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Multi-scale water cycle predictions using the community WRF-Hydro
modeling system
May 2, 2017
D. Gochis, W. Yu, A. Dugger, J. McCreight, K. Sampson, D. Yates,A. RafieeiNasab, L. Karsten, L. Read, L. Pan, Y. Zhang
National Center for Atmospheric Research
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Purpose
Purpose: Provide a update of multi-scale water cycle modeling capabilities using the community WRF-Hydro system and description of recent prediction applications
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Scientific Imperative for WRF-Hydro:
Addressing water cycle prediction questions:1. How do horizontal routing processes impact the partitioning of water and energy
at the land-atmosphere interface?
2. How does organization of fine-scale heterogeneity impact boundary layer exchange and atmospheric circulation features?
3. How will eco-hydrologic processes evolve under various disturbance mechanisms such as landscape and climatic change?
4. What is the ‘coupled-system’ predictability of extreme hydrological events and how does physical process representation impact the lead-time dependence of forecast skill?
5. NOAA-National Water Model: Providing a framework for ‘total water prediction’
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Water Cycle Modeling and Prediction within the WRF-Hydro System:
Great Colorado Flood of 11-15 Sept. 2013
Accumulated Precipitation (shaded colors)100m gridded streamflow (points)
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WRF-Hydro within an Operational Forecasting Workflow:
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1. Forecasts of water flow everywhere all the time
Moving Beyond Point Flow Forecasts
Current efforts are demonstrating the feasibility of Operational Quantitative StreamflowForecasting (QSF):
– NSSL-FLASH, WRF-Hydro, LISFLOOD (UK), RAPID
– Spatial resolutions > 100m better
– Allows cycling from QPE and forecasting from QPN/QPF
– Emphasis on 0-6 hr gap
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1. Forecasts of water flow everywhere all the time: The NOAA National Water Model
Snow Water Equivalent (SNEQV): March 1, 2015 Total Column % Saturation (“SOILSAT”): Sept. 13, 2013
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1. Forecasts of water flow everywhere all the time: The NOAA National Water Model
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2. Coupled system flux predictions:
• Variability in surface fluxes are strongly coupled to convective initiation and cloud formation. Complex, non-linear feedback require coupled system resporesentation
FRNG_1km_cloudwater_tskin_NARR_7_18_2004_1800z FRNG_1km_cloudwater_tskin_WRF-Hydro_rtg_7_18_2004_1800z
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3. Moving beyond ‘natural flows’ towards explicit accounting of infrastructure
Design storm streamflow capture by Barker Reservoir and Gross Reservoirs. Colorado Front Range
Including the control effects of, and impacts on infrastructure:
– Dams and reservoirs (passive and actively managed)
– Overbank storage and attenuation
– Diversion structures, headgates
– Levees, dikes
– Failures of infrastructure (exceeding design capacity)
* Needs Infrastructure & Operations
Data Standards
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4. Probabilistic Framework for Meaningful Risk Forecasting
HMT SE Flooding July 28, 2013
Quantify analysis and forecast uncertainty to provide meaningful risk guidance
Provide forecasters and decision makers with probabilities of:
• Locations and time of rapid river stage increase
• Duration of high waters and inundation
Requires maximizing the utility of High Performance Computing (HPC)
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5. Hydro-system Dynamics
Improving representation of landscape dynamics essential to flood risks:• Geomorphological:
– Bank stability– Sediment transport/deposition– Debris flows
• Land cover change due fire, urbanization, ag/silvaculture
* Needs improved channel, soils andland cover geospatial data
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Overarching WRF-Hydro System Objectives
A community-based, supported coupling architecture designed to provide:
