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Accurate flood risk assessment requires a comprehensive understanding of flood sensitivity to regional drivers and climate factors. This paper presents the scaling of floods (duration, peak, volume) with geomorphologic characteristics of the basin (i.e., drainage area, slope, elevation) and precipitation patterns (rainfall accumulation, variability). Long‐term daily streamflow observations over the 20th and early 21st centuries from Hydro‐Climatic Data Network streamgages across the conterminous United States are used to create a flood event database based on their flood stage information. Antecedent daily rainfall accumulation and variability corresponding to these floods are computed using Global Historical Climatology Network daily data set. Two Bayesian scaling models are developed, and the spatial organization of scaling exponents is investigated. The baseline model quantifies the scaling of floods to geomorphologic characteristics. The dynamic model quantifies the scaling of floods to antecedent precipitation distribution which is further conditioned on geomorphologic characteristics. Results show that small and low‐elevation basins have a stronger response to antecedent rainfall distribution in amplifying flood peaks, while high‐elevation steeper basins have a lower response for flood duration and volume. The dynamic models demonstrate that there are significant variations in the flood scaling rates, with the largest rates up to 40% and 4.5% for flood duration, 64% and 44% for peak, and 98% and 40% for volume found across the Northeast, Coastal Southeast, and Northwest with intensifying rainfall accumulation and variability, respectively. This study advances flood predictions by better informing the flood attributes in the context of dynamical land‐atmosphere perturbations. Plain Language Summary: The relationships between floods, physical properties of river basins, and the spatio‐temporal distribution of precipitation define the regional flood scaling laws. Improved regional flood scaling models are required for updating flood risk scoring tools to help insurers and decision‐makers in the light of anthropogenic climate changes and natural variability of Earth systems. Two scaling models are developed here to examine how the physical properties of basins and precipitation patterns can inform flood attributes across the conterminous United States (US). Using long records of streamflow and precipitation observations, we identified different scaling relationships between the catchment factors, preceding precipitation, and the duration, peaks, and volume of floods. Larger basins located at higher elevations are found to have lower scaling rates with flood peaks when interacting with the preceding precipitation patterns. Overall, low‐elevation flat basins across the Eastern, Southern, and Northwestern US have a larger positive response to precipitation in amplifying flood attributes. These new scaling laws can help advance our understanding of controlling mechanisms for flash floods and mega‐floods, which is required for a dynamic flood risk assessment in a changing environment. Key Points: Two Bayesian scaling models are developed to understand the sensitivity of floods to geomorphologic characteristics and catchment rainfallLow‐elevation and flat basins with different sizes have greater scaling to rainfall in amplifying flood duration and volumesLow‐elevation and small basins with different steepness show larger sensitivity to rainfall distribution that enhances flood peaks [ABSTRACT FROM AUTHOR]

Global flood models integrate flood maps of constant probability in space, ignoring the correlation between sites and thus potentially misestimating the risk posed by extreme events. Stochastic flood models alleviate this issue through the simulation of flood events with a realistic spatial structure, yet their proliferation at large scales has historically been inhibited by data quality and computer availability. In this paper, we show, for the first time, the efficacy of modeled river discharge reanalyses in the characterization of flood spatial dependence in the absence of a dense stream gauge network. While global hydrological models may show poor correspondence with absolute observed river flows, we find that the rate at which they can simulate the joint occurrence of relative flow exceedances at two given locations is broadly similar to when a gauge‐based statistical model is used. Evidenced over the United States, flood events simulated using observed gauge data from the U.S. Geological Survey versus those generated using modeled streamflows have similar (i) distributions of site‐to‐site correlation strength, (ii) relationships between event size and return period, and, importantly, (iii) loss distributions when incorporated into a continental‐scale flood risk model. Extremal dependence is generally quantified less accurately on larger rivers, in arid climates, in mountainous terrain, and for the rarest high‐magnitude events. However, local‐scale errors are shown to broadly cancel each other out when combined, producing an unbiased flood spatial dependence model. These findings suggest that building accurate stochastic flood models worldwide may no longer be a distant aspiration. Plain Language Summary: Global flood risk is commonly estimated through flood inundation maps with a defined probability of occurrence. These flood simulations have a key drawback in that they fail to capture the spatial patterns exhibited during real flood events, instead modeling the same probability of flooding on every river at once. Solutions which rely on networks of gauged river flow observations will necessarily break down in the majority of the world's regions which lack such a resource. In this paper, we use historic river flows simulated by global rainfall‐runoff models (rather than observed flows) into a statistical model which captures the spatial correlation of flow extremes. If we examine the relative flow exceedance probabilities from these hydrological models rather than the volumetric flow values, flood events are generated which exhibit similar characteristics to those when gauged flow observations are used. Crucially, the simulation‐ and observation‐generated flood events produce near‐identical losses to buildings in the United States. The implications of this are that true stochastic flood risk models, which account for spatial dependence, can proliferate globally via the generation of realistic flood event sets from hydrological models. Key Points: Large‐scale flood hazard models typically neglect to represent the spatial dependence of real flood eventsRelative flow exceedances simulated by the fusion of global hydrological with statistical models reproduce gauge‐driven flood event setsAt the continental scale, key characteristics of a flood risk model are indistinguishable when driven with observed versus modeled flow data [ABSTRACT FROM AUTHOR]

