Rare Floods Research Articles

Adaptive Operating Rules for Flood Control of a Multi-Purpose Reservoir

Almost all multipurpose reservoirs in Romania were put into operation 30–50 years ago or even earlier. Meanwhile, a large volume of hydrologic data has been collected, and the initial design flood should be reconsidered. Consequently, the operating rules of flow control structures (bottom gates and weir gates) should be re-examined, mainly for medium and low-frequency floods. The design flood is not unique, being characterized by different shapes and time to peak, which has consequences for flood mitigation rules. Identifying the critical design flood is an important preliminary step, although it is usually neglected in flood management. Simulating the operation of the Stânca–Costești reservoir on the Prut River, it was found that the design flood corresponding to the maximum value of the compactness coefficient is the most difficult to mitigate considering the specific conditions of the dam and the reservoir: the prescribed conservation level in the reservoir, and the design flood volume of medium and rare floods that far exceeds the flood control volume. These conditions can jeopardize both dam safety and downstream flood protection. The main steps of the proposed approach are as follows: (1) developing the hydraulic model; (2) statistical processing of the registered floods and defining critical design floods for different AEPs (Annual Exceedance Probabilities); (3) deriving optimal operation rules based on a simulation-optimization model; (4) implementing real-time adaptive operation of the mechanical outlets; and (5) critically assessing the operating rules after the event. Based on the hydrological forecast, if necessary, new outlets are put into operation while keeping the ones already activated. Based on the hydrological forecast and properly operated, the safety of the Stânca–Costești dam is guaranteed even in the event of a 0.1% CC (Climate Change) flood. However, for floods greater than 1% magnitude, the carrying capacity of the downstream riverbed is exceeded. The main gaps addressed in this paper are the following: (1) the establishment of critical design floods, and (2) the adaptive operating rules of outlet devices aimed at optimizing flood control results, using short-term flood forecasts.

Reconstruction of the Dynamics of a Catastrophic Crater Lake Outburst Flood, Changbaishan‐Tianchi Volcano

AbstractReconstruction of the catastrophic drainage following the Millennium Eruption (ME) of Changbaishan‐Tianchi volcano in 946 ± 20 CE is of great significance, as it contributes to improving the regional maximum flood record and develop rare flood risk analysis. However, limited knowledge exists concerning the failure mode, magnitude, and transport processes of the outburst flooding. In this work, we present a whole system model that describes the paleohydrology of catastrophic drainage using geological records along the downstream valley. The model encompasses the crater lake dynamics, an approximation of the breach erosion process and flood propagation downstream. The boulder competence method was used to constrain by reasonable flow parameters, while mitigating the uncertainty caused by the ambiguous geological paleostage indicators. Paleohydrologic analysis indicates that at least 1 km3 of water was released from the caldera, with the vertical breach erosion rates as high as 34 m/hr. Volcanic activity during the ME II may have directly contributed to triggering of the flood event. The local hydrodynamic response of the downstream riverbed captures the dynamic migration patterns of sediments across spatio‐temporal scales, offering a comprehensive interpretation of the specific scouring surfaces observed in the geological profile. The analysis of simulated inundation boundaries reveals that not all recorded inundations can be attributed to the crater lake outburst event. Reconstructions of megafloods based on downstream constraints on flood stage, velocity and discharge can help to infer and constrain the dynamics of dam failure mechanisms, and also contribute to our understanding of these complex paleohydrologic events.

Evaluation of key flood risk drivers under climate change using a bottom-up approach

