Annual Exceedance Probability Research Articles

Advancing process-based flood frequency analysis for assessing flood hazard and population flood exposure

Recent studies have showcased the use of process-based hydrological models with Stochastic Storm Transposition (SST) techniques to conduct Flood Frequency Analysis (FFA). This framework, referred hereby FFA-SST, has proved to be a robust strategy to estimate peak flows of specific annual exceedance probability (e.g., 100-year peak flow) that can reflect natural and anthropogenic disturbances, including changes in land use and meteorological patterns. With the objective of advancing the FFA-SST framework, this study presents for the first time the use of a spatially-resolved Integrated Surface-Subsurface Hydrological Model (ISSHM) to conduct FFA-SST. This allows us to extend the analysis from peak flow responses to flood extent, enabling a unique view and analysis of flood hazard and population flood exposure at the basin scale. As a proof-of-concept, we used the ISSHM, Amanzi–ATS, and the SST model, RainyDay, to conduct FFA-SST by simulating the flood response to 5000 annual synthetic storm events in a ∼2000km2 Southeast Texas watershed. We demonstrate that ATS, without site-specific calibration, provides a robust process-based representation of peak flows, flood extent, streamflow, evapotranspiration, soil moisture content, and water storage changes. Our results and analyses, covering frequency curves up to a 500-year return period for peak flows, basin inundation fractions, and the number of people exposed to flooding, offer a unique perspective to analyze flood impacts across spatial scales. Overall, this study provides critical insights for flood risk management by extending the FFA-SST framework to include both flood hazard and population flood exposure analyses at the basin scale. Such an approach will empower stakeholders and disaster emergency agencies with a more comprehensive understanding of flood impacts across the entire basin domain, facilitating informed decision-making for flood risk assessment and management.

Seismic hazard study for the duration of ground-motions triggered by intraslab earthquakes

AbstractThis paper presents a probabilistic seismic hazard analysis for the duration of ground motions in Mexico City caused by intraslab earthquakes. The study implies establishing criteria to measure the duration of ground motions and developing predictive equations (GMPEs) for its estimation. Focus is placed on the relative significant duration, $$D_{Sr}$$ D Sr , which is based on the accumulation of energy of ground motions. The study extends to the development of hazard curves that allow estimating the annual probability of exceedance of said parameter. The scope is limited to ground motions caused by intraslab earthquakes occurring in Mexico and that engineeringly affect its capital, Mexico City, which is in a geographical region where the effects of soil-dynamic amplification are manifested considerably. A strong-motion database that includes 1517 horizontal accelerograms, with peak ground acceleration, $$PGA$$ PGA , greater than or equal to 3 cm/s2, recorded during 21 earthquakes from 1994 to 2023 were used to develop the GMPEs. The quantitative description of the time, size, and spatial distribution of the seismic-activity occurrence was defined using a catalog of 46 earthquakes that occurred from 1900 to 2023. The earthquakes have moment magnitude, $$M_{w}$$ M w , greater than or equal to 6. With the tools developed, amplitude- and duration-based hazard-consistent accelerograms can be simulated and used in nonlinear dynamic analyses (NDAs) of structures. This study includes an application example where three sites in Mexico City are selected to compute inelastic response spectra. Hysteretic-energy demands up to 5 times higher are found after a comparison between the inelastic-response spectra from hazard-consistent accelerograms and those provided by current Mexican regulations.

Modelling the effect of cascade reservoir regulation on ice-jam flooding

In regulated rivers, dams and their reservoirs are the main means to change the flow conditions, and strategic reservoir operations can be an important measure for mitigating ice-jam flooding (IJF). Previous studies have shown the effect of single reservoir operation on IJF probabilities, but little is known about the effect of cascade reservoir operation on IJF occurrences and severity. This study presents a probabilistic modelling framework that couples machine learning (LSTM, Long Short-Term Memory) and river ice (RIVICE) models to assess the effects of regulated and naturalized flows on IJF backwater levels. The trained LTSM shows good performance in both validation (RMSE = 398.88, R = 0.86 and NSE = 0.67) and testing (RMSE = 366.57, R = 0.84 and NSE = 0.71) periods and the calibrated RIVICE can well simulate backwater levels caused by three IJFs from 2008 to 2010 with total volumes of incoming ice for 0.4 million m3, 0.55 million m3 and 0.32 million m3. The completed framework was used to assess IJF hazard along the Sanhuhekou bend, a reach of the upper Yellow River, whose hydraulic regime is impacted by eight upstream reservoirs. The effect of naturalized and regulated flows on IJF are compared from two aspects. Firstly, comparing the median of naturalized and regulated IJF backwater level ensembles, the results show that regulation increases the IJF backwater levels. Secondly, comparing the extreme values of naturalized and regulated IJF backwater level ensembles, results show that regulation can increase the hazard of IJF inundation when annual exceedance probability (AEP) is less severe than 1:100 and regulation can decrease the hazard of IJF when AEP is more severe than 1:125.

