Spherical Semivariogram Models Research Articles

Internal structures of the Last Glacial Period multi-phase cold-climate aeolian dunes, Moravian Sahara, Czechia

Aeolian dunes in the Moravian Sahara developed during at least two distinct cold periods of the Last Glacial Period (LGP). Their two-phase development was previously deduced solely from the different orientations of dune crests without knowing the dunes' internal structure. Therefore, the aim of this study is to reveal the internal structure of the multi-generational dune system, developing a relationship between these dunes and bedrock. A combination of data was obtained from boreholes and ground-penetrating radar (GPR). GPR profiles were measured in places where the boreholes were drilled to obtain high-resolution combination data. Furthermore, models of aeolian sediment thickness and ground-water level were made using a spatial interpolation method (ordinary kriging with spherical semivariogram model) to interpret the GPR results and to investigate a relationship between the bedrock and dune patterns. Internal dune structures confirm the hypothesis of multi-generational development of this dune field. In addition, the aeolian sediment distribution was influenced by the bedrock topography. Finally, this study confirms that a change in LGP atmospheric circulation over Central Europe is the main controlling factor in forming the Moravian Sahara dune field.

Spatio-temporal distribution of the leaf crown borer Eupalamides cyparissias in coconut (Cocus nucifera L.) plantation in a tropical region

The leaf crown borer Eupalamides cyparissias (Fabricius, 1777) is an agricultural pest that deserves attention in the cultivation of coconut (Cocos nucifera L.) and other economically important palm trees (Areacaceae). Several studies address the use of geostatistics to understand the attack of pest insects in agriculture, but there are no studies on the spatial and temporal patterns of E. cyparissias in coconut trees. The presence or absence of the borer was verified monthly in all coconut plants of a plot measuring 12.5 hectares, from July 2018 to October 2020. The occurrence of the borer was verified by the appearance of two main symptoms in the coconut trees. Dispersion patterns of E. cyparissias were studied using semivariograms to determine the most suitable spatial distribution model for the species. Kriging maps were created. Exponential and spherical semivariogram models showed the best fit to the patterns of spatial dispersion of the borer, showing that the attack on the coconut plantation occurs in an aggregate, and in foci, with a range of 20 to 160 metres. The climatic data had no significant correlation with the infestation rate. These results show that geostatistics are an important tool to define reliable sampling plans for integrated pest management. It is useful to assess the spatial and temporal distribution of E. cyparissias

Spatial variability of dendrometric parameters in a native tree Mabea fistulifera Mart. and its relationship with soil physical properties

Mabea fistulifera Mart. (common name: canudo-de-pito) belongs to the Euphorbiaceae family and is a native tree species in Brazil showing a high potential to recover degraded lands. This study aimed to evaluate the distribution and spatial correlation between the dendrometric parameters of the M. fistulifera plants and the physical attributes of the soil through geostatistics. The study was carried out at the Paulista State University (UNESP), in the city of Selvíria, MS, Brazil, in a typical dystrophic Red Oxisol with a clayey texture. The following properties were analyzed: for soil, penetration resistance, gravimetric moisture, particle density, and, for plants, circumference and diameter at breast height (measured at 130 cm above the ground), tree height, and total volume of the tree. An experiment grid was introduced with 35 sample points spaced 13 m x 13 m. Two soil samples were taken at each point, at 0.00 - 0.10 m and 0.10 - 0.20 m depth. Descriptive data analysis and spatial dependence analysis were carried out through semivariogram adjustments and kriging maps. The dendrometric properties of the species M. fistulifera and the soil gravimetric moisture content showed spatial dependence. The spherical semivariogram model best explained the spatial structure of circumference at breast height, diameter at breast height, tree volume, and soil gravimetric moisture. There was an emphasis on the correlation between the total volume of the tree as a function of the diameter at breast height, showing a moderate spatial dependence. Furthermore, the tree diameter at breast height proved to be a good indicator for determining the total height of the M. fistulifera tree.

