Abstract
One of the most important impacts of a future warmer climate is the projected increase in the frequency and intensity of extreme rainfall events. This increasing trend in extreme rainfall is seen in both the observational record and climate model projections. However, a thorough review of the recent scientific literature paints a complex picture in which the intensification of rainfall extremes depends on a multitude of factors. While some projected rainfall indices follow the Clausius-Clapeyron relationship scaling of an increase in rainfall per 1°C of warming, there is substantial evidence that this scaling depends on rainfall extremes frequency, with longer return period events seeing larger increases, leading to super Clausius-Clapeyron scaling in some cases. The intensification of extreme rainfall events is now well documented at the daily scale but is less clear at the subdaily scale. In recent years, climate model simulations at a finer spatial and temporal resolution, including convection-permitting models, have provided more reliable projections of subdaily rainfall. Recent analyses indicate that rainfall scaling may also increase as a function of duration, such that shorter-duration, longer return period events will likely see the largest rainfall increases in a warmer climate. This has broad implications on the design and the use of rainfall intensity–duration–frequency (IDF) curves, for which both an overall increase in magnitude and a steepening can now be predicted. This paper also presents an overview of measures that have been adopted by various governing bodies to adapt IDF curves to the changing climate. Current measures vary from multiplying historical design rainfall by a simple constant percentage to modulating correction factors based on return periods and to scaling them to the Clausius-Clapeyron relationship based on projected temperature increases. All of these current measures fail to recognize a possible super Clausius-Clapeyron scaling of extreme rainfall and, perhaps more importantly, the increasing scaling toward shorter-duration rainfall and the most extreme rainfall events that will significantly impact stormwater runoff in cities and in small rural catchments. This paper discusses the remaining scientific gaps and offers technical recommendations for practitioners on how to adapt IDF curves to improve climate resilience.
Introduction
The design of most hydraulic engineering infrastructures is based on local intensity–duration–frequency (IDF) curves, which provide extreme rainfall intensity (mm/h) values for various durations (minutes to days) and return periods (years). Typically, IDF curves cover rainstorm durations from to 24 h, with return periods from 2 to . These are normally sufficient to cover the needs of most applications dealing with streamflow, runoff routing, and floods and, therefore, help design infrastructures resilient to extreme rainfalls. Indeed, IDF curves are widely employed in stormwater management and engineering applications across the world (e.g., Akan 1993; Ferguson 1998; Seybert 2006).
IDF curves are best suited for hydrological studies in urban and small rural catchments. They can also be used for larger catchments with concentration times exceeding 24 h. However, when dealing with catchments in mountainous or cold areas, snowmelt and rain-on-snow events can become the driving processes that generate design flood events, requiring different methods such as continuous hydrological modeling and flood frequency analyses. This paper does not address such cases but focuses exclusively on rainfall-generated flood events, for which IDF curves are the main design tool.
These curves are typically developed by using an extreme value distribution locally, which is fitted to historical annual maxima series of accumulated rainfall data of various durations. Most countries have their own governmental bodies or agencies responsible for producing IDF curves at the national level through standard procedures [e.g., Environment and Climate Change Canada (ECCC 2019) and the National Oceanic and Atmospheric Administration (NOAA) Atlas 14 in the United States (Perica et al. 2018)]. An example of IDF curves from ECCC is provided in Fig. 1.
HE.1943-5584.0002122/asset/80b9c287-6540-4298-91f8-898aeef8453e/assets/images/large/figure1.jpg)
Most IDF curves used operationally rely on a stationarity assumption, in which the climate remains stable over time. This implies a stationary spatial and temporal structure of rainfall (Cheng and AghaKouchak 2014; Myhre et al. 2019). This hypothesis has long been challenged, given the impact of low-frequency internal climate variability (natural variations in climate due to sea surfaces temperature anomalies, such as the Atlantic Multidecadal Oscillation and El Nino Southern Oscillation) on rainfall and temperature patterns (Milly et al. 2008; Ouarda et al. 2019). In addition, the intensity and frequency of extreme rainfalls are affected by the anthropogenic forcing resulting from greenhouse gas (GHG) emissions (Fadhel et al. 2017; Mamoon et al. 2019; Myhre et al. 2019). Indeed, robust increases in the magnitude of daily rainfall were identified in both observations and climate models over the last half of the 20th century (Min et al. 2011; Westra et al. 2013), with an expected intensification of rainfall extremes in the decades to come (Donat et al. 2016). There is also considerable evidence that short-duration (subdaily) rainfall extremes are becoming more intense, as highlighted by Westra et al. (2014) and Fowler et al. (2021a, b), resulting in increased flood risk (Fowler et al. 2021c). This increase in rainfall intensity will potentially lead to a reduction in water security, as water will not redistribute from the soil to the surface reservoirs, leading to plant water stress and issues in agriculture (Eekhout et al. 2018).
The observed and projected intensification of daily and subdaily extreme rainfall calls into question the applicability of historical IDF curves, which do not explicitly consider extreme rainfall nonstationarity, in designing infrastructure that will operate in future climate conditions. This is especially true for many engineering structures, which, for the most part, will still be in operation by the end of the 21st century (Arnbjerg-Nielsen 2012; Cheng and AghaKouchak 2014; Mailhot and Duchesne 2010; Yilmaz et al. 2014). For instance, Cheng and AghaKouchak (2014) suggest that a stationary climate assumption might lead to an underestimation of extreme rainfalls by as much as 60%, increasing flood risk and failure risk in infrastructure systems. We can affirm that most existing stormwater runoff infrastructures are ill-adapted, and design strategies, including climate change adaptation, need to be implemented (Lopez-Cantu and Samaras 2018; Myhre et al. 2019). IDF curves are a critical part of the design process and require updating to account for an evolving climate and reduce infrastructure vulnerability (ASCE 2018; Watt and Marsalek 2013; Yan et al. 2021).
Fig. 2 shows a typical conceptualization (Kind 2012; Merisalu et al. 2021) of the tradeoff between investment and risk that optimizes the total cost of infrastructure, such as stormwater drainage systems. Infrastructure design attempts to balance construction costs (solid blue curve) against damages if the designed return period flow is exceeded during the lifespan of the element (dashed red curve). There is a theoretical optimum of the total cost (dashed black curve) upon which the return period flow is selected, as shown by the orange arrow in the current climate.
HE.1943-5584.0002122/asset/94a7b9d2-14e4-4369-b634-d352f3b96ab8/assets/images/large/figure2.jpg)
The science (as reviewed in this work) shows, with a high level of certainty, that the projected increases in extreme rainfall lead to an increased likelihood of exceeding the return period design flow, therefore increasing the damage risk. This can be seen in Fig. 2, with the shift of the damage curve to the right (solid red curve), which in turn moves the optimal total cost to the right (green arrow). The theoretical cost of the underdesigned infrastructure element is outlined by the difference in height between the orange and green arrows. Thus, building climate-resilient infrastructure through a larger investment in the initial construction cost (blue curve) could ultimately lead to a lower total cost (black curve) over the lifespan of the infrastructure elements, considering their typical long service life. For stormwater drainage infrastructure, the challenge is to quantify the expected impacts of climate change on the red curve, in other words, the projected changes in IDF curves.
A simple illustration of the cost-benefit of adapting to climate change consists in oversizing a culvert by choosing a larger diameter to provide more conveyance. While this oversizing generates a larger initial cost (blue curve), the replacement of a whole section of a washed-away road following an extreme storm event (exceeding the culvert’s design capacity) will be more expensive overall.
The importance of larger initial investments is clearly illustrated with the current state of most infrastructures in North America. For instance, the latest ASCEs’ Infrastructure Report Card (ASCE 2017) gave the overall infrastructure of the United States a D+ rating (i.e., poor, at-risk). Similarly, the latest Canadian Infrastructure Report Card (CIRC 2019) underlines that about 30% of Canada’s water infrastructure [e.g., drinking water, wastewater, stormwater, and, to some extent, roads (culverts), and bridges] is in very poor, poor, or fair conditions. A large part of existing infrastructures is so scheduled for renewal in the short term, and replacements are expected to have a lifespan of between 50 and . Climate change adaptation must therefore be incorporated in planning and designing new infrastructure projects in order to handle the upcoming increases in rainfall extremes during the projects’ lifespan. This means that new infrastructure projects require investing more capital in the short term for future-proofing, which implies that difficult financial decisions will have to be made. However, the cost of not adapting to climate change is more expensive than the cost of adapting to climate change (Chambwera et al. 2014). This should compel stakeholders to integrate climate change considerations into water and infrastructure management (Wasko et al. 2021).
The aim of this study is threefold: (1) provide a critical review of recent studies on the evolutive trends of extreme rainfall under a warmer climate; (2) assess how these findings will influence the IDF curves in the future; and then (3) propose a nonstationarity model of IDF curves that is easily accessible for different regions of the world for design and operational purposes. An overview of the science on the expected impacts of climate change on rainfall extremes is provided in the next section. A discussion on current scientific gaps, current governmental agency guidelines, and new technical recommendations from an operational perspective is then presented, followed by a conclusion and our final remarks. The overarching motivation of this work is to introduce technological support for adaptation in the area of infrastructure design by providing key findings on how to adapt IDF curves in the context of climate change.
Overview of Science
Theoretical Background and Climate Modeling of Extreme Rainfall Events
Mechanisms Leading to Extreme Rainfall Events
Rainfall is the outcome of a long chain of physical processes, starting with the evaporation of water into the air as water vapor, which then condenses by binding to nucleation sites (such as dust particles and aerosols) when an air parcel rises to form clouds, from which rainfall ultimately falls under gravity (Trenberth et al. 2003). Three types of rainfall can be produced, depending on the different ways leading to the condensation of the atmospheric water vapor (Trenberth et al. 2003; Poujol et al. 2020, 2021). Firstly, orographic rainfall occurs when the air is forced to rise due to the presence of a mountain, leading to the condensation of water through adiabatic cooling, creating clouds and then periodic rainfall on the windward side of a mountain (Roe 2005). Secondly, stratiform rainfall originates from the rise of large air masses induced by weather fronts and is often associated with meteorological systems that move over long distances, producing long mild showers over large areas (Houze 1997). Thirdly, convective rainfall arises from the fast rise of a local air mass under a conditionally unstable atmosphere caused by local differential heating, leading to cumulonimbus clouds from which short and local heavy rainfalls are produced, sometimes in the form of hail (Houze 1997). An algorithm was recently proposed to separate these three types of rainfall in climate model simulations (Poujol et al. 2020). Many studies argue that the hydrological cycle is currently intensifying and will continue to intensify under global warming (Huntington 2006; Trenberth 2011), although this hypothesis is debated (Koutsoyiannis 2020). In a future warmer world, a larger amount of water vapor in the atmosphere could potentially lead to an increase in the three types of rainfall (Trenberth et al. 2003). Of the three forms, convective rainfall, which is responsible for most subdaily rainfall extremes, is expected to increase the most, according to a mechanism in which fewer (but larger) storms generating heavier rainfall events could take place in a warmer future (Dai et al. 2020).
Rainfall extremes occur in organized atmospheric phenomena, such as mesoscale convective systems (MCS) (Prein et al. 2017a), tropical and extratropical cyclones (Gutmann et al. 2018), atmospheric rivers (Huang et al. 2020), and squall lines (Purr et al. 2019), in which deep convection (characterized as an atmospheric parcel that is lifted above the 500 hPa pressure level) is central. According to the atmospheric scales of motion of these phenomena, only high-resolution climate models, such as those that are regional and convection-permitting, are capable of simulating these processes and simulating subdaily extremes comparable to observations (Fowler et al. 2021a).
Clausius-Clapeyron Relationship
The capacity of the atmosphere to contain water is governed by the Clausius-Clapeyron (CC) equation, according to which the saturation of specific humidity increases by a rate of 7% per degree Celsius of warming (Westra et al. 2014). Assuming constant relative humidity, an increase in temperature could lead to an increase in the water vapor available to generate rainfall, but the relation is not that straightforward. At the global scale, global climate models predict an increase in mean precipitation by approximately 2%–3% per degree Celsius of warming (Held and Soden 2006), which can be explained by global energy constraints (Allen and Ingram 2002). However, this limit is not uniform and depends on the geographical location, as well as on the rainfall duration and intensity. According to climate model projections, future changes in extreme rainfalls seem to follow and exceed the CC rate in certain cases, even in regions where the mean rainfall is projected to decrease (Aalbers et al. 2018; Prein et al. 2017b).
A warmer atmosphere can hold more moisture (), which can potentially lead to more rainfall. Many studies suggest that extreme rainfall may scale with the increasing precipitable water content as the atmosphere gets warmer (Drobinski et al. 2018). Subsequently, this scaling between extreme rainfall and temperature will be called rainfall scaling. However, this scaling of rainfall with respect to temperature seems to be sensitive to rainfall intensity. Analyses of observed time series from different weather stations indicate that daily rainfall extremes follow the CC rate (Fischer and Knutti 2016; Guerreiro et al. 2018; Westra et al. 2013), while hourly rainfall extremes seem to exceed this rate (i.e., the super CC rate; Berg et al. 2013; Lenderink and van Meijgaard 2008, 2010; Panthou et al. 2014; Westra et al. 2014).
