Prof. Dr. D. Schröder
Page 1
GIS in Hydrology and Water ResourceManagement - ENWAT
Tutorial: ArcGIS Tools for Hydrology – Groundwater Vulnerability Map
In this tutorial, a groundwater vulnerability map will be created. Based on different factor
maps, e.g. groundwater depth, soil media, aquifer media, slope, and land cover an index map
will be created, where a higher index indicate a higher vulnerability. For the first four
parameters a deterministic approach will be used, i.e. the rating for the different input classes
has been fixed by expert knowledge. For the last one the rating will be determined based on
observed data. Most of the analysis will be based on raster maps. The study area is the Rems
watershed.
The main tasks of this tutorial are:
 Creating weight maps
 Using zonal statistics
 Combining different weight maps using raster algebra
As input data the following raster and vector layers are available:
 Groundwater table depth
 Soil media map
 Aquifer media map
 DEM
 Corine Land Cover map
 Well locations with measured NO2 +NO3 –N data
All data is with respect to the Datum of the Hauptdreiecksnetz and uses Gauss-Krüger
projection (zone 3). For all the raster map you will create, you should use the same extent and
resolution. As the DEM is already a grid this map should be used as reference.
Workflow:
Step 1: Calculating weight maps (deterministic method)
a) Depth to Water
For the groundwater depth there are only three classes. In the valley we have depths of 0-2.5m
or 2.5 -5m, where in all other areas the depths is more than 5m, typically less than 10m.
The rating for the depth of water is given by
Range (meter)
0 – 2.5
2.5 - 5
5 - 10
10 - 15
15 - 25
25 - 35
35+
Rating
10
9
7
5
3
2
1
With the overall weight of 5.
- rasterize the map using the depth attribute
- reclassify the map according to the table above
Prof. Dr. D. Schröder
Page 2
GIS in Hydrology and Water ResourceManagement - ENWAT
b) soil media
In the study area the dominant soils are clay loam, loam, nonshrinking clay, sandy loam,
shrinking clay, and silty loam. Some areas behave highly variable, and some are settlement
areas. The hydrological relevant class is coded in the attribute table in the column soil_class.
The rating for the soil media is given by
Type
Rating
Thin or Absent
10
Gravel
10
Sand
9
Peat
8
Shrinking and/or Aggregated Clay
7
Sandy Loam
6
Loam
5
Silty Loam
4
Clay Loam
3
Muck
2
Nonshrinking and Nonaggregated Clay 1
With the overall weight of 2.
- create a lookup table for relevant values of the study area using ArcCatalog and ArcMap.
Fix a meaningful value for those classes not specified in the table above.
- join the lookup table with the soil media data
- rasterize the map using the rating values
c) Aquifer Media
Here a classification of the permeability is already given in the column durchl_cla:
Class
2.2
3.1
3.2
4.1
4.2
5.1
5.2
6.1
rating
10
8
6
4
3
2
2
1
With the overall weight of 3.
- add a new field to the attribute table
- select the rows class by class and use the field calculator to assign the rating value to the
selected features
- rasterize the map using the rating values
d) Topography (Percent Slope)
The rating for the slopes is given in the following table:
Range
Rating
0 - 2
10
2 - 6
9
6 - 12
5
12 - 18
3
18+
1
With the overall weight of 1.
Prof. Dr. D. Schröder
Page 3
GIS in Hydrology and Water ResourceManagement - ENWAT
- create the slopes from the DEM
- reclassify the slopes according to the table above by setting the values in the classify dialog
of the reclassify tool manually
Remark: to create the rating maps different workflows have been used. This is to show that
there are many different tools available and that the same goal can be achieved using different
workflows. Of course all the methods are equivalent, so it is up to you to use the method
which suited you most in a real project!
Step 2: Calculating weight maps (statistical method)
So far the land cover has been not considered in our analysis. Of course it will affect the
vulnerability of the groundwater too. But how to set the ratings for the different land uses? If
there are observations, we can fix a rating scale using zonal statistics. Here the statistics of an
input map (mean, standard deviation etc.) for each zone defined e.g. by the same land cover,
is calculated.
The data set well contains measurements of the NO2 +NO3 –N ground-water quality in 1/100
milligrams per litre for 95 wells in the study area. We will use this data to define a rating for
the land cover using the CLC data set. We will calculate for each land cover the mean of the
NO2 +NO3 –N contamination to define a rating scale.
- rasterize the well data set using the n-value
- to simplify the analysis, we will only use three land cover classes:
 Urban/ settlement: CLC code between 100 and 199
 Agriculture: CLC code between 200 and 299
 Forest: CLC code between 300 and 399
Add a new field to the CLC attribute table and populate it with the first digit of the CLC
2000 code column.
- use Zonal statistics from the Spatial Analyst tool box. The zones are defined by the new
attribute of the CLC data set where as the value raster is defined by the rasterized well layer.
Use the Mean to graph.
- from the calculated mean values, you can now fix the rating values (remark: in a real project,
you should first test if the differences in the mean values are statistically significant. For that
e.g. the Student’s test can be used. All the input value you need, i.e. mean values and
variances, have been calculated in the table by the zonal statistics tool. So by exporting the
table to Excel, the test can easily be done)
- rasterize the map using the new rating attribute
- now the rating for the land cover can be combined with the other data to a vulnerability map
using e.g. an overall weight of 5.
Step 3: Calculating the index map
Now we can combine the 5 input layers to get the final index map
index = groundwaterdepth * wD + soilmedia * wS + aquifer * wA + slope * wSL
+ landcover * wL
using the map calculator:
Prof. Dr. D. Schröder
Page 4
GIS in Hydrology and Water ResourceManagement - ENWAT