Deterministic storm transposition has been a longstanding component of probable maximum precipitation estimation, while stochastic storm transposition (SST) is gaining interest as a probabilistic flood hazard methodology. One example is within the Federal Emergency Management Agency’s (FEMA) National Flood Insurance Program (NFIP) to modernize methodologies for estimating flood hazard and risk throughout the United States using SST. A key challenge in the widespread and systematic application of SST is objectively delineating transposition domains, geographic regions over which rainfall is roughly homogeneous and can be transposed to accurately represent a watershed’s extreme precipitation frequency and intensity characteristics. This study introduces the spatial L-moments of annual maxima (SLAM) procedure, a novel hypothesis testing approach for defining transposition domains. SLAM assesses the similarity of L-moment statistics (L-mean, L-Coefficient of variation, L-skewness, and L-kurtosis) between a watershed’s mean annual rainfall maxima and those at transposition locations within a proposed domain. SLAM generates statistically backed homogeneous domains around each of the pilot watersheds. We found that SST-based rainfall frequency estimates using these “SLAM domains” align closely with empirical precipitation return periods for three of the four pilot watersheds, and that the domains are large enough to support the estimation of long return period rainfall. The sole exception is an NFIP pilot watershed that exhibits substantial precipitation variability due to orography. In that case, SLAM results can be interpreted as evidence that SST procedures must be adapted to be used effectively there. These results highlight the potential of SLAM to improve SST-based extreme rainfall and flood hazard assessment and to support FEMA.

Understanding how the space‐time properties of extreme rainfall evolve in response to temperature changes is essential for assessing water‐related hazard risks. However, future sub‐daily rainfall fields, which are the primary trigger of flash floods, are not readily available across locations and warming scenarios due to the high computational cost of convection‐permitting climate models. As an alternative, we develop a Gamma‐based Spatial Quantile Mapping (GSQM) method that uses air temperature as a covariate to morph observed rainfall fields and project an archive of plausible future rainfall fields. When combined with the Stochastic Storm Transposition (SST) method, which estimates rainfall frequency at arbitrary spatial scales using gridded rainfall data, the GSQM‐SST framework provides a computationally efficient approach to project future changes in regional rainfall extremes. Using Beijing as a case study, we employ 22 years of 1‐km hourly rainfall and temperature data to demonstrate this approach. First, the observed scalings governing changes in rainfall fields with temperature are identified across various rainfall intensities. These scalings are then used to morph rainfall intensity, spatial extent, and heterogeneity. Finally, future rainfall extremes for 2‐ to 100‐year return levels under several regional warming levels are estimated by integrating the GSQM and SST methods. Results indicate that hourly extreme rainfall events tend to intensify while becoming more spatially concentrated, resulting in a 4% increase in return levels under 1C warming in Beijing. The GSQM‐SST approach provides a flexible and physically informed method to project future sub‐daily rainfall extremes, with potential application in regional flood risk assessment.

Conventional probable maximum precipitation (PMP) methods face several limitations, including uncertainty in estimating the upper bound, subjectivity in storm maximization, and the assumption of a stationary climate. To address these limitations, we propose a new approach that estimates PMP with specific annual exceedance probabilities (AEPs) across varying climate periods. Our approach integrates a stochastic rainfall generator (StormLab) with a novel nonstationary generalized extreme value (GEV) model and is applied to the upper Red River basin in the south-central United States. StormLab simulated 10 000 years of high-resolution (6 h, 0.03°) precipitation fields from 1901 to 2100, driven by 50 ensembles of a global climate model (GCM). We then fitted the GEV model to the simulated precipitation annual maxima to derive nonstationary PMP estimates. StormLab was further coupled with a hydrologic model estimate probable maximum flood (PMF) in major tributaries. Our approach estimates PMP for areas ranging from 10 to 20 000 mi 2 and durations from 6 to 360 h. Results show a 15%–25% increase in PMP (with an AEP of 10 −4 ) from 2020 to 2100 across various spatial and temporal scales. Higher increases of 35% and 36% are projected for PMF in two major tributaries with the same AEP. This study underscores the value of using stochastic rainfall models and GCM large ensembles to inform PMP and PMF analysis in a changing climate.

