QGIS Tutorial...WaterEurope QGIS step by step tutorial 6 3. Catchment Delineation We recommend structuring the layers in your GIS-Project using groups. You can add a new group with

WaterEurope QGIS step by step tutorial 2

QGIS

Frank Molkenthin, BTU Cottbus-Senftenberg

Version 12 Nov 2019 WaterEurope2020

QGIS Tutorial


1. Introduction

QGIS Description

QGIS is a free and open-source cross-platform Geographical Information System (GIS). Initiated in

2002 the software version 1.0 has been released in 2009, actual LTR version in 2019 is 3.4. QGIS is

an open system including GIS geo-algorithms libraries such as GDAL, GRASS and SAGE and a long

list of additional plugins to support general as well as discipline oriented functionalities such as

support for several hydroinformatics modelling tools.

Software Version

This tutorial is using QGIS version 3.4.6. Maderia (Long term release) English version on a Windows

operation system with metric system. The tutorial might work also with older or newer versions of

QGIS. Please check the release notes in case of questions or version problems. The tutorial is using

besides the standard QGIS / GDAL tools the core plugin GRASS 7 as basic additional library. This

tutorial is not using any other libraries and plugins. However, the proposed workflow is not the

only way of the GIS analysis of the VAR catchment region. Other combinations of geo-algorithms

from different libraries and plugins will lead to similar results.

Update 09 Nov 2019: Tutorial was tested successfully with QGIS 3.10 Coruna (Latest Release) incl.

GRASS 7.6.1. We recommend to use the Long term release of QGIS as most stable version. For

installation, please use the Standalone Installer Version (not the network installer) and always start

the software with the “QGIS Desktop *.*.* with Grass 7.*.*” version. Case study and Data

The QGIS tutorial is using the WaterEurope caste study area of the Var river catchment nearby the

city of Nice/France using the 300 m resolution DEM, the 300 m landuse data and the 2000m soil

type data for the Var Catchment. In addition, the available rainfall station data are used for the

pre-processing of the hydrological analysis.

Tasks and Goals

This QGIS tutorial integrates all pre-processing steps for catchment delineation and hydrological

pre-analysis of the Var catchment structured in five work packages, described in chapter 2.-6.:

Project Preparation

Catchment Delineation

Rainfall Station Location Analysis

Landuse Data Analysis

Soil Data Analysis

Individual Exercise WaterEurope 2020

The QGIS tutorial has been adapted as individual exercise for WaterEurope 2020 (orange colour).

The work load has been reduced by skipping the longest flow path part of Step 10 and proposal of

skipping repetition of analysis steps for more than 2 sub-catchments.


Result Data

Results of the work packages are structured in tables to be used later for the hydrological analysis.

The Var catchment will be subdivided and analysed by five sub-catchments and related geospatial

and thematic numbers have to be calculated. The individual exercise Water Europe 2020 is

reduced to two sub-catchments (Tinee and Upper Var) and skipping the longest flow path part.

Sub-Catchments: geospatial parameters

value / sub catchment Tinee Upper Var Esteron Vesubie Lower Var

area [km**2]

longest flow path [km]

avg. slope [m/m] in %

Rainfall Gauges: area contribution Thiessen polygons <-> sub-catchment in [km**2]

gauge / sub catchment Tinee Upper Var Esteron Vesubie Lower Var

Carros

Levens

Roquesteron

Puget

Guillaumes

St Martin

Landuse: area contribution landuse <-> sub-catchment in [km**2]


forest [km**2]

semi-natural [km**2]

agricultural [km**2]

artificial surfaces [km**2]

water bodies [km**2]

wetlands [km**2]

Soil Type Data: area contribution soil type <-> sub-catchment in [km**2]


clay loam

loam

sandy clay loam

sandy loam

silt loam

silty clay loam


2. Project Preparation

Step 1: Creation of a QGIS Project

Please start “QGIS Desktop with Grass” and create a new Project (Menu Project - > New). Create a

new folder for the project in your file system with a suitable folder name. This folder will be named

GISProjectFolder in this tutorial. Update the project properties (Project -> Properties), esp. the CRS

of the project to NTF (Paris) / France II (EPSG: 27572). Save the project in your GISProjectFolder

folder as *.qgs or *.qgz file (Project -> Save As). Please save your project after each of the following

workflow step (Project -> Save or floppy icon).

