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Compound floods due to intense rainfall and storm surges in coastal areas have shown an increasing trend in some parts of the world, and many studies suggested a strong link with climate change. Yet, such link has not been fully explored and quantitively assessed. In this paper, we demonstrate the development and application of a nonstationary framework to determining different compound scenarios, where individual drivers and their interactions have altered under climate change. The framework has been applied to one of the most flood-prone areas: the Ho Chi Minh City of Vietnam, to help analyze the present and future compound flood risks in both the dry and wet seasons driven by the joint effect from heavy inland rainfall and high skew surge. Over the period of 1980–2017, the two drivers are found to be significantly correlated in March and April, corresponding to the transition from dry-to-wet seasons. We also find that the commonly-used traditional multivariate statistical models underestimate the flood magnitudes for both the current (represented by 2020) and future (represented by 2050) scenarios, when compared with the results produced by the nonstationary methods. In addition, the results reveal that the dry season is expected to receive more floods triggered by the increased intensity and frequency of rainfall extremes, with the magnitude reaching a similar level to that of the wet season. This is in line with the climate projections under RCP4.5 and 8.5 scenarios although the duration of dry spells is expected to increase and the total annual rainfall to decrease in Vietnam. The simulated flood inundations indicate remarkable increases in flood magnitude and extension, especially at the locations identified as low risk by the stationary models.

Critical infrastructures are more exposed than ever to natural hazards in a changing climate. To understand and manage risk, failure cascades across large, real-world infrastructure networks, and their impact on people, must be captured. Bridging established methods in both infrastructure and risk modelling communities, we develop an open-source modelling framework which integrates a network-based interdependent infrastructure system model into the globally consistent and spatially explicit natural hazard risk assessment platform CLIMADA. The model captures infrastructure damages, triggers failure cascades and estimates resulting basic service disruptions for the dependent population. It flexibly operates on large areas with publicly available hazard, exposure and vulnerability information, for any set of infrastructure networks, hazards and geographies of interest. In a validated case study for 2018’s Hurricane Michael across three US states, the model reproduced important failure dynamics among six infrastructure networks, and provided a novel spatial map where people were likely to experience disruptions in access to healthcare, loss of power and other vital services. Our generalized approach allows for a view on infrastructure risks and their social impacts also in areas where detailed information and risk assessments are traditionally scarce, informing humanitarian activities through hotspot analyses and policy frameworks alike., Reliability Engineering & System Safety, 234, ISSN:0951-8320

Coastal flooding is driven by the combination of (high) tide and storm surge, the latter being caused by strong winds and low pressure in tropical and extratropical cyclones. The combination of storm surge and the astronomical tide is defined as the storm tide. To gain an understanding of the threat posed by coastal flooding and to identify areas that are especially at risk, now and in the future, it is crucial to accurately model coastal inundation. Most models used to simulate the coastal inundation scale follow a simple planar approach, referred to as bathtub models. The main limitations of this type of models are that they implicitly assume an infinite flood duration, and they do not capture relevant physical processes. In this study we develop a method to generate hydrographs called HGRAPHER, and we provide a global dataset of storm tide hydrographs based on time series of storm surges and tides derived from the Global Tide and Surge Model (GTSM) forced with the ERA5 reanalysis wind and pressure fields. These hydrographs represent the typical shape of an extreme storm tide at a certain location along the global coastline. We test the sensitivity of the HGRAPHER method with respect to two main assumptions that determine the shape of the hydrograph, namely the surge event sampling threshold and coincidence in the time of the surge and tide maxima. The hydrograph dataset can be used to move away from planar inundation modelling techniques towards dynamic inundation modelling techniques across different spatial scales.

Rainfall-induced landslides cause frequent disruptions to critical infrastructure in mountainous countries. Climate change is altering rainfall patterns and localizing extreme rainfall events, increasing the occurrence of landslides. For planning climate-resilient critical infrastructure in landslide-prone regions, it is urgent to understand the changing landslide susceptibility in relation to changing rainfall extremes and spatially overlay them with critical infrastructure to determine risk zones. As such, areas requiring financial reinforcements can be prioritized. In this paper, we develop a framework linking changing rainfall extremes to landslide susceptibility and intensity of critical infrastructure — exemplified on a national scale using Nepal as a case study. First, we define a set of 21 different unique rainfall indices that describe extreme and localized rainfall. Second, we prepare a new annual (2016–2020) inventory of 107,900 landslides in Nepal mapped on PlanetScope satellite imagery. Next, we prepare a landslide susceptibility map by training a random forest model using the collected extreme rainfall indices and landslide locations in combination with spatial data on topography. Fourth, we construct a gridded critical infrastructure spatial density map that quantifies the intensity of infrastructure (i.e., transportation, energy, telecommunication, waste, water, health, and education) at each grid location using OpenStreetMap. The landslide susceptibility map classified Nepal's topography into low (36 %), medium (33 %), and (32 %) high rainfall-triggered landslide susceptibility zones. The landslide susceptibility map had an average area under the receiver characteristic curve value of 0.94. Finally, we overlay the landslide susceptibility map with the critical infrastructure intensity to identify areas needing financial reinforcement. Our framework reasonably mapped critical infrastructure hotspots in Nepal prone to landslides on a 1 km grid. The hotspots are mainly concentrated along major national highways and in provinces 4, 3, and 1, highlighting the need for improved land management practices. These hotspots need spatial prioritization regarding climate-resilient critical infrastructure financing and slope conservation policies. The research data, output maps, and code are publicly released via an ArcGIS WebApp and GitHub repository. The framework is scalable and can be used for developing infrastructure financing strategies for landslide mountain regions and countries.

Transportation infrastructures are generally designed to have multi-decadal service lives. Transport infrastructure design, however, is largely based on historical conditions. Yet, in the face of global warming, we are likely going to experience more intense and frequent extreme events, which may put infrastructure at severe risk. In this study, we comprehensively analyze the exposure of road and railway infrastructure assets to changes in precipitation return periods globally. Under ~2 degrees of warming in mid-century (RCP 8.5 scenario), 43.6% of the global transportation assets are expected to experience at least a 25% decrease in design return period of extreme rainfall (a 33% increase in exceedance probability), which may increase to 69.9% under ~4 degrees of warming by late-21st century. To accommodate for such increases, we propose to incorporate a safety factor for climate change adaptation during the transportation infrastructure design process to ensure transportation assets will maintain their designed risk level in the future. Our results show that a safety factor of 1.2 would work sufficient for most regions of the world for quick design process calculations following the RCP4.5 path.

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 characterise management shortcomings, hazard, exposure, vulnerability and impacts of all events; 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, link for review: https://dataservices.gfz-potsdam.de/panmetaworks/review/923c14519deb04f83815ce108b48dd2581d57b90ce069bec9c948361028b8c85/).

Sub-seasonal forecasts are challenging for numerical weather prediction (NWP) and machine learning models alike. Forecasting two-meter temperature (t2m) with a lead-time of two or more weeks requires a forward model to integrate multiple complex interactions, like oceanic and land surface conditions leading to predictableweather patterns. NWPmodels represent these interactions imperfectly, meaning that in certain conditions, errors accumulate and model predictability deviates from real predictability, often for poorly understood reasons. To advance that understanding, this paper corrects conditional errors in NWP forecasts with an artificial neural network (ANN). The ANN post-processes ECMWF extended-range summer temperature forecasts by learning to correct the ECMWF-predicted probability that monthly t2m in western and central Europe exceeds the climatological median. Predictors are objectively selected from ECMWF forecasts themselves, and from states at initialization, i.e. the ERA5 reanalysis. The latter allows the ANN to account for sources of predictability that are biased in the NWP model itself. We attribute ANN-corrections with two explainable AI tools. This reveals that certain erroneous forecasts relate to tropical west Pacific sea surface temperatures at initialization. We conjecture that the atmospheric teleconnection following this source of predictability is imperfectly represented by the ECMWF model. Correcting the associated conditional errors with the ANN improves forecast skill.

