Transcript Global Flood and Drought Prediction

Global Flood and Drought Prediction
Nathalie Voisin and Dennis P. Lettenmaier
Department of Civil and Environmental Engineering
University of Washington
Seattle, USA
www.ektopia.co.uk/ektopia/ images/parisflood.jpg
Outline
1.
Background and Objective
2.
Data and models
…Or why I think GCM met data are more appropriate for
global streamflow forecasting for now
3.
Toward developing global hydrology forecast capability
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4.
Approach
Data Processing : bias correction and downscaling of the
forecasts
Preliminary results: Evaluation of the bias correction and the
downscaling on two specific events
Future work
-1Background
Need for flood prediction globally?
www.dartmouth.edu/~floods, Dartmouth Flood Observatory
Global Floods and Droughts
• Floods
– $50-60 billion USD /year, worldwide ( United Nations University)
– 520+ million people impacted per year worldwide
– Estimates of up to 25,000 annual deaths
Mostly in developing countries; Mozambique in 2000 and
2001, Vietnam and others (Mekong) in 2000.
• Droughts
– 1988 US Drought: $40 billion (1988 drought: NCDC )
– Famine in many countries: 200,000 people killed in
Ethiopia in 1973-74
Source: United Nations University, http://update.unu.edu/archive/issue32_2.htm
http://www.unu.edu/env/govern/ElNIno/CountryReports/inside/ethopia/Executive%20Summary/Executive%20Summary-txt.html
1988 drought: NCDC : http://lwf.ncdc.noaa.gov/oa/reports/billionz.html
Objective
Predict streamflow and associated hydrologic variables, soil
moisture, runoff, evaporation and snow water equivalent :
1. At a global scale
– Spatial consistency
– To cover ungauged or poorly gauged basins
2. Time scales:
– Short term for floods
– Seasonal (or longer) for drought
3. Freely disseminate information for agriculture, energy, food
security ,and protection of life and property
-2Data and models
Meteorological Data
- Surface observations:
Uneven global coverage
Various attempts to grid globally
We use Adam et al. (2006) 1979-1999 (0.5 degrees) and ERA-40
- Precipitation derived from satellite:
Various products available, mostly either passive microwave
and/or infra-red
Issue with climatology and consistency ( especially important for
seasonal prediction)
- Climate Models: ECMWF and NCEP
Re-analysis products, for at least 25 years
Ensemble forecast products
Quasi all or all required input data for our hydrologic model
available
SWE
Soil Moist
Evap
Runoff
Precip
Meteorological Data - Comparison
…Or why I think GCM met data are more appropriate for global streamflow
forecasting for now
Comparison
of 19971999
monthly
basin
averages
SWE
Soil Moist
Evap
Runoff
Precip
Meteorological Data - Comparison
…Or why I think GCM met data are more appropriate for global streamflow
forecasting for now
SWE
Soil Moist
Evap
Runoff
Precip
Meteorological Data - Comparison
…Or why I think GCM met data are more appropriate for global streamflow
forecasting for now
Meteorological Data
In this paper , I conclude that :
(1) Satellite precip need more “calibration” based on
topography and in the ITCZ
(2) Great hope that satellite will bring the “observed”
small scale spatial variability available. Need
longer climatology to be able to perform bias
correction in order to correct for (1)
(3) Missing data, issue in real time forecasting
The Hydrologic Model VIC
- Already calibrated and
validated at 2 degree
resolution on over 26 basins
worldwide
(Nijjsen et al. 2001)
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Calibrated and validated at
0.5 degree over the Arctic
domain
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Ongoing with UW and
Princeton globally at 0.5
degree resolution
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Routing at 0.5 degree derived
from SRTM30 and from the
manually corrected global
direction file from Doell and
Lehner (2002)
Routed Basins
-3Toward developing global hydrology
forecast capability
Forecast System Schematic *
local scale (1/2 degree)
weather inputs
Hydrologic
model spin up
ECMWF ERA40
(or
Analysis)