Step 2: Load DEM Data

Copy the provided 300 m resolution DEM (topo300.asc) in your GISProjectFolder folder. Add the

DEM to your project as raster layer (Layer -> Add Layer -> Add Raster Layer or related icon) using

the project CRS NTF (Paris) / France II (EPSG: 27572) as layer CRS (right Mouse click on layer after

adding: Set CRS -> Set Layer CRS).

Step 3: Load Rainfall Station Location

Copy the provided rainfall station point shape file (provided as *.zip archive) in your

GISProjectFolder folder and extract it. Add the shape file as vector layer to your project (Layer ->

Add Layer -> Add Vector Layer or related icon). The shape file is using the project CRS NTF (Paris) /

France II (EPSG: 27572) as layer CRS.

Step 4: Load Landuse Data

Copy the provided 300 m resolution landuse raster data file landuse.asc and the related legend

table landuse_legend.txt and the prepared reclassification file landuse_reclassification_rules.txt in

your GISProjectFolder folder. Add the DEM to your project as raster layer (Layer -> Add Layer ->

Add Raster Layer or related icon) using the project CRS NTF (Paris) / France II (EPSG: 27572) as

layer CRS (right Mouse click on layer after adding: Set CRS -> Set Layer CRS).

Step 5: Load Soil Type Data

Copy the provided 2000 m resolution soil type raster data file soiltype.asc and the related legend

table soiltype_legend.txt in your GISProjectFolder folder. Add the DEM to your project as raster

layer (Layer -> Add Layer -> Add Raster Layer or related icon) using the project CRS NTF (Paris) /

France II (EPSG: 27572) as layer CRS (right Mouse click on layer after adding: Set CRS -> Set Layer

CRS).

Step 6: Update the Visualization

Change the visualization style for the loaded data in a suitable way using the Menu (Layer ->

Properties or right Mouse click on layer for layer menu). The sub-mask Style / Symbology offers the

visualization option of the layer e.g. to singleband pseudocolor as render type. Please consider the

min/max values.


3. Catchment Delineation We recommend structuring the layers in your GIS-Project using groups. You can add a new group

with the related icon Add Group in the head of the layer panel. All generated layers of the

catchment delineation work package might be handled in a separated, specific group.

Step 7: Fill Sinks

Generate a depressionless DEM by filling the sinks of the DEM. You will find the related

geo-algorithm in Processing -> Toolbox. In the Processing Toolbox use GRASS -> Raster (r.*) ->

r.fill.dir. Input layer is the original DEM. As results store the Depressionless DEM and the Flow

direction in your folder as files with related names and keep the flag Open output file after running

algorithm active. This flag can be deactivated for Problem Areas, this result can be kept temporary.

GRASS tool Raster r.fill.dir

Input: original DEM

Result: DepressionlessDEM

FlowDirection

You can analyse the sinks by calculate the difference of the depressionless DEM and the original

DEM using the Raster Calculator.


Step 8: Catchment Delineation

The main catchment delineation operation in QGIS is using the GRASS tool r.watershed as

watershed basin analysis program. This command combines several working steps in one tool and

generates several output results. As elevation input use the depressionless DEM. The four optional

raster data input are not needed in this tutorial. The r.watershed tool is combined with calculation

of parameters for the Universal Soil Loss Equation (USLE), input and output for this feature can be

ignored/deactivated.

The minimum size of exterior watershed basin is a threshold, as smaller the value, as more

sub-basins/sub-catchments will be identified and vice versa. You can play with this input parameter

to analyse the impact to the sub-catchment results. A suitable threshold value for the Var DEM

with 300 m resolution is 2500 to receive the main five sub-catchments.

Please store as result files the number of cells that drain through each cell (flow accumulation),

drainage direction, unique label for each watershed basin (sub-watersheds) and stream segments.

All other output results can be ignored, feel free to generate and analyse them.

GRASS tool Raster r.watershed

Input: DepressionlessDEM

Parameter:

Minimum size of exterior

watershed basin: 2500

Single Flow Direction D8

Results: FlowAccumulation

DrainageDirection

StreamSegments

WatershedBasins


Step 9: Sub-Catchment Specification and Extraction

The different sub-catchment are specified and extracted from the full catchment based on the

results of Step 8: (unique labels for each watershed basin).

The watershed basins raster data result

distinguished the main sub-catchments Tinee,

Esteron, Upper Var and Vesubie. The remaining

watershed basins downstreams are dissolved

towards the sub-catchment Lower Var.

The extraction will be done using vector data

operations. The existing watershed basin raster data

is converted to vector data using the GRASS

command r.to.vect. We recommend to use shape

files (compatibility to other GIS tools) as vector data

file type to store the vector data polygons.

GRASS tool Raster r.to.vect

Input: WatershedBasins (result of Step 8:)

Parameter: feature type: area

Results: watershed basins polygons (as shape file)


The resulting polygons are extracted and dissolved towards the five sub-catchment polygons. The

polygon(s) of each of the five sub-catchments are selected (e.g. with the select feature icon or

within the Attribute Table) and using the GRASS operation v.extract copied to a new vector data set.

The selection can be also done via SQL command within the v.extract command. The individual

exercise Water Europe 2020 is reduced to two sub-catchments (Tinee and Upper Var), you can skip

the extraction for the other three sub-catchments Esteron, Vesubie and Lower Var.

GRASS tool Vector v.extract

Input: watershed basins polygons

Parameter: selection of the relevant polygon of the sub-catchment

Selected feature only flag activated

(or SQL statement e.g. cat = catchment_id)

Advanced Parameter:

Dissolve common boundary flag activated

v.out.ogr output type: area

Results: sub-catchment polygons


Lower Var sub-catchment part can be skipped in the individual exercise Water Europe 2020.

In opposite to the other four sub-catchment the Lower Var sub-catchments consists of several

polygons, all the related polygons will be extracted in one new vector data layer and dissolved to

one new vector data layer with only one polygon (menu Vector -> Geoprocessing -> Dissolve).

End of skipping part.

Finally, the five resulting vector data layers for the different sub-catchments are merged in one

vector data layer for later analysis. The resulting vector data layer contains five polygons. (In the

individual exercise Water Europe 2020 two polygons!)

QGIS Menu Vector -> Data Management Tools -> Merge vector Layers

Input: five sub-catchment polygon layers

Results: vector data layer with the five sub-catchment polygons


Step 10: Sub-Catchment Analysis

Each sub-catchment will be analysed individually. The area of the sub-catchments can be

calculated in the Attribute Table for the related vector data polygons using the Field Calculator with

the Create a new field flag and decimal number as output filed type as well as using as expression

$area (row number: Geometry). The same operation can be done for the merged vector data layer

to receive the five values in one attribute table.


To determine the mean slope of each sub-catchment the DEM of the sub-catchment has to be

extracted from the original DEM using Menu Raster -> Extraction -> Clip Raster by Mask Layer

QGIS Menu Raster -> Extraction -> Clip Raster by Mask Layer

Input: original topo300 layer

Parameter: sub-catchment polygon as mask layer

deactivated: Match the extend of the clipped raster ….

Result: DEM of the sub-catchment

The slope of the sub-catchments can be analysed with the menu tool Raster -> Analysis -> Slope.

QGIS Menu Raster -> Analysis -> Slope

Input: DEM of a sub-catchment

Parameter: activated: Slope expressed as percent instead of degrees

Results: slope of the sub-catchment

The average slope is accessible by the statistical values of the properties information of the

resulting raster data.


