Return Period Analysis Research Articles

Multi-covariate non-stationary flood frequency analysis for assessing flood hazard over the Mahanadi River basin, India

ABSTRACT Due to climate change and anthropogenic activities, the river’s peak flow is changing, which prompts exploring the non-stationary flood frequency analysis. Most of the past studies modelled the non-stationarity of floods, considering time and/or a single dominant covariate influencing the flood flows, but they are inadequate. This study proposes a multi-covariate non-stationary flood frequency model (MC-NSFFM) and investigates the influence of different covariates, such as rainfall, modified reservoir index (MRI), and climate indices, for the Mahanadi River basin in India. The models’ performances are evaluated by comparing them with the results of stationary and single-covariate non-stationary flood frequency models (SC-NSFFMs) using standard performance measures and return period analysis. The study showed that the MC-NSFFMs performed better than stationary and SC-NSFFMs. The flood return levels corresponding to the MC-NSFFMs are 4% to 22% higher than the stationary models, indicating the rise in flood risks due to climate change and anthropogenic activities.

Seismicity and fractal analysis in Aswan region, southern Egypt

Seismic activity in Aswan is influenced by the complex interactions of tectonic plates, the accumulation of stress, and the presence of geological fault systems. It revealed that epicenters are well distributed along four fault segments in a conjugate pattern, indicating a prominent E-W compressional stress. This research aims to explore the characteristics of seismicity and seismotectonics, with a focus on assessing their implications for risk reduction and disaster management in this densely populated region. A data review from the Egyptian National Seismological Network (ENSN) identified 464 earthquakes occurred between 2000 and 2021, with local magnitudes ranging from 0.3 to 4.4, and depths up to 25 km. The calculated Gutenberg-Richter b-value is approximately 0.87 ± 0.05, indicating a gradual stress accumulation. The current analysis shows a more consistent level of moderate seismic activity, unlike previous studies in Aswan region that reported a wide range of b-values from 0.554 to 1.07. This suggests that while earlier research captured a wider range of seismic behaviors, recent data indicates a stabilization in earthquake frequency and intensity. Additionally, the fractal dimension (Dc) calculated at 1.57 ± 0.04 shows an intermediate level of complexity and reflecting a clustering pattern of earthquakes. The variations in the b-value with different magnitudes and depths signify the involvement of active smaller faults, responsible for earthquakes up to magnitude 2.2, which then transition to fractured zones inducing earthquakes up to magnitude 2.5. This transition is followed by a decline in seismic activity, indicating regions that are potentially more likely to experience larger earthquakes. Moreover, stress disparities at various depths contribute to smaller earthquakes within the 5–10 km depth range. Return period analysis suggests that the earthquakes of magnitude 3.7 or higher are expected to occur approximately once every decade in Aswan. These findings are of utmost importance for earthquake risk reduction, hazard assessment, and the sustainable development of Aswan area.

Probability Distribution Analysis of Annual Maximum Precipitation in the Niger Delta: A Critical Step towards Effective Flood Risk Management

Aims: The study aimed to analyze historical maximum precipitation data from seven cities in the Niger Delta to assess vulnerability to extreme rainfall events, identify optimal probability distribution models for future predictions, and provide insights for flood risk management strategies. Study Design: This is a retrospective analytical study based on historical precipitation data. Place and Duration of Study: The study was conducted across seven major cities in the Niger Delta region of Nigeria, utilizing data collected over several decades. Methodology: Historical maximum precipitation data were statistically analyzed to determine the best-fit probability distribution models. The Kolmogorov-Smirnov (KS) test and Mean Squared Prediction Error (MSPE) were used to validate the suitability of distributions, including Log-Normal, Normal, Log-Pearson III, and Generalized Extreme Value (GEV) for each city. Return periods of 10, 25, 50, and 100 years were calculated to predict future precipitation trends and assess flood risks. Results: Significant variations in annual maximum precipitation were observed across the cities, with Calabar recording the highest peak at 4062.7mm. The Log-Normal distribution was the best fit for Akure and Calabar, while the Normal distribution best described rainfall in Benin City. Log-Pearson III was optimal for Owerri, Umuahia, and Uyo, and GEV best fitted Port Harcourt’s data. High P-values (>0.87) indicated good model fits across the cities. Return period analysis suggested greater risks of extreme precipitation events in coastal cities like Calabar and Port Harcourt. Conclusion: The study underscores the importance of tailored, city-specific flood management strategies in the Niger Delta to mitigate the impact of extreme rainfall events, particularly in the face of climate change.

