A review of the global soil property maps for Earth system models

Abstract. Soil is an important regulator of Earth system processes, but remains one of the least well-described data layers in Earth system models (ESMs). We reviewed global soil property maps from the perspective of ESMs, including soil physical and chemical and biological properties, which can also offer insights to soil data developers and users. These soil datasets provide model inputs, initial variables, and benchmark datasets. For modelling use, the dataset should be geographically continuous and scalable and have uncertainty estimates. The popular soil datasets used in ESMs are often based on limited soil profiles and coarse-resolution soil-type maps with various uncertainty sources. Updated and comprehensive soil information needs to be incorporated into ESMs. New generation soil datasets derived through digital soil mapping with abundant, harmonized, and quality-controlled soil observations and environmental covariates are preferred to those derived through the linkage method (i.e. taxotransfer rule-based method) for ESMs. SoilGrids has the highest accuracy and resolution among the global soil datasets, while other recently developed datasets offer useful compensation. Because there is no universal pedotransfer function, an ensemble of them may be more suitable for providing derived soil properties to ESMs. Aggregation and upscaling of soil data are needed for model use, but can be avoided by using a subgrid method in ESMs at the expense of increases in model complexity. Producing soil property maps in a time series still remains challenging. The uncertainties in soil data need to be estimated and incorporated into ESMs.

[1]  Alexander J. Winkler,et al.  Developments in the MPI‐M Earth System Model version 1.2 (MPI‐ESM1.2) and Its Response to Increasing CO2 , 2019, Journal of advances in modeling earth systems.

[2]  A. Ercan,et al.  Integrating global land-cover and soil datasets to update saturated hydraulic conductivity parameterization in hydrologic modeling. , 2018, The Science of the total environment.

[3]  Mario Guevara,et al.  No silver bullet for digital soil mapping: country-specific soil organic carbon estimates across Latin America , 2018, SOIL.

[4]  Shaoning Lv,et al.  Analysis of soil hydraulic and thermal properties for land surface modeling over the Tibetan Plateau , 2018, Earth System Science Data.

[5]  Zhiyong Wu,et al.  An Integration Approach for Mapping Field Capacity of China Based on Multi-Source Soil Datasets , 2018, Water.

[6]  M. Kearney,et al.  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 , 2018, Journal of Hydrology.

[7]  N. Hanan,et al.  HYSOGs250m, global gridded hydrologic soil groups for curve-number-based runoff modeling , 2018, Scientific Data.

[8]  Raphael A. Viscarra Rossel,et al.  Proximal sensing for soil carbon accounting , 2018 .

[9]  J. R. Wilson,et al.  The GFDL Global Atmosphere and Land Model AM4.0/LM4.0: 2. Model Description, Sensitivity Studies, and Tuning Strategies , 2018 .

[10]  J. Hartmann,et al.  Compiling and Mapping Global Permeability of the Unconsolidated and Consolidated Earth: GLobal HYdrogeology MaPS 2.0 (GLHYMPS 2.0) , 2018 .

[11]  Q. Duan,et al.  Parameter optimization for carbon and water fluxes in two global land surface models based on surrogate modelling , 2018 .

[12]  Bertrand Guenet,et al.  Large Differences in Global and Regional Total Soil Carbon Stock Estimates Based on SoilGrids, HWSD, and NCSCD: Intercomparison and Evaluation Based on Field Data From USA, England, Wales, and France , 2018 .

[13]  J. Bouma,et al.  Pedotransfer Functions in Earth System Science: Challenges and Perspectives , 2017 .

[14]  Gerard B. M. Heuvelink,et al.  Soil legacy data rescue via GlobalSoilMap and other international and national initiatives , 2017, GeoResJ.

[15]  Xing Yuan,et al.  Do Lateral Flows Matter for the Hyperresolution Land Surface Modeling? , 2017 .

[16]  A. McBratney,et al.  GlobalSoilMap - Digital Soil Mapping from Country to Globe : Proceedings of the Global Soil Map 2017 Conference, July 4-6, 2017, Moscow, Russia , 2017 .

[17]  Kurt Christian Kersebaum,et al.  Impact analysis of climate data aggregation at different spatial scales on simulated net primary productivity for croplands , 2017 .

[18]  Jonathon S. Wright,et al.  Evaluation of the Common Land Model (CoLM) from the Perspective of Water and Energy Budget Simulation: Towards Inclusion in CMIP6 , 2017 .

