Climate Change Detection and Modeling in Hydrology

Detection of a change is defined as the process of demonstrating that climate or a system affected by climate has changed in some defined statistical sense, without providing a reason for that change. Attribution is defined as the process of evaluation of the relative contribution of multiple causal factors to a change or event with an assignment of statistical confidence. However, the observed changes must be able to be detected (IPCC 2010). Attribution to a change in climatic conditions includes the assessments that attribute an observed change in a variable of interest to a specific observed change in climate conditions based on the process knowledge and relative importance of a change in climate condition in determining the observed impacts (Hao et al. 2008; Liu and Xia 2011). The associated confidence levels should be evaluated for the data, model, methods, and the factors used in the study (IPCC 2010). Seibert et al. (2010) used the three different approaches for change detection modeling employing a modified version of the HBV (Hydrologiska Byrans Vattenbalansavdelning) model (Bergstrom 1976, 1992) to conclude that catchment-scale runoff increases following severe wildfire. The application of the HBV model as a change detection tool indicated the increases in peak flows following severe wildfire and the related road building and harvesting of the dead and damaged forest vegetations. The parameter uncertainty of various parameter sets is commonly known in hydrologic and climatologic modeling. It is an issue seldom addressed in modeling approaches for detecting changes (Pappenberger and Beven 2006; Seibert and McDonnell 2010). Employing a large number of parameter sets rather than a single set of parameter values facilitates the assessment of the associated uncertainty. The detection of climate change impacts on the observed climate and elements of the hydrological cycle have made a great progress, recently (Amiri and Eslamian, 2010). Based on the climate model simulation, the optimal methods have been used to detect the responses of observed change to Green House Gas emissions from the other external forcing at large spatial scales. Presently, the detection of anthropogenic influence is not yet possible for all of the climate variables. It is still difficult to attribute the observed changes in climate

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