SAT-Data Actuele verdamping en verdampingstekort

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1 SAT-Data Actuele verdamping en verdampingstekort Minisymposium What's happening with SAT WATER Joris Timmermans (ITC) 22 april 214 Kees de Gooijer (HKV) Observant, Amersfoort

2 Overview Evapotranspiration introduction Remote sensing introduction Objective Data flows Cloudfree algorithm Cloudy data algorithm Data (pre) processing steps Data Merging Kwaliteitsborging Operationele dienstverlening

3 Latent Heat (Phase change) Precipitation Why Evapotranspiration Evapotranspiration (ET) refers to evaporation (from water or soil surfaces) and biospheric transpiration Remote Sensing scales are too large to separate the two ET is often considered plays a central role in the water, energy and carbon cycles, and is common to all three cycles: Wind Advection Biochemical Processes 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.

4 Why Remote Sensing Field measurements Different equipment Eddy covariance Scintillometry Bowen Ratio Number is increasing CarboEurope, FLUXNET Limited coverage Remote sensing satellites Global coverage

5 Which satellite data? SORCE Envisat Aura/Aqua/Terra QuikScat IKONOS THEOS CBERS SeaWiFS SPOT 4, 5 SPIN-2 SeaWinds Orbview 2, 3 TRMM DMC ACRIMSAT ERBS Radarsat ALOS EROS A1 Grace QuickBird Toms-EP UARS Jason Landsat 7

6 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!

7 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

8 Data flows

9 Cloudfree conditions: SEBS Energy Balance E R R n n G o H 4 1 R T T 4 swd G Rn C exp( LAI ) a a R n H E G o The difference in the Surface Energy Balance methods is the way that they calculate the Sensible Heat Single Source parameterization SEBAL, SEBI H Problem aero a a p ra r a and T aero can not be measured from space (yet). Two source models (TSEB, TSTIM, ALEXI) C H C a Problem model is too complex for implementation for global coverage p T T T T T T c r ac a r s ac r a as Ta Tc Ts Ta r a

10 For calculation of H SEBS needs to take account of the state of the atmosphere: SEBS Sensible Heat: MOS eqs. H ini, u* ini (MOS, m =, h =) z m z h o - a z u d H L z L d z z d z C ku H h h h h p a * ln L z L d z z d z k u u m m m m * ln kgh u C L p v 3 *

11 SEBS: Evaporative fraction Constrain evapotranspiration estimates using hypothetical limiting cases Evaporative fraction E E E wet r 1 1 Ewet Ewet H H dry H H wet wet r E R G n wet H wet is calculated by Penman Monteith (potential LE) H dry = R n -G (latent heat is set to zero)

12 LE+H [W/m 2 ] EF [] H [W/m 2 ] H [W/m 2 ] Time [H] Daily Evapotranspiration In some cases evaporative fraction displays a diurnal dependence DOY [#] DOY [#] Apr-28 DOY [#] 18-Jul DOY [#] : 6: 12: 18: time : 6: 12: 18: time Li, Kang et al. 28; Zhong, Ma et al. 29 multiple remote sensing images needed for calculating daily evapotranspiration Geostationary satellites have low spatial resolution

13 SEBS: Upscaling to daily E E daily 24 R n G E t dt 24 t R t G t dt w i n Rn, i G i w, i t i E E E wet r 1 1 Ewet Ewet H H dry H H For Satdata. the effective daily evaporative fraction is calculated 24 mean( i ) E 1 wet wet re R t 24 R G t R G 24 daily n, i, i i w i w n n wet G

14 Cloudy conditions Harmony meteorological model Based on ECMWF/HIRLAM data Reanalysis/Assimilation of meteorological data Air temp RH Radiation.. Forecast ECMWF meteorological forecast system Data Assimilation, obs LE/H/Rn HIRLAM meteorological forecast system Data Assimilation, obs HARMONIE meteorological forecast system Data Assimilation, obs

15 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

16 Estimating Biophysical Parameters Geostationary Orbiting LandSAF (LAI, fc, Albedo) ε= ε s (1 f c ) + ε v f c Orbiting Satellites MODIS LAI, Emissivity, at coarse res. NDVI at high res P 25m = F NDVI P 1km

17 Disaggregation Biophysical Parameters Attributing subpixel variability to MODIS data LAI landsaf (i, j) =< LAI > landsaf W landsa < LAI 1 n LAI MODIS(i, j) Albedo, Leaf Area Index, fractional vegetation cover W landsaf = LAI MODIS(i, j) < LAI > MODIS

18 Identification Cloudy Conditions 8 time frames [6:-9: 9:-1: 1:-11: 11:- 12: 12:-13: 12:-14: 14:-15: 15: -18:] Select best observation within time frame (collocated DSSF/DSLF/LST observations)

19 Identification of Thermodynamic Parameters

20 Collocation with Meteorological Data Meteorological Data, Harmony Tref (2m), Uref (1m), Pref, RH(ref)

21 Evapotranspiration outputs

22 Merged product

23 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 E pot uit HARMONIE Actuele verdamping Vergelijking met E act uit HARMONIE Vergelijking met LSA-SAF E act

24 Kwaliteitsborging

25 Kwaliteitsborging

26 Kwaliteitsborging: tijdreeksen E pot vs E Mak Lelystad Heino Hupsel De Kooy Twente Vlissingen

27 Kwaliteitsborging (operationeel) Bewaking van de operationele reken- en dataprocessen Geautomatiseerde controles op inhoud met behulp van Delft-FEWS

28 Datalevering SLA met SAT-Water Resolutie 25x25 meter en 8x8 meter Nederland (excl. open water) plus 1 km buiten de landsgrens Formaat: NetCDF Getest binnen Delft-FEWS FTP en OpenDAP Binnen 48 uur beschikbaar (conform contract) In praktijk binnen 12 uur (25x25 meter) en 24 uur (8x8 meter) 1 maand historie beschikbaar

29 Contact en/of vragen Kees de Gooijer (HKV): Joris Timmermans (ITC): Helpdesk: