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| Titel |
Impact of modellers' decisions on hydrological a priori predictions |
| VerfasserIn |
H. M. Holländer, H. Bormann, T. Blume, W. Buytaert, G. B. Chirico, J.-F. Exbrayat, D. Gustafsson, H. Hölzel, T. Krauße, P. Kraft, S. Stoll, G. Blöschl, H. Flühler |
| 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 ; 18, no. 6 ; Nr. 18, no. 6 (2014-06-04), S.2065-2085 |
| Datensatznummer |
250120375
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| Publikation (Nr.) |
 copernicus.org/hess-18-2065-2014.pdf |
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| Zusammenfassung |
In practice, the catchment hydrologist is often confronted with the task of
predicting discharge without having the needed records for calibration.
Here, we report the discharge predictions of 10 modellers – using the
model of their choice – for the man-made Chicken Creek catchment (6 ha,
northeast Germany, Gerwin et al., 2009b) and we analyse how well they
improved their prediction in three steps based on adding information prior
to each following step. The modellers predicted the catchment's hydrological
response in its initial phase without having access to the observed records.
They used conceptually different physically based models and their modelling
experience differed largely. Hence, they encountered two problems: (i) to
simulate discharge for an ungauged catchment and (ii) using models that were
developed for catchments, which are not in a state of landscape
transformation. The prediction exercise was organized in three steps: (1)
for the first prediction the modellers received a basic data set
describing the catchment to a degree somewhat more complete than usually
available for a priori predictions of ungauged catchments; they did not
obtain information on stream flow, soil moisture, nor groundwater response
and had therefore to guess the initial conditions; (2) before the second
prediction they inspected the catchment on-site and discussed their first
prediction attempt; (3) for their third prediction they were offered
additional data by charging them pro forma with the costs for obtaining this
additional information.
Holländer et al. (2009) discussed the range of predictions obtained in
step (1). Here, we detail the modeller's assumptions and decisions in
accounting for the various processes. We document the prediction progress as
well as the learning process resulting from the availability of added
information. For the second and third steps, the progress in prediction
quality is evaluated in relation to individual modelling experience and
costs of added information.
In this qualitative analysis of a statistically small number of predictions
we learned (i) that soft information such as the modeller's system
understanding is as important as the model itself (hard information), (ii)
that the sequence of modelling steps matters (field visit, interactions
between differently experienced experts, choice of model, selection of
available data, and methods for parameter guessing), and (iii) that added
process understanding can be as efficient as adding data for improving
parameters needed to satisfy model requirements. |
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