approaches to seasonal drought prediction bradfield lyon conagua workshop 24-26 nov, 2014 mexico...
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Approaches to SeasonalDrought Prediction
Bradfield Lyon
CONAGUA Workshop24-26 Nov, 2014
Mexico City, Mexico
- 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.).
Drought Prediction
What do we want to predict?
Sources of Predictive Skill Sea Surface Temperatures
a) Tropical Pacific (El Niño, La Niña) b) Tropical Atlantic
Seager et al. 2009 May to October
Sources of Predictive Skill Sea Surface Temperatures
Climate Model* Skillin Seasonal Rainfall
PredictionsCorrelation (Fcst, Obs)
1982-2010
* North AmericanMulti-Model Ensemble
(NMME, 6 climate models)
Jan-Mar Apr-Jun
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 comparesaccumulated, precipitation to historical values, expressing differences as a normal distribution.
SPI6(Jun)
SPI6(Jul)
Jan Feb Mar Apr May Jun
5 of the 6 months are in common large persistence
JUL Feb Mar Apr May Jun
To make a forecast of SPI6 one month ahead,the picture looks like this:
Number of months with lagged correlation > 0.6 for the 12-month SPI
Sources of Predictive Skill The “Initial Condition”
Lyon et al. 2012, JAMC
Sources of Predictive Skill The “Initial Condition”
Yaqui Water System
Inflow data courtesy of José Luis Minjares
Accumulated inflow in March a potentialpredictor annual inflow…
IN
FL
OW
(
x10^
6 m
^3)
Sources of Predictive Skill The “Initial Condition”
Use accumulatedinflow in March to
predict yearly inflow
Inflows to the Yaqui System
Which Indicator is Best?
-7000
-5000
-3000
-1000
1000
3000
5000
7000
1965 1970 1975 1980 1985 1990 1995 2000 2005
-2.5
-2
-1.5
-1
-0.5
0
0.5
1
1.5
2
2.5
Inflow
SPI-12
Yaqui Water System InflowDeparture from Average:Comparison with SPI-12
Water years 1965-2007
Yaqui Water System
Inflow data courtesy of José Luis Minjares
r = 0.7
The one most relevant for a specific use
** *
Model Soil Moisture vs. Various SPI IndicatorsExample from the Eastern US (1950-200)
Layer 3
Layer 2
Layer 1
“VIC” Land Surface Model
www.hydro.washington.edu VIC soil moisture data courtesy of Justin Sheffield, Princeton University
Correlation “VIC” Soil Moisture and SPI
Which Indicator is Best?
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
-5000
-3000
-1000
1000
3000
5000
7000
1965 1970 1975 1980 1985 1990 1995 2000 2005
-2.5
-2
-1.5
-1
-0.5
0
0.5
1
1.5
2
2.5
Inflow
SPI-12
Tailored Forecasts
Drought & Agricultural Impacts in Sri Lanka (1960 – 2000)
Lyon et al., 2009, JAMC
• 40 yrs. of agricultural impacts data available at the district level.
• Which meteorological drought indicator is most closely associated with drought impacts to agriculture?
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
GCM FcstPRCP, Wind Statistical
ModelHistoricalInflows
IRI Seasonal FcstPr(Below-Normal Rainfall)
Tailored Forecasts
2-Mo. Lead Fcst for end of June 2010
2-Mo. Lead Fcst for end of June 2011
Low Risk High
Towards a Water Sector Impact Forecast for Mexico
Index of Water SectorVulnerability
Drought Index,Water ImpactRelationship:
IdentifyThresholds
IssuedApril 2010
IssuedApril 2011
Drought IndexForecast
ProbabilisticWater Supply
ImpactForecast
[ V + Pr(< threshold) ] [ 1+ Pr(< threshold) ] R =
0 ≤ V ≤ 1, 0 ≤ R ≤ 1
With Carolina Neri, UNAM
Drought Index ForecastProb. SPI6 < -1Issued in April
Low Risk High Low Risk High
ForJun 2011
= Probabilistic Water Impact Risk
Forecast Issued in April For
Jun 2010
ObsJun 2010
Observed SPI6in June
Dry Wet Dry Wet
+ Water Vulnerability
ForJun 2011
ObsJun 2011
ForJun 2010
Available Today Forecasts of 3, 6, 9 and 12-month SPI
Dec Prob. SPI12 < threshold
Dec SPIBest Estimate
Dec SPI1210% probability
Dec SPI12Best Estimate
Interactive: User selects Index, Thresholds,Probabilities of interest…
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.
AcknowledgementsThis work has been supported in part by the Modeling, Analysis, Predictions and Projections (MAPP) program at NOAA, which is gratefully acknowledged.
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
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Longer Time Scale Variations
A Simple Separation of Time Scales
The majority of the variation in rainfall is from
one year to the next…