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Abstract While there is growing attention toward the changes in flood magnitude and frequency, little is known about the way climate change could impact flood duration. Here we focus on 378 streamgages across the eastern United States to develop statistical models that allow the description of the year-to-year changes in flood duration above two National Weather Service (NWS) flood severity levels (i.e., minor and moderate). We use climate-related variables (i.e., basin- and season-averaged precipitation and temperature) as predictors, and show that they can be used to describe the inter-annual variability in seasonal flood durations for both NWS flood severity levels. We then use the insights from the understanding of the historical changes to provide an assessment of the projected changes in flood durations using global climate models from the Coupled Model Intercomparison Project Phase 6 and multiple shared socio-economic pathways. Our results show that the eastern United States is projected to experience longer flood durations, especially in winter (i.e., the main flood season) and under higher emission scenarios.

Abstract The results of probabilistic event attribution studies depend on the choice of the extreme value statistics used in the analysis, particularly with the arbitrariness in the selection of appropriate thresholds to define extremes. We bypass this issue by using the Extended Generalized Pareto Distribution (ExtGPD), which jointly models low precipitation with a generalized Pareto distribution and extremes with a different Pareto tail, to conduct daily precipitation attribution across the contiguous United States (CONUS). We apply the ExtGPD to 12 general circulation models from the Coupled Model Intercomparison Project Phase 6 and compare counterfactual scenarios with and without anthropogenic emissions. Observed precipitation by the Climate Prediction Center is used for evaluating the GCMs. We find that greenhouse gases rather than natural variability can explain the observed magnitude of extreme daily precipitation, especially in the temperate regions. Our results highlight an unambiguous linkage of anthropogenic emissions to daily precipitation extremes across CONUS.

Abstract Urban areas have been shown to impact rainfall by altering both its intensity and spatial structure at sub‐hourly and sub‐kilometer scales. However, there is currently no clear understanding of the precise pattern of change and the mechanisms that drive these changes. Since the hydrological response in urban areas is highly sensitive to such rainfall properties, understanding these changes is critical to improving our ability to assess urban flood risk. We use 7 years of high‐resolution weather radar data (4‐ or 5‐min and 1 km) to analyze changes in patterns of rainfall intensity, spatial structure, and storm evolution across eight urban areas within Europe and the United States. The use of the same methodology across the different cities enables a consistent comparison among them. We track convective rainfall events using a storm tracking algorithm and assess changes in rainfall properties in the upwind, center, and downwind regions of each city. We also investigate changes in the frequency of storm initiations, terminations, splitting, and merging. Our results show that urban areas act to intensify rainfall—mostly over them, but sometimes on their peripheries. Overall, larger cities tend to show the largest rainfall enhancements. Our findings highlight that rainfall spatial structure is altered over the urban core; usually resulting in more spatially concentrated rainfall. We also observe increased storm initiations over most cities and increased storm splitting over one. Given that demographic projections show that future urban population will increase, our results point toward an increased future flood risk in growing cities.

Abstract Understanding projected changes in flooding across the contiguous United States (CONUS) helps increase our capability to adapt to and mitigate against this hazard. Here, we assess future changes in flooding across CONUS using outputs from 28 global climate models and four scenarios of the Coupled Model Intercomparison Project Phase 6. We find that CONUS is projected to experience an overall increase in flooding, especially under higher emission scenarios; there are subregional differences, with the Northeast and Southeast (Great Plains of the North and Southwest) showing higher tendency towards increasing (decreasing) flooding due to changes in flood processes at the seasonal scale. Moreover, even though trends may not be detected in the historical period, these projected future trends highlight the current needs for incorporating climate change in the future infrastructure designs and management of the water resources.

