Approaches to SeasonalDrought Prediction

Bradfield Lyon

CONAGUA Workshop24-26 Nov, 2014

Mexico City, Mexico

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.).

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

As input to a reservoir management tool…

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

(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