Stochastic Storm Transposition Research Articles

Advancing process-based flood frequency analysis for assessing flood hazard and population flood exposure

Recent studies have showcased the use of process-based hydrological models with Stochastic Storm Transposition (SST) techniques to conduct Flood Frequency Analysis (FFA). This framework, referred hereby FFA-SST, has proved to be a robust strategy to estimate peak flows of specific annual exceedance probability (e.g., 100-year peak flow) that can reflect natural and anthropogenic disturbances, including changes in land use and meteorological patterns. With the objective of advancing the FFA-SST framework, this study presents for the first time the use of a spatially-resolved Integrated Surface-Subsurface Hydrological Model (ISSHM) to conduct FFA-SST. This allows us to extend the analysis from peak flow responses to flood extent, enabling a unique view and analysis of flood hazard and population flood exposure at the basin scale. As a proof-of-concept, we used the ISSHM, Amanzi–ATS, and the SST model, RainyDay, to conduct FFA-SST by simulating the flood response to 5000 annual synthetic storm events in a ∼2000km2 Southeast Texas watershed. We demonstrate that ATS, without site-specific calibration, provides a robust process-based representation of peak flows, flood extent, streamflow, evapotranspiration, soil moisture content, and water storage changes. Our results and analyses, covering frequency curves up to a 500-year return period for peak flows, basin inundation fractions, and the number of people exposed to flooding, offer a unique perspective to analyze flood impacts across spatial scales. Overall, this study provides critical insights for flood risk management by extending the FFA-SST framework to include both flood hazard and population flood exposure analyses at the basin scale. Such an approach will empower stakeholders and disaster emergency agencies with a more comprehensive understanding of flood impacts across the entire basin domain, facilitating informed decision-making for flood risk assessment and management.

Investigating Utilization of Activated Distributed Storage Networks for Peak Flow Reduction Using Stochastic Storm Transposition

Investigating Utilization of Activated Distributed Storage Networks for Peak Flow Reduction Using Stochastic Storm Transposition

CON-SST-RAIN: Continuous Stochastic Space–Time Rainfall generation based on Markov chains and transposition of weather radar data

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 Influence of Typhoon Events on the Design Storm for the Shanghai Metropolitan Area in the Yangtze River Delta, China

Given the fact that the high frequency of extreme weather events globally, in particular typhoons, has more of an influence on flood forecasting, there is a great need to further understand the impact of typhoon events on design storms. The main objectives of this paper are to examine the magnitude, occurrence, and mechanism of typhoon events in southeast coastal China and their contribution to the design storm study. We take Shanghai, which is a typical metropolitan region in the Yangtze River Delta, China, as an example. The impact of typhoons on the rainfall frequency analysis is quantitatively evaluated using stochastic storm transposition (SST)-based intensity–duration–frequency (IDF) estimates with various temporal and spatial structures under different return periods. The results show that there is significant variability in the storm magnitude within the transposition domain across different durations, highlighting the spatiotemporal heterogeneity over the coastal area. Moreover, the probability of random storm transposition exhibits an uneven distribution. The frequency of typhoon rainfall events within the transposition domain is notably high, and there is considerable variability in the structure of rainfall. Typhoon rainfall amplifies the intensity of design storms, and its contribution increases with return periods. The variability in design storms increases accordingly. Based on the advantages of SST, which retains the spatiotemporal structure of the rainfall in the generated scenarios, the overall framework provides an effective way to examine the impact of diverse characteristics of typhoon rainfall on frequency analysis and facilitate a deeper exploration of the direct impact of various types of extreme storms on the intensity, spatial, and temporal distributions of design storms amidst evolving environmental conditions over this metropolitan region.