The scaling of hourly rainfall extremes is sensitive to temperature. In the studies by Drobinski et al. (2018) and Prein et al. (2017b), super CC rates were found at low to moderate temperatures (around 10°C–20°C), but lower and negative scaling rates were found above approximately 20°C, likely due to a reduction in relative humidity at warmer temperatures. Interesting reviews of temperature scaling of extreme subdaily rainfalls are presented by Westra et al. (2014) and Fowler et al. (2021a). Scaling above the CC rate is locally possible with dynamical feedbacks involving convective processes, and different mechanisms have been proposed: cold pool collisions (Haerter et al. 2018; Moseley et al. 2016), intensification of updrafts in the cloud core (Loriaux et al. 2013), or moisture-driven increases in large-scale ascent (Nie et al. 2018). Although the scaling rate found in observations is sensitive to the methodology employed, and its translation to a warmer climate is disputed (Chan et al. 2016; Prein et al. 2017b; Zhang et al. 2017), the hypothesis according to which extreme subdaily rainfall events are expected to increase at the CC scaling rate () and above for shorter-duration and longer return periods is generally accepted based on observations and climate modeling (Förster and Thiele 2020). This provides a few hints about how IDF curves should be adjusted in the future.
Ability of Climate Models to Simulate Extreme Rainfall
To reliably project the future evolution of IDF curves in a warmer climate, climate models must be able to simulate a wide range of rainfall extremes (2- to 100-year return periods) of different durations ( to 24 h) reasonably well. The ability of climate models to simulate rainfall extremes is dependent on their horizontal resolution, which determines the way convection is represented. Global climate models (GCMs) and regional climate models (RCMs) operating at resolutions equal to (or coarser than) 10 km have to use parameterizations to represent deep convection, which occurs on a scale of a few kilometers. The parameterization of convection, which is essential to represent subgrid-scale processes, is a well-known source of uncertainty in climate models, and it affects their ability to simulate rainfall extremes, especially at the subdaily scales (Berg et al. 2019; Kendon et al. 2012).
About ago, convection-permitting climate models (CPM) operating at horizontal resolutions emerged as powerful tools that considerably improved the simulation of shorter-duration (up to subhourly) rainfall extremes (Meredith et al. 2020; Vergara-Temprado et al. 2021; Meredith et al. 2021), with implications for water management (Orr et al. 2021). At such fine spatial resolutions, the deep convection, which is essential for a realistic simulation of rainfall extremes, is explicitly simulated, allowing one to switch off deep convection parameterization in CPMs (Ban et al. 2021). When compared with RCMs (with horizontal resolutions between 10 and 50 km), CPMs show significant added value for the simulation of rainfall extremes at the hourly scales (Luu et al. 2020), as well as the diurnal cycle of rainfall (Ban et al. 2014; Fumière et al. 2020; Lind et al. 2020). However, CPMs are relatively new models that are still considered to be in their infancy, which explains why they still suffer from several biases, such as their precipitation being too intense (Kendon et al. 2014) and being too dry and too warm over some continental regions (Berthou et al. 2020; Barlage et al. 2021). Many of these biases are currently addressed through active ongoing model developments that include calibration and the addition (or improvements) of some model components, including groundwater processes and turbulent schemes (Kendon et al. 2021). To eliminate these biases, bias correction is generally recommended, but the lack of reliable high-resolution (both spatially and temporally) gridded datasets restrains its wide use (Argüeso et al. 2013).
It is generally accepted that heavy daily rainfall (around the 95th percentile) is well simulated by global and regional climate models using horizontal resolutions coarser than 10 km (Iles et al. 2020), while subdaily and subhourly rainfall extremes are now well simulated with CPMs operating at resolutions (Lind et al. 2020; Meredith et al. 2020; Vergara-Temprado et al. 2021). Due to their high computational requirements, CPM climate simulations can only be computed over small regions of the globe, and only relatively short decadal simulations are possible, making the estimation of longer return periods only possible by extrapolating from fitted distributions (Cannon and Innocenti 2019).
In order to project future climate changes with climate models, two methods are currently adopted. The more common of these consists in comparing a climate simulation time window of typically 20 or forced by historical GHG concentrations with another one of the same length forced by a perturbed GHG concentration from a projected socioeconomic scenario. Thus, the future change often referred to as the climate change delta is associated with a specific GHG scenario forcing. Because climate models have different climate sensitivities and biases, an ensemble of climate models is needed to cover some of the sources of uncertainty. The downsides of this top-down method are that large ensembles of climate simulations are required to cover model uncertainty, and ensembles of simulations should be repeated for each socioeconomic scenario. Furthermore, this method also mixes the thermodynamics and dynamics aspects of climate change. The second method, the so-called pseudoglobal warming method (Schär et al. 1996), has recently been gaining interest in the atmospheric science community. It consists of perturbing the driving field (often a reanalysis) of an RCM by a certain factor, which is often taken from changes in temperature and humidity from an ensemble of GCMs (Prein et al. 2017b). Thus, the control RCM simulation can be compared with that forced with the perturbed driving field to assess changes related to that perturbation. This method has the advantage that the atmospheric circulation remains the same in the pseudofuture and that only the thermodynamic aspect of climate change is perturbed. Additionally, the method limits driving model biases in the case of a CPM being forced at its lateral boundary by biased global or regional climate models. However, future changes in the large-scale atmospheric circulation are not taken into account in this method, and the sequence of events is unchanged due to global warming [see the review by Adachi and Tomita (2020) for more details on the different methods, which can be adopted to constrain limited area models].
Projected Changes in Extreme Rainfall Events
A literature review of the projected changes in extreme rainfall events from climate models is provided in the following sections. This review focuses on the recent studies that have been conducted with the Representative Concentration Pathway (RCP) GHG emissions scenarios (IPCC 2013; Meinshausen et al. 2011). Considering the importance of the subdaily duration in the development of IDF curves, these more recent studies also took into account projections from higher spatiotemporal resolution climate models needed to investigate change over shorter durations (i.e., CPM and RCM simulations).
Daily Time Scale
Fischer et al. (2014) used 15 CMIP5 GCMs to analyze the forced response of heavy rainfall (Rx1day) changes. They found that disagreements between GCM projections of heavy rainfall primarily arise from internal variability and that the forced response of each GCM agrees remarkably well with one another. They estimated that changes in heavy rainfall at midlatitudes are robust among GCMs at about . Fischer and Knutti (2015) estimated that about 18% of the heavy daily rainfall (99.9th percentile, expected approximately once in every ) is attributable to the observed temperature increase of 0.85°C, with this number moving to 40% for a 2°C of warming. For higher extremes, the percentage of attribution increases even more. Pfahl et al. (2017) decomposed the forced response of daily regional scale heavy rainfall (Rx1day) into thermodynamic and dynamic contributions. They showed that thermodynamics alone would lead to a spatially homogeneous increase of around , which is consistent across GCMs, while the dynamic contribution modifies the regional responses, amplifying their increase in the Asian monsoon region but weakening them in the Mediterranean and South African regions.
Bao et al. (2017) were able to accurately and robustly reproduce observed apparent scaling between heavy daily rainfalls and near-surface temperature with an ensemble of RCMs over Australia. Projections from the same RCMs show future heavy daily rainfalls that are increasing at higher rates than those inferred from the observed scaling, although the strongest rainfall events (99.9th percentile) scaled significantly faster at rates between 6% and 15%, depending on RCM configurations. Analyzing an ensemble of GCM projections according to water availability, Tabari (2020) found an intensification of extreme daily rainfall that increases as return periods become longer (less frequent), which converges asymptotically toward 6.5%/°C over all climatic regions. However, the author identified that this intensification increases with water availability from dry to wet regions and also with the seasonal cycle of water availability from summer to winter. The latter study emphasizes that updating the IDF curves will require considering water availability and will not be the same for all regions.
Kharin et al. (2013) investigated the change in the daily 20-year return period using a multimodel average over 29 CMIP5 models between the reference (1986–2005) and future (2071–2100) periods. Looking at the global median value over all land grid points, they found that the 20-year historical intensity corresponds to a 6-year event in the future, or roughly a 3- to 4-fold increase in frequency. Martel et al. (2020) obtained similar results for two GCM large ensembles of simulations when looking at the daily 20-year return period. For the 100-year event, they found that the historical intensity corresponds roughly to a 20-year event in the future or a five-fold increase in frequency.
Subdaily Time Scale
There is a general expectation that (very) short-duration rainfall extremes will become more intense in the next decades (e.g., Arnbjerg-Nielsen et al. 2015; Chandra et al. 2015; Fadhel et al. 2017; Forestieri et al. 2018; Khazaei 2021; Lima et al. 2016; Morrison et al. 2019; Moustakis et al. 2021), with an increase in frequency (Hosseinzadehtalaei et al. 2020; Myhre et al. 2019) and for longer return periods (Ganguli and Coulibaly 2019; Hosseinzadehtalaei et al. 2018; Martel et al. 2020; Ragno et al. 2018).
Berg et al. (2019) used a subset of the Euro-CORDEX 12 km RCM ensemble to evaluate and project the future evolution of hourly rainfall extremes. They highlighted that most RCMs underestimate the 10-year return period rainfall for durations of up to a few hours but performed better at longer durations (12 h). They also identified a strong relationship of extreme hourly rainfall changes with temperature across different subregions of Europe, emission scenarios, and future time periods, although the scaling varied considerably (1%–10%) between different combinations of GCMs and RCMs.
Berg et al. (2019) also highlighted the limitations in the ability of RCMs to simulate subdaily rainfall extremes, which are significantly improved in CPMs due to their explicit representation of deep convection (Ban et al. 2020; Ban et al. 2014; Kendon et al. 2012). An intensification of hourly rainfall extremes that are becoming progressively greater in intensity was revealed in many studies using CPMs over the UK (Fosser et al. 2020; Kendon et al. 2014), the Alps (Ban et al. 2020; Ban et al. 2015), Belgium (Vanden Broucke et al. 2019), Germany (Knist et al. 2020), Africa (Kendon et al. 2019), and Canada (Cannon and Innocenti 2019). Generally, the intensification is greater with CPMs than with RCMs, especially in the summer, when convection plays an important role in the production of rainfall extremes (Fosser et al. 2020; Kendon et al. 2017, 2014, 2019; Knist et al. 2020; Tabari et al. 2016; Vanden Broucke et al. 2019), a fact that was recently confirmed using an ensemble of 12 different CPMs in autumn but not in summer (Pichelli et al. 2021), although some studies found the opposite to be true (Ban et al. 2020, 2015).
In most studies, the simulated increase in intensification with CPMs stays below the CC rate (Ban et al. 2015; Hodnebrog et al. 2019; Vergara-Temprado et al. 2021). However, super CC rates were found in certain cases for the largest extremes (Fosser et al. 2020; Hodnebrog et al. 2019; Knist et al. 2020; Lenderink et al. 2021; Mantegna et al. 2017), in seasons other than summer (Ban et al. 2020), over Africa (Kendon et al. 2019), and for the shorter-duration extremes (Cannon and Innocenti 2019). Helsen et al. (2020) found small differences in the future increase in hourly rainfall between two different CPMs. They concluded that the future increase in hourly rainfall is more dependent on the region than on the CPMs. In addition, the scaling of the extreme rainfall intensification with temperature was revealed to be sensitive to the region (Hodnebrog et al. 2019; Prein et al. 2017b), to the topography (Helsen et al. 2020; Vanden Broucke et al. 2019), and to moisture availability (Prein et al. 2017b). Focusing on subhourly rainfall extremes, Vergara-Temprado et al. (2021) found an intensification of the extremes that grew with the intensity of the events but that remained below the CC rate, and that was slightly dependent on the rainfall durations over many European regions. Analyzing an ensemble of the same CPM over the UK, Fosser et al. (2020) found a stronger intensification, above the CC rate, of summer hourly rainfall with their CPMs compared to their driving 12 km ensemble. They revealed that the climate change signal of the extreme hourly rainfall intensification across their CPMs seemed to converge, likely because of the more realistic representation of local storm dynamics with CPMs. This fact led them to the conclusion that CPMs can potentially reduce the long-standing future extreme rainfall uncertainties associated with deep convection parameterization. Finally, recent studies using CPMs suggested that the urban heat islands of some megacities create a more unstable atmosphere, which increases vertical uplift and moisture convergence and, therefore, extreme rainfall intensity (Li et al. 2020) and frequency (Marelle et al. 2020).
Sources of Uncertainty
Uncertainties related to projected rainfall extremes are important, which ultimately translate into uncertain future IDF curves (Butcher and Zi 2019). The main sources of uncertainty are related to (1) the choice of the GHG emission scenario and the level of warming, (2) the difference in climate model parameters and structures, as well as the potentially critical issue of the underestimation of heavy-to-extreme rainfall events, (3) the internal climate variability (unforced variability due to the chaotic nature of the climate system), and (4) the downscaling and postprocessing of climate simulations.
