 |
| Titel |
Separating precipitation and evapotranspiration from noise – a new filter routine for high-resolution lysimeter data |
| VerfasserIn |
A. Peters, T. Nehls, H. Schonsky, G. Wessolek |
| Medientyp |
Artikel
|
| Sprache |
Englisch
|
| ISSN |
1027-5606
|
| Digitales Dokument |
URL |
| Erschienen |
In: Hydrology and Earth System Sciences ; 18, no. 3 ; Nr. 18, no. 3 (2014-03-28), S.1189-1198 |
| Datensatznummer |
250120315
|
| Publikation (Nr.) |
 copernicus.org/hess-18-1189-2014.pdf |
|
|
|
|
|
| Zusammenfassung |
| Weighing lysimeters yield the most precise and realistic measures
for evapotranspiration (ET) and precipitation (P),
which are of great importance for many questions regarding soil and
atmospheric sciences. An increase or a decrease of the system mass
(lysimeter plus seepage) indicates P or ET. These real
mass changes of the lysimeter system have to be separated from
measurement noise (e.g., caused by wind). A promising approach to filter
noisy lysimeter data is (i) to introduce a smoothing routine, like
a moving average with a certain averaging window, w, and then (ii)
to apply a certain threshold value, δ, accounting for
measurement accuracy, separating significant from insignificant
weight changes. Thus, two filter parameters are used, namely w and
δ. In particular, the time-variable noise due to wind as well as strong
signals due to heavy precipitation pose challenges for such noise-reduction
algorithms. If w is too small, data noise might be
interpreted as real system changes. If w is too wide, small weight
changes in short time intervals might be disregarded. The same
applies to too small or too large values for δ. Application
of constant w and δ leads either to unnecessary losses of
accuracy or to faulty data due to noise. The aim of this paper is to
solve this problem with a new filter routine that is appropriate
for any event, ranging from smooth evaporation to strong wind and
heavy precipitation. Therefore, the new routine uses adaptive w
and δ in dependence on signal strength and noise (AWAT – adaptive
window and adaptive threshold filter). The AWAT filter,
a moving-average filter and the Savitzky–Golay filter with constant
w and δ were applied to real lysimeter data comprising the
above-mentioned events. The AWAT filter was the only filter that
could handle the data of all events very well. A sensitivity study
shows that the magnitude of the maximum threshold value has
practically no influence on the results; thus only the maximum
window width must be predefined by the user. |
| |
|
| Teil von |
|
|
|
|
|
|
|
|