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  1. Abstract

    Dry and wet extremes (i.e., droughts and floods) are the costliest hydrologic hazards for infrastructure and socio-environmental systems. Being closely interconnected and interdependent extremes of the same hydrological cycle, they often occur in close succession with the potential to exacerbate hydrologic risks. However, traditionally this is ignored and both hazards are considered separately in hydrologic risk assessments; this can lead to an underestimation of critical infrastructure risks (e.g., dams, levees, dikes, and reservoirs). Here, we identify and characterize consecutive dry and wet extreme (CDW) events using the Standardized Precipitation Evapotranspiration Index, assess their multi-hazard hydrologic risks employing copula models, and investigate teleconnections with large-scale climate variability. We identify hotspots of CDW events in North America, Europe, and Australia where the total numbers of CDW events range from 20 to 30 from 1901 to 2015. Decreasing trends in recovery time (i.e., time between termination of dry extreme and onset of wet extreme) and increasing trends in dry and wet extreme severities reveal the intensification of CDW events over time. We quantify that the joint exceedance probabilities of dry and wet extreme severities equivalent to 50-year and 100-year univariate return periods increase by several folds (up to 20 and 54 for 50-year and 100-year return periods, respectively) when CDW events and their associated dependence are considered compared to their independent and isolated counterparts. We find teleconnections between CDW and Niño3.4; at least 80% of the CDW events are causally linked to Niño3.4 at 50% of the grid locations across the hotspot regions. This study advances the understanding of multi-hazard hydrologic risks from CDW events and the presented results can aid more robust planning and decision-making.

     
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  2. Abstract

    Coastal areas are subject to the joint risk associated with rainfall‐driven flooding and storm surge hazards. To capture this dependency and the compound nature of these hazards, bivariate modelling represents a straightforward and easy‐to‐implement approach that relies on observational records. Most existing applications focus on a single tide gauge–rain gauge/streamgauge combination, limiting the applicability of bivariate modelling to develop high‐resolution space–time design events that can be used to quantify the dynamic, that is, varying in space and time, compound flood hazard in coastal basins. Moreover, there is a need to recognize that not all extreme events always come from a single population, but can reflect a mixture of different generating mechanisms. Therefore, this paper describes an empirical approach to develop design storms with high‐resolution in space and time (i.e., ~5 km and hourly) for different joint annual exceedance probabilities. We also stratify extreme rainfall and storm surge events depending on whether they were caused by tropical cyclones (TCs) or not. We find that there are significant differences between the TC and non‐TC populations, with very different dependence structures that are missed if we treat all the events as coming from a single population. While we apply this methodology to one basin near Houston, Texas, our approach is general enough to make it applicable for any coastal basin exposed to compounding flood hazards from storm surge and rainfall‐induced flooding.

     
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  3. Abstract

    Compound flooding may result from the interaction of two or more contributing processes, which may not be extreme themselves, but in combination lead to extreme impacts. Here, we use statistical methods to assess compounding effects from storm surge and multiple riverine discharges in Sabine Lake, TX. We employ several trivariate statistical models, including vine‐copulas and a conditional extreme value model, to examine the sensitivity of results to the choice of data pre‐processing steps, statistical model setup, and outliers. We define a response function that represents water levels resulting from the interaction between discharge and surge processes inside Sabine Lake and explore how it is affected by including or ignoring dependencies between the contributing flooding drivers. Our results show that accounting for dependencies leads to water levels that are up to 30 cm higher for a 2% annual exceedance probability (AEP) event and up to 35 cm higher for a 1% AEP event, compared to assuming independence. We also find notable variations in the results across different sampling schemes, multivariate model configurations, and sensitivity to outlier removal. Under data constraints, this highlights the need for testing various statistical modelling approaches, while the choice of an optimal approach remains subjective.

     
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  4. Abstract

    The release of new and updated sea‐level rise (SLR) information, such as from the Intergovernmental Panel on Climate Change (IPCC) Assessment Reports, needs to be better anticipated in coastal risk and adaptation assessments. This requires risk and adaptation assessments to be regularly reviewed and updated as needed, reflecting the new information but retaining useful information from earlier assessments. In this paper, updated guidance on the types of SLR information available is presented, including for sea‐level extremes. An intercomparison of the evolution of the headline projected ranges across all the IPCC reports show an increase from the fourth and fifth assessments to the most recent “Special Report on the Ocean and Cryosphere in a Changing Climate” assessment. IPCC reports have begun to highlight the importance of potential high‐end sea‐level response, mainly reflecting uncertainties in the Greenland/Antarctic ice sheet components, and how this might be considered in scenarios. The methods that are developed here are practical and consider coastal risk assessment, adaptation planning, and long‐term decision‐making to be an ongoing process and ensure that despite the large uncertainties, pragmatic adaptation decisions can be made. It is concluded that new sea‐level information should not be seen as an automatic reason for abandoning existing assessments, but as an opportunity to review (i) the assessment's robustness in the light of new science and (ii) the utility of proactive adaptation and planning strategies, especially over the more uncertain longer term.

    This article is categorized under:

    Assessing Impacts of Climate Change > Scenario Development and Application

     
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  5. Abstract. Flooding is of particular concern in low-lying coastal zones that are prone to flooding impacts from multiple drivers, such as oceanographic (storm surge and wave), fluvial (excessive river discharge), and/or pluvial (surface runoff). In this study, we analyse, for the first time, the compound flooding potential along the contiguous United States (CONUS) coastline from all flooding drivers, using observations and reanalysis data sets. We assess the overall dependence from observations by using Kendall's rank correlation coefficient (τ) and tail (extremal) dependence (χ). Geographically, we find the highest dependence between different drivers at locations in the Gulf of Mexico, southeastern, and southwestern coasts. Regarding different driver combinations, the highest dependence exists between surge–waves, followed by surge–precipitation, surge–discharge, waves–precipitation, and waves–discharge. We also perform a seasonal dependence analysis (tropical vs. extra-tropical season), where we find higher dependence between drivers during the tropical season along the Gulf and parts of the East Coast and stronger dependence during the extra-tropical season on the West Coast. Finally, we compare the dependence structure of different combinations of flooding drivers, using observations and reanalysis data, and use the Kullback–Leibler (KL) divergence to assess significance in the differences of the tail dependence structure. We find, for example, that models underestimate the tail dependence between surge–discharge on the East and West coasts and overestimate tail dependence between surge–precipitation on the East Coast, while they underestimate it on the West Coast. The comprehensive analysis presented here provides new insights on where the compound flooding potential is relatively higher, which variable combinations are most likely to lead to compounding effects, duringwhich time of the year (tropical versus extra-tropical season) compoundflooding is more likely to occur, and how well reanalysis data capture thedependence structure between the different flooding drivers. 
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  6. Abstract. In coastal regions, floods can arise through a combination of multipledrivers, including direct surface run-off, river discharge, storm surge, andwaves. In this study, we analyse compound flood potential in Europe andenvirons caused by these four main flooding sources using state-of-the-artdatabases with coherent forcing (i.e. ERA5). First, we analyse thesensitivity of the compound flooding potential to several factors: (1)sampling method, (2) time window to select the concurrent event of theconditioned driver, (3) dependence metrics, and (4) wave-driven sea leveldefinition. We observe higher correlation coefficients using annual maximathan peaks over threshold. Regarding the other factors, our results showsimilar spatial distributions of the compound flooding potential. Second, thedependence between the pairs of drivers using the Kendall rank correlationcoefficient and the joint occurrence are synthesized for coherent patterns ofcompound flooding potential using a clustering technique. This quantitativemulti-driver assessment not only distinguishes where overall compound floodingpotential is the highest, but also discriminates which driver combinations aremore likely to contribute to compound flooding. We identify that hotspots ofcompound flooding potential are located along the southern coast of the NorthAtlantic Ocean and the northern coast of the Mediterranean Sea. 
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