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Nominated as an Outstanding Ph.D. thesis by the University of Zaragoza, Spain
For researchers looking for innovative methods of systems identification for data-driven analysis of a karst aquifer and a polar glacier
How to build models for nonlinear natural systems to improve the management, simulate the behavior, predict future states and offer useful help to generate knowledge in subsequent research
It shows a progressive identification from linear to nonlinear identification, to get the best of both into a block model, which emulates the dynamics of the natural system with high efficiency
This thesis tackles fundamental questions concerning the discharge of a pre-Pyrenean karst aquifer system and an Antarctic glacier system, utilizing a system engineering methodology and data-driven approach. It presents for the first time a simplified and effective linear transfer function for karst aquifers. The author provides detailed wavelet spectrum results, which reveal certain non-linearities in drought periods. In addition, structures based on Hammerstein-Wiener blocks have yielded a nonlinear model that is substantially more efficient than its linear counterparts. Another pioneering finding is the use of wavelet coherence between glacier discharge and air temperature to estimate SEC (Seasonal Effective Core) boundaries. The yearly SEC is essential to obtaining a model based on Hammerstein-Wiener structures, which offers considerably higher efficiency. Moreover, two different types of glacier dynamics have been discovered (overdamped and overshoot), depending on the annual cycle and the SEC average temperature.