Impacts of temporal/spatial rainfall heterogeneities on peak runoff distribution and intensities for an urban river basin of south China

AbstractIn this research, a modeling approach of rainfall generator coupled with high resolution rainfall products were proposed to generate designed rainfall events under multiple spatial and temporal distributions, which was then employed to analyze the impacts of spatial and temporal rainfall heterogeneities on peak runoff for watersheds. Three scenarios were developed under multiple degrees of impermeable underlying surface areas within an urban watershed in south China. Detailed runoff processes were analyzed through the adoption of a distributed hydrological model (GSSHA). A covariance analysis method combined with rainfall spatio‐temporal heterogeneity characteristic were used to quantify heterogeneity effects on peak runoff. Results indicated that coupling short period (2008–2016) remotely rainfall data and RainyDay results could successfully reproduce designed rainfall events, spatio‐temporal heterogeneity of rainfall contributed significantly to the peak runoff, which was greater than those by rainfall duration and capacity, and the increase in impermeable underlying surface enhanced the complexities of the effects. Over each rainfall duration with increasing rainfall return period, the indicator of rainfall peak coefficient (RWD) would decrease and then increase. Regarding the total rainfall center (tg), 25 mm/h threshold rainfall spatial coverage (A25) decreased with increasing imperviousness, 1‐h maximum rainfall (Rmax) surged with increasing imperviousness at rainfall duration of 2 and 24 h. Innovations of this research lied in: combination of a rainfall generator model based on a stochastic storm transposition technique and remote‐sensing rainfall data to generate designed rainfall events, a rainfall spatial and temporal heterogeneities index system was developed to reveal how the changing characteristics of rainfall distribution and the impacts on peak runoff, and in‐depth analysis of the impacts on runoff peak under multiple urban development scenarios for increasing capability in flood control/prevention.

Using a physics-based hydrological model and storm transposition to investigate machine-learning algorithms for streamflow prediction

Machine learning (ML) algorithms have produced remarkable advances in streamflow prediction, exceeding the performance of calibrated conceptual and physics-based hydrological models that have been developed over many decades. ML algorithms seem to overcome the issue of errors known to be present in rainfall and streamflow estimates that have hindered the performance of hydrological models for decades. In this paper, we propose a methodology for testing and benchmarking ML algorithms using artificial data generated by physically-based hydrological models. Our approach makes it possible to design controlled numerical experiments that can improve our understanding of this new generation of black-box models. We conducted a diagnostics study to demonstrate our methodology in which we attempted to determine if ML algorithms can identify a function relating streamflow and rainfall. This exercise combined the implementation of the distributed hillslope-link hydrological model (HLM) on a 4,385 km2 basin driven by precipitation fields created using the stochastic storm transposition (SST) framework, and an advanced deep learning algorithm based on gated recurrent unit (GRU)-Attention neural networks. The data generated allowed us to create prediction scenarios that are equivalent to the hindcast and real-time forecast problems. We proposed a set of scale-independent performance metrics to evaluate the results of our experiment and found that the GRU can correctly identify a predictive function for all analyzed locations in the river network. We concluded that under the circumstances tested in this study, deep learning can identify the transformation function when trained in Hindcast Mode, making it a powerful tool to determine the streamflow response of a basin to predetermined rainfall scenarios. However, it fails to significantly outperform the predictions of temporal persistence when tested in Forecast Mode.

Sub-Hourly to Daily Rainfall Intensity-Duration-Frequency Estimation Using Stochastic Storm Transposition and Discontinuous Radar Data

Frequency analysis of rainfall data is essential in the design and modelling of hydrological systems but is often statistically limited by the total observation period. With advances in weather radar technology, frequency analysis of areal rainfall data is possible at a higher spatial resolution. Still, the observation periods are short relative to established rain gauge networks. A stochastic framework, “stochastic storm transposition” shows great promise in recreating rainfall statistics from radar rainfall products, similar to rain gauge-derived statistics. This study estimates intensity–duration–frequency (IDF) relationships at both point and urban catchment scales. We use the stochastic storm transposition framework and a single high-resolution, 17-year long (however, discontinuous), radar rainfall dataset. The IDF relations are directly compared to rain gauge statistics with more than 40 years of observation, and rainfall extremes derived from the original, and untransposed, radar dataset. An overall agreement is discovered, however, with some discrepancies in short-duration storms due to scaling errors between gauge and radar.

