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Spatial 3D-visualization of groundwater quality Gooi en Vechtstreek.

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Presentation on theme: "Spatial 3D-visualization of groundwater quality Gooi en Vechtstreek."— Presentation transcript:

1 Spatial 3D-visualization of groundwater quality Gooi en Vechtstreek

2 How does it work? Temporal attribute Spatial attributes Qualitaive attribute s Interpolation 3D scatter plot (...estimation of chemical species’ concentrations at unsampled locations, using the available data at sampled locations) (Data selection...)

3 Problem definition: Data related factors Data related factorsDescriptionProblemQuestion Temporal heterogeneity Our dataset contains data from 1900 to 2006 Groundwater system conditions changed over this last century(biblio) Which are the temporal clusters that best represent the current conditions? Dimension of the dataset More data = more dense scattering = more reliability of the 3D view Most of the chemical species’ datasets are not abundant Are the most abundant chemical species correlated with the ones with less data? Monitoring points with multiple samples Certain scattered points are associated with 2 or more concentration values These concentrations have same spatial location (x,y,z), but different age (dd/mm/yy) For all of these points it’s opportune to select the average value of concentration? Or the youngest? 1) Which are the datasets that best represent the current groundwater quality?

4 Problem definition: Software related factors Software related factors DescriptionProblemQuestion Spatial heterogenity of scattered data The most homogeneous is the spatial distribution of the points, the most reliable is the interpolation (BIBLIO). Our data are randomly located (sometimes clumped points, sometimes blank areas). How can we estimate the reliability of the 3D visualizations in all the parts of the studied area? Different frequency of ranges of concentration Some ranges of concentration are more frequent than others. The quality of the interpolation can be influenced by the frequency of the ranges of concentration. Which are the most suitable algorithms to represent frequent and less frequent ranges of concentration? 2) What is the best way (interpolation) to visualize in 3D the groundwater composition in Gooi and Vechtstreek?

5 Data related factors:

6 Dimension of the datasets and temporal heterogeneity According to Du (2008) and Schot and van der Wal (1992) it is reasonable to assume that the hydrological conditions did not vary appreciably over the last four decades. Therefore, it’s evident that cluster 4 and 5 are the most suitable as they best represent the current groundwater quality

7 Visualizing the concentrations of chloride, and bicarbonates (the most abundant in terms of cluster 4 and 5) it’s beneficial to represent the distribution of several other species according to their correlation factor (PEARSON CORRELATION ANALYSIS) Phosphates need to be included because of their particular environmental interest: 1)strictly related with one of the most preponderant land use: agriculture 2)Have a great influence on the type of wetland plant species

8 In light of these considerations, in order to reproduce the most accurate visualizations of the current groundwater quality, it’s opportune to : 1)create new datasets of Cl, HCO 3 and PO 4 containing temporal clusters 4 and 5 (beneficial to increase the spatial coverage) 2)select the average value of concentration for all the data with the same coordinates (X, Y, depth) (as it reinforces the robustness of the concentration values). Research question 1 “Which are the datasets that best represent the current groundwater quality?” answered

9 Software related factors:

10 1) Spatial heterogenity of the scattered data The variation of the density of the scattered points along the studied area, is one of the main factors that influences the reliability of the visualizations.

11 Reliability of chlorine’s 3D visualization

12 Reliability of carbonates’ 3D visualization

13 Reliability of phosphates’ 3D visualization

14 2) Performances of the interpolators: The performance of these interpolation methods depend also on the value of concentration they are visualizing in the form of isosurfaces. 3 interpolators will be tested: Inverse Distance (ID) Local Polynomial (LP) Data Metric (DM)

15 Performance of the interpolators: [Cl]=40mg/l Cl concentration from cluster 1 1) For ID, many violet/blue points are spread far away from the isosurfaces that should represent them. 2)LP produces isosurfaces that better interpret the information of the scattered points at low and intermediate depth 3)LP interpolation reproduce isosurfaces in areas where the concentration of the data tend to be zero 4)DM is strictly correlated to ID...but in some cases lacks precision: The violet and blue scattered points represent ranges of concentration from 15 to 65 mg/l. ABUNDANT AND SPREAD

16 Performance of the interpolators: [Cl]=1500mg/l The yellow and green points represent ranges of concentration from 1200 to 1700 mg/l. FEW AND LESS DENSE POINTS 1)ID algorithm interpolates successfully our dataset as most of the yellow/green points are located in proximity to the isosurfaces 2) LP’s isosurfaces are correlated with the ones of ID. But not as precise.... 3) LP extends isosurfaces also in correspondence of blank areas 4) DM’s isosurfaces don’t not correctly reproduce the concentration 1500 mg/l

17 ... for carbonates and phosphates the interpolation methods behave in the same way as we observed for chloride: INVERSE DISTANCE: 1) represents succesfully ranges of concentration with low frequency. 2) performs the best estimation at high levels of depth (very low/ low density) LOCAL POLYNOMIAL: 1) succesfully represents ranges of concentration with high frequency in correspondence to low depth values (intermediate/high density) DATA METRIC: failed in both of the cases. We do not exclude the possibility that both of the suitable interpolators (ID and LP), could be used in the same visualization in order to provide the most reliable 3D groundwater quality view in all of the cells.

18 Therefore, knowing the behaviour of each interpolation method allow us to assign to each cell the most appropriate interpretation of all the ranges of concentrations. [Cl]= 40 mg/l[Cl]= 1500mg/l [HCO 3 ]= 80mg/l[HCO 3 ]=550mg/l [PO 4 ]=0.8mg/l[PO 4 ]=2mg/l Research question 2 “What is the best way (interpolation) to visualize the 3D groundwater quality in the studied area?” answered

19 Thanks to data analysis and software analysis we obtained the most reliable 3D visualizations of the groundwater quality in the area of Gooi en Vechtstreek with the available dataset Conclusions:


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