While soybeans are among the most consumed crops in the world, most of its production lies in the US, Brazil, and Argentina. The concentration of soybean growing regions in the Americas renders the supply chain vulnerable to regional disruptions. In 2012, anomalous hot and dry conditions occurring simultaneously in these regions led to low soybean yields, which drove global soybean prices to all-time records. In this study, we explore climate change impacts on simultaneous extreme crop failures as the one from 2012. We develop a hybrid model, coupling a process-based crop model with a machine learning model, to improve the simulation of soybean production. We assess the frequency and magnitude of events with similar or higher impacts than 2012 under different future scenarios, evaluating anomalies both with respect to present day and future conditions to disentangle the impacts of (changing) climate variability from the long-term mean trends. We find long-term trends in mean climate increase the frequency of 2012 analogs by 11–16 times and the magnitude by 4–15% compared to changes in climate variability only depending on the global climate scenario. Conversely, anomalies like the 2012 event due to changes in climate variability show an increase in frequency in each country individually, but not simultaneously across the Americas. We deduce that adaptation of the crop production practice to the long-term mean trends of climate change may considerably reduce the future risk of simultaneous soybean losses across the Americas.

Seasonal rainfall forecasts provide information several months ahead to support decision making. These forecasts may use dynamic, statistical, or hybrid approaches, but their comparative value is not well understood over Central America. This study conducts a regional evaluation of seasonal rainfall forecasts focusing on two of the leading dynamic climate ensembles: the Copernicus Climate Change Service seasonal forecasting system (C3S) and the North American Multimodel Ensemble (NMME). We compare the multimodel ensemble mean and individual model predictions of seasonal rainfall over key wet season periods in Central America to better understand their relative forecast skill at the seasonal scale. Three types of rainfall forecasts are compared: direct dynamic rainfall predictions from the C3S and NMME ensembles, a statistical approach using the lagged observed sea surface temperature (SST), and an indirect hybrid approach, driving a statistical model with dynamic ensemble SST predictions. Results show that C3S and NMME exhibit similar regional variability with strong performance in the northern Pacific part of Central America and weaker skill primarily in eastern Nicaragua. In the northern Pacific part of the region, the models have high skill across the wet season. Indirect forecasts can outperform the direct rainfall forecasts in specific cases where the direct forecasts have lower predictive power (e.g., eastern Nicaragua during the early wet season). The indirect skill generally reflects the strength of SST associations with rainfall. The indirect forecasts based on Tropical North Atlantic SSTs are best in the early wet season and the indirect forecasts based on Niño3.4 SSTs are best in the late wet season when each SST zone has a stronger association with rainfall. Statistical predictions are competitive with the indirect and direct forecasts in multiple cases, especially in the late wet season, demonstrating how a variety of forecasting approaches can enhance seasonal forecasting.

To support Mozambique in the mitigation and management of droughts, the World Food Programme (WFP) is seeking to implement innovative approaches to protect people's livelihood who face drought risk. The approach that has potential of closing the humanitarian funding gap is Forecast-based Financing (FbF). FbF enables anticipatory actions against droughts using seasonal forecasts, which are implemented to reduce impacts in the critical window between a forecast and an event. An important step for leveraging seasonal forecasting information to implement FbF is the development of an operational trigger system for drought anticipatory action. Our study describes WFP's FbF system for droughts being tested in Mozambique using the ECMWF 7-month rainfall ensemble forecast. We showed that the forecasting information has skill to detect most of the drought events, measured by the Standardized Precipitation Index (SPI) with lead time varying from 6 to 1 month. We observed that information of different lead times produces a marginal improvement on the average skill of the predictions. After deriving probability values that can trigger anticipatory action, we found that the overall system yields a mean Hit Rate and False Alarm Ratio of 74 % and 59 %, respectively. However, there are opportunities for reducing the false alarm ratio and increasing the hit rate when focusing on key specific lead times. We believe that implementing FbF against droughts based on forecast information can help humanitarian organizations to reduce losses to livelihoods and cost of humanitarian operations while supporting communities in a more dignified manner.

Traditional measurements of the Secchi depth (zSD) and Forel-Ule colour were collected alongside modern radiometric measurements of ocean clarity and colour, and in-situ measurements of chlorophyll-a concentration (Chl-a), on four Atlantic Meridional Transect (AMT) cruises. These data were used to evaluate historic and modern optical techniques for monitoring Chl-a, and to evaluate remote-sensing algorithms. Historic and modern optical measurements were broadly consistent with current understanding, with Secchi depth inversely related to Forel-Ule colour and to beam and diffuse attenuation, positively related to the ratio of blue to green remote-sensing reflectance and euphotic depth. The relationship between Secchi depth and Forel-Ule on AMT was found to be in closer agreement to historical relationships when using data of the Forel-Ule colour of infinite depth, rather than the Forel-Ule colour of the water above the Secchi disk at half zSD. Over the range of 0.03-2.95 mg m-3, Chl-a was tightly correlated with these optical variables, with the ratio of blue to green remote-sensing reflectance explaining the highest amount of variance in Chl-a (89%), closely followed by the Secchi depth (85%) and Forel-Ule colour (71-81%, depending on the scale used). Existing algorithms that predict Chl-a from these variables were evaluated, and found to perform well, albeit with some systematic differences. Remote sensing algorithms of Secchi depth were in good agreement with in-situ data over the range of values collected (8.5 - 51.8 m, r2>0.77, unbiased root mean square differences around 4.5 m), but with a slight positive bias (2.0 - 5.4 m). Remote sensing algorithms of Forel-Ule agreed well with Forel-Ule colour data of infinite water (r2>0.68, mean differences zSD, and propose a path forward to include the effect of wind in current Secchi depth theory. We discuss the benefits and challenges of collecting measurements of the Secchi depth and Forel-Ule colour and propose future directions for research. Our dataset is made publicly available to support the research community working on the topic.

Around 80 % of global soybean supply is produced in southeast South America (SESA), central Brazil (CB) and the United States (US) alone. This concentration of production in few regions makes global soybean supply sensitive to spatially compounding harvest failures. Weather variability is a key driver of soybean variability, with soybeans being especially vulnerable to hot and dry conditions during the reproductive growth stage in summer. El Niño–Southern Oscillation (ENSO) teleconnections can influence summer weather conditions across the Americas, presenting potential risks for spatially compounding harvest failures. Here, we develop causal structural models to quantify the influence of ENSO on soybean yields via mediating variables like local weather conditions and extratropical sea surface temperatures (SSTs). We show that soybean yields are predominately driven by soil moisture conditions in summer, explaining ∼50 %, 18 % and 40 % of yield variability in SESA, CB and the US respectively. Summer soil moisture is strongly driven by spring soil moisture, as well as by remote extratropical SST patterns in both hemispheres. Both of these soil moisture drivers are again influenced by ENSO. Our causal models show that persistent negative ENSO anomalies of −1.5 standard deviation (SD) lead to a −0.4 SD soybean reduction in the US and SESA. When spring soil moisture and extratropical SST precursors are pronouncedly negative (−1.5 SD), then estimated soybean losses increase to −0.9 SD for the US and SESA. Thus, by influencing extratropical SSTs and spring soil moisture, persistent La Niñas can trigger substantial soybean losses in both the US and SESA, with only minor potential gains in CB. Our findings highlight the physical pathways by which ENSO conditions can drive spatially compounding events. Such information may increase preparedness against climate-related global soybean supply shocks.

