A China data set of soil properties for land surface modeling
01 Jun 2013-Journal of Advances in Modeling Earth Systems (American Geophysical Union)-Vol. 5, Iss: 2, pp 212-224
TL;DR: Li et al. as mentioned in this paper used the polygon linkage method to derive the spatial distribution of soil properties and linked the profile attribute database and soil map under the framework of the Genetic Soil Classification of China which avoids uncertainty in taxon referencing.
Abstract: A comprehensive 30x30 arc-second resolution gridded soil characteristics data set of China has been developed for use in the land surface modeling. It includes physical and chemical attributes of soils derived from 8979 soil profiles and the Soil Map of China (1:1,000,000). We used the polygon linkage method to derive the spatial distribution of soil properties. The profile attribute database and soil map are linked under the framework of the Genetic Soil Classification of China which avoids uncertainty in taxon referencing. Quality control information (i.e., sample size, soil classification level, linkage level, search radius and texture) is included to provide confidence information for the derived soil parameters. The data set includes 28 attributes for 8 vertical layers at the spatial resolution of 30x30 arc-seconds. Based on this data set, the estimated storage of soil organic carbon in the upper 1 m of soil is 72.5 Pg, total N is 6.6 Pg, total P is 4.5 Pg, total K is 169.9 Pg, alkali-hydrolysable N is 0.55 Pg, available P is 0.03 Pg, and available K is 0.61 Pg. These estimates are reasonable compared with previous studies. The distributions of soil properties are consistent with common knowledge of Chinese soil scientists and the spatial variations over large areas are well represented. The data set can be incorporated into land models to better represent the role of soils in hydrological and biogeochemical cycles in China.
Citations
More filters
TL;DR: Improvements in the relative accuracy considering the amount of variation explained, in comparison to the previous version of SoilGrids at 1 km spatial resolution, range from 60 to 230%.
Abstract: This paper describes the technical development and accuracy assessment of the most recent and improved version of the SoilGrids system at 250m resolution (June 2016 update). SoilGrids provides global predictions for standard numeric soil properties (organic carbon, bulk density, Cation Exchange Capacity (CEC), pH, soil texture fractions and coarse fragments) at seven standard depths (0, 5, 15, 30, 60, 100 and 200 cm), in addition to predictions of depth to bedrock and distribution of soil classes based on the World Reference Base (WRB) and USDA classification systems (ca. 280 raster layers in total). Predictions were based on ca. 150,000 soil profiles used for training and a stack of 158 remote sensing-based soil covariates (primarily derived from MODIS land products, SRTM DEM derivatives, climatic images and global landform and lithology maps), which were used to fit an ensemble of machine learning methods-random forest and gradient boosting and/or multinomial logistic regression-as implemented in the R packages ranger, xgboost, nnet and caret. The results of 10-fold cross-validation show that the ensemble models explain between 56% (coarse fragments) and 83% (pH) of variation with an overall average of 61%. Improvements in the relative accuracy considering the amount of variation explained, in comparison to the previous version of SoilGrids at 1 km spatial resolution, range from 60 to 230%. Improvements can be attributed to: (1) the use of machine learning instead of linear regression, (2) to considerable investments in preparing finer resolution covariate layers and (3) to insertion of additional soil profiles. Further development of SoilGrids could include refinement of methods to incorporate input uncertainties and derivation of posterior probability distributions (per pixel), and further automation of spatial modeling so that soil maps can be generated for potentially hundreds of soil variables. Another area of future research is the development of methods for multiscale merging of SoilGrids predictions with local and/or national gridded soil products (e.g. up to 50 m spatial resolution) so that increasingly more accurate, complete and consistent global soil information can be produced. SoilGrids are available under the Open Data Base License.
2,228 citations
TL;DR: SoilGrids1km provides an initial set of examples of soil spatial data for input into global models at a resolution and consistency not previously available and results of regression modeling indicate that the most useful covariates for modeling soils at the global scale are climatic and biomass indices, lithology, and taxonomic mapping units derived from conventional soil survey.
