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This study presents a new method, the MYRIAD-Hazard Event Sets Algorithm (MYRIAD-HESA), that compiles historically-based multi-hazard event sets. MYRIAD-HESA is a fully open-access method that can create multi-hazard event sets from any hazard events that occur on varying time, space, and intensity scales. In the past, multi-hazards have predominately been studied on a local or continental scale, or have been limited to specific hazard combinations, such as the combination between droughts and heatwaves. Therefore, we exemplify our approach by compiling a global multi-hazard event set database, spanning from 2004 to 2017, which includes eleven hazards from varying hazard classes (e.g. meteorological, geophysical, hydrological and climatological). This global database provides new scientific insights on the frequency of different multi-hazard events and their hotspots. Additionally, we explicitly incorporate a temporal dimension in MYRIAD-HESA, the time-lag. The time-lag, or time between the occurrence of hazards, is used to determine potentially impactful events that occurred in close succession. Varying time-lags have been tested in MYRIAD-HESA, and are analysed using North America as a case study. Alongside the MYRIAD-HESA, the multi-hazard event sets, MYRIAD-HES, is openly available to further increase the understanding of multi-hazard events in the disaster risk community. The open-source nature of MYRIAD-HESA provides flexibility to conduct multi-risk assessments by, for example, incorporating higher resolution data for an area of interest. [ABSTRACT FROM AUTHOR]

This study presents a new method, the MYRIAD-Hazard Event Sets Algorithm (MYRIAD-HESA), that compiles historically-based multi-hazard event sets. MYRIAD-HESA is a fully open-access method that can create multi-hazard event sets from any hazard events that occur on varying time, space, and intensity scales. In the past, multi-hazards have predominately been studied on a local or continental scale, or have been limited to specific hazard combinations, such as the combination between droughts and heatwaves. Therefore, we exemplify our approach by compiling a global multi-hazard event set database, spanning from 2004 to 2017, which includes eleven hazards from varying hazard classes (e.g. meteorological, geophysical, hydrological and climatological). This global database provides new scientific insights on the frequency of different multi-hazard events and their hotspots. Additionally, we explicitly incorporate a temporal dimension in MYRIAD-HESA, the time-lag. The time-lag, or time between the occurrence of hazards, is used to determine potentially impactful events that occurred in close succession. Varying time-lags have been tested in MYRIAD-HESA, and are analysed using North America as a case study. Alongside the MYRIAD-HESA, the multi-hazard event sets, MYRIAD-HES, is openly available to further increase the understanding of multi-hazard events in the disaster risk community. The open-source nature of MYRIAD-HESA provides flexibility to conduct multi-risk assessments by, for example, incorporating higher resolution data for an area of interest. [ABSTRACT FROM AUTHOR]

El Niño Southern Oscillation (ENSO) is the most dominant interannual signal of climate variability and has a strong influence on climate over large parts of the world. In turn, it strongly influences many natural hazards (such as hurricanes and droughts) and their resulting socioeconomic impacts, including economic damage and loss of life. However, although ENSO is known to influence hydrology in many regions of the world, little is known about its influence on the socioeconomic impacts of floods (i.e., flood risk). To address this, we developed a modeling framework to assess ENSO's influence on flood risk at the global scale, expressed in terms of affected population and gross domestic product and economic damages. We show that ENSO exerts strong and widespread influences on both flood hazard and risk. Reliable anomalies of flood risk exist during El Niño or La Niña years, or both, in basins spanning almost half (44%) of Earth's land surface. Our results show that climate variability, especially from ENSO, should be incorporated into disaster-risk analyses and policies. Because ENSO has some predictive skill with lead times of several seasons, the findings suggest the possibility to develop probabilistic flood-risk projections, which could be used for improved disaster planning. The findings are also relevant in the context of climate change. If the frequency and/or magnitude of ENSO events were to change in the future, this finding could imply changes in flood-risk variations across almost half of the world's terrestrial regions. [ABSTRACT FROM AUTHOR]

