Multi-variate Analysis of National Scale Hydrologic Simulations & Predictions: NFIE 2015

Transcription

1 Multi-variate Analysis of National Scale Hydrologic Simulations & Predictions: NFIE 2015 NCAR: D. Gochis, A. Dugger, L. Pan, W. Yu, K. Sampson, J. McCreight, Y. Zhang, D. Yates U. Texas-Austin: M. Somos, F. Salas, D. Maidment U. Arizona: T. Lahmers; U. Iowa: M. ElSaadani Jul. 16, 2015

2 Outline: NFIE Operational Workflow and Model Configuration NFIE Model Evaluation: Verification tools & data Model correlation & bias Precipitation bias Process evaluation Community WRF-Hydro System people

3 Acknowledgements: A big team! U. Alabama and NWC: Local host NSSL: development and provision of real-time radar data ESRL&NCEP: high resolution data assimilation & NWP forecasts Unidata: real-time data service USGS/EPA/Horizon, Inc.: NHDPlus support NSF XSEDE/TACC/Yellowstone supercomputing support for R&D and operational model runs Cedric David: RAPID upgrade and support BYU-Tethys: Operational display CUAHSI/NSF-Hydrology/EarthCube & NOAA/NWC, many local institutions: Sponsorship Community WRF-Hydro System people

4 NFIE-2015 System Configurations Community WRF-Hydro System people

5 WRF-Hydro/RAPID for the Community National Flood Interoperability Experiment (NFIE) Operational MRMS QPE and HRRR QPF Experimental WRF-Hydro 1. Goal: Produce real-time flood information on the NHDPlus river network as inputs for FEMA flood mapping 2. Collaborators: UT-Austin, CUAHSI, NWS-OHD, NCAR, Unidata, ESRI, Microsoft 3. WRF-Hydro and RAPID codes set up on UT-Austin stampede system River flow in the Mississippi River Basin Water Mgt. netcdf to NHD+ 4. Mapping of land model grid runoff to NHDPlus2 catchments defined a priori using ArcGIS tools Data Services 5. Benchmark model runs with 4,3,2 and 1km land model grids all feasible RAPID NHDPlus Channel Flow Source: Modeled and observed streamflow Community WRF-Hydro System people

6 NFIE Forecasting Workflow: 1. Model Upgrade and Implementation: RAPID and WRF-Hydro/NoahMP 2. Land Model Spinup: WRF-Hydro/NoahMP driven by 5 years of regridded NLDAS forcing with GFS model background 3. WRF-Hydro Forcing Data Engine: Acquire real-time feed of NSSL/NCEP MRMS (Unidata LDM) and ESRL-HRRR (ftp) 4. WRF-Hydro Forcing Data Engine: Forcing data regridding using ESMF regridding tools and NCAR/ncl scripts.transfer data to NSF XSEDE/U. Texas TACC ( stampede ) 5. Model Execution: WRF-Hydro/NoahMP on stampede.provide gridded runoff and water balance variables in netcdf format 6. Conversion of netcdf gridded runoff from WRF-Hydro/NoahMP to NHDPlusv2 reach-based channel inflows for RAPID using tailored ArcGIS geo-processing scripts 7. Model Execution: RAPID on stampede provide reach (point) netcdf output of river flow on NHDPlus network. Community WRF-Hydro System people

7 NFIE Forecasting Workflow: (continued ) 7. IRODS data service: Data shipped to RNCI every 3 hrs (forcings, gridded runoff, channel flows) 8. Tethys Web Application: Upload to CKAN server at BYU (visualization and discovery) Community WRF-Hydro System people

8 NFIE WRF-Hydro/NoahMP Set-up: NHDPlusV2-Encompassing Domain 3km NoahMP land model only: No terrain routing (only offline channel routing by RAPID) No reservoirs 2011 NLCD land cover type NRCS STATSGO, 1km soils Climatological vegetation structure Spin-up: 5 year continuous run: Regridded NLDAS2/GFS background Downscaling of T/RH/SW/Press. is now underway for sensitivity testing Community WRF-Hydro System people

9 NFIE Evaluations: 5-yr Benchmark Evaluation Using rwrfhydro 2001 USGS vs NLCD Land cover specification Community WRF-Hydro System people

10 NFIE Default Set-up Computational Requirement: WRF-Hydro/NoahMP Runtime: 128 cores, U. Texas- XSEDE stampede, 3km NoahMP No routing (default NFIE configuration): ~9 min wall clock for 14 hr simulation time RAPID Runtime: 1.5 minutes for one 14 hr forecast cycle on one node (16 cores) Model output is thinned to following variables, with emphasis on water budget: NHPlus Channel Flows from RAPID SW, LW, ground heat flux, sensible heat flux, evaporation, transpiration, soil moisture (4 layers), surface runoff, subsurface runoff, snow depth, snow water equivalent, precipitation, surface skin temperature Community WRF-Hydro System people

