Al-Awadhi SA and Garthwaite PH (2006) Quantifying expert opinion for modelling fauna habitat distributions. Computational Statistics 21(1), 121–140.
Barrett D, Couch C, Metcalfe D, Lytton L, Adhikary D and Schmidt R (2013) Methodology for bioregional assessments of the impacts of coal seam gas and coal mining development on water resources. A report prepared for the Independent Expert Scientific Committee on Coal Seam Gas and Large Coal Mining Development through the Department of the Environment. Department of the Environment, Australia.
Bastin L, Cornford D, Jones R, Heuvelink GB, Pebesma E, Stasch C, Nativi S, Mazzetti P and Williams M (2013) Managing uncertainty in integrated environmental modelling: the UncertWeb framework. Environmental Modelling & Software Thematic Issue on the Future of Integrated Modeling Science and Technology 39, 116–134.
Bedrick EJ, Christensen R and Johnson W (1996) A new perspective on priors for generalized linear models. Journal of the American Statistical Association 91(436), 1450–1460.
Bettonvil B and Kleijnen JP (1997) Searching for important factors in simulation models with many factors: sequential bifurcation. European Journal of Operational Research 96, 180–194.
Bredehoeft J (2005) The conceptualization model problem--surprise. Hydrogeology Journal 13, 37–46.
Caers J (2011) Modeling uncertainty in the Earth sciences. Wiley-Blackwell, Hoboken, New Jersey.
Clemen RT and Winkler RL (1999) Combining probability distributions from experts in risk analysis. Risk Analysis 19(2), 187–203.
Crosbie R, Peeters L and Carey H (2016) Groundwater modelling. Submethodology M07 from the Bioregional Assessment Technical Programme. Department of the Environment and Energy, Bureau of Meteorology, CSIRO and Geoscience Australia, Australia. http://data.bioregionalassessments.gov.au/submethodology/M07.
de Marsily G, Delay F, Goncalves J, Renard P, Teles V and Violette S (2005) Dealing with spatial heterogeneity. Hydrogeology Journal 13, 161–183.
Denham R and Mengersen K (2007) Geographically assisted elicitation of expert opinion for regression models. Bayesian Analysis 2(1), 99–135.
Doherty J and Hunt RJ (2009) Two statistics for evaluating parameter identifiability and error reduction. Journal of Hydrology 366, 119–127.
Ford JH, Hayes KR, Henderson BL, Lewis S and Baker PA (2016) Systematic analysis of water-related hazards associated with coal resource development. Submethodology M11 from the Bioregional Assessment Technical Programme. Department of the Environment and Energy, Bureau of Meteorology, CSIRO and Geoscience Australia, Australia. http://data.bioregionalassessments.gov.au/submethodology/M11.
Gan Y, Duan Q, Gong W, Tong C, Sun Y, Chu W, Ye A, Miao C and Di Z (2014) A comprehensive evaluation of various sensitivity analysis methods: a case study with a hydrological model. Environmental Modelling & Software 51, 269–285.
Garthwaite PH, Al-Awadhi SA, Elfadaly FG and Jenkinson DJ (2013) Prior distribution elicitation for generalized linear and piecewise-linear models. Journal of Applied Statistics 40, 59–75.
Garthwaite PH, Kadane JB and O'Hagan A (2005) Statistical methods for eliciting probability distributions. Journal of the American Statistical Association 100, 680–701.
Gramacy RB (2013) laGP: Local approximate Gaussian process regression. R package version 1.
Gramacy RB and Apley DW (2015) Local Gaussian process approximation for large computer experiments. Journal of Computational and Graphical Statistics 24(2), 561–578. DOI: 10.1080/10618600.2014.914442.
Gupta HV, Clark MP, Vrugt JA, Abramowitz G and Ye M (2012) Towards a comprehensive assessment of model structural adequacy. Water Resources Research 48, W08301. DOI:10.1029/2011WR011044.