Our understanding of the key drivers of change in flood risk due to climate change remains incomplete. Here, to understand and quantify the key drivers of change in flood risk, we present a framework to undertake a ‘bottom-up’ (‘scenario-neutral’) climate change impact study on flood risk using an event-based flood model that considers non-stationarity in rainfall extremes and catchment wetness. A key advantage of this approach is that by using an event-based model, which explicitly represents key flood drivers, we can directly understand how changes in these drivers will influence changes in flood peaks. The utility of this modelling framework is demonstrated by applying it to one temperate and one tropical catchment in Australia, focusing on the sensitivity of frequent (1 in 5 annual exceedance probability, AEP) and rare (1 in 50 AEP) flood peaks to changes in thermodynamic and dynamic drivers of rainfall extremes, and changes in catchment processes, which are quantified by changes in rainfall losses.Response surfaces of the catchment sensitivity are first produced and demonstrate that increases in rainfall intensity drive increases in the flood peak. Smaller flood peaks in drier catchments are most sensitive to changes in rainfall losses, with the modulating impact of changes in losses decreasing as the runoff ratio increases. Climate model projections of these drivers are then superimposed on these surfaces to identify plausible shifts in flood risk under climate change. Several climate model ensemble members imply a decrease in future flood peaks, most notably 14 % of ensemble members suggest future decreases for the 1 in 5 AEP event for the temperate catchment. Despite large agreement on the direction of change, we found large variability in the expected magnitude of change for both frequent and rare events with flood peaks projected to increase between 0 % and 45 % for the tropical catchment and between −10 % to more than 50 % for the temperate catchment under RCP8.5 in 2085, highlighting the deep uncertainty associated with projecting flood risk under climate change. We believe this study acts as proof of concept for how a bottom-up climate change assessment can be undertaken within an event-based flood modelling framework, and provides insights to help us better understand and communicate the key drivers of changes to flood risk into the future.

Recharge and dynamics of the Tamanrasset alluvial aquifer (Algerian Sahara)

Abstract. Population growth and agricultural intensification are ever more demanding in water resources, and threaten their quality and quantity, especially in semi-arid and arid areas. The Sahara desert is typical of these multiple constraints and rapid changes making sustainable management of water resources a major issue. The wide regional aquifers have nearly fossil water. In contrast, alluvial aquifers associated with small wadis are recharged nearly each year by the rare floods. In the case of Wadi Tamanrasset, in southern Algeria, the alluvial aquifer is exploited intensively because of its accessibility and its good chemical quality. The physical conditions commanding flow and recharge of groundwater, and especially the transfer from surface to groundwater, were studied by combining hydrodynamics and isotopic tracers (18O, 2H). Monthly monitoring of groundwater level in around thirty wells was complemented by automatic recorders with hourly time steps since March 2016 in three wells, along a 600 m transect perpendicular to the wadi bed. The slow response of the water table is visible only two months after the first flood; its maximum rise was 1 m in 2016. Isotopic analyses of rainwater on a daily time scale, of the wadi water on a 1 h time scale during runoff and of groundwater (two complete campaigns in the dry and wet seasons and a specific monitoring of the transect every month) have provided additional information on surface-groundwater transfer.

Unraveling the interplay of human decisions and flood risk: An agent-based modeling approach

Flooding is a frequent and highly destructive natural disaster that requires effective flood management policies. This paper addresses the limitations of traditional flood risk assessment methods, focusing on the interplay between human decisions and flood risk using an integrated approach. Agent-Based Modeling (ABM) is employed to capture the social behavior of government, households, and farmers in three flood management scenarios. The study is conducted in the Fall River watershed, Idaho, USA, over a 30-year period. The results highlight the important influence of crop selection decisions on flood damage outcomes, with alfalfa emerging as the preferred crop due to its profitability and lower damage potential (around 67 % crop loss compared to more than 81 % for crops like corn, barley, and wheat). The levee scenario is identified as the most effective strategy, significantly reducing flood damage by more than 37 % and generating the highest average annual profit for farmers. The study emphasizes the need for comprehensive flood risk management strategies that address both frequent and rare flood occurrences in mitigating flood damage and promoting community resilience. By integrating human-flood interactions into flood risk assessment, this study offers valuable insights for Policymakers and stakeholders can utilize the research implications to develop effective strategies that mitigate damage and enhance community resilience.

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.”