1. An extensible multi-scale & multi-physics land-atmosphere modeling capability for conservative, coupled and uncoupled assimilation & prediction of major water cycle components such as precipitation, soil moisture, snowpack, groundwater, streamflow, inundation
2. ‘Accurate’ and ‘reliable’ streamflow prediction across scales (from 0-order headwater catchments to continental river basins & minutes to seasons)
3. A robust framework for land-atmosphere coupling studies
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WRF-Hydro Operates in 2 Major Modes: Coupled or Uncoupled to an Atmospheric Model
• Uncoupled mode critical for spinup, data assimilation and model calibration
• Coupled mode critical for land-atmosphere coupling research and long-term predictions
• Model forcing and feedback components mediated by WRF-Hydro:
• Forcings: T, Press, Precip., wind, radiation, humidity, BGC-scalars
• Feedbacks: Sensible, latent, momentum, radiation, BGC-scalars
One-way (‘uncoupled’) →
Two-way (‘coupled’) ↔
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Version 4.0 physics components:
• physics-based runoff processes
Overland Flow -Diffusive waveKinematic*Catchment aggregation*
Groundwater Flow –Boussinesq flowCatchment aggregation*
Channel Flow –Diffusive waveKinematic*Reach-based Muskingam*
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WRF-Hydro v4.0 Physics Components:
• Optional conceptual ‘catchment’ modeling support:– Benchmarking simple versus complex model structures
– Enable very rapid ‘first-guess’ forecasts with reduced runtime/computational demand
– Bucket discharge gets distributed to channel network channel routing (e.g. NWM & RAPID coupling)
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DA with WRF Hydro
Current capabilities• Ensemble DA:
• Offline WRF Hydro + DART =“HydroDART”
• Ensemble generation: • Initial state & parameter perturbation,
ensemble runs
Future capabilities• Variational DA and/or nudging:
• Faster & computationally cheaper for large-scale applications.
• Variational DA not rank-deficient• Other kinds of DA (hybrid, MLEF, …)• Bias-aware filtering / Two-stage bias estimation
(Friedland, 1969; Dee and de Silva, 1998; De Lannoy et al., 2007)
Open loop
DA
0.0
2.5
5.0
7.5
0.0
0.5
1.0
May 15 Jun 01 Jun 15 Jul 01 Jul 15 Aug 012012
Str
ea
mflow
(cub
ic m
ete
rs p
er
se
co
nd
)
Obs 95% uncertOpen loopOpen loop meanEns. meanEns. spread
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HydroDART Overview
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‘WRF-Hydro’ Process Permutations and System Features:
• ~180 possible ‘physics’ component configurations for streamflow prediction: – 3 up-to-date column physics land models (Noah,
NoahMP, CLM)– 3 overland flow schemes (Diffusive Wave,
Kinematic Wave, Direct basin aggregation)– 4 lateral/baseflow groundwater schemes
(Boussinesq shallow-saturated flow, 2d aquifer model, Direct Aggregation Storage-Release: pass-through or exponential model
– 5 channel flow schemes: Diffusive wave, Kinematic Wave, RAPID-Muskingam, Custom Network Muskingam/Muskingam Cunge
• Simple level-pool reservoir with management• Data Assimilation:
– DART, filter-based hydrologic data assimilation– Nudging-based streamflow
Ensemble Flood Forecasting in the Southeast U.S. with WRF-Hydro2014 WRF User’s Workshop, K. Mahoney (NOAA-ESRL)
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WRF-Hydro System-Level Coupling Capabilities
Completed:• Stand-alone, ‘Un-coupled’ (1-d Noah & NoahMP land model driver)• Coupled with the Weather Research and Forecasting Model WRF-ARW)• Coupled with LIS (WRF-Hydro v1.0, LISv6.1)• Coupled into DART…
In Progress:• NOAA/NEMS (NOAA Environmental Modeling System-Cecilia DeLuca) • Update of LIS coupling to LIS v7/WRF-Hydro v2.1• Coupled with CLM under CESM coupler (working on recent release of CLM
in WRF)
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‘WRF-Hydro’ Software Features:
• Modularized F90/95 (and later) • Coupling options are specified at compilation and WRF-Hydro is
compiled as a new library in WRF when run in coupled mode• Physics options are switch-activated though a
namelist/configuration file• Options to output sub-grid state and flux fields to standards-based