[19]  J. Melton,et al.  Tiling soil textures for terrestrial ecosystem modelling via clustering analysis: a case study with CLASS-CTEM (version 2.1) , 2017 .

[20]  T. Hengl,et al.  3D soil hydraulic database of Europe at 250 m resolution , 2017 .

[21]  T. Hengl,et al.  Soil Property and Class Maps of the Conterminous US at 100 meter Spatial Resolution based on a Compilation of National Soil Point Observations and Machine Learning , 2017, 1705.08323.

[22]  Philippe Lagacherie,et al.  Using quantile regression forest to estimate uncertainty of digital soil mapping products , 2017 .

[23]  T. Hengl,et al.  Mapping the global depth to bedrock for land surface modeling , 2017 .

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

[25]  Michael Herbst,et al.  A global data set of soil hydraulic properties and sub-grid variability of soil water retention and hydraulic conductivity curves, link to model result files in NetCDF format , 2017 .

[26]  Arnaud J.A.M. Temme,et al.  S‐World: A Global Soil Map for Environmental Modelling , 2017 .

[27]  Zong-Liang Yang,et al.  Effects of soil‐type datasets on regional terrestrial water cycle simulations under different climatic regimes , 2016 .

[28]  Dominique Arrouays,et al.  GlobalSoilMap France: High-resolution spatial modelling the soils of France up to two meter depth. , 2016, The Science of the total environment.

[29]  J.G.B. Leenaars,et al.  WoSIS: providing standardised soil profile data for the world , 2016 .

[30]  Simon F. B. Tett,et al.  Global evaluation of gross primary productivity in the JULES land surface model v3.4.1 , 2016 .

[31]  J. Fung,et al.  Updated global soil map for the Weather Research and Forecasting model and soil moisture initialization for the Noah land surface model , 2016 .

[32]  C. Folberth,et al.  Uncertainty in soil data can outweigh climate impact signals in global crop yield simulations , 2016, Nature Communications.

[33]  N. Batjes Harmonized soil property values for broad-scale modelling (WISE30sec) with estimates of global soil carbon stocks , 2016 .

[34]  Peter A. Troch,et al.  Implementing and Evaluating Variable Soil Thickness in the Community Land Model, Version 4.5 (CLM4.5) , 2016 .

[35]  J. Yeluripati,et al.  Impact of Spatial Soil and Climate Input Data Aggregation on Regional Yield Simulations , 2016, PloS one.

[36]  Florian Pappenberger,et al.  Improving weather predictability by including land-surface model parameter uncertainty , 2016 .

[37]  J. Pelletier,et al.  A gridded global data set of soil, intact regolith, and sedimentary deposit thicknesses for regional and global land surface modeling , 2016 .

[38]  Yujie He,et al.  Toward more realistic projections of soil carbon dynamics by Earth system models , 2016 .

[39]  Michail D. Vrettas,et al.  Toward a new parameterization of hydraulic conductivity in climate models: Simulation of rapid groundwater fluctuations in Northern California , 2015 .

[40]  Peijun Shi,et al.  Age‐dependent forest carbon sink: Estimation via inverse modeling , 2015 .

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

[42]  Luis Samaniego,et al.  Influence of soil textural properties on hydrologic fluxes in the Mississippi river basin , 2015 .

[43]  G. Heuvelink,et al.  Mapping Soil Properties of Africa at 250 m Resolution: Random Forests Significantly Improve Current Predictions , 2015, PloS one.

[44]  James S. Famiglietti,et al.  Toward hyper‐resolution land‐surface modeling: The effects of fine‐scale topography and soil texture on CLM4.0 simulations over the Southwestern U.S. , 2015 .

[45]  Randal D. Koster,et al.  An updated treatment of soil texture and associated hydraulic properties in a global land modeling system , 2014 .

[46]  G. Tóth,et al.  New generation of hydraulic pedotransfer functions for Europe , 2014, European journal of soil science.

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

[48]  Wei Gong,et al.  Multi-objective parameter optimization of common land model using adaptive surrogate modeling , 2014 .

[49]  Yongjiu Dai,et al.  Comparison of Aggregation Ways on Soil Property Maps , 2014 .

[50]  Fang Wang,et al.  An overview of BCC climate system model development and application for climate change studies , 2014, Journal of Meteorological Research.

[51]  Dawen Yang,et al.  Impacts of climate change and vegetation dynamics on runoff in the mountainous region of the Haihe River basin in the past five decades , 2014 .

