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Accurate modeling of infiltration losses is vital for runoff estimation and thus the development of flood design/protection criteria and water management schemes, etc. In design flood practices, the initial abstraction and constant loss (IACL) method has been widely applied due to its simplicity. However, due to a lack of physical equivalent properties, the IACL method is often subject to issues in parametrization and has large dependency on calibration efforts for storm events. Despite the wide range/variability of IACL values, a single set of IA and CL values is normally adopted for specific flood frequency, which may introduce uncertainty and bias in resulting peak streamflow. In this study, we identified a total of 2,036 rainfall-runoff events for 18 watersheds in North Central Texas to estimate the total losses with their IA and CL components based on time-series of mean areal precipitation (MAP) and streamflow data. Threshold behavior is found for all studied subbasins between the summation of gross rainfall and antecedent soil moisture versus runoff depth: below the threshold, runoff depth is minimal; whereas above it, runoff is largely linearly correlated with the summation of rainfall and antecedent soil moisture. This finding provides a convenient way to estimate/predict total loss or runoff depth given MAP and antecedent soil moisture. In addition, this study shows that the IA and CL values can be approximated by the gamma and Weibull distributions, respectively. The fitted distributions of IA and CL values can be applied in a Monte Carlo simulation framework to stochastically simulate numerous rainfall-runoff events for a flood frequency analysis. [ABSTRACT FROM AUTHOR]

Radar-based quantitative precipitation estimate (QPE) serves as input for flood forecasting, and its importance gets magnified during catastrophic storms, e.g., Hurricane Harvey in 2017. The record-breaking rainfall from Hurricane Harvey covered vast spatial extents and lasted for a 5-day period, providing a unique chance for evaluating radar errors, especially their spatiotemporal dependence. Using the rainfall data of Hurricane Harvey, the authors utilize a new method for sampling ground-based rainfall measurements over radar pixels (i.e., spatial reference rainfall) based on subpixel rainfall variability. The new method aims to enlarge the sample size and allow for compressively evaluating the QPE. Two hourly QPE products, the Next Generation Weather Radar (NEXRAD) Stage IV and Multiradar Multisensor (MRMS), are chosen for the evaluation due to their roles in major flood forecasting activities; and a dense rain gauge network covering the whole of Harris County, Texas, provides the spatial rainfall reference in this analysis. Comparative analyses are conducted based on Hurricane Harvey and other two flood-inducing storms occurring in 2015 and 2016 over Harris County. The results imply that the Stage IV and MRMS overestimate and underestimate, respectively, the total rainfall by a small factor, while both QPEs tend to overestimate very light precipitation. In addition, the study suggests that the spatial correlation of radar error from both QPEs be described as powered exponential functions of interpixel distance. This study also includes hydrologic simulations for an urban watershed, demonstrating the importance of both the accuracy and spatial resolution of QPE in representing the mean areal precipitation (MAP) over catchments. The insight gained from this study provides guidance for further improving the QPE performance, and the new sampling approach for spatial reference rainfall can be applied to comprehensively evaluate long-term radar rainfall data. [ABSTRACT FROM AUTHOR]

• Re-infiltration is comprehensively assessed under various conditions with parameters. • A calibration scheme is coupled with WRF-Hydro to improve efficiency and accuracy. • Re-infiltration is important for flat areas with clayey soils during flood events. WRF-Hydro (Weather Research and Forecasting model-Hydrological modeling system), as the core engine of the United States National Water Model (NWM), has now been used in many hydrometeorological applications throughout the world. One important feature that WRF-Hydro introduced is to allow infiltration excess ("ponded water") for subsequent lateral re-distribution and soil re-infiltration, which is a major enhancement in terms of physical realism. Nonetheless, how well WRF-Hydro models re-infiltration is largely unknown, because this process is difficult to be directly measured. To gain an in-depth understanding of re-infiltration process under different hydrometeorological/geographical conditions with model parameter settings, we start conducting a series of idealized numerical experiments using 18 watersheds in North Central Texas as a testbed. Next, the model is automatically calibrated to best quantify re-infiltration amounts during two major storms (2010 Tropical Storm Hermine and 2015 May Event), which is accomplished by coupling the dynamically dimensioned search (DDS) algorithm with WRF-Hydro to achieve optimal calibration efficiency. The results show that re-infiltration has quite substantial impacts on streamflow simulation in WRF-Hydro, especially for areas with flat terrains and soils with high clay content. Among all examined factors, precipitation, saturated hydraulic conductivity (K sat) and runoff partition parameter (REFKDT) are found to impose relatively higher impacts on both re-infiltration ratio and runoff coefficient. It is also found that the runoff coefficient and the re-infiltration ratio are positively correlated based on results from both hypothetical and real events, indicating re-infiltration effects can become more pronounced as flood potential increases. These findings collectively show the significance of representing the re-infiltration process in flood forecasting. Models that do not incorporate this process may be over-calibrated to compensate errors originated from the missing process. [ABSTRACT FROM AUTHOR]

Legionella, a large group of environmental Gram-negative bacteria, represents an occasional cause of pneumonia. We analyzed the microbiological and clinical features of 33 consecutive cases of Legionella infections that occurred at the University of Texas MD Anderson Cancer Center, Houston, TX, from 2002 to 2014. The Legionella strains were isolated from bronchoscopy specimens (32 strains) and a blood culture (1 strain) and were identified by sequencing analysis of the full-length 16S rRNA gene. The 33 strains involved 12 Legionella species or subspecies: 15 strains of L. pneumophila subsp. pneumophila, 3 strains of L. pneumophila subsp. fraseri or L. pneumophila subsp. pascullei, 4 strains of "L. donaldsonii," 3 strains of L. micdadei, and one each of L. bozemanae, L. feeleii, L. gormanii, L. longbeachae, L. maceachernii, L. parisiensis, L. sainthelensi, and Legionella sp. strain D5382. All patients except one asymptomatic carrier showed pneumonia, including one with concurrent bacteremia. Nine patients died, with this infection being the immediate cause of death in six. Twenty-seven patients had underlying hematologic malignancies. Twenty-three patients were leukopenic. Six patients were recipients of allogeneic hematopoietic stem cell transplant, with their infections caused by five Legionella species. Together, these results suggest that diverse Legionella species infect patients with cancer in the Houston area and its vicinity. The five cases of pneumonia due to L. donaldsonii and Legionella sp. D5382 are likely the first reports of human infection with these organisms., (Copyright © 2015, American Society for Microbiology. All Rights Reserved.)