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

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

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 Parameters original version

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

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

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 Polygon Hydrologic Response Units (HRUs) Grid Cell Hydrologic Response Units (HRUs)

15. Grid Complexity

16. 3rd HRU DIMENSION

17. PRMS

18. MODEL DRIVING VARIABLES - TEMPERATURE - PRECIPITATION - max and min daily - lapse rate varied monthly or daily - 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 tmax(hru) = obs_tmax(hru_tsta) - tcrx(mo) tmin(hru) = obs_tmin(hru_tsta) - tcrx(mo) tcrx(mo) = [ tmax_lapse(mo) * elfac(hru)] - For each HRU where elfac(hru) = [hru_elev - tsta_elev(hru_tsta)] / 1000. tmax_adj(hru)

22. Precipitation-Elevation Relations

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

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

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

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

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

30. Manual PCOR Computation

31. Auto Elevation Lapse Rate PCOR Computation For each HRU 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 ------ -------precip lapse rate by month [pmo_rate(mo)] hru_psta hru_plaps

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

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

34. XYZ Distribution

35. San Juan Basin Observation Stations 37 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.

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. 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 Precip and temp stations

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

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 Low SLP High SLP Sea Level Pressure Patterns Mean Daily PRCP (mm/day) Mean Daily Precipitation 0 1 2 3 4 5 6 7

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

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

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

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

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

49. Satellite Image for Snow-Covered Area Computation

57. Statistical Downscaling: Predictors (daily data from NCEP re-analysis) total column precipitable water 500 hPa geopotential height meridional component of wind (from 500 hPa height field) Predictands precipitation occurrence wet-day amount (PRCP) (NWS obs.; 3 station ave.) Calibration Period (1987-1995)Validation Period (1979-1986) Multiple Linear Regression by season (DJF, MAM, JJA, SON)

58. Statistical Downscaling: Multiple Linear Regression by season (DJF, MAM, JJA, SON) Calibration Period (1987-1995)Validation Period (1979-1986) Predictands temperature (TMAX, TMIN) (NWS obs.; 3 station ave.) Predictors (daily data from NCEP re-analysis) mean sea level pressure zonal component geostrophic wind at sea level total column precipitable water 500 hPa geopotential height

59. Dynamical Downscaling RegCM2 (Giorgi et al., 1993, 1996)  Period: 1979-1988  Boundary conditions: NCEP Reanalysis  52 km grid (Lambert conformal projection)

60. Representative Elevation of Atmospheric Model Output based on Regional Station Observations

61. Nash-Sutcliff Coefficient of Efficiency Scores Simulated vs Observed Daily Streamflow

62. Animas River, CO Simulated Q with station data (S_3) and downscaled data (N_ds) from NCEP reanalysis

63. PRMS

64. INTERCEPTION net_precip = [ hru_precip * (1. - covden)] + (PTF * covden) PTF = hru_precip - (max_stor - intcp_stor) ----- Throughfall Losses from intcp_stor Rain - Free water surface evaporation rate Snow - % of potet rate for sublimation Net precipitation PTF = 0. if [ hru_precip <= (max_stor - intcp_stor)] if [ hru_precip > (max_stor - intcp_stor)]

65. PRMS

66. Various Concepts of ET vs Soil Moisture

67. Transpiration vs Soil Moisture Content and Weather Conditions

68. Potential Evapotranspiration (potet) - Pan Evaporation - Hamon - Jensen - Haise potet(hru) = epan_coef(mo) * pan_evap potet(hru) = hamon_coef(mo) * dyl 2 * vdsat potet(hru) = jh_coef(mo) * --------------- (tavf(hru) - jh_coef_hru) * rin

69. Computed ET (AET) as function of PET and Soil Texture PRMS to PRMS/MMS SMAV = soil_moist SMAX = soil_moist_max RECHR = soil_rechr REMX = soil_rechr_max

70. Actual Evapotranspiration (actet) - f ( antecedent conditions, soil type) - Taken first from Recharge Zone & then Lower Zone - actet period ( months transp_beg to transp_end) transp_beg - start actet on HRU when  tmax_sum(hru) > transp_tmax(hru) transp_end - end actet

71. Mirror Lake, NH GW - ET Relations

72. PRMS

73. SOIL ZONE Recharge Zone (soil_rechr_max) Lower Zone excs (soil_moist > soil zone field capacity) sroff soil_moist_max (rooting depth) soil_to_gw excs - soil_to_gw to subsurface reservoir to ground-water reservoir

74. Soil Texture vs Available Water- Holding Capacity

75. SOIL MOISTURE ACCRETION - DAILY MODE - STORM MODE infil(hru) = net_precip(hru) - sroff(hru) Point Infil (fr) fr = dI/dt = ksat * [1. + (ps /  fr)] Areal Infil (fin) qrp = (.5 * net_precip 2 / fr ) net_precip < fr qrp = net_precip - (.5 * fr) Otherwise fin = net_precip - qrp

76. PRMS

77. STREAMFLOW Integration of a variety of runoff generation processes Surface Runoff Subsurface Flow (Interflow) Baseflow

78. ANIMAS RIVER, CO SURFACE GW SUBSURFACE PREDICTED MEASURED

79. EAST FORK CARSON RIVER, CA SUBSURFACE GW SURFACE

80. PRMS

81. SURFACE RUNOFF MECHANISMS

82. Variable-Source Area Concept

83. Contributing Area vs Basin Moisture Index

84. SURFACE RUNOFF (SRO) Contributing-Area Concept - Linear Scheme (by HRU) - Non-linear Scheme (by HRU) ca_percent = carea_min + [(carea_max - carea_min) ca_percent = smidx_coef * 10. (smidx_exp * smidx) where smidx = soil_moist(hru) + (net_precip(hru) / 2.) sroff(hru) = ca_percent * net_precip(hru) ---------------* (soil_rechr/soil_rechr_max)]

85. Surface Runoff Contributing Area vs Soil Moisture Index (nonlinear approach)

86. Surface Runoff Contributing Area vs Soil Moisture Index (nonlinear)

87. PRMS

88. SUBSURFACE FLOW = IN - (ssrcoef_lin * S) - dS dt IN Subsurface Reservoir ssr_to_gw = ssr2gw_rate * S ssrmax_coef () ssr2gw_exp -----(ssrcoef_sq * S 2 )

89. PRMS

90. GROUND-WATER FLOW gwres_flow= gwflow_coeff * soil_to_gw + ssr_to_gw Ground-water Reservoir gwres_sink = gwsink_coef * gwres_stor gwres_stor

91. Qbase = gwflow_coef x gwres_stor Q0Q0 QtQt Q t = Q 0 e -kt gwflow_coef = k Estimating GW Reservoir Parameters Daily recharge SEP fits interannual variation in Q base outflow inflow

92. 3rd HRU DIMENSION

93. Relation of HRUs and Subsurface and GW Reservoirs Surface ( 6 hrus ) Subsurface ( 2 reservoirs ) Ground water (1 reservoir)