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Evapotranspiration is one of the important components of the hydrological cycle; its estimation and analysis are crucial for better assessment of the hydrology of natural and reconstructed landscapes. The complexity of the evapotranspiration process have imposed some limitations on previously developed evapotranspiration estimation models. Limited literature exists on the modeling of actual evapotranspiration (AET), which depends on the available soil moisture, and is more complicated than the potential evapotranspiration . This book presents the modeling and analysis of AET using data driven techniques and wavelet analysis using a case study of an experimental reconstructed landscape. Data driven modeling techniques studied in this book include genetic programming (GP), artificial neural networks (ANNs), and multilinear regression (MLR). This book sheds some light on the utility of the data driven techniques and wavelet analysis for modeling and studying the AET process. This book is especially useful to hydrological practitioners, modelers, and researchers with interests in inductive modeling and data mining of hydrological processes.

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