Mathematical Modeling of Watershed Hydrology

By Vijay P. Singh, D. Woolhiser. 2002

870 citations recorded by the source corpus.

Subfields: Environmental Engineering, Global and Planetary Change, Water Science and Technology

View Paper: https://www.semanticscholar.org/paper/7ea67fa0b9b6abb49f52c2c7967a942f81e6606c

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In Environmental Engineering

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Citations in the corpus

Generic error model for calibration and uncertainty estimation of hydrological models (2008, background): “Singh and Woolhiser [2002] describe this fact as one of the major limitations of current watershed models.”
Entropy theory for derivation of infiltration equations (2010, methodology): “As a result a large number of infiltration equations have been developed, some of which are now commonly applied in hydrologic modeling and have been included in popular watershed hydrology models [Singh, 1989, 1995; Singh and Frevert, 2002a, 2002b, 2006; Singh and Woolhiser, 2002].”
Extension of the representative elementary watershed approach for cold regions via explicit treatment of energy related processes (2006, neutral): “The hydrological literature is replete with reviews and discussions of the advantages and limitations of physically-based distributed models (Beven, 1989, 1993, 1996, 2002; Grayson et al., 1992; Smith et al., 1994; Woolhiser, 1996; Refsgaard et al., 1996; Singh and Woolhiser, 2002).”
Dynamically dimensioned search algorithm for computationally efficient watershed model calibration (2007, background): “Gupta et al. [1998] and Singh and Woolhiser [2002] note that the automatic calibration methodology has a number of important parts including: (1) the selection of appropriate calibration data, (2) the definition of the objective function that measures the error between model predictions and the…”
HydroTest: A web-based toolbox of evaluation metrics for the standardised assessment of hydrological forecasts (2007, background): “Numerous other opportunities are also being explored, such as the potential for significant savings to be made in terms of model development time and effort, or the need to model nonlinear systems where traditional parameter estimation techniques are not convenient (Singh and Woolhiser, 2002).”