Standardization of Dual-Doppler Radar–Derived Wind Fields during a Hurricane Landfall

Abstract A new methodology for standardizing radar-derived elevated dual-Doppler (DD)-synthesized wind maps to the near surface is presented, leveraging the spatial variability found within the horizontal wind speed fields. The methodology is applied to a dataset collected by Texas Tech University (TTU) using two TTUKa-band mobile radar systems during the landfall of Hurricane Delta (2020) in coastal Louisiana. Relevant portions of the DD wind fields are extracted from multiple heights between 100 and 400 m above ground level, combined into 10-min segments and standardized to a reference height of 10 m and an open exposure roughness length of 0.03 m. Extractions from these standardized wind fields are compared and validated against the standardized wind measurements from a micronet of seven TTU StickNet platforms providing “ground truth” within the DD analysis domain. The validation efforts confirm the developed DD wind field standardization methodology yields robust results with correlation coefficients greater than 0.88 and mean biases less than 1%. The results of this study provide a new means for incorporating elevated DD radar data into new and existing surface wind field analysis systems geared toward generating a wind field of record during a hurricane landfall. Significance Statement This work presents a new methodology for using radar-generated wind fields during a hurricane landfall to support the construction of a surface wind field analysis. Because radar-based wind measurements are inherently elevated above the surface and because the wind conditions well above the ground do not directly translate to the wind conditions experienced at the ground, a method for standardizing the elevated radar wind fields to the surface provides tremendous value to generating a spatially continuous wind field of record during a hurricane landfall event to better inform event response and recovery efforts. In addition, the need for detailed wind fields of record from landfalling hurricanes that approach structural design limits is critical as a single design-level storm can alter building code return period analysis. Detailed wind fields for these high-impact events can directly inform associated updates in building codes ultimately contributing to a more resilient built environment.

Spatio-temporal analysis of meteorological drought return periods in a Mediterranean arid region, the center of Morocco

ABSTRACT Morocco has recently witnessed a surge in drought occurrences, leading to considerable socioeconomic damages. The standardized precipitation index (SPI) at 3- and 12-month timescales was employed to assess spatiotemporal drought characteristics across the Marrakech-Safi arid region. Mann-Kendall and Sen's slope methods were adopted to assess SPI trends. Furthermore, the Gumbel Copula was employed to construct the joint distribution function of three drought variables: duration, severity, and intensity. The spatial distribution of drought return period for different scenarios was carried out. Results revealed notable interannual fluctuations of wet and drought cycles, with the driest years being 1983 and 2005 and a mixture of positive and negative trends of SPI-12 and SPI-3 over the 1972–2018 period. Nevertheless, statistical significance was observed in only 50% of the SPI-12 trends and 27% of the SPI-3 trends. The drought severity in the region was highest in the plain area, with droughts lasting 6–20 months for 75% of all drought events for SPI-12. The coastal strip exhibited the lowest drought recurrences. Furthermore, dry periods exhibited greater temporal variability over shorter timescales. Finally, trivariate return periods of drought events were the longer, averaging 93 months for SPI-12.

Kelayakan usaha tani Hutan Kemasyarakatan (HKM) pola monokultur dan agroforestri di Desa Air Terjun Kabupaten Kerinci

The Community Forest Area (HKm) of Air Terjun Village is an area that provides direct real benefits for farmers who are located around the community forest area and has natural wealth that can be developed to provide farmers with income for the family economy. This research was conducted in Air Terjun Village, Siulak District, Kerinci Regency. This research aims to determine the feasibility of a cinnamon monoculture business and a potato, coffee and cinnamon agroforestry business from the use of areas within community forests. Data collection was carried out using structured interviews using questionnaires. Determination of respondents was carried out using the Snowball method with data analysis using cost analysis, revenue analysis, income analysis, internal analysis of return and payback period analysis. The results of the research showed that the cultivation of cinnamon monoculture planting patterns and cinnamon, coffee and potato agroforestry in Air Terjun Village, Kerinci Regency had decent/profitable results. However, the profits and return on capital are faster in agroforestry planting patterns because the agroforestry planting pattern relies on 3 types of plants, whereas the monoculture planting pattern only relies on 1 type of plant.

