Flash Flood Forecasting by Statistical Learning in the Absence of Rainfall Forecast: A Case Study

2009 ◽  
pp. 98-107 ◽  
Author(s):  
Mohamed Samir Toukourou ◽  
Anne Johannet ◽  
Gérard Dreyfus
Keyword(s):  
Statistical Learning ◽  
Flash Flood ◽  
Flood Forecasting ◽  
Rainfall Forecast ◽  
Flash Flood Forecasting

scholarly journals The Ensemble Framework For Flash Flood Forecasting (EF5) v1.2: description and case study

2020 ◽  
Vol 13 (10) ◽  
pp. 4943-4958
Author(s):  
Zachary L. Flamig ◽  
Humberto Vergara ◽  
Jonathan J. Gourley
Keyword(s):  
Water Balance ◽  
Flash Flood ◽  
Flood Forecasting ◽  
Ensemble Forecasts ◽  
Routing Schemes ◽  
Catchment Areas ◽  
The Us ◽  
Water Balance Models ◽  
Flash Flood Forecasting

Abstract. 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.

scholarly journals The Ensemble Framework For Flash Flood Forecasting (EF5) v1.2: Description and Case Study

2020 ◽  
Author(s):  
Zachary L. Flamig ◽  
Humberto Vergara ◽  
Jonathan J. Gourley
Keyword(s):  
Water Balance ◽  
Flash Flood ◽  
Flood Forecasting ◽  
Streamflow Prediction ◽  
Ensemble Forecasts ◽  
Routing Schemes ◽  
Catchment Areas ◽  
Water Balance Models ◽  
Flash Flood Forecasting

Abstract. 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 U.S. 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 near real time. A study on flash flood scale basins was conducted over the conterminous United States using gauged basins with catchment areas less than 1,000 km2. The results of the study show that the three uncalibrated water balance models linked to kinematic wave routing are skillful in streamflow prediction.

A spatially distributed flash flood forecasting model

2008 ◽  
Vol 23 (4) ◽  
pp. 464-478 ◽  
Author(s):  
G BLOSCHL ◽  
C RESZLER ◽  
J KOMMA
Keyword(s):  
Flash Flood ◽  
Flood Forecasting ◽  
Forecasting Model ◽  
Spatially Distributed ◽  
Flash Flood Forecasting

2D hydraulic model integration to real-time flash flood forecasting chain

2012 ◽  
Author(s):  
C Girard ◽  
T Godfroy ◽  
M Erlich ◽  
E David ◽  
C Sorbet ◽  
...  
Keyword(s):  
Real Time ◽  
Flash Flood ◽  
Flood Forecasting ◽  
Hydraulic Model ◽  
Model Integration ◽  
Flash Flood Forecasting

scholarly journals Evaluation of radar-based precipitation estimates for flash flood forecasting in the Three Gorges Region

2015 ◽  
Vol 368 ◽  
pp. 89-95 ◽  
Author(s):  
Z. Li ◽  
D. Yang ◽  
Y. Hong ◽  
Y. Qi ◽  
Q. Cao
Keyword(s):  
Flash Flood ◽  
Critical Role ◽  
Flood Forecasting ◽  
Three Gorges ◽  
Distributed Hydrological Model ◽  
Ground Clutter ◽  
Three Gorges Region ◽  
Physically Based ◽  
Precipitation Estimation ◽  
Flash Flood Forecasting

Abstract. Spatial rainfall pattern plays a critical role in determining hydrological responses in mountainous areas, especially for natural disasters such as flash floods. In this study, to improve the skills of flood forecasting in the mountainous Three Gorges Region (TGR) of the Yangtze River, we developed a first version of a high-resolution (1 km) radar-based quantitative precipitation estimation (QPE) consideration of many critical procedures, such as beam blockage analysis, ground-clutter filter, rain type identification and adaptive Z–R relations. A physically-based distributed hydrological model (GBHM) was established and further applied to evaluate the performance of radar-based QPE for regional flood forecasting, relative to the gauge-driven simulations. With two sets of input data (gauge and radar) collected during summer 2010, the applicability of the current radar-based QPE to rainstorm monitoring and flash flood forecasting in the TGR is quantitatively analysed and discussed.

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