GHG Emission Scenario
One of the largest sources of uncertainty, as we progress further into the 21st century, is the selection of a GHG emission scenario (Hawkins and Sutton 2011). The RCP8.5 scenario has been considered by many as the business-as-usual scenario reflecting a future with no effort in reducing GHG. Current emissions are close to the RCP8.5 scenario’s projected emissions and are likely to remain so for at least the next decade. However, many scientists now argue that the RCP8.5 emissions projected by the end of the century are unlikely to happen as they would require unrealistic increases in fossil fuel use (Hausfather and Peters 2020). Nevertheless, a middle-of-the-road scenario such as the RCP4.5 is seen as too optimistic by many (Sanford et al. 2014; Schwalm et al. 2020). In short, the GHG emission scenario will remain a major source of uncertainty. Using the higher-emission scenarios is a conservative approach in dealing with this uncertainty.
Furthermore, the rate at which extreme rainfall increases does not directly depend on the emissions scenario (Pendergrass et al. 2015; Srivastav et al. 2014) but rather on the actual level of warming reached (Pendergrass et al. 2015; Seneviratne et al. 2016). Using a fixed level of global warming can be a way to circumvent the selection of a particular GHG emission scenario (Cannon et al. 2020; Seneviratne et al. 2016). However, knowing the time horizon at which a given threshold will be reached remains useful to engineers. For instance, some stormwater control measures can be designed with a relatively short lifespan of a few decades. In that case, a closer time horizon could be selected accordingly, also offering the potential for retrofitting at the end of the service life of the control measure.
Climate Models
Climate models such as GCMs and RCMs are not able to accurately simulate the magnitude and location of convective storms because of their coarse resolution (RCMs are typically coarser than ) and constitute a large part of the uncertainty in designing IDF curves for the future climate, particularly for shorter rainfall durations (Hosseinzadehtalaei et al. 2018; Schardong and Simonovic 2019).
However, CPMs, through their finer spatial resolution (horizontal grid spacing ), can improve the representation of subdaily rainfall both in intensity and duration at the regional to local scale (Ban et al. 2015; Kendon et al. 2014; Prein et al. 2015), therefore providing additional confidence in future IDF curves derived from CPMs (Cannon and Innocenti 2019; Mantegna et al. 2017). This has important implications for the building and infrastructure sectors (Kendon et al. 2017).
Internal Climate Variability
Many studies have shown that internal variability can mask the short- and long-term climate change signal for rainfall projections at both local and regional scales (Aalbers et al. 2018; Deser et al. 2012; Donat et al. 2020; Sanderson et al. 2018; Thompson et al. 2015). This variability becomes an important contributor to the uncertainty of the change in extreme rainfall due to warming, explaining about 20% of the intermodel spread in extreme rainfall change over extratropical land (Pendergrass et al. 2015). At the local scale, internal climate variability will dominate extreme rainfall uncertainty up to the end of the century in many parts of the world (Martel et al. 2018). For instance, the intensification of rainfall extremes can be distinctly different between two realizations of the same climate model at the regional scale (Aalbers et al. 2018; Fischer et al. 2014).
To account for internal variability and to quantify the epistemic uncertainty of the model structure in future extreme rainfall changes, it is recommended to use multimember ensembles (one climate model with several realizations that differ by their initial conditions) as well as multimodel ensembles (different GCMs with different RCMs) (Berg et al. 2019; Mailhot et al. 2007). Poschlod et al. (2021) suggest that a convection-permitting, single-model, initial-condition large ensemble would be a very valuable tool to improve the analysis of extreme rainfall and its natural variability.
From an operational point of view, engineering design has a long history of using methods based on the statistical theory of extreme value to extrapolate large return periods (e.g., 50- and 100-year events) from relatively short observation records (e.g., of data), leading to significant epistemic uncertainty (Ball et al. 2019; Katz 2013; Schulz and Bernhardt 2016). Single realizations from climate models are subject to a similar level of uncertainty, considering the 20- or 30-year time windows used to ensure stationarity over a given future time horizon (Aalbers et al. 2018). Large ensembles of climate simulations generated from the same climate model and external forcing provide a larger sample, allowing longer empirical annual maximum series (e.g., annual maximum series), reducing the epistemic uncertainty (Martel et al. 2020). These large ensembles of climate simulations have recently been gaining in popularity both with RCMs (e.g., Aalbers et al. 2018; Leduc et al. 2019; Mizuta et al. 2017) and GCMs (Deser et al. 2012; Kay et al. 2015; Sigmond and Fyfe 2016), providing more robust (i.e., lower epistemic uncertainty) projected changes for rare rainfall extreme events.
Downscaling and Postprocessing
The scale dependence of rainfall extremes associated with the coarse resolution of climate models (GCMs and even RCMs) limits the use of raw simulations to analyze changes in subdaily rainfall extremes at the local scale (or at a site-specific scale, such as for urban infrastructure). By extension, the investigation regarding the changes to IDF curves is limited by the ability of climate models to simulate such shorter-duration rainfall extremes and their changes that will be introduced by climate change. GCMs (and even RCMs and CPMs) have specific model biases that must be considered and often removed in impact studies. As a result, an analysis of changes in local rainfall extremes often requires statistical postprocessing of climate simulations. Statistical postprocessing is widely used to develop projections of rainfall extremes at the regional and local scale (Fluixá-Sanmartín et al. 2019; Hosseinzadehtalaei et al. 2020; Khazaei 2021; Ning et al. 2015; Schoof and Robeson 2016). However, Fadhel et al. (2017) found that the uncertainty in the projected rainfall change compared to the rainfall in the current climate varies according to the selected reference period for the statistical postprocessing. This aspect of the uncertainty of benchmarking periods in postprocessing future rainfall projections is largely ignored by the engineering community. Already, CPMs provide improved estimates of extreme subdaily rainfall that are likely to further improve in the near future, and hopefully, the postprocessing step will soon become unnecessary in some cases.
Discussion
This discussion begins with a summary of the current knowledge on rainfall extremes based on a recent literature review, followed by an analysis of current guidelines to adapt the IDF curves under a warmer climate. Afterward, some adapted guidelines for practitioners and stakeholders are suggested to fill the scientific gap in the existing resources. Finally, key concerns regarding the detection of the climate change signal and its limitations for decision-making are provided.
What We Know
Since the 1950s, extreme rainfall events have become more intense and more frequent in many regions of the world (Westra et al. 2013, 2014). Scientists expect this trend in rainfall extremes to continue with global warming due to the association with the CC relationship (Li et al. 2021), leading to heavier rainfall events.
However, caution is required when applying the rate regionally because there is some evidence of an increase in the CC rate for shorter-duration (hourly or subdaily) extremes when the temperature is in the range of approximately 12°C–22°C. Cases of super CC scaling are noted in North America (e.g., Nie et al. 2018), Europe (e.g., Lenderink et al. 2017; Wood and Ludwig 2020), and Australia (e.g., Bao et al. 2017; Mantegna et al. 2017). This is partly attributable to (1) an increase in the likelihood of convective versus stratiform rainfall occurrence as the temperature increases and (2) to the properties of convective rainfall itself (Westra et al. 2014).
There is also evidence of a limitation to the rate of increase (even a decrease) in rainfall intensities with increasing temperatures above in some regions (Ghausi and Ghosh 2020; Pendergrass 2018). This appears to be associated with a decrease in moisture availability at high temperatures, even though the mechanism(s) leading to the moisture deficits are not fully understood to date (Westra et al. 2014).
There is an overwhelming consensus that extreme rainfall will intensify at the global and regional scales (e.g., Collins et al. 2013; Field et al. 2012; IPCC 2013; Kendon et al. 2019; Martel et al. 2020; Sillmann et al. 2013). The occurrence of extreme rainfall events in many regions (particularly in Europe and North America) has contributed to an increase in the number of related studies over the last few years [Fig. 3(a)]. These studies aim to assess projected changes in rainfall extremes better and to understand better the driving processes (e.g., Innocenti et al. 2019; Kirchmeier-Young and Zhang 2020; Myhre et al. 2019; Wood and Ludwig 2020). Many international initiatives have also emerged to improve the understanding regarding the relationships between global warming, atmospheric circulation, and extreme rainfall events, such as the INTElligent use of climate models for adaptatioN to nonStationary hydrological Extremes (INTENSE) project (Blenkinsop et al. 2018).
HE.1943-5584.0002122/asset/c11905b9-98ae-46cf-a44f-307d84520108/assets/images/large/figure3.jpg)
As discussed previously, many studies have projected increases in extreme rainfall statistics for the coming decades (Table S1 ), with an increasing trend correlated with the frequency of occurrence, as well as with the occurrence of the shortest-duration rainfall events [Fig. 3(c)]. A large number of the referenced studies are primarily based on GCM and RCM simulations [Fig. 3(b)] and on the use of more than one climate model [83%; Fig. 3(d)]; however, only a few integrate a multimember ensemble of the same climate model [26%; Fig. 3(d)]. Greater changes in rainfall extremes are projected for the subdaily timescale than for the daily timescale, especially for shorter durations [Fig. 3(b)] (Cannon and Innocenti 2019; Forestieri et al. 2018; Fowler et al. 2021a; Huo et al. 2021; Martel et al. 2020; Rajczak and Schär 2017; Westra et al. 2014; Wood and Ludwig 2020). More significant changes are projected in rare extreme rainfall events when compared to the common rainfall extreme indices, such as the daily annual maximum rainfall (Rx1day) [Fig. 3(b)] (Butcher and Zi 2019; Cannon and Innocenti 2019; Hosseinzadehtalaei et al. 2020; Huo et al. 2021; Kharin et al. 2018; Li et al. 2021). In particular, the 20-year daily rainfall event is projected to become 3–4 times more frequent (Kharin et al. 2013; Martel et al. 2020), while the 100-year daily rainfall event will be between 4 and 5 times more frequent (Martel et al. 2020), indicating a stronger amplification of rainfall extremes over longer return periods.
Analysis of Current Guidelines for Adaptation
The need to adapt IDF curves is now well recognized by many organizations. For example, the guidelines for Canadian water resources practitioners (CSA 2019) highlight the necessity to update the IDF curves more frequently than in the past because climate change is expected to induce an increased intensity and frequency of rainfall extremes in most areas over the next decades.
However, a more frequent update of IDF curves only accounts for changes over the recent past and does not cover the projected future changes in rainfall intensity. The uncertainties around the projected future climate are the main reason why projected changes in rainfall extremes are not included in IDF curves. The following subsections give local, regional, and national examples of guidelines, as well as standards for the modification of IDF curves to account for the future climate. Although these adaptation strategies might be imperfect (based on the current state of scientific knowledge previously discussed), they constitute an interesting step as they cover the lifespan of new infrastructure while improving its resilience.
Simple Constant Percentage Increase
The simplest adaptation strategy is the constant percentage increase, in which the same safety factor is used for all extreme rainfall design values. This method is currently used by some countries and regions. For instance, Belgium and the United Kingdom respectively apply an increase of 30% (Madsen et al. 2014; Willems 2011) and 20% (UK Department for Infrastructure 2020) on all rainfall extremes. Similar factors are also enforced by Canada’s provincial governmental bodies, such as in the Province of Quebec (18%; MDDELCC 2017) and in the City of Moncton, New Brunswick (20%; EPWDR 2011).
Adaptive Percentage Increase
The adaptive percentage increase approach recognizes that future increases in rainfall extremes are not uniform and depend on various factors, such as temperature increases (as represented by future time horizons) and rainfall frequency. Different adaptive percentage increase strategies have been identified in different countries. One strategy that is used at the national level by Denmark for rainfall design (Arnbjerg-Nielsen 2008; Gregersen et al. 2014) is to implement different safety factors based on the frequency of the event selected in the design (i.e., 20%, 30%, and 40% increases are added to the 2-, 10-, and 100-year return periods, respectively). The Swedish Water and Wastewater Association (Madsen et al. 2014; Svenskt Vatten 2011) recommends a fixed percentage increase, with an adaptive variation between 5% and 30%, depending on the region. The UK Department for Infrastructure (2020) has selected a unique factor of 20% at the national level with incremental safety factors for rainfall design across all of England based on the time horizon (i.e., up to 10%, 20%, and 40% by 2040, 2070, and 2115, respectively; Defra 2020).
Percentage Increases Based on the Clausius-Clapeyron Relationship
The third approach is to apply a percentage increase based on the projected increase in warming. This follows the rationale behind the Clausius-Clapeyron relationship by providing correction factors based on the most likely local or regional increase in temperature. The future rainfall intensity can be computed as Eq. (1)where and = reference and future rainfall intensities, respectively; = rainfall scaling factor (%) based on the CC relationship; and = projected change in local temperature. The Australian Rainfall-Runoff guidelines (ARR; Ball et al. 2019) recommends a 5% increase per degree Celsius of warming while the Canadian Standard Association (CSA 2019) recommends a value of . However, CSA (2019) acknowledges that shorter-duration events could follow a super CC relationship, implying that a larger rate than of warming may be applied, depending on the area. This is also supported by multiple studies, as previously discussed.
