Publications

2019

The effect of univariate bias adjustment on multivariate hazard estimates Journal article

Authors: Jakob Zscheischler and Erich M. Fischer and Stefan Lange
Journal: Earth System Dynamics
Bias adjustment is often a necessity in estimating climate impacts because impact models usually rely on unbiased climate information, a requirement that climate model outputs rarely fulfil. Most currently used statistical bias-adjustment methods adjust each climate variable separately, even though impacts usually depend on multiple potentially dependent variables. Human heat stress, for instance, depends on temperature and relative humidity, two variables that are often strongly correlated. Whether univariate bias-adjustment methods effectively improve estimates of impacts that depend on multiple drivers is largely unknown, and the lack of long-term impact data prevents a direct comparison between model outputs and observations for many climate-related impacts. Here we use two hazard indicators, heat stress and a simple fire risk indicator, as proxies for more sophisticated impact models. We show that univariate bias-adjustment methods such as univariate quantile mapping often cannot effectively reduce biases in multivariate hazard estimates. In some cases, it even increases biases. These cases typically occur (i) when hazards depend equally strongly on more than one climatic driver, (ii) when models exhibit biases in the dependence structure of drivers and (iii) when univariate biases are relatively small. Using a perfect model approach, we further quantify the uncertainty in bias-adjusted hazard indicators due to internal variability and show how imperfect bias adjustment can amplify this uncertainty. Both issues can be addressed successfully with a statistical bias adjustment that corrects the multivariate dependence structure in addition to the marginal distributions of the climate drivers. Our results suggest that currently many modeled climate impacts are associated with uncertainties related to the choice of bias adjustment. We conclude that in cases where impacts depend on multiple dependent climate variables these uncertainties can be reduced using statistical bias-adjustment approaches that correct the variables' multivariate dependence structure.

Compound climate events transform electrical power shortfall risk in the Pacific Northwest Journal article

Authors: SWD Turner and N Voisin and J Fazio and D Hua and M Jourabchi
Journal: Nature communications
Power system reliability is sensitive to climate-driven variations in both energy demand and water availability, yet the combined effect of these impacts is rarely evaluated. Here we show that combined climate change impacts on loads and hydropower generation may have a transformative effect on the nature and seasonality of power shortfall risk in the U.S. Pacific Northwest. Under climate change, potential shortfall events occur more readily, but are significantly less severe in nature. A seasonal reversal in shortfall risk occurs: winter shortfalls are eradicated due to reduced building heating demands, while summer shortfalls multiply as increased peak loads for day-time cooling coincide with impaired hydropower generation. Many of these summer shortfalls go unregistered when climate change impacts on loads and hydropower dispatch are analyzed in isolation—highlighting an important role of compound events.

Growing Spatial Scales of Synchronous River Flooding in Europe Journal article

Authors: Wouter R Berghuijs and Scott T Allen and Shaun Harrigan and James W Kirchner
Journal: Geophysical Research Letters
Abstract River flooding is a common hazard, causing billions of dollars in annual losses. Flood impacts are shaped by the spatial scale over which different rivers flood simultaneously, but this dimension of flood risk remains largely unknown. Using annual flood data from several thousand European rivers, we demonstrate that the flood synchrony scale—the distance over which multiple rivers flood near synchronously—far exceeds the size of individual drainage basins and varies regionally by more than an order of magnitude. These data also show that flood synchrony scales have grown by about 50% over the period 1960–2010. Detrended flood synchrony values are serially correlated, implying that years with spatially extensive floods tend to follow one another. These findings reveal that flood risks are correlated well beyond the individual drainage basins for which flood hazards are typically assessed and managed.

