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Jonathan J. Gourley
and
Humberto Vergara

Abstract

New operational tools for monitoring flash flooding based on radar quantitative precipitation estimates (QPEs) have become available to U.S. National Weather Service forecasters. Herman and Schumacher examined QPE exceedance thresholds for several tools and compared them to each other, to flash flood reports (FFRs), and to flash flood warnings. The Next Generation Radar network has been updated with dual-polarization capabilities since the publication of Herman and Schumacher, which has changed the characteristics of the derived QPEs. Updated thresholds on Multi-Radar Multi-Sensor version 12 products that are associated to FFRs are provided and thus can be used as guidance by the operational forecasting community and other end-users of the products.

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Humberto Vergara
,
Jonathan J. Gourley
, and
Michael Erickson

Abstract

Uncertainty in quantitative precipitation forecasts (QPFs) from numerical weather prediction (NWP) models manifests in errors in the amounts of rainfall, storm structure, storm location, and timing, among other precipitation characteristics. In flash flood forecasting applications, errors in the QPFs can translate into significant uncertainty in forecasts of surface water flows and their impacts. In particular, the QPF errors in location and structure result in errors on flow paths, which can be highly detrimental in identifying locations susceptible to flash flood impacts. To account for this type of uncertainty, the neighboring pixel ensemble technique (NPET) was devised and implemented as a postprocessing algorithm of deterministic or ensemble outputs from a distributed hydrologic model. The aim of the technique is to address displaced hydrologic responses resulting from location biases in QPFs using a probabilistic approach. NPET identifies a sampling region surrounding each forecast pixel and builds an ensemble of surface water flow values considering the pixel’s physiographic similarities. The probabilistic information produced with NPET can be calibrated through a set of tunable parameters that are adjusted to account for NWP-specific QPF error characteristics. The utility of NPET is demonstrated for the Ellicott City flash flood event on 27 May 2018, using products and tools routinely used in the U.S. National Weather Service for warning operations. Results from this case demonstrate that NPET effectively conveys uncertainty information about QPF precipitation location in a hydrologic context.

Significance Statement

This study introduces a new method suitable for operational use called the neighboring pixel ensemble technique (NPET). NPET is an algorithm that generates ensemble-based streamflow forecasts accounting for the location uncertainties in quantitative precipitation forecasts (QPFs) without the requirement of multiple hydrologic model runs. NPET is capable of this feat through probabilistic assimilation of a priori QPF displacement information and its uncertainty. The application of NPET with the Flooded Locations and Simulated Hydrographs (FLASH) project shows the technique could be beneficial for flash flood warning operations in the U.S. National Weather Service (NWS). It is envisioned that the application of NPET with Warn-on-Forecast System (WoFS)-forced FLASH outputs will further enhance the quality of flash flood forecasts that support NWS warning operations.

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Xinxuan Zhang
,
Emmanouil N. Anagnostou
, and
Humberto Vergara

Abstract

Satellite-retrieved precipitation has the potential to support flood modeling in mountainous areas. However, to reach this potential satellite estimates need to be corrected for the severe underestimation exhibited in orography-induced heavy precipitation events (HPEs). This paper assesses an existing satellite precipitation error correction technique driven by high-resolution numerical weather prediction (NWP) simulations of HPEs in complex terrain. The study is based on NOAA Climate Prediction Center morphing technique (CMORPH) high-resolution precipitation estimates of six such events induced by hurricane landfalls in the southern Appalachian mountainous region. A distributed hydrological model (Coupled Routing and Excess Storage model) is applied to evaluate the impact of the proposed satellite precipitation error correction on flood simulations for 20 basins of various sizes in this mountainous region. The results demonstrate significant improvements due to the NWP-based adjustment technique in terms of both the precipitation error characteristics and corresponding runoff simulations. These improvements are shown to be comparable to those from the postprocessed gauge-adjusted CMORPH precipitation product, which is promising for advancing hydrologic uses of satellite rainfall in mountainous areas lacking ground observations.

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Yixin Wen
,
Terry Schuur
,
Humberto Vergara
, and
Charles Kuster

Abstract

Quantitative precipitation estimates (QPE) at high spatiotemporal resolution are essential for flash flood forecasting, especially in urban environments and headwater areas. An accurate quantification of precipitation is directly related to the temporal and spatial sampling of the precipitation system. The advent of phased array radar (PAR) technology, a potential next-generation weather radar, can provide updates that are at least 4–5 times faster than the conventional WSR-88D scanning rate. In this study, data collected by the Norman, Oklahoma (KOUN), WSR-88D radar with ~1-min temporal resolution are used as an approximation of data that a future PAR system could provide to force the Ensemble Framework for Flash Flood Forecasting (EF5) hydrologic model. To assess the effect of errors resulting from temporal and spatial sampling of precipitation on flash flood warnings, KOUN precipitation data (1-km/1-min resolution) are used to generate precipitation products at other spatial/temporal resolutions commonly used in hydrologic models, such as those provided by conventional WSR-88D radar (1 km/5 min), space-based observations (10-km/30-min), and hourly rainfall products (1 km/60 min). The effect of precipitation sampling errors on flash flood warnings are then examined and quantified by using discharge simulated from KOUN (1 km/1 min) as truth to assess simulations conducted using other generated coarser spatial/temporal resolutions of other precipitation products. Our results show that 1) observations with coarse spatial and temporal sampling can cause large errors in quantification of the amount, intensity, and distribution of precipitation; 2) time series of precipitation products show that QPE peak values decrease as the temporal resolution gets coarser; and 3) the effect of precipitation sampling error on flash flood forecasting is large in headwater areas and decrease quickly as drainage area increases.

