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PRECIPITATION-RUNOFF MODELING SYSTEM (PRMS)

PRECIPITATION-RUNOFF MODELING SYSTEM (PRMS). MODELING OVERVIEW & DAILY MODE COMPONENTS. BASIC HYDROLOGIC MODEL. Q = P - ET ± S. Components. Runoff Precip Met Vars Ground Water

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PRECIPITATION-RUNOFF MODELING SYSTEM (PRMS)

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  1. PRECIPITATION-RUNOFF MODELING SYSTEM(PRMS) MODELING OVERVIEW & DAILY MODE COMPONENTS

  2. BASIC HYDROLOGIC MODEL Q = P - ET ± S Components Runoff Precip Met Vars Ground Water Soil Moisture Reservoirs Basin Chars Snow & Ice Water use Soil Moisture

  3. SUGGESTED REGERENCE ON WATERSHED MODELING - Overview chapters on basic concepts - 25 Models, each a chapter with discussions of model components and assumptions

  4. PRMS

  5. PRMS Parametersoriginal version

  6. PRMSParameters MMS Version

  7. PRMS Features • Modular Design • Deterministic • Distributed Parameter • Daily and Storm Mode • Variable Time Step • User Modifiable • Optimization and Sensitivity Analysis

  8. SPATIAL CONSIDERATIONS LUMPED MODELS - No account of spatial variability of processes, input, boundary conditions, and system geometry DISTRIBUTED MODELS - Explicit account of spatial variability of processes, input, boundary conditions, and watershed characteristics QUASI-DISTRIBUTED MODELS - Attempt to account for spatial variability, but use some degree of lumping in one or more of the modeled characteristics.

  9. GRID-BASED MODELS TOPMODEL Distributed Approaches - Explicit grid to grid - Statistical distribution ----(topgraphic index)

  10. Fully Coupled 1-D unsat and 3-D sat flow model

  11. HYDROLOGIC RESPONSE UNITS (HRUs)

  12. Distributed Parameter Approach Hydrologic Response Units - HRUs HRU Delineation Based on: - Slope - Aspect - Elevation - Vegetation - Soil - Precip Distribution

  13. HRUs

  14. HRU DELINEATION AND CHARACTERIZATION Grid Cell Hydrologic Response Units (HRUs) Polygon Hydrologic Response Units (HRUs)

  15. Grid Complexity

  16. 3rd HRU DIMENSION

  17. PRMS

  18. MODEL DRIVING VARIABLES - TEMPERATURE - max and min daily - lapse rate varied monthly or daily - PRECIPITATION - spatial and elevation adjustment - form estimation

  19. MODEL DRIVING VARIABLES - SOLAR RADIATION - measured data extrapolated to slope-aspect of each HRU - when no measured data, then estimated using temperature, precip, and potential solar radiation - max daily temperature procedure - daily temperature range procedure

  20. Max Temperature-Elevation Relations

  21. TEMPERATURE For each HRU tmax(hru) = obs_tmax(hru_tsta) - tcrx(mo) tmin(hru) = obs_tmin(hru_tsta) - tcrx(mo) where tcrx(mo) = [ tmax_lapse(mo) * elfac(hru)] - tmax_adj(hru) elfac(hru) = [hru_elev - tsta_elev(hru_tsta)] / 1000.

  22. Precipitation-Elevation Relations

  23. Mean Daily Precipitation Schofield Pass (10,700 ft) vs Crested Butte (9031 ft) Mean daily precip, in. MONTH

  24. Precipitation Gage Catch Error vs Wind Speed (Larsen and Peck, 1972) Rain (shield makes little difference) Snow (shielded) Snow (unshielded)

  25. Precipitation Gauge Intercomparison Rabbit Ears Pass, Colorado

  26. PRECIPITATION For each HRU - DEPTH hru_precip(hru) = precip(hru_psta) * pcor(mo) pcor(mo) = Rain_correction or Snow_correction

  27. PRECIPITATION For each HRU - FORM (rain, snow, mixture of both) RAIN tmin(hru) > tmax_allsnow tmax(hru) > tmax_allrain(mo) SNOW tmax(hru) <= tmax_allsnow

