@article {51550,
	title = {Application of copula modelling to the performance assessment of reconstructed watersheds},
	volume = {26},
	year = {2012},
	pages = {16 pages },
	publisher = {Stochastic Environmental Research and Risk Assessment },
	abstract = {The existence of interdependence among environmental variables has been qualitatively known for centuries. Recent studies have shown that copula modelling can provide a simple, yet powerful framework for modelling interdependence among hydrological data; however, still there are several studies which use outdated and superficial methods to perform this task. By considering the current state of knowledge, this study tries to introduce a pragmatic procedure to perform copula modelling in real-world problems. Our study uses copula modelling to find a notion for conditional quantities of the maximum annual water deficit with respect to the annual cumulative evapotranspiration. Therefore, by having an estimate for the annual cumulative evapotranspiration, the hydrological performance of the reconstructed watershed can be assessed even in nearby ungauged reconstructed watersheds with similar physical characteristics and reclamation strategy. Several competitive models are developed for joint description of these variables in a prototype reclaimed oil-sands mining site in northern Alberta, Canada. The developed joint models are compared and analyzed according to their convergence feasibility, overall accuracy, tail behaviour and a goodness-of-fit test. Our study concludes that copula modelling can be considered as a powerful option in practitioners{\textquoteright} toolkit. For the case under consideration, the Gumbel-Houguaard structure provides the most credible model of dependence. Moreover, our study provides some initial supports for the application of minimum distance methods for copula parameter estimation.[},
	keywords = {evaporation / evapotranspiration, hydrology, model / modeling, UofS, watershed},
	url = {http://search.proquest.com/docview/915870413},
	author = {Nazemi, A. and Elshorbagy, A.}
}
