Soil Database of 1:1,000,000 Digital Soil Survey and Reference System of the Chinese Genetic Soil Classification System

Shi, Xinjie; Yu, Dongsheng; Warner, Eric; Pan, Xinfu; Petersen, G. W.; Gong, Zhefeng; Weindorf, David C.

doi:10.2136/sh2004.4.0129

Cited by 291 publications

(146 citation statements)

References 1 publication

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“…Natural factors include soil type classified by the genetic soil classification of China (Shi et al, 2004), lithology, vegetation type, elevation and slope; human factors include road influences (buffer of roads), settlement influences (buffer of settlements), gross domestic product (GDP) density, and population density. It is difficult to quantify human activities as a detailed spatial unit.…”

Section: Driving Factors Data Setsmentioning

confidence: 99%

Characterization and interaction of driving factors in karst rocky desertification: a case study from Changshun, China

Zhang

2014

Solid Earth

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Abstract. As the most severe ecological issue in southwest China, karst rocky desertification (KRD) has both threatened and constrained regional sustainable development. Comprehensively understanding the relationship between the evolution of KRD and relevant driving data would provide more information to combat KRD in such complex karst environments. Past studies have been limited in quantifying the relative importance of driving factors influencing fine-scale KRD evolution, and have also lacked insight into their interactive impacts. To address these issues, we have used geographical information system techniques and a geographical detector model to explore the spatial consistency of driving factors and their interactions in relation to the evolution of KRD. Changshun County in China was selected as a representative area for the study. Nine relevant driving factors, including both natural and anthropogenic factors, were studied in regard to their relationships with KRD transformation between 2000 and 2010. Our results demonstrate the relative importance of driving data in influencing the improvement and deterioration of KRD. Lithology, soil type and road influence are identified as the leading factors. Interestingly, to our study at least, there is no significant difference between the impacts of natural and anthropogenic factors influencing KRD improvement, and even natural factors have a higher impact on KRD deterioration. Factors were found to enhance the influence of each other for KRD transformation. In particular, the results show a non-linearly enhanced effect between driving factors, which significantly aggravates KRD. New information found in our study helps to effectively control and restore areas afflicted by KRD.

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Section: Driving Factors Data Setsmentioning

confidence: 99%

Characterization and interaction of driving factors in karst rocky desertification: a case study from Changshun, China

Zhang

2014

Solid Earth

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“…Figure 2 illustrates the data abundance of the five model variables. The data for soil sand content is a 10 km by 10 km raster data set constructed from soil profiles via spatial interpolation (Oberthür et al, 1999;Shi et al, 2004Shi et al, , 2006. Although a certain proportion of the immense spatial variation in soil properties may be lost after spatial interpolation (Goovaerts, 2001;van Bodegom et al, 2002), the gridded soil data are still the most detailed of the five model inputs.…”

Section: Pdfs Of the Model Input Variablesmentioning

confidence: 99%

Uncertainties in estimating regional methane emissions from rice paddies due to data scarcity in the modeling approach

Zhang

Huang

et al. 2014

Geosci. Model Dev.

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Abstract. Rice paddies are a major anthropogenic source of the atmospheric methane. However, because of the high spatial heterogeneity, making accurate estimations of the methane emission from rice paddies is still a big challenge, even with complicated models. Data scarcity is one of the substantial causes of the uncertainties in estimating the methane emissions on regional scales. In the present study, we discussed how data scarcity affected the uncertainties in model estimations of rice paddy methane emissions, from county/provincial scale up to national scale. The uncertainties in methane emissions from the rice paddies of China was calculated with a local-scale model and the Monte Carlo simulation. The data scarcities in five of the most sensitive model variables, field irrigation, organic matter application, soil properties, rice variety and production were included in the analysis. The result showed that in each individual county, the within-cell standard deviation of methane flux, as calculated via Monte Carlo methods, was 13.5-89.3 % of the statistical mean. After spatial aggregation, the national total methane emissions were estimated at 6.44-7.32 Tg, depending on the base scale of the modeling and the reliability of the input data. And with the given data availability, the overall aggregated standard deviation was 16.3 % of the total emissions, ranging from 18.3-28.0 % for early, late and middle rice ecosystems. The 95 % confidence interval of the estimation was 4.5-8.7 Tg by assuming a gamma distribution. Improving the data availability of the model input variables is expected to reduce the uncertainties significantly, especially of those factors with high model sensitivities.

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“…This database is composed of records of 2473 typical soil profiles across China (see Figure 1), each of which represents soil species in the Genetic Soil Classification of China (GSCC) [Shi et al, 2004b]. Each soil profile is divided into A, B and C horizons as depth increases.…”

Section: Data Sourcesmentioning

confidence: 99%

Patterns of soil nitrogen storage in China

Tian

Wang

Liu

et al. 2006

Global Biogeochemical Cycles

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We have investigated the storage and spatial distribution of soil nitrogen (N) in China based on a data set of 2480 soil profiles and a map of Chinese soil types at a spatial resolution of 1:1,000,000. Our estimate indicates that the total N storage in China is 8.29 × 1015 g, representing 5.9–8.7% of the total global N storage. The total N storage in China is on average or slightly above the average of its share in the global N storage, even though low nitrogen content soils cover a large area in China. N density varies substantially with soil types and regions. Peat soils in the southeast of Tibet, southwest China, show the highest averaged N density with a value of 7314.9 g/m3 among all soil types. This is more than 30 times of the lowest N density of brown desert soils in the western desert and arid region. The highest N storages among all the soil types are the felty soil in southeast of Tibet, dark‐brown earths in northeast China, and red earths in southeast China with values of 921.1, 611.4, and 569.6 Tg, respectively. N density also varies with land cover types in China. Wetlands in southwest China exhibit the highest N density at 6775.9 g/m3 and deserts in northwest China have the least at 447.5 g/m3. Our analysis also indicates that land cover types are poor predictors of N content. Further research is needed to examine how transformation from organic agriculture to increased use of fertilizers and pesticides has influenced N storage in China.

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Soil Database of 1:1,000,000 Digital Soil Survey and Reference System of the Chinese Genetic Soil Classification System

Cited by 291 publications

References 1 publication

Characterization and interaction of driving factors in karst rocky desertification: a case study from Changshun, China

Characterization and interaction of driving factors in karst rocky desertification: a case study from Changshun, China

Uncertainties in estimating regional methane emissions from rice paddies due to data scarcity in the modeling approach

Patterns of soil nitrogen storage in China

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