Journals

Stochastische Simulation und Sicherheitsforschung für Hydrosysteme

  1. 2019 (submitted)

    1. González-Inostroza P, Rahmann C, Alvarez R, Haas J, Nowak W, Rehtanz C. The Role of Virtual Inertial Response of Energy Storage Systems for Ensuring Frequency Stability in Future Power Systems. submitted to Energy Policy. 2019;
    2. Moreno-Leiva S, Haas J, Junne T, Valencia F, Godin H, Kracht W, et al. Renewable energy in copper production: a review on systems design and methodological approaches. Journal of Cleaner Production. 2019;
    3. Haas J, Khalighi J, de la Fuente A, Gerbersdorf S, Nowak W, Chen P-J. Floating photovoltaic plants: ecological impacts versus hydropower operation flexibility. Energy Conversion and Management. 2019;
    4. Haas J, Khalighi J, de la Fuente A, Gerbersdorf S, Nowak W, Chen P-J. Floating photovoltaic plants: ecological impacts versus hydropower operation flexibility. Energy Conversion and Management. 2019;
    5. Sinsbeck M, Cook E, Nowak W. Sequential Design of Computer Experiments for the Computation of Bayesian Model Evidence. SIAM Journal of Uncertainty Quantification. 2019;
    6. Haas J, Moreno-Leiva S, Junne T, Chen P-J, Pamparana G, Nowak W, et al. Copper mining: 100% solar electricity by 2030? submitted to Applied Energy. 2019;
    7. Haas J, Moreno-Leiva S, Junne T, Chen P-J, Pamparana G, Nowak W, et al. Copper mining: 100% solar electricity by 2030? submitted to Applied Energy. 2019;
  2. 2019 (Submitted)

    1. Bakhshipour AE, Dittmer U, Haghighi A, Nowak W. Hybrid green-blue-gray decentralized urban drainage systems design, a simulation-optimization framework. Journal of Environmental Management. 2019;
  3. 2019 (submitted)

    1. Keller J, Hendricks Franssen H-J, Nowak W. Pilot Point Ensemble Kalman Filter for geostatistical1 inversion and data assimilation. Water Resources Research. 2019;
    2. Guisandez I, Perez-Diaz JI, Nowak W, Haas J. Should environmental constraints be considered in long-term hydro scheduling? Journal of Electrical Power & Energy Systems. 2019;
    3. Xiao S, Oladyshkin S, Nowak W. Reliability analysis with conditional importance sampling based on adaptive Kriging. Reliability Engineering & System Safety. 2019;
    4. Kröker I, Oladyshkin S. Arbitrary Multi-Resolution Multi-Wavelet-based Polynomial Chaos Expansion for Data-Driven Uncertainty Quantification. Reliability Engineering & System Safety. 2019;
    5. Sinsbeck M, Nowak W. No exploratory phase required: Identifying Gaussian process hyper parameters in sequential design of computer experiments at the example of Bayesian inverse problems. SIAM/ASA Journal on Uncertainty Quantification. 2019;
    6. Xiao S, Reuschen S, Köse G, Oladyshkin S, Nowak W. Estimation of small failure probabilities based on thermodynamic integration and parallel tempering. Mechanical Systems and Signal Processing. 2019;
    7. Dibak C, Nowak W, Dürr F, Rothermel K. Using Surrogate Models and Data Assimilation for Efficient Mobile Simulations. Pervasive and Mobile Computing. 2019;
  4. 2019 (accepted)

    1. Motavita DF, Chow R, Guthke A, Nowak W. Assessing the impact of calibration and validation data choices on hydrological model performance: a model stress-test. Journal of Hydrology. 2019;
    2. Haas J, Hagen D, Nowak W. Energy storage and transmission systems to save the fish? Minimizing hydropeaking for little extra-cost. Sustainable Energy Technologies and Assessments. 2019;
    3. Chow R, Jeremy B, Jürnjakob D, Wöhling T, Nowak W. Evaluating subsurface parameterization to simulate hyporheic exchange: The Steinlach River Test Site. Groundwater. 2019;
  5. 2019