We used a calibrated coupled climate-hydrological model to simulate Meuse discharge over the late Holocene (4000-3000 BP and 1000-2000 AD). We then used this model to simulate discharge in the twenty-first century under SRES emission scenarios A2 and B1, with and without future land use change. Mean discharge and medium-sized high-flow (e.g. Q) frequency are higher in 1000-2000 AD than in 4000-3000 BP; almost all of this increase can be attributed to the conversion of forest to agriculture. In the twentieth century, mean discharge and the frequency of medium-sized high-flow events are higher than in the nineteenth century; this increase can be attributed to increased (winter half-year) precipitation. Between the twentieth and twenty-first centuries, anthropogenic climate change causes a further increase in discharge and medium-sized high-flow frequency; this increase is of a similar order of magnitude to the changes over the last 4,000 years. The magnitude of extreme flood events (return period 1,250-years) is higher in the twenty-first century than in any preceding period of the time-slices studied. In contrast to the long-term influence of deforestation on mean discharge, changes in forest cover have had little effect on these extreme floods, even on the millennial timescale. [ABSTRACT FROM AUTHOR]

Abstract: In this study we investigate the relative importance of changes in land use and climate on suspended sediment yield (SY) on millennial timescales in the Meuse basin. We use a spatially distributed soil erosion and sediment delivery model (WATEM/SEDEM) to simulate SY in three time-periods: 4000–3000 BP (minimal anthropogenic influence); 1000–2000 AD (includes land use and climate change); and the 21st Century. Changes in climate are based on climate model output (ECBilt-CLIO-VECODE). For the 21st Century the model is forced according to two emission scenarios of the Intergovernmental Panel on Climate Change (IPCC), namely the SRES scenarios A2 and B1. These scenarios lie towards the higher and lower end of the full IPCC scenario range respectively. For 4000–3000 BP the basin is assumed to be almost fully forested; for 1000–2000 AD land use is reconstructed using CORINE data, historical sources, and land use modelling; and for the 21st Century land use is based on the European land use change project EURURALIS. Whilst rainfall erosivity increases by only 3% between 4000–3000 BP and 1000–2000 AD, SY increases from ca. 92000 Mg a−1 to ca. 306000 Mg a−1. This model prediction is in agreement with the limited regional multi-proxy data available. Our simulations show that almost all of this increase is due to the conversion of forest to agricultural land. Over the period 1000–1900 AD, SY shows a significant increasing trend, with a peak of ca. 388000 Mg a−1 in the 19th Century (due to continuing deforestation). In the 20th Century, reforestation and rapid urbanisation result in a decrease to ca. 281000 Mg a−1. Sensitivity analyses show that although land use change acts as the primary control on long-term changes in SY, the sensitivity of SY to changes in climate increases as the percentage of deforested land increases. For the 21st Century the results are highly sensitive to the scenarios used. Due to relatively large increases in rainfall erosivity, SY increases by 12% compared to the 20th Century according to scenario A2, or by 8% according to B1. However, the associated land use change scenarios cause decreases in SY by 26% (A2) and 46% (B1). The net effect is thus a decrease of SY. This study highlights the potentially significant efficacy of land use planning as a tool to mitigate the negative effects of soil erosion and sediment delivery to rivers. [Copyright &y& Elsevier]

Abstract: We have coupled a climate model (ECBilt-CLIO-VECODE) and a hydrological model (STREAM) offline to simulate palaeodischarge of nineteen rivers (Amazon, Congo, Danube, Ganges, Krishna, Lena, Mackenzie, Mekong, Meuse, Mississippi, Murray–Darling, Nile, Oder, Rhine, Sacramento–San Joaquin, Syr Darya, Volga, Volta, Zambezi) for three time-slices: Early Holocene (9000–8650 BP), Mid-Holocene (6200–5850 BP) and Recent (1750–2000 AD). To evaluate the model''s skill in retrodicting broad changes in mean palaeodischarge we have compared the model results with palaeodischarge estimates from multi-proxy records. We have compared the general trends inferred from the proxy data with statistical differences in modelled discharge between the three periods, thereby developing a technique to assess the level of agreement between the model and proxy data. The quality of the proxy data for each basin has been classed as good, reasonable or low. Of the model runs for which the proxy data were good or reasonable, 72% were in good agreement with the proxy data, and 92% were in at least reasonable agreement. We conclude that the coupled climate-hydrological model performs well in simulating mean discharge in the time-slices studied. The discharge trends inferred from the proxy and model data closely follow latitudinal and seasonal variations in insolation over the Holocene. For a number of basins for which agreement was not good we have identified specific mechanisms which could be responsible for the discrepancy, primarily the absence of the Laurentide ice sheet in our model. In order to use the model in an operational sense within water management studies it would be useful to use a higher spatial resolution and a daily time-step. [Copyright &y& Elsevier]

