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| Titel |
Don’t trust a rain gauge. Trust three of them. |
| VerfasserIn |
Christian Chwala, Svenja Reineke, Harald Kunstmann |
| Konferenz |
EGU General Assembly 2016
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| Medientyp |
Artikel
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| Sprache |
en
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 18 (2016) |
| Datensatznummer |
250134009
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| Publikation (Nr.) |
 EGU/EGU2016-14682.pdf |
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| Zusammenfassung |
| Despite the existence of advanced precipitation remote sensing techniques - using radar,
microwave links or satellites - rain gauges still provide what is considered to be the truth
about rainfall. However, rain gauges are prone to errors themselves. In particular failures of
the mechanics and electronics or blockage due to debris, can cause large biases and data gaps.
That is, a single gauge cannot be trusted. Hence we have investigated the use of multiple
gauges at one location.
In summer 2015 the KIT Campus Alpin carried out the intensive measurements campaign
ScaleX to investigate atmospheric, hydrologic and biogeochemical processes over a large
range of scales. For the observation of the high spatio-temporal variability of rainfall, we have
installed a dense network of 22 rain gauge sites, each equipped with three equal tipping
buckets. Five sites have a spacing of 250 m and cover the area of our wireless soil moisture
network. The remaining 17 sites have a spacing of approximately 2.5 km and cover our target
catchment with a size of 70 km2.
Using the redundancy provided by the three equal gauges per site, we are able to easily
identify failing rain gauges automatically. This results in a high data availability at the gauge
sites and very reliable high resolution spatial rainfall information which will be used by the
accompanying ScaleX modeling of soil moisture and streamflow.
We will show the advantages of having redundant information on each rain gauge site and
describe our automated processing of this information. Furthermore we will present analyses
of the spatial decorrelation of rainfall using the rain gauge network and additional
polarimetric weather radar data. |
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