scholarly journals The FLASH Project: Improving the Tools for Flash Flood Monitoring and Prediction across the United States

2017 ◽  
Vol 98 (2) ◽  
pp. 361-372 ◽  
Author(s):  
Jonathan J. Gourley ◽  
Zachary L. Flamig ◽  
Humberto Vergara ◽  
Pierre-Emmanuel Kirstetter ◽  
Robert A. Clark ◽  
...  
Keyword(s):  
United States ◽  
Real Time ◽  
Flash Flood ◽  
Rank Correlation ◽  
The United States ◽  
Hydrologic Model ◽  
Computationally Efficient ◽  
Modeling Framework ◽  
Flood Prediction ◽  
Peak Discharges

Abstract This study introduces the Flooded Locations and Simulated Hydrographs (FLASH) project. FLASH is the first system to generate a suite of hydrometeorological products at flash flood scale in real-time across the conterminous United States, including rainfall average recurrence intervals, ratios of rainfall to flash flood guidance, and distributed hydrologic model–based discharge forecasts. The key aspects of the system are 1) precipitation forcing from the National Severe Storms Laboratory (NSSL)’s Multi-Radar Multi-Sensor (MRMS) system, 2) a computationally efficient distributed hydrologic modeling framework with sufficient representation of physical processes for flood prediction, 3) capability to provide forecasts at all grid points covered by radars without the requirement of model calibration, and 4) an open-access development platform, product display, and verification system for testing new ideas in a real-time demonstration environment and for fostering collaborations. This study assesses the FLASH system’s ability to accurately simulate unit peak discharges over a 7-yr period in 1,643 unregulated gauged basins. The evaluation indicates that FLASH’s unit peak discharges had a linear and rank correlation of 0.64 and 0.79, respectively, and that the timing of the peak discharges has errors less than 2 h. The critical success index with FLASH was 0.38 for flood events that exceeded action stage. FLASH performance is demonstrated and evaluated for case studies, including the 2013 deadly flash flood case in Oklahoma City, Oklahoma, and the 2015 event in Houston, Texas—both of which occurred on Memorial Day weekends.

Real-Time Flash Flood Forecasting

2018 ◽  
Author(s):  
Jonathan J. Gourley ◽  
Robert A. Clark
Keyword(s):  
United States ◽  
Real Time ◽  
Flash Flood ◽  
Weather Prediction ◽  
The United States ◽  
Flood Forecasting ◽  
Flash Floods ◽  
Hydrologic Model ◽  
The World ◽  
Flash Flood Forecasting

Flash floods are one of the world’s deadliest and costliest weather-related natural hazards. In the United States alone, they account for an average of approximately 80 fatalities per year. Damages to crops and infrastructure are particularly costly. In 2015 alone, flash floods accounted for over $2 billion of losses; this was nearly half the total cost of damage caused by all weather hazards. Flash floods can be either pluvial or fluvial, but their occurrence is primarily driven by intense rainfall. Predicting the specific locations and times of flash floods requires a multidisciplinary approach because the severity of the impact depends on meteorological factors, surface hydrologic preconditions and controls, spatial patterns of sensitive infrastructure, and the dynamics describing how society is using or occupying the infrastructure. Real-time flash flood forecasting systems rely on the observations and/or forecasts of rainfall, preexisting soil moisture and river-stage states, and geomorphological characteristics of the land surface and subsurface. The design of the forecast systems varies across the world in terms of their forcing, methodology, forecast horizon, and temporal and spatial scales. Their diversity can be attributed at least partially to the availability of observing systems and numerical weather prediction models that provide information at relevant scales regarding the location, timing, and severity of impending flash floods. In the United States, the National Weather Service (NWS) has relied upon the flash flood guidance (FFG) approach for decades. This is an inverse method in which a hydrologic model is run under differing rainfall scenarios until flooding conditions are reached. Forecasters then monitor observations and forecasts of rainfall and issue warnings to the public and local emergency management communities when the rainfall amounts approach or exceed FFG thresholds. This technique has been expanded to other countries throughout the world. Another approach, used in Europe, relies on model forecasts of heavy rainfall, where anomalous conditions are identified through comparison of the forecast cumulative rainfall (in space and time) with a 20-year archive of prior forecasts. Finally, explicit forecasts of flash flooding are generated in real time across the United States based on estimates of rainfall from a national network of weather radar systems.

scholarly journals Evaluation of Tools Used for Monitoring and Forecasting Flash Floods in the United States

2012 ◽  
Vol 27 (1) ◽  
pp. 158-173 ◽  
Author(s):  
Jonathan J. Gourley ◽  
Jessica M. Erlingis ◽  
Yang Hong ◽  
Ernest B. Wells
Keyword(s):  
United States ◽  
Flash Flood ◽  
The United States ◽  
Flash Floods ◽  
Urban Setting ◽  
Flash Flooding ◽  
Flood Prediction ◽  
Critical Success ◽  
Monitoring And Forecasting ◽  
First Time