Geostatistical Analysis of Apricot Shot Hole Disease and Influence Factors in District Nagar, Gilgit-Baltistan, Pakistan

Apricot (Prunus armeniaca L.) is the most common, high-income generative fruit and consequently plays an important part in the region's socio-economic growth. However, apricots are susceptible to several diseases and pests that cause lower yields and significant financial losses, including apricot shot hole disease brought on by Wilsonomyces carpophilus, a polyphagous fungus, creates a serious threat to stone fruits, particularly the apricot tree. The current study used descriptive statistics and geostatistical approaches to evaluate the geographical distribution of the apricot shot hole disease (5 valleys, 30 orchards, and 480 apricot trees) in district Nagar, Gilgit-Baltistan, Pakistan. From each apricot tree, 150 apricot fruits, a total of 72,000, were randomly selected to assess the incidence, disease index, and severity. Results revealed that the disease is spatially distributed within the study area, varying within valleys and orchards. The range of disease prevalence was 50.0-67.70%, incidence 56.97-64.01%, index 32.49-40.56% and mean severity 0.21-0.24%. Geostatistical techniques were used to predict the spatial dependency class. Results revealed that disease prevalence had a nugget/sill ratio of 0.915, inferring weak spatial dependence, whereas incidence, disease index, and severity inferred moderate spatial dependence with nugget/sill ratios of 0.479, 0.628, and 0.393, respectively. Moreover, the spherical semivariogram model, trend analysis graph, and GIS maps indicate the region's spatial distribution of apricot shot hole disease. This information linked to metrological data (temperature, precipitation, and relative humidity) seemingly favored the apricot shot hole disease development during the growing period. Evaluation of geostatistical mapping strategies in monitoring spatial distributions of apricot shot hole disease in a field setting will improve the decision support for disease management, selection of resistant variety, and improve sanitation condition of apricot orchards. Continuous climate and pest monitoring are essential for creating an efficient disease warning system for farmers and other organizations involved in agriculture to prevent future plant epidemics.

Spatial Variability of Groundwater Quality for Freshwater Production in a Semi-Arid Area

Presenting groundwater quality assessment for different usages using one index is helpful to monitor the quality of this invaluable resource and reduce the cost of freshwater production, particularly in arid and semi-arid regions. The drinking groundwater quality index (DGWQI) is one the best indicators for groundwater quality assessment. Therefore, the purpose of the present research was to assess and map the groundwater quality of an aquifer for freshwater production in a semi-arid region, using GIS-based spatial analysis of DGWQI. For this goal, mean data from 70 wells collected during 2010–2018 were used. Results showed that total dissolved solids (TDS), electrical conductivity (EC), and total hardness (TH) had the highest impact on groundwater quality that exceed the permissible range for drinking purposes. Results also revealed that 42% of samples had a DGWQI value between 0 and 100 (appropriate quality class). Sensitivity analysis determined that Mg2+, EC, and TDS with highest mean variation indexes of 18.98, 20.68, and 19.04, respectively, were the most sensitive parameters in the calculation of DGWQI. According to R2 and RMSE, the ordinary kriging and spherical semi-variogram model had good performance for spatial analysis for all DGWQI, Mg2+, EC, and TDS. The DGWQI map showed that in the southern parts the groundwater (50% of the area) had unsuitable quality for drinking.

PEDOTRANSFER FUNCTION TO ESTIMATE THE SOIL STRUCTURAL “S” INDEX AND SPATIAL VARIABILITY IN AN OXISOL WITHIN A LIVESTOCK FARMING SYSTEM

ABSTRACT Various attributes are often required to build soil quality indicators. However, determining these attributes is time-consuming and requires several specific devices. Thus, it is desirable to develop indexes that express soil quality based on easily determined attributes. The objective of this study was to investigate the spatial variability of the S index and its correlation with other physical and chemical soil attributes to generate a pedotransfer function for estimating the S index. A georeferenced sampling mesh covering 1.4 ha and employing 71 points was installed. The soil samples were collected in 0.00–0.15 m and 0.15–0.30 m deep layers to determine the physical and chemical attributes. The results indicated that the S index was correlated with porosity, carbon stocks, cation exchange capacity, and particle size fractions. However, macroporosity, microporosity, and sand content were the most suitable attributes for the construction of pedotransfer functions. A principal component analysis indicated that the S index was representative of 9.4% and 11.5% of the total variability in the dataset in the respective soil layers. Spherical semivariogram models showed that the S index was spatially dependent and ranged between 84–188 m. The S index maps estimated by the pedotransfer function resemble the observed S values; therefore, the function can be applied in spatial variability studies.

