Introduction to the special issue : historical and projected climatic changes to Australian natural hazards

Australia’s size and varied climates mean that it is affected by a range of weather-related natural hazards, including tropical and extra-tropical storms and associated extreme wind and hail, coastal and inland floods, heatwaves and bushfires. These hazards cause multiple human and environmental impacts, and collectively account for 93 % of Australian insured losses (Schuster 2013). In addition, drought—often treated distinctly from other hazards due to its more gradual onset—can cause substantial reductions in agricultural productivity, and places stress on municipal and industrial water resources and natural ecosystems. Evidence is building that the frequency and cost of natural hazards are increasing both in Australia (Insurance Council of Australia 2013; Schuster 2013) and globally (Munich Re 2014). However, understanding the cause of these changes has proved to be difficult, with increases in reporting rates (Munich Re 2014), changes in societal exposure and vulnerability (Bouwer 2011; Neumayer and Barthel 2011) and anthropogenic climate change (IPCC 2013) all potentially playing a role in explaining the observed changes. Yet although the potential causes are many, correct attribution of the observed changes is necessary in order to identify appropriate policy responses, and to predict how the frequency and severity of natural hazards might change in the future. This Special Issue focuses on the specific role of large-scale climatic changes on the observed and future incidence of Australian natural hazards. The Special Issue is divided into seven papers, each covering a major class of climate-influenced natural hazard: floods, drought, storms (including wind and hail), coastal extremes, bushfires, heatwaves and frost. The work was initiated by the Working Group on Trends and Extremes from the Australian Water and Energy Exchanges (OzEWEX) initiative, which is a regional hydroclimate project run under the auspices of the Global Energy and Water Exchanges (GEWEX) initiative

Global‐Scale Prediction of Flood Timing Using Atmospheric Reanalysis

The annual timing of flood events is a useful indicator to study the interaction between atmospheric and catchment processes in generating floods. This paper presents an assessment of the seasonal timing of floods for 7,894 gauging locations across the globe over a common period from 1981 to 2010. The averaged ordinal date of annual maximum streamflow is then estimated for ungauged locations following a two‐stage prediction scheme. The first stage identifies regions that share a common climatic predictor of flood timing by analyzing the similarity of flood timing with seven climate variables. These variables represent precipitation timing and snowmelt dynamics and are derived from a global climate reanalysis data set. Homogeneous regions in terms of the dominant predictor are generalized in the second stage through a rule‐based classification. The classification partitions the world into 10 hydroclimate classes, where each class has flood timing predicted using the most relevant climate predictor. Using this relatively simple and interpretable model structure, flood timing could be predicted with a global mean absolute error of approximately 32 days while maintaining consistency across large regions. Potential applications of the developed map include better understanding of climatic drivers of flooding and benchmarking the performance of global hydrological models in simulating the processes relevant to flooding.Plain Language SummaryTiming of annual maximum streamflow is a useful index to relate flood occurrence to appropriate flood generation processes. This study presents an assessment of flood timing across 7,894 gauging locations globally over the period from 1981 to 2010. The averaged date of annual maximum streamflow is compared to seven climate predictors, identifying regions that are likely to share a common flood generation process. These homogeneous regions are generalized across the globe using a gridded data set of daily precipitation and temperature. To derive a global map of flood timing, the date of annual maximum streamflow is predicted for both gauged and ungauged locations, using a linear function of the most important climate predictor in each region.Key PointsObservation‐based analysis of the timing of annual maximum streamflow indicates large‐scale patterns over the common 1981–2010 periodRegional patterns of flood generation mechanisms are highlighted through a comparison between high‐flow timing and the timing of seven climate predictorsA prediction of flood timing was made for the global land mass using an atmospheric reanalysis data setPeer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/152703/1/wrcr24292_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/152703/2/wrcr24292.pd