In this paper we seek to understand the nature of flood spatial dependence over the conterminous United States. We extend an existing conditional multivariate statistical model to enable its application to this large and heterogenous region and apply it to a 40‐year data set of ~2,400 U.S. Geological Survey gauge series records to simulate 1,000 years of U.S. flooding comprising more than 63,000 individual events with realistic spatial dependence. A continental‐scale hydrodynamic model at 30 m resolution is then used to calculate the economic loss arising from each of these events. From this we are able to compute the probability that different values of U.S. annual total economic loss due to flooding are exceeded (i.e., a loss‐exceedance curve). Comparing these data to an observed flood loss‐exceedance curve for the period 1988–2017 shows a reasonable match for annual losses with probability below 10% (e.g., >1 in 10‐year return period). This analysis suggests that there is a 1% chance of U.S. annual fluvial flood losses exceeding $78Bn in any given year, and a 0.1% chance of them exceeding $136Bn. Analysis of the set of stochastic events and losses yields new insights into the nature of flooding and flood risk in the United States. In particular, we confirm the strong relationship between flood affected area and event peak magnitude, but show considerable variability in this relationship between adjacent U.S. regions. The analysis provides a significant advance over previous national flood risk analyses as it gives the full loss‐exceedance curve instead of simply the average annual loss. Plain Language Summary: Traditional flood risk analyses make the assumption that flow probability (the chance that a given river discharge is exceeded) does not vary within river catchments within an event. Real floods, however, do not look like this: In some places flooding is more severe than in others. Over a few tens of kilometers of river assuming the same event return period everywhere is perfectly fine, but over larger areas it breaks down. At national scales traditional risk analyses can only estimate the average annual loss. To estimate the total annual losses that might occur in more extreme flooding years the risk analysis needs to be based on more realistic spatial patterns of flooding. In this paper we use a sophisticated statistical model, based on U.S. Geological Survey river flow data, to simulate 1,000 years of spatially realistic U.S. flooding comprising more than 63,000 individual events. By calculating the damage for each event as a dollar value, we are able to estimate the probability of the United States experiencing particular levels of annual flood losses. We show that there is a 1% chance of U.S. annual fluvial flood losses exceeding $78Bn in any given year, and a 0.1% chance of them exceeding $136Bn. Key Points: 1,000 years of realistic U.S. flood patterns, comprising >63,000 individual events, are simulated using a statistical modelMonetary losses for each event are calculated using a continental hydrodynamic model at 30 m resolutionThe analysis suggests that there is a 1% chance of U.S. annual fluvial flood losses exceeding $78Bn in any given year [ABSTRACT FROM AUTHOR]

