Return Period Of Events Research Articles

Hydrodynamic exposure – on the quest to deriving quantitative metrics for mariculture sites

This work attempts to define metrics for hydrodynamic exposure, using known oceanographic variables to provide a universal site assessment method for mariculture structures. Understanding environmental conditions driving open-ocean mariculture siting is crucial in establishing consistent ocean governance, minimizing adverse environmental impacts, and facilitating economically sustainable farm operations. To provide a metric of oceanic conditions and associated requirements for structural design and operation of aquaculture systems, six Exposure Indices (EI) are proposed that consider physical energy levels related to hydrodynamic forces at a site. Four of the proposed indices consider only environmental conditions, while the other two also consider the dimensions of the gear that is exposed to the external loads. These indices are: Exposure Velocity (EV), Exposure Velocity at Reference Depth (EVRD), Specific Exposure Energy (SEE), Depth-integrated Energy Flux (DEF), Structure-centered Depth-integrated Energy (SDE), and a Structure-centered Drag-to-Buoyancy Ratio (SDBR). While these indices are derived with a focus on aquaculture structures, they may also have applications for estimating biological stressors and operational challenges. The proposed exposure indices were evaluated for a range of known aquaculture sites around the world. A sensitivity analysis was conducted that quantified the relationship between the exposure indices and storm event return period. At a regional scale, hindcast numerical data for the German Bight combined with calculations of 50-year extreme values were used to calculate and map each proposed index spatially. Resulting maps showed that exposure is not simply a function of distance from shore. The six indices show plausible performance regarding the objective assessment of aquaculture sites. The authors herein present the indices to the aquaculture and ocean engineering communities for discussion, application, and potential adoption of one or more of the proposed indices.

Probabilistic risk analysis of local verification of Load Model 1 in Eurocode for Soil-Steel Composite bridges in Sweden

Bridges must be designed to ensure safety for all users. At the same time, the design should be performed with an appropriate risk level. In Sweden, Soil-Steel Composite bridges (SSCB) are the most common bridge type. For SSCB, local verification of Load Model 1 in Eurocode is most often governing the design. The objective of this study was to investigate whether local verification of LM1 load case could be modified without decreasing the agreed risk level in Eurocode. Weight in motion measurements from real traffic were extrapolated with Rice formula. Monte Carlo simulations were used to simulate the 1000-year return period event to obtain the acceptable risk level as prescribed in Eurocode. The results show that the local verification of LM1 is conservative, considering the acceptable risk level in Eurocode. With a modified implementation of local verification, this paper shows that a potential saving of up to 14% in terms of economic cost and CO2-equivalents is possible. A modified implementation of local verification of LM1 in Eurocode for SSCB is proposed, which could reduce the climate impact by up to 14% associated to the construction of new SSCB in Sweden.

Hydrological and hydrodynamic modelling for flood management: A case study of the Yamuna River Basin in Delhi

Study regionYamuna River (Delhi), India. Study focusThe anthropogenic activities within the vicinity of the floodplain reduce the river's margin and subsequently alter the magnitude of the river's flow. The encroachment of riverbeds leads to waterlogging and flooding in urban areas, thereby causing damage to property, human life, etc. It necessitates a comprehensive study of the floodplain and changes in its proximity such as encroachment of floodplains to carry out any further activities with certainty. This study employs a two-dimensional model to simulate the Yamuna River's (YR) hydrodynamic characteristics, focusing on India's Delhi region. New hydrological insightsSimulated flood flows are employed to evaluate floods of once in 10, 20, 25, and 30-year return periods using the flood frequency analysis for 1951–2013. The model validation results indicated that the model could mimic the flood depth in YR. Simulation results revealed that the floodplain's encroachment had increased the severity of the floods. The increase in the extremeness of flooding events, i.e., from once in a 10-year return period to a 30-year return period event, is expected to increase the areas at risk of floods by 12 %. The model also offers a potential platform for evaluating other alternatives, such as further encroachment, for a business-as-usual scenario or for restoring the Yamuna floodplains. With such a comprehensive perspective, floodplains' role enhances river basin resilience to climate and anthropogenic changes and increases flood safety.

