Medium-range ensemble flood forecast inundation maps: The case of the tidal Delaware River near Philadelphia

Restricted (Penn State Only)
Author:
Gomez Sanchez, Michael Josue
Graduate Program:
Civil Engineering
Degree:
Master of Science
Document Type:
Master Thesis
Date of Defense:
July 11, 2017
Committee Members:
  • Alfonso Ignacio Mejia, Thesis Advisor
Keywords:
  • Hydrologic modeling
  • Hydraulic modeling
  • Flood inundation mapping
  • Medium-range flood forecast
  • Water-level statistical postprocessing
  • River-estuary transition zone
  • Forecast verification
Abstract:
We investigate the ability to enhance flood inundation medium-range (0-7 days) forecast maps through weather ensembles and statistically water surface elevation (WSEL) postprocessing. To generate the flood forecast maps, a one-dimensional hydraulic model (HEC-RAS) is coupled to a regional hydrological ensemble prediction system (RHEPS). The RHEPS is in this case comprised by: i) weather ensembles from the National Center for Environmental Prediction Global Ensemble Forecast System Reforecast version 2 program; ii) distributed hydrological model (HL-RDHM); iii) quantile regression (QR) as the statistical postprocessor and iv) verification strategy. The coupled hydrometeorological-hydraulic system is tested in the riverine-estuarine transition zone of the Delaware River near the city of Philadelphia, Pennsylvania. The approach is used to generate 2-hourly high-resolution flood inundation forecast maps at lead times from 0 to 7 days, over the period 2008-2013. To comprehensively and rigorously verify the forecast maps, the following four different sets of flood maps are generated: i) observed, ii) deterministic, iii) raw ensemble, and iv) postprocessed ensemble. The observed map is generated by forcing the hydraulic model with streamflow and water level observations at the boundary conditions and tributaries of the model. The deterministic and raw ensemble maps use hydrometeorological deterministic and ensemble medium-range forecasts as the forcing, respectively. Given that tide and storm surge forecast were not available for the study period, we force the hydraulic model with observed water levels at the downstream boundary for the deterministic and raw ensemble forecast maps. Lastly, the postprocessed maps are generated by using QR to postprocess the raw ensemble forecasts at individual cross-sections of the hydraulic model. Results show that the tidal fluctuations of the estuary are highly influential on the forecast skill in the transition zone. Nevertheless, upstream of the head of the tide hydrometeorologic uncertainties are dominant and can cause relatively high errors and biases in the flood inundation forecasts, especially for the later lead times. Moreover, the raw ensembles flood inundation forecasts show higher skill than the deterministic flood inundation forecasts, with higher improvement at lead times from 3 to 7 days. Furthermore, statistical postprocessing improves the skill of the raw ensemble flood inundation forecasts, with an evident improvement across all lead times but is higher at the later lead times. In conclusion, we find that the medium-range flood forecast maps can be skillful and thus provide an alternative for representing and communicating medium-range forecasts. We find that statistical postprocessing can improve the skill of the forecast maps. This may turn out to be a viable approach for bias correcting flood maps in ungauged reaches, which is required for continental scale flood mapping.