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Critical infrastructures are more exposed than ever to natural hazards in a changing climate. To understand and manage risk, failure cascades across large, real-world infrastructure networks, and their impact on people, must be captured. Bridging established methods in both infrastructure and risk modelling communities, we develop an open-source modelling framework which integrates a network-based interdependent infrastructure system model into the globally consistent and spatially explicit natural hazard risk assessment platform CLIMADA. The model captures infrastructure damages, triggers failure cascades and estimates resulting basic service disruptions for the dependent population. It flexibly operates on large areas with publicly available hazard, exposure and vulnerability information, for any set of infrastructure networks, hazards and geographies of interest. In a validated case study for 2018’s Hurricane Michael across three US states, the model reproduced important failure dynamics among six infrastructure networks, and provided a novel spatial map where people were likely to experience disruptions in access to healthcare, loss of power and other vital services. Our generalized approach allows for a view on infrastructure risks and their social impacts also in areas where detailed information and risk assessments are traditionally scarce, informing humanitarian activities through hotspot analyses and policy frameworks alike., Reliability Engineering & System Safety, 234, ISSN:0951-8320

Limited global gas emissions reductions would lead to exceeding 1.5˚C and even 2˚C of global warming compared to pre-industrial levels. Yet many emission scenarios included in the last IPCC report feature a decrease in Global Mean Temperature (GMT) after peak warming, even suggesting that levels compatible with the Long-Term Temperature Goal of the Paris Agreement could be reached again within the 21st century. In contrast with the prominence of overshoot pathways in the literature, their implications for adaptation planning have not been discussed. Many factors are taken into account when it comes to decision-making about adaptation, however it could be argued that the perspective of seeing in the future Global Mean Temperature decrease after peak warming constitutes a disincentive to deploy adaptation measures dimensioned against impacts to be expected at peak warming. In this study, we take the viewpoint of a decisionmaker who is trying to determine the extent to which they should adapt their assets to optimize costs. We assume that this decision is made in 2040, when global warming has just reached 1.5˚C and that the decisionmaker has full knowledge of the future evolution of GMT and resulting potential impacts from 1-in-100-year tropical cyclones on their assets. We consider three idealized pathways: two overshoot pathways in which GMT peaks in 2060 (at 1.65 or 1.8˚C) before coming back to 1.5 in 2080, and a third one in which it stabilizes at 1.65˚C as of 2060. We then compare the sum of the one-off costs of implementing adaptation measures and of the expected damages from tropical cyclones on the assets for two options: the decisionmaker decides to adapt against the level of damages expected at 1.5˚C, or at Peak Warming (1.65 or 1.8˚C). We also assess the sensitivity of the results to the evolving perceived value of the assets via a discount or growth (inflation) rate. We find that adapting to impacts at peak warming is more cost-efficient for adaptation measures characterized by high efficiency (effectiveness divided by costs), while adapting to impacts at 1.5˚C is more cost-efficient for measures with low efficiency. In contrast, which option is more cost-efficient does not depend on the GMT pathway, although the differences in total costs between the two options become stronger in pathways that reach higher levels of global warming. Higher discount rates constitute incentives to adapt to lower levels of global warming, whereas this is the opposite for higher growth rates. Overall, these results suggest that the perspective of decreasing GMT in the future plays a limited role in adaptation decisionmaking, and that it should not be perceived as an incentive not to deploy adaptation measures.

