Combined assimilation of streamflow and satellite soil moisture with the particle filter and geostatistical modeling

Abstract Assimilation of satellite soil moisture and streamflow data into a distributed hydrologic model has received increasing attention over the past few years. This study provides a detailed analysis of the joint and separate assimilation of streamflow and Advanced Scatterometer (ASCAT) surface soil moisture into a distributed Sacramento Soil Moisture Accounting (SAC-SMA) model, with the use of recently developed particle filter-Markov chain Monte Carlo (PF-MCMC) method. Performance is assessed over the Salt River Watershed in Arizona, which is one of the watersheds without anthropogenic effects in Model Parameter Estimation Experiment (MOPEX). A total of five data assimilation (DA) scenarios are designed and the effects of the locations of streamflow gauges and the ASCAT soil moisture on the predictions of soil moisture and streamflow are assessed. In addition, a geostatistical model is introduced to overcome the significantly biased satellite soil moisture and also discontinuity issue. The results indicate that: (1) solely assimilating outlet streamflow can lead to biased soil moisture estimation; (2) when the study area can only be partially covered by the satellite data, the geostatistical approach can estimate the soil moisture for those uncovered grid cells; (3) joint assimilation of streamflow and soil moisture from geostatistical modeling can further improve the surface soil moisture prediction. This study recommends that the geostatistical model is a helpful tool to aid the remote sensing technique and the hydrologic DA study.

[1]  Hongxiang Yan,et al.  Improving Soil Moisture Profile Prediction With the Particle Filter-Markov Chain Monte Carlo Method , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[2]  Klaus Scipal,et al.  Soil moisture-runoff relation at the catchment scale as observed with coarse resolution microwave remote sensing , 2005 .

[3]  Victor Koren,et al.  Assimilation of streamflow and in situ soil moisture data into operational distributed hydrologic models: Effects of uncertainties in the data and initial model soil moisture states , 2011 .

[4]  R. Stouffer,et al.  Stationarity Is Dead: Whither Water Management? , 2008, Science.

[5]  M. Drusch,et al.  Estimation of Radiative Transfer Parameters from L‐Band Passive Microwave Brightness Temperatures Using Advanced Data Assimilation , 2013 .

[6]  Daniel Cooley,et al.  A comparison of a traditional geostatistical regression approach and a general Gaussian process approach for spatial prediction , 2014 .

[7]  Lifeng Luo,et al.  A Multiscale Ensemble Filtering System for Hydrologic Data Assimilation. Part I: Implementation and Synthetic Experiment , 2009 .

[8]  Dawei Han,et al.  Misrepresentation and amendment of soil moisture in conceptual hydrological modelling , 2016 .

[9]  Hongxiang Yan,et al.  A regional Bayesian hierarchical model for flood frequency analysis , 2015, Stochastic Environmental Research and Risk Assessment.

[10]  Xing Yuan,et al.  Microwave remote sensing of short‐term droughts during crop growing seasons , 2015 .

[11]  K. Mo,et al.  Continental-scale water and energy flux analysis and validation for the North American Land Data Assimilation System project phase 2 (NLDAS-2): 1. Intercomparison and application of model products , 2012 .

[12]  G. Lannoy,et al.  The importance of parameter resampling for soil moisture data assimilation into hydrologic models using the particle filter , 2011 .

[13]  Gift Dumedah,et al.  Assessing model state and forecasts variation in hydrologic data assimilation , 2014 .

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

[15]  Michael H. Cosh,et al.  Assimilation of Remotely Sensed Soil Moisture and Snow Depth Retrievals for Drought Estimation , 2014 .

[16]  H. Moradkhani,et al.  Hydrologic modeling in dynamic catchments: A data assimilation approach , 2016 .

[17]  Soroosh Sorooshian,et al.  Dual state-parameter estimation of hydrological models using ensemble Kalman filter , 2005 .

[18]  Lucy Marshall,et al.  Detecting non-stationary hydrologic model parameters in a paired catchment system using data assimilation , 2016 .

[19]  Youfei Zheng,et al.  Impact of quality control of satellite soil moisture data on their assimilation into land surface model , 2014 .

