High Return Periods Research Articles

Integrating Long and Short‐Term Time Dependencies in Simulation‐Based Seismic Hazard Assessments

AbstractConventional probabilistic seismic hazard analysis (PSHA) only considers mainshock events and uses a time‐independent earthquake rupture forecast to describe the occurrence of mainshocks. This approach neglects the long‐term time‐dependency of mainshocks on specific fault segments, the interaction of adjacent faults, and the spatial and temporal clustering of aftershocks. This study integrates a recently proposed advanced fault‐based PSHA framework with an aftershock simulator based on the Epidemic‐Type Aftershock Sequence (ETAS) model, to overcome the aforementioned limitations. Central Italy is used as a case study to illustrate the proposed framework. The case‐study hazard curves show that including state‐of‐the‐art advances in fault‐based PSHA may increase the ground‐motion amplitudes for low return periods, and decrease the ground‐motion amplitudes for high return periods, relative to a conventional PSHA approach. The magnitude of ground‐motion estimate increases due to aftershock inclusion is dependent on the selected site and return period. Sensitivity analyses of the results demonstrate that: (a) close to modeled faults, high‐return‐period hazard estimates are more affected by fault‐related inputs than the aftershock inclusion; (b) close to modeled faults, the influence of aftershock inclusion is higher for low return periods than high ones; (c) away from modeled faults, aftershocks inclusion greatly affects the variance of the hazard results; and (d) fault interaction has a limited effect on the variance of the hazard estimates compared to other analysis inputs. This study could help with the future implementation of complex fault modeling approaches and aftershock hazard in seismic risk models.

Responses of urban flood processes to local land use using a high-resolution numeric model

Rapid increase of urban impervious surface, resulting from unplanned urban sprawl, leads to the frequent occurrence of urban flood disasters caused by abnormal precipitation in flood season. In this work, a coupled urban rain flood model and normal pipe network model is employed to simulate the city inundation and pipe network hydrodynamics under different local land use types. The results shows that the peak surface inundation and pipe network outlet flow after transformation were 4.36%–42.43% and 0.01%–1.34% higher than those before transformation for P ≤ 20, and its corresponding values reached 1.24%–5.59% and 27.26%–35.21% for P > 20. The change in land use types in local areas significantly impacted inundation of the whole surface under normal rainfall (P < 20), which was also verified by the water recession coefficient. In addition, land use transformation slightly affected urban inundation for P ≥ 100, which indicates that low-impact development facilities were almost ineffective in addressing inundation under extreme rainfall. A significant impact on the pipe network discharge was observed under the high rainfall return period (P ≥ 20). The recession coefficient obtained via coupled model simulations can be effectively applied in the urban flood recession process.

Numerical Comparison of the Hydrological Response of Different Permeable Pavements in Urban Area

Floods are becoming more frequent, especially in urban environments where most of the surface is waterproofed. Permeable pavement (PP) can be applied as low impact development (LID) systems for runoff mitigation in urban areas. Their effectiveness can be assessed, case by case, by numerical simulations. In this study, the effectiveness of mitigating runoff of different permeable pavements has been evaluated. In particular, porous asphalt (PA), pervious concrete (PC), permeable interlocking concrete pavement (PICP) and grid pavement (GP) have been investigated using EPA Storm Water Management Model (SWMM) software. To this aim, a car parking area located in the University Campus of Palermo (Italy) has been taken as a case study, considering several scenarios, each having a different percentage and planimetric layout of a PP type combined with an impermeable pavement. All the scenarios were tested assuming four synthetic rainfall events, referring to return periods of 5, 10, 50 and 100 years, and a real high return period event that occurred in Palermo in 2020. The results showed that amongst the different PPs considered, only the PA, bounded at the bottom by an impermeable layer, was practically ineffective. The other three PPs, proved to be effective in a noticeable way and furthermore for each scenario studied, they proved to bear almost the same mitigated runoff. The results proved appreciable differences in runoff as a function of the location of the PP over the study area.

