper we show that integrated environmental modelling (IEM) techniques can be used to generate a catastrophe model for groundwater flooding. Catastrophe models are probabilistic models based upon sets of events representing the hazard and weights their likelihood with the impact of such an event happening which is then used to estimate future financial losses. These probabilistic loss estimates often underpin re-insurance transactions. Modelled loss estimates can vary significantly, because of the assumptions used within the models. A rudimentary insurance-style catastrophe model for groundwater flooding has been created by linking seven individual components together. Each component is linked to the next using an open modelling framework (i.e. an implementation of OpenMI). Finally, we discuss how a flexible model integration methodology, such as described in this paper, facilitates a better understanding of the assumptions used within the catastrophe model by enabling the interchange of model components created using different, yet appropriate, assumptions. IEM techniques are used to generate a catastrophe model for groundwater flooding.Groundwater catastrophe model is developed for the Marlborough and Berkshire downs.Documentation of model components and data increases transparency and understanding.OpenMI provided a flexible and repeatable framework for Catastrophe model generation.

Integrated environmental modeling (IEM) provides a systematic way to couple models for integrated analysis. Coupled models in IEM often exchange data at runtime for time-step based executions. It is a challenge to track which raw observations or intermediate data exchanged at runtime contribute to individual model outputs. Time-step level provenance is needed to audit the trail of model execution or perform diagnosis in case of anomalies. This paper introduces a method to support provenance awareness in IEM. It suggests that individual models should expose necessary interfaces for provenance capturing in IEM environments. The provenance is represented using the W3C PROV model for interoperability. Fine-grained provenance is inferred based on coarse-grained provenance and temporal characteristics of computations of numerical time marching models. The approach is implemented in OpenMI-compliant models. A case study of model provenance tracking and inference on the watershed runoff simulation scenario illustrates the applicability of the approach. Model provenance tracking in integrated environmental modelling.Inference for fine-grained model provenance.Provenance awareness for OpenMI.

Integrated environmental modelling (IEM) couples interdependent environmental models and data together to solve complex environmental problems. There are two major modelling frameworks for IEM: component based framework, and service oriented framework. This paper suggests to take the best of both to couple modelling components and services together. The result is a hybrid method to leverage Open Modelling Interface (OpenMI) and WebSocket for coupling components and services together to provide a more flexible IEM environment.

This paper builds on reverse combinatorial auctions theory and its selected environmental applications, which were presented at ISESS 2013 and ISESS 2015. It provides an approach for calculating the sensitivity and proposals for necessary adjustments of CombinatoRial Auction Body Software System (CRAB), which makes its use for the relevant decision-making tasks more user friendly. Two possibilities are suggested. The first approach is appropriate for cases with relatively small numbers of subjects, where it is possible to compute all feasible solutions ordered by total cost. In such cases it is possible to analyse changes of coalition structures with increasing the cost. The second one suggests modification of the CRAB software, which would make it possible to analyse cases with high numbers of feasible coalition structures located between the optimal coalition (i.e. the cost-effective one) and the structure consisting of individual projects. This approach is appropriate for complex real applications involving setting of cost levels.

Several global datasets are available for environmental modelling, but information provided is hardly used for decision-making at a country-level. Here we propose a method, which relies on global sensitivity analysis, to improve local relevance of environmental indicators from global datasets. This method is tested on nitrogen use framework for two contrasted case studies: mixed dairy supply chains in Rwanda and the Netherlands. To achieve this, we evaluate how indicators computed from a global dataset diverge from same indicators computed from survey data. Second, we identify important input parameters that explain the variance of indicators. Subsequently, we fix non-important ones to their average values and substitute important ones with field data. Finally, we evaluate the effect of this substitution. This method improved relevance of nitrogen use indicators; therefore, it can be applied to any environmental modelling using global datasets to improve their relevance by prioritizing important parameters for additional data collection.

The KUBUS conference centre at the Helmholtz Centre for Environmental Research in Leipzig, Germany, hosted the Sixth Biennial Congress on Environmental Modelling and Software from July 1st to 5th, 2012. The meeting welcomed more than 430 scientists from 33 countries under the overarching theme "Managing Resources of a Limited Planet: Pathways and Visions under Uncertainty". Here, we report on the outcomes of the congress, but most of all, we provide insights from behind the scenes, showing how the meeting was developed along four key objectives: (i) to provide an excellent environment for scientific exchange, (ii) to support young academics and early career scientists, (iii) to perform outreach activities to promote the event (and environmental modelling) among broader public and (iv) to act as an environmentally friendly meeting. Thus, this commentary suggests a blueprint and provides templates and ideas for upcoming similar conferences.

