An Unexpected Journey in Hydrological Forecasting

Witness the unpredictable nature of hydrological forecasting, where droughts can swiftly turn into devastating floods. This research delves into the challenges we faced in predicting 2021 summer flood event in west Germany, highlighting the need for resilience in the face of nature's extremes.
Published in Earth & Environment
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Have you ever had an unforeseen turn in your research? That's precisely what happened to me!  For years, I had been working on developing an operational soil moisture drought forecasting system in for Germany. However, the devastating 2021 European Summer flood, which claimed the lives of 134 people, particularly in the Ahr Valley, abruptly shifted my research focus towards the opposite end of hydrological extremes – extreme flood prediction.
This tragic event  illuminated the challenges inherent in predicting hydrological extremes. With limited historical data, extending only as far back as the late 20th century, forecasting events as rare as millennial floods becomes a formidable task. The scarcity of data introduces significant uncertainty into the statistical models used for extrapolation, impeding accurate intensity prediction.  Additionally, weather predictions, observation errors, and modeling complexities compound this uncertainty, while non-stationary data and unpredictable precipitation patterns add further layers of complexity.

Our journey towards predicting extreme floods faced its own set of hurdles, particularly evident in our initial attempt to forecast the estimated 8,000-year flood in the Ahr River. We quickly recognized that the existing German-wide daily hydrological model wasn't suitable  for such extreme events.  To overcome this challenge, we made significant adaptations, transitioning to hourly hydrological modeling and calibration tailored specifically for flood forecasting. Additionally, we enhanced our weather forecasting inputs,  leveraging high-resolution forecasts from the German Weather Service to replace the coarser atmospheric forecasts from the ECMWF.

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While our water level predictions didn't perfectly match the estimated flood peak, analysis identified a high (>50%) chance of exceeding 100-year flood levels at the gauge 17 hours beforehand. Yet, this alone wouldn't aid flood managers needing  more detailed impact assessments to safeguard lives and infrastructure. Collaboration with GFZ colleagues  proved instrumental. We integrated near-real-time flood forecasting into our modeling, providing detailed impact forecasts for buildings and infrastructure at 10-meter resolution, along with probabilistic lead-time maps.

Teamwork was pivotal in this endeavour, with my  UFZ colleagues dedicating tireless hours within the first week after the flood to transition to hourly data and recalibrate models.A draft paper was ready by summer 2022, but impact forecasting and visualizations required further effort, highlighting the dedication needed for impactful research. Thanks to all co-authors!

The journey from disaster indication to publication involved addressing reviewer feedback and emphasising the research's ultimate goal of enabling more effective early warnings and decision-making for flood management.  We acknowledge transparent peer review, like Nature Communication's, which we believe is crucial as it exposes both the strengths and weaknesses of a publication, fostering accountability, trust, and integrity within the scientific community  [peer review file]. 

The research lays the groundwork for  advancements like the UN's EW4ALL initiative, focusing on workflow integration, uncertainty communication, and preparedness. By pushing hydrological modeling and forecasting boundaries and fostering collaboration, the research aims to provide actionable insights that can potentially save lives and minimise damage during extreme events.

Link to the paper: [https://www.nature.com/articles/s41467-024-48065-y]

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