Flood Generation Mechanisms Research Articles

Integrated risk analysis for urban flooding under changing climates

Urban flooding poses significant threats to human lives and urban development worldwide, while the impact of climate change on urban flooding remains unclear. To systematically analyze the variabilities of hydrological patterns and urban flooding under changing climates, this study coupled the downscaled general circulation model (GCM) projection with hydrologic-hydraulic modeling, with consideration of both the low-greenhouse gas (GHG) emission scenario of SSP1–2.6 and the high-GHG emission scenario of SSP5–8.5. Results demonstrated that the original GCM projection effectively captured the changing trend of rainfall patterns in the given area, with an overestimation of rainfall peaks. The GCM downscaling through the k-nearest neighbors (kNN)-based analog method significantly improved the model accuracy. Scenario analysis indicated that climate change significantly affected the regional hydrology, with the precipitation, surface runoff, and floods increasing by a maximum value of 17.10%, 12.66%, and 63.26%, respectively. The interannual comparison demonstrated that the temporal variability in precipitation and flood intensified with the increase in GHG emissions during 2025–2100, suggesting the uncertainty of long-term climate forecasts. According to flood risk analysis, long-term and short-term floods exhibit varied changing trends across climate scenarios, despite the strong correlations between precipitation and runoff, implying the complexity of flood generation mechanisms. The methods and findings herein provide valuable insights for policymakers and practitioners, to cope with the increasing urban flood risk within the context of evolving environment.

Deciphering the Role of Total Water Storage Anomalies in Mediating Regional Flooding

AbstractRegional floods result from various flood generation mechanisms. Traditional analyses mainly link flooding to extreme rainfall, with limited input from soil moisture. Total water storage (TWS) is a holistic measure of basin wetness, including additional storage components from surface water, snow, and groundwater. Utilizing a new 5‐day Gravity Recovery and Climate Experiment and its Follow On (GRACE(‐FO)) data set, we investigated the linkage between short‐term TWS anomaly (TWSA) and regional flooding. The 5‐day TWSA solutions revealed flood signals missed by monthly TWSA solutions. Global basins exhibit distinct storage‐discharge co‐evolution patterns, offering new insights into flood mechanisms and propensity. Our bivariate event analyses show the annual maximum river discharges co‐occur more often with the TWSA maxima than with precipitation in many basins. Further analyses revealed TWSA's time‐lagged effect on river discharge, particularly in basins susceptible to floods triggered by saturation‐excess runoff. The 5‐day TWSA provides a new source of information for enhancing global flood preparedness.

Mechanisms of spring freshet generation in southern Quebec, Canada

Seasonal forecasting of spring floods in snow-covered basins is challenging due to the ambiguity in the driving processes, uncertain estimations of antecedent catchment conditions, and the choice of predictor variables. In this study, we attempt to improve the prediction of spring flow peaks in southern Quebec, Canada, by studying the preconditioning mechanisms of runoff generation and their impact on inter-annual variations in the timing and magnitude of spring peak flow. Historical observations and simulated data from a hydrological and snowmelt model were used to study the antecedent conditions that control flood characteristics in 12 unregulated snow-dominated catchments. Maximum snow accumulation (peak SWE), snowmelt and rainfall volume and intensity, soil moisture, river baseflow, and the air freezing index, a proxy for soil freezing, were estimated during the pre-flood period. Stepwise multiple linear regression was used to identify the most relevant predictors and assess their relative contribution to interannual variability flood variability. Peak SWE was not by itself a strong predictor of spring flood magnitude and timing. The snowmelt rate during the pre-flood period was the most ubiquitous and skillful predictor of spring flood magnitude, followed by the rainfall intensity. The ‘soil memory’ effect, represented here by the simulated soil moisture content and freezing depth, was generally poorly related to flood characteristics. SWE and snowmelt dynamics dominated the interannual variability of flood magnitude in the southern and agricultural basins while rainfall intensity had a stronger influence in the northern, snowier forested basins. More complex machine learning models (random forest and support vector regression) did not perform better than the simple linear models to predict peakflow from antecedent hydroclimatic factors. These results highlight the impact of climate and land cover and use on spring flood generation mechanism and the moderate predictability potential of spring floods based on antecedent hydrological factors.

