10 th Sept 201 Luca Brocca IRPI-CNR Luca Brocca T. Moramarco, S. Barbetta, A. Tarpanelli, S. Camici, C. Massari, G. Zucco, C. Corradini, P. Maccioni, L. Ciabatta SOIL MOISTURE: A KEY VARIABLE FOR LINKING SMALL SCALE CATCHMENT HYDROLOGY TO GLOBAL SCALE APPLICATIONS Research Institute for Geo-Hydrological Protection (IRPI- CNR), Perugia, Italy 50th anniversary symposium: State of the art measurements of catchment-scale hydrological processes http://hydrology.irpi.cnr.i 10 th Sept 2015
Transcript
Luca BroccaT. Moramarco, S. Barbetta, A. Tarpanelli, S. Camici, C. Massari, G. Zucco,
C. Corradini, P. Maccioni, L. Ciabatta
SOIL MOISTURE:A KEY VARIABLE FOR LINKING
SMALL SCALE CATCHMENT HYDROLOGY TO GLOBAL SCALE APPLICATIONS
Research Institute for Geo-Hydrological Protection (IRPI-CNR), Perugia, Italy
50th anniversary symposium:State of the art measurements of catchment-scale hydrological processes
http://hydrology.irpi.cnr.it
10 th Sept 2015
10th Sept 2015Luca Brocca
IRPI-CNR
Soil moisture is a key variable of the climate system.
Soil moisture generally refers to the amount of water stored in the unsaturated soil zone, although its exact definition can vary depending on the context, i.e. whether it is defined in relative, absolute or indirect terms, and depending on the reference storage.
What is soil moisture?
10th Sept 2015Luca Brocca
IRPI-CNR
Casentino basincentral Italy
30% increase of soil moisture produces a
8-fold increase of peak discharge!
FLOOD
DROUGHT
WEATHER PREDICTION
CLIMATE SYSTEM
LANDSLIDES
CROP PRODUCTION
Why soil moisture?
10th Sept 2015Luca Brocca
IRPI-CNR
ANTECEDENT WETNESS
CONDITIONSBrocca et al., 2009 JHE;
Massari et al., 2014 HESS; Tramblay et al.,
2012; …
SOIL MOISTURE SPATIAL-TEMPORAL
VARIABILITYBrocca et al., 2007 JoH; 2009 GEOD; 2010; 2014
WRR; Zucco et al., 2014; …
FLOOD FREQUENCY ANALYSIS
Camici et al., 2011 WRR
SOIL MOISTURE & LANDSLIDE
PREDICTIONBrocca et al., 2012 RS; Ponziani et al., 2012
LASL
RAINFALL-RUNOFF MODELLING
Brocca et al., 2011 HYP; 2013 HESS; Tayfur
et al., 2015 WARM
SOIL MOISTURE MODELLING
Brocca et al., 2008 HYP; 2014 HYP; Lacava
et al., 2012
SOIL MOISTURE & DROUGHT
MONITORINGMaccioni et al., 2014 JHE; Rahmani et al.,
2015 JAG
REMOTE SENSING VALIDATION
Brocca et al., 2011 RSE; Dorigo et al., 2015 RSE;
Wagner et al., 2013 IEEE TGRS; …
SOIL MOISTURE DATA ASSIMILATION
Brocca et al., 2010 HESS; 2012 IEEE TGRS; Massari et al., 2015 RS
GEOPHYSICAL METHODS
Calamita et al., 2012 JoH; 2015 JoH
SOIL MOISTURE FOR SOIL EROSION
Todisco et al., 2015 HESS
COSMIC-RAY NEUTRONS
Franz et al., 2015 GRL
SOIL MOISTURE & CLIMATE CHANGE
Camici et al., 2014 JHE; Ciabatta et al., 2015
JoH
NUMERICAL WEATHER
PREDICTIONCapecchi & Brocca et
al., 2014 METZET
FROM SURFACE TO ROOT-ZONE MODELLING
Brocca et al., 2010 RSE; Manfreda et al., 2014
HESS
SM2RAINBrocca et al., 2013 GRL;
2014 JGR; Massari et al., 2014 AWR;
Ciabatta et al., 2015 JHM; 2015 JAG
10-year of research on soil moisture
10th Sept 2015Luca Brocca
IRPI-CNR
Soil moisture monitoring with in situ and remote sensing
Understanding the spatial-temporal variability of soil moisture at different spatial scales
Assimilation of in situ and remote sensing soil moisture measurements into rainfall-runoff modelling
Detecting rainfall from the bottom up: using soil moisture observations for measuring rainfall (SM2RAIN)
Storyline
2014GRL paper
2010HESS paper
2007JoH paper
2005 2015
10th Sept 2015Luca Brocca
IRPI-CNRSoil moisture monitoring
IN SITU(TDR, FDR, Gravimetric, Geophysical methods,
The scale issue (for RS validation)!25 August 2015
10th Sept 2015Luca Brocca
IRPI-CNR
~25 kmsatellitepixels
Typical catchment size for hydrological studies.
