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| Titel |
Large-scale hydrological modelling by using modified PUB recommendations: the India-HYPE case |
| VerfasserIn |
I. G. Pechlivanidis, B. Arheimer |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1027-5606
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| Digitales Dokument |
URL |
| Erschienen |
In: Hydrology and Earth System Sciences ; 19, no. 11 ; Nr. 19, no. 11 (2015-11-17), S.4559-4579 |
| Datensatznummer |
250120851
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| Publikation (Nr.) |
 copernicus.org/hess-19-4559-2015.pdf |
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| Zusammenfassung |
| The scientific initiative Prediction in Ungauged Basins (PUB) (2003–2012 by
the 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 to large domains and large
data samples in a multi-basin manner, to explore emerging spatial patterns or
learn from comparative hydrology. However, such modelling involves additional
sources of uncertainties caused by the inconsistency between input data sets,
i.e. particularly regional and global databases. This may lead to inaccurate
model parameterisation and erroneous process understanding. In order to
bridge the gap between the best practices for flow predictions in single
catchments and multi-basins at the large scale, 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 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, identify
challenges and recommend ways to overcome them. We describe the work process
related to (a) errors and inconsistencies in global databases, unknown human
impacts, and poor data quality; (b) robust approaches to identify model
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 physiographical gradient over the subcontinent, a single
model can describe the spatial variability in dominant hydrological processes
at the catchment scale. In addition, spatial model deficiencies are used to
identify potential improvements of the model concept. Eventually, through
simultaneous calibration using numerous gauges, the median Kling–Gupta
efficiency for river flow increased from 0.14 to 0.64. We finally demonstrate
the potential of multi-basin modelling for comparative hydrology using PUB,
by grouping the 6000 subbasins based on similarities in flow signatures to
gain insights into the spatial patterns of flow generating processes at the large
scale. |
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