Huub Scholten

Quality assurance in model based water management - review of existing practice and outline of new approaches

Quality assurance (QA) is defined as protocols and guidelines to support the proper application of models. In the water management context we classify QA guidelines according to how much focus is put on the dialogue between the modeller and the water manager as: (Type 1) Internal technical guidelines developed and used internally by the modellers organisation; (Type 2) Public technical guidelines developed in a public consensus building process; and (Type 3) Public interactive guidelines developed as public guidelines to promote and regulate the interaction between the modeller and the water manager throughout the modelling process. State-of-the-art QA practices vary considerably between different modelling domains and countries. It is suggested that these differences can be explained by the scientific maturity of the underlying discipline and differences in modelling markets in terms of volume of jobs outsourced and level of competition. The structure and key aspects of new generic guidelines and a set of electronically based supporting tools that are under development within the HarmoniQuA project are presented. Model credibility can be enhanced by a proper modeller-manager dialogue, rigorous validation tests against independent data, uncertainty assessments, and peer reviews of a model at various stages throughout its development.

A methodology to support multidisciplinary model-based water management

Quality assurance in model based water management is needed because of some frequently perceived shortcomings, e.g. a lack of mutual understanding between modelling team members, malpractice and a tendency of modellers to oversell model capabilities. Initiatives to support quality assurance focus on single domains and often follow a textbook approach with guidelines and checklists. A modelling process involves a complex set of activities executed by a team. To manage this complex, usually multidisciplinary process, to guide users through it and enhance the reproducibility of modelling work a software product has been developed, aiming at supporting the full modelling process by offering an ontological knowledge base (KB) and a Modelling Support Tool (MoST). The KB consists of a generic part for modelling, but also parts specific for various water management domains, for different types of users and for different levels of modelling complexity. MoSTs guiding component filters relevant knowledge from the KB depending on the user profile and needs. Furthermore, MoST supports different types of users by monitoring what they actually do and by producing customized reports for diverse audiences. In this way MoST facilitates co-operation in teams, modelling project audits and re-use of experiences of previous modelling projects.

Responses of Mytilus edulis L. to varying food concentrations: testing EMMY, an ecophysiological model

In this paper a complex ecophysiological model is presented, which aims to simulate individual growth and reproduction of Mytilus edulis L. The model includes feedback mechanisms in the acquisition and metabolism of natural food sources and partitioning of carbon and nitrogen to the internal state variables: somatic tissue, storage, organic shell matrix, blood and gametes before and after spawning. The model was calibrated with statistical distributions for 38 parameters. The resulting a posteriori parameter sets were applied in a validation procedure. First inputs of one system were used to produce model outcomes with uncertainty bands in order to compare these with system observations not used for calibration. In a second validation step, the model was run with inputs of two different ecosystems. The results of this step were promising, but no acceptable growth could be predicted for the system with low seston and food concentrations.

The ecophysiological response of mussels (Mytilus edulis) in mesocosms to a range of inorganic nutrient loads: simulations with the model EMMY

EMMY is an ecophysiological model of the growth and reproduction of a single mussel (Mytilus edulis L.). It contains feedback loops in the uptake and metabolism of food and in the partitioning of carbon and nitrogen to the internal state variables somatic tissue, storage, organic shell matrix and gametes. In this paper EMMY is used to simulate individual mussel growth in a series of mesocosm experiments with different inorganic nutrient loads (N and P). The experiments explore the impact of eutrophication reduction scenarios on mussel growth under defined and controlled conditions.In earlier studies EMMY was calibrated using expert knowledge on growth and reproduction during a period of 5 years. The resulting calibrated model was validated for system inputs and observations of three ecosystems with significantly different food and silt concentrations. EMMY reproduced the mussel growth sufficiently accurate in ecosystems with moderate or high food concentrations. In this study EMMY was adapted in order to cope with low food concentrations, then recalibrated (using the original calibration data and procedure) and applied without further calibration to 3 replicated mesocosm experiments. The EMMY simulations in this study show the ecophysiological response of mussels to different food (phytoplankton and detritus) concentrations. It is concluded that the mussels can adapt to significantly reduced food concentrations, due to inorganic nutrient load reduction, and still maintain growth.

Serving many at once

Simulation modelling in ecology is a field that is becoming increasingly compartmentalized. Here we propose a Database Approach To Modelling (DATM) to create unity in dynamical ecosystem modelling with differential equations. In this approach the storage of ecological knowledge is independent of the language and platform in which the model will be run. To create an instance of the model, the information in the database is translated and augmented with the language and platform specifics. This process is automated so that a new instance can be created each time the database is updated. We describe the approach using the simple Lotka-Volterra model and the complex ecosystem model for shallow lakes PCLake, which we automatically implement in the frameworks OSIRIS, GRIND for MATLAB, ACSL, R, DUFLOW and DELWAQ. A clear advantage of working in a database is the overview it provides. The simplicity of the approach only adds to its elegance. Scientific and educational experience with the proposed Database Approach To Modelling (DATM) shows the following:It facilitated overview of and insight in the model by developers and users.Allowed for a much more dynamic scientific development of the model.Allowed for a direct implementation of these developments in multiple platforms.

