Articles | Volume 5, issue 2
https://doi.org/10.5194/soil-5-137-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/soil-5-137-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Review article
| 
05 Jul 2019
Review article |
| 05 Jul 2019
| 05 Jul 2019
A review of the global soil property maps for Earth system models
Yongjiu Dai
CORRESPONDING AUTHOR
Southern Marine Science and Engineering Guangdong Laboratory
(Zhuhai), Guangdong Province Key Laboratory for Climate Change and Natural
Disaster Studies, School of Atmospheric Sciences, Sun Yat-sen University,
Guangzhou, China
Southern Marine Science and Engineering Guangdong Laboratory
(Zhuhai), Guangdong Province Key Laboratory for Climate Change and Natural
Disaster Studies, School of Atmospheric Sciences, Sun Yat-sen University,
Guangzhou, China
Nan Wei
Southern Marine Science and Engineering Guangdong Laboratory
(Zhuhai), Guangdong Province Key Laboratory for Climate Change and Natural
Disaster Studies, School of Atmospheric Sciences, Sun Yat-sen University,
Guangzhou, China
Qinchuan Xin
School of Geography and Planning, Sun Yat-sen University, Guangzhou,
China
Hua Yuan
Southern Marine Science and Engineering Guangdong Laboratory
(Zhuhai), Guangdong Province Key Laboratory for Climate Change and Natural
Disaster Studies, School of Atmospheric Sciences, Sun Yat-sen University,
Guangzhou, China
Shupeng Zhang
Southern Marine Science and Engineering Guangdong Laboratory
(Zhuhai), Guangdong Province Key Laboratory for Climate Change and Natural
Disaster Studies, School of Atmospheric Sciences, Sun Yat-sen University,
Guangzhou, China
Shaofeng Liu
Southern Marine Science and Engineering Guangdong Laboratory
(Zhuhai), Guangdong Province Key Laboratory for Climate Change and Natural
Disaster Studies, School of Atmospheric Sciences, Sun Yat-sen University,
Guangzhou, China
Xingjie Lu
Southern Marine Science and Engineering Guangdong Laboratory
(Zhuhai), Guangdong Province Key Laboratory for Climate Change and Natural
Disaster Studies, School of Atmospheric Sciences, Sun Yat-sen University,
Guangzhou, China
Dagang Wang
School of Geography and Planning, Sun Yat-sen University, Guangzhou,
China
Fapeng Yan
College of Global Change and Earth System Science, Beijing Normal
University, Beijing, China
Related authors
Jiahao Shi, Hua Yuan, Wanyi Lin, Wenzong Dong, Hongbin Liang, Zhuo Liu, Jianxin Zeng, Haolin Zhang, Nan Wei, Zhongwang Wei, Shupeng Zhang, Shaofeng Liu, Xingjie Lu, and Yongjiu Dai
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-77, https://doi.org/10.5194/essd-2024-77, 2024
Preprint under review for ESSD
Short summary
Short summary
Flux tower data are widely recognized as benchmarking data for land surface models, but insufficient emphasis on and deficiency in site attribute data limits their true value. We collect site-observed vegetation, soil, and topography data from various sources. The final dataset encompasses 90 sites globally with relatively complete site attribute data and high-quality flux validation data. This work has provided more reliable site attribute data, benefiting land surface model development.
Qingliang Li, Gaosong Shi, Wei Shangguan, Vahid Nourani, Jianduo Li, Lu Li, Feini Huang, Ye Zhang, Chunyan Wang, Dagang Wang, Jianxiu Qiu, Xingjie Lu, and Yongjiu Dai
Earth Syst. Sci. Data, 14, 5267–5286, https://doi.org/10.5194/essd-14-5267-2022, https://doi.org/10.5194/essd-14-5267-2022, 2022
Short summary
Short summary
SMCI1.0 is a 1 km resolution dataset of daily soil moisture over China for 2000–2020 derived through machine learning trained with in situ measurements of 1789 stations, meteorological forcings, and land surface variables. It contains 10 soil layers with 10 cm intervals up to 100 cm deep. Evaluated by in situ data, the error (ubRMSE) ranges from 0.045 to 0.051, and the correlation (R) range is 0.866-0.893. Compared with ERA5-Land, SMAP-L4, and SoMo.ml, SIMI1.0 has higher accuracy and resolution.
Ziqi Lin, Yongjiu Dai, Umakant Mishra, Guocheng Wang, Wei Shangguan, Wen Zhang, and Zhangcai Qin
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2022-232, https://doi.org/10.5194/essd-2022-232, 2022
Manuscript not accepted for further review
Short summary
Short summary
Spatial soil organic carbon (SOC) data is critical for predictions in carbon climate feedbacks and future climate trends, but no conclusion has yet been reached on which dataset to be used for specific purposes. We evaluated the SOC estimates from five widely used global soil datasets and a regional permafrost dataset, and identify uncertainties of SOC estimates by region, biome, and data sources, hoping to help improve SOC/soil data in the future.
Yaoping Wang, Jiafu Mao, Mingzhou Jin, Forrest M. Hoffman, Xiaoying Shi, Stan D. Wullschleger, and Yongjiu Dai
Earth Syst. Sci. Data, 13, 4385–4405, https://doi.org/10.5194/essd-13-4385-2021, https://doi.org/10.5194/essd-13-4385-2021, 2021
Short summary
Short summary
We developed seven global soil moisture datasets (1970–2016, monthly, half-degree, and multilayer) by merging a wide range of data sources, including in situ and satellite observations, reanalysis, offline land surface model simulations, and Earth system model simulations. Given the great value of long-term, multilayer, gap-free soil moisture products to climate research and applications, we believe this paper and the presented datasets would be of interest to many different communities.
Qinchuan Xin, Yongjiu Dai, and Xiaoping Liu
Biogeosciences, 16, 467–484, https://doi.org/10.5194/bg-16-467-2019, https://doi.org/10.5194/bg-16-467-2019, 2019
Short summary
Short summary
Terrestrial biosphere models that simulate both leaf dynamics and canopy photosynthesis are required to understand vegetation–climate interactions. A time-stepping scheme is proposed to simulate leaf area index, phenology, and gross primary production via climate variables. The method performs well on simulating deciduous broadleaf forests across the eastern United States; it provides a simplified and improved version of the growing production day model for use in land surface modeling.
Jiahao Shi, Hua Yuan, Wanyi Lin, Wenzong Dong, Hongbin Liang, Zhuo Liu, Jianxin Zeng, Haolin Zhang, Nan Wei, Zhongwang Wei, Shupeng Zhang, Shaofeng Liu, Xingjie Lu, and Yongjiu Dai
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-77, https://doi.org/10.5194/essd-2024-77, 2024
Preprint under review for ESSD
Short summary
Short summary
Flux tower data are widely recognized as benchmarking data for land surface models, but insufficient emphasis on and deficiency in site attribute data limits their true value. We collect site-observed vegetation, soil, and topography data from various sources. The final dataset encompasses 90 sites globally with relatively complete site attribute data and high-quality flux validation data. This work has provided more reliable site attribute data, benefiting land surface model development.
Bangjun Cao, Yaping Shao, Xianyu Yang, Xin Yin, and Shaofeng Liu
Atmos. Chem. Phys., 24, 275–285, https://doi.org/10.5194/acp-24-275-2024, https://doi.org/10.5194/acp-24-275-2024, 2024
Short summary
Short summary
Our novel scheme enhances large-eddy simulations (LESs) for atmosphere–land interactions. It couples LES subgrid closure with Monin–Obukhov similarity theory (MOST), overcoming MOST's limitations. Validated over diverse land surfaces, our approach outperforms existing methods, aligning well with field measurements. Robustness is demonstrated across varying model resolutions. MOST's influence strengthens with decreasing grid spacing, particularly for sensible heat flux.
Qingliang Li, Gaosong Shi, Wei Shangguan, Vahid Nourani, Jianduo Li, Lu Li, Feini Huang, Ye Zhang, Chunyan Wang, Dagang Wang, Jianxiu Qiu, Xingjie Lu, and Yongjiu Dai
Earth Syst. Sci. Data, 14, 5267–5286, https://doi.org/10.5194/essd-14-5267-2022, https://doi.org/10.5194/essd-14-5267-2022, 2022
Short summary
Short summary
SMCI1.0 is a 1 km resolution dataset of daily soil moisture over China for 2000–2020 derived through machine learning trained with in situ measurements of 1789 stations, meteorological forcings, and land surface variables. It contains 10 soil layers with 10 cm intervals up to 100 cm deep. Evaluated by in situ data, the error (ubRMSE) ranges from 0.045 to 0.051, and the correlation (R) range is 0.866-0.893. Compared with ERA5-Land, SMAP-L4, and SoMo.ml, SIMI1.0 has higher accuracy and resolution.
Ziqi Lin, Yongjiu Dai, Umakant Mishra, Guocheng Wang, Wei Shangguan, Wen Zhang, and Zhangcai Qin
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2022-232, https://doi.org/10.5194/essd-2022-232, 2022
Manuscript not accepted for further review
Short summary
Short summary
Spatial soil organic carbon (SOC) data is critical for predictions in carbon climate feedbacks and future climate trends, but no conclusion has yet been reached on which dataset to be used for specific purposes. We evaluated the SOC estimates from five widely used global soil datasets and a regional permafrost dataset, and identify uncertainties of SOC estimates by region, biome, and data sources, hoping to help improve SOC/soil data in the future.
Shuang Ma, Lifen Jiang, Rachel M. Wilson, Jeff P. Chanton, Scott Bridgham, Shuli Niu, Colleen M. Iversen, Avni Malhotra, Jiang Jiang, Xingjie Lu, Yuanyuan Huang, Jason Keller, Xiaofeng Xu, Daniel M. Ricciuto, Paul J. Hanson, and Yiqi Luo
Biogeosciences, 19, 2245–2262, https://doi.org/10.5194/bg-19-2245-2022, https://doi.org/10.5194/bg-19-2245-2022, 2022
Short summary
Short summary
The relative ratio of wetland methane (CH4) emission pathways determines how much CH4 is oxidized before leaving the soil. We found an ebullition modeling approach that has a better performance in deep layer pore water CH4 concentration. We suggest using this approach in land surface models to accurately represent CH4 emission dynamics and response to climate change. Our results also highlight that both CH4 flux and belowground concentration data are important to constrain model parameters.
Yaoping Wang, Jiafu Mao, Mingzhou Jin, Forrest M. Hoffman, Xiaoying Shi, Stan D. Wullschleger, and Yongjiu Dai
Earth Syst. Sci. Data, 13, 4385–4405, https://doi.org/10.5194/essd-13-4385-2021, https://doi.org/10.5194/essd-13-4385-2021, 2021
Short summary
Short summary
We developed seven global soil moisture datasets (1970–2016, monthly, half-degree, and multilayer) by merging a wide range of data sources, including in situ and satellite observations, reanalysis, offline land surface model simulations, and Earth system model simulations. Given the great value of long-term, multilayer, gap-free soil moisture products to climate research and applications, we believe this paper and the presented datasets would be of interest to many different communities.
