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Simulating future land use exposure to extreme floods in metropolitan areas based on an integrated framework

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The process of urban land use change is influenced by the interactions of various stakeholders who have conflicting values and priorities. These interactions, which are characterized by a strong competition for advantageous land locations, can be represented by an agent-based model in order to better understand, analyze and forecast possible future urban land use patterns. The objective of this study is to develop and implement Agent iCity, an agent-based model that simulates the process of urban land-use change by using irregular spatial units at a cadastral scale and by incorporating the interactions of the key stakeholders. The simulation outcomes are generated for two scenarios of the process of urban land use change as it occurs under the conditions of different urban growth policies. The model is implemented on municipal cadastral and land use data for part of the City of Chilliwack, Canada, a city that has experienced rapid growth in the last decade. The results indicate that that relative household incomes and property values drive the changes in urban land use patterns as households search for affordable homes in suitable neighbourhoods. The developed Agent iCity model can assist urban planners in better understanding and analysis of the changes in urban land use patterns.
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This paper describes the design and implementation of an Agent-Based Model (ABM) used to simulate land use change on household farms in the Northern Ecuadorian Amazon (NEA). The ABM simulates decision-making processes at the household level that is examined through a longitudinal, socio-economic and demographic survey that was conducted in 1990 and 1999. Geographic Information Systems (GIS) are used to establish spatial relationships between farms and their environment, while classified Landsat Thematic Mapper (TM) imagery is used to set initial land-use/land-cover conditions for the spatial simulation, assess from-to land-use/land-cover change patterns, and describe trajectories of land use change at the farm and landscape levels. Results from prior studies in the NEA provide insights into the key social and ecological variables, describe human behavioral functions, and examine population–environment interactions that are linked to deforestation and agricultural extensification, population migration, and demographic change. Within the architecture of the model, agents are classified as active or passive. The model comprises four modules, i.e., initialization, demography, agriculture, and migration that operate individually, but are linked through key household processes. The main outputs of the model include a spatially-explicit representation of the land use/land cover on survey and non-survey farms and at the landscape level for each annual time-step, as well as simulated socio-economic and demographic characteristics of households and communities. The work describes the design and implementation of the model and how population–environment interactions can be addressed in a frontier setting. The paper contributes to land change science by examining important pattern–process relations, advocating a spatial modeling approach that is capable of synthesizing fundamental relationships at the farm level, and links people and environment in complex ways.
Article
Traditional approaches to studying human–environment interactions often ignore individual-level information, do not account for complexities, or fail to integrate cross-scale or cross-discipline data and methods, thus, in many situations, resulting in a great loss in predictive or explanatory power. This article reports on the development, implementation, validation, and results of an agent-based spatial model that addresses such issues. Using data from Wolong Nature Reserve for giant pandas (China), the model simulates the impact of the growing rural population on the forests and panda habitat. The households in Wolong follow a traditional rural lifestyle, in which fuelwood consumption has been shown to cause panda habitat degradation. By tracking the life history of individual persons and the dynamics of households, this model equips household agents with “knowledge” about themselves, other agents, and the environment and allows individual agents to interact with each other and the environment through their activities in accordance with a set of artificial-intelligence rules. The households and environment coevolve over time and space, resulting in macroscopic human and habitat dynamics. The results from the model may have value for understanding the roles of socioeconomic and demographic factors, for identifying particular areas of special concern, and for conservation policy making. In addition to the specific results of the study, the general approach described here may provide researchers with a useful general framework to capture complex human–environment interactions, to incorporate individual-level information, and to help integrate multidisciplinary research efforts, theories, data, and methods across varying spatial and temporal scales.
Article
This paper describes a spatial planning model combining a multi-agent simulation (MAS) approach with cellular automata (CA). The model includes individual actor behaviour according to a bottom-up modelling concept. Spatial planning intentions and related decision making of planning actors is defined by agents. CA is used to infer the knowledge needed by the agents to make decisions about the future of a spatial organisation in a certain area. The innovative item of this approach offers a framework for modelling complex land use planning process by extending CA approach with MAS. The modelling approach is demonstrated by the implementation of a pilot model using JAVA and the SWARM agent modelling toolkit. The pilot model itself is applied to a study area near the city of Nijmegen, The Netherlands.
Article
Our study investigates the possible effects of recent land-use changes on the frequency regime of floods for reclaimed lands. We modelled the runoff concentration behaviour of a reclaimed area of 76 km2, located in the Po River plain near the city of Bologna (northern Italy), through the combined application of a semi-distributed rainfall-runoff model, which captures the key features of surface and sub-surface flows, and a hydrodynamic streamflow routing model. Three land use data from 1955, 1980 and 1992 were available. We implemented the rainfall-runoff model to the three land-use scenarios and analysed the hydrological–hydraulic behaviour of the study area for numerous rainfall events associated with different recurrence interval. The results of our study show a rather significant sensitivity of the flood frequency regime of the reclaimed land to land-use changes, and the sensitivity tends to increase as the recurrence interval of the rainfall event decreases.
