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Deep Learning based assessment of groundwater level development in Germany until 2100

Wunsch, Andreas; Liesch, Tanja; Broda, Stefan

Abstract (englisch):
Clear signs of climate stress on groundwater resources have been observed in recent years even in generally water-rich regions such as Germany. Severe droughts, resulting in decreased groundwater recharge, led to declining groundwater levels in many regions and even local drinking water shortages have occurred in past summers. We investigate how climate change will directly influence the groundwater resources in Germany until the year 2100. For this purpose, we use a machine learning groundwater level forecasting framework, based on Convolutional Neural Networks, which has already proven its suitability in modelling groundwater levels. We predict groundwater levels on more than 120 wells distributed over the entire area of Germany that showed strong reactions to meteorological signals in the past. The inputs are derived from the RCP8.5 scenario of six climate models, pre-selected and pre-processed by the German Meteorological Service, thus representing large parts of the range of the expected change in the next 80 years. Our models are based on precipitation and temperature and are carefully evaluated in the past and only wells with models reaching high forecasting skill scores are included in our study. ... mehr


Volltext §
DOI: 10.5445/IR/1000139509
Veröffentlicht am 29.10.2021
Originalveröffentlichung
DOI: 10.5194/egusphere-egu21-9590
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Angewandte Geowissenschaften (AGW)
Publikationstyp Vortrag
Publikationsdatum 30.04.2021
Sprache Englisch
Identifikator KITopen-ID: 1000139509
Veranstaltung vEGU21 (2021), Online, 19.04.2021 – 30.04.2021
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