Mapping groundwater quality in the Netherlands

Pebesma, Edzer; Kwaadsteniet, J.W. de

doi:10.1016/s0022-1694(97)00027-9

Cited by 58 publications

(30 citation statements)

References 36 publications

(66 reference statements)

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“…Geo-statistical methods can be used to describe the spatial variability of environment and reveal the spatial heterogeneity and spatial pattern of natural phenomena (Pebesma et al，1997). The semi-variogram model and Kriging interpolation are the two main geo-statistical methods used in this analysis (Jin et al,1999;Sousa et al, 1999;Desbarats et al, 2002;Vijendra et al, 2004;Tong et al, 2007;Wang et al, 2007;Yue et al, 2009;Hu et al2001Hu et al , 2009Husam,2010).…”

Section: Geo-statistical Methods and Processingmentioning

confidence: 99%

An Analysis on the Inter-annual Spatial and Temporal Variation of the Water Table Depth and Salinity in Hetao Irrigation District, Inner Mongolia, China

Yang

et al. 2011

Computer and Computing Technologies in Agriculture IV

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Abstract. Long-term Yellow River irrigation and the unique natural conditions in the Heitao Irrigation District (HID) Inner Mongolia, China, has led to serious environmental problems such as the shallower groundwater table and soil secondary salinization, etc. The conflicts among socio-economic development, water shortage and environmental degradation have become increasingly critical. By using the statistical methods, geo-statistical methods and ArcGIS9.0, we analyze the temporal and spatial variation of depth to water table (DWT) and groundwater salinity in the three different irrigation seasons in 2001, 2002 and 2003 respectively. The results show that DWT and groundwater salinity has formed a ribbon distribution after the long-term Yellow River irrigation. DWT is medium spatial correlative and the average spatial autocorrelation distance is 18.5km; the groundwater salinity is strong spatial correlative and the average spatial autocorrelation distance is 12.5km. The inter-annual distribution of DWT and groundwater salinity in 2001 is quite similar with it in 2002 and 2003. The DWT in western area, eastern area and a small part of middle area are shallower than other area in HID. The average DWT in March reached maximum and its minimum is in November each year. There are two high salinity degree zones (M>5000mg/l and even some other M>30000mg/l). The shallower groundwater salinity in the southeast and northwest are higher than that of in the middle part of HID. The shallower water table depth is, the higher the salinity of groundwater will be; the deeper water table depth is, the lower the salinity of groundwater will be.

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Section: Geo-statistical Methods and Processingmentioning

confidence: 99%

An Analysis on the Inter-annual Spatial and Temporal Variation of the Water Table Depth and Salinity in Hetao Irrigation District, Inner Mongolia, China

Yang

et al. 2011

Computer and Computing Technologies in Agriculture IV

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“… Using glyphs (i.e., small icons), the shape, size or colour of which can be mapped to different components of an uncertain variable. A special case of this is the use of "confidence triangles" (e.g., [36]), which visualize the estimated spread of data by dividing the image into squares, each of which is divided into two triangles. The lower triangle is assigned a colour representing the lower bound of the variable, and the upper triangle is coloured according to the upper bound of the variable.…”

Section: Extensions To the Symbology Encoding Standardmentioning

confidence: 99%

Communicating Thematic Data Quality with Web Map Services

Blower

Masó

Diaz

et al. 2015

IJGI

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Geospatial information of many kinds, from topographic maps to scientific data, is increasingly being made available through web mapping services. These allow georeferenced map images to be served from data stores and displayed in websites and geographic information systems, where they can be integrated with other geographic information. The Open Geospatial Consortium's Web Map Service (WMS) standard has been widely adopted in diverse communities for sharing data in this way. However, current services typically provide little or no information about the quality or accuracy of the data they serve. In this paper we will describe the design and implementation of a new "quality-enabled" profile of WMS, which we call "WMS-Q". This describes how information about data quality can be transmitted to the user through WMS. Such information can exist at many levels, from entire datasets to individual measurements, and includes the many different ways in which data uncertainty can be expressed. We also describe proposed extensions to the Symbology Encoding specification, which include provision for visualizing uncertainty in raster data in a number of different ways, including contours, shading and bivariate colour maps. We shall

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“…This method has been applied to similar problems (e.g. Myers et al, 1982;Pebesma and De Kwaadsteniet, 1997). Information on land use and soil type were taken from topographical and soil maps, while information on phreatic levels, infiltration and seepage were taken from Schot (1989), and was treated as known variables.…”

Section: Case Studymentioning

confidence: 99%

Uncertainties in spatially aggregated predictions from a logistic regression model

Horssen

Pebesma

Schot

2002

Ecological Modelling

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This paper presents a method to assess the uncertainty of an ecological spatial prediction model which is based on logistic regression models, using data from the interpolation of explanatory predictor variables. The spatial predictions are presented as approximate 95% prediction intervals. The prediction model is based on logistic regression analysis of field data of a wetland area in the central parts of the Netherlands. The model predicts block average probability of occurrences of 78 wetland plant species for 500 m × 500 m blocks. The explanatory variables comprise groundwater chemistry, hydrological characteristics, and land use management. The uncertainty of the spatial model output is assumed to be a function of the uncertainty in the estimated regression coefficients and uncertainty in the interpolated values of explanatory variables. Monte Carlo analysis was used to assess the model output error due to uncertainty in both the regression coefficients and the explanatory variables. Correlation between errors in regression coefficients and spatial autocorrelation in explanatory variables are accounted for in the Monte Carlo analysis. Spatial patterns of the relative contribution of uncertainty of the regression coefficients to the total model uncertainty are presented. The patterns of the relative contributions of uncertainty to the total model uncertainty give information on the most effective way to reduce error, i.e. either by reducing uncertainty in the regression coefficients or in the interpolated input patterns. The spatial patterns and values of the 95% prediction intervals vary widely between species but are in general large and the relative contribution of the uncertainty of the regression coefficients is in general large (over 80%).

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Mapping groundwater quality in the Netherlands

Cited by 58 publications

References 36 publications

An Analysis on the Inter-annual Spatial and Temporal Variation of the Water Table Depth and Salinity in Hetao Irrigation District, Inner Mongolia, China

An Analysis on the Inter-annual Spatial and Temporal Variation of the Water Table Depth and Salinity in Hetao Irrigation District, Inner Mongolia, China

Communicating Thematic Data Quality with Web Map Services

Uncertainties in spatially aggregated predictions from a logistic regression model

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