This study synthesizes the current understanding of the hydrological, impact, and adaptation processes underlying drought‐to‐flood events (i.e., consecutive drought and flood events), and how they interact. Based on an analysis of literature and a global assessment of historic cases, we show how drought can affect flood risk and assess under which circumstances drought‐to‐flood interactions can lead to increased or decreased risk. We make a distinction between hydrological, socio‐economic and adaptation processes. Hydrological processes include storage and runoff processes, which both seem to mostly play a role when the drought is a multiyear event and when the flood occurs during the drought. However, which process is dominant when and where, and how this is influenced by human intervention needs further research. Processes related to socio‐economic impacts have been studied less than hydrological processes, but in general, changes in vulnerability seem to play an important role in increasing or decreasing drought‐to‐flood impacts. Additionally, there is evidence of increased water quality problems due to drought‐to‐flood events, when compared to drought or flood events by themselves. Adaptation affects both hydrological (e.g., through groundwater extraction) or socio‐economic (e.g., influencing vulnerability) processes. There are many examples of adaptation, but there is limited evidence of when and where certain processes occur and why. Overall, research on drought‐to‐flood events is scarce. To increase our understanding of drought‐to‐flood events we need more comprehensive studies on the underlying hydrological, socio‐economic, and adaptation processes and their interactions, as well as the circumstances that lead to the dominance of certain processes. This article is categorized under:Science of Water > Hydrological ProcessesScience of Water > Water Extremes [ABSTRACT FROM AUTHOR]

The article presents a study which explores the globally consistent local-scale assessment of future tropical cyclone (TC) risk. It mentions about the unique and innovative methodology enables globally consistent comparison of TC risk in both time and space and can be easily adapted to accommodate alternative climate scenarios and time periods.

To improve coastal adaptation and management, it is critical to better understand and predict the characteristics of sea levels. Here, we explore the capabilities of artificial intelligence, from four deep learning methods to predict the surge component of sea-level variability based on local atmospheric conditions. We use an Artificial Neural Networks, Convolutional Neural Network, Long Short-Term Memory layer (LSTM) and a combination of the latter two (ConvLSTM), to construct ensembles of Neural Network (NN) models at 736 tide stations globally. The NN models show similar patterns of performance, with much higher skill in the mid-latitudes. Using our global model settings, the LSTM generally outperforms the other NN models. Furthermore, for 15 stations we assess the influence of adding complexity more predictor variables. This generally improves model performance but leads to substantial increases in computation time. The improvement in performance remains insufficient to fully capture observed dynamics in some regions. For example, in the tropics only modelling surges is insufficient to capture intra-annual sea level variability. While we focus on minimising mean absolute error for the full time series, the NN models presented here could be adapted for use in forecasting extreme sea levels or emergency response. [ABSTRACT FROM AUTHOR]

Traditionally, people have inhabited places with ready access to fresh water. Today, over 50% of the global population lives in urban areas, and water can be directed via tens of kilometres of pipelines. Still, however, a large part of the world's population is directly dependent on access to natural freshwater sources. So how are inhabited places related to the location of freshwater bodies today? We present a high-resolution global analysis of how close present-day populations live to surface freshwater. We aim to increase the understanding of the relationship between inhabited places, distance to surface freshwater bodies, and climatic characteristics in different climate zones and administrative regions. Our results show that over 50% of the world's population lives closer than 3 km to a surface freshwater body, and only 10% of the population lives further than 10 km away. There are, however, remarkable differences between administrative regions and climatic zones. Populations in Australia, Asia, and Europe live closest to water. Although populations in arid zones live furthest away from freshwater bodies in absolute terms, relatively speaking they live closest to water considering the limited number of freshwater bodies in those areas. Population distributions in arid zones show statistically significant relationships with a combination of climatic factors and distance to water, whilst in other zones there is no statistically significant relationship with distance to water. Global studies on development and climate adaptation can benefit from an improved understanding of these relationships between human populations and the distance to fresh water. [ABSTRACT FROM AUTHOR]