netcdf point and grid files• Fully-parallelized to HPC systems (e.g. NCAR supercomputer) and
‘good’ scaling performance• Ported to Intel, IBM and MacOS systems and a variety of compilers
(pg, gfort, ifort)
Wei Yu (RAL) – lead engineer
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The WRF-Hydro Workflow
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WRF-Hydro Implementation Workflow:
Collect geospatial terrain and
hydrographic data
Prepare:Land model grids (WPS)Routing Grids/Networks
(ArcGIS)
Conduct uncoupled model runs-physics selection
-calibration-assimilation &/or spinup
Execute uncoupled forecast cycles:
Nowcasts, NWP QPF
Execute coupled-model forecast cycles
Create output forecast & evaluation products
Collect & Prepare Meteorological
Forcings:(uncoupled runs)
Prepare Atmospheric Model:
(coupled runs)
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Model System and Components:
• GIS Pre-processor – Physiographic data processing
• Meteorological Forcing Engine (MFE) – Met. Pre-processing
• Core WRF-Hydro Model – Model physics
• Hydro-DART – Data assimilation
• Rwrfhydro – Analysis, verification, visualization
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WRF-Hydro Setup and Parameterization:Python Pre-Processing Toolkit: K. Sampson - developer
• Python-based scripts
• ESRI ArcGIS geospatial processing functions
– Support of multiple terrain datasets
• NHDPlus, Hydrosheds, EuroDEM
Outputs: topography, flowdirection, watersheds, gridded channels, river reaches, lakes, various parameters
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Meteorological Forcing Engine:• NLDAS, NARR analyses• QPE products: MPE, StgIV,
NCDC-served, dual-pol, Q3/MRMS, gauge analyses, CMOPRH, TRMM, GPM
• NOAA QPF products: GFS, NAM, RAP, HRRR, ExREF
• Nowcast (NCAR Trident/TITAN)
• NOHRSC SNODAS
• ESMF/ncl regridding tools
Boulder
Ft. CarsonAurora
Long’s Peak (~14,200’)
Pikes Peak (~14,300’)
Regridded MPE precipitation during the 2013 Colorado FloodsUnidata IDV display
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NWM: Meteorological Forcing Engine (MFE): Examples
Blended MRMS-HRRR Precipitation
Seasonally-varying MRMS RQI
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Visual forecast products…Web map service interfaces: GoogleMaps/Earth , ESRI ArcGIS, OpenLayers
GoogleEarth, GoogleMaps. ArcGISWMS display
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Rwrfhydro Evaluation, Verification and Visualization Tools
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• Set of R tools to support WRF-Hydro pre- and post-processing
• Open source, community tool
• Full documentation and training vignettes
• Major Features:
– Domain visualization
– Remote sensing & geospatial data prep
– Output post-processing
– Observation data acquisition and processing
– Model output evaluation and visualization
– Generally model agnostic
• Developed in parallel with NWM v1.0
https://github.com/mccreigh/rwrfhydro
Rwrfhydro: R package for Hydrological Model Evaluation
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Rwrfhydro: R package for WRF-Hydro Model Evaluationhttps://github.com/mccreigh/rwrfhydro
Dataset Data type/format rwrfhydro functionality
Climate
GHCN point obs download, format/process, statistics
SNOTEL point obs download, format/process, statistics
Snow
SNOTEL point obs download, format/process, statistics
SNODAS raster download, regrid, statistics
MODIS SCA raster download, mosaic/resample, statistics
Soil Moisture
SCAN point obs download, format/process
NASMD point obs download, format/process (in process)
Energy
Ameriflux point obs download, format/process, statistics
ARM point obs download, format/process (in process)
MODIS ET, TRAD, ALBEDO raster download, mosaic/resample, statistics
Streamflow
USGS point obs download, format/process, DA prep, statistics
CO DWR point obs download, format/process, statistics
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Rwrfhydro Model Calibration Toolkit
Developed by A. Dugger with contributions from Logan Karsten and A. RafieeiNasab
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• Domain subsetting tools
• Parameter Sensitivity
Analysis
• Calibration:
• ‘Supervised’ Dynamically Dimension Search
(DDS) method (Tolson, B. A., and C. A.