Development of a convolutional neural network based regional flood frequency analysis model for South-east Australia

Development of a convolutional neural network based regional flood frequency analysis model for South-east Australia

Modelling non-stationarity in extreme rainfall using large-scale climate drivers

Flood estimates used in engineering design are commonly based on intensity–duration–frequency (IDF) curves derived from historical extreme rainfalls. Under global warming these extreme rainfalls are increasing, threatening the capacity of existing infrastructure to resist failure as IDF curves traditionally assume no change in rainfall magnitude. Hence, there is a need to investigate the implications of non-stationarity in extreme rainfalls used to derive IDFs across storm durations and annual exceedance probabilities (AEPs). One way of doing this is to incorporate covariates into the fitted probability distribution. However, there are few studies which examine non-stationarity in extreme rainfall using large-scale climate drivers as covariates, with little consensus on which covariate is the most appropriate. Here we evaluate non-stationarity in extreme rainfall across different durations, from the 1 in 5 AEP to the 1 in 100 AEP, using potential large-scale climate drivers including global and continental mean temperature, continental diurnal temperature range, continental dewpoint temperature, continental precipitable water, the Indian Ocean Dipole, the El Niño Southern Oscillation, and the Southern Annular Mode. These covariates are linked to the three parameters of the Generalized Extreme Value distribution to identify the most appropriate form of non-stationary probability model. We analyse 16 different storm durations from 6 min to 7 day annual maxima across 46 stations in Australia. Based on the Akaike Information Criteria, precipitable water is the superior covariate at a large proportion of stations irrespective of storm duration. However, when the modelled quantile changes are inspected, only global temperature is able to adequately capture the variability in changes across both storm duration and annual exceedance probability. Further, when regional average values of mean temperature, diurnal temperature range, dewpoint temperature and precipitable water were used as covariates, there was no improvement in the model performance compared to continental-wide covariates, particularly for short durations. The results using a mean global temperature covariate show that stationary historical rainfall quantile estimates are underestimated by 12 % – 9 % for frequent (1 in 5 AEP) and 23 % – 13 % for rare (1 in 100 AEP) short duration (6 min – 30 min) events. Moving forward our results suggests infrastructure design needs to incorporate non-stationarity in IDFs to ensure infrastructure and flood planning levels are not under-designed.

Integrating social vulnerability into high-resolution global flood risk mapping

High-resolution global flood risk maps are increasingly used to inform disaster risk planning and response, particularly in lower income countries with limited data or capacity. However, current approaches do not adequately account for spatial variation in social vulnerability, which is a key determinant of variation in outcomes for exposed populations. Here we integrate annual average exceedance probability estimates from a high-resolution fluvial flood model with gridded population and poverty data to create a global vulnerability-adjusted risk index for flooding (VARI Flood) at 90-meter resolution. The index provides estimates of relative risk within or between countries and changes how we understand the geography of risk by identifying ‘hotspots’ characterised by high population density and high levels of social vulnerability. This approach, which emphasises risks to human well-being, could be used as a complement to traditional population or asset-centred approaches.

