We have over 12,000 students, from over 100 countries, within one of the safest campuses in the UK


93% of Lancaster students go into work or further study within six months of graduating

Home > Research > Publications & Outputs > Regionalization as a learning process
View graph of relations

« Back

Regionalization as a learning process

Research output: Contribution to journalJournal article


Article numberARTN W11419
Journal publication date17/11/2009
JournalWater Resources Research
Number of pages13
Original languageEnglish


This paper deals with the uncertainty involved in geographical migration of hydrological model structures, commonly known as regionalization. Regionalization relies on the hypothesis that calibrated parameter sets from a donor catchment can be useful to predict discharge of an ungauged catchment. However, since every catchment is unique, model parameters need to be adapted for differences between a calibration and a prediction catchment, either by transformation or further selection. This process is inherently uncertain. Model parameters, and therefore the required changes, do not exactly represent quantities that we can measure or calculate. This paper outlines an approach to learn about how model parameters should be transformed between a gauged and an ungauged catchment. The approach consists of an iterative process, in which a model structure is applied successively to gauged catchments. After each step, parameter behavior is evaluated as a function of catchment properties and intercatchment similarities. The method is illustrated with an application of a customized version of TOPMODEL to a set of catchments in the Ecuadorian Andes. First, parameter sets are generated for a donor catchment. This model ensemble is then used to predict the discharge of the other catchments, after applying a stochastic parameter transformation to account for the uncertainty in the model migration. The parameter transformation is then evaluated and improved before further application. The case study shows that accurate predictions can be made for predicted basins. At the same time, knowledge is gained about model behavior and potential model limitations.