El Niño‐Southern Oscillation (ENSO) is an important driver of interannual climate variability with increasing attention for its impacts on water and flood management. The impact of ENSO on basin‐scale floods during the TRMM period (1998–2013) is examined by using the streamflow outputs from the Dominant river Routing Integrated with VIC Environment model (DRIVE). Significant simultaneous correlations between flood indices and Niño 3.4 appear in many flood‐prone river basins during peak flood months across both the tropics and midlatitudes especially for flood frequency and flood duration. Gauged by significant lag‐correlations between floods and Niño 3.4, significant ENSO‐leading‐floods relations are found as well in many river basins in South America, south and southeastern Asia, and northern Africa. These ENSO‐floods‐relations can greatly enhance understanding of physical mechanisms relevant to the ENSO impact and may also improve the skills of basin‐scale monthly‐to‐seasonal flood forecast, thus allowing for better preparedness and management of flood risks. Plain Language Summary: As a naturally occurring phenomenon involving quasi‐periodic variation of air pressure and sea surface temperature, the ENSO impacts global floods through its modulation of atmospheric circulations, weather, and precipitation. Previous studies on ENSO‐floods‐relations are often limited in supporting practical flood disaster preparedness and risk analyses because of the lack of a "complete" reconstruction of past flood events likely due to scattered gauge‐based streamflow data and/or coarse‐resolution retrospective simulations. Here flood events at model grids and for individual river basins are identified using the outputs from the Dominant river Routing Integrated with VIC Environment model (DRIVE) with a high spatial resolution (0.125° × 0.125°) driven by the state‐of‐the‐art satellite precipitation product TMPA. Simultaneous and lagged responses of basin‐scale floods to ENSO are explored to provide an improved knowledge of the ENSO effect on global floods and a guidance for flood disaster planning and risk management for the regions consistently impacted by ENSO. Key Points: Simultaneous correlations between flood indices and Nino 3.4 appear in many flood‐prone river basins in the tropics and midlatitudesENSO‐leading‐floods relations gauged by significant lag‐correlations between floods and Nino 3.4 can also be seen in many river basinsThese ENSO‐floods‐relations can greatly help our efforts of exploring basin‐scale monthly‐to‐seasonal flood predictability [ABSTRACT FROM AUTHOR]

We investigate hydrology during a past climate slightly warmer than the present: the last interglacial (LIG). With daily output of preindustrial and LIG simulations from eight new climate models we force hydrological model PCR‐GLOBWB and in turn hydrodynamic model CaMa‐Flood. Compared to preindustrial, annual mean LIG runoff, discharge, and 100‐yr flood volume are considerably larger in the Northern Hemisphere, by 14%, 25%, and 82%, respectively. Anomalies are negative in the Southern Hemisphere. In some boreal regions, LIG runoff and discharge are lower despite higher precipitation, due to the higher temperatures and evaporation. LIG discharge is much higher for the Niger, Congo, Nile, Ganges, Irrawaddy, and Pearl and lower for the Mississippi, Saint Lawrence, Amazon, Paraná, Orange, Zambesi, Danube, and Ob. Discharge is seasonally postponed in tropical rivers affected by monsoon changes. Results agree with published proxies on the sign of discharge anomaly in 15 of 23 sites where comparison is possible. Plain Language Summary: It is still uncertain how the water cycle will respond to a warmer climate in the coming decades. To increase our understanding of the relationships between climate and hydrology, we study the past climate of the last interglacial, which was slightly warmer than the present. We present the results of a modeling approach, showing that while Northern Hemisphere precipitation was higher during the last interglacial, discharge of rivers was even higher, and floods were even larger. On the contrary, in the Southern Hemisphere precipitation, discharge and floods were lower. We show that, for some regions, precipitation, discharge, and floods do not have the same direction of change. The seasonal timing of discharge also changes for some large basins of the Northern Hemisphere. Finally, for 23 sites, we compare our results to geological evidence. These results form a useful term of comparison to both projections of the future and geological studies of past hydrology. Key Points: We present the first modeling of hydrology and floods for the last interglacialBoreal precipitation and runoff are higher than preindustrial, and boreal river discharge and flood volume are (in %) even higherMost of the discharge occurs later in the year in large river basins of the Northern Hemisphere [ABSTRACT FROM AUTHOR]

