Spatial distribution of greenhouse gas concentrations in arid and semi-arid regions: A case study in East Asia

https://doi.org/10.1016/j.jaridenv.201301001 Get rights and content

Abstract

Land degradation and global warming are currently highly active research topics. Land degradation can both change land cover and surface climate and significantly influence atmospheric circulation. Researches have verified that carbon dioxide (CO2) and methane (CH4) are major greenhouse gases (GHG) in the atmosphere and are directly affected by human activity. However, to date, there is no research on the spatial distribution of GHG concentrations and also no research on how land degradations affect GHG concentrations in arid and semi-arid regions. In this study, we used GHG data from the ENVIronment SATellite (ENVISAT) and the Greenhouse gases Observing Satellite (GOSAT), the Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) data from the MODerate resolution Imaging Spectroradiometer (MODIS) and precipitation data from ground stations to analyze the way land degradation affects GHG concentrations in northern China and Mongolia, which exhibit the most serious land degradation process in East Asia. Our research revealed that the CO2 and CH4 concentrations (XCO2 and XCH4) increased from 2003 to 2009 and then decreased into 2011. We used geostatistics to predict and simulate the spatial distribution of XCO2 and XCH4 and found that the distribution of XCO2 displays a seasonal trend and is primarily affected by plant photosynthesis , soil respiration and precipitation. As the distribution of XCH4 is mainly affected by the sources' distribution, microbial processes, LST and submarine hydrate, the CH4 concentration presents no obvious seasonal changes and the high XCH4 values are primarily found in northeast and southeast China. Land degradation increases the concentration of GHG: the correlation coefficient between NDVI and XCO2 is R2 = 0.76 (P < 0.01) and the value between NDVI and XCH4 is R2 = 0.75 (P < 0.01).

Highlights

► We used ENVISAT and GOSAT data to calculate the greenhouse gases concentration changes in desertified area from 2003 to 2011. ► We analyzed the reasons that affect the distribution of GHGs concentration seasonally. ► We revealed the relationships between GHGs concentration and desertification.

Introduction

Climate change is one of the great challenges of the 21st century (IPCC et al., 2011); the average surface temperature has increased by 0.74 °C over the past 100 years (1906–2005). The increasing concentration of greenhouse gases (GHG) in the atmosphere has been verified as the most important reason for global warming, which is a major environmental concern and a prominent research topic (Wu and Shi, 2011; Zhang et al., 2005). According to the analysis of the World Data Centre for Greenhouse Gases (WDCGG, http://gaw.kishou.go.jp/wdcgg/wdcgg.html), the average global CO2 concentration (XCO2) in 2010 was 389.0 ppm, which is 11.9 ppm more than in 2004; this figure has increased 39% from the pre-industrial global level of 280.0 ppm. The average CH4 concentration (XCH4) was 1808 ppb in 2010, which represents an increase of 158% from approximately 700 ppb in the pre-industrial era (WMO Greenhouse Gas Bulletin, 2004–2010).
Current GHG emission rates may escalate in the future due to population growth and changing diets. Wu and Shi (2011) recognized that the rapid development of the tourism industry can also increase the emission of GHG. Land degradation caused by unviable agricultural practices is another source of increased GHG emissions (Dutt and Gonzalez, 2011).
The degradation of vegetation and soils in drylands, also referred to as desertification, is thought to be a serious threat to the sustainability of human habitation. The reduction in vegetation cover that accompanies desertification has also led to soil erosion (Hassan, 2004). The rise in global atmospheric temperatures may also increase the frequency of droughts in the middle latitudes during the summer months (Ci and Yang, 2010). The land degradation through unviable agricultural practices and land use has also resulted in the increased emission of GHG (Hulme and Kelly, 1993).
Climate change may adversely affect biodiversity and exacerbate desertification due to increases in evapotranspiration and likely decreases in rainfall in drylands. However, because CO2 is the raw material of photosynthesis and is also a major resource for plant productivity, efficient water use in arid and semi-arid areas will significantly increase for some dryland species that may respond favorably to increases in CO2. The contrasting responses of different dryland plants to increasing CO2 and temperatures may lead to changes in the species' composition and abundance (Zafar et al., 2005).
An adequate understanding of the sources and sinks of GHG and their feedbacks is a prerequisite for the reliable prediction of the climate of our planet. However, our current understanding of this is inadequate due to the lack of accurate time-series data. While measurements of fluxes and ground-based measurements of CO2 and CH4 are highly accurate, they are sparse and inefficient. Launching satellites to collect GHG data can solve this issue quite well. At present, SCIAMACHY (SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY) aboard ENVISAT (ENVIronmental SATellite), launched in 2002 but lost in April 2012, and TANSO (Thermal And Near infrared Sensor for carbon Observation) aboard GOSAT (Greenhouse gases Observing SATellite), launched in 2009, are the instruments measuring NIR radiation in appropriate absorption bands at approximately 0.76, 1.6, and 2.0 μm, with sufficient spectral resolutions to retrieve XCO2 and XCH4 (Reuter et al., 2010).
Due to the infrequency of sustained periods of carbon uptake, there is no research on the spatial distribution of GHG concentrations in arid and semi-arid regions based on remote sensing data for that time frame. To fill this gap, the aims of this study were as follows: first, to analyze changes in XCO2 and XCH4 from 2003 to 2011 using ENVISAT SCIAMACHY and GOSAT TANSO data; second, to analyze the spatial distributions of XCO2 and XCH4 in the study area in 2010 based on the TANSO data and using the Ordinal Kriging method; and finally, using Normalized Difference Vegetation Index (NDVI) data from the MODerate resolution Imaging Spectroradiometer (MODIS) combined with precipitation data and land surface temperature (LST) data to analyze the spatial distribution of GHG concentrations. We know that human activities are the most important source of GHG (IPCC et al., 2011). However, here we will only discuss the relationships between NDVI and GHG concentrations.

