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An automated calibration framework and open source tools for 3D lake hydrodynamic models
Baracchini, T., Hummel, S., Verlaan, M., Cimatoribus, A., Wüest, A., & Bouffard, D. (2020). An automated calibration framework and open source tools for 3D lake hydrodynamic models. Environmental Modelling and Software, 134, 104787 (16 pp.). https://doi.org/10.1016/j.envsoft.2020.104787
Active learning for anomaly detection in environmental data
Russo, S., Lürig, M., Hao, W., Matthews, B., & Villez, K. (2020). Active learning for anomaly detection in environmental data. Environmental Modelling and Software, 134, 104869 11 pp.). https://doi.org/10.1016/j.envsoft.2020.104869
Gamified online survey to elicit citizens' preferences and enhance learning for environmental decisions
Aubert, A. H., & Lienert, J. (2019). Gamified online survey to elicit citizens' preferences and enhance learning for environmental decisions. Environmental Modelling and Software, 111, 1-12. https://doi.org/10.1016/j.envsoft.2018.09.013
An open-source data manager for network models
Knox, S., Tomlinson, J., Harou, J. J., Meier, P., Rosenberg, D. E., Lund, J. R., & Rheinheimer, D. E. (2019). An open-source data manager for network models. Environmental Modelling and Software, 122, 104538 (16 pp.). https://doi.org/10.1016/j.envsoft.2019.104538
A review of water-related serious games to specify use in environmental Multi-Criteria Decision Analysis
Aubert, A. H., Bauer, R., & Lienert, J. (2018). A review of water-related serious games to specify use in environmental Multi-Criteria Decision Analysis. Environmental Modelling and Software, 105, 64-78. https://doi.org/10.1016/j.envsoft.2018.03.023
Modelling characteristics of the urban form to support water systems planning
Bach, P. M., Deletic, A., Urich, C., & McCarthy, D. T. (2018). Modelling characteristics of the urban form to support water systems planning. Environmental Modelling and Software, 104, 249-269. https://doi.org/10.1016/j.envsoft.2018.02.012
A multi-lake comparative analysis of the General Lake Model (GLM): stress-testing across a global observatory network
Bruce, L. C., Frassl, M. A., Arhonditsis, G. B., Gal, G., Hamilton, D. P., Hanson, P. H., … Hipsey, M. R. (2018). A multi-lake comparative analysis of the General Lake Model (GLM): stress-testing across a global observatory network. Environmental Modelling and Software, 102, 274-291. https://doi.org/10.1016/j.envsoft.2017.11.016
Transforming data into knowledge for improved wastewater treatment operation: a critical review of techniques
Corominas, L., Garrido-Baserba, M., Villez, K., Olsson, G., Cortés, U., & Poch, M. (2018). Transforming data into knowledge for improved wastewater treatment operation: a critical review of techniques. Environmental Modelling and Software, 106, 89-103. https://doi.org/10.1016/j.envsoft.2017.11.023
A python framework for multi-agent simulation of networked resource systems
Knox, S., Meier, P., Yoon, J., & Harou, J. J. (2018). A python framework for multi-agent simulation of networked resource systems. Environmental Modelling and Software, 103, 16-28. https://doi.org/10.1016/j.envsoft.2018.01.019
Accelerating Bayesian inference in hydrological modeling with a mechanistic emulator
Machac, D., Reichert, P., Rieckermann, J., Del Giudice, D., & Albert, C. (2018). Accelerating Bayesian inference in hydrological modeling with a mechanistic emulator. Environmental Modelling and Software, 109, 66-79. https://doi.org/10.1016/j.envsoft.2018.07.016
Groundwater recharge predictions in contrasted climate: the effect of model complexity and calibration period on recharge rates
Moeck, C., von Freyberg, J., & Schirmer, M. (2018). Groundwater recharge predictions in contrasted climate: the effect of model complexity and calibration period on recharge rates. Environmental Modelling and Software, 103, 74-89. https://doi.org/10.1016/j.envsoft.2018.02.005
A toolkit for climate change analysis and pattern recognition for extreme weather conditions – case study: California-Baja California Peninsula
Ashraf Vaghefi, S., Abbaspour, N., Kamali, B., & Abbaspour, K. C. (2017). A toolkit for climate change analysis and pattern recognition for extreme weather conditions – case study: California-Baja California Peninsula. Environmental Modelling and Software, 96, 181-198. https://doi.org/10.1016/j.envsoft.2017.06.033
Appraisal of data-driven and mechanistic emulators of nonlinear simulators: the case of hydrodynamic urban drainage models
Carbajal, J. P., Leitão, J. P., Albert, C., & Rieckermann, J. (2017). Appraisal of data-driven and mechanistic emulators of nonlinear simulators: the case of hydrodynamic urban drainage models. Environmental Modelling and Software, 92, 17-27. https://doi.org/10.1016/j.envsoft.2017.02.006
Patterns of streamflow regimes along the river network: the case of the Thur river
Doulatyari, B., Betterle, A., Radny, D., Celegon, E. A., Fanton, P., Schirmer, M., & Botter, G. (2017). Patterns of streamflow regimes along the river network: the case of the Thur river. Environmental Modelling and Software, 93, 42-58. https://doi.org/10.1016/j.envsoft.2017.03.002
Combined analysis of time-varying sensitivity and identifiability indices to diagnose the response of a complex environmental model
Ghasemizade, M., Baroni, G., Abbaspour, K., & Schirmer, M. (2017). Combined analysis of time-varying sensitivity and identifiability indices to diagnose the response of a complex environmental model. Environmental Modelling and Software, 88, 22-34. https://doi.org/10.1016/j.envsoft.2016.10.011
Bayesian parameter inference for individual-based models using a Particle Markov Chain Monte Carlo method
Kattwinkel, M., & Reichert, P. (2017). Bayesian parameter inference for individual-based models using a Particle Markov Chain Monte Carlo method. Environmental Modelling and Software, 87, 110-119. https://doi.org/10.1016/j.envsoft.2016.11.001
Fast mechanism-based emulator of a slow urban hydrodynamic drainage simulator
Machac, D., Reichert, P., Rieckermann, J., & Albert, C. (2016). Fast mechanism-based emulator of a slow urban hydrodynamic drainage simulator. Environmental Modelling and Software, 78, 54-67. https://doi.org/10.1016/j.envsoft.2015.12.007
Global parameter optimization for biokinetic modeling of simple batch experiments
Mašić, A., Udert, K. M., & Villez, K. (2016). Global parameter optimization for biokinetic modeling of simple batch experiments. Environmental Modelling and Software, 85, 356-373. https://doi.org/10.1016/j.envsoft.2016.06.015
Model bias and complexity - understanding the effects of structural deficits and input errors on runoff predictions
Del Giudice, D., Reichert, P., Bareš, V., Albert, C., & Rieckermann, J. (2015). Model bias and complexity - understanding the effects of structural deficits and input errors on runoff predictions. Environmental Modelling and Software, 64, 205-214. https://doi.org/10.1016/j.envsoft.2014.11.006
Setting up a hydrological model of Alberta: data discrimination analyses prior to calibration
Faramarzi, M., Srinivasan, R., Iravani, M., Bladon, K. D., Abbaspour, K. C., Zehnder, A. J. B., & Goss, G. G. (2015). Setting up a hydrological model of Alberta: data discrimination analyses prior to calibration. Environmental Modelling and Software, 74, 48-65. https://doi.org/10.1016/j.envsoft.2015.09.006