2-D rainfall fields play a critical role in assessing urban flood impacts and planning drainage systems. High-resolution rainfall fields, obtained from remote sensing devices such as weather radar and satellites, are not largely available and are even more limited for rainfall and flood frequency applications. One method that can be used to estimate extreme rainfall frequency—even with limited data—is Stochastic Storm Transposition (SST), which transposes observed rainfall fields within a region. In the context of climate change, there is a need to alter the observed rainfall fields to account for nonstationary changes in storm intensity and structure. Here, we suggest using Spatial Quantile Mapping (SQM) to modify the intensities and structures of rainfall fields with temperature as a covariate to generate an archive of plausible rainfall fields, which can then be used within SST as an input to assess changes in rainfall and floods. We take Beijing city as a case study, employing 22 years of 1 km hourly downscaled rainfall from CMORPH and near-surface air temperature data from ERA5, to demonstrate the effectiveness of this approach. Initially, SST is run under the current climate and validated for the 2- to 100-year rainfall return levels compared with those of 21 stations within Beijing city. Subsequently, according to the observed relationships between hourly rainfall and temperature, the rainfall fields are modified by the SQM method to fit future temperature conditions. Ultimately, the future extreme rainfall intensities, ranging from 2- to 100-year return levels, are obtained through the integration of the SST and SQM methods. The results indicate that the combined SST-SQM approach can be efficiently used to estimate future rainfall extremes in a changing climate.

The EPA's Storm Water Management Model (SWMM) has been applied across the globe for citywide stormwater modeling due to its robustness and versatility. Recent research indicated that SWMM, with proper setup, can be applied in the description of more dynamic flow conditions, such as rapid inflow conditions. However, stormwater systems often have geometric discontinuities that can pose challenges to SWMM model accuracy, and this issue is poorly explored in the current literature. The present work evaluates the performance of SWMM 5 in the context of a real-world stormwater tunnel with a geometric discontinuity. Various combinations of spatiotemporal discretization are systematically evaluated along with four pressurization algorithms, and results are benchmarked with another hydraulic model using tunnel inflow simulations. Results indicated that the pressurization algorithm has an important effect on SWMM's accuracy in conditions of sudden diameter changes. From the tested pressurization algorithms, the original Preissmann slot algorithm was the option that yielded more representative results for a wider range of spatiotemporal discretizations. Regarding spatiotemporal discretization options, intermediate discretization, and time steps that lead to Courant numbers equal to one performed best. Interestingly, the traditional SWMM's link-node approach also presented numerical instabilities despite having low continuity errors. Results indicated that although SWMM can be effective in simulating rapid inflow conditions in tunnels, situations with drastic geometric changes need to be carefully evaluated so that modeling results are representative.

Although major floods in the Lower Mississippi River Basin (LMRB) are primarily driven by clusters of extreme storms rather than isolated events, the role of storm clustering in LMRB flood hazard remains underexplored. We show that floods driven by compound space-time storm clustering have higher peaks, volumes, and durations compared to those from isolated storms or single-mode clustering. Under future climate conditions (2070 to 2100; SSP3-7.0 scenario), projections indicate a 26% rise in the number of extreme storms, an 8% increase in average storm precipitation, and a 17% reduction in dry intervals preceding flood peaks. The dominant flood type is projected to shift from those produced by isolated storms (38%) to ones produced by compound storm clustering (49%)-the type of floods that have been responsible for catastrophic floods historically. These findings highlight the need to incorporate storm clustering and its changes into design flood analysis and management strategies for the LMRB.

Understanding the changing flood magnitude and frequency is crucial for managing flood risks. Current flood projections are hindered by uncertain rainfall changes and inadequate representation of spatial-temporal rainfall characteristics from global climate models. Here we develop a framework for flood projection integrating large ensemble climate model outputs and dynamic downscaling simulations. By unveiling the shifting importance of changes in atmospheric circulation and water vapor to rainfall extremes of varying magnitudes, a scaling ratio is derived to constrain rainfall projections from thermodynamics-perturbed experiments. Applying this framework to the North China Plain, we reveal substantial rainfall increases partially attributed to more favorable synoptic conditions. This leads to a nearly fivefold rise in population exposure to moderate and high flood depths (i.e., 0.5 similar to 2 m). The region with heightened flood risk is spatially offset from that with rainfall increases. These findings highlight an urgency for improved flood risk management in the densely populated region.

