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Design flood estimation is a fundamental task in hydrology. In this research, we propose a machine-learning-based approach to estimate design floods globally. This approach involves three stages: (i) estimating at-site flood frequency curves for global gauging stations using the Anderson–Darling test and a Bayesian Markov chain Monte Carlo (MCMC) method; (ii) clustering these stations into subgroups using a K-means model based on 12 globally available catchment descriptors; and (iii) developing a regression model in each subgroup for regional design flood estimation using the same descriptors. A total of 11 793 stations globally were selected for model development, and three widely used regression models were compared for design flood estimation. The results showed that (1) the proposed approach achieved the highest accuracy for design flood estimation when using all 12 descriptors for clustering; and the performance of the regression was improved by considering more descriptors during training and validation; (2) a support vector machine regression provided the highest prediction performance amongst all regression models tested, with a root mean square normalised error of 0.708 for 100-year return period flood estimation; (3) 100-year design floods in tropical, arid, temperate, cold and polar climate zones could be reliably estimated (i.e. <±25 % error), with relative mean bias (RBIAS) values of −0.199, −0.233, −0.169, 0.179 and −0.091 respectively; (4) the machine-learning-based approach developed in this paper showed considerable improvement over the index-flood-based method introduced by Smith et al. (2015, https://doi.org/10.1002/2014WR015814) for design flood estimation at global scales; and (5) the average RBIAS in estimation is less than 18 % for 10-, 20-, 50- and 100-year design floods. We conclude that the proposed approach is a valid method to estimate design floods anywhere on the global river network, improving our prediction of the flood hazard, especially in ungauged areas.

Changes in annual maximum flood (AMF), which are usually detected using simple trend tests (e.g., Mann‐Kendall test (MKT)), are expected to change design‐flood estimates. We propose an alternate framework to detect significant changes in design‐flood between two periods and evaluate it for synthetically generated AMF from the Log‐Pearson Type‐3 (LP3) distribution due to changes in moments associated with flood distribution. Synthetic experiments show MKT does not consider changes in all three moments of the LP3 distribution and incorrectly detects changes in design‐flood. We applied the framework on 31 river basins spread across the United States. Statistically significant changes in design‐flood quantiles were observed even without a significant trend in AMF and basins with statistically significant trend did not necessarily exhibit statistically significant changes in design‐flood. We recommend application of the framework for evaluating changes in design‐flood estimates considering changes in all the moments as opposed to simple trend tests. Plain Language Summary Any statistically significant change in the design‐flood quantile between two periods needs to account for changes in moments (such as mean, variance, and skewness) of the underlying flood distribution. Simple trend tests cannot be relied upon to identify basins undergoing changes in their design‐flood as they fail to capture changes in all the relevant moments. The proposed framework investigates statistically significant changes in design‐flood by considering changes in all moments of the flood distribution. Key Points Design‐flood quantile estimation, a function of moments of flood variable, is vital for water infrastructure design Temporal trend tests do not account for changes in all moments associated with any flood distribution The proposed framework tests the hypothesis of statistically significant change in design‐flood quantiles between two periods

In the Indian Himalayas, the rapid development of hydroelectric projects leverages perennial water sources but raises concerns regarding the safety of these installations in the face of climate change. This study investigates the Bajoli-Holi Hydroelectric Project on the River Ravi, focusing on the potential impacts of glacial lake outburst floods (GLOFs) alongside inflow design floods on spillway capacity. Given the high likelihood of breaching in moraine-dammed lakes near glacier snouts, our analysis employs advanced hydrodynamic modelling and hydro-meteorological assessments to evaluate the combined flood risks. Utilizing Arc-GIS for catchment delineation and the MIKE 11 model for sensitivity analysis, we emphasize the need for integrated flood risk management strategies in the design phase of hydropower projects. The findings in the study area of Bajoli-Holi dam highlight the critical importance of accounting for GLOFs in spillway capacity assessments in addition to the inflow design flood, to enhance the hydrological security of these essential energy infrastructures.