(1)
Future IDF Curves
Some cities (e.g., NYC 2017; City of Vancouver 2018) or regions [e.g., Ontario (MTO 2016) and Newfoundland and Labrador (Finnis and Daraio 2018) in Canada] have already adopted future IDF curves for different time horizons (e.g., 2040 and 2070). The future IDF curves are usually an updated version of historical curves (obtained through standard methods) but upscaled based on the increase in rainfall extremes projected by the climate models. While it allows providing an adaptive percentage increase for both of the different durations and return periods, this approach often relies on GCM spatio-temporal resolutions, which are too coarse to produce reliable regional IDF curves.
Many studies attempt to provide regional future IDF curves by using either GCM [e.g., Canada (Srivastav et al. 2014); India (Chandra et al. 2015); Iran (Khazaei 2021); and the United States (Butcher and Zi 2019; Ragno et al. 2018)], or RCM/CPM [Australia (Mantegna et al. 2017); Belgium (Hosseinzadehtalaei et al. 2018); Canada (Ganguli and Coulibaly 2019); England (Fadhel et al. 2017); Italy (Forestieri et al. 2018); South Korea (Lima et al. 2016); and Spain (Fluixá-Sanmartín et al. 2019)] simulations. However, these future IDF curves are rarely adopted by governmental agencies and remain unused by practitioners.
Overall, most of the currently adopted guidelines fail to recognize the larger projected increases in extreme rainfall toward shorter and less frequent events. Climate model outputs can be used to explore rainfall characteristics/statistics; CPMs are particularly suited to investigate subdaily and subhourly extreme rainfall events. Furthermore, it is recommended to use multimember ensembles of climate models to reduce uncertainty (i.e., climate models and internal variability) in the projected values of rainfall extremes.
Suggested Adapted Guidelines for Practice
First, IDF curves based on past observations should be computed using the latest historical records. IDF curves are often decades-old [e.g., see the ECCC (2019) and NOAA’s Atlas 14 (Bonnin et al. 2006)], and so do not even account for recent climate trends. Multiple guidelines thoroughly explain how to perform a rainfall frequency analysis from historical records to produce IDF curves (e.g., CSA 2019). Practitioners thus have some resources at their disposal to ensure that the IDF curves they use are up to date (e.g., less than old could serve as a general rule of thumb) before using them in any new design applications.
Then, for IDF curves covering the future climate, it is necessary to account for the intensification of rainfall extremes of shorter duration and of longer return periods. From our literature review, we found that the approach based on the CC relationship has strong potential but requires some modifications to integrate the expectation of intensification of projected rainfall extremes with frequency (as the return period increases) and with shorter durations (subdaily). Therefore, we propose to adapt Eq. (1) as followswhere = projected future rainfall intensity of duration and return period ; = reference period rainfall intensity of duration and return period ; = adjustment factor for return period ; = adjustment factor for duration shorter than 24 h ; = rainfall scaling (%/°C) for the 24-h 2-year return period rainfall event; and = projected change in seasonal mean temperature (°C).
(2)
This equation is based on the expected rainfall scaling () of the 24-h 2-year return period rainfall (), which has the smallest intensity in typical IDF curves. This value is equal to the median Rx1day value, which is one of the most common extreme rainfall indices studied in climate science (Table S1 ). Local Rx1day values can be computed easily and reliably using climate model outputs.
Because Rx1day is generally well represented in GCMs (Alexander and Arblaster 2017), we computed worldwide factors based on 26 CMIP5 GCM simulations (see Table S2 for the list of GCMs used). Only the first member (r1i1p1) of each GCM ensemble simulation was selected for the reference (1981–2000) and future (2081–2100) periods. The seasonal (December-January-February, DJF; March-April-May, MAM; June-July-August, JJA; and September-October-November, SON) Rx1day and mean temperature were calculated for both the reference and future periods and then remapped on a common grid to compute the multimodel ensemble mean. The scaling rates were obtained through the ratio of the projected changes of Rx1day (Fig. 4) and mean temperature (Fig. 5). The scaling rate for the RCP8.5 scenario is shown in Fig. 6 (the scaling rate of the RCP4.5 scenario is shown in Fig. S1 ). Similar to those by Seneviratne et al. (2016), the scaling ratios are found to be roughly similar for both the RCP4.5 and RCP8.5 emission scenarios.
HE.1943-5584.0002122/asset/9b326f56-1e19-4739-a9ca-032f494d62b2/assets/images/large/figure4.jpg)
HE.1943-5584.0002122/asset/80293ef7-0ace-4db7-a76e-55a74a3876a4/assets/images/large/figure5.jpg)
HE.1943-5584.0002122/asset/8a79d0b5-5b9b-4de8-8e01-fc6a171264ac/assets/images/large/figure6.jpg)
The projected change in mean seasonal temperature (), needed to compute Eq. (2), is shown for the RCP8.5 scenario over the 2081–2100 period in Fig. 5. Results for other future time horizons (2021–2040, 2041–2060, and 2061–2080) are available in Figs. S2 –S4 . By specifying a projected change in mean seasonal temperature (), the practitioner can determine the horizon of interest-based on the infrastructure’s expected lifespan (e.g., up to the middle or the end of the 21st century). As discussed previously, the RCP8.5 business-as-usual scenario was preferred over the more optimistic RCP scenarios (e.g., RCP4.5) because it matches the current emissions (Sanford et al. 2014; Schwalm et al. 2020) and provides more conservative design criteria.
The seasonal and spatial heterogeneity of the changes in maximum daily rainfall presented (Fig. 4) and in the scaling rates (Fig. 6) are the consequence of multiple factors, which include large-scale atmospheric circulation changes that drive moisture convergence and local changes of near-surface air temperature and atmospheric conditions that affect convection (O’Gorman and Schneider 2009; Donat et al. 2016). Generally, extreme precipitation increases with temperature in moist, energy-limited environments and decreases in dry, moisture-limited environments (Prein et al. 2017b). Moreover, during the wet season, when there is a moisture surplus, extreme precipitation increases close to or above the Clausius-Clapeyron rate, while the increase is smaller during the dry season (Tabari 2020).
Fig. 6 presents the scaling of the median Rx1day corresponding to the 24-h 2-year return period rainfall. A strong worldwide seasonal variation is found in the scaling rates, depending on the time of occurrence of the annual daily maximum rainfall. We recommend selecting a scaling rate based on the season that displays the largest annual daily maximum rainfall (Fig. 7). Most regions project positive scaling factors, with some level of regional variability. For instance, the scaling rates of most regions in Eastern North America vary between 4%/°C and 6%/°C for the winter (DJF) and autumn (SON). Warm regions with less available moisture (e.g., Southern Spain, the Maghreb, South Africa, and Australia) exhibit negative rates.
HE.1943-5584.0002122/asset/3d834427-da95-45fc-9566-d7bf2ecf40d3/assets/images/large/figure7.jpg)
For Eq. (2), we recommend using the and scaling provided in Figs. 5 and 6, respectively, or to recompute local scaling factors using RCM and CPM ensembles according to the data availability and the practitioner’s level of climate expertise.
Based on the literature review reported previously, it is currently not feasible to compute precise and reliable estimates of the duration () and frequency () factors. This is especially true for subhourly durations, for which high-resolution CPMs are required. The use of CPM ensembles, such as the one presented by Pichelli et al. (2021) over a domain centered over the Alps, will eventually allow estimating these two factors more accurately at the local scale. Considering the rate at which more and longer CPM simulations are being generated, reliable estimations of both factors are expected to be possible in the next decade. While a single CPM simulation could still be used to provide an initial estimation for these factors, it should be done with precaution, considering the different sources of uncertainties previously discussed. However, even an uncertain estimation would prove to be a better alternative than the status quo when it comes to building resilient infrastructure.
We can nonetheless provide the range of plausible values for each factor. Both factors are positive and equal to or greater than 1. The value of each factor will progressively increase above 1 as the rainfall duration decreases (from the 24-h reference duration) or as the rainfall return period increases (from the 2-year reference return period). The upper limit of both factors is given by their physical interpretation: coefficient values larger than 1.5 are unlikely as they would lead to an improbable super CC as compared to observations and climate model projections. Just as is the case for the scaling rate, these values will be region-dependent. A value of 1 indicates no amplification of rainfall extremes for durations shorter than 24 h () and return periods longer than 2 years ().
Improvements will continue to be made in climate modeling, and it is expected that better spatial and temporal resolutions of climate models will lead to lower uncertainty in their simulations as the 21st century progresses. For these reasons, we strongly recommend that all the Eq. (2) components be periodically revisited (e.g., every ) by using state-of-the-art climate models and ensemble simulations. Similar to the updating of IDF curves based on historical observations, the nonstationarity of the future climate must be considered, which will ultimately lead to a reduction of the overall uncertainty in infrastructure design.
Finally, the practitioner should keep in mind that no-regret strategies are likely a good option when dealing with climate uncertainty, as their excess adaptation cost can be relatively small when compared to potential consequences. Using the same analogy as before, choosing a culvert that can convey a 20-year flow or a 100-year flow is only a matter of choosing a slightly larger diameter. This is a much smaller cost than having to rebuild a section of road that has been washed away due to an underdesigned culvert.
Detection of the Climate Change Signal
Various trend detection methods were explored to determine if an anthropogenic climate change signal can be detected by using the observation networks of rain gauges. Globally, approximately two-thirds of daily rainfall stations display increasing trends for the latter half of the 20th century (Min et al. 2011; Westra et al. 2013). There is also considerable evidence of greater increases in subdaily rainfall extremes (Fowler et al. 2021a; Westra et al. 2014).
However, natural variability can represent a considerable source of uncertainty and could mask the climate change signal at both the local and regional scales (Deser et al. 2012; Fischer et al. 2013; Martel et al. 2018). Martel et al. (2018) show that the detection of a significant trend at the regional scale could be delayed until the middle of the 21st century in many parts of the world. In the case of local trends, the role of natural variability is even greater, and the detection of a significant trend could be further delayed until the end of the century.
The studies used in this paper lead to an overwhelming consensus: extreme daily and subdaily rainfall events are expected to increase significantly in response to a warmer climate (Fig. 3 and Table S1 ). Thus, for regions where no trend is observed in rainfall data, a seemingly absent trend should not prevent the implementation of adaptation measures against climate change, especially for infrastructures with a long service life.
Conclusion
Due to the observable shift in the recent climate and the expected changes in the future, engineers cannot continue to rely on codes, standards, and professional guidelines that are solely based on historical climate information when designing structures with a long lifespan. In such cases, failing to consider the impact of climate change could be considered as a breach of an engineer’s standard of care (ASCE 2018; Engineers Canada 2018). Addressing the challenges of an evolving climate is necessary to ensure that any new infrastructure provides a service level adapted to both current and future climates.
This paper provides a literature review on extreme rainfall events, looking at trends in both the recent observational records as well as in future projections from climate models, including global climate models (GCMs), regional climate models (RCMs), and convection-permitting models (CPMs). From this review, a strong consensus emerges on the increase of extreme rainfall events in a warmer climate over most land regions of the world. GCMs and RCMs provide consistent patterns of increasing extreme rainfall events at the daily and subdaily (for RCMs) scales, and CPMs have recently opened a window on subhourly rainfall extreme events, a time scale especially relevant to many engineering applications. The intensification of extreme rainfall in a warmer climate is a settled scientific issue, and the challenges are now to determine the rates of change and factors governing these rates.
The patterns of increasing extreme rainfall events are complex and regionally dependent. State-of-the-art climate models cannot currently provide reliable estimates of the scaling factors of subhourly and longer return period extremes of precipitation with temperature, but they are rapidly improving. However, some important conclusions can be drawn with the current state of knowledge, and actions can be taken today to mitigate the upcoming impacts of climate change better. Over most of the land areas, the scaling of the annual maximum rainfall (Rx1day) appears to be slightly below but close to the Clausius-Clapeyron scaling. However, the scaling of extreme rainfall is frequency- and duration-dependent, with lower frequency (longer return period) and shorter-duration rainfall experiencing larger scaling, including up to super Clausius-Clapeyron scaling in many parts of the world. This frequency-duration dependency is particularly critical to engineers who routinely use design rainfall with very long return periods () and subhourly durations, which will be the most affected by potential super Clausius-Clapeyron scaling.
Adaptation strategies have already been put in place by some regulatory/legislative bodies with respect to IDF curves used by engineers. These strategies are a step in the right direction, but they do not recognize the impact of frequency, and especially duration, on the amplification of future extreme rainfall, with potentially very large increases for subhourly and very low-frequency rainfall events. It is hoped that this work will be useful in helping shape better guidelines for the adaptation of IDF curves for a future warmer climate. Future work should focus on improving the representation of extreme rainfall events in climate models, reducing the uncertainty, and increasing the robustness of the local scaling factors to integrate the frequency and duration of extreme events in rainfall design.
Supplemental Materials
File (supplemental materials_he.1943-5584.0002122_martel.pdf)
- Download
- 1.29 MB
Data Availability Statement
All data, models, and code generated or used during the study appear in the published article.
Acknowledgments
Philippe Lucas-Picher is supported by the French National Research Agency under the future investment program, ANR-18-MPGA-0005. This study was also partially funded by the Natural Sciences and Engineering Research Council of Canada (NSERC) through François Brissette’s and Richard Arsenault’s respective Discovery Grants. We acknowledge the World Climate Research Programme’s Working Group on Coupled Modeling, which is responsible for CMIP, and we thank the climate modeling groups (listed in Table S2 ) for producing and making available their model output. For CMIP, the USDOE’s Program for Climate Model Diagnosis and Intercomparison provided coordinating support and led the development of software infrastructure in partnership with the Global Organization for Earth System Science Portals. The authors also extend their gratitude to the anonymous reviewers whose comments helped shape the paper in its current form.
References
Aalbers, E. E., G. Lenderink, E. van Meijgaard, and B. J. J. M. van den Hurk. 2018. “Local-scale changes in mean and heavy precipitation in Western Europe, climate change or internal variability?” Clim. Dyn. 50 (11): 4745–4766. https://doi.org/10.1007/s00382-017-3901-9.
Adachi, S. A., and H. Tomita. 2020. “Methodology of the constraint condition in dynamical downscaling for regional climate evaluation: A review.” J. Geophys. Res.: Atmos. 125 (11): e2019JD032166. https://doi.org/10.1029/2019JD032166.
Akan, O. A. 1993. Urban stormwater hydrology: A guide to engineering calculations. Boca Raton, FL: CRC Press.
Alexander, L. V., and J. M. Arblaster. 2017. “Historical and projected trends in temperature and precipitation extremes in Australia in observations and CMIP5.” Weather Clim. Extremes 15 (Mar): 34–56. https://doi.org/10.1016/j.wace.2017.02.001.
Allen, M. R., and W. J. Ingram. 2002. “Constraints on future changes in climate and the hydrologic cycle.” Nature 419 (6903): 228–232. https://doi.org/10.1038/nature01092.
Argüeso, D., J. P. Evans, and L. Fita. 2013. “Precipitation bias correction of very high resolution regional climate models.” Hydrol. Earth Syst. Sci. 17 (11): 4379–4388. https://doi.org/10.5194/hess-17-4379-2013.
Arnbjerg-Nielsen, K. 2008. Forventede ændringer i ekstrem regn som følge af klimaændringer (Anticipated changes in extreme rainfall due to climate change). [In Danish.]. Copenhagen, Denmark: Water Pollution Committee of the Society of Danish Engineers.
Arnbjerg-Nielsen, K. 2012. “Quantification of climate change effects on extreme precipitation used for high resolution hydrologic design.” Urban Water J. 9 (2): 57–65. https://doi.org/10.1080/1573062X.2011.630091.
Arnbjerg-Nielsen, K., L. Leonardsen, and H. Madsen. 2015. “Evaluating adaptation options for urban flooding based on new high-end emission scenario regional climate model simulations.” Clim. Res. 64 (1): 73–84. https://doi.org/10.3354/cr01299.
ASCE. 2017. Infrastructure report card: A comprehensive assessment of America’s infrastructure. Reston, VA: ASCE.
ASCE. 2018. “American Society of Civil Engineers—Policy statement 360—Impact of climate change.” Accessed February 1, 2021. https://www.asce.org/issues-and-advocacy/public-policy/policy-statement-360---impact-of-climate-change/.
Ball, J., M. Basbister, R. Nathan, W. WEeks, E. Weinmann, M. Retallick, and I. Testoni. 2019. Australian rainfall and runoff: A guide to flood estimation, 1526. Symonston, ACT, Australia: Commonwealth of Australia (Geoscience Australia).
Ban, N., J. Rajczak, J. Schmidli, and C. Schär. 2020. “Analysis of Alpine precipitation extremes using generalized extreme value theory in convection-resolving climate simulations.” Clim. Dyn. 55 (1): 61–75. https://doi.org/10.1007/s00382-018-4339-4.
Ban, N., J. Schmidli, and C. Schär. 2014. “Evaluation of the convection-resolving regional climate modeling approach in decade-long simulations.” J. Geophys. Res.: Atmos. 119 (13): 7889–7907. https://doi.org/10.1002/2014JD021478.
Ban, N., J. Schmidli, and C. Schär. 2015. “Heavy precipitation in a changing climate: Does short-term summer precipitation increase faster?” Geophys. Res. Lett. 42 (4): 1165–1172. https://doi.org/10.1002/2014GL062588.
Ban, N. J., et al. 2021. “The first multi-model ensemble of regional climate simulations at kilometer-scale resolution. Part I: Evaluation of precipitation.” Clim. Dyn. 57: 275–302. https://doi.org/10.1007/s00382-021-05708-w.
Bao, J., S. C. Sherwood, L. V. Alexander, and J. P. Evans. 2017. “Future increases in extreme precipitation exceed observed scaling rates.” Nat. Clim. Change 7 (2): 128–132. https://doi.org/10.1038/nclimate3201.
Barlage, M., F. Chen, R. Rasmussen, Z. Zhang, and G. Miguez-Macho. 2021. “The importance of scale-dependent groundwater processes in land-atmosphere interactions over the central United States.” Geophys. Res. Lett. 48 (5): e2020GL092171. https://doi.org/10.1029/2020GL092171.
Berg, P., O. B. Christensen, K. Klehmet, G. Lenderink, J. Olsson, C. Teichmann, and W. Yang. 2019. “Summertime precipitation extremes in a EURO-CORDEX 0.11° ensemble at an hourly resolution.” Nat. Hazards Earth Syst. Sci. 19 (4): 957–971. https://doi.org/10.5194/nhess-19-957-2019.
Berg, P., C. Moseley, and J. O. Haerter. 2013. “Strong increase in convective precipitation in response to higher temperatures.” Nat. Geosci. 6 (3): 181–185. https://doi.org/10.1038/ngeo1731.
Berthou, S., E. J. Kendon, S. C. Chan, N. Ban, D. Leutwyler, C. Schär, and G. Fosser. 2020. “Pan-European climate at convection-permitting scale: A model intercomparison study.” Clim. Dyn. 55 (1): 35–59. https://doi.org/10.1007/s00382-018-4114-6.
Blenkinsop, S., et al. 2018. “The INTENSE project: Using observations and models to understand the past, present and future of sub-daily rainfall extremes.” Adv. Sci. Res. 15: 117–126. https://doi.org/10.5194/asr-15-117-2018.
Bonnin, G. M. M., D. Martin, B. Lin, T. Parzybok, M. Yekta, and D. Riley. 2006. NOAA Atlas 14—Precipitation-frequency atlas of the United States—Volume 2 version 3: Delaware, District of Columbia, Illinois, Indiana, Kentucky, Maryland, New Jersey, North Carolina, Ohio, Pennsylvania, South Carolina, Tennessee, Virginia, West Virginia. Silver Spring, MD: National Oceanic and Atmospheric Administration—National Weather Service.
Butcher, J. B., and T. Zi. 2019. “Efficient method for updating IDF curves to future climate projections.” Preprint, submitted June 11, 2019. https://arxiv.org/abs/1906.04802.
Cannon, A. J., and S. Innocenti. 2019. “Projected intensification of sub-daily and daily rainfall extremes in convection-permitting climate model simulations over North America: Implications for future intensity–duration–frequency curves.” Nat. Hazards Earth Syst. Sci. 19 (2): 421–440. https://doi.org/10.5194/nhess-19-421-2019.
Cannon, A. J., D. I. Jeong, X. Zhang, and F. Zwiers. 2020. Climate-resilient buildings and core public infrastructure: An assessment of the impact of climate change on climatic design data in Canada, 113. Gatineau, QC, Canada: Environment and Climate Change Canada.
Chambwera, M., G. Heal, C. Dubeux, S. Hallegatte, L. Leclerc, A. Markandya, B. A. McCarl, R. Mechler, and J. E. Neumann. 2014. “Chapter 17—Economics of adaptation.” In Climate change 2014: Impacts, adaptation, and vulnerability. Part A: Global and sectoral aspects. Contribution of working group II to the fifth assessment report of the IPCC. Cambridge, UK: Cambridge University Press.
Chan, S. C., E. J. Kendon, N. M. Roberts, H. J. Fowler, and S. Blenkinsop. 2016. “Downturn in scaling of UK extreme rainfall with temperature for future hottest days.” Nat. Geosci. 9 (1): 24–28. https://doi.org/10.1038/ngeo2596.
Chandra, R., U. Saha, and P. P. Mujumdar. 2015. “Model and parameter uncertainty in IDF relationships under climate change.” Adv. Water Resour. 79 (May): 127–139. https://doi.org/10.1016/j.advwatres.2015.02.011.
Cheng, L., and A. AghaKouchak. 2014. “Nonstationary precipitation intensity-duration-frequency curves for infrastructure design in a changing climate.” Sci. Rep. 4 (1): 7093. https://doi.org/10.1038/srep07093.
CIRC (Canadian Infrastructure Report Card). 2019. Monitoring the state of Canada’s core public infrastructure. Ottawa: Association of Consulting Engineering Companies Canada.
City of Vancouver. 2018. Engineering design manual, 348. Vancouver, BC, Canada: City of Vancouver.
Collins, M., R. Knutti, J. Arblaster, J.-L. Dufresne, T. Fichefet, P. Friedlingstein, X. Gao, W. J. Gutowski, T. Johns, and G. Krinner. 2013. “Long-term climate change: Projections, commitments and irreversibility.” In Proc., Climate Change 2013-The Physical Science Basis: Contribution of Working Group I to the 5th Assessment Report of the Intergovernmental Panel on Climate Change, 1029–1136. Cambridge, UK: Cambridge University Press.
CSA (Canadian Standards Association). 2019. Technical guide: Development, interpretation and use of rainfall intensity-duration-frequency (IDF) information: Guideline for Canadian water resources practitioners. Mississauga, ON, Canada: CSA.
Dai, A., R. M. Rasmussen, C. Liu, K. Ikeda, and A. F. Prein. 2020. “A new mechanism for warm-season precipitation response to global warming based on convection-permitting simulations.” Clim. Dyn. 55 (1): 343–368. https://doi.org/10.1007/s00382-017-3787-6.
Defra. 2020. “Guidance—Flood and coastal risk projects, schemes and strategies: Climate change allowance.” Accessed February 1, 2021. https://www.gov.uk/guidance/flood-and-coastal-risk-projects-schemes-and-strategies-climate-change-allowances.
Deser, C., R. Knutti, S. Solomon, and A. S. Phillips. 2012. “Communication of the role of natural variability in future North American climate.” Nat. Clim. Change 2 (11): 775–779. https://doi.org/10.1038/nclimate1562.
Donat, M. G., A. L. Lowry, L. V. Alexander, P. A. O’Gorman, and N. Maher. 2016. “More extreme precipitation in the world’s dry and wet regions.” Nat. Clim. Change 6 (5): 508–513. https://doi.org/10.1038/nclimate2941.
Donat, M. G., J. Sillmann, and E. M. Fischer. 2020. “Chapter 3—Changes in climate extremes in observations and climate model simulations. From the past to the future.” In Climate Extremes and their implications for impact and risk assessment, edited by J. Sillmann, S. Sippel, and S. Russo, 31–57. Amsterdam, Netherlands: Elsevier.
Drobinski, P., et al. 2018. “Scaling precipitation extremes with temperature in the Mediterranean: Past climate assessment and projection in anthropogenic scenarios.” Clim. Dyn. 51 (3): 1237–1257. https://doi.org/10.1007/s00382-016-3083-x.
ECCC (Environment and Climate Change Canada). 2019. Documentation on Environment and Climate Canada rainfall intensity-duration-frequency (IDF) tables and graphs, 12. Gatineau, QC, Canada: ECCC.
Eekhout, J. P. C., J. E. Hunink, W. Terink, and J. de Vente. 2018. “Why increased extreme precipitation under climate change negatively affects water security.” Hydrol. Earth Syst. Sci. 22 (11): 5935–5946. https://doi.org/10.5194/hess-22-5935-2018.
Engineers Canada. 2018. Public guidelines: Principles for climate adaptation and mitigation for engineers, 38. Ottawa: Engineers Canada.
EPWDR (Engineering and Public Works Department of Riverview). 2011. Storm water design criteria manual for municipal services, 88. Riverview, NB, Canada: EPWDR.
Fadhel, S., M. A. Rico-Ramirez, and D. Han. 2017. “Uncertainty of intensity–duration–frequency (IDF) curves due to varied climate baseline periods.” J. Hydrol. 547 (Apr): 600–612. https://doi.org/10.1016/j.jhydrol.2017.02.013.
Ferguson, B. K. 1998. Introduction to stormwater: Concept, purpose, design. New York: Wiley.
Field, C. B., V. Barros, T. F. Stocker, and Q. Dahe. 2012. Managing the risks of extreme events and disasters to advance climate change adaptation: Special report of the intergovernmental panel on climate change. Cambridge, UK: Cambridge University Press.
Finnis, J., and J. Daraio. 2018. Projected impacts of climate change for the province of Newfoundland & Labrador—2018 update, 198. St. John’s, Canada: Memorial Univ. of Newfoundland.