The Mediterranean (MED) Basin is a climate change hotspot that has seen drying and a pronounced increase in heatwaves over the last century. At the same time, it is experiencing increased heavy precipitation during wintertime cold spells. Understanding and quantifying the risks from compound events over the MED is paramount for present and future disaster risk reduction measures. Here, we apply a novel method to study compound events based on dynamical systems theory and analyse compound temperature and precipitation events over the MED from 1979 to 2018. The dynamical systems analysis quantifies the strength of the coupling between different atmospheric variables over the MED. Further, we consider compound warm–dry anomalies in summer and cold–wet anomalies in winter. Our results show that these warm–dry and cold–wet compound days are associated with large values of the temperature–precipitation coupling parameter of the dynamical systems analysis. This indicates that there is a strong interaction between temperature and precipitation during compound events. In winter, we find no significant trend in the coupling between temperature and precipitation. However in summer, we find a significant upward trend which is likely driven by a stronger coupling during warm and dry days. Thermodynamic processes associated with long-term MED warming can best explain the trend, which intensifies compound warm–dry events. [ABSTRACT FROM AUTHOR]

The Mediterranean (MED) basin is a climate change hot-spot that has seen drying and a pronounced increase in heatwaves over the last century. At the same time, it is experiencing increasing heavy precipitation during wintertime cold spells. Understanding and quantifying the risks from compound events over the MED is paramount for present and future disaster risk reduction measures. Here, we apply a novel method to study compound events based on dynamical systems theory and analyse compound temperature and precipitation anomalies over the MED from 1979 to 2018. The dynamical systems analysis measures the strength of the coupling between different atmospheric variables over the MED. Further, we consider compound hot-dry days in summer and cold-wet days in winter. Our results show that these hot-dry and cold-wet compound days are associated with maxima in the temperature-precipitation coupling parameter of the dynamical systems analysis. This indicates that there is a strong interaction between temperature and precipitation during compound events. In summer, we find a significant upward trend in the coupling between temperature and precipitation over 1979-2018, which is likely driven by a stronger coupling during hot and dry days. Thermodynamic processes associated with long-term MED warming can best explain the trend. No such trend is found for wintertime cold-wet compound events. Our findings suggest that long- term warming strengthens the coupling of temperature and precipitation which intensifies hot-dry compound events. [ABSTRACT FROM AUTHOR]

We assess the suitability of ECMWF Integrated Forecasting System (IFS) data for the global modeling of tropical cyclone (TC) storm surges. We extract meteorological forcing from the IFS at a 0.225° horizontal resolution for eight historical TCs and simulate the corresponding surges using the global tide and surge model. Maximum surge heights for Hurricanes Irma and Sandy are compared with tide gauge observations, with R2-values of 0.86 and 0.74 respectively. Maximum surge heights for the other TCs are in line with literature. Our case studies demonstrate that a horizontal resolution of 0.225° is sufficient for the large-scale modeling of TC surges. By upscaling the meteorological forcing to coarser resolutions as low as 1.0°, we assess the effects of horizontal resolution on the performance of surge modeling. We demonstrate that coarser resolutions result in lower-modeled surges for all case studies, with modeled surges up to 1 m lower for Irma and Nargis. The largest differences in surges between the different resolutions are found for the TCs with the highest surges. We discuss possible drivers of maximum surge heights (TC size, intensity, and coastal slope and complexity), and find that coastal complexity and slope play a more profound role than TC size and intensity alone. The highest surges are found in areas with complex coastlines (fractal dimension > 1.10) and, in general, shallow coastlines. Our findings show that using high-resolution meteorological forcing is particularly beneficial for areas prone to high TC surges, since these surges are reduced the most in coarse-resolution datasets. [ABSTRACT FROM AUTHOR]