Article
There is a lack of information on the environmental effects of urban change and the dynamics of greenspace. Such information is essential for a better understanding of the sustainability of urban development processes, both planned and unplanned. We therefore investigated the changes in land use and land cover of 11 residential areas in Merseyside, UK, using aerial photographs taken in 1975 and 2000. We then modeled how these changes would alter three important environmental parameters: surface temperature, runoff of rainfall, and greenspace diversity. These changes were then related to the socio-economic status of the areas, as measured by an index of multiple deprivation. The comparisons revealed a loss of greenspace in all 11 case study sites Overall, the more affluent, low density areas lost more greenspace, especially of tree cover. A major cause was infill development whereby gardens were built over. However, greenspace was also lost in already densely built-up, deprived areas due to the reuse of derelict land. As a consequence, the models used in this study predicted negative environmental impacts for all areas. The results emphasize the need to critically review concepts such as urban densification and give more weight to the preservation and management of urban greenspaces.
Article
Land use change modelling, especially if done in a spatially-explicit, integrated and multi-scale manner, is an important technique for the projection of alternative pathways into the future, for conducting experiments that test our understanding of key processes in land use changes. Land-use change models should represent part of the complexity of land use systems. They offer the possibility to test the sensitivity of land use patterns to changes in selected variables. They also allow testing of the stability of linked social and ecological systems, through scenario building. To assess current progress in this field, a workshop on spatially explicit land-use/land-cover models was organised within the scope of the Land-Use and Land Cover Change project (LUCC). The main developments presented in this special issue concern progress in: 1) Modelling of drivers of land-use change; 2) modelling of scale dependency of drivers of land use change; 3) modelling progress in predicting location versus quantity of land-use change; 4) the incorporation of biophysical feedbacks in land-use change models.
Article
Global sensitivity indices for rather complex mathematical models can be efficiently computed by Monte Carlo (or quasi-Monte Carlo) methods. These indices are used for estimating the influence of individual variables or groups of variables on the model output.
Article
This paper compares two land change models in terms of appropriateness for various applications and predictive power. Cellular Automata Markov (CA_Markov) and Geomod are the two models, which have similar options to allow for specification of the predicted quantity and location of land categories. The most important structural difference is that CA_Markov has the ability to predict any transition among any number of categories, while Geomod predicts only a one-way transition from one category to one alternative category. To assess the predictive power, each model is run several times to predict land change in central Massachusetts, USA. The models are calibrated with information from 1971 to 1985, and then the models predict the change from 1985 to 1999. The method to measure the predictive power: 1) separates the calibration process from the validation process, 2) assesses the accuracy at multiple resolutions, and 3) compares the predictive model vis-à-vis a null model that predicts pure persistence. Among 24 model runs, the predictive models are more accurate than the null model at resolutions coarser than two kilometres, but not at resolutions finer than one kilometre. The choice of the options account for more variation in accuracy of runs than the choice of the model per se. The most accurate model runs are those that did not use spatial contiguity explicitly. For this particular study area, the added complexity of CA_Markov is of no benefit.
Article
It is widely known that watershed hydrology is dependent on many factors, including land use, climate, and soil conditions. But the relative impacts of different types of land use on the surface water are yet to be ascertained and quantified. This research attempted to use a comprehensive approach to examine the hydrologic effects of land use at both a regional and a local scale. Statistical and spatial analyses were employed to examine the statistical and spatial relationships of land use and the flow and water quality in receiving waters on a regional scale in the State of Ohio. Besides, a widely accepted watershed-based water quality assessment tool, the Better Assessment Science Integrating Point and Nonpoint Sources (BASINS), was adopted to model the plausible effects of land use on water quality in a local watershed in the East Fork Little Miami River Basin. The results from the statistical analyses revealed that there was a significant relationship between land use and in-stream water quality, especially for nitrogen, phosphorus and Fecal coliform. The geographic information systems (GIS) spatial analyses identified the watersheds that have high levels of contaminants and percentages of agricultural and urban lands. Furthermore, the hydrologic and water quality modeling showed that agricultural and impervious urban lands produced a much higher level of nitrogen and phosphorus than other land surfaces. From this research, it seems that the approach adopted in this study is comprehensive, covering both the regional and local scales. It also reveals that BASINS is a very useful and reliable tool, capable of characterizing the flow and water quality conditions for the study area under different watershed scales. With little modification, these models should be able to adapt to other watersheds or to simulate other contaminants. They also can be used to study the plausible impacts of global environmental change. In addition, the information on the hydrologic effects of land use is very useful. It can provide guidelines not only for resource managers in restoring our aquatic ecosystems, but also for local planners in devising viable and ecologically-sound watershed development plans, as well as for policy makers in evaluating alternate land management decisions.
Article
Scale issues have significant implications for the analysis of social and biophysical processes in complex systems. These same scale implications are likewise considerations for the design and application of models of landcover change. Scale issues have wide-ranging effects from the representativeness of data used to validate models to aggregation errors introduced in the model structure. This paper presents an analysis of how scale issues affect an agent-based model (ABM) of landcover change developed for a research area in the Midwest, USA. The research presented here explores how scale factors affect the design and application of agent-based landcover change models.
Predicting temporal patterns in urban development from remote imagery
  • Howes
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Summary for policymakers
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An Integrated Framework for Evaluating the Effectiveness of Land-use based Flood Management Strategy in Urban Areas (A Case Study in Taipei City
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Ku, C.A., 2016b. An Integrated Framework for Evaluating the Effectiveness of Land-use based Flood Management Strategy in Urban Areas (A Case Study in Taipei City, Taiwan, PhD thesis, University of Cambridge, UK).
Summary for policymakers
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Modeling the environmental impacts of urban land use and land cover change—a study in Merseyside, UK
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