Date

2010-01-01

Author

Yılmaz, Koray Kamil
Gupta, Hoshin V
Sorooshian, Soroosh

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Hydrologic models use relatively simple mathematical equations to conceptualize and aggregate the complex, spatially distributed, and highly interrelated water, energy, and vegetation processes in a watershed. A consequence of process aggregation is that the model parameters often do not represent directly measurable entities and must, therefore, be estimated using measurements of the system inputs and outputs. During this procedure, known as model calibration, the parameters are adjusted so that the behavior of the model approximates, as closely and consistently as possible, the observed response of the hydrologic system over some historical period of time. This Chapter reviews the current state-of-the-art of model calibration in watershed hydrology with special emphasis on our own contributions in the last few decades. We discuss the historical background that has led to current perspectives and review different approaches for manual and automatic single- and multi-objective parameter estimation. In particular, we highlight the recent developments in the calibration of distributed hydrologic models using parameter dimensionality reduction sampling, parameter regularization, and parallel computing. Finally, this chapter concludes with a short summary of methods for assessment of parameter uncertainty, including recent advances in Markov chain Monte Carlo sampling and sequential data assimilation methods based on the Ensemble Kalman Filter.

URI

http://www.worldscientific.com/worldscibooks/10.1142/7783
https://hdl.handle.net/11511/82597

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Department of Geological Engineering, Book / Book chapter