Abstract This paper evaluates, for the first time, flash-flood guidance (FFG) values and recently developed gridded FFG (GFFG) used by the National Weather Service (NWS) to monitor and predict imminent flash flooding, which is the leading storm-related cause of death in the United States. It is envisioned that results from this study will be used 1) to establish benchmark performance of existing operational flash-flood prediction tools and 2) to provide information to NWS forecasters that reveals how the existing tools can be readily optimized. Sources used to evaluate the products include official reports of flash floods from the NWS Storm Data database, discharge measurements on small basins available from the U.S. Geological Survey, and witness reports of flash flooding collected during the Severe Hazards Analysis and Verification Experiment. Results indicated that the operational guidance values, with no calibration, were marginally skillful, with the highest critical success index of 0.20 occurring with 3-h GFFG. The false-alarm rates fell and the skill improved to 0.34 when the rainfall was first spatially averaged within basins and then reached 50% of FFG for 1-h accumulation and exceeded 3-h FFG. Although the skill of the GFFG values was generally lower than that of their FFG counterparts, GFFG was capable of detecting the spatial variability of reported flash flooding better than FFG was for a case study in an urban setting.

scholarly journals Can Remote Sensing Technologies Capture the Extreme Precipitation Event and Its Cascading Hydrological Response? A Case Study of Hurricane Harvey Using EF5 Modeling Framework

2020 ◽  
Vol 12 (3) ◽  
pp. 445 ◽  
Author(s):  
Mengye Chen ◽  
Soumaya Nabih ◽  
Noah S. Brauer ◽  
Shang Gao ◽  
Jonathan J. Gourley ◽  
...  
Keyword(s):  
United States ◽  
Remote Sensing ◽  
Flood Control ◽  
Flash Flood ◽  
The United States ◽  
Precipitation Event ◽  
United States Geological Survey ◽  
Hydrological Response ◽  
Modeling Framework ◽  
Precipitation Measurement

A new generation of precipitation measurement products has emerged, and their performances have gained much attention from the scientific community, such as the Multi-Radar Multi-Sensor system (MRMS) from the National Severe Storm Laboratory (NSSL) and the Global Precipitation Measurement Mission (GPM) from the National Aeronautics and Space Administration (NASA). This study statistically evaluated the MRMS and GPM products and investigated their cascading hydrological response in August of 2017, when Hurricane Harvey brought historical and record-breaking precipitation to the Gulf Coast (>1500 mm), causing 107 fatalities along with about USD 125 billion worth of damage. Rain-gauge observations from Harris County Flood Control District (HCFCD) and stream-gauge measurements by the United States Geological Survey (USGS) were used as ground truths to evaluate MRMS, GPM and National Centers for Environmental Prediction (NCEP) gauge-only data by using statistical metrics and hydrological simulations using the Ensemble Framework for Flash Flooding Forecast (EF5) model. The results indicate that remote sensing technologies can accurately detect and estimate the unprecedented precipitation event with their near-real-time products, and all precipitation products produced good hydrological simulations, where the Nash–Sutcliff model efficiency coefficients (NSCE) were close to 0.9 for both the MRMS and GPM products. With the timeliness and seamless coverage of MRMS and GPM, the study also demonstrated the capability and efficiency of the EF5 framework for flash flood modeling over the United States and potentially additional international domains.

scholarly journals A collaborative multiyear, multimodel assessment of seasonal influenza forecasting in the United States

2019 ◽  
Vol 116 (8) ◽  
pp. 3146-3154 ◽  
Author(s):  
Nicholas G. Reich ◽  
Logan C. Brooks ◽  
Spencer J. Fox ◽  
Sasikiran Kandula ◽  
Craig J. McGowan ◽  
...  
Keyword(s):  
Public Health ◽  
United States ◽  
Real Time ◽  
Forecast Accuracy ◽  
The United States ◽  
Public Health Response ◽  
Public Health Decision ◽  
Health Decision Making ◽  
Health Response ◽  
Health Decision

Influenza infects an estimated 9–35 million individuals each year in the United States and is a contributing cause for between 12,000 and 56,000 deaths annually. Seasonal outbreaks of influenza are common in temperate regions of the world, with highest incidence typically occurring in colder and drier months of the year. Real-time forecasts of influenza transmission can inform public health response to outbreaks. We present the results of a multiinstitution collaborative effort to standardize the collection and evaluation of forecasting models for influenza in the United States for the 2010/2011 through 2016/2017 influenza seasons. For these seven seasons, we assembled weekly real-time forecasts of seven targets of public health interest from 22 different models. We compared forecast accuracy of each model relative to a historical baseline seasonal average. Across all regions of the United States, over half of the models showed consistently better performance than the historical baseline when forecasting incidence of influenza-like illness 1 wk, 2 wk, and 3 wk ahead of available data and when forecasting the timing and magnitude of the seasonal peak. In some regions, delays in data reporting were strongly and negatively associated with forecast accuracy. More timely reporting and an improved overall accessibility to novel and traditional data sources are needed to improve forecasting accuracy and its integration with real-time public health decision making.

