Yu Zhang1 and Haksu Lee2,3

Assimilation of Satellite QPE in A Distributed
Hydrologic Model for Flood Predictions
Yu Zhang1 and Haksu Lee2,3
1Office
of Hydrologic Development
NOAA National Weather Service
2Office
of Climate, Weather and Water
NOAA National Weather Service
3Len
Technologies, Oak Hil, VA
1
Outline
Ø  NWS
flood forecast in the US
Ø  Products
and services
Ø  Elements of forecast operation
Ø  Hydrologic
l 
l 
l 
experiment
Objectives
Experiment Layout
Observations
Ø Summary
and implications for global flood
monitoring
2
Flood Forecast in the US
Ø  NOAA National
Weather Service responsible for
forecasts for riverine floods in the US
Ø  Primary products
Ø 
Stage Forecast
Flood Warnings
Ø  Flash Flood Warnings
Ø  Inundation extent
Ø 
Ø 
Flood outlook
Minor-moderate Flooding
3
Elements of NWS Forecast
Hydrologic Models
Ø  Forcing inputs
Ø Precipitation, temperature, etc
Ø  Reservoir inflow
Ø  Observed river stages from USGS
Ø  Forecaster interventions (assimilation)
Ø 
4
NWS Hydrologic Models
Ø  Lumped
Model
Ø 
Prediction only at designated outlets w/ uniform forcing
Ø 
SAC-SMA (runoff) and Unit Hydrograph (routing)
Ø 
Carefully calibrated at each outlet
Ø  Distributed
Ø 
Model
Ø 
Gridded (1,2 and 4 km mesh)
Ø 
Multiple runoff mechanisms (SAC-SMA, SAC-HTET, API, etc)
Ø 
Kinematic routing
Ø 
A priori parameters from physiographic data
Both models among best performers in the Distributed
Model Inter-comparison Projects (DMIPs)
5
Precipitation Inputs
Ø  Requirements:
Ø  Accurate,
Ø  Existing
Ø  Fused
Quantitative Precipitation Estimates (QPEs)
multi-sensor data
Ø Primarily
Ø Limited
Ø  Issues
Ø  Satellite
low latency, and spatially contiguous
radar and gauge observations
satellite input (Hydro-estimator)
with gaps and quality in radar/gauge data
QPEs
Ø  In
particular Global Precipitation Measurement (GPM)
Ø  Expected to help fill gaps and improve the accuracy in
radar/gauge sparse areas
6
Radar Coverage Gaps
Summertime
Wintertime
• More severe radar coverage gaps during winter time
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Data Assimilation
Ø  Data Assimilation
:
Ø  Systematic
ways of adjusting model using
observations
Ø  Mimics forecast interventions
Ø  4D
Variational Assimilation (Var) techniques
developed at OHD
Ø  3D
+ time dimension = 4D
Ø  Assimilates
Ø Streamflow,soil
moisture, etc
Ø Forcings (precip, PET)
Ø  Experimental
at River Forecast Centers
8
Simulation Experiment
Ø  Motivations
Ø  Understand
the accuracy of flood forecast driven
by satellite QPEs
Ø  Assess the impacts of streamflow assimilation on
forecast accuracy
Ø  Experiment Layout
Ø  Simulate flood events using distributed model
Ø  Ingest satellite QPE (CMORPH)
Ø  Use 4D Var to assimilate
Ø  Streamflow, QPEs and Potential ET
Ø  Evaluate the simulation at different forecast horizons
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Study Setting and Data sets
Ø 
TIFM7
Ø 
TIFM7
Ø  Elk River near Tiff City, Mo
Ø  Drainage Area: 2258 km2
Ø  LANAG: subbasin (619 km2)
QPEs
Ø 
TIFM7
LANAG
Ø 
Ø 
CPC-MORPH (CMORPH)
Ø  8km, 30-min
Multisensor QPE (MQPE)
Ø  4km, hourly
HL-RDHM (a research prototype of
NWS DHM)
Ø 
Ø 
Ø 
4-km grid mesh
Hourly time step
A priori parameters
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Hydrologic Results
Ø  Flood
of 3-10, March, 2004
Ø  Baseline
Simulations (w/o assimilation)
Ø  DA results
Ø 
Ø 
From assimilation of streamflow at outlet and QPEs
Performance at outlet and interior points
Ø  Raw
and adjusted soil moisture (UZFWC)
11
Baseline Simulations
Mar 3-10, 2004 Flood
Pos. bias from CMORPH
TIFM7
Neg. bias from MPE
LANAG
Pos. bias more severe
for CMORPH over the
interior point
Time (hours from 0Z, 2004/03/03)
Using satellite QPE alone may lead to large
bias in predicted streamflow
12
Assimilation Results
Mar 3-10, 2004 Flood
CMORPH: nearly identical
traces after assimilation
TIFM7
MQPE: negative bias
remains
LANAG
Both CMORPH and MQPE
yielded flood peak higher
than observed
CMORPH bias is improved
but remains positive
Assimilation at outlet markedly improves the
prediction at interior points
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Model State Adjustments
UZFWM (upper zone free water)
CMORPH:
Note large differences before
and after assimilation –
heavy adjustment due to
poor performance
MQPE:
Minor adjustments –
baseline results relatively
good
Baseline
Assimilation
Major adjustments to the CMORPH-based model
states to compensate for precip errors
08/14/10
14
Summary and Findings
Ø 
Hydrologic experiment
Ø 
Ø 
Ø 
Ø 
Baseline simulations:
Ø 
Ø 
CMORPH vs. operational MQPE
Assimilate streamflow at outlet
Examine results at interior points
Large positive discharge bias in CMORPH-based results
Assimilation results:
Ø 
Ø 
Ø 
Appreciable improvements after assimilation at interior
point
Better results from CMORPH
Heavy adjustments for CMORPH-driven soil moisture
states
15
Implications for Global Flood
Monitoring and Forecast
Ø 
Satellite QPEs
Ø 
Ø 
Ø 
Can be subject to substantial errors
Bias magnified in streamflow predictions
Data assimilation
Ø 
Ø 
Ø 
Infusing in situ/remotely sensed observations
Ø  Streamflow/soil moisture/inundation extent
Shown to help suppress large positive bias
Useful for monitoring floods in drainages where
limited observations are available
16