HYDROLOGIST
too coarse for hydrological
applications !
The scale issue (for hydrology)!
10th Sept 2015Luca Brocca
IRPI-CNRFilling the scale gapCOSMOS rover: cosmic-ray neutrons
12 km
12 km 22 surveys in 5 months: ~300 measures/5 hours
Also GPS (see Kristine Larson), Geophysics methods (EMI, Resistivity)
10th Sept 2015Luca Brocca
IRPI-CNR
What is the relation between point and area-averaged soil moisture
measurements?
PLOT SCALE 400-9000 m2
CEN
TRA
L IT
ALY
Brocca et al., 2009 (GEOD)
SMALL CATCHMENT
SCALE ~50 km2
20
25
30
35
40
45
50
20 30 40 50
Mean soil moisture (%)"R
epre
sent
ativ
e" s
ite s
oil m
oist
ure
(%) Castel Rigone
Casale BelfioreVal di Rosa
Brocca et al., 2010 (WRR)
CATCHMENT SCALE~250 km2
Brocca et al., 2012 (JoH)
USA
Cosh et al., 2006 (JoH)
AFRICA
de Rosnay et al., 2009 (JoH)
ASIA
Zhao et al., 2010 (HYP)
Soil moisture temporal stability
Brocca et al., 2010 (WRR)
REMOTE SENSINGGlobal scale(~1000 km²)
MODELLINGCatchment
scale(~100 km²)
IN SITUPlot scale(~100
m²)
10th Sept 2015Luca Brocca
IRPI-CNRSoil moisture information content
Simply matching mean and variance
Different land models show substantial differences
“Large differences are typical between soil moisture estimates from different climate models […] in modelling studies [], the temporal anomalies of soil moisture are usually of greater interest as most of the informative content of soil moisture data is not in their absolute values, but in their temporal dynamics”
10th Sept 2015Luca Brocca
IRPI-CNRAbsolute soil moisture vs anomalies
ABSOLUTE SOIL MOISTURE
TEMPORAL MEAN: time-invariant component
TEMPORAL ANOMALIES: time-varying component
10th Sept 2015Luca Brocca
IRPI-CNR
ABSOLUTE SM ANOMALIES RELATIVE SM
Absolute soil moisture vs anomalies
For large scale and spatial heterogeneous soil moisture network (France, Spain, Switzerland, Australia) the time invariant component (green bar) is the major contributor to the total spatial variance.
Australia France
Italy Spain
Switzerland USA
Spain
Total variabilityTime invariant comp. (temp. mean)Time variant comp. (anomalies)Covariance
Network size between 200 and 150000 km²
Absolute and anomaly soil moisture data behave very differently.
How to use this understanding for remote sensing validation and in
hydrological applications (e.g., data assimilation)?
