Forecasting Evaporative Demand across the Conterminous US

Michael HobbinsDave StreubelKevin Werner

Outline and Background

• ETrc forecasts

• Improving streamflow forecasts

• Evaporative demand: Upper constraint on actual evapotranspiration (ET). Numerous metrics, either physically based or more simple temperature-only based including:– Reference crop ET (ETrc)

– Pan Evaporation (Epan)– Potential ET

• provide forecasts of ETrc that are scientifically sound, web-disseminated, fine-resolution, accurate, and daily-to-weekly;

• develop a 30-year climatology to add context to these forecasts.

1. Forecasting ETrc across NWS Western Region

1. Forecasting ETrc across NWS Western RegionPenman-Monteith ETrc (standard FAO-56 formulation)

Weighted combination of radiative and advective drivers.

reference crop is specified:• well-watered grass,• actively growing,• 0.12 m in height,• completely shading the ground,• albedo of 0.23.

ETrc = reference crop ETλ = latent heat of vaporization

T = 2-m air temperatureΔ = desat/dT at T

γ = psychrometric constantQn = net available energy for ETesat = saturated vapor pressure

ea = actual vapor pressureU2 = 2-m wind speed

ETrc then multiplied by factors describing soil moisture, stress, and phenology, to yield an actual ET estimate, e.g.:

1. Forecasting ETrc across NWS Western RegionTwo set of model drivers

1. North American Land Data Assimilation System (NLDAS)

• Air temperature at 2-m elevation• Specific humidity at 2-m• Down-welling short-wave radiation• Down-welling long-wave radiation• Station pressure• Wind speed at 10-m

• Hourly time-step• 0.125-deg (~12 km) resolution• 1980 through present

2. National Digital Forecast Database (NDFD)

• Air temperature at 2-m• Dewpoint temperature at 2-m• Wind speed at 2-m• Areal extent of cloud cover

• Hourly, 3-hourly, or 6-hourly time-steps• 2.5-km / 5-km resolution HRAP grid

Forecast data

Reanalysis data for climatology

1. Forecasting ETrc across NWS Western RegionMean annual Penman-Monteith ETrc

Also

available at

multi-day

time-steps.

NLDAS = North American Land Data Assimilation System

1. Forecasting ETrc across NWS Western RegionNLDAS-forced climatologies of ETrc

-Computed for each day (1980 – present)

1. Forecasting ETrc across NWS Western RegionNLDAS-driven climatology and NDFD-driven forecasts

Climatology surface, specific to date and tailored to local area

Forecast surface, generated at Weather

Forecast Office

+

CBRF

C &

W

R-SS

DW

FO

PRO

DU

CT

ETrc forecast grid

• 2.5-km resolution• daily/weekly outlook

ETrc point forecast

• value-added• historical

context• spatial context

ETrc climatology grids• NLDAS-derived• 0.125o resolution• CONUS-wide• daily / weekly• moving window /

static weeks

meanvarianceminimum90% exceedancemedian (50%)10% exceedancemaximum

GFEscript

e.g., http://www.wrh.noaa.gov/sto/et

WFO

FO

RECA

ST

+ + +Input forecast grids• NDFD-derived• 2.5-km resolution• hourly

Wind Temperature Dewpoint Sky cover

1. Forecasting ETrc across NWS Western RegionProduct delivery

1. Forecasting ETrc across NWS Western RegionProduct delivery: Forecast ETrc (FRET) webpage

FRET website for Sacramento, CAhttp://www.wrh.noaa.gov/forecast/evap/FRET/FRET.php?wfo=sto

Operational status:• running at 12 NWS-WR WFOs:• soon at rest of NWS Western Region,

• eventually CONUS-wide (in line with NWS 2020 Goal #2),

• experimental period ends 06/30/2011.

1. Forecasting ETrc across NWS Western RegionProject status: 12 Weather Forecast Offices

1. Forecasting ETrc across NWS Western RegionProject status

Forecastoperations

• real-time• daily/weekly

Climatology• Jan 1980 – Dec 2009• high resolution• unbiased wrt forecasts

ETrc(climo)

ETrc(forecast)

statistical analysis

ETrc(t)

Value-added ETrc(forecast)

experimental www publication

www publication

feedback from users

verification

system spread

1. Forecasting ETrc across NWS Western RegionUses of ET-related reanalyses, real time analyses, and forecasts

• Drought analyses, reanalyses:• ongoing drought monitoring• forecast drought development• historical drought trends

(e.g., improved PDSI-analyses in Hobbins et al., [2008])• US Drought Monitor

• No explicit ET related input

• Demand-planning and management for:• Agriculture – irrigation scheduling• Municipal utilities – water management• Trans-mountain diversions• Reservoir operations

• Hydrologic science community

• Private industry – e.g., recreation, estimating water needed to support proposed developments

Reanalysis

Daily gridded time series

Multi-decadal analysis

Trend analysis

Real time analysis

Daily updating gridded time series

Consistent with reanalysis

Anomaly calculation

Forecasts

Days 1-5 based on NDFD

Seasonal forecasts

• Improve streamflow forecast skill at daily operational and seasonal time-scales;

• By improving ET-treatment in river forecast operations;

• By replacing the current, static evaporative driver of the Sac-SMA model with a physically based, accurate, and temporally dynamic driver.

