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

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

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

PRMS

PRMS Parameters original version

PRMS Parameters MMS Version

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

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.

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

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

HYDROLOGIC RESPONSE UNITS (HRUs)

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

HRUs

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

Grid Complexity

3rd HRU DIMENSION

PRMS

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

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

Max Temperature-Elevation Relations

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)] / tmax_adj(hru)

Precipitation-Elevation Relations

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

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

Precipitation Gauge Intercomparison Rabbit Ears Pass, Colorado

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

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

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) * []

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

Manual PCOR Computation

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

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

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)

XYZ Distribution

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.

Precipitation-Elevation Relations

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

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

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

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

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

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)

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

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

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

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

Satellite Image for Snow-Covered Area Computation

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 ( )Validation Period ( ) Multiple Linear Regression by season (DJF, MAM, JJA, SON)

Statistical Downscaling: Multiple Linear Regression by season (DJF, MAM, JJA, SON) Calibration Period ( )Validation Period ( ) 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

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

Representative Elevation of Atmospheric Model Output based on Regional Station Observations

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

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

PRMS

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)]

PRMS

Various Concepts of ET vs Soil Moisture

Transpiration vs Soil Moisture Content and Weather Conditions

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

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

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

Mirror Lake, NH GW - ET Relations

PRMS

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

Soil Texture vs Available Water- Holding Capacity

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

PRMS

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

ANIMAS RIVER, CO SURFACE GW SUBSURFACE PREDICTED MEASURED

EAST FORK CARSON RIVER, CA SUBSURFACE GW SURFACE

PRMS

SURFACE RUNOFF MECHANISMS

Variable-Source Area Concept

Contributing Area vs Basin Moisture Index

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)]

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

Surface Runoff Contributing Area vs Soil Moisture Index (nonlinear)

PRMS

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 )

PRMS

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

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

3rd HRU DIMENSION

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