[52]  M. Iredell,et al.  The NCEP Climate Forecast System Version 2 , 2014 .

[53]  Hua Yuan,et al.  A global soil data set for earth system modeling , 2014 .

[54]  J. Randerson,et al.  Changes in soil organic carbon storage predicted by Earth system models during the 21st century , 2013 .

[55]  A. Hursthouse,et al.  Harmonisation of physical and chemical methods for soil management in Cork Oak forests - Lessons from collaborative investigations , 2013 .

[56]  Baoyuan Liu,et al.  Development of a China Dataset of Soil Hydraulic Parameters Using Pedotransfer Functions for Land Surface Modeling , 2013 .

[57]  A. Zhu,et al.  A China data set of soil properties for land surface modeling , 2013 .

[58]  Ian N. Harman,et al.  The land surface model component of ACCESS: description and impact on the simulated surface climatology , 2013 .

[59]  Y. Fan,et al.  Global Patterns of Groundwater Table Depth , 2013, Science.

[60]  Bill X. Hu,et al.  Coupling a groundwater model with a land surface model to improve water and energy cycle simulation , 2012 .

[61]  Z. Libohova,et al.  Equal-area spline functions applied to a legacy soil database to create weighted-means maps of soil organic carbon at a continental scale , 2012 .

[62]  J. Randerson,et al.  Causes of variation in soil carbon simulations from CMIP5 Earth system models and comparison with observations , 2012 .

[63]  J. Canadell,et al.  The Northern Circumpolar Soil Carbon Database: spatially distributed datasets of soil coverage and soil carbon storage in the northern permafrost regions , 2012 .

[64]  S. Jeffrey,et al.  Aerosol- and greenhouse gas-induced changes in summer rainfall and circulation in the Australasian region: a study using single-forcing climate simulations , 2012 .

[65]  Jun Qin,et al.  Parameterizing soil organic carbon’s impacts on soil porosity and thermal parameters for Eastern Tibet grasslands , 2012, Science China Earth Sciences.

[66]  Hua Yuan,et al.  A soil particle-size distribution dataset for regional land and climate modelling in China , 2012 .

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

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

[69]  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 .

[70]  Budiman Minasny,et al.  Necessary meta-data for pedotransfer functions , 2011 .

[71]  Mathieu Javaux,et al.  Using Pedotransfer Functions to Estimate the van Genuchten–Mualem Soil Hydraulic Properties: A Review , 2010 .

[72]  Vivek K. Arora,et al.  The Effect of Terrestrial Photosynthesis Down Regulation on the Twentieth-Century Carbon Budget Simulated with the CCCma Earth System Model , 2009 .

[73]  B. Minasny,et al.  Digital Soil Map of the World , 2009, Science.

[74]  V. Stolbovoi,et al.  Soil-geographical database of Russia , 2008 .

[75]  Kevin R. Gurney,et al.  Interannual variations in continental‐scale net carbon exchange and sensitivity to observing networks estimated from atmospheric CO2 inversions for the period 1980 to 2005 , 2008 .

[76]  Budiman Minasny,et al.  Quantitative models for pedogenesis — A review , 2008 .

[77]  T. Chase,et al.  Representing a new MODIS consistent land surface in the Community Land Model (CLM 3.0) , 2007 .

[78]  Scott C. Doney,et al.  Natural Variability in a Stable, 1000-Yr Global Coupled Climate–Carbon Cycle Simulation , 2006 .

[79]  P. Thornton,et al.  Ecosystem model spin-up: Estimating steady state conditions in a coupled terrestrial carbon and nitrogen cycle model , 2005 .

[80]  M. Vanclooster,et al.  Sensitivity of the SWAT model to the soil and land use data parametrisation : a case study in the thyle catchment, belgium , 2005 .

[81]  Gerd Sparovek,et al.  A National Soil Profile Database for Brazil Available to International Scientists , 2005 .

[82]  I. C. Prentice,et al.  A dynamic global vegetation model for studies of the coupled atmosphere‐biosphere system , 2005 .

[83]  Guangxing Wang,et al.  Up-scaling methods based on variability-weighting and simulation for inferring spatial information across scales , 2004 .

[84]  Chris Moran,et al.  ASRIS: the database , 2003 .

[85]  B. Minasny,et al.  Digital Soil Mapping , 2017 .

[86]  R. Dickinson,et al.  The Common Land Model , 2003 .

[87]  Kumiko Takata,et al.  Development of the minimal advanced treatments of surface interaction and runoff , 2003 .

