1. Distributed Hydrologic Modeling Presented by: Paul McKee and Mike Shultz West Gulf River Forecast Center DHM/HL-RDMH Workshop 2007 WGRFC Forecast Point Application and Results

2. WGRFC http://www.srh.noaa.gov/wgrfc Overview  What’s the interest for WGRFC?  Testing Objectives and Strategy  Model Calibraton  Operational Implementation  Forecast Applications/Results

3. WGRFC http://www.srh.noaa.gov/wgrfc Overview and status Development and calibration of basin models Development and calibration of basin models  26 total basins  25 available for operational forecasting  1 nested basin Visual Inspection and qualitative analysis for model comparison Visual Inspection and qualitative analysis for model comparison Implementation into real-time river forecast operations Implementation into real-time river forecast operations

4. WGRFC http://www.srh.noaa.gov/wgrfc Distributed Modeling What’s the interest? Research indicates the greatest improvement occurs for basins with: Research indicates the greatest improvement occurs for basins with:  Non-uniform rainfall distributions  Irregular shaped basins (Long and narrow)  Non-uniform soil type and land use  Relatively large impervious areas which cause a rapid surface runoff response  Increased accuracy of event timing  Stream flow prediction at interior points  Distributed parameter inputs utilizes more data complexity as available

5. WGRFC http://www.srh.noaa.gov/wgrfc Basin Response Times WGRFC Study Area Hydrologic Response Times  6 hours or less  20%  12 hours or less  47%  18 hours or less  65%  24 hours or less  74%

6. WGRFC http://www.srh.noaa.gov/wgrfc Application Objectives Test basin setup procedures Test basin setup procedures Examine calibration strategies Examine calibration strategies Evaluate simulations compared to lumped model Evaluate simulations compared to lumped model Provide feedback to OH for prototype Provide feedback to OH for prototype Assist with developing requirements for an operational DHM (OSIP process) Assist with developing requirements for an operational DHM (OSIP process)

7. WGRFC http://www.srh.noaa.gov/wgrfc Application Strategy Basin Development/ Setup Headwater Headwater  Limitations with lumped model  VAR basins to utilize SAC parameters estimated using ab_opt Diversity Diversity  size, shape, terrain, landuse/cover, soils  Varied time-to-peak response  (DA: 75-400mi2; peak times: 6-60hr) Interior stream gages Interior stream gages

8. WGRFC http://www.srh.noaa.gov/wgrfc Basin Calibration Strategy Approach similar to lumped model Approach similar to lumped model Manual “expert” process; parameter estimation/ optimization tools unavailable Manual “expert” process; parameter estimation/ optimization tools unavailable Use ab_opt estimated SAC parameters for scalar adjustments Use ab_opt estimated SAC parameters for scalar adjustments Simulation comparisons: Simulation comparisons:  Apriori, ab_opt, “expert” calibration, lumped

9. WGRFC http://www.srh.noaa.gov/wgrfc DHM Basin Calibration Constraints/Limitations Global scalar adjustment of parameter grids; conserves relative diff. between grid cells Global scalar adjustment of parameter grids; conserves relative diff. between grid cells Lumped values only for PCTIM, ADIMP, RIVA (grids unavailable) Lumped values only for PCTIM, ADIMP, RIVA (grids unavailable) Unknown effect of apriori grid outliers on calibration results (ie. sensitivity) Unknown effect of apriori grid outliers on calibration results (ie. sensitivity) Difficult to keep simple… build complexity as needed Difficult to keep simple… build complexity as needed

10. WGRFC http://www.srh.noaa.gov/wgrfc Calibration Sensitivities?? Possible outliers in apriori param grids? Large relative differences of grid values? LZFPM apriori grid QPE error, both location and amt. Grid resolution vs. available data? Outlier?

