Natural hazards in Australia : floods

Floods are caused by a number of interacting factors, making it remarkably difficult to explain changes in flood hazard. This paper reviews the current understanding of historical trends and variability in flood hazard across Australia. Links between flood and rainfall trends cannot be made due to the influence of climate processes over a number of spatial and temporal scales as well as landscape changes that affect the catchment response. There are also still considerable uncertainties in future rainfall projections, particularly for sub-daily extreme rainfall events. This is in addition to the inherent uncertainty in hydrological modelling such as antecedent conditions and feedback mechanisms. Research questions are posed based on the current state of knowledge. These include a need for high-resolution climate modelling studies and efforts in compiling and analysing databases of sub-daily rainfall and flood records. Finally there is a need to develop modelling frameworks that can deal with the interaction between climate processes at different spatio-temporal scales, so that historical flood trends can be better explained and future flood behaviour understood

Uncertainties of statistical downscaling from predictor selection: Equifinality and transferability

The nonhomogeneous hidden Markov model (NHMM) statistical downscaling model, 38 catchments in southeast Australia and 19 general circulation models (GCMs) were used in this study to demonstrate statistical downscaling uncertainties caused by equifinality to and transferability. That is to say, there could be multiple sets of predictors that give similar daily rainfall simulation results for both calibration and validation periods, but project different amounts (or even directions of change) of rainfall changing in the future. Results indicated that two sets of predictors (Set 1 with predictors of sea level pressure north-south gradient, u-wind at 700 hPa, v-wind at 700 hPa, and specific humidity at 700 hPa and Set 2 with predictors of sea level pressure north-south gradient, u-wind at 700 hPa, v-wind at 700 hPa, and dewpoint temperature depression at 850 hPa) as inputs to the NHMM produced satisfactory results of seasonal rainfall in comparison with observations. For example, during the model calibration period, the relative errors across the 38 catchments ranged from 0.48 to 1.76% with a mean value of 1.09% for the predictor Set 1, and from 0.22 to 2.24% with a mean value of 1.16% for the predictor Set 2. However, the changes of future rainfall from NHMM projections based on 19 GCMs produced projections with a different sign for these two different sets of predictors: Set 1 predictors project an increase of future rainfall with magnitudes depending on future time periods and emission scenarios, but Set 2 predictors project a decline of future rainfall. Such divergent projections may present a significant challenge for applications of statistical downscaling as well as climate change impact studies, and could potentially imply caveats in many existing studies in the literature

Scientific, societal and pedagogical approaches to tackle the impact of climate change on marine pollution

Marine pollution impacts coastal nations around the world, and more so: (a) in confined maritime areas with significant marine traffic, (b) where exploitation of natural and mineral resources is taking place, or (c) in regions witnessing pressure from tourism, local population growth, and industry. In this work, Digital Elevation Models, hydrographic, and climatic data are used together with computer simulations to understand the control of climate change on marine pollution. The results show that different climate change signals can potentially alter the flow and concentration of pollution in the European Seas, when compared to the present day. Ultimately, this work identifies the main sources of marine pollution as: (1) rivers and streams near cities and industrialised areas, (2) coastal areas experiencing sudden demographic pressures, (3) offshore shipping lanes in which oil and other marine debris are released, and (4) areas of rugged seafloor where industrial fishing takes place. This paper finishes by describing new educational material prepared to teach school children around the world. It explains why how a new training curriculum and e-game developed by Sea4All can be crucial in future Environmental Education and Education for a Sustainable Development

Comparison of various climate change projections of eastern Australian rainfall

The Australian eastern seaboard is a distinct climate entity from the interior of the continent, with different climatic influences on each side of the Great Dividing Range. Therefore, it is plausible that downscaling of global climate models could reveal meaningful regional detail, or ‘added value’, in the climate change signal of mean rainfall change in eastern Australia un-der future scenarios. However, because downscaling is typically done using a limited set of global climate models and downscaling methods, the results from a downscaling study may not represent the range of uncertainty in plausible projected change for a region suggested by the ensemble of host global climate models. A complete and unbiased representation of the plausible changes in the climate is essential in producing climate projections useful for future planning. As part of this aim it is important to quantify any differences in the change signal between global climate models and downscaling, and understand the cause of these differ-ences in terms of plausible added regional detail in the climate change signal, the impact of sub-sampling global climate models and the effect of the downscaling models themselves. Here we examine rainfall projections in eastern Australia under a high emissions scenario by late in the century from ensembles of global climate models, two dynamical downscaling models and one statistical downscaling model. We find no cases where all three downscaling methods show the same clear regional spatial detail in the change signal that is distinct from the host models. However, some downscaled projections suggest that the eastern seaboard could see little change in spring rainfall, in contrast to the substantial rainfall decrease inland. The change signal in the downscaled outputs is broadly similar at the large scale in the various model outputs, with a few notable exceptions. For example, the model median from dynamical downscaling projects a rainfall increase over the entirety of eastern Australia in autumn that is greater than the global models. Also, there are some instances where a downscaling method produces changes outside the range of host models over eastern Australia as a whole, thus ex-panding the projected range of uncertainty. Results are particularly uncertain for summer, where no two downscaling studies clearly agree. There are also some confounding factors from the model configuration used in downscaling, where the particular zones used for statis-tical models and the model components used in dynamical models have an influence on results and produce additional uncertainty

Increased Cortical Thickness in Sports Experts: A Comparison of Diving Players with the Controls

