Technical
Methodology
Outlined in this document is a guide as to how the data used in the project for identifying the
locations for a hydrological corridor in Morocco, was attained, processed and manipulated. Such
information is useful for any future projects or for a full understanding on what was done in this
project, and for further exploration and manipulation of the data, should our results be
unsatisfactory or need to be replicated.
Climate mitigation strategies consultancy training group
Jordy van ’t Hull, Elise Droste, Elaine Sellwood, Roos Ottink and Ilona van der Kroef
Forum building – Wageningen University
Droevendaalsesteeg 2
Building 102
6708 PB Wageningen
+31682348752
[email protected]
Client
Name
Company relation
Address
Postal code
Phone
E-mail
: Sander de Haas
: Justdiggit
: Rokin 69
: 1012 KL
: 06 44988559
: [email protected]
Contractor
Climate mitigation and adaption strategies academic consultancy
Authors
: Elaine Sellwood (contact person)
Jordy van ’t Hull
Elise Droste
Roos Ottink
Ilona van der Kroef
Department
: Master Earth and Environment and Master Climate studies
Phone
: +31682348752
E-mail
: [email protected]
Cooperation
Supervisor
Experts
: Ronald Hutjes- Associate professor land atmosphere interactions, WUR
: Dr. ir. Jetse Stoorvogel- Associate professor Soil-land use interactions, WUR
: Dr. Jeroen Schoorl- Associate professor Soil geography and landscape, WUR
: Dr. ir. Luuk Fleskens- Associate professor Soil physics and land management
WUR
: Dr. Judith Klostermann- Researcher of Climate change and adaptive land
and water management at Alterra
: Mohammed Messouli- PhD of Hydrology, oceanography and remote
sensing at Cadi Ayyad University, Marrakech
: Dr. Christopher Taylor- Meteorologist at the Centre for Ecology and
Hydrology (Natural Environment Research Council in Wallingford
Reference picture title page: Justdiggit presentation for stakeholders
Date: June 2016
Table of Contents
1.
Digital Elevation Model (DEM) and derivatives ........................................................... 4
2.
Geology map and data ............................................................................................... 5
3.
Soil map and data ...................................................................................................... 5
4.
Potential Soil Organic Carbon Restoration data .......................................................... 6
5.
Potential Vegetation data .......................................................................................... 7
6.
Climatic variables and administrative and physical boundaries ................................... 8
7.
Population data ....................................................................................................... 11
8.
Exposed Economic Stock data ................................................................................... 11
9.
Map combining: ....................................................................................................... 12
10. Mesoscale meteorology models ............................................................................. 14
References ...................................................................................................................... 16
1. Digital Elevation Model (DEM) and derivatives
Source of original DEM
ASTER GDEM, website: https://asterweb.jpl.nasa.gov/gdem.asp.
After first registering as a user for this website, you will be directed to a page where you can select
file type to download- search for ASTER GDEM in the search box. Then input the coordinates for the
chosen area (here they were between 30 to 34°N, -5 to -10°W), and then select the ASTER files you
wish to download. You will receive the data as raster files.
For a complete step-by-step guide for how to download an ASTER then go to:
http://www.geos.ed.ac.uk/homes/mattal/ASTER12.pdf .
Program/ software:
We used ArcMap GIS software for the displaying and processing of this data.
Uploading and pre-processing
The ASTER rasters were loaded into and displayed in GIS. A quick description of how to do this is
outlined below:
Open ArcMap software and a new blank map.
In the ‘catalogue’ window, set a new folder connection to the folder where your DEM tiles
are.
Select the drop down symbol to see the tiles in the folder.
Click and drag the tiles in to the ‘Layers’ window to display them.
The ‘Mosaic’ tool was used to stitch the tiles together into one DEM. For this, you need to select
the tool and input each of the tiles which were downloaded.
The DEM was clipped (using the ‘Clip’ tool) to make the area smaller – area extent coordinates now:
30 to 34N, -4 to -10W.
