A pragmatic parameterisation and calibration approach to model hydrology and water quality of agricultural landscapes and catchments. (August 2020)

• Record Type: Journal Article
• Authors: Ghahramani, Afshin; Freebairn, David M.; Sena, Dipaka R.; Cutajar, Justin L.; Silburn, David M.
• Is Part Of: Environmental modelling & software. Volume 130(2020)
• Publication Date: 2020-08
• Subjects: Great barrier reef (GBR) -- Hydrological modelling -- Water quality -- HowLeaky -- Nitrogen -- Phosphorus -- Pesticide -- PEST -- Calibration -- Uncertainty -- Sensitivity; 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.2020.104733
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: Australian and Queensland Government's Reef 2050 Water Quality Improvement Plan has set targets for improving the water quality entering the Great Barrier Reef lagoon. Given the large public investment and the deficit of data linking on-farm land management to changes in environmental outcomes, there is a need for a robust and efficient methods of quantifying links between land management and water quality. This paper explores a pragmatic approach to making this link using available data. We demonstrate that a simple parameterisation process is suitable for estimating hydrology and water quality across a wide range of land uses and management practices in agricultural landscapes. However, a manually calibrated model may still require the analysis of parameters to reduce error variances and evaluate uncertainties. Confidence in estimating hydrology and water quality in descending order is: runoff, sediment, nitrogen, phosphorous, and pesticide losses, reflecting the availability of data and inherent error propagation. Highlights: A pragmatic modelling by Howleaky is suitable to link management to water quality. A manually calibrated model may still require a reduction in error variances. Data availability and inherent error propagation determined confidence in modelling.