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Department of Civil and Environmental Engineering

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Presentation on theme: "Department of Civil and Environmental Engineering"— Presentation transcript:

1 Department of Civil and Environmental Engineering
A multi-model hydrologic ensemble for seasonal streamflow forecasting in the western U.S. Ted Bohn, Andy Wood, Ali Akanda and Dennis P. Lettenmaier Department of Civil and Environmental Engineering for HEPEX Workshop Foothills Lab, NCAR, Boulder, Colorado July 19-22, 2005

2 OUTLINE experimental western U.S. forecasting system
expansion to multiple hydrologic model framework research issues

3 Experimental W. US Hydrologic Forecast System
Objective: To create a model-based testbed for evaluating potential sources of improvement in seasonal forecasts since inception of regression/ESP methods operational seasonal climate forecasts (model-based and otherwise) greater real-time availability of station data computing advances new satellite-based products (primarily snow cover) distributed, physical hydrologic modeling for macroscale regions

4 Experimental W. US Hydrologic Forecast System
NCDC met. station obs. up to 2-4 months from current local scale (1/8 degree) weather inputs soil moisture snowpack Hydrologic model spin up SNOTEL Update streamflow, soil moisture, snow water equivalent, runoff 25th Day, Month 0 1-2 years back LDAS/other real-time met. forcings for spin-up gap Hydrologic forecast simulation Month INITIAL STATE SNOTEL / MODIS* Update ensemble forecasts ESP traces (40) CPC-based outlook (13) NCEP CFS ensemble (N) NSIPP-1 ensemble (9) * experimental, not yet in real-time product

5 Experimental W. US Hydrologic Forecast System
Snowpack Initial Condition Soil Moisture Initial Condition

6 monthly hydrographs targeted statistics e.g., runoff volumes spatial forecast maps

7 Seasonal Hydrologic Forecast Uncertainty in Western US
Importance of uncertainty in ICs vs. climate vary with lead time … IC error low climate fcst error high IC error high climate fcst error low Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Uncertainty Forecast actual perfect data, model streamflow volume forecast period model + data uncertainty low high ESP addresses climate uncertainty, but the single model/calibration framework doesn’t address IC uncertainty -- ignoring calibration issues at moment – assuming reasonably well calibrated models can be further adjusted via bias-correction -- don’t forget issues to do w/ estimating inputs as an ensemble … hence importance of model & data errors also vary with lead time.

8 Expansion to multiple-model framework
Seasonal Climate Forecast Data Sources CCA NOAA CAS OCN CPC Official Outlooks SMLR CA Coupled Forecast System VIC Hydrology Model NASA NSIPP/GMAO dynamical model ESP ENSO UW ENSO/PDO

9 Expansion to multiple-model framework
Multiple Hydrologic Models CCA NOAA CAS OCN CPC Official Outlooks Model 1 SMLR CA Coupled Forecast System Model 2 NASA NSIPP/GMAO dynamical model Model 3 weightings calibrated via retrospective analysis (regression, bayesian, …) ESP ENSO UW ENSO/PDO

10 SIMMA: “Standard Interface Multi-Model Array”
(the intended design) Wrapper Script Set up simulation parameters Translate forcings (if necessary) Run model Translate output Forcings (ALMA, NetCDF) PET ... SAC NOAH VIC Model 1 Wrapper Model 2 Wrapper Model N Wrapper Post-Processing (ALMA, NetCDF) Compare Combine Route

11 SIMMA Features Common forcings (ALMA, NetCDF)
Common output format (ALMA, NetCDF) Easy to compare results Wrapper Scripts Handle translation Handle model-specific processing Minimal changes to model code Easy to update a model Modular Easy to add a new model Re-use code

12 ALMA Standards - Forcings
Name Units Description SWdown W/m2 Surface incident shortwave radiation LWdown W/m2 Surface incident longwave radiation Tair K Near surface air temperature Qair kg/kg Near surface specific humidity PSurf Pa Surface air pressure Rainf kg/m2 Rainfall rate Snowf kg/m2 Snowfall rate Wind m/s Near surface wind speed