11. WGRFC http://www.srh.noaa.gov/wgrfc DHM Calibration early tools and utility limitations XDMS – 1 st generation, display/ no editing of parameter grids XDMS – 1 st generation, display/ no editing of parameter grids Stat_q – text output, no graphics Stat_q – text output, no graphics Parameter Estimation/ Optimization tools Parameter Estimation/ Optimization tools  Enhances expert calib.  Automated parameter sensitivity anal.  Graphics of statistical analyisis

12. WGRFC http://www.srh.noaa.gov/wgrfc DHM OBS LMP GETT2: May 5, 2006 S. Fork San Gabriel R.Georgetown Integrating DHM into operations Forecast Mode - Runs once per hour; no operational modifications applied - View DMS and lumped simulations in operational forecast software… ensemble? - No verification; qualitative analysis

13. WGRFC http://www.srh.noaa.gov/wgrfc Basin Studies Geologic Areas Hill Country (S.C. TX) Hill Country (S.C. TX) Urban Development Urban Development Gulf Coastal Plains Gulf Coastal Plains Blackland Prairie (N. TX) Blackland Prairie (N. TX) Piney Woods (E. TX) Piney Woods (E. TX)

14. WGRFC http://www.srh.noaa.gov/wgrfc Test Basins Locations Replace with updated map Blackland Prairie Hill Country Coastal Plains Piney Woods Urban

15. WGRFC http://www.srh.noaa.gov/wgrfc DHM Test Basins Varied basin size, terrain, land-use/cover, soils DA: 75 – 400 mi2 Peak times: 6 – 60 hr

16. WGRFC http://www.srh.noaa.gov/wgrfc Basin Characteristics BasinDrainage Area (mi2) Avg Hillslope (m/m) Time to Peak (hrs) SOLT2336.001382 GNVT2 78 78.005016 KNLT2349.02747 ATIT2326.01689 HBMT2 95 95.00103 MTPT2168.000917 Blackland Hill Country Gulf Coast Piney Woods Urban Hill C./Urban

17. WGRFC http://www.srh.noaa.gov/wgrfc Study Basin: KNLT2 Sandy Creek - Kingsland semi-regular shape, fast response semi-regular shape, fast response steep slope (0.0274) steep slope (0.0274) drainage area: 346 mi2 drainage area: 346 mi2 avg. time to peak: 7 hrs avg. time to peak: 7 hrs 2 interior stream gages 2 interior stream gages  OXDT2 (147 mi2) –Willow City  SNBT2 (155 mi2) –Click

18. WGRFC http://www.srh.noaa.gov/wgrfc KNLT2 time period RMS(CMS)R DHMApriori10/1/97-12/31/0315.080.77 DHMCalibrated 1/1/96 – 12/31/04 9.690.80 Lumped (6 hr) 1/1/96 – 12/31/04 9.810.86 KNLT2 Calibration

19. WGRFC http://www.srh.noaa.gov/wgrfc KNLT2: Apr 2004 TS investigating nested basins, interior points DHM OBS OXDT2 SNBT2 KNLT2 US DS

20. WGRFC http://www.srh.noaa.gov/wgrfc KNLT2: Apr 2004 WY OBS DHM LMP

21. WGRFC http://www.srh.noaa.gov/wgrfc KNLT2: May 2006 RT OBS DHM LMP

22. WGRFC http://www.srh.noaa.gov/wgrfc KNLT2: Mar 2007 RT OBS DHM LMP

23. WGRFC http://www.srh.noaa.gov/wgrfc ATIT2: Mar 29, 2006 RT Onion Creek – Austin OBS DHM LMP DA: 326 mi2 T2P: 9 hr

24. WGRFC http://www.srh.noaa.gov/wgrfc ATIT2: May 5, 2006 RT OBS DHM LMP

25. WGRFC http://www.srh.noaa.gov/wgrfc ATIT2: June 2006 RT

26. WGRFC http://www.srh.noaa.gov/wgrfc ATIT2: Mar 2007 RT

27. WGRFC http://www.srh.noaa.gov/wgrfc ATIT2: Apr 2007 RT

28. WGRFC http://www.srh.noaa.gov/wgrfc ATIT2: May 2007 RT

29. WGRFC http://www.srh.noaa.gov/wgrfc MTPT2: June 21, 2006 RT DA: 168 mi2 T2P: 17 hr

30. WGRFC http://www.srh.noaa.gov/wgrfc MTPT2: June 21, 2006 RT Tres Palacios R.- Midfield DHM OBS LMP DA: 168 mi2 T2P: 17 hr

