1 / 45

Precipitation

Precipitation. ERS 482/682 Small Watershed Hydrology. Watershed definitions. watershed ridge or stretch of high land dividing the areas drained by different rivers or river systems (e.g., Continental Divide) the area drained by a river or river system waterbody

lazaro
Download Presentation

Precipitation

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Precipitation ERS 482/682 Small Watershed Hydrology

  2. Watershed definitions • watershed • ridge or stretch of high land dividing the areas drained by different rivers or river systems (e.g., Continental Divide) • the area drained by a river or river system • waterbody • geographically defined portion of navigable waters, waters of the contiguous zone, and ocean waters under the lakes, wetlands, coastal waters, and ocean waters (NRC 2001) • watershed management (per Lee MacDonald, CSU) • the art and science of managing the land and water resources of a drainage basin for the production and protection of water supplies, water resources, and water-dependent resources

  3. Precipitation • Water that falls to the earth (and reaches it) • Rain • Snow • Ice pellets (sleet) • Hail • Drizzle

  4. Process of precipitation • Global circulation • Formation of precipitation • uplift • temperature

  5. Global circulation • Distribution of solar radiation intensity Figure 3-4: Dingman (2002)

  6. Global circulation • Earth’s rotation Figure 4.1: Manning (1987)

  7. Formation of precipitation See Appendix D for more detail • Water vapor importation • Cooling of air to dewpoint temperature • Condensation • Growth of droplets or crystals

  8. Air cooling • Cyclonic uplift Figures 4.2 and 4.3: Manning (1987)

  9. Air cooling • Thunderstorm uplift Figure 4.4: Manning (1987) Figure 4-7: Dingman (2002)

  10. Air cooling • Orographic uplift Figure 4.5: Manning (1987)

  11. Condensation Figure 2.1: Hornberger et al. (1998)

  12. Condensation Assumption: Pressure is constant Figure 2.1: Hornberger et al. (1998)

  13. Formation of droplets Condensation requires condensation nuclei Figure D-7: Dingman (2002)

  14. Measuring precipitation • Units • Depth (L) • Intensity (L T-1) Figure 2-2; Dunneand Leopold (1978)

  15. Precipitation characteristics • Typical precipitation intensities <1”/hr • General rule: longer storm duration  lower average intensity

  16. Figure 4-51 (a): Dingman (2002)

  17. Figure 4-51(c): Dingman (2002)

  18. Precipitation characteristics • Typical precipitation intensities <1”/hr • General rule: longer storm duration  lower average intensity • Larger area  lower average intensity

  19. ~1 mi Rainfall amounts between 5:30 and 11:00 MDT on 7/28/97 for Fort Collins, CO (http://www.cira.colostate.edu/ramm/jw/flood/flood0.htm)

  20. Precipitation characteristics • Typical precipitation intensities <1”/hr • General rule: longer storm duration  lower average intensity • Larger area  lower average intensity • Cannot extrapolate directly from point to area; must correct for area! • Extremely variable in time and space!!! • more precipitation  less relative variability

  21. Precipitation-gage networks • World Meteorological Association recommendations: Table 4-6 (Dingman text) • Need ~ 1 gage every km2 (250 acres) to get error under ~10%

  22. Figure 4-31Dingman text

  23. Precision How close can we get to the true value? • Precision improves with: • Increasing density of gage network • Extending period of measurement • Increase in time and cost!

  24. Hershfield (1961) Std dev of the 24-hr maximums 24-hr PMP 15 Mean of 24-hr annual maximumsover period of record Extremes • Probable maximum precipitation (PMP) • “theoretically the greatest depth of precipitation for a given duration that is physically possible over a given size of storm area at a particular geographical location at a certain time of year” • Available in HMRs (Fig. 16.2 V&L (1996))

  25. Extremes • Probable maximum precipitation (PMP) • General guidelines: • Critical storm size  basin size • Critical duration  time of concentration • Significance: • Used to determine the probable maximum flood (PMF) • PMF is used to • Design dam spillways • Locate essential public utilities

  26. Extremes • Depth-Duration-Frequency analysis (DDF) • Determine point rainfall depth for storm of particular • Return period (e.g., 25-year, 100-year, etc.) • Duration (e.g., 1-hr, 2-hr, 6-hr, 24-hr, etc.)

