1 developing objective climate drought monitoring and prediction – a ctb project kingtse mo team...
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Developing objective climate drought monitoring and
prediction – A CTB project
Kingtse Mo
Team Leader
Drought NIDIS
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Drought monitoring:http://www.cpc.ncep.noaa.gov/products/Drought
Drought briefing:
Second Thursday each month
Call in is available
Atmospheric variables: NARR
Hydrological variables: ensemble NLDAS: Mosac, Noah, VIC and NARR
Prediction: NAEF, (ESP, CFS downscaling)
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Partners and contributors
CPC: Kingtse Mo, Wanru Wu, Muthuvel Chelliah, Wei Shi,Yun Fan,Huug van den Dool
EMC: the NAEF forecast group,
Ken Mitchell, Jesse Ming, Youlong Xia
GSFC/NASA: Brian Cosgrove and Chuck Alonge
University of Washington: Andy Wood, Dennis Lettenmaier
Princeton University: Eric Wood, Lifeng Luo, Justin Scheffield
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For drought assessment
• Are we able to use the North American Land data Assimilation Systems (NLDAS) to develop early drought warning system?
• Do we need more than one index to assess drought?
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Conditions
• Reliability: Agreement among the NLDAS systems.
• Consistency: All different indices/NLDAS should be able to select strong drought events.
• Long term data to obtain representative probability distribution functions
• Availability : Operational in near real time.
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Drought Indices
• Meteorological drought: Precipitation deficit. Index: Standardized Precipitation Index• Hydrological drought: Streamflow or runoff
deficit Index: Standardized runoff index• Agricultural drought: soil water storage deficit Index: SM percentile (to be determined)
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Data sets
• VIC - 0.5 degrees from 1915-2003 (Maurer et. al. 2002, Thanks! Andy Wood)
• Noah- 0.125 degrees from 1948-2001 from Fan and van den Dool
• Time scales: Monthly means from 1950-2000
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What is the soil moisture probability distribution function ?
• Bi-modal (D’Odorico and Porporato 2004)
• Beta distribution (Schiffield 2004)
A non parametric method will be used to determine the soil moisture distribution function based on the monthly mean VIC data from 1915-2003
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There are 4 types of distribution
Unimodal: Type 1 and 3 :and type 3 is close to the Gamma distribution
Bimodal: Type 2 and 4: Type 2 has two peaks and the second peak for type 4 is a shoulder
Type 1 Type 3
Type 2Type 4 To get smooth
distribution function, more points are needed.
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SM total SM anomalies
SM PDF for selected RFC: For SM anomalies, PDF is Gaussian
Type 1:
Southeast RFC
Type 2:
Mid Atlantic RFC
Type 3:
Colorado RFC
Type 4:
Ohio RFC
Red :winter
Blue summer
Green Spring
red crosses Fall
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Uncertainties among the NLDAS
• Models: VIC and Noah with the common period from 1950-2000.
• Compute SM percentiles for each model
A) Form SM standardized anomalies with respect to its own monthly climatology.
B) Obtain percentiles based on Gaussian probability distribution function for each month
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SM percentile difference between VIC and Noah
•Differences are regional dependent
•Over the areas east of 90W, differences are small.
•Over the areas west of 90W, differences are large.
•The RMS error is larger than 25%: the difference between one drought class to another
CorrRMS
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Two reasons:
a) SM is more persistent over the west region so SM at deeper levels play a role. That depends on model soil structure & parameters
b) Difference in precipitation. Less stations over the western mountains and different ways to grid data
Corr SPI3 VIC,noah RMS SPI3 VIC,Noah
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The need for ensemble NLDAS
Total SM percentile for selected River Forecast Center areas
Vic(Blue), Noah (black)
From 1950-2001
For RFC lower Mississippi, the VIC and the Noah agree well
For Missouri basin, there are large differences
3 month running mean soil moisture percentiles
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More than one index is needed
SM % are more smooth. SM has longer memory and events occur 2-3 months later than P and last longer
SPI has higher frequency: more events and shorter duration
Corr(SPI,SRI)=0.87
Corr(SRI,SM)=0.72
Corr(SPI,SM)=0.52
Longer record is needed
Colorado RFC
SM
SPI6
SRI6
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RFC: Southeast
All indices are similar.
They are likely to pick up same events
Corr(spi,sri)=0.91
Corr(sri,SM)=0.73
Corr(spi,sm)=0.63
SM
SPI6
SRI6
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Precipitation anom for the water year 2006- 2007
The dipole with wet Great -Plains & Dry SE persists from Jan 2007 to JAS
2. A weak to normal NA monsoon
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Jan 2007
Droughts as measured by the SPI6SPI<-0.8 enter drought conditions
3-cell pattern
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Conclusions
• The uncertainties of NLDAS are larger over the western region than areas east of 90W.
• Over areas east of 90W, different indices based on P, SM or runoff are likely to pick up same drought events.
• Over the west region, uncertainties are too large to select drought events for less than 3 months over 0.5 degree boxes
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What do we need?
NLDAS data : from different models & forcing
• How many samples are needed?
• Are differences in the NLDAS caused by model or forcing ?
• What is the best way to consolidate them to form ensemble?
Better precipitation analyses
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1. Overall, all indices pick up major drought events
2. SM (Black) droughts have longer duration
SM%
SRI
SPI
SM%
SRI
SPI
SM%
SRI
SPI
SM%
SRI
SPI
SM%
SRI
SPI
SM%
SRI
SPI
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Precipitation and SPI6
Drought
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SM percentiles for
the Colorado River Fcst Ct
1. NLDAS over the western region differs too much to analyze “low flow ‘ cases on the scales less than 3 months
2. VIC has more high frequency components than the Noah.
3. For droughts on the time scales 6-months or longer, the differences are smaller
Black-Noah
Blue VIC
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Runoff
• Runoff obeys Gamma distribution similar to precipitation.
• For drought on different time scales, they can be represented by standardized runoff indices computed the same way as the SPIs. We will have 3, 6, 12 and 24 month SRIs
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Areas with bi modal distribution coincide with areas with strong seasonal cycle
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Probability Distribution function
For each grid point : • Pool all months together and use 9 grid points in
the 1x1 deg. box centered at that grid point : there are 9X89X 12= 9612 data points
• From 9612 points, we determined the histogram and normalized to 1.
• Group grid points into different types using simple cluster analysis. (All points in the same type correlate with each other with correlation greater than 0.8)
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Precipitation and SM (32-36N)
3 cell pattern persisted for 2007
Can we declare drought for the SE? YES
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Probability Distribution function
For each grid point : • Pool all months together and use 9 grid points in
the 1x1 deg. box centered at that grid point : there are 9X89X 12= 9612 data points
• From 9612 points, we determined the histogram and normalized to 1.
• Group grid points into different types using simple cluster analysis. (All points in the same type correlate with each other with correlation greater than 0.8)