Reading List

Selected publications

General Hydrology

Hydrology - General references
  • Dingman L (2015) Physical Hydrology, 3rd Ed, Waveland Press.
  • Beven KJ (2012) Rainfall-Runoff Modelling: The Primer, 2nd Ed, Wiley-Blackwell.
Hydrological Modelling Debates
  • Clark MP, Kavetski D, and Fenicia F (2011) Pursuing the method of multiple working hypotheses for hydrological modeling, Water Resources Research, 47, W09301.
  • Fatichi S, Vivoni ER, Ogden FL, Ivanov VY, Mirus B, Gochis D, Downer DW, Camporese M, Davison JH, Ebel B, Jones N, Kim J, Mascaro G, Niswonger R, Restrepo P, Rigon R, Shen C, Sulis M, and Tarboton D (2016) An overview of current applications, challenges, and future trends in distributed process-based models in hydrology, Journal of Hydrology, 537, 45-60,
  • McDonnell JJ, Sivapalan M, Vaché K, Dunn S, Grant G, Haggerty R, Hinz C, Hooper R, Kirchner J, Roderick ML, Selker J, and Weiler M (2007) Moving beyond heterogeneity and process complexity: A new vision for watershed hydrology, Water Resources Research, 43, W07301, doi:10.1029/2006WR005467.
  • Grayson RB, Moore ID, and McMahon TA (1992) Physically based hydrologic modeling: 2. Is the concept realistic? Water Resources Research, 28(10), 2659–2666, doi:10.1029/92WR01259.
Flexible Hydrological Models
  • 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.
  • 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.
  • 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.
  • Clark MP, Slater AG, Rupp DE, Woods RA, Vrugt JA, Gupta HV, Wagener T, and Hay LE (2008) Framework for Understanding Structural Errors (FUSE): A modular framework to diagnose differences between hydrological models, Water Resources Research, 44, W00B02, doi:10.1029/2007WR006735.
Hydrological Model Development
  • Santos L, Thirel G, and Perrin C (2018) Continuous state-space representation of a bucket-type rainfall-runoff model: A case study with the GR4 model using state-space GR4 (version 1.0), Geoscientific Model Development, 11, 1591-1605,
  • Perrin C, Michel C, and Andréassian V (2003) Improvement of a parsimonious model for streamflow simulation, Journal of Hydrology, 279(1-4), 275-289,
  • Lindström G, Johansson B, Persson M, Gardelin M, and Bergström S (1997) Development and test of the distributed HBV-96 hydrological model, Journal of Hydrology, 201(1–4), 272-288,
Hydrological Models and Real Catchments
  • 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.
  • 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.
  • 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.
  • Krueger T, Freer J, Quinton JN, Macleod CJA, Bilotta GS, Brazier RE, Butler P, and Haygarth PM (2010) Ensemble evaluation of hydrological model hypotheses, Water Resources Research, 46, W07516, doi:10.1029/2009WR007845.


Numerical Methods in Hydrology
  • 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 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.
  • 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, 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.
Optimization Methods in Hydrology
  • 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.
  • 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.
  • 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.
  • Tolson BA and Shoemaker CA (2007) Dynamically dimensioned search algorithm for computationally efficient watershed model calibration, Water Resources Research, 43, W01413, doi:10.1029/2005WR004723.
  • Duan Q, Sorooshian S, and Gupta V (1992) Effective and efficient global optimization for conceptual rainfall-runoff models, Water Resources Research, 28(4), 1015–1031, doi:10.1029/91WR02985.

