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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
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
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
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
The effect of ambiguous prior knowledge on Bayesian model parameter inference and prediction
Rinderknecht, S. L., Albert, C., Borsuk, M. E., Schuwirth, N., Künsch, H. R., & Reichert, P. (2014). The effect of ambiguous prior knowledge on Bayesian model parameter inference and prediction. Environmental Modelling and Software, 62, 300-315. https://doi.org/10.1016/j.envsoft.2014.08.020
Constructing, evaluating and visualizing value and utility functions for decision support
Reichert, P., Schuwirth, N., & Langhans, S. (2013). Constructing, evaluating and visualizing value and utility functions for decision support. Environmental Modelling and Software, 46, 283-291. https://doi.org/10.1016/j.envsoft.2013.01.017
Combining expert knowledge and local data for improved service life modeling of water supply networks
Scholten, L., Scheidegger, A., Reichert, P., & Maurer, M. (2013). Combining expert knowledge and local data for improved service life modeling of water supply networks. Environmental Modelling and Software, 42, 1-16. https://doi.org/10.1016/j.envsoft.2012.11.013
Calibration of computationally demanding and structurally uncertain models with an application to a lake water quality model
Dietzel, A., & Reichert, P. (2012). Calibration of computationally demanding and structurally uncertain models with an application to a lake water quality model. Environmental Modelling and Software, 38(December), 129-146. https://doi.org/10.1016/j.envsoft.2012.05.007
Bridging uncertain and ambiguous knowledge with imprecise probabilities
Rinderknecht, S. L., Borsuk, M. E., & Reichert, P. (2012). Bridging uncertain and ambiguous knowledge with imprecise probabilities. Environmental Modelling and Software, 36, 122-130. https://doi.org/10.1016/j.envsoft.2011.07.022
A parallelization framework for calibration of hydrological models
Rouholahnejad, E., Abbaspour, K. C., Vejdani, M., Srinivasan, R., Schulin, R., & Lehmann, A. (2012). A parallelization framework for calibration of hydrological models. Environmental Modelling and Software, 31(May), 28-36. https://doi.org/10.1016/j.envsoft.2011.12.001
A comparison of different rule-based statistical models for modeling geogenic groundwater contamination
Amini, M., Abbaspour, K. C., & Johnson, C. A. (2010). A comparison of different rule-based statistical models for modeling geogenic groundwater contamination. Environmental Modelling and Software, 25(12), 1650-1657. https://doi.org/10.1016/j.envsoft.2010.05.014
A generic framework for deriving process stoichiometry in environmental models
Reichert, P., & Schuwirth, N. (2010). A generic framework for deriving process stoichiometry in environmental models. Environmental Modelling and Software, 25(10), 1241-1251. https://doi.org/10.1016/j.envsoft.2010.03.002
A GIS-based tool for modelling large-scale crop-water relations
Liu, J. (2009). A GIS-based tool for modelling large-scale crop-water relations. Environmental Modelling and Software, 24(3), 411-422. https://doi.org/10.1016/j.envsoft.2008.08.004
Using MODAWEC to generate daily weather data for the EPIC model
Liu, J., Williams, J. R., Wang, X., & Yang, H. (2009). Using MODAWEC to generate daily weather data for the EPIC model. Environmental Modelling and Software, 24(5), 655-664. https://doi.org/10.1016/j.envsoft.2008.10.008
Concepts of decision support for river rehabilitation
Reichert, P., Borsuk, M., Hostmann, M., Schweizer, S., Spörri, C., Tockner, K., & Truffer, B. (2007). Concepts of decision support for river rehabilitation. Environmental Modelling and Software, 22(2), 188-201. https://doi.org/10.1016/j.envsoft.2005.07.017