Fei Jiang, Hengmao Wang, Jing M. Chen, Weimin Ju, Xiangjun Tian, Shuzhuang Feng, Guicai Li, Zhuoqi Chen, Shupeng Zhang, Xuehe Lu, Jane Liu, Haikun Wang, Jun Wang, Wei He, and Mousong Wu
Atmos. Chem. Phys., 21, 1963–1985, https://doi.org/10.5194/acp-21-1963-2021, https://doi.org/10.5194/acp-21-1963-2021, 2021
Short summary
Short summary
We present a 6-year inversion from 2010 to 2015 for the global and regional carbon fluxes using only the GOSAT XCO2 retrievals. We find that the XCO2 retrievals could significantly improve the modeling of atmospheric CO2 concentrations and that the inferred interannual variations in the terrestrial carbon fluxes in most land regions have a better relationship with the changes in severe drought area or leaf area index, or are more consistent with the previous estimates about drought impact.
Richard Essery, Hyungjun Kim, Libo Wang, Paul Bartlett, Aaron Boone, Claire Brutel-Vuilmet, Eleanor Burke, Matthias Cuntz, Bertrand Decharme, Emanuel Dutra, Xing Fang, Yeugeniy Gusev, Stefan Hagemann, Vanessa Haverd, Anna Kontu, Gerhard Krinner, Matthieu Lafaysse, Yves Lejeune, Thomas Marke, Danny Marks, Christoph Marty, Cecile B. Menard, Olga Nasonova, Tomoko Nitta, John Pomeroy, Gerd Schädler, Vladimir Semenov, Tatiana Smirnova, Sean Swenson, Dmitry Turkov, Nander Wever, and Hua Yuan
The Cryosphere, 14, 4687–4698, https://doi.org/10.5194/tc-14-4687-2020, https://doi.org/10.5194/tc-14-4687-2020, 2020
Short summary
Short summary
Climate models are uncertain in predicting how warming changes snow cover. This paper compares 22 snow models with the same meteorological inputs. Predicted trends agree with observations at four snow research sites: winter snow cover does not start later, but snow now melts earlier in spring than in the 1980s at two of the sites. Cold regions where snow can last until late summer are predicted to be particularly sensitive to warming because the snow then melts faster at warmer times of year.
Qinchuan Xin, Yongjiu Dai, and Xiaoping Liu
Biogeosciences, 16, 467–484, https://doi.org/10.5194/bg-16-467-2019, https://doi.org/10.5194/bg-16-467-2019, 2019
Short summary
Short summary
Terrestrial biosphere models that simulate both leaf dynamics and canopy photosynthesis are required to understand vegetation–climate interactions. A time-stepping scheme is proposed to simulate leaf area index, phenology, and gross primary production via climate variables. The method performs well on simulating deciduous broadleaf forests across the eastern United States; it provides a simplified and improved version of the growing production day model for use in land surface modeling.
Gerhard Krinner, Chris Derksen, Richard Essery, Mark Flanner, Stefan Hagemann, Martyn Clark, Alex Hall, Helmut Rott, Claire Brutel-Vuilmet, Hyungjun Kim, Cécile B. Ménard, Lawrence Mudryk, Chad Thackeray, Libo Wang, Gabriele Arduini, Gianpaolo Balsamo, Paul Bartlett, Julia Boike, Aaron Boone, Frédérique Chéruy, Jeanne Colin, Matthias Cuntz, Yongjiu Dai, Bertrand Decharme, Jeff Derry, Agnès Ducharne, Emanuel Dutra, Xing Fang, Charles Fierz, Josephine Ghattas, Yeugeniy Gusev, Vanessa Haverd, Anna Kontu, Matthieu Lafaysse, Rachel Law, Dave Lawrence, Weiping Li, Thomas Marke, Danny Marks, Martin Ménégoz, Olga Nasonova, Tomoko Nitta, Masashi Niwano, John Pomeroy, Mark S. Raleigh, Gerd Schaedler, Vladimir Semenov, Tanya G. Smirnova, Tobias Stacke, Ulrich Strasser, Sean Svenson, Dmitry Turkov, Tao Wang, Nander Wever, Hua Yuan, Wenyan Zhou, and Dan Zhu
Geosci. Model Dev., 11, 5027–5049, https://doi.org/10.5194/gmd-11-5027-2018, https://doi.org/10.5194/gmd-11-5027-2018, 2018
Short summary
Short summary
This paper provides an overview of a coordinated international experiment to determine the strengths and weaknesses in how climate models treat snow. The models will be assessed at point locations using high-quality reference measurements and globally using satellite-derived datasets. How well climate models simulate snow-related processes is important because changing snow cover is an important part of the global climate system and provides an important freshwater resource for human use.
Qianyu Li, Xingjie Lu, Yingping Wang, Xin Huang, Peter M. Cox, and Yiqi Luo
Biogeosciences, 15, 6909–6925, https://doi.org/10.5194/bg-15-6909-2018, https://doi.org/10.5194/bg-15-6909-2018, 2018
Short summary
Short summary
Land-surface models have been widely used to predict the responses of terrestrial ecosystems to climate change. A better understanding of model mechanisms that govern terrestrial ecosystem responses to rising atmosphere [CO2] is needed. Our study for the first time shows that the expansion of leaf area under rising [CO2] is the most important response for the stimulation of land carbon accumulation by a land-surface model: CABLE. Processes related to leaf area should be better calibrated.
Xingjie Lu, Ying-Ping Wang, Yiqi Luo, and Lifen Jiang
Biogeosciences, 15, 6559–6572, https://doi.org/10.5194/bg-15-6559-2018, https://doi.org/10.5194/bg-15-6559-2018, 2018
Short summary
Short summary
How long does C cycle through terrestrial ecosystems is a critical question for understanding land C sequestration capacity under future rising atmosphere [CO2] and climate warming. Under climate change, previous conventional concepts with a steady-state assumption will no longer be suitable for a non-steady state. Our results using the new concept, C transit time, suggest more significant responses in terrestrial C cycle under rising [CO2] and climate warming.
Fapeng Yan, Wei Shangguan, Jing Zhang, and Bifeng Hu
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2018-103, https://doi.org/10.5194/essd-2018-103, 2018
Revised manuscript not accepted
Short summary
Short summary
A depth-to-bedrock map of China was produced at 100-meter resolution based on 6,382 observations and 133 covariates.
This map is the most detailed and accurate one at the national scale.
The uncertainty map was provided as a map quality reference.
The ensemble machine learning model explains 57% of variation in spatial distribution.
The four most important covariates for the map production are topographic wetness index, landform, topographic openness index, and slope.
Dagang Wang, Guiling Wang, Dana T. Parr, Weilin Liao, Youlong Xia, and Congsheng Fu
Hydrol. Earth Syst. Sci., 21, 3557–3577, https://doi.org/10.5194/hess-21-3557-2017, https://doi.org/10.5194/hess-21-3557-2017, 2017
Short summary
Short summary
Land surface models bear substantial biases. To reduce model biases, we apply a simple but efficient bias correction method to a land surface model. We first derive a relationship between observations and model simulations, and apply this relationship in the application period. While the bias correction method improves model-based estimates without improving the model physical parameterization, results do provide guidance for physically based model development effort.
Yiqi Luo, Zheng Shi, Xingjie Lu, Jianyang Xia, Junyi Liang, Jiang Jiang, Ying Wang, Matthew J. Smith, Lifen Jiang, Anders Ahlström, Benito Chen, Oleksandra Hararuk, Alan Hastings, Forrest Hoffman, Belinda Medlyn, Shuli Niu, Martin Rasmussen, Katherine Todd-Brown, and Ying-Ping Wang
Biogeosciences, 14, 145–161, https://doi.org/10.5194/bg-14-145-2017, https://doi.org/10.5194/bg-14-145-2017, 2017
Short summary
Short summary
Climate change is strongly regulated by land carbon cycle. However, we lack the ability to predict future land carbon sequestration. Here, we develop a novel framework for understanding what determines the direction and rate of future change in land carbon storage. The framework offers a suite of new approaches to revolutionize land carbon model evaluation and improvement.
Shengyun Chen, Wenjie Liu, Qian Zhao, Lin Zhao, Qingbai Wu, Xingjie Lu, Shichang Kang, Xiang Qin, Shilong Chen, Jiawen Ren, and Dahe Qin
The Cryosphere Discuss., https://doi.org/10.5194/tc-2016-80, https://doi.org/10.5194/tc-2016-80, 2016
Revised manuscript not accepted
Short summary
Short summary
Experimental warming was manipulated using open top chambers in alpine grassland ecosystem in the permafrost regions of the Qinghai-Tibet Plateau. The results revealed variations of earlier thawing, later freezing and longer freezing-thawing periods in shallow soil. Further, the estimated permafrost table declined under the warming scenarios. The work will be helpful to evaluate the stability of Qinghai-Tibet Railway/Highway and estimate the release of carbon under the future climate warming.
Related subject area
Soils and water
Intensive agricultural management-induced subsurface accumulation of water-extractable colloidal P in a Vertisol
Perspectives on the misconception of levitating soil aggregates
Combining lime and organic amendments based on titratable alkalinity for efficient amelioration of acidic soils
Optimized fertilization using online soil nitrate data
Sequestering carbon in the subsoil benefits crop transpiration at the onset of drought
Pesticide transport through the vadose zone under sugarcane in the Wet Tropics, Australia
Reproducibility of the wet part of the soil water retention curve: a European interlaboratory comparison
The higher relative concentration of K+ to Na+ in saline water improves soil hydraulic conductivity, salt-leaching efficiency and structural stability
Agricultural use of compost under different irrigation strategies in a hedgerow olive grove under Mediterranean conditions – a comparison with traditional systems
Potential of natural language processing for metadata extraction from environmental scientific publications
Soil and crop management practices and the water regulation functions of soils: a qualitative synthesis of meta-analyses relevant to European agriculture
Effects of innovative long-term soil and crop management on topsoil properties of a Mediterranean soil based on detailed water retention curves
Polyester microplastic fibers affect soil physical properties and erosion as a function of soil type
Modelling the effect of catena position and hydrology on soil chemical weathering
Long-term impact of cover crop and reduced disturbance tillage on soil pore size distribution and soil water storage
Effective hydraulic properties of 3D virtual stony soils identified by inverse modeling
Biochar alters hydraulic conductivity and impacts nutrient leaching in two agricultural soils
Impact of freeze–thaw cycles on soil structure and soil hydraulic properties
Added value of geophysics-based soil mapping in agro-ecosystem simulations
Particulate macronutrient exports from tropical African montane catchments point to the impoverishment of agricultural soils
Saturated and unsaturated salt transport in peat from a constructed fen
Sensitivity analysis of point and parametric pedotransfer functions for estimating water retention of soils in Algeria
Water in the critical zone: soil, water and life from profile to planet
Deriving pedotransfer functions for soil quartz fraction in southern France from reverse modeling
Morphological dynamics of gully systems in the subhumid Ethiopian Highlands: the Debre Mawi watershed
Characterization of stony soils' hydraulic conductivity using laboratory and numerical experiments
Quantification of the impact of hydrology on agricultural production as a result of too dry, too wet or too saline conditions
Sediment concentration rating curves for a monsoonal climate: upper Blue Nile
Nonstationarity of the electrical resistivity and soil moisture relationship in a heterogeneous soil system: a case study
Interactions between organisms and parent materials of a constructed Technosol shape its hydrostructural properties
Potential effects of vinasse as a soil amendment to control runoff and soil loss
Quantification of the inevitable: the influence of soil macrofauna on soil water movement in rehabilitated open-cut mined lands
Coupled cellular automata for frozen soil processes
Shouhao Li, Shuiqing Chen, Shanshan Bai, Jinfang Tan, and Xiaoqian Jiang
SOIL, 10, 49–59, https://doi.org/10.5194/soil-10-49-2024, https://doi.org/10.5194/soil-10-49-2024, 2024
Short summary
Short summary
The distribution of water-extractable colloids with soil profiles of 0–120 cm was investigated in a Vertisol under high-intensity agricultural management. A large number of experimental data show that colloidal phosphorus plays an important role in apatite transport throughout the profile. Thus, it is crucial to consider the impact of colloidal P when predicting surface-to-subsurface P loss in Vertisols.
