Brocca et al.: Using the soil as a natural raingauge
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SOIL AS A NATURAL RAINGAUGE: ESTIMATING GLOBAL RAINFALL
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FROM SATELLITE SOIL MOISTURE DATA
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SUPPLEMENTARY MATERIAL
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Luca Brocca1*, Luca Ciabatta1, Christian Massari1, Tommaso Moramarco1,
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Sebastian Hahn2, Stefan Hasenauer2, Richard Kidd2, Wouter Dorigo2, Wolfgang Wagner2
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Vincenzo Levizzani3
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Research Institute for Geo-Hydrological Protection, National Research Council, Perugia, Italy
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Department of Geodesy and Geoinformation, Vienna University of Technology, Vienna, Austria
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Institute of Atmospheric Sciences and Climate, National Research Council, Bologna, Italy
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April 2014
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Re-submitted to Journal of Geophysical Research
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Ph.D. Luca Brocca, Research Institute for Geo-Hydrological Protection, National Research Council,
Via Madonna Alta 126, 06128 Perugia, Italy. E-mail: luca.brocca@irpi.cnr.it.
Brocca et al.: Using the soil as a natural raingauge
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SUPPLEMENTARY MATERIAL
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The same results obtained by using the GPCC product as benchmark and shown in the main
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section of the manuscript are here reported after comparison against the GPCP and ERAI products.
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Specifically, Figures S1 and S2 show the correlation maps, and Figure S3 the RMSE and BIAS.
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The overall results are in good accordance with those derived from the comparison against GPCC
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with the exception of the correlation map between TRMM-RT and GPCP that shows significantly
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higher values (average R=0.634). However, this result is influenced by the use of similar data
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sources for the development of the two rainfall products (TRMM-RT and GPCP).
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Figure S4 shows the cumulative density function of the temporal standard deviation, i.e., the
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standard deviation of the rainfall timeseries of each pixel, for the 5-day rainfall data obtained
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through the different rainfall products analyzed in this study. This figure allows understanding if the
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significantly lower RMSE values obtained through the SM-derived rainfall products are due to their
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tendency to provide a less temporally-variable rainfall timeseries. Figure S5 shows the Taylor
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diagram illustrating the statistics of the comparison between each rainfall product and the GPCC
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dataset for 5-day accumulated rainfall. The comparison is carried out by considering all grid points
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as unique timeseries and only dates on which all datasets are available in the validation period
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2010-2011.
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Figures S6 and S7 show the maps of the accumulated rainfall in the validation period 2010-
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2011 for the GPCC, ASCAT, TRMM-RT, AMSR-E and SMOS rainfall products to highlight the
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geographical bias among the products.
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Finally, Figure S8 shows the categorical metrics for the comparison against the GPCP and
ERAI products.
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Figure S1:
Same as in Figure 3 but using GPCP product as benchmark.
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Figure S2:
Same as in Figure 3 but using ERAI product as benchmark.
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Figure S3: Same as in Figure 4 but using GPCP product (upper panels) and the ERAI product
(lower panels) as benchmark.
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Figure S4: Cumulative Density Function (CDF) of the 5-day rainfall temporal standard
deviation of each pixel (13,237) for the seven rainfall products used in this study, i.e., the 3
benchmark datasets (GPCC, GPCP and ERAI), the 3 soil moisture derived products (from ASCAT,
AMSR-E and SMOS), and the TRMM-RT product.
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Figure S5: Taylor diagram illustrating the statistics (by considering 5-day rainfall) of the
comparison between each rainfall product (ASCAT, AMSR-E, SMOS and TRMM-RT) and the
GPCC benchmark dataset. The comparison is carried out by considering all grid points as unique
timeseries and only dates on which all datasets are available in the validation period 2010-2011.
Each symbol indicates the correlation value (angle), the temporal standard deviation (radial distance
to the origin point), and the centered root mean square error, RMSE (distance to the white circle
marked “GPCC”).
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Figure S6: Accumulated rainfall (in mm) for the validation period 2010-2011 for the GPCC
(top), ASCAT-derived (middle) and TRMM-RT (bottom) products. The colorbar is truncated at
5000 mm for a better visualization of the spatial rainfall pattern.
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Figure S7: Same as in Figure S5 but for AMSR (middle) and SMOS (bottom) derived rainfall
products. In the top the accumulated rainfall from GPCC is reported as benchmark.
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Figure S8: Same as in Figure 5 but using GPCP product (upper panels) and the ERAI product
(lower panels) as benchmark.
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Data Sources
The datasets used in this study are freely available and can be downloaded from the following
web sites:
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ASCAT soil moisture:
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http://rs.geo.tuwien.ac.at/products/
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AMSR-E soil moisture:
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ftp://hydro1.sci.gsfc.nasa.gov/data/s4pa/WAOB/LPRM_AMSRE_SOILM2.002/
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SMOS soil moisture:
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http://catds.ifremer.fr/Products/Available-products-from-CPDC
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TRMM-RT rainfall:
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http://gdata1.sci.gsfc.nasa.gov/daac-bin/G3/gui.cgi?instance_id=TRMM_3B42RT
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GPCP rainfall:
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ftp://rsd.gsfc.nasa.gov/pub/1dd-v1.2/
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GPCC rainfall:
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ftp://ftp-anon.dwd.de/pub/data/gpcc/html/download_gate.html
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ERA-Interim rainfall (and soil temperature):
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http://www.ecmwf.int/research/era/do/get/era-interim
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