Hankin R (2013a) A multivariate generalization of the emulator package. Viewed 3 May 2016, http://cran.r-project.org/web/packages/multivator/multivator.pdf
Hankin R (2013b) Bayesian emulation of computer programs. Viewed 3 May 2016, http://cran.r-project.org/web/packages/emulator/emulator.pdf
Hansen C, Behie G, Bier A, Brooks K, Chen Y, Helton J, Hommel S, Lee K, Lester B, Mattie P, Mehta S, Miller S, Sallaberry C, Sevougian S and Vo P (2014) Uncertainty and sensitivity analysis for the nominal scenario class in the 2008 performance assessment for the proposed high-level radioactive waste repository at Yucca Mountain, Nevada. Reliability Engineering & System Safety Special Issue: Performance Assessment for the Proposed High-Level Radioactive Waste Repository at Yucca Mountain, Nevada 122, 272–296.
Hayes K (2011) Uncertainty and uncertainty analysis methods. Issues in quantitative and qualitative risk modelling with application to import risk assessment. ACERA project (0705). Report no. 102467. Viewed 4 May 2016, http://cebra.unimelb.edu.au/__data/assets/pdf_file/0008/1290473/0705a_final-report.pdf.
Helton JC and Davis FJ (2003) Latin hypercube sampling and the propagation of uncertainty in analyses of complex systems. Reliability Engineering & System Safety 81, 23–69.
Helton J, Hansen C and Swift P (2014) Performance assessment for the proposed high-level radioactive waste repository at Yucca Mountain, Nevada. Reliability Engineering & System Safety Special Issue: Performance Assessment for the Proposed High-Level Radioactive Waste Repository at Yucca Mountain, Nevada 122, 1–6.
Henderson B, Hayes KR, Mount R, Schmidt RK, O'Grady A, Lewis S, Holland K, Dambacher J, Barry S and Raiber M (2016) Developing the conceptual model of causal pathways. Submethodology M05 from the Bioregional Assessment Technical Programme. Department of the Environment and Energy, Bureau of Meteorology, CSIRO and Geoscience Australia, Australia. http://data.bioregionalassessments.gov.au/submethodology/M05.
Higdon D, Gattiker J, Williams B and Rightley M (2008) Computer model calibration using high-dimensional output. Journal of the American Statistical Association 103, 570–583.
Hill MC and Tiedeman CR (2007) Effective groundwater model calibration. Wiley and Sons, New York.
Hooten M, Leeds W, Fiechter J and Wikle C (2011) Assessing first-order emulator inference for physical parameters in nonlinear mechanistic models. Journal of Agricultural, Biological, and Environmental Statistics 16, 475–494.
Kadane JB, Dickey JM, Winkler RL, Smith WS and Peters SC (1980) Interactive elicitation of opinion for a normal linear model. Journal of the American Statistical Association 75(372), 845–854.
Kadane JB and Wolfson LJ (1998) Experiences in elicitation. Journal of the Royal Statistical Society: Series D (The Statistician) 47, 3–19.
Kennedy MC and O'Hagan A (2001) Bayesian calibration of computer models. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 63, 425–464.
Kleijnen JP (2009) Kriging metamodeling in simulation: a review. European Journal of Operational Research 192, 707–716.
Kloprogge P, van der Sluijs JP and Petersen AC (2011) A method for the analysis of assumptions in model-based environmental assessments. Environmental Modelling & Software Thematic Issue on the Assessment and Evaluation of Environmental Models and Software 26, 289–301.
Kuhnert PM, Martin TG and Griffiths SP (2010) A guide to eliciting and using expert knowledge in Bayesian ecological models. Ecology Letters 13(7), 900–914.
Kynn M (2008) The ‘heuristics and biases’ bias in expert elicitation. Journal of the Royal Statistical Society: Series A (Statistics in Society) 171, 239–264.
Leeds WB, Wikle CK, Fiechter J, Brown J and Milliff RF (2013) Modeling 3-D spatio-temporal biogeochemical processes with a forest of 1-D statistical emulators. Environmetrics 24, 1–12.
Low-Choy S, O'Leary R and Mengersen K (2009) Elicitation by design in ecology: using expert opinion to inform priors for Bayesian statistical models. Ecology 90, 265–277.
Liu Q and Homma T (2009) A new computational method of a moment-independent uncertainty importance measure. Reliability Engineering & System Safety Special Issue on Sensitivity Analysis 94, 1205–1211.