Accounting for hydroclimatic properties in flood frequency analysis procedures

Abstract. Flood hazard is typically evaluated by computing extreme flood probabilities from a flood frequency distribution following nationally defined procedures in which observed peak flow series are fit to a parametric probability distribution. These procedures, also known as flood frequency analysis, typically recommend only one probability distribution family for all watersheds within a country or region. However, large uncertainties associated with extreme flood probability estimates (>50-year flood or Q50) can be further biased when fit to an inappropriate distribution model because of differences in the tails between distribution families. Here, we demonstrate that hydroclimatic parameters can aid in the selection of a parametric flood frequency distribution. We use L-moment diagrams to visually show the fit of gaged annual maxima series across the United States, grouped by their Köppen climate classification and the precipitation intensities of the basin, to a general extreme value (GEV), log normal 3 (LN3), and Pearson 3 (P3) distribution. Our results show that in real space basic hydroclimatic properties of a basin exert a significant influence on the statistical distribution of the annual maxima. The best-fitted family distribution shifts from a GEV towards an LN3 distribution across a gradient from colder and wetter climates (Köppen group D, continental climates) towards more arid climates (Köppen group B, dry climates). Due to the diversity of hydrologic processes and flood-generating mechanisms among watersheds within large countries like the United States, we recommend that the selection of distribution model be guided by the hydroclimatic properties of the basin rather than relying on a single national distribution model.

Dominant flood types in mountains catchments: Identification and change analysis for the landscape planning

The classification of floods may be a supporting tool for decision-makers in regard to water management, including flood protection. The main objective of this work is the classification of flood generation mechanisms in 28 catchments of the upper Vistula basin. A significant innovation in this study lies in the utilization of decision trees for flood classification. The methodology has so far been applied in the Alpine region. The analysis reveals that peak daily precipitation in the catchments mainly occurs in summer, particularly from June to August. Maximal daily snowmelt typically happens at the end of winter (March to April) and occasionally in November. Winter peaks are observed in March to April and, in some areas, in November to December, while summer peaks occur in May and, in specific catchments, in October. Higher peak flows for annual floods are noted in March to April and June to August. Most annual floods in the Upper Vistula basin are classified as Rain-on-Snow Floods (RoSFs) or Lowland River Floods (LRFs). LRFs contribute from 19% to almost 72%, while RoSFs range from 18% to 75%. In Season 1 (summer), most seasonal floods are identified as LRFs (51%–100%), with very few as RoSFs (0%–46.9%). In Season 2 (winter), the opposite pattern is observed, with most RoSFs (48.4%–97.9%) and fewer LRFs (0%–20.6%). While there are changes in flood patterns, they are not statistically significant. Conducted studies and obtained results can be useful for the preparation of flood prevention documentation and for flood management in general.

Quantifying the relative contributions of different flood generating mechanisms to floods across CONUS

The occurrence of flooding in a catchment is attributable to various mechanisms. Several researchers have used linear models to quantify the relative contributions of different flood generating mechanisms. However, the use of linear models for this quantification may not be suitable given the complex and nonlinear processes from climate to floods. In this study, we used nonlinear machine learning models with interpretability methods to quantify the contributions of multiple flood generating mechanisms to floods across the continental United States (CONUS). Four flood generating mechanisms were considered comprehensively, which varied in terms of the influence of antecedent soil moisture condition or snowmelt on flood generation. These mechanisms involved three common mechanisms (rainfall, rainfall excess, and snowmelt/rain on snow) and one complex mechanism (effective precipitation) that accounts for snowmelt/rain on snow lost to unsaturated soil zone. Consistent with previous studies, we observed that rainfall alone (i.e., without considering other hydrometeorological processes) is seldom the dominant mechanism for flood generation. Floods in most catchments are largely attributable to rainfall excess and snowmelt. Unlike previous studies, we noted that in the midwest and northeastern CONUS, snowmelt/rain on snow upon saturated soil was the greatest contributor to local flood generation. This finding highlights that both snowmelt and soil moisture condition affect flood generation in the midwest and northeastern CONUS, instead of only snowmelt as has been reported previously. Overall, this study provides new insights into flood generating mechanisms in CONUS and demonstrates the potential of applying interpretable machine learning to examine flood generating mechanisms.