Risk-informed design of debris-flow mitigation at Cheekeye Fan

The Cheekeye Fan, located within the District of Squamish, is prone to debris-flow hazards that pose unacceptable risk to development. This article describes how a debris-flow barrier that would protect existing and proposed development was designed to achieve locally-adopted risk tolerance criteria. A risk assessment showed that the barrier should manage debris flows with volumes up to 2.8 Mm3 (1:10,000-year return-period events) to achieve tolerable risk, and that debris flows with volumes below 0.2 Mm3 (10 to 30-year events) can pass the barrier through an outlet without exceeding risk tolerance thresholds. The local government specifies that tolerable debris-flow risks be reduced ‘As Low As Reasonably Practicable’ (ALARP), defined in this project as the point where the cost of additional mitigation measures is grossly disproportionate to the benefits gained. By estimating the disproportionality ratio for potential auxiliary measures, this study shows that the barrier reduces risk ALARP without additional measures in place. The authors believe that new development approval on Cheekeye fan would not be possible without the risk-informed decision-making process described in this article.

Retrofit of grass swales with outflow controls for enhancing drainage capacity

Reduction of runoff flow peaks and volumes is one of the performance objectives of grass swales in the context of Green Stormwater Infrastructure (GSI). Towards this end, a study of the feasibility of using a retrofitted swale outlet control weir (SOCW) to reduce runoff volume and peak flow, by enhancing swale runoff storage and infiltration into swale soils, was conducted in Luleå, Northern Sweden. Experimental field work consisted of 43 irrigation-driven runoff experiments, mimicking rainfall events with return periods between 1- to 50-years, with a constant intensity and duration of 30 min, in a 30-m long grass swale section. Experimental results confirmed that, under the tested conditions, swales with the retrofitted outflow control, reduced runoff volumes and peak flows. Such reductions ranged from 32 percentage points (for 2-year) to 1 and 4 percentage points (for 50-year return period) for runoff volumes and peak flows, respectively. Outcomes of scenarios with outflow controls clearly indicated a decreasing performance with increasing flow rates (and irrigation event return periods). Furthermore, the retrofitted swale controlled the outflow release during less frequent 20 to 50-year events, which would contribute to reducing flood risks in downstream urban areas.

Comparison of the calculated frost event return period based on copula models under climate change: a case study of Chadegan region in Isfahan province- Iran

Comparison of the calculated frost event return period based on copula models under climate change: a case study of Chadegan region in Isfahan province- Iran

Rainfall-Runoff Simulation for Ungauged Watershed: A Case of Bessre Watershed, Duhok Province, Iraq

The scarcity of measured hydrological data poses a challenge in many developing countries, stemming from insufficiently established gauging stations. Due to the mentioned issue, it is crucial to develop models capable of conducting reliable simulations of runoff behavior, particularly for ungauged catchments. Understanding the intricate relationships in rainfall-runoff modeling is essential for estimating peak flows, a critical aspect in formulating water resources management strategies, which can aid in water resource management and planning. In areas prone to floods performing, an extensive hydrological study becomes necessary. This study determined the outflow discharge at the outlet point of the Bessre Valley Ungauged Catchment (41.4 km2) using the Watershed Modeling System, used by reliable hydrological standards as a graphical interface integrating with the Hydrologic Modeling System (HEC-HMS). Bessre Valley watershed is one of the flood-prone watersheds in the Duhok governorate, mainly due to the terrain’s steep slopes at the upper north and east of the catchment. The catchment was delineated by a Geographic Information System (GIS). Its properties were extracted from a 12.5 m × 12.5 m Digital Elevation Model (DEM), which evaluates the hydrological response of a watershed to two significant storm events: a real rainfall event in March 2020 and a hypothetical 100-year return period event by dividing the watershed into ten sub-basins. Achieving a Nash-Sutcliffe efficiency of 0.895 indicates a high accuracy between observed and simulated peak flows of the real rainfall event of March 2020, underscoring the model's reliability for hydrological predictions. Also, comparing the HEC-HMS model and the Rational Method of (100 YRP event) for calculating peak discharges revealed a mere 2.2% error. Furthermore, the study explores the potential for building additional dams based on discharge volumes from specific sub-basins to enhance flood control and water storage capabilities.