Sports experts represent a population of people who have acquired expertise in sports training and competition. Recently, the number of studies on sports experts has increased; however, neuroanatomical changes following extensive training are not fully understood. In this study, we used cortical thickness measurement to investigate the brain anatomical characteristics of professional divers with extensive training experience. A comparison of the brain anatomical characteristics of the non-athlete group with those of the athlete group revealed three regions with significantly increased cortical thickness in the athlete group. These regions included the left superior temporal sulcus, the right orbitofrontal cortex and the right parahippocampal gyrus. Moreover, a significant positive correlation between the mean cortical thickness of the right parahippocampal gyrus and the training experience was detected, which might indicate the effect of extensive training on diving players' brain structure

Maternal body mass index, gestational weight gain, and the risk of overweight and obesity across childhood : An individual participant data meta-analysis

Background Maternal obesity and excessive gestational weight gain may have persistent effects on offspring fat development. However, it remains unclear whether these effects differ by severity of obesity, and whether these effects are restricted to the extremes of maternal body mass index (BMI) and gestational weight gain. We aimed to assess the separate and combined associations of maternal BMI and gestational weight gain with the risk of overweight/obesity throughout childhood, and their population impact. Methods and findings We conducted an individual participant data meta-analysis of data from 162,129 mothers and their children from 37 pregnancy and birth cohort studies from Europe, North America, and Australia. We assessed the individual and combined associations of maternal pre-pregnancy BMI and gestational weight gain, both in clinical categories and across their full ranges, with the risks of overweight/obesity in early (2.0-5.0 years), mid (5.0-10.0 years) and late childhood (10.0-18.0 years), using multilevel binary logistic regression models with a random intercept at cohort level adjusted for maternal sociodemographic and lifestylerelated characteristics. We observed that higher maternal pre-pregnancy BMI and gestational weight gain both in clinical categories and across their full ranges were associated with higher risks of childhood overweight/obesity, with the strongest effects in late childhood (odds ratios [ORs] for overweight/obesity in early, mid, and late childhood, respectively: OR 1.66 [95% CI: 1.56, 1.78], OR 1.91 [95% CI: 1.85, 1.98], and OR 2.28 [95% CI: 2.08, 2.50] for maternal overweight; OR 2.43 [95% CI: 2.24, 2.64], OR 3.12 [95% CI: 2.98, 3.27], and OR 4.47 [95% CI: 3.99, 5.23] for maternal obesity; and OR 1.39 [95% CI: 1.30, 1.49], OR 1.55 [95% CI: 1.49, 1.60], and OR 1.72 [95% CI: 1.56, 1.91] for excessive gestational weight gain). The proportions of childhood overweight/obesity prevalence attributable to maternal overweight, maternal obesity, and excessive gestational weight gain ranged from 10.2% to 21.6%. Relative to the effect of maternal BMI, excessive gestational weight gain only slightly increased the risk of childhood overweight/obesity within each clinical BMI category (p-values for interactions of maternal BMI with gestational weight gain: p = 0.038, p <0.001, and p = 0.637 in early, mid, and late childhood, respectively). Limitations of this study include the self-report of maternal BMI and gestational weight gain for some of the cohorts, and the potential of residual confounding. Also, as this study only included participants from Europe, North America, and Australia, results need to be interpreted with caution with respect to other populations. Conclusions In this study, higher maternal pre-pregnancy BMI and gestational weight gain were associated with an increased risk of childhood overweight/obesity, with the strongest effects at later ages. The additional effect of gestational weight gain in women who are overweight or obese before pregnancy is small. Given the large population impact, future intervention trials aiming to reduce the prevalence of childhood overweight and obesity should focus on maternal weight status before pregnancy, in addition to weight gain during pregnancy.Peer reviewe

Consensus-based care recommendations for adults with myotonic dystrophy type 1

Purpose of review Myotonic dystrophy type 1 (DM1) is a severe, progressive genetic disease that affects between 1 in 3,000 and 8,000 individuals globally. No evidence-based guideline exists to inform the care of these patients, and most do not have access to multidisciplinary care centers staffed by experienced professionals, creating a clinical care deficit. Recent findings The Myotonic Dystrophy Foundation (MDF) recruited 66 international clinicians experienced in DM1 patient care to develop consensus-based care recommendations. MDF created a 2-step methodology for the project using elements of the Single Text Procedure and the Nominal Group Technique. The process generated a 4-page Quick Reference Guide and a comprehensive, 55-page document that provides clinical care recommendations for 19 discrete body systems and/or care considerations. Summary The resulting recommendations are intended to help standardize and elevate care for this patient population and reduce variability in clinical trial and study environments. Described as “one of the more variable diseases found in medicine,” myotonic dystrophy type 1 (DM1) is an autosomal dominant, triplet-repeat expansion disorder that affects somewhere between 1:3,000 and 1:8,000 individuals worldwide.1 There is a modest association between increased repeat expansion and disease severity, as evidenced by the average age of onset and overall morbidity of the condition. An expansion of over 35 repeats typically indicates an unstable and expanding mutation. An expansion of 50 repeats or higher is consistent with a diagnosis of DM1. DM1 is a multisystem and heterogeneous disease characterized by distal weakness, atrophy, and myotonia, as well as symptoms in the heart, brain, gastrointestinal tract, endocrine, and respiratory systems. Symptoms may occur at any age. The severity of the condition varies widely among affected individuals, even among members of the same family. Comprehensive evidence-based guidelines do not currently exist to guide the treatment of DM1 patients. As a result, the international patient community reports varied levels of care and care quality, and difficulty accessing care adequate to manage their symptoms, unless they have access to multidisciplinary neuromuscular clinics. Consensus-based care recommendations can help standardize and improve the quality of care received by DM1 patients and assist clinicians who may not be familiar with the significant variability, range of symptoms, and severity of the disease. Care recommendations can also improve the landscape for clinical trial success by eliminating some of the inconsistencies in patient care to allow more accurate understanding of the benefit of potential therapies