Eucalyptus spp. volume determined through geospatial interpolation

Planning and managing forest resources is fundamental for increasing the productivity of forest products and by­products. This study evaluates the use of the ordinary kriging interpolator in the estimation of standing wood vol­ume in Eucalyptus spp. plantations in Paragominas city, Para State, Brazil. For this, we used data from the precut forest inventory, processed by the ordinary kriging method. Measurements took place in 27 georeferenced fields, with plots of 504 m², where trees were rigorously cubed. Geostatistical modeling was performed using ordinary kriging (spherical semivariogram model) with isotropic and anisotropic interpolators, seeking to map the volume of the area. To this end, experimental semivariograms were obtained through the Geostatistical Analyst tool of ArcGIS 10.1 software. Interpolation presented satisfactory results regarding plot volume mapping, in which the average volume fitted by ordinary kriging was statistically similar to the average volume obtained by the forest inventory, 11.3517 m³ and 11.3842 m³, respectively, showing efficiency with an adjusted coefficient of determina­tion of 0.94. The use of ordinary kriging interpolation proved to be effective in plot volume estimation in the forest stands under study.

Characterization of spatial variability of vertisol micronutrients by geostatistical techniques in Deccan Plateau of India

In vertisols, accounting for the spatial variability of micronutrients is important for sustainable agriculture. In this study, the assessment of spatial variability maps is carried out by the geostatistical technique in SpaceStat 4.0®. A total of 68 random soil samples were collected from small-scale agricultural lands from Kalaburagi, Karnataka, India. The chemical analysis for iron (Fe), manganese (Mn), copper (Cu), and zinc (Zn) was carried out in microwave plasma-atomic emission spectroscopy. The coefficient of variation (CV) showed different micronutrients variability (CV > 35%). The significant correlation is among Cu with Fe and Mn (r = 0.753 and 0.258, respectively). The Box–Cox transformation converted the raw data to normal distribution efficiently. Spherical semivariogram model defined the spatial structure for all micronutrients. The nugget/sill ratio specifies that the Zn showed strong spatial dependence and rest micronutrients moderate. Ordinary kriging is applied for generating maps. The spatial variability maps exhibited different distribution pattern; maps generated are utilized as initial guidance for site-specific management practices and the amount of fertilizer application rate planned in the vertisols. The obtained range and spatial distribution maps act as the baseline in this region for administration planners.

Spatiotemporal evaluation of Xiphopenaeus kroyeri (Heller, 1862) abundance in a region of the Southwest Atlantic shelf

Several activities, such as port and fishing operations, exploit coastal marine ecosystems around the world, creating significant environmental impact. Since these activities often overlap spatially, they can interfere with each other. This paper aims to evaluate the spatiotemporal variability in the relative abundance of the Atlantic seabob shrimp (Xiphopenaeus kroyeri), caught in the central coastal marine region of the State of São Paulo, Brazil, where port activity takes place next to environmental protection areas. Commercial fishing data from a 6-year period (2009–2014) were used to perform this evaluation. Generalized linear models (GLMs) were applied to the catch and effort data, and model coefficients were used to calculate the catch per unit effort (CPUE, kg/fishing day), which was standardized per square nautical mile and year. Semivariogram and kriging techniques were used to evaluate the existence of spatial dependence in the data and to estimate the standardized CPUE in unsampled areas. Isotropic spherical semivariogram models were fitted using the least squares method, and they showed a pattern of spatial dependence for all years. Variation in Atlantic seabob relative abundance within the vessel demarcation area of the Port of Santos was similar to that in the area used for dumping dredged material, although the latter was fished more frequently, thus creating larger catches. Therefore, commercial fishing data can be used to evaluate the spatiotemporal variation in the relative abundance of marine species in coastal regions. The results suggest that fishing and port activities interfere with one another in the region of the Port of Santos.

Mapping Azteca barbifex Forel (Hymenoptera: Formicidae) dispersal in georeferenced orange (Citrus sinensis [L.] Osbeck) orchard in the Eastern Amazon, Brazil

Damage to orange trees (Citrus sinensis [L.] Osbeck) as a result of Azteca barbifex Forel infestation in orchards of the Eastern Amazon, Brazil, has caused concern among farmers. The presence or absence of A. barbifex ants was sampled in a georeferenced orchard with 4600 plants from June 2007 to June 2008, and the six most representative months of infestation were analyzed. Dispersion patterns of A. barbifex were studied using semivariograms to determine the most suitable spatial distribution model of the species and kriging maps were drawn. Spherical semivariogram models best fit the spatial dispersion patterns of A. barbifex, showing that the patterns of damage resulted from attack foci. The density of A. barbifex nests increased during the dry period, with drastic reduction in the rainy season. The results show that geostatistics is a useful tool for evaluating A. barbifex spatial and temporal distributions and to define reliable sampling plans for use in integrated pest management.