Revealing the Key Drivers Conducive to the “Once‐In‐A‐Century” 2021 Peninsular Malaysia Flood

AbstractIn December 2021, Super Typhoon Rai caused significant devastation to the South Philippines and East Malaysia. In the meantime, an unprecedented flood event occurred in Peninsular Malaysia at 2,000 km west of the typhoon's path, causing comparable socioeconomic impacts as Rai. Record‐breaking 3‐day precipitation was received by Peninsular Malaysia during 16–18 December. Based on the storm tracking results, this study identified two mesoscale convective systems (MCSs) that were directly responsible for the flooding. The two MCSs were directly initiated by a tropical depression and sustained by an elongated easterly water vapor corridor originating from the Super Typhoon Rai. The return period and joint frequency analysis of key drivers indicate that the 3‐day downpour was more severe than a “once‐in‐a‐century” event. Historical records suggest such anomalous moisture channel has become more frequent in Southeast Asia, which alarms heightened attention in forecasting winter flood.

Direct Sampling for Spatially Variable Extreme Event Generation in Resampling‐Based Stochastic Weather Generators

AbstractResampling‐based weather generators simulate new time series of weather variables by reordering the observed values such that the statistics of the simulated data are consistent with the observed ones. These generators are fully data‐driven, easy to implement, and capable of reproducing the dynamics among weather variables. However, although the simulated time series is new, the weather fields produced at arbitrary time steps are replicas of those found in observations, limiting the spatial variability of simulations and preventing the generation of extreme weather fields beyond the range of observed values. To address these limitations, we propose the integration of the Direct Sampling algorithm—a data‐driven method for producing simulations—into resampling‐based weather generators. By incorporating Direct Sampling as a post‐processing step on the outputs of the weather generator, we enhance the spatial variability of the generated weather fields and enable the generation of extreme weather fields. We introduce an approach for generating out‐of‐sample extreme weather fields using Direct Sampling. This method involves utilizing a set of control points in conjunction with Direct Sampling, where the values of these control points are informed by return period analysis. The proposed approach is validated using precipitation, temperature, and cloud cover weather fields in a region of northwest India. The experimental results confirm that Direct Sampling enhances the spatial variability of the weather fields and facilitates the generation of out‐of‐sample precipitation fields that accurately adhere to the spatial statistics provided by return precipitation level maps, as well as the observed precipitation weather field employed in the analysis.

Implications of climate change on surface temperature in North Indian states: evidence from CMIP6 model ensembles

Introduction: The significant impact of climate change on temperature is an important topic of discussion as it rises globally. Hence, the present study is designed to investigate the profound influence of climate change on temperature by considering the North Indian States.Methods: In this comprehensive case study, CMIP6 models are used to investigate temperature behaviour in the states of North India under 1.5°C and 2°C warming scenarios. Comparisons are made between observed surface temperature data from the Climatic Research Unit (CRU) and ensemble mean simulations from CMIP6.Results and Discussion: Results indicate that CMIP6 ensemble mean simulations effectively depict observed climatological patterns of surface temperature with negligible discrepancies. Under both the 1.5°C and 2°C warming scenarios, extreme temperatures show an increase compared to the preindustrial and present periods, suggesting an elevated risk of future severe heat events. Temperature changes relative to the preindustrial period are around 1.5°C, 3°C, and 4.5°C for the present, 1.5°C, and 2°C scenarios, respectively. Return period analysis shows a significant temperature rise of approximately 4.5° over a return time of 60 years. These findings highlight the importance of climate models, valuable for impact studies, and emphasize the need to accurately enhance future model iterations’ precision in simulating regional climates. Urgent climate change mitigation strategies are vital to curb temperature rise and mitigate potential adverse impacts on the region.Conclusion: The study provides critical insights into North India’s climate behavior, underscoring the significance of proactive measures to effectively address climate change challenges within the region.