This paper reports the development of a ∼30 m resolution two-dimensional hydrodynamic model of the conterminous U.S. using only publicly available data. The model employs a highly efficient numerical solution of the local inertial form of the shallow water equations which simulates fluvial flooding in catchments down to 50 km2 and pluvial flooding in all catchments. Importantly, we use the U.S. Geological Survey (USGS) National Elevation Dataset to determine topography; the U.S. Army Corps of Engineers National Levee Dataset to explicitly represent known flood defenses; and global regionalized flood frequency analysis to characterize return period flows and rainfalls. We validate these simulations against the complete catalogue of Federal Emergency Management Agency (FEMA) Special Flood Hazard Area (SFHA) maps and detailed local hydraulic models developed by the USGS. Where the FEMA SFHAs are based on high-quality local models, the continental-scale model attains a hit rate of 86%. This correspondence improves in temperate areas and for basins above 400 km2. Against the higher quality USGS data, the average hit rate reaches 92% for the 1 in 100 year flood, and 90% for all flood return periods. Given typical hydraulic modeling uncertainties in the FEMA maps and USGS model outputs (e.g., errors in estimating return period flows), it is probable that the continental-scale model can replicate both to within error. The results show that continental-scale models may now offer sufficient rigor to inform some decision-making needs with dramatically lower cost and greater coverage than approaches based on a patchwork of local studies. [ABSTRACT FROM AUTHOR]

In this paper, we describe a new method of modeling coastal inundation arising from storm surge by coupling a widely used storm surge model (ADCIRC) and an urban flood inundation model (FloodMap). This is the first time the coupling of such models is implemented and tested using real events. The method offers a flexible and efficient procedure for applying detailed ADCIRC storm surge modeling results along the coastal boundary (with typical resolution of ∼100 m) to FloodMap for fine resolution inundation modeling (<5 m). The coastal inundation during Hurricane Sandy was simulated at both the city (New York City) and subregional (lower Manhattan) scales with various resolutions. Results obtained from the ADCIRC and coupled ADCIRC-FloodMap simulations were compared with the recorded (high water marks) and derived (inundation extent based on the planar method) data from FEMA. At the city scale, coupled ADCIRC-FloodMap modeling demonstrates improved prediction over ADCIRC modeling alone for both the extent and depth of inundation. The advantage of the coupled model is further illustrated in the subregional modeling, using a mesh resolution of 3 m which is substantially finer than the inland mesh resolution used by ADCIRC (>70 m). In further testing, we explored the effects of mesh resolution and roughness specification. Results agree with previous studies that fine resolution is essential for capturing intricate flow paths and connectivity in urban topography. While the specification of roughness is more challenging for urban environments, it may be empirically optimized. The successful coupling of ADCIRC and FloodMap models for fine resolution coastal inundation modeling unlocks the potential for undertaking large numbers of probabilistically based synthetic surge events for street-level risk analysis. [ABSTRACT FROM AUTHOR]

Regionalization approaches are widely used in water resources engineering to identify hydrologically homogeneous groups of watersheds that are referred to as regions. Pooled information from sites (depicting watersheds) in a region forms the basis to estimate quantiles associated with hydrological extreme events at ungauged/sparsely gauged sites in the region. Conventional regionalization approaches can be effective when watersheds (data points) corresponding to different regions can be separated using straight lines or linear planes in the space of watershed related attributes. In this paper, a kernel-based Fuzzy c-means (KFCM) clustering approach is presented for use in situations where such linear separation of regions cannot be accomplished. The approach uses kernel-based functions to map the data points from the attribute space to a higher-dimensional space where they can be separated into regions by linear planes. A procedure to determine optimal number of regions with the KFCM approach is suggested. Further, formulations to estimate flood quantiles at ungauged sites with the approach are developed. Effectiveness of the approach is demonstrated through Monte-Carlo simulation experiments and a case study on watersheds in United States. Comparison of results with those based on conventional Fuzzy c-means clustering, Region-of-influence approach and a prior study indicate that KFCM approach outperforms the other approaches in forming regions that are closer to being statistically homogeneous and in estimating flood quantiles at ungauged sites. [ABSTRACT FROM AUTHOR]