  28. [ ] tmax(hru) - tmax_allsnow prmx = * adjmix_rain(mo) (tmax(hru) - tmin(hru) PRECIPITATION For each HRU - FORM (rain, snow, mixture of both) MIXTURE OTHER Precipitation Form Variable Snowpack Adjustment

  29. PCORComputation Precipitation Distribution Methods(module) • Manual (precip_prms.f) • Auto Elevation Lapse Rate (precip_laps_prms.f) • XYZ (xyz_dist.f)

  30. PCORComputation • Manual

  31. PCORComputation hru_plaps • Auto Elevation Lapse Rate For each HRU hru_psta hru_psta = precip station used to compute hru_precip [ hru_precip = precip(hru_psta) * pcor] hru_plaps = precip station used with hru_psta to compute ------ -------preciplapse rate by month [pmo_rate(mo)]

  32. PCORComputation Auto Elevation Lapse Rate Parameters pmn_mo elv_plaps padj_sn or padj_rn

  33. PCORComputation pmn_mo(hru_plaps) - pmn_mo(hru_psta) pmo_rate(mo) = elv_plaps(hru_plaps)-elv_plaps(hru_psta) For each HRU • Auto Elevation Lapse Rate hru_elev - elv_plaps(hru_psta) adj_p = pmo_rate * pmn_mo(hru_psta) snow_adj(mo) = 1. + (padj_sn(mo) * adj_p) if padj_sn(mo) < 0. then snow_adj(mo) = - padj_sn(mo)

  34. XYZ Distribution

  35. XYZ Spatial Redistribution of Precip and Temperature 1. Develop Multiple Linear Regression (MLR) equations (in XYZ) for PRCP, TMAX, and TMIN by month using all appropriate regional observation stations. San Juan Basin Observation Stations 37

  36. Precipitation-Elevation Relations

  37. XYZ Distribution Exhaustive Search Analysis - Select best station subset from all stations - Estimate gauge undercatch error for snow events - Select precipitation frequency station set

  38. Precip and temp stations XYZ Spatial Redistribution 2. Daily mean PRCP, TMAX, and TMIN computed for a subset of stations (3) determined by the Exhaustive Search analysis to be best stations 3. Daily station means from (2) used with monthly MLR xyz relations to estimate daily PRCP, TMAX, and TMIN on each HRU according to the XYZ of each HRU

  39. P1 Mean Station Precipitation P2 P3 Elevation 2-D Example XYZ and Rain Day Frequency Slope from MLR Precipitation in the mean station set Precipitation in the frequency station set but not the mean station set Mean station set elevation

  40. Application of XYZ Methodology Chesapeake Bay Subdivide the monthly MLRs by Sea Level Pressure (SLP) patterns using a map-pattern classification procedure Sea Level Pressure Patterns Low SLP High SLP

  41. Application of XYZ Methodology Chesapeake Bay PRCP subdivided by SLP Mean Daily Precipitation 0 1 2 3 4 5 6 7 Sea Level Pressure Patterns Mean Daily PRCP (mm/day) Low SLP High SLP

  42. SOLAR RADIATION For each HRU swrad(hru) = ( pot_rad(hru) / pot_horad ) * orad /cos_slp(hru) where orad is observerd sw radiation pot_rad and pot_horad are computed from hru slope, aspect, & latitude - Missing orad is computed byeither - obs_tmax - SolarRad relation - [obs_tmax - obs_tmin] --> sky cover --> SolarRad relation

  43. Degree-Day Solar Radiation Estimation Procedure (non precip day) For days with precip, daily value is multiplied by a seasonal adjustment factor

  44. ccov tmax - tmin Temperature-Range Radiation Estimation Procedure (non precip day) ccov = ccov_slope(mo) * (obs_tmax – obs_tmin) + ccov_intcp(mo) orad/pot_rad = crad_coef + (1. – crad_coef) * [(1. – ccov)** crad_exp] crad_coef and crad_exp from Thompson, 1976, WRR For days with precip, daily value is multiplied by a seasonal adjustment factor

  45. DRIVING VARIABLE INPUT SOURCES • Point measurement data • Radar data • Satellite data • Atmospheric model data

  46. RADAR DATA NEXRAD vs S-POL, Buffalo Creek, CO

  47. Satellite Image for Snow-Covered Area Computation

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