Bivariate rainfall frequency analysis in an urban Watershed: Combining copula theory with stochastic storm transposition

Extreme rainfall is a critical “agent” driving flash floods in urban areas. In rainfall frequency analysis (RFA), however, storms are usually assumed to be uniform in space and fixed in time. Spatially and temporally uniform design storms and area reduction factors are oftentimes used in conjunction with RFA results in engineering practice for infrastructure design and planning. The consequences of such assumptions are poorly understood. This study examines how spatiotemporal rainfall heterogeneity impacts RFA, using a newly-introduced bivariate framework consisting of copula theory and stochastic storm transposition (SST). A large number of regionally-extreme storms with specific features—rainfall depth, duration, intensity, and level of intra-storm spatial organization—were collected. Rainfall intensity–durationfrequency (IDF) estimates exhibiting these bivariate features were then generated by synthesizing long records of rainfall via SST. The results show that dependencies exist among spatiotemporal storm characteristics. Bivariate frequency results exhibit smaller uncertainties but more complex physical meanings that the results from conventional methods. In particular, the highly spatially-organized storms play a leading role in frequency estimates.

The impact of the spatiotemporal structure of rainfall on flood frequency over a small urban watershed: an approach coupling stochastic storm transposition and hydrologic modeling

Abstract. The role of rainfall space–time structure, as well as its complex interactions with land surface properties, in flood response remains an open research issue. This study contributes to this understanding, specifically for small (<15 km2) urban watersheds. Using a flood frequency analysis framework that combines stochastic storm transposition (SST)-based rainfall scenarios with the physically based distributed Gridded Surface Subsurface Hydrologic Analysis (GSSHA) model, we examine the role of rainfall spatial and temporal variability in flood frequency across drainage basin scales in the highly urbanized Dead Run watershed (14.3 km2), Maryland, USA. The results show the complexities of flood response within several subwatersheds for both short (<50 years) and long (>100 years) rainfall return periods. The impact of impervious area on flood response decreases with increasing rainfall return period. For extreme storms, the maximum discharge is closely linked to the spatial structure of rainfall, especially storm core spatial coverage. The spatial heterogeneity of rainfall increases flood peak magnitudes by 50 % on average at the watershed outlet and its subwatersheds for both small and large return periods. The framework of SST–GSSHA-coupled frequency analysis also highlights the fact that spatially distributed rainfall scenarios are needed in quick-response flood frequency, even for relatively small basin scales.

Connecting Hydrometeorological Processes to Low‐Probability Floods in the Mountainous Colorado Front Range

AbstractEstimating the probabilities of rare floods in mountainous watersheds is challenging due to the hydrometeorological complexity of seasonally varying snowmelt and soil moisture dynamics, as well as spatiotemporal variability in extreme precipitation. Design storm methods and statistical flood frequency analyses often overlook these complexities and how they shape the probabilities of rare floods. This study presents a process‐based approach that combines gridded precipitation, stochastic storm transposition (SST), and physics‐based distributed rainfall‐runoff modeling to simulate flood peak and volume distributions up to the 10,000‐year recurrence interval and to provide insights into the hydrometeorological drivers of those events. The approach is applied to a small mountainous watershed in the Colorado Front Range in the United States. We show that storm transposition in the Front Range can be justified under existing definitions of regional precipitation homogeneity. The process‐based results show close agreement with a statistically based mixture distribution that considers underlying flood drivers. We further demonstrate that antecedent conditions and snowmelt drive frequent peak discharges and rarer flood volumes, while the upper tail of the flood peak distribution appears to be controlled by heavy rainfall and rain‐on‐snow. In particular, we highlight the important role of early fall extreme rainfall in controlling rare flood peaks (but not volumes), despite only one such event having been observed in recent decades. Notwithstanding issues related to the accuracy of gridded precipitation datasets, these findings highlight the potential of SST and process‐based modeling to help understand the relationships between flood drivers and flood frequencies.

The Upper Tail of Precipitation in Convection‐Permitting Regional Climate Models and Their Utility in Nonstationary Rainfall and Flood Frequency Analysis

AbstractComputational advances have made atmospheric modeling at convection‐permitting (≤4 km) grid spacings increasingly feasible. These simulations hold great promise in the projection of climate change impacts including rainfall and flood extremes. The relatively short model runs that are currently feasible, however, inhibit the assessment of the upper tail of rainfall and flood quantiles using conventional statistical methods. Stochastic storm transposition (SST) and process‐based flood frequency analysis are two approaches that together can help to mitigate this limitation. SST generates large numbers of extreme rainfall scenarios by temporal resampling and geospatial transposition of rainfall fields from relatively short data sets. Coupling SST with process‐based flood frequency analysis enables exploration of flood behavior at a range of spatial and temporal scales. We apply these approaches with outputs of 13‐year simulations of regional climate to examine changes in extreme rainfall and flood quantiles up to the 500‐year recurrence interval in a medium‐sized watershed in the Midwestern United States. Intensification of extreme precipitation across a range of spatial and temporal scales is identified in future climate; changes in flood magnitudes depend on watershed area, with small watersheds exhibiting the greatest increases due to their limited capacity to attenuate flood peaks. Flood seasonality and snowmelt are predicted to be earlier in the year under projected warming, while the most extreme floods continue to occur in early summer. Findings highlight both the potential and limitations of convection‐resolving climate models to help understand possible changes in rainfall and flood frequency across watershed scales.