Recently, Nature-based Solutions (NbS) have received increasing attention for their potential to contribute to climate change mitigation, as well as disaster risk reduction and adaptation to climate change. Previous research has used a variety of methods to assess NbS and ecosystem- based risk reduction. The overarching question we aim to answer is: How many people do coastal ecosystems protect from the impacts of tropical cyclones and resulting storm surges? The combination of event-based risk modelling and ecosystem modelling data is a novel approach. This research uses the probabilistic model CLIMADA and ecosystem service data to quantify the coastal protection provided by coastal ecosystems. First, a baseline of the number of people impacted by tropical cyclones in the low-elevation coastal zone globally and the number of people simultaneously within the protection distance of coastal habitats is established. Next, the baseline is compared with historical habitat and population data from 1992. Looking to the future, we investigate changes in coastal protection under climate change (SSP585 in 2050). Finally, scenarios of different options for human action in protecting, managing, and restoring nature in the near future (2050) are appraised: continued forest conversion, agroforestry, mangrove restoration, and reforestation. Currently, the annual average number of people in the global low-elevation coastal zone protected from tropical cyclones by coastal habitats is 13.84 million, which corresponds to approximately a quarter of all people impacted annually by tropical cyclones in this zone. Historically, the share of protected people has decreased by approximately 4%, both due to population developments and habitat loss. With climate change, the average annual number of people impacted will increase by up to 40%, however, there is a slight decrease in the share of people protected by coastal ecosystems. Protecting, managing, and restoring nature is important to prevent a further decrease in the protection provided by coastal ecosystems globally, but especially on a local scale. While the number of people protected globally only increases slightly across the nature management and protection scenarios, protection in individual countries can increase by around 30% under reforestation or mangrove restoration, and around 5% under agroforestry. These findings form an important basis for NbS policy and use for disaster risk reduction and adaptation to climate change, e.g. by highlighting areas which have both a need for protection and a potential for NbS.

Weather-related events were responsible for nearly 95 per cent of all disaster displacement recorded over the last decade. An average of 21.5 million internal displacements were triggered per year by weather related hazards. Although displacement can be a “short-term” pre-emptive evacuation measure that effectively prevent injuries or loss of lives, people whose home or livelihoods are destroyed or threatened might be forced into medium to long-term displacement.Risk assessment for displacement could help inform anticipatory action that protects people from the harmful impacts of being displaced. Past studies used CLIMADA (CLIMate ADAptation), an open-source probabilistic natural catastrophe risk assessment platform, to estimate the displacement risk in future for weather related hazards taking in account climate change variation in intensity and frequency and population growth. The platform also enables the implementation of impact forecasting for displacement for impending tropical cyclone events, with the possibility to transfer the implementation to other hazards and impact types.However, displacement is much more complex and context-dependent. The modelling assumptions may not be able to represent all the drivers and complex processes of displacement. From our modelling experience we will shed some light on the potential of probabilistic risk assessment for displacement, and the importance of uncertainty and sensitivity analysis that quantify the confidence of model outputs. We will identify and discuss the current scientific gaps of displacement risk modelling, and the way forward to support decision making process in mitigating displacement risk.

Human societies rely on the existence of functioning global ecosystems, which are threatened by a combination of gradual changes and extreme events. Among the latter, natural hazards such as wildfires or floods can play a *functional* role for ecosystems, with plant and animal species requiring regular disturbance in their life-cycle in order to thrive, but beyond a threshold, the extreme events might cause ecosystem degradation.Here we map and project the risk of tropical cyclones on coastal ecosystems worldwide, using the probabilistic risk model CLIMADA to describe the vulnerability of global terrestrial ecosystems to tropical cyclones. First, a baseline for the current climate conditions is used to determine whether ecosystems are resilient, dependent, or vulnerable to tropical cyclones. We show that most ecosystems in the tropics are at least resilient to lower-intensity storms, but only a few ecosystems are not vulnerable to high-intensity storms. Second, the changes in tropical cyclone frequency under the high-emission scenario RCP8.5 in 2050 are used to determine which ecosystems are at risk. We show that while the global increase in the frequency of strong storms is the most threatening effect, several ecosystems with a dependency relationship are also at risk of locally decreasing frequency of low to middle-intensity storms.Our study paves the way for a better understanding of the functional and vital relationship between extreme weather events and ecosystems at a global scale, and how regime shifts under climate change might threaten them. This can prove useful to improve ecosystem management and design appropriate nature-based protection measures in a rapidly changing climate.

