Translating aboveground cosmic-ray neutron intensity to high-frequency soil moisture profiles at sub-kilometer scale

Above-ground cosmic-ray neutron measurements provide an opportunity to infer soil moisture at the sub-kilometer scale. Initial efforts to assimilate those measurements have shown promise. This study expands such analysis by investigating (1) how the information from aboveground cosmic-ray neutrons can constrain the soil moisture at distinct depths simulated by a land surface model, and (2) how changes in data availability (in terms of retrieval frequency) impact the dynamics of simulated soil moisture profiles. We employ ensemble data assimilation techniques in a "nearly-identical twin" experiment applied at semi-arid shrubland, rainfed agricultural field, and mixed forest biomes in the USA. The performance of the Noah land surface model is compared with and without assimilation of observations at hourly intervals, as well as every 2 days. Synthetic observations of aboveground cosmic-ray neutrons better constrain the soil moisture simulated by Noah in root-zone soil layers (0–100cm), despite the limited measurement depth of the sensor (estimated to be 12–20cm). The ability of Noah to reproduce a "true" soil moisture profile is remarkably good, regardless of the frequency of observations at the semi-arid site. However, soil moisture profiles are better constrained when assimilating synthetic cosmic-ray neutron observations hourly rather than every 2 days at the cropland and mixed forest sites. This indicates potential benefits for hydrometeorological modeling when soil moisture measurements are available at a relatively high frequency. Moreover, differences in summertime meteorological forcing between the semi-arid site and the other two sites may indicate a possible controlling factor to soil moisture dynamics in addition to differences in soil and vegetation properties.

[1]  Andrew E. Suyker,et al.  Annual carbon dioxide exchange in irrigated and rainfed maize-based agroecosystems , 2005 .

[2]  Rafael Rosolem,et al.  The Effect of Atmospheric Water Vapor on Neutron Count in the Cosmic-Ray Soil Moisture Observing System , 2013 .

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

[4]  J. Whitaker,et al.  Distance-dependent filtering of background error covariance estimates in an ensemble Kalman filter , 2001 .

[5]  Y. Xue,et al.  Modeling of land surface evaporation by four schemes and comparison with FIFE observations , 1996 .

[6]  W. James Shuttleworth,et al.  Ecosystem‐scale measurements of biomass water using cosmic ray neutrons , 2013 .

[7]  D. McLaughlin,et al.  Assessing the Performance of the Ensemble Kalman Filter for Land Surface Data Assimilation , 2006 .

[8]  R. Scott,et al.  Measuring soil moisture content non‐invasively at intermediate spatial scale using cosmic‐ray neutrons , 2008 .

[9]  J. Wallace,et al.  Calibration and correction procedures for cosmic‐ray neutron soil moisture probes located across Australia , 2014 .

[10]  A. Dai Increasing drought under global warming in observations and models , 2013 .

[11]  W. James Shuttleworth,et al.  Land surface modeling inside the Biosphere 2 tropical rain forest biome , 2010 .

[12]  Kevin W. Manning,et al.  The community Noah land surface model with multiparameterization options (Noah-MP): 2. Evaluation over global river basins , 2011 .

[13]  J.L. Anderson,et al.  Ensemble Kalman filters for large geophysical applications , 2009, IEEE Control Systems.

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

[15]  T. Jackson,et al.  Field observations of soil moisture variability across scales , 2008 .

[16]  Irena Hajnsek,et al.  A Network of Terrestrial Environmental Observatories in Germany , 2011 .

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

[18]  D. Baldocchi Assessing the eddy covariance technique for evaluating carbon dioxide exchange rates of ecosystems: past, present and future , 2003 .

[19]  J. M. Sabater,et al.  From near-surface to root-zone soil moisture using different assimilation techniques , 2007 .

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

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

[22]  J. Dudhia,et al.  Coupling an Advanced Land Surface–Hydrology Model with the Penn State–NCAR MM5 Modeling System. Part I: Model Implementation and Sensitivity , 2001 .

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

[24]  A. P. Annan,et al.  Electromagnetic determination of soil water content: Measurements in coaxial transmission lines , 1980 .

[25]  X. Zeng,et al.  Natural and drought scenarios in an east central Amazon forest: Fidelity of the Community Land Model 3.5 with three biogeochemical models , 2011 .