The Wyoming Survey for H-alpha. I. Initial Results at z ~ 0.16 and 0.24

The Wyoming Survey for H-alpha, or WySH, is a large-area, ground-based, narrowband imaging survey for H-alpha-emitting galaxies over the latter half of the age of the Universe. The survey spans several square degrees in a set of fields of low Galactic cirrus emission. The observing program focuses on multiple dz~0.02 epochs from z~0.16 to z~0.81 down to a uniform (continuum+line) luminosity at each epoch of ~10^33 W uncorrected for extinction (3sigma for a 3" diameter aperture). First results are presented here for 98+208 galaxies observed over approximately 2 square degrees at redshifts z~0.16 and 0.24, including preliminary luminosity functions at these two epochs. These data clearly show an evolution with lookback time in the volume-averaged cosmic star formation rate. Integrals of Schechter fits to the extinction-corrected H-alpha luminosity functions indicate star formation rates per co-moving volume of 0.009 and 0.014 h_70 M_sun/yr/Mpc^3 at z~0.16 and 0.24, respectively. The formal uncertainties in the Schechter fits, based on this initial subset of the survey, correspond to uncertainties in the cosmic star formation rate density at the >~40% level; the tentative uncertainty due to cosmic variance is 25%, estimated from separately carrying out the analysis on data from the first two fields with substantial datasets.Comment: To appear in the Astronomical Journa

Compounding heatwave-extreme rainfall events driven by fronts, high moisture, and atmospheric instability

Heatwaves have been shown to increase the likelihood and intensity of extreme rainfall occurring immediately afterward, potentially leading to increased flood risk. However, the exact mechanisms connecting heatwaves to extreme rainfall remain poorly understood. In this study, we use weather type data sets for Australia and Europe to identify weather patterns, including fronts, cyclones, and thunderstorm conditions, associated with heatwave terminations and following extreme rainfall events. We further analyze, using reanalysis data, how atmospheric instability and moisture availability change before and after the heatwave termination depending on whether the heatwave is followed by extreme rainfall, as well as the location of the heatwave. We find that most heatwaves terminate during thunderstorm and/or frontal conditions. Additionally, atmospheric instability and moisture availability increase several days before the heatwave termination; but only if heatwaves are followed by extreme rainfall. We also find that atmospheric instability and moisture after a heatwave are significantly higher than expected from climatology for the same time of the year, and that highest values of instability and moisture are associated with highest post-heatwave rainfall intensities. We conclude that the joint presence of high atmospheric instability, moisture, as well as frontal systems are likely to explain why rainfall is generally more extreme and likely after heatwaves, as well as why this compound hazard is mainly found in the non-arid mid and high latitudes. An improved understanding of the drivers of these compound events will help assess potential changing impacts in the future

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

Continuous rainfall simulation: 2. A regionalized daily rainfall generation approach

This paper is the second of two in the current issue that presents a framework for simulating continuous (uninterrupted) rainfall sequences at both gaged and ungaged locations. The ultimate objective of the papers is to present a methodology for stochastically generating continuous subdaily rainfall sequences at any location such that the statistics at a range of aggregation scales are preserved. In this paper we complete the regionalized algorithm by adopting a rationale for generating daily sequences at any location by sampling daily rainfall records from "nearby" gages with statistically similar rainfall sequences.The approach consists of two distinct steps: first the identification of a set of locations with daily rainfall sequences that are statistically similar to the location of interest, and second the development of an algorithm to sample daily rainfall from those locations. In the first step, the similarity between all bivariate combinations of 2708 daily rainfall records across Australia were considered, and a logistic regression model was formulated to predict the similarity between stations as a function of a number of physiographic covariates. Based on the model results, a number of nearby locations with adequate daily rainfall records are identified for any ungaged location of interest (the "target" location), and then used as the basis for stochastically generating the daily rainfall sequences. The continuous simulation algorithm was tested at five locations where long historical daily rainfall records are available for comparison, and found to perform well in representing the distributional and dependence attributes of the observed daily record. These daily sequences were then used to disaggregate to a subdaily time step using the rainfall state-based disaggregation approach described in the first paper, and found to provide a good representation of the continuous rainfall sequences at the location of interest. Copyright 2012 by the American Geophysical Union.Rajeshwar Mehrotra, Seth Westra, Ashish Sharma and Ratnasingham Srikantha

Natural hazards in Australia : sea level and coastal extremes

The Australian coastal zone encompasses tropical, sub- and extra-tropical climates and accommodates about 80 % of Australia’s population. Sea level extremes and their physical impacts in the coastal zone arise from a complex set of atmospheric, oceanic and terrestrial processes that interact on a range of spatial and temporal scales and will be modified by a changing climate, including sea level rise. This review details significant progress over recent years in understanding the causes of past and projections of future changes in sea level and coastal extremes, yet a number of research questions, knowledge gaps and challenges remain. These include efforts to improve knowledge on past sea level extremes, integrate a wider range of processes in projections of future changes to sea level extremes, and focus efforts on understanding long-term coastline response from the combination of contributing factors

