| description abstract | The past few decades have witnessed the development of numerous catchment models, often with increasing structural complexity and mathematical sophistication. While such models have certainly provided a better understanding of catchments and associated processes, they are also often catchment-specific, region-specific, or process-specific. Serious concerns on this modeling trend have been increasingly raised in recent times and, consequently, the need for a generic catchment classification framework in hydrology has been emphasized. There have indeed been some attempts to advance the idea of such a classification framework. Such studies have investigated different ways of developing a framework, including river morphology, river regimes, hydroclimatic factors, landscape and land use parameters, hydrologic similarity indexes, hydrologic signatures, ecohydrologic factors, geostatistical properties, entropy, nonlinear and chaotic properties, data mining, and other relevant characteristics and methods. Although useful in their own ways, these studies are largely inadequate for a generic classification framework. In addition to the limitations that exist in each of the different forms, a coherent effort to bring these disparate forms together for a workable classification is also missing. This study highlights the challenges that the existing approaches pose in the development of a generic classification framework. It argues for an appropriate basis, a suitable methodology, and key components for such a framework. In particular, it discusses the vital role of | |
