Details zur Publikation

Kategorie Textpublikation
Referenztyp Zeitschriften
DOI 10.5194/hess-18-2773-2014
Titel (primär) How to identify groundwater-caused thermal anomalies in lakes based on multi-temporal satellite data in semi-arid regions
Autor Mallast, U.; Gloaguen, R.; Friesen, J. ORCID logo ; Rödiger, T.; Geyer, S. ORCID logo ; Merz, R.; Siebert, C. ORCID logo
Quelle Hydrology and Earth System Sciences
Erscheinungsjahr 2014
Department CATHYD; CIWAS
Band/Volume 18
Heft 7
Seite von 2773
Seite bis 2787
Sprache englisch
UFZ Querschnittsthemen RU2;
Abstract Information on groundwater discharge over large spatial scales are essential for groundwater management particularly in (semi-) arid regions. If discharge areas are known, direct measurements over larger spatial scales are complicated to obtain by conventional means, why thermal remote sensing is increasingly applied to localize and quantify groundwater discharge. In this context, mostly unconsidered is (i) the influence of surface-runoff that can negatively affect groundwater focused studies and (ii) the representativeness of remotely sensed groundwater discharge based on single thermal images, against the background of discharge intermittency. Addressing these issues we apply a multi-temporal SST data approach based on 19 Landsat ETM+ band 6.2 (high gain) data from the year 2000 until 2002 at the example of the (semi-)arid vicinity of Dead Sea. To be independent of auxiliary rain data we develop a novel approach to identify surface-runoff influenced images solely using image statistics. Compared to foregoing rain events the result reveals a general influence-time of at least two days, but also that a simple time-difference criterion to exclude possible surface-runoff is not advisable. In the second part of the study we evaluate the significance of six statistical measures calculated on a per-pixel basis on the remaining 12 surface-runoff uninfluenced sea-surface-temperature (SST) data using in situ discharge measurements of the Israel Hydrological Service (IHS). We found that the spatial patterns of the standard deviation and range on the SST data series best fit to the IHS observed discharge locations and hence are suitable for detecting groundwater discharge areas.
dauerhafte UFZ-Verlinkung
Mallast, U., Gloaguen, R., Friesen, J., Rödiger, T., Geyer, S., Merz, R., Siebert, C. (2014):
How to identify groundwater-caused thermal anomalies in lakes based on multi-temporal satellite data in semi-arid regions
Hydrol. Earth Syst. Sci. 18 (7), 2773 - 2787 10.5194/hess-18-2773-2014