    1. Oladyshkin S, Nowak W. The connection between Bayesian Inference and Information Theory for model selection, information gain and experimental design. Entropy. 2019;
    2. Cheng K, Lu Z. Time-variant reliability analysis based on high dimensional model representation. Reliability Engineering & System Safety. 2019;188:310–9.
    3. Cheng K, Lu Z, Chaozhang K. Gradient-enhanced high dimensional model representation via Bayesian inference. Knowledge-Based Systems. 2019;
    4. Cheng K, Lu Z, Zhen Y. Multi-level multi-fidelity sparse polynomial chaos expansion based on Gaussian process regression. Computer Methods in Applied Mechanics and Engineering. 2019;349:360–77.
    5. Bürger R, Kröker I. Computational uncertainty quantification for some strongly degenerate parabolic convection–diffusion equations. Journal of Computational and Applied Mathematics [Internet]. 2019;348:490–508. Available from: http://www.sciencedirect.com/science/article/pii/S037704271830551X
    6. J. Salgado, Oladyshkin S, Osmancevic E, Janotte F. Kalibrierung von Rechennetzmodellen anhand probabilistischer Bayes‘scher Verfahren. DWGV energie wasser-praxis. 2019;2:16–21.
    7. Bakhshipour AE, Bakhshizadeh M, Dittmer U, Haghighi A, Nowak W. Hanging Gardens Algorithm to Generate Decentralized Layouts for Urban Drainage Systems Optimization. Journal of Water Resources Planning and Management. 2019;145(9):04019034.
    8. Chow R, Hao W, Jeremy B, Jürnjakob D, Wöhling T, Nowak W. Sensitivity of simulated hyporheic exchange to river bathymetry: The Steinlach River Test Site. Groundwater. 2019;57(3):378–91.
    9. Haas J, Nowak W, Palma-Behnke R. Multi-objective planning of energy storage technologies for a fully renewable system: implications for the main stakeholders in Chile. Energy Policy. 2019;126:494–506.
    10. Köppel M, Franzelin F, Kröker I, Oladyshkin S, Santin G, Wittwar D, et al. Comparison of data-driven uncertainty quantification methods for a carbon dioxide storage benchmark scenario. Computational Geosciences. 2019;23(2):339–54.
    11. Höge M, Guthke A, Nowak W. The Hydrologist’s Guide to Bayesian Model Selection, Averaging and Combination. Journal of Hydrology [Internet]. 2019;572:96–107. Available from: http://www.sciencedirect.com/science/article/pii/S0022169419301532
    12. Anindito Y, Haas J, Olivares MA, Kern J, Nowak W. A new solution to mitigate hydropeaking? Batteries versus re-regulation reservoirs. Journal of Cleaner Production. 2019;210:477–89.
    13. Pamparana G, Kracht W, Haas J, Ortiz JM, Nowak W, Palma-Behnke R. Studying the integration of solar energy into the operation of a semi-autogenous grinding mill. Part I: framework, model development and effect of solar irradiance forecasting. Minerals Engineering. 2019;
    14. Pamparana G, Kracht W, Haas J, Ortiz JM, Nowak W, Palma-Behnke R. Studying the integration of solar energy into the operation of a semi-autogenous grinding mill. Part II: effect of ore hardness variability, geometallurgical modeling and demand side management. Minerals Engineering. 2019;
    15. Wang Y, Xiao S, Lu Z. An efficient method based on Bayes’ theorem to estimate the failure-probability-based sensitivity measure. Mechanical Systems and Signal Processing. 2019;115:607–20.
    16. Most S, Bolster D, Bijeljic B, Nowak W. Trajectories as training images to simulate advective-diffusive, non-Fickian transport. Water Resources Research. 2019;55:3465–80.
    17. Bode F, Reed P, Reuschen S, Nowak W. Search Space Representation and Reduction Methods to Enhance Multi-Objective Water Supply Monitoring Design. Water Resources Research. 2019;55(3):2257–78.
    18. Rodriguez-Pretelin A, Nowak W. Dynamic re-distribution of pumping rates in well fields to counter transient problems in groundwater production. Groundwater for Sustainable Development. 2019;8:606–16.
    19. González-Nicolás A, Cihan A, Petrusak R, Zhou Q, Trautz R, Riestenberg D, et al. Pressure management via brine extraction in geological CO2 storage: Adaptive optimization strategies under poorly characterized reservoir conditions. International Journal of Greenhouse Gas Control. 2019;83:176–85.
    20. Motavita DF, Chow R, Guthke A, Nowak W. The Comprehensive Differential Split-Sample Test: A stress-test for hydrological model robustness under climate variability. Journal of Hydrology. 2019;573:501–15.
    21. Pamparana G, Kracht W, Haas J, Ortiz JM, Nowak W, Palma-Behnke R. Studying the integration of solar energy into the operation of a semi-autogenous grinding mill. Part I: framework, model development and effect of solar irradiance forecasting. Minerals Engineering. 2019;137:68–77.
    22. Pamparana G, Kracht W, Haas J, Ortiz JM, Nowak W, Palma-Behnke R. Studying the integration of solar energy into the operation of a semi-autogenous grinding mill. Part II: effect of ore hardness variability, geometallurgical modeling and demand side management. Minerals Engineering. 2019;137:53–67.
  6. 2018

