Making Women Visible in Disasters: Problematising the Private Domain

Gender awareness and sensitivity in disaster research and management remains uncommon and tends to focus on the developing rather than the developed world. This paper uses a feminist oral geography to present some findings about women's experiences in two floods in Scotland. It is conceptualised around public and private (masculinised and feminised) space, problematising the private domain and presenting it, in the feminist research tradition, as a legitimate object of research. It shows the ordinary and everyday to be more opaque and complex than usually imagined and makes recommendations for their recognition and incorporation into disaster management. While there is a specific focus on the private domain of the home, this is not intended to reinforce gender stereotypes but simply to recognise the reality of many of the women interviewed. It concludes that disaster research generally has yet to advance much beyond the earliest stages of feminist studies which merely sought to make women visible in society.     

A RESERVOIR OPERATING MODEL WITH INORGANIC QUALITY CONSTRAINTS FOR THE TRUCKEE RIVER1

ABSTRACT: This study investigated low flow augmentation as a means of meeting inorganic water quality standards for the Truckee River at the California-Nevada state line. A digital inorganic water quality model was combined with a deterministic dynamic reservoir operating model in an iterative process which allowed the optimization of releases subject to selected inorganic water quality constraints as well as downstream demands. Results from model runs with varied flow and river loading data indicate that flow augmentation may be a feasible and relatively inexpensive way of meeting standards for this system except in time of severe drought.     

A novel method for mapping reefs and subtidal rocky habitats using artificial neural networks

Reefs and subtidal rocky habitats are sites of high biodiversity and productivity which harbour commercially important species of fish and invertebrates. Although the conservation management of reef associated species has been informed using species distribution models (SDM) and community based approaches, to date their use has been constrained to specific regions where the locality and spatial extent of reefs is well known. Much of the world's subtidal habitats remain either undiscovered or unmapped, including coasts of intense human use. Consequently, to facilitate a stronger understanding of species-environmental relationships there is an urgent need for a cost and time effective standard method to map reefs at fine spatial resolutions across broad geographical extents. We used bathymetric data (∼250 m resolution) to calculate the local slope and curvature of the seabed. We then constructed artificial neural networks (ANNs) to forecast the probability of reef occurrence within grid cells as a function of bathymetric and slope variables. Testing over an independent data set not used in training showed that ANNs were able to accurately predict the location of reefs for 86% of all grid cells (Kappa = 0.63) without over fitting. The ANN with greatest support, combining bathymetric values of the target grid cell with the slope of adjacent grid cells, was used to map inshore reef locations around the Southern Australian coastline (∼250 m resolution). Broadly, our results show that reefs are identifiable from coarse-scale bathymetry data of the seabed. We anticipate that our research technique will strengthen systematic conservation planning tools in many regions of the world, by enabling the identification of rocky substratum and mapping in localities that remain poorly surveyed due to logistics or monetary constraints.     

Operationalizing risk perception and preparedness behavior research for a multi-hazard context

Increasingly, citizens are being asked to take a more active role in disaster risk reduction (DRR), as decentralization of hazard governance has shifted greater responsibility for hazard preparedness actions onto individuals. Simultaneously, the taxonomy of hazards considered for DRR has expanded to include medical and social crises alongside natural hazards. Risk perception research emerged to support decision-makers with understanding how people characterize and evaluate different hazards to anticipate behavioral response and guide risk communication. Since its inception, the risk perception concept has been incorporated into many behavioral theories, which have been applied to examine preparedness for numerous hazard types. Behavioral theories have had moderate success in predicting or explaining preparedness behaviors; however, they are typically applied to a single hazard type and there is a gap in understanding which theories (if any) are suited for examining multiple hazard types simultaneously. This paper first reviews meta-analyses of behavioral theories to better understand performance. Universal lessons learnt are summarized for survey design. Second, theoretically based preparedness studies for floods, earthquakes, epidemics, and terrorism are reviewed to assess the conceptual requirements for a ‘multi-hazard’ preparedness approach. The development of an online preparedness self-assessment and learning platform is discussed.     

The Risks from Flooding: Which Risks and Whose Perception?

Four main groups are considered in relation to the risk from flooding: the engineers involved in the design of flood alleviation schemes, emergency planners, the public, including both the population at risk from flooding and the rest of the population who will bear all or most of the cost of flood alleviation schemes and the researchers, such as geographers and economists concerned with flood hazards and scheme appraisal. It is argued that these different groups vary significantly in their selection and definition of risks from flooding as a focus of concern and that their definition of risk influences their expectations about future events and the appropriate response to those events. But the different groups share two tendencies: the expectation that the future will be a replication of the past; and the neglect of "uncertain uncertainties" in favour of known uncertainties of risk.