Luca Brocca, Christian Massari, Luca Ciabatta, Tommaso Moramarco Research Institute for Geo-Hydrological Protection (IRPI-CNR), Perugia, Italy …and many.

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Luca Brocca, Christian Massari, Luca Ciabatta, Tommaso Moramarco Research Institute for Geo-Hydrological Protection (IRPI-CNR), Perugia, Italy …and many others: W. Wagner, W. Dorigo, S. Hahn, S. Hasenauer, C. Albergel, P. De Rosnay, S. Puca, S. Gabellani, R. Parinussa, R. De Jeu, P. Matgen, D. Penna, J. Martinez-Fernandez, V. Levizzani, …

MOISST 2015 Stillwater 2 nd June 2015 Brocca Luca INTRODUCTIONINTRODUCTION Casentino basin central Italy 30% increase of soil moisture produces a 8-fold increase of peak discharge! Runoff generation Brocca et al. (2010, HESS+WRR, 2012 TGRS, …) Landslide prediction Brocca et al. (2012, RS) Numerical Weather Prediction Capecchi & Brocca (2014, METZET) Erosion modelling Todisco et al. (2015, HESSD) Plant production Bolten et al. (2010, JSTARS) Water quality modelling Han et al. (2012, HYP) Drought monitoring Rahmani et al. (2015, JAG, in press)

MOISST 2015 Stillwater 2 nd June 2015 Brocca Luca Soil moisture is needed by all GEO Social Benefit Areas and was ranked the second top priority parameter (behind precipitation) in a year 2010 GEO report on "Critical Earth Observation Priorities". 01a_SummaryBrochure.pdfINTRODUCTIONINTRODUCTION

MOISST 2015 Stillwater 2 nd June 2015 Brocca Luca

MOISST 2015 Stillwater 2 nd June 2015 Brocca Luca 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”. What is SM2RAIN?

MOISST 2015 Stillwater 2 nd June 2015 Brocca Luca precipitation surface 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

MOISST 2015 Stillwater 2 nd June 2015 Brocca Luca calibration validation 0.75<R<0.95 In situ soil moisture observations SM2RAIN: in situ observations R fRMSE R

MOISST 2015 Stillwater 2 nd June 2015 Brocca Luca SM2RAIN: in situ observations Soil moisture variations 64% Drainage 30% Percentage contribution to the total simulated rainfall of the different components of the water balance Actual Evapotranspiration 4% Surface runoff 2%

MOISST 2015 Stillwater 2 nd June 2015 Brocca Luca

MOISST 2015 Stillwater 2 nd June 2015 Brocca Luca CRASH TEST: timeseries Central Italy Central Australia Southern USA Siberia Congo

MOISST 2015 Stillwater 2 nd June 2015 Brocca Luca 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 ) Global scale: real satellite observations

MOISST 2015 Stillwater 2 nd June 2015 Brocca Luca Global monthly rainfall from ASCAT

MOISST 2015 Stillwater 2 nd June 2015 Brocca Luca Application to QUIKSCAT, AMSR Improved algorithm Distributed calibration Application of filtering techniques (e.g. wavelet) Application to AMSR2 (C+X-band radiometer) Application to QUIKSCAT (Ku-band scatterometer),

MOISST 2015 Stillwater 2 nd June 2015 Brocca Luca ERA-Interim as benchmark 5-day cumulated ASCAT+QUIKSCAT vs TMPA-RT The correlation is 25% higher than TMPA real time rainfall product  0.640

MOISST 2015 Stillwater 2 nd June 2015 Brocca Luca

MOISST 2015 Stillwater 2 nd June 2015 Brocca Luca ASCAT SMOS Estimation of 5-day rainfall for 2-year data ASCAT+ SMOS R increases from 0.65 (0.63) to 0.78 RMSE decreases from 28.0 (30.0) to 22.8 mm (28%) Mean values SMOS + ASCAT

MOISST 2015 Stillwater 2 nd June 2015 Brocca Luca Merging Metop-A and B Metop-A Metop-B Rainfall events detected only by considering both sensors Metop A and B Blu: MetopA+MetopB Red: MetopA Black: rainfall

MOISST 2015 Stillwater 2 nd June 2015 Brocca Luca Merging Metop-A and B Ciabatta et al. (2015) – JHM Median R= 0.44 T=0 (no SWI application) Data Period: 2-may-2013  31-dec-2014 Metop A+B improves by 14% wrt single sensors Metop A+B improves by 37% wrt Ciabatta et al. JHM (calibration period) Metop-AMetop-B Metop-A+B Time step: 1-day