    1. Cheng K, Lu Z. Sparse polynomial chaos expansion based on D-MORPH regression. Applied Mathematics and Computation. 2018;323:17–30.
    2. Cheng K, Lu Z. Adaptive sparse polynomial chaos expansions for global sensitivity analysis based on support vector regression. Computers & Structures. 2018;194:86–96.
    3. Praditia T, Helmig R, Hajibeygi H. Multiscale formulation for coupled flow-heat equations arising from single-phase flow in fractured geothermal reservoirs. Computational Geosciences [Internet]. 2018;22(5):1305–22. Available from: https://doi.org/10.1007/s10596-018-9754-4
    4. Haas J, Cebulla F, Nowak W, Rahmann C, Palma R. A multi-service approach for planning the optimal mix of energy storage technologies in a fully renewable power supply. Energy Conversion and Management [Internet]. 2018;178:355–68. Available from: https://www.sciencedirect.com/science/article/pii/S0196890418310987
    5. Bode F, Ferre T, Zigelli N, Emmert M, Nowak W. Reconnecting Stochastic Methods with Hydrogeological Applications: A Utilitarian Uncertainty Analysis and Risk Assessment Approach for the Design of Optimal monitoring Networks. Water Resources Research. 2018;54:2270–97.
    6. Rahmann C, Mayol C, Haas J. Dynamic control strategy in partially-shaded photovoltaic power plants for improving the frequency of the electricity system. Journal of Cleaner Production [Internet]. 2018;202C:109–19. Available from: https://www.sciencedirect.com/science/article/pii/S0959652618322996?via%3Dihub
    7. Mehne J, Nowak W. Predicting the battery core temperature: explanatory power of  measurement quantities under different uncertainty scenarios. Journal of Energy Storage. 2018;18:476–84.
    8. Rodriguez-Pretelin A, Nowak W. Integrating transient behavior as a new dimension to WHPA delineation. Advances in Water Resources. 2018;119:178–87.
    9. Höge M, Wöhling T, Nowak W. A Primer for Model Selection: The Decisive Role of  Model Complexity. Water Resources Research. 2018;
    10. Haas J, Palma-Behnke R, Valencia F, Araya P, D’iaz-Ferrán G, Telsnig T, et al. Sunset or sunrise? Understanding the barriers and options for the massive deployment of solar technologies in Chile. Energy Policy [Internet]. 2018;112:399–414. Available from: http://linkinghub.elsevier.com/retrieve/pii/S0301421517306249
    11. Mohammadi F, Kopmann R, Guthke A, Oladyshkin S, Nowak W. Bayesian selection of hydro-morphodynamic models under computational  time constraints. Advances in Water Resources [Internet]. 2018;117:53–64. Available from: http://www.sciencedirect.com/science/article/pii/S0309170817311909
    12. Xiao S, Lu Z, Wang P. Multivariate Global Sensitivity Analysis Based on Distance Components Decomposition. Risk Analysis. 2018;38(12):2703–21.
    13. Gosses M, Nowak W, Wöhling T. Explicit treatment for Dirichlet, Neumann and Cauchy      boundary conditions in POD-based reduction of groundwater models. Advances in Water Resources. 2018;115:160–71.
    14. Sun AY, González-Nicolás A, Jeong H, Templeton TC. Metamodeling-based approach for risk assessment and cost estimation: Application to geological carbon sequestration planning. Computers & Geosciences [Internet]. 2018;113(https://doi.org/10.1016/j.cageo.2018.01.006):70–80. Available from: https://www.sciencedirect.com/science/article/pii/S0098300417305393
    15. Oladyshkin S, Nowak W. Incomplete statistical information limits the utility of higher-order polynomial chaos expansions. Reliability Engineering & System Safety. 2018;169:137–48.
    16. Wang P, Lu Z, Xiao S. Variance-based sensitivity analysis with the uncertainties of the input variables and their distribution parameters. Communication in Statistics-Simulation and Computation. 2018;47(4):1103–25.
    17. Wang Y, Xiao S, Lu Z. A new efficient simulation method based on Bayes’ theorem and importance sampling Markov chain simulation to estimate the failure-probability-based global sensitivity. Aerospace Science and Technology. 2018;79:364–72.
    18. Chen Z, G’omez-Hernández JJ, Xu T, Zanini A. Joint identification of contaminant source and aquifer geometry in a sandbox experiment with the restart ensemble Kalman filter. Journal of Hydrology. 2018;564:1074–84.
    19. Wang P, Lu Z, Zhang K, Xiao S, Yue Z. Copula-based decomposition approach for the derivative-based sensitivity of variance contributions with dependent variables. Reliability Engineering & System Safety. 2018;169:437–50.
    20. Darscheid P, Guthke A, Ehret U. A Maximum-Entropy Method to Estimate Discrete Distributions  from Samples Ensuring Nonzero Probabilities. Entropy [Internet]. 2018;20(8). Available from: http://www.mdpi.com/1099-4300/20/8/601
    21. Cebulla F, Haas J, Eichman J, Nowak W, Mancarella P. How much Electrical Energy Storage do we need? A synthesis for the U.S., Europe, and Germany. Journal of Cleaner Production [Internet]. 2018;181:449–59. Available from: https://www.sciencedirect.com/science/article/pii/S0959652618301665
    22. Xiao S, Lu Z, Wang P. Global sensitivity analysis based on distance correlation for structural systems with multivariate output. Engineering Structures. 2018;167:74–83.
    23. Xiao S, Lu Z, Wang P. Multivariate global sensitivity analysis for dynamic models based on energy distance. Structural and Multidisciplinary Optimization. 2018;57(1):279–91.
    24. Xiao S, Lu Z. Global sensitivity analysis based on Gini’s mean difference. Structural and Multidisciplinary Optimization. 2018;58(4):1523–35.
    25. Xiao S, Lu Z. Reliability analysis by combining higher-order unscented transformation and fourth-moment method. ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering. 2018;4(1):04017034.
    26. Xu T, G’omez-Hernández JJ. Simultaneous identification of a contaminant source and hydraulic conductivity via the restart normal-score ensemble Kalman filter. Advances in Water Resources. 2018;112:106–23.
    27. Gosses M, Nowak W, Wöhling T. Explicit treatment for Dirichlet, Neumann and Cauchy      boundary conditions in POD-based reduction of groundwater models. Advances in Water Resources. 2018;
  7. 2017 (submitted)

    1. Bode F, Binning PJ, Nowak W. An Analytical Approach for Positioning a Single Monitoring Well to Reliably Detect Point-Source Contaminant Plumes. Water Resources Research. 2017;
    2. Gosses M, Nowak W, Wöhling T. Explicit treatment for Dirichlet, Neumann and Cauchy boundary conditions in POD-based reduction of groundwater models. Advances in Water Resources. 2017;
  8. 2017