10th Sept 2015Luca Brocca
IRPI-CNRIn situ vs remote sensing
Median correlation ~0.6-0.7
~1500 measurement stations / 40 networks
10th Sept 2015Luca Brocca
IRPI-CNRIn situ & RS for RR modelling
Satellite vs modelled soil moisture
In situ soil moisture as initial condition of RR modelling
Tramblay et al., 2012 (HESS)
Brocca et al., 2009 (JHE)
In situ soil moisture measurement at an experimental plot are used to set the initial conditions of an event-based rainfall-runoff model with successfully results.
Satellite and modelled soil moisture data are in good agreement for a period of 25 years!
137km²
60km²
13km²
R²
10th Sept 2015Luca Brocca
IRPI-CNRA Simplified Continuous RR model
Advantages1) No need of continuous rainfall and evapotranspiration datasets.Good in poorly gauged areas!2) Parsimony and simplicity.Good for operational purposes!
In rainfall-runoff modelling …In the last decades a number studies performed data assimilation experiments and tested different techniques and approaches for soil moisture assimilation within rainfall-runoff modelling …In situ soil moisture Satellite soil moisture
Loumagne et al., 2001 (HSJ) Pauwels et al., 2001, 2002 (JoH, HYP)
Aubert et al., 2003 (JoH) Francois et al., 2003 (HSJ)
Anctil et al., 2008 (JoH) Crow et al., 2005 (GRL)
Brocca et al. 2009 (JHE) Brocca et al., 2010, 2012, 2013 (HESS, IEEE TGRS, IGARSS)
Lee et al. 2011 (AWR) Draper et al., 2011 (HESS)
Matgen et al. 2011 (AWR) Chen et al., 2011, 2014 (AWR, JHM)
Massari et al., 2014 (HESS) Matgen et al., 2012 (AWR)
Alvarez-Garreton et al., 2014, 2015 (JoH, HESS)
Wanders et al., 2014 (HESS)
Lievens et al., 2015 (RSE)
Corato et al., 2015 (RSE)
Soil moisture data assimilation
From 2010However, few studies demonstrated the value of assimilating real soil moisture data for improving runoff prediction
and there are still many controversial issues to be solved…
Observations1) In situ2) Satellite data3) Land surface model data
Observation error1) Temporal variability of
the obs. error2) Spatial correlation
between the observations3) Masking
“Cooking” techniques
The problem is often not the ingredients but the cooking
technique …
Model error1) Model error covariance estimation (i.e. EnkF: ensemble size)2) What to perturb. (parameters, inputs, states etc …)3) How to perturb (amount of perturbation)
A complex recipe?
10th Sept 2015Luca Brocca
IRPI-CNR
Bias handling
Filtering Temporalvariability
Spatialvariability
What toperturb
Biascorrection
Ensemblesize
Ensembleverification
Given 1 RR model (e.g., HBV), 1 observation dataset (e.g., SMOS), and 1 assimilation technique (e.g., EnKF), we can obtain 2300different results!!!
The task can be even more difficult if we consider different
catchments, climatic, soil, land use conditions, ….
… for a complex topic?
Only changing the cooking techniques
10th Sept 2015Luca Brocca
IRPI-CNRTiber River BasinBasin Area (km2)Tevere at Ponte Felcino 2080Nestore at Marsciano 725Chiani at Morrano 457Topino at Bevagna 440Marroggia at Azzano 258Niccone at Migianella 137
Rainfall-runoff data from 1989 at hourly time resolution
6 sub-catchments(140-2080 km²)
A systematic study…
10th Sept 2015Luca Brocca
IRPI-CNR…toward data assimilation guidelines
10th Sept 2015Luca Brocca
IRPI-CNR
RAINFALL SOIL MOISTURE
The soil moisture variations are strongly related to the amount of rainfall falling into the soil. Therefore, we can use soil moisture observations for estimating rainfall by considering the “soil as a natural raingauge”.
Doing hydrology backward
10th Sept 2015Luca Brocca
IRPI-CNR
Is it raining?
radar raingauge
Remote sensing of rainfall
TOP-DOWN PERSPECTIVE
BOTTOM-UP PERSPECTIVE: CAN WE USE SOIL MOISTURE DATA TO INFER THE AMOUNT OF
WATER FALLING INTO THE SOIL?