Abstract Despite advances in drought early warning systems, forecast information is rarely used for triggering and financing early actions, such as cash transfer. Scaling up cash transfer pay-outs, and overcoming the barriers to actions based on forecasts, requires an understanding of costs resulting from False Alarms, and the potential benefits associated with appropriate early interventions. On this study, we evaluate the potential cost-effectiveness of cash transfer responses, comparing the relative costs of ex-ante cash transfers during the maize growing season to ex-post cash transfers after harvesting in Kenya. For that, we developed a forecast model using Fast-and Frugal Trees that unravels early warning relationships between climate variability, vegetation coverage, and maize yields at multiple lead times. Results indicate that our models correctly forecast low maize yield events 85% of the time across the districts studied, some already six months before harvesting. The models' performance improves towards the end of the growing season driven by a decrease of 29% in the probability of False Alarms. Overall, we show that timely cash transfers ex-ante to a disaster can often be more cost-effective than investing in ex-post expenditures. Our findings suggest that early response can yield significant cost savings, and can potentially increase the effectiveness of existing cash transfer systems. Graphical abstract Unlabelled Image Highlights • Drought early warning systems are rarely used for triggering early actions, such as cash transfer. • We evaluate the cost-effectiveness of cash transfers ex-ante (forecast-based) and ex-post. • We developed a forecast model using Machine Learning named Fast-and-Frugal Trees. • We show that our models predict the majority of the low maize yield events in all districts studied. • Our results indicate that cash transfers ex-ante can often be more cost-effective than ex-post. [ABSTRACT FROM AUTHOR]

Water-related disasters, such as fluvial floods and cyclonic storm surges, are a major concern in the world's mega-delta regions. Furthermore, the simultaneous occurrence of extreme discharges from rivers and storm surges could exacerbate flood risk, compared to when they occur separately. Hence, it is of great importance to assess the compound risks of fluvial and coastal floods at a large scale, including mega-deltas. However, most studies on compound fluvial and coastal flooding have been limited to relatively small scales, and global-scale or large-scale studies have not yet addressed both of them. The objectives of this study are twofold: to develop a global coupled river-coast flood model; and to conduct a simulation of compound fluvial flooding and storm surges in Asian mega-delta regions. A state-of-the-art global river routing model was modified to represent the influence of dynamic sea surface levels on river discharges and water levels. We conducted the experiments by coupling a river model with a global tide and surge reanalysis data set. Results show that water levels in deltas and estuaries are greatly affected by the interaction between river discharge, ocean tides and storm surges. The effects of storm surges on fluvial flooding are further examined from a regional perspective, focusing on the case of Cyclone Sidr in the Ganges-Brahmaputra-Meghna Delta in 2007. Modeled results demonstrate that a >3 m storm surge propagated more than 200 km inland along rivers. We show that the performance of global river routing models can be improved by including sea level dynamics. [ABSTRACT FROM AUTHOR]

An accurate understanding of flood risk and its drivers is crucial for effective risk management. Detailed risk projections, including uncertainties, are however rarely available, particularly in developing countries. This paper presents a method that integrates recent advances in global-scale modeling of flood hazard and land change, which enables the probabilistic analysis of future trends in national-scale flood risk. We demonstrate its application to Indonesia. We develop 1000 spatially-explicit projections of urban expansion from 2000 to 2030 that account for uncertainty associated with population and economic growth projections, as well as uncertainty in where urban land change may occur. The projections show that the urban extent increases by 215%–357% (5th and 95th percentiles). Urban expansion is particularly rapid on Java, which accounts for 79% of the national increase. From 2000 to 2030, increases in exposure will elevate flood risk by, on average, 76% and 120% for river and coastal floods. While sea level rise will further increase the exposure-induced trend by 19%–37%, the response of river floods to climate change is highly uncertain. However, as urban expansion is the main driver of future risk, the implementation of adaptation measures is increasingly urgent, regardless of the wide uncertainty in climate projections. Using probabilistic urban projections, we show that spatial planning can be a very effective adaptation strategy. Our study emphasizes that global data can be used successfully for probabilistic risk assessment in data-scarce countries. [ABSTRACT FROM AUTHOR]

