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Widespread changes to climate-sensitive systems are placing increased demands on risk assessments as a foundation for managing risk. Recent attention to compounding and cascading risks, deep uncertainty, and “bottom-up” risk assessment frameworks have foregrounded the need to account for systemic complexity in risk assessment methodology. We describe the sources of systemic complexity and highlight the role of risk assessments as a formal sense-making device that enables learning and organizing knowledge of the dynamic interplay between the climate-sensitive system and its (climatological) environment. We highlight boundary judgments as a core concern of risk assessments, helping to create islands of analytical and cognitive tractability in a complex, uncertain, and ambiguous world. We then point to three key concepts—boundary critique, multi-methodology, and second-order learning—as critical elements of contemporary risk assessment practice, and we weave these into an overarching framework to better account for systemic complexity in the assessment of climate risk.

Contains fulltext : 252135.pdf (Publisher’s version ) (Open Access), BACKGROUND: Left bundle branch area pacing (LBBAP) has recently been introduced as a physiological pacing technique with synchronous left ventricular activation. It was our aim to evaluate the feasibility and learning curve of the technique, as well as the electrical characteristics of LBBAP. METHODS AND RESULTS: LBBAP was attempted in 80 consecutive patients and electrocardiographic characteristics were evaluated during intrinsic rhythm, right ventricular septum pacing (RVSP) and LBBAP. Permanent lead implantation was successful in 77 of 80 patients (96%). LBBAP lead implantation time and fluoroscopy time shortened significantly from 33 ± 16 and 21 ± 13 min to 17 ± 5 and 12 ± 7 min, respectively, from the first 20 to the last 20 patients. Left bundle branch (LBB) capture was achieved in 54 of 80 patients (68%). In 36 of 45 patients (80%) with intact atrioventricular conduction and narrow QRS, an LBB potential (LBB(pot)) was present with an LBB(pot) to onset of QRS interval of 22 ± 6 ms. QRS duration increased significantly more during RVSP (141 ± 20 ms) than during LBBAP (125 ± 19 ms), compared to 130 ± 30 ms without pacing. An even clearer difference was observed for QRS area, which increased significantly more during RVSP (from 32 ± 16 µVs to 73 ± 20 µVs) than during LBBAP (41 ± 15 µVs). QRS area was significantly smaller in patients with LBB capture compared to patients without LBB capture (43 ± 18 µVs vs 54 ± 21 µVs, respectively). In patients with LBB capture (n = 54), the interval from the pacing stimulus to R‑wave peak time in lead V6 was significantly shorter than in patients without LBB capture (75 ± 14 vs 88 ± 9 ms, respectively). CONCLUSION: LBBAP is a safe and feasible technique, with a clear learning curve that seems to flatten after 40-60 implantations. LBB capture is achieved in two-thirds of patients. Compared to RVSP, LBBAP largely maintains ventricular electrical synchrony at a level close to intrinsic (narrow QRS) rhythm.

Anthropogenic climate change is expected to affect global river flow. Here, we analyze time series of low, mean, and high river flows from 7250 observatories around the world covering the years 1971 to 2010. We identify spatially complex trend patterns, where some regions are drying and others are wetting consistently across low, mean, and high flows. Trends computed from state-of-the-art model simulations are consistent with the observations only if radiative forcing that accounts for anthropogenic climate change is considered. Simulated effects of water and land management do not suffice to reproduce the observed trend pattern. Thus, the analysis provides clear evidence for the role of externally forced climate change as a causal driver of recent trends in mean and extreme river flow at the global scale.

A large number of recent studies have aimed at understanding short-duration rainfall extremes, due to their impacts on flash floods, landslides and debris flows and potential for these to worsen with global warming. This has been led in a concerted international effort by the INTENSE Crosscutting Project of the GEWEX (Global Energy and Water Exchanges) Hydroclimatology Panel. Here, we summarize the main findings so far and suggest future directions for research, including: the benefits of convection-permitting climate modelling; towards understanding mechanisms of change; the usefulness of temperature-scaling relations; towards detecting and attributing extreme rainfall change; and the need for international coordination and collaboration. Evidence suggests that the intensity of long-duration (1 day+) heavy precipitation increases with climate warming close to the Clausius-Clapeyron (CC) rate (6-7% K-1), although large-scale circulation changes affect this response regionally. However, rare events can scale at higher rates, and localized heavy short-duration (hourly and sub-hourly) intensities can respond more strongly (e.g. 2 × CC instead of CC). Day-to-day scaling of short-duration intensities supports a higher scaling, with mechanisms proposed for this related to local-scale dynamics of convective storms, but its relevance to climate change is not clear. Uncertainty in changes to precipitation extremes remains and is influenced by many factors, including large-scale circulation, convective storm dynamics andstratification. Despite this, recent research has increased confidence in both the detectability and understanding of changes in various aspects of intense short-duration rainfall. To make further progress, the international coordination of datasets, model experiments and evaluations will be required, with consistent and standardized comparison methods and metrics, and recommendations are made for these frameworks. This article is part of a discussion meeting

The quantification of flood risk in estuarine regions relies on accurate estimation of flood probability, which is often challenging due to the rareness of hazardous flood events and their multi-causal (or “compound”) nature. Failure to consider the compounding nature of estuarine floods can lead to significant underestimation of flood risk in these regions. This study provides a comparative review of alternative approaches for estuarine flood estimation – namely, traditional univariate flood frequency analysis applied to both observed historical data and simulated data, as well as multivariate frequency analysis applied to flood events. Three specific implementations of the above approaches are evaluated on a case study – the estuarine portion of Swan River in Western Australia – highlighting the advantages and disadvantages of each approach. The theoretical understanding of the three approaches, combined with findings from the case study, enable the generation of guidance on method selection for estuarine flood probability estimation, recognizing issues such as data availability, the complexity of the application/analysis process, the location of interest within the estuarine region, the computational demands, and whether or not future conditions need to be assessed.

Multiple plausible future scenarios are being used increasingly in preference to a single deterministic or probabilistic prediction of the future in the long-term planning of water resources systems. These scenarios enable the determination of the robustness of a system—the consideration of performance across a range of plausible futures—and allow an assessment of which possible future system configurations result in a greater level of robustness. There are many approaches to selecting scenarios, and previous studies have observed that the choice of scenarios might affect the estimated robustness of the system. However, these observations have been anecdotal and qualitative. This paper develops a systematic, quantitative methodology for exploring the influence of scenario selection on the robustness and the ranking of decision alternatives. The methodology is illustrated on the Lake Problem. The quantitative results obtained confirm the qualitative observations of previous works, showing that the selection of scenarios is important, as it has a large influence on the robustness value calculated for each decision alternative. However, we show that it has a relatively small influence on how those decision alternatives are ranked. This implies that despite the difference in robustness values, similar decision outcomes will be reached in this case study, regardless of the basis on which the scenarios are obtained. It is also revealed that the impact of the scenarios on the robustness values is due to complex interactions with the system model and robustness metrics., Policy Analysis

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 concentrati