1

Overland and Channel Routing in the Distributed Model

Lecture 4a

Yu Zhang

2

Outline• Conceptual model

• Parameter estimation– Connectivity– Slopes– Channel hydraulic properties

• Local customization steps

3

Overland flow routed independently for each

hillslope

(adapted from Chow et al., 1988)

HRAP Cell (~ 4 km x 4 km) Uniform, conceptual hillslopes within a modeling unit are assumed

• Drainage density illustrated is ~1.1 km/km2• Number of hillslopes depends on drainage density

Conceptual channel provides cell-

to-cell link

Overland flow routed independently for each

hillslope

(adapted from Chow et al., 1988)

HRAP Cell (~ 4 km x 4 km) Uniform, conceptual hillslopes within a modeling unit are assumed

• Drainage density illustrated is ~1.1 km/km2• Number of hillslopes depends on drainage density

Conceptual channel provides cell-

to-cell link

Real HRAP Cell

Conceptual Config

Cell-to-cell channel routing

Routing Model

hillslope

channel

4

Fast runoff components• Surface• Direct• Impervious

Slow runoff components• Interflow• Supplemental baseflow• Primary baseflow

Hillslope routing

Channel routing

Separate Treatment of Fast and Slow Runoff

HRAP Cell

5

ABRFC ~33,000 cells

MARFC ~14,000 cells

• OHD delivers baseline HRAP resolution connectivity, channel slope, and hillslope slope grids for each CONUS RFC on the basis of higher resolution DEM data.

HRAP Cell-to-cell Connectivity Examples

6

Representative Slopes Are Extracted from Higher Resolution DEMS(North Fork of the American River (850 km2))

Slopes from 30-m DEM

Hillslope Slope (1/2 HRAP Resolution)Average = 0.15Slopes of all DEM cells within the HRAP pixel are averaged.

Main Channel Slope (1/2 HRAP Resolution)Average = 0.06Channel slopes are assigned based on a representative channel with the closest drainage area.

Local Channel Slope (1/2 HRAP Resolution)Average = 0.11

Slope (m/m)

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Main

Tributary

Main Channel Slope vs. Local Channel Slope

(1) Slopes of each stream segment are calculated on the DEM grid

(2) Model pixel slopes are assigned from representative segments (DEM cell) that most closely match either the cell’s cumulative or local drainage area.

Segment Slopes (m/m)

Cell slope -> pixel-wise local slopec

Cell slope -> pixel-wise main slopec

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Hillslope and Channel Routing

• Conceptual Framework

• Parameters needed

• How to assign the parameters– Training provided in workshop 2

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Hillslope Routing

sh Rx

qL

t

h

35

352

hqhn

SDq s

h

h

q = discharge per unit area of hillslopeh = average overland flow depthRs = fast runoff from water balanceSh = hillslope slopenh = hillslope roughnessD = drainage densityLh = hillslope length

Momentum:

Conceptual Hillslope

DLh 2

1x

Continuity:

• Kinematic Wave– Koren et al. (2004)

• Independent routing for each hillslope element

• Only routes fast runoffGrid Pixel

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Channel Routing

Momentum:

cLx

Continuity:

• Kinematic Wave– Koren et al. (2004)

• Routes – fast runoff from hillslope

– Slow runoffGrid Pixel

c

cgL L

fRq

x

Q

t

Ah

mqAqQ 0

Q = channel dischargeA = channel cross-sectional areaqLh = overland flow rate at the hillslope outletRg = slow runoff component from the water balanceFc = grid cell areaLc = channel length within a cell

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Kinematic Wave vs. Unit Hydrograph

•Typically qm > 1: faster flood propagation at high flows.•If qm == 1, channel flow similar to a unit hydrograph with uniform runoff

0

200

400

600

800

1000

1200

1400

0 20 40 60 80

Time (hours)

Flo

w (

cms)

KW 12.7

KW 25.4

KW 50.8mm

2 x KW 25.4 mm

0.5 * KW 25.4

UG Peak Time

Smaller flood delayed

Larger flood accelerated

Treating KW 25.4 like UG

qmAQ q0

Same q0,qm

Runoff = 50.8 mm

Runoff = 12.7 mm

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Parameters Needed

• Hillslope Routing– Hillslope (rutpix_SLOPH)– Roughness (rutpix_ROUGH)– Drainage Density (rutpix_DS)

• Channel Routing– Need q0 and qm in Q=q0Aqm

– Two Channel-Flood Plain Models provided• Rating Curve• Channel Shape

– Both produced good results in our applications.

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‘Rating Curve’ Model

• Direct estimation of q0 and qm• at a USGS gauge using measurement data• Use geomorphologic relationships to derive

spatially variable values (see Koren, 2004 for details)

• Parameters– rutpix_Q0CHN (q0)– rutpix_QMCHN (qm)

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‘Channel Shape’ Model

• Assume expoential relationship between top width (B) and depth (H)

• Estimate and at a USGS gauge using streamflow measurement data

• Use geomorphologic relationships to derive spatially variable a values (see Koren, 2004 for details)

• Compute q0 and qm as a function of and , channel slope (Sc) and channel roughness (nc)

• Required parameters– Rutpix_SLOPC (channel slope)– Rutpix_ROUGC (channel roughness)– Rutpix_BETAC (beta factor in )– Rutpix_ALPHC (alpha factor …)

B H

= 1

< 1

> 1

= 0

B H

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Assign Distributed Routing Parameters

