Professor Dmitri Kavetski

Professor Dmitri Kavetski
 Position Professor
 Org Unit Environmental Engineering
 Email dmitri.kavetski@adelaide.edu.au
 Telephone +61 8 8313 1710
 Location Floor/Room 1 ,  Engineering North ,   North Terrace

  • Biography/ Background

    One of the main contributions of Dmitri's research work has been the development of Bayesian Total Error Analysis (BATEA) - a comprehensive framework for parameter estimation and probabilistic prediction accounting for data and model uncertainties. BATEA is currently applied in hydrological modelling, including in the operational seasonal forecasting system of the Australian Bureau of Meteorology. Additional appplications are developing in river system modelling, irrigation modelling and other areas of environmental engineering.

    Dmitri's broader interests include mathematical modelling of surface and subsurface hydrological systems, development of numerically robust rainfall-runoff and snow models, and more generally the design of robust and computationally efficient numerical algorithms for nonlinear differential equations and for nonlinear optimization.

    Dmitri's international collaborations include the Swiss Federal Institute for Aquatic Science and Technology (EAWAG), CEMAGREF (Paris and Lyon, France), US National Center for Atmospheric Research (NCAR), Environmental Hydraulics Institute of Cantabria (Santander, Spain), and other institutions worldwide.

    From 2012 onwards, Dmitri joined the School of Civil, Environmental and Mining Engineering at the University of Adelaide.

     

    Research interests

    Bayesian inference and prediction in hydrology and environmental modelling. Development of novel Bayesian and Monte Carlo techniques for parameter estimation, uncertainty analysis and predictive applications. Areas of application have included hydrological models and river system models.

    Modelling in hydrology and environmental engineering. Selection of governing equations and process representations in hydrological and snow models, including scientifically defensible hypothesis testing. Model development, optimisation and testing.

    Applied numerical and statistical analysis in environmental engineering. Design and implementation of accurate, robust and computationally efficient numerical algorithms and software. Solution of nonlinear differential equations, numerical integration, nonlinear optimisation and others. Areas of application have included Richards equation for groundwater simulations, rainfall-runoff models, CO2 geosequestration models, and others.


    Academic activities

    2012 - present                   

    Professor of Civil and Environmental Engineering, School of Civil, Environmental and Mining Engineering, University of Adelaide, SA, Australia
    2010 - 2012 Senior Research Fellow, School of Engineering, University of Newcastle, NSW, Australia
    2007 - 2010
    		 Research Fellow, School of Engineering, University of Newcastle, NSW, Australia
    2004 - 2007
    		 Postdoctoral Research, Department of Civil and Environmental Engineering, Princeton University, NJ, USA

     

    Education

    2000 - 2005 (awarded 2006)                                    

    Doctor of Philosophy in Engineering (Environmental), University of Newcastle, NSW, Australia                                                                               Title: Analysis of data and model uncertainty and numerical robustness in environmental modelling

    1996 - 1999 (awarded 2000)

    Bachelor of Engineering (Environmental), University of Newcastle, Australia

     

    Editorial boards

    2010 - 2014                         Associate Editor for Water Resources Research (American Geophysical Union)

     

  • Awards & Achievements

    Major recent awards and nominations

    • 2021 Best Paper Award in Statistical Hydrology (STAHY) (International Commission on Statistical Hydrology (ICSH) of the International Association of Hydrological Sciences (IAHS))
    • 2016 Outstanding Publication in Hydrological Modeling (US National Center for Atmospheric Research)
    • 2012 South Australia Young Tall Poppy Science Award (Australian Institute of Policy and Science)
    • 2011 Editors’ Choice Award in Water Resources Research (American Geophysical Union)
    • 2011 Editors’ Citation for Excellence in Refereeing for Water Resources Research
    • 2011 Researcher of the Year Award, University of Newcastle, Australia
    • 2011 Vice-Chancellor’s Award for Research Excellence, University of Newcastle
    • 2010 Pro Vice-Chancellor’s Award for Research Excellence, Faculty of Engineering and Built Environment, University of Newcastle

  • Research Funding

    Year Project Chief Investigators
    Funding Body
    		 Amount
    2019-2021 Delivering robust hydrological predictions for Australia’s water resource challenges Thyer, Kavetski, Maier, Westra, Simmons, Jakeman, Croke, Gupta

    Australian Research Council (ARC)