The Grubbs-Beck test is recommended by the federal guidelines for detection of low outliers in flood flow frequency computation in the United States. This paper presents a generalization of the Grubbs-Beck test for normal data (similar to the Rosner (1983) test; see also Spencer and McCuen (1996)) that can provide a consistent standard for identifying multiple potentially influential low flows. In cases where low outliers have been identified, they can be represented as 'less-than' values, and a frequency distribution can be developed using censored-data statistical techniques, such as the Expected Moments Algorithm. This approach can improve the fit of the right-hand tail of a frequency distribution and provide protection from lack-of-fit due to unimportant but potentially influential low flows (PILFs) in a flood series, thus making the flood frequency analysis procedure more robust. [ABSTRACT FROM AUTHOR]

Trends in flood magnitudes vary across the conterminous USA (CONUS). There have been attempts to identify what controls these regionally varying trends, but these attempts were limited to certain—for example, climatic—variables or to smaller regions, using different methods and datasets each time. Here we attribute the trends in annual maximum streamflow for 4,390 gauging stations across the CONUS in the period 1960–2010, while using a novel combination of methods and an unprecedented variety of potential controlling variables to allow large‐scale comparisons and minimize biases. Using process‐based flood classification and complex networks, we find 10 distinct clusters of catchments with similar flood behavior. We compile a set of 31 hydro‐climatological and land use variables as predictors for 10 separate Random Forest models, allowing us to find the main controls the flood magnitude trends for each cluster. By using Accumulated Local Effect plots, we can understand how these controls influence the trends in the flood magnitude. We show that hydro‐climatologic changes and land use are of similar importance for flood magnitude trends across the CONUS. Static land use variables are more important than their trends, suggesting that land use is able to attenuate (forested areas) or amplify (urbanized areas) the effects of climatic changes on flood magnitudes. For some variables, we find opposing effects in different regions, showing that flood trend controls are highly dependent on regional characteristics and that our novel approach is necessary to attribute flood magnitude trends reliably at the continental scale while maintaining sensitivity to regional controls. Key Points: A wide variety of controls are necessary to explain flood magnitude trends across the United States between 1960 and 2010Climatic changes and land cover conditions are of similar importance for flood magnitude trends at the regional scaleControls on flood trends can have highly nonlinear effects and can have opposing effects in different hydro‐climatological subregions [ABSTRACT FROM AUTHOR]

Many studies investigate river floods by analyzing annual maximum series that record the largest flow of each year, including many within‐bank events inconsequential for human communities. Fewer focus on larger floods, especially at the continental scale. Using 473 streamgages across the conterminous United States with near‐natural flow from 1966 to 2015, we characterized the seasonality, occurrence, and climatic associations of the 10 largest and 2 largest floods at each site. These are often overbank events that have ecosystem functions and pose risks to humans. We grouped sites into 14 clusters corresponding to climatic and physiographic regions and characterized their flood seasonality at a monthly resolution using a probabilistic method. We then evaluated annual occurrence regionally and nationally, and seasonal occurrence regionally, by complementing a traditional approach to trend analyses with a novel method based on expected occurrence. Relationships between flood occurrence and climate indices were also investigated. Large floods have strong seasonality in some regions, but in areas with numerous flood‐generating mechanisms, seasonality is more complex. There is little evidence nationally that large riverine floods are more or less frequent than expected in recent years and only two regions show significant trends in annual counts; few show seasonal trends. We found some regional relationships between flood counts and climate indices, annually and seasonally; nationally the Pacific North American pattern is related to annual counts of the 2 largest floods. Large‐flood occurrence was generally stable across the United States in the last five decades; this may or may not continue with projected warming. Plain Language Summary: Flood studies usually focus on records of the largest streamflow of each year because they are widely available and provide relatively large samples. However, the largest flow of each year is not necessarily big enough to inundate the floodplain and pose risks to human communities. Our study investigated only the largest observed floods in the record, ones that occur roughly every five to fifty years. We used a set of 473 gages that measure near‐natural river flow across the United States. We studied many aspects of these consequential events at regional and national scales for the period 1966–2015, including seasonality, changes over time, and associations with known climate patterns. We found that the seasonality of large floods is strong in some regions, but weak or complex in others where floods have a variety of causes. There is little evidence that large floods have become more or less frequent with time, but we found some regional and national associations between large‐flood occurrence and known climate patterns such as the Pacific North American pattern. Large‐flood occurrence has generally been stable in the last five decades, but continued re‐evaluation of the historical record is important as new data are collected. Key Points: First broad characterization of large‐flood occurrence in the conterminous United States in the modern hydroclimate, including seasonalityBoth traditional and novel methods to assess temporal distribution of large floodsFew significant temporal trends, but significant relations to climate indices for some regions and seasons [ABSTRACT FROM AUTHOR]

