A multi-model approach to assess the impact of catchment characteristics on spatial water quality in the Great Barrier Reef catchments
datasetposted on 13.12.2019 by SHUCI LIU, DONGRYEOL RYU, JAMES ANGUS, Anna Lintern, Danlu Guo, David Waters, ANDREW WESTERN
Datasets usually provide raw data for analysis. This raw data often comes in spreadsheet form, but can be any collection of data, on which analysis can be performed.
Water quality monitoring programs often collect large amounts of data with limited attention given to the assessment of the dominant drivers of spatial and temporal water quality variations at the catchment scale. This study aims to: a) identify the influential catchment characteristics affecting spatial variability in water quality, and b) develop predictive models to estimate average concentration of water quality constituents. Tropical catchments in the Great Barrier Reef area, Australia were used as a case study. Water quality monitoring data (i.e. sediments, nutrients and salinity) from 32 sites together with 58 candidate catchment characteristics were used to construct statistical models. This data set contains 58 catchment characteristics and 9 time-averaged water quality constituents' concentration at 32 GBR catchments.