Evidence is accumulating that global change is altering species distributions. Yet, detailed knowledge is missing about the relative and joint contribution of different drivers to observed species responses. Here, we implemented an impact attribution framework based on counterfactual simulations to assess the impact of climate and land use change on occupancy dynamics of North American breeding birds. We used a Bayesian framework to fit process-explicit dynamic occupancy models to long-term survey data for 159 species from 1995 to 2019, and quantified predictive performance using spatial and temporal cross-validation. We then assessed the relative importance and effect direction of climate and land use change while accounting for model predictive accuracy. Results indicate that climate change negatively affected 90 % of the species and land use change negatively impacted 96 %. Climate change emerged as more important than land use change for driving changes in occupancy across species. Remarkably, the effects of both drivers were mostly antagonistic rather than acting additively or synergistically. Climate was the most important driver for bird communities in the western USA, while land use change dominated in the southeast, and combined climate and land use change in the northeast. Our analysis demonstrates that recent changes in North American bird distributions are shaped by multiple global change drivers acting in concert. The effect of recent climate and land use change were mostly antagonistic, and thus trends in bird occupancy dynamics could not be understood by studying the impact of those drivers in isolation. By disentangling the effects of climate and land use change on biodiversity trends, impact attribution approaches can improve our understanding of global change impacts and can support conservation planning and more accurate and realistic projections of biodiversity response to global change.

This paper describes the climate-related forcings (CRFs), i.e. change in climate comprising the atmosphere and the ocean, coastal water levels, and atmospheric composition (CO2 and methane concentrations), provided as input data within the “b” part of the third simulation round of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP3b). While ISIMIP3a comprises historical impact models simulations forced by observational Direct Human Forcings (DHF), such as changes in population and asset distributions, land use, fishing efforts, agricultural and water management driven by socio-economic development or climate protection strategies, and observational CRF, the ISIMIP3b CRFs are based on climate model simulations generated within the sixth phase of the Coupled Model Intercomparison Project (CMIP6). In a first set of experiments covering the pre-industrial (1601–1849) and historical period (1850–2014) (ISIMIP3b, group I) the CMIP6-based CRFs for the historical period are combined with historical observation-based DHF also considered in ISIMIP3a. These group I simulations allow for the quantification of impacts of historical climate change by comparison to simulations where the observational DHF are combined with simulated pre-industrial CRFs. In addition, the impacts of observed changes in CRFs can be compared to the impacts of simulated changes in CRFs by comparing the ISIMIP3a simulations to the ISIMIP3b, group I simulations. The second group of experiments (ISIMIP3b, group II) comprises future projections assuming constant observational direct human forcings at 2015 levels to estimate the impact of climate change given today's DHF for the low emission scenario SSP1-2.6, the high and the very high emission scenarios SSP3-7.0, SSP5-8.5, and reference simulations based on pre-industrial CRF, respectively. The very high emissions scenarios and the assumption of fixed present day DHF particularly allow for testing the scalability of impacts in terms of global temperature change. The provided CRFs comprise atmospheric CO2 and CH4 concentrations, atmospheric and oceanic climate data, coastal water levels, tropical cyclone (TC) tracks and their associated wind speed and precipitation fields. In addition to the CRFs data, this paper describes the experiments belonging to group I and II and the rationale behind them. Another set of future projections accounting for changing DHFs (ISIMIP3b, group III) is in preparation and will be described in another paper.

Successful recovery from extreme weather events is key to avoid long-term poverty implications. Yet, in disaster prone regions, there may not always be enough time to recover between events. There is a common narrative that the resulting incomplete recoveries aggravate adverse impacts, but approaches allowing for a systematic quantitative assessment are missing. Here, we extend an agent-based model to study welfare effects in the Philippines depending on household exposure and income. We find that incomplete recoveries increase cumulative consumption and well-being losses across the study period 2000-2018 by 40%. While low-income households suffer the highest well-being losses, the effect of incomplete recoveries is most relevant for middle-income households. Consequently, losses can be critically underestimated when drawing conclusions about the impacts of recurrent events based on the impacts of individual events. Accounting for incomplete recoveries may help to better prepare for an intensification of extreme events under climate change.