    Discovery Project

    		 $381k
    2018-2020 Subseasonal streamflow forecasting Thyer and Kavetski Australian Bureau of Meteorology $426k
    2014-2017 A robust integrated streamflow forecasting framework for Australian water information and management agencies Kavetski, Thyer, Kuczera, Tuteja, Shin, Seed, Lerat, Tibaldi, Clark, Wood

    Australian Research Council (ARC)

    Linkage Project

    		 $270k
    2011-2013 The development of IWWS operating rules project Kuczera and Kavetski Water Corporation of Western Australia (WCWA)
    		 $468k
    2011-2013 Robust optimization of urban drought security for an uncertain climate Kuczera, Kavetski, Kiem

    National Climate Change Adaptation Research Facility (NCCARF)

    Australia

    		 $217k
    2010-2012 Robust streamflow prediction by improving the identification of hydrological model structure
    		 Kavetski, Kuczera, Thyer, Franks

    Australian Research Council (ARC)

    Discovery Project

    		 $240k
    2010-2013 An integrated modelling approach for the efficient management of irrigated landscapes Kuczera, Kavetski, Thyer, Franks, Selle, Githui, Thayalakumaran

    Australian Research Council (ARC)

    Linkage Project

    with Department of Primary Industries (Victoria)

    		 $185k
    2010-2012 Adapting Bayesian Total Error Analysis to river systems modelling Kuczera, Thyer, Kavetski

    CSIRO

    Flagship Project

    		 $200k
    2010-2011 Supply of Bayesian Total Error Analysis Kuczera, Thyer, Kavetski Australian Bureau of Meteorology (BoM) $125k
    2010-2011 Improving flood forecasting via robust handling of data and model uncertainties in hydrologic predictions Thyer, Kavetski, Kuczera, Franks, Renard, Andreassian, Perrin, Lang, Sauquet

    Australian Department of Innovation, Industry, Science and Research (DIISR)

    International Science Linkages

    		 $20k
    2008-2010 Urban water systems project Kuczera, Thyer, Rodriguez, Kavetski

    eWater CRC

    Core project

    		 $1,911k
    2007-2011 Research Fellowship grant Kavetski University of Newcastle, Australia $491k

  • Publications

    Book Chapters

    Kavetski D (2018) Parameter estimation and predictive uncertainty quantification in hydrological modelling, Book Chapter 25-1 in Duan Q et al (eds) Handbook of Hydrometeorological Ensemble Forecasting, Springer-Verlag, doi: 10.1007/978-3-642-40457-3_25-1.

    Kuczera G, Kavetski D, Renard B and Thyer M (2016) Bayesian methods, Book Chapter 23 in Singh VJ, Chow VT (eds) Handbook of Applied Hydrology, McGraw-Hill.

    Kavetski D, Franks SW and Kuczera G (2002) Confronting input uncertainty in environmental modeling. Book Chapter in Duan Q et al (eds) Calibration of Watershed Models, Washington, AGU Series, vol. 6, pp. 49-68.

     

    Journal Articles

    Volpi E, Grimaldi S, Aghakouchak A, Castellarin A, Chebana F, Papalexiou SM, Aksoy H, Bardossy A, Cancelliere A, Chen Y, Deidda R, Haberlandt U, Eris E, Fischer S, Frances F, Kavetski D, Kjeldsen TR, Kochanek K, Langousis A, Orduna LM, Montanari A, Nerantzaki SD, Ouarda TBMJ, Prosdocimi I, Ragno E, Rajulapati CR, Requena AI, Ridolfi E, Sadegh M, Schumann A, Sharma A (2024). The legacy of STAHY: Milestones, achievements, challenges, and open problems in statistical hydrology. Hydrological Sciences Journal, doi:10.1080/02626667.2024.2385686

    Thyer M, Gupta H, Westra S, McInerney D, Maier HR, Kavetski D, Jakeman A, Croke B, Simmons C, Partington D, Shanafield M, Tague C (2024) Virtual Hydrological Laboratories: Developing the next generation of conceptual models to support decision making under change, Water Resources Research, 60(4), doi:10.1029/2022WR034234.

    McInerney D, Thyer M, Kavetski D, Westra S, Maier HR, Shanafield M, Barry Croke B, Gupta H, Bennett B, Leonard M (2024) Neglecting hydrological errors can severely impact predictions of water resource system performance, Journal of Hydrology, 634. doi:10.1016/j.jhydrol.2024.130853.

