Hier finden Sie die Veröffentlichungen des Teilprojektes 1 an der Ruhr-Universität Bochum. Eine Übersicht aller Veröffentlichungen, die innerhalb der SPATE-Forschungsgruppe erschienen sind, finden Sie hier.
Fischer, S., und Schumann, A. (2023): Generation of type-specific synthetic design flood hydrographs. Hydrological Sciences Journal. doi.org/10.1080/02626667.2023.2195560
Schnurr, A., und Fischer, S. (2022): Generalized Ordinal Pattern allowing for Ties and their Application in Hydrology. Computational Statistics & Data Analyses 171, https://doi.org/10.1016/j.csda.2022.107472.
Fischer, S., und Schumann, A. (2022): Handling of the Stochastic Uncertainty of Flood Statistics in Regionalisation Approaches. Hydrological Sciences Journal. https://doi.org/10.1080/02626667.2022.2091410
Merz, B., Basso, S., Fischer, S., Lun, D., Blöschl, G., Merz, R., Guse, B., Viglione, A., Vorogushyn, S., Macdonald, E., Wietzke, L. und Schumann, A. (2022): Understanding heavy tails of flood peak distributions. Water Resources Research.
Schnurr, A., und Fischer, S. (2022): An Ordinal Procedure to Detect Change Points in the Dependence Structure between Non-Stationary Time Series. Eng. Proc. 2022, 18, 14. https://doi.org/10.3390/engproc2022018014
Brunner, M. I., und Fischer, S. (2022): Snow-influenced floods are more strongly connected in space than purely rainfall-driven floods. Environ. Res. Lett. 17 104038, https://doi.org/10.1088/1748-9326/ac948f.
Lun, D., Fischer, S., Viglione, A., und Blöschl, G. (2022): Significance testing of rank cross-correlations between autocorrelated time series with short-range dependence. Journal of Applied Statistics. DOI: 10.1080/02664763.2022.2137115.
Fischer, S., Lun, D., Schumann, A., und Blöschl, G. (2022): Detecting flood-type-specific flood-rich and flood-poor periods in peaks-over-threshold series with application to Bavaria (Germany). Stochastic Environmental Research and Risk Assessment. DOI: 10.1007/s00477-022-02350-8
Fischer, S., und Schumann, A. (2021): Regionalisation of flood frequencies based on flood type-specific mixture distributions. Journal of Hydrology X, 13, DOI: 10.1016/j.hydroa.2021.100107.
Barion, D., Buchholz, O., Einfalt, T., Fischer, S., Johann, G., Leutnant, D., Meyer, B., Mudersbach, C., Piroth, K. Ross, U., Scheibel. M., und Schumann, A. (2021): HKC-Werkstattbericht 2021: Umgang mit hydrologischen Belastungsgrößen in Zeiten des Klimawandels - Hochwasser und Starkregen. Hochwasserkompetenzzentrum e.V., Download.
Fischer, S., und Schumann, A. (2021): Multivariate Flood Frequency Analysis in Large River Basins Considering Tributary Impacts and Flood Types. Water Resources Research 53, DOI: 10.1029/2020WR029029
Fischer, S., Bühler, P., und Schumann, A. (2021): Impact of Flood Types on Superposition of Flood Waves and Flood Statistics Downstream. Journal of Hydrologic Engineering, DOI: 10.1061/(ASCE)HE.1943-5584.0002103.
Fischer, S., Schumann, A., und Bühler, P. (2021): A statistics-based flood event separation. Journal of Hydrology X, 10, 100070,DOI: 10.1016/j.hydroa.2020.100070.
Fischer, S., und Schumann, A. (2021): Multivariate Flood Frequency Analysis in Large River Basins Considering Tributary Impacts and Flood Types. Water Resources Research 57 (8), DOI: 10.1029/2020WR029029.
Fischer, S., Bühler, P., Büttner, U. und Schumann, A. (2020): The use of maximum entropy to increase the informational content of hydrological networks by additional gauges, Hydrological Sciences Journal, DOI: 10.1080/02626667.2020.1802028.
Lun, D., Fischer, S., Viglione, A., und Blöschl, G. (2020): Detecting flood‐rich and flood‐poor periods in annual peak discharges across Europe. Water Resources Research, 56, e2019WR026575, DOI: 10.1029/2019WR026575.
Oppel, H., und Fischer, S. (2020): A new unsupervised learning method to assess clusters of temporal distribution of rainfall and their coherence with flood types. Water Resources Research, 56, e2019WR026511, DOI: 10.1029/2019WR026511
Krug, A., Primo, C., Fischer, S., Schumann, A., und Ahrens, B. (2020): On the temporal variability of widespread rain-on-snow floods. Meteorologische Zeitschrift, DOI: 10.1127/metz/2020/0989.
Fischer, S., Schumann, A., und Bühler, P. (2019): Timescale-based flood typing to estimate temporal changes in flood frequencies. Hydrological Sciences Journal, DOI: 10.1080/02626667.2019.1679376.
Tarasova, L., Merz, R., Kiss, A., Basso, S., Blöschl, G., Merz, B., Viglione, A., Plötner, S., Guse, B., Schumann, A., Fischer, S., Ahrens, B., Anwar, F., Bárdossy, A., Bühler, P., Haberlandt, U., Kreibich, H., Krug, A., Lun, D., Müller-Thomy, H., Pidoto, R., Primo Ramos, C., Seidel, J., Vorogushyn, S., Wietzke, L. (2019): Causative classification of river flood events, WIREs Water, DOI: 0.1002/wat2.1353.
Fischer, S., und Schumann, A. (2019): Spatio-temporal consideration of the impact of flood types on flood statistics. Stochastic Environmental Research and Risk Assessment, DOI: 10.1007/s00477-019-01690-2.
Fischer, S., und Schumann, A. (2018): A distribution-free ordinal classification of floods based on moments. Hydrological Sciences Journal 63 (11), 1605-1618, DOI: 10.1080/02626667.2018.1525614.
Fischer, S., und Schumann, A. (2018): Berücksichtigung von Starkregen in der Niederschlagsstatistik. Hydrologie und Wasserbewirtschaftung 62 (4), 248-256, DOI: 10.5675/HyWa_2018,4_2.
Fischer, S. (2018): A seasonal mixed-POT model to estimate high flood quantiles from different event types and seasons. Journal of Applied Statistics, 45 (15), 2831-2847, DOI: 10.1080/02664763.2018.1441385.
Schumann, A., und Fischer, S. (2017): Flood risk and flood processes in a changing environment. European Water 57: 19-25, 2017.
Fischer,S., Schumann, A., und Schnurr, A. (2017): Ordinal Pattern Dependence Between Hydrological Time Series. Journal of Hydrology 548, 536-551, DOI: 10.1016/j.jhydrol.2017.03.029.
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Letzte Änderung: 08. Mai. 2023