A Statistical-Distributed Hydrologic Model for Flash Flood Forecasting
Experimental Real-time Seasonal Hydrologic Forecasting
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Transcript of Experimental Real-time Seasonal Hydrologic Forecasting
Experimental Real-time Seasonal Hydrologic Forecasting
Andrew WoodDennis P. Lettenmaier
University of Washington
presented:AMS Conference on Applied Climatology, 2002
Portland, OR May 2002
Project Overview
Research Objective:
To produce monthly to seasonal snowpack, streamflow, runoff & soil moisture forecasts for continental scale river basins
Underlying rationale/motivation:
1.Global numerical weather prediction / climate models (e.g. GSM) take advantage of SST – atmosphere teleconnections
2.Hydrologic models add soil-moisture – streamflow influence (persistence)
Topics
1. Approach2. Columbia River basin (summer 2001) results3. Ongoing Work4. Comments
climate model forecastmeteorological outputs
• ~1.9 degree resolution (T62)• monthly total P, avg T
Use 3 steps: 1) statistical bias correction 2) downscaling and disaggregation3) hydrologic simulation
General Approach
hydrologic model inputs
streamflow, soil moisture,snowpack,runoff• 1/8-1/4 degree resolution
• daily P, Tmin, Tmax
Models: 1. Global Spectral Model (GSM) ensemble forecasts from NCEP/EMC
• forecast ensembles available near beginning of each month, extend 6 months beginning in following month
• each month:• 210 ensemble members define GSM climatology for
monthly Ptot & Tavg• 20 ensemble members define GSM forecast
Models: 2. VIC Hydrologic Model
domain slide
Flow Routing Network
One Way Coupling of GSM and VIC models
a) bias correction: climate model climatology observed climatology
b) spatial interpolation: GSM (1.8-1.9 deg.) VIC (1/8 deg)
c) temporal disaggregation (via resampling of observed patterns):monthly daily
a. b. c.
0
5
10
15
20
25
30
0 1Probability
Te
mp
era
ture
TGSM
TOBS
Bias Example:
JFM precipitation from Parallel Climate Model (DOE)
climate model vs. “observed” distributions at climate model scale (T42)
Dealing with bias using a climatology-based correction
Note: we apply correction to both forecast ensemble and climatology ensemble itself (to use as a baseline)
Downscaling: add spatial VIC-scale variability
observed mean fields
(1/8-1/4 degree)
monthly GSManomaly (T62)
VIC-scale monthly forecast
interpolated to VIC scale
note:month m, m = 1-6ens e, e = 1-20
Lastly, temporal disaggregation…
for each VIC-scale monthly forecast value, e.g.:
-5
5
15
25
35
Mon1
Mon2
Mon3
Mon4
Mon5
Mon6
deg
C
-5
5
15
25
35
Mon1
Mon2
Mon3
Mon4
Mon5
Mon6
de
g C
Simulations
start of month 0 end of month 6
Forecast Productsstreamflow soil moisture
runoffsnowpack
VIC model spin-upVIC forecast ensemble
climate forecast
information (from GSM)
VIC climatology ensemble
1-2 years back
NCDC met. station obs. up to
2-4 months from
current
LDAS/other met.
forcings for remaining
spin-up
data sources
Columbia River Basin Application
Initial Conditions
late-May SWE &water balance
Initial Conditions
late-May SWE &water balance(percentiles)
May climate forecastobservedforecast
forecastmedians
May snowpack forecast
hindcast“observed”
forecast
forecast medians
May runoff & soil moisture forecasthindcast “observed”forecast
forecastmedians
May streamflow forecast
Ongoing Work: Assessment and Expansion
Tercile Prediction “Hit Rate”
e.g., GSM Ensemble “Forecast” Average, January
(based on retrospectiveperfect-SST ensemble forecasts)
Masked for local significance
U.S. West-wide Hydrologic Forecasting
Summary Comments
climate-hydrology model forecasting method has potential hydrologic persistence was most important in the CRB
example
bias-correction of climate model outputs (using a climate model hindcast climatology) is critical
access to quality met data for hydrologic model initialization is also essential