Gina Garland, John Koestel, Alice Johannes, Olivier Heller, Sebastian Doetterl, Dani Or, and Thomas Keller
SOIL, 10, 23–31, https://doi.org/10.5194/soil-10-23-2024, https://doi.org/10.5194/soil-10-23-2024, 2024
Short summary
Short summary
The concept of soil aggregates is hotly debated, leading to confusion about their function or relevancy to soil processes. We propose that the use of conceptual figures showing detached and isolated aggregates can be misleading and has contributed to this skepticism. Here, we conceptually illustrate how aggregates can form and dissipate within the context of undisturbed soils, highlighting the fact that aggregates do not necessarily need to have distinct physical boundaries.
Birhanu Iticha, Luke M. Mosley, and Petra Marschner
SOIL, 10, 33–47, https://doi.org/10.5194/soil-10-33-2024, https://doi.org/10.5194/soil-10-33-2024, 2024
Short summary
Short summary
Little effort has been made to develop methods to calculate the application rates of lime combined with organic amendments (OAs) needed to neutralise soil acidity and achieve the desired pH for plant growth. The previous approach of estimating appropriate lime and OA combinations based on field trials is time-consuming and costly. Hence, we developed and successfully validated a new method to calculate the amount of lime or OAs in combined applications required to ameliorate acidity.
Yonatan Yekutiel, Yuval Rotem, Shlomi Arnon, and Ofer Dahan
EGUsphere, https://doi.org/10.5194/egusphere-2023-2775, https://doi.org/10.5194/egusphere-2023-2775, 2023
Short summary
Short summary
A new soil nitrate monitoring system that was installed in cultivated field enabled, for the first-time, controlling the nitrate concentration across the soil profile. Frequent adjustment of fertilizer and water application followed the actual dynamic variation in nitrate concentration across the soil profile. Hence, significant reduction in fertilizer application was achieved while preserving optimal crop yield.
Maria Eliza Turek, Attila Nemes, and Annelie Holzkämper
SOIL, 9, 545–560, https://doi.org/10.5194/soil-9-545-2023, https://doi.org/10.5194/soil-9-545-2023, 2023
Short summary
Short summary
In this study, we systematically evaluated prospective crop transpiration benefits of sequestering soil organic carbon (SOC) under current and future climatic conditions based on the model SWAP. We found that adding at least 2% SOC down to at least 65 cm depth could increase transpiration annually by almost 40 mm, which can play a role in mitigating drought impacts in rain-fed cropping. Beyond this threshold, additional crop transpiration benefits of sequestering SOC are only marginal.
Rezaul Karim, Lucy Reading, Les Dawes, Ofer Dahan, and Glynis Orr
SOIL, 9, 381–398, https://doi.org/10.5194/soil-9-381-2023, https://doi.org/10.5194/soil-9-381-2023, 2023
Short summary
Short summary
The study was performed using continuous measurement of temporal variations in soil saturation and of the concentration of pesticides along the vadose zone profile and underlying alluvial aquifers at sugarcane fields in the Wet Tropics of Australia. A vadose zone monitoring system was set up to enable the characterization of pesticide (non-PS II herbicides) migration with respect to pesticide application, sugarcane growing period, and, finally, rainwater infiltration.
Benjamin Guillaume, Hanane Aroui Boukbida, Gerben Bakker, Andrzej Bieganowski, Yves Brostaux, Wim Cornelis, Wolfgang Durner, Christian Hartmann, Bo V. Iversen, Mathieu Javaux, Joachim Ingwersen, Krzysztof Lamorski, Axel Lamparter, András Makó, Ana María Mingot Soriano, Ingmar Messing, Attila Nemes, Alexandre Pomes-Bordedebat, Martine van der Ploeg, Tobias Karl David Weber, Lutz Weihermüller, Joost Wellens, and Aurore Degré
SOIL, 9, 365–379, https://doi.org/10.5194/soil-9-365-2023, https://doi.org/10.5194/soil-9-365-2023, 2023
Short summary
Short summary
Measurements of soil water retention properties play an important role in a variety of societal issues that depend on soil water conditions. However, there is little concern about the consistency of these measurements between laboratories. We conducted an interlaboratory comparison to assess the reproducibility of the measurement of the soil water retention curve. Results highlight the need to harmonize and standardize procedures to improve the description of unsaturated processes in soils.
Sihui Yan, Tibin Zhang, Binbin Zhang, Tonggang Zhang, Yu Cheng, Chun Wang, Min Luo, Hao Feng, and Kadambot H. M. Siddique
SOIL, 9, 339–349, https://doi.org/10.5194/soil-9-339-2023, https://doi.org/10.5194/soil-9-339-2023, 2023
Short summary
Short summary
The paper provides some new information about the effects of different relative concentrations of K+ to Na+ at constant electrical conductivity (EC) on soil hydraulic conductivity, salt-leaching efficiency and pore size distribution. In addition to Ca2+ and Mg2+, K+ plays an important role in soil structure stability. These findings can provide a scientific basis and technical support for the sustainable use of saline water and control of soil quality deterioration.
Laura L. de Sosa, María José Martín-Palomo, Pedro Castro-Valdecantos, and Engracia Madejón
SOIL, 9, 325–338, https://doi.org/10.5194/soil-9-325-2023, https://doi.org/10.5194/soil-9-325-2023, 2023
Short summary
Short summary
Olive groves are subject to enormous pressure to meet the social demands of production. In this work, we assess how an additional source of organic carbon and an irrigation control can somehow palliate the effect of olive grove intensification by comparing olive groves under different management and tree densities. We observed that a reduced irrigation regimen in combination with compost from the oil industry's own waste was able to enhance soil fertility under a water conservation strategy.
Guillaume Blanchy, Lukas Albrecht, John Koestel, and Sarah Garré
SOIL, 9, 155–168, https://doi.org/10.5194/soil-9-155-2023, https://doi.org/10.5194/soil-9-155-2023, 2023
Short summary
Short summary
Adapting agricultural practices to future climatic conditions requires us to synthesize the effects of management practices on soil properties with respect to local soil and climate. We showcase different automated text-processing methods to identify topics, extract metadata for building a database and summarize findings from publication abstracts. While human intervention remains essential, these methods show great potential to support evidence synthesis from large numbers of publications.
Guillaume Blanchy, Gilberto Bragato, Claudia Di Bene, Nicholas Jarvis, Mats Larsbo, Katharina Meurer, and Sarah Garré
SOIL, 9, 1–20, https://doi.org/10.5194/soil-9-1-2023, https://doi.org/10.5194/soil-9-1-2023, 2023
Short summary
Short summary
European agriculture is vulnerable to weather extremes. Nevertheless, by choosing well how to manage their land, farmers can protect themselves against drought and peak rains. More than a thousand observations across Europe show that it is important to keep the soil covered with living plants, even in winter. A focus on a general reduction of traffic on agricultural land is more important than reducing tillage. Organic material needs to remain or be added on the field as much as possible.
Alaitz Aldaz-Lusarreta, Rafael Giménez, Miguel A. Campo-Bescós, Luis M. Arregui, and Iñigo Virto
SOIL, 8, 655–671, https://doi.org/10.5194/soil-8-655-2022, https://doi.org/10.5194/soil-8-655-2022, 2022
Short summary
Short summary
This study shows how an innovative soil and crop management including no-tillage, cover crops and organic amendments is able to improve the topsoil physical quality compared to conventional management for rainfed cereal cropping in a semi-arid Mediterranean area in Navarre (Spain).
Rosolino Ingraffia, Gaetano Amato, Vincenzo Bagarello, Francesco G. Carollo, Dario Giambalvo, Massimo Iovino, Anika Lehmann, Matthias C. Rillig, and Alfonso S. Frenda
SOIL, 8, 421–435, https://doi.org/10.5194/soil-8-421-2022, https://doi.org/10.5194/soil-8-421-2022, 2022
Short summary
Short summary
The presence of microplastics in soil environments has received increased attention, but little research exists on the effects on different soil types and soil water erosion. We performed two experiments on the effects of polyester microplastic fiber on soil properties, soil aggregation, and soil erosion in three agricultural soils. Results showed that polyester microplastic fibers affect the formation of new aggregates and soil erosion and that such effects are strongly dependent on soil type.
Vanesa García-Gamero, Tom Vanwalleghem, Adolfo Peña, Andrea Román-Sánchez, and Peter A. Finke
SOIL, 8, 319–335, https://doi.org/10.5194/soil-8-319-2022, https://doi.org/10.5194/soil-8-319-2022, 2022
Short summary
Short summary
Short-scale soil variability has received much less attention than at the regional scale. The chemical depletion fraction (CDF), a proxy for chemical weathering, was measured and simulated with SoilGen along two opposite slopes in southern Spain. The results show that differences in CDF could not be explained by topography alone but by hydrological parameters. The model sensitivity test shows the maximum CDF value for intermediate precipitation has similar findings to other soil properties.
Samuel N. Araya, Jeffrey P. Mitchell, Jan W. Hopmans, and Teamrat A. Ghezzehei
SOIL, 8, 177–198, https://doi.org/10.5194/soil-8-177-2022, https://doi.org/10.5194/soil-8-177-2022, 2022
Short summary
Short summary
We studied the long-term effects of no-till (NT) and winter cover cropping (CC) practices on soil hydraulic properties. We measured soil water retention and conductivity and also conducted numerical simulations to compare soil water storage abilities under the different systems. Soils under NT and CC practices had improved soil structure. Conservation agriculture practices showed marginal improvement with respect to infiltration rates and water storage.
Mahyar Naseri, Sascha C. Iden, and Wolfgang Durner
SOIL, 8, 99–112, https://doi.org/10.5194/soil-8-99-2022, https://doi.org/10.5194/soil-8-99-2022, 2022
Short summary
Short summary
We simulated stony soils with low to high volumes of rock fragments in 3D using evaporation and multistep unit-gradient experiments. Hydraulic properties of virtual stony soils were identified under a wide range of soil matric potentials. The developed models for scaling the hydraulic conductivity of stony soils were evaluated under unsaturated flow conditions.
Danielle L. Gelardi, Irfan H. Ainuddin, Devin A. Rippner, Janis E. Patiño, Majdi Abou Najm, and Sanjai J. Parikh
SOIL, 7, 811–825, https://doi.org/10.5194/soil-7-811-2021, https://doi.org/10.5194/soil-7-811-2021, 2021
Short summary
Short summary
Biochar is purported to alter soil water dynamics and reduce nutrient loss when added to soils, though the mechanisms are often unexplored. We studied the ability of seven biochars to alter the soil chemical and physical environment. The flow of ammonium through biochar-amended soil was determined to be controlled through chemical affinity, and nitrate, to a lesser extent, through physical entrapment. These data will assist land managers in choosing biochars for specific agricultural outcomes.
