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Using Convolutional Neural Networks To Evaluate Long‐Term Groundwater Trends In Germany

Wunsch, Andreas ORCID iD icon; Broda, Stefan; Liesch, Tanja

Abstract (englisch):

Even water‐rich regions like Germany are more and more subject to climate stress. Severe droughts, followed by reduced
groundwater recharge and decreasing groundwater levels resulted in recent years in local shortages in drinking water
supply. We use a machine learning groundwater forecasting framework based on convolutional neural networks to
investigate the direct impact of climate change on Germany’s groundwater resources until 2100. Groundwater levels at 118
monitoring sites ‐ showing strong reactions to meteorological signals in the past and are spread across Germany ‐ were
predicted. The input data is taken from the RCP8.5 scenario with six climate models pre‐selected and pre‐processed by the
German meteorological service, covering a large part of the range of expected changes over the next 80 years. Our models
are using solely precipitation and temperature as input, are carefully evaluated using past observations and all achieve high
predictive skill scores. Only effects of natural climate change are considered, as reliable future input data on highly uncertain
human factors such as increased groundwater extraction or irrigation effects are basically unavailable. ... mehr

Zugehörige Institution(en) am KIT Institut für Angewandte Geowissenschaften (AGW)
Publikationstyp Vortrag
Publikationsdatum 09.09.2021
Sprache Englisch
Identifikator KITopen-ID: 1000139507
Veranstaltung 48th IAH Congress (2021), Brüssel, Belgien, 06.09.2021 – 10.09.2021
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