The effect of climate change on snow avalanches is widely unknown. Various studies indicate that a rise of temperature and extreme precipitation events will influence the release and the flow regime of snow avalanches. To compare the consequences of these potential changes on snow avalanche hazard and risk with the current situation, we have developed a framework to model avalanche risk at a regional scale. In a first step, we combined an algorithm to delineate potential release areas using a high-resolution terrain model and a forest layer and modeled three hazard scenarios for the current climate situation in a region in central Switzerland. The runout modelling was carried out with the RAMMS::LSHIM Large Scale Hazard Indication Mapping algorithm implemented in the recently released high parameterised version RAMMS::Extended.For modelling climate change effects on snowfall intensity and snow pack temperature, we used down-scaled data from the Swiss climate change scenarios CH2018 as input for the snow- and surface model "SNOWPACK''. The results of six different model chains within the RCP8.5 emission scenario and a hundred year (from year 2000 to 2100) long data set provided the input to simulate the course of over 600 future winters. For these hypothetical future winters, we applied extreme value statistics to determine the future changes of the three-day maxima of snowfall. This maxima were used to derive the potential future avalanche fracture depth. We used the output of SNOWPACK for various snow layers to take the effect of changing snow temperatures on the flow regime into account. Furthermore, we considered the rise of the zero degree line to restrict potential future avalanche release zones.The so-derived changing avalanche hazard disposition maps were used as input for the probabilistic, Python-based risk assessment platform CLIMADA to quantitatively assess the risk to buildings. The resulting maps depict the impacts of climate change on snow avalanche risk by highlighting areas where adaptation measures might be needed and thereby provide a basis for risk appraisal options and risk management strategies considering climate change.

Tropical cyclone (TC) risks are expected to increase with climate change and socio-economic development and are subject to substantial uncertainties. We thus assess future global TC risk drivers and perform a systematic uncertainty and sensitivity analysis. We combine synthetic TCs downscaled from CMIP6 global climate models (GCMs) for several emission scenarios with economic growth factors derived from the Shared Socioeconomic Pathways (SSPs) and a wide range of vulnerability functions. We find a non-linear effect between climate change and socio-economic development that drives the future TC risk. Furthermore, we show that the choice of GCM affects the output uncertainty most among all varied model input factors. Finally, we discover a positive correlation between climate sensitivity and TC risk increase. We conclude that uncertainty and sensitivity analysis are powerful tools to improve the information value of climate-risk models, producing transparent output and providing a comprehensive context to quantitative results for robust decision-making.

Extratropical winter windstorms are among the most significant natural hazards in Europe in terms of fatalities and economic losses, and windstorm impacts projections under climate change in Europe are considerably uncertain. This study combines state-of-the-art climatic projections from 29 global climate models participating in CMIP6, with the open-source weather and climate impact-risk assessment model CLIMADA to obtain a set of relevant projections for future windstorm-induced damages over Europe. Spatial patterns of the future changes in windstorm damages projected by the multi-model ensemble show a median increase in the damages in northwestern and northern-central Europe, and a median decrease over the rest of Europe, in agreement with an eastward extension of the North Atlantic storm track into Europe. We combine all 29 available climate models in an ensemble of opportunity approach and find evidence for an overall increase in future windstorm loss events, with events with return periods of 100 years under current climate becoming events with return periods of less than 20 years under future SSP585 climate. Using an uncertainty-sensitivity quantification analysis, we find that the climate model uncertainty dominates the uncertainty in the projections of damages related to frequent events, but that stochastic uncertainty hinders the uncertainty quantification for more extreme events. Our findings demonstrate the importance of climate model uncertainty for the CMIP6 projections of extratropical winter windstorms in Europe, and emphasize the increasing need for risk mitigation and management due to extreme weather in the future.

Modelling the risk of natural hazards for society, ecosystems, and the economy is subject to strong uncertainties, even more so in the context of a changing climate, evolving societies, growing economies, and declining ecosystems. Here we present a new feature of the climate risk modelling platform CLIMADA which allows to carry out global uncertainty and sensitivity analysis. CLIMADA underpins the Economics of Climate Adaptation (ECA) methodology which provides decision makers with a fact-base to understand the impact of weather and climate on their economies, communities, and ecosystems, including appraisal of bespoke adaptation options today and in future. We apply the new feature to an ECA analysis of risk from tropical cyclone storm surge to people in Vietnam to showcase the comprehensive treatment of uncertainty and sensitivity of the model outputs, such as the spatial distribution of risk exceedance probabilities or the benefits of different adaptation options. We argue that broader application of uncertainty and sensitivity analyses will enhance transparency and inter-comparison of studies among climate risk modellers and help focus future research. For decision-makers and other users of climate risk modelling, uncertainty and sensitivity analysis has the potential to lead to better-informed decisions on climate adaptation. Beyond provision of uncertainty quantification, the presented approach does contextualise risk assessment and options appraisal, and might be used to inform the development of story-lines and climate adaptation narratives.