[26]  R. Ibbitt,et al.  Hydrological data assimilation with the ensemble Kalman filter: Use of streamflow observations to update states in a distributed hydrological model , 2007 .

[27]  H. Mooney,et al.  Modeling the Exchanges of Energy, Water, and Carbon Between Continents and the Atmosphere , 1997, Science.

[28]  R. E. Kalman,et al.  New Results in Linear Filtering and Prediction Theory , 1961 .

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

[30]  W. James Shuttleworth,et al.  Towards a comprehensive approach to parameter estimation in land surface parameterization schemes , 2013 .

[31]  A. Scott Denning,et al.  Simulated and observed fluxes of sensible and latent heat and CO2 at the WLEF‐TV tower using SiB2.5 , 2003 .

[32]  R. Koster,et al.  Comparison and assimilation of global soil moisture retrievals from the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR‐E) and the Scanning Multichannel Microwave Radiometer (SMMR) , 2007 .

[33]  Xubin Zeng,et al.  Sensitivity of the NCEP/Noah land surface model to the MODIS green vegetation fraction data set , 2006 .

[34]  Peter E. Thornton,et al.  Improvements to the Community Land Model and their impact on the hydrological cycle , 2008 .

[35]  Jeffrey L. Anderson,et al.  The Data Assimilation Research Testbed: A Community Facility , 2009 .

[36]  Xubin Zeng,et al.  Improving snow processes in the Noah land model , 2010 .

[37]  Wade T. Crow,et al.  Assimilating remote sensing observations of leaf area index and soil moisture for wheat yield estimates: An observing system simulation experiment , 2012 .

[38]  S. Seneviratne,et al.  A regional perspective on trends in continental evaporation , 2009 .

[39]  S. Seneviratne Climate science: Historical drought trends revisited , 2012, Nature.

[40]  W. James Shuttleworth,et al.  A fully multiple-criteria implementation of the Sobol' method for parameter sensitivity analysis , 2012 .

[41]  A. Pitman The evolution of, and revolution in, land surface schemes designed for climate models , 2003 .

[42]  Scott D. Miller,et al.  Seasonal drought stress in the Amazon: Reconciling models and observations , 2008 .

[43]  R. Knight,et al.  Soil Moisture Measurement for Ecological and Hydrological Watershed‐Scale Observatories: A Review , 2008 .

[44]  Revising the Ensemble-Based Kalman Filter Covariance for the Retrieval of Deep-Layer Soil Moisture , 2010 .

[45]  Günter Blöschl,et al.  On the spatial scaling of soil moisture , 1999 .

[46]  W. James Shuttleworth,et al.  Calibrating the Simple Biosphere Model for Amazonian Tropical Forest Using Field and Remote Sensing Data. Part I: Average Calibration with Field Data , 1989 .

[47]  W. J. Shuttleworth,et al.  COSMOS: the COsmic-ray Soil Moisture Observing System , 2012 .

[48]  M. Zreda,et al.  Footprint diameter for a cosmic‐ray soil moisture probe: Theory and Monte Carlo simulations , 2013 .

[49]  T. Başar,et al.  A New Approach to Linear Filtering and Prediction Problems , 2001 .

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

[51]  Rafael Rosolem,et al.  A universal calibration function for determination of soil moisture with cosmic-ray neutrons , 2012 .

[52]  Jeffrey L. Anderson An Ensemble Adjustment Kalman Filter for Data Assimilation , 2001 .

[53]  T. Stocker,et al.  Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation. A Special Report of Working Groups I and II of IPCC Intergovernmental Panel on Climate Change , 2012 .

[54]  R. Dickinson,et al.  The land surface climatology of the community land model coupled to the NCAR community climate model , 2002 .

[55]  P. Houtekamer,et al.  Data Assimilation Using an Ensemble Kalman Filter Technique , 1998 .

[56]  R. O N A L,et al.  The annual cycles of CO 2 and H 2 O exchange over a northern mixed forest as observed from a very tall tower , 2003 .

[57]  Wade T. Crow,et al.  A land surface data assimilation framework using the land information system : Description and applications , 2008 .

[58]  Geir Evensen,et al.  The Ensemble Kalman Filter: theoretical formulation and practical implementation , 2003 .

