The Ensemble Framework For Flash Flood Forecasting (EF5) was developed specifically for improving hydrologic predictions to aid in the issuance of flash flood warnings by the US National Weather ...Service. EF5 features multiple water balance models and two routing schemes which can be used to generate ensemble forecasts of streamflow, streamflow normalized by upstream basin area (i.e., unit streamflow), and soil saturation. EF5 is designed to utilize high-resolution precipitation forcing datasets now available in real time. A study on flash-flood-scale basins was conducted over the conterminous United States using gauged basins with catchment areas less than 1000 km2. The results of the study show that the three uncalibrated water balance models linked to kinematic wave routing are skillful in simulating streamflow.
Extreme flooding impacts millions of people that live within the
Amazon floodplain. Global hydrological models (GHMs) are frequently used to
assess and inform the management of flood risk, but ...knowledge on the skill
of available models is required to inform their use and development. This
paper presents an intercomparison of eight different GHMs freely available
from collaborators of the Global Flood Partnership (GFP) for simulating
floods in the Amazon basin. To gain insight into the strengths and
shortcomings of each model, we assess their ability to reproduce daily and
annual peak river flows against gauged observations at 75 hydrological
stations over a 19-year period (1997–2015). As well as highlighting regional variability in the accuracy of simulated streamflow, these results indicate that (a) the meteorological input is the dominant control on the accuracy of both daily and annual maximum river flows, and (b) groundwater and routing calibration of Lisflood based on daily river flows has no impact on the ability to simulate flood peaks for the chosen river basin. These findings have important relevance for applications of large-scale hydrological models, including analysis of the impact of climate variability, assessment of the influence of long-term changes such as land-use and anthropogenic climate change, the assessment of flood likelihood, and for flood forecasting systems.
THE FLASH PROJECT Gourley, Jonathan J.; Flamig, Zachary L.; Vergara, Humberto ...
Bulletin of the American Meteorological Society,
02/2017, Letnik:
98, Številka:
2
Journal Article
Recenzirano
Odprti dostop
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.
Celotno besedilo
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BFBNIB, DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
A method for probabilistic flash flood forecasting Hardy, Jill; Gourley, Jonathan J.; Kirstetter, Pierre-Emmanuel ...
Journal of hydrology (Amsterdam),
October 2016, 2016-10-00, 20161001, Letnik:
541
Journal Article
Recenzirano
Odprti dostop
•A novel method for probabilistic flash flood forecasting is explained.•We input a stormscale NWP ensemble into a distributed hydrological model.•The method proved successful in forecasting flash ...flooding many hours in advance.•The method positively forecasted specific basin scales impacted by flash flooding.•The method yielded probabilistic information about the hydrologic response.
Flash flooding is one of the most costly and deadly natural hazards in the United States and across the globe. This study advances the use of high-resolution quantitative precipitation forecasts (QPFs) for flash flood forecasting. The QPFs are derived from a stormscale ensemble prediction system, and used within a distributed hydrological model framework to yield basin-specific, probabilistic flash flood forecasts (PFFFs). Before creating the PFFFs, it is important to characterize QPF uncertainty, particularly in terms of location which is the most problematic for hydrological use of QPFs. The SAL methodology (Wernli et al., 2008), which stands for structure, amplitude, and location, is used for this error quantification, with a focus on location. Finally, the PFFF methodology is proposed that produces probabilistic hydrological forecasts. The main advantages of this method are: (1) identifying specific basin scales that are forecast to be impacted by flash flooding; (2) yielding probabilistic information about the forecast hydrologic response that accounts for the locational uncertainties of the QPFs; (3) improving lead time by using stormscale NWP ensemble forecasts; and (4) not requiring multiple simulations, which are computationally demanding.
Extreme precipitation is one of the most devastating forms of atmospheric phenomenon, causing severe damage worldwide, and is likely to intensify in strength and occurrence in a warming climate. This ...contribution gives an overview of the potential and challenges associated with using weather radar data to investigate extreme precipitation. We illustrate this by presenting radar data sets for Germany, the U.S. and the UK that resolve small-scale heavy rainfall events of just a few km2 with return periods of 5 years or more. Current challenges such as relatively short radar records and radar-based QPE uncertainty are discussed. An example from a precipitation climatology derived from the German weather radar network with spatial resolution of 1 km reveals the necessity of radars for observing short-term (1-6 h) extreme precipitation. Only 17.3% of hourly heavy precipitation events that occurred in Germany from 2001 to 2018 were captured by the rain gauge station network, while 81.8% of daily events were observed. This is underlined by a similar study using data from the UK radar network for 2014. Only 36.6% (52%) of heavy hourly (daily) rain events detected by the radar network were also captured by precipitation gauging stations. Implications for the monitoring of hydrologic extremes are demonstrated over the U.S. with a continental-scale radar-based reanalysis. Hydrologic extremes are documented over ∼1000 times more locations than stream gauges, including in the majority of ungauged basins. This underlines the importance of high-resolution weather radar observations for resolving small-scale rainfall events, and the necessity of radar-based climatological data sets for understanding the small-scale and high-temporal resolution characteristics of extreme precipitation.
•Innovative macro scale approach to kinematic wave parameter estimation.•Geophysical factors’ statistical analysis consistent with fluvial hydraulics theory.•Large sample verification of hydrologic ...simulations shows good and consistent skill.•Methodology enables hydrologic modeling in ungauged basins at global scale.
This study presents a methodology for the estimation of a-priori parameters of the widely used kinematic wave approximation to the unsteady, 1-D Saint–Venant equations for hydrologic flow routing. The approach is based on a multi-dimensional statistical modeling of the macro scale spatial variability of rating curve parameters using a set of geophysical factors including geomorphology, hydro-climatology and land cover/land use over the Conterminous United States. The main goal of this study was to enable prediction at ungauged locations through regionalization of model parameters. The results highlight the importance of regional and local geophysical factors in uniquely defining characteristics of each stream reach conforming to physical theory of fluvial hydraulics. The application of the estimates is demonstrated through a hydrologic modeling evaluation of a deterministic forecasting system performed on 1672 gauged basins and 47,563 events extracted from a 10-year simulation. Considering the mean concentration time of the basins of the study and the target application on flash flood forecasting, the skill of the flow routing simulations is significantly high for peakflow and timing of peakflow estimation, and shows consistency as indicated by the large sample verification. The resulting a-priori estimates can be used in any hydrologic model that employs the kinematic wave model for flow routing. Furthermore, probabilistic estimates of kinematic wave parameters are enabled based on uncertainty information that is generated during the multi-dimensional statistical modeling. More importantly, the methodology presented in this study enables the estimation of the kinematic wave model parameters anywhere over the globe, thus allowing flood modeling in ungauged basins at regional to global scales.
This article focuses on conceptual and methodological developments allowing the integration of physical and social dynamics leading to model forecasts of circumstance‐specific human losses during a ...flash flood. To reach this objective, a random forest classifier is applied to assess the likelihood of fatality occurrence for a given circumstance as a function of representative indicators. Here, vehicle‐related circumstance is chosen as the literature indicates that most fatalities from flash flooding fall in this category. A database of flash flood events, with and without human losses from 2001 to 2011 in the United States, is supplemented with other variables describing the storm event, the spatial distribution of the sensitive characteristics of the exposed population, and built environment at the county level. The catastrophic flash floods of May 2015 in the states of Texas and Oklahoma are used as a case study to map the dynamics of the estimated probabilistic human risk on a daily scale. The results indicate the importance of time‐ and space‐dependent human vulnerability and risk assessment for short‐fuse flood events. The need for more systematic human impact data collection is also highlighted to advance impact‐based predictive models for flash flood casualties using machine‐learning approaches in the future.
Abstract
This study quantifies the skill of the National Weather Service’s (NWS) flash flood guidance (FFG) product. Generated by River Forecast Centers (RFCs) across the United States, local NWS ...Weather Forecast Offices compare estimated and forecast rainfall to FFG to monitor and assess flash flooding potential. A national flash flood observation database consisting of reports in the NWS publication Storm Data and U.S. Geological Survey (USGS) stream gauge measurements are used to determine the skill of FFG over a 4-yr period. FFG skill is calculated at several different precipitation-to-FFG ratios for both observation datasets. Although a ratio of 1.0 nominally indicates a potential flash flooding event, this study finds that FFG can be more skillful when ratios other than 1.0 are considered. When the entire continental United States is considered, the highest observed critical success index (CSI) with 1-h FFG is 0.20 for the USGS dataset, which should be considered a benchmark for future research that seeks to improve, modify, or replace the current FFG system. Regional benchmarks of FFG skill are also determined on an RFC-by-RFC basis. When evaluated against Storm Data reports, the regional skill of FFG ranges from 0.00 to 0.19. When evaluated against USGS stream gauge measurements, the regional skill of FFG ranges from 0.00 to 0.44.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
Flood disasters have significant impacts on the development of communities globally. This study describes a public cloud-based flood cyber-infrastructure (CyberFlood) that collects, organizes, ...visualizes, and manages several global flood databases for authorities and the public in real-time, providing location-based eventful visualization as well as statistical analysis and graphing capabilities. In order to expand and update the existing flood inventory, a crowdsourcing data collection methodology is employed for the public with smartphones or Internet to report new flood events, which is also intended to engage citizen-scientists so that they may become motivated and educated about the latest developments in satellite remote sensing and hydrologic modeling technologies. Our shared vision is to better serve the global water community with comprehensive flood information, aided by the state-of-the-art cloud computing and crowd-sourcing technology. The CyberFlood presents an opportunity to eventually modernize the existing paradigm used to collect, manage, analyze, and visualize water-related disasters.
•We elaborate the global flood disaster community cyber-infrastructure (CyberFlood).•Cloud-based service is integrated to provide map and statistic visualization.•A crowdsourcing methodology is employed to enable the public to report flood events.•This CyberFlood presents an opportunity to modernize the way of managing flood data.
Despite flash flooding being one of the most deadly and costly weather-related natural hazards worldwide, individual datasets to characterize them in the United States are hampered by limited ...documentation and can be difficult to access. This study is the first of its kind to assemble, reprocess, describe, and disseminate a georeferenced U.S. database providing a long-term, detailed characterization of flash flooding in terms of spatiotemporal behavior and specificity of impacts. The database is composed of three primary sources: 1) the entire archive of automated discharge observations from the U.S. Geological Survey that has been reprocessed to describe individual flooding events, 2) flash-flooding reports collected by the National Weather Service from 2006 to the present, and 3) witness reports obtained directly from the public in the Severe Hazards Analysis and Verification Experiment during the summers 2008–10. Each observational data source has limitations; a major asset of the unified flash flood database is its collation of relevant information from a variety of sources that is now readily available to the community in common formats. It is anticipated that this database will be used for many diverse purposes, such as evaluating tools to predict flash flooding, characterizing seasonal and regional trends, and improving understanding of dominant flood-producing processes. We envision the initiation of this community database effort will attract and encompass future datasets.
Celotno besedilo
Dostopno za:
BFBNIB, DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
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