    Laugesen R, Thyer M, McInerney D and Kavetski D (2023) Flexible forecast value metric suitable for a wide range of decisions: application using probabilistic subseasonal streamflow forecasts, Hydrology and Earth System Sciences, 27(4), doi:10.5194/hess-27-873-2023.

    Dal Molin M, Kavetski D, Albert C and Fenicia F (2023) Exploring signature-based model calibration for streamflow prediction in ungauged basins, Water Resources Research, 59(7), doi:10.1029/2022WR031929.

    Renard B, McInerney D, Westra S, Leonard M, Kavetski D, Thyer M and Vidal JP (2023) Floods and heavy precipitation at the global scale: 100-year analysis and 180-year reconstruction, Journal of Geophysical Research: Atmospheres, 128(9). doi:10.1029/2022JD037908.

    Renard B, Thyer M, McInerney D, Kavetski D, Leonard M, and Westra S (2022) A hidden climate indices modeling framework for multi-variable space-time data, Water Resources Research, 58(2). doi:10.1029/2021WR030007.

    Qin Y, Kavetski D, Kuczera G, McInerney D, Yang T, and Guo Y (2022) Technical Note: Can Gauss-Newton algorithms outperform stochastic optimization algorithms when calibrating a highly parameterized hydrological model? A case study using SWAT, Water Resources Research, 58(11). doi:10.1029/2021WR031532.

    McInerney D, Thyer M, Kavetski D, Laugesen R, Woldemeskel F, Tuteja N, Kuczera G (2022) Seamless streamflow forecasting at daily to monthly scales: MuTHRE lets you have your cake and eat it too, Hydrology and Earth System Sciences, 26(21), 5669-5683, doi:10.5194/hess-26-5669-2022.

    Prieto C, Le Vine N, Kavetski D, Fenicia F, Scheidegger A and Vitolo C (2022) An exploration of Bayesian identification of dominant hydrological mechanisms in ungauged catchments, Water Resources Research, 58(3), doi:10.1029/2021WR030705.

    Partington D, Thyer M, Shanafield M, McInerney D, Westra S, Maier H, Simmons C, Croke B, Jakeman AJ, Gupta H, Kavetski D (2022) Predicting wildfire induced changes to runoff: A review and synthesis of modeling approaches, Wiley Interdisciplinary Reviews: Water, 9(5), 1-25. doi:10.1002/wat2.1599.

    Fenicia F and Kavetski D (2021) Behind every robust result is a robust method: Perspectives from a case study and publication process in hydrological modelling, Hydrological Processes, 35(8), doi:10.1002/hyp.14266.

    Dal Molin M, Kavetski D, Fenicia F (2021) SuperflexPy 1.3.0: An open-source Python framework for building, testing, and improving conceptual hydrological models, Geoscientific Model Development, 14(11), 7047-7072. doi:10.5194/gmd-14-7047-2021.

    McInerney D, Thyer M, Kavetski D, Laugesen R, Woldemeskel F, Tuteja N, Kuczera G (2021) Improving the reliability of sub-seasonal forecasts of high and low flows by using a flow-dependent non-parametric model. Water Resources Research, 57(11), doi:10.1029/2020WR029317.

    Hunter J, Thyer M, McInerney D, Kavetski D (2021) Achieving high-quality probabilistic predictions from hydrological models calibrated with a wide range of objective functions, Journal of Hydrology, 603, 1-22, doi:10.1016/j.jhydrol.2021.126578.

    Prieto C, Kavetski D, Le Vine N, Álvarez C, Medina R (2021) Identification of dominant hydrological mechanisms using Bayesian inference, multiple statistical hypothesis testing and flexible models. Water Resources Research, 57, doi:10.1029/2020WR028338.

    McInerney D, Thyer M, Kavetski D, Laugesen R, Tuteja N, Kuczera G (2020) Multi-temporal hydrological residual error modelling for seamless sub-seasonal streamflow forecasting, Water Resources Research, 56, doi:10.1029/2019WR026979.

    Lerat J, Thyer M, McInerney D, Kavetski D, Woldemeskel F, Pickett-Heaps C, Shin D, Feikema P (2020) A robust approach for calibrating a daily rainfall-runoff model to monthly streamflow data, Journal of Hydrology, 591, doi:10.1016/j.jhydrol.2020.125129.