Using Physically Based Synthetic Peak Flows to Assess Local and Regional Flood Frequency Analysis Methods

AbstractAlthough prior studies have evaluated the role of sampling errors associated with local and regional methods to estimate peak flow quantiles, the investigation of epistemic errors is more difficult because the underlying properties of the random variable have been prescribed using ad‐hoc characterizations of the regional distributions of peak flows. This study addresses this challenge using representations of regional peak flow distributions derived from a combined framework of stochastic storm transposition, radar rainfall observations, and distributed hydrologic modeling. The authors evaluated four commonly used peak flow quantile estimation methods using synthetic peak flows at 5,000 sites in the Turkey River watershed in Iowa, USA. They first used at‐site flood frequency analysis using the Pearson Type III distribution with L‐moments. The authors then pooled regional information using (1) the index flood method, (2) the quantile regression technique, and (3) the parameter regression. This approach allowed quantification of error components stemming from epistemic assumptions, parameter estimation method, sample size, and, in the regional approaches, the number of pooled sites. The results demonstrate that the inability to capture the spatial variability of the skewness of the peak flows dominates epistemic error for regional methods. We concluded that, in the study basin, this variability could be partially explained by river network structure and the predominant orientation of the watershed. The general approach used in this study is promising in that it brings new tools and sources of data to the study of the old hydrologic problem of flood frequency analysis.

Process-based flood frequency analysis in an agricultural watershed exhibiting nonstationary flood seasonality

Abstract. Floods are the product of complex interactions among processes including precipitation, soil moisture, and watershed morphology. Conventional flood frequency analysis (FFA) methods such as design storms and discharge-based statistical methods offer few insights into these process interactions and how they “shape” the probability distributions of floods. Understanding and projecting flood frequency in conditions of nonstationary hydroclimate and land use require deeper understanding of these processes, some or all of which may be changing in ways that will be undersampled in observational records. This study presents an alternative “process-based” FFA approach that uses stochastic storm transposition to generate large numbers of realistic rainstorm “scenarios” based on relatively short rainfall remote sensing records. Long-term continuous hydrologic model simulations are used to derive seasonally varying distributions of watershed antecedent conditions. We couple rainstorm scenarios with seasonally appropriate antecedent conditions to simulate flood frequency. The methodology is applied to the 4002 km2 Turkey River watershed in the Midwestern United States, which is undergoing significant climatic and hydrologic change. We show that, using only 15 years of rainfall records, our methodology can produce accurate estimates of “present-day” flood frequency. We found that shifts in the seasonality of soil moisture, snow, and extreme rainfall in the Turkey River exert important controls on flood frequency. We also demonstrate that process-based techniques may be prone to errors due to inadequate representation of specific seasonal processes within hydrologic models. If such mistakes are avoided, however, process-based approaches can provide a useful pathway toward understanding current and future flood frequency in nonstationary conditions and thus be valuable for supplementing existing FFA practices.

Storm Catalog‐Based Analysis of Rainfall Heterogeneity and Frequency in a Complex Terrain

AbstractUrban development, topographic relief, and coastal boundaries can all exert influences on storm hydroclimatology, making rainfall and flood frequency analysis a major challenge. This study explores heterogeneity in extreme rainfall in the Baltimore Metropolitan region at small spatial scales using hydrometeorological analyses of major storm events in combination with hydroclimatological analyses based on storm catalogs developed using a 16‐year record of high‐resolution bias‐corrected radar rainfall fields. Our analyses demonstrate the potential for rainfall frequency methods using storm catalogs combined with stochastic storm transposition (SST); procedures are implemented for Dead Run, a small (14.3 km2) urban watershed located within the Baltimore Metropolitan area. The results point to the pronounced impact of complex terrain (including the Chesapeake Bay to the east, mountainous terrain to the west and urbanization in the region) on the regional rainfall climatology. Warm‐season thunderstorm systems are shown to be the dominant mechanism for generating extreme, short‐duration rainfall that leads to flash flooding. The SST approach is extended through the implementation of a multiplier field that accounts for spatial heterogeneities in extreme rainfall magnitude. SST‐based analyses demonstrate the need to consider rainfall heterogeneity at multiple scales when estimating the rainfall intensity‐duration‐frequency relationships.