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Jonathan J. Gourley
,
Yang Hong
,
Zachary L. Flamig
,
Jiahu Wang
,
Humberto Vergara
, and
Emmanouil N. Anagnostou

Abstract

This study evaluates rainfall estimates from the Next Generation Weather Radar (NEXRAD), operational rain gauges, Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks Cloud Classification System (PERSIANN-CCS) in the context as inputs to a calibrated, distributed hydrologic model. A high-density Micronet of rain gauges on the 342-km2 Ft. Cobb basin in Oklahoma was used as reference rainfall to calibrate the National Weather Service’s (NWS) Hydrology Laboratory Research Distributed Hydrologic Model (HL-RDHM) at 4-km/l-h and 0.25°/3-h resolutions. The unadjusted radar product was the overall worst product, while the stage IV radar product with hourly rain gauge adjustment had the best hydrologic skill with a Micronet relative efficiency score of −0.5, only slightly worse than the reference simulation forced by Micronet rainfall. Simulations from TRMM-3B42RT were better than PERSIANN-CCS-RT (a real-time version of PERSIANN-CSS) and equivalent to those from the operational rain gauge network. The high degree of hydrologic skill with TRMM-3B42RT forcing was only achievable when the model was calibrated at TRMM’s 0.25°/3-h resolution, thus highlighting the importance of considering rainfall product resolution during model calibration.

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Manabendra Saharia
,
Pierre-Emmanuel Kirstetter
,
Humberto Vergara
,
Jonathan J. Gourley
,
Yang Hong
, and
Marine Giroud

Abstract

Flash floods, a subset of floods, are a particularly damaging natural hazard worldwide because of their multidisciplinary nature, difficulty in forecasting, and fast onset that limits emergency responses. In this study, a new variable called “flashiness” is introduced as a measure of flood severity. This work utilizes a representative and long archive of flooding events spanning 78 years to map flash flood severity, as quantified by the flashiness variable. Flood severity is then modeled as a function of a large number of geomorphological and climatological variables, which is then used to extend and regionalize the flashiness variable from gauged basins to a high-resolution grid covering the conterminous United States. Six flash flood “hotspots” are identified and additional analysis is presented on the seasonality of flash flooding. The findings from this study are then compared to other related datasets in the United States, including National Weather Service storm reports and a historical flood fatalities database.

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Viviana Maggioni
,
Humberto J. Vergara
,
Emmanouil N. Anagnostou
,
Jonathan J. Gourley
,
Yang Hong
, and
Dimitrios Stampoulis

Abstract

This study uses a stochastic ensemble-based representation of satellite rainfall error to predict the propagation in flood simulation of three quasi-global-scale satellite rainfall products across a range of basin scales. The study is conducted on the Tar-Pamlico River basin in the southeastern United States based on 2 years of data (2004 and 2006). The NWS Multisensor Precipitation Estimator (MPE) dataset is used as the reference for evaluating three satellite rainfall products: the Tropical Rainfall Measuring Mission (TRMM) real-time 3B42 product (3B42RT), the Climate Prediction Center morphing technique (CMORPH), and the Precipitation Estimation from Remotely Sensed Imagery Using Artificial Neural Networks–Cloud Classification System (PERSIANN-CCS). Both ground-measured runoff and streamflow simulations, derived from the NWS Research Distributed Hydrologic Model forced with the MPE dataset, are used as benchmarks to evaluate ensemble streamflow simulations obtained by forcing the model with satellite rainfall corrected using stochastic error simulations from a two-dimensional satellite rainfall error model (SREM2D). The ability of the SREM2D ensemble error corrections to improve satellite rainfall-driven runoff simulations and to characterize the error variability of those simulations is evaluated. It is shown that by applying the SREM2D error ensemble to satellite rainfall, the simulated runoff ensemble is able to envelope both the reference runoff simulation and observed streamflow. The best (uncorrected) product is 3B42RT, but after applying SREM2D, CMORPH becomes the most accurate of the three products in the prediction of runoff variability. The impact of spatial resolution on the rainfall-to-runoff error propagation is also evaluated for a cascade of basin scales (500–5000 km2). Results show a doubling in the bias from rainfall to runoff at all basin scales. Significant dependency to catchment area is exhibited for the random error propagation component.

