The Flash Flood Potential Index (FFPI) Project at BTV - PowerPoint PPT Presentation

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The Flash Flood Potential Index (FFPI) Project at BTV

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  1. The Flash Flood Potential Index (FFPI) Project at BTV Nathan Foster Warm Season Workshop 5/2/12

  2. Flash Flooding Overview • We all know the factors that determine the occurrence of flash flooding • Rainfall rates • Thunderstorm training • Antecedent conditions • Non-meteorological factors also help determine the occurrence of flash flooding: • Soil type (sandy soil vs. clay) • Land use (open water vs. urban development) • Slope (mountainous terrain vs. flat land) • Forest canopy density • We have conceptual models of these non-meteorological factors. Can they be quantified?

  3. FFPI Background • FFPI was created by Greg Smith, Senior Hydrologist at the Colorado Basin River Forecast Center • A simple numerical index tied to runoff response potential • Attempts to account for land-surface features that affect hydrologic response (non-meteorological factors) • Forest canopy density • Slope • Land cover (land use) • Soil type • Meant to account for multiple characteristics in a river basin and highlight its susceptibility to flash flooding

  4. FFPI Background Represent real world digitally, through a GIS For each layer, reclassify and index the input on a 1-10 scale to represent FFPI Average all input data for each grid cell over the analysis area

  5. FFPI Background Final step, average across FFMP stream basins We monitor rainfall and flash flooding potential on a basin by basin basis Knowing the underlying susceptibility of these basins to flash flooding could speed up the warning decision making process and increase lead time Also, could help us decide when not to warn, which could potentially decrease false alarms

  6. BTV FFPI - Forest Canopy Density Percentage of forest canopy cover Important to flash flooding because dense forest cover will intercept falling rain and slow its ground arrival Ranges from 0-100% Plenty of national forest land in mountains, high percentage there

  7. BTV FFPI - Forest Canopy Density • Reclassified as: • 91-100 = 1 • 81-90 = 2 • 71-80 = 3 • 61-70 = 4 • 51-60 = 5 • 41-50 = 6 • 31-40 = 7 • 21-30 = 8 • 11-20 = 9 • 0-10 = 10

  8. BTV FFPI - Slope Slope calculated from a digital elevation model Important to flash flooding because a higher slope will increase the runoff speed and cause water to be funneled into one area and collect Weighted 3x higher than other layers

  9. BTV FFPI – Land Cover • Affects flash flooding because varying land cover types affect runoff potential and rate of absorption into the ground • Land cover types: • 11 = Open water • 21 = Developed, open space • 22 = Developed, low intensity • 23 = Developed, medium intensity • 24 = Developed, high intensity • 31 = Barren land (rock/sand/clay) • 41 = Deciduous forest • 42 = Evergreen forest • 43 = Mixed forest • 52 = Shrub/scrub • 71 = Grassland/herbaceous • 81 = Pasture/hay • 82 = Cultivated crops • 90 = Woody wetlands • 95 = Emergent herbaceous wetlands

  10. BTV FFPI – Land Cover • Reclassified: • 11 (Open water) = 1 • 21 (Developed, open space) = 7 • 22 (Developed, low intensity) = 8 • 23 (Developed, medium intensity) = 9 • 24 (Developed, high intensity) = 10 • 31 (Barren land (rock/sand/clay))) = 8 • 41 (Deciduous forest) = 5 • 42 (Evergreen forest) = 3 • 43 (Mixed forest) = 4 • 52 (Shrub/scrub) = 6 • 71 (Grassland/herbaceous) = 6 • 81 (Pasture/hay) = 5 • 82 (Cultivated crops) = 5 • 90 (Woody wetlands) = 2 • 95 (Emergent herbaceous wetlands) = 2

  11. BTV FFPI – Hydrologic Soil Group • Hydrologic soil groups are based on estimates of runoff potential. Soils are assigned to one of four groups according to the rate of water infiltration when the soils are not protected by vegetation, are thoroughly wet, and receive precipitation from long-duration storms. • Reclassified: • Group A. Soils having a high infiltration rate (low runoff potential) when thoroughly wet. These consist mainly of deep, well drained to excessively drained sands or gravelly sands. These soils have a high rate of water transmission. • Group B. Soils having a moderate infiltration rate when thoroughly wet. These consist chiefly of moderately deep or deep, moderately well drained or well drained soils that have moderately fine texture to moderately coarse texture. These soils have a moderate rate of water transmission. • Group C. Soils having a slow infiltration rate when thoroughly wet. These consist chiefly of soils having a layer that impedes the downward movement of water or soils of moderately fine texture or fine texture. These soils have a slow rate of water transmission. • Group D. Soils having a very slow infiltration rate (high runoff potential) when thoroughly wet. These consist chiefly of clays that have a high shrink-swell potential, soils that have a high water table, soils that have a claypan or clay layer at or near the surface, and soils that are shallow over nearly impervious material. These soils have a very slow rate of water transmission.

  12. BTV FFPI – Impervious Surfaces • An impervious surface increases the flash flooding threat because all water is converted to runoff • Impervious surfaces range from 0-100% • Reclassified: • 91-100 = 10 • 81-90 = 9 • 71-80 = 8 • 61-70 = 7 • 51-60 = 6 • 41-50 = 5 • 31-40 = 4 • 21-30 = 3 • 11-20 = 2 • 0-10 = 1

  13. GSP FFPI – Average Values

  14. GSP FFPI – FFMP Basin Average Minimum Value: 2.8 Tributary to Little River

  15. Summary and Future Work FFPI attempts to quantify the relative susceptibility of a stream basin to flash flooding, regardless of meteorological factors When compared to past flash flood events, there appears to be a positive correlation suggesting FFPI would provide beneficial information Important to remember, flash flooding can occur anywhere regardless of FFPI FFPI is not meant to be a predictive indicator, rather another piece of information to consider during flash flood warning operations (confidence modifier) Perhaps it will prove most beneficial in areas where there is a relative minimum of information (sparsely populated areas, lack of gage data, poor radar coverage, etc.)

  16. Summary and Future Work Next step is to implement operationally Would probably get the most use and be most efficient if it can be viewed directly in AWIPS A basic web page version. KML. Additional datasets? Movement downstream

  17. References • FFPI Resources • Greg Smith, CBRFC • Blair Halloway, Jim Brewster, NWS Binghamton, NY • GIS Datasets • Seamless Data Warehouse (http://seamless.usgs.gov/) • State Soil Geographic (STATSGO) data (http://www.nws.noaa.gov/oh/hrl/dmip/soil.html)