Long short‐term memory (LSTM) networks represent one of the most prevalent deep learning (DL) architectures in current hydrological modeling, but they remain black boxes from which process understanding can hardly be obtained. This study aims to demonstrate the potential of interpretive DL in gaining scientific insights using flood prediction across the contiguous United States (CONUS) as a case study. Two interpretation methods were adopted to decipher the machine‐captured patterns and inner workings of LSTM networks. The DL interpretation by the expected gradients method revealed three distinct input‐output relationships learned by LSTM‐based runoff models in 160 individual catchments. These relationships correspond to three flood‐inducing mechanisms—snowmelt, recent rainfall, and historical rainfall—that account for 10.1%, 60.9%, and 29.0% of the 20,908 flow peaks identified from the data set, respectively. Single flooding mechanisms dominate 70.7% of the investigated catchments (11.9% snowmelt‐dominated, 34.4% recent rainfall‐dominated, and 24.4% historical rainfall‐dominated mechanisms), and the remaining 29.3% have mixed mechanisms. The spatial variability in the dominant mechanisms reflects the catchments' geographic and climatic conditions. Moreover, the additive decomposition method unveils how the LSTM network behaves differently in retaining and discarding information when emulating different types of floods. Information from inputs within previous time steps can be partially stored in the memory of LSTM networks to predict snowmelt‐induced and historical rainfall‐induced floods, while for recent rainfall‐induced floods, only recent information is retained. Overall, this study provides a new perspective for understanding hydrological processes and extremes and demonstrates the prospect of artificial intelligence‐assisted scientific discovery in the future. Key Points: The expected gradients method was used to retrieve flooding mechanisms learned by long short‐term memory networks in 160 catchments in the United StatesSnowmelt, recent rainfall, and historical rainfall induce 10.1%, 60.9%, and 29.0% of the 20,908 identified flow peaks, respectivelyThe additive decomposition method unveiled models' distinct behaviors in retaining and discarding information for different flood types [ABSTRACT FROM AUTHOR]

Hydrometeorological flood generating processes (excess rain, short rain, long rain, snowmelt, and rain‐on‐snow) underpin our understanding of flood behavior. Knowledge about flood generating processes improves hydrological models, flood frequency analysis, estimation of climate change impact on floods, etc. Yet, not much is known about how climate and catchment attributes influence the spatial distribution of flood generating processes. This study aims to offer a comprehensive and structured approach to close this knowledge gap. We employ a large sample approach (671 catchments across the contiguous United States) and evaluate how catchment attributes and climate attributes influence the distribution of flood processes. We use two complementary approaches: A statistics‐based approach which compares attribute frequency distributions of different flood processes; and a random forest model in combination with an interpretable machine learning approach (accumulated local effects [ALE]). The ALE method has not been used often in hydrology, and it overcomes a significant obstacle in many statistical methods, the confounding effect of correlated catchment attributes. As expected, we find climate attributes (fraction of snow, aridity, precipitation seasonality, and mean precipitation) to be most influential on flood process distribution. However, the influence of catchment attributes varies both with flood generating process and climate type. We also find flood processes can be predicted for ungauged catchments with relatively high accuracy (R2 between 0.45 and 0.9). The implication of these findings is flood processes should be considered for future climate change impact studies, as the effect of changes in climate on flood characteristics varies between flood processes. Key Points: Flood generating processes are mostly influenced by climate attributesWhich attributes are influential varies between different flood processes and climate regionsMix of flood generating processes can be predicted for ungauged catchments with high accuracy [ABSTRACT FROM AUTHOR]