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Steven M. Martinaitis
,
Katie A. Wilson
,
Nusrat Yussouf
,
Jonathan J. Gourley
,
Humberto Vergara
,
Tiffany C. Meyer
,
Pamela L. Heinselman
,
Alan Gerard
,
Kodi L. Berry
,
Andres Vergara
, and
Justin Monroe

Abstract

There are ongoing efforts to move beyond the current paradigm of using deterministic products driven by observation-only data to make binary warning decisions. Recent works have focused on severe thunderstorm hazards, such as hail, lightning, and tornadoes. This study discusses one of the first steps toward having probabilistic information combined with convective-scale short-term precipitation forecasts available for the prediction and warning of flash flooding. Participants in the Hydrometeorology Testbed–MRMS Hydrology (HMT-Hydro) experiment evaluated several probabilistic-based hydrologic model output from the probabilistic Flooded Locations and Simulated Hydrographs (PRO-FLASH) system during experimental real-time warning operations. Evaluation of flash flood warning performance combined with product surveys highlighted how forecasters perceived biases within the probabilistic information and how the different probabilistic approaches influenced warnings that were verified versus those that were unverified. The incorporation of the Warn-on-Forecast System (WoFS) ensemble precipitation forecasts into the PRO-FLASH product generation provided an opportunity to evaluate the first coupling of subhourly convective-scale ensemble precipitation forecasts with probabilistic hydrologic modeling at the flash flood warning time scale through archived case simulations. The addition of WoFS precipitation forecasts resulted in an increase in warning lead time, including four events with ≥29 min of additional lead time but with increased probabilities of false alarms. Additional feedback from participants provided insights into the application of WoFS forecasts into warning decisions, including how flash flood expectations and confidence evolved for verified flash flood events and how forecast probabilistic products can positively influence the communications of the potential for flash flooding.

Open access
Humberto Vergara
,
Yang Hong
,
Jonathan J. Gourley
,
Emmanouil N. Anagnostou
,
Viviana Maggioni
,
Dimitrios Stampoulis
, and
Pierre-Emmanuel Kirstetter

Abstract

Uncertainty due to resolution of current satellite-based rainfall products is believed to be an important source of error in applications of hydrologic modeling and forecasting systems. A method to account for the input’s resolution and to accurately evaluate the hydrologic utility of satellite rainfall estimates is devised and analyzed herein. A radar-based Multisensor Precipitation Estimator (MPE) rainfall product (4 km, 1 h) was utilized to assess the impact of resolution of precipitation products on the estimation of rainfall and subsequent simulation of streamflow on a cascade of basins ranging from approximately 500 to 5000 km2. MPE data were resampled to match the Tropical Rainfall Measuring Mission’s (TRMM) 3B42RT satellite rainfall product resolution (25 km, 3 h) and compared with its native resolution data to estimate errors in rainfall fields. It was found that resolution degradation considerably modifies the spatial structure of rainfall fields. Additionally, a sensitivity analysis was designed to effectively isolate the error on hydrologic simulations due to rainfall resolution using a distributed hydrologic model. These analyses revealed that resolution degradation introduces a significant amount of error in rainfall fields, which propagated to the streamflow simulations as magnified bias and dampened aggregated error (RMSEs). Furthermore, the scale dependency of errors due to resolution degradation was found to intensify with increasing streamflow magnitudes. The hydrologic model was calibrated with satellite- and original-resolution MPE using a multiscale approach. The resulting simulations had virtually the same skill, suggesting that the effects of rainfall resolution can be accounted for during calibration of hydrologic models, which was further demonstrated with 3B42RT.

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Nusrat Yussouf
,
Katie A. Wilson
,
Steven M. Martinaitis
,
Humberto Vergara
,
Pamela L. Heinselman
, and
Jonathan J. Gourley

Abstract

The goal of the National Oceanic and Atmospheric Administration’s (NOAA) Warn-on-Forecast (WoF) program is to provide frequently updating, probabilistic model guidance that will enable National Weather Service (NWS) forecasters to produce more continuous communication of hazardous weather threats (e.g., heavy rainfall, flash floods, damaging wind, large hail, and tornadoes) between the watch and warning temporal and spatial scales. To evaluate the application of this WoF concept for probabilistic short-term flash flood prediction, the 0–3-h rainfall forecasts from NOAA National Severe Storms Laboratory’s (NSSL) experimental WoF System (WoFS) were integrated as the forcing to the NWS operational hydrologic modeling core within the Flooded Locations and Simulated Hydrographs (FLASH) system. Initial assessment of the potential impacts of probabilistic short-term flash flood forecasts from this coupled atmosphere–hydrology (WoFS-FLASH) modeling system were evaluated in the 2018 Hydrometeorology Testbed Multi-Radar Multi-Sensor Hydrology experiment held in Norman, Oklahoma. During the 3-week experiment period, a total of nine NWS forecasters analyzed three retrospective flash flood events in archive mode. This study will describe specifically what information participants extracted from the WoFS-FLASH products during these three archived events, and how this type of information is expected to impact operational decision-making processes. Overall feedback from the testbed participants’ evaluations show promise for the coupled NSSL WoFS-FLASH system probabilistic flash flood model guidance to enable earlier assessment and detection of flash flood threats and to advance the current warning lead time for these events.

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