Nonlinear Dynamics and Chaos in Hydrologic Systems: Recent Developments and Future Directions

During the last two decades or so, studies on the applications of the concepts of nonlinear dynamics and chaos to hydrologic systems and processes have been on the rise. Earlier studies on this topic focused mainly on the investigation and prediction of chaos in rainfall, river flow and lake volume data, and further advances were made during the subsequent years through applications of the concepts to other problems (e.g. data disaggregation, missing data estimation and reconstruction of system equations) and other processes (e.g. rainfall-runoff and sediment transport). During the same period, some of the important issues in the applications of chaos theory to real hydrologic data (e.g. data size, noise, zeros) were addressed and comparisons with other methods (e.g. stochastic methods, artificial neural networks) as to their applicability and performance for modeling and prediction of hydrologic processes were also made. The outcomes of these studies are certainly encouraging, especially considering the exploratory stage of the concepts in hydrology, although they continue to be under certain criticism mainly on the basis of the possible ‘blind’ applications of the lessunderstood’ chaos concepts without recognizing their potential limitations for real hydrologic data.