New evidence of high-magnitude Holocene floods in the Purlung Tsangpo River, southeastern Tibetan Plateau

Catastrophic flood events are the most devastating natural hazard in the world, and direct human observation or instrumental/systematic measurement cannot capture the full range of their frequency-magnitudes. It is essential to reconstruct large-magnitude, rare floods that are recorded in landforms and sediments. Recently, palaeohydrology studies associated with Holocene palaeofloods in Himalayan rivers have been gaining attention. Exceptional Holocene outburst floods have been invoked as a highly efficient erosion agent contributing to the rapid incision of the Tsangpo Gorge. Despite its significance, palaeoflood records upstream of Tsangpo Gorge have rarely been explored. This study presents a series of flood deposits consisting of laminated silt and sand or repeated cross-bedding sets, which points to high-magnitude flood. Inverse hydraulic modelling of the step-backwater method based on HEC-RAS software was applied to acquire the water surface profile under varying peak discharge scenarios. The discharges of palaeofloods were estimated by relating elevations of flood features to water surface profiles calculated for the reach. The result provides a minimum estimate of the peak discharge of approximately 3.70 × 104 m3/s with a maximal depth of up to 47 m. The overestimation of the peak discharge due to post-event channel incision is up to 3000 m3/s. Seven charcoal 14C dates give a narrow age range between 2.2 and 2.6 ka cal BP. This flood occurred only a few hundred years after a period of climate transition at ∼ 3 ka BP, which was prone to hydrological extremes. Since the reconstructed palaeodischarge is significantly beyond the scope of either rainfall floods or glacial lake outburst floods, it can be inferred that the breaching of an upstream landslide dam is the most reasonable origin.

SatVITS‐Flood: Satellite Vegetation Index Time Series Flood Detection Model for Hyperarid Regions

AbstractWe present the satellite vegetation index time series model for detecting historical floods in ungauged hyperarid regions (SatVITS‐Flood). SatVITS‐Flood is based on observations that floods are the primary cause of local vegetation expansion in hyperarid regions. To detect such expansion, we used two time‐series metrics: (a) trend change detection from the Breaks For Additive Season and Trend and (b) a newly developed seasonal change metric based on Temporal Fourier Analysis (TFA) and the growing‐season integral anomaly (TFA‐GSIanom). The two metrics complement each other by capturing changes in perennial plant species following extreme, rare floods and ephemeral vegetation changes following more frequent floods. Metrics were derived from the time series of the normalized difference vegetation index, the modified soil‐adjusted vegetation index, and the normalized difference water index, acquired from MODIS, Landsat, and Advanced Very High‐Resolution Radiometer. The timing of the change was compared with the date of the flood and the magnitude of change with its volume and duration. We tested SatVITS‐Flood in three regions on different continents with 40‐year‐long, systematic, reliable gauge data. Our results indicate that SatVITS‐Flood can predict flood occurrence with an accuracy of 78% and precision of 67% (Recall = 0.69 and F1 = 0.68; p < 0.01), and the flood volume and duration with NSE of 0.79 (RMSE = 15.4 × 106 m3 event−1), and R2 of 0.69 (RMSE = 5.7 days), respectively. SatVITS‐Flood proved useful for detecting historical floods and may provide valuable long‐term hydrological information in poorly documented areas, which can help understand the impacts of climate change on the hydrology of hyperarid regions.

Diverging projections for flood and rainfall frequency curves

Flood damages are projected to increase with climate change due to intensification of extreme rainfalls, with water scarcity also projected to increase due to longer periods of drought leading to less runoff from flood events. In order to adapt to climate change, precise and robust projections of flood events are required of a magnitude (or rarity) relevant to engineering design and water resources planning. However, due to the complexity of catchment-specific processes which influence flood response, many studies disagree on the direction and magnitude of the projected change: some studies project increases in flood magnitude, even above projected increases in rainfall maxima, while other studies project decreases. Using a combination of continentally downscaled rainfall projections for Australia and rainfall-runoff models locally calibrated to rare floods using a novel objective function, we show that projected changes in flooding do not follow projected changes in extreme rainfall, with changes in flooding depending on region, runoff mechanism, and event severity. Decreases in frequent flooding up to the 1 in 5 annual exceedance probability are projected across the continent, with increases only projected for frequent flooding in some tropical regions, diverging from the projected increases in rainfall maxima across a majority of the continent. For rare events of annual exceedance probability 1 in 50, the flood magnitude is projected to increase across the northern and eastern coasts by the end of the century – commensurate with the median projected increase of 20% in rainfall maxima. However, decreases in both rainfall maxima and flooding are still projected for south-western Australia, even for the rarest events. The regional coherence of our results suggests the patterns in the projected changes may be applicable to other tropical, temperate, and arid regions around the world.

