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• W4250028511 abstract "Abstract. The Prediction in Ungauged Basins (PUB) scientific initiative (2003–2012 by IAHS) put considerable effort into improving the reliability of hydrological models to predict flow response in ungauged rivers. PUB's collective experience advanced hydrologic science and defined guidelines to make predictions in catchments without observed runoff data. At present, there is a raised interest in applying catchment models for large domains and large data samples in a multi-basin manner. However, such modelling involves several sources of uncertainties, which may be caused by the imperfectness of input data, i.e. particularly regional and global databases. This may lead to inaccurate model parameterisation and incomplete process understanding. In order to bridge the gap between the best practices for single catchments and large-scale hydrology, we present a further developed and slightly modified version of the recommended best practices for PUB by Takeuchi et al. (2013). By using examples from a recent HYPE hydrological model set-up on the Indian subcontinent, named India-HYPE v1.0, we explore the recommendations, indicate challenges and recommend quality checks to avoid erroneous assumptions. We identify the obstacles, ways to overcome them and describe the work process related to: (a) errors and inconsistencies in global databases, unknown human impacts, poor data quality, (b) robust approaches to identify parameters using a stepwise calibration approach, remote sensing data, expert knowledge and catchment similarities; and (c) evaluation based on flow signatures and performance metrics, using both multiple criteria and multiple variables, and independent gauges for blind tests. The results show that despite the strong hydro-climatic gradient over the subcontinent, a single model can adequately describe the spatial variability in dominant hydrological processes at the catchment scale. Eventually, during calibration of India-HYPE, the median Kling–Gupta Efficiency for river flow increased from 0.14 to 0.64. To sum up, we demonstrate that by using the further developed PUB recommendations in processed-based large-scale models, the predictions can be consistent in both space and time for multiple basins. We describe and argue for the suggested work process when approaching the large scale with multi-basins and big datasets. Some useful methods are presented and examples of results are given." @default.
• W4250028511 created "2022-05-12" @default.
• W4250028511 creator A5051099106 @default.
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• W4250028511 date "2015-03-10" @default.
• W4250028511 modified "2023-09-27" @default.
• W4250028511 title "Large-scale hydrological modelling by using modified PUB recommendations: the India-HYPE case" @default.
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• W4250028511 doi "https://doi.org/10.5194/hessd-12-2885-2015" @default.
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