Abstract: Background: Soils are widely recognized as a non-renewable natural resource and as biophysical carbon sinks. As such, there is a growing requirement for global soil information. Although several global soil information systems already exist, these tend to suffer from inconsistencies and limited spatial detail. Methodology/Principal Findings: We present SoilGrids1km — a global 3D soil information system at 1 km resolution — containing spatial predictions for a selection of soil properties (at six standard depths): soil organic carbon (g kg21), soil pH, sand, silt and clay fractions (%), bulk density (kg m23), cation-exchange capacity (cmol+/kg), coarse fragments (%), soil organic carbon stock (t ha21), depth to bedrock (cm), World Reference Base soil groups, and USDA Soil Taxonomy suborders. Our predictions are based on global spatial prediction models which we fitted, per soil variable, using a compilation of major international soil profile databases (ca. 110,000 soil profiles), and a selection of ca. 75 global environmental covariates representing soil forming factors. Results of regression modeling indicate that the most useful covariates for modeling soils at the global scale are climatic and biomass indices (based on MODIS images), lithology, and taxonomic mapping units derived from conventional soil survey (Harmonized World Soil Database). Prediction accuracies assessed using 5–fold cross-validation were between 23–51%. Conclusions/Significance: SoilGrids1km provide an initial set of examples of soil spatial data for input into global models at a resolution and consistency not previously available. Some of the main limitations of the current version of SoilGrids1km are: (1) weak relationships between soil properties/classes and explanatory variables due to scale mismatches, (2) difficulty to obtain covariates that capture soil forming factors, (3) low sampling density and spatial clustering of soil profile locations. However, as the SoilGrids system is highly automated and flexible, increasingly accurate predictions can be generated as new input data become available. SoilGrids1km are available for download via http://soilgrids.org under a Creative Commons Non Commercial license.
894 citations
Cites background from "A China data set of soil properties..."
...gov/technical/nasis/), LUCAS Topsoil Survey database [19], Africa Soil Profiles database [20], Mexican National soil profile database [21], Brazilian national soil profile database [22], Chinese soil profile database [23], and the soil profile archive from the Canadian Soil Information System [24]....
[...]
TL;DR: The Global Soil Dataset (GSDE) as discussed by the authors provides soil information such as soil particle-size distribution, organic carbon, and nutrients, and quality control information in terms of confidence level at 30″ × 30″ horizontal resolution and for eight vertical layers to a depth of 2.3 m.
Abstract: We developed a comprehensive, gridded Global Soil Dataset for use in Earth System Models (GSDE) and other applications. The GSDE provides soil information, such as soil particle-size distribution, organic carbon, and nutrients, and quality control information in terms of confidence level at 30″ × 30″ horizontal resolution and for eight vertical layers to a depth of 2.3 m. The GSDE is based on the Soil Map of the World and various regional and national soil databases, including soil attribute data and soil maps. We used a standardized data structure and data processing procedures to harmonize the data collected from various sources. We then used a soil type linkage method (i.e., taxotransfer rules) and a polygon linkage method to derive the spatial distribution of the soil properties. To aggregate the attributes of different compositions of a mapping unit, we used three mapping approaches: the area-weighting method, the dominant soil type method, and the dominant binned soil attribute method. The data set can also be aggregated to a lower resolution. In this paper, we only show the vertical and horizontal variations of sand, silt and clay contents, bulk density, and soil organic carbon as examples of the GSDE. The GSDE estimates of global soil organic carbon stock to the depths of 2.3, 1, and 0.3 m are 1922.7, 1455.4, and 720.1 Gt, respectively. This newly developed data set provides more accurate soil information and represents a step forward to advance earth system modeling.
400 citations
TL;DR: This finding indicates that the implementation of the ecological restoration projects in China has significantly increased ecosystem C sequestration across the country and demonstrates that these restoration projects have substantially contributed to CO2 mitigation in China.
Abstract: The long-term stressful utilization of forests and grasslands has led to ecosystem degradation and C loss. Since the late 1970s China has launched six key national ecological restoration projects to protect its environment and restore degraded ecosystems. Here, we conducted a large-scale field investigation and a literature survey of biomass and soil C in China's forest, shrubland, and grassland ecosystems across the regions where the six projects were implemented (∼16% of the country's land area). We investigated the changes in the C stocks of these ecosystems to evaluate the contributions of the projects to the country's C sink between 2001 and 2010. Over this decade, we estimated that the total annual C sink in the project region was 132 Tg C per y (1 Tg = 1012 g), over half of which (74 Tg C per y, 56%) was attributed to the implementation of the projects. Our results demonstrate that these restoration projects have substantially contributed to CO2 mitigation in China.
400 citations
Macquarie University1, Imperial College London2, Brown University3, Leiden University4, University of New South Wales5, VU University Amsterdam6, University of Western Sydney7, Estonian University of Life Sciences8, Aarhus University9, University of Minnesota10, Smithsonian Tropical Research Institute11, University of California, Riverside12
TL;DR: Although the reliability of global soil datasets lags behind that of climate datasets, the results nonetheless provide compelling evidence that both can be jointly used in broad-scale analyses, and that effects uniquely attributable to soil properties are important determinants of leaf photosynthetic traits and rates.
