Transcript Diapositiva 1

Luca Brocca1, Tommaso Moramarco1,
Wolfgang Wagner2, Wouter Dorigo2,
Clement Albergel3, Simone Gabellani4
1 Research Institute for Geo-Hydrological Protection, CNR, Perugia, Italy
2 Department of Geodesy and Geoinformation, TU-Wien, Vienna, Austria
3 ECMWF, Reading, UK
4 CIMA research foundation, Savona, Italy
Date : 2013/03/20
http://hydrology.irpi.cnr.it
[email protected]
DOING HYDROLOGY BACKWARDS
RAINFALL
SOIL MOISTURE
Infiltration
evapotranspiration
SM2RAIN – “Doing hydrology backward”
relative saturation
Evapoprecipitation runoff transpiration drainage
soil
depth
Inverting for p(t):
Assuming:
Brocca et al., 2013 (GRL)
+
+
Soil water
balance
equation
SM2RAIN
MODEL TESTING
SOIL WATER BALANCE MODEL
VAL D’AOSTA
SOIL WATER BALANCE
MODEL
e(t):
evapotranspiration
s(t):
saturation
excess
Wmax
W(t)
NS= 0.905 NS(lnSD)= 0.911 NS(radSD)= 0.912 RQ= 0.906 RMSE= 0.017
0.35
CENTRAL
ITALY
0.3
soil moisture (m3m-3)
f(t):
infiltration
0.25
0.2
0.15
obs
sim
0.1
sup
30
25
rain (mm/h)
g(t):
percolation
0.05
35
20
15
10
5
Brocca et al., 2013 (HYP), 2013 (VZJ)
0
01/01/10
01/04/10
01/07/10
01/10/10
HOURLY SYNTHETIC DATA
1-hour
1-day
DAILY SYNTHETIC DATA
1-day
5-day
IN SITU OBSERVATIONS
Three sites in Italy, Spain and
France with hourly rainfall and
soil moisture observations are
selected
Estimation of daily rainfall for
1-year data
Italy
NS=0.82
Spain
NS=0.89
France
NS=0.81
SATELLITE DATA – ASCAT (4-years)
Italy
NS=0.57
Spain
NS=0.62
Estimation of
4-day rainfall
for 4-year data
SM2RAIN
VALIDATION OF
SATELLITE SOIL
MOISTURE
PRODUCTS
RAINFALL OBSERVATIONS (ITALY)
Precipitation input interpolated by
GRISO model:
Spatial resolution 2 km temporal
resolution 1 hour
Raingauges: ~ 3000
Temporal step: 5 - 10 min
GRISO Interpolation
1) Maintains the observed punctual rain value on the
raingauge spatial position in the interpolated field.
2) Maintains the mean value of the punctual rain
observations as the mean value of the interpolated
field.
HOURLY OBSERVATIONS FOR THE PERIOD Courtesy by CIMA (Gabellani Simone, …)
2010-2011  ASCAT GRID
SATELLITE AND MODELLED SOIL
MOISTURE DATA
1) ASCAT TU-Wien (FTP)
2) AMSR-E LPRM - asc,
desc, asc+desc (VUA)
3) ESA – CCI SM product
4) ERA-Land (ECMWF)
and
1) TRMM 3B42v7
(standard satellite
rainfall product)
ASCAT GRID ~ 12.5 km
CORRELATION MAPS: ASCAT SV AMSR-E
ASCAT
1-day
3-day
5-day
AMSR-E descending
1-day
3-day
5-day
CORRELATION MAPS: ASCAT SV AMSR-E
ASCAT
1-day
3-day
5-day
AMSR-E ascending + descending
1-day
3-day
5-day
ASCAT 2010-2011/10/03
5-day
5-day
5-day
AMSR-E
ASCAT 2010-2011
CORRELATION MAPS: ASCAT SV AMSR-E
ASCAT vs AMSR-E vs ESA-CCI
1 January 2010 – 31 December 2010 (3-day cumulated rainfall)
ESA-CCI
ASCAT
AMSR-E
merged
ASCAT vs ERA-Land vs TRMM
1 January 2010 – 31 December 2010 (3-day cumulated rainfall)
TRMM
ASCAT
ERA
Land
ASCAT
vs ERA
ASCAT vs AMSR-E vs ERA-Land vs ESA-CCI
1 January 2010 – 31 December 2010 (5-day cumulated rainfall)
PINK: ASCAT win!