Fischer, E. M., U. Beyerle, and R. Knutti. 2013. “Robust spatially aggregated projections of climate extremes.” Nat. Clim. Change 3 (12): 1033–1038. https://doi.org/10.1038/nclimate2051.
Fischer, E. M., and R. Knutti. 2015. “Anthropogenic contribution to global occurrence of heavy-precipitation and high-temperature extremes.” Nat. Clim. Change 5 (6): 560–564. https://doi.org/10.1038/nclimate2617.
Fischer, E. M., and R. Knutti. 2016. “Observed heavy precipitation increase confirms theory and early models.” Nat. Clim. Change 6 (11): 986–991. https://doi.org/10.1038/nclimate3110.
Fischer, E. M., J. Sedláček, E. Hawkins, and R. Knutti. 2014. “Models agree on forced response pattern of precipitation and temperature extremes.” Geophys. Res. Lett. 41 (23): 8554–8562. https://doi.org/10.1002/2014GL062018.
Fluixá-Sanmartín, J., A. Morales-Torres, I. Escuder-Bueno, and J. Paredes-Arquiola. 2019. “Quantification of climate change impact on dam failure risk under hydrological scenarios: A case study from a Spanish dam.” Nat. Hazards Earth Syst. Sci. 19 (10): 2117–2139. https://doi.org/10.5194/nhess-19-2117-2019.
Forestieri, A., E. Arnone, S. Blenkinsop, A. Candela, H. Fowler, and L. V. Noto. 2018. “The impact of climate change on extreme precipitation in Sicily, Italy.” Hydrol. Processes 32 (3): 332–348. https://doi.org/10.1002/hyp.11421.
Förster, K., and L.-B. Thiele. 2020. “Variations in sub-daily precipitation at centennial scale.” npj Clim. Atmos. Sci. 3 (1): 13. https://doi.org/10.1038/s41612-020-0117-1.
Fosser, G., E. J. Kendon, D. Stephenson, and S. Tucker. 2020. “Convection-permitting models offer promise of more certain extreme rainfall projections.” Geophys. Res. Lett. 47 (13): e2020GL088151. https://doi.org/10.1029/2020GL088151.
Fowler, H. J., et al. 2021a. “Anthropogenic intensification of short-duration rainfall extremes.” Nat. Rev. Earth Environ. 2 (2): 107–122. https://doi.org/10.1038/s43017-020-00128-6.
Fowler, H. J., et al. 2021b. “Towards advancing scientific knowledge of climate change impacts on short-duration rainfall extremes.” Philos. Trans. R. Soc. A 379 (2195): 20190542. https://doi.org/10.1098/rsta.2019.0542.
Fowler, H. J., C. Wasko, and A. F. Prein. 2021c. “Intensification of short-duration rainfall extremes and implications for flood risk: Current state of the art and future directions.” Philos. Trans. R. Soc. A 379 (2195): 20190541. https://doi.org/10.1098/rsta.2019.0541.
Fumière, Q., M. Déqué, O. Nuissier, S. Somot, A. Alias, C. Caillaud, O. Laurantin, and Y. Seity. 2020. “Extreme rainfall in Mediterranean France during the fall: Added value of the CNRM-AROME convection-permitting regional climate model.” Clim. Dyn. 55 (1): 77–91. https://doi.org/10.1007/s00382-019-04898-8.
Ganguli, P., and P. Coulibaly. 2019. “Assessment of future changes in intensity-duration-frequency curves for Southern Ontario using North American (NA)-CORDEX models with nonstationary methods.” J. Hydrol.: Reg. Stud. 22 (Apr): 100587. https://doi.org/10.1016/j.ejrh.2018.12.007.
Ghausi, S. A., and S. Ghosh. 2020. “Diametrically opposite scaling of extreme precipitation and streamflow to temperature in South and Central Asia.” Geophys. Res. Lett. 47 (17): e2020GL089386. https://doi.org/10.1029/2020GL089386.
Gregersen, I. B., M. A. Sunyer Pinya, H. Madsen, S. Funder, J. Luchner, D. Rosbjerg, and K. Arnbjerg-Nielsen. 2014. Past, present, and future variations of extreme precipitation in Denmark. Lyngby, Denmark: DTU Environment.
Guerreiro, S. B., H. J. Fowler, R. Barbero, S. Westra, G. Lenderink, S. Blenkinsop, E. Lewis, and X.-F. Li. 2018. “Detection of continental-scale intensification of hourly rainfall extremes.” Nat. Clim. Change 8 (9): 803–807. https://doi.org/10.1038/s41558-018-0245-3.
Gutmann, E. D., R. M. Rasmussen, C. Liu, K. Ikeda, C. L. Bruyere, J. M. Done, L. Garrè, P. Friis-Hansen, and V. Veldore. 2018. “Changes in hurricanes from a 13-yr convection-permitting pseudo–global warming simulation.” J. Clim. 31 (9): 3643–3657. https://doi.org/10.1175/JCLI-D-17-0391.1.
Haerter, J. O., S. J. Boeing, O. Henneberg, and S. B. Nissen. 2018. “Reconciling cold pool dynamics with convective self-organization.” Preprint, submitted October 12, 2018. http://arXiv:1810.05518.
Hausfather, Z., and G. P. Peters. 2020. “Emissions–The ‘business as usual’ story is misleading.” Nature 577: 618–620. https://doi.org/10.1038/d41586-020-00177-3.
Hawkins, E., and R. Sutton. 2011. “The potential to narrow uncertainty in projections of regional precipitation change.” Clim. Dyn. 37 (1): 407–418. https://doi.org/10.1007/s00382-010-0810-6.
Held, I. M., and B. J. Soden. 2006. “Robust responses of the hydrological cycle to global warming.” J. Clim. 19 (21): 5686–5699. https://doi.org/10.1175/JCLI3990.1.
Helsen, S., et al. 2020. “Consistent scale-dependency of future increases in hourly extreme precipitation in two convection-permitting climate models.” Clim. Dyn. 54 (3): 1267–1280. https://doi.org/10.1007/s00382-019-05056-w.
Hodnebrog, Ø., L. Marelle, K. Alterskjær, R. R. Wood, R. Ludwig, E. M. Fischer, T. B. Richardson, P. M. Forster, J. Sillmann, and G. Myhre. 2019. “Intensification of summer precipitation with shorter time-scales in Europe.” Environ. Res. Lett. 14 (12): 124050. https://doi.org/10.1088/1748-9326/ab549c.
Hosseinzadehtalaei, P., H. Tabari, and P. Willems. 2018. “Precipitation intensity–duration–frequency curves for central Belgium with an ensemble of EURO-CORDEX simulations, and associated uncertainties.” Atmos. Res. 200 (Feb): 1–12. https://doi.org/10.1016/j.atmosres.2017.09.015.
Hosseinzadehtalaei, P., H. Tabari, and P. Willems. 2020. “Climate change impact on short-duration extreme precipitation and intensity–duration–frequency curves over Europe.” J. Hydrol. 590 (Nov): 125249. https://doi.org/10.1016/j.jhydrol.2020.125249.
Houze, R. A., Jr. 1997. “Stratiform precipitation in regions of convection: A meteorological paradox?” Bull. Am. Meteorol. Soc. 78 (10): 2179–2196. https://doi.org/10.1175/1520-0477(1997)078%3C2179:SPIROC%3E2.0.CO;2.
Huang, X., D. L. Swain, and A. D. Hall. 2020. “Future precipitation increase from very high resolution ensemble downscaling of extreme atmospheric river storms in California.” Sci. Adv. 6 (29): eaba1323. https://doi.org/10.1126/sciadv.aba1323.
Huntington, T. G. 2006. “Evidence for intensification of the global water cycle: Review and synthesis.” J. Hydrol. 319 (1–4): 83–95. https://doi.org/10.1016/j.jhydrol.2005.07.003.
Huo, R., L. Li, H. Chen, C.-Y. Xu, J. Chen, and S. Guo. 2021. “Extreme precipitation changes in Europe from the last millennium to the end of the twenty-first century.” J. Clim. 34 (2): 567–588. https://doi.org/10.1175/JCLI-D-19-0879.1.
Iles, C. E., R. Vautard, J. Strachan, S. Joussaume, B. R. Eggen, and C. D. Hewitt. 2020. “The benefits of increasing resolution in global and regional climate simulations for European climate extremes.” Geosci. Model Dev. 13 (11): 5583–5607. https://doi.org/10.5194/gmd-13-5583-2020.
Innocenti, S., A. Mailhot, M. Leduc, A. J. Cannon, and A. Frigon. 2019. “Projected changes in the probability distributions, seasonality, and spatiotemporal scaling of daily and subdaily extreme precipitation simulated by a 50-member ensemble over Northeastern North America.” J. Geophys. Res.: Atmos. 124 (19): 10427–10449. https://doi.org/10.1029/2019JD031210.
IPCC (Intergovernmental Panel on Climate Change). 2013. “Climate change 2013: The physical science basis.” In Proc., Contribution of Working Group I to the 5th Assessment Report of the Intergovernmental Panel on Climate Change. New York: Cambridge University Press.
Katz, R. W. 2013. “Statistical methods for nonstationary extremes.” In Extremes in a changing climate: Detection, analysis and uncertainty, 15–37. Dordrecht, Netherlands: Springer.
Kay, J. E., et al. 2015. “The community earth system model (CESM) large ensemble project: A community resource for studying climate change in the presence of internal climate variability.” Bull. Am. Meteorol. Soc. 96 (8): 1333–1349. https://doi.org/10.1175/BAMS-D-13-00255.1.
Kendon, E. J., N. Ban, N. M. Roberts, H. J. Fowler, M. J. Roberts, S. C. Chan, J. P. Evans, G. Fosser, and J. M. Wilkinson. 2017. “Do convection-permitting regional climate models improve projections of future precipitation change?” Bull. Am. Meteorol. Soc. 98 (1): 79–93. https://doi.org/10.1175/BAMS-D-15-0004.1.
Kendon, E. J., A. F. Prein, C. A. Senior, and A. Stirling. 2021. “Challenges and outlook for convection-permitting climate modelling.” Philos. Trans. R. Soc. A 379 (2195): 20190547. https://doi.org/10.1098/rsta.2019.0547.
Kendon, E. J., N. M. Roberts, H. J. Fowler, M. J. Roberts, S. C. Chan, and C. A. Senior. 2014. “Heavier summer downpours with climate change revealed by weather forecast resolution model.” Nat. Clim. Change 4 (7): 570–576. https://doi.org/10.1038/nclimate2258.
Kendon, E. J., N. M. Roberts, C. A. Senior, and M. J. Roberts. 2012. “Realism of rainfall in a very high-resolution regional climate model.” J. Clim. 25 (17): 5791–5806. https://doi.org/10.1175/JCLI-D-11-00562.1.
Kendon, E. J., R. A. Stratton, S. Tucker, J. H. Marsham, S. Berthou, D. P. Rowell, and C. A. Senior. 2019. “Enhanced future changes in wet and dry extremes over Africa at convection-permitting scale.” Nat. Commun. 10 (1): 1794. https://doi.org/10.1038/s41467-019-09776-9.
Kharin, V. V., G. M. Flato, X. Zhang, N. P. Gillett, F. Zwiers, and K. J. Anderson. 2018. “Risks from climate extremes change differently from 1.5°C to 2.0°C depending on rarity.” Earth’s Future 6 (5): 704–715. https://doi.org/10.1002/2018EF000813.
Kharin, V. V., F. W. Zwiers, X. Zhang, and M. Wehner. 2013. “Changes in temperature and precipitation extremes in the CMIP5 ensemble.” Clim. Change 119 (2): 345–357. https://doi.org/10.1007/s10584-013-0705-8.
Khazaei, M. R. 2021. “A robust method to develop future rainfall IDF curves under climate change condition in two major basins of Iran.” Theor. Appl. Climatol. 144 (1): 179–190. https://doi.org/10.1007/s00704-021-03540-0.
Kind, J. M. 2012. “Economically efficient flood protection standards for the Netherlands.” J. Flood Risk Manage. 7 (2): 103–117. https://doi.org/10.1111/jfr3.12026.
Kirchmeier-Young, M. C., and X. Zhang. 2020. “Human influence has intensified extreme precipitation in North America.” Proc. Natl. Acad. Sci. 117 (24): 13308–13313. https://doi.org/10.1073/pnas.1921628117.
Knist, S., K. Goergen, and C. Simmer. 2020. “Evaluation and projected changes of precipitation statistics in convection-permitting WRF climate simulations over Central Europe.” Clim. Dyn. 55 (1): 325–341. https://doi.org/10.1007/s00382-018-4147-x.
Koutsoyiannis, D. 2020. “Revisiting the global hydrological cycle: Is it intensifying?” Hydrol. Earth Syst. Sci. 24 (8): 3899–3932. https://doi.org/10.5194/hess-24-3899-2020.
