1. 1 Mike Smith OHD/HL Hydrologic Science and Modeling Branch Introduction Lecture 1 DHM/HL-RDHM Workshop ABRFC June 7, 2007

2. 2 Overview Introductions Acknowledgements Review of Goals Expectations Strategy

3. 3 Attendees Norm Bingham NERFC Paula Cognitore MARFC Tom Adams OHRFC Jonathon Atwell SERFC Jeff Dobur SERFC Eric Jones LMRFC Katelyn Schnieda LMRFC Paul McKee WGRFC Mike Shultz WGRFC Eugene Derner MBRFC Ed Clark CBRFC Craig Peterson CBRFC Pete Fickenscher CNRFC Kevin Berghoff NWRFC Kevin Werner WR Kris Lander CR Diane Cooper SR Randy Rieman HSD JJ Gourley NSSL Suzanne Van Cooten NSSL Prafulla Pokhrel U. Arizona Michael Thiemann RTi Mike Pierce ABRFC John Schmidt ABRFC Bill Lawrence ABRFC ABRFC others

4. 4 Workshop Objectives To train RFC personnel how to set-up and run the new AWIPS-NWSRFS DHM operation. To train RFC personnel how to use the HL- RDHM and related tools to parameterize and calibrate the DHM. To provide an overview of the vision and plan to use distributed models for RFC and WFO river and water resources forecasting operations. To provide an overview of the science and systems R&D for NWS distributed modeling, obtain feedback, and promote collaborative development. “If you aim at nothing, you are sure to hit it!”

5. 5 Goals and Expectations Potential –History Lumped modeling took years and is a good example We’re second to do operational forecasting with dist. models –Expectations ‘As good or better than lumped’ Limited, but growing experience with calibration May not yet show (statistical) improvement in all cases due to errors and insufficient spatial variability of precipitation and basin features… but is proper future direction! –New capabilities Gridded water balance values and variables e.g., soil moisture Flash Flood e.g., DHM-TF Land Use- Land cover changes

6. 6 (a) Lumped Basin (c) Basin disaggregated Into 16 cells (d) Basin disaggregated into 100 cells (b) Basin disaggregated into 4 cells “Truth Scale” and “Truth Simulation” Expectations: Effect of Data Errors and Modeling Scale

7. 7 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 the benefits of fine scale modeling. In some cases, they may make the results worse than lumped simulations. Simulation error compared to fully distributed ‘Truth’ is simulation from 100 sub- basin model clean data

8. 8 Rationale for Distributed Modeling Scientific motivation –Finer scales > better results –Data availability Field requests NOAA Water Resources Program NIDIS Flash flood improvements Goals and Expectations

9. 9 Applicability Distributed models applicable everywhere Issues –Data availability and quality needed to realize benefits –Parameterization –Calibration –Run-time mods/assimilation Goals and Expectations

10. 10 R&D Implementation Use Distributed Modeling Strategy

11. 11 Strategy for R&D OHD Parameterization: SAC-SMA, Snow-17, routing Calibration: manual, auto, spatially variable Assimilation: streamflow, soil moisture New process models DMIP 1, 2 Data analysis Link to dynamic routing RFCs WFOs Partners DMIP 1, 2 MOPEX Collaborative Univ. Research Partners NOHRSC RTi Prototype testing of models, calibration, new science, etc Components

12. 12 XDMS and other applications AWIPS Oper. Baseline OB7.2/OB8.1 HL-RDHM and tools Display xmrg grids, calibration Calibration of baseline DHM; Generate gridded FFG; Prototype new capabilities OB7.2 → Feb, 2007 OB8.1 → July 2007 DHM IFP D-2D Grids display Display time series OFS Runs DHM ArcView extensions Calibration (CAP) Strategy for Implementation (1) Distributed Model SAC-runoff Kinematic routing No snow DHM Approach for OB7.2/ 8.1

