SMOS soil moisture assimilation for improved hydrologic simulation in the Murray Darling Basin, Australia

Abstract This study explores the benefits of assimilating SMOS soil moisture retrievals for hydrologic modeling, with a focus on soil moisture and streamflow simulations in the Murray Darling Basin, Australia. In this basin, floods occur relatively frequently and initial catchment storage is known to be key to runoff generation. The land surface model is the Variable Infiltration Capacity (VIC) model. The model is calibrated using the available streamflow records of 169 gauge stations across the Murray Darling Basin. The VIC soil moisture forecast is sequentially updated with observations from the SMOS Level 3 CATDS (Centre Aval de Traitement des Donnees SMOS) soil moisture product using the Ensemble Kalman filter. The assimilation algorithm accounts for the spatial mismatch between the model (0.125°) and the SMOS observation (25 km) grids. Three widely-used methods for removing bias between model simulations and satellite observations of soil moisture are evaluated. These methods match the first, second and higher order moments of the soil moisture distributions, respectively. In this study, the first order bias correction, i.e. the rescaling of the long term mean, is the recommended method. Preserving the observational variability of the SMOS soil moisture data leads to improved soil moisture updates, particularly for dry and wet conditions, and enhances initial conditions for runoff generation. Second or higher order bias correction, which includes a rescaling of the variance, decreases the temporal variability of the assimilation results. In comparison with in situ measurements of OzNet, the assimilation with mean bias correction reduces the root mean square error (RMSE) of the modeled soil moisture from 0.058 m3/m3 to 0.046 m3/m3 and increases the correlation from 0.564 to 0.714. These improvements in antecedent wetness conditions further translate into improved predictions of associated water fluxes, particularly runoff peaks. In conclusion, the results of this study clearly demonstrate the merit of SMOS data assimilation for soil moisture and streamflow predictions at the large scale.

[1]  Eleanor J. Burke,et al.  Using area-average remotely sensed surface soil moisture in multipatch land data assimilation systems , 2001, IEEE Trans. Geosci. Remote. Sens..

[2]  R. H. Brooks,et al.  Hydraulic properties of porous media , 1963 .

[3]  R. Koster,et al.  Assessing the Impact of Horizontal Error Correlations in Background Fields on Soil Moisture Estimation , 2003 .

[4]  D. Lettenmaier,et al.  Surface soil moisture parameterization of the VIC-2L model: Evaluation and modification , 1996 .

[5]  Robain De Keyser,et al.  Can ASCAT-derived soil wetness indices reduce predictive uncertainty in well-gauged areas? A comparison with in situ observed soil moisture in an assimilation application , 2012 .

[6]  Yann Kerr,et al.  Soil moisture retrieval from space: the Soil Moisture and Ocean Salinity (SMOS) mission , 2001, IEEE Trans. Geosci. Remote. Sens..

[7]  A. Sahoo,et al.  Improving soil moisture retrievals from a physically-based radiative transfer model , 2014 .

[8]  G. Evensen.  Sequential data assimilation with a nonlinear quasi‐geostrophic model using Monte Carlo methods to forecast error statistics , 1994 .

[9]  Matthias Drusch,et al.  Observation operators for the direct assimilation of TRMM microwave imager retrieved soil moisture , 2005 .

[10]  Ahmad Al Bitar,et al.  Comparison Between SMOS, VUA, ASCAT, and ECMWF Soil Moisture Products Over Four Watersheds in U.S. , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[11]  Randal D. Koster,et al.  On the Nature of Soil Moisture in Land Surface Models , 2009 .

[12]  Randal D. Koster,et al.  Bias reduction in short records of satellite soil moisture , 2004 .

[13]  Alina Barbu,et al.  The SURFEXv7.2 land and ocean surface platform for coupled or offline simulation of earth surface variables and fluxes , 2012 .

[14]  Wolfgang Wagner,et al.  Assimilation of ASCAT near-surface soil moisture into the SIM hydrological model over France , 2011 .