Comparative Study of Low Flow Frequency Analysis Using Bivariate Copula Model at Soyanggang Dam and Chungju Dam

A univariate analysis that relies solely on precipitation data in low flow frequency analysis is a technique to express meteorological drought, so it is limited to analyzing the characteristics of hydrological drought related to available water resources. In addition, if the data for the model calibration are insufficient, the uncertainty of a single variable limits the construction of a reliable model. To improve this problem, a frequency analysis was performed by constructing a bivariate copula model as a multivariate model with a high correlation between variables targeting reservoir inflows. The methodology utilizes the theory of runs to identify low flow events, establishing a threshold based on the mandatory regional water supply plan, and determining the low flow duration and cumulative water deficit. The Gumbel copula function, effective in capturing correlations between hydrological variables, was applied to derive a joint bivariate probability distribution, facilitating the calculation of combined low flow event return periods. This study compared low flow frequencies at Soyanggang dam (’74–’22) and Chungju dam (’86–‘22), which are in the same Han River basin but have different capacities and water demands, using a bivariate copula model. The top four extreme low flow events for the two adjacent dam basins did not occur in the same year and, in the years of the extreme low flow events at one of the two dam basins, there was an insignificant magnitude at the remaining dam basin. This result is noteworthy because it shows that the possibility of extreme low flow events appearing simultaneously in both watersheds is not as high as expected. The operational efficiency can be improved by setting the coordinated operation rules of the two reservoirs using the copula dependency structure.

A multi‐model likelihood analysis of unprecedented extreme rainfall along the east coast of Australia

AbstractA large stretch of the east coast of Australia experienced unprecedented rainfall and flooding over a two‐week period in early 2022. It is difficult to reliably estimate the likelihood of such a rare event from the relatively short observational record, so an alternative is to use data from an ensemble prediction system (e.g., a seasonal or decadal forecast system) to obtain a much larger sample of simulated weather events. This so‐called ‘UNSEEN’ method has been successfully applied in several scientific studies, but those studies typically rely on a single prediction system. In this study, we use data from the Decadal Climate Prediction Project to explore the model uncertainty associated with the UNSEEN method by assessing 10 different hindcast ensembles. Using the 15‐day rainfall total averaged over the river catchments impacted by the 2022 east coast event, we find that the models produce a wide range of likelihood estimates. Even after excluding a number of models that fail basic fidelity tests, estimates of the event return period ranged from 320 to 1814 years. The vast majority of models suggested the event is rarer than a standard extreme value assessment of the observational record (297 years). Such large model uncertainty suggests that multi‐model analysis should become part of the standard UNSEEN procedure.

Evaluating the impacts of climate change and land-use change on future droughts in northeast Thailand

The impacts of climate change (CC) on droughts are well documented, but the effects of land-use change (LUC) are poorly understood. This study compares the projected individual and combined impacts of these stressors on future droughts (2021–2050), with respect to baseline (1981–2010) in one of the major tributaries of the Mekong River. LUC impacts on hydrological droughts are minimal compared to CC, with the latter expected to shorten the recurrence interval of a 20-year return period event to every 14 years. Both CC and LUC have significant impacts on agricultural droughts with heightened sensitivity. ‘Once in a Decade’ agricultural droughts will be 40% (35%) longer and 88% (87%) more severe under the CC (LUC) scenario. Under both stressors, the events occurring every 20 years will be twice as frequent. Results highlight the intensification of future droughts and the urgency for actions to mitigate/adapt to climate change and manage land use. Future policy shall holistically address agricultural water management, sustainable land use management, and crop management to cope with future droughts. We recommend developing resilient agricultural practices, enhanced water resource management strategies, and incorporating drought risk into land-use planning to mitigate the compounded impacts of CC and LUC.