Shoemaker: 2007)…..Implementation of
Shuffled Complex Evolution (in progress)
• Split sample calibration/validation
• Multiple criteria monitoring (NSE, RMSE, %
bias, correlation, KGE)
• Automated Rwrfhydro-NWM workflow
• Re-validation of full domain results
Rwrfhydro Model Calibration Toolkit
NWM v1.0 (5-yr NLDAS Percent Bias)
Developed by A. Dugger with contributions from Logan Karsten and A. RafieeiNasab
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WRF-Hydro Support Services
• Web Page:– Code distribution (GIT
repository)– Documentation (v2, 120 pages)– Test cases (coupled and
uncoupled)– Script Library (file prep,
reformatting, viz)– ArcGIS preparation tools– Email help support (staff limited)
http://www.ral.ucar.edu/projects/wrf_hydro/
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WRF-Hydro Support Services
• Training classes:– Semi-annual WRF tutorial
training sessions (short 1-hr system overviews)
– University hosted visits (~1-2/yron the order of 1-3 days)
– International training seminars and colloquia (~1-2/yr, on the order of 1-3 days)
http://www.ral.ucar.edu/projects/wrf_hydro/
1st European Fully Coupled Atmospheric-Hydrological Modeling and WRF-Hydro Users workshop, U. of Calabria, Italy, June 2014
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Current WRF-Hydro Applications around the world:1. Operational Streamflow Forecasting:
– U.S. National Weather Service, National Water Center– Israeli Hydrological Service– State of Colorado-Upper Rio Grande River Basin (CWCB, NSSL)– NCAR-STEP Hydrometeorological Prediction Group– U. of Calabria reservoir inflow forecasting
2. Streamflow prediction research (U. Ankara, Arizona State U., Karlsruhe Inst. Tech.)3. Diagnosing climate change impacts on water resources
– Himalayan Mountain Front (Bierknes Inst.)– Colorado Headwaters (U. Colorado)– Bureau of Reclamation Dam Safety Group (USBR,NOAA/CIRES)
4. Diagnosing land-atmosphere coupling behavior in mountain-front regions of the U.S. and Mexico (Arizona State U., U. Arizona)
5. Diagnosing the impacts of disturbed landscapes on coupled hydrometeorlogical predictions– Western U.S. Fires (USGS)– West African Monsoon (Karlsruhe Inst. Tech)– S. America Paraná river (U. Arizona)– Texas Dust Emissions (Texas A&M U.)– Landslide Hazard Modeling (USGS)
6. Hydrologic Data Assimilation, WRF-Hydro/DART coupling
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Acknowledgements
External Contributors• K. Mahoney (CU-CIRES)
• Brian Cosgrove (NOAA/OHD)
• B. Fersch, T. Rummler (KIT-Germany)
• Alfonso Senatore (U. Calabria-Italy)
• A. Parodi and E. Fiori (CIMA-Italy)
• Amir Givati and Erik Fredj (Israeli Hydr. Service)
• Lu Li (Bierknes Inst.)
• Col. State Univ. CHILL-team
• Logan Karsten (NOHRSC)
• Sujay Kumar, Christa Peters-Lidard (NASA-Goddard)
• Peirong Lin, Z.-Liang Yang (U. Texas-Austin)
• I. Yucel, (U. Ankara-Turkey)
Support provided by:• NSF- NCAR-STEP program, EarthCube, ETBC, WSC• NOAA-OHD• NASA-IDS• CUAHSI• DOE-ESM• USBR WaterSmart & Dam Safety Programs• Colorado Water Conservation Board• Texas Dept. of Environmental Quality & Texas A&M U.
NCAR Development, Evaluation and Advising Team: Wei Yu, David Yates, Kevin Sampson, Aubrey Dugger, James McCreight, Mike Barlage, Yongxin Zhang, Mukul Tewari, Roy Rasmussen, Andy Wood, Fei Chen, Martyn Clark, Matthias Steiner
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End
WRF-Hydro: http://www.ral.ucar.edu/projects/wrf_hydro/