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The  scientific initiative Prediction in Ungauged Basins (PUB) (2003–2012 bythe IAHS) put considerable effort into improving the reliability of hydrologicalmodels to predict flow response in ungauged rivers. PUB's collectiveexperience advanced hydrologic science and defined guidelines to makepredictions in catchments without observed runoff data. At present, there isa raised interest in applying catchment models to large domains and largedata samples in a multi-basin manner, to explore emerging spatial patterns orlearn from comparative hydrology. However, such modelling involves additionalsources of uncertainties caused by the inconsistency between input data sets,i.e. particularly regional and global databases. This may lead to inaccuratemodel parameterisation and erroneous process understanding. In order tobridge the gap between the best practices for flow predictions in singlecatchments and multi-basins at the large scale, we present a furtherdeveloped and slightly modified version of the recommended best practices forPUB by Takeuchi et al. (2013). By using examples from a recent HYPE (Hydrological Predictions for the Environment)hydrological model set-up across 6000 subbasins for the Indian subcontinent,named India-HYPE v1.0, we explore the PUB recommendations, identifychallenges and recommend ways to overcome them. We describe the work processrelated to  (a) errors and inconsistencies in global databases, unknown humanimpacts, and poor data quality; (b) robust approaches to identify modelparameters using a stepwise calibration approach, remote sensing data, expertknowledge, and catchment similarities; and (c) evaluation based on flowsignatures and performance metrics, using both multiple criteria and multiplevariables, and independent gauges for "blind tests". The results show thatdespite the strong physiographical gradient over the subcontinent, a singlemodel can describe the spatial variability in dominant hydrological processesat the catchment scale. In addition, spatial model deficiencies are used toidentify potential improvements of the model concept. Eventually, throughsimultaneous calibration using numerous gauges, the median Kling–Guptaefficiency for river flow increased from 0.14 to 0.64. We finally demonstratethe potential of multi-basin modelling for comparative hydrology using PUB,by grouping the 6000 subbasins based on similarities in flow signatures togain insights into the spatial patterns of flow generating processes at the largescale

Large-scale hydrological modelling by using modified PUB recommendations: the India-HYPE case