Global prediction of extreme floods in ungauged watersheds

Floods are one of the most common natural disasters, with a disproportionate impact in developing countries that often lack dense streamflow gauge networks1. Accurate and timely warnings are critical for mitigating flood risks2, but hydrological simulation models typically must be calibrated to long data records in each watershed. Here we show that artificial intelligence-based forecasting achieves reliability in predicting extreme riverine events in ungauged watersheds at up to a five-day lead time that is similar to or better than the reliability of nowcasts (zero-day lead time) from a current state-of-the-art global modelling system (the Copernicus Emergency Management Service Global Flood Awareness System). In addition, we achieve accuracies over five-year return period events that are similar to or better than current accuracies over one-year return period events. This means that artificial intelligence can provide flood warnings earlier and over larger and more impactful events in ungauged basins. The model developed here was incorporated into an operational early warning system that produces publicly available (free and open) forecasts in real time in over 80 countries. This work highlights a need for increasing the availability of hydrological data to continue to improve global access to reliable flood warnings.

Theoretical Boundaries of Annual Flood Risk for Single-Family Homes Within the 100-Year Floodplain

Special flood hazard areas (SFHAs), defined as having an annual probability of occurrence of 1 percent or above, are used by U.S. Federal Emergency Management Agency (FEMA) to demarcate areas within which flood insurance purchase is required to secure a mortgage. However, quantifying flood risk within SFHAs can be challenging due to the lack of modeled flood depth data for all return periods. To address this issue, this research quantifies flood risk indicated by average annual loss (AAL) within the A Zone—the subset of the SFHA where wave heights can potentially range from 0 to 3 feet. The methodology resolves the Gumbel quantile function for four distinct flooding cases (i.e., locations flooded at return periods exceeding 1.58-, 10-, 25-, and 50-year return period events) and generates synthetic flood hazard parameters for these cases within the 100-year floodplain, as well as with additional elevation above the base flood elevation (BFE), known as freeboard, for single-family homes with different attributes. The results indicate that for single-family homes in the A Zone, with the lowest floor elevated to the BFE, the AAL ranges from 0.3 to 1 percent of the building replacement cost value. Adding one foot of freeboard reduces flood risk by over 90% if the annual flood risk is between the minimum and 25th percentiles and the 100-year flood depth is less than two feet. The demonstrated approach helps enhance flood resilience in the A Zone, demonstrating the feasibility of proactive measures to protect communities.

Quantifying the compound hazard of freezing rain and wind gusts across CONUS

The co-occurrence of freezing rain, ice accumulation and wind gusts (FZG) poses a significant hazard to infrastructure and transportation. However, quantification of the frequency and intensity of FZG is challenged by the lack of direct icing measurements. In this work, we evaluate and then apply an energy balance model to high-frequency data collected during 2005–2022 to derive hourly ice accumulation at 883 stations across the contiguous USA. These estimates are combined with wind gust observations to compute time series of hourly FZG hazard magnitude using the Sperry–Piltz Ice Accumulation (SPIA) index. Results are evaluated using Storm Reports of damage and economic disruption. The hourly SPIA estimates are also used to (i) derive a geospatial atlas of the hazard including the 50 yr return period event intensities for each US state derived using superstations, and (ii) describe storylines of significant events in terms of meteorological drivers and socioeconomic impacts. The highest values of SPIA during the 18 yr study period occur in a region extending from the Southern Great Plains over the Midwest into the densely populated Northeast. States in these regions also have high 50 yr return period maximum radial ice accumulation of 3–5 cm and co-occurring wind gusts >30 ms−1. These values are comparable to past estimates for the 500 yr event which may imply this hazard has been previously underestimated. This atlas can be used to inform optimal FZG hazard mitigation strategies for each state/region.

Improving precipitation estimates for Turkey with multimodel ensemble: a comparison of nonlinear artificial neural network method with linear methods

AbstractEnsemble analysis is proven to provide advantages in climate change impact assessment based on outputs from climate models. Ensembled series are shown to outperform single-model assessments through increased consistency and stability. This study aims to test the improvement of precipitation estimates through the use of ensemble analysis for south and southwestern Turkey which is known to have complex climatic features due to varying topography and interacting climate forcings. The analysis covers an evaluation of the performance of eight regional climate models (RCMs) from the EUR-11 domain available from the CORDEX database. The historical outputs are evaluated for their representativeness of the current climate of the Mediterranean region and its surroundings in Turkey through a comparison with long-term monthly precipitation time series obtained from ground-based precipitation observations by the use of statistical performance indicators and Taylor diagrams. This is followed by a comparative evaluation of three ensemble methodologies, simple average of the models, multiple linear regression for superensemble, and artificial neural networks (ANN). The analysis results show that the overall performance of ensembled time series is better compared to individual RCMs. ANN generally provided the best performance when all RCMs are used as inputs. Improvement in the performance of ensembling due to the use of nonlinear models is further confirmed by fuzzy inference systems (FIS). Both ANN and FIS generated monthly precipitation time series with higher correlations with those of observations. However, extreme events are poorly represented in the ensembled time series, and this may result in inefficiency in the design of various water structures such as spillways and storm water drainage systems that are based on high return period events.