The projection of the DEM was corrected to WGS UTM 1984 through using ‘Project raster’
tool.
The resolution of the initial DEM was 30m which was much too fine for quick calculations.
The DEM was then resampled to 100m resolution, which still enables representation of
morphological and topographical features.
As most DEM files contain missing values or data gaps, the process of sink and void filling was
undertaken to ensure the smooth running of later functions, using the ‘Fill’ tool.
The area of sea was removed using the ‘SetNull’ tool.
Analysis/ manipulation
The derivatives of: slope, aspect, flow direction and flow accumulation were then calculated with the
corresponding tools, in this order.
Next, catchments were determined to enable faster and more representative calculations to be
conducted.
Two main river catchments were identified in the study area from images and maps on
Google: the Oued Tensift and the Oum Er-Rbia basins. These were identified and then clipped from
our flow accumulation DEM to create 5 new sub-catchment shape files.
The steps how we achieved this are outlined below:
Identify main river catchments from images on Google and on our DEM/ flow accumulation
maps
Create new shape file: locate the file you want to cut in the catalogue window, right hand
click on the layer, and click create new shape file.
In the pop-up window, set the coordinate system to WGS UTM 1984 and select feature type
to ‘polygon’.
Select the editor toolbar- then click on ‘start editing’
Display the ‘Create feature’ toolbar (click on the last button on the editor toolbar)
Under ‘Construction tools’ in the create feature toolbar, click ‘polygon’
Start drawing your shape file on the DEM layer, roughly following the shape in the image
from Google. When finished, select ‘Stop editing’ in the editor toolbar.
Select the ‘Clip data’ tool- input the newly created shape file and DEM data file which you
want to take the data from.
Results/Outputs
With these 5 sub-catchments cut from the flow accumulation DEM, we were able to locate the
discharge points of the major rivers in the catchments, and find the flow accumulation values at
these points.
2. Geology map and data
Source
USGS: http://ngmdb.usgs.gov/ngmdb/ngmdb_home.html.
Program/software
ArcMap GIS software was used for the displaying and processing of this data.
Analysis/ manipulation
A literature research was conducted to investigate what type of geology would influence the regreening projects the most. Then we checked with the geology map whether and where this geology
occurs in our study. In other words: if the geology would have an influence on this project.
Results/Outputs
We found that karst areas can have large influences on the drainage of the soil and that large
groundwater aquifers can be found under karstic areas. However, since karst is mainly located in the
Atlas mountains (slightly outside the scope of our study area), we will not take this into account in
further analysis or results.
3. Soil map and data
Source
The Harmonised World Soil Database (HWSD) downloadable program and corresponding dataset
was used from: http://webarchive.iiasa.ac.at/Research/LUC/External-World-soil-database/HTML/
No need to register as this data and application is available for everyone.
Program/ software
The HWSD viewer was used to initially display, explore and locate our relevant data. Then the
selected data was loaded into ArcMap GIS software for the processing of this data.
Uploading and pre-processing
The data was loaded into ArcMap 10.3.1 by following the next steps:
Download the zip-file, as well as the two mdb-files
Open ArcCatalog and add an OLE DB (for this I used the instructions on this website:
http://resources.arcgis.com/en/help/main/10.2/index.html#//005s00000023000000)
Open ArcMap and add the raster that was provided with the HWSD-data, called hwsd.bil
Clip the raster to show only Morocco in Data Management Tools -> Raster -> Raster
Processing -> Clip
If the raster is unprojected: run Define Projection and choose World Geodetic System 1984
Run the tool Build Raster Attribute Table for the added raster
Right click on the raster in the table of contents and choose Joins and Relates -> Join
In the Join-window click the second drop down (number 1) and choose the field Value
For the drop down number 2, click on the “open folder” on the right and choose to add the
table HWSD_Data from your database connections (situated in the HWSD you downloaded
earlier)
For the 3rd dropdown choose the field MU_GLOBAL
Keep the radio button selected and choose Keep all records
Click OK
If wanted, more joins can be added to the raster. For instance, we joined the D_SYMBOL to
show the actual soil types (names) on the map, instead of only the abbreviations
Analysis/ manipulation
Now you have uploaded the HWSD data into ArcMap, you can show maps on several variables by
changing the symbology of the raster:
Right-click on the raster in table of contents -> select properties
Go to the symbology tab
Choose Unique Values on the top-left panel, here you can change the variable you want to
show on your map and what colour-scheme you would like to use
Results/Outputs
Clipped soil map of the Morocco area with the following variables: soil types, available water
capacity, drainage class, soil depth, texture class, topsoil bulk density, topsoil organic carbon, topsoil
pH-H2O.
