Computational 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.
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.
more » « less- Award ID(s):
- 1749638
- NSF-PAR ID:
- 10449542
- Publisher / Repository:
- DOI PREFIX: 10.1029
- Date Published:
- Journal Name:
- Water Resources Research
- Volume:
- 57
- Issue:
- 4
- ISSN:
- 0043-1397
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
more » « less
Abstract -
more » « less
Abstract Urban 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 amultiplier 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. -
more » « less
Abstract High temporal and spatial resolution precipitation datasets are essential for hydrological and flood modeling to assist water resource management and emergency responses, particularly for small watersheds, such as those in Hawai‘i in the United States. Unfortunately, fine temporal (subdaily) and spatial (<1 km) resolutions of rainfall datasets are not always readily available for applications. Radar provides indirect measurements of the rain rate over a large spatial extent with a reasonable temporal resolution, while rain gauges provide “ground truth.” There are potential advantages to combining the two, which have not been fully explored in tropical islands. In this study, we applied kriging with external drift (KED) to integrate hourly gauge and radar rainfall into a 250 m × 250 m gridded dataset for the tropical island of O‘ahu. The results were validated with leave-one-out cross validation for 18 severe storm events, including five different storm types (e.g., tropical cyclone, cold front, upper-level trough, kona low, and a mix of upper-level trough and kona low), and different rainfall structures (e.g., stratiform and convective). KED-merged rainfall estimates outperformed both the radar-only and gauge-only datasets by 1) reducing the error from radar rainfall and 2) improving the underestimation issues from gauge rainfall, especially during convective rainfall. We confirmed the KED method can be used to merge radar with gauge data to generate reliable rainfall estimates, particularly for storm events, on mountainous tropical islands. In addition, KED rainfall estimates were consistently more accurate in depicting spatial distribution and maximum rainfall value within various storm types and rainfall structures.
Significance Statement The results of this study show the effectiveness of utilizing kriging with external drift (KED) in merging gauge and radar rainfall data to produce highly accurate, reliable rainfall estimates in mountainous tropical regions, such as O‘ahu. The validated KED dataset, with its high temporal and spatial resolutions, offers a valuable resource for various types of rainfall-related research, particularly for extreme weather response and rainfall intensity analyses in Hawai’i. Our findings improve the accuracy of rainfall estimates and contribute to a deeper understanding of the performance of various rainfall estimation methods under different storm types and rainfall structures in a mountainous tropical setting.
-
null (Ed.)Abstract Some of the most intense convective storms on Earth initiate near the Sierras de Córdoba mountain range in Argentina. The goal of the RELAMPAGO field campaign was to observe these intense convective storms and their associated impacts. The intense observation period (IOP) occurred during November–December 2018. The two goals of the hydrometeorological component of RELAMPAGO IOP were 1) to perform hydrological streamflow and meteorological observations in previously ungauged basins and 2) to build a hydrometeorological modeling system for hindcast and forecast applications. During the IOP, our team was able to construct the stage–discharge curves in three basins, as hydrological instrumentation and personnel were successfully deployed based on RELAMPAGO weather forecasts. We found that the flood response time in these river locations is typically between 5 and 6 h from the peak of the rain event. The satellite-observed rainfall product IMERG-Final showed a better representation of rain gauge–estimated precipitation, while IMERG-Early and IMERG-Late had significant positive bias. The modeling component focuses on the 48-h simulation of an extreme hydrometeorological event that occurred on 27 November 2018. Using the Weather Research and Forecasting (WRF) atmospheric model and its hydrologic component WRF-Hydro as an uncoupled hydrologic model, we developed a system for hindcast, deterministic forecast, and a 60-member ensemble forecast initialized with regional-scale atmospheric data assimilation. Critically, our results highlight that streamflow simulations using the ensemble forecasting with data assimilation provide realistic flash flood forecast in terms of timing and magnitude of the peak. Our findings from this work are being used by the water managers in the region.more » « less
-
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.more » « less
