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| Titel |
Disinformative data in large-scale hydrological modelling |
| VerfasserIn |
A. Kauffeldt, S. Halldin, A. Rodhe, C.-Y. Xu, I. K. Westerberg |
| 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 ; 17, no. 7 ; Nr. 17, no. 7 (2013-07-22), S.2845-2857 |
| Datensatznummer |
250018940
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| Publikation (Nr.) |
 copernicus.org/hess-17-2845-2013.pdf |
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| Zusammenfassung |
| Large-scale hydrological modelling has become an important tool for the
study of global and regional water resources, climate impacts, and
water-resources management. However, modelling efforts over large spatial
domains are fraught with problems of data scarcity, uncertainties and
inconsistencies between model forcing and evaluation data. Model-independent
methods to screen and analyse data for such problems are needed. This study
aimed at identifying data inconsistencies in global datasets using a
pre-modelling analysis, inconsistencies that can be disinformative for
subsequent modelling. The consistency between (i) basin areas for different
hydrographic datasets, and (ii) between climate data (precipitation and
potential evaporation) and discharge data, was examined in terms of how well
basin areas were represented in the flow networks and the possibility of
water-balance closure. It was found that (i) most basins could be well
represented in both gridded basin delineations and polygon-based ones, but
some basins exhibited large area discrepancies between flow-network datasets
and archived basin areas, (ii) basins exhibiting too-high runoff
coefficients were abundant in areas where precipitation data were likely
affected by snow undercatch, and (iii) the occurrence of basins exhibiting
losses exceeding the potential-evaporation limit was strongly dependent on
the potential-evaporation data, both in terms of numbers and geographical
distribution. Some inconsistencies may be resolved by considering sub-grid
variability in climate data, surface-dependent potential-evaporation
estimates, etc., but further studies are needed to determine the reasons for
the inconsistencies found. Our results emphasise the need for pre-modelling
data analysis to identify dataset inconsistencies as an important first step
in any large-scale study. Applying data-screening methods before modelling
should also increase our chances to draw robust conclusions from subsequent
model simulations. |
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