We propose the TEMPORANA framework for analysis of integrated scenarios aiming at studying the possible effects on the society of sustainable innovation policies. The framework consists of a set of advanced temporal queries for scenario analysis and a software application. Integrated scenarios are possible future states of different aspects of the world (e.g., energy, environment, technology, economy, societal system) representing plausible conditions under different assumptions. Temporal scenario queries aim at detecting a specified behavior for the system over time and, hence, at verifying that a temporal property holds.

Every year, the Editors of Environmental Modelling and Software select ten outstanding reviewers who have provided exceptional contributions to the journal. These ‘Outstanding Reviewer Award’ recipients are shortlisted by the Editors based on the rigor, constructiveness and timeliness of their reviews in addition to the number of reviews performed.

Bayesian networks (BNs), also known as Bayesian belief networks or Bayes nets, are a kind of probabilistic graphical model that has become very popular to practitioners mainly due to the powerful probability theory involved, which makes them able to deal with a wide range of problems. The goal of this review is to show how BNs are being used in environmental modelling. We are interested in the application of BNs, from January 1990 to December 2010, in the areas of the ISI Web of Knowledge related to Environmental Sciences. It is noted that only the 4.2% of the papers have been published under this item. The different steps that configure modelling via BNs have been revised: aim of the model, data pre-processing, model learning, validation and software. Our literature review indicates that BNs have barely been used for Environmental Science and their potential is, as yet, largely unexploited.

International audience; This letter details the collective views of a number of independent researchers on the technical assessment and evaluation of environmental models and software. The purpose is to stimulate debate and initiate action that leads to an improved quality of model development and evaluation, so increasing the capacity for models to have positive outcomes from their use. As such, we emphasize the relationship between the model evaluation process and credibility with stakeholders (including funding agencies) with a view to ensure continued support for modelling efforts. Many journals, including EM&S, publish the results of environmental modelling studies and must judge the work and the submitted papers based solely on the material that the authors have chosen to present and on how they present it. There is considerable variation in how this is done with the consequent risk of considerable variation in the quality and usefulness of the resulting publication. Part of the problem is that the review process is reactive, responding to the submitted manuscript. In this letter, we attempt to be proactive and give guidelines for researchers, authors and reviewers as to what constitutes best practice in presenting environmental modelling results. This is a unique contribution to the organisation and practice of model-based research and the communication of its results that will benefit the entire environmental modelling community. For a start, our view is that the community of environmental modellers should have a common vision of minimum standards that an environmental model must meet. A common vision of what a good model should be is expressed in various guidelines on Good Modelling Practice. The guidelines prompt modellers to codify their practice and to be more rigorous in their model testing. Our statement within this letter deals with another aspect of the issue it prompts professional journals to codify the peer-review process. Introducing a more formalized approach to peer-review may discourage reviewers from accepting invitations to review given the additional time and labour requirements. The burden of proving model credibility is thus shifted to the authors. Here we discuss how to reduce this burden by selecting realistic evaluation criteria and conclude by advocating the use of standardized evaluation tools as this is a key issue that needs to be tackled. (C) 2010 Elsevier Ltd. All rights reserved.

Models used to aid decision-making often incorporate knowledge from various disciplines to provide an overarching assessment of the impact of different management decisions. Such models generally require numerous parameters from varying sources; many of which are not known with certainty. Rapid increases in model size and complexity, particularly in the case of integrated models used to assist decision-making, pose new challenges for effective sensitivity analysis. In particular, the sensitivity that is of interest is often that of the decision being made to the model's varying inputs and parameters. The Management Option Rank Equivalence (MORE) method of sensitivity analysis has been developed specifically for use with models used to assist decision-making. The method operates on the assumption that model outputs will result in a ranking of management options. Where models are used to assist decision-making it is important to ensure that the solution is robust and that rankings will not alter with small changes in model inputs. MORE uses numerical optimisation methods in order to determine the smallest and largest changes in model inputs and parameters that will result in a change of the ranking of management options. This allows a translation of the set of acceptable model outcomes into a corresponding range of model inputs, thus allowing decision-makers to directly assess whether current certainties of model inputs are adequate for differentiating between management options. The MORE method is demonstrated using a mathematical test model, as well as a case study of the MSM-BIGMOD flow and salinity model of the River Murray in South-Eastern Australia.