Indirect and Direct Impacts of Typhoon In-Fa (2021) on Heavy Precipitation in Inland and Coastal Areas of China: Synoptic-Scale Environments and Return Period Analysis

Abstract In July 2021, Typhoon In-Fa (TIF) triggered a significant indirect heavy precipitation event (HPE) in central China and a direct HPE in eastern China. Both these events led to severe disasters. However, the synoptic-scale conditions and the impacts of these HPEs on future estimations of return periods remain poorly understood. Here, we find that the remote HPE that occurred ∼2200 km ahead of TIF over central China was a predecessor rain event (PRE). The PRE unfolded under the equatorward entrance of the upper-level westerly jet. This event, which encouraged divergent and adiabatic outflow in the upper level, subsequently intensified the strength of the upper-level westerly jet. In contrast, the direct HPE in eastern China was due primarily to the long duration and slow movement of TIF. The direct HPE occurred in areas situated less than 200 km from TIF’s center and to the left of TIF’s propagation trajectory. Anomaly analyses reveal favorable thermodynamic and dynamic conditions and abundant atmospheric moisture that sustained TIF’s intensity. A saddle-shaped pressure field in the north of eastern China and peripheral weak steering flow impeded TIF’s movement northward. Hydrologically, the inclusion of these two HPEs in the historical record leads to a decrease in the estimated return periods of similar HPEs. Our findings highlight the potential difficulties that HPEs could introduce for the design of hydraulic engineering infrastructure as well as for the disaster mitigation measures required to alleviate future risk, particularly in central China.

Characterization and return period analysis of meteorological drought under the humid subtropical climate of Manipur, northeast India

Monitoring drought characteristics is crucial for understanding drought behaviour and developing effective mitigation plans. In this study, we analyze the characteristics of meteorological droughts in the eastern Himalayan region by utilizing both the Standardized Precipitation Index (SPI) and Copula functions. In this study, we utilized monthly rainfall data spanning 35 years to estimate three critical characteristics of droughts: duration (D), severity (S), and Intensity (I). To determine the best fit marginal distribution of each univariate drought characteristic, we employed five commonly used probability distribution functions (PDFs). We conducted Kolmogorov-Smirnov (K–S) and Anderson-Darling (A-D) tests.The bivariate modelling for the joint D-S, S–I, and I-D datasets involves fitting Archimedean families such as Frank, Clayton, Gumbel, and Joe copulas. To perform the trivariate modelling, two meta elliptical copulas, including Normal and Frank, and two Archimedean families, namely Clayton and Gumbel, are fitted using the test statistics BIC (Bayesian Information Criterion) and AIC (Akaike Information Criterion). The cross-validation process using Maximum Likelihood Estimation (MLE) is employed to identify the most appropriate Copula model based on its goodness of fit. This step is crucial for selecting the best model to accurately describe the joint behaviour of drought characteristics. Once the best-fit Copula model is determined, it is utilized to estimate the return period of various drought characteristics, thereby facilitating the investigation of their joint return period. Furthermore, the distribution of S, D, and I classes is categorized into different return periods (T) to facilitate drought management planning.The findings revealed moderate drought conditions were recorded for SPI 1 and SPI 3 with a 2–5 years return period. For SPI 1, this drought class remains seasonal even for higher return periods. Further, the drought class transitions from seasonal to quarter for SPI 3 and a return period of 10–50 years. Regarding SPI 6 and SPI 12, the drought class is seasonal for a return period of 2 years, but it later progresses into the quarter to long-term drought class.