  27. Collect/calculate data (e.g., annual maximum) Rank the data Plot frequency distribution (histogram) Plot probability distribution (divide by N observations) Evaluate normality yes no Calculate cumulative probability Transform data Plot on normal probability paper Estimate recurrence intervals or depths Extremes • Depth-Duration-Frequency analysis (DDF)

  28. Rank the data Plot frequency distribution (histogram) Plot probability distribution (divide by N observations) Evaluate normality yes no Calculate cumulative probability Transform data Plot on normal probability paper Estimate recurrence intervals or depths Extremes • Depth-Duration-Frequency analysis (DDF) Collect/calculate data (e.g., annual maximum)

  29. Rank the data Plot frequency distribution (histogram) Plot probability distribution (divide by N observations) Evaluate normality yes no Calculate cumulative probability Transform data Plot on normal probability paper Estimate recurrence intervals or depths Extremes • Depth-Duration-Frequency analysis (DDF) Collect/calculate data (e.g., annual maximum)

  30. Rank the data Plot frequency distribution (histogram) Plot probability distribution (divide by N observations) Evaluate normality yes no Calculate cumulative probability Transform data Plot on normal probability paper Estimate recurrence intervals or depths Extremes • Depth-Duration-Frequency analysis (DDF) Collect/calculate data (e.g., annual maximum)

  31. Discrete vs. continuous data • Discrete data can only take on discrete values within a range • Continuous data can take on any value within a range

  32. Rank the data Plot frequency distribution (histogram) Plot probability distribution (divide by N observations) Evaluate normality yes no Calculate cumulative probability Transform data Plot on normal probability paper Estimate recurrence intervals or depths Extremes • Depth-Duration-Frequency analysis (DDF) Collect/calculate data (e.g., annual maximum)

  33. Rank the data Plot frequency distribution (histogram) Plot probability distribution (divide by N observations) Evaluate normality yes no Calculate cumulative probability Transform data Plot on normal probability paper Estimate recurrence intervals or depths Extremes • Depth-Duration-Frequency analysis (DDF) Collect/calculate data (e.g., annual maximum)

  34. estimated by Normal distribution • 2-parameter distribution: • Mean () • Standard deviation ()  data are symmetric estimated by s

  35. Rank the data Plot frequency distribution (histogram) Plot probability distribution (divide by N observations) Evaluate normality yes no Calculate cumulative probability Transform data Plot on normal probability paper Estimate recurrence intervals or depths Extremes • Depth-Duration-Frequency analysis (DDF) Collect/calculate data (e.g., annual maximum)

  36. Plot cumulative probability • Calculate cumulative probability for the sorted (i.e., ranked) data points with plotting position formula: m = rank n = number of observations - Weibull:

  37. log scale

  38. Rank the data Plot frequency distribution (histogram) Plot probability distribution (divide by N observations) Evaluate normality yes no Calculate cumulative probability Transform data Plot on normal probability paper Estimate recurrence intervals or depths Extremes • Depth-Duration-Frequency analysis (DDF) Collect/calculate data (e.g., annual maximum)

  39. Lognormal distribution • Plotting the log of the data resembles a normal distribution • Mean (LX) is estimated by • Standard deviation (LX) is estimated bytaking the std. dev. of the ln xidata:

  40. Rank the data Plot frequency distribution (histogram) Plot probability distribution (divide by N observations) Evaluate normality yes no Calculate cumulative probability Transform data Plot on normal probability paper log, ln, Estimate recurrence intervals or depths Extremes • Depth-Duration-Frequency analysis (DDF) Collect/calculate data (e.g., annual maximum)

  41. Rank the data Plot frequency distribution (histogram) Plot probability distribution (divide by N observations) Evaluate normality yes no Calculate cumulative probability Transform data Plot on normal probability paper Estimate recurrence intervals or depths Extremes • Depth-Duration-Frequency analysis (DDF) Collect/calculate data (e.g., annual maximum)

  42. Rank the data Plot frequency distribution (histogram) Plot probability distribution (divide by N observations) Evaluate normality yes no Calculate cumulative probability Transform data Plot on normal probability paper Estimate recurrence intervals or depths Extremes • Depth-Duration-Frequency analysis (DDF) Collect/calculate data (e.g., annual maximum)

  43. Rank the data Plot frequency distribution (histogram) Plot probability distribution (divide by N observations) Evaluate normality yes no Calculate cumulative probability Transform data Plot on normal probability paper Estimate recurrence intervals or depths Extremes • Depth-Duration-Frequency analysis (DDF) Collect/calculate data (e.g., annual maximum)

  44. non-exceedence probability = 1 – EP • Calculate mean (AVERAGE), standard deviation (STDEV) and use NORMINV function in Excel Note: If you have transformed your data, you should use the mean and std dev of the transformed data andUNTRANSFORM the result!!!

  45. Extremes • Depth-Duration-Frequency analysis (DDF) • Determine point rainfall depth for storm of particular • Return period (e.g., 25-year, 100-year, etc.) • Duration (e.g., 1-hr, 2-hr, 6-hr, 24-hr, etc.) • Adjust point estimate to areal estimate • Equation 4-29 or Figure 4-52 or Figures 16.10 and 16.13 of Viessman and Lewis (1996)

More Related