Improved daily estimates of relative humidity at high resolution across Germany: A random forest approach

The lack of readily available methods for estimating high-resolution near-surface relative humidity (RH) and the incapability of weather stations to fully capture the spatiotemporal variability can lead to exposure misclassification in studies of environmental epidemiology. We therefore aimed to predict German-wide 1 × 1 km daily mean RH during 2000-2021. RH observations, longitude and latitude, modelled air temperature, precipitation and wind speed as well as remote sensing information on topographic elevation, vegetation, and the true color band composite were incorporated in a Random Forest (RF) model, in addition to date for capturing the temporal variations of the response-explanatory variables relationship. The model achieved high accuracy (R2 = 0.83) and low errors (Root Mean Square Error (RMSE) of 5.07%, Mean Absolute Percentage Error (MAPE) of 5.19% and Mean Percentage Error (MPE) of - 0.53%), calculated via ten-fold cross-validation. A comparison of our RH predictions with measurements from a dense monitoring network in the city of Augsburg, South Germany confirmed the good performance (R2 ≥ 0.86, RMSE ≤ 5.45%, MAPE ≤ 5.59%, MPE ≤ 3.11%). The model displayed high German-wide RH (22y-average of 79.00%) and high spatial variability across the country, exceeding 12% on yearly averages. Our findings indicate that the proposed RF model is suitable for estimating RH for a whole country in high-resolution and provide a reliable RH dataset for epidemiological analyses and other environmental research purposes.

Decision-support tools to build climate resilience against emerging infectious diseases in Europe and beyond

Climate change is one of several drivers of recurrent outbreaks and geographical range expansion of infectious diseases in Europe. We propose a framework for the co-production of policy-relevant indicators and decision-support tools that track past, present, and future climate-induced disease risks across hazard, exposure, and vulnerability domains at the animal, human, and environmental interface. This entails the co-development of early warning and response systems and tools to assess the costs and benefits of climate change adaptation and mitigation measures across sectors, to increase health system resilience at regional and local levels and reveal novel policy entry points and opportunities. Our approach involves multi-level engagement, innovative methodologies, and novel data streams. We take advantage of intelligence generated locally and empirically to quantify effects in areas experiencing rapid urban transformation and heterogeneous climate-induced disease threats. Our goal is to reduce the knowledge-to-action gap by developing an integrated One Health-Climate Risk framework.

High-resolution spatiotemporal modeling of daily near-surface air temperature in Germany over the period 2000-2020

The commonly used weather stations cannot fully capture the spatiotemporal variability of near-surface air temperature (T_air), leading to exposure misclassification and biased health effect estimates. We aimed to improve the spatiotemporal coverage of T_air data in Germany by using multi-stage modeling to estimate daily 1 × 1 km minimum (T_min), mean (T_mean), maximum (T_max) T_air and diurnal T_air range during 2000-2020. We used weather station T_air observations, satellite-based land surface temperature (LST), elevation, vegetation and various land use predictors. In the first stage, we built a linear mixed model with daily random intercepts and slopes for LST adjusted for several spatial predictors to estimate T_air from cells with both T_air and LST available. In the second stage, we used this model to predict T_air for cells with only LST available. In the third stage, we regressed the second stage predictions against interpolated T_air values to obtain T_air countrywide. All models achieved high accuracy (0.91 ≤ R² ≤ 0.98) and low errors (1.03 °C ≤ Root Mean Square Error (RMSE) ≤ 2.02 °C). Validation with external data confirmed the good performance, locally, i.e., in Augsburg for all models (0.74 ≤ R² ≤ 0.99, 0.87 °C ≤ RMSE ≤ 2.05 °C) and countrywide, for the T_mean model (0.71 ≤ R² ≤ 0.99, 0.79 °C ≤ RMSE ≤ 1.19 °C). Annual T_mean averages ranged from 8.56 °C to 10.42 °C with the years beyond 2016 being constantly hotter than the 21-year average. The spatial variability within Germany exceeded 15 °C annually on average following patterns including mountains, rivers and urbanization. Using a case study, we showed that modeling leads to broader T_air variability representation for exposure assessment of participants in health cohorts. Our results indicate the proposed models as suitable for estimating nationwide T_air at high resolution. Our product is critical for temperature-based epidemiological studies and is also available for other research purposes.

The existential risk space of climate change

Climate change is widely recognized as a major risk to societies and natural ecosystems but the high end of the risk, i.e., where risks become existential, is poorly framed, defined, and analyzed in the scientific literature. This gap is at odds with the fundamental relevance of existential risks for humanity, and it also limits the ability of scientific communities to engage with emerging debates and narratives about the existential dimension of climate change that have recently gained considerable traction. This paper intends to address this gap by scoping and defining existential risks related to climate change. We first review the context of existential risks and climate change, drawing on research in fields on global catastrophic risks, and on key risks and the so-called Reasons for Concern in the reports of the Intergovernmental Panel on Climate Change. We also consider how existential risks are framed in the civil society climate movement as well as what can be learned in this respect from the COVID-19 crisis. To better frame existential risks in the context of climate change, we propose to define them as those risks that threaten the existence of a subject, where this subject can be an individual person, a community, or nation state or humanity. The threat to their existence is defined by two levels of severity: conditions that threaten (1) survival and (2) basic human needs. A third level, well-being, is commonly not part of the space of existential risks. Our definition covers a range of different scales, which leads us into further defining six analytical dimensions: physical and social processes involved, systems affected, magnitude, spatial scale, timing, and probability of occurrence. In conclusion, we suggest that a clearer and more precise definition and framing of existential risks of climate change such as we offer here facilitates scientific analysis as well societal and political discourse and action.