Tree Rings as Proxies of Historical Runoff in a National Park in Northern Mexico: A Major Ecosystem Service Provider

A dendrochronological network of conifers (Pinus leiophylla, Pinus cembroides, Pinus engelmannii) was developed in the Cumbres de Majalca National Park (CMNP) in Chihuahua, Mexico, to reconstruct historical runoff patterns and examine the impact of ocean–atmosphere phenomena. The CMNP plays a vital role as a runoff source for Conchos River tributaries and groundwater recharge for Chihuahua City and nearby populations. The ring-width chronologies displayed a common signal from 1859 to 2021, with the highest association found between P. engelmannii and P. leiophylla (r = 0.65) and the lowest between P. cembroides and P. engelmannii (r = 0.55). The first principal component explained 75.7% of the variance, and among the species, P. leiophylla exhibited the highest correlation (0.624, p < 0.05) with the accumulated streamflow records from the previous November to July, allowing the construction of a bootstrapped model for runoff reconstruction. The reconstructed streamflow spanned from 1859 to 2014, with an average of 2.732 × 108 m3. Periods of low runoff occurred in 1860–1880, 1940–1960, and 1994–2014, while extreme wet years with high runoff occurred in 1865, 1884, and 1987. The interannual streamflow variability correlated significantly with ENSO indices (SOI, MEI, TRI, and sea surface temperature anomalies), particularly during the winter–spring seasons, indicating that warm phases of the ENSO increased precipitation and runoff. The analysis of return periods revealed probabilities for specific runoff volumes, enabling stakeholders to use the information to develop effective strategies for sustainable water allocation and utilization in the region.

Estimation of multivariate design quantiles for drought characteristics using joint return period analysis, Vine copulas, and the systematic sampling method

Abstract The Wei River Basin has suffered from severe droughts. It is essential to build drought relief projects to cope with drought disasters. Traditionally, design quantiles have been estimated using univariate analysis, in which multiple characteristics of hydrological events are not considered. To design the more appropriate hydrological projects for the case study area, the Wei River Basin, it is essential to conduct research on multivariate analysis allowing multiple characteristics to be considered simultaneously. Here, the authors focus on hydrological drought (the basis for designing a hydrological project), and a framework to calculate the joint design quantiles of three drought characteristics is proposed. The most likely design quantiles relating to a specific return period, reflecting the highest occurrence probabilities among multiple combinations of variables, are derived by the maximum joint probability density function. Results show that compared to univariate analysis, design quantiles calculated via joint return period yield infrastructure with a smaller total storage capacity in the study area relating to a specific return period, i.e., reduces the economic input while maintaining the project safety. Proposed methods bring new sights to the design project. However, multi-method comparisons considering more uncertainties, inherent laws, investment, and other limited factors are still vital.

Assessing the long-term impact of climate change on olive crops and olive fly in Andalusia, Spain, through climate indices and return period analysis

The objective of this study is to investigate the long-term effects of climate change on olive crops in Andalusia, one of the most important olive growing areas worldwide, using tailored climate indices and return period analysis. Both the climatic indices and the occurrence probabilities of bad years in terms of olive yield and olive fly risk are being calculated for the reference period 1971–2000 and for the near (2031–2060) and distant future (2071–2100) under two emission scenarios using an ensemble of five high-resolution, bias-adjusted Regional Climate Models. Three threshold-based temperature related indices (SPR32, SU36, SU40) as well as mean temperature and precipitation related climatic indices (SPRTX, WINRR) have been calculated, revealing increasing changes in the near and distant future and pointing out the challenges faced by the olive sector due to climate change; a robust increase of the threshold-based indices are found in central and northern Andalusia, mainly in the provinces of Seville, Cordoba and Jaen. Although the changes of the precipitation-related index are less pronounced for the near future, robust decreases are projected for the distant future period under a high emission scenario. The identification of the bad years is based on observational data from five monitoring stations in Andalusia (Malaga, Granada, Sevilla, Cordoba and Jaen). The role of certain meteorological parameters (precipitation, temperature, relative humidity) is investigated and the return levels of interest for the calculation of the occurrence probabilities are estimated by the upper tail of the reference distribution. The results indicate an overall tendency for increased occurrence probability of bad years in terms of olive yield due to future decreased precipitation; bad year occurrences with return periods of below 2 years may increase by about 20 % by 2060. Spatial variability is also evident with overall larger changes in western Andalusia. The range of the olive fly is expected to decrease in the northern areas of Andalusia.