31. WGRFC http://www.srh.noaa.gov/wgrfc HBMT2: June 2006 RT DA: 95 mi2 T2P: 3 hr

32. WGRFC http://www.srh.noaa.gov/wgrfc HBMT2: June 2006 RT

33. WGRFC http://www.srh.noaa.gov/wgrfc GNVT2: Mar 2007 RT DA: 78 mi2 T2P: 16 hr

34. WGRFC http://www.srh.noaa.gov/wgrfc GNVT2: Apr 2007 RT

35. WGRFC http://www.srh.noaa.gov/wgrfc GNVT2: May 2007 RT

36. WGRFC http://www.srh.noaa.gov/wgrfc Study Basin: SOLT2 Pine Island Bayou – Sour Lake Irregular shape, slow response Irregular shape, slow response Mild slope (0.0013) Mild slope (0.0013) Drainage area: 336 sq. mi. Drainage area: 336 sq. mi. Avg. time to peak: 48-60 hrs Avg. time to peak: 48-60 hrs

37. WGRFC http://www.srh.noaa.gov/wgrfc SOLT2 time period RMS(CMS)R DHMApriori1/1/96-12/31/0319.550.86 DHMCalibrated1/1/96-12/31/0319.070.86 Lumped (6 hr) 10/1/00 – 9/30/04 17.820.91 SOLT2 Calibration

38. WGRFC http://www.srh.noaa.gov/wgrfc SOLT2: Feb 2002 WY DHM OBS LMP

39. WGRFC http://www.srh.noaa.gov/wgrfc SOLT2: Jun 2004 TS DHM OBS LMP double peak *notice 2 separate areas of heavy rainfall

40. WGRFC http://www.srh.noaa.gov/wgrfc SOLT2: Nov 2003 TS DHM OBS LMP

41. WGRFC http://www.srh.noaa.gov/wgrfc SOLT2: Mar 2006 RT

42. WGRFC http://www.srh.noaa.gov/wgrfc SOLT2: Oct 2006 RT

43. WGRFC http://www.srh.noaa.gov/wgrfc Study Conclusions Questions/Concerns of DHM at WGRFC Difficult to calibrate peak flows Difficult to calibrate peak flows Model errors and uncertainties tend to increase at smaller scales Model errors and uncertainties tend to increase at smaller scales Does SAC model error compound for each grid cell (diffused with lumped)? Does SAC model error compound for each grid cell (diffused with lumped)? Gridded data for all parameters may be too much complexity (ie. zones?) Gridded data for all parameters may be too much complexity (ie. zones?) QPE most sensitive parameter… spatial and magnitude errors explain false peaks and compound peak flow errors QPE most sensitive parameter… spatial and magnitude errors explain false peaks and compound peak flow errors

44. WGRFC http://www.srh.noaa.gov/wgrfc Expected Effect of Data Errors and Modeling Scale How much is “too much” resolution and complexity? Relative Sub-basin Scale A/A k 110 100 10 15 20 25 30 0 5 Relative error, Ek, % (lumped) (distributed) Noise 0% 25% 50% 75% Data errors (noise) may mask benefits of fine scale modeling. In some cases, may make results worse than lumped simulations. Simulation error compared to fully distributed ‘Truth’ is simulation from 100 sub- basin model clean data Graphic courtesy of Mike Smith, OHD

45. WGRFC http://www.srh.noaa.gov/wgrfc Current Study Conclusions Benefits of DHM at WGRFC Timing of rising limbs well-simulated (variety of DAs and spatially distributed QPE) Timing of rising limbs well-simulated (variety of DAs and spatially distributed QPE) Outperforms lumped model for irregulary shaped basins Outperforms lumped model for irregulary shaped basins Full utilization of gridded QPE Full utilization of gridded QPE Understanding model biases and limitations useful for operational forecasting Understanding model biases and limitations useful for operational forecasting

46. WGRFC http://www.srh.noaa.gov/wgrfc Model Application Spectrum hypothetical use within WGRFC operations LumpedDHM Influencing factors basin type basin response basin shape rainfall distribution flow volume headwater fast irregular non-uniform small mainstem slow regular uniform large model ensemble tool?