The Drying Peace–Athabasca Delta, Canada: Review and Synthesis of Cryo-Hydrologic Controls and Projections to Future Climatic Conditions

The Peace–Athabasca Delta (PAD) in northern Alberta, Canada is one of the world’s largest inland freshwater deltas, home to large populations of waterfowl, muskrat, beaver, and free-ranging wood bison. The delta region has been designated a Ramsar wetland of international importance and is largely located within the Wood Buffalo National Park, itself being a UNESCO World Heritage Site. Indigenous residents have depended on the delta for centuries to sustain their culture and lifeways. In the past five decades, the PAD has experienced prolonged dry periods in-between rare floods, accompanied by reduction in the area covered by lakes and ponds that provide habitat for aquatic life. Recharge of the higher-elevation, or “perched”, basins depends on overland flooding generated by major spring ice jams that occasionally form in the lower reaches of the Peace and Athabasca Rivers and in their various distributaries. Indigenous Traditional Knowledge and Historical Records for the unregulated Athabasca River are relatively scarce, but conclusively demonstrate the role of ice jams in replenishing perched basins of the Athabasca sector of the PAD. Similar information, coupled with extensive hydrometric and observational data for the regulated Peace River have enabled elucidation of the physical mechanisms that lead to ice-jam flooding of the Peace sector and assessment of regulation impacts on flood frequency. Such understanding can inform design of remedial strategies to moderate or arrest the drying trend of the delta. Climate-related projections to future scenarios suggest reduced frequency of ice-jam floods, albeit with uncertainty.

Identifying discontinuities of flood frequency curves

Discontinuities in flood frequency curves, here referred to as flood divides, hinder the estimation of rare floods. In this paper we develop an automated methodology for the detection of flood divides from observations and models, and apply it to a large set of case studies in the USA and Germany. We then assess the reliability of the PHysically-based Extreme Value (PHEV) distribution of river flows to identify catchments that might experience a flood divide, validating its results against observations. This tool is suitable for the identification of flood divides, with a high correct detection rate especially in the autumn and summer seasons. It instead tends to indicate the emergence of flood divides not visible in the observations in spring and winter. We examine possible reasons of this behavior, finding them in the typical streamflow dynamics of the concerned case studies. By means of a controlled experiment we also re-evaluate detection capabilities of observations and PHEV after discarding the highest maxima for all cases where both empirical and theoretical estimates display flood divides. PHEV mostly confirms its capability to detect a flood divide as observed in the original flood frequency curve, even if the shortened one does not show it. These findings prove its reliability for the identification of flood divides and set the premises for a deeper investigation of physiographic and hydroclimatic attributes controlling the emergence of discontinuities in flood frequency curves.