[15]  D. Lawrence,et al.  Parameterization improvements and functional and structural advances in Version 4 of the Community Land Model , 2011 .

[16]  B. Choudhury,et al.  Effect of surface roughness on the microwave emission from soils , 1979 .

[17]  Hoshin Vijai Gupta,et al.  Decomposition of the mean squared error and NSE performance criteria: Implications for improving hydrological modelling , 2009 .

[18]  Arnaud Mialon,et al.  The SMOS Soil Moisture Retrieval Algorithm , 2012, IEEE Transactions on Geoscience and Remote Sensing.

[19]  Wade T. Crow,et al.  The Optimality of Potential Rescaling Approaches in Land Data Assimilation , 2013 .

[20]  Wade T. Crow,et al.  Relevance of time‐varying and time‐invariant retrieval error sources on the utility of spaceborne soil moisture products , 2005 .

[21]  Ahmad Al Bitar,et al.  Copula-Based Downscaling of Coarse-Scale Soil Moisture Observations With Implicit Bias Correction , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[22]  M. Ek,et al.  Hyperresolution global land surface modeling: Meeting a grand challenge for monitoring Earth's terrestrial water , 2011 .

[23]  Arnaud Mialon,et al.  SMOS CATDS level 3 global products over land , 2010, Remote Sensing.

[24]  Niko E. C. Verhoest,et al.  The importance of the spatial patterns of remotely sensed soil moisture in the improvement of discharge predictions for small-scale basins through data assimilation , 2001 .

[25]  N. Verhoest,et al.  State and bias estimation for soil moisture profiles by an ensemble Kalman filter: Effect of assimilation depth and frequency , 2007 .

[26]  Ann Henderson-Sellers,et al.  Biosphere-atmosphere Transfer Scheme (BATS) for the NCAR Community Climate Model , 1986 .

[27]  Yann Kerr,et al.  Validation of Soil Moisture and Ocean Salinity (SMOS) Soil Moisture Over Watershed Networks in the U.S. , 2012, IEEE Transactions on Geoscience and Remote Sensing.

[28]  Wade T. Crow,et al.  Improving hydrologic predictions of a catchment model via assimilation of surface soil moisture , 2011 .

[29]  P. Houser,et al.  Assimilation and downscaling of satellite observed soil moisture over the Little River Experimental Watershed in Georgia, USA , 2013 .

[30]  Eric F. Wood,et al.  An efficient calibration method for continental‐scale land surface modeling , 2008 .

[31]  Wade T. Crow,et al.  Beyond triple collocation: Applications to soil moisture monitoring , 2014 .

[32]  P. Cox,et al.  The Joint UK Land Environment Simulator (JULES), model description – Part 1: Energy and water fluxes , 2011 .

[33]  Wade T. Crow,et al.  Towards the estimation root-zone soil moisture via the simultaneous assimilation of thermal and microwave soil moisture retrievals , 2010 .

[34]  Jeffrey P. Walker,et al.  Improved Understanding of Soil Surface Roughness Parameterization for L-Band Passive Microwave Soil Moisture Retrieval , 2009, IEEE Geoscience and Remote Sensing Letters.

[35]  Christoph Rüdiger,et al.  Can SMOS Data be Used Directly on the 15-km Discrete Global Grid? , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[36]  J. M. Sabater,et al.  Sensitivity of L-band NWP forward modelling to soil roughness , 2011 .

[37]  P. Rosnay,et al.  Benchmarking of L-band soil microwave emission models , 2013 .

[38]  Kenneth W. Harrison,et al.  A comparison of methods for a priori bias correction in soil moisture data assimilation , 2012 .

[39]  Philippe Richaume,et al.  Evaluation of SMOS Soil Moisture Products Over Continental U.S. Using the SCAN/SNOTEL Network , 2012, IEEE Transactions on Geoscience and Remote Sensing.