Probabilistic Methods

Probability and Statistics - General references
  • Ang AHS and Tang WH (2007) Probability Concepts in Engineering, with applications to Civil and Environmental Engineering, 2nd Ed, Wiley.
  • Box GEP and Tiao GC (1973) Bayesian Inference in Statistical Analysis, Addison-Wesley.
  • Gelman A, Carlin JB, Stern HS, Dunson DB, and Vehtari A (2013) Bayesian Data Analysis, 3rd Ed, Taylor & Francis.
  • Press W (2019) Opinionated Lessons in Statistics,
Probabilistic Methods in Hydrology - General references
  • 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.
  • 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.
Residual Error Modelling in Hydrology
  • 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.
  • McInerney D, Thyer M, Kavetski D, Lerat J, and Kuczera G (2017) Improving probabilistic prediction of daily streamflow by identifying Pareto optimal approaches for modeling heteroscedastic residual errors, Water Resources Research, 53, 2199–2239, doi:10.1002/2016WR019168.
  • Schoups G and Vrugt JA (2010) A formal likelihood function for parameter and predictive inference of hydrologic models with correlated, heteroscedastic, and non‐Gaussian errors, Water Resources Research, 46, W10531, doi:10.1029/2009WR008933.
Decomposition of Uncertainty in Hydrology
  • Renard B, Kavetski D, Leblois E, Thyer M, Kuczera G, and Franks SW (2011) Toward a reliable decomposition of predictive uncertainty in hydrological modeling: Characterizing rainfall errors using conditional simulation. Water Resources Research, 47, W11516, doi:10.1029/2011WR010643.
  • 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.
  • Reichert P and Mieleitner J (2009) Analyzing input and structural uncertainty of nonlinear dynamic models with stochastic, time-dependent parameters, Water Resources Research, 45, W10402, doi:10.1029/2009WR007814.
  • 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) Bayesian analysis of input uncertainty in hydrological modelling: 1. Theory, Water Resources Research, 42, W03407.
Signature-Domain Inference - Probabilistic approaches
  • 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.
Prediction in Ungauged Basins - Probabilistic approaches
  • Prieto C, Le Vine N, Kavetski D, García E, and Medina R (2019) Flow prediction in ungauged catchments using probabilistic Random Forests regionalization and new statistical adequacy tests, Water Resources Research, in press.
Probabilistic Predictions in Hydrology - Operational applications
  • 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.

Sensitivity Analysis

Sensitivity Analysis - General references
  • Saltelli A, Ratto M, Andres T, Campolongo F, Cariboni J, Gatelli D, et al. (2008) Global Sensitivity Analysis, Wiley-Interscience.
  • Sobol IM (1993) Sensitivity estimates for nonlinear mathematical models, Math. Model. Comput. Exp., Engl. Transl., 1(4), 407–414.
  • Morris MD (1991) Factorial sampling plans for preliminary computational experiments, Technometrics, 33(2), 161–174.
Sensitivity Analysis in Hydrology
  • Mai J and Tolson BA (2019) Model Variable Augmentation (MVA) for diagnostic assessment of sensitivity analysis results, Water Resources Research, in press.
  • Cuntz M, Mai J, Samaniego L, Clark MP, Wulfmeyer V, Branch O, et al. (2016) The impact of standard and hard-coded parameters on the hydrologic fluxes in the Noah-MP land surface model, Journal of Geophysical Research: Atmospheres, 1–25.
  • Cuntz M, Mai J, Zink M, Thober S, Kumar R, Schäfer D, et al. (2015) Computationally inexpensive identification of noninformative model parameters by sequential screening, Water Resources Research, 51(8), 6417–6441.
  • Pianosi F and Wagener T (2015) A simple and efficient method for global sensitivity analysis based on cumulative distribution functions, Environmental Modelling & Software, (67), 1–11.
  • Razavi S and Gupta HV (2015) What do we mean by sensitivity analysis? The need for comprehensive characterization of “Global” sensitivity in Earth and Environmental Systems Models, Water Resources Research, 3070-3092.
  • Rakovec O, Hill MC, and Clark MP (2014) Distributed Evaluation of Local Sensitivity Analysis (DELSA), with application to hydrologic models, Water Resources Research, 50, 409–426.
  • Göhler M, Mai J, and Cuntz M (2013) Use of eigendecomposition in a parameter sensitivity analysis of the Community Land Model, Journal of Geophysical Research: Biogeosciences, 118(2), 904–921.