Application of hydroclimatic drought indicators in the transboundary Prut River basin Journal article

Authors: Vera Potopová and Valeriu Cazac and Boris Boincean and Josef Soukup and Miroslav Trnka
Journal: Theoretical and Applied Climatology
The transboundary Prut River basin (PRB) is one of the most drought vulnerable areas in the Republic of Moldova, Romania, and Ukraine. The main objective of this study was to identify the response of hydrological drought to climatic conditions and cropping practice in a region with insufficient water resources. The presented work takes advantage of the development of statistical tools to analyze existing data, as well as the collection of qualitative and quantitative hydroclimatic datasets for each sub-basin region. The study also provides survey results of the impacts of climate change on agricultural water management, including agricultural water requirements and water availability, and the transition of these impacts to cropping practice. The multi-dimensional attributes of hydrological drought are defined according to the standardized streamflow index (SSI) and water-level standardized anomaly index (SWI). The standardized precipitation evapotranspiration index (SPEI) was selected for the assessment of the impact of climate drought control on hydrological drought. The streamflow/water river level is determined more by the climatic water balance deficit of the previous 6 months than over longer periods. The lag times between climatic and hydrological drought are short, which can cause a hydrological drought to occur in the same season as the climatic drought that caused it. Summer streamflow droughts are most closely linked to SPEI in the same month. Summer streamflow drought in upstream areas can impact streamflow at the outlet within the same month. Winter streamflow droughts are related to longer SPEI accumulation periods resulting from snow cover. The synthesis of findings from the river basin shown that concurrent compound climate events have much more severe impact on crop failures compared to their individual occurrence. Adjustments to sowing time (15%), the introduction of more drought resistant cultivars (11%), the use of crop protection measures (9%), and shifting to new crops (8%) seem to be minor and moderate adaptation practices employed by farmers.

2018

Dependence between high sea-level and high river discharge increases flood hazard in global deltas and estuaries Journal article

Authors: Philip J Ward and Anaïs Couasnon and Dirk Eilander and Ivan D Haigh and Alistair Hendry and Sanne Muis and Ted I E Veldkamp and Hessel C Winsemius and Thomas Wahl
Journal: Environmental Research Letters
When river and coastal floods coincide, their impacts are often worse than when they occur in isolation; such floods are examples of 'compound events'. To better understand the impacts of these compound events, we require an improved understanding of the dependence between coastal and river flooding on a global scale. Therefore, in this letter, we: provide the first assessment and mapping of the dependence between observed high sea-levels and high river discharge for deltas and estuaries around the globe; and demonstrate how this dependence may influence the joint probability of floods exceeding both the design discharge and design sea-level. The research was carried out by analysing the statistical dependence between observed sea-levels (and skew surge) from the GESLA-2 dataset, and river discharge using gauged data from the Global Runoff Data Centre, for 187 combinations of stations across the globe. Dependence was assessed using Kendall's rank correlation coefficient (τ) and copula models. We find significant dependence for skew surge conditional on annual maximum discharge at 22% of the stations studied, and for discharge conditional on annual maximum skew surge at 36% of the stations studied. Allowing a time-lag between the two variables up to 5 days, we find significant dependence for skew surge conditional on annual maximum discharge at 56% of stations, and for discharge conditional on annual maximum skew surge at 54% of stations. Using copula models, we show that the joint exceedance probability of events in which both the design discharge and design sea-level are exceeded can be several magnitudes higher when the dependence is considered, compared to when independence is assumed. We discuss several implications, showing that flood risk assessments in these regions should correctly account for these joint exceedance probabilities.

A Copula-Based Bayesian Network for Modeling Compound Flood Hazard from Riverine and Coastal Interactions at the Catchment Scale: An Application to the Houston Ship Channel, Texas Journal article

Authors: Anaïs Couasnon and Antonia Sebastian and Oswaldo Morales-Nápoles
Journal: Water
Traditional flood hazard analyses often rely on univariate probability distributions; however, in many coastal catchments, flooding is the result of complex hydrodynamic interactions between multiple drivers. For example, synoptic meteorological conditions can produce considerable rainfall-runoff, while also generating wind-driven elevated sea-levels. When these drivers interact in space and time, they can exacerbate flood impacts, a phenomenon known as compound flooding. In this paper, we build a Bayesian Network based on Gaussian copulas to generate the equivalent of 500 years of daily stochastic boundary conditions for a coastal watershed in Southeast Texas. In doing so, we overcome many of the limitations of conventional univariate approaches and are able to probabilistically represent compound floods caused by riverine and coastal interactions. We model the resulting water levels using a one-dimensional (1D) steady-state hydraulic model and find that flood stages in the catchment are strongly affected by backwater effects from tributary inflows and downstream water levels. By comparing our results against a bathtub modeling approach, we show that simplifying the multivariate dependence between flood drivers can lead to an underestimation of flood impacts, highlighting that accounting for multivariate dependence is critical for the accurate representation of flood risk in coastal catchments prone to compound events

Mapping Dependence Between Extreme Rainfall and Storm Surge Journal article

Authors: Wenyan Wu and Kathleen Mcinnes and Julian O'grady and Ron Hoeke and Michael Leonard and Seth Westra
Journal: Journal of Geophysical Research: Oceans
Dependence between extreme storm surge and rainfall can have significant implications for flood risk in coastal and estuarine regions. To supplement limited observational records, we use reanalysis surge data from a hydrodynamic model as the basis for dependence mapping, providing information at a resolution of approximately 30 km along the Australian coastline. We evaluated this approach by comparing the dependence estimates from modeled surge to that calculated using historical surge records from 79 tide gauges around Australia. The results show reasonable agreement between the two sets of dependence values, with the exception of lower seasonal variation in the modeled dependence values compared to the observed data, especially at locations where there are multiple processes driving extreme storm surge. This is due to the combined impact of local bathymetry as well as the resolution of the hydrodynamic model and its meteorological inputs. Meteorological drivers were also investigated for different combinations of extreme rainfall and surge—namely rain‐only, surge‐only, and coincident extremes—finding that different synoptic patterns are responsible for each combination. The ability to supplement observational records with high‐resolution modeled surge data enables a much more precise quantification of dependence along the coastline, strengthening the physical basis for assessments of flood risk in coastal regions.

The role of atmospheric rivers in compound events consisting of heavy precipitation and high storm surges along the Dutch coast Journal article

Authors: Nina Ridder and Hylke de Vries and Sybren Drijfhout
Journal: Natural Hazards and Earth System Sciences
Atmospheric river (AR) systems play a significant role in the simultaneous occurrence of high coastal water levels and heavy precipitation in the Netherlands. Based on observed precipitation values (E-OBS) and the output of a numerical storm surge model (WAQUA/DSCMv5) forced with ERA-Interim sea level pressure and wind fields, we find that the majority of compound events (CEs) between 1979 and 2015 have been accompanied by the presence of an AR over the Netherlands. In detail, we show that CEs have a 3 to 4 times higher chance of occurrence on days with an AR over the Netherlands compared to any random day (i.e. days without knowledge on presence of an AR). In contrast, the occurrence of a CE on a day without AR is 3 times less likely than on any random day. Additionally, by isolating and assessing the prevailing sea level pressure (SLP) and sea surface temperature (SST) conditions with and without AR involvement up to 7 days before the events, we show that the presence of ARs constitutes a specific type of forcing conditions that (i) resemble the SLP anomaly patterns during the positive phase of the North Atlantic Oscillation (NAO+) with a north–south pressure dipole over the North Atlantic and (ii) cause a cooling of the North Atlantic subpolar gyre and eastern boundary upwelling zone while warming the western boundary of the North Atlantic. These conditions are clearly distinguishable from those during compound events without the influence of an AR which occur under SLP conditions resembling the East Atlantic (EA) pattern with a west–east pressure dipole over northern Europe and are accompanied by a cooling of the West Atlantic. Thus, this study shows that ARs are a useful tool for the early identification of possible harmful meteorological conditions over the Netherlands and supports an effort for the establishment of an early warning system.

Future climate risk from compound events Journal article

Authors: Jakob Zscheischler and Seth Westra and Bart J J M van den Hurk and Sonia I Seneviratne and Philip J Ward and Andy Pitman and Amir AghaKouchak and David N Bresch and Michael Leonard and Thomas Wahl and Xuebin Zhang
Journal: Nature Climate Change
Floods, wildfires, heatwaves and droughts often result from a combination of interacting physical processes across multiple spatial and temporal scales. The combination of processes (climate drivers and hazards) leading to a significant impact is referred to as a ‘compound event'. Traditional risk assessment methods typically only consider one driver and/or hazard at a time, potentially leading to underestimation of risk, as the processes that cause extreme events often interact and are spatially and/or temporally dependent. Here we show how a better understanding of compound events may improve projections of potential high-impact events, and can provide a bridge between climate scientists, engineers, social scientists, impact modellers and decision-makers, who need to work closely together to understand these complex events.

Contrasting biosphere responses to hydrometeorological extremes: revisiting the 2010 western Russian heatwave Journal article

Authors: M Flach and S Sippel and F Gans and A Bastos and A Brenning and M Reichstein and M D Mahecha
Journal: Biogeosciences
Combined droughts and heatwaves are among those compound extreme events that induce severe impacts on the terrestrial biosphere and human health. A record breaking hot and dry compound event hit western Russia in summer 2010 (Russian heatwave, RHW). Events of this kind are relevant from a hydrometeorological perspective, but are also interesting from a biospheric point of view because of their impacts on ecosystems, e.g., reductions in the terrestrial carbon storage. Integrating both perspectives might facilitate our knowledge about the RHW. We revisit the RHW from both a biospheric and a hydrometeorological perspective. We apply a recently developed multivariate anomaly detection approach to a set of hydrometeorological variables, and then to multiple biospheric variables relevant to describe the RHW. One main finding is that the extreme event identified in the hydrometeorological variables leads to multidirectional responses in biospheric variables, e.g., positive and negative anomalies in gross primary production (GPP). In particular, the region of reduced summer ecosystem production does not match the area identified as extreme in the hydrometeorological variables. The reason is that forest-dominated ecosystems in the higher latitudes respond with unusually high productivity to the RHW. Furthermore, the RHW was preceded by an anomalously warm spring, which leads annually integrated to a partial compensation of 54% (36% in the preceding spring, 18% in summer) of the reduced GPP in southern agriculturally dominated ecosystems. Our results show that an ecosystem-specific and multivariate perspective on extreme events can reveal multiple facets of extreme events by simultaneously integrating several data streams irrespective of impact direction and the variables' domain. Our study exemplifies the need for robust multivariate analytic approaches to detect extreme events in both hydrometeorological conditions and associated biosphere responses to fully characterize the effects of extremes, including possible compensatory effects in space and time.

2017

Compounding effects of sea level rise and fluvial flooding Journal article

Authors: Hamed R Moftakhari and Gianfausto Salvadori and Amir AghaKouchak and Brett F Sanders and Richard A Matthew
Journal: Proceedings of the National Academy of Sciences
Sea level rise (SLR), a well-documented and urgent aspect of anthropogenic global warming, threatens population and assets located in low-lying coastal regions all around the world. Common flood hazard assessment practices typically account for one driver at a time (e.g., either fluvial flooding only or ocean flooding only), whereas coastal cities vulnerable to SLR are at risk for flooding from multiple drivers (e.g., extreme coastal high tide, storm surge, and river flow). Here, we propose a bivariate flood hazard assessment approach that accounts for compound flooding from river flow and coastal water level, and we show that a univariate approach may not appropriately characterize the flood hazard if there are compounding effects. Using copulas and bivariate dependence analysis, we also quantify the increases in failure probabilities for 2030 and 2050 caused by SLR under representative concentration pathways 4.5 and 8.5. Additionally, the increase in failure probability is shown to be strongly affected by compounding effects. The proposed failure probability method offers an innovative tool for assessing compounding flood hazards in a warming climate.

Multivariate Statistical Modelling of Compound Events via Pair-Copula Constructions: Analysis of Floods in Ravenna Journal article

Authors: Emanuele Bevacqua and Douglas Maraun and Ingrid Hobæk Haff and Martin Widmann and Mathieu Vrac
Journal: Hydrology and Earth System Sciences
Compound events (CEs) are multivariate extreme events in which the individual contributing variables may not be extreme themselves, but their joint – dependent – occurrence causes an extreme impact. Conventional univariate statistical analysis cannot give accurate information regarding the multivariate nature of these events. We develop a conceptual model, implemented via pair-copula constructions, which allows for the quantification of the risk associated with compound events in present-day and future climate, as well as the uncertainty estimates around such risk. The model includes predictors, which could represent for instance meteorological processes that provide insight into both the involved physical mechanisms and the temporal variability of compound events. Moreover, this model enables multivariate statistical downscaling of compound events. Downscaling is required to extend the compound events' risk assessment to the past or future climate, where climate models either do not simulate realistic values of the local variables driving the events or do not simulate them at all. Based on the developed model, we study compound floods, i.e. joint storm surge and high river runoff, in Ravenna (Italy). To explicitly quantify the risk, we define the impact of compound floods as a function of sea and river levels. We use meteorological predictors to extend the analysis to the past, and get a more robust risk analysis. We quantify the uncertainties of the risk analysis, observing that they are very large due to the shortness of the available data, though this may also be the case in other studies where they have not been estimated. Ignoring the dependence between sea and river levels would result in an underestimation of risk; in particular, the expected return period of the highest compound flood observed increases from about 20 to 32 years when switching from the dependent to the independent case.

Dependence of drivers affects risks associated with compound events Journal article

Authors: Jakob Zscheischler and Sonia I Seneviratne
Journal: Science Advances
Compound climate extremes are receiving increasing attention because of their disproportionate impacts on humans and ecosystems. However, risks assessments generally focus on univariate statistics. We analyze the co-occurrence of hot and dry summers and show that these are correlated, inducing a much higher frequency of concurrent hot and dry summers than what would be assumed from the independent combination of the univariate statistics. Our results demonstrate how the dependence structure between variables affects the occurrence frequency of multivariate extremes. Assessments based on univariate statistics can thus strongly underestimate risks associated with given extremes, if impacts depend on multiple (dependent) variables. We conclude that a multivariate perspective is necessary to appropriately assess changes in climate extremes and their impacts and to design adaptation strategies.

Multivariate anomaly detection for Earth observations: a comparison of algorithms and feature extraction techniques Journal article

Authors: M Flach and F Gans and A Brenning and J Denzler and M Reichstein and E Rodner and S Bathiany and P Bodesheim and Y Guanche and S Sippel and M D Mahecha
Journal: Earth System Dynamics
Today, many processes at the Earth's surface are constantly monitored by multiple data streams. These observations have become central to advancing our understanding of vegetation dynamics in response to climate or land use change. Another set of important applications is monitoring effects of extreme climatic events, other disturbances such as fires, or abrupt land transitions. One important methodological question is how to reliably detect anomalies in an automated and generic way within multivariate data streams, which typically vary seasonally and are interconnected across variables. Although many algorithms have been proposed for detecting anomalies in multivariate data, only a few have been investigated in the context of Earth system science applications. In this study, we systematically combine and compare feature extraction and anomaly detection algorithms for detecting anomalous events. Our aim is to identify suitable workflows for automatically detecting anomalous patterns in multivariate Earth system data streams. We rely on artificial data that mimic typical properties and anomalies in multivariate spatiotemporal Earth observations like sudden changes in basic characteristics of time series such as the sample mean, the variance, changes in the cycle amplitude, and trends. This artificial experiment is needed as there is no gold standard for the identification of anomalies in real Earth observations. Our results show that a well-chosen feature extraction step (e.g., subtracting seasonal cycles, or dimensionality reduction) is more important than the choice of a particular anomaly detection algorithm. Nevertheless, we identify three detection algorithms (k-nearest neighbors mean distance, kernel density estimation, a recurrence approach) and their combinations (ensembles) that outperform other multivariate approaches as well as univariate extreme-event detection methods. Our results therefore provide an effective workflow to automatically detect anomalies in Earth system science data.

2016

A global quantification of compound precipitation and wind extremes Journal article

Authors: Olivia Martius and Stephan Pfahl and Clément Chevalier
Journal: Geophysical Research Letters
textcopyright2016. American Geophysical Union. All Rights Reserved. The concomitant occurrence of extreme precipitation and winds can have severe impacts. Here this concomitant occurrence is quantified globally using ERA-Interim reanalysis data. A logistic regression model is used to determine significant changes in the odds of precipitation extremes given a wind extreme that occurs on the same day, the day before, or the day after. High percentages of cooccurring wind and precipitation extremes are found in coastal regions and in areas with frequent tropical cyclones, with maxima of more than 50% of concomitant events. Strong regional-scale variations in this percentage are related to the interaction of weather systems with topography resulting in Föhn winds, gap winds, and orographic drying and the structure and tracks of extratropical and tropical cyclones. The percentage of concomitant events increases substantially if spatial shifts by one grid point are taken into account. Such spatially shifted but cooccurring events are important in insurance applications.

2015

Analysis of a compounding surge and precipitation event in the Netherlands Journal article

Authors: Bart J J M van den Hurk and Erik van Meijgaard and Paul de Valk and Klaas-Jan van Heeringen and Jan Gooijer
Journal: Environmental Research Letters
Hydrological extremes in coastal areas in the Netherlands often result from a combination of anomalous (but not necessarily extreme) conditions: storm surges preventing the ability to discharge water to the open sea, and local precipitation generating excessive water levels in the inland area. A near-flooding event in January 2012 occurred due to such a combination of (mild) extreme weather conditions, by which free discharge of excessive water was not possible for five consecutive tidal periods. An ensemble of regional climate model simulations (covering 800 years of simulation data for current climate conditions) is used to demonstrate that the combined occurrence of the heavy precipitation and storm surge in this area is physically related. Joint probability distributions of the events are generated from the model ensemble, and compared to distributions of randomized variables, removing the potential correlation. A clear difference is seen. An inland water model is linked to the meteorological simulations, to analyze the statistics of extreme water levels and its relationship to the driving forces. The role of the correlation between storm surge and heavy precipitation increases with inland water level up to a certain value, but its role decreases at the higher water levels when tidal characteristics become increasingly important. The case study illustrates the types of analyzes needed to assess the impact of compounding events, and shows the importance of coupling a realistic impact model (expressing the inland water level) for deriving useful statistics from the model simulations.

Increasing risk of compound flooding from storm surge and rainfall for major US cities Journal article

Authors: Thomas Wahl and Shaleen Jain and Jens Bender and Steven D Meyers and Mark E Luther
Journal: Nature Climate Change
When storm surge and heavy precipitation co-occur, the potential for flooding in low-lying coastal areas is often much greater than from either in isolation. Knowing the probability of these compound events and understanding the processes driving them is essential to mitigate the associated high-impact risks1, 2. Here we determine the likelihood of joint occurrence of these two phenomena for the contiguous United States (US) and show that the risk of compound flooding is higher for the Atlantic/Gulf coast relative to the Pacific coast. We also provide evidence that the number of compound events has increased significantly over the past century at many of the major coastal cities. Long-term sea-level rise is the main driver for accelerated flooding along the US coastline3, 4; however, under otherwise stationary conditions (no trends in individual records), changes in the joint distributions of storm surge and precipitation associated with climate variability and change also augment flood potential. For New York City (NYC)—as an example—the observed increase in compound events is attributed to a shift towards storm surge weather patterns that also favour high precipitation. Our results demonstrate the importance of assessing compound flooding in a non-stationary framework and its linkages to weather and climate.

The co-incidence of storm surges and extreme discharges within the Rhine–Meuse Delta Journal article

Authors: W J Klerk and Hessel C Winsemius and W J van Verseveld and A M R Bakker and F L M Diermanse
Journal: Environmental Research Letters
The Netherlands is a low-lying coastal area and therefore threatened by both extreme river$backslash$r discharges from the Meuse and Rhine rivers and storm surges along the North Sea coastline. To date,$backslash$r in most flood risk analyses these two hazardous phenomena are considered independent. However, if$backslash$r there were a dependence between high sea water levels and extreme discharges this might result in$backslash$r higher design water levels, which might consequently have implications for flood protection policy$backslash$r in the Netherlands. In this study we explore the relation between high sea water levels at Hoek van$backslash$r Holland and high river discharges at Lobith. Different from previous studies, we use physical models$backslash$r forced by the same atmospheric forcing leading to concomitant and consistent time series of storm$backslash$r surge conditions and river discharge. These time series were generated for present day conditions as$backslash$r well as future climate projections and analysed for dependence within the upper tails of their$backslash$r distribution. In this study, dependence between the discharge at Lobith and storm surge at Hoek van$backslash$r Holland was found, and the dependence was highest for a lag of six days between the two processes.$backslash$r As no significant dependence of the threats was found for cases without time lag, there is no need$backslash$r for considering dependence in flood protection and policy making. Although future climate change is$backslash$r expected to lead to more extreme conditions in river discharges, we cannot conclude from this study$backslash$r that it will change the magnitude of the dependence for extreme conditions.

2014

Modeling dependence between extreme rainfall and storm surge to estimate coastal flooding risk Journal article

Authors: Feifei Zheng and Seth Westra and Michael Leonard and Scott A Sisson
Journal: Water Resources Research
Accounting for dependence between extreme rainfall and storm surge can be critical for cor- rectly estimating coastal flood risk. Several statistical methods are available for modeling such extremal dependence, but the comparative performance of these methods for quantifying the exceedance probabil- ity of rare coastal floods is unknown. This paper compares three classes of statistical methods—threshold- excess, point process, and conditional—in terms of their ability to quantify flood risk. The threshold-excess method offers approximately unbiased estimates for dependence parameters, but its application for quanti- fying flood risk is limited because it is unable to handle situations where only one of the two variables is extreme. In contrast, the point process method (with the logistic and negative logistic models) and the con- ditional method describe the full distribution of extremes, but they overestimate and underestimate the dependence strength, respectively. We conclude that the point process method is the most suitable approach for modeling dependence between extreme rainfall and storm surge when the dependence is rel- atively strong, while none of the three methods produces satisfactory results for bivariate extremes with very weak dependence. It is therefore important to take the bias of each method into account when apply- ing them to flood estimation problems. A case study is used to demonstrate the three statistical methods and illustrate the implication of dependence to flood risk.

Impact of large-scale climate extremes on biospheric carbon fluxes: An intercomparison based on MsTMIP data Journal article