In urban water quality management, several models are connected and integrated for analysing the fate of pollutants from the sources in the urban catchment to the final recipient; classical problems connected with the selection and calibration of parameters are amplified by the complexity of the modelling approach increasing their uncertainty. The present paper aims at studying the influence of reductions in available data on the modelling response uncertainty with respect to the different integrated modelling outputs (both considering quantity and quality variables). At this scope, a parsimonious integrated home-made model has been used allowing for analysing the combinative effect of data availability regarding the different parts of the integrated urban drainage system; the uncertainty analysis approach has been applied to an experimental catchment in Bologna (Italy). The number of available data points has been fictitiously reduced obtaining data sets ranging between 25% and 100% of the actually measured data. For each of the data sets, uncertainty has been evaluated and its propagation from the upstream sub-model to the downstream ones has been assessed. The present study demonstrates that model calibration and modelling efficiency assessment may induce the operator to be excessively confident in the model results when available data are scarce. Quite the opposite is indeed true, that limited data availability increases modelling uncertainty. A conclusion of this article is that uncertainty analysis should always be conducted in order to effectively evaluate model reliability.

The procedures involved in model development may be set out as a ten step process, beginning with defining the purpose of the model and ending with evaluation of the appropriateness and utility of the completed model. This process, recently outlined by Jakeman et al. [Jakeman, A.J., Letcher, R.A., Norton, J.P., 2006. Ten iterative steps in development and evaluation of environmental models. Environmental Modelling and Software 21, 602-614], is often iterative as model development is a continuous process that refines and improves the intended capacity of the model. Here, the ten steps of model development are critiqued and applied using a process-based biogeochemical model of aquatic systems, with examples from two case studies: a model of phytoplankton succession and nutrient concentrations in the Swan-Canning Estuary (Western Australia) and a model of sediment and nutrient transport and transformation in the Fitzroy Estuary and Keppel Bay (Queensland).

Jakeman et al. (Jakeman, A.J., Letcher, R.A., Norton, J.P., 2006. Ten iterative steps in development and evaluation of environmental models. Environmental Modelling & Software 21, 602-614) outline ten steps in the pursuit of good practice in model development and application to increase the credibility and impact of results from environmental models. This paper shows how the ten steps of model development are relevant to numerical groundwater modelling, using a model of a data-rich coastal groundwater system near Bowen in Queensland, Australia as an example. The model is Geographic Information System-based and estimates the dynamical water balance using Darcy's Law. The method, which is generally applicable to data-rich aquifers, proved cost and time effective and provided important insights to the groundwater dynamics of the area.

Uncertainty can hamper the stringency of commitments under cap and trade schemes. We assess how well intensity targets, where countries' permit allocations are indexed to future realised GDP, can cope with uncertainties in a post-Kyoto international greenhouse emissions trading scheme. We present some empirical foundations for intensity targets and derive a simple rule for the optimal degree of indexation to GDP. Using an 18-region simulation model of a 2020 global cap-and-trade treaty under multiple uncertainties and endogenous commitments, we estimate that optimal intensity targets could achieve global abatement as much as 20 per cent higher than under absolute targets, and even greater increases in welfare measures. The optimal degree of indexation to GDP would vary greatly between countries, including super-indexation in some advanced countries, and partial indexation for most developing countries. Standard intensity targets (with one-toone indexation) would also improve the overall outcome, but to a lesser degree and not in all cases. Although target indexation is no magic wand for a future global climate treaty, gains from reduced cost uncertainty might justify increased complexity, framing issues and other potential downsides of intensity targets.

Many environmental processes can be modelled as transient convection–diffusion–reaction problems. This is the case, for instance, of the operation of activated-carbon filters. For industrial applications there is a growing demand for 3D simulations, so efficient linear solvers are a major concern. We have compared the numerical performance of two families of incomplete Cholesky factorizations as preconditioners of conjugate gradient iterations: drop-tolerance and prescribed-memory strategies. Numerical examples show that the former are computationally more efficient, but the latter may be preferable due to their predictable memory requirements.