Improved Transferability of Data-Driven Damage Models Through Sample Selection Bias Correction

Damage models for natural hazards are used for decision making on reducing and transferring risk. The damage estimates from these models depend on many variables and their complex sometimes nonlinear relationships with the damage. In recent years, data-driven modeling techniques have been used to capture those relationships. The available data to build such models are often limited. Therefore, in practice it is usually necessary to transfer models to a different context. In this article, we show that this implies the samples used to build the model are often not fully representative for the situation where they need to be applied on, which leads to a "sample selection bias." In this article, we enhance data-driven damage models by applying methods, not previously applied to damage modeling, to correct for this bias before the machine learning (ML) models are trained. We demonstrate this with case studies on flooding in Europe, and typhoon wind damage in the Philippines. Two sample selection bias correction methods from the ML literature are applied and one of these methods is also adjusted to our problem. These three methods are combined with stochastic generation of synthetic damage data. We demonstrate that for both case studies, the sample selection bias correction techniques reduce model errors, especially for the mean bias error this reduction can be larger than 30%. The novel combination with stochastic data generation seems to enhance these techniques. This shows that sample selection bias correction methods are beneficial for damage model transfer.

Uncertainty and sensitivity of flood risk calculations for a dike ring in the south of the Netherlands

A central tool in risk management is the exceedance-probability loss (EPL) curve, which denotes the probabilities of damages being exceeded or equalled. These curves are used for a number of purposes, including the calculation of the expected annual damage (EAD), a common indicator for risk. The model calculations that are used to create such a curve contain uncertainties that accumulate in the end result. As a result, EPL curves and EAD calculations are also surrounded by uncertainties. Knowledge of the magnitude and source of these uncertainties helps to improve assessments and leads to better informed decisions. This study, therefore, performs uncertainty and sensitivity analyses for a dike-ring area in the Netherlands, on the south bank of the river Meuse. In this study, a Monte Carlo framework is used that combines hydraulic boundary conditions, a breach growth model, an inundation model, and a damage model. It encompasses the modelling of thirteen potential breach locations and uncertainties related to probability, duration of the flood wave, height of the flood wave, erodibility of the embankment, damage curves, and the value of assets at risk. The assessment includes uncertainty and sensitivity of risk estimates for each individual location, as well as the dike-ring area as a whole. The results show that for the dike ring in question, EAD estimates exhibit a 90% percentile range from about 8 times lower than the median, up to 4.5 times higher than the median. This level of uncertainty can mainly be attributed to uncertainty in depth-damage curves, uncertainty in the probability of a flood event and the duration of the flood wave. There are considerable differences between breach locations, both in the magnitude of the uncertainty, and in its source. This indicates that local characteristics have a considerable impact on uncertainty and sensitivity of flood damage and risk calculations.

Projections of future extreme weather losses under changes in climate and exposure

Many attempts are made to assess future changes in extreme weather events due to anthropogenic climate change, but few studies have estimated the potential change in economic losses from such events. Projecting losses is more complex as it requires insight into the change in the weather hazard but also into exposure and vulnerability of assets. This article discusses the issues involved as well as a framework for projecting future losses, and provides an overview of some state-of-the-art projections. Estimates of changes in losses from cyclones and floods are given, and particular attention is paid to the different approaches and assumptions. All projections show increases in extreme weather losses due to climate change. Flood losses are generally projected to increase more rapidly than losses from tropical and extra-tropical cyclones. However, for the period until the year 2040, the contribution from increasing exposure and value of capital at risk to future losses is likely to be equal or larger than the contribution from anthropogenic climate change. Given the fact that the occurrence of loss events also varies over time due to natural climate variability, the signal from anthropogenic climate change is likely to be lost among the other causes for changes in risk, at least during the period until 2040. More efforts are needed to arrive at a comprehensive approach that includes quantification of changes in hazard, exposure, and vulnerability, as well as adaptation effects.

Financing climate change adaptation

This paper examines the topic of financing adaptation in future climate change policies. A major question is whether adaptation in developing countries should be financed under the 1992 United Nations Framework Convention on Climate Change (UNFCCC), or whether funding should come from other sources. We present an overview of financial resources and propose the employment of a two-track approach: one track that attempts to secure climate change adaptation funding under the UNFCCC; and a second track that improves mainstreaming of climate risk management in development efforts. Developed countries would need to demonstrate much greater commitment to the funding of adaptation measures if the UNFCCC were to cover a substantial part of the costs. The mainstreaming of climate change adaptation could follow a risk management path, particularly in relation to disaster risk reduction. 'Climate-proofing' of development projects that currently do not consider climate and weather risks could improve their sustainability.

Some rationales for risk sharing and financing adaptation

Current climate variability and anticipated climate change challenge our water systems and our financial resources. The sharing of economic losses due to weather related hazards and the sharing of costs that result from protecting lives and property take place in different forms, but are currently insufficient. In this paper we discuss three different rationales for financing disaster losses through public and private arrangements, as well as options for financing adaptation, with a special focus on water management. We propose that financial arrangements for risk sharing and climate change adaptation should be reconsidered, in a more structured approach, to be able to deal with both disaster losses and the costs that arise because of climate change adaptation, e.g. for water management, in both developing and developed countries.