• Information needed•Parameters estimated at an outlet pixel•Drainage area•Connectivity•Geomorphologic relationships. .

q0qm

Extrapolate

Estimate

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Validation

Derive q0 and qm

Validate against

locally derived values

Extrapolate upstream

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Validation

WTTO2

TALO2

WTTO2 (1645 km2)

Predicted values (p) based on estimatesfor TALO2 (2484 km2) compared with local fits (l)

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Validation

KNSO2

TALO2

Predicted values (p) based on estimatesfor TALO2 (2484 km2) compared with local fits (l)

KNSO2 (285 km2)

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Validation

CAVES

TALO2

Predicted values (p) based on estimatesfor TALO2 (2484 km2) compared with local fits (l)

CAVES (90 km2)

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Validation

SPRIN

TALO2

Predicted values (p) based on estimatesfor TALO2 (2484 km2) compared with local fits (l)

SPRIN(37 km2)

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Customization Procedures (User Manual Chapter 9)

• Determine outlet pixel – XDMS

• Update Connectivity to incorporate this outlet• Adjust cell areas to match USGS drainage area

– cellarea

• Download USGS flow measurement • Derive routing parameters for a given outlet

– Interactively using a R script

• Distribute values to upstream grids– genpar

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Determine Outlet pixel

• Connectivity on grid scale can be inaccurate– Can not rely on proximity between USGS outlet coordinate and HRAP

location itself

– Visual inspection is needed

– Finer resolution grid helps improve accuracy

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1 TIFM7 Elk River near Tiff City Mo 22582 POWEL Big Sugar Creek near Powell MO 3653 LANAG Indian Creek near Lanagan MO 619

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3

User must choose which cell is the best outlet for this basin.

Gauge Name Area (km2)ID

4 km resolution does not allow accurate selection of an outlet for this subbasin because

HRAP vs. ½ HRAP Implementation

2 km resolution allows more accurate delineation

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2258 km2

285 km2

795 km2

HRAP Cell Connectivity

Adjust Cell AreasPercent errors in representing basins with 4 km resolution pixels.• Open squares represent errors due to resolution only. • Black diamonds represent errors due to resolution and connectivity.• We correct for these errors by adjusting cell areas in the model so that the sum of the model cell areas matches the USGS reported area at the basin outlet.

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Add Outlet to Connectivity

Change this number when adding outlets

User defined header lines

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Obtain USGS Flow Measurements

• Several times a year, USGS provides measurements of– Discharge

– Cross-sectional Area (wetted)

– Width

– Depth

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Outletmeas_manual.R User Options

#---(1)--- input file namefile.list<-"/fs/hsmb5/hydro/users/sreed/flow_measurements/dmip2/talo2meas3_29_07.d"

#---(2)--- user specified weight exponent for regressionQwt.qa<-1 # for Q-AQwt.ab<-1 # for A-BQwt.n <-1 # for Manning's n

#---(3)--- User specified relative weights for each of the USGS data quality flagsws<-c(1,1,1,1,1)

#---------------------------------# Code Description # ---------------------------------# E Excellent the data is within 2% (percent) of the actual flow# G Good the data is within 5% (percent) of the actual flow# F Fair the data is within 8% (percent) of the actual flow# P Poor the data are not within 8% (percent) of the actual flow# -1 Missing# The ws vector is ordered as above c(E,G,F,P,-1)

#---(4)--- graph optionsplot_quality=Tnew_graphics=T

#---(5)--- info for the channel shape methodslope=0.002

#reread_data=TRUE

#--- (6)--- output file namesfile.out<-"param.final.d"

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R Scripts Provided to Assist with Flow Measurement Analysis

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R Scripts for Deriving Routing Parameters

• Outletmeas_manual.R •Reads USGS measurements •Assigns weighting factors according to discharge

•To better match the results at high flows during regression •Perform weighted regression for

•Rating Curve method (Q ~A)•Channel Shape method (A~B)

•Generate plots •Enable user to make adjustments

•Derived parameters are saved to a file for later use

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Distribute Parameters Upstream using Genpar

• Features of Genpar– Needs a base grid

– Modifies the entire area upstream of an outlet

– Able to handle multiple outlets

Assign values to entire upstream area Overwrite values for sub-basins

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Genpar Input Deck

#genpar.card#enter the connectivity file nameconnectivity = /fs/hsmb5/hydro/users/zhangy/RDHM/Genpar/sequence/abrfc_var_adj.con#specify an input location for parameter gridsinput-path = /fs/hsmb5/hydro/rms/parameterslx/abrfc#specificy an output locationoutput-path = /fs/hsmb5/hydro/users/zhangy/RDHM/Genpar/output#replace/update the existing grid or output the grid to the output-path, true or false#overwrite-existing-grid = false##create a new grid instead of modify existing grid, the boundary in this# case is the boundary of all selected basins, true or falsecreate-new-grid = true##if the create-new-grid is true, the grid will be created in this window.#if this window is not consistent with the window from the connectivity,#the windows are combined into a big window that contains both subwindows.#window-in-hrap = 480 505 298 306 ## Name of the parameter to be created, available names are:# slopc rougc betac alphc sloph ds rough Q0CHN QMCHM # They are case insensitive#genpar-id = slopc#genpar-id = rougc#genpar-id = alphc#the next line specifies the parameter for which values will be generatedgenpar-id = q0chn#genpar-id = qmchn#next line is an example input information for q0chn grid generationgenpar-data = TALO2 0.31 1.2 Table 9.3 tells you what to

put here