A National‐Scale Coastal Flood Hazard Assessment for the Atoll Nation of Tuvalu

AbstractAtoll nations such as Tuvalu are considered to be amongst those most vulnerable to the effects of climate change. Here we present a national‐scale coastal flood hazard assessment for Tuvalu based on high‐resolution Light Detection and Ranging (LiDAR) topography and bathymetry. We follow a fully probabilistic approach, considering sea level anomalies, tides, and extreme wave conditions from a mixed climate (i.e., from distant extra‐tropical storms and local tropical cyclones). Nearshore processes such as wave setup and runup are also accounted for. Hazard maps were calculated for the present sea level, as well as for sea level rise projections corresponding to different shared socioeconomic pathways (SSP2 4.5 and SSP5 8.5) and time horizons (2060 and 2100). With a mean elevation of 1.55 m above mean sea level (1.37 m above mean high water spring) >25% of land area is inundated once every 5 years and >50% of land area floods once every 100 years nationally. Results indicate that present day 1‐in‐50 years floods (>45% of land area flooded) will occur more than once every 5 years by 2060 (annual exceedance probability >20%), even under the moderate SSP2 4.5 sea level rise projections. Results of this study highlight the pressing need for ambitious and large‐scale adaptation solutions which are commensurate with projected sea level rise and marine hazard impacts. The methodologies presented in this paper can easily be applied to other low‐lying islands in the tropical Pacific, where mixed climates (i.e., regular and TC conditions) and non‐linear nearshore processes dominate extreme water levels and flooding.

FORECASTING OF FLOOD EVENTS FOR WATER RESOURCES MANAGEMENT IN GUCHA-MIGORI RIVER BASIN, KENYA

Floods are devastating natural disasters frequently occurring in many river basins such as Gucha-Migori River Basin, Kenya. However, non-structural countermeasures such as hydrologic modelling, flood proofing, and continuous forecasting have not been fully explored and implemented to reduce risk, damage, and vulnerabilities of flood events. The major challenge in flood forecasting is the selection of the relevant probability distribution. The main objective of the study was to forecast flood events in Gucha-Migori River Basin for integrated water resource management. Daily hydrological datasets between 1969 and 2015 were obtained from Water Resources Authority. After the ranking of independent flood events had been achieved, return periods and flood frequencies were computed. The relationship between flood magnitudes and their respective frequencies was through modelling using probability distribution functions (PDFs). The evaluated PDFs include Normal Distribution, Log-Normal Distribution, Gumbel Distribution, and Log-Person Type III Distribution. Coefficient of Determination (R2), Goodness of Fit test, and Best-Fit Distribution Curve guided selection of the suitable distribution model. The suitable probability distribution model out of the tested four was then applied to predict P-percent annual exceedance probabilities corresponding to specific flood magnitudes. From the results, out of the 132 flood events that occurred in the period 1969 to 2015, only 42 were identified as independent flood events. Based on the frequency analysis, the highest and lowest, and the average and standard deviation of the recorded independent flood events were 423.90, 240.8, 284.80 m3/s, and 45.54 respectively. It was revealed that the probability of a flood event of a magnitude equal to or exceeding the lowest (240.8 m3/s) and the highest (423.90 m3/s) occurring for a particular year were 0.98 and 0.02 respectively. Gumbel distribution curve was selected to be the best fit for the Gucha-Migori River Basin. According to the derived Gumbel's formula, the estimated magnitude for the return period 1.05, 1.11, 1.25, 2, 5, 10, 25, 50, 100, and 200 years were 221.38, 231.92, 245.77, 278.01, 321.39, 350.11, 386.39, 413.31, 440.03, and 466.65 m3/s. This research provides useful information for enhanced determination of probability occurrence of flood events and thus proper water resources planning and management strategies in the Gucha-Migori river basin.

A systematic review of climate change science relevant to Australian design flood estimation

Abstract. In response to flood risk, design flood estimation is a cornerstone of planning, infrastructure design, setting of insurance premiums, and emergency response planning. Under stationary assumptions, flood guidance and the methods used in design flood estimation are firmly established in practice and mature in their theoretical foundations, but under climate change, guidance is still in its infancy. Human-caused climate change is influencing factors that contribute to flood risk such as rainfall extremes and soil moisture, and there is a need for updated flood guidance. However, a barrier to updating flood guidance is the translation of the science into practical application. For example, most science pertaining to historical changes to flood risk focuses on examining trends in annual maximum flood events or the application of non-stationary flood frequency analysis. Although this science is valuable, in practice, design flood estimation focuses on exceedance probabilities much rarer than annual maximum events, such as the 1 % annual exceedance probability event or even rarer, using rainfall-based procedures, at locations where there are few to no observations of streamflow. Here, we perform a systematic review to summarize the state-of-the-art understanding of the impact of climate change on design flood estimation in the Australian context, while also drawing on international literature. In addition, a meta-analysis, whereby results from multiple studies are combined, is conducted for extreme rainfall to provide quantitative estimates of possible future changes. This information is described in the context of contemporary design flood estimation practice to facilitate the inclusion of climate science into design flood estimation practice.