    1. Cheng K, Lu Z, Wei Y, Shi Y, Zhou Y. Mixed kernel support vector regression for global sensitivity analysis. Mechanical Systems and Signal Processing. 2017;96:201–14.
    2. Cheng K, Lu Z, Zhou Y, Shi Y, Wei Y. Global sensitivity analysis using support vector regression. Applied Mathematical Modelling. 2017;49:587–98.
    3. Köppel M, Kröker I, Rohde C. Intrusive uncertainty quantification for hyperbolic-elliptic systems governing two-phase flow in heterogeneous porous media. Comput Geosci. 2017;21(4):807--832.
    4. Xiao S, Lu Z. Structural reliability sensitivity analysis based on classification of model output. Aerospace Science and Technology. 2017;71:52–61.
    5. Xiao S, Lu Z, Qin F. Estimation of the generalized Sobol’s sensitivity index for multivariate output model using unscented transformation. Journal of Structural Engineering. 2017;143(5):06016005.
    6. Bode F, Nowak W, Emmert M, Zigelli N. Optimale Grundwassermessstellennetze: Multi-kriterielle  Optimierung als Entscheidungshilfe. gwf Wasser+Abwasser. 2017;158(07–08):111–21.
    7. Cihan A, Birkholzer JT, Trevisan L, González-Nicolás A, Illangasekare T. Investigation of representing hysteresis in macroscopic models of two-phase flow in porous media using intermediate scale experimental data. Water Resources Research [Internet]. 2017;53(1):199–221. Available from: http://dx.doi.org/10.1002/2016WR019449
    8. Wirtz D, Nowak W. The rocky road to extended simulation frameworks covering uncertainty, inversion, optimization and control. Environmental Software and Modelling. 2017;93:180–92.
    9. Carpentier D, Haas J, Olivares M, de la Fuente A. Modeling the Multi-Seasonal Link between the Hydrodynamics of a Reservoir and Its Hydropower Plant Operation. Water [Internet]. 2017;9(6):367. Available from: http://www.mdpi.com/2073-4441/9/6/367
    10. Wang P, Lu Z, Xiao S. A generalized separation for the variance contributions of input variables and their distribution parameters. Applied Mathematical Modelling. 2017;47:381–99.
    11. Namhata A, Zhang L, Dilmore RM, Oladyshkin S, Nakles DV. Modeling Pressure Changes due to Migration of Fluids into the Above Zone Monitoring Interval of a Geologic Carbon Storage Site. International Journal of Greenhouse Gas Control. 2017;56:30–42.
    12. Haas J, Cebulla F, Karl-Kiên C, Nowak W, Palma-Behnke R, Rahmann C, et al. Challenges and trends of energy storage expansion planning for flexibility provision in power systems - a review. Renewable & Sustainable Energy Reviews [Internet]. 2017;80:603–619. Available from: http://www.sciencedirect.com/science/article/pii/S1364032117308377
    13. González-Nicolás A, Baú D, Cody BM. Application of binary permeability fields for the study of CO2 leakage from geological carbon storage in saline aquifers of the Michigan Basin,. Mathematical Geosciences [Internet]. 2017; Available from: https://doi.org/10.1007/s11004-017-9706-x
    14. Xiao S, Lu Z, Xu Liyang. Multivariate sensitivity analysis based on the direction of eigen space through principal component analysis. Reliability Engineering & System Safety. 2017;165:1–10.
    15. González-Nicolás A, Trevisan L, Illangasekare TH, Cihan A, Birkholzer JT. Enhancing capillary trapping effectiveness through proper time scheduling of injection of supercritical CO2 in heterogeneous formations. Greenhouse Gases: Science and Technology [Internet]. 2017;7(2):339–52. Available from: http://dx.doi.org/10.1002/ghg.1646
    16. Agada SS, Geiger S, ElSheikh A, Oladyshkin S. Data-driven surrogates for rapid simulation and optimization of WAG injection in fractured carbonate reservoirs. Petroleum Geoscience. 2017;23(2):270–83.
    17. Mehne J, Nowak W. Improving temperature predictions for Li-ion batteries: data assimilation with a stochastic extension of a physically-based, thermo-electrochemical model. Journal of Energy Storage [Internet]. 2017;12:288–96. Available from: http://www.sciencedirect.com/science/article/pii/S2352152X16302328
    18. Sinsbeck M, Nowak W. Sequential Design of Computer Experiments for the Solution of Bayesian Inverse Problems. SIAM /ASA Journal of Uncertainty Quantification. 2017;5(1):640–64.
    19. Moreno-Leiva S, D’iaz-Ferrán G, Haas J, Telsnig T, D’iaz-Alvarado FA, Palma-Behnke R, et al. Towards solar power supply for copper production in Chile: Assessment of global warming potential using a life-cycle approach. Journal of Cleaner Production [Internet]. 2017;164:242--249. Available from: http://linkinghub.elsevier.com/retrieve/pii/S0959652617312076
    20. Wirtz D, Nowak W. The rocky road to universal scientific simulation frameworks. Environmental Software and Modelling. 2017;93:180–92.
    21. Emmert M, Zigelli N, Haakh F, Bode F, Nowak W. Risikobasiertes Grundwassermonitoring für Wasserschutzgebiete. energie | wasser-praxis. 2017;67(8):68–71.
    22. Pamparana G, Kracht W, Haas J, D’iaz-Ferrán G, Palma-Behnke R, Román R. Integrating photovoltaic solar energy and a battery energy storage system to operate a semi-autogenous grinding mill. Journal of Cleaner Production [Internet]. 2017;165:273–80. Available from: http://linkinghub.elsevier.com/retrieve/pii/S0959652617315536
    23. Guthke A. Defensible Model Complexity: A Call for Data-Based and Goal-Oriented Model Choice. Groundwater [Internet]. 2017;55(5). Available from: http://dx.doi.org/10.1111/gwat.12554
    24. Trevisan L, Pini R, Cihan A, Birkholzer JT, Zhou Q, González-Nicolás A, et al. Imaging and quantification of spreading and trapping of carbon dioxide in saline aquifers using meter-scale laboratory experiments. Water Resources Research [Internet]. 2017;53(1):485–502. Available from: http://dx.doi.org/10.1002/2016WR019749
  9. 2016