Natural hazards pose significant risks to people and assets in many regions of the world. Quantifying associated risks iscrucial for many applications such as adaptation option appraisal and insurance pricing. However, traditional risk assessment approaches have focused on the impacts of single hazards, ignoring the effects of multi-hazard risks and potentially leading to underestimations or overestimations of risks. In this work, we present a framework for modelling multi-hazard risks globally in a consistent way, considering hazards, exposures, vulnerabilities, and assumptions on recovery. We illustrate the approach using river floods and tropical cyclones impacting people and physical assets on a global scale in a changing climate. To ensure physical consistency, we combine single hazard models that were driven by the same climate model realizations. Our results show that incorporating common physical drivers and recovery considerably alters the multi-hazard risk. We finally demonstrate how our framework can accommodate more than two hazards and integrate diverse assumptions about recovery processes based on a national case study. This framework is implemented in the open-source climate risk assessment platform CLIMADA and can be applied to various hazards and exposures, providing a more comprehensive approach to risk management than conventional methods.

Global hydrological models (GHMs) are widely used to assess the impact of climate change on streamflow, floods, and hydrological droughts. For the ‘model evaluation and impact attribution’ part of the current round of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP3a), modelling teams generated historical simulations based on observed climate and direct human forcings with updated model versions. Here we provide a comprehensive evaluation of daily and maximum annual discharge based on ISIMIP3a simulations from nine GHMs by comparing the simulations to observational data from 644 river gauge stations. We also assess low flows and the effects of different river routing schemes. We find that models can reproduce variability in daily and maximum annual discharge, but tend to overestimate both quantities, as well as low flows. Models perform better at stations in wetter areas and at lower elevations. Discharge routed with the river routing model CaMa-Flood can improve the performance of some models, but for others, variability is overestimated, leading to reduced model performance. This study indicates that areas for future model development include improving the simulation of processes in arid regions and cold dynamics at high elevations. We further suggest that studies attributing observed changes in discharge to historical climate change using the current model ensemble will be most meaningful in humid areas, at low elevations, and in places with a regular seasonal discharge as these are the regions where the underlying dynamics seem to be best represented.

This paper describes the rationale and the protocol of the first component of the third simulation round of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP3a, http://www.isimip.org, last access: 2 November 2023) and the associated set of climate-related and direct human forcing data (CRF and DHF, respectively). The observation-based climate-related forcings for the first time include high-resolution observational climate forcings derived by orographic downscaling, monthly to hourly coastal water levels, and wind fields associated with historical tropical cyclones. The DHFs include land use patterns, population densities, information about water and agricultural management, and fishing intensities. The ISIMIP3a impact model simulations driven by these observation-based climate-related and direct human forcings are designed to test to what degree the impact models can explain observed changes in natural and human systems. In a second set of ISIMIP3a experiments the participating impact models are forced by the same DHFs but a counterfactual set of atmospheric forcings and coastal water levels where observed trends have been removed. These experiments are designed to allow for the attribution of observed changes in natural, human, and managed systems to climate change, rising CH4 and CO2 concentrations, and sea level rise according to the definition of the Working Group II contribution to the IPCC AR6.

Global flood impacts have risen in recent decades. While increasing exposure was the dominant driver of surging impacts, counteracting vulnerability reductions have been detected, but were too weak to reverse this trend. To assess the ongoing progress on vulnerability reduction, we combine a recently available dataset of flooded areas derived from satellite imagery for 913 events with four global disaster databases and socio-economic data. Event-specific flood vulnerabilities for assets, fatalities and displacements reveal a lack of progress in reducing global flood vulnerability from 2000-2018. We examine the relationship between vulnerabilities and human development, inequality, flood exposure and local structural characteristics. We find that vulnerability levels are significantly lower in areas with good structural characteristics and significantly higher in low developed areas. However, socio-economic development was insufficient to reduce vulnerabilities over the study period. Nevertheless, the strong correlation between vulnerability and structural characteristics suggests further potential for adaptation through vulnerability reduction. Global vulnerability to flooding did not decrease between 2000-2018 despite ongoing socio-economic development as flood adaptation remained low in less developed areas, suggests an analysis combining satellite observations and socio-economic data.