Holocene Thermal Maximum paleofloods improve flood frequency analyses in the lower Tennessee River Basin (USA)

Extreme floods are underrepresented in instrumented flow records. Consequently, flood frequency model estimates of extreme floods contain large uncertainty. The lower Tennessee River (LTR) Basin is located within the southeastern United States and is a large, regulated, and socio-economically significant North American river. The flood-of-record for the LTR occurred in 1867 CE. The 1867 flood-of-record is a design flow for the dams built and managed by the Tennessee Valley Authority. Here we develop and apply an extreme paleoflood chronology and use it to improve flood frequency model estimates of large, rare floods. We created an extreme paleoflood chronology that contained paleodischarge estimates for six extreme paleofloods, dating back to 5900 years before present, for a ∼2 km segment of the LTR. We obtained paleoflood hydrologic data from two sources. These included field sampling and analysis of slackwater deposits found in a rock shelter located on a limestone bluff and published extreme paleoflood data from a terrace located within the study segment.Placed within the context of the extreme paleoflood chronology, the 1867 flood-of-record was the largest flood to occur within the last 2000 years and coincided with the conclusion of the cooler, wetter Little Ice Age. The three highest magnitude extreme floods, however, occurred during a peak in Holocene temperatures 6000 to 5000 years ago, during the mid-Holocene Thermal Maximum. Paleodischarge estimates for the three mid-Holocene extreme floods were three times larger than the estimated discharge of the 1867 CE flood-of-record. Different iterations of a Bayesian flood frequency model showed removing the three most extreme, mid-Holocene paleofloods resulted in a rarer annual exceedance probability for the 1867 CE flood-of-record, changing it from an 0.002 AEP (500-year event) to an 0.0007 AEP (1300-year event) and underestimation of annual exceedance probabilities for design flows commonly used in river management and infrastructure design, including the 100-yr, 500-yr, 1000-yr, and 10,000-yr floods. These results suggest that flood frequency models may underestimate design flows if historic-floods-of-record are assumed to be the most extreme floods within these analyses. This finding has implications for regulated rivers worldwide, which now includes most large rivers. The previously unknown, extreme mid-Holocene paleofloods, which were found to be larger than all known modern or paleofloods, significantly altered the estimates of the final flood frequency model. This finding makes a compelling case for seeking out more extreme flood paleodischarge estimates from the mid-Holocene in rivers of the Northern Hemisphere, where the mid-Holocene Thermal Maximum's occurrence is well substantiated. Flood data from the mid-Holocene Thermal Maximum may help improve flood frequency model-based estimates of extreme floods by minimizing risk associated with flood “unknown unknowns.”

Understanding the Impact of Precipitation Bias‐Correction and Statistical Downscaling Methods on Projected Changes in Flood Extremes

AbstractThis study evaluates five bias correction and statistical downscaling (BCSD) techniques for daily precipitation and examines their impacts on the projected changes in flood extremes (i.e., 1%, 0.5%, and 0.2% floods). We use climate model outputs from the Coupled Model Intercomparison Project Phase 6 (CMIP6) to conduct hydrologic simulations across watersheds in Iowa and determine historical and future flood extreme estimates based on generalized extreme value distribution fitting. Projected changes in these extremes are examined with respect to four Shared Socioeconomic Pathways (SSPs) alongside five BCSD techniques. We find the magnitude of the estimates of future annual exceedance probabilities (AEPs) are expected to increase under all SSPs, especially for the emission scenarios with higher greenhouse gases concentrations (i.e., SSP370 and SSP585). Our results also suggest the choice of BCSD impacts the magnitude of the projected changes, with the SSPs that play a more limited role compared to the choice of downscaling method. The variability in projected flood changes across Iowa is similar across the downscaling technique but increases as the AEP increases. Our findings provide insights into the impact of downscaling techniques on flood extremes' projections and useful information for climate planning across the state.