4. Potential Soil Organic Carbon Restoration data
Source
The four maps (current-, natural-, 2050 potential- and 2050 business as usual SOC scenarios) were
received from dr. ir. Luuk Fleskens, who produced these maps during his current project (no
publication yet).
Program/ software
ArcMap GIS software was used for the displaying and processing of this data.
Uploading and pre-processing
See uploading of files in to ArcMap as described in the DEM and Derivatives chapter.
Analysis/ manipulation
The four maps were visually compared in ArcMap and statistics were gathered (maximum, minimum,
mean and standard deviation). This was used to indicate the change of SOC in the future, when we
would perform restoration measures or when we would continue like we are doing now.
Results/Outputs
Comparing the maps has shown that, when measures to restore SOC to natural levels are conducted,
the SOC will increase from current levels, in the year 2050. This supports the sustainability of the regreening project of Justdiggit.
5. Potential Vegetation data
Data source
MODIS NASA land data products and services.
Uploading and pre-processing
Obtaining MODIS Leaf area index (LAI) and Fraction of photosynthetically active radiation
product
Product code:
MCD15A2
1-kilometer grid on a Sinusoidal grid
Every 8 days available
Monthly images of 2015 (we used the first available image in the month)
Removing errors from the LAI layer of every image
Software package:
ERDAS IMAGINE® (Data cannot be opened in ArcMap)
The layers consist of digital numbers which mainly range between 1 and 60. However, some
pixels have a value of almost 256. These values are probably errors and are removed from
the data layers.
[EITHER 0 IF (Layer_n > 245) OR Layer_n OTHERWISE]
Average yearly LAI
Software package:
ERDAS IMAGINE®
Modeller tool underneath the TOOLS tab. Images of every month added as raster.
The formula underneath STACK LAYERS is applied, which calculate statistics per grid cell.
[STACK MEAN (layer_1, Layer_2,…. Layer_n)]
The output have been set on calculate statistics without 0 values and data type single float.
Digital number values to LAI
Software package:
ERDAS IMAGINE®
Modeller tool underneath the TOOLS tab. The mean yearly LAI is going to be recalculated to
the LAI values. In the specification of the MODIS product, the scale factor of 0.1 is used to
calculate the LAI.
Analysis/manipulation
Classification system for potential LAI
Software package:
ArcGIS 10.3.1
In Plessis (1999), the main determining factors for total vegetation (NDVI) are rainfall and soil
type in an arid area; therefore, these variables are used for classification of the potential LAI.
Rainfall is classified by the system of FAO, which separate hyper arid (0-100 mm/yr), arid
areas (100-300 mm/yr), and semi-arid areas (300-800 mm/yr) (FAO, 1989). The class semiarid is further subdivided in semi-arid (1) (300-400) and semi-arid (2) (400-800), as this class
is most present in the data set. The rainfall layer and soil type layer are combined with the
combine tool in the spatial analyst toolbox.
LAI signatures
Software Package:
ArcGIS 10.3.1
For every combined classification, a data signature of a natural vegetated area is made with
the signature editor, see table 1 for the mean values). Google Earth is used to determine the
location of natural vegetated area. These signatures are applied to related the classes.
