Can next-generation soil data products improve soil moisture modelling at the continental scale? An assessment using a new microclimate package for the R programming environment

Abstract Accurate models of soil moisture are vital for solving core problems in meteorology, hydrology, agriculture and ecology. The capacity for soil moisture modelling is growing rapidly with the development of high-resolution, continent-scale gridded weather and soil data together with advances in modelling methods. In particular, the GlobalSoilMap.net initiative represents next-generation, depth-specific gridded soil products that may substantially increase soil moisture modelling capacity. Here we present an implementation of Campbell’s infiltration and redistribution model within the NicheMapR microclimate modelling package for the R environment, and use it to assess the predictive power provided by the GlobalSoilMap.net product Soil and Landscape Grid of Australia (SLGA, ∼100 m) as well as the coarser resolution global product SoilGrids (SG, ∼250 m). Predictions were tested in detail against 3 years of root-zone (3–75 cm) soil moisture observation data from 35 monitoring sites within the OzNet project in Australia, with additional tests of the finalised modelling approach against cosmic-ray neutron (CosmOz, 0–50 cm, 9 sites from 2011 to 2017) and satellite (ASCAT, 0–2 cm, continent-wide from 2007 to 2009) observations. The model was forced by daily 0.05° (∼5 km) gridded meteorological data. The NicheMapR system predicted soil moisture to within experimental error for all data sets. Using the SLGA or the SG soil database, the OzNet soil moisture could be predicted with a root mean square error (rmse) of ∼0.075 m3 m−3 and a correlation coefficient (r) of 0.65 consistently through the soil profile without any parameter tuning. Soil moisture predictions based on the SLGA and SG datasets were ≈ 17% closer to the observations than when using a chloropleth-derived soil data set (Digital Atlas of Australian Soils), with the greatest improvements occurring for deeper layers. The CosmOz observations were predicted with similar accuracy (r = 0.76 and rmse of ∼0.085 m3 m−3). Comparisons at the continental scale to 0–2 cm satellite data (ASCAT) showed that the SLGA/SG datasets increased model fit over simulations using the DAAS soil properties (r ∼ 0.63 & rmse 15% vs. r 0.48 & rmse 18%, respectively). Overall, our results demonstrate the advantages of using GlobalSoilMap.net products in combination with gridded weather data for modelling soil moisture at fine spatial and temporal resolution at the continental scale.

[1]  F. Janzen,et al.  Hydric conditions during incubation influence phenotypes of neonatal reptiles in the field , 2015 .

[2]  G. C. Packard,et al.  Influence of Moisture, Temperature, and Substrate on Snapping Turtle Eggs and Embryos , 1987 .

[3]  T. McVicar,et al.  Wind speed climatology and trends for Australia, 1975–2006: Capturing the stilling phenomenon and comparison with near‐surface reanalysis output , 2008 .

[4]  W. Beckman,et al.  Behavioral implications of mechanistic ecology , 1973, Oecologia.

[5]  Balaji Rajagopalan,et al.  Are we unnecessarily constraining the agility of complex process‐based models? , 2015 .

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

[7]  R. Koster,et al.  Variance and Predictability of Precipitation at Seasonal-to-Interannual Timescales , 2000 .

[8]  Gaylon S. Campbell,et al.  Soil physics with BASIC :transport models for soil-plant systems , 1985 .

[9]  M. Uddstrom,et al.  Soil moisture simulation by JULES in New Zealand: verification and sensitivity tests , 2014 .

[10]  Christoph Rudiger,et al.  Towards soil property retrieval from space: Proof of concept using in situ observations , 2014 .

[11]  G. Hornberger,et al.  A Statistical Exploration of the Relationships of Soil Moisture Characteristics to the Physical Properties of Soils , 1984 .

[12]  Marvin N. Wright,et al.  SoilGrids250m: Global gridded soil information based on machine learning , 2017, PloS one.

[13]  Ary A. Hoffmann,et al.  Microclimate modelling at macro scales: a test of a general microclimate model integrated with gridded continental‐scale soil and weather data , 2014 .

[14]  Pedro Viterbo,et al.  An Improved Land Surface Parameterization Scheme in the ECMWF Model and Its Validation. , 1995 .

[15]  R.A.M. de Jeu,et al.  Comparison of remotely sensed and modelled soil moisture data sets across Australia , 2016 .

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

[17]  David Clifford,et al.  The Australian three-dimensional soil grid: Australia’s contribution to the GlobalSoilMap project , 2015 .

[18]  A. Western,et al.  The Effect of Soil and Vegetation Parameters in the ECMWF Land Surface Scheme , 2004 .

[19]  M. Kearney,et al.  microclim: Global estimates of hourly microclimate based on long-term monthly climate averages , 2014, Scientific Data.

[20]  Luca Ridolfi,et al.  On the spatial and temporal links between vegetation, climate, and soil moisture , 1999 .

[21]  S. Seneviratne,et al.  Recent decline in the global land evapotranspiration trend due to limited moisture supply , 2010, Nature.

[22]  On the use of elevation, altitude, and height in the ecological and climatological literature , 2013, Oecologia.

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

[24]  G. Heuvelink,et al.  SoilGrids1km — Global Soil Information Based on Automated Mapping , 2014, PloS one.

[25]  Jeffrey P. Walker,et al.  Towards soil property retrieval from space: An application with disaggregated satellite observations , 2015 .

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

[27]  J. Qu,et al.  Satellite remote sensing applications for surface soil moisture monitoring: A review , 2009 .

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

[29]  M. Kearney,et al.  NicheMapR – an R package for biophysical modelling: the microclimate model , 2017 .

[30]  John Mitchell,et al.  Thermal Model for Prediction of a Desert Iguana’s Daily and Seasonal Behavior , 1973 .

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

[32]  Jeffrey P. Walker,et al.  Towards soil property retrieval from space: A one-dimensional twin-experiment , 2013 .

[33]  Philippe Lagacherie,et al.  GlobalSoilMap.net – A New Digital Soil Map of the World , 2010 .

[34]  Mauricio Zambrano-Bigiarini,et al.  A model-independent Particle Swarm Optimisation software for model calibration , 2013, Environ. Model. Softw..

[35]  Eva Borbas,et al.  Development of a Global Infrared Land Surface Emissivity Database for Application to Clear Sky Sounding Retrievals from Multispectral Satellite Radiance Measurements , 2008 .

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

[37]  W. Wagner,et al.  A Method for Estimating Soil Moisture from ERS Scatterometer and Soil Data , 1999 .

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

[39]  D. Jupp,et al.  Estimating one-time-of-day meteorological data from standard daily data as inputs to thermal remote sensing based energy balance models , 1999 .

[40]  Mike Grundy,et al.  Soil and landscape grid of Australia. , 2015 .

[41]  Valerie Isham,et al.  Some models for rainfall based on stochastic point processes , 1987, Proceedings of the Royal Society of London. A. Mathematical and Physical Sciences.

[42]  Jesse A. Logan,et al.  A model for diurnal variation in soil and air temperature , 1981 .

[43]  Ying Gao,et al.  The Soil Moisture Active Passive Experiments (SMAPEx): Toward Soil Moisture Retrieval From the SMAP Mission , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[44]  C. Rudiger,et al.  Towards soil hydraulic parameter retrieval from Land Surface Models using near-surface soil moisture data , 2011 .

[45]  J. Goudriaan,et al.  Modelling diurnal patterns of air temperature, radiation wind speed and relative humidity by equations from daily characteristics , 1996 .