Frederic Leuther and Steffen Schlüter
SOIL, 7, 179–191, https://doi.org/10.5194/soil-7-179-2021, https://doi.org/10.5194/soil-7-179-2021, 2021
Short summary
Short summary
Freezing and thawing cycles are an important agent of soil structural transformation during the winter season in the mid-latitudes. This study shows that it promotes a well-connected pore system, fragments dense soil clods, and, hence, increases the unsaturated conductivity by a factor of 3. The results are important for predicting the structure formation and hydraulic properties of soils, with the prospect of milder winters due to climate change, and for farmers preparing the seedbed in spring.
Cosimo Brogi, Johan A. Huisman, Lutz Weihermüller, Michael Herbst, and Harry Vereecken
SOIL, 7, 125–143, https://doi.org/10.5194/soil-7-125-2021, https://doi.org/10.5194/soil-7-125-2021, 2021
Short summary
Short summary
There is a need in agriculture for detailed soil maps that carry quantitative information. Geophysics-based soil maps have the potential to deliver such products, but their added value has not been fully investigated yet. In this study, we compare the use of a geophysics-based soil map with the use of two commonly available maps as input for crop growth simulations. The geophysics-based product results in better simulations, with improvements that depend on precipitation, soil, and crop type.
Jaqueline Stenfert Kroese, John N. Quinton, Suzanne R. Jacobs, Lutz Breuer, and Mariana C. Rufino
SOIL, 7, 53–70, https://doi.org/10.5194/soil-7-53-2021, https://doi.org/10.5194/soil-7-53-2021, 2021
Short summary
Short summary
Particulate macronutrient concentrations were up to 3-fold higher in a natural forest catchment compared to fertilized agricultural catchments. Although the particulate macronutrient concentrations were lower in the smallholder agriculture catchment, because of higher sediment loads from that catchment, the total particulate macronutrient loads were higher. Land management practices should be focused on agricultural land to reduce the loss of soil carbon and nutrients to the stream.
Reuven B. Simhayov, Tobias K. D. Weber, and Jonathan S. Price
SOIL, 4, 63–81, https://doi.org/10.5194/soil-4-63-2018, https://doi.org/10.5194/soil-4-63-2018, 2018
Short summary
Short summary
Lab experiments were performed to understand solute transport in peat from an experimental fen. Transport was analyzed under saturated and unsaturated conditions using NaCl (salt). We tested the applicability of a physical-based model which finds a wide consensus vs. alternative models. Evidence indicated that Cl transport can be explained using a simple transport model. Hence, use of the physical transport mechanism in peat should be evidence based and not automatically assumed.
Sami Touil, Aurore Degre, and Mohamed Nacer Chabaca
SOIL, 2, 647–657, https://doi.org/10.5194/soil-2-647-2016, https://doi.org/10.5194/soil-2-647-2016, 2016
M. J. Kirkby
SOIL, 2, 631–645, https://doi.org/10.5194/soil-2-631-2016, https://doi.org/10.5194/soil-2-631-2016, 2016
Short summary
Short summary
The review paper surveys the state of the art with respect to water in the critical zone, taking a broad view that concentrates on the global range of natural soils, identifying some areas of currently active research.
Jean-Christophe Calvet, Noureddine Fritz, Christine Berne, Bruno Piguet, William Maurel, and Catherine Meurey
SOIL, 2, 615–629, https://doi.org/10.5194/soil-2-615-2016, https://doi.org/10.5194/soil-2-615-2016, 2016
Short summary
Short summary
Soil thermal conductivity in wet conditions can be retrieved together with the soil quartz content using a reverse modelling technique based on sub-hourly soil temperature observations at three depths below the soil surface.
A pedotransfer function is proposed for quartz, for the considered region in France.
Gravels have a major impact on soil thermal conductivity, and omitting the soil organic matter information tends to enhance this impact.
Assefa D. Zegeye, Eddy J. Langendoen, Cathelijne R. Stoof, Seifu A. Tilahun, Dessalegn C. Dagnew, Fasikaw A. Zimale, Christian D. Guzman, Birru Yitaferu, and Tammo S. Steenhuis
SOIL, 2, 443–458, https://doi.org/10.5194/soil-2-443-2016, https://doi.org/10.5194/soil-2-443-2016, 2016
Short summary
Short summary
Gully erosion rehabilitation programs in the humid Ethiopian highlands have not been effective, because the gully formation process and its controlling factors are not well understood. In this manuscript, the severity of gully erosion (onsite and offsite effect), the most controlling factors (e.g., ground water elevation) for gully formation, and their arresting mechanisms are discussed in detail. Most data were collected from the detailed measurements of 13 representative gullies.
Eléonore Beckers, Mathieu Pichault, Wanwisa Pansak, Aurore Degré, and Sarah Garré
SOIL, 2, 421–431, https://doi.org/10.5194/soil-2-421-2016, https://doi.org/10.5194/soil-2-421-2016, 2016
Short summary
Short summary
Determining the behaviour of stony soils with respect to infiltration and storage of water is of major importance, since stony soils are widespread across the globe. The most common procedure to overcome this difficulty is to describe the hydraulic characteristics of a stony soils in terms of the fine fraction of soil corrected for the volume of stones present. Our study suggests that considering this hypothesis might be ill-founded, especially for saturated soils.
Mirjam J. D. Hack-ten Broeke, Joop G. Kroes, Ruud P. Bartholomeus, Jos C. van Dam, Allard J. W. de Wit, Iwan Supit, Dennis J. J. Walvoort, P. Jan T. van Bakel, and Rob Ruijtenberg
SOIL, 2, 391–402, https://doi.org/10.5194/soil-2-391-2016, https://doi.org/10.5194/soil-2-391-2016, 2016
Short summary
Short summary
For calculating the effects of hydrological measures on agricultural production in the Netherlands a new comprehensive and climate proof method is being developed: WaterVision Agriculture (in Dutch: Waterwijzer Landbouw). End users have asked for a method that considers current and future climate, which can quantify the differences between years and also the effects of extreme weather events.
Mamaru A. Moges, Fasikaw A. Zemale, Muluken L. Alemu, Getaneh K. Ayele, Dessalegn C. Dagnew, Seifu A. Tilahun, and Tammo S. Steenhuis
SOIL, 2, 337–349, https://doi.org/10.5194/soil-2-337-2016, https://doi.org/10.5194/soil-2-337-2016, 2016
Short summary
Short summary
In tropical monsoonal Africa, sediment concentration data in rivers are lacking. Using occasional historically observed sediment loads, we developed a simple method for prediction sediment concentrations. Unlike previous methods, our techniques take into account that sediment concentrations decrease with the progression of the monsoon rains. With more testing, the developed method could improve sediment predictions in monsoonal climates.
Didier Michot, Zahra Thomas, and Issifou Adam
SOIL, 2, 241–255, https://doi.org/10.5194/soil-2-241-2016, https://doi.org/10.5194/soil-2-241-2016, 2016
Short summary
Short summary
This study focuses on temporal and spatial soil moisture changes along a toposequence crossed by a hedgerow, using ERT and occasional measurements. We found that the relationship between ER and soil moisture had two behaviors depending on soil heterogeneities. ER values were consistent with occasional measurements outside the root zone. The shift in this relationship was controlled by root system density and a particular topographical context in the proximity of the hedgerow.
Maha Deeb, Michel Grimaldi, Thomas Z. Lerch, Anne Pando, Agnès Gigon, and Manuel Blouin
SOIL, 2, 163–174, https://doi.org/10.5194/soil-2-163-2016, https://doi.org/10.5194/soil-2-163-2016, 2016
Short summary
Short summary
This paper addresses the evolution of engineered soils (i.e., Technosols). The formation of such soils begins with proportional mixing of urban waste. Technosols are particularly well suited for investigating the role of organisms in soil function development. This is because they provide a controlled environment where the soil development can be monitored over time.
Organisms and their interaction with parent materials positively affect the structure of Technosols.
Z. Hazbavi and S. H. R. Sadeghi
SOIL, 2, 71–78, https://doi.org/10.5194/soil-2-71-2016, https://doi.org/10.5194/soil-2-71-2016, 2016
Short summary
Short summary
This study evaluates the influences of vinasse waste of sugarcane industries on runoff and soil loss at small plot scale. Laboratory results indicated that the vinasse at different levels could not significantly (P > 0.05) decrease the runoff amounts and soil loss rates in the study plots compared to untreated plots. The average amounts of minimum runoff volume and soil loss were about 3985 mL and 46 g for the study plot at a 1 L m−2 level of vinasse application.
S. Arnold and E. R. Williams
SOIL, 2, 41–48, https://doi.org/10.5194/soil-2-41-2016, https://doi.org/10.5194/soil-2-41-2016, 2016
Short summary
Short summary
Soil water models are used to design cover systems for containing hazardous waste following mining. Often, soil invertebrates are omitted from these calculations, despite playing a major role in soil development (nutrient cycling) and water pathways (seepage, infiltration). As such, soil invertebrates can influence the success of waste cover systems. We propose that experiments in glasshouses, laboratories and field trials on mined lands be undertaken to provide knowledge for these models.
R. M. Nagare, P. Bhattacharya, J. Khanna, and R. A. Schincariol
SOIL, 1, 103–116, https://doi.org/10.5194/soil-1-103-2015, https://doi.org/10.5194/soil-1-103-2015, 2015
Cited articles
Arora, V. K., Boer, G. J., Christian, J. R., Curry, C. L., Denman, K. L.,
Zahariev, K., Flato, G. M., Scinocca, J. F., Merryfield, W. J., and Lee, W. G.:
The Effect of Terrestrial Photosynthesis Down Regulation on the
Twentieth-Century Carbon Budget Simulated with the CCCma Earth System Model,
J. Climate, 22, 6066–6088, 2009.
Arrouays, D., Leenaars, J. G. B., Richer-de-Forges, A. C., Adhikari, K.,
Ballabio, C., Greve, M., Grundy, M., Guerrero, E., Hempel, J., Hengl, T.,
Heuvelink, G., Batjes, N., Carvalho, E., Hartemink, A., Hewitt, A., Hong,
S.-Y., Krasilnikov, P., Lagacherie, P., Lelyk, G., Libohova, Z., Lilly, A.,
McBratney, A., McKenzie, N., Vasquez, G. M., Mulder, V. L., Minasny, B.,
Montanarella, L., Odeh, I., Padarian, J., Poggio, L., Roudier, P., Saby, N.,
Savin, I., Searle, R., Solbovoy, V., Thompson, J., Smith, S., Sulaeman, Y.,
Vintila, R., Rossel, R. V., Wilson, P., Zhang, G.-L., Swerts, M., Oorts, K.,
Karklins, A., Feng, L., Ibelles Navarro, A. R., Levin, A., Laktionova, T.,
Dell'Acqua, M., Suvannang, N., Ruam, W., Prasad, J., Patil, N., Husnjak, S.,
Pásztor, L., Okx, J., Hallett, S., Keay, C., Farewell, T., Lilja, H.,
Juilleret, J., Marx, S., Takata, Y., Kazuyuki, Y., Mansuy, N., Panagos, P.,
Van Liedekerke, M., Skalsky, R., Sobocka, J., Kobza, J., Eftekhari, K.,
Alavipanah, S. K., Moussadek, R., Badraoui, M., Da Silva, M., Paterson, G.,
Gonçalves, M. d. C., Theocharopoulos, S., Yemefack, M., Tedou, S.,
Vrscaj, B., Grob, U., Kozák, J., Boruvka, L., Dobos, E., Taboada, M.,
Moretti, L., and Rodriguez, D.: Soil legacy data rescue via GlobalSoilMap
and other international and national initiatives, GeoResJ, 14, 1–19,
https://doi.org/10.1016/j.grj.2017.06.001, 2017.
