David N. Bresch

Last Name

Bresch

First Name

David N.

ORCID

0000-0002-8431-4263

Organisational unit

09576 - Bresch, David Niklaus / Bresch, David Niklaus

Show all metadata

Search Results

Publications1 - 10 of 93

Applying big data beyond small problems in climate research
Knüsel, Benedikt; Zumwald, Marius; Baumberger, Christoph; et al. (2019)
Nature Climate Change
Commercial success of big data has led to speculation that big-data-like reasoning could partly replace theory-based approaches in science. Big data typically has been applied to ‘small problems’, which are well-structured cases characterized by repeated evaluation of predictions. Here, we show that in climate research, intermediate categories exist between classical domain science and big data, and that big-data elements have also been applied without the possibility of repeated evaluation. Big-data elements can be useful for climate research beyond small problems if combined with more traditional approaches based on domain-specific knowledge. The biggest potential for big-data elements, we argue, lies in socioeconomic climate research.
Mapping urban temperature using crowd-sensing data and machine learning
Zumwald, Marius; Knüsel, Benedikt; Bresch, David N.; et al. (2021)
Urban Climate
Understanding the patterns of urban temperature a high spatial and temporal resolution is of large importance for urban heat adaptation and mitigation. Machine learning offers promising tools for high-resolution modeling of urban heat, but it requires large amounts of data. Measurements from official weather stations are too sparse but could be complemented by crowd-sensed measurements from citizen weather stations (CWS). Here we present an approach to model urban temperature using the quantile regression forest algorithm and CWS, open government and remote sensing data. The analysis is based on data from 691 sensors in the city of Zurich (Switzerland) during a heat wave using data from for 25-30th June 2019. We trained the model using hourly data from for 25-29th June (n = 71,837) and evaluate the model using data from June 30th (n = 14,105). Based on the model, spatiotemporal temperature maps of 10 × 10 m resolution were produced. We demonstrate that our approach can accurately map urban heat at high spatial and temporal resolution without additional measurement infrastructure. We furthermore critically discuss and spatially map estimated prediction and extrapolation uncertainty. Our approach is able to inform highly localized urban policy and decision-making.
A natural hazard risk modelling approach to human displacement - frontiers & challenges
Meiler, Simona; Mühlhofer, Evelyn; Lüthi, Samuel; et al. (2025)
Environmental Research: Climate
Extreme weather is increasingly driving human displacement worldwide, a trend expected to worsen with climate change. Quantifying global displacement risk is thus crucial for assessing potential impacts and informing long-term strategies to build more resilient societies, and reducing this risk. One approach involves leveraging classic probabilistic risk modelling methods that hinge on the interplay of hazard, exposure, and vulnerability. Here, we present a methodological stocktaking of these natural-hazard risk models as applied to human displacement. Specifically, we present a globally consistent displacement risk model from multiple hazards under present-day and future conditions. We model population displacement from tropical cyclone winds, coastal floods, river floods, and droughts under present, optimistic, and pessimistic future climate conditions for the middle and end of the century, assuming constant exposure and vulnerability. Our results reveal that current displacement risk is on the order of 30 million annual average displacements (AAD). By 2100, global displacement risk could increase by 75% (157%) under optimistic (pessimistic) climate scenarios. While our risk model makes methodological advances through its global setup, utilisation of two risk frameworks and state-of-the-art datasets, we also highlight current challenges in displacement risk modelling. For instance, our approach primarily models displacement as the direct result of loss of homes from sudden-onset hazards. While we begin to incorporate indirect drivers, such as livelihood loss in river floods and droughts, the model still omits important social, political, and economic dimensions. Nevertheless, as our model adopts a modular design, continuous updates enable the inclusion of additional hazards, improved data, and integration of these broader dimensions. This stocktaking represents a concerted research effort, and our modelling framework may help inform global discussions in international climate negotiations, including those related to Loss and Damage, national action plans, policy development, and other climate adaptation strategies, provided appropriate data and context are applied.
Uncertainty and sensitivity analysis for probabilistic, global modelling of future tropical cyclone risk
Meiler, Simona; Ciullo, Alessio; Bresch, David N.; et al. (2023)
14th International Conference on Applications of Statistics and Probability in Civil Engineering(ICASP14)
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, growing economies, evolving societies, and declining ecosystems. Here we apply a new feature of the CLIMADA climate risk modelling platform, which allows carrying out global uncertainty and sensitivity analysis. We showcase the comprehensive treatment of uncertainty and sensitivity of CLIMADAﾒs outputs for the assessment of future global tropical cyclone (TC) risk. Our results show that socio-economic development contributes more strongly to TC risk increase in the future and is a more uncertain risk driver than climate change. Besides, we find that exposure scaling based on the Shared Socioeconomic Pathways (SSP) is the input variable with the most significant impact on TC risk change calculations. In conclusion, we argue that a thorough and systematic assessment of future global TC risk will help focus forthcoming research efforts and enable better-informed adaptation decisions and mitigation strategies.
