Deutsch
 
Datenschutzhinweis Impressum
  DetailsucheBrowse

Datensatz

DATENSATZ AKTIONENEXPORT

Freigegeben

Buchkapitel

Projecting the consequences of climate change on river ecosystems

Urheber*innen

Kiesel,  J.
External Organizations;

/persons/resource/bfguse

Guse,  Björn
4.4 Hydrology, 4.0 Geosystems, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum;

Bormann,  H.
External Organizations;

Externe Ressourcen
Es sind keine externen Ressourcen hinterlegt
Volltexte (frei zugänglich)
Es sind keine frei zugänglichen Volltexte in GFZpublic verfügbar
Ergänzendes Material (frei zugänglich)
Es sind keine frei zugänglichen Ergänzenden Materialien verfügbar
Zitation

Kiesel, J., Guse, B., Bormann, H. (2019): Projecting the consequences of climate change on river ecosystems. - In: Sabater, S., Elosegi, A., Ludwig, R. (Eds.), Multiple Stressors in River Ecosystems: Status, Impacts and Prospects for the Future, Amsterdam : Elsevier, 281-301.
https://doi.org/10.1016/B978-0-12-811713-2.00016-9


Zitierlink: https://gfzpublic.gfz-potsdam.de/pubman/item/item_3635896
Zusammenfassung
River ecosystems are influenced by natural processes and anthropogenic impacts at different spatial and temporal scales. Understanding these complex interactions for current conditions is the first requirement to be able to simulate the impact of changes in a catchment on river ecosystems. Climate change causes alterations of hydrologic patterns such as seasonal flow and extreme flows. Knowing the magnitude and frequency of these alterations is the second requirement for simulating climate change implications in river ecosystems. The third requirement is an appropriate implementation of these multiple processes in a model cascade; for example, ranging from hydrological via hydraulic models to biological predictions. Scenario simulations with a model cascade detect the impact of changes in stressors on river ecosystems at different spatial and temporal scales. Uncertain aspects within this model cascade require a special focus in order to obtain reliable predictions.