Talks and Poster Presentations (with Proceedings-Entry):
C Künzer, Z. Bartalis, M. Schmidt, D. Zhao, W. Wagner:
"Trend Analyses Of A Global Soil Moisture Time Series Derived From ERS-1/-2 Scatterometer Data: Floods, Droughts And Long Term Changes";
Talk: International Society for Photogrammetry and Remote Sensing XXIst Congress,
- 2008-07-11; in: "Proceedings",
Vol. XXXVII. Part B7
Soil moisture is a governing parameter in many complex environmental processes from the disciplines of meteorology, hydrology and agriculture. Since rainfall is partitioned into runoff and infiltration, the soil moisture content allows for direct information on further infiltration capability and expected runoff behavior. Spatial and temporal soil moisture variability are thus important factors to be included into predictive agricultural, hydrological and climate models. Furthermore, long term soil moisture pattern analyses can support the derivation of regional trends. In this paper we present the results of analyses of the 15 year long, remote sensing based soil moisture time series of TU Wien. Based on ERS scatterometer derived data soil moisture has been derived at a spatial resolution of 50km, and a temporal resolution of 3-4 days globally since 1992. This time series is currently being extended and reprocessed with 25km Metop Ascat derived data. We have processed the time series with respect to global anomaly derivation, whereas an anomaly in the soil moisture dataset depicts "wetter than normal" or "drier than normal" conditions with respect to the long term mean. Findings indicate that extreme events such as confirmed floods and droughts are clearly represented in the dataset. Anomaly analyses in months prior to known extreme events indicate that the time series holds a strong potential for flood early warning activities. Furthermore, long term trend derivation allows to depict regions, which have become significantly wetter or drier over the course of the last 15 years. Trends investigated for Mongolia and Australia correlate with trends from in-situ station data. We consider the TU Wien time series to have a high potential for further detailed global long term trend analyses.
Soil moisture, Time series, ERS scatterometer, Floods, Droughts, Climate change
Electronic version of the publication:
Created from the Publication Database of the Vienna University of Technology.