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| Titel |
Water balance estimation in high Alpine terrain by combining distributed modeling and a neural network approach (Berchtesgaden Alps, Germany) |
| VerfasserIn |
G. Kraller, M. Warscher, H. Kunstmann, S. Vogl, T. Marke, U. Strasser |
| 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 ; 16, no. 7 ; Nr. 16, no. 7 (2012-07-09), S.1969-1990 |
| Datensatznummer |
250013359
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| Publikation (Nr.) |
 copernicus.org/hess-16-1969-2012.pdf |
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| Zusammenfassung |
| The water balance in high Alpine regions is often characterized by
significant variation of meteorological variables in space and time, a
complex hydrogeological situation and steep gradients. The system is even
more complex when the rock composition is dominated by soluble limestone,
because unknown underground flow conditions and flow directions lead to
unknown storage quantities. Reliable distributed modeling cannot be
implemented by traditional approaches due to unknown storage processes at
local and catchment scale. We present an artificial neural network extension
of a distributed hydrological model (WaSiM-ETH) that allows to account for
subsurface water transfer in a karstic environment. The extension was
developed for the Alpine catchment of the river "Berchtesgadener Ache"
(Berchtesgaden Alps, Germany), which is characterized by extreme topography
and calcareous rocks. The model assumes porous conditions and does not
account for karstic environments, resulting in systematic mismatch of modeled
and measured runoff in discharge curves at the outlet points of neighboring
high alpine subbasins. Various precipitation interpolation methods did not
allow to explain systematic mismatches, and unknown subsurface hydrological
processes were concluded as the underlying reason. We introduce a new method
that allows to describe the unknown subsurface boundary fluxes, and account
for them in the hydrological model. This is achieved by an artificial neural
network approach (ANN), where four input variables are taken to calculate the
unknown subsurface storage conditions. This was first developed for the high
Alpine subbasin Königsseer Ache to improve the monthly water balance. We
explicitly derive the algebraic transfer function of an artificial neural net
to calculate the missing boundary fluxes. The result of the ANN is then
implemented in the groundwater module of the hydrological model as boundary
flux, and considered during the consecutive model process. We tested several
ANN setups in different time increments to investigate ANN performance and to
examine resulting runoff dynamics of the hydrological model. The ANN with
5-day time increment showed best results in reproducing the observed water
storage data (r2 = 0.6). The influx of the 20-day ANN showed best
results in the hydrological model correction. The boundary influx in the
subbasin improved the hydrological model, as performance increased from
NSE = 0.48 to NSE = 0.57 for subbasin Königsseetal, from NSE = 0.22
to NSE = 0.49 for subbasin Berchtesgadener Ache, and from NSE = 0.56 to
NSE = 0.66 for the whole catchment within the test period. This combined
approach allows distributed quantification of water balance components
including subsurface water transfer. |
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