Coastal flood risk is expected to increase as a result of climate change effects, such as sea level rise, and socioeconomic growth. To support policymakers in making adaptation decisions, accurate flood risk assessments that account for the influence of complex adaptation processes on the developments of risks are essential. In this study, we integrate the dynamic adaptive behavior of homeowners within a flood risk modeling framework. Focusing on building-level adaptation and flood insurance, the agent-based model (DYNAMO) is benchmarked with empirical data for New York City, USA. The model simulates the National Flood Insurance Program (NFIP) and frequently proposed reforms to evaluate their effectiveness. The model is applied to a case study of Jamaica Bay, NY. Our results indicate that risk-based premiums can improve insurance penetration rates and the affordability of insurance compared to the baseline NFIP market structure. While a premium discount for disaster risk reduction incentivizes more homeowners to invest in dry-floodproofing measures, it does not significantly improve affordability. A low interest rate loan for financing risk-mitigation investments improves the uptake and affordability of dry-floodproofing measures. The benchmark and sensitivity analyses demonstrate how the behavioral component of our model matches empirical data and provides insights into the underlying theories and choices that autonomous agents make.

The climate of High Mountain Asia (HMA) has changed in recent decades. While the temperature is consistently increasing at a higher rate than the global warming rate, precipitation changes are inconsistent, with substantial temporal and spatial variation. Climate warming will have enormous consequences for hydroclimatic extremes. For the higher altitudes of the HMA, which are a significant source of water for the large rivers in Asia, often trends are calculated using a limited number of in situ observations mainly observed in valleys. This study explores the changes in mean, extreme, and compound-extreme climate variables and their seasonality along the full altitudinal range in HMA using daily ERA5 reanalysis data (1979–2018). Our results show that winter warming and summer wetting dominate the interior part of HMA. The results indicate a coherent significant increasing trend in the occurrence of heatwaves across all regions in HMA. The number of days with heavy precipitation shows more significant trends in southern and eastern basins than in other areas of HMA. The dry period occurrence shows a distinct demarcation between lower- and higher-altitude regions and is increasing for most basins. Although precipitation and temperature show variable tendencies, their compound occurrence is coherent in the monsoon-dominated basins. These changes in indicators of climatic extremes may imply substantial increases in the future occurrence of hazards such as floods, landslides, and droughts, which in turn impact economic production and infrastructure.

Accurate age-depth models for marine sediment cores are crucial for understanding of paleo-oceanographic and -climatic changes derived from these archives. To date, information on bulk sediment composition is largely ignored as a potential source of information to improve age-depth models. Here, we explore how bulk sediment composition can be used qualitatively to improve age-depth models. We developed the BomDia, algorithm, which produces age-depth models with realistic sediment accumulation rates that co-vary in harmony with the bulk sediment composition. We demonstrate that changes in the marine versus terrigenous sediment deposition based on bulk sediment composition, can be used to significantly improve age-depth models of hemipelagic marine deposits. Based on two marine records - each containing more than twenty radiocarbon (AMS 14C) dated levels - we show that the mean error of prediction of unused AMS 14C ages significantly improves from 3.9% using simple linear interpolation, to 2.4% (p = 0.003) when bulk sediment composition is included. The BomDia age modelling approach provides a powerful statistical tool to assess the validity of age control points used and also may assist in the detection of hiatuses. Testing and further development of the BomDia algorithm may be needed for application in other than hemipelagic depositional settings. Key Points The accuracy and predictive quality of age-depth models for marine hemipelagic cores improves when bulk sediment composition is included The BomDia algorithm produces age-depth models with realistic accumulation rates which co-vary with bulk sediment composition The Bomdia algorithm uses a Monte Carlo approach to assess age-depth model uncertainty Plain Language Summary The age-depth relationship in a marine sediment core is known as an age-depth model. To build an age-depth model, that is to produce a continuous age-depth relationship, an accurate and precise prediction of ages in between age control points is needed. A new algorithm, which includes information on the bulk sediment composition in the prediction of ages, has been developed and tested for this purpose. The calcium carbonate content of the sediments is calibrated versus the log ratio of titanium and calcium as obtained from resolution X-ray fluorescence and used as model input for bulk sediment composition. We tested the algorithm using data from two tropical hemipelagic cores and find that the algorithm, which we refer to as BomDia (‘a good dynamic interpolation algorithm’), produces robust age-depth models with predictive power and realistic sedimentation rates that co-vary with sediment composition. Further application and testing of the algorithm to a wider variety of other than tropical hemipelagic sedimentary settings is recommended.

Accurate forecasts of solar irradiance are required for the large-scale integration of solar photovoltaic (PV) systems. Fluctuations of energy generation in the order of minutes can lead to issues on the electricity grid, therefore reliable forecasts of minute-to-minute irradiance variability are required. However, state of the art numerical weather predictions (NWP) deliver forecasts at a much coarser temporal resolution, e.g. hourly, missing crucial information on meteorological variability such as clouds. In this work we present a methodology to forecast minute-to-minute irradiance variability in terms of its probability density function (PDF) based on hourly NWP results, by applying statistical postprocessing using machine learning. The algorithm is tested using the 2.5 × 2.5 km2 HARMONIE-AROME (HA) mesoscale model as input, with 1-minute irradiance observations for 18 meteorological stations throughout the Netherlands used as a ground truth. The applicability of the algorithm to 31 × 31 km2 global-scale models is investigated using ERA5 reanalysis data, which yields comparable accuracies. We find that almost half of the inaccuracy of the postprocessed result is due to errors in the radiation forecast of the NWP model used as input. Finally, the proposed post-processing algorithm is compared to the next generation weather models based on high resolution Large Eddy Simulation (LES), at 75 m horizontal grid spacing, on a case study spanning four days. While LES underestimates values of high irradiance due to lack of 3D radiative effects, it enables detailed analysis of cloud and irradiance dynamics at high spatial and temporal resolution unreachable by statistical postprocessing.

Quote: “What I hear, I forget. What I see, I remember. What I do, I understand.” (Xunzi, ∼300 BCE). Modelling complex interactions involving climatic features, socio-economic vulnerability or responses, and long impact transmissions is associated with substantial uncertainty. Physical climate storylines are proposed as an approach to explore complex impact transmission pathways and possible alternative unfoldings of event cascades under future climate conditions. These storylines are particularly useful for climate risk assessment for complex domains, including event cascades crossing multiple disciplinary or geographical borders. For an effective role in climate risks assessments, development guidelines are needed to consistently develop and interpret the storyline event analyses. This paper elaborates on the suitability of physical climate storyline approaches involving climate event induced shocks propagating into societal impacts. It proposes a set of common elements to construct the event storylines. In addition, criteria for their application for climate risk assessment are given, referring to the need for storylines to be physically plausible, relevant for the specific context, and risk-informative. Apart from an illustrative gallery of storyline examples found in literature, three examples of varying scope and complexity are presented in detail, all involving the potential impact on European socio-economic sectors induced by remote climate change features occurring far outside the geographical domain of the European mainland. The storyline examples illustrate the application of the proposed storyline components and evaluate the suitability of the criteria defined in this paper. It thereby contributes to a rigorous design and application of event-based climate storyline approaches.

Rainfall-induced hazards, such as landslides, debris flows, and floods, cause significant damage to railway infrastructure. However, an accurate assessment of rainfall-induced hazard risk to railway infrastructure is limited by the lack of regional and asset-tailored vulnerability curves. This study aims to use multisource empirical damage data to generate vulnerability curves and assess the risk of rainfall-induced hazards to railway infrastructure. The methodology is exemplified through a case study of the Chinese national railway infrastructure. Regional- and national-level vulnerability curves are derived based on historical railway damage records. These curves are combined with the daily precipitation data and the railway infrastructure market value to estimate regional- and national-level risk. The results show large variations in the shape of the vulnerability curves across the different regions. The railway infrastructure in Northeast and Northwest China is more vulnerable to rainfall-induced hazards due to low protection standards. The expected annual damage (EAD) ranges from 1.88 to 5.98 billion RMB for the Chinese railway infrastructure, with a mean value of 3.91 billion RMB. However, the risk to railway infrastructure in China shows high spatial differences due to the spatially variations of precipitation characteristics, exposure distribution, and vulnerability curves. The South, East, and Central provinces have a high risk of rainfall-induced hazards, resulting in the average EADs of 184 million RMB, 176 million RMB, and 156 million RMB, respectively, whereas the risks in the Northeast and Northwest provinces are estimated to be relatively lower. The usage of multisource empirical data enables risk assessments that include spatial details for each region. These risk assessments are highly necessary for effective decision making to achieve infrastructure resilience.

