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Abstract. Conventional rainfall frequency analysis faces several limitations. These include difficulty incorporating relevant atmospheric variables beyond precipitation and limited ability to depict the frequency of rainfall over large areas that is relevant for flooding. This study proposes a storm-based model of extreme precipitation frequency based on the atmospheric water balance equation. We developed a storm tracking and regional characterization (STARCH) method to identify precipitation systems in space and time from hourly ERA5 precipitation fields over the contiguous United States from 1951 to 2020. Extreme “storm catalogs” were created by selecting annual maximum storms with specific areas and durations over a chosen region. The annual maximum storm precipitation was then modeled via multivariate distributions of atmospheric water balance components using vine copula models. We applied this approach to estimate precipitation average recurrence intervals for storm areas from 5000 to 100 000 km2 and durations from 2 to 72 h in the Mississippi Basin and its five major subbasins. The estimated precipitation distributions show a good fit to the reference data from the original storm catalogs and are close to the estimates from conventional univariate GEV distributions. Our approach explicitly represents the contributions of water balance components in extreme precipitation. Of these, water vapor flux convergence is the main contributor, while precipitable water and a mass residual term can also be important, particularly for short durations and small storm footprints. We also found that ERA5 shows relatively good water balance closure for extreme storms, with a mass residual on average 10 % of precipitation. The approach can incorporate nonstationarities in water balance components and their dependence structures and can benefit from further advancements in reanalysis products and storm tracking techniques. 

Abstract The Global Precipitation Measurement (GPM) constellation of spaceborne sensors provides a variety of direct and indirect measurements of precipitation processes. Such observations can be employed to derive spatially and temporally consistent gridded precipitation estimates either via data-driven retrieval algorithms or by assimilation into physically based numerical weather models. We compare the data-driven Integrated Multisatellite Retrievals for GPM (IMERG) and the assimilation-enabled NASA-Unified Weather Research and Forecasting (NU-WRF) model against Stage IV reference precipitation for four major extreme rainfall events in the southeastern United States using an object-based analysis framework that decomposes gridded precipitation fields into storm objects. As an alternative to conventional “grid-by-grid analysis,” the object-based approach provides a promising way to diagnose spatial properties of storms, trace them through space and time, and connect their accuracy to storm types and input data sources. The evolution of two tropical cyclones are generally captured by IMERG and NU-WRF, while the less organized spatial patterns of two mesoscale convective systems pose challenges for both. NU-WRF rain rates are generally more accurate, while IMERG better captures storm location and shape. Both show higher skill in detecting large, intense storms compared to smaller, weaker storms. IMERG’s accuracy depends on the input microwave and infrared data sources; NU-WRF does not appear to exhibit this dependence. Findings highlight that an object-oriented view can provide deeper insights into satellite precipitation performance and that the satellite precipitation community should further explore the potential for “hybrid” data-driven and physics-driven estimates in order to make optimal usage of satellite observations. 

Abstract Many existing models that predict landslide hazards utilize ground-based sources of precipitation data. In locations where ground-based precipitation observations are limited (i.e., a vast majority of the globe), or for landslide hazard models that assess regional or global domains, satellite multisensor precipitation products offer a promising near-real-time alternative to ground-based data. NASA’s global Landslide Hazard Assessment for Situational Awareness (LHASA) model uses the Integrated Multisatellite Retrievals for Global Precipitation Measurement (IMERG) product to issue hazard “nowcasts” in near–real time for areas that are currently at risk for landsliding. Satellite-based precipitation estimates, however, can contain considerable systematic bias and random error, especially over mountainous terrain and during extreme rainfall events. This study combines a precipitation error modeling framework with a probabilistic adaptation of LHASA. Compared with the routine version of LHASA, this probabilistic version correctly predicts more of the observed landslides in the study region with fewer false alarms by high hazard nowcasts. This study demonstrates that improvements in landslide hazard prediction can be achieved regardless of whether the IMERG error model is trained using abundant ground-based precipitation observations or using far fewer and more scattered observations, suggesting that the approach is viable in data-limited regions. Results emphasize the importance of accounting for both random error and systematic satellite precipitation bias. The approach provides an example of how environmental prediction models can incorporate satellite precipitation uncertainty. Other applications such as flood and drought monitoring and forecasting could likely benefit from consideration of precipitation uncertainty. 

Estimating 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.

 

Abstract. Floods are the product of complex interactions among processes includingprecipitation, soil moisture, and watershed morphology. Conventional floodfrequency analysis (FFA) methods such as design storms and discharge-basedstatistical methods offer few insights into these process interactions andhow they “shape” the probability distributions of floods. Understanding andprojecting flood frequency in conditions of nonstationary hydroclimate andland use require deeper understanding of these processes, some or all ofwhich may be changing in ways that will be undersampled in observationalrecords. This study presents an alternative “process-based” FFA approachthat uses stochastic storm transposition to generate large numbers ofrealistic rainstorm “scenarios” based on relatively short rainfall remotesensing records. Long-term continuous hydrologic model simulations are usedto derive seasonally varying distributions of watershed antecedentconditions. We couple rainstorm scenarios with seasonally appropriateantecedent conditions to simulate flood frequency. The methodology is appliedto 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 accurateestimates of “present-day” flood frequency. We found that shifts in theseasonality of soil moisture, snow, and extreme rainfall in the Turkey Riverexert important controls on flood frequency. We also demonstrate thatprocess-based techniques may be prone to errors due to inadequaterepresentation of specific seasonal processes within hydrologic models. Ifsuch mistakes are avoided, however, process-based approaches can provide auseful pathway toward understanding current and future flood frequency innonstationary conditions and thus be valuable for supplementing existing FFApractices.