A reference architecture for Farm Software Ecosystems

Display Omitted We mould the concept Software Ecosystems to the agricultural domain.We propose a reference architecture for Farm Software Ecosystems.Our reference architecture describes an organizational and technical infrastructure.We motivate that our reference architecture can improve farm enterprise integration.Our reference architecture is used to review some existing initiatives. Smart farming is a management style that includes smart monitoring, planning and control of agricultural processes. This management style requires the use of a wide variety of software and hardware systems from multiple vendors. Adoption of smart farming is hampered because of a poor interoperability and data exchange between ICT components hindering integration. Software Ecosystems is a recent emerging concept in software engineering that addresses these integration challenges. Currently, several Software Ecosystems for farming are emerging. To guide and accelerate these developments, this paper provides a reference architecture for Farm Software Ecosystems. This reference architecture should be used to map, assess design and implement Farm Software Ecosystems. A key feature of this architecture is a particular configuration approach to connect ICT components developed by multiple vendors in a meaningful, feasible and coherent way. The reference architecture is evaluated by verification of the design with the requirements and by mapping two existing Farm Software Ecosystems using the Farm Software Ecosystem Reference Architecture. This mapping showed that the reference architecture provides insight into Farm Software Ecosystems as it can describe similarities and differences. A main conclusion is that the two existing Farm Software Ecosystems can improve configuration of different ICT components. Future research is needed to enhance configuration in Farm Software Ecosystems.

Integrating ICT Applications for Farm Business Collaboration Processes Using FI Space

Agri-Food Supply Chain Networks are required to increase production and to be transparent while reducing environmental impact. This challenges farm enterprises to innovate their production processes. These processes need to be supported by advanced ICT components that are developed by multiple vendors and owned by different enterprises. These ICT components should use future internet technologies to enable integration. To make these future internet technologies easily available, the European Commission started a Future Internet Public-Private Partnership Programme (FI-PPP). As part of this FI-PPP the FIspace project started with the development of a cloud-based platform to support business to business collaboration, enabling the formation of domain specific Software Ecosystems. These FIspace Software Ecosystems should drive the development of integrated and extensible collaboration services together with an initial set of domain applications that can support business processes and enables collaboration in networks. The FIspace Platform will enable integration of legacy systems, extending their functionality. This paper presents two use case scenarios for the smart application of pesticides to protect crops and keep them safe for diseases. These scenarios provide architectural descriptions how FIspace Platform configurations can support these processes. Based on these specific use case scenarios, generic use case scenarios and architectural descriptions are derived. These results can be used to develop FIspace Platform configurations supporting collaboration processes in other domains.

An Ontological Framework for Model-Based Problem-Solving

Multidisciplinary projects to solve real world problems of increasing complexity are more and more plagued by obstacles such as miscommunication between modellers with different disciplinary backgrounds and bad modelling practices. To tackle these difficulties, a body of knowledge on problems, on modelling and on models to solve problems, has been made explicit and organised in ontological knowledge bases, which are structured in layers, ranging from generic to detailed and specific. This approach facilitates the solution of the ‘language’ and communication problem between team members from different disciplines, between the project team and its commissioner and other stakeholders and also makes parts of the knowledge reusable. Finally, we developed tools, available as Web-based services, that enable to fill the knowledge bases and support modelling projects (guidance from the knowledge base, logbook and project management). The modelling approach, the ontologies and the tools together constitute a more complete and better modelling framework.

Software Ecosystems for the Life Sciences Application Domains

Software ecosystems (SECOs) are gaining importance in and have been applied to different application domains. In this paper we focus on the needs for SECOs for the life science application domains. Similar to other domains the life science application domains also witnesses the emergence and application of an increased application of software engineering. An important trigger for this is the need for an increased need of smart and interconnected systems which is primarily realized through software. In this context, we can observe the need and benefits of the adoption of software ecosystems for the life sciences application domains such as smart logistics, farming technology, smart agro-food production, environmental services, and geoinformation systems. This paper provides a discussion on the state-of-the-practice of SECOs in the life sciences applications domains and identifies selected key problems.

Output-Classes for Faculty-Based Design-Oriented Research on Digital Learning Resources in Higher Education

Digital learning resources are essentially knowledge-intensive information systems. In higher education, advance of interactive digital learning resources will primarily require design-oriented efforts by faculty. Growth of knowledge on digital learning resources and learning objects in higher education implies design, realization, use, implementation and evaluation of digital learning resources as well as sharing knowledge in scientific publications. Over the last fifteen years a range of projects involving such design-oriented activities in natural, engineering, management, decision and information sciences has been carried out. There is a growing body of literature on various design-related research approaches in educational research and in information systems research. However, for faculty-based design-oriented research the question what knowledge should be shared in scientific publications is not yet clearly answered. This article relates this question to the transdisciplinary nature of faculty-based design-oriented research. The article proposes an answer to this question in terms of a set of result types or output-classes. For each output-class it is explained why a contribution in that class can provide a valuable addition to the body of knowledge on digital learning resources in higher education.