Abstract Understanding changes in the hazard component of climate risk is important to inform societal resilience planning in a changing climate. Here, we examine local changes in wind speed, rainfall, and flooding related to tropical cyclones (TCs) and compare them across statistical and dynamical modeling approaches. Our focus region is the Delaware River Basin, located in the northeastern United States. We pair event‐based downscaling with large ensemble climate model information to capture the details of extreme TC wind, rain, and flooding, and their likelihood, in a changing climate. We identify local TCs in the Community Earth System Model 2 Large Ensemble (CESM2‐LENS). We find fewer TCs in the future, but these future storms have higher wind speeds and are wetter. We also find that TCs produce heavier 3‐day precipitation distributions than all other summertime weather events, with TCs constituting a larger percentage of the upper tail of the full precipitation distribution. With this information, we identify a small collection of 200‐year return events and compare the resulting TC rain and wind across dynamical and statistical downscaling methods. We find that dynamical downscaling produces peak rain rates far higher than CESM or the statistical downscaling method. It can also produce quite different future changes in precipitation totals for the small set of events considered here. This leads to vastly different flood responses. Overall, our results highlight the need to interpret future changes of event‐based simulations in the context of downscaling method limitations.

Abstract Structural connectivity describes how landscapes facilitate the transfer of matter and plays a critical role in the flux of water, solutes, and sediment across the Earth’s surface. The strength of a landscape’s connectivity is a function of climatic and tectonic processes, but the importance of these drivers is poorly understood, particularly in the context of climate change. Here, we provide global estimates of structural connectivity at the hillslope level and develop a model to describe connectivity accounting for tectonic and climate processes. We find that connectivity is primarily controlled by tectonics, with climate as a second order control. However, we show climate change is projected to alter global-scale connectivity at the end of the century (2070 to 2100) by up to 4% for increasing greenhouse gas emission scenarios. Notably, the Ganges River, the world’s most populated basin, is projected to experience a large increase in connectivity. Conversely, the Amazon River and the Pacific coast of Patagonia are projected to experience the largest decreases in connectivity. Modeling suggests that, as the climate warms, it could lead to increased erosion in source areas, while decreased rainfall may hinder sediment flow downstream, affecting landscape connectivity with implications for human and environmental health.

Abstract Extreme flood events have regional differences in their generating mechanisms due to the complex interaction of different climate and catchment processes. This study aims to examine the capability of climate drivers to capture year‐to‐year variability in global flood extremes. Here, we use a statistical attribution approach to model seasonal and annual maximum daily discharge for 7,886 stations worldwide, using season‐ and basin‐averaged precipitation and temperature as predictors. The results show robust performance of our seasonal climate‐informed models in describing the inter‐annual variability in seasonal and annual maximum discharges regardless of the geographical region, climate type, basin size, degree of regulation, and impervious area. The developed models enable the assessment of the sensitivity of flood discharge to precipitation and temperature changes, indicating their potential to reliably project changes in the magnitude of flood extremes.

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

Abstract The detection of nonstationarities in partial duration time series (PDS) depends on several factors, including the length of the time series, the selected statistical test, and the heaviness of the tail of the distribution. Because of the more limited attention received in the literature when compared to the trend detection on block maxima variables, we perform a Monte Carlo simulation study to evaluate the performance of different approaches: Spearman's rho, Mann–Kendall, ordinary least squares (OLS), Sen's slope estimator (SEN), and the nonstationary generalized Pareto distribution fit to identify the presence of trends in PDS records characterized by different sample sizes (n), shape parameter (ξ) and degrees of nonstationarity. The results point to a power gain for all tests by increasing n and the degree of nonstationarity and by reducing ξ. The use of a nonparametric test is recommended in samples with a high positive skew. Furthermore, the use of sampling rates greater than one to increase the PDS sample size is encouraged, especially when dealing with small records. The use of SEN to estimate the magnitude of a trend is preferable over OLS due to its slightly smaller probability of occurrence of type S error when ξ is positive.

Abstract We explore the projected changes in flood impacts across Iowa (central United States) and the associated uncertainties by forcing a hydrologic model with downscaled global climate model outputs and four Shared Socioeconomic Pathways. Our results point to projected increasing magnitude and variability in flooding across the state, especially for high‐emission scenarios. Next, we partition the flood impacts' projections into: (a) the response of the global climate models to anthropogenic forcing, (b) scenario uncertainty due to emissions, and (c) internal climate variability. We find scenario uncertainty plays a small role, while climate model uncertainty and internal climate variability dominate the flood impacts' projections, with the contribution of model uncertainty increasing toward the end of this century. Insights from our work can be utilized by stakeholders to understand the current limitations of flood impact projections and provide suggestions about where modelers should focus efforts to reduce uncertainty.

Abstract River flooding has large societal and economic impacts across Africa. Despite the importance of this topic, little is known about the main flood generating mechanisms in Africa. This study is based on 13,815 flood events that occurred between 1981 and 2018 in 529 catchments. These flood events are classified to identify the different flood drivers: excess rains, long rains and short rains. Out of them, excess rains on saturated soils in Western Africa, and long rains for catchments in Northern and Southern Africa, are the two dominant mechanisms, contributing to more than 75% of all flood events. The aridity index is strongly related to the spatial repartition of the different flood generating processes showing the climatic controls on floods. Few significant changes were detected in the relative importance of these drivers over time, but the rather short time series available prevent a robust assessment of flood driver changes in most catchments. The major implication of these results is to underline the importance of soil moisture dynamics, in addition to rainfall, to analyze the evolution of flood hazards in Africa.

There is high confidence that precipitation extremes are projected to become more frequent and severe and, to a lesser extent, that their seasonality may change. However, these precipitation characteristics are dealt with separately, without examining whether magnitude and seasonality are jointly projected to change. Here we assess how the seasonality and magnitude of precipitation extremes are jointly projected to change for different climate scenarios. We perform analyses at the global scale using nine global climate models and four different emission scenarios. We identify large areas of the globe where the magnitude of the extremes is expected to increase as the emissions increase; at the same time, large changes in the seasonality of these extremes are projected to impact regions mainly located in the tropical and sub-tropical areas. These changes could impact our response and mitigation efforts and our resilience against such phenomena in response to climate change.

Urbanization has a strong signal on the hydrologic cycle, leading to reduced infiltration and faster and larger runoff. However, the detailed analysis of the contribution of urbanization to different quantiles of discharge is still lacking; particularly, less is known in watersheds that have been experiencing such large and rapid urbanization as those in China. Here, we focus on the Wenyu watershed, a rapidly urbanizing basin located in the Beijing metropolitan area. Using a statistical attribution framework, we examine the hydrological response to the increasing urbanization across a wide range of discharge quantiles, from low to high flows; moreover, we perform analyses at the seasonal scale to capture differences in the physical processes at play during the year. Results indicate that the selected GAMLSS (generalized additive models for location, scale, and shape) models can capture well the variability in streamflow in this highly urbanized basin, with the average Spearman correlation coefficients between observations and the median of the fitted models of 0.84, 0.79, and 0.81 in spring, summer, and winter, respectively. Overall, urbanization played a different role in the different seasons and discharge quantiles. More specifically, we find its strongest impact to be in winter and spring, and for low and median quantiles. The role of precipitation is the strongest in summer, and it increases as we move toward the upper tail of the discharge distribution, especially above the 55th percentile, for which precipitation is selected as the only important predictor. Recycled water, on the other hand, tends to play a more dominant role in winter and spring. HIGHLIGHTS Urbanization has a strong signal on the hydrologic cycle; however, less is known in watersheds that have been experiencing such large and rapid urbanization. In this research, we focus on a fast urbanizing basin, and the hydrological responses to the increasing urbanization have been examined.; The quantification of the impacts of drivers for different discharge quantiles and seasons has been examined.; Urbanization plays a decreasing role towards the upper tail of the distribution.;

How tropical cyclones have varied in intensity and frequency in the past is not well known as longer records are rare. Here, the authors show that changes in observing practices explain the recorded century scale increase in Atlantic major hurricane frequency, and recent increases are not part of a century-scale trend.

