TY - JOUR
T1 - Prospects for flash flood forecasting in mountainous regions - An investigation of Tropical Storm Fay in the Southern Appalachians
AU - Tao, Jing
AU - Barros, Ana P.
N1 - Funding Information:
This research was supported in part by a NASA Earth System Science Fellowship with the first author, and NASA Grant NNX1010H66G and NOAA Grant NA08OAR4310701 with the second author. We are grateful to Laurence Lee of the National Weather Service, Greer South Carolina for his help and insight.
Copyright:
Copyright 2018 Elsevier B.V., All rights reserved.
PY - 2013/12/9
Y1 - 2013/12/9
N2 - The sensitivity of Quantitative flash-Flood Estimates (QFEs) and Quantitative flash-Flood Forecasts (QFFs) to Quantitative Precipitation Estimates (QPEs) and Quantitative Precipitation Forecasts (QPFs) was investigated in three headwater catchments with different topographic and hydro-geomorphic characteristics during the passage of Tropical Storm Fay, 2008 over the Southern Appalachian Mountains in North Carolina, USA. QFEs and QFFs were generated by a high-resolution hydrologic model (250×250m2) with coupled surface-subsurface physics and rainfall forcing from the Next Generation Multi-sensor QPE (Q2) spatial rainfall (1×1km2) product, and from National Digital Forecast Database (NDFD) operational QPF product (5×5km2). Optimal QPE products (Q2+) were derived by assimilating rainfall observations from a high density raingauge network through adaptive bias correction. Deterministic QFEs simulated by the hydrologic model agree well with streamgauge observations (15-min intervals) regarding total water volume and peak flow with Nash-Sutcliffe (NS) coefficients 0.8-0.9, thus suggesting that the model without calibration captures well the dominant flash-flood physics. The propagation of uncertainty in storm rainfall to rainfall-runoff response was subsequently evaluated through model simulations forced by Monte Carlo replications of the QPEs to generate QFE distributions. Analysis of the joint QPE-QFE distributions shows that flood response at the catchment scale is highly non-linear, and exhibits strong dependence on basin physiography, initial soil moisture conditions (transient basin storage capacity), the space-time organization of runoff generation and conveyance mechanisms, and in particular interflow dynamics, with respect to the space-time structure of rainfall. QFFs for 6- to 1-h lead times using precipitation composites of Q2 QPE and NDFD QPF to drive the hydrology model in operational mode exhibited ubiquitous lack of skill yielding consistently negative NS scores. An experiment consisting of merging satellite-like observations into operational QPE/QPF showed significant improvement in QFF performance (e.g. 5-50% relative NS increases), especially when the timing of satellite overpass is such that it captures transient episodes of heavy rainfall during the event. Future advances in QFF remain principally constrained by progress in QPE and QPF at the spatial resolution necessary to resolve rainfall-interflow dynamics in mountainous regions.
AB - The sensitivity of Quantitative flash-Flood Estimates (QFEs) and Quantitative flash-Flood Forecasts (QFFs) to Quantitative Precipitation Estimates (QPEs) and Quantitative Precipitation Forecasts (QPFs) was investigated in three headwater catchments with different topographic and hydro-geomorphic characteristics during the passage of Tropical Storm Fay, 2008 over the Southern Appalachian Mountains in North Carolina, USA. QFEs and QFFs were generated by a high-resolution hydrologic model (250×250m2) with coupled surface-subsurface physics and rainfall forcing from the Next Generation Multi-sensor QPE (Q2) spatial rainfall (1×1km2) product, and from National Digital Forecast Database (NDFD) operational QPF product (5×5km2). Optimal QPE products (Q2+) were derived by assimilating rainfall observations from a high density raingauge network through adaptive bias correction. Deterministic QFEs simulated by the hydrologic model agree well with streamgauge observations (15-min intervals) regarding total water volume and peak flow with Nash-Sutcliffe (NS) coefficients 0.8-0.9, thus suggesting that the model without calibration captures well the dominant flash-flood physics. The propagation of uncertainty in storm rainfall to rainfall-runoff response was subsequently evaluated through model simulations forced by Monte Carlo replications of the QPEs to generate QFE distributions. Analysis of the joint QPE-QFE distributions shows that flood response at the catchment scale is highly non-linear, and exhibits strong dependence on basin physiography, initial soil moisture conditions (transient basin storage capacity), the space-time organization of runoff generation and conveyance mechanisms, and in particular interflow dynamics, with respect to the space-time structure of rainfall. QFFs for 6- to 1-h lead times using precipitation composites of Q2 QPE and NDFD QPF to drive the hydrology model in operational mode exhibited ubiquitous lack of skill yielding consistently negative NS scores. An experiment consisting of merging satellite-like observations into operational QPE/QPF showed significant improvement in QFF performance (e.g. 5-50% relative NS increases), especially when the timing of satellite overpass is such that it captures transient episodes of heavy rainfall during the event. Future advances in QFF remain principally constrained by progress in QPE and QPF at the spatial resolution necessary to resolve rainfall-interflow dynamics in mountainous regions.
KW - Hydrological prediction model
KW - Mountain watersheds
KW - Quantitative flash-Flood Estimate (QFE)
KW - Quantitative flash-Flood Forecast (QFF)
KW - Quantitative Precipitation Estimate (QPE)
KW - Quantitative Precipitation Forecast (QPF)
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U2 - 10.1016/j.jhydrol.2013.02.052
DO - 10.1016/j.jhydrol.2013.02.052
M3 - Article
AN - SCOPUS:84888030875
VL - 506
SP - 69
EP - 89
JO - Journal of Hydrology
JF - Journal of Hydrology
SN - 0022-1694
ER -