Spatial Analysis of Granulometry and Humic Substances of an Ecological Restoration Area in the Brazilian Atlantic Forest

The present study aimed at spatially analyzing granulometry and humic substances (HS) of an ecological restoration area in the Brazilian Atlantic Forest. In the study area, 49 points were systematically distributed, and soil was collected at each point, at a depth of 0-10 cm. The soil attributes evaluated were: total clay, total sand, silt, and organic carbon in the fulvic acid fraction (C-FAF), humic acid fraction (C-HAF), and humic fraction (C-HUM). Semivariogram analysis was performed for each variable, with spherical, exponential, and Gaussian semivariogram models tested. In order to choose the best model, the following parameters were used: Akaike Information Criterion (AIC), determination coefficient (R²), root mean square error (RMSE), Willmott concordance index (d), and degree of spatial dependence (DSD %). Mapping was performed using the Ordinary Kriging method. The attributes showed spatial dependence. The spherical model was adjusted for all variables. From spatialization, the attributes, except clay, showed a heterogeneous distribution, which may be due to the high range value. The distribution of soil texture depended mainly on soil class and relief. A correlation was observed in the distribution of the silt and C-HUM attributes. The ecological restoration area had HS content values close to those reported in studies conducted under conditions similar to those in the present study. Therefore, it appears that the study area is probably undergoing recovery; however, a thorough and continuous study of the area is essential for confirmation.

Modelling the spatial dependence of the rainfall erosivity index in the Brazilian semiarid region

Abstract: The objective of this work was to model the spatial dependence and to map the rainfall erosivity index (EI30) in the semiarid region of Brazil. Registers of monthly erosivity from 210 rainfall stations were used, with daily time series equal to or greater than 15 years. Based on the values of the EI30, a spatial dependence model was made by adjusting the semivariogram. From the semivariogram models, erosivity isoline maps were generated with a kriging interpolator. According to the historical data series, the maximum monthly average value of the EI30 was observed in March, and the annual value ranged from 1,439 to 5,864 MJ mm ha-1 per year, classified as low and moderate, respectively. The highest EI30 values were obtained in the northern and southern extremes of the semiarid region. Average spatial dependence was observed for rainfall erosivity, in most months, especially with the spherical semivariogram model. The range of erosivity varied from 62 to 1,508 km for the monthly EI30 and was of approximately 1,046 km for the annual one. The applied model, with the validation of the semivariograms using the jackknife test, allows the spatialization of the EI30 for the semiarid region of Brazil.

A Spatial Analysis of GEOID03 and GEOID09 in Connecticut

Abstract The National Geodetic Survey (NGS) recommends using a hybrid geoid model to derive orthometric heights from ellipsoid heights. The accuracy of GEOID03 and GEOID09 were assessed independently in Connecticut. The present research analyses the spatial behavior of residuals derived from the comparison of differential levelled NAVD 88 orthometric heights and GPS-derived orthometric heights (using GEOID03 & GEOID09) at 72 benchmarks in Connecticut. Both geometrical and geostatistical analyses were performed on the residuals. A planar regression model indicates a weak spatial relation for residuals derived from GEOID03. This weakness was not noted in the analysis of residuals derived from GEOID09. Results of a four-parameter regression model does not indicate any need for a correction surface. A kriging surface was created with a fitted spherical semivariogram model and suggests GEOID09 captures more spatial variability of geoid undulation than GEOID03 in Connecticut.

Geodetic mass balance record with rigorous uncertainty estimates deduced from aerial photographs and lidar data – Case study from Drangajökull ice cap, NW Iceland

