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| Titel |
Deriving root zone storage capacity from Earth observation |
| VerfasserIn |
Lan Wang-Erlandsson, Hongkai Gao, Wim Bastiaanssen, Jonas Jägermeyr, Patrick Keys, Line Gordon, Hubert Savenije |
| Konferenz |
EGU General Assembly 2015
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| Medientyp |
Artikel
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| Sprache |
Englisch
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 17 (2015) |
| Datensatznummer |
250104697
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| Publikation (Nr.) |
 EGU/EGU2015-4129.pdf |
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| Zusammenfassung |
| The root zone storage capacity (SR) is a critical, yet uncertain parameter in hydrological and
land surface modelling, ecological and biogeochemical studies, and even investigations on
shallow landslide and soil erosion. Unbiased and detailed observations of rooting depth
worldwide are not available, but observation-based evaporation and precipitation data with
global coverage are increasing in quality. Recently, the Mass Curve Technique (MCT, an
engineering method for reservoir design) has been successfully employed to estimate root
zone storage capacities at the catchment scale. The method assumes that vegetation adjusts its
root zone storage capacity to the smallest required to bridge critical dry periods.
Here, we adapt and use MCT with satellite-based evaporation and state-of-the-art
precipitation data to derive gridded SR at the global scale. Because ecosystems appear
to adapt to drought return periods of 10-40 years, the SR are normalized using
Gumbel distribution with accompanying sensitivity analyses. Upon implementing the
estimated SR in a global hydrological model, we find that the SR correctly allow for
simulated dry-season evaporation in contrast to the simulation results achieved
using look-up table rooting depths. Correlating SR with climate indices further
reveal different ecosystem strategies to cope with drought. Comparing the estimated
SR to previous estimates of global rooting depth shows that our SR estimate is
realistic and can correct for bias in regions where root depth field data are scarce.
In contrast to earlier attempts to quantify root zone storage capacity, this method
does not require soil or vegetation information, is model independent, and makes
few assumptions. This study presents an “observation-based” root zone storage
capacity at the global scale, directly implementable in hydrological and land surface
models. The dataset can potentially remediate current parameterization biases in
land surface models and in particular improve dry season simulations. In addition,
the simple method can easily be used at regional scales using other satellite-based
evaporation input datasets where such are of better quality than the available global data. |
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