In recent years, numerous flood events have caused loss of life, widespread disruption, and damage across the globe. These devastating impacts highlight the importance of a better understanding of flood generating processes, their impacts, and their variability under climate and landscape changes. Here, we argue that the ability to better model flooding is underpinned by the grand challenge of understanding flood generation mechanisms and potential impacts. To address this challenge, the World Meteorological Organization-Global Energy and Water Exchanges (GEWEX) Hydrometeorology Panel (GHP) aims to establish a Global Flood Crosscutting project to propagate flood modeling and research knowledge across regions and to synthesize results at the global scale. This paper outlines a framework for understanding the dynamics and impacts of runoff generation processes and a rationale for the role of a Global Flood Crosscutting project to address these challenges. Within this Global Flood Crosscutting project, we will establish a common terminology and methods to enable the global research community to exchange knowledge and experiences, and to design experiments toward developing actionable recommendations for more effective flood management practices and policies for improved resilience. This harmonization of rich perspectives across disciplines will foster the co-production of knowledge primed to advance flood research, particularly in the current period of heightened climate variability and rapid change. It will create a new transdisciplinary paradigm for flood science, wherein different dimensions of mechanistic understanding and processes are rigorously considered alongside socioeconomic impacts, early warning communications, and longer-term adaptation to alleviate flood risks in society.

Urban expansion and the increasing frequency and intensity of extreme precipitation events bring new challenges to stormwater collection systems. One underrecognized issue is the occurrence of transient flow conditions that lead to adverse multiphase flow interactions (AMFI): essentially, the formation, collapse, and uncontrolled release of air pockets within stormwater system flows. While the fundamental physics of AMFI have been evaluated in laboratory experiments and idealized modeling studies, much less is known about their development in real or simulated stormwater networks, and about the roles played by rainfall and network properties. A necessary precursor to AMFI is the development of pressurized flow conditions within a network. The goal of this study is to understand how spatiotemporal rainfall variability affects the occurrence of pressurized conditions in a stormwater drainage network in the Richmond district of San Francisco, California. High-resolution bias-corrected radar rainfall fields for 24 recent storms were used as the independent variable of EPA-SWMM simulations. Model analyses indicate that the incidence of pressurized flow increases with storm intensity, and is more sensitive to rainfall temporal variability than spatial variability. This research provides a reference for analyzing AMFI precursors in other networks and may have important implications for the improvement of stromwater infrastructures.

Accurate flood early warnings are critical to minimize damage and loss of life. Current large‐scale operational forecasting systems, however, have limited accuracy, description of uncertainty, and computational efficiency. While Artificial intelligence (AI) can address these limitations in principle, the accuracy and reliability of AI forecasts have thus far proven insufficient. Here we present a novel hybrid framework that integrates AI‐based machinery termed Errorcastnet (ECN) with the National Water Model (NWM) to showcase the potential of ensemble AI flood forecasts over the contiguous U.S. ECN boosts prediction accuracy four‐ to six‐fold across lead times of 1–10 days, while providing uncertainty quantification. It also outperforms Google's state‐of‐the‐art global AI model. ECN‐based forecasts offer superior economic value (up to four‐fold) for decision‐making as compared to those from NWM alone. ECN performs well in varied ecoregions, physiography, and land management conditions. The framework is computationally efficient, enabling national‐scale ensemble forecasts in minutes.