13. 13 DHM Mods DHM IFP D-2D DHM Grid Editor ASM-maintained application, enhanced Field-developed application, enhanced AWIPS Operational Baseline OB8.2 Grids display HSMB prototype, enhanced Display distributed time series Distributed Model SAC-runoff Kinematic routing No snow OB8.2 → Jan 2008 (CAP) DHM Approach for OB8.2 Strategy for Implementation (2) XDMS and other applications HL-RDHM and tools Display xmrg grids, calibration Calibration of baseline DHM; Generate gridded FFG; Prototype new capabilities ArcView extensions Calibration

14. 14 Retire OHD-developed application ASM-maintained application, enhanced Field-developed application, enhanced HSMB prototype, enhanced OB8.3 → June 2008 OB9 → June 2009 (CAP) DHM Approach for OB8.3, OB9 Strategy for Implementation (3) DHM Mods DHM IFP GFE AWIPS Operational Baseline OB8.3 Basic Grid Editor and display (replaces D-2D and DHM-Grid Editor) DHM Grid Editor Distributed Model SAC-runoff Kinematic routing No snow XDMS and other applications HL-RDHM and tools Display xmrg grids, calibration Calibration of baseline DHM; Generate gridded FFG; Prototype new capabilities ArcView extensions Calibration

15. 15 Strategy: Use Use with, not instead of, lumped model at same time step (Example BLUO2) Part of natural progression to finer scales Lumped 6-hr Lumped 1-hour Distributed 1-hour Calibration is good training process for forecasting Current: –DHM: operation in NWS for headwaters, locals –HL-RDHM: Large area, soil moisture, FFG, etc Feedback to OHD

16. 16 DHM/HL-RDHM Workshop A.DHM and HL-RDHM Overview B.Capabilities 1.SAC-SMA and SAC-HT 2.Snow-17 3.Hillslope and channel routing 4.Manual and auto calibration Overview of Capabilities

17. 17 HL-RDHM SAC-SMA, SAC-HT Channel routing SNOW -17 P, T & ET surface runoff rain + melt Flows and state variables base flow Hillslope routing SAC-SMA Channel routing P& ET surface runoff rain Flows and state variables base flow Hillslope routing DHM Mods Auto Calibration DHM-TF ForecastingCalibration

18. 18 1. Sacramento Soil Moisture Accounting Model Source: U. Arizona

19. 19 UZTWC UZFWC LZTWC LZFSC LZFPC UZTWC UZFWC LZTWC LZFSC LZFPC SMC1 SMC3 SMC4 SMC5 SMC2 Sacramento Model Storages Sacramento Model Storages Physically-based Soil Layers and Soil Moisture 1. Modified Sacramento Soil Moisture Accounting Model In each grid and in each time step, transform conceptual soil water content to physically-based water content SAC-HT Soil moisture Soil temperature

20. 20 1. SAC-HT Background Originally developed for the NOAH Land Surface Model –Documented improvements Koren, V., others, 1999. A parameterization of snowpack and frozen ground intended for NCEP weather and climate models. J Geo. Research, Vol. 104, Designed as replacement of the existing conceptual SAC-SMA frozen ground option. –Does not need calibration –Generates soil moisture and temperature versus depth –Can be used with local soil properties to adjust soil moisture to local conditions.

21. 21 Soil temperature Soil moisture Computed and observed soil Moisture and temperature: Valdai, Russia, 1972-1978 Validation of Modified Sacramento Model 1. SAC-HT

22. 22 Validation of Modified Sacramento Model Comparison of observed, non-frozen ground, and frozen ground simulations: Root River, MN Observed Frozen ground Non frozen ground 2. SAC-HT

23. 23 NOAA Water Resources Program: Prototype Products Soil moisture (m 3 /m 3 ) HL-RDHM soil moisture for April 5m 2002 12z 2. SAC-HT