Abstract: Aim The influence of soil properties on photosynthetic traits in higher plants is poorly quantified in comparison with that of climate. We address this situation by quantifying the unique and joint contributions to global leaf-trait variation from soils and climate. Location Terrestrial ecosystems world-wide. Methods Using a trait dataset comprising 1509 species from 288 sites, with climate and soil data derived from global datasets, we quantified the effects of 20 soil and 26 climate variables on light-saturated photosynthetic rate (Aarea), stomatal conductance (gs), leaf nitrogen and phosphorus (Narea and Parea) and specific leaf area (SLA) using mixed regression models and multivariate analyses. Results Soil variables were stronger predictors of leaf traits than climatic variables, except for SLA. On average, Narea, Parea and Aarea increased and SLA decreased with increasing soil pH and with increasing site aridity. gs declined and Parea increased with soil available P (Pavail). Narea was unrelated to total soil N. Joint effects of soil and climate dominated over their unique effects on Narea and Parea, while unique effects of soils dominated for Aarea and gs. Path analysis indicated that variation in Aarea reflected the combined independent influences of Narea and gs, the former promoted by high pH and aridity and the latter by low Pavail. Main conclusions Three environmental variables were key for explaining variation in leaf traits: soil pH and Pavail, and the climatic moisture index (the ratio of precipitation to potential evapotranspiration). Although the reliability of global soil datasets lags behind that of climate datasets, our results nonetheless provide compelling evidence that both can be jointly used in broad-scale analyses, and that effects uniquely attributable to soil properties are important determinants of leaf photosynthetic traits and rates. A significant future challenge is to better disentangle the covarying physiological, ecological and evolutionary mechanisms that underpin trait–environment relationships.
245 citations
Cites methods from "A China data set of soil properties..."
...In brief, we first assembled geolocated soil profiles from several soil phosphorus datasets (e.g. Batjes, 2011a; Shangguan et al., 2013; Tóth et al., 2013)....
[...]
References
More filters
Book•
14 Jan 2000TL;DR: This textbook is notable in emphasizing that the mechanisms underlying plant physiological ecology can be found at the levels of biochemistry, biophysics, molecular biology and whole-plant physiology, well-suited to assess the costs, benefits and consequences of modifying plants for human needs, and to evaluate the role of plants in ecosystems.
Abstract: -- Plant Science The growth, reproduction and geographical distribution of plants are profoundly influenced by their physiological ecology: the interaction with the surrounding physical, chemical and biological environments. This textbook is notable in emphasizing that the mechanisms underlying plant physiological ecology can be found at the levels of biochemistry, biophysics, molecular biology and whole-plant physiology. At the same time, the integrative power of physiological ecology is well-suited to assess the costs, benefits and consequences of modifying plants for human needs, and to evaluate the role of plants in ecosystems. Plant Physiological Ecology begins with the primary processes of carbon metabolism and transport, plant-water relations, and energy balance. After considering individual leaves and whole plants, these physiological processes are then scaled up to the level of the canopy. Subsequent chapters discuss mineral nutrition and the ways in which plants cope with nutrient-deficient or toxic soils. The book then looks at patterns of growth and allocation, life-history traits, and interactions between plants and other organisms. Later chapters deal with traits that affect decomposition of plant material and with plant physiological ecology at the level of ecosystems and global environmental processes. Plant Physiological Ecology features numerous boxed entries that provide extended discussions of selected issues, a glossary, and numerous references to the primary and review literature. The significant new text is suitable for use in plant ecology courses, as well as classes ranging from plant physiology to plant molecular
3,574 citations
TL;DR: A meta-analysis of a regional acidification phenomenon in Chinese arable soils that is largely associated with higher N fertilization and higher crop production is presented, likely to threaten the sustainability of agriculture and affect the biogeochemical cycles of nutrients and also toxic elements in soils.
Abstract: Soil acidification is a major problem in soils of intensive Chinese agricultural systems. We used two nationwide surveys, paired comparisons in numerous individual sites, and several long-term monitoring-field data sets to evaluate changes in soil acidity. Soil pH declined significantly ( P + ) per hectare per year, and base cations uptake contributed a further 15 to 20 kilomoles of H + per hectare per year to soil acidification in four widespread cropping systems. In comparison, acid deposition (0.4 to 2.0 kilomoles of H + per hectare per year) made a small contribution to the acidification of agricultural soils across China.
