Characterising groundwater use by vegetation using a surface energy balance model and satellite observations of land surface temperature. (June 2016)

• Record Type: Journal Article
• Authors: Gow, L.J.; Barrett, D.J.; Renzullo, L.J.; Phinn, S.R.; O'Grady, A.P.
• Is Part Of: Environmental modelling & software. Volume 80(2016:Jun.)
• Page Start: 66
• Page End: 82
• Publication Date: 2016-06
• Subjects: Groundwater-dependent vegetation -- Land surface temperature -- Surface energy balance modelling -- Satellite remote sensing; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2016.02.021
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: This study presents a novel 'model-data' approach to detect groundwater-dependent vegetation (GDV), through differences in modelled and observed land surface temperatures (LST) in space and time. Vegetation groundwater use is inferred where modelled LST exceeds observed LST by more than a threshold determined from consideration of systematic and random errors in model and observations. Modelled LST was derived from a surface energy balance model and LST observations were obtained from Terra-MODIS thermal imagery. The model-data approach, applied in the Condamine River Catchment, Queensland, Australia, identified GDV coincident to existing mapping. GDV were found to use groundwater up to 48% of the time and for as many as 56 consecutive days. Under driest of conditions, groundwater was estimated to contribute up to 0.2 mm h −1 to total ET for GDV. The ability to both detect the location and water-use dynamics of GDV is a significant advancement on previous remote-sensing GDV methods. Highlights: Model-data LST differences were used to map GDV. The location of mapped GDV was consistent with independent data. Vegetation water use dynamics depends on surface and groundwater availability. Groundwater use increased with decreasing surface soil moisture availability.