Unveiling flood-generating mechanisms using circular statistics-based machine learning approach without the need for discharge data during inference

Abstract Understanding the drivers of flooding is essential for flood disaster prevention. However, conventional flood prediction methods are hindered by their reliance on local discharge data, which can be constrained by limited spatial resolution. To address this limitation, we present a machine learning model that can categorize floods without requiring discharge data during inference. We first use circular statistics to calculate the relative importance of three candidate flood-generating mechanisms. Global land areas are classified into three primary categories and eight sub-categories based on the proportion of relative importance. A random forest model is then applied to identify the flood types by assuming that the discharge data is unavailable. The findings from circular statistics highlight that globally, soil moisture excess is the most influential driver of floods followed by extreme precipitation and snowmelt, with an average relative importance of 0.535, 0.387, and 0.078, respectively. The RF model performs well in resembling the three primary flood categories with an accuracy of 0.701 and a F1-score of 0.692 in 10-fold cross-validation. The trained gridded-based model provides a swift and efficient approach for analyzing flood mechanisms, even in limited discharge scenarios, allowing for rapid insights.

Model-based assessment of flood generation mechanisms over Poland: The roles of precipitation, snowmelt, and soil moisture excess

Hydrometeorological variability, such as changes in extreme precipitation, snowmelt, or soil moisture excess, in Poland can lead to fluvial flooding. In this study we employed the dataset covering components of the water balance with a daily time step at the sub-basin level over the country for 1952–2020. The data set was derived from the previously calibrated and validated Soil & Water Assessment Tool (SWAT) model for over 4000 sub-basins. We applied the Mann Kendall test and circular statistics-based approach on annual maximum floods and various potential flood drivers to estimate the trend, seasonality, and relative importance of each driver. In addition, two sub-periods (1952–1985 and 1986–2020) were considered to examine changes in flood mechanism in the recent decades. We show that floods in the northeast Poland were decreasing, while in the south the trend showed a positive behavior. Moreover, the snowmelt is a primary driver of flooding across the country, followed by soil moisture excess and precipitation. The latter seemed to be the dominant driver only in a small, mountain-dominated region in the south. Soil moisture excess gained importance mainly in the northern part, suggesting that the spatial pattern of flood generation mechanisms is also governed by other features. We also found a strong signal of climate change in large parts of northern Poland, where snowmelt is losing importance in the second sub-period in favor of soil moisture excess, which can be explained by the temperature warming and diminishing role of snow processes.

Flood generating mechanisms investigation and rainfall threshold identification for regional flood early warning

A cost effective and easily applied methodological approach for the identification of the main factors involved in flood generation mechanisms and the development of rainfall threshold for incorporation in flood early warning systems at regional scale is proposed. The methodology was tested at the Pinios upstream flood-prone area in Greece. High frequency monitoring rainfall and water level/discharge time-series were investigated statistically. Based on the results, the study area is impacted by “long-rain floods” triggered by several days long and low-intensity precipitation events in the mountainous areas, that saturate the catchment and cause high flow conditions. Time lag between the peaks of rainfall and water level was 17–25 h. The relationship between cumulative rainfall Rsum on the mountainous areas and maximum water level MaxWL of the river at the particular river site can be expressed as: MaxWL = 1.55ln(Rsum) − 3.70 and the rainfall threshold estimated for the mountainous stations can be expressed as: Rsum = 20.4*D0.3, where D is the duration of the event. The effect of antecedent moisture conditions prior each event was limited to the decrease of the time lag between rainfall and water level response. The limitations of the specific methodological approach are related to the uncertainties that arise due to the other variables contributing to the complex flood generating mechanisms not considered (e.g., the effect of snowmelt and air temperature, soil characteristics, the contribution of tributaries, or the inadequate maintenance of river network that may cause debris accumulation and river bank failure).

Changing Seasonality of Annual Maximum Floods over the Conterminous US: Potential Drivers and Regional Synthesis

Understanding the flood-generating mechanisms that influence flood seasonality in a region provides information on setting up relevant contingency measures. Although former studies estimated flood seasonality at regional/continental scale, limited/no studies have investigated the climate/basin drivers that influence the changes in flood seasonality. Considering this, the current study performed two analyses: (1) estimated the changes in the seasonality of annual maximum floods (AMF) between pre- and post-1970 across Hydroclimate Data Network basins over the conterminous US, and (2) identified the predictors that influence the change in the seasonality from a set of climate and geomorphic variables. Significant changes in the AMF seasonality were noted for approximately half of the basins in the eastern US, but low to no change was found in most basins in the central/western US. We found, except in the Northeast and mid-Atlantic basins, a decrease in the seasonality index, indicating floods arriving more uniformly is typically associated with an increase in the precipitation days in basins. On the other hand, increase in the seasonality index, indicating floods occurring more concentrated in time, is typically associated with an increase in the extreme precipitation in basins. Among the basin characteristics, elevation has a more dominant role than the drainage area in changing the flood seasonality. Elevation affects the form of precipitation, particularly in the western US, because floods arrive more distributed over the year (i.e., decrease in flood seasonality index), which potentially indicates increased warming resulting in early snowmelt.