A framework for quantifying climate-informed heavy rainfall change: Implications for adaptation strategies

To understand the influence of climate change on heavy rainfalls and reduce the consequential multidimensional risks, we develop a climate-informed and adaptation strategies-related framework by using the information on heavy rainfalls and various socioeconomic factors. For this purpose, we firstly quantify the spatiotemporal characteristics of heavy rainfalls with various durations (1 h to multiple days) and return periods (2-year to 50-year) for the flood-prone country Cambodia, as a case study, during the historical period (1980–2005), mid-century (2040–2065), and late-century (2070–2095), using the latest three hourly climate model datasets under RCP 8.5 and 1 hourly ERA5 reanalysis datasets. A novel conditional artificial neural network (CANN) model is employed for temporal disaggregation to obtain the monthly maximum of 1 hourly rainfall in the future periods and subsequently, a zero-inflated generalized extreme value function (ZIGEV) is applied for extreme value analysis (EVA) to obtain rainfall intensity with different return periods. Secondly, the province-level flood risk change maps are developed based on a novel flood risk change index. The combination of CANN and ZIGEV performs better in EVA than traditional approaches by reducing the uncertainty from the stationarity assumption of temporal disaggregation and bias in the disaggregated rainfall. Rainfall intensity is projected to increase more in higher return periods and shorter durations towards the late-century, predominantly over Southern and Central Cambodia. Projected rainfall intensity-duration-frequency (IDF) curves in the capital city, Phnom Penh, reveal that the occurrence frequency of heavy rainfall in a given duration (e.g., 48 h) is likely to become ~10-fold in the mid-century. Results of province-level flood risk change maps indicate that Southeastern and Northwestern regions should be prioritized for employing adaption strategies. Our results will assist the policymakers in further mapping the flood susceptibility and vulnerability in different spatiotemporal scales across various communities and localities in the country and beyond.

Climate change increased the compound extreme precipitation-flood events in a representative watershed of the Yangtze River Delta, China

A compound perspective on hydrological extreme events is of paramount significance as it may lead to damages with larger losses. In this study, an integrated framework, based on downscaled climate variables and hydrological model, i.e. the Soil and Water Assessment Tool, was applied to generate extreme precipitation (Rx1day) and extreme streamflow (Sx1day) series under historical and future climate conditions. Then the potential impacts of climate change for univariate and bivariate joint frequency of extreme precipitation and flood in Xitiaoxi River Basin (XRB), a representative watershed of the Yangtze River Delta, are detected. The compound risk of extreme precipitation and flood under different levels of joint return period for historical and projected periods is estimated by copula‐based two-dimensional approaches. The Rx1day and Sx1day under future scenarios changed by − 0.4% to 11.7% and 0.7% to 20.4%, respectively, compared to historical period based on univariate frequency analysis, indicating the increasing magnitude of the flood in the future. Climate change with different emission scenarios all have a driving effect on the rising coactivity of extreme precipitation and flood under compound flooding frequency analysis. In addition, the enhancement of climate change to extreme events is more apparent for extremes with higher return period and under the periods of 2080s. Moreover, the flood frequency designs are deduced by bivariate joint distribution are safer than that by univariate distribution. This study may provide actionable insights to formulate the planning scheme of flood control and disaster reduction under the changing environment.

Fitting flood frequency distributions using the annual maximum series and the peak over threshold approaches

Flood frequency plays an important role in the design of hydraulic structures as well as in the management of fisheries and aquatic resources. There are two types of flood frequency analyses, namely the annual maximum series (AMS) analysis and the partial duration series analysis (or peak over threshold, POT). The POT analysis consists of studying discharge data above a specific threshold (or truncation level), whereas the AMS method uses maximum annual discharge data. Both the AMS (generalized extreme value – GEV distribution) and POT (generalized Pareto – GP and exponential – Exp distributions) were used to calculate flood frequencies for four hydrometric stations within the Miramichi River basin of New Brunswick. A simple method was proposed for the selection of truncation levels, that is, values corresponding to 1, 1.5 and 2 flood counts per year. Considering multiple truncation levels in the POT analysis has the advantage of providing more results that are used to identify which level provides a better fit of the flood data. Both the GEV (AMS) and GP (POT) distributions best represented flood data within the Miramichi River whereas the Exp (POT) distribution did not fit well the data, especially for floods with high return periods (>25 years). Results showed the truncation level at a flood count of 1 (highest truncation level) for the POT method, generally provided a better fit of floods with high return periods (>25 years). Moreover, lower truncation levels tended to provide flood estimates with less uncertainties (lower coefficient of variation, as tested using a jackknife technique). Finally, results showed that both the AMS and POT methods are complementary in flood frequency analyses. The AMS is the more classic approach to flood frequency analyses; however, the POT provides a better characterization of floods (e.g. magnitude, duration and flood volume).