This part can be skipped in Water Europe 2020!!

The longest flow length of the sub-catchments can be analysed using the GRASS addon tool r.lfp.

Start the GRASS GIS 7 tool and create a new GRASS Mapset. This addon has to be installed in a

GRASS shell using the command g.extension in the shell or via the menu Settings -> Addons

extensions -> Install extensions from addons. After installation run r.lfp from the GRASS shell. As

input the drainage direction raster data (see Step 8:) and the OutletPoints vector data has to be

integrated in the GRASS mapset (Import raster data and Import vector data).

GRASS GIS 7 command r.lfp

Input: required: DrainageDirection

required: name of result map

optional: name of the outlet vector map

Results: longest flow path as vector map (line feature)

The result map can be exported as shape file (or any other suitable vector data format) and added

to QGIS project. The length of the different lines are calculated with the FieldCalculator as

described in Step 10: using $length as expression for the new attribute. Please consider the length

of the Lower Var sub-catchment might include the length of the Upper Var sub-catchment, so the

difference of both values is the final longest flow path of the lower Var sub-catchment.

End of skipping part.


4. Rainfall Station Location Analysis The impact of the measured rainfall at the five rainfall station to the Var river sub-catchments will

be analysed using the Thiessen Polygons area contribution to the sub-catchments. The application

of Thiessen polygons is suitable for flat regions (such as the Netherlands). The impact of mountains

and hill ranges such as in the river Var catchment is not considered by this type of interpolation in

this tutorial.

Step 11: Thiessen Polygons

The Thiessen polygons for the six rainfall stations are generated using the GRASS GIS 7 command

Vector v.voronoi. By default the resulting Voronoi regions / Thiessen polygons are covering the

region in the bounding box of the rainfall stations, to cover the whole river catchment region,

define the region extent by the DEM raster layer.

GRASS tool Vector v.voronoi

Input: rainfall station point vector layer

Parameter: region extent: select extent from DEM raster layer

Result: Thiessen polygon vector layer


Step 12: Intersection of Sub-Catchments and Thiessen Polygons

The geo-spatial relationship of the generated Thiessen polygons and the sub-catchment polygons

is defined by the overlay method intersection. The resulting polygons are analysed towards the

area, these area values in km**2 are the results of the GIS processing part.

The 6 Thiessen polygons are intersected with the 5 sub-catchment polygon leading to the 30

polygons with the combination of rainfall station/sub-catchment. The area of these intersection

polygons can be calculated with the FieldCalculator as described in Step 10:.

The individual exercise WaterEurope 2020 can be reduced to the rainfall station analysis for the

two sub-catchments Tinee and Upper Var.

QGIS Menu tool Vector -> Geoprocessing Tools -> Intersection

Input: Thiessen polygon vector layer

vector layer with five sub-catchment polygons

Results: rainfall station/sub-catchment polygons


5. Landuse Data Analysis

Step 13: Landuse Reclassification

In total 41 classes structured on three levels, classify the available landuse data (see

landuse_legend.txt). To simplify the analysis, the landuse will be reclassified using the 3rd level

classes for the analysis. The GRASS method r.class is used for this purpose, the reclassification rules

are defined in the provided file landuse_reclassification_rules.txt.

GRASS tool Raster r.reclass

Input: landuse raster layer

Parameter: reclassification file landuse_reclassification_rules.txt

Result: landuse reclassified raster layer


Step 14: Intersection of Landuse polygons and Sub-Catchments

The contribution of the different landuse classes to the sub-catchments is analysed using the

vector data operation intersection. For this purpose, the reclassified landuse data are converted to

a vector data layer (see r.to.vect tool used in Step 9:):


Input: landuse reclassified raster layer


Results: landuse reclassified vector layer

The resulting vector data layer can be simplified by dissolving the layer for the six landuse classes.