Extent of saltwater intrusion and freshwater exploitability in the coastal Vietnamese Mekong Delta assessed by gauging records and numerical simulations

Climate change-driven sea level rise has intensified salinity intrusion (SI) in deltas worldwide, posing significant threats to the exploitation of freshwater resources. In the Vietnamese Mekong Delta (VMD), the third largest delta globally, SI is a recurring challenge along the coastline, degrading freshwater resources for agricultural and domestic use and affecting socio-economic development. In this paper, we investigate the spatiotemporal extent of salinity intrusion in the Ben Tre Province, the hotspot of salinity disaster within the VMD. Long-term salinity monitoring data (25 years from 1996 to 2020) has been analyzed, and a 1D (Mike 11) coupled with 3D hydrodynamic model (Mike 3) was developed. Three scenarios were used to investigate the freshwater resources exploitation: (i) the year of investigation (2021), (ii) 2021 to 2030 climate change impacts, considering different annual exceedance probability of the upstream Mekong discharge (i.e., average flow, relatively low, low and very low), and (iii) extreme salinity intrusion (i.e., the 2016 condition). Our results indicated that salinity patterns are well-stratified at the beginning and end of the dry season but well-mixed during the middle period. Furthermore, over the last 25 years, SI has progressively increased and started earlier in the dry season. The modeling scenarios for SI have also revealed a growing complexity in the exploitation of freshwater resources, highlighting challenges related to timing, depth, and geographical location. The exceedance probability scenarios disclosed higher and deeper salinity intrusion along the channel in VMD, ranging from 50 % to 95 %. This poses significant limitations on the feasibility of freshwater exploitation throughout the Ben Tre Province. Under the current trajectory of climate change, the 2030 scenario anticipates salinity intrusion reaching further inland from the 2021 scenario. This is likely to exacerbate the existing challenges in freshwater resource exploitation, even with comprehensive water infrastructure. We, therefore, propose several management strategies to adapt to salinity intrusion: storing freshwater in main rivers, maintaining consistent operation of water infrastructure systems, and encouraging water-saving distribution and exploitation methods. Moreover, we also recommend supporting the development of new drought-tolerant crop patterns.

Predicting the peak flow and assessing the hydrologic hazard of the Kessem Dam, Ethiopia using machine learning and risk management centre-reservoir frequency analysis software

Abstract Flooding due to overtopping during peak flow in embankment dams primarily causes dam failure. The Kessem River watershed of the Awash basin in the Rift Valley of the Afar region in Ethiopia was studied intricately to predict the causes of the Kessem Dam safety using machine learning predictive models and risk management centre-reservoir frequency analysis. Recently developed recurrent neural network predictive models with hybrid Soil Conservation Service Curve Number (SCS-CN) were used for simulation of river flow. Peak daily inflow to the reservoir is predicted to be 467.72, 435.88, and 513.55 m3/s in 2035, 2061, and 2090, respectively. The hydrologic hazard analysis results show 2,823.57 m3/s and 935.21 m; 2,126.3 m3/s and 934.18 m; and 11,491.1 m3/s and 942.11 m peak discharge and maximum reservoir water level during the periods of 2022–2050, 2051–2075, and 2076–2100, respectively, for 0.0001 annual exceedance probability. The Kessem Dam may potentially be overtopped by a flood with a return period of about 10,000 years during the period of 2076–2100. Quantitative hydrologic risk assessment of the dam is used for dam safety evaluation to decide whether the existing structure provides an adequate level of safety, and if not, what modifications are necessary to improve the dam's safety.

Investigation of Ambient Seismic Noise in Different Region of Georgia

Georgia, like the whole South Caucasus, is a tectonically and structurally complex region. It is one of the active segments of the Alpine-Himalayan belt, therefore it is important to assess the seismic hazard for Georgia. At the regional scale, this assessment is evaluated by applying probabilistic seismic hazard analysis that identifies the annual probability of exceedance of various ground motion levels defined in terms of selected ground motion intensity measures, such as PGA or spectral accelerations (SA) corresponding to various return periods related to possible future earthquake scenarios for a site represented by soil classes A according to EC8, Euro-code 8-EN 1998-1 (1998). At the local scale, seismic hazard assessment is made by analyzing the geological, geomorphological, geotechnical and geophysical characteristics of the site, as it is well established that the incoming seismic motion can change in amplitude, frequency, and duration due the site-specific local characteristics. That is the subject of micro-zonation investigation. Site – specific local characteristics are presented by the following parameters: Dominant frequency, Vs,30 (average shear-wave velocity to a depth of 30 meters) and amplification factor. In this work, we presented results of geophysical survey assessing local site conditions by dominant frequency that allows identification of similar seismic response areas. For this purpose, seismic noise records have been used first time in Georgia.

