Description: As the negative impacts of hydrological extremes increase in large parts of the world, a better understanding of the drivers of change in risk and impacts is essential for effective flood and drought risk management and climate adaptation. However, there is a lack of comprehensive, empirical data about the processes, interactions and feedbacks in complex human-water systems leading to flood and drought impacts. To fill this gap, we present an IAHS Panta Rhei benchmark dataset containing socio-hydrological data of paired events, i.e. two floods or two droughts that occurred in the same area (Kreibich et al. 2017, 2019). The contained 45 paired events occurred in 42 different study areas (in three study areas we have data on two paired events), which cover different socioeconomic and hydroclimatic contexts across all continents. The dataset is unique in covering floods and droughts, in the number of cases assessed and in the amount of qualitative and quantitative socio-hydrological data contained. References to the data sources are provided in 2022-002_Kreibich-et-al_Key_data_table.xlsx where possible. Based on templates, we collected detailed, review-style reports describing the event characteristics and processes in the case study areas, as well as various semi-quantitative data, categorised into management, hazard, exposure, vulnerability and impacts. Sources of the data were classified as follows: scientific study (peer-reviewed paper and PhD thesis), report (by governments, administrations, NGOs, research organisations, projects), own analysis by authors, based on a database (e.g. official statistics, monitoring data such as weather, discharge data, etc.), newspaper article, and expert judgement. The campaign to collect the information and data on paired events started at the EGU General Assembly in April 2019 in Vienna and was continued with talks promoting the paired event data collection at various conferences. Communication with the Panta Rhei community and other flood and drought experts identified through snowballing techniques was important. Thus, data on paired events were provided by professionals with excellent local knowledge of the events and risk management practices.

Description: Private flood precautionary measures have proven to reduce flood damage effectively. Integration of these measures into flood response systems can improve flood risk management in high-risk areas such as Ho Chi Minh City (HCMC). Since uptake of such measures is voluntary, it is important to know what drives householders to implement precautionary measures. In this study, we developed a framework representing the uptake of private precautionary measures based on protection motivation theory and the transtheoretical model. Using empirical survey data collected from 1000 flood-prone households in HCMC, we implemented lasso and elastic-net regression to identify the drivers of private precaution. The measures were classified into structural measures and non-structural measures based on whether structural changes to the building were required. The households were classified into proactive and reactive households based on whether their decision to reduce risk (i.e. uptake of precautionary measures) was preceded by experiencing a flood. The data-driven model revealed that the household's level of education, the degree of belief in the government to implement regional flood protection measures and the degree of belief that in case of flooding one has to deal with the consequences of flooding by themselves positively influence the proactive uptake of non-structural measures. Among the households that experienced flooding before implementing the measures, the uptake was found to be driven by the severity of the experienced damage. For the same group of households, perceiving a high severity of future flood impacts was found to negatively influence the uptake of structural flood precautionary measures. These results highlight that efforts to improve the implementation of private precautionary measures should consider the socio-economic characteristics of the members of the household, their past flood experience and their perception of flood risk management for communicating flood risk and incentivizing private precautionary measures.

Description: Risk management has reduced vulnerability to floods and droughts globally1,2, yet their impacts are still increasing3. An improved understanding of the causes of changing impacts is therefore needed, but has been hampered by a lack of empirical data4,5. On the basis of a global dataset of 45 pairs of events that occurred within the same area, we show that risk management generally reduces the impacts of floods and droughts but faces difficulties in reducing the impacts of unprecedented events of a magnitude not previously experienced. If the second event was much more hazardous than the first, its impact was almost always higher. This is because management was not designed to deal with such extreme events: for example, they exceeded the design levels of levees and reservoirs. In two success stories, the impact of the second, more hazardous, event was lower, as a result of improved risk management governance and high investment in integrated management. The observed difficulty of managing unprecedented events is alarming, given that more extreme hydrological events are projected owing to climate change

