SAT-Data Actuele verdamping en verdampingstekort Minisymposium ‘What's happening with SAT‐WATER’
Joris Timmermans (ITC) Kees de Gooijer (HKV)
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22 april 2014 Observant, Amersfoort
Overview • Evapotranspiration introduction • Remote sensing introduction • Objective • Data flows – Cloudfree algorithm – Cloudy data algorithm
• Data (pre) processing steps – Data Merging
• Kwaliteitsborging • Operationele dienstverlening www.hkv.nl
Why Evapotranspiration Evapotranspiration (ET) refers to evaporation (from water or soil surfaces) and biospheric transpiration –
Precipitation
ET is often considered plays a central role in the water, energy and carbon cycles, and is common to all three cycles:
Latent Heat
•
Remote Sensing scales are too large to separate the two
(Phase change)
•
Wind
Advection
Biochemical Processes
• •
•
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Quantification of terrestrial ET helps determine the biological environment and its water use efficiency ET is of primary interest to water resources management in practice because many end users need ET to estimate the loss of useable water from the soil column and to help determine plant water stress for – – –
drought assessment, agricultural irrigation management, forest fire susceptibility.
Knowledge of surface ET helps in understanding the formation of summertime convective precipitation patterns.
Why Remote Sensing •
Field measurements – Different equipment • Eddy covariance • Scintillometry • Bowen Ratio
– Number is increasing • CarboEurope, FLUXNET
– Limited coverage
•
Remote sensing – satellites – Global coverage
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Which satellite data? Aura/Aqua/Terra
Envisat
SORCE QuikScat
THEOS IKONOS CBERS
SeaWiFS
SPOT 4, 5 SPIN-2
SeaWinds
TRMM
Orbview 2, 3 DMC
ACRIMSAT EROS A1
ERBS Radarsat
ALOS Toms-EP
Grace
QuickBird
UARS
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Jason
Landsat 7
Remote sensing introduction
• Sun-synchronous satellites – High res, ‘low’ frequency
• Geostationary satellites – High freq, ‘low’ res
• Evapotranspiration is highly affected by land surface temperature: – Thermal bands are required! www.hkv.nl
Objective
• To create an evapotranspiration product that combines the best of different satellite types: – Combine Data from Geostationary and Orbiting Satellites • Improve observation frequency • Improve resolutions • Decrease uncertainties
– Combine Evapotranspiration estimated from satellites with Meteorological model • Facilitate estimation of land-heat fluxes • Improve accuracy • Provide gap-free image
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Data flows
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Cloudfree conditions: SEBS • Energy Balance E Rn Go H
Rn 1 Rswd aTa4 T04
Rn
H E Go
G0 Rn C exp( LAI )
• The difference in the Surface Energy Balance methods is the way that they calculate the Sensible Heat • Single Source parameterization T T • SEBAL, SEBI H a C p aero a Ta ra • Problem – ra and Taero can not be measured from space (yet).
– Two source models
• (TSEB, TSTIM, ALEXI) • Problem
T T T T H aC p c a s a rac ras rac
– model is too complex for implementation for global coverage
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Tc Ts
ra Ta
SEBS Sensible Heat: • For calculation of H SEBS needs to take account of the state of the atmosphere: z d0 z d0 z0 h 0 a h h ln ku* C p z0 h L L u z d0 z d0 z0 m m u * ln m k z0 m L L C pu*3 v L kgH H
z0m u
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z0h
a
o-
z d
Hini, u*ini
(MOS, m=0, h=0)
MOS eqs.
H
SEBS: Evaporative fraction
• Constrain evapotranspiration estimates using hypothetical limiting cases – Evaporative fraction E E H H wet E r 1 wet 1 Ewet Ewet H dry H wet
r Ewet Rn G
• Hwet is calculated by Penman Monteith (potential LE) • Hdry = Rn-G0 (latent heat is set to zero)
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Daily Evapotranspiration
1
400
5 10 15 20
200 0 -200 50
100
150
200 DOY [#]
250
300
5 Time [H]
H [W/m2]
• In some cases evaporative fraction displays a diurnal dependence 0.5
10 0
15
-0.5
20
350
50
100
H [W/m2]
09-Apr-2008 5 10 15 20
400 200 0 100
150
200 DOY [#]
250
300
350
LE+H [W/m 2]
1
6
0.5
5
10
-0.5
0 20
350
-1
2
-1
1 0
-1.5
-1
-2
-2
200 15
300 18-Jul-2008
4
5 400
250
3
-200
600
200 DOY [#]
0
EF []
50
150
-2.5
-3
-200 50
100
150
200 DOY [#]
250
300
350
-4
-3 00:00
06:00
12:00 time
18:00
00:00
06:00
12:00 time
18:00
– Li, Kang et al. 2008; Zhong, Ma et al. 2009 – multiple remote sensing images needed for calculating daily evapotranspiration • Geostationary satellites have low spatial resolution
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SEBS: Upscaling to daily E Rn G0
R t G0 t R G0,i dt i n,i ti Edaily E t dt t n i w w 0 0 24
r
24
E E H H wet E 1 wet 1 Ewet Ewet H dry H wet
r Ewet Rn G0
• For Satdata. – the effective daily evaporative fraction is calculated 24
mean( i ) 0
Edaily
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1
w
24
t
0
w
Rn,i G0,i ti i
24
Rn G0 0
Cloudy conditions
• Harmony meteorological model – Based on ECMWF/HIRLAM data – Reanalysis/Assimilation of meteorological data • • • •
Air temp RH Radiation ..