Rare flood scenarios for a rapidly growing high-mountain city: Pokhara, Nepal

Abstract. Pokhara (ca. 850 m a.s.l.), Nepal's second-largest city, lies at the foot of the Higher Himalayas and has more than tripled its population in the past 3 decades. Construction materials are in high demand in rapidly expanding built-up areas, and several informal settlements cater to unregulated sand and gravel mining in the Pokhara Valley's main river, the Seti Khola. This river is fed by the Sabche glacier below Annapurna III (7555 m a.s.l.), some 35 km upstream of the city, and traverses one of the steepest topographic gradients in the Himalayas. In May 2012 a sudden flood caused >70 fatalities and intense damage along this river and rekindled concerns about flood risk management. We estimate the flow dynamics and inundation depths of flood scenarios using the hydrodynamic model HEC-RAS (Hydrologic Engineering Center’s River Analysis System). We simulate the potential impacts of peak discharges from 1000 to 10 000 m3 s−1 on land cover based on high-resolution Maxar satellite imagery and OpenStreetMap data (buildings and road network). We also trace the dynamics of two informal settlements near Kaseri and Yamdi with high potential flood impact from RapidEye, PlanetScope, and Google Earth imagery of the past 2 decades. Our hydrodynamic simulations highlight several sites of potential hydraulic ponding that would largely affect these informal settlements and sites of sand and gravel mining. These built-up areas grew between 3- and 20-fold, thus likely raising local flood exposure well beyond changes in flood hazard. Besides these drastic local changes, about 1 % of Pokhara's built-up urban area and essential rural road network is in the highest-hazard zones highlighted by our flood simulations. Our results stress the need to adapt early-warning strategies for locally differing hydrological and geomorphic conditions in this rapidly growing urban watershed.

Comprehensive space–time hydrometeorological simulations for estimating very rare floods at multiple sites in a large river basin

Abstract. Estimates for rare to very rare floods are limited by the relatively short streamflow records available. Often, pragmatic conversion factors are used to quantify such events based on extrapolated observations, or simplifying assumptions are made about extreme precipitation and resulting flood peaks. Continuous simulation (CS) is an alternative approach that better links flood estimation with physical processes and avoids assumptions about antecedent conditions. However, long-term CS has hardly been implemented to estimate rare floods (i.e. return periods considerably larger than 100 years) at multiple sites in a large river basin to date. Here we explore the feasibility and reliability of the CS approach for 19 sites in the Aare River basin in Switzerland (area: 17 700 km2) with exceedingly long simulations in a hydrometeorological model chain. The chain starts with a multi-site stochastic weather generator used to generate 30 realizations of hourly precipitation and temperature scenarios of 10 000 years each. These realizations were then run through a bucket-type hydrological model for 80 sub-catchments and finally routed downstream with a simplified representation of main river channels, major lakes and relevant floodplains in a hydrologic routing system. Comprehensive evaluation over different temporal and spatial scales showed that the main features of the meteorological and hydrological observations are well represented and that meaningful information on low-probability floods can be inferred. Although uncertainties are still considerable, the explicit consideration of important processes of flood generation and routing (snow accumulation, snowmelt, soil moisture storage, bank overflow, lake and floodplain retention) is a substantial advantage. The approach allows for comprehensively exploring possible but unobserved spatial and temporal patterns of hydrometeorological behaviour. This is of particular value in a large river basin where the complex interaction of flows from individual tributaries and lake regulations are typically not well represented in the streamflow observations. The framework is also suitable for estimating more frequent floods, as often required in engineering and hazard mapping.

Development of a kriging-based regional flood frequency analysis technique for South-East Australia

Flood is one of the most common natural disasters, which also triggers other natural disasters such as erosion and landslides. Flood damage can be minimised by ensuring optimum design of drainage infrastructure and other flood management tasks, which depends largely on reliable estimation of flood quantiles. This study investigates flood quantile estimation in ungauged catchments using a kriging-based regional flood frequency analysis (RFFA) technique. Three main research objectives are addressed in this study. Firstly, kriging-based RFFA models are developed using 558 catchments from eastern Australia in the range of frequent to rare flood quantiles (2, 5, 10, 20, 50 and 100 years of average recurrence intervals (ARIs)). Secondly, a validation of the models by adopting a leave-one-out (LOO) validation technique is undertaken to identify the best and the worst performing catchments across eastern Australia. Finally, a detailed comparison is made for the kriging-based RFFA technique with a generalised least-squares-based quantile regression technique, known as ‘RFFE model 2016’ using the same dataset to evaluate whether there are general patterns of the performance in different catchments. The study shows that for eastern Australia (a) the developed kriging-based RFFA model is a viable alternative for flood quantile estimation in ungauged catchments, (b) the 10-year ARI model Q10 performs best among the six quantiles, which is followed by the models Q5 and Q20, and (c) the kriging-based RFFA model is found to outperform the ‘RFFE model 2016’.

