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

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

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

Model Selection Criteria Problem objectives Problem objectives Data constraints Data constraints Time and space scales of application Time and space scales of application

Lumped Model Approach TANK MODEL

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

TOPMODEL Distributed Process Conceptualization Statistical Distribution of Topographic Index ln(a/tanB)

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

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.

PRMS

PRMS Variations PRMS_WET PRMS_ISO PRMS_Yakima PRMS_Jena PRMS- MODFLOW

PRMS Parameters original version

PRMS Parameters MMS Version

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

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)

Dill Basin, Germany 750 km 750 km 2 Land Use Sub-basins Topography

Topographic Pixelated PRMS -- HRU Delineation

Grid Complexity

3rd HRU DIMENSION

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

PRMS HRU resolution SSR resolution GWR resolution

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)] tmax_adj(hru) elfac(hru) = [hru_elev - tsta_elev(hru_tsta)] / For each HRU where

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

Catch Ratio Equations WMO Study

Catch Ratio WMO Study

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) Manual (precip_prms.f) Auto Elevation Lapse Rate (precip_laps_prms.f) Auto Elevation Lapse Rate (precip_laps_prms.f) XYZ (xyz_dist.f) XYZ (xyz_dist.f) PCOR Computation

Manual Manual PCOR Computation

Auto Elevation Lapse Rate 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 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.

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

Z PRCP 2. PRCP mru = slope*Z mru + intercept where PRCP mru is PRCP for your modeling response unit Z mru is mean elevation of your modeling response unit x One predictor (Z) example for distributing daily PRCP from a set of stations: 1.For each day solve for y-intercept intercept = PRCP sta - slope*Z sta where PRCP sta is mean station PRCP and Z sta is mean station elevation slope is monthly value from MLRs Plot mean station elevation (Z) vs. mean station PRCP Slope from monthly MLR used to find the y-intercept XYZ Methodology

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

Precipitation Distribution Methods (module) precip_dist2_prms - weights measured precipitation from two or more stations by the inverse of the square of the distance between the centroid of an HRU and each station location PCOR Computation

Precipitation Distribution Methods (module) ide_prms - Combines XYZ_prms and an inverse distance squared approach but allows you to select which months to apply each approach. You can also limit the number of stations used for the inverse distance computation to the nearest X stations. PCOR Computation

SOLAR RADIATION - drad and horad computed from table of 13 values for each HRU and a horizontal surface - Table generated from hru slope, aspect, & latitude - Missing data computed by obs_tmax - SolarRad relation [obs_tmax - obs_tmin] --> sky cover --> SolarRad relation For each HRU daily_potsw(hru) = ( drad(hru) / 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

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

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

Satellite Image for Snow-Covered Area Computation

Statistical Downscaling Atmospheric Models Multiple linear regression equations developed for selected climate stations Multiple linear regression equations developed for selected climate stations Predictors chosen from over 300 NCEP variables (< 8 chosen for given equation) Predictors chosen from over 300 NCEP variables (< 8 chosen for given equation) Predictands are maximum and minimum temperature, precipitation occurrence, and precipitation amounts Predictands are maximum and minimum temperature, precipitation occurrence, and precipitation amounts Stochastic modeling of the residuals in the regression equations to provide ensemble time series Stochastic modeling of the residuals in the regression equations to provide ensemble time series 11,000 Climate Station Locations NCEP Model Nodes Collaboratively with U. of Colorado

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

Performance Measures Coefficient of Efficiency E Nash and Sutcliffe, 1970, J. of Hydrology Widely used in hydrology Range – infinity to +1.0 Overly sensitive to extreme values

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

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

Various Concepts of ET vs Soil Moisture

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 S tmax_sum(hru) > transp_tmax(hru) transp_end - end actet

Point Evapotranspiration Comparison Eddy correlation Jensen-Haise Aspen Park, CO ET, inches

WORKSHOP ON REGIONAL CLIMATE PREDICTION AND DOWNSCALING TECHNIQUES FOR SOUTH AMERICA Basin Evapotranspiration Comparison Jensen-Haise RegCM2 Animas River Basin, Colarado

Mirror Lake, NH GW - ET Relations

PRMS

Distribution, Flow, and Interaction of Water

SOIL ZONE ( Original Version) Recharge Zone (soil_rechr_max) Lower Zone excs (soil_moist > soil zone field capacity) sroff soil_moist_max (rooting depth) soil2gw_max excs - soil_to_gw to subsurface reservoir to ground-water reservoir

Original and Revised Soil Zone

Original PRMS Conceptualization SRO

Revised PRMS Conceptualization

Soil Zone Structure and Flow Computation Sequence

wp fc sat soil_moist_max = fc -wp sat_threshold = sat -fc Capillary Reservoir Gravity Reservoir Preferential-Flow Reservoir pref_flow_stor slow_stor pref_flow_thresh = sat_threshold * (1.0 – pref_flow_den) pref_flow_max = sat_threshold – pref_flow_thresh soil_moist soil_rechr soil_zone_max = sat_threshold + soil_moist_max ssres_stor = slow_stor + pref_flow_stor

Soil Zone Water Flux

Soil Zone Module

HYDROLOGIC RESPONSE UNITS (HRUs)

Cascading Flow

HRUs AS FLOW PLANES & CHANNELS (Storm Mode)

OVERLAND FLOW PLANES channel Overland Flow Plane 1.0 } } ∆x Pervious Precipitation excess Unit overland flow % Impervious % Pervious Impervious Precipitation excess

CASCADING FLOW PLANES 3 Overland Flow Path Channel Segment Overland Flow Plane Grass/Agriculture Bare Ground/Rock Trees Shrubs length width Channel Junction

Soil Texture vs Available Water-Holding Capacity

Infiltration - DAILY MODE - STORM MODE infil(hru) = net_precip(hru) - sroff(hru) Point Infil (fr) fr = dI/dt = ksat * [1. + (ps / S 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 GENERATION 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) * (soil_rechr/soil_rechr_max)] ca_percent = smidx_coef * 10. (smidx_exp * smidx) where smidx = soil_moist(hru) + (net_precip(hru) / 2.) sroff(hru) = ca_percent * net_precip(hru)

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

Surface Runoff Contributing Area vs Soil Moisture Index (nonlinear)

STARKWEATHER COULEE, ND Depression Storage Prairie Pothole Region

DEPRESSION STORAGE ESTIMATION (BY HRU) USING THE GIS WEASEL (AREA & VOLUME)

Depression Store Hydrology DEPRESSION STORES (flowing and closed) HRU 1 HRU 2 STORAGE HRU FLOW S GW PET FLOW

PRMS

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

PRMS

GROUND-WATER FLOW gwres_flow= gwflow_coeff * gwres_stor soil_to_gw + ssr_to_gw Ground-water Reservoir gwres_sink = gwsink_coef * 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) Assumes No Cascade Flow