[59]  Jeffrey P. Walker,et al.  Upscaling sparse ground‐based soil moisture observations for the validation of coarse‐resolution satellite soil moisture products , 2012 .

[60]  Jeffrey P. Walker,et al.  Extended versus Ensemble Kalman Filtering for Land Data Assimilation , 2002 .

[61]  L. M. Berliner,et al.  A Bayesian tutorial for data assimilation , 2007 .

[62]  Bailing Li,et al.  Improving estimated soil moisture fields through assimilation of AMSR-E soil moisture retrievals with an ensemble Kalman filter and a mass conservation constraint , 2011 .

[63]  J. D. Tarpley,et al.  Implementation of Noah land surface model advances in the National Centers for Environmental Prediction operational mesoscale Eta model , 2003 .

[64]  Dara Entekhabi,et al.  An ensemble‐based reanalysis approach to land data assimilation , 2005 .

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

[66]  J. D. Tarpley,et al.  The multi‐institution North American Land Data Assimilation System (NLDAS): Utilizing multiple GCIP products and partners in a continental distributed hydrological modeling system , 2004 .

[67]  Jeffrey P. Walker,et al.  THE GLOBAL LAND DATA ASSIMILATION SYSTEM , 2004 .

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

[69]  Jeffrey L. Anderson A Local Least Squares Framework for Ensemble Filtering , 2003 .

[70]  D. Lawrence,et al.  Regions of Strong Coupling Between Soil Moisture and Precipitation , 2004, Science.

[71]  Paul R. Houser,et al.  Requirements of a global near-surface soil moisture satellite mission: accuracy, repeat time, and spatial resolution , 2004 .

[72]  D. Entekhabi,et al.  Land data assimilation and estimation of soil moisture using measurements from the Southern Great Plains 1997 Field Experiment , 2001 .

[73]  J. Willis,et al.  Meridional overturning circulation and heat transport observations in the Atlantic Ocean [in 'state of the Climate in 2012'] , 2013 .

[74]  M. Susan Moran,et al.  Carbon dioxide exchange in a semidesert grassland through drought‐induced vegetation change , 2010 .

[75]  Harrie-Jan Hendricks Franssen,et al.  Correction of systematic model forcing bias of CLM using assimilation of cosmic-ray Neutrons and land surface temperature: a study in the Heihe Catchment, China , 2014 .

[76]  Richard J. Beckman,et al.  A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output From a Computer Code , 2000, Technometrics.

[77]  Dara Entekhabi,et al.  NASA's Soil Moisture Active Passive (SMAP) Mission and Opportunities for Applications Users , 2013 .

[78]  M. D. McKay,et al.  A comparison of three methods for selecting values of input variables in the analysis of output from a computer code , 2000 .

[79]  Jeffrey L. Anderson EXPLORING THE NEED FOR LOCALIZATION IN ENSEMBLE DATA ASSIMILATION USING A HIERARCHICAL ENSEMBLE FILTER , 2007 .

[80]  Rafael Rosolem,et al.  Measurement depth of the cosmic ray soil moisture probe affected by hydrogen from various sources , 2012 .

[81]  Wade T. Crow,et al.  An adaptive ensemble Kalman filter for soil moisture data assimilation , 2007 .

[82]  Douglas E. Ahl,et al.  Effects of aggregated classifications of forest composition on estimates of evapotranspiration in a northern Wisconsin forest , 2002 .

[83]  H. Hendricks Franssen,et al.  Accuracy of the cosmic‐ray soil water content probe in humid forest ecosystems: The worst case scenario , 2013 .

[84]  Kevin W. Manning,et al.  The community Noah land surface model with multiparameterization options (Noah-MP): 1. Model description and evaluation with local-scale measurements , 2011 .

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

[86]  T. Ferré,et al.  Field Validation of a Cosmic‐Ray Neutron Sensor Using a Distributed Sensor Network , 2012 .

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

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

[89]  Nirmala Murthy,et al.  Summary for Policymakers , 2007 .

[90]  Kenneth J. Davis,et al.  The annual cycles of CO2 and H2O exchange over a northern mixed forest as observed from a very tall tower , 2003 .

[91]  D. P. DEE,et al.  Bias and data assimilation , 2005 .

[92]  Stefan Rahmstorf,et al.  A decade of weather extremes , 2012 .