Section snippets

Study region

The study area was located in East Asia between 30°–50°N and 73.5°–134.5°E, covering approximately 7.46 ×ばつ 106 km2 of northern China and Mongolia (Fig. 1. The regions that covered by land cover data are the study area). This region exhibits the most serious desertification in East Asia. It is also one of the areas most highly prone to sand dust storms in Asia (Guo et al., 2012).
In China, the area of desertification is 2.62 ×ばつ 106 km2, accounting for 27.31% of all terrestrial land. Nearly all of

Annual changes of GHG concentrations in East Asia

We calculated the annual value of GHG concentrations using SCIAMACHY and TANSO from 2003 to 2011. Fig. 5 demonstrates that XCO2 and XCH4 increased from 2003 to 2009 and then clearly decreased until 2010. XCH4 continued to decrease but XCO2 exhibited a small increase between 2010 and 2011. The significant turning point appears in 2010; this is primarily due to the different levels of precision of SCIAMACHY and TANSO. The evaluated precisions of the retrieved column abundances for the single

Conclusions

The reduction of GHG emissions arising from land cover change and land degradation–that is, the reduction of emissions from the conversion and degradation of vegetation–is an issue of crucial significance to the future. Therefore, it is important to understand the monthly trends of GHG concentrations and the spatial distribution of these concentrations at a regional scale. This study selected northern China and Mongolia as its study regions. Spaceborne GHG monitoring data were employed to

Acknowledgments

This study was supported by National Natural Science Foundation of China (41201159, Study on the effect mechanism of commercial center pattern on traffic carbon emissions in Shenyang city. PI: Assistant Researcher Jing Li, Chinese Academy of Sciences, China). We thank the GOSAT Project of Japan and NASA for the use of their data in this study.

References (50)

  • J. Schneider et al.

    Land cover classification of tundra environments in the Arctic Lena Delta based on Landsat 7 ETM+ data and its application for upscaling of methane emissions

    Remote Sensing of Environment

    (2009)
  • K. Schneider et al.

    Ambiguous effects of grazing intensity on surface soil moisture: a geostatistical case study from a steppe environment in Inner Mongolia, PR China

    Journal of Arid Environments

    (2008)
  • M. Stellmes et al.

    Dryland observation at local and regional scale – comparison of Landsat TM/ETM+ and NOAA AVHRR time series

    Remote Sensing of Environment

    (2010)
  • Z.M. Wan

    New refinements and validation of the MODIS Land-Surface Temperature/Emissivity products

    Remote Sensing of Environment

    (2008)
  • K.J. Wessels et al.

    Assessing the effects of human-induced land degradation in the former homelands of northern South Africa with a 1 km AVHRR NDVI time-series

    Remote Sensing of Environment

    (2004)
  • Z.G. Bai et al.

    Proxy global assessment of land degradation

    Soil Use and Management

    (2008)
  • S.K. Chong et al.

    How do soils breathe?

    Golf Course Management

    (2003)
  • L.J. Ci et al.

    Desertification and Its Control in China

    (2010)
  • R.J. Cicerone et al.

    Biogeochemical aspects of atmospheric methane

    Global Biogeochemical Cycles

    (1988)
  • P.J. Crutzen et al.

    A Two-dimensional photochemical model of the atmosphere 2: the tropospheric budgets of the anthropogenic chlorocarbons CO, CH4, CH3Cl and the effect of various NOx sources on tropospheric Ozone

    Journal of Geophysical Research

    (1983)
  • Z.H. Duan et al.

    Estimate of total CO2 output from desertified sandy land in China

    Atmospheric Environment

    (2001)
  • V. Dutt et al.

    Human control of climate change

    Climatic Change

    (2011)
  • E.M. Elhadi et al.

    Landscape change and sandy desertification monitoring and assessment: a case study in Northern Shaanxi Province, China

    Journal of American Science

    (2010)
  • M.M. Elhag

    Causes and Impact of Desertification in the Butana Area of Sudan

    (2006)
  • A. Elmzoughi et al.

    Using long term NOAA-AVHRR NDVI time-series data for desertification monitoring in South Tunisia

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