Existing stochastic rainfall generators (SRGs) are typically limited to relatively small domains due to spatial stationarity assumptions, hindering their usefulness for flood studies in large basins. This study proposes StormLab, an SRG that simulates precipitation events at 6‐hr and 0.03° resolution in the Mississippi River Basin (MRB). The model focuses on winter and spring storms caused by water vapor transport from the Gulf of Mexico—the key flood‐generating storm type in the basin. The model generates anisotropic spatiotemporal noise fields that replicate local precipitation structures from observed data. The noise is transformed into precipitation through parametric distributions conditioned on large‐scale atmospheric fields from a climate model, reflecting spatial and temporal nonstationarity. StormLab can produce multiple realizations that reflect the uncertainty in fine‐scale precipitation arising from a specific large‐scale atmospheric environment. Model parameters were fitted monthly from December–May, based on storms identified from 1979 to 2021 ERA5 reanalysis data and Analysis of Record for Calibration (AORC) precipitation. StormLab then generated 1,000 synthetic years of precipitation events based on 10 CESM2 ensemble simulations. Empirical return levels of simulated annual maxima agree well with AORC data and show an overall increase in 1‐ to 500‐year events in the future period (2022–2050). To our knowledge, this is the first SRG simulating nonstationary, anisotropic high‐resolution precipitation over continental‐scale river basins, demonstrating the value of conditioning such stochastic models on large‐scale atmospheric variables. StormLab provides a wide range of extreme precipitation scenarios for design floods in the MRB and can be further extended to other large river basins. Key Points A nonstationary stochastic rainfall generator driven by water vapor transport is proposed to simulate storms in the Mississippi Basin 1,000 synthetic years of precipitation events were simulated based on large‐scale atmospheric variables from a global climate model Simulations showed an increase in 1‐ to 500‐year precipitation in the future (2022–2050) compared to the historical period (1979–2021)

The frequency analysis method is commonly used to calculate design floods. Under the double challenge of the non‐stationary situation under the changing environment and the inadequate length of flood series, developing a new method to integrate the historical extraordinary floods into the non‐stationary frequency analysis is essential. First, the Multi‐Model Ensemble projections of temperature and precipitation based on Global Climate Model outputs were employed to drive the Soil & Water Assessment Tool hydrological model for runoff simulation. Then, the Integrated Time‐Varying Moment (ITVM) model was developed to re‐analyze the design floods based on the Pearson‐III distribution. The calibrated SWAT model can satisfactorily simulate the rainfall‐runoff relationship in the Yalong River basin. The developed ITVM model is effective to conduct the design flood frequency analysis to cope with the problems of insufficient length and non‐stationarity of the flood series. The design flood values of Maidilong station show an obvious increase, with variations of 6.5%–9.4%, 2.9%–12.3%, and 16%–33.7% for SSP1‐2.6, SSP2‐4.5, and SSP5‐8.5, respectively. The significant increase of low frequencies (p = 0.2%, p = 0.1%) floods, especially for SSP5‐8.5 scenario, requires more attention, as the increased floods may exceed the discharge capacity of the reservoir determined at the design stage.

Seasonal design floods and operational water levels are critical for high-efficient water resource utilization. In this study, statistical and rational analyses methods were applied to divide the flood season based on seasonal rainfall patterns. The Mann–Kendall test and Theil–Sen analysis were used to detect trend changes in the observed flow series. Both stationary and nonstationary flood frequency analysis methods were conducted to estimate seasonal design floods. The Three Gorges Reservoir (TGR) in the Yangtze River, China, was selected as the case study. Results show that the TGR flood season could be divided into four periods: the reservoir drawdown period (1 May–20 June), the Meiyu flood period (21 June–31 July), the transition period (1 August–10 September), and the Autumn Rain refill period (11 September–31 October). Trend analyses indicate that the flow series at the TGR dam site exhibited a decreasing trend in recent decades. Upstream reservoir regulation has significantly reduced inflow discharges of TGR, and the nonstationary seasonal 1000-year design floods in the transition period are decreased by about 20%, and the flood control water level could rise from 145 m to 157 m, which can generate 2.288 billion kW h more hydropower (16.57% increase) while maintaining unchanged flood prevention standards. This study provides valuable insights into the TGR operational water level in the flood season and highlights the necessity of considering the regulation impact of upstream reservoirs for design floods and reservoir operational water levels.