Future changes in the occurrence of flood events can be estimated using multi‐model ensembles to inform adaption and mitigation strategies. In the near future, these estimates could be used to guide the updating of exceedance probabilities for flood control design and water resources management. However, the estimate of return levels from ensemble experiments represents a challenge: model runs are affected by biases and uncertainties and by inconsistencies in simulated peak flows when compared with observed data. Moreover, extreme value distributions are generally fit to ensemble members individually and then averaged to obtain the ensemble fit with loss of information. To overcome these limitations, we propose a Bayesian hierarchical model for assessing changes in future peak flows, and the uncertainty coming from global climate, global impact models and their interaction. The model we propose allows use of all members of the ensemble at once for estimating changes in the parameters of an extreme value distribution from historical to future peak flows. The approach is applied to a set of grid‐cells in the eastern United States to the full and to a constrained version of the ensemble. We find that, while the dominant source of uncertainty in the changes varies across the domain, there is a consensus on a decrease in flood magnitudes toward the south. We conclude that projecting future flood magnitude under climate change remains elusive due to large uncertainty mostly coming from global models and from the intrinsic uncertain nature of extreme values. Key Points: A Bayesian hierarchical model is developed to assess the changes in future flood magnitude and quantify uncertainty in a single stepFuture flood magnitude at selected sites over the eastern United States decreases in the south of the domain with varying uncertaintyA constrained ensemble based on how well model runs replicate timing of observed peak flows yields similar results to the full ensemble [ABSTRACT FROM AUTHOR]

This study reports a new and significantly enhanced analysis of US flood hazard at 30 m spatial resolution. Specific improvements include updated hydrography data, new methods to determine channel depth, more rigorous flood frequency analysis, output downscaling to property tract level, and inclusion of the impact of local interventions in the flooding system. For the first time, we consider pluvial, fluvial, and coastal flood hazards within the same framework and provide projections for both current (rather than historic average) conditions and for future time periods centered on 2035 and 2050 under the RCP4.5 emissions pathway. Validation against high‐quality local models and the entire catalog of FEMA 1% annual probability flood maps yielded Critical Success Index values in the range 0.69–0.82. Significant improvements over a previous pluvial/fluvial model version are shown for high‐frequency events and coastal zones, along with minor improvements in areas where model performance was already good. The result is the first comprehensive and consistent national‐scale analysis of flood hazard for the conterminous US for both current and future conditions. Even though we consider a stabilization emissions scenario and a near‐future time horizon, we project clear patterns of changing flood hazard (3σ changes in 100 years inundated area of −3.8 to +16% at 1° scale), that are significant when considered as a proportion of the land area where human use is possible or in terms of the currently protected land area where the standard of flood defense protection may become compromised by this time. Plain Language Summary: We develop a method to estimate past, present, and future flood risk for all properties in the conterminous United States whether affected by river, coastal or rainfall flooding. The analysis accounts for variability within environmental factors including changes in sea level rise, hurricane intensity and landfall locations, precipitation patterns, and river discharge. We show that even for a conservative climate change trajectory we can expect locally significant changes in the land area at risk from floods by 2050, and by this time defenses protecting 2,200 km2 of land may be compromised. The complete dataset has been made available via a website (https://floodfactor.com/) created by the First Street Foundation in order to increase public awareness of the threat posed by flooding to safety and livelihoods. Key Points: First complete high‐resolution flood hazard analysis of conterminous US flood risk from all major sources (fluvial, pluvial, and coastal)In validation tests the model achieved Critical Success Index scores of 0.69–0.82, similar to many local custom‐built 2D modelsBy 2050, flood hazard increases for the Eastern seaboard and Western states, but decreases or changes little for the center and South‐West [ABSTRACT FROM AUTHOR]