Abstract While spatial heterogeneity of riverine nitrogen (N) loading is predominantly driven by the magnitude of basin‐wide anthropogenic N input, the temporal dynamics of N loading are closely related to the amount and timing of precipitation. However, existing studies do not disentangle the contributions of heavy precipitation versus non‐heavy precipitation predicted by future climate scenarios. Here, we explore the potential responses of N loading from the Mississippi Atchafalaya River Basin to precipitation changes using a well‐calibrated hydro‐ecological model and Coupled Model Intercomparison Project Phase 5 climate projections under two representative concentration pathway (RCP) scenarios. With present agricultural production and management practices, N loading could increase up to 30% by the end of the 21st century under future climate scenarios, half of which would be driven by heavy precipitation. Particularly, the RCP8.5 scenario, in which heavy precipitation and drought events become more frequent, would increase N loading disproportionately to projected increases in river discharge. N loading in spring would contribute 41% and 51% of annual N loading increase under the RCP4.5 and RCP8.5 scenarios, respectively, most of which is related to higher N yield due to increases in heavy precipitation. Anthropogenic N inputs would be increasingly susceptible to leaching loss in the Midwest and the Mississippi Alluvial Plain regions. Our results imply that future climate change alone, including more frequent and intense precipitation extremes, would increase N loading and intensify the eutrophication of the Gulf of Mexico over this coming century. More effective nutrient management interventions are needed to reverse this trend.

The use of a long-term and high-quality precipitation dataset is crucial for hydrologic modeling and flood risk management. This study evaluates the Analysis of Period of Record for Calibration (AORC) dataset, a newly released product with high temporal and spatial resolutions. Our study region is centered on Louisiana because of the major flooding it has been experiencing. We compare the AORC hourly precipitation to other widely used gridded rainfall products and rain-gauge observations. To evaluate the performance of rainfall products according to different weather conditions causing severe flooding, we stratify the analyses depending on whether precipitation is associated with a tropical cyclone (TC) or not. Compared to observations, our results show that the AORC has the highest correlation coefficients (i.e., values above 0.75) with respect to observations among all rainfall products for both TC and non-TC periods. When the skill metric is decomposed into the potential skill and biases, the AORC clearly shows the highest potential skill with relatively small biases for the whole period. In addition, the AORC performs better for the TC period compared to the non-TC ones. Our results suggest that AORC precipitation shows good potential to be viable for hydrologic modeling and simulations of TC and non-TC events.

Abstract Riverine flooding associated with landfalling tropical cyclones (TCs) in the western North Pacific basin is responsible for some of the most severe socioeconomic losses in East Asian countries. However, little is known about the spatial and temporal patterns of TC flooding and its climate controls, which constrain the predictive understandings of flood risk in this highly populated region. We provide a climatological characterization of TC flooding over China based on an exceptional network of stream gauging stations across the entire country. The most extreme floods in central and northeastern China are associated with TCs despite infrequent TC visits in these regions. Temporal variations in TC flooding demonstrate a mixture of controls tied to surface temperature anomalies in the northern hemisphere. The established links between TC flooding and climate controls present a potentially predictive tool of TC flood risk over China and other East Asian countries under future climate conditions.

Floods represent a major natural hazard in Africa, causing over 27 000 fatalities during the period 1950–2019. Despite its relevance, little is known about changes in flood hazard across this continent due to the lack of long-term high-quality streamflow records. Here we use a newly assembled discharge dataset of African rivers, and provide a long-term comprehensive view of flood hazard across this continent. We show that the annual maximum peak discharge does not exhibit a monotonic pattern, but overall decreasing trends prior to 1980 and increasing trends afterwards, especially in western and southern Africa. Our results indicate that these differing trends can be ascribed to changes in extreme precipitation around 1980. Moreover, these changes in intense precipitation pre/post 1980 are due to increased thunderstorm activity associated with enhanced convective available potential energy and zonal vertical shear driven by cooling temperature trends over western Africa. The changes in flood hazard in southern Africa can be tied to changes in Namibia low-level jet. Therefore, the observed increase in flooding since 1980 suggests that it would be beneficial to improve the monitoring, modeling and communication of flood hazard to reduce the socio-economic impacts of these events.

