Articles | Volume 18, issue 7
Hydrol. Earth Syst. Sci., 18, 2773–2787, 2014
https://doi.org/10.5194/hess-18-2773-2014
Hydrol. Earth Syst. Sci., 18, 2773–2787, 2014
https://doi.org/10.5194/hess-18-2773-2014

Research article 31 Jul 2014

Research article | 31 Jul 2014

How to identify groundwater-caused thermal anomalies in lakes based on multi-temporal satellite data in semi-arid regions

U. Mallast1, R. Gloaguen3, J. Friesen4, T. Rödiger2, S. Geyer2, R. Merz2, and C. Siebert2 U. Mallast et al.
  • 1Helmholtz Centre for Environmental Research, Department Groundwater Remediation, 06120 Halle, Germany
  • 2Helmholtz Centre for Environmental Research, Department Catchment Hydrology, 06120 Halle, Germany
  • 3Helmholtz Institute Freiberg of Resource Technology, Remote Sensing Group, 09599 Freiberg, Germany
  • 4Helmholtz Centre for Environmental Research, Department Computational Hydrosystems, 04318 Leipzig, Germany

Abstract. The deduction by conventional means of qualitative and quantitative information about groundwater discharge into lakes is complicated. Nevertheless, at least for semi-arid regions with limited surface water availability, this information is crucial to ensure future water availability for drinking and irrigation purposes.

Overcoming this lack of discharge information, we present a satellite-based multi-temporal sea-surface-temperature (SST) approach. It exploits the occurrence of thermal anomalies to outline groundwater discharge locations using the example of the Dead Sea. Based on a set of 19 Landsat Enhanced Thematic Mapper (ETM+) images 6.2 (high gain), recorded between 2000 and 2002, we developed a novel approach which includes (i) an objective exclusion of surface-runoff-influenced data which would otherwise lead to erroneous results and (ii) a temporal SST variability analysis based on six statistical measures amplifying thermal anomalies caused by groundwater.

After excluding data influenced by surface runoff, we concluded that spatial anomaly patterns of the standard deviation and range of the SST data series spatially fit best to in situ observed discharge locations and, hence, are most suitable for detecting groundwater discharge sites.

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