The global impacts of river floods are substantial and rising. Effective adaptation to the increasing risks requires an in-depth understanding of the physical and socioeconomic drivers of risk. Whereas the modeling of flood hazard and exposure has improved greatly, compelling evidence on spatiotemporal patterns in vulnerability of societies around the world is still lacking. Due to this knowledge gap, the effects of vulnerability on global flood risk are not fully understood, and future projections of fatalities and losses available today are based on simplistic assumptions or do not include vulnerability. We show for the first time (to our knowledge) that trends and fluctuations in vulnerability to river floods around the world can be estimated by dynamic highresolution modeling of flood hazard and exposure. We find that rising per-capita income coincided with a global decline in vulnerability between 1980 and 2010, which is reflected in decreasing mortality and losses as a share of the people and gross domestic product exposed to inundation. The results also demonstrate that vulnerability levels in low- and high-income countries have been converging, due to a relatively strong trend of vulnerability reduction in developing countries. Finally, we present projections of flood losses and fatalities under 100 individual scenario and model combinations, and three possible global vulnerability scenarios. The projections emphasize that materialized flood risk largely results from human behavior and that future risk increases can be largely contained using effective disaster risk reduction strategies. [ABSTRACT FROM AUTHOR]

The western North-Atlantic coast experienced major coastal floods in recent years. Coastal floods are primarily composed of tides and storm surges due to tropical (TCs) and extra-tropical cyclones (ETCs). We present a reanalysis from 1988 to 2015 of extreme sea levels that explicitly include TCs for the western North-Atlantic coastline. Validation shows a good agreement between modeled and observed sea levels and demonstrates that the framework can capture large-scale variability in extreme sea levels. We apply the 28-year reanalysis to analyze spatiotemporal patterns. Along the US Atlantic coasts the contribution of tides can be significant, with the average contribution of tides during the 10 largest events up to 55% in some locations, whereas along the Mexican Southern Gulf coast, the average contribution of tides over the largest 10 events is generally below 25%. At the US Atlantic coast, ETCs are responsible for 8.5 out of the 10 largest extreme events, whereas at the Gulf Coast and Caribbean TCs dominate. During the TC season more TC-driven events exceed a 10-year return period. During winter, there is a peak in ETC-driven events. Future research directions include coupling the framework with synthetic tropical cyclone tracks and extension to the global scale. [ABSTRACT FROM AUTHOR]

Studies show that climate variability drives interannual changes in meteorological variables in Europe, which directly or indirectly impacts crop production. However, there is no climate-based decision model that uses indices of atmospheric oscillation to predict agricultural production risks in Europe on multiple time-scales during the growing season. We used Fast-and-Frugal trees to predict sugar beet production, applying five large-scale indices of atmospheric oscillation: El Niño Southern Oscillation, North Atlantic Oscillation, Scandinavian Pattern, East Atlantic Pattern, and East Atlantic/West Russian pattern. We found that Fast-and-Frugal trees predicted high/low sugar beet production events in 77% of the investigated regions, corresponding to 81% of total European sugar beet production. For nearly half of these regions, high/low production could be predicted six or five months before the start of the sugar beet harvesting season, which represents approximately 44% of the mean annual sugar beet produced in all investigated areas. Providing early warning of crop production shortages/excess allows decision makers to prepare in advance. Therefore, the use of the indices of climate variability to forecast crop production is a promising tool to strengthen European agricultural climate resilience. [ABSTRACT FROM AUTHOR]