Urbanization substantially modifies surface water and energy cycles. Compared to natural vegetation, paved urban surfaces produce more runoff, trap more heat, and lower evapotranspiration. At the same time, increased heatwaves and rainfall due to climate change are amplified in urban areas due to feedbacks between cities and meteorological processes. Land surface models, the part of atmospheric models tasked with modeling the earth’s surface and hydrology, lack the fine-scale, ecohydrologic process representation in cities to capture important feedbacks between urbanization, hydrology, and near surface energy partitioning. For example, tree cover that shades pavement and enhances evapotranspiration is ubiquitous amongst many cities worldwide, but contemporary land surface models cannot allow for tree canopy to extend over pavements. Further, lateral transfers of surface water from impervious to permeable surfaces are critical for runoff reduction, like routing of rainfall to natural vegetation, but are similarly not represented. Lack of ecohydrologic processes is problematic because we are unable to predict the impact of increasingly common greening initiatives that feature both nature-based solutions, like increased tree cover, and green infrastructure practices, like permeable pavements and green roofs. These practices are targeted to reduce runoff and urban heat, but will likely modify other urban atmospheric processes like rainfall in unknown ways. Unfortunately, potential connections between urban greening initiatives and resulting changes to the urban climate have not been explored rigorously at city scale. In this project, we use Noah-MP for Heterogenous Urban Environments (HUE), a new land surface model capable of resolving fine-scale ecohydrologic processes like urban tree cover shading pavements and routing of surface water to permeable surfaces with multiple landcover types per grid cell (e.g. a mosaicking scheme) in urban spaces. We use HUE to examine the impact of widespread climate adaptation policy in multi-year WRF regional climate simulations centered on the coastal city of Milwaukee, Wisconsin, USA at convective permitting scales. Different landcover configurations that represent cases of city-wide greening are interpreted from an ambitious real-world regional urban greening “master plan.” We show that more greening leads to a reduction of runoff throughout the warm season, although partitioning of runoff reduction between evapotranspiration and deep drainage varies year to year. We also examine how changes in sensible and latent heat fluxes affect near surface meteorology within the city, generally increasing humidity and decreasing air temperatures. These differences are especially apparent during days of strong lake-breeze coupling between Milwaukee and nearby Lake Michigan. We further show that urban greening leads changes in rainfall event totals, peak intensities, and seasonal averages. While only for a single city, our results highlight that widespread urban greening changes not only urban hydrology but also urban hydrometeorology. This highlights that the evaluation of urban greening initiatives worldwide is critical for climate change adaptation and mitigation.

Urban regions substantially modify both surface energy and hydrologic cycles. Despite the linkage between urban hydrology and energy cycles, modern coupled land-atmosphere models do not represent common urban hydrologic features like runoff routing from impervious to pervious surfaces or tree canopy that shades pavements. We compare three urban surface models in the Weather and Research Forecasting model across multiple hydrometeorological events in Milwaukee, Wisconsin: a widely used slab urban model (Noah-MP Bulk Parameterization; Typical), multiple land covers per grid cell (Noah-MP Mosaic; Mosaic), and a model which adds sub-grid water transfers between land cover types (Noah-MP HUE; HUE). Inclusion of urban hydrology and vegetation (HUE) increases albedo and emissivity, reducing available energy in our study region. Ambient soil moisture conditions cause divergent responses in HUE simulations: warming when soil water is limited and cooling when ample soil water is available for evapotranspiration. Comparison against observed 2m air temperature and specific humidity show increased skill in the HUE model simulations, especially compared to the Typical model. Noah-MP HUE presents a stride in understanding how urban hydrology influences city-scale meteorology and a pathway to examine urban hydrologic greening initiatives more wholistically in regional atmospheric models.

Satellite‐based precipitation observations can provide near‐global coverage with high spatiotemporal resolution in near‐realtime. Their utility, however, is hindered by oftentimes large uncertainties that vary substantially in space and time. This problem is particularly pronounced in regions which lack dense ground‐based measurements to quantify or reduce such uncertainty. Since this uncertainty is, by definition, a random process, probabilistic representations are needed to advance their operational application. Ensemble methods, in which uncertainty is depicted via multiple realizations of precipitation fields, have been widely used in numerical weather and climate prediction, but rarely in satellite contexts. Creating such an ensemble dataset is challenging due to the complexity of observational uncertainties and the scarcity of “ground truth” to characterize them. In this study, we attempt to resolve these two challenges and propose the first quasi‐global (covering all continental land masses within 50°N‐50°S) satellite‐only ensemble precipitation dataset (STREAM‐Sat), derived entirely from NASA's Integrated Multi‐SatellitE Retrievals for Global Precipitation Measurement (IMERG) and GPM's radar‐radiometer combined precipitation product (2B‐CMB). No ground‐based measurements are used to generate STREAM‐Sat, and it is suitable for near‐realtime use without extending the 4‐hr latency and 0.1°, 30‐min spatiotemporal resolution of IMERG Early. We compare STREAM‐Sat against several precipitation datasets, including global satellite‐based, rain gage‐based, atmospheric reanalysis, and merged products. While our proposed approach faces some limitations and is not universally superior to the comparison datasets in all respects, it does hold relative advantages due to its unique combination of accuracy, resolution, rainfall spatiotemporal structure, latency, and utility in hydrologic and hazard applications.

