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Continuously growing cities significantly modify the entire environment through air pollution and modification of land surface, resulting altered energy budget and land-atmosphere exchange processes over built-up areas. These effects mainly appear in cities or metropolitan areas, leading to the Urban Heat Island (UHI) phenomenon, which occurs due to the temperature difference between the built-up areas and their cooler surroundings. The Weather Research and Forecasting (WRF) mesoscale model coupled to multilayer urban canopy parameterisation is used to investigate this phenomenon for Budapest and its surroundings with actual land surface properties. In this paper the basic ideas of our research and the methodology in brief are presented. The simulation is completed for one week in summer 2015 with initial meteorological fields from Global Forecasting System (GFS) outputs, under atmospheric conditions of weak wind and clear sky for the Pannonian Basin. Then, to improve the WRF model and its settings, the calculated skin temperature is compared to the remotely sensed measurements derived from satellites Aqua and Terra, and the temporal and spatial bias values are estimated.

Continuously growing cities significantly modify the entire environment through air pollution and modification of land surface, resulting altered energy budget and land-atmosphere exchange processes over built-up areas. These effects mainly appear in cities or metropolitan areas, leading to the Urban Heat Island (UHI) phenomenon, which occurs due to the temperature difference between the built-up areas and their cooler surroundings. The Weather Research and Forecasting (WRF) mesoscale model coupled to multilayer urban canopy parameterisation is used to investigate this phenomenon for Budapest and its surroundings with actual land surface properties. In this paper the basic ideas of our research and the methodology in brief are presented. The simulation is completed for one week in summer 2015 with initial meteorological fields from Global Forecasting System (GFS) outputs, under atmospheric conditions of weak wind and clear sky for the Pannonian Basin. Then, to improve the WRF model and its settings, the calculated skin temperature is compared to the remotely sensed measurements derived from satellites Aqua and Terra, and the temporal and spatial bias values are estimated. [ABSTRACT FROM AUTHOR]

Abstract: The modelling of land use change is a way to analyse future scenarios by modelling different pathways. Application of spatial data of different scales coupled with socio-economic data makes it possible to explore and test the understanding of land use change relations. In the EU-FP7 research project PASHMINA (Paradigm Shift modelling and innovative approaches), three storylines of future transportation paradigm shifts towards 2040 are created. These storylines are translated into spatial planning strategies and modelled using the cellular automata model LUCIA. For the modelling, an Eastern Danish case area was selected, comprising of the Copenhagen metropolitan area and its hinterland. The different scenarios are described using a range of different descriptive GIS datasets. These include mapping of accessibility based on public and private transportation, urban density and structure, and distribution of jobs and population. These indicators are then incorporated in the model calculations as factors determining urban development, related to the scenario outlines. The results calculated from the scenarios reveals the great difference in urban distribution that different spatial planning strategies can produce, changing the shape of the urban landscape. The scenarios visualized showed to outline different planning strategies that could be implemented, creating a more homogenous urban structure targeted at a reduction of transportation work and thus energy consumption. This will lead to less impact on climate from transportation based on a more optimal localization and transport infrastructure strategy. [Copyright &y& Elsevier]

This paper presents an intraurban cellular automata model that is an extension to White and Engelen's pioneering model. The paper's main contribution is to distinguish between agglomerative eff ects, determined by the attraction of the neighbourhood, and disagglomerative eff ects, driven by land prices, or land aff ordability. In order to do that, social heterogeneity is introduced in the model at the intraurban level. As a result, we can simulate both the evolution of land use and land prices. An application of the model and a sensitivity analysis indicate that neighborhood infl uence is the main driving force of cities' spatial confi gurations. Prices, however, exert an important countereff ect. Actually, the higher the infl uence of land prices, the faster land succession is observed. Finally, an important conclusion of the model is that intraurban models should not fail to diff erentiate actors by income level. [ABSTRACT FROM AUTHOR]

Abstract: Urban planners are faced with the decision of what planning policy to pursue in order to achieve the best possible future. Many cities in developed nations use comprehensive models that simulate various aspects of the urban system, capable of predicting implications of a given set of policy inputs, to assist the planning process. However, in developing countries, demographic and socioeconomic data with appropriate spatial disaggregation are difficult to obtain. This constrains the development of such comprehensive urban models to support planning decisions. In the absence of models, the plan-making process usually inclines towards a more intuitive approach. Using simplified urban models adapted to the data constraints, this paper explores the prospects of enhancing planning in developing countries, with the aim of shifting the plan-making process from being purely intuitive towards being more scientific. The SIMPLAN (SIMplified PLANning) modelling suite has been developed for the case study city of Ahmedabad, India (the calibration per se is not discussed) to test alternative urban planning policies (combinations for land use and transport) for the year 2021. Model outputs are evaluated for key economic, environmental and social indicators. It should be noted that such a research study, in the context of developing countries, represents a first generation of studies/models, owing to the simplicity of the model structure and its accompanying limitations and data availability constraints. The modelling framework developed in this study has a visually driven user interface. This makes the model easy to understand, operate and update. Due to this attribute, it allows local planning authorities to carry out testing of several alternative planning policies themselves, without having the need to outsource modelling work to private consulting firms, usually at much higher cost. Key model outputs indicate that dispersing cities proves to be economically beneficial to society as a whole. Compact development may prove to be better in terms of environmental and social aspects, but it may be possible to tackle the undesirable effects of dispersal by appropriate combinations of planning and management measures. The modelling outputs informed the wider debate on compact vs. dispersed urban forms. It was shown that neither of these diametrically opposite forms provide an outright ‘win–win’ solution. They are likely to perform differently in different economies and sociocultural contexts. Therefore, it would appear that each city needs to test out the pros and cons of such alterative urban planning policies before pursing a plan for the future. Learning from such modelling exercises, cities can prepare their own tailor-made policy that best satisfies their objectives, making the planning process more rigorous and transparent. [Copyright &y& Elsevier]

Empirical models designed to simulate and predict urban land-use change in real situations are generally based on the utilization of statistical techniques to compute the land-use change probabilities. In contrast to these methods, artificial neural networks arise as an alternative to assess such probabilities by means of non-parametric approaches. This work introduces a simulation experiment on intra-urban land-use change in which a supervised back-propagation neural network has been employed in the parameterization of several biophysical and infrastructure variables considered in the simulation model. The spatial land-use transition probabilities estimated thereof feed a cellular automaton (CA) simulation model, based on stochastic transition rules. The model has been tested in a medium-sized town in the Midwest of Sao Paulo State, Piracicaba. A series of simulation outputs for the case study town in the period 1985-1999 were generated, and statistical validation tests were then conducted for the best results, based on fuzzy similarity measures. [ABSTRACT FROM AUTHOR]

A soil–vegetation–atmosphere transfer scheme based on a one-dimensional boundary layer model has been developed to study the sensitivity of boundary layer growth on the surface temperature fields and land-use types. Full interaction between the surface and atmosphere is achieved by representing the surface and turbulent mixing processes by using a Land Air Parameterization Scheme (LAPS) and the Blackadar''s local closure PBL scheme. Sensitivity tests are performed by simulating planetary boundary layer structures over different underlying surfaces, soil textures and soil moisture contents using the meteorological data for July 17, 1999 in Philadelphia, PA. Also, a simpler surface parameterization used in the MM5 model results in a significantly higher surface temperature as well as the PBL depth over the urban area than those obtained by the LAPS parameterization in which a new method, including a combination of aggregated fluxes and parameters in calculating the surface temperature, is applied. [Copyright &y& Elsevier]