    McInerney D, Kavetski D, Thyer M, Lerat J and Kuczera G (2019) Benefits of explicit treatment of zero flows in probabilistic hydrological modeling of ephemeral catchments, Water Resources Research, 55, 11035-11060, doi:10.1029/2017WR024148.

    Prieto C, Le Vine N, Kavetski D, Garcia E and Medina R (2019) Flow prediction in ungauged catchments using probabilistic Random Forests regionalization and new statistical adequacy tests, Water Resources Research, 55(5), 4364-4392, doi:10.1029/2018WR023254.

    Kavetski D, Fenicia F, Reichert P and Albert C (2018) Signature-domain calibration of hydrological models using Approximate Bayesian Computation: Theory and comparison to existing applications, Water Resources Research, 54(6), 4059–4083, doi:10.1002/2017WR020528.

    Fenicia F, Kavetski D, Reichert P and Albert C (2018) Signature-domain calibration of hydrological models using Approximate Bayesian Computation: Empirical analysis of fundamental properties, Water Resources Research, 54(6), 3958–3987, doi:10.1002/2017WR021616.

    Qin Y, Kavetski D and Kuczera G (2018) A robust Gauss-Newton algorithm for the optimization of hydrological models: Benchmarking against industry-standard algorithms, Water Resources Research, 54, 9637–9654. doi:10.1029/2017WR022489.

    Qin Y, Kavetski D and Kuczera G (2018) A robust Gauss-Newton algorithm for the optimization of hydrological models: From standard Gauss-Newton to robust Gauss-Newton, Water Resources Research, 54, 9655–9683. https://doi.org/10.1029/2017WR022488.

    Kavetski D, Qin Y and Kuczera G (2018) The fast and the robust: Trade-offs between optimization robustness and cost in the calibration of environmental models, Water Resources Research, 54, 9432–9455. doi:10.1029/2017WR022051.

    McInerney D, Thyer M, Kavetski D, Bennett B, Lerat J, Gibbs M and Kuczera G (2018) A simplified approach to produce probabilistic hydrological model predictions, Environmental Modelling and Software, 109, 306-314, doi:10.1016/j.envsoft.2018.07.001.

    Woldemeskel F, McInerney D, Lerat J, Thyer M, Kavetski D, Shin D, Tuteja N and Kuczera G (2018) Evaluating residual error approaches for post-processing monthly and seasonal streamflow forecasts, Hydrological and Earth System Sciences, 22, 6257-6278, doi:10.5194/hess-2018-214.

    Hostache R, Chini M, Giustarini L, Neal J, Kavetski D, Wood M, Corato G, Pelich RM and Matgen P (2018) Near real-time assimilation of SAR derived flood maps for improving flood forecasts, Water Resources Research, 54(8), 5516–5535, doi:10.1029/2017wr022205.

    McInerney D, Thyer M, Kavetski D, Githui F, Thayalakumaran T, Liu M and Kuczera G (2018) The importance of spatio-temporal variability in irrigation inputs for hydrological modelling of irrigated catchments, Water Resources Research, 54(9), 6792–6821, doi:10.1029/2017WR022049.

    Gibbs MS, McInerney D, Humphrey G, Thyer MA, Maier HR, Dandy GC and Kavetski D (2018) State updating and calibration period selection to improve dynamic monthly streamflow forecasts for an environmental flow management application, Hydrology and Earth System Sciences, 22(1), 871-887, doi:10.5194/hess-22-871-201.

    Henn B, Clark MP, Kavetski D, Newman AJ, Hughes M, McGurk B and Lundquist JD (2018) Spatiotemporal patterns of precipitation inferred from streamflow observations across the Sierra Nevada mountain range, Journal of Hydrology, 556, 993-1012, doi:10.1016/j.jhydrol.2016.08.009.

    McInerney D, Thyer M, Kavetski D, Lerat J and Kuczera G (2017) Improving probabilistic prediction of daily streamflow by identifying Pareto optimal approaches for modelling heteroscedastic residual errors, Water Resources Research, 53(3), 2199–2239, doi:10.1002/2016WR019168.

    Schaefli B and Kavetski D (2017) Bayesian spectral likelihood for hydrological parameter inference, Water Resources Research, 53(8), 6857–6884, doi:10.1002/2016WR019465.

