1 Developing objective climate drought monitoring and prediction – A CTB project Kingtse Mo Team Leader Drought NIDIS

2 Drought monitoring: 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)

3 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

4 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?

5 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.

6 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)

7 Data sets VIC degrees from (Maurer et. al. 2002, Thanks! Andy Wood) Noah degrees from from Fan and van den Dool Time scales: Monthly means from

8 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

9 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 4To get smooth distribution function, more points are needed.

10 SM totalSM 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

11 Uncertainties among the NLDAS Models: VIC and Noah with the common period from 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

12 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

13 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

14 The need for ensemble NLDAS Total SM percentile for selected River Forecast Center areas Vic(Blue), Noah (black) From 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

15 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

16 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

17 Precipitation anom for the water year The dipole with wet Great - Plains & Dry SE persists from Jan 2007 to JAS 2. A weak to normal NA monsoon

18 Jan 2007 Droughts as measured by the SPI6 SPI<-0.8 enter drought conditions 3-cell pattern

19 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

20 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

21 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

22 Precipitation and SPI6 Drought

23 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

24 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

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

26 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)

27 Precipitation and SM (32-36N) 3 cell pattern persisted for 2007 Can we declare drought for the SE? YES

28 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)