Satellite soil moisture data assimilation into the Australian Water Resources Assessment modelling system Luigi Renzullo 1 , Brent Henderson 2 , Warren Jin 2 , Jean-Michel Perraud 1 , Matthew Stenson 1 , Albert van Dijk 3 CSIRO LAND AND WATER 1 CSIRO Land and Water 2 CSIRO Computational Informatics 3 Australian National University, Fenner School 6 th WMO Symposium on Data Assimilation 7 – 11 Oct 2013, University of Maryland, USA
Water Resources Information in Australia • Commonwealth Water Act 2007 • //www.bom.gov.au/water/ Australian Bureau of Meteorology (BoM) • Mandate: ” Manage Australia’s water resources information …”; • new responsibilities; new BoM Water Division formed. • National water accounts & assessments Water balance across Australia • Water Information Research & Development Alliance (2000-2006) • WIRADA: An R & D initiative between the BoM and CSIRO ; • partnership of $50M over 5 years ( July 2008 – June 2013 ) • Australian Water Resources Assessment (AWRA) system • Comprehensive reconstruction of the water balance for the whole country • Scale and accuracy acceptable for water resources management www.bom.gov.au/water/awra/2012/ AWRA Report 2012 Net water Net water NWA 2012 www.bom.gov.au/water/nwa/2012/ using producing 6th WMO Data Assimilation Symposium | Luigi Renzullo | 7- 11 Oct 2013 Maryland, USA
AWRA system A ustralian W ater R esources A ssessment modelling system • Developed CSIRO-BoM for reporting on WRA and NWA System model components AWRA Landscape model ( AWRA-L ) – Hybrid land surface model / conceptual RR model – Daily time step short-wave short-wave minimum minimum maximum maximum short-wave short-wave minimum minimum maximum maximum precipitation precipitation – precipitation precipitation 0.05-degree resolution grid radiation radiation temperature temperature temperature temperature radiation radiation temperature temperature temperature temperature across continent – Top-layer ( S0 ), shallow root ( Ss ) & saturated area saturated area available available ET ET surface soil surface soil ET ET energy energy deep root ( Sd ) soil layers fraction fraction fraction fraction AWRA River model ( AWRA-R ) deep-rooted deep-rooted deep-rooted deep-rooted vegetation vegetation shallow root shallow root maximum maximum vegetation vegetation – zone zone Node-link model (simplified sourceRivers ) transpiration transpiration surface surface AWRA Groundwater model ( AWRA-G ) water water maximum maximum – Models aquifer dynamics SW-GW processes (incl. lateral deep root deep root root uptake root uptake zone zone transfer between cells, SW-GW interactions, recharge from overbank flows, models impact of extraction, ..) vegetation vegetation adjustment adjustment ground ground water water 6th WMO Data Assimilation Symposium | Luigi Renzullo | 7- 11 Oct 2013 Maryland, USA
AWRA system: data assimilation objectives Overall goal: Develop and deploy a modelling environment to integrate surface measurement and remote sensing data systems for comprehensive water balance * e.g. Streamflow, water table, bore data, reservoir data; Available water vegetation indices, soil moisture, land surface temperature Specific goals of this study: Evaluate assimilation satellite soil moisture retrievals on soil water representation in AWRA-L * Assess active and passive remotely-sensed soil moisture retrievals constraint on AWRA-L top-layer and shallow root-zone moisture estimates. Volumetric (m 3 m -3 ) * Evaluate modelling against in situ measurements & cosmic-ray 0.20 0 0.05 0.10 0.15 data Pixel counts Soil layer thickness (x 10 4 ) Top soil Method summary: 6 Shallow root 4 Deep root Sequential updating of AWRA-L model states (soil water 2 storages) using the Ensemble Kalman Filter (EnKF) based on 0 / / perturbed forcing and triple collocation for errors on satellite 0 20 cm 4 m 6 m 8 m soil moisture products 6th WMO Data Assimilation Symposium | Luigi Renzullo | 7- 11 Oct 2013 Maryland, USA