Leduc, M., et al. 2019. “The ClimEx project: A 50-member ensemble of climate change projections at 12-km resolution over Europe and Northeastern North America with the Canadian Regional Climate Model (CRCM5).” J. Appl. Meteorol. Climatol. 58 (4): 663–693. https://doi.org/10.1175/JAMC-D-18-0021.1.
Lenderink, G., R. Barbero, J. M. Loriaux, and H. J. Fowler. 2017. “Super-Clausius–Clapeyron scaling of extreme hourly convective precipitation and its relation to large-scale atmospheric conditions.” J. Clim. 30 (15): 6037–6052. https://doi.org/10.1175/JCLI-D-16-0808.1.
Lenderink, G., and E. van Meijgaard. 2008. “Increase in hourly precipitation extremes beyond expectations from temperature changes.” Nat. Geosci. 1 (8): 511–514. https://doi.org/10.1038/ngeo262.
Lenderink, G., and E. van Meijgaard. 2010. “Linking increases in hourly precipitation extremes to atmospheric temperature and moisture changes.” Environ. Res. Lett. 5 (2): 025208. https://doi.org/10.1088/1748-9326/5/2/025208.
Lenderink, G. R., H. de Vries, H. J. Fowler, R. Barbero, B. van Ulft, and E. van Meijgaard. 2021. “Scaling and responses of extreme hourly precipitation in three climate experiments with a convection-permitting model.” Philos. Trans. R. Soc. A 379 (2195): 20190544. https://doi.org/10.1098/rsta.2019.0544.
Li, C., F. Zwiers, X. Zhang, G. Li, Y. Sun, and M. Wehner. 2021. “Changes in annual extremes of daily temperature and precipitation in CMIP6 models.” J. Clim. 34 (9): 3441–3460. https://doi.org/10.1175/JCLI-D-19-1013.1.
Li, Y., H. J. Fowler, D. Argüeso, S. Blenkinsop, J. P. Evans, G. Lenderink, X. Yan, S. B. Guerreiro, E. Lewis, and X.-F. Li. 2020. “Strong intensification of hourly rainfall extremes by urbanization.” Geophys. Res. Lett. 47 (14): e2020GL088758. https://doi.org/10.1029/2020GL088758.
Lima, C. H. R., H.-H. Kwon, and J.-Y. Kim. 2016. “A Bayesian beta distribution model for estimating rainfall IDF curves in a changing climate.” J. Hydrol. 540 (Sep): 744–756. https://doi.org/10.1016/j.jhydrol.2016.06.062.
Lind, P., et al. 2020. “Benefits and added value of convection-permitting climate modeling over Fenno-Scandinavia.” Clim. Dyn. 55 (7): 1893–1912. https://doi.org/10.1007/s00382-020-05359-3.
Lopez-Cantu, T., and C. Samaras. 2018. “Temporal and spatial evaluation of stormwater engineering standards reveals risks and priorities across the United States.” Environ. Res. Lett. 13 (7): 074006. https://doi.org/10.1088/1748-9326/aac696.
Loriaux, J. M., G. Lenderink, S. R. De Roode, and A. P. Siebesma. 2013. “Understanding convective extreme precipitation scaling using observations and an entraining plume model.” J. Atmos. Sci. 70 (11): 3641–3655. https://doi.org/10.1175/JAS-D-12-0317.1.
Luu, L. N., R. Vautard, P. Yiou, and J.-M. Soubeyroux. 2020. “Evaluation of convection-permitting extreme precipitation simulations for the south of France.” Earth Syst. Dyn. Discuss. 1–24. https://doi.org/10.5194/esd-2020-77.
Madsen, H., D. Lawrence, M. Lang, M. Martinkova, and T. R. Kjeldsen. 2014. “Review of trend analysis and climate change projections of extreme precipitation and floods in Europe.” J. Hydrol. 519 (Nov): 3634–3650. https://doi.org/10.1016/j.jhydrol.2014.11.003.
Mailhot, A., and S. Duchesne. 2010. “Design criteria of urban drainage infrastructures under climate change.” J. Water Resour. Plann. Manage. 136 (2): 201–208. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000023.
Mailhot, A., S. Duchesne, D. Caya, and G. Talbot. 2007. “Assessment of future change in intensity–duration–frequency (IDF) curves for Southern Quebec using the Canadian Regional Climate Model (CRCM).” J. Hydrol. 347 (1–2): 197–210. https://doi.org/10.1016/j.jhydrol.2007.09.019.
Mamoon, A. A., A. Rahman, and N. E. Joergensen. 2019. “Assessment of climate change impacts on IDF curves in Qatar using ensemble climate modeling approach.” In Hydrology in a changing world: Challenges in modeling, edited by S. K. Singh and C. T. Dhanya, 153–169. Cham, Switzerland: Springer.
Mantegna, G. A., C. J. White, T. A. Remenyi, S. P. Corney, and P. Fox-Hughes. 2017. “Simulating sub-daily intensity-frequency-duration curves in Australia using a dynamical high-resolution regional climate model.” J. Hydrol. 554 (Nov): 277–291. https://doi.org/10.1016/j.jhydrol.2017.09.025.
Marelle, L., G. Myhre, B. M. Steensen, Ø. Hodnebrog, K. Alterskjær, and J. Sillmann. 2020. “Urbanization in megacities increases the frequency of extreme precipitation events far more than their intensity.” Environ. Res. Lett. 15 (12): 124072. https://doi.org/10.1088/1748-9326/abcc8f.
Martel, J.-L., A. Mailhot, and F. Brissette. 2020. “Global and regional projected changes in 100-yr subdaily, daily, and multiday precipitation extremes estimated from three large ensembles of climate simulations.” J. Clim. 33 (3): 1089–1103. https://doi.org/10.1175/JCLI-D-18-0764.1.
Martel, J.-L., A. Mailhot, F. Brissette, and D. Caya. 2018. “Role of natural climate variability in the detection of anthropogenic climate change signal for mean and extreme precipitation at local and regional scales.” J. Clim. 31 (11): 4241–4263. https://doi.org/10.1175/JCLI-D-17-0282.1.
MDDELCC (Ministère du Développement durable, de l’Environnement et de la Lutte contre les changements climatiques). 2017. Manuel de calcul et de conception des ouvrages municipaux de gestion des eaux pluviales, 125. [In French.] Québec: MDDELCC.
Meinshausen, M., et al. 2011. “The RCP greenhouse gas concentrations and their extensions from 1765 to 2300.” Clim. Change 109 (1): 213. https://doi.org/10.1007/s10584-011-0156-z.
Meredith, E., U. Ulbrich, H. W. Rust, and H. Truhetz. 2021. “Present and future diurnal hourly precipitation in 0.11° EURO-CORDEX models and at convection-permitting resolution.” Environ. Res. Commun. 3 (5): 055002. https://doi.org/10.1088/2515-7620/abf15e.
Meredith, E. P., U. Ulbrich, and H. W. Rust. 2020. “Subhourly rainfall in a convection-permitting model.” Environ. Res. Lett. 15 (3): 034031. https://doi.org/10.1088/1748-9326/ab6787.
Merisalu, J., J. Sundell, and L. Rosén. 2021. “A framework of risk-based cost-benefit analysis for decision support on hydrogeological risks in underground construction.” Geosciences 11 (2): 82. https://doi.org/10.3390/geosciences11020082.
Milly, P. C. D., J. Betancourt, M. Falkenmark, R. M. Hirsch, Z. W. Kundzewicz, D. P. Lettenmaier, and R. J. Stouffer. 2008. “Stationarity is dead: Whither water management?” Science 319 (5863): 573. https://doi.org/10.1126/science.1151915.
Min, S.-K., X. Zhang, F. W. Zwiers, and G. C. Hegerl. 2011. “Human contribution to more-intense precipitation extremes.” Nature 470 (7334): 378–381. https://doi.org/10.1038/nature09763.
Mizuta, R., et al. 2017. “Over 5,000 years of ensemble future climate simulations by 60-km global and 20-km regional atmospheric models.” Bull. Am. Meteorol. Soc. 98 (7): 1383–1398. https://doi.org/10.1175/BAMS-D-16-0099.1.
Morrison, A., G. Villarini, W. Zhang, and E. Scoccimarro. 2019. “Projected changes in extreme precipitation at sub-daily and daily time scales.” Global Planet. Change 182 (Nov): 103004. https://doi.org/10.1016/j.gloplacha.2019.103004.
Moseley, C., C. Hohenegger, P. Berg, and J. O. Haerter. 2016. “Intensification of convective extremes driven by cloud–cloud interaction.” Nat. Geosci. 9 (10): 748–752. https://doi.org/10.1038/ngeo2789.
Moustakis, Y., S. M. Papalexiou, C. J. Onof, and A. Paschalis. 2021. “Seasonality, intensity and duration of rainfall extremes change in a warmer climate.” Earth’s Future 9 (3): e2020EF001824. https://doi.org/10.1029/2020EF001824.
MTO (Ministry of Transportation of Ontario). 2016. Provincial engineering memo. Toronto: MTO.
Myhre, G., et al. 2019. “Frequency of extreme precipitation increases extensively with event rareness under global warming.” Sci. Rep. 9 (1): 16063. https://doi.org/10.1038/s41598-019-52277-4.
Nie, J., A. H. Sobel, D. A. Shaevitz, and S. Wang. 2018. “Dynamic amplification of extreme precipitation sensitivity.” Proc. Natl. Acad. Sci. 115 (38): 9467–9472. https://doi.org/10.1073/pnas.1800357115.
Ning, L., E. E. Riddle, and R. S. Bradley. 2015. “Projected changes in climate extremes over the Northeastern United States.” J. Clim. 28 (8): 3289–3310. https://doi.org/10.1175/JCLI-D-14-00150.1.
NYC (New York City). 2017. “Preliminary climate resiliency guidelines.” In Mayor’s office of recovery and resiliency, 72. New York: NYC.
O’Gorman, P. A., and T. Schneider. 2009. “The physical basis for increases in precipitation extremes in simulations of 21st-century climate change.” Proc. Natl. Acad. Sci. 106 (35): 14773–14777. https://doi.org/10.1073/pnas.0907610106.
Orr, H. G., M. Ekström, M. B. Charlton, K. L. Peat, and H. J. Fowler. 2021. “Using high-resolution climate change information in water management: A decision-makers’ perspective.” Philos. Trans. R. Soc. A 379 (2195): 20200219. https://doi.org/10.1098/rsta.2020.0219.
Ouarda, T. B. M. J., L. A. Yousef, and C. Charron. 2019. “Non-stationary intensity-duration-frequency curves integrating information concerning teleconnections and climate change.” Int. J. Climatol. 39 (4): 2306–2323. https://doi.org/10.1002/joc.5953.
Panthou, G., A. Mailhot, E. Laurence, and G. Talbot. 2014. “Relationship between surface temperature and extreme rainfalls: A multi-time-scale and event-based analysis.” J. Hydrometeorol. 15 (5): 1999–2011. https://doi.org/10.1175/JHM-D-14-0020.1.
Pendergrass, A. G. 2018. “What precipitation is extreme?” Science 360 (6393): 1072–1073. https://doi.org/10.1126/science.aat1871.
Pendergrass, A. G., F. Lehner, B. M. Sanderson, and Y. Xu. 2015. “Does extreme precipitation intensity depend on the emissions scenario?” Geophys. Res. Lett. 42 (20): 8767–8774. https://doi.org/10.1002/2015GL065854.
Perica, S., S. Pavlovic, M. St. Laurent, C. Trypaluk, D. Unruh, and O. Wilhite. 2018. NOAA Atlas 14 - Precipitation-frequency atlas of the United States volume 11 version 2.0: Texas, 283. Silver Spring, MD: National Oceanic and Atmospheric Administration—National Weather Service.
Pfahl, S., P. A. O’Gorman, and E. M. Fischer. 2017. “Understanding the regional pattern of projected future changes in extreme precipitation.” Nat. Clim. Change 7 (6): 423. https://doi.org/10.1038/nclimate3287.
Pichelli, E., et al. 2021. “The first multi-model ensemble of regional climate simulations at kilometer-scale resolution. Part 2: Historical and future simulations of precipitation.” Clim. Dyn. 56: 3581–3602. https://doi.org/10.1007/s00382-021-05657-4.
Poschlod, B., R. Ludwig, and J. Sillmann. 2021. “Ten-year return levels of sub-daily extreme precipitation over Europe.” Earth Syst. Sci. Data 13 (3): 983–1003. https://doi.org/10.5194/essd-13-983-2021.
Poujol, B., P. A. Mooney, and S. P. Sobolowski. 2021. “Physical processes driving intensification of future precipitation in the mid- to high latitudes.” Environ. Res. Lett. 16 (3): 034051. https://doi.org/10.1088/1748-9326/abdd5b.
Poujol, B., S. P. Sobolowski, P. A. Mooney, and S. Berthou. 2020. “A physically based precipitation separation algorithm for convection-permitting models over complex topography.” Q. J. R. Meteorol. Soc. 146 (727): 748–761. https://doi.org/10.1002/qj.3706.