Coastal infrastructure, such as roadways, is particularly vulnerable to water stressors. An excellent example of such a vulnerable system is the State Route AL-180, located in Fort Morgan Peninsula, Alabama. While much attention has been devoted to studying the effects of extreme hydrological stressors on roadways, the effects of moderate rain events, tides, and elevated groundwater levels (GWLs) on the saturation of pavements in low-lying areas have been mostly ignored. GWL predictions typically apply analytical, numerical, or data-intensive empirical formulations. Typically, data-intensive formulations can be computationally intensive and involve numerous assumptions. In this effort, we propose a relatively simpler new data-driven methodology for groundwater level estimation in coastal roadways. The method is used to evaluate the impacts of pavement exposure to elevated shallow GWL for the AL-180 highway. The modeling framework is based on GWL response, which is influenced by rainfall, distance to tidal water bodies, tidal level fluctuations, and groundwater storage characteristics. Water level data collected from two shallow wells along AL-180 were used to calibrate the proposed model, and a third well dataset was used for validation. Results show that the Nash-Sutcliffe efficiency (NSE) varied from 0.65 to 0.73 when the first two locations were considered, and for the validation location, the NSE was 0.56. The projected GWL data also indicated sections of AL-180 may be fully saturated 100 % of the time because of sea level rise. The proposed modeling framework is a good tool to guide decision-making processes for coastal roadway management. However, the results are constrained by simplified assumptions, such as uniform soil characteristics, no lateral flows, and limited spatial validation.

Existing stochastic rainfall generators (SRGs) are typically limited to relatively small domains due to spatial stationarity assumptions, hindering their usefulness for flood studies in large basins. This study proposes StormLab, an SRG that simulates precipitation events at 6-hr and 0.03 degrees resolution in the Mississippi River Basin (MRB). The model focuses on winter and spring storms caused by water vapor transport from the Gulf of Mexico-the key flood-generating storm type in the basin. The model generates anisotropic spatiotemporal noise fields that replicate local precipitation structures from observed data. The noise is transformed into precipitation through parametric distributions conditioned on large-scale atmospheric fields from a climate model, reflecting spatial and temporal nonstationarity. StormLab can produce multiple realizations that reflect the uncertainty in fine-scale precipitation arising from a specific large-scale atmospheric environment. Model parameters were fitted monthly from December-May, based on storms identified from 1979 to 2021 ERA5 reanalysis data and Analysis of Record for Calibration (AORC) precipitation. StormLab then generated 1,000 synthetic years of precipitation events based on 10 CESM2 ensemble simulations. Empirical return levels of simulated annual maxima agree well with AORC data and show an overall increase in 1- to 500-year events in the future period (2022-2050). To our knowledge, this is the first SRG simulating nonstationary, anisotropic high-resolution precipitation over continental-scale river basins, demonstrating the value of conditioning such stochastic models on large-scale atmospheric variables. StormLab provides a wide range of extreme precipitation scenarios for design floods in the MRB and can be further extended to other large river basins.

In this study, we present CON-SST-RAIN, a novel stochastic space–time rainfall generator specialized for model-based urban drainage design and planning. CON-SST-RAIN is based on Markov Chains for sequences of dry/rainy days and uses stochastic storm transposition (SST) to generate realistic rainfall fields from weather radar data. CON-SST-RAIN generates continuous areal rainfall time series at arbitrary lengths. We propose a method for updating the Markov Chains by each passing year to better incorporate low-frequency variation in inter-annual rainfall values. The performance of CON-SST-RAIN is tested against multi-year records from rain gauges at both point and catchment scales. We find that updating the Markov Chains has a significant impact on the inter-annual variation of rainfall, but has little effect on mean annual/seasonal precipitation and dry/wet spell lengths. CON-SST-RAIN shows good preservation of extreme rain rates (including sub-hourly values) compared to observed rain gauge data and the original SST framework.