Abstract. In this paper we describe how recent high-resolution digital elevation models (DEMs) can be used to extract glacier surface DEMs from old aerial photographs and to evaluate the uncertainty of the mass balance record derived from the DEMs. We present a case study for Drangajökull ice cap, NW Iceland. This ice cap covered an area of 144 km2 when it was surveyed with airborne lidar in 2011. Aerial photographs spanning all or most of the ice cap are available from survey flights in 1946, 1960, 1975, 1985, 1994 and 2005. All ground control points used to constrain the orientation of the aerial photographs were obtained from the high-resolution lidar DEM. The lidar DEM was also used to estimate errors of the extracted photogrammetric DEMs in ice- and snow-free areas, at nunataks and outside the glacier margin. The derived errors of each DEM were used to constrain a spherical semivariogram model, which along with the derived errors in ice- and snow-free areas were used as inputs into 1000 sequential Gaussian simulations (SGSims). The simulations were used to estimate the possible bias in the entire glaciated part of the DEM and the 95 % confidence level of this bias. This results in bias correction varying in magnitude between 0.03 m (in 1975) and 1.66 m (in 1946) and uncertainty values between ±0.21 m (in 2005) and ±1.58 m (in 1946). Error estimation methods based on more simple proxies would typically yield 2–4 times larger error estimates. The aerial photographs used were acquired between late June and early October. An additional seasonal bias correction was therefore estimated using a degree-day model to obtain the volume change between the start of 2 glaciological years (1 October). This correction was largest for the 1960 DEM, corresponding to an average elevation change of −3.5 m or approx. three-quarters of the volume change between the 1960 and the 1975 DEMs. The total uncertainty of the derived mass balance record is dominated by uncertainty in the volume changes caused by uncertainties of the SGSim bias correction, the seasonal bias correction and the interpolation of glacier surface where data are lacking. The record shows a glacier-wide mass balance rate of Ḃ = −0.26 ± 0.04 m w.e. a−1 for the entire study period (1946–2011). We observe significant decadal variability including periods of mass gain, peaking in 1985–1994 with Ḃ = 0.27 ± 0.11 m w.e. a−1. There is a striking difference when Ḃ is calculated separately for the western and eastern halves of Drangajökull, with a reduction of eastern part on average ∼ 3 times faster than the western part. Our study emphasizes the need for applying rigorous geostatistical methods for obtaining uncertainty estimates of geodetic mass balance, the importance of seasonal corrections of DEMs from glaciers with high mass turnover and the risk of extrapolating mass balance record from one glacier to another even over short distances.

Characterizing spatial variability of soil properties in salt affected coastal India using geostatistics and kriging

Soil salinization is a major problem affecting 955 Mha globally and 7 Mha in India. Soil properties vary spatially and knowing the extent of spatial variability of soil physicochemical characteristics is highly essential for management of these soils and crop cultivation. This study was conducted in salt-affected coastal parts of eastern India, with the following objectives: (i) to explore the spatial variability of soil properties (soil electrical conductivity (ECe), soil pH, soil organic carbon (SOC), available soil nitrogen, available soil phosphorus, and available soil potassium) and fitting the semivariogram models; (ii) to estimate the values of soil properties at unsampled locations using geostatistical tools; and (iii) to prepare the spatial maps of soil properties using parameters of best fit semivariogram model and interpolation by ordinary kriging technique. A total of 132 soil samples were collected. Gaussian, exponential, circular, spherical, K-Bessel, and spherical semivariogram models were found to be the best fit for assessing the spatial variability in ECe, soil pH, SOC, available soil nitrogen, available soil phosphorus, and available soil potassium, respectively. The best fit model parameters were used to create the spatial maps for these soil properties by ordinary kriging. It was concluded that geostatistical and kriging tools can be used to estimate the value of soil properties at unsampled locations and ultimately to develop spatial maps for site-specific nutrient management.

SOIL MOISTURE SPACE-TIME ANALYSIS TO SUPPORT IMPROVED CROP MANAGEMENT

The knowledge of the water content in the soil profile is essential for an efficient management of crop growth and development. This work aimed to use geostatistical techniques in a spatio-temporal study of soil moisture in an Oxisol in order to provide that information for improved crop management. Data were collected in a coffee crop area at São Roque de Minas, in the upper São Francisco River basin, MG state, Brazil. The soil moisture was measured with a multi-sensor capacitance (MCP) probe at 10-, 20-, 30-, 40-, 60- and 100-cm depths between March and December, 2010. After adjusting the spherical semivariogram model using ordinary least squares, best model, the values were interpolated by kriging in order to have a continuous surface relating depth x time (CSDT) and the soil water availability to plant (SWAP). The results allowed additional insight on the dynamics of soil water and its availability to plant, and pointed to the effects of climate on the soil water content. These results also allowed identifying when and where there was greater water consumption by the plants, and the soil layers where water was available and potentially explored by the plant root system.