2. Dynamic Evaporative Demand in the Sac-SMA model at CBRFCGoals

1. North American Land Data Assimilation System (NLDAS)

• Air temperature at 2-m elevation• Specific humidity at 2-m• Down-welling short-wave radiation• Down-welling long-wave radiation• Station pressure• Wind speed at 10-m

• Hourly time-step• 0.125-deg (~12 km) resolution

2. National Digital Forecast Database (NDFD)

• Air temperature at 2-m• Dewpoint temperature at 2-m• Wind speed at 2-m• Areal extent of cloud cover

• Hourly, 3-hourly, or 6-hourly time-steps• 2.5-km / 5-km resolution HRAP grid

Forecast data

Data for reanalyses of Epan, streamflow

2. Dynamic Evaporative Demand in the Sac-SMA model at CBRFCDrivers

Modifies Penman equation to replicate the enhanced characterization of radiative and advective dynamics of evaporation pans.

• weighted combination of radiative and advective drivers.

• synthesizes monthly Epan observations well.

Epan = synthetic pan evaporationλ = latent heat of vaporizationU2 = 2-m wind speedfq(U2) = vapor transfer function or “wind function”esat = saturated vapor pressureea = actual vapor pressureΔ = desat/dT at air temperatureaP = ratio of effective surface areas for heat and water-vapor transfer in a panγ = psychrometric constantQn = net available energy for Epan

2. Dynamic Evaporative Demand in the Sac-SMA model at CBRFCPenPan equation

2. Dynamic Evaporative Demand in the Sac-SMA model at CBRFCMean annual PenPan, 1980-2009

max, min daily Epan (1980-2009)mean daily Epan (1980-2009)1983 daily Epan

current, static evaporative demand

Dai

ly E

pan (

mm

)

2. Dynamic Evaporative Demand in the Sac-SMA model at CBRFCPenPan for Animas River at Durango, CO, 1980-2009

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec0

50

100

150

200

Observed Simulated, static Eo Simulated, dynamic Eo

Dai

ly m

ean

stre

amflo

w, m

3/se

c

2. Dynamic Evaporative Demand in the Sac-SMA model at CBRFCStreamflow for Animas River at Durango, CO, 1983

Future Work

• Address CBRFC operational issues (e.g., calibration, forecast mechanics)

• Temporal / spatial variability and trends (e.g., what drivers are dominating?)

• Apply evaporative demand forecasting methods to seasonal forecasts

• Real time ETrc accumulated anomalies as applied to drought

• Application to water management, drought monitor, etc.

Evaporative Demand as a metric of drought2002 drought examined across CONUS

30

1i

30

1i

E

E

Sep

Aprirc

Sep

Aprircirc

rc

ET

ETET

indexET

4/1 4/21 5/11 5/31 6/20 7/10 7/30 8/19 9/8 9/280

2

4

6

8

10

0

20

40

60

80

100

ObservedClimatologicalAccumulated difference

Date in 2002

Dai

ly E

Trc

(mm

)

X(t)

, acc

umul

ating

dai

ly E

Trc

dif-

fere

nce

(mm

)

30

1i

30

1i

E

E

Sep

Aprirc

Sep

Aprircirc

ET

ETET

X

Evaporative Demand as a metric of drought2002 drought examined at Lakewood, CO

> 0 positive anomaly in evaporative demand, DROUGHT X = 0 expected evaporative demand < 0 negative anomaly in evaporative demand

Evaporative Demand as a metric of drought2002 drought examined at Lakewood, CO

Double-mass curve: accumulating ETrc, Apr 1 – Sep 30, 2002

0 200 400 600 800 1000 12000

200

400

600

800

1000

1200

1:1

Climatological ETrc, Apr 1 - Sept 30 (mm)

Obs

erve

d ET

rc ,

Apr

1 -

Sep

30 (m

m)

Uncertainty concept

atmdatmddd

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dd

atmddpan

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atm

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Further workTemporal variability drivers in Epan

contribution to the variability in Epan of uncertainties in individual drivers varying independently

sensitivities are derived analytically from model

formulation

variances and covariances are derived empirically

contribution from the interdependence of all possible pairs of drivers

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1

222

432

TeeUfT

eUff

TLRTR

PRf

T

E

asatqsatqT

dntoa

raddpT

pan

Further workTemporal variability drivers in Epan, 1980 - 2009

Sens

itivi

tyCo

varia

nce

Varia

nce

Annual January JulyDecomposing variability in Epan – e.g., U2 and U2-SWdn

Annual

January

July

Further workMost significant drivers of Epan variability, 1980 - 2009

Annual

January

July

T

T

T

q

SWdn

SWdn

SWdn

covs

covs

covs

U10

q

q

Further workTrends in Epan, 1980 - 2009

Annual

January

July

mean, max, min daily Epan (1980-2009)1983 daily Epan

current, static E0

Epan across DRGC2H, 1980 - 2009 (& 1983)

Streamflow at DRGC2H, 1983

Mean annual Epan, 1980 - 2009

Skill-test of simulation at DRGC2H, 1980 - 2009

Modifies Penman equation to replicate the enhanced characterization of radiative and advective dynamics of evaporation pans.

• weighted combination of radiative and advective drivers.

• synthesizes monthly Epan observations well.

PenPan equation of synthetic pan evaporation

Dynamic evaporative demand across CBFC: DRGC2H test-basin