    Fenicia F, Kavetski D, Savenije HHG and Pfister L (2016) From spatially variable streamflow to distributed hydrological models: Analysis of key modeling decisions, Water Resources Research, 52(2), 954-989, doi:10.1002/2015WR017398.

    Henn B, Clark MP, Kavetski D, McGurk B, Painter TH and Lundquist JD (2016) Combining snow, streamflow, and precipitation gauge observations to infer basin-mean precipitation, Water Resources Research, 52, 8700–8723, doi:10.1002/2015WR018564.

    Giustarini L, Hostache R, Kavetski D, Chini M, Corato G, Schlaffer S and Matgen P (2016) Probabilistic flood mapping using synthetic aperture radar data, IEEE Transactions on Geoscience and Remote Sensing, 54(12), 6958-6969.

    Hill MC, Kavetski D, Clark M, Ye M, Arabi M, Lu D, Foglia L and Mehl S (2016) Practical use of computationally frugal model analysis methods, Groundwater, 54, 159–170.

    Henn B, Clark MP, Kavetski D and Lundquist JD (2015) Estimating mountain basin-mean precipitation from streamflow using Bayesian inference, Water Resources Research, 51, 8012-8033.

    Clark MP, Nijssen B, Lundquist JD, Kavetski D, Rupp DE, Woods RA, Freer JE, Gutmann ED, Wood AW, Brekke LD, et al. (2015) A unified approach for process-based hydrologic modeling: 1. Modeling concept, Water Resources Research, 51(4), 2498-2514.

    Clark MP, Nijssen B, Lundquist JD, Kavetski D, Rupp DE, Woods RA, Freer JE, Gutmann ED, Wood AW, Gochis DJ, et al. (2015) A unified approach for process-based hydrologic modeling: 2. Model implementation and case studies, Water Resources Research, 51(4), 2515-2542.

    Lockart N, Kavetski D and Franks SW (2015) A new stochastic model for simulating daily solar radiation from sunshine hours, International Journal of Climatology, 35(6), 1090-1106.

    Wrede S, Fenicia F, Martínez-Carreras N, Juilleret J, Hissler C, Krein A, Savenije HHG, Uhlenbrook S, Kavetski D and Pfister L (2015) Towards more systematic perceptual model development: a case study using 3 Luxembourgish catchments, Hydrological Processes, 29(12), 2731-2750.

    Evin G, Thyer M, Kavetski D, McInerney D and Kuczera G (2014) Comparison of joint versus postprocessor approaches for hydrological uncertainty estimation accounting for error autocorrelation and heteroscedasticity, Water Resources Research, 50(3), 2350-2375; AGU Research Highlight (2014).

    Westra S, Thyer M, Leonard M, Kavetski D and Lambert M (2014) A strategy for diagnosing and interpreting hydrological model nonstationarity, Water Resources Research, 50(6), 5090-5113; AGU Research Highlight (2014).

    Pagano TC, Wood AW, Ramos MH, Cloke HL, Pappenberger F, Clark MP, Cranston M, Kavetski D, Mathevet T, Sorooshian S, et al. (2014) Challenges of operational river forecasting, Journal of Hydrometeorology, 15(4), 1692-1707.

    Fenicia F, Kavetski D, Savenije HHG, Clark MP, Schoups G, Pfister L and Freer J (2014) Catchment properties, function, and conceptual model representation: Is there a correspondence? Hydrological Processes, 28(4), 2451-2467, doi: 10.1002/hyp.9726.

    van Esse W, Perrin C, Booij M, Augustijn D, Fenicia F, Kavetski D and Lobligeois F (2013) The influence of conceptual model structure on model performance: A comparative study for 237 French catchments, Hydrology and Earth System Sciences, 17(10), 4227-4239.

    Evin G, Kavetski D, Thyer M and Kuczera G (2013) Pitfalls and improvements in the joint inference of heteroscedasticity and autocorrelation in hydrological model calibration, Water Resources Research, 49(7), 4518-4524, doi:10.1002/wrcr.20284.

    Ershadi A, McCabe MF, Jason PE, Mariethoz G and Kavetski D (2013) A Bayesian analysis of sensible heat flux estimation: Quantifying uncertainty in meteorological forcing to improve model prediction, Water Resources Research, 49(5), 2343-2358, doi: 10.1002/wrcr.20231.