– Forecast • LE/H/Rn
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ECMWF meteorological forecast system
Data Assimilation, obs
HIRLAM meteorological forecast system
Data Assimilation, obs
HARMONIE meteorological forecast system
Data Assimilation, obs
Data Preprocessing
• Data (pre) processing steps – Estimating Biophysical Parameters – Disaggregation Biophysical Parameters – Identification cloudy conditions – Identification of Thermodynamic Parameters – Collocation with Meteorological Data – Merging Datastreams
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Estimating Biophysical Parameters • Geostationary Orbiting – LandSAF (LAI, fc, Albedo)
𝜖= 𝜖𝑠 ∗ (1 − 𝑓𝑐 ) + 𝜖𝑣 ∗ 𝑓𝑐
• Orbiting Satellites – MODIS
• LAI, Emissivity, at coarse res. • NDVI at high res
𝑃250𝑚 = 𝐹 𝑁𝐷𝑉𝐼 ∗ 𝑃1𝑘𝑚
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Disaggregation Biophysical Parameters
• Attributing subpixel variability to MODIS data
𝐿𝐴𝐼𝑙𝑎𝑛𝑑𝑠𝑎𝑓 (𝑖, 𝑗) =< 𝐿𝐴𝐼 >𝑙𝑎𝑛𝑑𝑠𝑎𝑓 ∗ 𝑊𝑙𝑎𝑛𝑑𝑠𝑎 1 < 𝐿𝐴𝐼 ≥ ∑𝐿𝐴𝐼𝑀𝑂𝐷𝐼𝑆 (𝑖, 𝑗) 𝑛
• Albedo, Leaf Area Index, fractional vegetation cover
𝑊𝑙𝑎𝑛𝑑𝑠𝑎𝑓
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𝐿𝐴𝐼𝑀𝑂𝐷𝐼𝑆 (𝑖, 𝑗) = < 𝐿𝐴𝐼 >𝑀𝑂𝐷𝐼𝑆
Identification Cloudy Conditions
• 8 time frames – [06:00-09:00 09:00-10:00 10:00-11:00 11:0012:00 12:00-13:00 12:00-14:00 14:00-15:00 15:00 -18:00] – Select best observation within time frame (collocated DSSF/DSLF/LST observations)
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Identification of Thermodynamic Parameters
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Collocation with Meteorological Data
• Meteorological Data, Harmony – Tref (2m), Uref (10m), Pref, RH(ref)
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Evapotranspiration outputs
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Merged product
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Kwaliteitsborging (inhoud) • Ruimtelijke dekking en projectie met ruimtelijke en temporele patronen • Inhoudelijke tests Onbewolkt, bewolkt en combinatie <1,5 mm absoluut verschil en >1,5 mm relatief verschil – Potentiële verdamping • Vergelijking met Makkink (KNMI-stations) • Vergelijking met Epot uit HARMONIE – Actuele verdamping • Vergelijking met Eact uit HARMONIE • Vergelijking met LSA-SAF Eact www.hkv.nl
Kwaliteitsborging
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Kwaliteitsborging
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Kwaliteitsborging: tijdreeksen Epot vs EMak
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Lelystad
Heino
Hupsel
De Kooy
Twente
Vlissingen
Kwaliteitsborging (operationeel)
• Bewaking van de operationele reken- en dataprocessen • Geautomatiseerde controles op inhoud met behulp van Delft-FEWS
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Datalevering
• SLA met SAT-Water • Resolutie 250x250 meter en 8x8 meter • Nederland (excl. open water) plus 10 km buiten de landsgrens • Formaat: NetCDF Getest binnen Delft-FEWS • FTP en OpenDAP • Binnen 48 uur beschikbaar (conform contract) In praktijk binnen 12 uur (250x250 meter) en 24 uur (8x8 meter) • 1 maand historie beschikbaar www.hkv.nl
Contact en/of vragen
Kees de Gooijer (HKV):
[email protected] Joris Timmermans (ITC):
[email protected] Helpdesk:
[email protected] 0320 - 294242
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