Daniel B. Wright

Urbanization and Climate Change: An Examination of Nonstationarities in Urban Flooding

The authors examine the hydroclimatology, hydrometeorology, and hydrology of flooding in the Milwaukee metropolitan region of the upper midwestern United States. The objectives of this study are 1) to assess nonstationarities in flood frequency associated with urban transformation of land surface properties and climate change and 2) to examine how spatial heterogeneity in land surface properties and heavy rainfall climatology interact to determine floods in urbanizing areas. The authors focus on the Menomonee River basin, which drains much of the urban core of Milwaukee, and the adjacent Cedar Creek basin, where agricultural land use dominates. Results are based on analyses of bias-corrected, high-resolution (1-km 2 spatial resolution and 15-min time resolution) radar rainfall fields that are developed using the Hydro-NEXRAD system, rainfall observations from a network of 21 rain gauges in the Milwaukee metropolitan region, and discharge observations from 11 U.S. Geological Survey stream gauging stations. Both annual flood peak magnitudes and annual peaks over threshold flood counts have increased for the Menomonee River basin during the past five decades, and these trends are accompanied by a transition of flood events dominated by snowmelt (March‐April floods) to a regime in which warm season thunderstorms are the dominant floodproducing agents. The frequency of heavy rainfall events has increased significantly. The spatial distribution of rainfall for flood-producing storms in the Milwaukee study region exhibits striking spatial heterogeneity, with a maximum in the central portion of the Menomonee River basin. Storm event hydrologic response is determined by the interactions of spatial patterns of urbanization and rainfall distribution in the Menomonee River basin.

Extreme Flood Response: The June 2008 Flooding in Iowa

AbstractThe authors examine the hydroclimatology, hydrometeorology, and hydrology of extreme floods through analyses that center on the June 2008 flooding in Iowa. The most striking feature of the June 2008 flooding was the flood peak of the Cedar River at Cedar Rapids (3964 m3 s−1), which was almost twice the previous maximum from a record of 110 years. The spatial extent of extreme flooding was exceptional, with more U.S. Geological Survey stream gauging stations reporting record flood peaks than in any other year. The 2008 flooding was produced by a sequence of organized thunderstorm systems over a period of two weeks. The authors examine clustering and seasonality of flooding in the Iowa study region and link these properties to features of the June 2008 flood event. They examine the environment of heavy rainfall in Iowa during June 2008 through analyses of composite rainfall fields (15-min time interval and 1-km spatial resolution) developed with the Hydro-NEXRAD system and simulations using the Weat...

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.

Critical Examination of Area Reduction Factors

AbstractArea reduction factors (ARFs), which are used to convert estimates of extreme point rainfall to estimates of extreme area-averaged rainfall, are central to conventional flood risk assessment. Errors in the estimation of ARFs can result in large errors in subsequent estimates of design rainfall and discharge. This paper presents a critical examination of commonly used ARFs, particularly those from the U.S. Weather Bureau TP-29, demonstrating that they do not adequately represent the true properties of extreme rainfall. This lack of representativeness is due mainly to formulations that mix rainfall observations from different storms and different storm types. Storm catalogs developed from a 10-year high-resolution radar rainfall data are used set to estimate storm-centered ARFs for Charlotte, North Carolina. Storms are classified as either tropical or nontropical to demonstrate that storm type strongly influences spatial rainfall structure. While there appears to be some relationship between ARF str...

Effect of Spatially Distributed Small Dams on Flood Frequency: Insights from the Soap Creek Watershed

AbstractDams are ubiquitous in the United States, with more than 87,000 influencing streamflow across the nation. The significant majority of these dams are small and are often ignored in real-time...

Evaluating Hourly Rainfall Characteristics over the U.S. Great Plains in Dynamically Downscaled Climate Model Simulations using NASA-Unified WRF

Accurate simulation of extreme precipitation events remains a challenge in climate models. This study utilizes hourly precipitation data from ground stations and satellite instruments to evaluate rainfall characteristics simulated by the NASA-Unified Weather Research and Forecasting (NU-WRF) regional climate model at horizontal resolutions of 4, 12, and 24 km over the Great Plains of the United States. We also examined the sensitivity of the simulated precipitation to different spectral nudging approaches and the cumulus parameterizations. The rainfall characteristics in the observations and simulations were defined as a hourly diurnal cycle of precipitation and a joint probability distribution function (JPDF) between duration and peak intensity of precipitation events over the Great Plains in summer. We calculated a JPDF for each dataset and the overlapping area between observed and simulated JPDFs to measure the similarity between the two JPDFs. Comparison of the diurnal precipitation cycles between observations and simulations does not reveal the added value of high-resolution simulations. However, the performance of NU-WRF simulations measured by the JPDF metric strongly depends on horizontal resolution. The simulation with the highest resolution of 4 km shows the best agreement with the observations in simulating duration and intensity of wet spells. Spectral nudging does not affect the JPDF significantly. The effect of cumulus parameterizations on the JPDFs is considerable but smaller than that of horizontal resolution. The simulations with lower resolutions of 12 and 24 km show reasonable agreement but only with the high-resolution observational data that are aggregated into coarse resolution and spatially averaged.

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.

Applications of Radar-Based Rainfall Estimates to Urban Flood Studies

The United States has dense weather radar and rain gage networks that provide potentially useful rainfall inputs for a variety of hydrologic applications, espe…