1 / 31

Overland and Channel Routing in the Distributed Model

Overland and Channel Routing in the Distributed Model. Lecture 4a. Yu Zhang. Outline. Conceptual model Parameter estimation Connectivity Slopes Channel hydraulic properties Local customization steps. Routing Model. Real HRAP Cell. hillslope. Conceptual Config. channel.

isla
Download Presentation

Overland and Channel Routing in the Distributed Model

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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. Routing Model Real HRAP Cell hillslope Conceptual Config channel Cell-to-cell channel routing

  4. Separate Treatment of Fast and Slow Runoff HRAP Cell

  5. HRAP Cell-to-cell Connectivity Examples ABRFC ~33,000 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. MARFC ~14,000 cells

  6. Representative Slopes Are Extracted from Higher Resolution DEMS (North Fork of the American River (850 km2)) Slope (m/m) Main Channel Slope (1/2 HRAP Resolution) Average = 0.06 Channel slopes are assigned based on a representative channel with the closest drainage area. Slopes from 30-m DEM Hillslope Slope (1/2 HRAP Resolution) Average = 0.15 Slopes of all DEM cells within the HRAP pixel are averaged. Local Channel Slope (1/2 HRAP Resolution) Average = 0.11

  7. Tributary Main Main Channel Slope vs. Local Channel Slope Segment Slopes (m/m) Cell slope -> pixel-wise local slopec Cell slope -> pixel-wise main slopec • 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.

  8. Hillslope and Channel Routing • Conceptual Framework • Parameters needed • How to assign the parameters • Training provided in workshop 2

  9. Hillslope Routing • Kinematic Wave • Koren et al. (2004) • Independent routing for each hillslope element • Only routes fast runoff x Grid Pixel Conceptual Hillslope q = discharge per unit area of hillslope h = average overland flow depth Rs = fast runoff from water balance Sh = hillslope slope nh = hillslope roughness D = drainage density Lh = hillslope length Continuity: Momentum:

  10. Channel Routing • Kinematic Wave • Koren et al. (2004) • Routes • fast runoff from hillslope • Slow runoff x Grid Pixel Q = channel discharge A = channel cross-sectional area qLh = overland flow rate at the hillslope outlet Rg = slow runoff component from the water balance Fc = grid cell area Lc = channel length within a cell Continuity: Momentum:

  11. Kinematic Wave vs. Unit Hydrograph Larger flood accelerated Runoff = 50.8 mm Same q0,qm Runoff = 12.7 mm Treating KW 25.4 like UG Smaller flood delayed • Typically qm > 1: faster flood propagation at high flows. • If qm == 1, channel flow similar to a unit hydrograph with uniform runoff

  12. 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.

  13. ‘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)

  14. ‘Channel Shape’ Model • Assume expoential relationship between top width (B) and depth (H) • Estimate a and b 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 a and b, 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 = 1 b < 1 b > 1 b = 0

  15. Assign Distributed Routing Parameters • Information needed • Parameters estimated at an outlet pixel • Drainage area • Connectivity • Geomorphologic relationships. . Extrapolate qm q0 Estimate

  16. Validation Validate against locally derived values Extrapolate upstream Derive q0 and qm

  17. Validation WTTO2 (1645 km2) WTTO2 TALO2 Predicted values (p) based on estimates for TALO2 (2484 km2) compared with local fits (l)

  18. Validation KNSO2 (285 km2) KNSO2 TALO2 Predicted values (p) based on estimates for TALO2 (2484 km2) compared with local fits (l)

  19. Validation CAVES (90 km2) CAVES TALO2 Predicted values (p) based on estimates for TALO2 (2484 km2) compared with local fits (l)

  20. Validation SPRIN(37 km2) SPRIN TALO2 Predicted values (p) based on estimates for TALO2 (2484 km2) compared with local fits (l)

  21. 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

  22. 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

  23. HRAP vs. ½ HRAP Implementation Area (km2) ID Gauge Name 2 1 3 4 km resolution does not allow accurate selection of an outlet for this subbasin because 2 km resolution allows more accurate delineation User must choose which cell is the best outlet for this basin.

  24. 2258 km2 285 km2 795 km2 Adjust Cell Areas HRAP Cell Connectivity • Percent 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.

  25. Add Outlet to Connectivity Change this number when adding outlets User defined header lines

  26. Obtain USGS Flow Measurements • Several times a year, USGS provides measurements of • Discharge • Cross-sectional Area (wetted) • Width • Depth

  27. Outletmeas_manual.R User Options #---(1)--- input file name file.list<-"/fs/hsmb5/hydro/users/sreed/flow_measurements/dmip2/talo2meas3_29_07.d" #---(2)--- user specified weight exponent for regression Qwt.qa<-1 # for Q-A Qwt.ab<-1 # for A-B Qwt.n <-1 # for Manning's n #---(3)--- User specified relative weights for each of the USGS data quality flags ws<-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 options plot_quality=T new_graphics=T #---(5)--- info for the channel shape method slope=0.002 #reread_data=TRUE #--- (6)--- output file names file.out<-"param.final.d"

  28. R Scripts Provided to Assist with Flow Measurement Analysis

  29. 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

  30. 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

  31. Genpar Input Deck #genpar.card #enter the connectivity file name connectivity = /fs/hsmb5/hydro/users/zhangy/RDHM/Genpar/sequence/abrfc_var_adj.con #specify an input location for parameter grids input-path = /fs/hsmb5/hydro/rms/parameterslx/abrfc #specificy an output location output-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 false create-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 generated genpar-id = q0chn #genpar-id = qmchn #next line is an example input information for q0chn grid generation genpar-data = TALO2 0.31 1.2 Table 9.3 tells you what to put here

More Related