The City of Cedar Rapids was significantly affected by the June 2008 flood. However, little is known about the role anthropogenic warming during this event, not only in terms of hydrologic response but also of impacts. Here we use a continuous distributed hydrologic model forced with precipitation with and without external forcing and show that the impacts of this flood were likely magnified because of increased anthropogenic warming; compared to the baseline scenario with the external forcing removed, this event was ∼1.28-fold larger in flood extent, an approximate 3.4-time larger in the number of affected buildings, and an estimated 5.8- and 7.1-time larger in structural and content damage, respectively. While much of the effort up to this point has focused on the attribution of the physical hazard, our results highlight the cascading increase of the contribution of the external forcing (mainly from anthropogenic forcing) moving from hazard to human impacts.

Within the warm conveyor belt of extra-tropical cyclones, atmospheric rivers (ARs) are the key synoptic features which deliver the majority of poleward water vapour transport, and are associated with episodes of heavy and prolonged rainfall. ARs are responsible for many of the largest winter floods in the mid-latitudes resulting in major socioeconomic losses; for example, the loss from United Kingdom (UK) flooding in summer/winter 2012 is estimated to be about $1.6 billion in damages. Given the well-established link between ARs and peak river flows for the present day, assessing how ARs could respond under future climate projections is of importance in gauging future impacts from flooding. We show that North Atlantic ARs are projected to become stronger and more numerous in the future scenarios of multiple simulations from five state-of-the-art global climate models (GCMs) in the fifth Climate Model Intercomparison Project (CMIP5). The increased water vapour transport in projected ARs implies a greater risk of higher rainfall totals and therefore larger winter floods in Britain, with increased AR frequency leading to more flood episodes. In the high emissions scenario (RCP8.5) for 2074–2099 there is an approximate doubling of AR frequency in the five GCMs. Our results suggest that the projected change in ARs is predominantly a thermodynamic response to warming resulting from anthropogenic radiative forcing.

This paper examines the severity and extent of flooding caused by six predecessor rain events (PREs) over the Midwest United States. PREs are areas of heavy rainfall that occur about 1000 km ahead of landfalling tropical cyclones. While recent studies have mostly focused on the synoptic conditions associated with PREs, little is known about the hydrologic impacts of these events. Here, we use discharge data collected by the US Geological Survey to perform flood analyses at the regional scale. We first examine the number of stations at which a PRE caused an annual maximum flood peak. We then compute the flood ratio, defined as the ratio between the flood peak magnitude caused by the PRE and the at-site, expected 10 year flood peak magnitude. These results indicate that PREs are responsible for significant flooding over the Midwest United States, including highly populated areas like Chicago, Illinois and Detroit, Michigan.

In recent years, the United States has been severely affected by numerous tropical cyclones (TCs) which have caused massive damages. While media attention mainly focuses on coastal losses from storm surge, these TCs have inflicted significant devastation inland as well. Yet, little is known about the relationship between TC-related inland flooding and economic losses. Here we introduce a novel methodology that first successfully characterizes the spatial extent of inland flooding, and then quantifies its relationship with flood insurance claims. Hurricane Ivan in 2004 is used as illustration. We empirically demonstrate in a number of ways that our quantified inland flood magnitude produces a very good representation of the number of inland flood insurance claims experienced. These results highlight the new technological capabilities that can lead to a better risk assessment of inland TC flood. This new capacity will be of tremendous value to a number of public and private sector stakeholders dealing with disaster preparedness.