Hurricane Frances in Florida: Past and Future

Tropical cyclones (TC) are common high-impact weather events in the southeastern United States. One of the most intense TCs to hit north-central Florida in recent decades was Hurricane Frances (2004), which resulted in over $10B in damages. Given the growing concern about the relationship between anthropogenic climate change, precipitation, and TCs, we investigated how a Hurricane Frances-like event would affect north-central Florida in the late 21st century under an RCP- 8.5 scenario. Using a ‘pseudo’ global warming dataset based on the high-resolution Weather Research and Forecasting (WRF) model, we explore historical and future simulations of Hurricane Frances’s path, precipitation rates, and wind speeds. We discover that, across north-central Florida sub-daily to daily rainfall totals in the future simulation exceed that of the historical, having return period events in some cases reaching 500-year thresholds. We also find more broadly across the spatial domain of the hurricane that rainfall rates in all temporal categories and total rainfall accumulation increased significantly. Results for wind speeds were mixed, with an evident expansion of tropical storm winds, but a slight contraction of the spatial extent of strongest winds at the surface. We discuss the implications of these findings for decision-makers in Florida.

Seismic collapse performance of high-rise mass timber building with buckling-restrained braced frames

The present paper proposes a hybrid building system with the buckling-restrained brace and Glulam frames (BRBGF). For a highly seismic location of Vancouver, Canada, an 18-story building is designed with BRBGF as the lateral load-resisting system for the seismic hazard of NBC 2020. A nonlinear analytical model is developed and validated with the experimental data. Further, 30 pairs of ground motion records are selected to represent the complex seismicity of Southern British Columbia consisting of Shallow Crustal, Subduction Interface, and In-slab faulting mechanisms. The BRBGF system is found to achieve the target of maximum inter-story drift of 2.5% for the 2,475-year return period event. The building also passes the FEMA P695 criteria for acceptable collapse margin for seismic force modification factors of Rd=4 and Ro=1.2. The results from the study indicate the feasibility of high-rise timber buildings in highly seismic regions.

Hydrological reduction and control effect evaluation of sponge city construction based on one-way coupling model of SWMM-FVCOM: A case in university campus

The exacerbation of global warming, the frequent incidence of extreme weather events, and the rapid urbanization have collectively contributed to the heightened prevalence of flooding in urban areas. As a result of this challenge, sponge city (SPC) has been adopted in China as an efficient means of preventing and controlling urban floods. To evaluate the hydrological reduction and control effect of sponge city construction (SPCC) within a university campus, a one-way coupled model integrating one-dimensional sewer hydrodynamic model (SWMM) and two-dimensional surface flow model (FVCOM), namely SWMM-FVCOM model, was established. The Nash-Sutcliffe efficiency (NSE) of the SWMM were greater than 0.75 under there rainfalls with different intensity, indicating the good reliability and stability of this model could be used in the subsequent simulation. An analysis of drainage capacity and the risk of urban flooding was conducted using this model before and after the implementation of SPCC, considering six rainfall scenarios. Implementing SPCC demonstrated an effective performance in mitigating surface runoff, regulating inspection well overflow, and reducing overflow volume in the study region. However, the efficacy of runoff control diminished proportionally with the escalation of rainfall return period. Simultaneously, the implementation of low impact development (LID) measures can significantly decrease the extent and magnitude of surface inundations. The reduction rate of SPCC on the area of waterlogging ranged from 55.84% to 72.50%. But the control rate decreased with increasing rainfall return periods, demonstrating that adopting SPCC can effectively mitigate the severity of urban flooding resulting from low rainfall return period events (Tr < 20 years). This study can provide scientific foundation for environment managers to evaluate the impact of urban flooding prevention and control on runoff pollution mitigation when adopting the implementation of SPCC.

