The aftermath of three earthquakes has forced Christchurch to re-plan and rebuild. New perspectives of a sustainable city have arisen granting Christchurch the chance of becoming an example to the world. This work is centred on bioclimatic landscape design as a base for greening strategies. It deals with strategic landscape design adapted to a specific climate, from a user’s perspective. The investigation will be applied to Christchurch’s urban centres, assessing cultural adaptability to the local climate and implications for landscape design. Climatic data shows that humidity is not a local problem. However, the wind is the determinant. In Christchurch the solar radiation and the prevailing winds are the most important microclimatic variables, the latter intensifying the loss of surface heat, decreasing the radiant temperature and affecting thermal sensation.
The research objective is to explore design parameters at the street-scale and identify ways to maximise thermal comfort in outdoor spaces through design-based strategies. The investigation will apply methods of participant observation, depth interviews, climatic data collection and design experimentation based on thermal comfort models and computer simulation tools. Case study sites chosen for investigation are places with current levels of activity that may be anticipated in the rebuild of the central city. The research will have two main outcomes: improved understanding of local urban culture adaptation to microclimate, and a demonstration of how design can enhance adaption. These outcomes will inform designers and city managers about good design practices and strategies that can be used to ensure a long term liveable city.
Disasters are often followed by a large-scale stimulus supporting the economy through the built environment, which can last years. During this time, official economic indicators tend to suggest the economy is doing well, but as activity winds down, the sentiment can quickly change. In response to the damaging 2011 earthquakes in Canterbury, New Zealand, the regional economy outpaced national economic growth rates for several years during the rebuild. The repair work on the built environment created years of elevated building activity. However, after the peak of the rebuilding activity, as economic and employment growth retracts below national growth, we are left with the question of how the underlying economy performs during large scale stimulus activity in the built environment. This paper assesses the performance of the underlying economy by quantifying the usual, demand-driven level of building activity at this time. Applying an Input–Output approach and excluding the economic benefit gained from the investment stimulus reveals the performance of the underlying economy. The results reveal a strong growing underlying economy, and while convergence was expected as the stimulus slowed down, the results found that growth had already crossed over for some time. The results reveal that the investment stimulus provides an initial 1.5% to 2% growth buffer from the underlying economy before the growth rates cross over. This supports short-term economic recovery and enables the underlying economy to transition away from a significant rebuild stimulus. Once the growth crosses over, five years after the disaster, economic growth in the underlying economy remains buoyant even if official regional economic data suggest otherwise.
Globally, the maximum elevations at which treelines are observed to occur coincide with a 6.4 °C soil isotherm. However, when observed at finer scales, treelines display a considerable degree of spatial complexity in their patterns across the landscape and are often found occurring at lower elevations than expected relative to the global-scale pattern. There is still a
lack of understanding of how the abiotic environment imposes constraints on treeline patterns, the scales at which different effects are acting, and how these effects vary over large spatial extents. In this thesis, I examined abrupt Nothofagus treelines across seven degrees of
latitude in New Zealand in order to investigate two broad questions: (1) What is the nature and extent of spatial variability in Nothofagus treelines across the country? (2) How is this variation associated with abiotic variation at different spatial scales? A range of GIS, statistical, and atmospheric modelling methods were applied to address these two questions.
First, I characterised Nothofagus treeline patterns at a 15x15km scale across New Zealand using a set of seven, GIS-derived, quantitative metrics that describe different aspects of treeline position, shape, spatial configuration, and relationships with adjacent vegetation.
Multivariate clustering of these metrics revealed distinct treeline types that showed strong spatial aggregation across the country. This suggests a strong spatial structuring of the abiotic environment which, in turn, drives treeline patterns. About half of the multivariate treeline
metric variation was explained by patterns of climate, substrate, topographic and disturbance variability; on the whole, climatic and disturbance factors were most influential.
Second, I developed a conceptual model that describes how treeline elevation may
vary at different scales according to three categories of effects: thermal modifying effects, physiological stressors, and disturbance effects. I tested the relevance of this model for Nothofagus treelines by investigating treeline elevation variation at five nested scales (regional to local) using a hierarchical design based on nested river catchments. Hierarchical linear modelling revealed that the majority of the variation in treeline elevation resided at the broadest, regional scale, which was best explained by the thermal modifying effects of solar radiation, mountain mass, and differences in the potential for cold air ponding. Nonetheless, at finer scales, physiological and disturbance effects were important and acted to modify the regional trend at these scales. These results suggest that variation in abrupt treeline elevations
are due to both broad-scale temperature-based growth limitation processes and finer-scale stress- and disturbance-related effects on seedling establishment.
Third, I explored the applicability of a meso-scale atmospheric model, The Air
Pollution Model (TAPM), for generating 200 m resolution, hourly topoclimatic data for
temperature, incoming and outgoing radiation, relative humidity, and wind speeds. Initial assessments of TAPM outputs against data from two climate station locations over seven years showed that the model could generate predictions with a consistent level of accuracy for both sites, and which agreed with other evaluations in the literature. TAPM was then used to generate data at 28, 7x7 km Nothofagus treeline zones across New Zealand for January
(summer) and July (winter) 2002. Using mixed-effects linear models, I determined that both
site-level factors (mean growing season temperature, mountain mass, precipitation,
earthquake intensity) and local-level landform (slope and convexity) and topoclimatic factors (solar radiation, photoinhibition index, frost index, desiccation index) were influential in
explaining variation in treeline elevation within and among these sites. Treelines were
generally closer to their site-level maxima in regions with higher mean growing season
temperatures, larger mountains, and lower levels of precipitation. Within sites, higher
treelines were associated with higher solar radiation, and lower photoinhibition and
desiccation index values, in January, and lower desiccation index values in July. Higher treelines were also significantly associated with steeper, more convex landforms.
Overall, this thesis shows that investigating treelines across extensive areas at multiple study scales enables the development of a more comprehensive understanding of treeline variability and underlying environmental constraints. These results can be used to formulate new hypotheses regarding the mechanisms driving treeline formation and to guide the optimal choice of field sites at which to test these hypotheses.