Extreme Sea Level Estimation Combining Systematic Observed Skew Surges and Historical Record Sea Levels

AbstractThe estimation of sea levels corresponding to high return periods is crucial for coastal planning and for the design of coastal defenses. This paper deals with the use of historical observations, that is, events that occurred before the beginning of the systematic tide gauge recordings, to improve the estimation of design sea levels. Most of the recent publications dealing with statistical analyses applied to sea levels suggest that astronomical high tide levels and skew surges should be analyzed and modeled separately. Historical samples generally consist of observed record sea levels. Some extreme historical skew surges can easily remain unnoticed if they occur at low or moderate astronomical high tides and do not generate extreme sea levels. The exhaustiveness of historical skew surge series, which is an essential criterion for an unbiased statistical inference, can therefore not be guaranteed. This study proposes a model combining, in a single Bayesian inference procedure, information of two different natures for the calibration of the statistical distribution of skew surges: measured skew surges for the systematic period and extreme sea levels for the historical period. A data‐based comparison of the proposed model with previously published approaches is presented based on a large number of Monte Carlo simulations. The proposed model is applied to four locations on the French Atlantic and Channel coasts. Results indicate that the proposed model is more reliable and accurate than previously proposed methods that aim at the integration of historical records in coastal sea level or surge statistical analyses.

Flood frequency analysis based on a mixed GEV distribution with upper limit applied to the Hydrological Region No. 10 (Sinaloa), Mexico

Flood Frequency Analysis (FFA) processes the available record of annual maximum flows of a river, to estimate predictions associated with low probabilities of exceedance, its reciprocal is the return period (Tr) in years. These predictions are the design floods, with which all hydraulic works are planned, designed, and reviewed hydrologically, such as reservoirs, protective embankments, channel rectifications, bridges and urban drainage works. In this work, a novel method of the FFA is described and applied, which incorporates additional hydrometric information in a mixed General Extreme Values (GEV) distribution; this procedure is aimed to accurately define the flood of Tr = 1 000 years. Initially, the information on average annual flow and annual maximum flow of all the hydrometric stations that make up the homogeneous region under study are processed. The above, with the approach of the enveloping curves and with the objective of defining an enveloping curve with zero probability of exceedance; whereby, defines the extreme maximum flow that is approached as asymptote by the upper part of the mixed GEV, which avoids an unreal increase in predictions. Lastly, based on each record of floods, synthetic sequences of 1 500 values are generated and one with similarity to the available data and more than ten floods of Tr &gt; 150 years is chosen. To such a synthetic sequence a GEV distribution is adjusted, with the methods of moments L and LH, to select the one with the lowest standard error of fit. This distribution forms the lower part of the mixed GEV, until the point of inflection of Tr = 500 years. The described method was applied to the seven largest flood records of the Hydrological Region No. 10 (Sinaloa), Mexico and based on its results, the Conclusions were formulated, which highlight its advantages and suggest its application to estimate predictions of high return periods (100 ≤ Tr ≤ 1 000 years) more accurately, by incorporating regional hydrometric information.

ANALYSIS OF EARTHQUAKE-PRONE AREAS FOR DISASTER MITIGATION IN THE SUMATRA TRENCH AND SURROUNDINGS

Indonesia is situated at the junction of three active plates: Eurasia, Circum Pacific, and Indo-Australia.. Sumatran tectonics is controlled by the subduction of two plates, namely the Indo-Australian plate and the southeastern Eurasian plate. The research area is along the Sumatra Trench and the data used are from the United States Geological Survey in the last 50 years, the depth is 0-60 km, and the methodology used is the least squares method. Purpose of this study, to predict the occurrence of earthquakes with the aim of minimizing the impact of damage that occurs in earthquakes. The Locked Area has a high return period value, the influencing factor is the existence of a seismic gap, where the stress applied to this area will be isolated due to seamount subduction, so that it can withstand earthquakes. but one day, when the rock can no longer withstand the stress, it will cause an earthquake with large strength.