CYAN: ASCAT lost!
ASCAT: Pobs vs PTRMM
1 January 2010 – 31 December 2010 (5-day cumulated rainfall)
ASCAT
Pobs
ASCAT
PTRMM
ASCAT NOISE vs SM2RAIN
1 January 2010 – 31 December 2010 (3-day cumulated rainfall)
ASCAT
SM2RAIN vs RCM
TCM 2007-2008 - RMSE
ASCAT
AMSR-E
SM2RAIN 2010-2011/10/03
RAINFALL MAPS (5-day): ASCAT
SPATIAL CORRELATION
ASCAT
AMSR-E
ascending + descending
TIME SERIES (best pixel)
5-day cumulated rainfall
ASCAT
AMSR-E
descending
TIME SERIES (best pixel)
5-day cumulated rainfall
ASCAT
AMSR-E
ascending + descending
TIME SERIES (central ITALY)
5-day cumulated rainfall
ASCAT
AMSR-E
ascending + descending
TIME SERIES (south ITALY)
5-day cumulated rainfall
ASCAT
AMSR-E
ascending + descending
Hydrological
applications of the
ESA-CCI SM product
ESA-CCI vs MODELLED SM (central Italy)
Brocca et al., 2013 (JoH, under review)
ESA-CCI vs MODELLED SM (Morocco)
Tramblay et al., 2012 (HESS)
ANTECEDENT WETNESS CONDITIONS
SICILY –
EROSION
MODELLING
600
0.54
rR== 0.54
500
N = 25
S
400
300
200
600
rR = 0.63
0.63
N = 31
100
500
0
0.3
0.4
theta 5-10 cm
r (in situ vs WACMOS) = 0.81
r (in situ vs ASCAT) = 0.91
400
0.5
S
0.2
300
200
600
500
rR = 0.69
0.69
N = 19
S
400
0
0.2
0.25
0.3
theta WACMOS
300
200
100
0
0.25
100
0.35
theta ASCAT
0.35
CONCLUSIONS
The SM2RAIN method can be effectively
use to estimate rainfall from soil
moisture observations
The large-scale and long-term validation
of satellite soil moisture products can be
carried out by using the SM2RAIN
method
References cited
Brocca, L., Melone, F., Moramarco, T., Wagner, W. (2013). A new method for rainfall
estimation through soil moisture observations. Geophysical Research Letters,
40, doi:10.1002/grl.50173.
Brocca, L., Camici, S., Melone, F., Moramarco, T., Martinez-Fernandez, J., DidonLescot, J.-F., Morbidelli, R. (2013). Improving the representation of soil
moisture by using a semi-analytical infiltration model. Hydrological Processes,
in press, doi:10.1002/hyp.9766.
Brocca, L., Tarpanelli, A., Melone, F., Moramarco, T., Caudaro, M., Ratto, S., Ferraris,
S., Berni, N., Ponziani, F., Wagner, W., Melzer, T. (2013). Soil moisture
estimation in alpine catchments through modelling and satellite observations.
Vadose Zone Journal, in press, doi:10.2136/vzj2012.0102.
Brocca, L., Zucco, G., Moramarco, T., Morbidelli, R. (...). Modelling soil moisture
spatial-temporal variability at catchment scale. submitted to Journal of
Hydrology.
Tramblay, Y., Bouaicha, R., Brocca, L., Dorigo, W., Bouvier, C., Camici, S., Servat, E.
(2012). Estimation of antecedent wetness conditions for flood modelling in
Northern Morocco. Hydrology and Earth System Sciences, 16, 4375-4386,
doi:10.5194/hess-16-4375-2012.
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