Authors: Jakob Zscheischler and Anna M Michalak and Christopher Schwalm and Miguel D Mahecha and Deborah N Huntzinger and Markus Reichstein and Gwenaëlle Berthier and Philippe Ciais and Robert B Cook and Bassil El-Masri and Maoyi Huang and Akihiko Ito and Atul Jain and Anthony King and Huimin Lei and Chaoqun Lu and Jiafu Mao and Shushi Peng and Benjamin Poulter and Daniel Ricciuto and Xiaoying Shi and Bo Tao and Hanqin Tian and Nicolas Viovy and Weile Wang and Yaxing Wei and Jia Yang and Ning Zeng
Journal: Global Biogeochemical Cycles
Understanding the role of climate extremes and their impact on the carbon (C) cycle is increasingly a focus of Earth system science. Climate extremes such as droughts, heat waves, or heavy precipitation events can cause substantial changes in terrestrial C fluxes. On the other hand, extreme changes in C fluxes are often, but not always, driven by extreme climate conditions. Here we present an analysis of how extremes in temperature and precipitation, and extreme changes in terrestrial C fluxes are related to each other in 10 state-of-the-art terrestrial carbon models, all driven by the same climate forcing. We use model outputs from the North American Carbon Program Multi-scale Synthesis and Terrestrial Model Intercomparison Project (MsTMIP). A global-scale analysis shows that both droughts and heat waves translate into anomalous net releases of CO2 from the land surface via different mechanisms: Droughts largely decrease gross primary production (GPP) and to a lower extent total respiration (TR), while heat waves slightly decrease GPP but increase TR. Cold and wet periods have a smaller opposite effect. Analyzing extremes in C fluxes reveals that extreme changes in GPP and TR are often caused by strong shifts in water availability, but for extremes in TR shifts in temperature are also important. Extremes in net CO2 exchange are equally strongly driven by deviations in temperature and precipitation. Models mostly agree on the sign of the C flux response to climate extremes, but model spread is large. In tropical forests, C cycle extremes are driven by water availability, whereas in boreal forests temperature plays a more important role. Models are particularly uncertain about the C flux response to extreme heat in boreal forests.

2013

Quantifying the dependence between extreme rainfall and storm surge in the coastal zone Journal article

Authors: Feifei Zheng and Seth Westra and Scott A Sisson
Journal: Journal of Hydrology
The interaction between extreme rainfall and storm surge can be critical in determining flood risk in the coastal zone. This paper investigates the presence of dependence between these two processes along the Australian coastline using the most extensive observational records of rainfall and storm surge events currently available. A bivariate logistic threshold-excess model was employed to conduct the dependence study. Statistically significant dependence was observed for the majority of locations that were analysed, although regional variations as well as seasonal variations of the dependence strength are also apparent. The dependence remains significant even at distances of several hundred kilometres between the tide gauge and the rainfall gauge, indicating that dependence arises largely due to synoptic scale meteorological forcings. The strength of dependence varies as a function of storm burst duration, with an increase in dependence when going from one hour through to 24. h storm bursts. The dependence strength also varies with the lag between the extreme rainfall and the storm surge event, with the greatest level of dependence when extreme events occurred in the same time step for storm burst durations exceeding six hours, or for lags up to ??10. h for storm bursts durations below six hours. These findings have important implications for flood risk assessments in the coastal zone, showing that the two processes must be considered jointly if flood risk is to be quantified correctly. ?? 2013 Elsevier B.V.

2010

A new method to assess the risk of local and widespread flooding on rivers and coasts Journal article

Authors: R Lamb and C Keef and J Tawn and S Laeger and I Meadowcroft and S Surendran and P Dunning and C Batstone
Journal: Journal of Flood Risk Management
To date, national- and regional-scale flood risk assessments have provided valuable information about the annual expected consequences of flooding, but not the exposure to widespread concurrent flooding that could have damaging consequences for people and the economy. We present a new method for flood risk assessment that accommodates the risk of widespread flooding. It is based on a statistical conditional exceedance model, which is fitted to gauged data and describes the joint probability of extreme river flows or sea levels at multiple locations. The method can be applied together with data from models for flood defence systems and economic damages to calculate a risk profile describing the probability distribution of economic losses or other consequences aggregated over a region. The method has the potential to augment national or regional risk assessments of expected annual damage with new information about the likelihoods, extent and impacts of events that could contribute to the risk.