Mastrandrea M, Field C, Stocker T, Edenhofer O, Ebi K, Frame D, Held H, Kriegler E, Mach K, Matschoss P, Plattner G-K, Yohe G and Zwiers F (2010) Guidance note for lead authors of the IPCC Fifth Assessment Report on Consistent Treatment of Uncertainties. Intergovernmental Panel on Climate Change (IPCC). Viewed 4 May 2016, http://www.ipcc-wg2.gov/meetings/CGCs/Uncertainties-GN_IPCCbrochure_lo.pdf.
Morris DE, Oakley JE and Crowe JA (2014) A web-based tool for eliciting probability distributions from experts. Environmental Modelling & Software 52, 1–4.
Morris MD (1991) Factorial sampling plans for preliminary computational experiments. Technometrics 33, 161–174.
Murray JV, Goldizen AW, O’Leary RA, McAlpine CA, Possingham HP and Choy SL (2009) How useful is expert opinion for predicting the distribution of a species within and beyond the region of expertise? A case study using brush-tailed rock-wallabies Petrogale penicillata. Journal of Applied Ecology 46(4), 842–851.
Murray LM (2013) Bayesian state-space modeling on high-performance hardware using LibBi. arXiv preprint arXiv:1306.3277 28.
Nossent J (2012) Sensitivity and uncertainty analysis in view of the parameter estimation of a SWAT model of the River Kleine Nete, Belgium. PhD thesis, Vrije Universiteit Brussel, Vakgroep Hydrologie.
Nossent J, Elsen P and Bauwens W (2011) Sobol' sensitivity analysis of a complex environmental model. Environmental Modelling & Software 26, 1515–1525.
Oakley JE, Brennan A, Tappenden P and Chilcott J (2010) Simulation sample sizes for Monte Carlo partial EVPI calculations. Journal of Health Economics 29, 468–477.
Oakley J and O'Hagan A (2002) Bayesian inference for the uncertainty distribution of computer model outputs. Biometrika 89, 769–784.
Oakley JE and O'Hagan A (2010) SHELF: the Sheffield Elicitation Framework (Version 2.0) www.tonyohagan.co.uk/shelf
O’Grady AP, Mount R, Holland K, Sparrow A, Crosbie R, Marston F, Dambacher J, Hayes K, Henderson B, Pollino C and Macfarlane C (2016) Assigning receptors to water-dependent assets. Submethodology M03 from the Bioregional Assessment Technical Programme. Department of the Environment and Energy, Bureau of Meteorology, CSIRO and Geoscience Australia, Australia. http://data.bioregionalassessments.gov.au/submethodology/M03.
O'Hagan A (1998) Eliciting expert beliefs in substantial practical applications. The Statistician 47, 21–35.
O'Hagan A (2006) Bayesian analysis of computer code outputs: a tutorial. Reliability Engineering & System Safety 91, 1290–1300.
O'Hagan A (2012) Probabilistic uncertainty specification: overview, elaboration techniques and their application to a mechanistic model of carbon flux. Environmental Modelling & Software Thematic Issue on Expert Opinion in Environmental Modelling and Management 36, 35–48.
O'Hagan A, Buck C, Daneshkah A, Eiser J, Garthwaite P, Jenkinson D, Oakley J and Rakow T (2006) Uncertain judgements: eliciting experts’ probabilities. John Wiley & Sons, Ltd, Chichester.
O’Hagan A and Oakley JE (2004) Probability is perfect, but we can’t elicit it perfectly. Reliability Engineering and System Safety 85, 239–248.
Patt A (2009) Communicating uncertainty to policy makers. In: Baveye P, Mysiak J and Laba M (eds) Uncertainties in environmental modelling and consequences for policy making. Springer, New York, 231–251.
Peeters LJM, Dawes WR, Rachakonda PR, Pagendam DE, Singh RM, Pickett TW, Frery E, Marvanek SP and McVicar TR (2016) Groundwater numerical modelling for the Gloucester subregion. Product 2.6.2 for the Gloucester subregion from the Northern Sydney Basin Bioregional Assessment. Department of the Environment and Energy, Bureau of Meteorology, CSIRO and Geoscience Australia, Australia. http://data.bioregionalassessments.gov.au/product/NSB/GLO/2.6.2.
Plischke E, Borgonovo E and Smith CL (2013) Global sensitivity measures from given data. European Journal of Operational Research 226, 536–550.
Ransley TR and Smerdon BD (eds) (2012) Hydrostratigraphy, hydrogeology and system conceptualisation of the Great Artesian Basin. A technical report to the Australian Government from the CSIRO Great Artesian Basin Water Resource Assessment. CSIRO Water for a Healthy Country Flagship, Australia.