Soil type and precipitation class
LAI mean
Calcisols - Arid
Calcisols – Semi-Arid (1)
Calcisols – Semi-Arid (2)
Fluvisols - Arid
Fluvisols – Semi-Arid(1)
Kastanozems – Semi-Arid (1)
Kastanozems – Semi-Arid (2)
Leptosols - Arid
Leptosols- Semi-Arid (1)
Leptosols- Semi-Arid (2)
Luvisols – Arid
Luvisols- Semi-Arid (1)
Luvisols – Semi-Arid (2)
Phaeozems – Semi-Arid (1)
Planosols – Semi-Arid (1)
Regosols – Semi-Arid (1)
Vertisols – Semi-Arid (2)
0.69
0.86
1.57
0.19
0.82
1.19
1.86
0.63
1.25
1.75
0.13
1.85
1.31
0.63
1.76
0.95
1.90
Table 1: Mean LAI for soil type and precipitation
class combined.
Results/Outputs
The output is a dataset of potential LAI with a resolution of a half degree. This potential LAI is an
estimation for amount of vegetation after re-greening the area.
6. Climatic variables and administrative and physical boundaries
Source
Climate variables: ERA interim website, ECMWF:
http://apps.ecmwf.int/datasets/data/interim-full-moda/levtype=sfc/
Once creating an account, you are able to select the time range for the data you want, as well as the
variables you wish to have data for.
Administrative and physical boundaries data: Natural Earth Data:
http://www.naturalearthdata.com/. This website does not require an account and is freely available.
Program/ software
For the viewing, cropping, and pre-processing of these data sets, we used MatLab software, followed
by ArcMap GIS software. The folder that contains all the scripts, used input files, and created output
files will be provided to the commissioner. A detailed description of these files, their function, and
the set up of the directory is given below. This information is also included in a Word document
within the directory itself.
Uploading and pre-processing
Gridded datasets had to be obtained first form the sources given above. The MatLab scripts were
modified from a version written by Ronald Hutjes.
Directory set up
Main
directory
Directory name
matlab_NAGA
Description
Contains folders and files necessary to support
the MatLab scripts and outputs from our
project.
Subdirectories m_maps
convert_humidity
WFDEI
maps
mfluxx_Moroc_boundaries
ASCII_files_ArcGIS
Files
NAGA_climatology_boundaries.m
extract_data_GIS.m
extract_data_monthly_GIS.m
Maps_NatEarthData_Moroc.m
NAGA_allWFDEI_Moroc.mat
Log book on data processing in
Toolbox required to make maps in MatLab.
Toolbox required to calculate relative humidity.
Folder containing NetCDF files with data as
input for MatLab scripts. Data is obtained from
ERA Iterim database. It also contains a text file
with the units of all of the variables given in the
NetCDF (input) and ASCII (output) files.
Folder containing folders with downloaded
items from Natural Earth Data
(www.naturalearthdata.com), used to create
images with borders in the
NAGA_climatology_testmap.m script.
Folder output from NAGA_climatology.m
script, applied to Morocco. This folder contains
two more folders: one contains maps with only
administrative boundaries, the other contains
maps with both administrative boundaries and
roads.
Folder with ASCII files that are output from the
extract_data_GIS.m script. It contains a folder
named ASCII_Monthly_Averages, which
contains ASCII files with monthly averages (one
file for each month) for relative humidity and
surface temperature, and a folder named
ASCII_Total_Averages, which contains ASCII
files with total averages over the period
between 1979 and 2012 of all climatological
variables used in this project. In addition, it
contains a TextEdit file that contains
information on the units of the inputs and
outputs.
Script that calculates the monthly averages for
all climatological variables and plots them in
.jpg images that get saved in the main
directory. These images contain administrative
boundaries.
Script that calculates the total average of
different climatological variables and writes
them into an ASCII file.
Script that calculates the monthly averages for
relative humidity and surface temperature and
writes them into ASCII files. It can be further
adapted to perform the same calculations and
tasks for other variables.
File that saved all maps from Natural Earth
Data into an .m file.
File that saved all monthly average values of all
variables (precipitation, rainfall, snowfall,
relative humidity, wind, and surface
temperature) within the geographical study
area of Morocco.