Successful recoveries of households in the aftermath of extreme weather events are key to avoid long-term poverty implications. Yet, in frequently hit regions, there may not always be enough time for households to recover in-between recurrent events. There is a common narrative that the resulting incomplete recoveries aggravate adverse impacts, but there may also be counteracting mechanisms where a cluster of events leads to less destruction than a series of well-separated events because, after the first events, there are less assets left that can be destroyed by the subsequent events. To develop a systematic quantitative understanding for the interplay of the different mechanisms, we extend an agent-based household model to recurrent floods and study their welfare effects in the Philippines in dependence of household exposure and income. We find that incomplete recoveries increase cumulative consumption and well-being losses across the study period 2000-2018 by 50%. While low-income households suffer the highest well-being losses, lower-middle income households experience the largest relative increase in well-being (240%) and consumption losses (120%) due to incomplete recoveries. Our results show that the impacts of recurrent extreme weather events on households are not additive. In consequence, the well-being and consumption losses can be critically underestimated when concluding from the poverty implications of an individual event on the implications of recurrent events, as usually done in conventional disaster risk management. Thus, accounting for incomplete recoveries may allow to develop more effective risk management strategies and better prepare societies for an intensification of these events under global warming.

Global flood impacts have risen in recent decades and are projected to increase due to climate change and socio-economic expansion. While increasing exposure was the dominant driver of surging impacts, counteracting vulnerability reductions have been detected, but were too weak to reverse this trend. To assess the progress on vulnerability reduction in the 21st century, we combine newly available satellite observations of flooded areas for 913 events with four global disaster databases, and spatially-explicit data on structural and socio-economic characteristics. Event-specific flood vulnerabilities for assets, fatalities and displacements reveal a lack of progress in reducing global flood vulnerability from 2000—2018. Going beyond previous analyses on the income dependency of vulnerabilities, we examine their dependencies upon human development index, flood experience and local structural characteristics linked to the quality of governance. We find that vulnerability levels are significantly reduced in areas with better structural characteristics, while the effect of flood experience is limited to a reduction in flood mortality. Vulnerabilities are higher in low developed areas, but overall the effect of increasing socio-economic development was insufficient to significantly reduce vulnerabilities over the study period. Nevertheless, the strong dependence of vulnerability on structural characteristics reveals further potential for adaptation through vulnerability reduction.

Natural hazards are a driver of ecological dynamics as they alter individual species, community structure and entire ecosystems. Their ecological impact can be highly variable over space and time: perceived by some species as disastrous but vital for the survival of others. The ability of a system to cope with such events is called ecological resilience and depends on biotic and abiotic factors, as well as the disturbance legacies. With global climate change, intensity, frequency, and spatial distribution of natural hazards will change. As a result, understanding the ecological resilience is crucial to analyse implications of future changes. So far, studies investigated mainly the impact of one natural hazard on specific species, regions, or ecosystem services but an approach to investigate global patterns is still missing. In this thesis, the impact modelling platform CLIMADA was used to assess the current impact of four natural hazards (tropical cyclones, river floods, wildfires, European winter storms) on ecoregions on a global scale. For each natural hazard type, distinct patterns of a hazard’s impact on ecoregions were found with large differences among and within ecological realms and biomes. Based on the current hazard-ecosystem interactions, the relative change of hazard activity over the next 60 years was quantified by comparing current and future hazard patterns. Results indicated global changes in hazard activity with new patterns of tropical cyclone activity and river flood events for most ecoregions. To identify the implications of changing hazard regimes for ecoregions, recovery times of experimental field studies were analysed and linked to local return periods of tropical cyclones. Differences between modelled return periods and measured recovery times were found between the analysed sites, but a global scientific link was difficult to prove because of differences in site and hazard characteristics. The knowledge about hazard-ecosystem interactions combined with the implications of recovery times yields great potential to investigate upcoming changes through climate change for specific regions. Lastly, the findings of this thesis were applied on a set of ecologically important areas. Here again, all regions experienced natural hazards to some extent so far, but future intensities and frequencies will change radically for some areas. Additionally, the statistical tests imply a link between hazard occurrence and ecological status which has great potential for further application.

Some coastal environments facing climate change risks are starting to be managed with nature-based solutions (NBS). Strategies based on the rehabilitation of green infrastructures in coastal municipalities, such as renaturalization of seafronts, are considered adaptive to the effects of climate change but may cause misconceptions that could lead to social conflicts between the tourist sector and the society. A survey was carried out to study user perceptions on the effects of climate change, preferences for adaptation strategies, and the assessment of projects of dune reconstruction. We find that while beach users recognize the benefits of NBS for environmental conservation and storm protection, they show little concern about possible effects of climate change on recreational activity and have limited understanding about the protective capacity of NBS. Thus, a greater effort must be made to better explain the effects of climate change and the potential benefits of NBS in coastal risk management.