Auswirkungen des Klimawandels im Ausland – Risiken und Chancen für die Schweiz
Kohli, Anik; Steinemann, Myriam; Guyer, Madeleine; et al. (2018)
Future climate risk from compound events
Zscheischler, Jakob; Westra, Seth; van den Hurk, B.J.J.M.; et al. (2018)
Nature Climate Change
Joint knowledge production in climate change adaptation networks
Muccione, Veruska; Huggel, Christian; Bresch, David N.; et al. (2019)
Current Opinion in Environmental Sustainability
Infrastructure failure cascades quintuple risk of storm and flood-induced service disruptions across the globe
Mühlhofer, Evelyn; Bresch, David N.; Koks, Elco E. (2024)
One Earth
Society is dependent on critical infrastructure that provides basic services such as healthcare, mobility, communications, and power. Severe weather can damage these vital infrastructure assets, disrupting services. Such disruptions can further escalate due to system interdependencies. Although research increasingly evaluates physical risks to infrastructure assets, knowledge on service disruption risks from natural hazard-induced failure cascades across networked infrastructure systems remains limited. Here, we couple an open-source risk model with a complex network-based infrastructure module to simulate spatially explicit service disruptions from 700 historic floods and tropical cyclones in 30 countries. We find that failure cascades account for 64–89% of service disruptions, which also spread beyond the hazard footprint in nearly 3 out of 4 events. Disruption-affected population surpasses estimates of physically affected by up to ten-fold. We demonstrate that knowledge of the effect of infrastructure network designs, population distribution, wealth, and hazard characteristics can help prioritize systemic adaptation strategies over asset-focused ones.
Climate-resilient strategy planning using the SWOT methodology: A case study of the Japanese wind energy sector
Bachmann, Lisa; Lex, Ricarda; Regli, Florian; et al. (2024)
Climate Risk Management
As climate change leads to more frequent and intense extreme weather events, industry stakeholders and policymakers must assess their business strategies, practices, and entire sector policies under these uncertain conditions. Much recent research has integrated quantitative climate risk modeling into frameworks to engage policymakers and inform adaptation decisions in a general way, but relatively little attention has been devoted to extending this to strategic business and investment decisions. This falls short of identifying economic opportunities and threats in a wider socio-economic context, such as the development of new technologies or evolving political and regulatory environments. Here, a methodology is developed to integrate quantitative climate risk modeling with SWOT analysis (strengths, weaknesses, opportunities, and threats) which is commonly used in business and investment strategic planning. This moves the focus from avoidance of negative outcomes to prospective planning in an evolving environment. This methodology is illustrated with a case study of the Japanese wind energy sector, using open-access data and the open-source climate risk-assessment platform CLIMADA. This Climate risk assessment indicates threats from increasing damages to the wind energy infrastructure, as well as the profitability of typhoon-resistant wind turbines under present and future climate. Expert interviews and extensive literature research on opportunities and threats, however, also show that the transition towards renewable energies faces restrictive market dynamics, political and social hurdles, which set external conditions surpassing physically-informed dimensions. Beyond this illustrative case study, the methodology developed here bridges established concepts in climate risk modeling and strategic management and thus can be used to identify industry-centric ways forward for climate-resilient planning across a wide range of economic sectors.
Navigating and attributing uncertainty in future tropical cyclone risk estimates
Meiler, Simona; Kropf, Chahan Michael; McCaughey, Jamie W; et al. (2025)
Science Advances
Future tropical cyclone risks will evolve with climate change and socioeconomic development, entailing substantial uncertainties. An uncertainty and sensitivity analysis of these risks is vital, yet the chosen model setup influences outcomes. This study investigates how much future tropical cyclone risks are driven by climate and socioeconomic changes, quantifies uncertainty from propagating alternate representations of these systems through the risk modeling chain, and evaluates how strongly each model input contributes to output uncertainty. By comparing these three elements—drivers, uncertainty, and sensitivity—across four distinct tropical cyclone models, we derive findings generalizable beyond individual model setups. We find that average tropical cyclone risk will increase 1 to 5% by 2050 globally, with maximum increases ranging from 10 to 400% by 2100, depending on tropical cyclone model choice, region, and risk model inputs, while the dominant source of uncertainty shifts with modeling choices. Last, we differentiate between aleatory, epistemic, and normative uncertainties, offering guidance to reduce them and inform better decision-making.

Publications1 - 10 of 93

David N. Bresch

Last Name

First Name

ORCID

Organisational unit

Filters

Search Results