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| Titel |
The effects of the sub-grid variability of soil and land cover data on agricultural droughts in Germany |
| VerfasserIn |
Rohini Kumar, Luis Samaniego, Matthias Zink |
| Konferenz |
EGU General Assembly 2013
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| Medientyp |
Artikel
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| Sprache |
Englisch
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 15 (2013) |
| Datensatznummer |
250081667
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| Zusammenfassung |
| Simulated soil moisture from land surface or water balance models is increasingly
used to characterize and/or monitor the development of agricultural droughts at
regional and global scales (e.g. NLADS, EDO, GLDAS). The skill of these models to
accurately replicate hydrologic fluxes and state variables is strongly dependent on the
quality meteorological forcings, the conceptualization of dominant processes, and the
parameterization scheme used to incorporate the variability of land surface properties
(e.g. soil, topography, and vegetation) at a coarser spatial resolutions (e.g. at least
4Â km).
The goal of this study is to analyze the effects of the sub-grid variability of soil texture
and land cover properties on agricultural drought statistics such as duration, severity, and
areal extent. For this purpose, a process based mesoscale hydrologic model (mHM) is
used to create two sets of daily soil moisture fields over Germany at the spatial
resolution of (4 Ã 4)Â km2 from 1950 to 2011. These simulations differ from each other
only on the manner in which the land surface properties are accounted within the
model. In the first set, soil moisture fields are obtained with the multiscale parameter
regionalization (MPR) scheme (Samaniego, et. al. 2010, Kumar et. al. 2012), which explicitly
takes the sub-grid variability of soil texture and land cover properties into account.
In the second set, on the contrary, a single dominant soil and land cover class is
used for ever grid cell at 4 km. Within each set, the propagation of the parameter
uncertainty into the soil moisture simulations is also evaluated using an ensemble
of 100 best global parameter sets of mHM (Samaniego, et. al. 2012). To ensure
comparability, both sets of this ensemble simulations are forced with the same
fields of meteorological variables (e.g., precipitation, temperature, and potential
evapotranspiration).
Results indicate that both sets of model simulations, with and without the sub-grid
variability of soil texture and land cover properties, reconstruct the general features of the
large scale extreme drought events (e.g. 1976, 2003, 2007) reported in the literature. Results
also emphasize the importance of accounting for the parametric uncertainty for identifying
benchmark drought events based on simulated soil moisture. Drought statistics such as
duration, severity, and areal extent, estimated from both ensemble sets are currently under
investigation. |
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