1 Mike Smith, Victor Koren, Ziya Zhang, Brian Cosgrove, Zhengtao Cui, Naoki Mizukami OHD/HL Hydrologic Science and Modeling Branch Introduction Lecture 1 DHM/HL-RDHM Training MARFC July 21-24, 2009

2 Overview Expectations Strategy for use Results of DMIP 2 Overview of HL-RDHM capabilities

3 Goals and Expectations Potential –History Lumped modeling took years to enter operations and is a good example of implementation time 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 predictions, e.g., DHM-TF Frozen ground New evapotranspiration component to SAC-SMA

4 Strategy: Use Use with, not instead of, lumped model at same time step 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: AWIPS operation for headwaters, locals –HL-RDHM: Large area, soil moisture, SNOW-17, GFFG, DHM-TF, etc Feedback to OHD

5 Distributed Model Intercomparison Project (DMIP) Nevada California Texas Oklahoma Arkansas Missouri Kansas Elk River Illinois River Blue River American River Carson River Additional Tests in DMIP 1 Basins 1.Routing 2.Soil Moisture 3.Lumped vs. Distributed 4.Prediction mode Tests with Complex Hydrology 1.Snow, Rain/snow events 2.Soil Moisture 3.Lumped vs. Distributed Phase 2 Scope

6 Overall Results: Rmod, calibrated models, all periods ARS AZ1 AZ2 CEM DH1 DH2 EMC ILL LMP NEB OHD UAE UOK WHU ICL UAE UCI Median uncalb Median Calb VUB Results of DMIP 2: Oklahoma Basins

7 North Fork American River

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10 Calibrated and Uncalibrated Dist. Model Simulations of SWE Ebbets Pass Blake Sprat Creek Poison Flats Observed Calibrated Uncalibrated East Fork Carson River OHD Calibrated and Uncalibrated Simulations of SWE

11 North fork, calibrated, North Fork American River Modified R vs %Bias Results of Sierra Nevada Experiments Calb. and Ver. Periods Calibrated Models

12 DHM/HL-RDHM Workshop A.DHM and HL-RDHM Overview B.HL-RDHM and CHPS/FEWS C.Capabilities 1.SAC-SMA, SAC-HT, SAC-HT with new ET (in progress) 2.Snow-17 3.DHM-TF 4.Hillslope and channel routing 5.Manual and auto calibration Overview of Capabilities

13 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 AWIPS DHM Mods Auto Calb & ICP DHM-TF Forecasting Calibration/Forecasting (Available on AWIPS LAD)

14 Distributed Modeling and CHPS/FEWS CHPS BOC 1 will not have distributed modeling OHD will migrate HL-RDHM components to CHPS/FEWS Official OHD distributed model in CHPS/FEWS to come later

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

16 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 (Victor Koren) In each grid and in each time step, transform conceptual soil water content to physically-based water content SAC-HT Soil moisture products Soil temperature products

17 1. SAC-HT Background Originally developed for the Noah Land Surface Model 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.

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

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

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

21 Soil moisture for top 10 cm layer April 06, z Local Soil Texture 4km Gridded Soil Moisture Distributed Modeling for New Products and Services Time Local Soil Moisture “Long-term soil moisture forecasts, when used to manage livestock and forage production, can increase ranch profits by as much as 40% ($1.05/acre)”

22 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 –Updated for dry area effects (Victor) –New ZPERC values 3. Based on SSURGO + variable CN –Parameters for CONUS –Updated for dry area effects (Victor) Objective estimation procedure: produce spatially consistent and physically realistic parameter values

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

24 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 adjusted by using DEM and regional temperature lapse rate The areal depletion curve is modified depending on the topography of the basins. Other parameters are either replaced by physical properties or related to physical properties Melt factors can be related to topographic properties: slope & aspect … HL-RDHM and Distributed Snow-17

25 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.

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

27 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

28 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 USGS recently removed its measurements from the web pages; must specifically request them.