    1. Barth A, Bürger R, Kröker I, Rohde C. Computational uncertainty quantification for a clarifier-thickener model with several random perturbations: A hybrid stochastic Galerkin approach. Computers & Chemical Engineering [Internet]. 2016;89:11-- 26. Available from: http://www.sciencedirect.com/science/article/pii/S0098135416300503
    2. Vereecken H, Schnepf A, Hopmans JW, Or MJD, Roose T, Vanderborght J, et al. Modeling Soil Processes: Key challenges and new perspectives. Vadoze Zone Journal. 2016;15(5).
    3. Xu T, G’omez-Hernández JJ. Characterization of non-Gaussian conductivities and porosities with hydraulic heads, solute concentrations, and water temperatures. Water Resources Research. 2016;52(8):6111–36.
    4. Namhata A, Oladyshkin S, Dilmore RM, Zhang L, Nakles DV. Probabilistic Assessment of Above Zone Pressure Predictions at a Geologic Carbon Storage Site. Scientific Reports. 2016;6:39536.
    5. Xiao S, Lu Z, Xu Liyang. A new effective screening design for structural sensitivity analysis of failure probability with the epistemic uncertainty. Reliability Engineering & System Safety. 2016;156:1–14.
    6. Wöhling T, Geiges A, Nowak W. Optimal design of multi-type groundwater monitoring networks using easily accessible tools. Groundwater. 2016;54(6):861–70.
    7. Koch J, Nowak W. Identification of contaminant source architectures - A statistical inversion that emulates multi-phase physics in a computationally practicable manner. Water Resources Research. 2016;52(2):1009–25.
    8. Most S, Bijeljic B, Nowak W. Evolution and persistence of cross-directional statistical dependence during finite-P’eclet transport through a real porous medium. Water Resources Research. 2016;52(11):8920–37.
    9. Xu T, G’omez-Hernández JJ. Joint identification of contaminant source location, initial release time, and initial solute concentration in an aquifer via ensemble Kalman filtering. Water Resources Research. 2016;52(8):6587–95.
    10. Chow R, Frind ME, Frind EO, Jones JP, Sousa MR, Rudolph DL, et al. Delineating Baseflow Contribution Areas for Streams – A Model and Methods Comparison. Journal of Contaminant Hydrology. 2016;195:11–22.
    11. Lötgering-Lin O, Schöniger A, Nowak W, Gross J. Bayesian Model Selection helps to Choose Objectively between Thermodynamic Models: A Demonstration of Selecting a Viscosity-Model based on Entropy Scaling. Industrial & Engineering Chemistry Research. 2016;55(38):10191–207.
    12. Birkmann J, Wenzel F, Greiving S, Garschagen M, Vall’ee D, Nowak W, et al. Extreme Events, Critical Infrastructures, Human Vulnerability and Strategic Planning: Emerging Research Issues. Journal of Extreme Events. 2016;3(2):1650017(1-25).
    13. Schulte DO, Rühaak W, Oladyshkin S, Welsch B, Sass I. Optimization of Medium Deep Borehole Thermal Energy Storages. Energy Technology. 2016;(4):104–13.
    14. Zhang Y, Liu Y, Pau G, Oladyshkin S, Finsterle S. Evaluation of multiple reduced-order models to enhance confidence in global sensitivity analyses. International Journal of Greenhouse Gas Control. 2016;49:217–26.
    15. Flemisch B, Nordbotten JM, Nowak W, Raoof A. Special Issue on “‘NUPUS: Non-linearities and Upscaling in PoroUS Media’” (Editorial). Transport in Porous Media. 2016;114:237–40.
    16. Xiao S, Lu Z. Structural reliability analysis using combined space partition technique and unscented transformation. Journal of Structural Engineering. 2016;142(11):04016089.
    17. Rahmann C, Vittal V, Ascui J, Haas J. Mitigation Control against Partial Shading Effects in Large-scale PV Power Plants. IEEE Transactions on Sustainable Energy. 2016;7(1):173–80.
    18. Nowak W, Guthke A. Entropy-based experimental design for optimal model discrimination in the geosciences. Entropy. 2016;18(11):409.
  10. 2015

    1. Koch J, Nowak W. Predicting DNAPL mass discharge and contaminated site longevity probabilities: Conceptual model and high-resolution stochastic simulation. Water Resources Research. 2015;51(2):806–31.
    2. González-Nicolás A, Baú D, Cody BM, Alzraiee A. Stochastic and global sensitivity analyses of uncertain parameters affecting the safety of geological carbon storage in saline aquifers of the Michigan Basin. International Journal of Greenhouse Gas Control. 2015;37:99–114.
    3. Schöniger A, Wöhling T, Nowak W. A statistical concept to assess the uncertainty in Bayesian model weights and its impact on model ranking. Water Resources Research. 2015;51(9):7524–46.
    4. Xu T, G’omez-Hernández JJ. Inverse sequential simulation: Performance and implementation details. Advances in water resources. 2015;86:311–26.
    5. Olivares MA, Haas J, Palma-Behnke R, Benavides C. A framework to identify Pareto-efficient subdaily environmental flow constraints on hydropower reservoirs using a grid-wide power dispatch model. Water Resources Research [Internet]. 2015;51(5):3664–80. Available from: http://doi.wiley.com/10.1002/2014WR016215
    6. Schöniger A, Illman W, Wöhling T, Nowak W. Finding the Right Balance Between Groundwater Model Complexity and Experimental Effort via Bayesian Model Selection. Journal of Hydrology. 2015;531(1):96–110.
    7. Sinsbeck M, Nowak W. An optimal sampling rule for nonintrusive polynomial chaos expansions of expensive models. International Journal for Uncertainty Quantification. 2015;5(3):275–95.
    8. Haas J, Olivares MA, Palma-Behnke R. Grid-wide subdaily hydrologic alteration under massive wind power penetration in Chile. Journal of Environmental Management [Internet]. 2015;154:183–9. Available from: http://linkinghub.elsevier.com/retrieve/pii/S0301479715000857
    9. Gerbersdorf SU, Cimatoribus C, Class H, Engesser KH, Helbich S, Hollert H, et al. Anthropogenic Trace Compounds (ATCs) in aquatic habitats - research needs on sources, fate, detection and toxicity to ensure timely elimination strategies and risk management. Environment International. 2015;79:85–105.
    10. Enzenhöfer R, Nowak W, Binning PJ. Stakeholder-Objective Risk Model (STORM): Determining the aggregated risk of multiple contaminant hazards in groundwater well catchments. Advances in Water Resources. 2015;83:165–75.
    11. Bode F, Nowak W, Loschko M. Optimization for early-warning monitoring networks in well catchments should be multi-objective, risk-prioritized and robust against uncertainty. Transport in Porous Media. 2015;114:261–81.
    12. Xu T, G’omez-Hernández JJ. Inverse sequential simulation: A new approach for the characterization of hydraulic conductivities demonstrated on a non-Gaussian field. Water Resources Research. 2015;51(4):2227–42.
    13. Xu T, G’omez-Hernández JJ. Probability fields revisited in the context of ensemble Kalman filtering. Journal of Hydrology. 2015;531:40–52.
    14. González-Nicolás A, Baú D, Alzraiee A. Detection of potential leakage pathways from geological carbon storage by fluid pressure data assimilation. Advances in Water Resources. 2015;86:366–84.
    15. Sinsbeck M, Tartakovsky D. Impact of data assimilation on cost-accuracy tradeoff in multifidelity models. SIAM/ASA Journal of Uncertainty Quantification. 2015;3(1):954–68.
    16. Geiges A, Rubin Y, Nowak W. Interactive design of experiments: A priori global versus sequential optimization, revised under changing states of knowledge. Water Resources Research. 2015;51(10):7915–36.
    17. Wöhling T, Schöniger A, Gayler S, Nowak W. Bayesian model averaging to explore the worth of data for soil-plant model selection and prediction. Water Resources Research. 2015;51(4):2825–46.
    18. Nowak W, Bode F, Loschko M. A multi-objective optimization concept for risk-based early-warning monitoring networks in well catchments. Procedia Environmental Sciences. 2015;25:191–8.
    19. Cody BM, Baú D, González-Nicolás A. Stochastic injection-strategy optimization for the preliminary assessment of candidate geological storage sites. Hydrogeology Journal. 2015;23(6):1229–45.
    20. Baú D, Cody BM, González-Nicolás A. An iterative global pressure solution for the semi-analytical simulation of geological carbon sequestration. Computational Geosciences. 2015;19(4):781–9.
  11. 2014

    1. Bürger R, Kröker I, Rohde C. A hybrid stochastic Galerkin method for uncertainty quantification  applied to a conservation law modelling a clarifier-thickener unit. ZAMM Z Angew Math Mech [Internet]. 2014;94(10):793–817. Available from: http://dx.doi.org/10.1002/zamm.201200174
    2. Schöniger Anneli, Wöhling T, Samaniego L, Nowak W. Model selection on solid ground: rigorous comparison of nine ways to evaluate Bayesian evidence. Water Resources Research. 2014;50(12):9484–513.
    3. Kröker I, Nowak W, Rohde C. A stochastically and spatially adaptive parallel scheme for uncertain and non-linear two-phase flow problems. Computational Geosciences. 2014;19:269–84.
    4. Enzenhöfer R, Bunk T, Nowak W. Nine steps to risk-informed wellhead protection and management: A case study. Groundwater. 2014;52:161–74.
    5. Koch J, Nowak W. A method for implementing Dirichlet and third-type boundary conditions in PTRW simulations. Water Resources Research. 2014;50(2):1374–95.
    6. Karajan N, Otto D, Oladyshkin S, Ehlers M. Application of the polynomial chaos expansion to approximate the homogenised response of the intervertebral disc. Biomechanics and modeling in mechanobiology. 2014;13(5):1065–80.
  12. 2013

    1. Oladyshkin S, Class H, Nowak W. Bayesian updating via bootstrap filtering combined with data-driven polynomial chaos expansions:methodology and application to history matching for carbon dioxide storage in geological formations. Computational Geosciences. 2013;17(4):671–87.
    2. Xu T, G’omez-Hernández JJ, Li L, Zhou H. Parallelized ensemble Kalman filter for hydraulic conductivity characterization. Computers & geosciences. 2013;52:42–9.
    3. Leube P, de Barros FPJ, Nowak W, Rajagopal R. Towards optimal allocation of computer resources: trade-offs between uncertainty quantification,discretization and model reduction. Environmental Software and Modelling. 2013;50:97–107.
    4. Grathwohl P, Rügner H, Wöhling T, Osenbrück K, Schwientek M, Gayler S, et al. Catchments as Reactors: A comprehensive approach for water fluxes and solute turn-over            (Introduction to the Thematic Issue on Catchment Research). Environmental Earth Science. 2013;69(2):317–33.
    5. Wöhling T, Geiges A, Nowak W, Gayler S. Towards optimizing experiments for maximum-confidence model selection between different soil-plant models. Procedia Environmental Sciences. 2013;19:514–23.
    6. Grathwohl P, Rügner H, Wöhling T, Osenbrück K, Schwientek M, Gayler S, et al. Catchments as Reactors: A comprehensive approach for water fluxes and solute turn-over (Introduction to the Thematic Issue on Catchment Research). Environmental Earth Science. 2013;69(2):317–33.
    7. Nowak W, Litvinenko A. Kriging and spatial design accelerated by orders of magnitude: combining low-rank covariance approximations with FFT-techniques. Mathematical Geosciences. 2013;45(4):411–35.
    8. Xu T, G’omez-Hernández JJ, Zhou H, Li L. The power of transient piezometric head data in inverse modeling: An application of the localized normal-score EnKF with covariance inflation in a heterogenous bimodal hydraulic conductivity field. Advances in water resources. 2013;54:100–18.
    9. Oladyshkin S, Schröder P, Class H, Nowak W. Chaos Expansion based Bootstrap Filter to Calibrate CO2 Injection Models. Energy Procedia. 2013;40:398–407.
    10. Ashraf M, Oladyshkin S, Nowak W. Geological storage of CO2: global sensitivity analysis and risk assessment using arbitrary polynomial chaos expansion. International Journal of Greenhouse Gas Control. 2013;19:704–19.
  13. 2012