Description: Based on the global serious flood disasters data during 1980 to 2020 from the emergency events database (EM-DAT), we calculated flood disaster index (FDI) in each administrative region. Based on FDI and the relevant hydrological and meteorological data, terrain data, vegetation data and economic data, the random forest (RF) algorithm was applied to establish flood risk model to assess the global flood risk on grid scale. RF models were constructed at global and climatic zoning scales, respectively. The accuracy evaluation results showed that compared with the global model, the R〈sup〉2〈/sup〉 of the partition model increased by 12%, and the RMSE and MAE decreased by 33% and 21%, respectively. The model results could capture more than 70% of the historical flood events and more than 90% of the effected population and death toll in EM-DAT. The assessment results showed that the areas with middle-high risk of global flood serious disasters covered 43.79% of the Earth’s land surface which were located mainly in south, east and southeast Asia. The low risk areas are mainly located in the sparsely populated area above 60°N, the desert area in northern Africa and the plateau in western Australia. The results of factor importance showed that rainfall, economic conditions, topography and surface water and soil conditions are vital factors for the serious flood disasters in each climatic zone model, and topography is the dominant factor in cold climatic zone.

Description: The reseach investigates how to refine the ground meteorological observation network for greatly improving the PM2.5 concentration forecasts by identifying the sensitive areas for targeted observations associated with a total of 48 forecasts in eight heavy haze events during the years of 2016- 2018 over the Beijing-Tianjin-Hebei (BTH) region. The conditional non-linear optimal perturbation (CNOP) method is adopted to determine the sensitive area of the surface meteorological fields for each forecast and a total of 48 CNOP-type errors are obtained including wind, temperature, and water vapor mixing ratio components. It is found that, although all the sensitive areas tend to locate within and/or surrounding the BTH region, their specific distributions are dependent on the events and the start times of the forecasts. Based on these sensitive areas, the current ground meteorological stations within and surrounding the BTH region are refined to form a cost-effective observation network, which makes the relevant PM2.5 forecasts starting from different initial times for varying events assimilate fewer observations but overall achieve the forecasting skill comparable to, even higher than that obtained by assimilating all ground station observations. This network sheds light on that some of the current ground stations within and surrounding the BTH region are very useless for improving the PM2.5 forecasts in the BTH region and can be greatly scattered to avoid the thankless work.

Description: As the adverse impacts of hydrological extremes increase in many regions of the world, a better understanding of the drivers of changes in risk and impacts is essential for effective flood and drought risk management and climate adaptation. However, there is currently a lack of comprehensive, empirical data about the processes, interactions, and feedbacks in complex human–water systems leading to flood and drought impacts. Here we present a benchmark dataset containing socio-hydrological data of paired events, i.e. two floods or two droughts that occurred in the same area. The 45 paired events occurred in 42 different study areas and cover a wide range of socio-economic and hydro-climatic conditions. The dataset is unique in covering both floods and droughts, in the number of cases assessed and in the quantity of socio-hydrological data. The benchmark dataset comprises (1) detailed review-style reports about the events and key processes between the two events of a pair; (2) the key data table containing variables that assess the indicators which characterize management shortcomings, hazard, exposure, vulnerability, and impacts of all events; and (3) a table of the indicators of change that indicate the differences between the first and second event of a pair. The advantages of the dataset are that it enables comparative analyses across all the paired events based on the indicators of change and allows for detailed context- and location-specific assessments based on the extensive data and reports of the individual study areas. The dataset can be used by the scientific community for exploratory data analyses, e.g. focused on causal links between risk management; changes in hazard, exposure and vulnerability; and flood or drought impacts. The data can also be used for the development, calibration, and validation of socio-hydrological models. The dataset is available to the public through the GFZ Data Services (Kreibich et al., 2023, https://doi.org/10.5880/GFZ.4.4.2023.001).

Description: As the negative impacts of hydrological extremes increase in large parts of the world, a better understanding of the drivers of change in risk and impacts is essential for effective flood and drought risk management and climate adaptation. However, there is a lack of comprehensive, empirical data about the processes, interactions and feedbacks in complex human-water systems leading to flood and drought impacts. To fill this gap, we present an IAHS Panta Rhei benchmark dataset containing socio-hydrological data of paired events, i.e. two floods or two droughts that occurred in the same area (Kreibich et al. 2017, 2019). The contained 45 paired events occurred in 42 different study areas (in three study areas we have data on two paired events), which cover different socioeconomic and hydroclimatic contexts across all continents. The dataset is unique in covering floods and droughts, in the number of cases assessed and in the amount of qualitative and quantitative socio-hydrological data contained. References to the data sources are provided in 2023-001_Kreibich-et-al_Key_data_table.xlsx where possible. Based on templates, we collected detailed, review-style reports describing the event characteristics and processes in the case study areas, as well as various semi-quantitative data, categorised into management, hazard, exposure, vulnerability and impacts. Sources of the data were classified as follows: scientific study (peer-reviewed paper and PhD thesis), report (by governments, administrations, NGOs, research organisations, projects), own analysis by authors, based on a database (e.g. official statistics, monitoring data such as weather, discharge data, etc.), newspaper article, and expert judgement. The campaign to collect the information and data on paired events started at the EGU General Assembly in April 2019 in Vienna and was continued with talks promoting the paired event data collection at various conferences. Communication with the Panta Rhei community and other flood and drought experts identified through snowballing techniques was important. Thus, data on paired events were provided by professionals with excellent local knowledge of the events and risk management practices.