Soil moisture data sets ASCAT AMSR-E Average of descending ~9pm Descending ~1.30 am local time previous day and ascending pass only ~9 am current day 1. Vrije Universiteit Amsterdam (VUA) – NASA soil 2. Technische Universität Wien (TUW) soil relative wetness moisture products products • Estimates derived from passive microwave AMSR-E • Estimates derived from active microwave ASCAT backscatter brightness temperatures using the LPRM algorithm (Owe signal using the change detection algorithm (Wagner et al., et al., 2001, IEEE Trans. Geosci. Rem. Sens.) 1999, Rem. Sens. Environ.) • Our holding: 1 Jul 2002 – 30 Sep 2011. • Our holding: 1 Jan 2007 – 31 Dec 2011. • Volumetric soil moisture (m 3 m -3 ) • Surface degree of saturation (0-1) • 0.25 x 0.25 - NN resampling to 0.05 x 0.05 • 0.125 x 0.125 - NN resampling to 0.05 x 0.05 6th WMO Data Assimilation Symposium | Luigi Renzullo | 7- 11 Oct 2013 Maryland, USA • Top ~1-2cm soil layer • Top ~1-2cm soil layer
Continental error estimates: using triple collocation ( CDF* matched SM obs ) Relative wetness VUA – AMSR-E TUW – ASCAT Pattern and magnitude of errors appear consistent with others work, e.g. Dorigo et al., 2010, HESS 6th WMO Data Assimilation Symposium | Luigi Renzullo | 7- 11 Oct 2013 Maryland, USA
Continental satellite DA into AWRA-L AWRA-L Relative wetness for 7 July 2009 a f a − w 0 w 0 w 0 (median) Continental AWRA-L data assimilation IQR EnKF using perturbed forcing multiplicative perturbation on rainfall Additive perturbation on air temp and shortwave radiation Details in Renzullo et al., 2013, J Hydrol. (in prep) Simulations over the last 13 years. (~2-day turn around 6th WMO Data Assimilation Symposium | Luigi Renzullo | 7- 11 Oct 2013 Maryland, USA
Soil moisture assimilation Top layer relative wetness 1.0 ASCAT 0.8 0.6 0.4 Open loop Assimilation 0.2 Observations 0.0 1.0 Top layer relative wetness AMSR-E 0.8 0.6 0.4 0.2 0.0 1.0 Top layer relative wetness ASCAT & 0.8 AMSR-E 0.6 0.4 0.2 0.0 2010 2010 2010 2010 2011 May Jul Sep Nov Jan Mar 2011 6th WMO Data Assimilation Symposium | Luigi Renzullo | 7- 11 Oct 2013 Maryland, USA
Evaluation @ OzNet CosmOz OzFlux OzNet COSMOZ 0.6 0.6 Soil moisture [m 3 / m -3 ] AMSR-E x 0.4 0.4 Yanco 0.2 0.2 0.0 0.0 Jan Jan Mar Mar May May Jul Jul Sep Sep Nov Nov Jan Jan Date [2011] 6th WMO Data Assimilation Symposium | Luigi Renzullo | 7- 11 Oct 2013 Maryland, USA
Evaluation: AWRA-L top-layer SM estimation r a − r 0 / r 0 (a) (c) AMSR-E (b) ASCAT AMSRE + 30 30 30 ASCAT 20 20 20 10 10 10 0 0 0 100 x -10 0 0 0.2 0.4 0.6 0.8 0.2 0.4 0.6 0.8 0.2 0.4 0.6 0.8 r 0 r 0 r 0 45 OzNet top-layer (0-8 cm) open loop AMSR-E ASCAT 0.9 in situ measurement sites 0.8 Correlation Percentage relative difference between open-loop ( r 0 ) and 0.7 analysis ( r a ) correlation 0.6 Correlation between model 0.5 and in situ moisture for 1 July 2007 – 31 May 2011 0.4 M_1 M_3 M_5 M_7 Y_2 Y_4 Y_6 Y_8 Y_11 Y_13 Y_A3 Y_A9 Y_B3 Y_10 K_1 K_4 K_6 K_8 K_10 K_12 K_14 A_2 A_4 6th WMO Data Assimilation Symposium | Luigi Renzullo | 7- 11 Oct 2013 Maryland, USA
Evaluation: AWRA-L shallow root-zone 36 OzNet shallow root-zone (0-30 cm and 0-90 cm) in situ measurements Percentage relative difference between open-loop ( r 0 ) and analysis ( r a ) correlation Correlation between model and in situ moisture for 1 July 2007 – 31 May 2011 AMSR-E ASCAT AMSRE + ASCAT (a) (b) (c) r a − r 0 / r 0 30 30 30 0 – 30 cm 20 20 20 10 10 10 0 0 0 -10 0 0 (d) (e) (f) 90 90 90 0.2 0.4 0.6 0.8 0.2 0.4 0.6 0.8 70 70 70 0 – 90 cm 50 50 50 30 30 30 100 x 10 10 10 0 -10 0 0 -30 0 0 -50 0 -70 0 0 0.2 0.4 0.6 0.8 0.2 0.4 0.6 0.8 0.2 0.4 0.6 0.8 r 0 6th WMO Data Assimilation Symposium | Luigi Renzullo | 7- 11 Oct 2013 Maryland, USA
Evaluation: AWRA-L shallow root-zone Cumulative distribution of the analysis increments of the AWRA-L soil water storage states (normalised by the forecast states estimates) pooled across the OzNet site and only for those times when satellite SM were available for assimilation 6th WMO Data Assimilation Symposium | Luigi Renzullo | 7- 11 Oct 2013 Maryland, USA
Evaluation: AWRA-L shallow root-zone Evaluation against cosmic-ray probes (CosmOz) Soil moisture (m 3 m -3 ) CosmOz Daly Baldry 0.6 AWRA-L (95% ensemble range) 0.4 0.2 0.0 2011 2012 2011 2012 Soil moisture (m 3 m -3 ) Robson Tullochgorum 0.6 0.4 0.2 0.9 0.0 2011 2012 2011 2012 0.8 Soil moisture (m 3 m -3 ) Weany Yanco 0.6 Correlation 0.7 0.4 Baldry 0.2 0.6 Daly 0.0 Robson 0.5 Tullochgorum 2011 2012 2011 2012 Tumbarumba 0.4 Weany Yanco 0.3 0 500 1000 1500 2000 2500 6th WMO Data Assimilation Symposium | Luigi Renzullo | 7- 11 Oct 2013 Maryland, USA Depth (mm)
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