Snow avalanches are recurring natural hazards that affect the population and infrastructure in mountainous regions, such as in the recent avalanche winters of 2018 and 2019, when considerable damage was caused by avalanches throughout the Alps. Hazard decision makers need detailed information on the spatial distribution of avalanche hazards and risks to prioritize and apply appropriate adaptation strategies and mitigation measures and thus minimize impacts. Here, we present a novel risk assessment approach for assessing the spatial distribution of avalanche risk by combining large-scale hazard mapping with a state-of-the-art risk assessment tool, where risk is understood as the product of hazard, exposure and vulnerability. Hazard disposition is modeled using the large-scale hazard indication mapping method RAMMS::LSHIM (Rapid Mass Movement Simulation::Large-Scale Hazard Indication Mapping), and risks are assessed using the probabilistic Python-based risk assessment platform CLIMADA, developed at ETH Zurich. Avalanche hazard mapping for scenarios with a 30-, 100- and 300-year return period is based on a high-resolution terrain model, 3 d snow depth increase, automatically determined potential release areas and protection forest data. Avalanche hazard for 40 000 individual snow avalanches is expressed as avalanche intensity, measured as pressure. Exposure is represented by a detailed building layer indicating the spatial distribution of monetary assets. The vulnerability of buildings is defined by damage functions based on the software EconoMe, which is in operational use in Switzerland. The outputs of the hazard, exposure and vulnerability analyses are combined to quantify the risk in spatially explicit risk maps. The risk considers the probability and intensity of snow avalanche occurrence, as well as the concentration of vulnerable, exposed buildings. Uncertainty and sensitivity analyses were performed to capture inherent variability in the input parameters. This new risk assessment approach allows us to quantify avalanche risk over large areas and results in maps displaying the spatial distribution of risk at specific locations. Large-scale risk maps can assist decision makers in identifying areas where avalanche hazard mitigation and/or adaption is needed., Natural Hazards and Earth System Sciences, 23 (6), ISSN:1561-8633, ISSN:1684-9981

Various studies show that changes in the climate system, such as temperature rise and extreme precipitation events, strongly influence gravity driven hazards. Within the research program "Climate Change Impacts on Alpine Mass Movements'', we develop a framework to model mass movement risk altered by climate and socio-economic drivers. In a first approach, we've modeled snow avalanche risk in Switzerland for the current climate situation and three avalanche hazard scenarios. For each of these scenarios we've considered different 3-day increases in snow height for avalanche formation, derived from meteorological stations. For the modelling we've applied the RAMMS::LSHIM Large Scale Hazard Indication Mapping algorithm combining the delineation of potential release areas from a high-resolution terrain model with a forest layer to depict the spatial distribution of avalanche impact for each of the chosen scenarios. To model possible climate change effects on snow avalanche hazard, we use down-scaled data from the CH2018 climate change scenarios as input for the model "SNOWPACK''. The so-derived changing avalanche hazard disposition is simulated with the RAMMS::LSHIM method and risks are analysed with the probabilistic, Python-based risk assessment platform CLIMADA using high resolution building layers to identify monetary assets and assign vulnerabilities. The results are spatio-temporally explicit risk maps, depicting changes of snow avalanche risks based on the combination of exposure and vulnerability information. These maps allow for the appraisal of appropriate risk management options and thereby contribute to decision support and highlight areas where adaptation measures to climate change might be needed.

Ultimately, human societies rely on the existence of functioning global ecosystems. Thus, avoiding the collapse of global ecosystems should be among the highest priorities of climate mitigation and adaptation efforts. However, "protecting" ecosystems is a challenge much more complex than avoiding adverse effects on human infrastructures, societies, economies or lives. For instance, natural hazards such as wildfires or floods can play a *functional* role for ecosystems, with species requiring those events in their life-cycle. Therefore simply trying to avoid the at-first-sight devastating effects of natural hazards on ecosystems can be counter-productive, and even be damaging. Here we present a statistical study made with the open-source, probabilistic risk model CLIMADA [1] about the frequency and magnitude distribution of several natural hazards affecting global terrestrial ecosystems. The hazard modelling is based on historical data augmented with probabilistic methods, and thus can be interpreted as providing a snap-shot of "current conditions". This can then be used as a baseline to be contrasted with future projections of climate change and socio-economic development. Further, this baseline can inform studies on the functional and vital relationship between natural hazards and ecosystems, which are necessary to design appropriate protection measures.CLIMADA: https://github.com/CLIMADA-project/climada_python [1] Aznar-Siguan, G. et al., GEOSCI MODEL DEV. 12, 7 (2019) 3085–97