Statistical Modeling and Applications of Joint Distributions for Significant Wave Height, Spectral Peak Period, and Peak Direction of Propagation: A Case Study in the Norwegian Sea

The estimation of long-term extreme response is a crucial task in the design of marine structures. The target extreme responses are typically defined by annual exceedance probabilities of 10−2 and 10−4. Various approaches can be employed for this purpose, with preference given to statistical long-term analysis, which involves aggregating the exceedance probabilities of all potential sea states contributing to the exceedance of the target extremes. A joint model encompassing important metocean parameters such as wind, waves, and current is often necessary. This study specifically focuses on waves and wave-induced responses. In characterizing short-term sea state conditions, significant wave height (Hs), spectral peak period (Tp) and peak direction of propagation (Φp) are identified as the most important sea state characteristics. The objective of this work is to present the results of the joint model for the three sea state parameters, i.e., Hs, Tp and Φp, at an offshore site in the Norwegian Sea. The conditional modeling approach is applied using long-term hindcast data, and different statistical models are discussed for fitting the marginal and conditional distributions. The fitted parameters for all directional sectors are provided, offering a comprehensive representation of the joint model for direct use in long-term response analysis. Two case studies are included to illustrate the application of the fitted joint model in long-term response analyses. The case studies identify the governing wave directions and the most important combinations of short-term sea state characteristics regarding the estimation of long-term extreme responses.

Framework for risk assessment of economic loss from structures damaged by rainfall-induced landslides using machine learning

ABSTRACT Given the increased frequency of extreme rainfall events, pre-disaster countermeasures against landslides triggered by heavy rainfall are important to enhance disaster resilience. This study presents a methodology for economic risk assessment of structures affected by rainfall-induced landslides using machine learning (ML). Random Forest and LightGBM algorithms were applied to develop ML-based landslide prediction models considering the spatial distributions of landslide conditioning and triggering factors. The rainfall index was calculated considering the temporal variation in rainfall and was used as a feature associated with rainfall intensity. The rainfall hazard curve, representing the relationship between the rainfall index and its annual exceedance probability, was statistically estimated using a generalised extreme value distribution. Rainfall-induced landslide susceptibility was assessed using an ML-based landslide prediction model and rainfall hazard curve. Finally, the risk curve associated with the economic loss from structures damaged by rainfall-induced landslides was estimated based on landslide susceptibility and structure distribution maps. In this study, LightGBM showed better prediction performance for evaluating rainfall-induced landslide susceptibility than Random Forest. An illustrative example is presented to demonstrate that the proposed methodology can be used to develop an appropriate risk-based disaster mitigation strategy.

A Design Water Discharge Maxima Forecasting Method Based on Observation Data Using Plotting Position Formulas

Plotting position formulas provide a non-parametric means to estimate the observed hydrological data probability distribution. In particular, using a plotting position formula, a plot of the estimated values from a theoretical parametric probability distribution can be compared with the observation data. It allows an examination of the adequacy of the fit provided by parametric probability distributions. However, results of calculating empirical annual probabilities of exceedance observed maxima water discharges show an increase in the divergence between the estimates obtained using the different plotting position formulas in case of more extreme events. Thereby, the choice of a relevant plotting position formula becomes a challenge. Different plotting position formulas may be admissible options. This article shows that the divergence between the plotting position estimates can be extrapolated to predict design maxima water discharges of low exceedance probabilities.