24. 24 Simulated using local soil properties Simulated w/o local soil properties Tailor Soil Moisture Simulations for Local Soil types Technique used in NOAA Water Resources Economics Study 2. SAC-HT

25. 25 HL-RDHM MOSAIC Source: Moreda et al., 2005. Lower 30cmUpper 10cm Comparison of Soil Moisture Estimates HL-RDHM: Higher Correlation 2. SAC-HT

26. 26 ‘We are also interested in the modified SAC model, particularly since we are somewhat “on the hook” to try to develop a soil moisture product (graphic) which conveys the current model states. This has been a recurring request (several years) which we have delayed, but was recently placed on a list in Central Region which specifies that we begin attempts to address this.” -John Halquist Use of SAC-HT for Soil Moisture to Meet RFC Needs

27. 27 SAC-SMA Parameters 1. Based on STATSGO + constant CN –Assumed “pasture or range land use” under “fair” hydrologic conditions –National coverage –Available now via CAP 2. Based on STATSGO + variable CN –National coverage –Being evaluated 3. Based on SSURGO + variable CN –Parameters for 25 states so far –Being evaluated Objective estimation procedure: produce spatially consistent and physically realistic parameter values

28. 28 Demonstration of scale difference between polygons in STATSGO and SSURGO SSURGO STATSGO Soils Data for SAC Parameters

29. 29 Status of SSURGO – Based SAC-SMA Parameter Derivation

30. 30 SSURGO and STATSGO SAC-SMA Parameters UZTWM- SSURGO UZTWM- STATSGO UZFWM-SSURGO UZFWM-STATSGO

31. 31 STATSGO and STATSGO Variable CN SAC-SMA Parameters STATSGO STATSGO Varable CN DIfference

32. 32 STATSGO vs SSURGO Results STATSGO vs SSURGO Results Hydrograph Comparison __ Observed flow __ SSURGO-based __ STATSGO-based TALO2

33. 33 Hydrograph Comparison __ Observed flow __ SSURGO-based __ STATSGO-based CAVESP STATSGO vs SSURGO Results STATSGO vs SSURGO Results

34. 34 2. Distributed SNOW-17 Model SNOW-17 model is run at each pixel (hourly ok) Gridded precipitation from multiple sensor products are provided at each pixel Gridded temperature inputs are provided by using DEM and regional temperature lapse rate The areal depletion curve is removed because of distributed approach Other parameters are either replaced by physical properties or related to physical properties Melt factors can be related to topographic properties: slope & aspect Parameters to be available through CAP … Distributed Snow-17

35. 35 Case Study 1: Juniata River Outlet, Juniata at Newport Saxton, Interior point Williamsburg, Interior point Model resolution 4km x 4km Total number of pixels =497 Watershed area = 8687 km 2 Model parameters = a priori Channel parameters are derived from USGS measurements at Newport. … Distributed Snow-17

36. 36 Flow simulation during snow periods Simulated and observed hydrographs generally show good agreement, with the exception of some events where flows are extremely low/high compared to observed. This may be due to quality of temperature data

37. 37 DEM Aspect Slope Vegetation Type Vegetation Percent Land Use Map MFMIN MFMAX Forest Cover MFMAX MFMIN Coniferous forest /persistent cloud cover 0.5 -0.70.2 - 0.4 Mixed forest Coniferous plus open and/or deciduous 0.8 – 1.20.1-0.3 Predominantly Deciduous1.0-1.40.2- 0.6 Open Areas flat terrain1.5-2.20.2-0.6 Mountainous terrain0.9-1.30.1-0.3 Computation of MFMAX and MFMIN Eric Anderson Rec’s.

38. 38 2. Distributed modeling and snow Parameterization of Distributed Snow-17 Min Melt Factor Max Melt Factor Derived from: 1.Aspect 2.Forest Type 3.Forest Cover, % 4.Anderson’s rec’s.

