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| Created: | Jul 15, 2026 at 9:55 p.m. (UTC) | |
| Last updated: | Jul 22, 2026 at 2:53 p.m. (UTC) | |
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Abstract
Flash floods often develop faster than a warning can be issued and acted upon, and their forecasting depends on driving a hydrologic model with quantitative precipitation forecasts (QPF). QPF carries substantial location uncertainty, and forecast displacement errors of up to 100 km can shift simulated streamflow to the wrong basin and underpredict peak flow in the basin at risk. Representing this uncertainty with an ensemble of QPF members is effective but requires one hydrologic model run per member, which is not feasible for real-time flash-flood operations. This work develops and implements a hydrofabric-based catchment-scale neighboring catchment ensemble technique (NCET), extending NPET to irregular NextGen catchments and real-world applications. NCET produces a probabilistic streamflow forecast from a single deterministic NextGen simulation by borrowing routed hydrographs from nearby, physiographically similar catchments, weighted by distance and filtered by catchment similarity. In place of the drainage-area criterion used in NPET, similarity is characterized from terrain, climate, land cover, and soil attributes derived by zonal statistics over the hydrofabric, and catchments are grouped by hydrologic response through clustering. The technique is demonstrated for the May 27, 2018 flash flood event in Ellicott City, Maryland, and the resulting ensembles are evaluated with the Continuous Ranked Probability Score (CRPS) and the CRPS skill-score. NCET offers a computationally inexpensive route to probabilistic flash-flood streamflow forecasting within the NextGen framework.
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This resource is shared under the Creative Commons Attribution CC BY.
http://creativecommons.org/licenses/by/4.0/
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