Continuidade espacial da condutividade hidráulica saturada do solo na bacia hidrográfica do Alto Rio Grande, MG

A distribuição espacial da condutividade hidráulica saturada (k0) é essencial para estudos relacionados a erosão do solo e escoamento superficial. Este trabalho objetivou estudar a continuidade espacial do atributo hidrológico do solo k o na Bacia Hidrográfica do Alto Rio Grande, MG, e realizar seu mapeamento por meio de técnicas geoestatísticas, utilizando dados sem e com transformação logarítmica. Os modelos de semivariograma esférico e exponencial foram ajustados ao semivariograma experimental pelo método dos Mínimos Quadrados Ordinários (MQO) e Mínimos Quadrados Ponderados (MQP). Técnicas de validação cruzada e preditiva foram aplicadas para a escolha do melhor modelo. Os resultados indicaram que, ao se trabalhar com o logaritmo dos dados de k o, houve redução de pontos discrepantes e de anisotropia, entretanto os valores mais elevados de grau de dependência espacial foram observados nos dados sem transformação. O ajuste do modelo esférico, por meio do método MQO, foi o que produziu melhor desempenho na modelagem da continuidade espacial de k0. Os mapas gerados a partir dos dados sem e com transformação logarítmica não apresentaram o mesmo comportamento na distribuição espacial dos valores, e o mapa dos dados na forma transformada teve o melhor detalhamento da distribuição espacial.

Variabilidade espacial das frações primárias e agregados de um Argissolo no estado do Ceará

A variabilidade espacial tem sido uma das preocupações de estudiosos desde o inicio do século, sendo o sistema de manejo do solo um fator que interfere na distribuição espacial das propriedades físicas do mesmo. O objetivo do trabalho foi caracterizar e determinar a variabilidade espacial de atributos físicos de um Argissolo Amarelo, utilizando métodos geoestatísticos e confecção de mapas de superfície pelo método da Krigagem, em duas camadas de solo. Foram realizadas coletas de solo em intervalos regulares de 2,5 m numa área de 400 m², sendo 81 pontos em cada uma das camadas (0-0,2 m e 0,2-0,4 m), totalizando 162 amostras que foram submetidas às determinações físicas e os dados obtidos analisados pela estatística descritiva para verificar a normalidade da distribuição de freqüência e posteriormente submetidos à geoestatística. O modelo de semivariograma que melhor se ajustou aos dados, na maioria das variáveis, foi o esférico. Verificou-se efeito pepita puro para as variáveis grau de floculação, na profundidade de 0-0,2 m e os agregados estáveis, na profundidade de 0,2-0,4 m.

Continuidade e distribuição espacial da umidade do solo em bacia hidrográfica da Serra da Mantiqueira

O entendimento associado à continuidade espacial da umidade do solo é imprescindível para compreensão e predição de fenômenos relacionados aos processos hidrológicos. A metodologia geoestatística tem sido empregada muitas vezes, para estudos desta natureza. Neste contexto, o semivariograma é utilizado para modelar a continuidade espacial de uma variável para, seu posterior mapeamento. O objetivo no presente estudo foi modelar a continuidade espacial da umidade do solo em uma bacia hidrográfica experimental na região da Serra da Mantiqueira, Minas Gerais, e posteriormente o mapeamento da umidade do solo ao longo do tempo. Os modelos de semivariograma exponencial, esférico e gaussiano, foram ajustados por Mínimos Quadrados Ordinários, Mínimos Quadrados Ponderados e Máxima Verossimilhança, testando sua aderência pelo grau de dependência espacial e validação cruzada. O modelo exponencial apresentou melhor ajuste em 59% dos casos. O grau de dependência espacial médio foi de 88%, conferindo forte grau de dependência à estrutura de continuidade espacial. O mapeamento da umidade do solo mostrou comportamento fortemente associado ao uso do solo e condições topográficas.

Spatial and temporal variability in soil CO 2–C emissions and relation to soil temperature at King George Island, maritime Antarctica

Few studies have examined the effects of temperature on spatial and temporal trends in soil CO 2–C emissions in Antarctica. In this work, we present in situ measurements of CO 2–C emissions and assess their relation with soil temperature, using dynamic chambers. We found an exponential relation between CO 2 emissions and soil temperature, with the value of Q 10 being close to 2.1. Mean emission rates were as low as 0.026 and 0.072 g of CO 2–C m −2 h −1 for bare soil and soil covered with moss, respectively, and as high as 0.162 g of CO 2–C m −2 h −1 for soil covered with grass, Deschampsia antarctica Desv. (Poaceae). A spatial variability analysis conducted using a 60-point grid, for an area with mosses ( Sannionia uncianata) and D. antarctica, yielded a spherical semivariogram model for CO 2–C emissions with a range of 1 m. The results suggest that soil temperature is a controlling factor on temporal variations in soil CO 2–C emissions, although spatial variations appear to be more strongly related to the distribution of vegetation types.