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| Titel |
Skill assessment of a global hydrological model in reproducing extreme flows |
| VerfasserIn |
Naze Candogan Yossef, L. P. H. Van Beek, J. C. J. Kwadijk, M. F. P. Bierkens |
| Konferenz |
EGU General Assembly 2010
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| Medientyp |
Artikel
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| Sprache |
Englisch
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 12 (2010) |
| Datensatznummer |
250039866
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| Zusammenfassung |
| This study investigates the skill of the Macro-scale Hydrological Model (MHM)
PCR-GLOBWB in reproducing past extremes in the discharges of 20 large rivers of the
world, as an initial step in assessing the prospect of using the model for hydrological
forecasting. The assessment provides a benchmark verification procedure with the
best possible meteorological forcing. Global terrestrial hydrology is simulated for
a historical period from 1958 until 2001 by forcing PCR-GLOBWB with daily
meteorological data obtained by downscaling the CRU dataset to daily fields using the
ERA-40 reanalysis, which can be considered to be the best representation of past
weather. Simulated discharge values are compared with observed monthly streamflow
records for a selection of 20 large river basins that represent all the continents,
a wide range of climatic zones and latitudes as well as a variety of precipitation
regimes.
Model skill is assessed in three ways. At first, the general performance of the
model in simulations is evaluated. MSE skill scores that provide a quality measure
relative to the mean climatology are calculated for each river basin, prior to and after
bias correction. The coefficient of determination (R2) and Nash and Sutcliffe’s
coefficient of efficiency (E) are also calculated. Secondly, model skill in reproducing
significantly higher and lower flows than the monthly normals is assessed in terms of
skill scores used for forecasts of categorical events. For each month, discharge
is classified into low, normal and high flow; where normal flow is defined as the
central 50% of the cumulative distribution. The skill in simulating these classes
is assessed by constructing categorical contingency tables and applying Gerrity
scores used in meteorology for ordinal categorical events. Thirdly, model skill in
reproducing flood and drought events is assessed. Floods and droughts are regarded
as simple binary events defined in terms of exceedences of threshold discharges
corresponding to five-year return periods. The skill is assessed by constructing binary
contingency tables for floods and droughts for each basin and applying Peirce’s skill
score.
The results show that the model does have skill in all three types of hindcasting. For most
basins, the model skill in simulating hydrographs is higher than the climatology; and it is
improved significantly by bias correction. The skill obtained by categorical hindcasts is quite
high compared to that of an imaginary unskilled system. The model also performs better than
an unskilled system in binary hindcasting, with a markedly higher skill in floods. The model
skill in simulating anomalous flows is higher than that in reproducing five-year floods and
droughts. Therefore, It can be said that the model can be used for forecasting anomalous
flows with a higher degree of confidence rather than exact discharges or extreme
events.
This assessment in hindcast represents a potential skill given the current MHM, with a
meteorological forcing based on observations. The true skill can be assessed in forecasting
mode using less certain meteorological forecasts from numerical weather prediction (NWP)
models. |
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