Transcript Drought Prediction

Approaches to Seasonal
Drought Prediction
Bradfield Lyon
CONAGUA Workshop
24-26 Nov, 2014
Mexico City, Mexico
Drought Prediction
What do we want to predict?
Drought Prediction
What do we want to predict?
- Precipitation (timescale? monthly, seasonal, annual...?)
- Soil Moisture (how deep a layer?)
- Stream flow / Inflow
- Groundwater Level
- Impacts
It depends on specific decisions:
• The best “Drought Index” is the one that is most closely associated
with the specific outcome/impact of interest.
A generalized drought prediction system needs to forecast several
indicators, which ultimately need to be related to specific variables of
interest (inflow, soil moisture, crop yield, etc.).
Sources of Predictive Skill
 Sea Surface Temperatures
a) Tropical Pacific (El Niño, La Niña)
Seager et al. 2009
b) Tropical Atlantic
May to October
Sources of Predictive Skill
 Sea Surface Temperatures
Climate Model* Skill
in Seasonal Rainfall
Predictions
Correlation (Fcst, Obs)
1982-2010
Jan-Mar
Apr-Jun
* North
American
Multi-Model Ensemble
(NMME, 6 climate models)
Jul-Sep
Oct-Dec
Sources of Predictive Skill
 The “Initial Condition”
 The July NADM is a good “first guess” of the October NADM…
Sources of Predictive Skill
 The “Initial Condition”
There is often month-to-month persistence in drought indicators
that can provide predictive information.
Consider the Standardized Precipitation Index (SPI). The SPI compares
accumulated, precipitation to historical values, expressing differences
as a normal distribution.
SPI6(Jun)
Jan
Feb
Mar
Apr
May
Jun
To make a forecast of SPI6 one month ahead,
the picture looks like this:
SPI6(Jul)
Feb
Mar
Apr
May
Jun
JUL
5 of the 6 months are in common  large persistence
Sources of Predictive Skill
 The “Initial Condition”
Number of months with lagged correlation > 0.6 for the 12-month SPI
Lyon et al. 2012, JAMC
Sources of Predictive Skill
 The “Initial Condition”
INFLOW ( x10^6 m^3)
Yaqui Water System
Inflow data courtesy of José Luis Minjares
Accumulated inflow
in March a potential
predictor annual
inflow…
Sources of Predictive Skill
 The “Initial Condition”
Inflows to the Yaqui System
Use accumulated
inflow in March to
predict yearly inflow
Which Indicator is Best?
 The one most relevant for a specific use
Yaqui Water System
Yaqui Water System Inflow
Departure from Average:
Comparison with SPI-12
Water years 1965-2007
7000
2.5
r = 0.7
5000
2
1.5
3000
1
0.5
1000
0
-1000 1965
-3000
1970
1975
1980
1985
1990
1995
2000
2005
-0.5
-1
-1.5
-5000
-7000
Inflow data courtesy of José Luis Minjares
-2
-2.5
Inflow
SPI-12
Which Indicator is Best?
Model Soil Moisture vs. Various SPI Indicators
Example from the Eastern US (1950-200)
“VIC” Land Surface Model
Correlation “VIC” Soil Moisture and SPI
*
*
www.hydro.washington.edu
*
VIC soil moisture data courtesy of Justin Sheffield, Princeton University
Tailored Forecasts
Inflows to the Yaqui System
Ideally, predictions of specific
outcomes are desired:
 reservoir inflow,
 crop yield,
 rangeland biomass, etc.
However, more general drought
indicators can be linked to specific
outcomes. This provides a calibration
of the index to something more
relevant to the user…
7000
2.5
2
5000
1.5
3000
1
0.5
1000
0
-1000 1965
-3000
1970
1975
1980
1985
1990
1995
2000
2005
-0.5
-1
-1.5
-5000
-7000
-2
-2.5
Inflow
SPI-12
Tailored Forecasts
 Drought & Agricultural Impacts in Sri Lanka (1960 – 2000)
• 40 yrs. of agricultural impacts
data available at the district
level.
• Which meteorological drought
indicator is most closely
associated with drought
impacts to agriculture?
Lyon et al., 2009, JAMC
Tailored Forecasts
1. Examine drought indicators and impact occurrences
2. Consider seasonality of drought & impacts
3. Quantify relationships between
drought indictors and impacts.
 Key for development of early
warning systems
Lyon et al., JAMC, 2009
Tailored Forecasts
IRI Seasonal Fcst
Pr(Below-Normal Rainfall)
GCM Fcst
PRCP, Wind
Historical
Inflows
Statistical
Model
Tailored Forecasts
As input to a reservoir management tool…
Towards a Water Sector
Impact Forecast for Mexico
2-Mo. Lead Fcst for end of June 2010
With Carolina Neri, UNAM
Drought Index
Forecast
Index of
Water Sector
Vulnerability
Drought Index,
Water Impact
Relationship:
Identify
Thresholds
Issued
April 2010
Probabilistic
Water Supply
Impact
Forecast
[ V + Pr(< threshold) ]
R = [ 1+ Pr(< threshold) ]
2-Mo. Lead Fcst for end of June 2011
Issued
April 2011
0 ≤ V ≤ 1, 0 ≤ R ≤ 1
Low
Risk
High
Drought Index Forecast
Prob. SPI6 < -1
Issued in April
For
Jun 2010
For
Jun 2011
+ Water Vulnerability
= Probabilistic
Water Impact Risk
Forecast
Issued in April
For
Jun 2010
Low
Observed SPI6
in June
For
Jun 2011
Risk
High
Obs
Jun 2010
Dry
Low
Risk
High
Obs
Jun 2011
Wet
Dry
Wet
Available Today
 Forecasts of 3, 6, 9 and 12-month SPI
Interactive: User selects Index, Thresholds,
Probabilities of interest…
Dec SPI12
SPI
Best Estimate
Dec SPI12
10% probability
Dec Prob.
SPI12 < threshold
Summary
• Droughts are not simply unpredictable, random events.
 There is identifiable skill in seasonal forecasts of several
meteorological drought indicators (and other variables).
 Skill is typically greatest in fall and winter, least in summer.
• Ultimately, we are interested in the likelihood of drought
impacts, not just forecasts of drought indicators.
• Thus, there is a need to calibrate drought indicators to
impacts in some fashion.
• Generation of drought risk forecasts will first require a
vulnerability assessment of a system to drought.
Acknowledgements
This work has been supported in part by the Modeling, Analysis, Predictions
and Projections (MAPP) program at NOAA, which is gratefully acknowledged.
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References
• Lyon, B., M. A. Bell, M. K. Tippett, A. Kumar, M. P. Hoerling, X. Quan, H. Wang,
2012: Baseline probabilities for the seasonal prediction of meteorological drought. J.
Appl. Meteor. Climatol., 51, 1222-1237.
• Lyon, B., L. Zubair, V. Ralapanawe, and Z. Yahiya, 2009: Finescale Evaluation of
Drought in a Tropical Setting: Case Study in Sri Lanka. J. Appl. Meteor. Climatol., 48,
77–88.
US-Mexico SPI Forecast and Monitoring Products from IRI
http://iridl.ldeo.columbia.edu/maproom/Global/Drought/N_America/index.html
Longer Time Scale Variations
(mm/mo.)
Annual Average Rainfall
Longer Time Scale Variations
A Simple Separation
of Time Scales
The majority of the
variation in rainfall is from
one year to the next…
Longer Time Scale Variations
Seasonality of Precipitation