A numerical study of a taut-moored point-absorber wave energy converter with a linear power take-off system under extreme wave conditions

Probably the biggest challenge for wave energy is to ensure survival in harsh offshore conditions, in order to reduce costs for offshore repair operations and downtime, and achieve economic viability. This work presents a reliable numerical tool that can be used to study the dynamics and survivability of wave energy converters in violent wave conditions, possibly cutting down the costs of experimental campaigns. Within the Smoothed Particle Hydrodynamics framework, this research identifies a detailed procedure to model a taut-moored point-absorber wave energy converter together with its inherent power take-off device, which seamlessly exploits its functions of energy harvesting and load bearing. A validation of the DualSPHysics code is provided by contrasting the numerical outcome with a thorough set of data obtained in physical tests with extreme waves, showing that the time-integrated numerical model can capture with good accuracy all the physics involved. The computational fluid dynamics tool is employed to perform a survivability study, modeling high-return period wave conditions for marine structures, and providing guidelines on how to create the numerically best setup to be used for design purposes. A real-like irregular sea state representation, comprising 500 waves, was used to draw insightful indications for the structure optimization to increase the structure’s life expectancy, or conversely, to reduce the initial and operational costs.

A combined hydrological and hydraulic modelling approach for the flood hazard mapping of the Po river basin

AbstractThe identification of flood prone areas is essential for a range of engineering, risk reduction and research applications. Here, we describe a combined hydrological and hydraulic modelling approach for the assessment of flood‐prone areas and we present the results obtained over the Po river (Northern Italy). Runoff and river discharges are calculated through the hydrological model CHyM driven by GRIPHO, a new precipitation dataset for Italy. River flow data are used to obtain flood hydrographs for the CA2D hydraulic model, which calculates flood hazard maps at a resolution of 90 m. Flood simulations are run over a re‐shaped HydroSHEDS digital elevation model that includes information of the channel geometry. Modeled flood hydrographs are compared with observed data for a choice of gauging stations, showing a good performance of the CHyM model. We validate the flood hazard maps against observed flood events and official hazard maps. For high return periods, modelled maps can correctly identify up to 67% of the flood extent, both on the Po River and on smaller tributaries, while performances are more variable for lower return periods. Overall, the proposed approach suggests a strong potential for further applications, such as flood hazard assessment under future climate scenarios.

Site or regional design wind speeds?

Estimates of high return period design wind speeds are required to achieve an acceptable probability of failure. These can be found by fitting an appropriate distribution to observed data but is complicated by spatial variation of the wind climate, therefore it is unclear which data are relevant at a particular location. We aim to frame this problem in terms of model selection and the bias variance trade-off. Existing models for incorporating site versus regionally averaged statistics are expressed in terms of key parameters, which allow the errors associated with each model to be derived. It is found that using a characteristic wind speed in codification combines the site statistics (low bias) with regionally averaged statistics (low variance). The return period of the characteristic wind speed is shown to act as a parameter controlling the relative weighting of these models. This insight is used to develop an optimal (minimum mean square error) estimator of the design wind speed, which is shown to vary based on both the available quantity of data at a particular site and how well this data corresponds to the regional average. Some practical advantages of this optimal model are then demonstrated at several South African stations.

Impact of Extreme Rainfall on Flood Hydrographs

Extreme rainfall is the main factor triggering flooding in various regions of the world including Indonesia. The increase in intensity and duration of current extreme rainfall is predicted as a result of global climate change. This paper aims to analyze the impact of extreme rainfall to the peak discharge of flood hydrographs at a watershed outlet in Palu, Sulawesi, Indonesia. Maximum daily rainfall data for the period 1990-1999 recorded at the Palu Meteorological Station, Central Sulawesi were selected using the Annual Maximum Series Method, and grouped into two types. Type I is the maximum daily rainfall data with extreme events and Type II is the maximum daily rainfall data without extreme events. Frequency analysis was applied to the two data groups using the best distribution method of: Normal, Normal Log, Pearson III Log, and Gumbel to obtain the design rainfall of each data group. In the next stage, the design rainfall transformation into a flood hydrograph is performed using the Nakayasu Synthetic Unit Hydrograph based on a number of return periods in one of the rivers flowing into Palu Bay, namely the Poboya River. The analysis results show that the design rainfall graphs with both extreme rainfall and without extreme rainfall are identical at the low return period and divergent at the high return period with a difference of up to 21.6% at the 1000-year return period. Correspondingly, extreme rainfall has a greater impact at the peak of the flood hydrograph with increasing return periods ranging from -1.28% to 26.81% over the entire return period.