Arrouays, D., Savin, I., Leenaars, J., and McBratney, A.: GlobalSoilMap –
Digital Soil Mapping from Country to Globe, CRC Press, London, UK, 2018.
Ballabio, C., Panagos, P., and Monatanarella, L.: Mapping topsoil physical
properties at European scale using the LUCAS database, Geoderma, 261,
110–123, 2016.
Batjes, N. H.: A taxotransfer rule-based approach for filling gaps in
measured soil data in primary SOTER databases, International Soil Reference
and Information Centre, Wageningen, the Netherlands, 2003.
Batjes, N. H.: ISRIC-WISE derived soil properties on a 5 by 5 arc-minutes
global grid. Report 2006/02, ISRIC- World Soil Information, Wageningen (with
data set), the Netherlands, 2006.
Batjes, N. H.: ISRIC-WISE harmonized global soil profile dataset (ver. 3.1).
Report 2008/02, ISRIC – World Soil Information, Wageningen, the Netherlands, 2008.
Batjes, N. H.: Harmonized soil property values for broad-scale modelling
(WISE30sec) with estimates of global soil carbon stocks, Geoderma, 269,
61-68, https://doi.org/10.1016/j.geoderma.2016.01.034, 2016.
Batjes, N. H., Ribeiro, E., van Oostrum, A., Leenaars, J., Hengl, T., and
Mendes de Jesus, J.: WoSIS: providing standardised soil profile data for the
world, Earth Syst. Sci. Data, 9, 1–14, https://doi.org/10.5194/essd-9-1-2017, 2017.
Best, M. J., Pryor, M., Clark, D. B., Rooney, G. G., Essery, R. L. H., Ménard, C. B., Edwards, J. M., Hendry, M. A., Porson, A., Gedney, N., Mercado, L. M., Sitch, S., Blyth, E., Boucher, O., Cox, P. M., Grimmond, C. S. B., and Harding, R. J.: The Joint UK Land Environment Simulator (JULES), model description – Part 1: Energy and water fluxes, Geosci. Model Dev., 4, 677–699, https://doi.org/10.5194/gmd-4-677-2011, 2011.
Bishop, T. F. A., McBratney, A. B., and Laslett, G. M.: Modelling soil
attribute depth functions with equal-area quadratic smoothing splines,
Geoderma, 91, 27–45, 1999.
Brunke, M. A., Broxton, P., Pelletier, J., Gochis, D., Hazenberg, P.,
Lawrence, D. M., Leung, L. R., Niu, G.-Y., Troch, P. A., and Zeng, X.:
Implementing and evaluating variable soil thickness in the Community Land
Model version 4.5 (CLM4.5), J. Climate, 29, 3441–3461,
https://doi.org/10.1175/JCLI-D-15-0307.1, 2016.
Chen, F. and Dudhia, J.: Coupling an advanced land surface-hydrology model
with the Penn State-NCAR MM5 modeling system, Part I: Model implementation
and sensitivity, Mon. Weather Revi., 129, 569–585, 2001.
Chen, Y., Yang, K., Tang, W., Qin, J., and Zhao, L.: Parameterizing soil
organic carbon's impacts on soil porosity and thermal parameters for Eastern
Tibet grasslands, Sci. China Earth Sci., 55, 1001–1011, https://doi.org/10.1007/s11430-012-4433-0, 2012.
Clapp, R. W. and Hornberger, G. M.: Empirical equations for some soil
hydraulic properties, Water Resour. Res., 14, 601–604, 1978.
Clark, D. B., Mercado, L. M., Sitch, S., Jones, C. D., Gedney, N., Best, M. J., Pryor, M., Rooney, G. G., Essery, R. L. H., Blyth, E., Boucher, O., Harding, R. J., Huntingford, C., and Cox, P. M.: The Joint UK Land Environment Simulator (JULES), model description – Part 2: Carbon fluxes and vegetation dynamics, Geosci. Model Dev., 4, 701–722, https://doi.org/10.5194/gmd-4-701-2011, 2011.
Cooper, M., Mendes, L. M. S., Silva, W. L. C., and Sparovek, G.: A national
soil profile database for brazil available to international scientists, Soil
Sci. Soci. Am. J., 69, 649–652, 2005.
Cox, P. M., Betts, R. A., Bunton, C. B., Essery, R. L. H., Rowntree, P. R.,
and Smith, J.: The impact of new land surface physics on the GCM sensitivity
of climate and climate sensitivity, Clim. Dynam., 15, 183–203, 1999.
Dai, Y., Zeng, X., Dickinson, R. E., Baker, I., Bonan, G. B., Bosilovich, M.
G., Denning, A. S., Dirmeyer, P. A., Houser, P. R., Niu, G., Oleson, K. W.,
Schlosser, C. A., and Yang, Z.: The Common Land Model, B. Am. Meteorol.
Soc., 84, 1013–1023, 2003.
Dai, Y., Shangguan, W., Duan, Q., Liu, B., Fu, S., and Niu, G.: Development
of a China Dataset of Soil Hydraulic Parameters Using Pedotransfer Functions
for Land Surface Modeling, J. Hydrometeorol., 14, 869–887, 2013.
De Lannoy, G. J. M., Koster, R. D., Reichle, R. H., Mahanama, S. P. P., and
Liu, Q.: An updated treatment of soil texture and associated hydraulic
properties in a global land modeling system, J. Adv. Model.
Earth Sy., 6, 957-979, https://doi.org/10.1002/2014ms000330, 2014.
Dickinson, R. E., Henderson-Sellers, A., and Kennedy, P. J.:
Biosphere-Atmosphere Transfer Scheme (BATS) Version 1e as Coupled to the
NCAR Community Climate Model, NCAR-TN-387+STR, National Center for
Atmospheric Research, Boulder, Colorado, USA, 88 pp., 1993.
Doney, S. C., Lindsay, K., Fung, I., and John, J.: Natural variability in a
stable, 1000-yr global coupled climate-carbon cycle simulation, J.
Climate, 19, 3033–3054, 2006.
Dy, C. Y. and Fung, J. C. H. C. J.: Updated global soil map for the Weather
Research and Forecasting model and soil moisture initialization for the Noah
land surface model, J. Geophys. Res.-Atmos., 121,
8777–8800, https://doi.org/10.1002/2015jd024558, 2016.
Elguindi, N., Bi, X., Giorgi, F., Nagarajan, B., Pal, J., Solmon, F.,
Rauscher, S., Zakey, A., O'Brien, T., Nogherotto, R., and Giuliani, G.:
Regional climatic model RegCM Reference Manual version 4.6, 33, ITCP, Trieste, Italy, 2014.
England, J. R. and Viscarra Rossel, R. A.: Proximal sensing for soil carbon accounting, SOIL, 4, 101–122, https://doi.org/10.5194/soil-4-101-2018, 2018.
Fan, Y., Li, H., and Miguez-Macho, G.: Global Patterns of Groundwater Table Depth, Science, 339, 940–943, https://doi.org/10.1126/science.1229881, 2013.
Guevara, M., Olmedo, G. F., Stell, E., Yigini, Y., Aguilar Duarte, Y., Arellano Hernàndez, C., Arévalo, G. E., Arroyo-Cruz, C. E., Bolivar, A., Bunning, S., Bustamante Cañas, N., Cruz-Gaistardo, C. O., Davila, F., Dell Acqua, M., Encina, A., Figueredo Tacona, H., Fontes, F., Hernández Herrera, J. A., Ibelles Navarro, A. R., Loayza, V., Manueles, A. M., Mendoza Jara, F., Olivera, C., Osorio Hermosilla, R., Pereira, G., Prieto, P., Ramos, I. A., Rey Brina, J. C., Rivera, R., Rodríguez-Rodríguez, J., Roopnarine, R., Rosales Ibarra, A., Rosales Riveiro, K. A., Schulz, G. A., Spence, A., Vasques, G. M., Vargas, R. R., and Vargas, R.: No silver bullet for digital soil mapping: country-specific soil organic carbon estimates across Latin America, SOIL, 4, 173–193, https://doi.org/10.5194/soil-4-173-2018, 2018.
FAO: Soil Map of the World, Vol. 110, UNESCO, Paris, France, 1981.
FAO: Digitized Soil Map of the World and Derived Soil Properties, FAO, Rome, Italy, 1995.
FAO: Digital soil map of the world and derived soil properties, FAO, Land
and Water Digital Media Series, CD-ROM, 2003a.
FAO: The Digitized Soil Map of the World Including Derived Soil Properties
(version 3.6), FAO, Rome, Italy, 2003b.
FAO/IIASA/ISRIC/ISS-CAS/JRC: Harmonized World Soil Database (version1.2),
FAO, Rome, Italy and IIASA, Laxenburg, Austria, 2012.
Farouki, O. T.: Thermal Properties of Soils. Monograph, No. 81-1, U.S. Army
Cold Regions Research and Engineering Laboratory, Hanover, NH, USA, 1981.
Folberth, C., Skalský, R., Moltchanova, E., Balkovič, J., Azevedo,
L. B., Obersteiner, M., and van der Velde, M.: Uncertainty in soil data can
outweigh climate impact signals in global crop yield simulations, Nat.
Commun., 7, 11872, https://doi.org/10.1038/ncomms11872, 2016.
Gessler, P. E., Moore, I. D., McKenzie, N. J., and Ryan, P. J.: Soil-landscape
modelling and spatial prediction of soil attributes, Int. J. Geogr. Inf. Syst., 9, 421–432, 1995.
Global Soil DataTask: Global Soil Data Products CD-ROM (IGBP-DIS),
International Geosphere-Biosphere Programme – Data and Information Services,
Available online at from the ORNL Distributed Active Archive Center, Oak
Ridge National Laboratory, Oak Ridge, Tennessee, USA, 2000.
Gong, W., Duan, Q., Li, J., Wang, C., Di, Z., Dai, Y., Ye, A., and Miao, C.: Multi-objective parameter optimization of common land model using adaptive surrogate modeling, Hydrol. Earth Syst. Sci., 19, 2409–2425, https://doi.org/10.5194/hess-19-2409-2015, 2015.
Gurney, K. R., Baker, D., Rayner, P., and Denning, S.: Interannual
variations in continental-scale net carbon exchange and sensitivity to
observing networks estimated from atmospheric CO2 inversions for the period
1980 to 2005, Global Biogeochem. Cy., 22, GB3025, https://doi.org/10.1029/2007GB003082,
2008.
Hagemann, S.: An Improved Land Surface Parameter Dataset for Global and
Regional Climate Models, MPI Report No. 336, 28 pp., 2002.
Hagemann, S., Botzet, M., Dümenil, L., and Machenhauer, B.: Derivation
of global GCM boundary conditions from 1 km land use satellite data, MPI
Report No. 289, 34 pp., 1999.
Hannam, J. A., Hollis, J. M., Jones, R. J. A., Bellamy, P. H., Hayes, S. E.,
Holden, A., Van Liedekerke, M. H., and Montanarella, L.: SPADE-2: The soil
profile analytical database for Europe, Version 2.0 Beta Version March 2009,
unpublished Report, 27 pp., 2009.
Hengl, T., de Jesus, J. M., MacMillan, R. A., Batjes, N. H., Heuvelink, G.