[20]  Rolf Reichle,et al.  Variational data assimilation of microwave radiobrightness observations for land surface hydrology applications , 2001, IEEE Trans. Geosci. Remote. Sens..

[21]  Wade T. Crow,et al.  Improving operational flood ensemble prediction by the assimilation of satellite soil moisture: comparison between lumped and semi-distributed schemes , 2014 .

[22]  Wade T. Crow,et al.  A new data assimilation approach for improving runoff prediction using remotely-sensed soil moisture retrievals , 2008 .

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

[24]  H. Moradkhani,et al.  Bayesian Model Averaging for Flood Frequency Analysis , 2014 .

[25]  Jasper A. Vrugt,et al.  Hydrologic data assimilation using particle Markov chain Monte Carlo simulation: Theory, concepts and applications (online first) , 2012 .

[26]  R. Reichle.  Data assimilation methods in the Earth sciences , 2008 .

[27]  Jasmeet Judge,et al.  A particle batch smoother for soil moisture estimation using soil temperature observations , 2015 .

[28]  Harrie-Jan Hendricks Franssen,et al.  Soil moisture and soil properties estimation in the Community Land Model with synthetic brightness temperature observations , 2014 .

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

[30]  D. McLaughlin,et al.  Hydrologic Data Assimilation with the Ensemble Kalman Filter , 2002 .

[31]  Chunlin Huang,et al.  Comparison of ensemble-based state and parameter estimation methods for soil moisture data assimilation , 2015 .

[32]  Hamid Moradkhani,et al.  Toward reduction of model uncertainty: Integration of Bayesian model averaging and data assimilation , 2012 .

[33]  Maosheng Zhao,et al.  Improvements to a MODIS global terrestrial evapotranspiration algorithm , 2011 .

[34]  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 .

[35]  Thomas J. Jackson,et al.  Validation of Advanced Microwave Scanning Radiometer Soil Moisture Products , 2010, IEEE Transactions on Geoscience and Remote Sensing.

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

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

[38]  M. Canty,et al.  Hydraulic parameter estimation by remotely-sensed top soil moisture observations with the particle filter , 2011 .

[39]  Rolf H. Reichle,et al.  Assimilation of passive and active microwave soil moisture retrievals , 2012 .

[40]  Seong Jin Noh,et al.  Advancing data assimilation in operational hydrologic forecasting: progresses, challenges, and emerging opportunities , 2012 .

[41]  H. Moradkhani,et al.  Analyzing the uncertainty of suspended sediment load prediction using sequential data assimilation , 2012 .

[42]  Soroosh Sorooshian,et al.  Model Parameter Estimation Experiment (MOPEX): An overview of science strategy and major results from the second and third workshops , 2006 .

[43]  Hongxiang Yan,et al.  Toward more robust extreme flood prediction by Bayesian hierarchical and multimodeling , 2016, Natural Hazards.

[44]  Luis Samaniego,et al.  Implications of Parameter Uncertainty on Soil Moisture Drought Analysis in Germany , 2013 .

[45]  Alina Barbu,et al.  Integrating ASCAT surface soil moisture and GEOV1 leaf area index into the SURFEX modelling platform: a land data assimilation application over France , 2013 .

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

[47]  Klaus Scipal,et al.  Assimilating scatterometer soil moisture data into conceptual hydrologic models at the regional scale , 2005 .

[48]  Eric F. Wood,et al.  Inverse streamflow routing , 2013 .

[49]  Hongxiang Yan.  Magnitude and Frequency of Floods for Rural, Unregulated Streams of Tennessee by L-Moments Method , 2012 .

[50]  Rafael Rosolem,et al.  The COsmic-ray Soil Moisture Interaction Code (COSMIC) for use in data assimilation , 2013 .

[51]  Dong-Jun Seo,et al.  Real-Time Variational Assimilation of Hydrologic and Hydrometeorological Data into Operational Hydrologic Forecasting , 2003 .

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

[53]  On the Muskingum-Cunge flood routing method , 1995 .

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

[55]  F. B. Desta,et al.  Experimental and numerical findings on the long‐term evolution of migrating alternate bars in alluvial channels , 2012 .

[56]  C. M. DeChant,et al.  Improving the characterization of initial condition for ensemble streamflow prediction using data assimilation , 2011 .