47. WGRFC http://www.srh.noaa.gov/wgrfc DHM Study Summary Timing of rising limbs well-simulated Timing of rising limbs well-simulated Generally performs as well or better than lumped model for headwater basins. DHM compliments the lumped model for ensemble forecasting. Mainstem river basins have not been tested… Operational DHM within OFS available… not presently setup Operational DHM within OFS available… not presently setup

48. WGRFC http://www.srh.noaa.gov/wgrfc What next?  Implement latest version of research prototype for calibration improvements:  finer grid  auto-calibration process  Setup downstream basins to test process for combined segments.  Verify streamflow at interior points  Transition basins from research model to operational DHM within OFS

49. WGRFC http://www.srh.noaa.gov/wgrfc A closer look Mike Shultz Calibration Calibration  Urban/Coastal basins (HBMT2, GBHT2)  Effects of Tropical Storm Allision  Effects of wastewater effluent discharges Overview/ Observation summary Overview/ Observation summary

50. WGRFC Calibration – a closer look Brays Bayou at Houston (HBMT2)

51. WGRFC Calibration – a closer look Greens Bayou at Houston (GBHT2)

52. WGRFC Greens Bayou – Houston (GBHT2) Statistical Analyses ================================================================================================= ================================================================================================= MULTI-YEAR STATISTICS MULTI-YEAR STATISTICS Abs. Abs. % % Obs. Sim. Obs. Sim. Obs. Sim. % RMS Nash-S. Modi. % % Obs. Sim. Obs. Sim. Obs. Sim. % RMS Nash-S. Modi. Bias Bias Qmean Qmean std std Cv Cv RMS (CMS) R r Rm Bias Bias Qmean Qmean std std Cv Cv RMS (CMS) R r Rm ------------------------------------------------------------------------------------------------------------------------------------------------- ------------------------------------------------------------------------------------------------------------------------------------------------- 13.476 59.115 3.683 4.179 18.88 25.94 5.126 6.207 521.6 19.21 0.674 -0.0356 0.491 13.476 59.115 3.683 4.179 18.88 25.94 5.126 6.207 521.6 19.21 0.674 -0.0356 0.491 Best line fit: Qobs = A+B*Qsim: A--> 1.63 (cms) B--> 0.491 Best line fit: Qobs = A+B*Qsim: A--> 1.63 (cms) B--> 0.491 ================================================================================================= ================================================================================================= YEARLY STATISTICS YEARLY STATISTICS Absolute Absolute Absolute Absolute % % Error Observed Simulated % RMS Nash-S. % % Error Observed Simulated % RMS Nash-S. Year Bias Bias (CMS) Qmean Qmean RMS (CMS) R r Year Bias Bias (CMS) Qmean Qmean RMS (CMS) R r -------------------------------------------------------------------------------------------------------------------------- -------------------------------------------------------------------------------------------------------------------------- 1997 -3.316 46.22 1.876 4.060 3.926 132.1 5.362 0.773 0.598 1997 -3.316 46.22 1.876 4.060 3.926 132.1 5.362 0.773 0.598 1998 -0.9055 46.30 1.854 4.004 3.967 211.1 8.453 0.840 0.652 1998 -0.9055 46.30 1.854 4.004 3.967 211.1 8.453 0.840 