Flood Frequency Analysis Using Mixture Distributions in Light of Prior Flood Type Classification in Norway

The fundamental assumption of flood frequency analysis is that flood samples are generated by the same flood generation mechanism (FGM). However, flood events are usually triggered by the interaction of meteorological factors and watershed properties, which results in different FMGs. To solve this problem, researchers have put forward traditional two-component mixture distributions (TCMD-T) without clearly linking each component distribution to an explicit FGM. In order to improve the physical meaning of mixture distributions in seasonal snow-covered areas, the ratio of rainfall to flood volume (referred to as rainfall–flood ratio, RF) method was used to classify distinct FGMs. Thus, the weighting coefficient of each component distribution was determined in advance in the rainfall–flood ratio based TCMD (TCMD-RF). TCMD-RF model was applied to 34 basins in Norway. The results showed that flood types can be clearly divided into rain-on-snow-induced flood, snowmelt-induced flood and rainfall-induced flood. Moreover, the design flood and associated uncertainties were also estimated. It is found that TCMD-RF model can reduce the uncertainties of design flood by 20% compared with TCMD-T. The superiority of TCMD-RF is attributed to its clear classification of FGMs, thus determining the weighting coefficients without optimization and simplifying the parameter estimation procedure of mixture distributions.

A Framework for Estimating the Probability Distribution of Event Runoff Coefficient in Ungauged Catchments

AbstractThe derived distribution method is a promising approach for flood estimation in ungauged catchments. In this approach, event runoff coefficient (ERC) is often adopted to behave as the runoff generation component since the behavior of ERC is closely related to flood generation mechanisms and its distribution reflects catchment climatological and landscape characteristics. However, there is a lack of understanding of how to transfer the information on ERC characteristics from gauged to ungauged catchments at catchment scale. Hence, here, we propose a generalized framework to estimate the probability distribution of ERC for ungauged catchments based on knowledge of the spatial and temporal controls on ERC from gauged catchments. Key components of the framework include cluster analysis based on ERC characteristics, linking clusters with catchment attributes, and constructing typical probability distribution of ERC conditioned on its temporal indicators. A total of 290,743 rainfall‐runoff events observed in 431 GB catchments during the period 1990–2014 have been employed to verify the framework. Good estimations are obtained with the median value of coefficient of determination (R2) reaching 0.85 across all test catchments. The results indicate that similar pre‐event catchment conditions may cause distinct runoff response in different catchments, thus predicting the correct spatial cluster is crucial to the estimation accuracy. This work sheds light on constructing a stochastic generator model of ERC according to its spatial pattern and temporal dynamic, facilitating a new alternative for flood estimation method in ungauged catchments.

Detecting flood-type-specific flood-rich and flood-poor periods in peaks-over-threshold series with application to Bavaria (Germany)

Previous studies suggest that flood-rich and flood-poor periods are present in many flood peak discharge series around the globe. Understanding the occurrence of these periods and their driving mechanisms is important for reliably estimating future flood probabilities. We propose a method for detecting flood-rich and flood-poor periods in peak-over-threshold series based on scan-statistics and combine it with a flood typology in order to attribute the periods to their flood-generating mechanisms. The method is applied to 164 observed flood series in southern Germany from 1930 to 2018. The results reveal significant flood-rich periods of heavy-rainfall floods, especially in the Danube river basin in the most recent decades. These are consistent with trend analyses from the literature. Additionally, significant flood-poor periods of snowmelt-floods in the immediate past were detected, especially for low-elevation catchments in the alpine foreland and the uplands. The occurrence of flood-rich and flood-poor periods is interpreted in terms of increases in the frequency of heavy rainfall in the alpine foreland and decreases of both soil moisture and snow cover in the midlands.