Bayesian networks (BNs) are an increasingly popular method of modelling uncertain and complex domains such as ecosystems and environmental management. At best, they provide a robust and mathematically coherent framework for the analysis of this kind of problems. However, there are certain pitfalls as well. In this paper, I summarise the pros and cons of the use of Bayesian networks especially in the context of environmental modelling and management. I will also give references to relevant publications, and introduce some software products that can be used to build Bayesian networks.

The contributions included in this special issue of Environmental Modelling & Software have been selected from the papers presented at the 2003 Modelling and Simulation Congress (MODSIM 2003) held in Townsville, Australia. MODSIM 2003 was the 15th biannual gathering of the Modelling and Simulation Society of Australia and New Zealand (MSSANZ), an organisation with more than 500 members from 50 countries. The theme of the congress was “Integrative Modelling of Biophysical, Social and Economic Systems for Resource Management Solutions” and the emphasis of the majority of the congress papers was on integration, namely using modelling techniques to bring together the natural environment, social considerations and economic improvement or provide information which can be used for integrative decision making.

Environmental modelling is done more and more by practising ecologists rather than computer scientists or mathematicians. This is because there is a broad spectrum of development tools available that allows graphical coding of complex models of dynamic systems and help to abstract from the mathematical issues of the modelled system and the related numerical problems for estimating solutions. In this contribution, we study how different modelling tools treat a test system, a highly non-linear predator-prey model, and how the numerical solutions vary. We can show that solutions (a) differ if different development tools are chosen but the same numerical procedure is selected; (b) depend on undocumented implementation details; (c) vary even for the same tool but for different versions; and (d) are generated but with no notifications on numerical problems even if these could be identified. We conclude that improved documentation of numeric methods used in the modelling software is essential to make sure that process based models formulated in terms of these modelling packages do not become ''black box'' models due to uncertainty in integration methods.

Environmental modelling languages are programming languages developed for building computer models simulating environmental processes. They come with database and visualization routines for the data used in the models. Environmental modelling languages provide the possibility to construct dynamic models, also called forward models, which are simulations run forward in time, where the state of the model at time t is defined as a function of its state in a time step preceding t. Nowadays, these modelling languages can deal with simulations in two spatial dimensions, but existing software does not support the construction of models in three dimensions. We describe concepts of an environmental modelling language supporting dynamic model construction in two and three spatial dimensions. The lateral dimension is represented by gridded maps, with a regular discretization, while the vertical dimension is represented by an irregular discretization in voxels. Universal spatial functions are described with these ent...

In this paper we develop a rule-based model for evaluation of regional environment based on both hard and soft data, where by hard data we mean the statistical measurements while by soft data we mean subjective appreciation of human beings of environmental issues. As people's feeling strongly depends on the social and economical characteristics of administrative regions where they live, we firstly use the hard data concerning these characteristics to do clustering in order to obtain clusters corresponding to regions with the homogeneous social and economical characteristics relatively. We then use the soft data, with the help of data-mining techniques, to develop rule-based models which show association between evaluated items of residents in the clusters. Finally, a relationship between hard data and soft data through an integrated model will be explored. It is shown that the soft data are rather reliable and we should integrate subjective knowledge learnt from soft data into modelling of environmental issues. c2004 Elsevier Science Inc. All rights reserved.

International audience; The development of models using multiple modelling paradigms is necessary to formulate and study current problems in environmental science. To simplify the coupling of those models, a formal basis for a high-level specification of such models must be set up. In this paper, we propose a discrete event system specification (DEVS) based modelling framework as a formal basis in environmental modelling. The formal framework ensures that the models are reusable and interoperable components with well defined interfaces. Moreover, a wide variety of modelling paradigms can be expressed in the DEVS formalism. We also extend the modelling paradigms that can be expressed in the DEVS framework with two techniques: Feedback-DEVS for the specification of supervised-learning models and Vector-DEVS for the specification of models in vector space. JDEVS is the Java implementation of the framework. It enables discrete event, general purpose, object oriented, component based, GIS connected, collaborative, visual simulation model development and execution. A Feedback-DEVS neural-network model and a cellular infiltration model are described as experiments using JDEVS. Those models are later coupled to show the new modelling scenarios enabled by the use of a formal framework and the flexibility of the software.