Societal awareness is a crucial factor driving floodplain dynamics. When modelling these dynamics, flood awareness decay is considered constant. However, empirical studies have shown that the intensity of an experienced event can influence awareness decay. Here we explore and model the influence of variable flood awareness decay on flood losses for two types of societies that cope with flooding by adopting structural (techno society) or nonstructural (green society) protection measures. We modified an established socio-hydrological model and performed three synthetic experiments with multiple scenarios of flood awareness decay, flood intensity, and frequency. We found that, when modelling techno societies, assuming a constant awareness decay leads to underestimating societal flood awareness after severe flood events. In contrast, overestimation of flood awareness occurs when using constant awareness decay for green societies. This might lead to overestimating the effects of human–flood dynamics, such as the levee effect and adaptation effects. QC 20230420

This study presents an event-based storyline framework to assess the influence of future climatic and socioeconomic conditions on coastal flood impacts to critical infrastructure. The framework combines well-established quantitative methods of sea level rise, coastal inundation, and critical infrastructure (CI) physical damage assessments into an integrated modelling approach. We apply our approach to re-imagine three historic events: storm Xaver, storm Xynthia, and a storm surge event along the coast of Emilia Romagna (Italy). Our results indicate that northern Germany would benefit mostly from coordinated adaptation action to reduce the flood impact, whereas the southwestern coast of France would find the highest damage reduction through asset-level ‘autonomous’ adaptation action. Our approach helps to improve the scientific understanding of how coastal flood risk are assessed and best managed, and forces a distillation of the science into an accessible narrative to support policymakers and asset owners to make progress towards more climate-resilient coastal communities.

Tropical cyclones (TCs) cause devastating damage to life and property. Historical TC data is scarce, complicating adequate TC risk assessments. Synthetic TC models are specifically designed to overcome this scarcity. While these models have been evaluated on their ability to simulate TC activity, no study to date has focused on model performance and applicability in TC risk assessments. This study performs the intercomparison of four different global-scale synthetic TC datasets in the impact space, comparing impact return period curves, probability of rare events, and hazard intensity distribution over land. We find that the model choice influences the costliest events, particularly in basins with limited TC activity. Modelled direct economic damages in the North Indian Ocean, for instance, range from 40 to 246 billion USD for the 100-yr event over the four hazard sets. We furthermore provide guidelines for the suitability of the different synthetic models for various research purposes., Nature Communications, 13 (1), ISSN:2041-1723

In the context of changing climate and increasing water demand, large-scale hydrological models are helpful for understanding and projecting future water resources across scales. Groundwater is a critical freshwater resource and strongly controls river flow throughout the year. It is also essential for ecosystems and contributes to evapotranspiration, resulting in climate feedback. However, groundwater systems worldwide are quite diverse, including thick multilayer aquifers and thin heterogeneous aquifers. Recently, efforts have been made to improve the representation of groundwater systems in large-scale hydrological models. The evaluation of the accuracy of these model outputs is challenging because (1) they are applied at much coarser resolutions than hillslope scale, (2) they simplify geological structures generally known at local scale, and (3) they do not adequately include local water management practices (mainly groundwater pumping). Here, we apply a large-scale hydrological model (CWatM), coupled with the groundwater flow model MODFLOW, in two different climatic, geological, and socioeconomic regions: the Seewinkel area (Austria) and the Bhima basin (India). The coupled model enables simulation of the impact of the water table on groundwater–soil and groundwater–river exchanges, groundwater recharge through leaking canals, and groundwater pumping. This regional-scale analysis enables assessment of the model's ability to simulate water tables at fine spatial resolutions (1 km for CWatM, 100–250 m for MODFLOW) and when groundwater pumping is well estimated. Evaluating large-scale models remains challenging, but the results show that the reproduction of (1) average water table fluctuations and (2) water table depths without bias can be a benchmark objective of such models. We found that grid resolution is the main factor that affects water table depth bias because it smooths river incision, while pumping affects time fluctuations. Finally, we use the model to assess the impact of groundwater-based irrigation pumping on evapotranspiration, groundwater recharge, and water table observations from boreholes.

Ports are embedded in different networks, including the local critical infrastructure network, the regional hinterland transport network and the global maritime transport network. These networks are exposed to a variety of natural hazards, which cause disruptions that can propagate to other network components, resulting in wider supply chain losses. However, the risks of such indirect network disruptions, or systemic risks, are often not considered in risk analyses of ports. We propose a systemic risk framework for different networks interconnected through ports, and describe the state-of-the-art risk modelling approaches to quantify systemic risks. In addition, we present a port risk layering framework that can help identify how resilience against systemic risks can be improved. As climate change will likely increase the occurrence of natural hazards to ports and transport networks, efforts to enhance system-wide resilience should be considered, alongside port adaptation, to prevent exacerbation of supply chain losses in the future.

The formalization of community forest tenure rights is expected to promote sustainable community forest management, and is seen as a way to combine objectives related to environmental conservation, livelihood improvement, and local self-determination. However, the formalization of forest tenure rights by itself, does not automatically result in the intended impacts. There is a need to better understand the conditions under which communities are able to use these rights to achieve positive outcomes across multiple dimensions. We formulated a simple theory of change that underlies the formalization of community forest tenure rights, and used it to assess the outcomes, bottlenecks and enabling conditions of community forest tenure models across the forested tropics. Based on this analysis, we identified ten conditions that need to be in place for community forest tenure rights formalization to achieve the intended impacts. The theory of change and associated conditions form a generic conceptual framework that can be used to inform policy and practice of actors supporting community forest tenure rights, including civil society organizations and government agencies.

Salinization poses a major challenge for modern agriculture with considerable areas being salt-affected worldwide. However, these lands can be cultivated by applying saline agriculture, involving soil, water and salt-tolerant crop management methods. The agricultural use of saline soils helps in addressing food security in times of population growth and climate change. Therefore, there is a need to map saline soils and examine conditions under which saline agriculture can be implemented. The aim of this study is to identify locations and surface area of saline soils. The potential areas for saline agricultural production are mapped using the QGIS software with a focus on the most promising lands for saline agriculture. To identify these most promising areas, we apply criteria of soil salinity, soil fertility, soil pH, water availability, presence of irrigation equipment, as well as depleted water basins. Our results show that the total area of salt-affected soils equals 17 million km2, but the largest potential for saline agriculture lays in saline soils above 4 dS/m ECe in non-depleted water basins totalling to 2 million km2. We conclude that further socio-economic analysis is needed to fully determine countries which should be prioritized in exploring the future potential for sustainable food production.