[40]  Huidong Jin,et al.  Continental satellite soil moisture data assimilation improves root-zone moisture analysis for water resources assessment , 2014 .

[41]  Philippe Richaume,et al.  SMOS Level 2 Retrieval Algorithm Over Forests: Description and Generation of Global Maps , 2013, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[42]  Eric F. Wood,et al.  Modeling ground heat flux in land surface parameterization schemes , 1999 .

[43]  Misako Kachi,et al.  Global Change Observation Mission (GCOM) for Monitoring Carbon, Water Cycles, and Climate Change , 2010, Proceedings of the IEEE.

[44]  E. Wood,et al.  Global Trends and Variability in Soil Moisture and Drought Characteristics, 1950–2000, from Observation-Driven Simulations of the Terrestrial Hydrologic Cycle , 2008 .

[45]  William A. Lahoz,et al.  Closing the Gaps in Our Knowledge of the Hydrological Cycle over Land: Conceptual Problems , 2014, Surveys in Geophysics.

[46]  D. Jones,et al.  High-quality spatial climate data-sets for Australia , 2009 .

[47]  Wolfgang Wagner,et al.  Inter-comparison of microwave satellite soil moisture retrievals over the Murrumbidgee Basin, southeast Australia , 2013 .

[48]  Wade T. Crow,et al.  The impacts of assimilating satellite soil moisture into a rainfall-runoff model in a semi-arid catchment , 2014 .

[49]  Thomas J. Jackson,et al.  Soil moisture retrieval from AMSR-E , 2003, IEEE Trans. Geosci. Remote. Sens..

[50]  Jeffrey P. Walker,et al.  A soil moisture and temperature network for SMOS validation in Western Denmark , 2011 .

[51]  Y. Kerr,et al.  Spatial distribution and possible sources of SMOS errors at the global scale , 2013 .

[52]  James R. Wang,et al.  Multifrequency Measurements of the Effects of Soil Moisture, Soil Texture, And Surface Roughness , 1983, IEEE Transactions on Geoscience and Remote Sensing.

[53]  G. Lannoy,et al.  Simultaneous estimation of model state variables and observation and forecast biases using a two-stage hybrid Kalman filter , 2012 .

[54]  D. Aubert,et al.  Sequential assimilation of soil moisture and streamflow data in a conceptual rainfall-runoff model , 2003 .

[55]  Luca Brocca,et al.  Assimilation of Surface- and Root-Zone ASCAT Soil Moisture Products Into Rainfall–Runoff Modeling , 2012, IEEE Transactions on Geoscience and Remote Sensing.

[56]  Y. Kerr,et al.  Evaluation of remotely sensed and modelled soil moisture products using global ground-based in situ observations , 2012 .

[57]  X. R. Liu,et al.  The Xinanjiang model. , 1995 .

[58]  A. B. Smith,et al.  The Murrumbidgee soil moisture monitoring network data set , 2012 .

[59]  Jeffrey P. Walker,et al.  Intercomparison of the JULES and CABLE land surface models through assimilation of remotely sensed soil moisture in southeast Australia , 2014 .

[60]  Antonino Maltese,et al.  Remote Sensing for Agriculture, Ecosystems, and Hydrology XII , 2006 .

[61]  Clemens Simmer,et al.  Effects of the Near-Surface Soil Moisture Profile on the Assimilation of L-band Microwave Brightness Temperature , 2006 .

[62]  S. Running,et al.  MODIS Leaf Area Index (LAI) And Fraction Of Photosynthetically Active Radiation Absorbed By Vegetation (FPAR) Product , 1999 .

[63]  D. Lettenmaier,et al.  A simple hydrologically based model of land surface water and energy fluxes for general circulation models , 1994 .

[64]  Bart Nijssen,et al.  Global Retrospective Estimation of Soil Moisture Using the Variable Infiltration Capacity Land Surface Model, 1980–93 , 2001 .