Development of flood inundation maps for the Chaliyar Basin, Kerala under climate change scenarios

Abstract Floods are one of the extreme events and widespread natural disasters that significantly affect the civil infrastructure and livelihoods of people. Recently, climate change has significantly altered the rainfall pattern and increased flood events worldwide, especially in India. Therefore, it has become essential to map potential flood inundation regions for various future extreme events to develop appropriate flood mitigation and management strategies. This study aims to develop flood inundation maps for different return periods under climate change scenarios for the Chaliyar basin, Kerala. The Hydrologic Engineering Center-Hydrologic Modelling System model was used to simulate streamflow under SSP2-4.5 and SSP5-8.5 scenarios. Later, flood inundation maps were developed for different return periods using the Hydrologic Engineering Center-River Analysis System model. It was observed that for the near future (2031–2040) and far future (2071–2080), simulated streamflow is higher for SSP5-8.5. However, the mid-future (2051–2060) resulted in a higher streamflow for SSP2-4.5 than the SSP5-8.5 scenario. A maximum of 19.52 m of water surface elevation occurred at Kizhupparamba during mid-future for SSP2-4.5, followed by 18.38 m of water surface elevation at Cheekode during the near future for SSP5-8.5, for 100-year return period events. This study showed that hydrologic and hydraulic models could be effectively combined for mapping the flood inundation areas.

Riverine flood risk assessment with a combined model chain in southeastern China

Climate change and rapid urbanization have exacerbated the occurrence and impact of floods. It is essential to carry out a quantitative flood risk assessment and manage the flood risk before a disaster occurs. This article presents a combined riverine flood risk model to obtain the exceedance probability loss (EPL) curve and expected annual damage (EAD) under the current climate. This model includes a rapid flood model and a flood damage model. It aims to simulate the flood risk and evaluate the flood damage at 10-, 30-, 50-, 100-, and 200-year return period events. The results show that: (1) The total inundation areas will sharply increase when the flood return periods are over 30 years. (2) The EAD is 1,476 million dollars in the Jiulong River Basin (JRB). When the flood return period is over 30 years, the total damage increases sharply. (3) The flood risk in the lower reaches of the JRB is higher than in the upper reaches when the flood event is beyond a 20-year return period. (4) Industrial sector damage is the largest, followed by tertiary industry, transportation, construction, agriculture, and infrastructure. The combined model chain can quickly assess the flood risk in the river basin and judge the flood risk in a watershed compared to the national level. In addition, it can be applied to other river basins, and it will provide actionable information for future flood risk management.

The Partial L-Moment of the Four Kappa Distribution

Statistical analysis of extreme events such as flood events is often carried out to predict large return period events. The behaviour of extreme events not only involves heavy-tailed distributions but also skewed distributions, similar to the four-parameter Kappa distribution (K4D). In general, this covers many extreme distributions such as the generalized logistic distribution (GLD), the generalized extreme value distribution (GEV), the generalized Pareto distribution (GPD), and so on. To utilize these distributions, we have to estimate parameters accurately. There are many parameter estimation methods, for example, Method of Moments, Maximum Likelihood Estimator, L-Moments, or partial L-Moments. Nowadays, no researchers have applied the partial L-Moments method to estimate the parameters of K4D. Therefore, the objective of this paper is to derive the partial L-Moments (PL-Moments) for K4D, namely the PL-Moments of the K4D in order to estimate hydrological extremes from censored data. The findings of this paper are formulas of parameter estimation for K4D based on the PL-Moments approach. We have derived the Partial Probability-Weighted Moments (PPWMs) of the K4D (β'r) and derive the estimation of parameters when separated by shape parameters (k,h) conditions i.e., case k&gt;-1 and h&gt;0, case k&gt;-1 and h=0 and case -1&lt;k&lt;-1/h and h&lt;0. Finally, we expect that the parameter estimate for K4D from this formula will help to make accurate forecasts. Doi: 10.28991/ESJ-2023-07-04-06 Full Text: PDF