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Range-dependent thresholds for global flood early warning.

Lorenzo Alfieri1, Ervin Zsoter2, Shaun Harrigan2

  • 1European Commission, Joint Research Centre (JRC), Ispra, Italy.

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Summary
This summary is machine-generated.

Developing new river flood early warning systems (EWS) using range-dependent thresholds improves flood detection and forecasting accuracy. This enhances disaster risk management by providing more reliable streamflow predictions up to six weeks in advance.

Keywords:
Early warning systemEnsemble forecastingFlood thresholdsGloFASGlobal hydrologyHydrologic model

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Area of Science:

  • Hydrology
  • Meteorology
  • Disaster Risk Management

Background:

  • Early warning systems (EWS) for river flooding are crucial for disaster risk management.
  • Ensemble Numerical Weather Predictions (NWP) enable skillful streamflow forecasts in large river basins.
  • Accurate estimation of warning thresholds is vital for effective flood detection across river networks and forecast horizons.

Purpose of the Study:

  • To develop and evaluate a novel approach for estimating warning thresholds that maintain statistical consistency with operational forecasts at all lead times.
  • To improve the accuracy and reliability of flood early warning systems, specifically within the Global Flood Awareness System (GloFAS).

Main Methods:

  • Utilized a 21-year forecast-consistent dataset to derive global warning thresholds with a forecast range up to six weeks.
  • Compared newly derived thresholds with those based on ERA5 atmospheric reanalysis data (1986-2017).
  • Assessed the consistency and skill of constant versus range-dependent thresholds for flood forecasting.

Main Results:

  • Constant 30-day thresholds, used in the current GloFAS, are consistent across the entire forecast range in only 30-40% of the river network.
  • Range-dependent thresholds, with weekly duration, demonstrate greater model consistency.
  • Range-dependent thresholds significantly improve flood monitoring and early warning skills, especially for longer forecast ranges.

Conclusions:

  • Time-invariant thresholds are inadequate for accurate flood forecasting across diverse river networks and forecast horizons.
  • Range-dependent thresholds offer a more suitable and effective alternative for enhancing flood early warning systems.
  • The novel approach improves statistical consistency and forecasting skills, leading to better disaster risk management capabilities.