The relative importance of antecedent soil moisture and precipitation in flood generation in the middle and lower Yangtze River basin

Abstract. Floods have caused severe environmental and socioeconomic losses worldwide in human history and are projected to exacerbate due to climate change. Many floods are caused by heavy rainfall with highly saturated soil; however, the relative importance of rainfall and antecedent soil moisture and how it changes from place to place has not been fully understood. Here we examined annual floods from more than 200 hydrological stations in the middle and lower Yangtze River basin. Our results indicate that the dominant factor in flood generation shifts from rainfall to antecedent soil moisture with the increase in watershed area. The ratio of the relative importance of antecedent soil moisture and daily rainfall (SPR) is positively correlated with topographic wetness index and has a negative correlation with the magnitude of annual floods. This linkage between watershed characteristics that are easy to measure and the dominant flood-generation mechanism provides a framework to quantitatively estimate potential flood risk in ungauged watersheds in the middle and lower Yangtze River basin.

Modeling riverine flood seasonality with mixtures of circular probability density functions

• We demonstrate a novel method for characterizing regional flood hazard. • We fit mixtures of von Mises distributions to model the seasons of river floods. • Nearly half of sites have multiple flood seasons. • Flood seasons differ when defined using annual maxima versus peaks over threshold. • Clustering sites yields spatial patterns that point to flood-generating mechanisms. Preparing for and minimizing the negative impacts of flooding requires knowing when floods are likely to occur. However, exploratory analysis of seasons may yield spurious conclusions, and models of flood seasonality can be misleading if they fail to account for the cyclical nature of flood data or the possibility of multiple flood seasons in a year. We modeled the arrival times of floods at 4,505 sites along rivers in the United States using statistical models comprised of weighted combinations of circular von Mises distributions. Circular distributions address the periodicity of arrival time data, while mixture models provide the flexibility to fit multiple seasons and a validation framework to test their significance. Nearly half of all sites we modeled optimized with at least a secondary flood season, a fact hidden by analyses that neglect or conflate multiple seasons. We found spatiotemporal patterns in the modeled dates of elevated flood hazard, which point to common flood-generating mechanisms. Partitioning sites by their modeled flood seasonalities yielded 6–7 clusters of sites along United States rivers with similar flood season numbers, dates, and durations. Results varied depending on whether floods were defined by annual maxima or peaks-over-threshold, and on the threshold chosen. Models of river flood seasonality and the spatial patterns they reveal are potentially useful in informing the timing of infrastructure maintenance, in risk-sharing via financial instruments, and in estimating the likelihood of compound events.

Winter runoff events pose an unquantified continental-scale risk of high wintertime nutrient export

Winters in snow-covered regions have warmed, likely shifting the timing and magnitude of nutrient export, leading to unquantified changes in water quality. Intermittent, seasonal, and permanent snow covers more than half of the global land surface. Warming has reduced the cold conditions that limit winter runoff and nutrient transport, while cold season snowmelt, the amount of winter precipitation falling as rain, and rain-on-snow have increased. We used existing geospatial datasets (rain-on-snow frequency overlain on nitrogen and phosphorous inventories) to identify areas of the contiguous United States (US) where water quality could be threatened by this change. Next, to illustrate the potential export impacts of these events, we examined flow and turbidity data from a large regional rain-on-snow event in the United States’ largest river basin, the Mississippi River Basin. We show that rain-on-snow, a major flood-generating mechanism for large areas of the globe (Berghuijs et al 2019 Water Resour. Res. 55 4582–93; Berghuijs et al 2016 Geophys. Res. Lett. 43 4382–90), affects 53% of the contiguous US and puts 50% of US nitrogen and phosphorus pools (43% of the contiguous US) at risk of export to groundwater and surface water. Further, the 2019 rain-on-snow event in the Mississippi River Basin demonstrates that these events could have large, cascading impacts on winter nutrient transport. We suggest that the assumption of low wintertime discharge and nutrient transport in historically snow-covered regions no longer holds. Critically, however, we lack sufficient data to accurately measure and predict these episodic and potentially large wintertime nutrient export events at regional to continental scales.