(see dissolve tool used in Step 9:):

QGIS Menu tool Vector -> Geoprocessing Tools -> Dissolve

Input: landuse reclassified vector layer

Parameter: dissolve field: value (landuse class index)

Results: dissolved landuse reclassified vector layer

The dissolved six landuse polygons are intersected with the 5 sub-catchment polygon leading to

the 30 polygons with the combination of landuse/sub-catchment (see intersection tool used in

Step 12:). The area of these intersection polygons can be calculate with the FieldCalculator as

described in Step 10:.


Input: dissolved reclassified landuse vector layer

vector layer with 5 sub-catchment polygons

Results: intersection landuse/sub-catchment

The individual exercise WaterEurope 2020 can be reduced to the landuse analysis for the two

sub-catchments Tinee and Upper Var.


6. Soil Data Analysis

Step 15: Intersection of Soil types and Sub-Catchments

The soil data analysis is using a similar strategy as the landuse data analysis. The contribution of

the different soil types to the sub-catchments is analysed using the vector overlay operation

intersection. For this purpose, the soil type raster data is converted to a vector data layer:


Input: soil data raster layer


Results: soil data vector layer

The resulting vector data layer can be simplified by dissolving the layer for the 6 soil type classes

using menu Vector -> Geoprocessing -> Dissolve.

QGIS Menu tool Vector -> Geoprocessing Tools -> Dissolve

Input: soil type vector layer

Parameter: dissolve field: value (soil type class index)

Results: dissolved soil type vector layer

The dissolved six soil type polygons are intersected with the five sub-catchment polygon leading to

the 30 polygons with the combination of soil type/sub-catchment. The area of these intersection

polygons can be calculate with the FieldCalculator as described in Step 10:


Input: dissolved soil type vector layer

vector layer with 5 sub-catchment polygons

Results: intersection soil type/sub-catchment

The individual exercise WaterEurope 2020 can be reduced to the soil type analysis for the two

sub-catchments Tinee and Upper Var.


Result Data

The results of the performed working steps are summarized in the tables below.

Sub-Catchments: geospatial parameters


area [km**2] 749,07 1087,47 451,71 393,39 150,12

longest flow path [km] 67,58 85,73 57,47 45,41 31,67

avg. slope [m/m] in % 39,97 32,62 26,78 38,45 22,06

Rainfall Gauges: area contribution Thiessen polygons <-> sub-catchment in [km**2]

gauge / sub catchment Tinee Upper Var Esteron Vesubie Lower Var

Carros 0,00 0,00 13,26 0,00 91,65

Levens 14,45 18,14 46,70 48,42 58,47

Roquesteron 0,09 75,03 203,80 0,00 0,00

Puget 0,70 418,46 187,95 0,00 0,00

Guillaumes 396,44 567,30 0,00 0,00 0,00

St Martin 337,36 8,52 0,00 344,97 0,00

Landuse: area contribution landuse <-> sub-catchment in [km**2]


forest [km**2] 263,25 393,39 271,35 196,47 50,31

semi-natural [km**2] 488,52 654,66 143,28 181,35 47,79

agricultural [km**2] 5,67 36,36 34,47 13,05 32,58

artificial surfaces [km**2] 1,80 2,43 1,80 2,34 15,12

water bodies [km**2] 0,36 0,00 0,00 0,09 3,33

wetlands [km**2] 0,00 1,62 0,09 0,00 0,00

Soil Type Data: area contribution soil type <-> sub-catchment in [km**2]


clay loam 107,95 295,94 92,03 84,40 18,05

loam 557,00 726,55 340,52 292,12 124,86

sandy clay loam 60,91 39,75 2,22 16,87 0,00

sandy loam 11,20 12,96 3,65 0,00 0,00

silt loam 12,00 8,00 12,00 0,00 4,00

silty clay loam 0,00 4,00 1,29 0,00 2,71

Documents

QGIS Tutorial...WaterEurope QGIS step by step tutorial 6 3. Catchment Delineation We recommend structuring the layers in your GIS-Project using groups. You can add a new group with