Several years back
soil moisture
snowpack
streamflow, soil moisture,
snow water equivalent, runoff
INITIAL
STATE
Later on:
CMORPH,
SNOTE
LMODIS,
AMSR-E,
Update
others
Hydrologic forecast
simulation
Ensemble Reforecasts
NCEP Reforecasts (Hamill 2006), bias
corrected w/r to ERA40 and
downscaled w/r to Adam et al. (2006)
observations
( NCEP GFS, ECMWF ESP)
NOWCASTS
Month 0
SEASONAL FORECASTS
(drought)
SHORT TERM
FORECASTS (flood)
* Similar experimental procedure as used by Wood et al (2005) West-wide
seasonal hydrologic forecast system
Spin Up
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ECMWF ERA40 reanalysis for retrospective
forecasting
Assume ERA40 is the truth
Later use ECMWF analysis field, bias
corrected to match ERA40 characteristics
The Meteorological Forecasts
Retrospective forecasting: Reforecasts
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Tom Hamill (2006) NOAA
NCEP-MRF, 1998 version
1979-present
15-day forecasts issued daily
15 member ensemble forecast
2.5 degree resolution
(Near) Real Time forecasting: ECMWF
and/or NCEP (future)
Data processing
The climatology statistics to be conserved in
the forecasts are :
- the frequency of occurrence of rain
- the peaks
- accumulated amounts (mean)
Data processing: Bias Correction
Non-exceedance probability plots (MRF in green, ERA40 in black )
Systematic Bias
Occurrence of Precipitation
Data processing: Bias Correction
Systematic bias
Using quantile-quantile
mapping technique
Occurrence of Precipitation
Data processing: Downscaling
1. Inverse square distance interpolation from 2.5
down to 0.5 degree resolution
2. Integration of observation based spatial
variability at 0.5 degree:
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Use observations based Adam et al. (2006) global
dataset (0.5 degree resolution)
Shifting :
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makes the Adam et al. average temperature field at 2.5
degree match ERA40,
Derive the temperature range for each 0.5 degree cell
within the 2.5 degree cell
Scaling of the precipitation and the wind field so that
the ratio Value(0.5)/Value(2.5) is conserved
Downscaling: observation based spatial
variability at 0.5 degree:
1. Choose the year that will give the variability:
• Choose randomly one single year (1979-1999) for all cells and all lead times
• For retrospective forecasting, choose the year of the forecast.
2. Rainy and non rainy days records from Adam et al. 2006 (averaged to 2.5
degrees) dataset, are classified and saved for the month of the lead time:
RecordRain(cell, month of fcst) and RecordNonRain(cell, month of fcst)
3. Shitfing and/or scaling:
If Raining: select randomly a record in the RecordRain database, the record is
conserved for the 4 variables.
Precip0.5=GFSbiascorrected,2.5 / Adam2.5(recordrain) * Adam0.5(recordrain)
Tavg0.5=GFSbiascorrected,2.5- Adam2.5(recordrain) + Adam0.5(recordrain)
Wind0.5=GFSbiascorrected,2.5 / Adam2.5(recordrain) * Adam0.5(recordrain)
4. Adjust Tmin and Tmax
DeltaTemp=TmaxAdam0.5(recordrain)-TminAdam0.5(recordrain)
Tmax0.5=Tavg0.5+1/2*DeltaTemp
and Tmin0.5=Tavg0.5-1/2*DeltaTemp
Downscaling
Bias corrected
GFS reforecast
2.5 degree
Adam et al.
Random rainy day ( in the mth of fcst)
0.5 degree 2.5 degree
Rain
Scaling: Normalize the 0.5 degree spatial
variability
Shifting: Save the (Value0.5-Value2.5)
No Rain
Shifting of temperature and scaling of wind based on any Non Rainy day
Downscaling
Notes:
Sometimes, bias corrected GFS reforecast calls for rain but there
is No rain in the Adam et al. dataset for the (randomly)
selected year. In this case, the 2.5 degree cell is downscaled
to 0.5 degree by inverse square distance interpolation. Tmin
and Tmax are derived from a randomly selected record.
If I was to select another year:
– No guarantee that there is rain in a different year
– The additional computing time and resources needed make it
not worth doing it
Downside of downscaling this way:
any spatially large rain event (> 2.5 degree like fronts, etc) is not
conserved because each 2.5 degree cell has an
independently randomly selected RainRecord.