    Hill MC, Faunt CC, Belcher WR, Sweetkind D, Tiedeman C and Kavetski D (2013) Knowledge, transparency, and refutability in groundwater models, an example from the Death Valley Regional Groundwater Flow System, Journal of Physics and Chemistry of the Earth, 64, 105-116.

    Lockart N, Kavetski D and Franks SW (2013) On the role of soil moisture in daytime evolution of temperatures, Hydrological Processes, 27, 3896-3904, doi: 10.1002/hyp.9525.

    Fenicia F, Pfister L, Kavetski D, Matgen P, Iffly JF, Hoffmann L and Uijlenhoet R (2012) Microwave links for rainfall estimation in an urban environment: Insights from an experimental setup in Luxembourg-City, Journal of Hydrology, 464-465, 69-78.

    Clark MP, Kavetski D and Fenicia F (2012) Reply to comment by Beven et al. on "Pursuing the method of multiple hypotheses for hydrological modeling", Water Resources Research, 48, W11802. AGU Research Highlight (Nov 2012).

    Renard B, Kavetski D, Leblois E, Thyer M, Kuczera G and Franks SW (2011) Towards a reliable decomposition of predictive uncertainty in hydrologic modeling: Characterizing rainfall errors using conditional simulation, Water Resources Research, 47, W11516.

    Fenicia F, Kavetski D and Savenije HHG (2011) Elements of a flexible framework for conceptual hydrological modeling at the catchment scale. Part 1. Motivation and theoretical development, Water Resources Research, 47, W11510.

    Kavetski D and Fenicia F (2011) Elements of a flexible framework for conceptual hydrological modeling at the catchment scale. Part 2. Application and experimental insights, Water Resources Research, 47, W11511.

    Clark MP, Kavetski D, Fenicia F (2011) Pursuing the method of multiple working hypotheses for hydrological modeling, Water Resources Research, Opinion Paper, 47, W09301.

    Kavetski D, Fenicia F and Clark MP (2011) Impact of temporal data resolution on parameter inference and model identification in conceptual hydrological models: Insights from an experimental catchment, Water Resources Research, 47, W05501.

    Kavetski D and Clark MP (2011) Numerical troubles in conceptual hydrology: Approximations, absurdities and impact on hypothesis-testing, Hydrological Processes, Invited Commentary, 25, 661-670.

    Clark MP, Hendrikx J, Slater A, Kavetski D, Anderson B, Cullen N, Kerr T, Orn Hreinsson E and Woods R (2011) Representing spatial variability of snow water equivalent in hydrologic and land-surface models: A review, Water Resources Research, 47, W07539.

    Clark MP, McMillan H, Collins D, Kavetski D and Woods RA (2011) Hydrological field data from a modeller's perspective: Part 2. Process-based evaluation of model hypotheses, Hydrological Processes, 400(1-2), 523-543.

    McMillan H, Jackson B, Clark M, Kavetski D, Woods R (2011) Rainfall uncertainty in hydrologic modelling: An evaluation of multiplicative error models, Journal of Hydrology, 400, 83-94.

    Thyer M, Leonard M, Kavetski D, Need S and Renard B (2011) The open source RFortran library for accessing R from Fortran, with applications in environmental modelling, Environmental Modelling and Software, 26, 219-234.

    Clark MP and Kavetski D (2010) Ancient numerical daemons of conceptual hydrological modeling: 1. Fidelity and efficiency of time stepping schemes, Water Resources Research, 46, W10510.

    Kavetski D and Clark MP (2010) Ancient numerical daemons of conceptual hydrological modeling: 2. Impact of time stepping schemes on model analysis and prediction, Water Resources Research, 46, W10511. AGU Research Highlight (Dec 2010), received 2011 Editor's Choice Award in WRR, recognized by the 2011 “Researcher of the Year” award at Univ of Newcastle.

    Renard B, Kavetski D, Kuczera G, Thyer M and Franks SW (2010) Understanding predictive uncertainty in hydrologic modelling: The challenge of identifying input and structural errors, Water Resources Research, 46, W05521, Featured Paper.

    Kuczera G, Kavetski D, Renard B and Thyer M (2010) A limited-memory acceleration strategy for Bayesian calibration of hydrological models, Water Resources Research, 46, W07602.