Statistical and hydrodynamic numerical modeling to quantify storm surge hazard: Comparison of approaches applied to U.S. North Atlantic coast

Estimating the storm surge magnitude and annual exceedance probability is a key element in the siting and design of coastal nuclear power plants in both the U.S. and France. However, differences in storm climatology, specifically the relative importance of tropical cyclones (TCs) versus extratropical storms (XTCs), have driven differences in estimation method development. This work compares purely statistical modeling with combined statistical and numerical simulation modeling approaches for extreme storm surge applied to the U.S. North Atlantic coast which is subject to both tropical and extratropical storms. Two frequency analysis methods are applied to observed water levels and compared to a copula-based joint probability analysis of TCs and automated frequency analysis of XTCs that is enriched with numerically simulated storms. One frequency analysis method is applied using (1) hourly at-site data and (2) hourly at-site data enriched with additional data from a homogeneous region. The other frequency analysis method is applied using (1) hourly at-site data and (2) hourly at-site data enriched with monthly water level maxima. Variables of interest used in the comparison are skew storm surge, maximum instantaneous storm surge, non-tidal residual and maximum seal level. The performance of the methods (mean surge and water level estimates and confidence intervals) depend on the variable of interest and, to some extent, on return period. Inclusion of additional information (e.g., regional water levels, and monthly maxima) in the frequency analysis methods does not have a large impact on estimated mean surge and water levels, but significantly reduces resulting confidence intervals (over 40% reduction in some cases). However, the confidence intervals still grow with increasing return period. Inclusion of simulated storms in the joint probability analysis results in significantly different mean surge and water level estimates (up to 25% higher than the frequency analysis in some cases). The joint probability analysis confidence intervals are wider than those for the frequency analysis methods lower return periods (e.g., 60%–80% wider at 100 years), but they grow much more slowly and are significantly narrower for higher return periods (e.g., 40%–60% narrower at 1 000 years). Although there are appreciable differences between the results documented in this paper, these are reasonable due to differences in the data and methods used in this comparison.

River Park Assessment: 2D Hydraulic Watercourse Modeling for Nature-based Solutions in Urban Area

Over time, fragmentation of semi-natural habitats in urban areas has become a pressing concern, disrupting ecological processes within cities. The focus on preserving open ecosystems has grown, highlighting the need to enhance resilience in urban riverside areas for effective ecosystem restoration. Comprehensive studies on river valleys, considering both hydrology and ecology, play a crucial role in urban river ecosystem development. Our article explores the potential of protective zones with urban vegetation and watercourses as Nature-based Solution within Krakow's ongoing riverine park system development. The study's cross-sections in the River Park area revealed dominant velocities ranging from 0.67 to 2.0 m s-1for SWQ (mean annual maximum flow) and below 0.67 m s-1for Q1% (1% annual exceedance probability flow). The hydrological analysis accurately captured the natural river bed channels' curvature, providing the basis for a two-dimensional mathematical model to visualize the hydraulic structure of protected sites. Integrating water and greenery management systems in urban areas offers significant potential for adapting to climate change, mitigating extreme weather events. Our research's novelty lies in applying 2D hydraulic modeling, demonstrating how River Parks can serve as climate change mitigation solutions in urban environments.

An Alternative Method for Estimating the Peak Flow for a Regional Catchment Considering the Uncertainty via Continuous Simulation

Estimating peak flow for a catchment is commonly undertaken using the design event method; however, this method does not allow for the understanding of uncertainty in the result. This research first presents a simplified method of fragments approach to rainfall disaggregation that ignores the need to consider seasonality, offering a greater diversity in storm patterns within the resulting sub-daily rainfall. By simulating 20 iterations of the disaggregated sub-daily rainfall within a calibrated continuous simulation hydrologic model, we were able to produce multiple long series of streamflow at the outlet of the catchment. With these data, we investigated the use of both the annual maximum and peaks over threshold approaches to flood frequency analysis and found that for a 1-in-100-year annual exceedance probability peak flow, the peaks over threshold method (333 m3/s ± 50 m3/s) was significantly less uncertain than the annual maximum method (427 m3/s ± 100 m3/s). For the 1-in-100-year annual exceedance probability, the median peak flow from the peaks over threshold method (333 m3/s) produced an outcome comparable to the design event method peak flow (328 m3/s), indicating that this research offers an alternative approach to estimating peak flow, with the additional benefit of understanding the uncertainty in the estimation. Finally, this paper highlighted the impact that length and period of streamflow has on peak flow estimation and noted that previous assumptions around the minimum length of gauged streamflow required for flood frequency analysis may not be appropriate in particular catchments.