forecasting evaporative demand across the conterminous us
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Forecasting Evaporative Demand across the Conterminous US. Michael Hobbins Dave Streubel Kevin Werner. Outline and Background. ET rc forecasts Improving streamflow forecasts - PowerPoint PPT PresentationTRANSCRIPT
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Forecasting Evaporative Demand across the Conterminous US
Michael HobbinsDave StreubelKevin Werner
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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
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• 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
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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.:
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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
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1. Forecasting ETrc across NWS Western RegionMean annual Penman-Monteith ETrc
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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)
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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
+
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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
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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.
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1. Forecasting ETrc across NWS Western RegionProject status: 12 Weather Forecast Offices
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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
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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
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• 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
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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
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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
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2. Dynamic Evaporative Demand in the Sac-SMA model at CBRFCMean annual PenPan, 1980-2009
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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
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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
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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.
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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
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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
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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)
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Uncertainty concept
atmdatmddd
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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
8834.30648303.54)(85.35
171.4098)(1
0115.04236142.131.1
1
222
432
TeeUfT
eUff
TLRTR
PRf
T
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asatqsatqT
dntoa
raddpT
pan
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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
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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
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Further workTrends in Epan, 1980 - 2009
Annual
January
July
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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