The Impact of Rainfall Space‐Time Structure in Flood Frequency Analysis

AbstractFlood hydrologic response is influenced by rainfall structure (i.e., variability in space and time). How this structure shapes flood frequency is unknown, and flood frequency analyses typically neglect or simplify potentially important aspects of rainfall variability. This study seeks to understand how rainfall structure impacts flood frequency. We use stochastic storm transposition combined with a 15‐year record of hourly, 4‐km2 radar rainfall to generate 10,000 synthetic extreme rain events. These events are resampled into four “scenarios” with differing spatial and temporal resolutions, which are used as input to a distributed hydrologic model. Analysis of variance is used to identify the proportions of total flood peak variability attributable to spatial and to temporal rainfall variability under two antecedent soil moisture conditions. We simulate peak discharges for recurrence intervals of 2 to 500 years for 1,343 subwatersheds ranging in size from 16 to 4,400 km2 in Turkey River in the Midwestern United States, which is situated in a typically humid continental climactic region. Antecedent soil moisture modulates the role of rainfall structure in simulated flood response, particularly for more frequent events and large watershed scales. Rainfall spatial structure is more important than temporal structure for drainage areas larger than approximately 2,000 km2 (approximately 200 km2) for wet (dry) initial soil conditions, especially when soils are dry, while the reverse is true for smaller subwatersheds. The results appear to be related to the differing propensities for surface and subsurface runoff production as a function of basin scale, event magnitude, and soil saturation. Our results suggest that hydrologic model‐based flood frequency analyses, and particularly efforts attempting to spanning a range of scales, must carefully consider rainfall structure.

A remote sensing-based tool for assessing rainfall-driven hazards

RainyDay is a Python-based platform that couples rainfall remote sensing data with Stochastic Storm Transposition (SST) for modeling rainfall-driven hazards such as floods and landslides. SST effectively lengthens the extreme rainfall record through temporal resampling and spatial transposition of observed storms from the surrounding region to create many extreme rainfall scenarios. Intensity-Duration-Frequency (IDF) curves are often used for hazard modeling but require long records to describe the distribution of rainfall depth and duration and do not provide information regarding rainfall space-time structure, limiting their usefulness to small scales. In contrast, RainyDay can be used for many hazard applications with 1–2 decades of data, and output rainfall scenarios incorporate detailed space-time structure from remote sensing. Thanks to global satellite coverage, RainyDay can be used in inaccessible areas and developing countries lacking ground measurements, though results are impacted by remote sensing errors. RainyDay can be useful for hazard modeling under nonstationary conditions.

Estimating the exceedance probability of extreme rainfalls up to the probable maximum precipitation

If risk-based criteria are used in the design of high hazard structures (such as dam spillways and nuclear power stations), then it is necessary to estimate the annual exceedance probability (AEP) of extreme rainfalls up to and including the Probable Maximum Precipitation (PMP). This paper describes the development and application of two largely independent methods to estimate the frequencies of such extreme rainfalls. One method is based on stochastic storm transposition (SST), which combines the “arrival” and “transposition” probabilities of an extreme storm using the total probability theorem. The second method, based on “stochastic storm regression” (SSR), combines frequency curves of point rainfalls with regression estimates of local and transposed areal rainfalls; rainfall maxima are generated by stochastically sampling the independent variates, where the required exceedance probabilities are obtained using the total probability theorem. The methods are applied to two large catchments (with areas of 3550km2 and 15,280km2) located in inland southern Australia. Both methods were found to provide similar estimates of the frequency of extreme areal rainfalls for the two study catchments. The best estimates of the AEP of the PMP for the smaller and larger of the catchments were found to be 10−7 and 10−6, respectively, but the uncertainty of these estimates spans one to two orders of magnitude. Additionally, the SST method was applied to a range of locations within a meteorologically homogenous region to investigate the nature of the relationship between the AEP of PMP and catchment area.