Incorporating nonstationarity in regional flood frequency analysis procedures to account for climate change impact

There now exists clear evidence of the impact of climate change on flooding, creating need for new methodologies for design flood estimation that account for warming induced nonstationarity. Current alternatives for nonstationary flood frequency analysis require the specification of a nonstationary probability model (often with time-varying parameters) that requires sufficient data to be stable. Reliance on the trend in observed data often ignores the dissimilar behaviour of frequent and rare quantiles under a warming climate. This, in turn, results in the direction of change to be consistent across both rare and frequent projected flood quantiles. However, results of multiple recent studies suggest that rarer floods are likely to increase while more frequent floods may decrease in magnitude into the future. In this study, we highlight this limitation of existing nonstationary flood frequency approaches and propose a novel nonstationary regional flood frequency analysis approach that captures the differing behaviour of more frequent and rare flood quantiles under a warming climate. The need for longer flood data to model nonstationarity is accommodated by pooling regional information which makes the projections more precise. Data for 105 Australian catchments are used to validate our proposed approach. Results show the effectiveness of the proposed method in capturing the variation of flood quantiles with varying average recurrence intervals in a changing climate. Finally, a few important statistics of future projections are also presented.

Reliable estimation of high floods: A method to select the most suitable ordinary distribution in the Metastatistical extreme value framework

Recent advances in the study of extreme values, namely the Metastatistical extreme value (MEV) framework, showed good performances for the estimation of extremes in several fields. Here we adopt MEV for flood frequency analysis and leverage its intrinsic property of allowing for the choice of the distribution which best describes ordinary peaks to improve flood estimation. To this end, we develop a non-parametric approach to select ex ante the most suitable distribution of ordinary peaks between Gamma and Log-Normal. The method relies on the tail ratio, which we define as the ratio between the empirical 99th and 95th percentile of the ordinary peaks, and is tested by using daily streamflow time series from 182 gauges in Germany. Based on the value of the tail ratio index, we choose either the Gamma or the Log-Normal distributions to represent the ordinary peaks in each gauge. The approach correctly identifies the most suitable distribution of ordinary peaks in a large majority of the analyzed basins, and is robust to changes of the considered dataset. The preliminary selection of the ordinary distribution based on the tail ratio index improves the estimation of frequent and rare floods with respect to MEV applied with a single distribution not tailored on the specific statistical properties of the ordinary peaks. Finally, by comparing the developed methodology with the standard Generalized Extreme Value (GEV) distribution, we show that we are able to reduce the estimation uncertainty of high flood quantiles.

PHEV! The PHysically-based Extreme Value distribution of river flows

Magnitude and frequency are prominent features of river floods informing design of engineering structures, insurance premiums and adaptation strategies. Recent advances yielding a formal characterization of these variables from a joint description of soil moisture and daily runoff dynamics in river basins are here systematized to highlight their chief outcome: the PHysically-based Extreme Value (PHEV) distribution of river flows. This is a physically-based alternative to empirical estimates and purely statistical methods hitherto used to characterize extremes of hydro-meteorological variables. Capabilities of PHEV for predicting flood magnitude and frequency are benchmarked against a standard distribution and the latest statistical approach for extreme estimation, by using both an extensive observational dataset and long synthetic series of streamflow generated for river basins from contrasting hydro-climatic regions. The analyses outline the domain of applicability of PHEV and reveal its fairly unbiased capabilities to estimate flood magnitudes with return periods much longer than the sample size used for calibration in a wide range of case studies. The results also emphasize reduced prediction uncertainty of PHEV for rare floods, notably if the flood magnitude-frequency curve displays an inflection point. These features, arising from the mechanistic understanding embedded in the novel distribution of the largest river flows, are key for a reliable assessment of the actual flooding hazard associated to poorly sampled rare events, especially when lacking long observational records.

