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

References

• Adelaide and Mount Lofty Ranges Natural Resources Management Board. 2009. “.” In: Water Allocation Plan - Barossa Prescribed Water Resources Area, Eastwood, South Australia: Adelaide and Mount Lofty Ranges Natural Resources Management Board, Government of South Australia.
   Google Scholar
• Allen, R. G., L. S. Pereira, D. Raes, and M. Smith. 1998. Crop Evapotranspiration: Guidelines for Computing Crop Water Requirements. Rome: Food and Agriculture Organization of the United Nations.
   Google Scholar
• Andréassian, V., C. Perrin, C. Michel, I. Usart-Sanchez, and J. Lavabre. 2001. “Impact of Imperfect Rainfall Knowledge on the Efficiency and the Parameters of Watershed Models.” Journal of Hydrology 250 (1–4): 206–223. doi:https://doi.org/10.1016/S0022-1694(01)00437-1.
   Web of Science ®Google Scholar
• Beesley, C. A., A. J. Frost, and J. Zajaczkowski 2009 13-17 July 2009. A Comparison of the BAWAP and SILO Spatially Interpolated Daily Rainfall Datasets. Paper presented at the 18th World IMACS Congress and MODSIM09 International Congress on Modelling and Simulation, Cairns, Australia.
   Google Scholar
• Black, D., P. Wallbrink, P. Jordan, D. Waters, C. Carroll, and J. Blackmore. 2011. Towards Best Practice Model Application - Generic Guidelines for Water Management Modelling. eWater CRC. https://ewater.org.au/document-library/.
   Google Scholar
• Chang, M., and R. Lee. 1974. “Objective Double-Mass Analysis.” Water Resources Research 10 (6): 1123–1126. doi:10.1029/WR010i006p01123.
   Web of Science ®Google Scholar
• Chapman, T. G. 2003. “Estimation of Evaporation in Rainfall-Runoff Models.” Modsim 2003: International Congress on Modelling and Simulation 1-4: 148–153.
   Google Scholar
• Chen, M. Y., W. Shi, P. P. Xie, V. B. S. Silva, V. E. Kousky, R. W. Higgins, and J. E. Janowiak. 2008. “Assessing Objective Techniques for Gauge-Based Analyses of Global Daily Precipitation.” Journal of Geophysical Research-Atmospheres 113 (D4): 113(D4. doi:https://doi.org/10.1029/2007JD009132.
   PubMed Web of Science ®Google Scholar
• Coron, L., G. Thirel, O. Delaigue, C. Perrin, and V. Andréassian. 2017. “The Suite of Lumped GR Hydrological Models in an R Package.” Environmental Modelling & Software 94: 166–171. doi:10.1016/j.envsoft.2017.05.002.
   Web of Science ®Google Scholar
• Department for Environment and Water. (2021). Methodology for Calculating Flow Through the River Murray barrages. Government of South Australia Through Department for Environment and Water, DEW Technical report 2021/04, Adelaide
   Google Scholar
• Guo, D., S. Westra, and H. R. Maier. 2017. “Impact of Evapotranspiration Process Representation on Runoff Projections from Conceptual Rainfall-Runoff Models.” Water Resources Research 53 (1): 435–454. doi:10.1002/2016WR019627.
   Web of Science ®Google Scholar
• Hughes, J., N. Potter, L. Zhang, and R. Bridgart. 2021. “Conceptual Model Modification and the Millennium Drought of Southeastern Australia.” Water 13 (5): 669. doi:10.3390/w13050669.
   Web of Science ®Google Scholar
• Jeffrey, S. J. 2006. “.” In Error Analysis for the Interpolation of Monthly Rainfall Used in the Generation of SILO Rainfall Datasets. Indooroopilly, Queensland: Department of Natural Resources and Water.
   Google Scholar
• Jeffrey, S. J., J. O. Carter, K. B. Moodie, and A. R. Beswick. 2001. “Using Spatial Interpolation to Construct a Comprehensive Archive of Australian Climate Data.” Environmental Modelling & Software 16 (4): 309–330. doi:10.1016/S1364-8152(01)00008-1.