Globally, flood risk accounts for about a third of all losses due to natural hazards. Several models have been developed to assess flood risk globally, mainly focusing on damage to buildings, particularly in urban areas. However, floods also cause hardship in rural areas due to their impacts on agricultural productivity. According to the FAO, crop production in the period 2003 to 2013 was the most affected agricultural sector by natural hazards in developing countries and about 60% of the damages are attributed to floods.If agricultural flood impacts are considered in the assessments, they are usually limited to local and regional analyses or flood prone areas are only overlaid with arable land. We address this gap by developing a model to assess the impacts of river floods on agricultural production at a global scale. In this study, we combine data of monthly flood inundation with a spatially explicit dataset on irrigated and rain fed crop areas and calculate potential crop yield using the LPJmL model. We assess the flood affected yield for four major crops: maize, cereals, rice and soybeans for the period 1960 to 1999, by analyzing for each crop which areas were inundated during the flood events. Our approach allows to identify global hotspot areas of flood affected agriculture. Preliminarily results show that examples for this can particularly be found for West Africa's agricultural production, a region that strongly relies on its own food production and which has frequently been affected by flooding. [ABSTRACT FROM AUTHOR]

Exposure to coastal flood events is increasing due to growing population and economic activities in delta areas. These developments go hand-in-hand with the loss and deterioration of ecosystems as well as increasing sea levels and subsiding lands. Ironically, these same ecosystems can play a buffering role in reducing flood hazard and mitigating the resulting impacts. The ability of ecosystems to reduce coastal flood risk has been emphasized in multiple studies, but their contribution at a global scale has never been quantified. Here, we evaluate the role of currently present coastal vegetation, both of mangroves and marshes, in mitigating coastal hazards through wave reduction. We do this by combining earth observation (EO) data with an assessment method for wave reduction based on numerical models. We show that 31% of the total global coastline is covered with vegetation. For about 5.5% of the total global coastlines, vegetation can provide a meaningful contribution to flood risk reduction, by lowering required levee crest height with at least 50 centimetres, while maintaining the same flood protection level. We calculated that, if levees were established along all coastlines susceptible to flooding, vegetation could considerably lower required levee crest height, with a resulting saving of 194 billion USD in investments, of which 37 billion USD in urban areas. [ABSTRACT FROM AUTHOR]

A dormant volcano suddenly awakens and bring chaos to a fictitious quiet town. Because of the delayed evacuation and poor emergency planning, inhabitants are left to themselves to survive through the unexpected series of consecutive events the volcanic eruption will generate: an earthquake, an avalanche (both prior to the eruption), the volcanic eruption and floods (lahars). While this sensational plot was created for the blockbuster movie Dante's Peak (1997), recent examples show that these "black-swan" type of events do occur and as a result have the potential to cause devastating consequences. Last December, more than 400 people were killed in a volcano-triggered tsunami. While extremely rare, this tsunami event was actually the second one the country faced, after the earthquake-triggered tsunami causing around 2 200 casualties. The consequences of the Pinatubo eruption in 1991, one of the most important of the 20th century, were aggravated by the coincidence with typhoon Yunua, creating deadly lahars and displacing several communities. Yet, spatial and temporal interactions from consecutive events are often neglected in regional or large-scale multi-hazard studies. Notwithstanding the large modeling complexity of such an endeavor, one might argue that the probability of occurrence of such series of events is extremely low and thus superfluous. In this study, we build a statistical model and incrementally increase its complexity to take into account event duration and correlation within or between hazard types to investigate the likelihood of consecutive events. Starting with the assumption of a Poisson process, we later deviate from such an assumption to include changes in the inter-event time distribution and memory effects. For example, earthquakes can be followed by aftershock sequences, creating a series of dependent events. Similarly, we can expect a correlation between major flood events and typhoon occurrence, or tsunami generation and earthquake occurrence. We collect and analyze occurrences of four hazard types in the Philippines from publicly available global databases for typhoons, earthquakes and volcanic eruptions and floods between 1980 to 2015 to calculate the parameters needed to force the model. We can therefore quantify the number of consecutive events that can be expected for an area exposed to multiple hazard types with such statistical characteristics. We also identify key challenges in the statistical modelling of consecutive events. Finally, our findings highlight the need to consider consecutive events in order to improve the resilience of emergency response plans or infrastructure in areas exposed to multiple hazards. [ABSTRACT FROM AUTHOR]