The challenge of estimating design flood magnitude under climate change has led to the development of multiple approaches to long‐term flood projection: stationarity, informed‐parameter (composed of both trend informed and climate informed), and hydrologic simulation. This is the first study to compare these approaches across a large set of hydro‐climatologically diverse basins located throughout the contiguous United States, grouped into distinct clusters based on catchment and flood characteristics. The comparison is achieved using a split‐sample test conducted over the historic period; the climate (specifically, precipitation) informed and hydrologic simulation (specifically, Sacramento Soil Moisture Accounting model) approaches are forced with observations and downscaled general circulation model (GCM) simulations. The results provide a quantitative perspective on key long‐term flood projection issues. The precipitation informed approach can be informative for projections of regional change; the hydrologic simulation approach can be informative for direction of change for a single basin. When forced with GCM outputs, precipitation informed is both more accurate and more precise than hydrologic simulation, after accounting for model error. For hydrologic simulation, forcing with GCMs will add positive bias for basins in the Rocky Mountains and Southwest and will add negative bias with large uncertainty across GCMs for basins in the lower Midwest, the South, and the mid‐Atlantic seaboard. The results illustrate the need for continued improvement in long‐term flood projection approaches and for design paradigms incorporating uncertainty. Key Points: Long‐term flood projection approaches are compared across 233 U.S. river basins using split‐sample tests over the historic periodPrecipitation informed is useful for regional change projections; hydrologic simulation is useful for direction of change in a single basinGeneral circulation model outputs as forcing introduces less additional bias for precipitation informed compared to hydrologic simulation [ABSTRACT FROM AUTHOR]

The 14 September 2015 Hildale, Utah, storm resulted in 20 flash flood fatalities, making it the most deadly natural disaster in Utah history; it is the quintessential example of the "paroxysmal precipitation of the desert". The measured peak discharge from Maxwell Canyon at a drainage area of 5.3 km2 was 266 m3/s, a value that exceeds envelope curve peaks for Utah. The 14 September 2015 flash flood reflects features common to other major flash flood events in the region, as well as unique features. The flood was produced by a hailstorm that was moving rapidly from southwest to northeast and intensified as it interacted with complex terrain. Polarimetric radar observations show that the storm exhibited striking temporal variability, with the Maxwell Canyon tributary of Short Creek and a small portion of the East Fork Virgin River basin experiencing extreme precipitation. Periods of extreme rainfall rates for the 14 September 2015 storm are characterized by KDP signatures of extreme rainfall in polarimetric radar measurements. Similar KDP signatures characterized multiple storms that have produced record and near‐record flood peaks in Colorado Plateau watersheds. The climatology of monsoon thunderstorms that produce flash floods exhibits striking spatial heterogeneities in storm occurrence and motion. The hydroclimatology of flash flooding in arid/semiarid watersheds of the southwestern United States exhibits relatively weak dependence on drainage basin area. Large flood peaks over a broad range of basin scales can be produced by small thunderstorms like the 14 September 2015 Hildale Storm, which pass close to the outlet. Key Points: The 14 September 2015 Hildale storm was a hailstorm that produced catastrophic flooding in southern UtahThe climatology of monsoon thunderstorms that produce flash floods in the southwestern United States exhibits large spatial heterogeneityRecord flood peaks over a wide range of basin scales are produced by "small," monsoon storms that pass close to the basin outlet [ABSTRACT FROM AUTHOR]

We examine the upper tail of flood peak distributions through analyses of annual peak observations from more than 8,000 U.S. Geological Survey (USGS) stream gaging stations and through hydrometeorological analyses of the storms that produce the most extreme floods. We focus on the distribution of the upper tail ratio, which is defined as the peak discharge for the flood of record at a stream gaging station divided by the sample 10‐year flood magnitude. The 14 June 1903 Heppner storm, which produced an upper tail ratio of 200, was the product of a hailstorm that formed along the Blue Mountains in eastern Oregon, a region dominated by snowmelt flooding. A striking contrast between record flood peaks and the larger distribution of annual flood peaks in the United States is in the seasonality of flood occurrence, with record floods reflecting a much stronger contribution from warm season thunderstorm systems. Mountainous terrain and intense convective rainfall are important elements of the geography and hydrometeorology of extreme upper tail ratio flood peaks. The distribution of upper tail ratio values for USGS stream gaging stations does not depend on basin area, a result which is consistent with scaling results based on extreme value theory. Downscaling simulations with the Weather Research and Forecasting model are used to examine the storm environment of the 1903 Heppner storm, along with two other record flood peaks near the Blue Mountains of eastern Oregon from the USGS miscellaneous flood record, the July 1956 Meyers Canyon flood and the July 1965 Lane Canyon flood. Key Points: The upper tail of flood peaks in the United States is examined through analyses of the upper tail ratio for annual flood peak observationsTwo important aspects of strange floods in the United States are location near mountainous terrain and intense thunderstorms as causative agentsA fundamental distinction between record floods in the United States and the broader population of annual flood peaks is their seasonal distribution [ABSTRACT FROM AUTHOR]