Historical and future changes in global flood magnitude - evidence from a model-observation investigation

To improve the understanding of trends in extreme flows related to flood events at the global scale, historical and future changes of annual maxima of 7 d streamflow are investigated, using a comprehensive streamflow archive and six global hydrological models. The models' capacity to characterise trends in annual maxima of 7 d streamflow at the continental and global scale is evaluated across 3666 river gauge locations over the period from 1971 to 2005, focusing on four aspects of trends: (i) mean, (ii) standard deviation, (iii) percentage of locations showing significant trends and (iv) spatial pattern. Compared to observed trends, simulated trends driven by observed climate forcing generally have a higher mean, lower spread and a similar percentage of locations showing significant trends. Models show a low to moderate capacity to simulate spatial patterns of historical trends, with approximately only from 12 % to 25 % of the spatial variance of observed trends across all gauge stations accounted for by the simulations. Interestingly, there are statistically significant differences between trends simulated by global hydrological models (GHMs) forced with observational climate and by those forced by bias-corrected climate model output during the historical period, suggesting the important role of the stochastic natural (decadal, inter-annual) climate variability. Significant differences were found in simulated flood trends when averaged only at gauged locations compared to those averaged across all simulated grid cells, highlighting the potential for bias toward well-observed regions in our understanding of changes in floods. Future climate projections (simulated under the RCP2.6 and RCP6.0 greenhouse gas concentration scenarios) suggest a potentially high level of change in individual regions, with up to 35 % of cells showing a statistically significant trend (increase or decrease; at 10 % significance level) and greater changes indicated for the higher concentration pathway. Importantly, the observed streamflow database under-samples the percentage of locations consistently projected with increased flood hazards under the RCP6.0 greenhouse gas concentration scenario by more than an order of magnitude (0.9 % compared to 11.7 %). This finding indicates a highly uncertain future for both flood-prone communities and decision makers in the context of climate change. © Author(s) 2020

Probabilistic estimation of multivariate streamflow using independent component analysis and climate information

A statistical estimation approach is presented and applied to multiple reservoir inflow series that form part of Sydney’s water supply system. The approach involves first identifying sources of interannual and interdecadal climate variability using a combination of correlation- and wavelet-based methods, then using this information to construct probabilistic, multivariate seasonal estimates using a method based on independent component analysis (ICA). The attraction of the ICA-based approach is that, by transforming the multivariate dataset into a set of independent time series, it is possible to maintain the parsimony of univariate statistical methods while ensuring that both the spatial and temporal dependencies are accurately captured. Based on a correlation analysis of the reservoir inflows with the original sea surface temperature anomaly data, the principal sources of variability in Sydney’s reservoir inflows appears to be a combination of the El Niño–Southern Oscillation (ENSO) phenomenon and the Pacific decadal oscillation (PDO). A multivariate ICA-based estimation model was then used to capture this variability, and it was shown that this approach performed well in maintaining the temporal dependence while also accurately maintaining the spatial dependencies that exist in the 11-dimensional historical reservoir inflow dataset.Seth Westra and Ashish Sharm

Nuclear factor κB-inducing kinase activation as a mechanism of pancreatic β cell failure in obesity

The nuclear factor κB (NF-κB) pathway is a master regulator of inflammatory processes and is implicated in insulin resistance and pancreatic β cell dysfunction in the metabolic syndrome. Whereas canonical NF-κB signaling is well studied, there is little information on the divergent noncanonical NF-κB pathway in the context of pancreatic islet dysfunction. Here, we demonstrate that pharmacological activation of the noncanonical NF-κB-inducing kinase (NIK) disrupts glucose homeostasis in zebrafish in vivo. We identify NIK as a critical negative regulator of β cell function, as pharmacological NIK activation results in impaired glucose-stimulated insulin secretion in mouse and human islets. NIK levels are elevated in pancreatic islets isolated from diet-induced obese (DIO) mice, which exhibit increased processing of noncanonical NF-κB components p100 to p52, and accumulation of RelB. TNF and receptor activator of NF-κB ligand (RANKL), two ligands associated with diabetes, induce NIK in islets. Mice with constitutive β cell-intrinsic NIK activation present impaired insulin secretion with DIO. NIK activation triggers the noncanonical NF-κB transcriptional network to induce genes identified in human type 2 diabetes genome-wide association studies linked to β cell failure. These studies reveal that NIK contributes a central mechanism for β cell failure in diet-induced obesity