In this work, we consider the preservation of a measurement for quantum systems interacting with an environment. Namely, a method of preserving an optimal measurement over a channel is devised, what we call channel coding of a quantum measurement in that operations are applied before and after a channel in order to protect a measurement. A protocol that preserves a quantum measurement over an arbitrary channel is shown only with local operations and classical communication without the use of a larger Hilbert space. Therefore, the protocol is readily feasible with present day's technologies. Channel coding of qubit measurements is presented, and it is shown that a measurement can be preserved for an arbitrary channel for both i) pairs of qubit states and ii) ensembles of equally probable states. The protocol of preserving a quantum measurement is demonstrated with IBM quantum computers., 9pages, 3 figures

Do you want to translate your work on extreme weather into human and economic impacts? Do you want to investigate the statistical risk from climate change to your region, country or planet? Do you need to identify vulnerable populations and find the most effective climate adaptation measures?The CLIMADA platform (CLIMate ADAptation) is built to support these analyses. The model is an open source, globally consistent, fully probabilistic risk assessment tool. It is designed with both academics and decision-makers in mind and is used in international financial planning, regional climate adaptation projects and impact forecasting.CLIMADA combines hazard, vulnerability and exposure data to produce risk assessments, allowing you to supply any (or none) of the data required. The model includes event data for hazards including tropical storm wind and surge, windstorms, earthquake, flood, drought, wildfire and agricultural risk, at different stages of maturity. It includes the LitPop exposure model for estimating economic and population exposure, and impact/vulnerability functions to combine them with hazards. It is suitable for case studies and climate studies.In this session we will present the model, highlight recent additions, and discuss our work supporting users in government, industry and the third sector. We want to hear questions from potential new users and collaborators and hope to spark conversations about new data sources, improved methodologies and integrations with other workflows.

Modelling societal, ecological, and economic costs of natural hazards in the context of climate change is subject to both strong aleatoric and ethical uncertainty. Dealing with these is challenging on several levels – from the identification and the quantification of the sources of uncertainty to their proper inclusion in the modelling, and the communication of these in a tangible way to both experts and non-experts. One particularly useful approach is global uncertainty and sensitivity analysis, which can help to quantify the confidence in the output values and identify the main drivers of the uncertainty while considering potential correlations in the model. Here we present applications of global uncertainty analysis, robustness quantification, and sensitivity analysis in natural hazard modelling using the new uncertainty module of the CLIMADA (CLIMate ADAptation) platform.CLIMADA is a fully open-source Python program that implements a probabilistic multi-hazard global natural catastrophe damage model, which also calculates averted damage (benefit) thanks to adaptation measures of any kind (from grey to green infrastructure, behavioral, etc.). With the new uncertainty module, one can directly and comprehensively inspect the uncertainty and sensitivity to input variables of various output metrics, such as the spatial distribution of risk exceedance probabilities, or the benefit-cost ratios of different adaptation measures. This global approach does reveal interesting parameter interplays and might provide valuable input for decision-makers. For instance, a study of the geospatial distribution of sensitivity indices for tropical cyclones damage indicated that the main driver of uncertainty in dense regions (e.g. cities) is the impact function (vulnerability), whereas in sparse regions it is the exposure (asset) layer. CLIMADA: https://github.com/CLIMADA-project/climada_python (1) Aznar-Siguan, G. et al., GEOSCI MODEL DEV. 12, 7 (2019) 3085–97(2) Bresch, D. N. and Aznar-Siguan., G., GEOSCI MODEL DEV. (2020), 1–20.