2,736 citations
TL;DR: In this article, the authors developed a model to simulate the dynamics of C, N, P, and S in cultivated and uncultivated grassland soils using a monthly time step.
Abstract: We have developed a model to simulate the dynamics of C, N, P, and S in cultivated and uncultivated grassland soils. The model uses a monthly time step and can simulate the dynamics of soil organic matter over long time periods (100 to 10,000 years). It was used to simulate the impact of cultivation (100 years) on soil organic matter dynamics, nutrient mineralization, and plant production and to simulate soil formation during a 10,000 year run. The model was validated by comparing the simulated impact of cultivation on soil organic matter C, N, P, and S dynamics with observed data from sites in the northern Great Plains. The model correctly predicted that N and P are the primary limiting nutrients for plant production and simulated the response of the system to inorganic N, P, and S fertilizer. Simulation results indicate that controlling the C:P and C:S ratios of soil organic matter fractions as functions of the labile P and S levels respectively, allows the model to correctly simulate the observed changes in C:P and C:S ratios in the soil and to simulate the impact of varying the labile P and S levels on soil P and S net mineralization rates.
1,321 citations
"A China data set of soil properties..." refers background in this paper
...These soil nutrients can be calculated by running the models for thousands of years until an equilibrium state is reached (also called as model ‘‘spin-up’’) [Kluzek, 2010; Parton et al., 1988; Wang et al., 2009; Xu and Prentice, 2008]....
[...]
TL;DR: In this article, root system sizes and shapes for different plant growth forms were predicted using data on above-ground plant sizes, climate and soil texture using regression models for three broad growth forms.
Abstract: Summary 1 In water-limited environments, the availability of water and nutrients to plants depends on environmental conditions, sizes and shapes of their root systems, and root competition. The goal of this study was to predict root system sizes and shapes for different plant growth forms using data on above-ground plant sizes, climate and soil texture. 2 A new data set of > 1300 records of root system sizes for individual plants was collected from the literature for deserts, scrublands, grasslands and savannas with ≤ 1000 mm mean annual precipitation (MAP). Maximum rooting depths, maximum lateral root spreads and their ratios were measured. 3 Root system sizes differed among growth forms and increased with above-ground size: annuals < perennial forbs = grasses < semi-shrubs < shrubs < trees. Stem succulents were as shallowly rooted as annuals but had lateral root spreads similar to shrubs. 4 Absolute rooting depths increased with MAP in all growth forms except shrubs and trees, but were not strongly related to potential evapotranspiration (PET). Except in trees, root systems tended to be shallower and wider in dry and hot climates and deeper and narrower in cold and wet climates. Shrubs were more shallowly rooted under climates with summer than winter precipitation regimes. 5 Relative to above-ground plant sizes, root system sizes decreased with increasing PET for all growth forms, but decreased with increasing MAP only for herbaceous plants. Thus relative rooting depths tended to increase with aridity, although absolute rooting depths decreased with aridity. 6 Using an independent data set of 20 test locations, rooting depths were predicted from MAP using regression models for three broad growth forms. The models succeeded in explaining 62% of the observed variance in median rooting depths. 7 Based on the data analysed here, Walter’s two-layer model of soil depth partitioning between woody and herbaceous plants appears to be most appropriate in drier regimes (< 500 mm MAP) and in systems with substantial winter precipitation.
1,216 citations
01 Jan 1989
TL;DR: A survey of physiological approaches to understand plantenvironment interactions from the functional perspective is given in this paper, where students are introduced to a wide range of techniques and make measurements on different plant species growing in the field or greenhouse.
Abstract: Lecture Course Objective: A survey of physiological approaches to understanding plantenvironment interactions from the functional perspective. Lectures cover physiological adaptation; limiting factors; resources acquisition/allocation; photosynthesis, carbon, energy balance; water use and relations; nutrient relations; linking physiology, stable isotope applications ecophysiology; stress physiology; life history, physiology; evolution of physiological performance; physiology population, community, ecosystem levels. Laboratory Course Objectives: The laboratory is focused on instructing you on observational and experimental approaches and methods used in plant physiological ecology. Students are introduced to a wide range of techniques and will make measurements on different plant species growing in the field or greenhouse (weeks 1-7). A group research project is required (weeks 912).
1,195 citations
"A China data set of soil properties..." refers background in this paper
...In the short term, recycling of nutrients from soil organic matter is the major direct source of soluble nutrients to the soils [Lambers et al., 2006]....
[...]