Climate change affects precipitation patterns. Here, we investigate whether its signals are already detectable in reported river flood damages. We develop an empirical model to reconstruct observed damages and quantify the contributions of climate and socio-economic drivers to observed trends. We show that, on the level of nine world regions, trends in damages are dominated by increasing exposure and modulated by changes in vulnerability, while climate-induced trends are comparably small and mostly statistically insignificant, with the exception of South & Sub-Saharan Africa and Eastern Asia. However, when disaggregating the world regions into subregions based on river-basins with homogenous historical discharge trends, climate contributions to damages become statistically significant globally, in Asia and Latin America. In most regions, we find monotonous climate-induced damage trends but more years of observations would be needed to distinguish between the impacts of anthropogenic climate forcing and multidecadal oscillations.

Already today, at about 1°C of global warming, we observe a regional intensification of extreme weather events. While the short-term economic impacts of these events are well documented, little is known about their impacts on economic growth in the long-term. Using a three-way fixed-effects panel model, an outlier-robust regression, and ``people exposed'' as exogenous predictor, we study the short-, medium-, and long-term impacts of tropical cyclones and fluvial floods on per-capita GDP growth for a set of 158 countries for the period 1971--2010. To understand how the observed impacts depend upon the countries' development level, we divide countries into four groups based on the inequality-adjusted human development index. We further decompose national gross domestic product (GDP) into i) its national income components and ii) its sectoral contributions enabling us to single out income components as well as sectors that are most important for impact transmission. On the global level, weather extremes of both categories have significant negative short-, medium-, and long-term impacts on economic growth; over 15 years, long-term growth losses from severe tropical cyclones accumulate to -6.6% and are about five times larger than growth losses from severe fluvial floods (-1.2%). For both event categories, we find growth losses to depend non-monotonously upon development level challenging the common narrative that development can generally protect against disaster losses. Across all levels of development, investment, international trade are the most important transmission channels. Further, in poor countries growth losses mainly result from declines in agriculture and manufacturing growth. Our results provide guidance and decision support for the implementation of evidence-based coping and adaptation strategies, for instance, for the design of National Adaptation Plans for developing countries.

Recent studies of past changes in precipitation patterns suggest regionally varying but clearly detectable trends of global warming on physical flood indicators such as river discharge. Whether these trends are also visible in economic flood losses, has not yet been clearly answered, as changes in trends of damage records may be induced by either climatic or socio-economic drivers. In general, the socioeconomic impact of an extreme weather event is composed of three components: The hazard, the exposure of socioeconomic values to the event, and the vulnerabilities of the values, i.e., their propensity or predisposition to be adversely affected. In this work, we separate the historically observed trends in economic losses from river floods into the three contributions. We then quantify the effect of each driver on the overall change in economic losses from river floods between 1980 - 2010 for different world regions. In particular, this allows us to determine in which regions anthropogenic warming has already contributed to the observed trends in damages. We use flood depth as biophysical hazard indicator calculated by combining discharge simulations from 12 global hydrological models of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) model ensemble with the river-routing and flood inundation model CaMa-Flood. The hydrological models are driven by observed weather data and our simulations account for present-day protection standards from the FLOPROS database. Asset losses are estimated by combining gridded asset data with state-of-the-art flood damage functions translating flood depth to the fraction of affected assets employing the open-source socioeconomic impact modelling framework CLIMADA. Trends in modeled historical flood damages are then compared to observational damages by Munich Re’s NATCATService database in order to explain residual differences in trends by the three types of drivers. We first show that the method permits to reproduce the year-to-year variability observed damages on the regional level. We identify changes in exposure as the main driver of rising damage trends, but also observe significant - rising as well as declining - trends in flood hazards in several regions. Thus, effects of anthropogenic climate change that have already shown to unfold in discharge patterns, partly manifest already in economic damages, too. Residual trends in observed losses, that cannot be explained by changes in the hazard and the exposure alone, are caused by changes in vulnerability that can be well explained with trends in GDP per capita. Mostly, rising regional income results in declining vulnerability to river floods, in particular in less developed world regions. However, we also find indications of maladaptation, i.e., in some regions, vulnerability increases with GDP per capita.

When recording action potentials (spikes) from many neurons simultaneously via multichannel micro-electrodes the overlapping of spikes from different neurons is a demanding problem for detection and classifi-cation of spikes (spike sorting). Since multichannel electrodes provide better possibilities to separate the superimposed waveforms, we refined an algorithm for separation of overlapping spikes for the use on multichannel recordings and tested it on simulated data with different numbers of signal channels and with several signal parameters. We show that the larger the number of signal channels the better the separation that may be achieved, especially under demanding recording conditions.