Retrospective Forecasting
Interests:
– Skills over different climate and basins
– Skills for different type of floods: short term strong
events ( fronts, extratropical or tropical storms) or
sustained rain events ( monsoons).
Events of interest:
– Event 1: End of January 1995 : flood in the Meuse and
Rhine basins (Europe)
– Event 2: July 1997: flood in the Oder Basin (Europe)
– Event 3: February 2000, flood in the Limpopo
– Event 4: July-September 2000, flood in the Mekong
Retrospective Forecasting of Short
Events
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Event 1: End of January
1995 : flood in the Meuse
and Rhine basins
(Europe)
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Event 3: February 2000,
flood in the Limpopo
1995/01/20 Rhine – Effect of SMSC
The Rhine Basin
1995/01/20 Rhine
Precipitation
w/o SMSC
SMSC
BC
SMSC
ERA40
LEAD 1
LEAD 2
LEAD 3
1995/01/20 Rhine
Runoff
w/o SMSC
SMSC
BC
SMSC
ERA40
LEAD 1
LEAD 2
LEAD 3
1995/01/20 Rhine
W/o SMSC
PRECIPITATION
With SMSC
ERA40
1995/01/20 Rhine
W/o SMSC
RUNOFF
With SMSC
ERA40
1995/01/20 Rhine
W/o SMSC
Change in SWE
With SMSC
ERA40
1995/01/20 Rhine
Basin Avg Hydrologic Variables Prediction (ERA40 in red, GFS in black,
NMC reanalysis in blue )
NO SMSC
SMSC
SMSC gives a few “crazy”
ensembles
BC
SMSCBC
BC improves SWE (?) but
does not do any miracle on
missed precip peaks ( timing
issue). Not significant
improvement here though …
2000/02/03 - Limpopo
Limpopo
2000/02/03 Limpopo – Effect of SMSC
W/o SMSC
PRECIPITATION
With SMSC
ERA40
2000/02/03 Limpopo – Effect of SMSC
W/o SMSC
Runoff
With SMSC
ERA40
2000/02/03 Limpopo – CTRL
PRECIPITATION without SMSC and BC
GFS ens avg NMC rean
ERA40
Conclusion
• Bias correction implemented
• Correction for occurrence of precip
implemented
• Downscaling implemented
• Output variables:
– By lead time: Precip, Runoff, Evap, Change in
SWE, Change in Soil Moisture, Degree Days
– 5 day accumulation: same
– Time series: of basin average hydrologic
variables and streamflow at selected stations
Conclusion on the Bias Correction
The bias correction :
• beneficial for ALL input variables (P,
Tavg,Wind)
• does not substitute for missed
precipitation/temperature peaks/lows BUT
the correction for the occurrence of rain
correction should help ( not shown )
• brings consistency between the control run
( model or observations, or both) and the
forecasts
Conclusion on the Downscaling
• A few ensembles get crazy values ( should check
the scaling factor)
• The location of the front is not really respected,
some information get lost ( maybe should
interpolate linearly first, then shift or scale)
• Adam et al. 2006 precipitation was disaggregated
statistically in time. Should I expect real
precipitation patterns (frontal shape, etc) within a
2.5 degree cell? Not appropriate for downscaling
in a tropical storm event ; linear interpolation
might be better in this case.
-4Future Work
Future Work
Retrospective forecasting:
– Investigate more events and fix the remaining
“bugs?”/make last improvements
– Extend the retrospective forecasts:
using archived ECMWF 10 day, monthly and seasonal
forecasts
– Predictions in forms of percentile and
anomalies with respect to the climatology
Future Work
Operational real time forecasting:
– Once/Twice a week for short term forecasts
– Use several climate model forecasts:
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ECMWF 10 day forecast
ECMWF monthly forecast
ECMWF seasonal forecast
GFS 6-10 day forecast
– Improvement of the initial conditions: e.g.
assimilation of satellite soil moisture; snow
Thank You!
2000/02/03 Limpopo – Effect of SMSC
Basin Avg Hydrologic Variables Prediction (ERA40 in red, GFS in black,
NMC reanalysis in blue )
NO SMSC
SMSC