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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

Kingtse.mo@noaa.gov

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?

• What will be the best drought indices to use in monitoring?

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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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Uncertainties among the NLDAS

• Models: VIC and Noah with the common period from 1950-2000 at 0.5 degrees.

• 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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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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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 over the western region

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

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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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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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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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