Isopluvial Analysis and Intensity Duration Frequency (IDF) Curves for different cities in India

Globally water resource management is becoming a priority for every Government. This is due to tremendous increase in population growth,urbanization, modern life style, increase in agricultural activities, industrialization etc. which is increasing the water demand day by day. This paper focuses on design storms studiesusing return period analysis technique (Isopluvial analysis) based on 129 well distributedSelf Recording Raingauge (SRRG) stations in the various climatic zones of India. The extreme rainfall values are taken for preparation of Isopluvial maps for return period of 2-year, 5- year, 10-year, 25-year, 50-year and 100-year at1-hour, 3-hour, 6-hour, 12-hour and 24-hour duration. Further, station wise Intensity Duration Frequency (IDF) curves are also prepared for 27 stations of the country. The Gumble distribution technique for extreme rainfall series is used for this study. Isopluvial maps & IDF curves are found very useful for Design engineers, hydrologists and hydraulic-consultants for the purpose of planning & designing of water resources related projects in the country forthe construction of any drainage network system, metro, road ways etc.and also, to some extend for the estimation of availability of water.
 

Drought Occurrence Probability Analysis Using Multivariate Standardized Drought Index and Copula Function Under Climate Change

This study aims to investigate the effect of climate change on the probability of drought occurrence in central Iran. To this end, a new drought index called Multivariate Standardized Drought Index (MSDI) was developed, which is composed of the Standardized Precipitation Evapotranspiration Index (SPEI) and the Standardized Soil Moisture Index (SSI). The required data included precipitation, temperature (from CRU TS), and soil moisture (from the ESA CCA SM product) on a monthly time scale for the 1980–2016 period. Moreover, future climate data were downloaded from CMIP6 models under the latest SSPs-RCPs emission scenarios (SSP1-2.6 and SSP5-8.5) for the 2020–2056 period. Based on the normalized root mean square error (NRMSE), Cramer-von mises statistic (Sn), and Nash Sutcliffe (NS) evaluation criteria, the Galambos and Clayton functions were selected to derive copula-based joint distribution functions in both periods. The results showed that more severe and longer droughts will occur in the future compared to the historical period and in particular under the SSP5-8.5 scenario. From the derived joint return period, a drought event with defined severity or duration will happen in a shorter return period as compared with the historical period. In other words, the joint return period indicated a higher probability of drought occurrence in the future period. Moreover, the joint return period analysis revealed that the return period of mild droughts will remain the same, while it will decrease for extreme droughts in the future.

Drought identification based on Palmer drought severity index and return period analysis of drought characteristics in Huaibei Plain China

The threshold level method for drought identification is challenging due to the problems of selection of drought index reflecting the drought process associated with water supply and demand as well as the underlying physical meaning of drought thresholds. The frequently used hydrological drought indices (e.g., runoff) are susceptible to being affected by human activities, and drought characteristics are incapable of revealing spatial and temporal comparability. Furthermore, the drought process with the same severity but a longer duration is more likely to be evaluated as a more severe event, which contradicts the actual drought situation. In this study, the Palmer drought severity index (PDSI) method, in which the meteorological factors less influenced by human activities were taken as the input, was adopted to determine the dry/wet states and the PDSI value at each period firstly. The dry/wet states were defined with dry period, wet period, transition period, transition period in dry spell, and transition period in a wet spell. Following that, drought identification criteria were established through the dry/wet states and PDSI value according to the consistency of the identified results and the actual drought situations. Particularly, drought severity and peak intensity were taken as drought characteristics in this paper, and the joint return periods of the characteristics were estimated based on the Gumbel-Hougaard copula function. And eventually, a case study was conducted in Huaibei Plain, China. The results showed that the most severe droughts identified by PDSI had a good consistency with the actual drought situations, drought severity and peak intensity were applicable to reflect the drought impacts. It is worth noting that the implications of the joint return period and the relationships among different types of them. The occurrence probability of a multi-characteristic drought event should be calculated by the integration of joint probability density function over the region corresponding to the event of interest, and the joint frequency of drought characteristics should not be used as the occurrence probability of the drought disaster losses greater (or less) than that caused by the drought with the same characteristics. In addition, drought processes identified by PDSI and standardized precipitation indices (SPI) from monthly and seasonal scales were compared, indicating the drought identified results through PDSI are almost consistent with the actual situations.

