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| Titel |
Constraining the hydrological model mHM using satellite retrieved soil moisture and streamflow data |
| VerfasserIn |
Matthias Zink, Juliane Mai, Oldrich Rakovec, Rohini Kumar, David Schäfer, Luis Samaniego |
| Konferenz |
EGU General Assembly 2017
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| Medientyp |
Artikel
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| Sprache |
en
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 19 (2017) |
| Datensatznummer |
250145782
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| Publikation (Nr.) |
 EGU/EGU2017-9752.pdf |
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| Zusammenfassung |
| Hydrological models are usually calibrated against observed streamflow at the catchment
outlet and thus they are conditioned by an integral catchment signal. Rakovec et al. 2016
(JHM) recently demonstrated that constraining model parameters against river discharge is a
necessary, but not a sufficient condition. Such a procedure ensures the fulfillment of the
catchment’s water balance but can lead to high predictive uncertainties of model internal
states, like soil moisture, or a lack in spatial representativeness of the model. However, some
hydrologic applications, as e.g. soil drought monitoring and prediction, rely on these
information.
Within this study we propose a framework in which the mesoscale Hydrologic
Model (mHM) is calibrated with soil moisture retrievals from various sources.
The aim is to condition the model on soil moisture (SM), while preserving good
performance in streamflow estimation. We identify the most appropriate objective
functions by conducting synthetic experiments. The hydrological model is constrained
using either satellite retrieved soil moisture as or a combination of streamflow and
soil moisture observations. The model is implemented on the native scale of the
observations, e.g., 25 km in case of the satellite retrieved ESA-CCI soil moisture.
The study is conducted in three distinct European basins (upper Sava, Neckar, and
upper Guadalquivir basin) ranging from snow domination to semi arid climatic
conditions.
Results obtained with the synthetic experiment indicate that objective functions focusing
on the temporal dynamics of SM are preferable to objective functions based on spatial
patterns or catchment average. Conditioning the hydrological model with satellite soil
moisture alone yields Nash-Sutcliffe Efficiencies (NSE) for streamflow ranging between 0.1
and 0.3, whereas the coefficient of correlation between modeled and satellite retrieved soil
moisture exceeds 0.7. Improvements in discharge estimation are achieved by consecutively
adding streamflow observations as calibration criteria which lead to NSEs exceeding 0.6.
In comparison calibrations using streamflow data alone exceed an NSE of 0.8. |
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