11 Verification Tool Description Community WRF-Hydro System people

12 Verification tools and data: rwrfhydro Verification data: Streamflow: USGS Gauges-II dataset Minimally humanimpacted Precipitation: GHCN data SNOTEL Ameriflux Network ET (pending)

13 Model Evaluation & Benchmarking:

14 NFIE-2015 WRF-Hydro/NoahMP & RAPID Model Analysis: MRMS-driven runs

15 Streamflow Correlations at GAGES-II Reference Basins WRF-Hydro/NoahMP WRF-Hydro/NoahMP/RAPID Good correlations in the Pacific NW, Central US Fairly good correlations in Eastern US (Appalachian) Poor correlations in the Rockies, arid Southwest, and Northern Plains Improved performance with RAPID routing in Eastern and Central US

16 Streamflow Correlations at GAGES-II Reference Basins WRF-Hydro/NoahMP WRF-Hydro/NoahMP/RAPID RAPID channel routing shows significant improvements over LSM-only at the hourly timescale

17 Streamflow Bias at GAGES-II Reference Basins WRF-Hydro/NoahMP/RAPID Generally low bias in the West (likely too early snowmelt) High bias in the Southeast and Central US Smaller biases in the Northeast How much is due to precipitation forcing and how much is due to the model?

18 Streamflow Performance at GAGES-II Reference Basins: Successes!

19 Streamflow Performance at GAGES-II Reference Basins: Problem Areas

20 NFIE-2015 WRF-Hydro/NoahMP & RAPID Process Evaluation

21 Process Evaluation: Fixing Snow Biases for Streamflow Forecasting Applications

22 Process Evaluation: Lateral Routing and Reservoir Storage WRF-Hydro v. USGS Streamflow Ocheyedan River Near Spencer, IA m 3 /s USGS Gauge NFIE-Hydro WRF-Hydro Images courtesy of Mohamed ElSaadani & Timothy Lahmers

23 Process Evaluation: Lateral Routing and Reservoir Storage Impact of lateral routing on Fourmile Creek in CO, consistent with Iowa findings Addition of reservoir storage on the Guadalupe River in TX Image courtesy of Fernando Salas & Marcelo Somos

24 Summary In most cases simulated uncalibrated NoahMP runoff is very flashy compared to observed streamflow why? Lack of routing processes Most regions of US show reasonable correlation.problems in southern Rockies, SW and north Plains Most regions have significant biases.why? Precipitation bias propagation? SW (Texas, NM, AZ) lack of channel losses, non-contributing areas? Piedmont, Florida. insufficient infiltration? Intermountain West. early melt-out snow bias (largely fixed)

25 NFIE-2015 WRF-Hydro/NoahMP Preliminary Forcing Data Analysis

26 NFIE Evaluations: Real-time outputs MRMS Precipitation Bias (HRRR evaluation in progress ) MRMS bias as compared to reference precip constructed from StageIV, the CMC/CPC North American analysis, and NLDAS2 (courtesy of David Kitzmiller, NOAA) MRMS bias as compared to GHCN gage data (200 points shown)

27 Summary Things to consider NFIE runs are largely uncalibrated Land model only runs without terrain routing can significantly affect the timescale of runoff responses and hydrograph structure Catchment aggregation and direct translation of soil column water can have significant shortcomings, particularly for slow, subsurface processes Forcing uncertainty in the western U.S. is significant and MRMS while improved likely faces challenges

28 Next Step: National Water Prediction Model Configurations WRF-Hydro IOC Configurations Analysis & Assimilation Short-Range Flood Prediction Medium Range Flow Prediction Long Range Water Resources Cycling Frequency Hourly 3-Hourly Daily ~Daily (x16) Forecast Duration - 3 hrs 0-2 days 0-10 days 0-30 days Spatial Discretization & Routing 1km/250m/NHDPlus Reach Meteorological Forcing MRMS blend/ HRRR-NAM bkgnd. 1km/250m/NHDPlus Reach Downscaled HRRR /RAP/NAM blend 1km/250m/NHDPlus Reach Short-range + Downscaled GFS 1 km/catchment /NHDPlus Reach Downscaled & bias-corrected CFS

29 Thank you. D. Gochis, A. Dugger, WRF-Hydro: Funding for WRF-Hydro provided by: NSF, NOAA-OHD, NASA-IDS, DOE-ESM