39. 39 2. Distributed SNOW-17 Completed Activities –Implementation of distributed SNOW-17 for the entire CONUS, proof of concept for computation of snow water equivalent and snow water covers –Use/test of CONUS wide forcings such as archived STAGE II and Stage IV data for 2002 cold season –Use and test of CONUS wide temperature from RUC model –Implement method of deriving gridded temperature for local application on river basin scales. Two methods are used: Disaggregation of MAT to grids by using DEM and basin wide lapse rate. Generating grids from gages within and near the basin –Implemented concepts of removing areal depletion curve and substituting by simple linear curve for a pixel level simulation –Generated a priori estimate of two major parameters MFMAX and MFMIN using properties of watershed

40. 40 Real HRAP Cell Hillslope model Cell-to-cell channel routing 3. Routing Model

41. 41 ABRFC ~33,000 cells MARFC ~14,000 cells OHD delivers baseline HRAP resolution connectivity, channel slope, and hillslope slope grids for each CONUS RFC HRAP cell-to-cell connectivity and slope grids are derived from higher resolution DEM data. HRAP Cell-to-cell Connectivity Examples

42. 42 3. Channel Routing Model Uses implicit finite difference solution technique Solution requires a unique, single-valued relationship between cross-sectional area (A) and flow (Q) in each grid cell (Q= q 0 A qm ) Distributed values for the parameters q0 and qm in this relationship are derived by using – USGS flow measurement data at selected points – Connectivity/slope data – Geomorphologic relationships Training on techniques to derive spatially distributed parameter grids is provided in this workshop

43. 43 4. Manual and Auto Calibration Adjustment of parameter scalar multipliers Use manual and auto adjustment as a strategy Parameters optimized: –SAC-SMA –Hillslope and channel routing Search algorithms –Simple local search –Next: Rosenbrock, others Objective function: Multi-scale

44. 44 48 56 3262 42 3044 40 44 42 32 36 42 40 24 28 1631 21 1522 20 22 21 16 18 21 20 Multiply each grid value by the samescalar factor. x 2 = Calibrate distributed model byuniformlyadjusting all grid values of each model parameter (i.e., multiply each parameter grid value by the same factor) 1.Manual: manually adjust thescalarfactors to get desired hydrograph fit. 2.Auto: use auto-optimization techniques to adjust scalar factors. Example:I th parameter out of N total model parameters Calibration Approach Preserve Spatial Pattern of Parameters

45. 45 HL-RDHM SAC-SMA, SAC-HT Channel routing SNOW -17 P, T & ET surface runoff rain + melt Flows and state variables base flow Hillslope routing Auto Calibration Execute these components in a loop to find the set of scalar multipliers that minimize the objective function

46. 46 Multi-Scale Objective Function (MSOF) Minimize errors over hourly, daily, weekly, monthly intervals (k=1,2,3,4…n…user defined) q = flow averaged over time interval k n = number of flow intervals for averaging m k = number of ordinates for each interval X = parameter set Weight: -Assumes uncertainty in simulated streamflow is proportional to the variability of the observed flow -Inversely proportional to the errors at the respective scales. Assume errors approximated by std. = Emulates multi-scale nature of manual calibration

47. 47 Average monthly flow Average weekly flow Average daily flow Hourly flow Calibration: MSOF Time Scales Multi-scale objective function represents different frequencies of streamflow and its use partially imitates manual calibration strategy

48. 48 Before autocalibration of a priori parameters After autocalibration Observed Example of HL-RDHM Auto Calibration: ELDO2 for DMIP 2 Arithmetic Scale Auto Calibration: Case 1

49. 49 Example of HL-RDHM Auto Calibration: ELDO2 for DMIP 2 Semi-Log Scale Auto Calibration: Case 1 Before autocalibration of a priori parameters After autocalibration Observed

50. 50 Before autocalibration of a priori parameters After autocalibration Observed Auto Calibration: Case 2 Example of HL-RDHM Auto Calibration: ELDO2 for DMIP 2 Arithmetic Scale