    1. Kröker I, Rohde C. Finite volume schemes for hyperbolic balance laws with multiplicative  noise. Appl Numer Math [Internet]. 2012;62(4):441--456. Available from: http://dx.doi.org/10.1016/j.apnum.2011.01.011
    2. Cirpka OA, Rolle M, Chiogna G, de Barros FPJ, Nowak W. Stochastic Evaluation of Mixing-Controlled Steady-State Plume Lengths in Two-Dimensional Heterogeneous Domains. Journal of Contaminant Hydrology. 2012;138–139:22–39.
    3. Oladyshkin S, Nowak W. Data-driven uncertainty quantification using the arbitrary polynomial chaos expansion. Reliability Engineering and System Safety. 2012;106:179–90.
    4. Oladyshkin S, Panfilov M. Open thermodynamic model for compressible multicomponent two-phase flow in porous media. Journal of Petroleum Science and Engineering. 2012;81:41–8.
    5. Troldborg M, Nowak W, Lange I, Santos M, Binning P, Bjerg PL. Application of Bayesian geostatistics for evaluation of mass discharge uncertainty at contaminated sites. Water Resources Research. 2012;48(W09535).
    6. Schöniger A, Nowak W, Franssen HJH. Parameter estimation by ensemble Kalman filters with transformed data: Approach and application to hydraulic tomography. Water Resources Research. 2012;48(W04502).
    7. Leube P, Geiges A, Nowak W. Bayesian assessment of the expected data impact on prediction confidence in optimal sampling design. Water Resources Research. 2012;48(W02501).
    8. Walter L, Binning PJ, Oladyshkin S, Flemisch B, Class H. Brine migration resulting from CO$_2$ injection into saline aquifers - An approach to risk estimation including various levels of uncertainty. International Journal of Greenhouse Gas Control. 2012;9:495–506.
    9. Oladyshkin S, Nowak W. Polynomial Response Surfaces for Probabilistic Risk Assessment and Risk Control via Robust Design (Book). Luo Y, editor. Novel Approaches and Their Applications in Risk Assessment, ISBN: 978-953-51-0519-0 [Internet]. 2012; Available from: /brokenurl#www.intechopen.com/books/
    10. Tartakovsky D, Nowak W, Bolster D. Introduction to the special issue on uncertainty quantification and risk assessment. Advances in Water Resources. 2012;36:1–2.
    11. Oladyshkin S, de Barros FPJ, Nowak W. Global sensitivity analysis: a flexible and efficient framework with an example from stochastic hydrogeology. Advances in Water Resources. 2012;37:10–22.
    12. Enzenhöfer R, Nowak W, Helmig R. Probabilistic Exposure Risk Assessment with Advective-Dispersive Well Vulnerability Criteria. Advances in Water Resources. 2012;36:121–32.
    13. Leube P, Nowak W, Schneider G. Temporal Moments revisited: Why there is there nobetter way for physically-based model reduction in time. Water Resources Research. 2012;48(W11527).
    14. de Barros FPJ, Dentz M, Koch J, Nowak W. Flow topology and scalar mixing in heterogeneous porous media. Geophysical Research Letters. 2012;39(L08404).
    15. Nowak W, Rubin Y, de Barros FPJ. A hypothesis-driven approach to optimal site investigation. Water Resources Research. 2012;48(W06509).
  14. 2011

    1. Cheng T, Wang Q, Ji X, Huang L, Xu T. Parallel computing method for groundwater flow simulation in porous media. Jisuanji Gongcheng yu Yingyong(Computer Engineering and Applications). 2011;47(20):234–7.
    2. Oladyshkin S, Panfilov M. Hydrogen penetration in water through porous medium: application to a radioactive waste storage site. Environmental Earth Sciences. 2011;64(4):989–99.
    3. Cirpka OA, de Barros FPJ, Chiogna G, Rolle M, Nowak W. Stochastic Flux-Related Analysis of Transverse Mixing in Two-Dimensional Heterogeneous Porous Media. Water Resources Research. 2011;47(W06515).
    4. Oladyshkin S, Class H, Helmig R, Nowak W. An Integrative Approach to Robust Design and Probabilistic Risk Assessment for CO$_2$ Storage in Geological Formations. Computer Geosciences. 2011;15(3):565–77.
    5. Oladyshkin S, Class H, Helmig R, Nowak W. A concept for data-driven probabilistic risk assessment and application to carbon dioxide storage in geological formations. Advances in Water Resources. 2011;34:1508–18.
    6. Walter L, Oladyshkin S, Class H, Darcis M, Helmig R. A study on pressure evolution in a channel system during CO$_2$ injection. Energy Procedia. 2011;4:3722–9.
    7. Cirpka OA, de Barros FPJ, Chiogna G, Nowak W. Probability Density Function of Steady-State Concentration in Two-Dimensional Heterogeneous Porous Media. Water Resources Research. 2011;47(W11523).
    8. Cirpka OA, de Barros FPJ, Chiogna G, Nowak W. Probability Density Function of Steady-State Concentration in Two-Dimensional    Heterogeneous Porous Media. Water Resour Res. 2011;47(W11523).
    9. de Barros FPJ, Bolster D, Sanchez-Vila X, Nowak W. A Divide and Conquer Approach to Cope with Uncertainty, Human Health Risk and Decision Making in Contaminant Hydrology. Water Resources Research. 2011;47(W05508).
    10. Hlawatsch M, Leube P, Nowak W, Weiskopf D. Flow Radar Glyphs-Static Visualization of Unsteady Flow with Uncertainty. IEEE Transactions on Visualization and Computer Graphics. 2011;17(12):1949–58.
  15. 2010

