Alexey Voinov

Modelling with stakeholders

Stakeholder engagement, collaboration, or participation, shared learning or fact-finding, have become buzz words and hardly any environmental assessment or modelling effort today can be presented without some kind of reference to stakeholders and their involvement in the process. This is clearly a positive development, but in far too many cases stakeholders have merely been paid lip service and their engagement has consequentially been quite nominal. Nevertheless, it is generally agreed that better decisions are implemented with less conflict and more success when they are driven by stakeholders, that is by those who will be bearing their consequences. Participatory modelling, with its various types and clones, has emerged as a powerful tool that can (a) enhance the stakeholders knowledge and understanding of a system and its dynamics under various conditions, as in collaborative learning, and (b) identify and clarify the impacts of solutions to a given problem, usually related to supporting decision making, policy, regulation or management. In this overview paper we first look at the different types of stakeholder modelling, and compare participatory modelling to other frameworks that involve stakeholder participation. Based on that and on the experience of the projects reported in this issue and elsewhere, we draw some lessons and generalisations. We conclude with an outline of some future directions.

Selecting among five common modelling approaches for integrated environmental assessment and management

The design and implementation of effective environmental policies need to be informed by a holistic understanding of the system processes (biophysical, social and economic), their complex interactions, and how they respond to various changes. Models, integrating different system processes into a unified framework, are seen as useful tools to help analyse alternatives with stakeholders, assess their outcomes, and communicate results in a transparent way. This paper reviews five common approaches or model types that have the capacity to integrate knowledge by developing models that can accommodate multiple issues, values, scales and uncertainty considerations, as well as facilitate stakeholder engagement. The approaches considered are: systems dynamics, Bayesian networks, coupled component models, agent-based models and knowledge-based models (also referred to as expert systems). We start by discussing several considerations in model development, such as the purpose of model building, the availability of qualitative versus quantitative data for model specification, the level of spatio-temporal detail required, and treatment of uncertainty. These considerations and a review of applications are then used to develop a framework that aims to assist modellers and model users in the choice of an appropriate modelling approach for their integrated assessment applications and that enables more effective learning in interdisciplinary settings. We review five common integrated modelling approaches.Model choice considers purpose, data type, scale and uncertainty treatment.We present a guiding framework for selecting the most appropriate approach.

Progress in integrated assessment and modelling

Environmental processes have been modelled for decades. However. the need for integrated assessment and modeling (IAM) has,town as the extent and severity of environmental problems in the 21st Century worsens. The scale of IAM is not restricted to the global level as in climate change models, but includes local and regional models of environmental problems. This paper discusses various definitions of IAM and identifies five different types of integration that Lire needed for the effective solution of environmental problems. The future is then depicted in the form of two brief scenarios: one optimistic and one pessimistic. The current state of IAM is then briefly reviewed. The issues of complexity and validation in IAM are recognised as more complex than in traditional disciplinary approaches. Communication is identified as a central issue both internally among team members and externally with decision-makers. stakeholders and other scientists. Finally it is concluded that the process of integrated assessment and modelling is considered as important as the product for any particular project. By learning to work together and recognise the contribution of all team members and participants, it is believed that we will have a strong scientific and social basis to address the environmental problems of the 21st Century.

Patuxent landscape model: integrated ecological economic modeling of a watershed

The Patuxent Landscape Model (PLM) is designed to simulate fundamental ecological processes on the watershed scale, in interaction with an economic component that predicts the land use patterns. The paper focuses on the ecological component of the PLM and describes how the spatial and structural rescaling can be instrumental for calibration of complex spatially distributed models. The PLM is based on a modified General Ecosystem Model (GEM) that is replicated across a grid of cells that compose the rasterized landscape. Different habitats and land use types translate into different parameter sets to be fed into GEM. Cells are linked by horizontal fluxes of material and information, driven mostly by the hydrologic flows. This approach provides additional flexibility in scaling up and down over a range of spatial resolutions and is essential to track the land use change patterns generated by the economic component. Structural modularity is another important feature that is implemented in the general purpose software packages (Spatial Modeling Environment and Collaborative Modeling Environment), that the PLM employs.

Modeling ecological and economic systems with STELLA: Part III

This special issue contains a group of eight modeling studies covering a range of ecological and economic systems and problems. The models were all developed using Stella®, an icon-based software package specifically designed for dynamic systems modeling. Models included in the special issue were built to describe and analyze: communities of organisms under the effects of random variability in disturbance rates and episodic disturbances; a spatially explicit metapopulation of white-tailed deer in a simulated landscape; a parcel level study of landcover change for smallholders in Altamira, Brazil; a discrete-time age structured single species fishery with dynamic total allowable catch limits; a metapopulation with an Alee effect driven to extinction by environmental stochasticity; seasonal deciduous forest growth to compare with remote sensing data (NDVI); herbivorous consumers in the Great Bay Estuary (New Hampshire); and dynamic patterns of deforestation in the Brazilian Amazon in relation to the value of ecosystem services. Most of the models described in this issue are available for download from http://iee.umces.edu/DMEES/Arch. A run time only version of Stella for Windows or the Macintosh is available for free to run the models from www.hps-inc.com.

Optimization methodology for land use patterns using spatially explicit landscape models

Spatially explicit ecosystem models allow the calculation of water and matter dynamics in a landscape as functions of spatial localization of habitat structures and matter input. For a mainly agricultural region we studied the nutrient balance as a function of different management schemes. For this purpose we formulated optimization tasks. This required the definition of performance criteria, which compare economic aspects, such like farmers income from harvest, with ecologic aspects, such like nutrient loss out of the watershed. The task was to calculate optimum land use maps and fertilizer application maps maximizing the performance criterion. We developed a framework of procedures for numerical optimization in spatially explicit dynamic ecosystem simulation models. The results were tested using Monte-Carlos simulation, which based on different stochastic generators for the independent control variables. Gradient free optimization procedures (Genetic Algorithms) were used to verify the simplifying assumptions. Parts of the framework offer tools for optimization with the computation effort independent of the size of the study area. As a result, important areas with high retention capabilities were identified and fertilizer maps were set up depending on soil properties. This shows that optimization methods even in complex simulation models can be a useful tool for a systematic analysis of management strategies of ecosystem use.

