Developing a predictive modelling capacity for a climate change-vulnerable blanket bog habitat: Assessing 1961-1990 baseline relationships

Aim: Understanding the spatial distribution of high priority habitats and developing predictive models using climate and environmental variables to replicate these distributions are desirable conservation goals. The aim of this study was to model and elucidate the contributions of climate and topography to the distribution of a priority blanket bog habitat in Ireland, and to examine how this might inform the development of a climate change predictive capacity for peat-lands in Ireland. Methods: Ten climatic and two topographic variables were recorded for grid cells with a spatial resolution of 1010 km, covering 87% of the mainland land surface of Ireland. Presence-absence data were matched to these variables and generalised linear models (GLMs) fitted to identify the main climatic and terrain predictor variables for occurrence of the habitat. Candidate predictor variables were screened for collinearity, and the accuracy of the final fitted GLM was evaluated using fourfold cross-validation based on the area under the curve (AUC) derived from a receiver operating characteristic (ROC) plot. The GLM predicted habitat occurrence probability maps were mapped against the actual distributions using GIS techniques. Results: Despite the apparent parsimony of the initial GLM using only climatic variables, further testing indicated collinearity among temperature and precipitation variables for example. Subsequent elimination of the collinear variables and inclusion of elevation data produced an excellent performance based on the AUC scores of the final GLM. Mean annual temperature and total mean annual precipitation in combination with elevation range were the most powerful explanatory variable group among those explored for the presence of blanket bog habitat. Main conclusions: The results confirm that this habitat distribution in general can be modelled well using the non-collinear climatic and terrain variables tested at the grid resolution used. Mapping the GLM-predicted distribution to the observed distribution produced useful results in replicating the projected occurrence of the habitat distribution over an extensive area. The methods developed will usefully inform future climate change predictive modelling for Irelan

Simulating the impact of urban development pathways on the local climate: A scenario-based analysis in the greater Dublin region,Ireland.

In this study, the impact of different urban development scenarios on neighbourhood climate are examined. The investigation considers the relative impact differing policy/planning choices will have on the local-scale climate across a city during a typical climatological year (TCY). The aim is to demonstrate a modelling approach which couples a climate-based land classification and simple urban climate modeland how this can be used to examine the impact differing urban forms and design strategies have on neigh-bourhood scale partitioning of energy and resulting consequences. Utilising the Surface Urban Energy and Water Balance (SUEWS) model (Järvi et al., 2011) hourly fluxes of sensible, latent and stored heat are simulated for an entire year under four different urban development scenarios. The land cover scenarios are based on those obtained by the MOLAND model for 2026 (Brennan et al., 2009) in our case study city Dublin (Ireland). MOLAND LULC are translated into local climate zones (Stewart and Oke, 2012) for examination. Subsequently, the types of building forms, vegetation type and coverage are modified based on realistic examples currently found across Dublin city. Our results focused on 2 principle aspects: the seasonality of energy partitioning with respect to vegetation and average diurnal partitioning of energy.Our analysis illustrates that compact scenarios are suitable form of future urban development in terms of reducing the spatial impact on the existing surface energy budget in Dublin. Design interventions which maintain the level of vegetation at a ratio ≥ 9:16 to artificial surfaces reduces the impact