Modellensembles zur Vorhersage von hydro-biogeochemischen Flüssen unter Klimawandel
Physik und Chemie der Atmosphäre
Physische Geographie
Zusammenfassung der Projektergebnisse
This project focused on the quantification of the uncertainty of hydrological and biogeochemical projections induced by structural model uncertainties. In the frame of various ensemble approaches, we identified the contribution of this structural uncertainty in relation to other uncertainty sources, e.g. that of model parameters, forcing data, spatial input data, or other impact models. Statistical bias correction is a necessary step before application of climate model projections to hydrological studies. One of the best-performing and hence most popular bias correction method is quantile mapping (QM). We presented a novel, general, and extensible analytical theory of the effect of QM on the climate change signal (CCS) of the distribution mean and quantiles. In a challenging application, we demonstrated that the theoretically predicted CCS modification well approximates the modification by the bias correction method. We further developed and implemented a pragmatically algorithm that homogenizes the station data of the German Weather Service (DWD) based on the history of station relocations. We applied QM to an ensemble of climate model simulations for the Fulda station and the period 1971-2000. The idea was to quantify the duration and severity of meteorological droughts for the Fulda catchment by means of the standardized precipitation index (SPI) and using extreme value theory. Future projections with the three Representative Concentration Pathways (RCP) 2.6, 4.5 and 8.5 were also investigated for the period 2021-2050. The results show an increase of up to 4 events and a decrease of up to 8 events in the future. RCP 8.5 shows a clear negative tendency in the number of events, but also more severe events. In this scenario, projected severity increases up to 40 % and duration up to 20 %. The uncertainty associated with the RCMs is larger than that associated with the RCPs. Aside from that work, we contributed to “Inter-Sectoral Impact Model Intercomparison Project” (ISIMIP) project through simulating discharge for a number of basins throughout the world. We analyzed low flows and droughts under climate change and addressed the 2003 heatwave in a multi-impact model study, including water resources, health impacts, or food production.
Projektbezogene Publikationen (Auswahl)
- 2015. SPOTting Model Parameters Using a Ready-Made Python Package. PLoS ONE 10, e0145180
Houska, T., Kraft, P., Chamorro-Chavez, A., Breuer, L.
(Siehe online unter https://doi.org/10.1371/journal.pone.0145180) - 2017. A comparison of changes in river runoff from multiple global and catchment-scale hydrological models under global warming scenarios of 1°C, 2°C and 3°C. Clim. Change 141, 577–595
Gosling, S.N., Zaherpour, J., Mount, N.J., Hattermann, F.F., Dankers, R., Arheimer, B., Breuer, L., Ding, J., Haddeland, I., Kumar, R., Kundu, D., Liu, J., van Griensven, A., Veldkamp, T.I.E., Vetter, T., Wang, X., Zhang, X.
(Siehe online unter https://doi.org/10.1007/s10584-016-1773-3) - 2017. A copula-based analysis of severityduration-frequency of droughts in six climatic regions of New Zealand. Journal of Hydrology (NZ) 56, 1–18
Singh, S.K., Chamorro, A., Srinivasan, M.S., Breuer, L.
- 2017. An ensemble analysis of climate change impacts on streamflow seasonality across 11 large river basins. Clim. Change 141, 401–417
Eisner, S., Flörke, M., Chamorro, A., Daggupati, P., Donnelly, C., Huang, J., Hundecha, Y., Koch, H., Kalugin, A., Krylenko, I., Mishra, V., Piniewski, M., Samaniego, L., Seidou, O., Wallner, M., Krysanova, V.
(Siehe online unter https://doi.org/10.1007/s10584-016-1844-5) - (2018), East Asian warm season temperature variations over the past two millennia, Nature Scientific Reports, 8 (1)
Huan Zhang, J. P. W., J. Luterbacher, E. Garcıa-Bustamante, F. González-Rouco, S. Wagner, E. ́ Zorita, K. Fraedrich, J. H. Jungclaus, F. C. Ljungqvist, X. Zhu, E. Xoplaki, F. Chen, J. Duan, Q. Ge, Z. Hao, M. Ivanov, L. Schneider, S. Talento, J. Wang, and B. Yang
(Siehe online unter https://doi.org/10.1038/s41598-018-26038-8) - 2018. Sources of uncertainty in hydrological climate impact assessment: a cross-scale study. Environ. Res. Lett. 13, 015006
Hattermann, F.F., Vetter, T., Breuer, L., Su, B., Daggupati, P., Donnelly, C., Fekete, B., Flörke, F., Gosling, S.N., P Hoffmann, Liersch, S., Masaki, Y., Motovilov, Y., Müller, C., Samaniego, L., Stacke, T., Wada, Y., Yang, T., Krysnaova, V.
(Siehe online unter https://doi.org/10.1088/1748-9326/aa9938)