    Kuczera G, Renard B, Thyer M and Kavetski D (2010) There are no hydrological monsters, just models and observations with large uncertainties! Hydrological Sciences Journal, 55, 980-991.

    Fenicia F, Wrede S, Kavetski D, Pfister L, Hoffmann L, Savenije H and McDonnell JJ (2010) Assessing the impact of mixing assumptions on the estimation of streamwater mean residence time, Hydrological Processes (Special Issue on Residence Times and Preferential Flows), 24(12), 1730-1741.

    Franks SW, Kavetski D, Lockart N (2010) Reply to comment on "On the recent warming in Murray-Darling: Land surface interactions misunderstood", Geophysical Research Letters, 37, L10403.

    Thyer M, Renard B, Kavetski D, Kuczera G, Franks SW and Srikanthan S (2009) Critical evaluation of consistency and predictive uncertainty in hydrological modelling: A case study using Bayesian total error analysis, Water Resources Research, 45, W00B14.

    Renard B, Kavetski D and Kuczera G (2009) Comment on "An integrated hydrologic Bayesian multimodel framework" by Ajami et al, Water Resources Research, 45, W03603.

    Lockart NA, Kavetski D and Franks SW (2009) On recent warming in Murray Darling Basin: Land surface interactions misunderstood, Geophysical Research Letters, 36(L24405).

    Nordbotten JM, Kavetski D, Celia MA and Bachu S (2009) A semi-analytic model estimating leakage associated with CO2 storage in large-scale multi-layered geological systems with multiple leaky wells, Environmental Science & Technology, 43, 743-749.

    Viswanathan H, Pawar R, Stauffer P, Kaszuba C, Carey S, Olsen T, Keating P, Kavetski D and Guthrie R (2008) Development of a hybrid process and system model for the assessment of wellbore leakage at a geologic CO2 sequestration site, Environmental Science & Technology, 42(19), 7280–7286.

    Kavetski D and Kuczera G (2007) Model smoothing strategies to remove micro-scale discontinuities and spurious secondary optima in objective functions in hydrological calibration, Water Resources Research, 43, W03411.

    Kavetski D, Kuczera G and Franks, SW (2006) Bayesian analysis of input uncertainty in hydrological modelling: 1. Theory, Water Resources Research, 42, W03407.

    Kavetski D, Kuczera G and Franks SW (2006) Bayesian analysis of input uncertainty in hydrological modelling: 2. Application, Water Resources Research, 42, W03408.

    Kuczera G, Kavetski D, Franks SW and Thyer M (2006) Towards a Bayesian total error analysis of conceptual rainfall-runoff models: Characterizing model error using storm-dependent parameters, Journal of Hydrology, 331(1-2), 161-177.

    Kavetski D, Kuczera G. and Franks SW (2006) Calibration of conceptual hydrological models revisited: 1. Overcoming numerical artefacts, Journal of Hydrology (Special issue on The model parameter estimation experiment - MOPEX), 320(1-2), 173-186.

    Kavetski D, Kuczera G and Franks SW (2006) Calibration of conceptual hydrological models revisited: 2. Improving optimisation and analysis, Journal of Hydrology (Special issue on The model parameter estimation experiment - MOPEX), 320(1-2), 187-201.

    Kavetski D, Binning P. and Sloan SW (2004) Truncation error and stability analysis of iterative and noniterative Thomas-Gladwell methods for nonlinear differential equations, International Journal for Numerical Methods in Engineering, 60: 2031–2043.

    Kavetski D, Kuczera G. and Franks SW (2003) Semidistributed hydrological modeling: A 'saturation path' perspective on TOPMODEL and VIC. Water Resources Research, 39(9), 1246-1253.

    Kavetski D, Binning P and Sloan SW (2002) Noniterative time stepping schemes with adaptive truncation error control for the solution of Richards equation. Water Resources Research, 38(10), 1211-1220.

    Kavetski D, Binning P and Sloan SW (2002) Adaptive backward Euler time stepping with truncation error control for numerical modelling of unsaturated fluid flow. International Journal for Numerical Methods in Engineering, 53, 1301-1322.

    Kavetski D, Binning P and Sloan SW (2001) Adaptive time stepping and error control in a mass conservative numerical solution of the mixed form of Richards equation. Advances in Water Resources, 24, 595-605.

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Entry last updated: Wednesday, 31 Jul 2024

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