The mechanisms leading to the occurrence of extreme weather and climate events are varied and complex. They generally encompass a combination of dynamic and thermodynamic processes, as well as drivers external to the climate system, such as anthropogenic greenhouse gas emissions and land use change. Here we present the ExtremeX multi-model intercomparison experiment, which was designed to investigate the contribution of dynamic and thermodynamic processes to recent weather and climate extremes. The numerical experiments are performed with three Earth system models: CESM, MIROC, and EC-Earth. They include control experiments with interactive atmosphere and land surface conditions, as well as experiments wherein the atmospheric circulation, soil moisture, or both are constrained using observation-based data. The temporal evolution and magnitude of temperature anomalies during heatwaves are well represented in the experiments with a constrained atmosphere. However, the magnitude of mean climatological biases in temperature and precipitation are not greatly reduced in any of the constrained experiments due to persistent or newly introduced biases. This highlights the importance of error compensations and tuning in the standard model versions. To show one possible application, ExtremeX is used to identify the main drivers of heatwaves and warm spells. The results reveal that both atmospheric circulation patterns and soil moisture conditions substantially contribute to the occurrence of these events. Soil moisture effects are particularly important in the tropics, the monsoon areas, and the Great Plains of the United States, whereas atmospheric circulation effects are major drivers in other midlatitude and high-latitude regions., Earth System Dynamics, 13 (3), ISSN:2190-4987, ISSN:2190-4979

Extreme rainfall associated with Jakarta floods has been observed to increase significantly in the 130-year historical data. Rainfall in the category heavy, very heavy, and extreme shows a trend to increase significantly in frequency and intensity, while the number of days of rain events continues to decline. The increase is more obvious in the rainy season and in the short duration (< 3 hours duration) rainfall classes. This increase is also more pronounced in the rainfall event that occurs at night and in the morning, which is different from past climatic conditions where intense rainfall occurred in the afternoon. Meanwhile during that time, the surface air temperature in Jakarta also increased dramatically by 1.4 times faster than the increase in global temperature. The dew point was observed to rise slightly, but the humidity actually decreased significantly. This trend is related to environmental changes due to the development of urban areas and the continued increase in the urban population. Compared to rural areas, there is a strong indication of the urban heat island phenomenon in Jakarta. This thesis finds that changes in the character of rainfall events and extreme rainfall in Jakarta are closely related to the increase in surface air temperature. The trend of increasing urban air temperature has increased the 2-3% chance of extreme rainfall events that trigger floods such as the Jakarta flood in 2020 and 2015 in the recent climate compared to the climatic conditions of 100 years ago. The continued increase in surface air temperature is associated with an increase in extreme rainfall intensity by about 14% in the Clausius-Clapeyron relationship. This indicated that a 1℃ increase in surface air temperature will increase about 14% of the water vapor holding capacity in the atmosphere. Increasing air temperature and surface drying in urban Jakarta, combined with increased atmospheric moisture content, promote intensified atmospheric convection, which contributes to increased intense rainfall. The observed historical trends in the characteristics of urban extreme rainfall and future climate projections from urban climate modeling indicate that the continued increase in temperature, population, and urban development will continue to cause these extreme problems to become more serious and riskier in the future.

Climate change-induced sea-level rise will lead to an increase in internal migration, whose intensity and spatial patterns will depend on the amount of sea-level rise; future socioeconomic development; and adaptation strategies pursued to reduce exposure and vulnerability to sea-level rise. To explore spatial feedbacks between these drivers, we combine sea-level rise projections, socioeconomic projections, and assumptions on adaptation policies in a spatially-explicit model (‘CONCLUDE’). Using the Mediterranean region as a case study, we find up to 20 million sea-level rise-related internal migrants by 2100 if no adaptation policies are implemented, with approximately three times higher migration in southern and eastern Mediterranean countries compared to northern Mediterranean countries. We show that adaptation policies can reduce the number of internal migrants by a factor of 1.4 to 9, depending on the type of strategies pursued; the implementation of hard protection measures may even lead to migration towards protected coastlines. Overall, spatial migration patterns are robust across all scenarios, with out-migration from a narrow coastal strip and in-migration widely spread across urban settings. However, the type of migration (e.g. proactive/reactive, managed/autonomous) depends on future socioeconomic developments that drive adaptive capacity, calling for decision-making that goes well beyond coastal issues.

We quantify the criticality of the world’s 1300 most important ports for global supply chains by predicting the allocation of trade flows on the global maritime transport network, which we link to a global supply-chain database to evaluate the importance of ports for the economy. We find that 50% of global trade in value terms is maritime, with low-income countries and small islands being 1.5 and 2.0 times more reliant on their ports compared to the global average. The five largest ports globally handle goods that embody >1.4% of global output, while 40 ports add >10% of domestic output of the economies they serve, predominantly small islands. We identify critical cross-border infrastructure dependencies for some landlocked and island countries that rely on specific ports outside their jurisdiction. Our results pave the way for developing new strategies to enhance the resilience and sustainability of port infrastructure and maritime trade.

Deriving reliable estimates of design water levels and wave conditions resulting from tropical cyclones is a challenging problem of high relevance for, among other things, coastal and offshore engineering projects and risk assessment studies. Tropical cyclone geometry and wind speeds have been recorded for the past few decades only, thus resulting in poorly reliable estimates of the extremes, especially in regions characterized by a low number of past tropical cyclone events. In this paper, this challenge is overcome by using synthetic tropical cyclone tracks and wind fields generated by the open-source tool TCWiSE (Tropical Cyclone Wind Statistical Estimation Tool) to create thousands of realizations representative of 1000 years of tropical cyclone activity for the Bay of Bengal. Each of these realizations is used to force coupled storm surge and wave simulations by means of the processed-based Delft3D Flexible Mesh Suite. It is shown that the use of synthetic tracks provides reliable estimates of the statistics of the first-order hazard (i.e., wind speed) compared to the statistics derived for historical tropical cyclones. Based on estimated wind fields, second-order hazards (i.e., storm surge and waves) are computed that are generated by the first-order hazard of wind. The estimates of the extreme values derived for wind speed, wave height and storm surge are shown to converge within the 1000 years of simulated cyclone tracks. Comparing second-order hazard estimates based on historical and synthetic tracks shows that, for this case study, the use of historical tracks (a deterministic approach) leads to an underestimation of the mean computed storm surge of up to −30 %. Differences between the use of synthetic versus historical tracks are characterized by a large spatial variability along the Bay of Bengal, where regions with a lower probability of occurrence of tropical cyclones show the largest difference in predicted storm surge and wave heights. In addition, the use of historical tracks leads to much larger uncertainty bands in the estimation of both storm surges and wave heights, with confidence intervals being +80 % larger compared to those estimated by using synthetic tracks (probabilistic approach). Based on the same tropical cyclone realizations, the effect that changes in tropical cyclone frequency and intensity, possibly resulting from climate change, may have on modeled storm surge and wave heights was computed. As a proof of concept, an increase in tropical cyclone frequency of +25.6 % and wind intensity of +1.6 %, based on literature values and without accounting for uncertainties in future climate projection, was estimated to possibly result in an increase in storm surge and wave heights of +11 % and +9 %, respectively. This suggests that climate change could increase tropical-cyclone-induced coastal hazards more than just the actual increase in maximum wind speeds.

Long-duration, sub-seasonal dry spells in combination with high temperature extremes during summer have led to extreme impacts on society and ecosystems in the past. Such events are expected to become more frequent due to increasing temperatures as a result of anthropogenic climate change. However, there is little information on how long-duration dry and hot spells are represented in global climate models (GCMs). In this study, we evaluate 33 CMIP5 (coupled model intercomparison project 5) GCMs in their representation of long-duration dry spells and temperatures during dry spells. We define a dry spell as a consecutive number of days with a daily precipitation of less than 1 mm. CMIP5 models tend to underestimate the persistence of dry spells in northern Europe, while a large variability exists between model estimates in central and southern Europe, where models have contrasting biases. Throughout Europe, we also find a large spread between models in their representation of temperature extremes during dry spells. In central and southern Europe this spread in temperature extremes between models is related to the representation of dry spells, where models that produce longer dry spells also produce higher temperatures, and vice versa. Our results indicate that this variability in model estimates is due to model differences and not internal variability. At latitudes between 50–60∘ N, the differences in the representation of persistent dry spells are strongly related to the representation of persistent anticyclonic systems, such as atmospheric blocking and subtropical ridges. Furthermore, models simulating a higher frequency of anticyclonic systems than ERA5 also simulate temperatures in dry spells that are between 1.4, and 2.8 K warmer than models with a lower frequency in these areas. Overall, there is a large spread between CMIP5 models in their representation of long-duration dry and hot events that is due to errors in the representation of large-scale anticyclonic systems in certain parts of Europe. This information is important to consider when interpreting the plausibility of future projections from climate models and highlights the potential value that improvements in the representation of anticyclonic systems may have for the simulation of impactful hazards.