[88]  Clayton V. Deutsch,et al.  A visualbasic program for histogram and variogram scaling , 2002 .

[89]  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 .

[90]  Thomas J. Jackson,et al.  Estimating soil water‐holding capacities by linking the Food and Agriculture Organization Soil map of the world with global pedon databases and continuous pedotransfer functions , 2000 .

[91]  Laerte Guimarães Ferreira,et al.  Predicting Soil Albedo from Soil Color and Spectral Reflectance Data , 2000 .

[92]  D. Verseghy,et al.  The Canadian land surface scheme (CLASS): Its history and future , 2000 .

[93]  Alex B. McBratney,et al.  Modelling soil attribute depth functions with equal-area quadratic smoothing splines , 1999 .

[94]  R. Betts,et al.  The impact of new land surface physics on the GCM simulation of climate and climate sensitivity , 1999 .

[95]  C. Rosenzweig,et al.  Land-Surface Model Development for the GISS GCM , 1997 .

[96]  Xiangming Xiao,et al.  Equilibrium responses of global net primary production and carbon storage to doubled atmospheric carbon dioxide: Sensitivity to changes in vegetation nitrogen concentration , 1997 .

[97]  D. Randall,et al.  A Revised Land Surface Parameterization (SiB2) for Atmospheric GCMS. Part I: Model Formulation , 1996 .

[98]  Paul E. Gessler,et al.  Soil-Landscape Modelling and Spatial Prediction of Soil Attributes , 1995, Int. J. Geogr. Inf. Sci..

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

[100]  Robert S. Webb,et al.  Specifying land surface characteristics in general circulation models: Soil profile data set and derived water‐holding capacities , 1993 .

[101]  R. Koster,et al.  Modeling the land surface boundary in climate models as a composite of independent vegetation stands , 1992 .

[102]  A. Henderson‐sellers,et al.  A global archive of land cover and soils data for use in general circulation climate models , 1985 .

[103]  G. Hornberger,et al.  Empirical equations for some soil hydraulic properties , 1978 .

[104]  George R. Blake,et al.  Thermal Properties of Soils , 1950 .

[105]  Dawen Yang,et al.  Development of a Physically Based Soil Albedo Parameterization for the Tibetan Plateau , 2018 .

[106]  Hyunseok Kim,et al.  Numerical Evaluation of JULES Surface Tiling Scheme with High-Resolution Atmospheric Forcing and Land Cover Data , 2018 .

[107]  C. Ballabio,et al.  Mapping topsoil physical properties at European scale using the LUCAS database , 2016 .

[108]  J.G.B. Leenaars,et al.  Towards the standardization and harmonization of world soil data , 2015 .

[109]  J. Randerson,et al.  Changes in soil organic carbon storage predicted by Earth system models during the 21 st century , 2013 .

[110]  Zong-Liang Yang,et al.  Technical description of version 4.5 of the Community Land Model (CLM) , 2013 .

[111]  Roland Hiederer,et al.  Global Soil Organic Carbon Estimates and the Harmonized World Soil Database , 2011 .

[112]  C. Perry,et al.  Regional Approach to Soil Property Mapping using Legacy Data and Spatial Disaggregation Techniques , 2010 .

[113]  V. Stolbovoi,et al.  Soil-geographic database of Russia , 2008 .

[114]  N. Batjes,et al.  ISRIC-WISE Harmonized Global Soil Profile Dataset (Ver. 3.1) , 2008 .

[115]  John M. Hollis,et al.  SPADE-2: THE SOIL PROFILE ANALYTICAL DATABASE , 2006 .

[116]  N. Batjes A taxotransfer rule-based approach for filling gaps in measured soil data in primary SOTER databases , 2003 .

[117]  Stefan Hagemann,et al.  An improved land surface parameter dataset for global and regional climate models , 2002 .

[118]  Dale A. Quattrochi,et al.  Fractal Characterization of Multitemporal Remote Sensing Data , 2000 .

[119]  A. Turner Analysis of soil , 2000 .

[120]  Michael Botzet,et al.  Derivation of global GCM boundary conditions from 1 km land use satellite data , 1999 .

[121]  Douglas A. Miller,et al.  A Conterminous United States Multilayer Soil Characteristics Dataset for Regional Climate and Hydrology Modeling , 1998 .

[122]  Ann Henderson-Sellers,et al.  Biosphere-atmosphere transfer scheme(BATS) version 1e as coupled to the NCAR community climate model , 1993 .