scholarly journals Development of a large-sample watershed-scale hydrometeorological data set for the contiguous USA: data set characteristics and assessment of regional variability in hydrologic model performance

2015 ◽  
Vol 19 (1) ◽  
pp. 209-223 ◽  
Author(s):  
A. J. Newman ◽  
M. P. Clark ◽  
K. Sampson ◽  
A. Wood ◽  
L. E. Hay ◽  
...  
Keyword(s):  
United States ◽  
Seasonal Cycle ◽  
Model Performance ◽  
The United States ◽  
Hydrologic Model ◽  
Low Flow ◽  
Reference Level ◽  
Hydrologic Response ◽  
Data Set ◽  
Wide Range

Abstract. We present a community data set of daily forcing and hydrologic response data for 671 small- to medium-sized basins across the contiguous United States (median basin size of 336 km2) that spans a very wide range of hydroclimatic conditions. Area-averaged forcing data for the period 1980–2010 was generated for three basin spatial configurations – basin mean, hydrologic response units (HRUs) and elevation bands – by mapping daily, gridded meteorological data sets to the subbasin (Daymet) and basin polygons (Daymet, Maurer and NLDAS). Daily streamflow data was compiled from the United States Geological Survey National Water Information System. The focus of this paper is to (1) present the data set for community use and (2) provide a model performance benchmark using the coupled Snow-17 snow model and the Sacramento Soil Moisture Accounting Model, calibrated using the shuffled complex evolution global optimization routine. After optimization minimizing daily root mean squared error, 90% of the basins have Nash–Sutcliffe efficiency scores ≥0.55 for the calibration period and 34% ≥ 0.8. This benchmark provides a reference level of hydrologic model performance for a commonly used model and calibration system, and highlights some regional variations in model performance. For example, basins with a more pronounced seasonal cycle generally have a negative low flow bias, while basins with a smaller seasonal cycle have a positive low flow bias. Finally, we find that data points with extreme error (defined as individual days with a high fraction of total error) are more common in arid basins with limited snow and, for a given aridity, fewer extreme error days are present as the basin snow water equivalent increases.

Emerging Synthetic Cannabinoids: Development and Validation of a Novel Liquid Chromatography Quadrupole Time-of-Flight Mass Spectrometry Assay for Real-Time Detection

2019 ◽  
Vol 44 (3) ◽  
pp. 207-217 ◽  
Author(s):  
Alex J Krotulski ◽  
Amanda L A Mohr ◽  
Barry K Logan
Keyword(s):  
United States ◽  
Mass Spectrometry ◽  
Liquid Chromatography ◽  
Real Time ◽  
Synthetic Cannabinoids ◽  
Time Of Flight ◽  
Forensic Toxicology ◽  
The United States ◽  
Synthetic Cannabinoid ◽  
Flight Mass Spectrometry

Abstract Synthetic cannabinoids pose significant threats to public health and safety, as their implications in overdose and adverse events continue to arise in United States and around the world. Synthetic cannabinoids have seen several generations of chemically diverse structural elements, impacting potency and effects. These factors create new analytical challenges for forensic laboratories. This report describes an efficient liquid chromatography/quadrupole time-of-flight mass spectrometry (LC-QTOF-MS) assay for the identification of synthetic cannabinoid parent compounds and metabolites, including real-time identification of emergent compounds, using a SCIEX TripleTOF® 5600+ with non-targeted SWATH® acquisition. Method validation evaluated precision/accuracy, limits of detection, interferences, processed sample stability and carryover, for which 19 parent compounds and 19 metabolites were tested. To demonstrate feasibility, de-identified blood sample extracts were acquired from a large forensic toxicology laboratory and analyzed using the validated LC-QTOF-MS assay. In mid-2018, 200 blood extracts were analyzed, demonstrating a 19% positivity rate with > 94% agreement rate with original testing. In addition, three newly discovered synthetic cannabinoids were identified, including 5F-MDMB-PICA, 4-cyano CUMYL-BUTINACA and 5F-EDMB-PINACA. These synthetic cannabinoids were previously unreported in forensic toxicology casework in the United States. 5F-MDMB-PICA has become the most prevalent synthetic cannabinoid in United States, as of early 2019. These results demonstrate the effectiveness of this assay and workflow in the identification and characterization of synthetic cannabinoids, as well as the usefulness of sample-mining using non-targeted mass acquisition by LC-QTOF-MS for the discovery of NPS. High resolution mass spectrometry should be considered when developing new or novel assays for synthetic cannabinoids.

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