Flood frequency analysis using radar rainfall fields and stochastic storm transposition

Flooding is the product of complex interactions among spatially and temporally varying rainfall, heterogeneous land surface properties, and drainage network structure. Conventional approaches to flood frequency analysis rely on assumptions regarding these interactions across a range of scales. The impacts of these assumptions on flood risk estimates are poorly understood. In this study, we present an alternative flood frequency analysis framework based on stochastic storm transposition (SST). We use SST to synthesize long records of rainfall over the Charlotte, North Carolina, USA metropolitan area by “reshuffling” radar rainfall fields, within a probabilistic framework, from a 10 year (2001–2010) high-resolution (15 min, 1 km2) radar data set. We use these resampled fields to drive a physics-based distributed hydrologic model for a heavily urbanized watershed in Charlotte. The approach makes it possible to estimate discharge return periods for all points along the drainage network without the assumptions regarding rainfall structure and its interactions with watershed features that are required using conventional methods. We develop discharge estimates for return periods from 10 to 1000 years for a range of watershed scales up to 110 km2. SST reveals that flood risk in the larger subwatersheds is dominated by tropical storms, while organized thunderstorm systems dominate flood risk in the smaller subwatersheds. We contrast these analyses with examples of potential problems that can arise from conventional frequency analysis approaches. SST provides an approach for examining the spatial extent of flooding and for incorporating nonstationarities in rainfall or land use into flood risk estimates.

Physically-based extreme flood frequency with stochastic storm transposition and paleoflood data on large watersheds

Summary Traditionally, deterministic flood procedures such as the Probable Maximum Flood have been used for critical infrastructure design. Some Federal agencies now use hydrologic risk analysis to assess potential impacts of extreme events on existing structures such as large dams. Extreme flood hazard estimates and distributions are needed for these efforts, with very low annual exceedance probabilities ( ⩽ 10 - 4 ) (return periods >10,000 years). An integrated data-modeling hydrologic hazard framework for physically-based extreme flood hazard estimation is presented. Key elements include: (1) a physically-based runoff model (TREX) coupled with a stochastic storm transposition technique; (2) hydrometeorological information from radar and an extreme storm catalog; and (3) streamflow and paleoflood data for independently testing and refining runoff model predictions at internal locations. This new approach requires full integration of collaborative work in hydrometeorology, flood hydrology and paleoflood hydrology. An application on the 12,000 km2 Arkansas River watershed in Colorado demonstrates that the size and location of extreme storms are critical factors in the analysis of basin-average rainfall frequency and flood peak distributions. Runoff model results are substantially improved by the availability and use of paleoflood nonexceedance data spanning the past 1000 years at critical watershed locations.

Estimating the frequency of extreme rainfall using weather radar and stochastic storm transposition

Spatial and temporal variability in extreme rainfall, and its interactions with land cover and the drainage network, is an important driver of flood response. “Design storms,” which are commonly used for flood risk assessment, however, are assumed to be uniform in space and either uniform or highly idealized in time. The impacts of these and other commonly-made assumptions are rarely considered, and their impacts on flood risk estimates are poorly understood. This study presents an alternate framework for rainfall frequency analysis that couples stochastic storm transposition (SST) with “storm catalogs” developed from a ten-year high-resolution (15-min, 1-km2) radar rainfall dataset for the region surrounding Charlotte, North Carolina, USA. The SST procedure involves spatial and temporal resampling from these storm catalogs to reconstruct the regional climatology of extreme rainfall. SST-based intensity–duration–frequency (IDF) estimates are driven by the spatial and temporal rainfall variability from weather radar observations, are tailored specifically to the chosen watershed, and do not require simplifying assumptions of storm structure. We are able to use the SST procedure to reproduce IDF estimates from conventional methods for four urban watersheds in Charlotte. We demonstrate that extreme rainfall can vary substantially in time and in space, with potentially important flood risk implications that cannot be assessed using conventional techniques. SST coupled with high-resolution radar rainfall fields represents a useful alternative to conventional design storms for flood risk assessment, the full advantages of which can be realized when the concept is extended to flood frequency analysis using a distributed hydrologic model.