B. M., Ribeiro, E., Samuel-Rosa, A., Kempen, B., Leenaars, J. G. B., Walsh,
M. G., and Gonzalez, M. R.: SoilGrids1km – Global Soil Information Based
on Automated Mapping, PLoS ONE, 9, e105992, https://doi.org/10.1371/journal.pone.0105992,
2014.
Hengl, T., Heuvelink, G. B. M., Kempen, B., Leenaars, J. G. B., Walsh, M.
G., Shepherd, K. D., Sila, A., MacMillan, R. A., Jesus, J. M. D., Tamene,
L., and Tondoh, J. E.: Mapping Soil Properties of Africa at 250 m
Resolution: Random Forests Significantly Improve Current Predictions, PLOS
ONE, 10, e0125814, 2015.
Hengl, T., Mendes de Jesus, J., Heuvelink, G. B. M., Ruiperez Gonzalez, M., Kilibarda, M., Blagotic, A., Shangguan, W., Wright, M. N., Geng, X., Bauer-Marschallinger,
B., Guevara, M. A., Vargas, R., MacMillan, R. A., Batjes, N. H., Leenaars,
J. G. B., Ribeiro, E., Wheeler, I., Mantel, S., and Kempen, B.:
SoilGrids250m: global gridded soil information based on Machine Learning,
PLOS One, 12, e0169748, https://doi.org/10.1371/journal.pone.0169748, 2017.
Hiederer, R. and Köchy, M.: Global Soil Organic Carbon Estimates and
the Harmonized World Soil Database, Publications Office of the European
Union, Luxembourg, 79 pp., 2012.
Hoffmann, H., Zhao, G., Asseng, S., Bindi, M., Biernath, C., Constantin, J.,
Coucheney, E., Dechow, R., Doro, L., Eckersten, H., Gaiser, T.,
Grosz, B., Heinlein, F., Kassie, B. T.,
Kersebaum, K.-C., Klein, C., Kuhnert, M., Lewan, E.,
Moriondo, M., Nendel, C., Priesack, E., Raynal, H., Roggero, P. P.,
Rötter, R. P., Siebert, S., Specka, X., Tao, F.,
Teixeira, E., Trombi, G., Wallach, D., Weihermüller, L.,
Yeluripati, J., and Ewert, F.: Impact of Spatial Soil and Climate Input Data
Aggregation on Regional Yield Simulations, PLOS One, 11, e0151782, https://doi.org/10.1371/journal.pone.0151782 2016.
Hugelius, G., Tarnocai, C., Broll, G., Canadell, J. G., Kuhry, P., and Swanson, D. K.: The Northern Circumpolar Soil Carbon Database: spatially distributed datasets of soil coverage and soil carbon storage in the northern permafrost regions, Earth Syst. Sci. Data, 5, 3–13, https://doi.org/10.5194/essd-5-3-2013, 2013.
IIASA: Harmonized World Soil Database (HWSD v 1.21), available at:
http://www.iiasa.ac.at/web/home/research/researchPrograms/water/HWSD.html, last access: 27 June 2019.
Instituto Nacional de Estadística y Geografía: Conjunto de Datos
de Perfiles de Suelos Escala 1 : 250 000 Serie II (Continuo Nacional), INEGI, Aguascalientes, Ags. Mexico, 2016.
ISRIC: WISE Soil Property Databases, available at: https://www.isric.org/explore/wise-databases, last access: 27 June 2019a.
ISRIC: SoilGrids, available at: http://www.soilgrids.org, last access: 27 June 2019b.
ISRIC: WoSIS, available at: https://www.isric.org/explore/wosis, last access: 27 June 2019c.
Ji, P., Yuan, X., and Liang, X.-Z.: Do Lateral Flows Matter for the
Hyperresolution Land Surface Modeling?, J. Geophys. Res.-Atmos., 122, 12077–12092, https://doi.org/10.1002/2017JD027366, 2017.
Johnston, R. M., Barry, S. J., Bleys, E., Bui, E. N., Moran, C. J., Simon,
D. A. P., Carlile, P., McKenzie, N. J., Henderson, B. L., Chapman, G.,
Imhoff, M., Maschmedt, D., Howe, D., Grose, C., and Schoknecht, N.: ASRIS:
the database, Aust. J. Soil Res., 416, 1021–1036, 2003.
Jordan, H., Tom, G., Jens, H., and Janine, B.: Compiling and Mapping Global
Permeability of the Unconsolidated and Consolidated Earth: GLobal
HYdrogeology MaPS 2.0 (GLHYMPS 2.0), Geophys. Res. Lett., 45,
1897–1904, https://doi.org/10.1002/2017GL075860, 2018.
Karssies, L.: CSIRO National Soil Archive and the National Soil Database
(NatSoil), No. v1 in Data Collection, CSIRO, Canberra, Australia, 2011.
Kearney, M. R. and Maino, J. L.: 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, J.
Hydrol., 561, 662–673, https://doi.org/10.1016/j.jhydrol.2018.04.040,
2018.
Koster, R. D. and Suarez, M. J.: Modeling the land surface boundary in
climate models as a composite of independent vegetation stands, J.
Geophys. Res.-Atmos., 97, 2697–2715, https://doi.org/10.1029/91JD01696,
1992.
Kowalczyk, E., Stevens, L., Law, R., Dix, M., Wang, Y., Harman, I., Haynes,
K., Srbinovsky, J., Pak, B., and Ziehn, T: The land surface model component
of ACCESS: description and impact on the simulated surface climatology,
Aust. Meteorol. Ocean., 63, 65–82, 2013.
Krinner, G., Viovy, N., de Noblet-Ducoudré, N., Ogée, J., Polcher, J., Friedlingstein, P., Ciais, P., Sitch, S., and Prentice, I. C.: A dynamic global vegetation model for studies of the coupled atmosphere-biosphere system, Global Biogeochem. Cy., 19, GB1015, https://doi.org/10.1029/2003gb002199, 2005.
Kuhnert, M., Yeluripati, J., Smith, P., Hoffmann, H., van Oijen, M.,
Constantin, J., Coucheney, E., Dechow, R., Eckersten, H., Gaiser, T., Grosz,
B., Haas, E., Kersebaum, K.-C., Kiese, R., Klatt, S., Lewan, E., Nendel, C.,
Raynal, H., Sosa, C., Specka, X., Teixeira, E., Wang, E., Weihermüller,
L., Zhao, G., Zhao, Z., Ogle, S., and Ewert, F.: Impact analysis of climate
data aggregation at different spatial scales on simulated net primary
productivity for croplands, Eur. J. Agron., 88, 41–52,
https://doi.org/10.1016/j.eja.2016.06.005, 2017.
Land-Atmosphere Interaction Research Group: available at: http://globalchange.bnu.edu.cn/research/data, last access: 27 June 2019.
Landon, J. R.: Booker Tropical Soil Manual, Longman Scientific
&Technical, New York, USA, 1991.
Lawrence, P. J. and Chase, T. N.: Representing a new MODIS consistent land
surface in the Community Land Model (CLM 3.0), J. Geophys.
Res., 112, G01023, https://doi.org/10.1029/2006JG000168, 2007.
Leenaars, J. G. B.: Africa Soil Profiles Database, Version 1.0. A
compilation of geo-referenced and standardized legacy soil profile data for
Sub Saharan Africa (with dataset), ISRIC report 2012/03, Africa Soil
Information Service (AfSIS) project and ISRIC – World Soil Information,
Wageningen, the Netherlands, 2012.
Lei, H., Yang, D., and Huang, M.: Impacts of climate change and vegetation
dynamics on runoff in the mountainous region of the Haihe River basin in the
past five decades, J. Hydrol., 511, 786–799,
https://doi.org/10.1016/j.jhydrol.2014.02.029, 2014.
Li, C., Lu, H., Yang, K., Wright, J. S., Yu, L., Chen, Y., Huang, X., and
Xu, S.: Evaluation of the Common Land Model (CoLM) from the Perspective of
Water and Energy Budget Simulation: Towards Inclusion in CMIP6, Atmosphere,
8, 141, https://doi.org/10.3390/atmos8080141, 2017.
Li, J., Duan, Q., Wang, Y.-P., Gong, W., Gan, Y., and Wang, C.: Parameter
optimization for carbon and water fluxes in two global land surface models
based on surrogate modelling, Int. J. Climatol., 38,
e1016–e1031, https://doi.org/10.1002/joc.5428, 2018.
Liang, X., Lettenmaier, D. P., Wood, E. F., and Burges, S. J.: A simple
hydrologically based model of land surface water and energy fluxes for
general circulation models, J. Geophys. Res.-Atmos.,
99, 14415–14428, https://doi.org/10.1029/94JD00483, 1994.
Livneh, B., Kumar, R., and Samaniego, L.: Influence of soil textural
properties on hydrologic fluxes in the Mississippi river basin, Hydrol.
Process., 29, 4638–4655, https://doi.org/10.1002/hyp.10601, 2015.
Looy, K. V., Bouma, J., Herbst, M., Koestel, J., Minasny, B., Mishra, U.,
Montzka, C., Nemes, A., Pachepsky, Y. A., Padarian, J., Schaap, M. G.,
Tóth, B., Verhoef, A., Vanderborght, J., Ploeg, M. J., Weihermüller,
L., Zacharias, S., Zhang, Y., and Vereecken, H.: Pedotransfer Functions in
Earth System Science: Challenges and Perspectives, Rev. Geophys.,
55, 1199–1256, https://doi.org/10.1002/2017RG000581, 2017.
Luo, Y., Ahlström, A., Allison, S. D., Batjes, N. H., Brovkin, V.,
Carvalhais, N., Chappell, A., Ciais, P., Davidson, E. A., Finzi, A.,
Georgiou, K., Guenet, B., Hararuk, O., Harden, J. W., He, Y., Hopkins, F.,
Jiang, L., Koven, C., Jackson, R. B., Jones, C. D., Lara, M. J., Liang, J.,
McGuire, A. D., Parton, W., Peng, C., Randerson, J. T., Salazar, A., Sierra,
C. A., Smith, M. J., Tian, H., Todd-Brown, K. E. O., Torn, M., van
Groenigen, K. J., Wang, Y. P., West, T. O., Wei, Y., Wieder, W. R., Xia, J.,
Xu, X., Xu, X., and Zhou, T. C. G. B.: Toward more realistic projections of
soil carbon dynamics by Earth system models, Global Biogeochem. Cy.,
30, 40–56, https://doi.org/10.1002/2015gb005239, 2016.
MacDonald, K. B. and Valentine, K. W. G.: CanSIS/NSDB, A general
description (Centre for Land and Biological Resources Research), Research
Branch, Agriculture Canada, Ottawa, Canada, 1992.
Mauritsen, T., Bader, J., Becker, T., Behrens, J.,
Bittner, M., Brokopf, R., Brovkin, V., Claussen, M.,
Crueger, T., Esch, M., Fast, I., Fiedler, S., Fläschner, D.,
Gayler, V., Giorgetta, M., Goll, D. S., Haak, H.,
Hagemann, S., Hedemann, C., Hohenegger, C., Ilyina, T.,
Jahns, T., Jimenez de la Cuesta Otero, D., Jungclaus, J., Kleinen, T.,
Kloster, S., Kracher, D., Kinne, S., Kleberg, D., Lasslop, G.,
Kornblueh, L., Marotzke, J., Matei, D., Meraner, K.,
Mikolajewicz, U., Modali, K., Möbis, B.,
Müller, W. A., Nabel, J. E. M. S., Nam, C. C. W., Notz, D.,
Nyawira, S.-S., Paulsen, H., Peters, K., Pincus, R.,
Pohlmann, H., Pongratz, J., Popp, M., Raddatz, T., Rast, S.,
Redler, R., Reick, C. H., Rohrschneider, T., Schemann, V., Schmidt, H.,
Schnur, R., Schulzweida, U., Six, K. D., Stein, L.,
Stemmler, I., Stevens, B., von Storch, J.-S., Tian, F., Voigt, A.,
de Vrese, P., Wieners, K.-H., Wilkenskjeld, S.,
Winkler, A., and Roeckner, E.: Developments in the MPI-M Earth System Model
version 1.2 (MPI-ESM 1.2) and its response to increasing CO2, J. Adv. Model. Earth Sy., 11, 998–1038, 2019.