The state of Iowa in the Central United States has experienced increasing flooding, with major events occurring most recently in 1993, 2008, 2011, and 2019. These floods caused over $23B in damage despite Iowa's three flood control reservoirs and expansive levee systems, suggesting the need for additional solutions. Iowa is home to over 4,000 small dams whose cumulative capacity more than double the state's current flood storage. These locations are operated passively, i.e., without the use of gated outlets to control basin storage utilization, thus limiting their flood mitigating potential. Here, the authors simulate gated outlets at 130 small dams within a 660 km2 watershed to (1) evaluate how effectively these storages can be activated across a watershed using gated outlets; and (2) quantify the utilization capacity of an activated distributed storage system for flow reduction. The authors used stochastic storm transposition to generate thousands of spatially variable rainfall events using Stage IV rainfall data within the Iowa domain at durations of 6, 12, 24, and 48 h and annual exceedance probabilities (AEPs) of 0.2, 0.1, 0.02, and 0.01. This expands the effective period of record, providing storms of various durations, intensities, and spatiotemporal distributions. An active management scheme was defined within the reservoir module of the hillslope link model designed to store water within the ponding locations. The study calculated the flow reductions that were achieved through this active scheme and found that flows were reduced for every rainfall duration and probability regardless of basin spatial scale. Reductions reached as high as 70% for a 6 h, 0.2 AEP event at a 93 km2 drainage area, while flows were reduced by roughly 12% for a 48 h, 0.01 AEP event at the basin outlet. This work establishes activated distributed storage as a meaningful flood reduction measure under realistic rainfall conditions at a variety of spatial scales.

Intense rain events and sprawling urbanization have contributed to more frequent flash flooding in cities, often due to the pressurization of drainage systems. Stormwater collection networks (SCNs) can become pressurized if their conveyance capacity is exceeded, leading to on-street flooding through backflow out of curb inlets. Due to the complexity of SCN geometry and spatiotemporal rainfall variability, studies evaluating pressurization in stormwater systems have previously been conducted for relatively simple geometries and inflow conditions. Thus, to date there have been few network-scale insights into how pressurization develops, making it difficult to understand drivers that influence pressurization: slope, roughness, connectivity, and inflow rate. The present work evaluates the process of SCN pressurization using numerical modeling through a systematic variation of these variables. Herein, three distinct pressurization mechanisms were identified by using EPA SWMM 5.1 to model idealized SCN topology and junction inflows. New nondimensional flow indexes (NDFIs) are proposed to characterize the pressurization conditions after an initially empty stormwater system reaches steady state under application of hydrographs. This study provides a basis for further systematic evaluation of factors influencing drainage system pressurization, guiding future actions to mitigate urban flash flooding.

The introduction of iterative ensemble smoothers (IES) for parameter calibration opens avenues for expanding parameter space in surface water hydrologic modeling. Here, we have introduced independent parameters into a model calibration experiment to estimate errors in rainfall forcing data. This approach has the potential to estimate rainfall errors using other hydrological observations and to improve model calibration. Using high-resolution rain gauge data, we estimated “real” rainfall errors across the Turkey River watershed at storm and daily scales. Tests on synthetic and real-world scenarios successfully estimated errors correlated with observed values – even at daily scales. However, a bias remained from model parameter compensation, and identifying errors was challenging for low precipitation and snowfall. Despite synthetic results showing good error correlation, the biases in parameter identification masked potential improvements in hydrological calibration. This study highlights the potential of IES to provide additional information on rainfall errors, even only using streamflow observations.

Hydrologists and civil engineers often use design storms to assess flash flood hazards in urban, rural, and mountainous catchments. These synthetic storms are not representations of real extreme rainfall events, but rather simplified versions parameterized to mimic extreme precipitation statistics often obtained from intensity-duration-frequency (IDF) curves. To construct design storms for the future climate, it is thus necessary first to recalculate IDF curves to represent rainfall under warmer conditions. We propose a framework for adjusting IDF curves and design storms to future climate conditions using the TENAX model, a novel statistical approach that can provide future short-duration precipitation return levels based on projected temperature changes. For most applications, information from climate models at the daily scale can be used to construct design storms at the sub-hourly scale without any downscaling or bias adjustment. Our approach is illustrated through a re-parameterization of the Chicago Design Storm (CDS) in the context of climate change. As a case study demonstration, we apply the TENAX model to data from the city of Zurich to calculate changes in the historical IDF curve for durations ranging from 10 min to 3 h. We then construct synthetic 100-year return period design storms based on the CDS for present and future climates and use the CAFlood model to produce flood inundation maps to assess changes in flood hazard. The codes for adapting design storms to climate change are simple to implement, easily applicable by practitioners, and made freely available.