   Web of Science ®Google Scholar
• Jones-Gill, A., and K. Savadamuthu 2014. Hydro-Ecological Investigations to Inform the Barossa PWRA WAP Review – Hydrology Report. Government of South Australia through Department of Environment Water and Natural Resources, Adelaide
   Google Scholar
• Jones, D. A., W. Wang, and R. Fawcett. 2009. “High-Quality Spatial Climate Data-Sets for Australia.” Australian Meteorological and Oceanographic Journal 58 (4): 233–248. doi:10.22499/2.5804.003.
   Google Scholar
• Kalnay, E., M. Kanamitsu, R. Kistler, W. Collins, D. Deaven, L. Gandin, M. Iredell, et al. 1996. “The NCEP/NCAR 40-Year Reanalysis Project.” Bulletin of the American Meteorological Society 77 (3): 437–471. doi:10.1175/1520-0477(1996)077<0437:Tnyrp>2.0.Co;2.
   Web of Science ®Google Scholar
• Kling, H., M. Fuchs, and M. Paulin. 2012. “Runoff Conditions in the Upper Danube Basin Under an Ensemble of Climate Change Scenarios.” Journal of Hydrology 424-425: 264–277. doi:10.1016/j.jhydrol.2012.01.011.
   Web of Science ®Google Scholar
• Lavery, B., A. Kariko, and N. Nicholls. 1992. “A Historical Rainfall Data Set for Australia.” Australian Meteorological Magazine 40 (1): 33–39.
   Google Scholar
• Lee, J., S. Kim, and H. Jun. 2018. “A Study of the Influence of the Spatial Distribution of Rain Gauge Networks on Areal Average Rainfall Calculation.” Water 10 (11): 1635. doi:10.3390/w10111635.
   Web of Science ®Google Scholar
• McMahon, T. A., and M. C. Peel. 2019. “Uncertainty in Stage–Discharge Rating Curves: Application to Australian Hydrologic Reference Stations Data.” Hydrological Sciences Journal 64 (3): 255–275. doi:10.1080/02626667.2019.1577555.
   Web of Science ®Google Scholar
• Merriam, C. F. 1937. “A Comprehensive Study of the Rainfall on the Susquehanna Valley.” Eos Transactions American Geophysical Union 18 (2): 471–476. doi:10.1029/TR018i002p00471.
   Google Scholar
• Michel, C. 1991. Hydrologie appliquée aux petits bassins ruraux. Antony, France: Cemagref.
   Google Scholar
• Morton, F. I. 1983. “Operational Estimates of Lake Evaporation.” Journal of Hydrology 66 (1–4): 77–100. doi:10.1016/0022-1694(83)90178-6.
   Web of Science ®Google Scholar
• Muggeo, V. 2008. “Segmented: An R Package to Fit Regression Models with Broken-Line Relationships.” R News 8: 20–25.
   Google Scholar
• Muggeo, V. M. R. 2003. “Estimating Regression Models with Unknown Break-Points.” Statistics in Medicine 22 (19): 3055–3071. doi:10.1002/sim.1545.
   PubMed Web of Science ®Google Scholar
• Nathan, R. J., and T. A. McMahon. 2017. “Recommended Practice for Hydrologic Investigations and Reporting.” Australasian Journal of Water Resources 21 (1): 3–19. doi:10.1080/13241583.2017.1362136.
   Web of Science ®Google Scholar
• Oudin, L., C. Perrin, T. Mathevet, V. Andréassian, and C. Michel. 2006. “Impact of Biased and Randomly Corrupted Inputs on the Efficiency and the Parameters of Watershed Models.” Journal of Hydrology 320 (1–2): 62–83. doi:https://doi.org/10.1016/j.jhydrol.2005.07.016.
   Web of Science ®Google Scholar
• Perrin, C., C. Michel, and V. Andréassian. 2003. “Improvement of a Parsimonious Model for Streamflow Simulation.” Journal of Hydrology 279 (1–4): 275–289. doi:https://doi.org/10.1016/S0022-1694(03)00225-7.