McBratney, A. B., Santos, M. L. M., and Minasny, B.: On digital soil
mapping, Geoderma, 117, 3–52, https://doi.org/10.1016/s0016-7061(03)00223-4, 2003.
McBratney, A. B., Minasny, B., and Tranter, G.: Necessary meta-data for
pedotransfer functions, Geoderma, 160, 627–629, 2011.
McGuire, A. D., Melillo, J. M., Kicklighter, D. W., Pan, Y. D., Xiao, X. M.,
Helfrich, J., Moore, B., Vorosmarty, C. J., and Schloss, A. L.: Equilibrium
responses of global net primary production and carbon storage to doubled
atmospheric carbon dioxide: sensitivity to changes in vegetation nitrogen
concentration, Global Biogeochem. Cy., 11, 173–189, 1997.
McLellan, I., Varela, A., Blahgen, M., Fumi, M. D., Hassen, A., Hechminet,
N., Jaouani, A., Khessairi, A., Lyamlouli, K., Ouzari, H.-I., Mazzoleni, V.,
Novelli, E., Pintus, A., Rodrigues, C., Ruiu, P. A., Pereira, C. S., and
Hursthouse, A.: Harmonisation of physical and chemical methods for soil
management in Cork Oak forests – Lessons from collaborative investigations,
African Journal of Environmental Science and Technology, 7, 386–401, 2013.
Melton, J. R., Sospedra-Alfonso, R., and McCusker, K. E.: Tiling soil textures for terrestrial ecosystem modelling via clustering analysis: a case study with CLASS-CTEM (version 2.1), Geosci. Model Dev., 10, 2761–2783, https://doi.org/10.5194/gmd-10-2761-2017, 2017.
Miller, D. A. and White, R. A.: A conterminous United States multilayer
soil characteristics dataset for regional climate and hydrology modeling,
Earth Interact., 2, 1–26, https://doi.org/10.1175/1087-3562(1998)002<0001:ACUSMS>2.3.CO;2, 1998.
Minasny, B., McBratney, A. B. and Salvador-Blanes, S.: Quantitative models
for pedogenesis – A review, Geoderma, 144, 140–157, 2008.
Moigne, P.: SURFEX scientific documentation, Centre National de Recherches
Meteorologiques, Toulouse and Grenoble, France, 2018.
Montzka, C., Herbst, M., Weihermüller, L., Verhoef, A., and Vereecken, H.: A global data set of soil hydraulic properties and sub-grid variability of soil water retention and hydraulic conductivity curves, Earth Syst. Sci. Data, 9, 529–543, https://doi.org/10.5194/essd-9-529-2017, 2017.
Mulder, V. L., Lacoste, M., Richer-de-Forges, A. C., and Arrouays, D.:
GlobalSoilMap France: High-resolution spatial modelling the soils of France
up to two meter depth, Sci. Total Environ., 573, 1352–1369,
https://doi.org/10.1016/j.scitotenv.2016.07.066, 2016.
National Soil Survey Office: Soil Map of China, China Map Press,
Beijing, 1995 (in Chinese).
Niu, G.-Y., Yang, Z.-L., Mitchell, K. E., Chen, F., Ek, M. B., Barlage, M.,
Kumar, A., Manning, K., Niyogi, D., Rosero, E., Tewari, M., and Xia, Y.: The
community Noah land surface model with multiparameterization options
(Noah-MP): 1. Model description and evaluation with local-scale
measurements, J. Geophys. Res.-Atmos., 116, D12110,
https://doi.org/10.1029/2010JD015139, 2011.
Odgers, N. P., Libohova, Z., and Thompson, J. A.: Equal-area spline
functions applied to a legacy soil database to create weighted-means maps of
soil organic carbon at a continental scale, Geoderma, 189–190, 153–163,
2012.
Oleson, K. W., Lawrence, D. M., Bonan, G. B., Drewniak, B., Huang, M., Koven, C. D., Levis, S., Li, F., Riley, W. J., Subin, Z. M., Swenson, S.C., Thornton, P. E.,
Bozbiyik, A., Fisher, R., Kluzek, E., Lamarque, J.-F., Lawrence, P. J., Leung, L. R.,
Lipscomb, W., Muszala, S., Ricciuto, D. M., Sacks, W., Sun, Y., Tang, J., and
Yang, Z.-L.: Technical Description of version 4.5 of the Community Land Model
(CLM). Ncar Technical Note NCAR/TN-503+STR, National Center for
Atmospheric Research, Boulder, CO, USA, 420 pp., 2013.
Orth, R., Dutra, E., and Pappenberger, F.: Improving Weather Predictability
by Including Land Surface Model Parameter Uncertainty, Mon. Weather
Rev., 144, 1551–1569, 2016.
Oz, B., Deutsch, C. V., and Frykman, P.: A visualbasic program for histogram
and variogram scaling, Comput. Geosci., 28, 21–31,
https://doi.org/10.1016/S0098-3004(01)00011-5, 2002.
Park, J., Kim, H.-S., Lee, S.-J., and Ha, T.: Numerical Evaluation of JULES
Surface Tiling Scheme with High-Resolution Atmospheric Forcing and Land
Cover Data, SOLA, 14, 19–24, https://doi.org/10.2151/sola.2018-004, 2018.
Pelletier, J. D., Broxton, P. D., Hazenberg, P., Zeng, X., Troch, P. A.,
Niu, G.-Y., Williams, Z., Brunke, M. A., and Gochis, D.: A gridded global data set of
soil, immobile regolith, and sedimentary deposit thicknesses for regional
and global land surface modeling, J. Adv. Modeling Earth
Sy., 8, 41–65, https://doi.org/10.1002/2015MS000526, 2016.
Pillar 5 Working Group: Implementation Plan for Pillar Five of the Global
Soil Partnership, FAO, Rome, Italy, 2017.
Pillar 4 Working Group: Plan of Action for Pillar Four of the Global Soil
Partnership, FAO, Rome, Italy, 2014.
Post, D. F., Fimbres, A., Matthias, A. D., Sano, E. E., Accioly, L.,
Batchily, A. K., and Ferreira, L. G.: Predicting Soil Albedo from Soil Color
and Spectral Reflectance Data, Soil Sci. Soc. Am. J., 64,
1027–1034, 2000.
Quattrochi, D. A., Emerson, C. W., Lam, N. S.-N., and Qiu, H.-L.: Fractal
Characterization of Multitemporal Remote Sensing Data, in: Modelling Scale
in Geographical Information System, edited by: Tate, N. and Atkinson, P.,
John Wiley & Sons, London, UK, 13–34, 2001.
Ramcharan, A., Hengl, T., Nauman, T., Brungard, C., Waltman, S., Wills, S.,
and Thompson, J.: Soil Property and Class Maps of the Conterminous United
States at 100-Meter Spatial Resolution, Soil Sci. Soc. Am.
J., 82, 186–201, https://doi.org/10.2136/sssaj2017.04.0122, 2018.
Ribeiro, E., Batjes, N. H., and Oostrum, A. V.: World Soil Information
Service (WoSIS) – Towards the standardization and harmonization of world
soil data, ISRIC – World Soil Information, Wageningen, the Netherlands, 2018.
Reynolds, C. A., Jackson, T. J., and Rawls, W. J.: 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, Water Resour. Res., 36, 3653–3662, 2000.
Romanowicz, A. A., Vanclooster, M., Rounsevell, M., and Junesse, I. L.:
Sensitivity of the SWAT model to the soil and land use data parametrisation:
a case study in the Thyle catchment, Belgium, Ecol. Model., 187,
27–39, 2005.
Rosenzweig, C. and Abramopoulos, F.: Land surface model development for the
GISS GCM, J. Climate, 10, 2040–2054, 1997.
Ross, C. W., Prihodko, L., Anchang, J., Kumar, S., Ji, W., and Hanan, N. P.:
HYSOGs250m, global gridded hydrologic soil groups for curve-number-based
runoff modeling, Scientific Data, 5, 180091, https://doi.org/10.1038/sdata.2018.91, 2018.
Rotstayn, L. D., Jeffrey, S. J., Collier, M. A., Dravitzki, S. M., Hirst, A. C., Syktus, J. I., and Wong, K. K.: Aerosol- and greenhouse gas-induced changes in summer rainfall and circulation in the Australasian region: a study using single-forcing climate simulations, Atmos. Chem. Phys., 12, 6377–6404, https://doi.org/10.5194/acp-12-6377-2012, 2012.
Saha, S., Moorthi, S., Wu, X., Wang, J., Nadiga, S., Tripp, P., Behringer,
D., Hou, Y.-T., Chuang, H.-y., Iredell, M., Ek, M., Meng, J., Yang, R.,
Mendez, M.P., Dool, H.v.d., Zhang, Q., Wang, W., Chen, M., and Becker, E.:
The NCEP Climate Forecast System Version 2, J. Climate 27,
2185–2208, 2014.
Sanchez, P. A., Ahamed, S., Carré, F., Hartemink, A. E., Hempel, J.,
Huising, J., Lagacherie, P., McBratney, A. B., McKenzie, N. J.,
Mendonça-Santos, M. d. L., Budiman Minasny, L. M., Okoth, P., Palm, C.
A., Sachs, J. D., Shepherd, K. D., Vågen, T.-G., Vanlauwe, B., Walsh, M.
G., Winowiecki, L. A., and Zhang, G.-L.: Digital soil map of the world,
Science, 325, 680–681, 2009.
Sellers, P. J., Randall, D. A., Collatz, G. J., Berry, J. A., Field, C. B.,
Dazlich, D. A., Zhang, C., Collelo, G. D., and Bounoua, L.: A revised land
surface parameterization (SiB2) for atmospheric GCMs, Part I: model
formulation, J. Climate, 9, 676–705, 1996.
Shangguan, W.: Comparison of aggregation ways on soil property maps, 20th
World Congress of Soil Science, 8–13 June 2019, Jeju, Korea, 2014.
Shangguan, W., Dai, Y., Liu, B., Ye, A., and Yuan, H.: A soil particle-size
distribution dataset for regional land and climate modelling in China,
Geoderma, 171–172, 85–91, 2012.
Shangguan, W., Dai, Y., Liu, B., Zhu, A., Duan, Q., Wu, L., Ji, D., Ye, A.,
Yuan, H., Zhang, Q., Chen, D., Chen, M., Chu, J., Dou, Y., Guo, J., Li, H.,
Li, J., Liang, L., Liang, X., Liu, H., Liu, S., Miao, C., and Zhang, Y.: A
China dataset of soil properties for land surface modeling, J.
Adv. Model. Earth Sy., 5, 212–224, https://doi.org/10.1002/jame.20026,
2013.