Flooding remains the most frequent natural hazard, affecting more people than any other weather-related disasters. The increase in frequency, duration and severity of floods in many parts of the world is projected to rise due to changes in climate, population and land use. Compound flooding, which occurs when multiple flood drivers interact to cause or exacerbate flooding, is a significant challenge in estuarine environments. However, the influence of flood drivers on compound flooding in estuaries remains poorly understood. To address this issue, the goal of this thesis is to improve flood risk assessments in estuarine environments by advancing our understanding and modelling of compound flood hazard. The thesis first identifies potential hotspots of compound flooding at the global scale by analyzing discharge and storm surge time series from state-of-the-art global models. The study is performed at 3,434 river mouths, encompassing all catchments along the coast with an upstream area larger than 1,000 km2. About 6% of the locations are classified as having the highest compound flooding potential. The results show that the dependence between river discharge and storm surge is significant in many areas, including the coasts of Madagascar, northern Morocco, northern Australia, Vietnam, and Taiwan. Furthermore, the study highlights the complex temporal dynamics between discharge and storm surge that cannot be captured by one indicator alone. Next, the thesis develops different probabilistic assessment methods to account for the dependence of flood drivers in flood risk assessment through two case studies. The first case study assesses the impact of the spatial dependence between river discharges and storm surge on the flood hazard quantification for a small catchment in Galveston Bay, Texas. The second case study expands the probabilistic assessment method into a computationally efficient probabilistic framework for calculating flood risk using Ho Chi Minh City as a case study. The framework allows for multiple dependence structures and is applied at a monthly time scale to account for seasonality. Lastly, the thesis moves beyond traditional expected annual damage (EAD) as the indicator of flood risk and discusses the many implications of spatial and temporal dynamics on disaster risk management. The development of plausible storylines and their impact on risk management decisions are explored within a serious game called Breaking the Silos. Breaking the Silos is a collaborative role-playing game designed to explore the impacts of these complexities on risk management decisions. Overall, the thesis provides important insights into the challenges of compound flooding in estuarine environments and proposes new methods to account for these challenges in flood risk assessments. The study highlights the need to include compound flooding in flood hazard assessments in locations where fluvial, pluvial, and coastal interactions are important. The thesis also emphasizes the need for disaster management measures to consider the spatial and temporal dynamics between hazards and their impact on residual risk.

Coastal river deltas are susceptible to flooding from pluvial, fluvial, and coastal flood drivers. Compound floods, which result from the co-occurrence of two or more of these drivers, typically exacerbate impacts compared to floods from a single driver. While several global flood models have been developed, these do not account for compound flooding. Local-scale compound flood models provide state-of-the-art analyses but are hard to scale to other regions as these typically are based on local datasets. Hence, there is a need for globally applicable compound flood hazard modeling. We develop, validate, and apply a framework for compound flood hazard modeling that accounts for interactions between all drivers. It consists of the high-resolution 2D hydrodynamic Super-Fast INundation of CoastS (SFINCS) model, which is automatically set up from global datasets and coupled with a global hydrodynamic river routing model and a global surge and tide model. To test the framework, we simulate two historical compound flood events, Tropical Cyclone Idai and Tropical Cyclone Eloise in the Sofala province of Mozambique, and compare the simulated flood extents to satellite-derived extents on multiple days for both events. Compared to the global CaMa-Flood model, the globally applicable model generally performs better in terms of the critical success index (−0.01–0.09) and hit rate (0.11–0.22) but worse in terms of the false-alarm ratio (0.04–0.14). Furthermore, the simulated flood depth maps are more realistic due to better floodplain connectivity and provide a more comprehensive picture as direct coastal flooding and pluvial flooding are simulated. Using the new framework, we determine the dominant flood drivers and transition zones between flood drivers. These vary significantly between both events because of differences in the magnitude of and time lag between the flood drivers. We argue that a wide range of plausible events should be investigated to obtain a robust understanding of compound flood interactions, which is important to understand for flood adaptation, preparedness, and response. As the model setup and coupling is automated, reproducible, and globally applicable, the presented framework is a promising step forward towards large-scale compound flood hazard modeling.

How might futures of land and water governance systems emerge from foresight processes? This paper explores how a multi-stakeholder group used foresight processes to reimagine landscape governance in the Philippines. Landscape governance as a natural resource management framework is not at all new, but it is gaining attention and momentum especially in developing countries. In 2018, alliances of Civil Society Organizations (CSOs) in the Philippines embarked on a landscape governance effort to influence policy and decision-making processes both at a landscape and national level. However, the alliance acknowledged that to better influence policy processes, the alliance has to tap and harness collective intelligence, and maximize their collective foresight. This case study focuses on exploring foresight methods, questioning underlying assumptions, reimagining future scenarios, and rethinking pathways to the futures of Philippine land use and water governance as two critical aspects of landscape governance. The emerging scenarios for land use posit the alliances’ aspirations for community-powered agricultural transformations for the next 50 years, and transformations that can promote human-conscious artificial intelligence, and biosphere-led ecosystems. On the other hand, the emerging scenarios for water governance detailed an eco-efficient, indigenous-centric, multi-purpose and cooperative-led water infrastructure, distribution and access systems. The foresight process revealed that diverse worldviews and narratives challenge and reframe default ways of knowing and embodying land use and water governance. Ultimately, the foresight process challenges how current landscape governance planning, and visioning provide power for the alliance to influence policy and decision-making processes in longer-time horizons.

Elevated flood risk due to sea level rise is expected to increase migration from coastal areas. This presents an enormous policy challenge given the hundreds of millions of people living in low-lying coastal areas globally. Despite its relevance, little empirical research has been done on what drives coastal residents to migrate or stay under increased flood risk. This study aims to improve the knowledge base on this topic by collecting and analyzing unique survey data of flood-prone coastal residents in Central Vietnam. To explain permanent migration intentions under increased flood risk, we present respondents with realistic scenarios of more frequent severe flooding and utilize a theoretical framework that incorporates flood risk indicators as well as key indicators of sustainable livelihoods. Results indicate that flood risk could play a major role in future migration behavior; permanent migration intentions strongly increase under the scenarios of increased flood risk. Several individual characteristics also play an important role in the migration decision. Regression analyses reveal that respondents who implemented in situ flood adaptation, and thereby reduced their flood risk, are less inclined to migrate. Past experiences during flooding such as evacuation or the reception of help from community members or the government are also strong predictors of migration intentions. Of the sustainable livelihood indicators, social capital plays the most important role, where a larger social network inside (outside) the place of residence is negatively (positively) related to migration intentions. We draw lessons from these insights for policymakers aiming to anticipate the challenge of sea level rise-induced migration.

In this manuscript, we present B-AMA (Basic dAta-driven Models for All), an easy, flexible, fully coded Python-written protocol for the application of data-driven models (DDM) in hydrology. The protocol, which is open source and freely available for academic and non-commercial purposes, has been realized to allow early career scientists, with a basic background in programming, to develop DDM ensuring that no stones are left unturned through their implementation. B-AMA embeds data splitting, feature selection, hyperparameter optimization, and performance metrics. A Jupyter notebook with a practical workflow is available to guide the users through the protocol employment, while visualization tools allow efficient investigation and communication of results. We tested B-AMA across four hydrological applications to explore DDM applicability across temporal resolutions, time series lengths, and autocorrelations. B-AMA showed great accuracy and reasonable computational time, making the protocol ideal for educational purposes and for the development of DDM-based forecasts of hydrological time-series.

