Ensemble flood forecasting with stochastic radar image extrapolation and a distributed hydrologic model

2009 ◽  
Vol 23 (4) ◽  
pp. 597-611 ◽  
Author(s):  
Sunmin Kim ◽  
Yasuto Tachikawa ◽  
Takahiro Sayama ◽  
Kaoru Takara
Keyword(s):  
Flood Forecasting ◽  
Hydrologic Model ◽  
Radar Image ◽  
Distributed Hydrologic Model

scholarly journals Mixed Variational-Monte Carlo Assimilation of Streamflow Data in Flood Forecasting: the Impact of Observations Spatial Distribution

10.29007/39wq ◽  
2018 ◽  
Author(s):  
Giulia Ercolani ◽  
Fabio Castelli
Keyword(s):  
Monte Carlo ◽  
Spatial Distribution ◽  
River Basin ◽  
Flood Forecasting ◽  
Hydrologic Model ◽  
Variational Monte Carlo ◽  
Distributed Hydrologic Model ◽  
Streamflow Data ◽  
The Impact

A mixed variational-Monte Carlo scheme is employed to assimilate streamflow data at multiple locations in a distributed hydrologic model for flood forecasting purposes. The goal of this work is to assess the role of the spatial distribution of the assimilation points in terms of forecasts accuracy. The area of study is Arno river basin, and the strategy of investigation is to focus on one single nearly-flood event, performing various assimilation experiments that differ only in number and location of the assimilation sites.

scholarly journals Limits to Flood Forecasting in the Colorado Front Range for Two Summer Convection Periods Using Radar Nowcasting and a Distributed Hydrologic Model

2013 ◽  
Vol 14 (4) ◽  
pp. 1075-1097 ◽  
Author(s):  
Hernan A. Moreno ◽  
Enrique R. Vivoni ◽  
David J. Gochis
Keyword(s):  
Lead Time ◽  
Flood Forecasting ◽  
Hydrologic Model ◽  
Colorado Front Range ◽  
Precipitation Forecast ◽  
Lead Times ◽  
Forecast Errors ◽  
Front Range ◽  
Distributed Hydrologic Model ◽  
Forecast Lead Time

Abstract Flood forecasting in mountain basins remains a challenge given the difficulty in accurately predicting rainfall and in representing hydrologic processes in complex terrain. This study identifies flood predictability patterns in mountain areas using quantitative precipitation forecasts for two summer events from radar nowcasting and a distributed hydrologic model. The authors focus on 11 mountain watersheds in the Colorado Front Range for two warm-season convective periods in 2004 and 2006. The effects of rainfall distribution, forecast lead time, and basin area on flood forecasting skill are quantified by means of regional verification of precipitation fields and analyses of the integrated and distributed basin responses. The authors postulate that rainfall and watershed characteristics are responsible for patterns that determine flood predictability at different catchment scales. Coupled simulations reveal that the largest decrease in precipitation forecast skill occurs between 15- and 45-min lead times that coincide with rapid development and movements of convective systems. Consistent with this, flood forecasting skill decreases with nowcasting lead time, but the functional relation depends on the interactions between watershed properties and rainfall characteristics. Across the majority of the basins, flood forecasting skill is reduced noticeably for nowcasting lead times greater than 30 min. The authors identified that intermediate basin areas [~(2–20) km2] exhibit the largest flood forecast errors with the largest differences across nowcasting ensemble members. The typical size of summer convective storms is found to coincide well with these maximum errors, while basin properties dictate the shape of the scale dependency of flood predictability for different lead times.

Evaluation of a physics-based distributed hydrologic model for flood forecasting

2004 ◽  
Vol 298 (1-4) ◽  
pp. 155-177 ◽  
Author(s):  
Baxter E. Vieux ◽  
Zhengtao Cui ◽  
Anubhav Gaur
Keyword(s):  
Flood Forecasting ◽  
Hydrologic Model ◽  
Distributed Hydrologic Model

scholarly journals Evaluation of a Distributed Streamflow Forecast Model at Multiple Watershed Scales

Water ◽  
2020 ◽  
Vol 12 (5) ◽  
pp. 1279
Author(s):  
Tyler Madsen ◽  
Kristie Franz ◽  
Terri Hogue
Keyword(s):  
Local Population ◽  
Model Performance ◽  
Forecast Model ◽  
Hydrologic Model ◽  
Laboratory Research ◽  
Discharge Data ◽  
Distributed Hydrologic Model ◽  
Verification Period ◽  
Silver Spring

Demand for reliable estimates of streamflow has increased as society becomes more susceptible to climatic extremes such as droughts and flooding, especially at small scales where local population centers and infrastructure can be affected by rapidly occurring events. In the current study, the Hydrology Laboratory-Research Distributed Hydrologic Model (HL-RDHM) (NOAA/NWS, Silver Spring, MD, USA) was used to explore the accuracy of a distributed hydrologic model to simulate discharge at watershed scales ranging from 20 to 2500 km2. The model was calibrated and validated using observed discharge data at the basin outlets, and discharge at uncalibrated subbasin locations was evaluated. Two precipitation products with nominal spatial resolutions of 12.5 km and 4 km were tested to characterize the role of input resolution on the discharge simulations. In general, model performance decreased as basin size decreased. When sub-basin area was less than 250 km2 or 20–40% of the total watershed area, model performance dropped below the defined acceptable levels. Simulations forced with the lower resolution precipitation product had better model evaluation statistics; for example, the Nash–Sutcliffe efficiency (NSE) scores ranged from 0.50 to 0.67 for the verification period for basin outlets, compared to scores that ranged from 0.33 to 0.52 for the higher spatial resolution forcing.

Continuous streamflow simulation with the HRCDHM distributed hydrologic model

2004 ◽  
Vol 298 (1-4) ◽  
pp. 61-79 ◽  
Author(s):  
Theresa M. Carpenter ◽  
Konstantine P. Georgakakos
Keyword(s):  
Hydrologic Model ◽  
Distributed Hydrologic Model ◽  
Streamflow Simulation

Parallelisation of a distributed hydrologic model

2005 ◽  
Vol 22 (1) ◽  
pp. 42 ◽  
Author(s):  
Zhengtao Cui ◽  
Baxter E. Vieux ◽  
Henry Neeman ◽  
Fekadu Moreda
Keyword(s):  
Hydrologic Model ◽  
Distributed Hydrologic Model
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