Assessing Coastal Road Flood Risk in Arctic Alaska, a Case Study from Hooper Bay

Rising waters and land subsidence are increasing relative sea levels in western and northern Alaska, forcing communities to relocate or armor in place. To appropriately plan and make equitable decisions, there is a need to forecast the risk of flood exposure in coastal Alaskan communities and to evaluate methods to mitigate that risk. This paper conducts use-inspired science to evaluate the current and future flood exposure of roads in Hooper Bay, Alaska, proposes a unit cost of flood exposure to estimate the cost of flooding, and compares various mitigation efforts including elevating roads and building dikes. Nine historic storms and their associated flood depths were subject to return-period analysis and modeled for several sea level rise scenarios. Based on the simulated road flood exposure (km hours/storm), and the storm-return period, an annual flood exposure (km hours/year) was computed. Then, the unit cost of flood exposure (USD/km hours) was determined as the ratio of the cost of flood mitigation (USD/year) to the annual flood exposure mitigated by the project. The analysis found that the unit cost of flood exposure, in conjunction with flood exposure calculations, does provide an approximate flood risk calculation, though a unitized cost of flood exposure needs to be divided into lump sum costs and materials costs. The analysis also found that dikes may be a more cost-effective alternative than road elevation. The flood risk calculation, based on the unit cost of flood exposure, could be made for all of the communities in a given region to identify those communities that face a high flood risk. Furthermore, if one divides the unit cost of flood exposure by the population, one obtains a cost/benefit ratio that potentially could be used to prioritize flood mitigation work.

Storm damage risk assessment for offshore cage culture

This study establishes a practical methodology for storm damage risk assessment and zoning for offshore cage culture by superimposing the vulnerability theory onto hydrodynamic modeling. The peak significant wave height is statistically correlated to the normalized fatigue load criteria at specified loading cycles to classify the destruction duration and damage state during storm events. The yield damage of the flotation pipe under cyclical wave oscillations was identified as the major failure mechanism of the sea cage. The structural failure risk would significantly decrease to 4.5-m waves, which is much lower than the manufacturing standard due to cumulative damage in a relatively short operation time. The wave-circulation model was applied to hindcast 112 high-impact typhoon scenarios in the study area for the return period analysis of the storm wave field. The damage risk distribution of the cage culture was mapped across the landscape to increase storm strength according to the vulnerability classification. The effectiveness of island shielding was explicitly revealed, and the leeward side was overall less risky as the return period increased, with the safe area shrinking inshore. For extreme conditions over a 100-year return period, the shield performance of leeward bays varied significantly due to the shoreline profile differences. Some existing cage deployment locations are at-risk relative to the structural resistance capability, whereas other unrecognized areas might be suitable for cage culture. These findings imply the necessity of storm risk assessment before proactive prevention measures and deployment zoning. The methodology may also be adapted to storm risk predictions for other facilities in the coastal ocean.

Copula‐Based Bivariate Return Period Analysis and Its Implication to Hydrological Design Event

AbstractIn univariate frequency analysis, the return period of an event has a one‐to‐one correspondence with its characteristic value, and the response of the hydraulic structure to hydrological load expressed by the hydrological event is monotonic. Thus, the design criteria of the hydraulic structure can be equivalently represented by the return period of the hydrological event, and consequently, design event‐based design parameters evaluated have been widely used in practical engineering. However, the monotonic correspondence between the return period of the hydrological event and the response of the hydraulic structure does not exist in the multivariate context, and hydrological load with a larger joint return period does not always produce a more unsafe response. Misunderstandings of concepts of return periods of hydrological event, and estimation of hydrological design events usually take place in multivariate frequency analysis. This study theoretically derives the relations between different types of joint return periods, joint return period and its marginal return periods, the occurrence of bivariate extreme events and their return periods, and then the theoretical framework is tested. Results from the case contribute to the understanding of bivariate return periods of hydrological event, and the results demonstrate that design criteria cannot be equivalently represented by joint return periods of hydrological load, and design parameters of the hydraulic structure should not be determined by multivariate hydrological design events.