“Top down” vs “bottom up”
10th Sept 2015Luca Brocca
IRPI-CNR
Ptrue=94 mmWith only two overpasses the bottom up approach provides a better estimate of the accumulated rainfall
Pbottom-up=(92-2)= 90 mm
TOP DOWN PERSPECTIVE
5 0 2 8 The underestimation is due to the satellite overpasses in period with low rainfall
Ptop-down=(5+0+2+8)*4= 60 mm
BOTTOM UP PERSPECTIVE
2
92
“Top down” vs “bottom up”
10th Sept 2015Luca Brocca
IRPI-CNR
precipitationsurface runoff
evapotranspiration
drainage
soil water capacity
relative saturation
Inverting for p(t):
= soil depth X porosity
Assuming: + +during rainfall
Soil water balance equation
SM2RAIN algorithm
10th Sept 2015Luca Brocca
IRPI-CNR
2013
2014
2015
SM2RAIN dataset from ASCAT, 0.25°, 2007-2013, freely available
SM2RAIN papers … so far!
10th Sept 2015Luca Brocca
IRPI-CNR
calibration validation0.75<R<0.95
In situ soil moisture observations
R
fRMSE fRMSE
R
Application to in situ observations …
10th Sept 2015Luca Brocca
IRPI-CNR
Correlation map between 5-day rainfall from GPCC and the rainfall product obtained from the application of SM2RAIN algorithm to ASCAT, AMSR-E and SMOS data plus TMPA 3B42RT(VALIDATION period 2010-2011)
… and to satellite data: global scale
10th Sept 2015Luca Brocca
IRPI-CNR
2007-2009 ERA-
Interim as benchmark
5-day cumulated
The correlation is 25% higher than TMPA real time rainfall product
0.504 0 .640
Integration of multiple datasets
SM2RAIN (ASCAT+QUIKSCAT)
TMPA (3B42RT)
Median correlation (+/- 50° lat. band) = 0.640
Median correlation (+/- 50° lat. band) = 0.504 TOP-DOWN
BOTTOM-UP
10th Sept 2015Luca Brocca
IRPI-CNR
Time step: 1-day
Bottom up + Top down
TOP-DOWN
BOTTOM-UP TOP-DOWN
BOTTOM-UP
Central Italy: R=0.86
10th Sept 2015Luca Brocca
IRPI-CNRFuture directions …Improving, testing, and integrating NEW monitoring techniques able to provide soil moisture measurements at catchment scale: COSMOS, GPS, Electromagnetic induction, Remote sensing (e.g., SMAP), …
Investigating the assimilation of in situ and satellite soil moisture observations in rainfall-runoff modelling for different basins, climates, …… also in contrast with conventional hydrological approaches (e.g., assimilation of river discharge)
SM2RAIN: from research to operational applications, thanks to funding from new research project starting in September: ESA SMOS+rainfall, ESA CCI, EUMETSAT H-SAF
10th Sept 2015Luca Brocca
IRPI-CNR… and open issuesHow to reduce the spatial scale gap between in situ measurements, modelling, and remote sensing? What is the role of soil moisture spatial variability? Absolute soil moisture or temporal anomalies? Spatial or temporal variability? Surface or root-zone measurements?
How much improvement can we expect from using in situ and satellite soil moisture observations in hydrological applications?Is it really useful? What is the role of soil moisture spatial variability?
Are we able to model/simulate soil moisture spatial variability?Models usually provide good simulation for soil moisture temporal evolution, but not in space
10th Sept 2015Luca Brocca
IRPI-CNR
This presentation is available for download at: http://hydrology.irpi.cnr.it/repository/public/presentations/2015/ Wageningen-l.-brocca
FOR FURTHER INFORMATIONURL: http://hydrology.irpi.cnr.it/people/l.brocca
