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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
Development of a mechanistic model (ERIMO-I) for analyzing the temporal dynamics of the benthic community of an intermittent Mediterranean stream
Schuwirth, N., Acuña, V., & Reichert, P. (2011). Development of a mechanistic model (ERIMO-I) for analyzing the temporal dynamics of the benthic community of an intermittent Mediterranean stream. Ecological Modelling, 222(1), 91-104. https://doi.org/10.1016/j.ecolmodel.2010.09.013
Use of steady-state concentration measurements in geostatistical inversion
Schwede, R. L., & Cirpka, O. A. (2009). Use of steady-state concentration measurements in geostatistical inversion. Advances in Water Resources, 32(4), 607-619. https://doi.org/10.1016/j.advwatres.2009.01.010
A mechanistic model of benthos community dynamics in the River Sihl, Switzerland
Schuwirth, N., Kühni, M., Schweizer, S., Uehlinger, U., & Reichert, P. (2008). A mechanistic model of benthos community dynamics in the River Sihl, Switzerland. Freshwater Biology, 53(7), 1372-1392. https://doi.org/10.1111/j.1365-2427.2008.01970.x
Comparing uncertainty analysis techniques for a SWAT application to the Chaohe Basin in China
Yang, J., Reichert, P., Abbaspour, K. C., Xia, J., & Yang, H. (2008). Comparing uncertainty analysis techniques for a SWAT application to the Chaohe Basin in China. Journal of Hydrology, 358(1–2), 1-23. https://doi.org/10.1016/j.jhydrol.2008.05.012
Hydrological modelling of the chaohe basin in china: statistical model formulation and Bayesian inference
Yang, J., Reichert, P., Abbaspour, K. C., & Yang, H. (2007). Hydrological modelling of the chaohe basin in china: statistical model formulation and Bayesian inference. Journal of Hydrology, 340(3-4), 167-182. https://doi.org/10.1016/j.jhydrol.2007.04.006
An efficient sampling technique for Bayesian inference with computationally demanding models
Reichert, P., Schervish, M., & Small, M. J. (2002). An efficient sampling technique for Bayesian inference with computationally demanding models. Technometrics, 44(4), 318-327. https://doi.org/10.1198/004017002188618518
On the necessity of using imprecise probabilities for modelling environmental systems
Reichert, P. (1997). On the necessity of using imprecise probabilities for modelling environmental systems. Water Science and Technology, 36(5), 149-156. https://doi.org/10.1016/S0273-1223(97)00469-1