0.652 1999 -1.798 50.78 1.041 2.051 2.014 136.5 2.799 0.802 0.644 1999 -1.798 50.78 1.041 2.051 2.014 136.5 2.799 0.802 0.644 2000 14.16 58.75 1.460 2.486 2.838 315.8 7.850 0.827 0.131 2000 14.16 58.75 1.460 2.486 2.838 315.8 7.850 0.827 0.131 2001 26.88 79.36 4.762 6.001 7.614 708.9 42.54 0.608 -0.310 2001 26.88 79.36 4.762 6.001 7.614 708.9 42.54 0.608 -0.310 2002 20.38 56.00 2.303 4.113 4.951 360.2 14.82 0.787 0.305 2002 20.38 56.00 2.303 4.113 4.951 360.2 14.82 0.787 0.305 2003 12.16 50.82 1.711 3.367 3.777 317.8 10.70 0.813 0.419 2003 12.16 50.82 1.711 3.367 3.777 317.8 10.70 0.813 0.419 ================================================================================================= ================================================================================================= MONTHLY STATISTICS MONTHLY STATISTICS Absolute Absolute Absolute Absolute % % Error Observed Simulated % RMS Nash-S. % % Error Observed Simulated % RMS Nash-S. Month Bias Bias (CMS) Qmean Qmean RMS (CMS) R r Month Bias Bias (CMS) Qmean Qmean RMS (CMS) R r ------------------------------------------------------------------------------------------------------------------------ ------------------------------------------------------------------------------------------------------------------------ 1 17.25 49.86 1.164 2.334 2.736 133.9 3.124 0.911 0.802 1 17.25 49.86 1.164 2.334 2.736 133.9 3.124 0.911 0.802 2 7.216 46.73 1.075 2.301 2.467 120.2 2.767 0.805 0.643 2 7.216 46.73 1.075 2.301 2.467 120.2 2.767 0.805 0.643 3 13.80 46.69 1.286 2.754 3.134 150.9 4.156 0.848 0.508 3 13.80 46.69 1.286 2.754 3.134 150.9 4.156 0.848 0.508 4 2.671 37.74 0.917 2.430 2.495 171.5 4.167 0.954 0.768 4 2.671 37.74 0.917 2.430 2.495 171.5 4.167 0.954 0.768 5 15.24 53.12 1.603 3.018 3.478 354.8 10.71 0.817 0.0504 5 15.24 53.12 1.603 3.018 3.478 354.8 10.71 0.817 0.0504 6 6.962 90.02 7.197 7.995 8.551 768.8 61.47 0.592 -0.365 6 6.962 90.02 7.197 7.995 8.551 768.8 61.47 0.592 -0.365 7 26.90 59.05 1.312 2.222 2.820 131.9 2.932 0.902 0.700 7 26.90 59.05 1.312 2.222 2.820 131.9 2.932 0.902 0.700 8 18.72 58.90 1.369 2.324 2.759 189.4 4.401 0.837 0.378 8 18.72 58.90 1.369 2.324 2.759 189.4 4.401 0.837 0.378 9 12.22 61.42 3.057 4.978 5.587 188.2 9.369 0.831 0.679 9 12.22 61.42 3.057 4.978 5.587 188.2 9.369 0.831 0.679 10 7.630 51.78 2.901 5.603 6.030 334.8 18.76 0.775 0.305 10 7.630 51.78 2.901 5.603 6.030 334.8 18.76 0.775 0.305 11 21.31 53.25 2.620 4.920 5.969 308.5 15.18 0.842 0.427 11 21.31 53.25 2.620 4.920 5.969 308.5 15.18 0.842 0.427 12 22.54 55.89 1.704 3.049 3.736 151.7 4.627 0.832 0.597 12 22.54 55.89 1.704 3.049 3.736 151.7 4.627 0.832 0.597 Tropical Storm Allison (June, 2001) R = 0.674 Years: 1997 - 2003 Years: 1997 – 2000, 2002 - 2003 R = 0.773 – 0.840 R = 0.608 Months: January – May, July - December R = 0.775 – 0.954 R = 0.592

53. WGRFC Observation Summary Geographic/ Topographic areas Geographic/ Topographic areas  Model performance with various watershed characteristics (slope, soils, vegetation, etc.) Experience… what works… what doesn’t Experience… what works… what doesn’t Modeling/ Calibration limitations Modeling/ Calibration limitations  Detention ponds  Unknown sources of inflow