MOISST 2015 Stillwater 2 nd June 2015 Brocca Luca Merging ASCAT and AMSR2 Lon,Lat: -5,40 Period: may 2013-dec 2014 ASCAT: Metop A+B AMSR2: Asc+Desc, X-band Benchmark: ERA-Interim AMSR2+ASCAT AMSR2 ASCAT Time step: 1-day R=0.59R=0.66 R=0.76

MOISST 2015 Stillwater 2 nd June 2015 Brocca Luca

MOISST 2015 Stillwater 2 nd June 2015 Brocca Luca SMOS+TMPA in Australia Period AWAP rainfall as benchmark Calibration with 3B42 5-day cumulated 3B42RT median R=0.714 SM2R SMOS median R=0.733 SM2R SMOS +3B42RT median R=0.757

MOISST 2015 Stillwater 2 nd June 2015 Brocca Luca ASCAT+H-SAF in Italy Time step: 1-day

MOISST 2015 Stillwater 2 nd June 2015 Brocca Luca

MOISST 2015 Stillwater 2 nd June 2015 Brocca Luca VALESCURE CATCHMENT (France) IN SITU OBSERVATIONS BLACK: simulated Q with raingauge+SM2RAIN data RED: simulated Q with raingauge data only GREEN: observed discharge (Q) BLUE: observed rainfall (P obs ) We obtained an increase in performance from NS=0.49 to NS=0.83 when SM2RAIN-derived rainfall is used for correcting rainfall observations during the flood event

MOISST 2015 Stillwater 2 nd June 2015 Brocca Luca Runoff simulation by using as input 1. Observed rainfall 2. SM2RAINASCAT 3. SM2RAINASCAT + Observed Rainfall ITALY: 4 catchments SATELLITE OBSERVATIONS Submitted for publication

MOISST 2015 Stillwater 2 nd June 2015 Brocca Luca Compelling evidence that it is possible to estimate rainfall from satellite soil moisture data The soil moisture derived products are found to estimate accurately the accumulated rainfall addressing the difficulties of estimating light rainfall from state-of-the-art products Merging multiple satellite soil moisture products allows to obtain good performance also at daily time scale The integration of the SM-derived products with state-of-the- art products provides a significant improvement, very useful for hydrological applications

MOISST 2015 Stillwater 2 nd June 2015 Brocca Luca

MOISST 2015 Stillwater 2 nd June 2015 Brocca Luca SM2RAIN: in situ observations On average, during rainfall the ratio of the sum of actual evapotranspiration and rainfall is less than 1% Potential Evapotranspiration Actual Evapotranspiration Potential Evapotranspirat ion during rainfall Actual Evapotranspirat ion during rainfall RAINFALL

MOISST 2015 Stillwater 2 nd June 2015 Brocca Luca SM2RAIN CRASH TEST 1)Global land surface simulation with the latest ECMWF land surface model (period ) driven by ERA-Interim atmospheric reanalysis 2)Extraction of modelled soil moisture data for the first three soil layers (0-7, 0-28, cm) 3)Application of SM2RAIN to modelled soil moisture data for each soil layer (0-7, 0-28, cm) 4)Comparison of SM2RAIN-derived rainfall with true rainfall data (from ERA-Interim) used to drive the land surface simulations

MOISST 2015 Stillwater 2 nd June 2015 Brocca Luca CRASH TEST: 1 layer vs 3 layers 1 st soil layer (0-7 cm) Root-zone (0-100 cm) Proxy of the investigation depth of satellite sensors Added-value of root-zone information, with 2 soil layers (0-28 cm) results are similar (median R=0.790).

MOISST 2015 Stillwater 2 nd June 2015 Brocca Luca P true =94 mm With only two overpasses the bottom up approach provides a better estimate of the accumulated rainfall P bottom-up =(92-2)= 90 mm “Top down” vs “Bottom up” perspective 5028 The underestimation is due to the satellite overpasses in period with low rainfall P top-down =( )*4= 60 mm 2 92

MOISST 2015 Stillwater 2 nd June 2015 Brocca Luca SM2RAIN dataset from ASCAT, 0.25°, , freely available SM2RAIN papers…so far!

MOISST 2015 Stillwater 2 nd June 2015 Brocca Luca SM2RAIN algorithm: extended version(s) precipitation surface runoff evapotranspiration drainage soil water capacity relative saturation = soil depth X porosity In collaboration with James Kirchner