Predictability of flow metrics calculated using a distributed hydrologic model across ecoregions and stream classes: implications for developing flow‐ecology relationships

Ecohydrology ◽  
2021 ◽  
Author(s):  
Michele C. Eddy ◽  
Benjamin Lord ◽  
Danielle Perrot ◽  
Luke M. Bower ◽  
Brandon K. Peoples
Water ◽  
2020 ◽  
Vol 12 (5) ◽  
pp. 1279
Author(s):  
Tyler Madsen ◽  
Kristie Franz ◽  
Terri Hogue

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.


2004 ◽  
Vol 298 (1-4) ◽  
pp. 61-79 ◽  
Author(s):  
Theresa M. Carpenter ◽  
Konstantine P. Georgakakos

Author(s):  
Zhengtao Cui ◽  
Baxter E. Vieux ◽  
Henry Neeman ◽  
Fekadu Moreda

2007 ◽  
Vol 12 (3) ◽  
pp. 336-344 ◽  
Author(s):  
Sunmin Kim ◽  
Yasuto Tachikawa ◽  
Kaoru Takara

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