Articles | Volume 20, issue 7
Hydrol. Earth Syst. Sci., 20, 2649–2667, 2016
https://doi.org/10.5194/hess-20-2649-2016
Hydrol. Earth Syst. Sci., 20, 2649–2667, 2016
https://doi.org/10.5194/hess-20-2649-2016

Research article 08 Jul 2016

Research article | 08 Jul 2016

A retrospective streamflow ensemble forecast for an extreme hydrologic event: a case study of Hurricane Irene and on the Hudson River basin

Firas Saleh, Venkatsundar Ramaswamy, Nickitas Georgas, Alan F. Blumberg, and Julie Pullen Firas Saleh et al.
  • Stevens Institute of Technology, Davidson Laboratory, Department of Civil, Environmental and Ocean Engineering, Hoboken NJ, 07030, USA

Abstract. This paper investigates the uncertainties in hourly streamflow ensemble forecasts for an extreme hydrological event using a hydrological model forced with short-range ensemble weather prediction models. A state-of-the art, automated, short-term hydrologic prediction framework was implemented using GIS and a regional scale hydrological model (HEC-HMS). The hydrologic framework was applied to the Hudson River basin ( ∼  36 000 km2) in the United States using gridded precipitation data from the National Centers for Environmental Prediction (NCEP) North American Regional Reanalysis (NARR) and was validated against streamflow observations from the United States Geologic Survey (USGS). Finally, 21 precipitation ensemble members of the latest Global Ensemble Forecast System (GEFS/R) were forced into HEC-HMS to generate a retrospective streamflow ensemble forecast for an extreme hydrological event, Hurricane Irene. The work shows that ensemble stream discharge forecasts provide improved predictions and useful information about associated uncertainties, thus improving the assessment of risks when compared with deterministic forecasts. The uncertainties in weather inputs may result in false warnings and missed river flooding events, reducing the potential to effectively mitigate flood damage. The findings demonstrate how errors in the ensemble median streamflow forecast and time of peak, as well as the ensemble spread (uncertainty) are reduced 48 h pre-event by utilizing the ensemble framework. The methodology and implications of this work benefit efforts of short-term streamflow forecasts at regional scales, notably regarding the peak timing of an extreme hydrologic event when combined with a flood threshold exceedance diagram. Although the modeling framework was implemented on the Hudson River basin, it is flexible and applicable in other parts of the world where atmospheric reanalysis products and streamflow data are available.

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Short summary
An operational framework was implemented to generate retrospective ensemble streamflow forecasts for an extreme hydrological event, Hurricane Irene. The implications of this work benefit streamflow forecast efforts and can be used for numerous applications, such as forecasting the water resources variability, predicting fate of water quality and climate change scenarios. Socio-economic analysis may be used to weigh on how improved forecasts prevent life loss and minimize property damage.