Shangguan, W., Dai, Y., Duan, Q., Liu, B., and Yuan, H.: A global soil data
set for earth system modeling, J. Adv. Model. Earth
Sy., 6, 249–263, 2014.
Shangguan, W., Hengl, T., Mendes de Jesus, J., Yuan, H., and Dai, Y.:
Mapping the global depth to bedrock for land surface modeling, J.
Adv. Model. Earth Sy., 9, 65–88, https://doi.org/10.1002/2016ms000686,
2017.
Shoba, S. A., Stolbovoi, V. S., Alyabina, I. O., and Molchanov, E. N.:
Soil-geographic database of Russia, Eurasian Soil Sci., 41, 907–913, https://doi.org/10.1134/s1064229308090019, 2008.
Singh, R. S., Reager, J. T., Miller, N. L., and Famiglietti, J. S.: Toward
hyper-resolution land-surface modeling: The effects of fine-scale topography
and soil texture on CLM4.0 simulations over the Southwestern U.S., Water
Resour. Res., 51, 2648–2667, https://doi.org/10.1002/2014WR015686, 2015.
Slevin, D., Tett, S. F. B., Exbrayat, J.-F., Bloom, A. A., and Williams, M.: Global evaluation of gross primary productivity in the JULES land surface model v3.4.1, Geosci. Model Dev., 10, 2651–2670, https://doi.org/10.5194/gmd-10-2651-2017, 2017.
Soil Landscapes of Canada Working Group: Soil Landscapes of Canada version
3.2., Agriculture and Agri-Food Canada, Ottawa, Ontario, Canada, 2010.
Soil Survey Staff (NRCS): United States Department of Agriculture: Web
Soil Survey, available at: http://websoilsurvey.nrcs.usda.gov/, last access: 1 January 2017.
Stoorvogel, J. J., Bakkenes, M., Temme, A. J. A. M., Batjes, N. H., and
Brink, B. J. E.: S-World: A Global Soil Map for Environmental Modelling,
Land Degrad. Dev., 28, 22–33, https://doi.org/10.1002/ldr.2656, 2017.
Takata, K., Emori, S., and Watanabe, T.: Development of the minimal advanced
treatments of surface interaction and runoff, Global Planet. Change, 38,
209–222, 2003.
Thompson, J. A., Prescott, T., Moore, A. C., Bell, J., Kautz, D. R., Hempel,
J. W., Waltman, S. W., and Perry, C. H.: Regional approach to soil property
mapping using legacy data and spatial disaggregation techniques, 19th World
Congress of Soil Science, Brisbane, Queensland, Australia, 2010,
Thornton, P. E. and Rosenbloom, N. A.: Ecosystem model spin-up: estimating
steady state conditions in a coupled terrestrial carbon and nitrogen cycle
model, Ecol. Model., 189, 25–48, 2005.
Tian, W., Li, X., Wang, X.-S., and Hu, B. X.: Coupling a groundwater model with a land surface model to improve water and energy cycle simulation, Hydrol. Earth Syst. Sci. Discuss., 9, 1163–1205, https://doi.org/10.5194/hessd-9-1163-2012, 2012.
Tifafi, M., Guenet, B., and Hatté, C.: 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, Global Biogeochem. Cy., 32, 42–56,
https://doi.org/10.1002/2017GB005678, 2018.
Todd-Brown, K. E. O., Randerson, J. T., Post, W. M., Hoffman, F. M., Tarnocai, C., Schuur, E. A. G., and Allison, S. D.: Causes of variation in soil carbon simulations from CMIP5 Earth system models and comparison with observations, Biogeosciences, 10, 1717–1736, https://doi.org/10.5194/bg-10-1717-2013, 2013.
Todd-Brown, K. E. O., Randerson, J. T., Hopkins, F., Arora, V., Hajima, T., Jones, C., Shevliakova, E., Tjiputra, J., Volodin, E., Wu, T., Zhang, Q., and Allison, S. D.: Changes in soil organic carbon storage predicted by Earth system models during the 21st century, Biogeosciences, 11, 2341–2356, https://doi.org/10.5194/bg-11-2341-2014, 2014.
Tóth, B., Weynants, M., Nemes, A., Makó, A., Bilas, G., and
Tóth, G.: New generation of hydraulic pedotransfer functions for Europe,
Eur. J. Soil Sci., 66, 226–238, https://doi.org/10.1111/ejss.12192, 2015.
Tóth, B., Weynants, M., Pásztor, L., and Hengl, T.: 3-D soil
hydraulic database of Europe at 250 m resolution, Hydrol. Process.,
31, 2662–2666, https://doi.org/10.1002/hyp.11203, 2017.
Trinh, T., Kavvas, M. L., Ishida, K., Ercan, A., Chen, Z. Q., Anderson, M.
L., Ho, C., and Nguyen, T.: Integrating global land-cover and soil datasets
to update saturated hydraulic conductivity parameterization in hydrologic
modeling, Sci. Total Environ., 631–632, 279–288,
https://doi.org/10.1016/j.scitotenv.2018.02.267, 2018.
Van Engelen, V. and Dijkshoorn, J.: Global and National Soils and Terrain
Digital Databases (SOTER), Procedures Manual, version 2.0. ISRIC Report
2012/04, ISRIC – World Soil Information, Wageningen, the Netherlands, 2012.
Vaysse, K. and Lagacherie, P.: Using quantile regression forest to estimate
uncertainty of digital soil mapping products, Geoderma, 291, 55–64,
https://doi.org/10.1016/j.geoderma.2016.12.017, 2017.
Vereecken, H., Weynants, M., Javaux, M., Pachepsky, Y., Schaap, M. G., and
Genuchten, M. T. V.: Using pedotransfer functions to estimate the van
Genuchten-Mualem soil hydraulic properties: a review, Vadose Zone J.,
9, 795–820, 2010.
Viscarra Rossel, R., Chen, C., Grundy, M., Searle, R., Clifford, D., and
Campbell, P.: The Australian three-dimensional soil grid: Australia's
contribution to the GlobalSoilMap project, Soil Res., 53, 845–864, 2015.
Verseghy, D.: The Canadian land surface scheme (CLASS): Itshistory and
future, Atmos. Ocean, 38, 1–13, 2000.
Vrettas, M. D. and Fung, I. Y.: Toward a new parameterization of hydraulic
conductivity in climate models: Simulation of rapid groundwater fluctuations
in Northern California, J. Adv. Model. Earth Sy., 7,
2105–2135, https://doi.org/10.1002/2015ms000516, 2016.
Wang, G., Gertner, G., and Anderson, A. B.: Up-scaling methods based on
variability-weighting and simulation for inferring spatial information
across scales, Int. J. Remote Sens., 25, 4961–4979,
2004.
Webb, R. S., Rosenzweig, C. E., and Levine, E. R.: Specifying land surface
characteristics in general circulation models: Soil profile data set and
derived water-holding capacities, Global Biogeochem. Cy., 7, 97–108, 1993.
Wilson, M. F. and Henderson-Sellers, A.: A global archive of land cover and
soils data for use in general circulation climate models, J.
Climatol., 5, 119–143, 1985.
Wu, L., Wang, A., and Sheng, Y.: Impact of Soil Texture on the Simulation of
Land Surface Processes in China, Climatic and Environmental Research, 19, 559–571, https://doi.org/10.3878/j.issn.1006-9585.2013.13055, 2014 (in
Chinese).
Wu, T., Song, L., Li, W., Wang, Z., Zhang, H., Xin, X., Zhang, Y., Zhang,
L., Li, J., Wu, F., Liu, Y., Zhang, F., Shi, X., Chu, M., Zhang, J., Fang,
Y., Wang, F., Lu, Y., Liu, X., Wei, M., Liu, Q., Zhou, W., Dong, M., Zhao,
Q., Ji, J., Li, L., and Zhou, M: An overview of BCC climate system model
development and application for climate change studies, J.
Meteorol. Res., 28, 34–56, 2014.
Wu, X., Lu, G., Wu, Z., He, H.,
Zhou, J., and Liu, Z.: An Integration Approach for Mapping Field Capacity of
China Based on Multi-Source Soil Datasets, Water, 10, 728, https://doi.org/10.3390/w10060728, 2018.
Zhang, W. L., Xu, A. G., Ji, H. J., Zhang, R. L., Lei, Q. L., Zhang, H. Z.,
Zhao, L. P., and Long, H. Y.: Development of China digital soil map at
1 : 50 000 scale, 19th World Congress of Soil Science, Soil Solutions for a
Changing World, 1–6 August 2010, Brisbane, Australia, 2010,
Zhao, H., Zeng, Y., Lv, S., and Su, Z.: Analysis of soil hydraulic and thermal properties for land surface modeling over the Tibetan Plateau, Earth Syst. Sci. Data, 10, 1031–1061, https://doi.org/10.5194/essd-10-1031-2018, 2018.
Zhao, M., Golaz, J.-C., Held, I. M., Guo, H., Balaji, V., Benson, R., Chen,
J.-H., Chen, X., Donner, L. J., Dunne, J. P., Dunne, K., Durachta, J., Fan,
S.-M., Freidenreich, S. M., Garner, S. T., Ginoux, P., Harris, L. M.,
Horowitz, L. W., Krasting, J. P., Langenhorst, A. R., Liang, Z., Lin, P.,
Lin, S.-J., Malyshev, S. L., Mason, E., Milly, P. C. D., Ming, Y., Naik, V.,
Paulot, F., Paynter, D., Phillipps, P., Radhakrishnan, A., Ramaswamy, V.,
Robinson, T., Schwarzkopf, D., Seman, C. J., Shevliakova, E., Shen, Z.,
Shin, H., Silvers, L. G., Wilson, J. R., Winton, M., Wittenberg, A. T.,
Wyman, B., and Xiang, B.: The GFDL Global Atmosphere and Land Model
AM4.0/LM4.0: 2. Model Description, Sensitivity Studies, and Tuning
Strategies, J. Adv. Model. Earth Sy., 10, 735–769,
https://doi.org/10.1002/2017MS001209, 2018.
Zheng, G., Yang, H., Lei, H., Yang, D., Wang, T., and Qin, Y.: Development
of a Physically Based Soil Albedo Parameterization for the Tibetan Plateau,
Vadose Zone J., 17, https://doi.org/10.2136/vzj2017.05.0102, 2018.
Zheng, H. and Yang, Z. L.: Effects of soil type datasets on regional
terrestrial water cycle simulations under different climatic regimes,
J. Geophys. Res.-Atmos., 121, 14387–14402, https://doi.org/10.1002/2016jd025187, 2016.
Zhou, T., Shi, P. J., Jia, G. S., Dai, Y. J., Zhao, X., Shangguan, W., Du,
L., Wu, H., and Luo, Y. Q.: Age-dependent forest carbon sink: Estimation via
inverse modeling, J. Geophys. Res.-Biogeo., 120,
2473–2492, https://doi.org/10.1002/2015jg002943, 2015.
Zöbler, L.: A world soil file for global climate modeling, NASA Tech.
Memo. 87802, NASA, New York, USA, 33 pp., 1986.
Short summary
Soil data are widely used in various Earth science fields. We reviewed soil property maps for Earth system models, which can also offer insights to soil data developers and users. Old soil datasets are often based on limited observations and have various uncertainties. Updated and comprehensive soil data are made available to the public and can benefit related research. Good-quality soil data are identified and suggestions on how to improve and use them are provided.
Soil data are widely used in various Earth science fields. We reviewed soil property maps for...