   Web of Science ®Google Scholar
• Peterson, T. J., M. Saft, M. C. Peel, and A. John. 2021. “Watersheds May Not Recover from Drought.” Science 372 (6543): 745–749. doi:10.1126/science.abd5085.
   PubMed Web of Science ®Google Scholar
• Peterson, T. J., C. Wasko, M. Saft, and M. C. Peel. 2020. “AWAPer: An R Package for Area Weighted Catchment Daily Meteorological Data Anywhere Within Australia.” Hydrological Processes 34 (5): 1301–1306. doi:10.1002/hyp.13637.
   Web of Science ®Google Scholar
• Pushpalatha, R., C. Perrin, N. Le Moine, T. Mathevet, and V. Andréassian. 2011. “A Downward Structural Sensitivity Analysis of Hydrological Models to Improve Low-Flow Simulation.” Journal of Hydrology 411 (1–2): 66–76. doi:https://doi.org/10.1016/j.jhydrol.2011.09.034.
   Web of Science ®Google Scholar
• R Core Team. 2021. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. https://www.R-project.org/.
   Google Scholar
• Richardson, C. W. 1977. A Model of Stochastic Structure of Daily Precipitation Over an Area. Hydrology paper no. 91. Fort Collins: Colorado State University.
   Google Scholar
• Tozer, C. R., A. S. Kiem, and D. C. Verdon-Kidd. 2012. “On the Uncertainties Associated with Using Gridded Rainfall Data as a Proxy for Observed.” Hydrology and Earth System Sciences 16 (5): 1481–1499. doi:10.5194/hess-16-1481-2012.
   Web of Science ®Google Scholar
• Vaze, J., P. Jordan, R. Beecham, A. Frost, and G. Summerell. 2011. Guidelines for Rainfall-Runoff Modelling: Towards Best Practice Model Application. eWater CRC.
   Google Scholar
• Vaze, J., D. A. Post, F. H. S. Chiew, J. M. Perraud, J. Teng, and N. R. Viney. 2011. “Conceptual Rainfall–Runoff Model Performance with Different Spatial Rainfall Inputs.” Journal of Hydrometeorology 12 (5): 1100–1112. doi:10.1175/2011JHM1340.1.
   Web of Science ®Google Scholar
• Wasko, C., J. B. Visser, R. Nathan, M. Ho, and A. Sharma. 2022. “Automating Rainfall Recording: Ensuring Homogeneity When Instruments Change.” Journal of Hydrology 609: 127758. doi:10.1016/j.jhydrol.2022.127758.
   Web of Science ®Google Scholar
• Welsh, W. D., J. Vaze, D. Dutta, D. Rassam, J. M. Rahman, I. D. Jolly, P. Wallbrink, et al. 2013. “An Integrated Modelling Framework for Regulated River Systems.” Environmental Modelling & Software 39: 81–102. doi:10.1016/j.envsoft.2012.02.022.
   Web of Science ®Google Scholar
• Wood, S. 2001. “Minimizing Model Fitting Objectives That Contain Spurious Local Minima by Bootstrap Restarting.” Biometrics 57 (1): 240–244. doi:10.1111/j.0006-341X.2001.00240.x.
   PubMed Web of Science ®Google Scholar
• Zajaczkowski, J., K. Wong, and J. Carter. 2013. “Improved Historical Solar Radiation Gridded Data for Australia.” Environmental Modelling & Software 49: 64–77. doi:10.1016/j.envsoft.2013.06.013.
   Web of Science ®Google Scholar
• Zhang, X. S., G. E. Amirthanathan, M. A. Bari, R. M. Laugesen, D. Shin, D. M. Kent, A. M. MacDonald, M. E. Turner, and N. K. Tuteja. 2016. “How Streamflow Has Changed Across Australia Since the 1950s: Evidence from the Network of Hydrologic Reference Stations.” Hydrology and Earth System Sciences 20 (9): 3947–3965. doi:10.5194/hess-20-3947-2016.
   Web of Science ®Google Scholar