Prein, A. F., W. Langhans, G. Fosser, A. Ferrone, N. Ban, K. Goergen, M. Keller, M. Tölle, O. Gutjahr, and F. Feser. 2015. “A review on regional convection-permitting climate modeling: Demonstrations, prospects, and challenges.” Rev. Geophys. 53 (2): 323–361. https://doi.org/10.1002/2014RG000475.
Prein, A. F., C. Liu, K. Ikeda, S. B. Trier, R. M. Rasmussen, G. J. Holland, and M. P. Clark. 2017a. “Increased rainfall volume from future convective storms in the US.” Nat. Clim. Change 7 (12): 880–884. https://doi.org/10.1038/s41558-017-0007-7.
Prein, A. F., R. M. Rasmussen, K. Ikeda, C. Liu, M. P. Clark, and G. J. Holland. 2017b. “The future intensification of hourly precipitation extremes.” Nat. Clim. Change 7 (1): 48–52. https://doi.org/10.1038/nclimate3168.
Purr, C., E. Brisson, and B. Ahrens. 2019. “Convective shower characteristics simulated with the convection-permitting climate model COSMO-CLM.” Atmosphere 10 (12): 810. https://doi.org/10.3390/atmos10120810.
Ragno, E., A. AghaKouchak, C. A. Love, L. Cheng, F. Vahedifard, and C. H. R. Lima. 2018. “Quantifying changes in future intensity-duration-frequency curves using multimodel ensemble simulations.” Water Resour. Res. 54 (3): 1751–1764. https://doi.org/10.1002/2017WR021975.
Rajczak, J., and C. Schär. 2017. “Projections of future precipitation extremes over Europe: A multimodel assessment of climate simulations.” J. Geophys. Res.: Atmos. 122 (20): 10,773–710,800. https://doi.org/10.1002/2017JD027176.
Roe, G. H. 2005. “Orographic precipitation.” Annu. Rev. Earth Planet. Sci. 33 (1): 645–671. https://doi.org/10.1146/annurev.earth.33.092203.122541.
Sanderson, B. M., K. W. Oleson, W. G. Strand, F. Lehner, and B. C. O’Neill. 2018. “A new ensemble of GCM simulations to assess avoided impacts in a climate mitigation scenario.” Clim. Change 146 (3): 303–318. https://doi.org/10.1007/s10584-015-1567-z.
Sanford, T., P. C. Frumhoff, A. Luers, and J. Gulledge. 2014. “The climate policy narrative for a dangerously warming world.” Nat. Clim. Change 4 (3): 164–166. https://doi.org/10.1038/nclimate2148.
Schär, C., C. Frei, D. Lüthi, and H. C. Davies. 1996. “Surrogate climate-change scenarios for regional climate models.” Geophys. Res. Lett. 23 (6): 669–672. https://doi.org/10.1029/96GL00265.
Schardong, A., and S. P. Simonovic. 2019. “Application of regional climate models for updating intensity-duration-frequency curves under climate change.” Int. J. Environ. Clim. Change 9 (5): 311–330. https://doi.org/10.9734/ijecc/2019/v9i530117.
Schoof, J. T., and S. M. Robeson. 2016. “Projecting changes in regional temperature and precipitation extremes in the United States.” Weather Clim. Extremes 11 (Mar): 28–40. https://doi.org/10.1016/j.wace.2015.09.004.
Schulz, K., and M. Bernhardt. 2016. “The end of trend estimation for extreme floods under climate change?” Hydrol. Processes 30 (11): 1804–1808. https://doi.org/10.1002/hyp.10816.
Schwalm, C. R., S. Glendon, and P. B. Duffy. 2020. “RCP8.5 tracks cumulative CO2 emissions.” Proc. Natl. Acad. Sci. 117 (33): 19656–19657. https://doi.org/10.1073/pnas.2007117117.
Seneviratne, S. I., M. G. Donat, A. J. Pitman, R. Knutti, and R. L. Wilby. 2016. “Allowable CO2 emissions based on regional and impact-related climate targets.” Nature 529 (7587): 477–483. https://doi.org/10.1038/nature16542.
Seybert, T. A. 2006. Stormwater management for land development. New York: Wiley.
Sigmond, M., and J. C. Fyfe. 2016. “Tropical Pacific impacts on cooling North American winters.” Nature Clim. Change 6 (10): 970–974. https://doi.org/10.1038/nclimate3069.
Sillmann, J., V. V. Kharin, F. W. Zwiers, X. Zhang, and D. Bronaugh. 2013. “Climate extremes indices in the CMIP5 multimodel ensemble. Part 2: Future climate projections.” J. Geophys. Res.: Atmos. 118 (6): 2473–2493. https://doi.org/10.1002/jgrd.50188.
Srivastav, R. K., A. Schardong, and S. P. Simonovic. 2014. “Equidistance quantile matching method for updating IDF curves under climate change.” Water Resour. Manage. 28 (9): 2539–2562. https://doi.org/10.1007/s11269-014-0626-y.
Svenskt Vatten. 2011. Nederbördsdata vid dimensionering och analys av avloppssystem (Rain Data for Design and Analysis of Urban Drainage Systems). [In Swedish.]. Stockholm, Sweden: Svenskt Vatten.
Tabari, H. 2020. “Climate change impact on flood and extreme precipitation increases with water availability.” Sci. Rep. 10 (1): 13768. https://doi.org/10.1038/s41598-020-70816-2.
Tabari, H., R. De Troch, O. Giot, R. Hamdi, P. Termonia, S. Saeed, E. Brisson, N. Van Lipzig, and P. Willems. 2016. “Local impact analysis of climate change on precipitation extremes: Are high-resolution climate models needed for realistic simulations?” Hydrol. Earth Syst. Sci. 20 (9): 3843–3857. https://doi.org/10.5194/hess-20-3843-2016.
Thompson, D. W. J., E. A. Barnes, C. Deser, W. E. Foust, and A. S. Phillips. 2015. “Quantifying the role of internal climate variability in future climate trends.” J. Clim. 28 (16): 6443–6456. https://doi.org/10.1175/JCLI-D-14-00830.1.
Trenberth, K. E. 2011. “Changes in precipitation with climate change.” Clim. Res. 47 (1–2): 123–138. https://doi.org/10.3354/cr00953.
Trenberth, K. E., A. Dai, R. M. Rasmussen, and D. B. Parsons. 2003. “The changing character of precipitation.” Bull. Am. Meteorol. Soc. 84 (9): 1205–1218. https://doi.org/10.1175/BAMS-84-9-1205.
UK Department for Infrastructure. 2020. Design of highway drainage system. Belfast, UK: Dept. for Infrastructure.
Vanden Broucke, S., H. Wouters, M. Demuzere, and N. P. M. van Lipzig. 2019. “The influence of convection-permitting regional climate modeling on future projections of extreme precipitation: Dependency on topography and timescale.” Clim. Dyn. 52 (9): 5303–5324. https://doi.org/10.1007/s00382-018-4454-2.
Vergara-Temprado, J., N. Ban, and C. Schär. 2021. “Extreme sub-hourly precipitation intensities scale close to the Clausius-Clapeyron rate over Europe.” Geophys. Res. Lett. 48 (3): e2020GL089506. https://doi.org/10.1029/2020GL089506.
Wasko, C., S. Westra, R. Nathan, H. G. Orr, G. Villarini, R. Villalobos Herrera, and H. J. Fowler. 2021. “Incorporating climate change in flood estimation guidance.” Philos. Trans. R. Soc. A 379 (2195): 20190548. https://doi.org/10.1098/rsta.2019.0548.
Watt, E., and J. Marsalek. 2013. “Critical review of the evolution of the design storm event concept.” Can. J. Civ. Eng. 40 (2): 105–113. https://doi.org/10.1139/cjce-2011-0594.
Westra, S., L. V. Alexander, and F. W. Zwiers. 2013. “Global increasing trends in annual maximum daily precipitation.” J. Clim. 26 (11): 3904–3918. https://doi.org/10.1175/JCLI-D-12-00502.1.
Westra, S., H. J. Fowler, J. P. Evans, L. V. Alexander, P. Berg, F. Johnson, E. J. Kendon, G. Lenderink, and N. M. Roberts. 2014. “Future changes to the intensity and frequency of short-duration extreme rainfall.” Rev. Geophys. 52 (3): 522–555. https://doi.org/10.1002/2014RG000464.
Willems, P. 2011. “Revision of urban drainage design rules based on extrapolation of design rainfall statistics.” In Proc., 12th Int. Conf. on Urban Drainage. London: International Water Association.
Wood, R. R., and R. Ludwig. 2020. “Analyzing internal variability and forced response of subdaily and daily extreme precipitation over Europe.” Geophys. Res. Lett. 47 (17): e2020GL089300. https://doi.org/10.1029/2020GL089300.
Yan, L., L. Xiong, C. Jiang, M. Zhang, D. Wang, and C. Y. Xu. 2021. “Updating intensity–duration–frequency curves for urban infrastructure design under a changing environment.” WIREs Water 8 (3): e1519. https://doi.org/10.1002/wat2.1519.
Yilmaz, A. G., I. Hossain, and B. J. C. Perera. 2014. “Effect of climate change and variability on extreme rainfall intensity–frequency–duration relationships: A case study of Melbourne.” Hydrol. Earth Syst. Sci. 18 (10): 4065–4076. https://doi.org/10.5194/hess-18-4065-2014.
Zhang, X., F. W. Zwiers, G. Li, H. Wan, and A. J. Cannon. 2017. “Complexity in estimating past and future extreme short-duration rainfall.” Nat. Geosci. 10 (4): 255–259. https://doi.org/10.1038/ngeo2911.
Information & Authors
Information
Published In
Journal of Hydrologic Engineering
Volume 26 • Issue 10 • October 2021
Copyright
This work is made available under the terms of the Creative Commons Attribution 4.0 International license, https://creativecommons.org/licenses/by/4.0/.
History
Published online: Aug 3, 2021
Published in print: Oct 1, 2021
Discussion open until: Jan 3, 2022
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.
Cited by
- Mohamed M. Morsy, Binata Roy, Yawen Shen, Jeffrey M. Sadler, Alexander B. Chen, Faria T. Zahura, Jonathan L. Goodall, Quantifying the Impact of Climate Change on Peak Stream Discharge for Watersheds of Varying Sizes in the Coastal Plain of Virginia, Journal of Hydrologic Engineering, 10.1061/JHYEFF.HEENG-6114, 29, 3, (2024).
- Amelia Peeples, Xiaoyu Li, Matthew Garcia, Philip Bedient, Flood Mitigation Using Channelization and Detention Given Changing Rainfall Conditions in Houston, Natural Hazards Review, 10.1061/NHREFO.NHENG-1861, 24, 4, (2023).
- Biruk Tagesse Lambe, Subrahmanya Kundapura, Recent Changes in Hydrometeorological Extremes in the Bilate River Basin of Rift Valley, Ethiopia, Journal of Hydrologic Engineering, 10.1061/JHYEFF.HEENG-5853, 28, 7, (2023).
- Vincent Crévolin, Elmira Hassanzadeh, Sarah-Claude Bourdeau-Goulet, Updating the intensity-duration-frequency curves in major Canadian cities under changing climate using CMIP5 and CMIP6 model projections, Sustainable Cities and Society, 10.1016/j.scs.2023.104473, 92, (104473), (2023).
- Angel Intriago, Paulo Galvão, Bruno Conicelli, Use of GIS and R to estimate climate change impacts on groundwater recharge in Portoviejo River watershed, Ecuador, Journal of South American Earth Sciences, 10.1016/j.jsames.2023.104288, 124, (104288), (2023).
- Baoxu Zhao, Taihua Wang, Dawen Yang, Shuyu Yang, Weiwei Lu, Jerasorn Santisirisomboon, The impacts of climatic and land surface characteristics on the storm-flood relationship in a tropical monsoon basin of Thailand, Journal of Hydrology, 10.1016/j.jhydrol.2022.128809, 616, (128809), (2023).
- Katherine E. Schlef, Kenneth E. Kunkel, Casey Brown, Yonas Demissie, Dennis P. Lettenmaier, Anna Wagner, Mark S. Wigmosta, Thomas R. Karl, David R. Easterling, Kimberly J. Wang, Baptiste François, Eugene Yan, Incorporating non-stationarity from climate change into rainfall frequency and intensity-duration-frequency (IDF) curves, Journal of Hydrology, 10.1016/j.jhydrol.2022.128757, 616, (128757), (2023).
- Sabrina Lanciotti, Elena Ridolfi, Fabio Russo, Francesco Napolitano, Intensity–Duration–Frequency Curves in a Data-Rich Era: A Review, Water, 10.3390/w14223705, 14, 22, (3705), (2022).
- Andrea Abbate, Monica Papini, Laura Longoni, Orographic Precipitation Extremes: An Application of LUME (Linear Upslope Model Extension) over the Alps and Apennines in Italy, Water, 10.3390/w14142218, 14, 14, (2218), (2022).
- Alcely Lau, Ali Behrangi, Understanding Intensity–Duration–Frequency (IDF) Curves Using IMERG Sub-Hourly Precipitation against Dense Gauge Networks, Remote Sensing, 10.3390/rs14195032, 14, 19, (5032), (2022).
- See more