Razavi S, Tolson BA and Burn DH (2012) Review of surrogate modeling in water resources. Water Resources Research 48, W07401. DOI: 10.1029/2011WR011527.
Refsgaard JC, van der Sluijs JP, Brown J and van der Keur P (2006) A framework for dealing with uncertainty due to model structure error. Advances in Water Resources 29, 1586–1597.
Saltelli A and Annoni P (2010) How to avoid a perfunctory sensitivity analysis. Environmental Modelling & Software 25, 1508–1517.
Saltelli A and Funtowicz S (2014) When all models are wrong. Issues in Science and Technology 30, 79–85.
Saltelli A and Marivoet J (1990) Non-parametric statistics in sensitivity analysis for model output: a comparison of selected techniques. Reliability Engineering & System Safety 28, 229–253.
Saltelli A, Annoni P, Azzini I, Campolongo F, Ratto M and Tarantola S (2010) Variance based sensitivity analysis of model output. Design and estimator for the total sensitivity index. Computer Physics Communications 181, 259–270.
Saltelli A, Pereira ÃG, Sluijs JPVd and Funtowicz S (2013) What do I make of your latinorum? Sensitivity auditing of mathematical modelling. International Journal of Foresight and Innovation Policy 9, 213–234.
Saltelli A, Ratto M, Andres T, Campolongo F, Cariboni J, Gatelli D, Saisana M and Tarantola S (2008) Global sensitivity analysis. The primer. John Wiley & Sons, West Sussex, England, 292p.
Shao Q, Lerat J, Brink H, Tomkins K, Yang A, Peeters L, Li M, Zhang L, Podger G and Renzullo LJ (2012) Gauge based precipitation estimation and associated model and product uncertainties. Journal of Hydrology 444–445, 100–112. DOI:10.1016/j.jhydrol.2012.04.009.
Sobol I (1976) Uniformly distributed sequences with an additional uniform property. USSR Computational Mathematics and Mathematical Physics 16, 236–242.
Sobol I (2001) Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates. Mathematics and Computers in Simulation The Second IMACS Seminar on Monte Carlo Methods 55, 271–280.
Spiegelhalter D, Pearson M and Short I (2011) Visualizing uncertainty about the future. Science 333, 1393–1400.
Tomkins KM (2014) Uncertainty in streamflow rating curves: methods, controls and consequences. Hydrological Processes 28(3), 464–481.
Turanyi T and Rabitz H (2000) Local methods. In: Saltelli A, Chan K and Scott M (eds) Sensitivity analysis. Wiley, New York, 81–99.
van der Sluijs JP, Craye M, Funtowicz S, Kloprogge P, Ravetz J and Risbey J (2005) Combining quantitative and qualitative measures of uncertainty in model-based environmental assessment: the NUSAP system. Risk Analysis 25, 481–492.
Van Loon E and Refsgaard A (2005) Guidelines for assessing data uncertainty in river basin management studies, HarmoniRiB Report. Geological Survey of Denmark and Greenland, Copenhagen. Viewed 4 May 2016, http://harmonirib.geus.info/xpdf/d_2-1_guidelines.pdf.
Viney N (2016) Surface water numerical modelling. Submethodology M06 from the Bioregional Assessment Technical Programme. Department of the Environment and Energy, Bureau of Meteorology, CSIRO and Geoscience Australia, Australia. http://data.bioregionalassessments.gov.au/submethodology/M06.
Voinov A and Shugart HH (2013) ‘Integronsters’, integral and integrated modeling. Environmental Modelling & Software Thematic Issue on the Future of Integrated Modeling Science and Technology 39, 149–158.
Walker WE, Harremoes P, Rotmans J, van der Sluijs JP, van Asselt MBA, Janssen P and Krayer von Krauss PM (2003) Defining uncertainty: a conceptual basis for uncertainty management in model-based decision support. Integrated Assessment 4, 5–17.
Young P (2000) Stochastic, dynamic modelling and signal processing: time variable and state dependent parameter estimation. In: Fitzgerald W, Smith R, Walden A and Young P (eds) Nonlinear and nonstationary signal processing. Cambridge University Press, Cambridge, U.K, 74–114.