Understanding Heavy Tails of Flood Peak Distributions

AbstractStatistical distributions of flood peak discharge often show heavy tail behavior, that is, extreme floods are more likely to occur than would be predicted by commonly used distributions that have exponential asymptotic behavior. This heavy tail behavior may surprise flood managers and citizens, as human intuition tends to expect light tail behavior, and the heaviness of the tails is very difficult to predict, which may lead to unnecessarily high flood damage. Despite its high importance, the literature on the heavy tail behavior of flood distributions is rather fragmented. In this review, we provide a coherent overview of the processes causing heavy flood tails and the implications for science and practice. Specifically, we propose nine hypotheses on the mechanisms causing heavy tails in flood peak distributions related to processes in the atmosphere, the catchment, and the river system. We then discuss to which extent the current knowledge supports or contradicts these hypotheses. We also discuss the statistical conditions for the emergence of heavy tail behavior based on derived distribution theory and relate them to the hypotheses and flood generation mechanisms. We review the degree to which the heaviness of the tails can be predicted from process knowledge and data. Finally, we recommend further research toward testing the hypotheses and improving the prediction of heavy tails.

An Overview of Flood Concepts, Challenges, and Future Directions

This review provides a broad overview of the current state of flood research, current challenges, and future directions. Beginning with a discussion of flood-generating mechanisms, the review synthesizes the literature on flood forecasting, multivariate and nonstationary flood frequency analysis, urban flooding, and the remote sensing of floods. Challenges and future flood research directions are outlined and highlight emerging topics where more work is needed to help mitigate flood risks. It is anticipated that the future urban systems will likely have more significant flood risk due to the compounding effects of continued climate change and land-use intensification. The timely prediction of urban floods, quantification of the socioeconomic impacts of flooding, and developing mitigation strategies will continue to be challenging. There is a need to bridge the scales between model capabilities and end-user needs by integrating multiscale models, stakeholder input, and social and citizen science input for flood monitoring, mapping, and dissemination. Although much progress has been made in using remote sensing for flood applications, recent and upcoming Earth Observations provide excellent potential to unlock additional benefits for flood applications. The flood community can benefit from more downscaled, as well as ensemble scenarios that consider climate and land-use changes. Efforts are also needed for data assimilation approaches, especially to ingest local, citizen, and social media data. Also needed are enhanced capabilities to model compound hazards and assess as well as help reduce social vulnerability and impacts. The dynamic and complex interactions between climate, societal change, watershed processes, and human factors often confronted with deep uncertainty highlights the need for transdisciplinary research between science, policymakers, and stakeholders to reduce flood risk and social vulnerability.

A ranking method for regionalization of watersheds

Identification of groups of watersheds with similar flood generation mechanisms and the same hydrological responses is an important step of regional flood frequency analysis which is called regionalization of watersheds. A group of watersheds with similar flood generation mechanisms and the same hydrological responses is called a homogeneous region. Theoretically, the homogeneity of regions is a requirement to fit the regional frequency distribution and estimate flood quantiles. However, it may be observed in some real-world cases that a regionalization with a more appropriate homogeneity condition results in less accurate estimates in comparison with a regionalization with lesszappropriate homogeneity. In this study, a ranking method is proposed to compare various regionalizations and select more suitable ones for regional flood frequency analysis. The proposed method is used to identify a suitable regionalization for regional flood frequency analysis by considering the homogeneity, quantile estimation accuracy, and average region size. The method was applied to Karun-e-bozorg basin in the southwest of Iran as a case study. The results show that the method has an acceptable performance in identifying a regionalization that provides suitable homogeneity, quantile estimation accuracy, and average region size. Also, the study results indicate that the ranking based on the homogeneity ratio is relatively compatible with the ranking based on the quantile estimation accuracy. In addition, according to the results, the most appropriate regionalization of 41 watersheds in the study area includes 3 regions provided by the combination of the clustering algorithms average-linkage and K-means.

Changes in Mechanisms and Characteristics of Western U.S. Floods Over the Last Sixty Years

AbstractClimate change is linked to changing precipitation, yet uncertainty remains as to whether and how hydroclimatic changes will influence flood frequency, magnitude, and timing. Part of this uncertainty is because few regional‐scale studies have analyzed changes in floods produced by different mechanisms. We identify six flood generating mechanisms (FGMs) based on prominent meteorological processes and moisture supply to the soil column in the Western U.S.; these include large‐scale frontal storms (atmospheric and non‐atmospheric rivers), monsoons, convective storms, snowmelt, and rain‐on‐snow. We assess trends in the frequency, magnitude, and timing of floods produced by different FGMs across 119 basins during 1960–2018. Overall, we find decreased frequency and magnitude of rain‐on‐snow‐driven floods, increased frequency and magnitude of convective‐storm‐driven floods, and a temporal shift to earlier in the year for snowmelt‐driven floods. The flood characteristics are generally stable for floods produced by other FGMs.