A detection problem: Sensitivity and uncertainty analysis of a land surface temperature approach to detecting dynamics of water use by groundwater-dependent vegetation. (November 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 85(2016:Nov.)
• Page Start: 342
• Page End: 355
• Publication Date: 2016-11
• Subjects: Groundwater-dependent vegetation -- Land surface temperature -- Surface energy balance modelling -- Sensitivity analysis -- Uncertainty analysis; 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.2016.09.003
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: Sustainable management of groundwater-dependent vegetation (GDV) requires the accurate identification of GDVs, characterisation of their water use dynamics and an understanding of associated errors. This paper presents sensitivity and uncertainty analyses of one GDV mapping method which uses temperature differences between time-series of modelled and observed land surface temperature (LST) to detect groundwater use by vegetation in a subtropical woodland. Uncertainty in modelled LST was quantified using the Jacobian method with error variances obtained from literature. Groundwater use was inferred where modelled and observed LST were significantly different using a Student's t -test. Modelled LST was most sensitive to low-range wind speeds (<1.5 m s −1 ), low-range vegetation height (<=0.5 m), and low-range leaf area index (<=0.5 m 2 m −2 ), limiting the detectability of groundwater use by vegetation under such conditions. The model-data approach was well-suited to detection of GDV because model-data errors were lowest for climatic conditions conducive to groundwater use. Highlights: Vegetation height, leaf area index and wind speed introduce greatest model errors. Sensitivity and uncertainty analysis improved the accuracy of model-data approach. Time-series analysis increased confidence in detected vegetation groundwater use. Vegetation groundwater use was detected with 95% confidence using this approach. Woodland vegetation in the study area used groundwater up to 38% of the time.