Position Paper: Modelling with stakeholders

Stakeholder engagement, collaboration, or participation, shared learning or fact-finding, have become buzz words and hardly any environmental assessment or modelling effort today can be presented without some kind of reference to stakeholders and their involvement in the process. This is clearly a positive development, but in far too many cases stakeholders have merely been paid lip service and their engagement has consequentially been quite nominal. Nevertheless, it is generally agreed that better decisions are implemented with less conflict and more success when they are driven by stakeholders, that is by those who will be bearing their consequences. Participatory modelling, with its various types and clones, has emerged as a powerful tool that can (a) enhance the stakeholders knowledge and understanding of a system and its dynamics under various conditions, as in collaborative learning, and (b) identify and clarify the impacts of solutions to a given problem, usually related to supporting decision making, policy, regulation or management. In this overview paper we first look at the different types of stakeholder modelling, and compare participatory modelling to other frameworks that involve stakeholder participation. Based on that and on the experience of the projects reported in this issue and elsewhere, we draw some lessons and generalisations. We conclude with an outline of some future directions.

Integrated ecological economic modeling of the patuxent river watershed, Maryland

Understanding the way regional landscapes operate, evolve, and change is a key area of research for ecosystem science. It is also essential to support the “place-based” management approach being advocated by the U.S. Environmental Protection Agency and other management agencies. We developed a spatially explicit, process-based model of the 2352 km2 Patuxent River watershed in Maryland to integrate data and knowledge over several spatial, temporal, and complexity scales, and to serve as an aid to regional management. In particular, the model addresses the effects of both the magnitude and spatial patterns of human settlements and agricultural practices on hydrology, plant productivity, and nutrient cycling in the landscape. The spatial resolution is variable, with a maximum of 200 × 200 m to allow adequate depiction of the pattern of ecosystems and human settlement on the landscape. The temporal resolution is different for various components of the model, ranging from hourly time steps in the hydrologic sector to yearly time steps in the economic land-use transition module. We used a modular, multiscale approach to calibrate and test the model. Model results show good agreement with data for several components of the model at several scales. A range of scenarios with the calibrated model shows the implications of past and alternative future land-use patterns and policies. We analyzed 18 scenarios including: (1) historical land-use in 1650, 1850, 1950, 1972, 1990, and 1997; (2) a “buildout” scenario based on fully developing all the land currently zoned for development; (3) four future development patterns based on an empirical economic land-use conversion model; (4) agricultural “best management practices” that lower fertilizer application; (5) four “replacement” scenarios of land-use change to analyze the relative contributions of agriculture and urban land uses; and (6) two “clustering” scenarios with significantly more and less clustered residential development than the current pattern. Results indicate the complex nature of the landscape response and the need for spatially explicit modeling.

Modular Ecosystem Modeling

The Library of Hydro-Ecological Modules (LHEM, http://giee.uvm.edu/LHEM) was designed to create flexible landscape model structures that can be easily modified and extended to suit the requirements of a variety of goals and case studies. The LHEM includes modules that simulate hydrologic processes, nutrient cycling, vegetation growth, decomposition, and other processes, both locally and spatially. Where possible the modules are formulated as STELLA models, which adds to transparency and helps reuse. Spatial transport processes are presented as C++ code. The modular approach takes advantage of the spatial modeling environment (http://giee.uvm.edu/SME3) that allows integration of various STELLA models and C++ user code, and embeds local simulation models into a spatial context. Using the LHEM/SME the Patuxent landscape model (PLM) was built to simulate fundamental ecological processes in the watershed scale driven by temporal (nutrient loadings, climatic conditions) and spatial (land use patterns) forcings. Local ecosystem dynamics were replicated across a grid of cells that compose the rasterized landscape. Different habitats and land use types translate into different modules and parameter sets. Spatial hydrologic modules link the cells together. These are also part of the LHEM and define horizontal fluxes of material and information. This approach provides additional flexibility in scaling up and down over a range of spatial resolutions. Model results show good agreement with data for several components of the model at several scales. Other applications include several subwatersheds of the Patuxent, the Gwynns Falls watershed in Baltimore, and others.  2003 Elsevier Ltd. All rights reserved.

Effectiveness of a participatory modeling effort to identify and advance community water resource goals in St. Albans, Vermont

Natural resource managers face complex challenges in addressing non-point source water pollution. A participatory modeling approach was applied in the St. Albans Bay watershed to identify the most effective phosphorus control options to achieve the load reductions required by the Lake Champlain Phosphorus Total Maximum Daily Load (TMDL). Stakeholders participated in the collection of data in the watershed, model creation, development of policy scenarios, and interpretation of model results. The participatory modeling approach employed in this study led to the identification of new solutions to an old water resource problem regarding phosphorus loads to streams and St. Albans Bay. The modeling process provided a perceived neutral atmosphere for discussing water pollution issues that have historically been divisive and provided participants with greater understanding of local environmental issues and reduced historic conflict among actors. This study highlights the importance of considering the dynamics of social and technical factors in the use of modeling in natural resource planning processes. The approach led to stakeholder agreement about problems and potential solutions generated in the modeling process. As the process ended, local decision makers were moving forward to implement solutions identified to be most cost-effective.