13 ALMA Standards - Outputs
Energy Balance Terms SWnet W/m^2 Net shortwave radiation LWnet W/m^2 Net longwave radiation Qle W/m^2 Latent heat flux Qh W/m^2 Sensible heat flux Qg W/m^2 Ground heat flux Qf W/m^2 Energy of fusion Qv W/m^2 Energy of sublimation Qa W/m^2 Advective energy DelSurfheat J/m^2 Change in surface heat storage DelColdCont J/m^2 Change in snow cold content Water Balance Terms Snowf kg/m^2s Snowfall rate Rainf kg/m^2s Rainfall rate Evap kg/m^2s Total Evapotranspiration Qs kg/m^2s Surface runoff Qsb kg/m^2s Subsurface runoff Qsm kg/m^2s Snowmelt DelSoilMoist kg/m^2 Change in soil moisture DelSWE kg/m^2 Change in snow water equivalent DelSurfStor kg/m^2 Change in Surface Water Storage DelIntercept kg/m^2 Change in interception storage Surface State Variables SnowT K Snow Surface Temperature VegT K Vegetation Canopy Temperature BaresoilT K Temperature of bare soil AvgSurfT K Average surface temperature RadT K Surface Radiative Temperature Albedo unitless Surface Albedo SWE kg/m^2 Snow Water Equivalent SWEVeg kg/m^2 SWE intercepted by vegetation SurfStor kg/m^2 Surface Water Storage Subsurface State Variables HLayerDepth m Hydrological Soil Layer Depth SoilMoist kg/m^2 Average layer soil moisture SoilTemp K Average layer soil temperature SMLiqFrac unitless Average layer fraction of liquid moisture SMFrozFrac unitless Average layer fraction of frozen moisture SoilWet unitless Total soil wetness Evaporation Variables PotEvap kg/m^2s Potential Evapotranspiration ECanop kg/m^2s Interception evaporation TVeg kg/m^2s Vegetation transpiration ESoil kg/m^2s Bare soil evaporation EWater kg/m^2s Open water evaporation RootMoist kg/m^2 Root zone soil moisture CanopInt kg/m^2 Total canopy water storage SubSnow kg/m^2s Snow sublimation SubSurf kg/m^2s Sublimation of the snow free area ACond m/s Aerodynamic conductance Cold Season Process Variables SnowFrac unitless Snow covered fraction IceFrac unitless Ice-covered fraction IceT m Sea-ice thickness Fdepth m Frozen soil depth Tdepth m Depth to soil thaw SAlbedo unitless Snow albedo SnowTProf K Temperature profile in the snow SnowDepth m Depth of snow layer

14 UW Multi-model Results
Arctic Basin application linearly combined CHASM, ECMWF, NOAH, and VIC based on snow cover simulation performance combination reduced annual runoff error West-wide forecast application our forecast-related multi-model results to date + gets you

15 SIMMA implementation “hardships”
Model Versions and Parameters: major experiment (PILPS, NLDAS) versions are a specialized branch off the main development tree. Reconciling desired combination of parameters, I/O, and model physics takes substantial effort. PILPS-2e version of NOAH used NetCDF I/O, which we adopted. NLDAS NOAH had desired CONUS domain, but we want physics from v No standard format for parameters: - Different versions of the parameters (for the same model) had different formats (sequential binary, arcinfo, xmrg, grib, etc). - Different versions sometimes were on different grids, or even different individual parameters within the same set were on different grids. Different model versions (for same model) use different parameters: - Some versions used different soil/veg classification schemes than others, requiring mapping them to the desired scheme. e.g. NLDAS parameters for an older version of NOAH than the desired one (2.7.1). We want parameters from NWS OHD for CONUS domain, not sure what version Some models (e.g. SAC) use conceptual parameters that lack an obvious relation to standard soil/veg types that are easily available. Efforts to standardize formats & datasets (forcing/output/parameter), and/or provide conversion tools to/from standards, will be useful.

16 UW forecast/nowcast application of SIMMA
Test Case Salmon R. at Whitebird, ID Future Applications Westwide forecast domain CONUS nowcast Starting Point NLDAS-grid implementations of VIC/NOAH/SAC

17 UW Real-time Daily Nowcast

18 Ted Bohn, tbohn@hydro.washington.edu
Research Issues Implementation Hurdles: grid specs, parameter inputs, model physics, I/O formats, etc. Combination methods in ungaged areas? (e.g., US nowcast) What are OBS? What if combinations that produce best streamflow do not also produce best SWE or soil moisture? Is use as diagnostic physical tool compromised? For more information: Ted Bohn,

19 END

20 NEW

21 Expansion to multiple-model framework
Our current research with multi-model simulations is promising: 4 land surface models used to simulate arctic basin hydrology, 100 km resolution following linear combination approach of Krishnamurti et al., (2000) weighting calibration based on simulation of snow-covered area results for streamflow and other hydrologic variables evaluated multi-model errors are lower than single model errors, in most cases (work by Ted Bohn at U. of Washington)

22 Expansion to multiple-model framework
annual discharge predictions

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