Input Variable Selection (IVS) is an essential step in the development of data-driven models and is particularly relevant in environmental modelling. While new methods for identifying important model inputs continue to emerge, each has its own advantages and limitations and no single method is best suited to all datasets and modelling purposes. Rigorous evaluation of new and existing input variable selection methods would allow the effectiveness of these algorithms to be properly identified in various circumstances. However, such evaluations are largely neglected due to the lack of guidelines or precedent to facilitate consistent and standardised assessment. In this paper, a new framework is proposed for the evaluation and inter-comparison of IVS methods which takes into account: (1) a wide range of dataset properties that are relevant to real world environmental data, (2) assessment criteria selected to highlight algorithm suitability in different situations of interest, and (3) a website for sharing data, algorithms and results (http://ivs4em.deib.polimi.it/). The framework is demonstrated on four IVS algorithms commonly used in environmental modelling studies and twenty-six datasets exhibiting different typical properties of environmental data. The main aim at this stage is to demonstrate the application of the proposed evaluation framework, rather than provide a definitive answer as to which of these algorithms has the best overall performance. Nevertheless, the results indicate interesting differences in the algorithms' performance that have not been identified previously. A framework for the evaluation of input variable selection algorithms is proposed.The framework consists of assessment criteria and twenty-six datasets.The framework is supported by a dedicated website (http://ivs4em.deib.polimi.it).Four popular IVS algorithms are considered for evaluation purposes.

NetLogo is a software platform for agent-based modelling that is increasingly used in ecological and environmental modelling. So far, for comprehensive analyses of agent-based models (ABMs) implemented in NetLogo, results needed to be written to files and evaluated by using external software, for example R. Ideally, however, it would be possible to call any R function from within a NetLogo program. This would allow sophisticated interactive statistical analysis of model structure and dynamics, using R functions and packages for generating certain statistical distributions and experimental design, and for implementing complex descriptive submodels within ABMs. Here we present an R extension of NetLogo. It consists of only nine new NetLogo primitives for sending data between NetLogo and R and for calling R functions (six additional primitives for debugging). We demonstrate the usage of the R extension with three short examples.

A major challenge in landscape and environmental modelling is to effectively visualize large amounts of time-series simulation output, often in various Geographic Information System (GIS) formats. We developed a software tool (LANDISVIEW), to easily visualize and animate time-series of ERDAS *.gis maps. The open source tool can also be used to generate batch files for FRAGSTATS, a widely used spatial analysis program.

Significant trends in the processing of geographical data require increasingly powerful software and hardware, consistent with the exploitation of parallel computing. Despite recent progress in technology, exploiting parallel processing is still difficult so that few applications have been developed in the environmental and geographical domains.

Since its introduction in 2002/2003, the current generation of the Delft-FEWS operational forecasting platform has found application in over forty operational centres. In these it is used to link data and models in real time, producing forecasts on a daily basis. In some cases it forms a building block of a country-wide national forecasting system using distributed client-server technology. In other cases it is applied at a much smaller scale on a simple desktop workstation, providing forecasts for a single basin. The flexibility of the software in open integration of models and data has additionally appealed to the research community. This paper discusses the principles on which the Delft-FEWS system has been developed, as well as a brief background of the architecture of the system and concepts used for storing and handling data. One of the key features of the system is its flexibility in integrating (third-party) models and data, and the available approaches to linking models and accessing data are highlighted. A brief overview of different applications of the system is given to illustrate how the software is used to support differing objectives in the domain of real time environmental modelling.

Integrated environmental modelling is gaining momentum for addressing grand scientific challenges such as monitoring the environment for change detection and forecasting environmental conditions along with the consequences for society. Such challenges can only be addressed by a multi-disciplinary approach, in which socio-economic, geospatial, and environmental information becomes inter-connected. However, existing solutions cannot be seamlessly integrated and current interaction paradigms prevent mainstream usage of the existing technology. In particular, it is still difficult to access and join harmonized data and processing algorithms that are provided by different environmental information infrastructures. In this paper we take a novel approach for integrated environmental modelling based on the notion of inter-linked resources on the Web. We present design practices for creating resource-oriented interfaces, driven by an interaction protocol built on the combination of valid linkages to enhance resource integration, accompanied by associated recommendations for implementation. The suggested resource-oriented approach provides a solution to the problems identified above, but still requires intense prototyping and experimentation. We discuss the central open issues and present a roadmap for future research., JRC.H.6-Digital Earth and Reference Data