[57]  C. M. DeChant,et al.  Radiance data assimilation for operational snow and streamflow forecasting , 2011 .

[58]  T. Hamill.  Interpretation of Rank Histograms for Verifying Ensemble Forecasts , 2001 .

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

[60]  Hongxiang Yan,et al.  Effects of Land Use Change on Hydrologic Response at a Watershed Scale, Arkansas , 2013 .

[61]  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.

[62]  Hamid Moradkhani,et al.  Examining the effectiveness and robustness of sequential data assimilation methods for quantification of uncertainty in hydrologic forecasting , 2012 .

[63]  Rafael Rosolem,et al.  Translating aboveground cosmic-ray neutron intensity to high-frequency soil moisture profiles at sub-kilometer scale , 2014 .

[64]  François Anctil,et al.  Sequential streamflow assimilation for short-term hydrological ensemble forecasting , 2014 .

[65]  T. Jackson,et al.  The USDA Natural Resources Conservation Service Soil Climate Analysis Network (SCAN) , 2007 .

[66]  H. Moradkhani.  Hydrologic Remote Sensing and Land Surface Data Assimilation , 2008, Sensors.

[67]  Soroosh Sorooshian,et al.  Evolution of ensemble data assimilation for uncertainty quantification using the particle filter‐Markov chain Monte Carlo method , 2012 .

[68]  Xin Li,et al.  Soil Moisture Estimation by Assimilating L-Band Microwave Brightness Temperature with Geostatistics and Observation Localization , 2015, PloS one.

[69]  S. Seneviratne,et al.  Investigating soil moisture-climate interactions in a changing climate: A review , 2010 .

[70]  Sujay V. Kumar,et al.  Assessing the Impact of L-Band Observations on Drought and Flood Risk Estimation: A Decision-Theoretic Approach in an OSSE Environment , 2014 .

[71]  Rolf H. Reichle,et al.  Connecting Satellite Observations with Water Cycle Variables Through Land Data Assimilation: Examples Using the NASA GEOS-5 LDAS , 2013, Surveys in Geophysics.

[72]  Kuolin Hsu,et al.  Uncertainty assessment of hydrologic model states and parameters: Sequential data assimilation using the particle filter , 2005 .

[73]  Luca Brocca,et al.  Data Assimilation of Satellite Soil Moisture into Rainfall-Runoff Modelling: A Complex Recipe? , 2015, Remote. Sens..

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

[75]  Yann Kerr,et al.  The SMOS Mission: New Tool for Monitoring Key Elements ofthe Global Water Cycle , 2010, Proceedings of the IEEE.

[76]  A. Doucet,et al.  Particle Markov chain Monte Carlo methods , 2010 .

[77]  Harry Vereecken,et al.  Multivariate and Multiscale Data Assimilation in Terrestrial Systems: A Review , 2012, Sensors.

[78]  Wade T. Crow,et al.  The Contributions of Precipitation and Soil Moisture Observations to the Skill of Soil Moisture Estimates in a Land Data Assimilation System , 2011 .

[79]  Dennis P. Lettenmaier,et al.  Soil Moisture Drought in China, 1950–2006 , 2011 .

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

[81]  Sujay V. Kumar,et al.  Multiscale assimilation of Advanced Microwave Scanning Radiometer–EOS snow water equivalent and Moderate Resolution Imaging Spectroradiometer snow cover fraction observations in northern Colorado , 2012 .

[82]  W. Wagner,et al.  Initial soil moisture retrievals from the METOP‐A Advanced Scatterometer (ASCAT) , 2007 .

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

[84]  Mohammad Najafi,et al.  Multi-model ensemble analysis of runoff extremes for climate change impact assessments , 2015 .

[85]  Eric F. Wood,et al.  Prospects for Advancing Drought Understanding, Monitoring, and Prediction , 2015 .

[86]  Sujay V. Kumar,et al.  Evaluating the utility of satellite soil moisture retrievals over irrigated areas and the ability of land data assimilation methods to correct for unmodeled processes , 2015 .

[87]  Xu Liang,et al.  Optimal multiscale Kalman filter for assimilation of near-surface soil moisture into land surface models , 2004 .