1 / 20

Overview of NWS Distributed Model (HL-RDHM)

Overview of NWS Distributed Model (HL-RDHM). Mike Smith Victor Koren, Seann Reed, Zhengtao Cui, Ziya Zhang, Fekadu Moreda, Yu Zhang. Model Structure. Operational Version. HL-RDHM. P& ET. P, T & ET. Auto Calibration. SNOW -17. rain. rain + melt. SAC-SMA. SAC-SMA, SAC-HT.

september-aguilar
Download Presentation

Overview of NWS Distributed Model (HL-RDHM)

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. Overview of NWS Distributed Model (HL-RDHM) Mike Smith Victor Koren, Seann Reed, Zhengtao Cui, Ziya Zhang, Fekadu Moreda, Yu Zhang

  2. Model Structure Operational Version HL-RDHM P& ET P, T & ET Auto Calibration SNOW -17 rain rain + melt SAC-SMA SAC-SMA, SAC-HT surface runoff surface runoff base flow base flow Hillslope routing Hillslope routing Channel routing Channel routing Mods Flows and state variables Flows and state variables Calibration Forecasting

  3. Sacramento Soil Moisture Accounting Model Courtesy of U. Arizona

  4. UZFWC UZFWC UZTWC UZTWC LZFPC LZFPC LZFSC LZFSC LZTWC LZTWC 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 Physically-based Soil Layers and Soil Moisture Sacramento Model Storages Sacramento Model Storages SMC1 SMC2 SMC3 SMC4 SMC5 Soil moisture Soil temperature

  5. Observed (white) and simulated (red) soil temperature and moisture at 20cm, 40cm, and 80cm depths. Valdai, Russia, 1971-1978 Soil Moisture Soil Temperature

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

  7. 2. SAC-SMA Parameters Objective estimation procedure:produce spatially consistent and physically realistic parameter values 1. Based on STATSGO; CN = f (hydro soil group only) • Assumed “pasture or range land use” under “fair” hydrologic conditions • National coverage • Available now via CAP 2. Based on STATSGO; CN = f(hydro soil group, landuse) • Global Land Cover Characterization (GLCC, 1 km) • National coverage • Being evaluated • Based on SSURGO; CN = f(hydro soil group, landuse) • National Land Cover Database (NLCD, 30 m) • Parameters for 25 states so far • Being evaluated

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

  9. 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=q0Aqm) • 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

  10. 4. Manual and Auto Calibration • Start with lumped 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: Kuzmin, Seo, and Koren, 2007. “Fast and Efficient Optimization of Hydrologic Model Parameters Using a Priori Estimates and Stepwise Line Search” Journal of Hydrology, in review • Objective function: Multi-scale

  11. 42 21 36 48 18 24 32 62 56 32 16 31 28 16 42 30 44 40 21 15 22 20 42 21 44 22 40 20 Calibration Approach Multiply each grid value by the same scalar factor. x 2 = Preserve Spatial Pattern of Parameters Example: I parameter out of N total model parameters th Calibrate distributed model by uniformly adjusting all grid values of each model parameter (i.e., multiply each parameter grid value by the same factor) 1. Manual: manually adjust the scalar factors to get desired hydrograph fit. 2. Auto: use auto - optimization techniques to adjust scalar factors .

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

  13. Multi-Scale Objective Function (MSOF) Emulates multi-scale nature of manual calibration • Minimize errors over hourly, daily, weekly, monthly intervals (k=1,2,3,4…n…userdefined) • q = flow averaged over time interval k • n = number of flow intervals for averaging • mk = 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. =

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

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

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

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

  18. How much do the parameters change? Example for SLOA4

  19. OHD Spatial and Temporal Scales for DMIP2 • “OHD” simulations • HRAP (~ 4 km) pixels for water balance calculations • Pixel main channels subdivided for accurate routing (usually 4 subreaches) • Basic time step: hourly (SAC percolation, hillslope routing, and channel routing functions may automatically shorten the time step under certain conditions) • Climatological PE and PE adjustments used (PE mid-month data interpolated to generate smoothly varying daily inputs) • “OH2” simulations • ½ HRAP pixel simulations for TALO2 and interior points only

  20. References • Koren et al., 2004. “Hydrology Laboratory Research Modeling System (HL-RMS) of the US National Weather Service” Journal of Hydrology, 291, 297-318 • Koren, 2005. “Physically-Based Parameterization of Frozen Ground Effects: Sensitivity to Soil Properties” VIIth IAHS Scientific Assembly, Session 7.2, Brazil, April. • Koren, 2003. Parameterization of Soil Moisture-Heat Transfer Processes for Conceptual Hydrological Models”, paper EAE03-A-06486 HS18-1TU1P-0390, AGU-EGU, Nice, France, April. • Mitchell, K., Koren, others, 2002. “Reducing near-surface cool/moist biases over snowpack and early spring wet soils in NCEP ETA model forecasts via land surface model upgrades”, Paper J1.1, 16th AMS Hydrology Conference, Orlando, Florida, January • Koren et al., 1999. “A parameterization of snowpack and frozen ground intended for NCEP weather and climate models”, J. Geophysical Research, 104, D16, 19,569-19,585. • Koren, et al., 1999. “Validation of a snow-frozen ground parameterization of the ETA model”, 14th Conference on Hydrology, 10-15 January 1999, Dallas, TX, by the AMS, Boston MA, pp. 410-413. • http://www.nws.noaa.gov/oh/hrl/frzgrd/index.html

More Related