54. WGRFC http://www.srh.noaa.gov/wgrfc Questions? Contacts for WGRFC Contacts for WGRFC  Bob Corby Robert.Corby@noaa.gov Robert.Corby@noaa.gov  Paul McKeePaul.Mckee@noaa.gov Paul.Mckee@noaa.gov  Mike ShultzMike.Shultz@noaa.gov Mike.Shultz@noaa.gov

55. WGRFC http://www.srh.noaa.gov/wgrfc Extra slides

56. WGRFC http://www.srh.noaa.gov/wgrfc Model Comparison Summary Lumped Model Lumped Model  Uses 6-hour time step  MAP computed; assumes uniform rainfall across the basin  Runoff applied to a unit hydrograph for the basin  Uses single SAC-SMA parameter across entire basins  Peak flow can be missed at basins that crest in less than 6 hours Distributed Model Distributed Model  Uses 1-hour time step  Uses 4km x 4km grids  Uses gridded QPE  SAC-SMA parameters estimated (i.e. soil type, vegetation type, land use, slope, etc.) for each grid cell  Hydrologic simulations computed using the kinematic wave technique

57. WGRFC http://www.srh.noaa.gov/wgrfc Understanding sources of error Gridded data sets Gridded data sets QPE spatial and magnitude errors QPE spatial and magnitude errors neighboring grids SAC paramX2X QPE location ZerrZ QPE amount 2A Relative differences b/t grids QPE mis-located where SAC param is half size QPE over-est by double error source

58. WGRFC http://www.srh.noaa.gov/wgrfc Factors that Affect DHM Simulations  Quality of calibration  QPE errors… location and amounts  Precipitation type (ie. no SNOW model)  Reservoirs/retention ponds  Method for mainstem river routing; currently no different from lumped  Rating Curve accuracy

59. WGRFC http://www.srh.noaa.gov/wgrfc LocationRiver BasinDrainage Area (sq mi) Time to Peak (hrs) Percent Bias Residual Mean Square (rms) Error Correlation Coefficient (GNVT2) Cowleech Fork 77.7 16 -27.17 9.08 0.66 Quinlan (QLAT2) South Fork 78.7 12 -24.76 13.59 0.64 Justin (DCJT2)Denton Creek 400.0 6 50.89 12.81 0.58 (MCKT2) East Fork 164.0 14 -17.71 8.79 0.81 (BVWT2)Sister Grove Creek 83.1 12 -52.36 4.86 0.60 Justiceburg (JTBT2) 1466.0 9 27.54 10.04 0.71 Pidcoke (PICT2)Cowhouse Creek 455.0 6 14.28 16.91 0.59 Lyons (LYNT2)Davidson Creek 195.0 18 23.44 7.77 0.79 (GETT2) 133.0 10 -23.38 10.68 0.68 (MDST2)Bedias Creek 321.0 21 -2.83 23.79 0.71 Splendora (SDAT2)Caney Creek 105.0 -1.96 4.48 0.83 Sour (SOLT2)Bayou 336.042 19.07 12.77 0.86 HILL COUNTRY Hunt (HNTT2) 288.0 3 15.62 15.56 0.66 Kingsland (KNLT2) 346.0 7 8.06 14.61 0.71 Laguna (UVAT2) 737.0<6 15.05 28.50 0.57 Bandera (BDAT2) 427.0<6 22.84 32.62 0.69 Fredericksburg (FRBT2) 369.0<6 -8.72 8.90 0.86 Midfield (MTPT2) 145.0 17 9.49 8.46 0.87 Refugio (REFT2) 690.0 39 61.74 15.90 0.79 Skidmore (SKMT2) 247.0 12 4.36 22.01 0.83 Schroeder (SCDT2)Coleto Creek 357.0 12 15.21 7.16 0.91 Sublime (SBMT2) 331.0 30 -2.17 20.97 0.75 URBAN AREAS Austin (ATIT2)Onion Creek321.0 9 14.47 32.39 0.51 Houston (HBMT2)Brays Bayou94.9 3 -30.03 13.32 0.88 Houston (GBHT2)Greens Bayou68.7 5 13.48 19.21 0.67