Calibration of the design wind load and snow load considering the historical climate statistics and climate change effects

In the present study, an updating of the probabilistic models used for calibrating the wind load, snow load and companion load factors for the National Building Code of Canada (NBCC) are presented. The update of probabilistic models for the extreme wind speed and ground snow depth is based on historical climatological data over Canadian sites. For the updating of the statistics of the extreme wind speed, the effect of mixed wind climate (i.e., thunderstorm winds and non-thunderstorm winds) is considered. Reliability analysis is carried out based on this update by considering different load combinations. The results indicate that, for an improved reliability consistency, the use of a high return period to specify the environmental loads and with a unit load factor is preferable to the use of 50 year return period and a load factor greater than one for structural design code making, if the coefficient of variation of the extreme climatological elements is spatially varying. Most importantly, based on these findings and the results from the climate change modelling results, sets of load scaling factors accounting for climate change effects are calibrated for different regions in Canada. Both the use of the target reliability index for a 50-year design working life as well as of the target minimum required annual reliability index within each of the 50-year design working life are considered for the reliability-based calibration. Specific recommendations on the load and companion load factors are suggested for a future edition of NBCC.

A Comparative Flood Frequency Analysis of High-Flow between Annual Maximum and Partial Duration Series at Sungai Langat Basin

Flood frequency analysis should consider small and frequent floods. Despite the complexities in partial duration series implementation, it can give a better flood estimation in a way that it does not exclude any significant high flow events, even if it is not the highest event of the year. This study employs the streamflow data recorded at Kajang station, Sungai Langat, Malaysia over a 36-year period spanning from 1978 to 2013. The paper attempts to conduct flood frequency analysis using two approaches, annual maximum and partial duration series. The optimal threshold value is selected to be 48.7 m3/s, where the dispersion index stabilizes at around 1, DI = 1 . The results have shown that generalized extreme value (GEV) distribution describes the annual maximum data while the lognormal (LN3) and generalized Pareto (GPA) distribution is chosen as the best fit distribution at Kajang station for a partial duration series. There is a slight difference between estimated streamflow magnitude when using GPA and LN3 for selected return periods, while a considerable difference was observed when using annual maximum at a higher return period. As a conclusion, PDS gives more relevant magnitude estimation rather than AMS. Flood frequency plays an important role in understanding the nature and magnitude of high flow, which in turn can assist relevant agencies in the design of hydrological structures and reduce flood impacts.

Spatio-temporal variability of dry and wet spells and their influence on crop yields

Spatial modelling of extreme dry (wet) spells is relevant to design agricultural insurances and establish risk management strategies. We characterised dry (wet) spells during the period 1971–2010 in 133 rainfall stations and investigated their impact on crop yields across Belgium. A day was considered as a dry (wet) day when the total amount of rainfall is below (above) 0.2 mm. A two-way ANOVA demonstrated significant differences between low and high yields across 12 agricultural regions in the number of consecutive dry (wet) days, rainfall amount during the wet days and their total during the crop growing season. Generalised Extreme Value distributions were fitted to the maxima series using maximum likelihood to estimate the distribution parameters. The number of consecutive dry (wet) days and rainfall amount was considered during the cropping season between March and October. We modelled maxima observed at rainfall stations by means of max-stable processes, an extension of multivariate extreme value theory. Spatial extreme modelling was performed through pairwise likelihood inference by a madogram and enabled 20-year return levels to be simulated across regions based on geographic coordinates and altitude as covariates. Spatial return levels confirmed the exceptionality of 2016 and 2018 as extreme wet and dry years with high return periods. The 2018 drought resulted in yield reductions of 74% for maize and 42% for potato, while the 2016 rainfall induced yield losses of up to 39% for winter cereals, 45% for maize and 53% for potato. The max-stable models provided a good fit to the joint behaviour of extremes in the number of consecutive dry (wet) days during the growing season as an indication for crop stress. The method allows for spatio-temporal characterisation of generalised extreme value distributions across regions, and provides a tool for weather based insurances and risk management.

