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

air planet

people

Hydrologic Prediction with the Community WRF-Hydro System

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

air planet people Hydrologic Prediction with the Community WRF-Hydro System

Outline:

NFIE Operational Workflow and Model Configuration

NFIE Model Evaluation:

Verification tools & data

Model correlation & bias

Precipitation bias

Process evaluation


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


NFIE-2015 System Configurations


WRF-Hydro/RAPID for the Community National Flood Interoperability Experiment (NFIE)

Experimental WRF-Hydro

Source: http://rapid-hub.org/

River flow in the Mississippi River Basin

Data Services

netCDF to

NHD+

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

4. Mapping of land model grid runoff to NHDPlus2 catchments defined a priori using ArcGIS tools

5. Benchmark model runs with 4,3,2 and 1km land model grids all feasible

Operational MRMS QPE and HRRR QPF

RAPID NHDPlus Channel Flow

Water Mgt.

Modeled and observed streamflow


NFIE Forecasting Workflow:

5. Model Execution: WRF-Hydro/NoahMP on stampede.provide gridded runoff and water balance variables in netcdf format

2. Land Model Spinup: WRF-Hydro/NoahMP driven by 5 years of regridded NLDAS forcing with GFS model background

1. Model Upgrade and Implementation: RAPID and WRF-Hydro/NoahMP

3. WRF-Hydro Forcing Data Engine: Acquire real-time feed of NSSL/NCEP MRMS (Unidata LDM) and ESRL-HRRR (ftp)

7. Model Execution: RAPID on stampedeprovide reach (point) netcdf output of river flow on NHDPlus network.

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)

6. Conversion of netcdf gridded runoff from WRF-Hydro/NoahMP to NHDPlusv2 reach-based channel inflows for RAPID using tailored ArcGIS geo-processing scripts


NFIE Forecasting Workflow: (continued)

8. Tethys Web Application: Upload to CKAN server at BYU (visualization and discovery)

7. IRODS data service: Data shipped to RNCI every 3 hrs (forcings, gridded runoff, channel flows)


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 2010-2014 continuous run:

Regridded NLDAS2/GFS background

Downscaling of T/RH/SW/Press. is now underway for sensitivity testing


NFIE Evaluations: 5-yr Benchmark Evaluation Using rwrfhydro

2001 USGS vs. 2011 NLCD Land cover specification


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


Verification Tool Description

air planet

people


Verification tools and data: rwrfhydro

Verification data:

Streamflow: USGS Gauges-II dataset

Minimally human-impacted

Precipitation: GHCN data

SNOTEL

Ameriflux Network ET (pending)

https://github.com/mccreigh/rwrfhydro, http://www.ral.ucar.edu/projects/wrf_hydro/

air planet

people


Model Evaluation & Benchmarking:

air planet

people


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

air planet

people


Streamflow Correlations at GAGES-II Reference Basins

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

WRF-Hydro/NoahMP WRF-Hydro/NoahMP/RAPID

air planet

people


Streamflow Correlations at GAGES-II Reference Basins

RAPID channel routing shows significant improvements over LSM-only at the hourly timescale

WRF-Hydro/NoahMP WRF-Hydro/NoahMP/RAPID

air planet

people


Streamflow Bias at GAGES-II Reference Basins

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?

WRF-Hydro/NoahMP/RAPID

air planet

people


Streamflow Performance at GAGES-II Reference Basins: Successes!

air planet

people


Streamflow Performance at GAGES-II Reference Basins: Problem Areas

air planet

people


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

air planet

people


Process Evaluation: Fixing Snow Biases for Streamflow Forecasting Applications

air planet

people


WRF-Hydro v. USGS Streamflow Ocheyedan River Near Spencer, IA

0605000

USGS Gauge NFIE-Hydro WRF-Hydro

m3/s

Images courtesy of Mohamed ElSaadani

& Timothy Lahmers

Process Evaluation: Lateral Routing and Reservoir Storage

air planet

people


Process Evaluation: Lateral Routing and Reservoir Storage

Addition of reservoir storage on the Guadalupe River in TX

Impact of lateral routing on Fourmile Creek in CO, consistent with Iowa findings

Image courtesy of Fernando Salas & Marcelo Somos

air planet

people


Summary

In most cases simulated uncalibrated NoahMP runoff is very flashy compared to observed streamflowwhy? 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)

air planet

people


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

air planet

people


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)

air planet

people


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

air planet

people


Next Step: National Water Prediction Model Configurations

Short-Range Flood Prediction

Long Range Water Resources

Analysis & Assimilation

Hourly 3-Hourly ~Daily (x16)

- 3 hrs

Cycling Frequency

WRF-Hydro IOC Configurations

0-2 days 0-30 days

Forecast Duration

1km/250m/NHDPlus Reach


1 km/catchment /NHDPlus Reach

Spatial Discretization & Routing

MRMS blend/ HRRR-NAM bkgnd.

Downscaled HRRR /RAP/NAM blend

Downscaled & bias-corrected CFS

Meteorological Forcing

Medium Range Flow Prediction

Daily

0-10 days


Short-range + Downscaled GFS

air planet

people


Thank you.

D. Gochis, [email protected]

A. Dugger, [email protected]

WRF-Hydro: http://www.ral.ucar.edu/projects/wrf_hydro/

Funding for WRF-Hydro provided by:

NSF, NOAA-OHD, NASA-IDS, DOE-ESM

Documents

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