[65]  Derek Karssenberg,et al.  The suitability of remotely sensed soil moisture for improving operational flood forecasting , 2013 .

[66]  W. Wagner,et al.  Improving runoff prediction through the assimilation of the ASCAT soil moisture product , 2010 .

[67]  A. Al Bitar,et al.  An improved algorithm for disaggregating microwave-derived soil moisture based on red, near-infrared and thermal-infrared data , 2010 .

[68]  Ahmad Al Bitar,et al.  Optimization of a Radiative Transfer Forward Operator for Simulating SMOS Brightness Temperatures over the Upper Mississippi Basin , 2015 .

[69]  D. Ryu,et al.  Multi-scale analysis of bias correction of soil moisture , 2014 .

[70]  J. Townshend,et al.  Global land cover classi(cid:142) cation at 1 km spatial resolution using a classi(cid:142) cation tree approach , 2004 .

[71]  S. Sorooshian,et al.  Effective and efficient global optimization for conceptual rainfall‐runoff models , 1992 .

[72]  Thomas J. Jackson,et al.  Estimating Effective Roughness Parameters of the L-MEB Model for Soil Moisture Retrieval Using Passive Microwave Observations From SMAPVEX12 , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[73]  J. Eitzinger,et al.  The ASCAT Soil Moisture Product: A Review of its Specifications, Validation Results, and Emerging Applications , 2013 .

[74]  Soroosh Sorooshian,et al.  Optimal use of the SCE-UA global optimization method for calibrating watershed models , 1994 .

[75]  Y. Kerr,et al.  Effective soil moisture sampling depth of L-band radiometry: A case study , 2010 .

[76]  H. Madsen,et al.  Assimilation of SMOS‐derived soil moisture in a fully integrated hydrological and soil‐vegetation‐atmosphere transfer model in Western Denmark , 2014 .

[77]  P. Cox,et al.  The Joint UK Land Environment Simulator (JULES), model description – Part 2: Carbon fluxes and vegetation dynamics , 2011 .

[78]  Lifeng Luo,et al.  Snow process modeling in the North American Land Data Assimilation System (NLDAS): 1. Evaluation of model‐simulated snow cover extent , 2003 .

[79]  W. J. Shuttleworth,et al.  Integration of soil moisture remote sensing and hydrologic modeling using data assimilation , 1998 .

[80]  Yann Kerr,et al.  Characterizing the dependence of vegetation model parameters on crop structure, incidence angle, and polarization at L-band , 2004, IEEE Transactions on Geoscience and Remote Sensing.

[81]  S. Schubert,et al.  MERRA: NASA’s Modern-Era Retrospective Analysis for Research and Applications , 2011 .

[82]  D. Lettenmaier,et al.  Evaluation of the land surface water budget in NCEP/NCAR and NCEP/DOE reanalyses using an off‐line hydrologic model , 2001 .

[83]  Niko E. C. Verhoest,et al.  Improvement of TOPLATS‐based discharge predictions through assimilation of ERS‐based remotely sensed soil moisture values , 2002, Hydrological Processes.

[84]  E. Wood,et al.  Bias correction of monthly precipitation and temperature fields from Intergovernmental Panel on Climate Change AR4 models using equidistant quantile matching , 2010 .

[85]  Grundy.  The Australian Soil Resource Information System , 2009 .

[86]  Jiancheng Shi,et al.  The Soil Moisture Active Passive (SMAP) Mission , 2010, Proceedings of the IEEE.

[87]  Gabrielle De Lannoy,et al.  Ensemble‐based assimilation of discharge into rainfall‐runoff models: A comparison of approaches to mapping observational information to state space , 2009 .

[88]  R. Koster,et al.  Global Soil Moisture from Satellite Observations, Land Surface Models, and Ground Data: Implications for Data Assimilation , 2004 .

[89]  Jetse D. Kalma,et al.  One-dimensional soil moisture profile retrieval by assimilation of near-surface observations: a comparison of retrieval algorithms , 2001 .