1. Linking typology clustering to river N and P loads to the NA Ocean
LOICZVIEW clustering tool applied to the European North Atlantic coastal zone
Linking typology clustering to river N and P loads to the NA Ocean
The Hague, July 2001
Natacha Brion

2. Introduction European coast of the North Atlantic Ocean
Outlet of large river systems
(Rhine, Elbe, Seine, Loire, …)
Dense population
Industrialized
Intense agriculture
Systems extremely influenced by human activity
> Impact on ecological functioning of the coastal system
Objective:
Find a classification of the different European Atlantic coastal environments based on typology data that can reflect the extend of the N and P load to the coastal zone.

3. Procedure abstract 1) Preparing the data to cluster: 2) Clustering:
Choose most relevant typology data linked to N and P loads and extract them from the LOICZ-Typology data base.
Import the data to an Excel sheet for appropriate filtration CSV file
Extract from the Budget data base, corresponding sites with river N and P load data + add new data from literature.
4) Add budget data to typology data CSV file
2) Clustering:
In LOICZVIEW: upload the dataset and select ONLY the typology data. Cluster the data.
3) Evaluation:
Visualize the clustered data and overlay the N-load data for evaluation.

4. Selecting Data to cluster: Typology
Region: all Europe
Data:
Mean annual precipitation
Temperature
Basin population density
Basin area
Basin runoff
% crop land coverage
Target cells: Coastal cell
>create the data file and open in Excel
Filtration: Remove all cells outside the geographic area of interest + remove cells with basin runoff = 0.
>save as a .CSV file

6. Selecting Data to cluster: N and P loads
Import the LOICZ budget data base in an excel sheet
Remove all cells outside the considered area
Result: Only 2 budgeted small estuaries in Ireland…
Add new data from literature for other estuaries : Final 9 estuaries

7. Data of annual N and P loads are introduced in the previous typology data sheet at the corresponding CELL_ID
> save as a>CSV file

9. Clustering
Upload the data file and select all data except DIN and DIP load
Make an MDL to estimate best number of clusters:

10. Cluster 0:
high SUB BASIN AREA
Cluster 1:
low TEMP CRU ANNAVG,
high PRECIP CRU TOTAL,
low SUB BASIN AREA
Cluster 2:
high SUB BASIN POPULATION DENSITY,
Cluster 3:
high TEMP CRU ANNAVG,
low SUB BASIN AREA,
low SUB BASIN POPULATION DENSITY
Cluster 4:
high CELL PERCNT CROPLAND,
Cluster 5:
medium TEMP CRU ANN AVG,
low PRECIP CRU TOTAL,

11. NITROGEN LOAD 1000 moles > year Class 0 > 5 000 000 4 points
Cluster 0: high BASIN AREA
Cluster 1: low TEMP, high PRECIP, low BASIN AREA
Cluster 2: high POPULATION DENSITY, low BASIN AREA
Cluster 3: high TEMP, low BASIN AREA, low POPULATION DENSITY
Cluster 4: high PERCNT CROPLAND, low BASIN AREA, low BASIN POPULATION DENSITY
Cluster 5: medium TEMP , low PRECIP , low BASIN AREA
NITROGEN LOAD
1000 moles > year
Class 0 > points
Class points
Class
Class 3 <
Class #0 Class #1 Class #2 Class #3
Cluster #
Cluster #
Cluster #
Cluster #
Cluster #
Cluster #

12. PHOSPHORUS LOAD 1000 moles / year Class 0 >100 000 8 points
Cluster 0: high BASIN AREA
Cluster 1: low TEMP, high PRECIP, low BASIN AREA
Cluster 2: high POPULATION DENSITY, low BASIN AREA
Cluster 3: high TEMP, low BASIN AREA, low POPULATION DENSITY
Cluster 4: high PERCNT CROPLAND, low BASIN AREA, low BASIN POPULATION DENSITY
Cluster 5: medium TEMP , low PRECIP , low BASIN AREA
PHOSPHORUS LOAD
1000 moles / year
Class 0 > points
Class
Class 2 <
Class #0 Class #1 Class #2
Cluster #
Cluster #
Cluster #
Cluster #
Cluster #
Cluster #