Rapid impact assessments immediately after disasters are crucial to enable rapid and effective mobilization of resources for response and recovery efforts. These assessments are often performed by analysing the three components of risk: hazard, exposure and vulnerability. Vulnerability curves are often constructed using historic insurance data or expert judgments, reducing their applicability for the characteristics of the specific hazard and building stock. Therefore, this paper outlines an approach to the creation of event-specific vulnerability curves, using Bayesian statistics (i.e., the zero-one inflated beta distribution) to update a pre-existing vulnerability curve (i.e., the prior) with observed impact data derived from social media. The approach is applied in a case study of Hurricane Dorian, which hit the Bahamas in September 2019. We analysed footage shot predominantly from unmanned aerial vehicles (UAVs) and other airborne vehicles posted on YouTube in the first 10 days after the disaster. Due to its Bayesian nature, the approach can be used regardless of the amount of data available as it balances the contribution of the prior and the observations.

Sub-daily extreme precipitation in Jakarta exhibits trends that are related to local temperature, seasonal tropical monsoon circulations, and other environmental drivers. An analysis of 81 years of hourly rainfall between 1900 and 2010 shows a significant increase of about doubling the number of short-duration rainfall events in the wet season. In recent decades, rainfall is found to be higher in intensity and shorter in duration relative to preceding decades. These short-duration rain showers develop typically between afternoon and late night or during early morning hours. Changing short-duration rainfall characteristics throughout the last century are partly attributed to changes in the surface environment of urban Jakarta. A recent temperature increase and land surface drying in the city, in combination with a small increase in the atmospheric moisture content, promote intensified atmospheric convection. A combination of rain gauge data with upper-air observations collected during 2002 – 2016 reveals that surface warming in the urbanized city accompanied by enhanced availability of moisture results in an increase in convective available potential energy, which contributes to enhanced intense precipitation. Super Clausius–Clapeyron scaling (CC) of high-intensity rainfall is attributed to high near-surface temperature and atmospheric moisture content in the morning. This super-CC scaling is present in a relatively small range of surface temperature values. Results of this study are in agreement with earlier findings exploring the intensification of extreme morning precipitation and a temporal shift of the diurnal convective maximum from late afternoon to late night/early morning in response to local warming. For a delta city such as Jakarta with abundant convection and heavy precipitation, a well-maintained rainfall database is crucial to assist urban flood early warning.

State-of-the-art flood hazard maps in coastal cities are often obtained from simulating coastal or pluvial events separately. This method does not account for the seasonality of flood drivers and their mutual dependence. In this article, we include the impact of these two factors in a computationally efficient probabilistic framework for flood risk calculation, using Ho Chi Minh City (HCMC) as a case study. HCMC can be flooded subannually by high tide, rainfall, and storm surge events or a combination thereof during the monsoon or tropical cyclones. Using long gauge observations, we stochastically model 10,000 years of rainfall and sea level events based on their monthly distributions, dependence structure and cooccurrence rate. The impact from each stochastic event is then obtained from a damage function built from selected rainfall and sea level combinations, leading to an expected annual damage (EAD) of $1.02 B (95th annual damage percentile of $2.15 B). We find no dependence for most months and large differences in expected damage across months ($36–166 M) driven by the seasonality of rainfall and sea levels. Excluding monthly variability leads to a serious underestimation of the EAD by 72–83%. This is because high-probability flood events, which can happen multiple times during the year and are properly captured by our framework, contribute the most to the EAD. This application illustrates the potential of our framework and advocates for the inclusion of flood drivers' dynamics in coastal risk assessments.

Whilst the last decades have seen a clear shift in emphasis from managing natural hazards to managing risk, the majority of natural-hazard risk research still focuses on single hazards. Internationally, there are calls for more attention for multi-hazards and multi-risks. Within the European Union (EU), the concepts of multi-hazard and multi-risk assessment and management have taken centre stage in recent years. In this perspective paper, we outline several key developments in multi-(hazard-)risk research in the last decade, with a particular focus on the EU. We present challenges for multi-(hazard-)risk management as outlined in several research projects and papers. We then present a research agenda for addressing these challenges. We argue for an approach that addresses multi-(hazard-)risk management through the lens of sustainability challenges that cut across sectors, regions, and hazards. In this approach, the starting point is a specific sustainability challenge, rather than an individual hazard or sector, and trade-offs and synergies are examined across sectors, regions, and hazards. We argue for in-depth case studies in which various approaches for multi-(hazard-)risk management are co-developed and tested in practice. Finally, we present a new pan-European research project in which our proposed research agenda will be implemented, with the goal of enabling stakeholders to develop forward-looking disaster risk management pathways that assess trade-offs and synergies of various strategies across sectors, hazards, and spatial scales.

Funding Information: This open-access publication was funded by the University of Freiburg. Funding Information: Financial support. The project is supported by the Wassernetzwerk Baden-Württemberg (Water Research Network of the State Baden-Württemberg), which is funded by the Ministerium für Wissenschaft, Forschung und Kunst Baden-Württemberg (Ministry of Science, Research and the Arts of the State Baden-Württemberg) (grant no. AZ. 7532.21/2.1.6) and Maa-ja vesitekniikan tuki ry foundation. Doris E. Wendt acknowledges her support as part of the NERC-funded Groundwater Drought Initiative (NE/R004994/1). Lucy J. Barker was supported by the Natural Environment Research Council (NE/R016429/1) as part of the UK-SCAPE programme delivering National Capability. The contributions of Mirek Trnka, Lenka Bartošová, and Jaak Jaagus have been supported by SustES – Adaptation strategies for sustainable ecosystem services and food security under adverse environmental conditions (CZ.02.1.01/0.0/0.0/16_019/0000797). Publisher Copyright: © 2022 Veit Blauhut et al. Drought events and their impacts vary spatially and temporally due to diverse pedo-climatic and hydrologic conditions, as well as variations in exposure and vulnerability, such as demographics and response actions. While hazard severity and frequency of past drought events have been studied in detail, little is known about the effect of drought management strategies on the actual impacts and how the hazard is perceived by relevant stakeholders. In a continental study, we characterised and assessed the impacts and the perceptions of two recent drought events (2018 and 2019) in Europe and examined the relationship between management strategies and drought perception, hazard, and impact. The study was based on a pan-European survey involving national representatives from 28 countries and relevant stakeholders responding to a standard questionnaire. The survey focused on collecting information on stakeholders' perceptions of drought, impacts on water resources and beyond, water availability, and current drought management strategies on national and regional scales. The survey results were compared with the actual drought hazard information registered by the European Drought Observatory (EDO) for 2018 and 2019. The results highlighted high diversity in drought perception across different countries and in values of the implemented drought management strategies to alleviate impacts by increasing national and sub-national awareness and resilience. The study identifies an urgent need to further reduce drought impacts by constructing and implementing a European macro-level drought governance approach, such as a directive, which would strengthen national drought management and mitigate damage to human and natural assets.