In this work, we characterize the performance of a deep convolutional neural network designed to detect and quantify chemical elements in experimental x-ray photoelectron spectroscopy data. Given the lack of a reliable database in literature, in order to train the neural network we computed a large (

The description of an open quantum system's decay almost always requires several approximations so as to remain tractable. In this Letter, we first revisit the meaning, domain, and seeming contradictions of a few of the most widely used of such approximations: (semigroup) Markovianity, linear response theory, Wigner-Weisskopf approximation, and rotating-wave approximation. Second, we derive an effective time-dependent decay theory and corresponding generalized quantum regression relations for an open quantum system linearly coupled to an environment. This theory covers all timescales and subsumes the Markovian and linear-response results as limiting cases. Finally, we apply our theory to the phenomenon of quantum friction.

The possibility to exploit quantum coherence to strongly enhance the efficiency of charge transport in solid state devices working at ambient conditions would pave the way to disruptive technological applications. In this work, we tackle the problem of the quantum transport of photogenerated electronic excitations subject to dephasing and on-site Coulomb interactions. We show that the transport to a continuum of states representing metallic collectors can be optimized by exploiting the ``superradiance'' phenomena. We demonstrate that this is a coherent effect which is robust against dephasing and electron-electron interactions in a parameters range that is compatible with actual implementation in few-monolayer transition-metal-oxide (TMO) heterostructures.

Transition-metal-oxide (TMO) heterostructures are promising candidates for building photon-harvesting devices which can exploit optimal quantum transport of charge excitations generated by light absorption. Here we address the explicit role of an electric field on the quantum transport properties of photo-excitations subject to dephasing in one-dimensional chains coupled to a continuum of states acting as a sink. We show that the average transfer time to the sink is optimized for suitable values of both the coupling strength to the sink and the electric field, thus fully exploiting the coherence-enhanced efficiency in the quantum transport regime achievable in few monolayers TMO heterostructures. The optimal coupling to the continuum remains approximately the same as that in absence of electric field and is characterizing the Superradiant Transition. On the other hand, the optimal electric field for which we provide estimates using an analytical expression is dependent on the initial state., Comment: 16 pages, 18 figures

In this work, we consider optimal state discrimination for a quantum system that interacts with an environment, i.e., states evolve under a quantum channel. We show the conditions on a quantum channel and an ensemble of states such that a measurement for optimal state discrimination is preserved. In particular, we show that when an ensemble of states with equal {\it a priori} probabilities is given, an optimal measurement can be preserved over any quantum channel by applying local operations and classical communication, that is, by manipulating the quantum states before and after the channel application. Examples are provided for illustration. Our results can be readily applied to quantum communication protocols over various types of noise., 7 pages, 3 figures

We show how random unitary dynamics arise from the coupling of an open quantum system to a static environment. Subsequently, we derive a master equation for the reduced system random unitary dynamics and study three specific cases: commuting system and interaction Hamiltonians, the short-time limit, and the Markov approximation., 5 pages, 1 figure

We show that macroscopic nonintegrable lattices of spins 1/2, which are often considered to be chaotic, do not exhibit the basic property of classical chaotic systems, namely, exponential sensitivity to small perturbations. We compare chaotic lattices of classical spins and nonintegrable lattices of spins 1/2 in terms of their magnetization responses to imperfect reversal of spin dynamics known as Loschmidt echo. In the classical case, magnetization exhibits exponential sensitivity to small perturbations of Loschmidt echoes, which is characterized by twice the value of the largest Lyapunov exponent of the system. In the case of spins 1/2, magnetization is only power-law sensitive to small perturbations. Our findings imply that it is impossible to define Lyapunov exponents for lattices of spins 1/2 even in the macroscopic limit. At the same time, the above absence of exponential sensitivity to small perturbations is an encouraging news for the efforts to create quantum simulators. The power-law sensitivity of spin 1/2 lattices to small perturbations is predicted to be measurable in nuclear magnetic resonance experiments., 12 pages, 8 figures, minor changes, new references

We derive general evolution equations describing the ensemble-average quantum dynamics generated by disordered Hamiltonians. The disorder average affects the coherence of the evolution and can be accounted for by suitably tailored effective coupling agents and associated rates which encode the specific statistical properties of the Hamiltonian's eigenvectors and eigenvalues, respectively. Spectral disorder and isotropically disordered eigenvector distributions are considered as paradigmatic test cases., Comment: 20 pages