Spatial 3D-visualization of groundwater quality Gooi en

Vechtstreek

How does it work?Temporal attribute Spatial attributesQualitaive attribute s

Interpolation

3D scatter plot

(...estimation of chemical species’ concentrations at unsampled locations, using the available data at sampled locations)

(Data selection...)

Problem definition: Data related factorsData related factors Description Problem Question

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?

Problem definition: Software related factors

Software related factors

Description Problem Question

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?

Data related factors:

Dimension of the datasets and temporal heterogeneity

NO2 SIO2 PO4 MN Mg Fe NO3 K NH4 Na SO4 Ca HCO3 Cl

Cluster 1(1901-1921) 42 6 26 19 35 42 43 1 43 3 39 35 9 73

Cluster 2(1922-1943) 205 107 10 191 112 199 211 1 191 77 215 118 223 230

Cluster 3(1944-1964) 439 228 245 428 237 442 444 5 429 222 444 224 459 497

Cluster 4(1965-1985) 1495 1032 1060 2007 2021 2121 3106 2902 3057 3035 2758 2982 3066 3126

Cluster 5(1986-2006) 94 1464 2639 2621 2919 2657 2000 2916 2451 2926 2820 2929 2884 2935

500

1500

2500

3500

4500

5500

6500

7500

Number of data for each chemical species

Num

ber o

f dat

a

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

• 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

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, HCO3 and PO4 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

Software related factors:

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.

Reliability of chlorine’s 3D visualization

Reliability of carbonates’ 3D visualization

Reliability of phosphates’ 3D visualization

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)

Performance of the interpolators: [Cl]=40mg/lCl 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

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

... 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.

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 [HCO3 ]= 80mg/l [HCO3]=550mg/l

[PO4]=0.8mg/l [PO4]=2mg/l

Research question 2 “What is

the best way (interpolation) to

visualize the 3D groundwater

quality in the studied area?”

answered

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: