Mike Bithell

Coupling agent-based models of subsistence farming with individual-based forest models and dynamic models of water distribution

Subsistence farming communities are dependent on the landscape to provide the resource base upon which their societies can be built. A key component of this is the role of climate and the feedback between rainfall, crop growth, land clearance and their coupling to the hydrological cycle. Temporal fluctuations in rainfall alter the spatial distribution of water availability, which in turn is mediated by soil-type, slope and landcover. This pattern ultimately determines the locations within the landscape that can support agriculture and controls sustainability of farming practices. The representation of such a system requires us to couple together the dynamics of human and ecological systems and landscape change, each of which constitutes a significant modelling challenge on its own. Here we present a proto-type coupled modelling system to simulate land-use change by bringing together three simple process models: (a) an agent-based model of subsistence farming; (b) an individual-based model of forest dynamics; and (c) a spatially explicit hydrological model which predicts distributed soil moisture and basin scale water fluxes. Using this modelling system we investigate how demographic changes influence deforestation and assess its impact on forest ecology, stream hydrology and changes in water availability.

Multi-agent modelling of climate outlooks and food security on a community garden scheme in Limpopo, South Africa

Seasonal climate outlooks provide one tool to help decision-makers allocate resources in anticipation of poor, fair or good seasons. The aim of the ‘Climate Outlooks and Agent-Based Simulation of Adaptation in South Africa’ project has been to investigate whether individuals, who adapt gradually to annual climate variability, are better equipped to respond to longer-term climate variability and change in a sustainable manner. Seasonal climate outlooks provide information on expected annual rainfall and thus can be used to adjust seasonal agricultural strategies to respond to expected climate conditions. A case study of smallholder farmers in a village in Vhembe district, Limpopo Province, South Africa has been used to examine how such climate outlooks might influence agricultural strategies and how this climate information can be improved to be more useful to farmers. Empirical field data has been collected using surveys, participatory approaches and computer-based knowledge elicitation tools to investigate the drivers of decision-making with a focus on the role of climate, market and livelihood needs. This data is used in an agent-based social simulation which incorporates household agents with varying adaptation options which result in differing impacts on crop yields and thus food security, as a result of using or ignoring the seasonal outlook. Key variables are the skill of the forecast, the social communication of the forecast and the range of available household and community-based risk coping strategies. This research provides a novel approach for exploring adaptation within the context of climate change.

Predictive systems ecology

Human societies, and their well-being, depend to a significant extent on the state of the ecosystems that surround them. These ecosystems are changing rapidly usually in response to anthropogenic changes in the environment. To determine the likely impact of environmental change on ecosystems and the best ways to manage them, it would be desirable to be able to predict their future states. We present a proposal to develop the paradigm of predictive systems ecology, explicitly to understand and predict the properties and behaviour of ecological systems. We discuss the necessary and desirable features of predictive systems ecology models. There are places where predictive systems ecology is already being practised and we summarize a range of terrestrial and marine examples. Significant challenges remain but we suggest that ecology would benefit both as a scientific discipline and increase its impact in society if it were to embrace the need to become more predictive.

Leafless roughness of complex tree morphology using terrestrial lidar

Strategies for extracting roughness parameters from riparian forests need to address the issue that the trees are more than just stems and that in large rivers flow can rise into the canopy. Remote sensing information with 3-D capabilities such as lidar can be used to extract information on trees. However, first and last pulse airborne lidar data are insufficient to characterize the complex vertical structure of vegetation because by definition, there are few data at intermediate levels. Terrestrial laser scanning (TLS) is used in this study to define complex structures at a millimetric scanning resolution for the purpose of extracting canopy parameters relevant for the parameterization of the flow resistance equations. We will mainly be concerned with the projected area of leafless trees, estimating the total tree dimensions using several different methods. These include manipulating mass point cloud data obtained from TLS to create stage-dependent projected areas through complex meshing techniques and voxelization. Stage-dependent projected areas were defined for natural and planted poplar forests in the riparian zone of the Garonne and Allier rivers in southern and central France, respectively. Roughness values for planted poplar forests dominant in many western European river floodplains range from Mannings n = 0.037–0.094 and n = 0.140–0.330 for below-canopy flow (2 m) and extreme in-canopy flow (8 m), respectively. Roughness values for natural poplar forests ranged from n = 0.066–0.210 and n = 0.202–0.720 for below-canopy flow (2 m) and extreme in-canopy flow (8 m), respectively.

Discrete-element modelling: methods and applications in the environmental sciences

This paper introduces a Theme Issue on discrete–element modelling, based on research presented at an interdisciplinary workshop on this topic organized by the National Institute of Environmental e–Science. The purpose of the workshop, and this collection of papers, is to highlight the opportunities for environmental scientists provided by (primarily) off–lattice methods in the discrete–element family, and to draw on the experiences of research communities in which the use of these methods is more advanced. Applications of these methods may be conceived in a wide range of situations where dynamic processes involve a series of fundamental entities (particles or elements) whose interaction results in emergent macroscale structures. Indeed, the capacity of these methods to reveal emergent properties at the meso– and macroscale, that reflect microscale interactions, is a significant part of their attraction. They assist with the definition of constitutive material properties at scales beyond those at which measurement and theory have been developed, and help us to understand self–organizing behaviours. The paper discusses technical issues including the contact models required to represent collision behaviour, computational aspects of particle tracking and collision detection, and scales at which experimental data are required and choices about modelling style must be made. It then illustrates the applicability of DEM and other forms of individual–based modelling in environmental and related fields as diverse as mineralogy, geomaterials, mass movement and fluvial sediment transport processes, as well as developments in ecology, zoology and the human sciences where the relationship between individual behaviour and group dynamics can be explored using a partially similar methodological framework.

Large Scale Agent-Based Modelling: A Review and Guidelines for Model Scaling

This chapter provides a review and examples of approaches to model scaling when constructing large agent-based models. A comparison is made between an aggregate ‘super-individual’ approach, as run on a single processor machine, and two different approaches to parallelisation of agent models run on multi-core hardware. Super-individuals provide a straightforward solution without much alteration of the model formulation and result in large improvements in model efficiency (speed and memory use). However, there are significant challenges to using a super-individual approach when relating super-individuals to individuals in time and space. Parallel computing approaches accept the requirement for large amounts of memory or CPU and attempt to solve the problem by distributing the calculation over many computational units. This requires some modification of the model software and algorithms to distribute the model components across multiple computational cores. This can be achieved in a number of different ways, two of which we illustrate further for the case of spatial models, an ‘agent-parallel’ and an ‘environment-parallel’ approach. However, the success of such approaches may also be affected by the complexity of the model (such as multiple agent types and agent interactions), as we illustrate by adding a predator to our example simulation. Between these two parallelisation approaches to the case study, the environment-parallel version of the model, written in C++ instead of Java, proved more efficient and successful at handling parallel processing of complex agent interactions. In conclusion, we use our experiences of creating large agent-based simulations to provide some general guidelines for best practice in agent-based model scaling.

Retrieval of vegetative fluid resistance terms for rigid stems using airborne lidar

Hydraulic resistance of riparian forests is an unknown but important term in flood conveyance modeling. Lidar has proven to be a very important new data source to physically characterize floodplain vegetation. This research outlines a recent campaign that aims to retrieve vegetation fluid resistance terms from airborne laser scanning to parameterize trunk roughness. Information on crown characteristics and vegetation spacing can be extracted for individual trees to aid in the determining of trunk stem morphology. Airborne lidar data were used to explore the potential to characterize some of the prominent tree morphometric properties from natural and planted riparian poplar zones such as tree position, tree height, trunk location, and tree spacing. Allometric equations of tree characteristics extrapolated from ground measurements were used to infer below-canopy morphometric variables. Results are presented from six riparian-forested zones on the Garonne and Allier rivers in southern and central France. The tree detection and crown segmentation (TDCS) method identified individual trees with 85% accuracy, and the TreeVaW method detected trees with 83% accuracy. Tree heights were overall estimated at both river locations with an RMSE error of around 19% for both methods, but crown diameter at the six sites produced large deviations from ground-measured values of above 40% for both methods. Total height-derived trunk diameters using the TDCS method produced the closest roughness coefficient values to the ground-derived roughness coefficients. The stem roughness values produced from this method fell within guideline values.