Description: Climate Responses to Tambora-Size Volcanic Eruption and the Impact of Warming ClimateThe climatic consequences of large volcanic eruptions depend on the direct radiative perturbation and the climate variability that amplifies or dampens the initial perturbation. Potential climate responses to future eruptions, however, have been rarely studied. Here we show perturbation of Tambora-size causes significant but no inter-scenario different global average climate responses, by using Community Earth System Model simulations under preindustrial and RCP8.5 scenarios. Regionally we find severe reduction in African and Asian-Australian monsoon rainfall and emerge of El Niño-like responses, largely due to the land-ocean thermal contrast mechanism. Global warming significantly amplifies such El Niño-like responses, which feed on the enhanced climatology atmospheric moisture and cause higher sensitivity of monsoon circulation to radiative forcing in the tropics. We also find prolonged Asian-Australian monsoon suppression associated with the enhanced westerly anomalies over the Pacific, suggesting the complexity of climate responses and feedbacks to external forcing under future climate.

Description: Metropolises such as Los Angeles, San Francisco, Tokyo, etc are faced with high earthquake risk, because their location within the actively deforming plate boundaries. The large population and density of infrastructure increase the earthquake risk exposure, and also make the earthquake monitoring difficult, due to various types of anthropogenic noise generated in cities and the logistical difficulties of instrument deployment.We have developed a deep-learning-based denoising algorithm, UrbanDenoiser, to filter out the strong urban seismological noise, and improve the capacity for earthquake monitoring in urban settings. UrbanDenoiser strongly suppresses noise relative to the signals, because it was trained on waveform datasets containing rich noise sources from the urban Long Beach dense array and high signal-to-noise ratio earthquake signals from the rural San Jacinto dense array. Application to urban seismic data shows that UrbanDenoiser can effectively suppress the high noise level at daytime, allowing us to work on the entire day’s data, not just during night when anthropogenic noise is lower, which doubles the utility of existing data. We apply UrbanDenoiser to a regional seismic network for the La Habra earthquake sequence in the urban area, and leads to an increased detection rate amounting to more than 4.5 times the number of detections in the Southern California Seismic Network catalog. Earthquake location using the denoised Long Beach data does not support the previous report of mantle seismicity beneath Los Angeles, but suggests a fault model featuring shallow creep, intermediate locking, and localized stress concentration at the base of the seismogenic zone.

Description: Characterizing the upper tail of flood peak distributions remains a challenge due to the elusive nature of extreme floods, particularly the key elements of flood-producing storms that are responsible for them. Here I examine the upper tail of flood peaks over China based on a comprehensive flood dataset that integrates systematic observations from 1759 stream gaging stations and 14,779 historical flood surveys. I show that flood peak distributions over China are associated with a mixture of rainfall-generation processes. The storms for the upper-tail floods (with recurrence interval beyond 50 years) are characterized with anomalous moisture transport and/or synoptic configurations, with respect to those responsible for annual flood peaks. Anomalous moisture transport (in terms of intensity, pathways, and durations) dictates the space-time rainfall dynamics (relative to the drainage networks) that subsequently lead to anomalous basin-scale flood response. I provide physical insights into extreme flood processes based on downscaling simulations using the Weather Research and Forecasting model driven by the 20〈sup〉th 〈/sup〉Century Reanalysis fields. Modeling analyses for a collective of extreme flood events highlight the role of interactions between complex terrain and large-scale environment in determining the spatial and temporal variability of extreme rainfall. My analyses contribute to improved understanding of the unprecedented flood hazards over China by establishing the nexus between atmospheric processes and basin-scale flood response. These knowledge gains can be potentially used to the upper tail of flood peak distributions.