Conventional green roofs have often been criticised for their limited water buffer capacity during extreme rainfall events and for their susceptibility to droughts when additional irrigation is unavailable. One solution to these challenges is to create an extra blue water retention layer underneath the green layer. Blue-green roofs allow more stormwater to be stored, and the reservoir can act as a water source for the green layer throughout capillary rises. An automated valve regulates the water level of the system. It can be opened to drain water when extreme precipitation is expected. Therefore, the water buffer capacity of the system during extreme rainfall events can be maximised by integrating precipitation forecasts as triggers for the operation of the valve. However, the added value of this forecast-based operation is yet unknown. Accordingly, in this study, we design and evaluate a hydrological blue-green roof model that utilises precipitation forecasts. We test its performance to capture (extreme) precipitation and to increase evapotranspiration and evaporative cooling under a variety of precipitation forecast-based decision rules. We show that blue-green roofs can capture between 70 % and 97 % of extreme precipitation (>20 mm/h) when set to anticipate ensemble precipitation forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF). This capture ratio is considerably higher than that of a conventional green roof without extra water retention (12 %) or that of a blue-green roof that does not use forecast information (i.e., valve always closed; 59 %). Moreover, blue-green roofs allow for high evapotranspiration rates relative to potential evapotranspiration on hot summer days (around 70 %), which is higher than from conventional green roofs (30 %). This serves to underscore the higher capacity of blue-green roofs to reduce heat stress. Using the city of Amsterdam as a case study, we show the high upscaling potential of the concept: on average, potentially suitable flat roofs cover 13.3 % of the total area of the catchments that are susceptible to pluvial flood risk. If the 90th percentile of the ECMWF forecast is used, an 84 % rainfall capture ratio can translate into capturing 11 % of rainfall in flood-prone urban catchments in Amsterdam.

Germany, Belgium and the Netherlands were hit by extreme precipitation and flooding in July 2021. This brief communication provides an overview of the impacts to large-scale critical infrastructure systems and how recovery has progressed. The results show that Germany and Belgium were particularly affected, with many infrastructure assets severely damaged or completely destroyed. Impacts range from completely destroyed bridges and sewage systems, to severely damaged schools and hospitals. We find that (large-scale) risk assessments, often focused on larger (river) flood events, do not find these local, but severe, impacts due to critical infrastructure failures. This may be the result of limited availability of validation material. As such, this brief communication not only will help to better understand how critical infrastructure can be affected by flooding, but also can be used as validation material for future flood risk assessments.

Sociohydrological models are increasingly used in flood risk analysis to reveal and understand the temporal dynamics in coupled human-flood systems. While most sociohydrological flood risk models are stylized and describe hypothetical human-flood systems, very few recent case studies employ empirical data to investigate real world systems. The mathematical representation of flooding processes in these models is often simplistic and does not reflect the current state of knowledge. This is due to the intricacy of human-flood interactions and the lack of sufficient and suitable historical data.We augment an existing, parsimonious sociohydrological flood risk model with a process-oriented flood loss model to integrate better understanding of flood damage processes into a sociohydrological modeling framework. Using Bayesian inference, we simulate the co-evolution of the flood risk system for companies located at the river Elbe in Dresden, Germany, over the course of 120 years. We compare model versions with differently complex process description on the basis of their loss prediction accuracy and uncertainty. This allows for exploring the added value of (i) resolving the inundation and damage process with more detail and (ii) accounting for heterogeneity across economic sectors. Apart from historical sociohydrological data, the proposed, augmented model versions are informed by object-level loss data, inundation maps, and spatial data, enhancing the pool of information available to the model. A leave-one-out cross-validation experiment shows that the augmented model versions increase the precision and reduce the uncertainty of company flood loss predictions in Dresden. In addition, the augmented models provide reliable loss predictions even in the absence of extensive historical flood loss data.The demonstrated model augmentation concept is not limited to the flood damage process but could be transferred to other processes within the human-flood system. For instance, by incorporating a dedicated model from protection motivation theory that describes how flood awareness and preparedness change after the occurrence of a damaging flood event.

Models that can integrate aspects of society such as institutions, perceptions and behaviours with aspects of the natural system such as rainfall, runoff and water quality may help us understand and manage complex human–water systems. In this paper, a socio-hydrological model is developed for the Black Volta (Mouhoun) watershed in Burkina Faso. The model captures the relationships between the awareness of water quality issues and capacity of local organizations, land use in the riparian zone, agricultural practices and suspended sediment concentration as an indicator of water quality. Scenarios are generated for the current situation and for plausible pathways to achieve improved water quality through different riparian land management strategies. Scenario comparison show how water quality improvements are generated if institutional support, resources and capacity of local level organizations are substantially increased compared to current levels.

Streamflow droughts are governed by different hydro-meteorological processes, whose relative importance may change over time, with potential impacts on drought severity. Here, we assess changes in the importance of different hydrological drought generation processes in the European Alps by applying a standardized drought type classification scheme to two time periods—one in the distant (1970–1993) and one in the recent past (1994–2017). Our findings show that changes in the relative importance of different drought generation processes are stronger in high-elevation catchments, where we detect clear changes in drought seasonality, than in low-elevation catchments. Furthermore, they suggest that changes in drought severity and generation processes are related because increasingly frequent snowmelt-deficit droughts in high-elevation catchments have larger deficits than droughts caused by decreasingly frequent cold temperatures. These changes might persist into the future, because of continuing decreases in snow cover and increases in evapotranspiration, with potential implications for water management., Geophysical Research Letters, 50 (2), ISSN:0094-8276, ISSN:1944-8007

The Last Interglacial (LIG; ca. 125 ka) is a period of interest for climate research as it is the most recent period of the Earth's history when the boreal climate was warmer than at present. Previous research, based on models and geological evidence, suggests that the LIG may have featured enhanced patterns of ocean storminess, but this remains hotly debated. Here, we apply state-of-the-art climate and hydrodynamic modeling to simulate changes in sea level extremes caused by storm surges, under LIG and pre-industrial climate forcings. Significantly higher seasonal LIG sea level extremes emerge for coastlines along northern Australia, the Indonesian archipelago, much of northern and eastern Africa, the Mediterranean Sea, the Gulf of Saint Lawrence, the Arabian Sea, the east coast of North America, and islands of the Pacific Ocean and of the Caribbean. Lower seasonal LIG sea level extremes emerge for coastlines along the North Sea, the Bay of Bengal, China, Vietnam, and parts of Central America. Most of these anomalies are associated with anomalies in seasonal sea level pressure minima and in eddy kinetic energy calculated from near-surface wind fields, and therefore seem to originate from anomalies in the meridional position and intensity of the predominant wind bands. In a qualitative comparison, LIG sea level extremes seem generally higher than those projected for future warmer climates. These results help to constrain the interpretation of coastal archives of LIG sea level indicators.

Drought is a persistent hazard that impacts the environment, people's livelihoods, access to education and food security. Adaptation choices made by people can influence the propagation of this drought hazard. However, few drought models incorporate adaptive behavior and feedbacks between adaptations and drought. In this research, we present a dynamic drought adaptation modeling framework, ADOPT-AP, which combines socio-hydrological and agent-based modeling approaches. This approach is applied to agropastoral communities in dryland regions in Kenya. We couple the spatially explicit hydrological Dryland Water Partitioning (DRYP) model with a behavioral model capable of simulating different bounded rational behavioral theories (ADOPT). The results demonstrate that agropastoralists respond differently to drought due to differences in (perceptions of) their hydrological environment. Downstream communities are impacted more heavily and implement more short-term adaptation measures than upstream communities in the same catchment. Additional drivers of drought adaptation concern socio-economic factors such as wealth and distance to wells. We show that the uptake of drought adaptation influences soil moisture (positively through irrigation) and groundwater (negatively through abstraction) and, thus, the drought propagation through the hydrological cycle.

Despite their economic importance, the risk that ports face from multiple natural hazards has not yet been monetised on a global scale. Here, we perform an asset-level risk analysis of global port infrastructure from multiple hazards, quantifying the risk to physical asset damages and logistics services (i.e. port-specific risk) and maritime trade flows at-risk (i.e. trade risk). We find that 86% majority of ports are exposed to more than three hazards. Globally, port-specific risk totals 7.5 USD bn per year, with 32% of the risk attributed to tropical cyclone impacts. In addition, 63.1 USD bn of trade is at-risk every year, with trade risk as a fraction of total trade being particularly high in Small Island Developing States. Our result underline that port resilience is determined by various critical factors, such as engineering standards, operational thresholds, recovery duration, that vary widely across ports, requiring tailored solutions to improve port resilience.