Urban Flood Analysis in Ungauged Drainage Basin Using Short-Term and High-Resolution Remotely Sensed Rainfall Records

Analyzing flooding in urban areas is a great challenge due to the lack of long-term rainfall records. This study hereby seeks to propose a modeling framework for urban flood analysis in ungauged drainage basins. A platform called “RainyDay” combined with a nine-year record of hourly, 0.1° remotely sensed rainfall data are used to generate extreme rainfall events. These events are used as inputs to a hydrological model. The comprehensive characteristics of urban flooding are reflected through the projection pursuit method. We simulate runoff for different return periods for a typical urban drainage basin. The combination of RainyDay and short-record remotely sensed rainfall can reproduce recent observed rainfall frequencies, which are relatively close to the design rainfall calculated by the intensity-duration-frequency formula. More specifically, the design rainfall is closer at high (higher than 20-yr) return period or long duration (longer than 6 h). Contrasting with the flood-simulated results under different return periods, RainyDay-based estimates may underestimate the flood characteristics under low return period or short duration scenarios, but they can reflect the characteristics with increasing duration or return period. The proposed modeling framework provides an alternative way to estimate the ensemble spread of rainfall and flood estimates rather than a single estimate value.

Does time-varying illiquidity matter for the Indian stock market? Evidence from high-frequency data

This article examines variations in illiquidity in the Indian stock market, using intraday data. Panel regression reveals prevalent day-of-the-week, month, and holiday effects in illiquidity across industries, especially during exogenous shock periods. Illiquidity fluctuations are higher during the second and third quarters. The ranking of most illiquid stocks varies, depending on whether illiquidity is measured using an adjusted or unadjusted Amihud measure. Using pooled quantile regression, we note that illiquidity plays an important asymmetric role in explaining stock returns under up- and down-market conditions in the presence of open interest and volatility. The impact of illiquidity is more severe during periods of extreme high and low returns. JEL Classification: G10, G12

A new empirical distribution for the design wave heights under the impact of typhoons

In the South China Sea, where storm surges are frequent, accurate calculation of the design wave heights is crucial to the coastal structures and subsequent disaster prevention and relief. The data sampling methods have developed from annual extreme value sampling to over threshold value sampling to use the precious observed wave heights more effectively. However, the problems still exist, resulting in "sharp peak" and "thick tail" for the over threshold data. And traditional design wave heights estimation models cannot fully fit the tail of the data. The strong typhoon also makes the variation patterns of associated wave height factors highly uncertain. To address these issues, this study proposes a Poisson—Gumbel-Pareto distribution model based on the cumulative distribution functions and the combined extreme value distribution theory. The new model not only takes the impacts of typhoon frequency on wave heights into consideration when the wave heights are over the threshold, it can better represent the peak of the data, and also makes the tail of the probability density function curves gradually descend, that is, the tail data can be fully utilized and fitted using this new method. Taking the measured wave and typhoon data from the Naozhou Ocean Observatory in the South China Sea from 1990 to 2016, the design wave heights in the western Guangdong waters have been calculated on the level of once-in-many-years, the design wave heights for 100-year and 200-year are 9.51m and 10.11m respectively. The analysis shows that the new empirical distribution improves the previous extreme value models' disadvantages, with reasonable and stable projections in the high return periods, which is especially favorable when dealing with extreme sea conditions.

EXTREME PRECIPITATION EVENTS AND ASSOCIATED RISK OF FAILURE IN HYDRAULIC PROJECTS IN THE STATE OF MATO GROSSO DO SUL, BRAZIL

It is necessary to know the extreme occurrences of precipitation in a region for satisfactory design of infrastructure projects. Tropical climate regions are characterized for their heavy rainfall events during the summer, and in recent years, as a result of climate change, such events are becoming recurrent. A theoretical probability distribution model is typically used to extrapolate extreme events for high return periods. In this study, it was verified whether probability distribution models are efficient for an estimation of extreme precipitation events for the normative recommended return period. Five cities in Mato Grosso do Sul State, located the midwest region of Brazil, that have been affected by disasters caused by heavy rainfall were adopted as a case study. The results illustrate that the recommendations of the return periods used for the design of hydraulic control structures are insufficient to avoid damage caused by precipitation events, thus lending a high breakdown risk to the structures.