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A comparison of numerical approaches for statistical inference with stochastic models
Bacci, M., Sukys, J., Reichert, P., Ulzega, S., & Albert, C. (2023). A comparison of numerical approaches for statistical inference with stochastic models. Stochastic Environmental Research and Risk Assessment, 37(8), 3041-3061. https://doi.org/10.1007/s00477-023-02434-z
Systematic handling of environmental fate data for model development - illustrated for the case of biodegradation half-life data
Hafner, J., Fenner, K., & Scheidegger, A. (2023). Systematic handling of environmental fate data for model development - illustrated for the case of biodegradation half-life data. Environmental Science and Technology Letters, 10(10), 859-864. https://doi.org/10.1021/acs.estlett.3c00526
Reducing sample size requirements by extending discrete choice experiments to indifference elicitation
Sriwastava, A., & Reichert, P. (2023). Reducing sample size requirements by extending discrete choice experiments to indifference elicitation. Journal of Choice Modelling, 48, 100426 (18 pp.). https://doi.org/10.1016/j.jocm.2023.100426
Application of stochastic time dependent parameters to improve the characterization of uncertainty in conceptual hydrological models
Bacci, M., Dal Molin, M., Fenicia, F., Reichert, P., & Šukys, J. (2022). Application of stochastic time dependent parameters to improve the characterization of uncertainty in conceptual hydrological models. Journal of Hydrology, 612, 128057 (19 pp.). https://doi.org/10.1016/j.jhydrol.2022.128057
Investigating the effect of pesticides on Daphnia population dynamics by inferring structure and parameters of a stochastic model
Palamara, G. M., Dennis, S. R., Haenggi, C., Schuwirth, N., & Reichert, P. (2022). Investigating the effect of pesticides on Daphnia population dynamics by inferring structure and parameters of a stochastic model. Ecological Modelling, 472, 110076 (13 pp.). https://doi.org/10.1016/j.ecolmodel.2022.110076
Confronting existing knowledge on ecological preferences of stream macroinvertebrates with independent monitoring data using a Bayesian multi-species distribution model
Vermeiren, P., Reichert, P., Graf, W., Leitner, P., Schmidt-Kloiber, A., & Schuwirth, N. (2021). Confronting existing knowledge on ecological preferences of stream macroinvertebrates with independent monitoring data using a Bayesian multi-species distribution model. Freshwater Science, 40(1), 202-220. https://doi.org/10.1086/713175
Characterizing fast herbicide transport in a small agricultural catchment with conceptual models
Ammann, L., Doppler, T., Stamm, C., Reichert, P., & Fenicia, F. (2020). Characterizing fast herbicide transport in a small agricultural catchment with conceptual models. Journal of Hydrology, 586, 124812 (15 pp.). https://doi.org/10.1016/j.jhydrol.2020.124812
Towards a comprehensive uncertainty assessment in environmental research and decision support
Reichert, P. (2020). Towards a comprehensive uncertainty assessment in environmental research and decision support. Water Science and Technology, 81(8), 1588-1596. https://doi.org/10.2166/wst.2020.032
Integrating uncertain prior knowledge regarding ecological preferences into multi-species distribution models: effects of model complexity on predictive performance
Vermeiren, P., Reichert, P., & Schuwirth, N. (2020). Integrating uncertain prior knowledge regarding ecological preferences into multi-species distribution models: effects of model complexity on predictive performance. Ecological Modelling, 420, 108956 (15 pp.). https://doi.org/10.1016/j.ecolmodel.2020.108956
Parameter estimation and predictive uncertainty quantification in hydrological modelling
Kavetski, D. (2019). Parameter estimation and predictive uncertainty quantification in hydrological modelling. In Q. Duan, F. Pappenberger, A. Wood, H. L. Cloke, & J. C. Schaake (Eds.), Handbook of hydrometeorological ensemble forecasting (pp. 481-522). https://doi.org/10.1007/978-3-642-39925-1_25
Exploring a copula-based alternative to additive error models—for non-negative and autocorrelated time series in hydrology
Wani, O., Scheidegger, A., Cecinati, F., Espadas, G., & Rieckermann, J. (2019). Exploring a copula-based alternative to additive error models—for non-negative and autocorrelated time series in hydrology. Journal of Hydrology, 575, 1031-1040. https://doi.org/10.1016/j.jhydrol.2019.06.006
SPUX: scalable particle Markov Chain Monte Carlo for uncertainty quantification in stochastic ecological models
Šukys, J., & Kattwinkel, M. (2018). SPUX: scalable particle Markov Chain Monte Carlo for uncertainty quantification in stochastic ecological models. In S. Bassini, M. Danelutto, P. Dazzi, G. R. Joubert, & F. Peters (Eds.), Advances in parallel computing: Vol. 32. Parallel computing is everywhere (pp. 159-168). https://doi.org/10.3233/978-1-61499-843-3-159
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
Integrating ecological theories and traits in process-based modeling of macroinvertebrate community dynamics in streams
Mondy, C. P., & Schuwirth, N. (2017). Integrating ecological theories and traits in process-based modeling of macroinvertebrate community dynamics in streams. Ecological Applications, 27(4), 1365-1377. https://doi.org/10.1002/eap.1530
Mechanistic modelling for predicting the effects of restoration, invasion and pollution on benthic macroinvertebrate communities in rivers
Paillex, A., Reichert, P., Lorenz, A. W., & Schuwirth, N. (2017). Mechanistic modelling for predicting the effects of restoration, invasion and pollution on benthic macroinvertebrate communities in rivers. Freshwater Biology, 62(6), 1083-1093. https://doi.org/10.1111/fwb.12927
The value of streamflow data in improving TSS predictions - Bayesian multi-objective calibration
Sikorska, A. E., Del Giudice, D., Banasik, K., & Rieckermann, J. (2015). The value of streamflow data in improving TSS predictions - Bayesian multi-objective calibration. Journal of Hydrology, 530, 241-254. https://doi.org/10.1016/j.jhydrol.2015.09.051
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
Sewer deterioration modeling with condition data lacking historical records
Egger, C., Scheidegger, A., Reichert, P., & Maurer, M. (2013). Sewer deterioration modeling with condition data lacking historical records. Water Research, 47(17), 6762-6779. https://doi.org/10.1016/j.watres.2013.09.010
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
Bridging the gap between theoretical ecology and real ecosystems: modeling invertebrate community composition in streams
Schuwirth, N., & Reichert, P. (2013). Bridging the gap between theoretical ecology and real ecosystems: modeling invertebrate community composition in streams. Ecology, 94(2), 368-379. https://doi.org/10.1890/12-0591.1