STARTER: a statistical GIS-based model for the prediction of snow avalanche susceptibility using terrain features—application to Alta Val Badia, Italian Dolomites

This article describes a methodology to localise areas with high potential towards natural snowpack instability under particular meteorological conditions, at the scale of an Alpine valley. Localisation is based on statistically relating known release areas of past avalanche events to maps of: (1) slope inclination, (2) slope orientation (aspect), (3) elevation, (4) distance from crest lines, (5) terrain roughness and (6) concavities/convexities. The maps have been built using two different GIS softwares while the statistical analyses have been performed with a specific software handling also Fuzzy Set theory algorithms. The results of the statistical analyses have been verified on test release—areas which have not been used as input data for the statistical analyses. Verification allowed to quantify how reliably the susceptibility values were calculated, to compare the values obtained using different combinations of terrain features and to finally decide on the most efficient combination. The susceptibility maps were calculated and verified for three different meteorological scenarios (given by three classes of snow depth). Verification has shown that the accuracy of the susceptibility maps was between 67% and 82%. The three susceptibility maps show a remarkable difference in the spatial pattern of the highest susceptibility pixels suggesting that for different meteorological scenarios different classes of terrain features need to be considered.The possibility to make combinations of terrain features and to assess and verify their statistical relationship with release areas of past avalanche events is the major original step made by STARTER. Linking those release areas to meteorological scenarios is an attempt to include in the analysis the combined influence of terrain features and meteorological conditions towards snowpack instability.     

Diatom assemblages from Adirondack lakes (New York,USA) and the development of inference models for retrospective environmental assessment

Detrended canonical coreespondence analysis (DCCA) was used to examine the relationships between diatom species distributions and environmental variables from 62 drainage lakes in the Adirondack region, New York (USA). The contribution of lakewater pH, Al_m (monomeric Al), NH₄, maximum depth, Mg, and DOC (dissolved organic carbon) were statistically significant in explaining the patterns of variation in the diatom species composition. Twenty-three and sixteen diatom taxa were identified as potential indicator species for pH and Al_m, respectively (i.e. a taxon with a strong statistical relationship to the environmental variable of interest, a well defined optimum, and a narrow tolerance to the variable of interest). Using weighted-averaging regression and calibration, predictive models were developed to infer lakewater pH (r ²=0.91), Al_m (r ²=0.83), DOC (dissolved organic carbon) (r ²=0.64), and ANC (acid neutralizing capacity; r ²=0.90). These variables are of key importance in understanding watershed acidification processes. These predictive models have been used in the PIRLA-II (Paleoecological Investigation of Recent Lake Acidification-II) project to answer policy-related questions concerning acidification, recovery, and fisheries loss.     

Modelling collective Yawuru values along the foreshore of Roebuck Bay,Western Australia using fuzzy logic

The cadastral model has played a key role in Indigenous dispossession in settler states. Yet, the recognition of Indigenous land rights, which has increased globally since the 1960s, frequently requires Indigenous communities to directly engage with this spatial model. In Australia, native title claimants must use entity-based models of space to delineate their traditional territories during the claim process. They must also engage with planning and development documents which use the cadastral model of space to assert and defend their rights following native title recognition. This is often problematic as Indigenous spatial ontologies emphasise complexity and continuity which is inimical to the ‘crisp’ representations of cadastral space.This study explores the potential of a fuzzy index modelling approach to represent cultural values using a case study from Broome, Western Australia. Sketch mapping, fuzzy index modelling and combinatory techniques were used to produce a model of several cultural values held by the Yawuru community for the in-town foreshore of Roebuck Bay. This model was overlaid on local planning documents to provide the Yawuru community with strategic intelligence for post native title governance. The experience of co-producing this model suggests that such techniques may assist Indigenous communities to engage with settler structures. This implies that policies which fail to extend analytical capacity to interested native title groups as part of programmes of spatial enablement continue to perpetuate historical processes of colonial domination.