Annual maximum peak discharge time series from 572 stations with a record of at least 75 years in the eastern United States are used to examine flood peak distributions from a regional perspective. The central issues of this study are (1) 'mixtures' of flood peak distributions, (2) upper tail properties of flood peaks, (3) scaling properties of flood peaks, (4) spatial heterogeneities of flood peak distributions, and (5) temporal nonstationarities of annual flood peaks. Landfalling tropical cyclones are an important element of flood peak distributions throughout the eastern United States, but their relative importance in the 'mixture' of annual flood peaks varies widely, and abruptly, in space over the region. Winter-spring extratropical systems and warm season thunderstorm systems also introduce distinct flood peak populations, with spatially varying control of flood frequency distributions over the eastern United States. We examine abrupt changes in the mean and variance of flood peak distributions through change point analyses and temporal trends in the flood peak records through nonparametric tests. Abrupt changes, rather than slowly varying trends, are typically responsible for nonstationarities in annual flood peak records in the eastern United States, and detected change points are often linked to regulation of river basins. Trend analyses for the 572 eastern United States gaging stations provide little evidence at this point (2009) for increasing flood peak distributions associated with human-induced climate change. Estimates of the location, scale, and shape parameters of the generalized extreme value (GEV) distribution provide a framework for examining scaling properties of flood peaks and upper tail properties of flood distributions. It is shown that anomalously large values of the GEV shape parameter estimates are linked to the role of tropical cyclones in controlling the upper tail of flood distributions. Scaling analyses of flood peaks highlight the heterogeneities in flood magnitudes over the region with maxima in scaled flood magnitudes in the high-elevation Appalachian Mountains and minima in the low-gradient Coastal Plain. [ABSTRACT FROM AUTHOR]

Many mountainous and high‐latitude regions have experienced more precipitation as rain rather than snow due to warmer winter temperatures. Further decreases in the annual snow fraction are projected under continued global warming, with potential impacts on flood risk. Here, we quantify the size of streamflow peaks in response to both seasonal and event‐specific rain‐fraction using stream gage observations from watersheds across the western United States. Across the study watersheds, the largest rainfall‐driven streamflow peaks are >2.5 times the size of the largest snowmelt‐driven peaks. Using a panel regression analysis of individual precipitation and snowmelt events, we show that the empirical streamflow response grows approximately exponentially as the liquid precipitation input increases, with rain‐dominated runoff leading to proportionately larger streamflow increases than snowmelt or mixed rain‐and‐snow runoff. We find that the response to changes in rain percentage is largest in the wettest watersheds, where wet antecedent conditions are important for increasing runoff efficiency. Similarly, the effect of rain percentage is larger across watersheds in the Northwest and West regions compared to watersheds in the Northern Rockies and Southwest regions. Overall, as a higher percentage of precipitation falls as rain, increases in the size of rainfall‐driven and "rain‐on‐snow"‐driven floods have the potential to more than offset decreases in the size of snowmelt‐driven floods. Key Points: Stream gages show an approximately exponential increase in streamflow peak size in response to lower snow fraction of runoffRainfall‐driven floods can be more than 2.5 times as large as snowmelt‐driven floods, so a shift toward more rain can increase flood riskSnow‐dominated and mixed‐regime watersheds could see larger floods under warmer winter conditions if precipitation amounts remain similar [ABSTRACT FROM AUTHOR]