Meta‐analysis of flow modeling performances—to build a matching system between catchment complexity and model types

Hydrological models play a significant role in modelling river flow for decision making support in water resource management. In the past decades, many researchers have made a great deal of efforts in calibrating and validating various models, with each study being focused on one or two models. As a result, there is a lack of comparative analysis on the performance of those models to guide hydrologists to choose appropriate models for the individual climate and physical conditions. This paper describes a two‐level meta‐analysis to develop a matching system between catchment complexity (based on catchment significant features (CSFs)) and model types. The intention is to use the available CSF information for choosing the most suitable model type for a given catchment. In this study, the CSFs include the elements of climate, soil type, land cover and catchment scale. Specific choices of model types in small and medium catchments are further explored with all CSF information obtained. In particular, it is interesting to find that semi‐distributed models are the most suitable model type for catchments with the area over 3000 km², regardless of other CSFs. The potential methodology for expanding the matching system between catchment complexity and model complexity is discussed. Copyright © 2014 John Wiley & Sons, Ltd.