    1. de Barros FPJ, Nowak W. On the Link Between Contaminant Source Release Conditions and Plume Prediction Uncertainty. Journal of Contaminant Hydrology. 2010;116:24–34.
    2. Nowak W. Measures of Parameter Uncertainty in Geostatistical Estimation and Geostatistical Optimal Design. Mathematical Geosciences. 2010;42(2):199–221.
    3. Troldborg M, Nowak W, Tuxen N, Bjerg PL, Helmig R, Binning PJ. Uncertainty evaluation of mass discharge estimates from a contaminated site using a fully Bayesian framework. Water Resources Research. 2010;46(12).
    4. Nowak W, de Barros FPJ, Rubin Y. Bayesian Geostatistical Design: Task-Driven Optimal Site Investigation when the Geostatistical Model is Uncertain. Water Resources Research. 2010;46(W03535).
  16. 2009

    1. Troldborg M, Nowak W, Tuxen N, Bjerg PL, Helmig R, Binning P. Quantifying uncertainty in mass discharge estimates from contaminated sites using a fully Bayesian framework. ModelCARE: Calibration and Reliability in Groundwater Modeling - Managing Groundwater and the Environment (20 - 23 September 2009, Wuhan, China) China University of Geosciences, 9/2009. 2009;
    2. Fritz J, Nowak W, Neuweiler I. Application of FFT-based Algorithms for Large-Scale Universal Kriging Problems. Mathematical Geosciences. 2009;51(5):199–221.
    3. Nowak W. Best unbiased ensemble linearization and the Quasi-Linear Kalman Ensemble Generator. Water Resources Research. 2009;45(W04431).
  17. 2008

    1. Vasin M, Lehmann P, Kaestner A, Hassanein R, Nowak W, Neuweiler I. Drainage in Heterogeneous Sand Columns with Different Geometric Structures. Advances in Water Resources. 2008;31(9):1205–20.
    2. Oladyshkin S, Royer JJ, Panfilov M. Effective solution through the streamline technique and HT-splitting for the $3$D dynamic analysis of the compositional flows in oil reservoirs. Transport in Porous Media. 2008;74(3):311–29.
    3. Schwede RL., Cirpka OA, Nowak W, Neuweiler I. Impact of sampling volume on the probability density function of steady state concentration. Water Resources Research. 2008;44(W12433).
    4. Nowak W, Schwede RL, Cirpka OA, Neuweiler I. Probability density functions of hydraulic head and velocity in three-dimensional heterogeneous porous media. Water Resources Research. 2008;44(W08452).
  18. 2007

    1. Oladyshkin S, Skachkov S, Panfilova I, Panfilov M. Upscaling fractured media and streamline HT-splitting in compositional reservoir simulation. Oil & Gas Science and Technology. 2007;62(2):137–46.
    2. Oladyshkin S, Panfilov M. Limit thermodynamic model for compositional gas-liquid systems moving in a porous medium. Transport in Porous Media. 2007;70(2):147–65.
    3. Oladyshkin S, Panfilov M. Streamline splitting between thermodynamics and hydrodynamics in compositional gas-liquid flow through porous media. Comptes rendus de l’Academie des sciences Mecanique. 2007;335(1):7–12.
  19. 2006

    1. Nowak W, Cirpka OA. Geostatistical Inference of Hydraulic Conductivity and Dispersivities from Hydraulic Heads and Tracer Data. Water Resources Research. 2006;42(W08416).
  20. 2005

    1. Li W, Nowak W, Cirpka OA. Geostatistical inverse modeling of transient pumping tests using temporal moments of drawdown. Water Resources Research. 2005;41(W08403).
    2. Rahman MA, Jose SC, Nowak W, Cirpka OA. Experiments on vertical transverse mixing in a large-scale heterogeneous model aquifer. Journal of Contaminant Hydrology. 2005;80(3):130–48.
    3. S. Oladyshkin MP. Modeling of two-phase macroflow with phase transitions and contract properties. Transactions of the Russian Academy of Engineering Sciences. 2005;5:34–6.
  21. 2004

    1. Cirpka OA, Nowak W. First-order variance of travel time in non-stationary formations. Water Resources Research. 2004;40(W03507).
    2. Cirpka OA, Bürger CM, Nowak W, Finkel M. Uncertainty and data worth analysis for the hydraulic design of funnel-and-gate systems in heterogeneous aquifers. Water Resources Research. 2004;40(W11502).
    3. Nowak W, Cirpka OA. A modified Levenberg-Marquardt Algorithm for Quasi-linear Geostatistical Inversing. Advances in Water Resources. 2004;27(7):737–50.
  22. 2003

    1. Cirpka OA, Nowak W. Dispersion on kriged hydraulic conductivity fields. Water Resources Research. 2003;39(2):1027.
    2. S. Oladyshkin, N.N. Bobkov YuPG, Kozyrev OR. Invariant immersing method applied to the problem to thermocapillary convection a viscous fluid in the plane channel. Transactions of the Russian Academy of Engineering Sciences Ser Applied Mathematics and Mechanics. 2003;4:26–31.
    3. Nowak W, Tenkleve S, Cirpka OA. Efficient computation of linearized cross-covariance and auto-covariance matrices of interdependent quantities. Mathematical Geology. 2003;35(1):53–66.
  23. 2002

    1. S. Oladyshkin, N.N. Bobkov YuPG, Kozyrev OR. Modeling of temperature and velocity field inside viscous fluid of finite thermoconductivity, moving inside a corner with free surface, under the action of Marangoni forces. Transactions of the Russian Academy of Engineering Sciences Ser Applied Mathematics and Mechanics. 2002;79–88.
  24. 2001

    1. S. Oladyshkin, N.N. Bobkov YuPG, Kozyrev OR. The numerical algorithm development in the problem of thermocapillary convection under the Marangoni forces action. Transactions of the Russian Academy of Engineering Sciences. 2001;2:28–39.
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