Designing floods in ungauged watersheds with limited data is a significant challenge in water conservancy projects. To address this, the method of calculating the design flood peak and flood volume using the weighted average method was proposed, which is based on the instantaneous unit hydrograph method and the inference formula method, combined with the characteristics of heavy rainfall floods in ungauged watersheds. The calculation results are analyzed in terms of reasonableness through the distribution pattern of the flood peak modulus under different frequencies of the constructed reservoirs, the relative error analysis, and the HEC-RAS model. Based on the one-day flood process of the adjacent basin, the calculation of deducing the design flood process using the hydrological comparison method was proposed. Taking the “Stormwater Runoff Chart” as the data source, the runoff generation, and concentration model was established with the design flood of Baludi Reservoir in the Gelangram River basin of Menglian, Yunnan Province as the research object. A comparative study of the results of the design floods calculated by different methods was carried out. The results show that the new method can well describe the rainstorm process. The method has better performance in the application to the design flood calculation of ungauged basins due to its consideration of the influence of subsurface conditions. The method not only reduces the construction cost but also improves the safety of the reservoir through a better-fitted design flood calculation.

The European Commission Flood Risk Directive review shows that while many nations have embraced the concepts of flood risk management, there is still quite more to do in delineating risk–cost-effective measures and developing cost estimates and financing of those measures. Not mentioned are the necessary changes to existing design standards and protocols which will have to change in order to properly encompass climate change and variability, with associated uncertainties. Adjustments in engineering design standards and changes in hazards are examined, based on trend detection in observational records and projections for the future. Issues of urban and transport (motorways and railways) drainage design are also examined. Furthermore, risk reduction strategies are discussed. Finally, a way of accounting for non-stationarity in determining design precipitation and design floods is tackled. Climate change adjustments in engineering design standards, such as design precipitation and design floods, are reviewed via examples from Europe.

The Upper Yangtze River (above Yichang) in China has constructed the world's largest reservoir group with the Three Gorges Reservoir (TGR) as the core, the operation of these reservoirs and future climate change will no doubt alter the downstream hydrological processes and pose a challenge to the downstream flood design. As Yichang Hydrologic Station is 44 km downstream of TGR, how the design flood at Yichang Station would be impacted in the future by climate and upstream reservoirs has rarely been investigated. In this study, the climate and upstream reservoirs effects on design flood at Yichang Station are evaluated under six future climate and reservoir scenarios (S1, S2, S3, S4, S5 and S6) with different combinations of summer precipitation anomaly (SPA) and reservoir index (RI), in which SPA is obtained from global climate models under the three emission scenarios (SSP1-2.6, SSP2-4.5 and SSP5-8.5) of CMIP6 and RI is calculated under the two reservoir conditions (RI at current level and RI at planning level). The SPA and RI of S1, S2, S3, S4, S5 and S6 are, respectively, substituted into the optimal nonstationary GEV probability model, and the corresponding 1000-year design floods are estimated by using average annual reliability method. Under the same future reservoir condition, the flood peak discharge, 3-day, 7-day, 15-day and 30-day flood volume (denoted as Qm, W3, W7, W15 and W30, respectively) under SSP2-4.5 and SSP5-8.5 are 0.2% ~ 2.5% larger than those under SSP1-2.6. The change rates of Qm, W3, W7, W15 and W30 under six scenarios relative to the stationary design flood values calculated by Changjiang Water Resources Commission range from −11.4% to −23.9%, and the reduction amount of Qm is more than 16,000 m3/s even under SSP5-8.5. Therefore, reservoirs impact on the design flood of Yichang Station is quite prominent.

Internationally, the occurrence and frequency of floods, along with the uncertainty involved in the estimation thereof, contribute to the practitioners' dilemma to make a single, justifiable decision when various design flood estimation methods are used. This article presents the further development of a Design Flood Estimation Tool (DFET) using Microsoft Visual Basic for Applications to assess the performance of event‐based design flood estimation methods in 48 gauged catchments in South Africa. The improved DFET proved to be an easy‐to‐use software tool for the rapid estimation and assessment of at‐site design floods in both gauged and ungauged catchments. In using a ranking‐based selection procedure, the Soil Conservation Service, Alternative Rational and Catchment Parameter methods provided the best estimates of the at‐site probabilistic flood peaks, while the Standard Design Flood method proved to be the least appropriate. Since the accuracy and uncertainty associated with each design flood method's key input parameters are unknown when applied in ungauged catchments, the incorporation of an ensemble event approach as part of the DFET calculation routines, is recommended. This will ensure that the key input parameters from an expected range of values are used to achieve probability neutrality between input rainfall and estimated runoff.