# Presentation: 912508

## Developing objective climate drought monitoring and prediction – A CTB project

- Kingtse Mo
- Team Leader
- Drought NIDIS

## 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) **

## 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

## 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?

## 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.

## 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)

## 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**

## 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

## There are 4 types of distribution

**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 2

- Type 4

**To get smooth distribution function, more points are needed.**

## SM PDF for selected RFC: For SM anomalies, PDF is Gaussian

- 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**

## Uncertainties among the NLDAS

- Models: VIC and Noah with the common period from 1950-2000.
- Compute SM percentiles for each model
- Form SM standardized anomalies with respect to its own monthly climatology.
- Obtain percentiles based on Gaussian probability distribution function for each month

## 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**

**Corr**

**RMS**

##  Two reasons:

**SM is more persistent over the west region so SM at deeper levels play a role. That depends on model soil structure & parameters**
** ****Difference in precipitation. Less stations over the western mountains and different ways to grid data**

**Corr SPI3 VIC,noah**

**RMS SPI3 VIC,Noah**

## 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**

## More than one index is needed

**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

## 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**

## 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

## Droughts as measured by the SPI6

**Jan 2007**

**Droughts as measured by the SPI6**
**SPI<-0.8 enter drought conditions**

**3-cell pattern**

## 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

## 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

## Overall, all indices pick up major drought events

- SM (Black) droughts have longer duration

- SM%
- SRI
- SPI

- SM%
- SRI
- SPI

- SM%
- SRI
- SPI

- SM%
- SRI
- SPI

- SM%
- SRI
- SPI

- SM%
- SRI
- SPI

## Precipitation and SPI6

**Drought**

##        SM percentiles for

-        SM percentiles for
-       the Colorado River Fcst Ct
**NLDAS over the western region differs too much to analyze “low flow ‘ cases on the scales less than 3 months**
**VIC has more high frequency components than the Noah.**
**For droughts on the  time scales 6-months or longer, the differences are smaller**

- Black-Noah
- Blue VIC

## 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

## Areas with bi modal distribution coincide with areas with strong seasonal cycle

## 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)

## Precipitation and SM (32-36N)

**3 cell pattern persisted for 2007**
**Can we declare drought for the SE? YES**

## 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)