Adding Confidence to Seasonal Drought Forecasts Using

Reference Evapotranspiration Anomalies

U.S. Drought Monitor Forum

April 15th, 2015

Reno, NV

Dan McEvoy,

Graduate Research

Assistant and PhD

Candidate

Desert Research Institute

and University of Nevada,

Reno

Photo credit: Steve Carver

What is evaporative demand (E0)?

(Figure: http://www.fao.org/docrep/s2022e/s2022e07.htm)

• Evapotranspiration rate that

could occur given an unlimited

water supply

• Potential evapotranspiration

• Atmospheric demand

• Reference evapotranspiration

• Often estimated using

temperature alone, but a

physically based model should

be used

Physically-based E0 contains valuable information related to drought dynamics

“ATMOSPHERIC THIRST”

Reference Evapotranspiration (ETo)

ASCE Standardized Reference ET (Allen et al. 1998, 2005):

𝐸𝑇𝑜 =Δ

Δ + 𝛾(1 + 0.34𝑢2)

𝑅𝑛 − 𝐺

𝜆+

𝛾

Δ + 𝛾 1 + 0.34𝑢2

900

𝑇 + 273𝑢2(𝑒𝑠 − 𝑒𝑎)

Advective component

Radiative component

Rn = net radiation (shortwave + longwave)

G = ground heat flux (assumed to be zero)

T = mean daily temperature

U2 = mean daily wind speed at 2-m

es = saturation vapor pressure ((es_tmax+es_tmin)/2)

ea = actual vapor pressure (from q and surface pressure)

λ = latent heat of vaporization

Δ = slope of saturation vapor pressure-temperature curve

γ = psychrometric constant

Cn = 900 (grass reference)

Cd = 0.34 (grass reference)

Gridded weather/climate data:

• Maximum and temperature (2 m)

• Specific or relative humidity (2 m)

• Wind speed (10 m)

• Downward shortwave radiation at

surface

Evaporative Demand Drought Index (EDDI)

(EDDI; Hobbins et al. 2011, McEvoy et

al 2014)

• EDDI: same standardization procedure as SPI with ETo as only input

• Based on the historical probability distribution for a given accumulation window

• EDDI identifies current western “snow drought” despite above normal

precipitation in some locations.

Represents previous 6 months, October – March

• 4-km spatial resolution

• Daily temporal resolution

• METDATA, 1979-present

• (Abatzoglou 2011)

• http://metdata.northwest

knowledge.net/)

EDDI and Snow Drought

Red: <50%, Brown: 50-69%, Yellow: 70-89%

EDDI as an Early Warning Indicator: 2012

Evaporative Stress Index

(1-month ESI)

Standardized Soil Moisture

Index (1-month SSI)

Standardized Precipitation

Index (1-month SPI)

1-month EDDI

(ESI data provided courtesy Martha Anderson and Chris Hain)

USDM

Motivation: Poor Skill in Precipitation Forecasts

From Saha et al. 2014: “Except for the first month (lead 0), which is essentially weather

prediction in the first two weeks, there is no skill at all, which is a sobering conclusion”

Other studies also highlight this problem: e.g., Lavers et al. 2009, Yuan et al. 2011, Yuan et al. 2013,

Wood et al. 2015

Motivation: Seasonal ETo Prediction

Previous research :

• Tian, D. and C. J. Martinez, 2014: Seasonal prediction of regional reference

evapotranspiration based on Climate Forecast System version 2. J. Hydrometeor., 15,

1166-1188.

Moderate

skill, cold

seasons

No skill,

warm

seasons

• Only AL,

GA, and FL

• No evaluation

of humidity

• No drought

perspective

(Karl and Koss, 1984)

CONUS Scale Analysis: Seasonal ETo Prediction

9 NCDC Climate Regions (Karl and Koss, 1984)

• CFSv2 Reforecast

• CFSR used as baseline observations

• Monthly mean Tmax, Tmin, specific

humidity, wind speed, solar radiation,

and precipitation

• Monthly mean ETo

• Deterministic skill: temporal anomaly

correlation (ensemble mean)

• Probabilistic skill: Heidke Skill score during

drought events

CFSv2 Reforecast Data (CFSRF)

(CFSv2: Saha et al., 2014)

• Reforecast period of record: 1982-2009 monthly means

• Spatial resolution: 1° x 1°

• Lead times used: 1 to 9 months

Season 1 ETo vs. PPT Regional Skill

• ETo forecasts almost always contain greater skill than PPT

• Certain regions and seasons contain a large skill gap

Lead 1-month, Season 1 AC

Case Study: 2002 Southwest Drought

• Forecasts initialized in March, 2002

• Spatial extent better predicted by ETo

• Severity poorly forecast by both metrics in southwest

Case Study: 2002 Southwest Drought

• ETo better

predicted D0-

D4 and D1-D4

percentages

• Poor prediction

of severe to

exceptional

drought, both

ETo and PPT

Red = ETo

Blue = PPT

Percent area of southwest region in given drought categories: AMJ 2002

ETo Predictability and ENSO

ENSO event:

Oceanic Niño Index

(ONI)

>1 or <-1

Reduce noise from

weak ENSO signal

76 events (months) of

336 possible

(ONI:

http://www.cpc.ncep.noaa.gov/products/analy

sis_monitoring/ensostuff/ensoyears.shtml)

ETo Predictability and ENSO

Conclusions and Ongoing Efforts

• Skill analysis with second set of

observations (NARR or ERA-40)

• Produce experimental real-time

seasonal ETo forecast from CFSv2

• Apply analysis to NMME Phase II

output

Thank You!

Questions?

mcevoyd@dri.edu

The use of ETo anomalies as a seasonal forecast tool could add confidence to

drought predictions when used in addition to PPT and other drought metrics.

NMME:

http://www.cpc.ncep.noaa.gov/prod

ucts/NMME/

CFSR E0 and Precipitation Anomalies

Climate

Forecast

System

Reanalysis

(CFSR)

(CFSR; Saha et al. 2010)