{"id":79261,"date":"2025-03-05T15:07:46","date_gmt":"2025-03-05T14:07:46","guid":{"rendered":"https:\/\/www.geographie.nat.fau.de\/?page_id=79261"},"modified":"2026-04-22T09:49:22","modified_gmt":"2026-04-22T07:49:22","slug":"ag-delight","status":"publish","type":"page","link":"https:\/\/www.geographie.nat.fau.de\/en\/ag-delight\/","title":{"rendered":"JRG DeLIGHT (Deep-learning-informed Glacio-Hydrological Threat)"},"content":{"rendered":"\n
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The DELIGHT Framework International Junior Research Group, funded by the Elite Network of Bavaria, is conducting research into developing a fully coupled suite of models that trace the entire hydrological pathway of glacierised catchments, from precipitation, through glaciers, to rivers. The research group will work on developing this suite of models by combining several existing process models and augmenting or replacing certain parts with neural networks to make the whole run within a sensible timeframe.<\/strong><\/p>\n<\/div><\/div>\n\n\n

Accurately predicting these glacio-hydrological hazards requires modelling of the entire hydrological process chain, something not before possible as it would take too long to run the simulations. With the advent of machine learning and the resulting possibility to greatly accelerate the modelling of physical systems, this fundamental barrier is now being lifted, making more accurate predictions feasible for the first time.<\/p>\n

As a third of the world\u2019s population live in catchments with glacierised headwaters and will consequently be exposed to changes in water quantity and quality as glaciers retreat, as well as the possibility of devastating glacial lake outburst floods (GLOFs) as water fills unstably dammed previously ice-covered depressions, we hope to contribute with our work to an improvement in accuracy of glacio-hydrological hazard predictions.<\/p>\n

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