Identifying sensitivities in flood frequency analyses using a stochastic hydrologic modeling system

Abstract. This study employs a stochastic hydrologic modeling framework to evaluate the sensitivity of flood frequency analyses to different components of the hydrologic modeling chain. The major components of the stochastic hydrologic modeling chain, including model structure, model parameter estimation, initial conditions, and precipitation inputs were examined across return periods from 2 to 100 000 years at two watersheds representing different hydroclimates across the western USA. A total of 10 hydrologic model structures were configured, calibrated, and run within the Framework for Understanding Structural Errors (FUSE) modular modeling framework for each of the two watersheds. Model parameters and initial conditions were derived from long-term calibrated simulations using a 100 member historical meteorology ensemble. A stochastic event-based hydrologic modeling workflow was developed using the calibrated models in which millions of flood event simulations were performed for each basin. The analysis of variance method was then used to quantify the relative contributions of model structure, model parameters, initial conditions, and precipitation inputs to flood magnitudes for different return periods. Results demonstrate that different components of the modeling chain have different sensitivities for different return periods. Precipitation inputs contribute most to the variance of rare floods, while initial conditions are most influential for more frequent events. However, the hydrological model structure and structure–parameter interactions together play an equally important role in specific cases, depending on the basin characteristics and type of flood metric of interest. This study highlights the importance of critically assessing model underpinnings, understanding flood generation processes, and selecting appropriate hydrological models that are consistent with our understanding of flood generation processes.

Connecting Hydrometeorological Processes to Low‐Probability Floods in the Mountainous Colorado Front Range

AbstractEstimating the probabilities of rare floods in mountainous watersheds is challenging due to the hydrometeorological complexity of seasonally varying snowmelt and soil moisture dynamics, as well as spatiotemporal variability in extreme precipitation. Design storm methods and statistical flood frequency analyses often overlook these complexities and how they shape the probabilities of rare floods. This study presents a process‐based approach that combines gridded precipitation, stochastic storm transposition (SST), and physics‐based distributed rainfall‐runoff modeling to simulate flood peak and volume distributions up to the 10,000‐year recurrence interval and to provide insights into the hydrometeorological drivers of those events. The approach is applied to a small mountainous watershed in the Colorado Front Range in the United States. We show that storm transposition in the Front Range can be justified under existing definitions of regional precipitation homogeneity. The process‐based results show close agreement with a statistically based mixture distribution that considers underlying flood drivers. We further demonstrate that antecedent conditions and snowmelt drive frequent peak discharges and rarer flood volumes, while the upper tail of the flood peak distribution appears to be controlled by heavy rainfall and rain‐on‐snow. In particular, we highlight the important role of early fall extreme rainfall in controlling rare flood peaks (but not volumes), despite only one such event having been observed in recent decades. Notwithstanding issues related to the accuracy of gridded precipitation datasets, these findings highlight the potential of SST and process‐based modeling to help understand the relationships between flood drivers and flood frequencies.

Downsizing parameter ensembles for simulations of rare floods

Abstract. For extreme-flood estimation, simulation-based approaches represent an interesting alternative to purely statistical approaches, particularly if hydrograph shapes are required. Such simulation-based methods are adapted within continuous simulation frameworks that rely on statistical analyses of continuous streamflow time series derived from a hydrological model fed with long precipitation time series. These frameworks are, however, affected by high computational demands, particularly if floods with return periods > 1000 years are of interest or if modelling uncertainty due to different sources (meteorological input or hydrological model) is to be quantified. Here, we propose three methods for reducing the computational requirements for the hydrological simulations for extreme-flood estimation so that long streamflow time series can be analysed at a reduced computational cost. These methods rely on simulation of annual maxima and on analysing their simulated range to downsize the hydrological parameter ensemble to a small number suitable for continuous simulation frameworks. The methods are tested in a Swiss catchment with 10 000 years of synthetic streamflow data simulated thanks to a weather generator. Our results demonstrate the reliability of the proposed downsizing methods for robust simulations of rare floods with uncertainty. The methods are readily transferable to other situations where ensemble simulations are needed.