The SWTools R package for SILO data acquisition, homogeneity testing and correction

ABSTRACT

Rainfall is a fundamental input to water resources models. Data products are making rainfall data available in application ready formats. There is a temptation to adopt these datasets directly without undertaking thorough quality assurance checks. The SWTools R package is introduced to download, summarise, check and, if necessary, correct rainfall data from a commonly used data product in Australia, the SILO database. A case study is presented where interpolation to extend a dataset after a station closed introduced non-homogeneity when compared to adjacent sites. SWTools functions are used to identify this change and apply corrections to the data. Calibrating a rainfall–runoff model to the datasets with and without corrections had a substantial influence on the simulated water availability in the catchment, with a 30% reduction in average runoff after the rainfall corrections. This case study highlights the importance of undertaking data quality assurance checks, particularly when applying a model outside the calibration period.

KEYWORDS:

• Non-homogeneity test
• SILO
• rainfall

Acknowledgements

Parts of this work were undertaken while some authors were employed by the Department for Environment and Water (DEW), South Australia (MG and MA) and the University of Adelaide (MG). Dr Daniel McCullough (DEW) is thanked for his contribution to the SILOThiessenShp() function. The authors thank the two anonymous reviewers for their constructive comments that improved this manuscript.

Disclosure statement

No potential conflict of interest was reported by the authors.

Data availability statement

The SWTools package is available from the Comprehensive R Archive Network. The Source code and code to produce the analyses in this manuscript are available at https://github.com/matt-s-gibbs/swtools and https://github.com/matt-s-gibbs/Rainfall-homogeneity-example., respectively.

Additional information

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Notes on contributors

Matt Gibbs

Matt Gibbs is a Senior Research Scientist (hydrologist) in CSIRO’s Water Resource Assessment team. Matt has 15 years’ experience in the fields of water resources, modelling and optimisation techniques based in academia, state and federal government. He has worked extensively on the River Murray, Lower Lakes and Coorong in South Australia, focusing on hydrological, hydraulic and water quality modelling to inform policy, infrastructure, and river operations decisions. Matt has recently published in the fields of river restoration, inundation and hydraulic modelling, environmental water management, uncertainty analysis, streamflow forecasting as well as salinity and blackwater modelling.

Mark Alcorn

Mark Alcorn is a Senior Hydrologist at Jacobs. He has over 15 years’ experience as a hydrologist, with much of this time in state government in South Australia. His main interest is in catchment hydrology, in particular the representation and impact of farm dams on hydrology and implications for water allocation planning.

Jai Vaze

Jai Vaze is an internationally recognised hydrologist developing methods to estimate runoff in ungauged catchments and studying the impacts of land use and climate change on catchment, regional and continental-scale water balance. He is currently a Research Team Leader (Water Resources Assessment Team) within Land and Water Business unit. He collaborates widely with leading scientists from Australian Universities and State Government Departments, and from France, the UK, the USA, Ireland, South Africa, China, Nepal, and India. He has worked (and continues to work) on and led many high impact projects that contribute to key national initiatives including the CSIRO Sustainable Yields projects, the AWRA project, the MDBA floodplain inundation modelling project, the eWater Source, SEACI, the Bioregional Assessments project and the Northern Australian Water Resources Assessment project.