Word document containing information on the
Matlab
MatLab scripts, their output, inputs, and
methodology.
Table 2: Description of various directories.
Inputs
The inputs for the scripts are:
NetCDF files in the WFDEI folder contain three dimensional data (lon, lat, time). These files
are obtained from ERA Interim database. These files are required by all scripts.
The script NAGA_climatology_boundaries.m needs gridded data from the maps folder, which
contains cultural and physical large scale data (1:10m), in order to create maps with different
types of boundaries. These files also contain this information in .shp files, which can be
loaded into ArcGIS. This information has been obtained from Natural Earth Data database.
The scripts “NAGA_climatology_boundaries.m” both need the m_maps toolbox in order to
produce the images/maps.
The “NAGA_climatology_boundaries.m”, “extract_data_GIS.m”, and
“extract_data_monthly_GIS.m” scripts need the convert_humidity file in order to perform
the calculations for relative humidity.
MatLab scripts
Extract_data_GIS.m
This script was adjusted from the “NAGA_climatology.m” script (see below), which was written by
Ronald Hutjes for the NAGA project in Kenya. Adjustments included alterations to the relevant
geographical area in Morocco, calculations that produced averages over the entire period of
observation measurements (instead of monthly averages), and functions that allow the processed
data to be written into an ASCII file.
The script “extract_data_GIS.m” was written in order to:
Load NetCDF data files with relevant climatological variable values
Select the relevant spatial study area, according to longitude and latitude
Averaging the values of the variable over the entire time period in which measurements of
this variable were made, maintaining the spatial variability
Writing the processed data into an ASCII file that can be imported and mapped in ArcGIS.
Average values for eastward and northward wind field and moisture field fluxes are calculated
separately from the other climatological variables, as these data files have different longitude and
latitude values.
After the ASCII files are created, they still need a header so that ArcGIS can read them. The
following header with information on the grid size and geographical area is used:
NCOLS 20
NROWS 20
XLLCORNER -13
YLLCORNER 27
dx 0.5
dy 0.5
NODATA_VALUE NaN
Extract_data_monthly_GIS.m
This script is an adapted version of the “NAGA_climatolgy.m” script, which was originally written by
Ronald Hutjes. It calculates the monthly average values for relative humidity and surface
temperature and writes these values into ASCII files; each monthly average over the geographical
area is saved in a separate ASCII file. After these files are created, they still need a header in order for
ArcGIS to read them. These were added manually according to the header shown above.
NAGA_climatology_boundaries.m
This script was originally written by Ronald Hutjes for the NAGA project in Kenya. It has been
adjusted in terms of selected geographical area and the NetCDF files that contain data on moisture
flux and wind fields are adapted to the area of Morocco, instead of Kenya.
This script produces 12 maps (.jpg) with monthly averages (one for each month of the year),
which include relative humidity, moisture flux, and precipitation data. The code that produces the jpg
images is adjusted in that they include several boundary types that are contained by the maps folder.
Outputs
The ASCII files produced by the “extract_data_GIS.m” and “extract_data_monthly_GIS.m” scripts
were saved in the matlab_NAGA file and were later moved to the ASCII_files_GIS folder. The jpg files
produced by the “NAGA_climatology_boundaries.m” script are also saved in the matlab_NAGA file
and were later moved to the mfluxx_Moroc_boundaries folder.
Analysis/ manipulation
The output maps allowed to study the range of values per variable and scaling for further map
combination. Scaling was done by finding the relation between the variable and the quality of plant
growth conditions, according to what was found in literature. The scale was then used in a linear
programming algorithm with other variables to obtain a number of optimum points for the initiation
projects of Justdiggit in Morocco.
Results/Outputs
Data in the ASCII files were visualised in ArcGIS and used in map combination. The jpg figures
produced, which included monthly averages for wind direction, precipitation, and relative humidity,
were used to gain a sense in the seasonality of these climatological variables and what the
implications may be for the purposes of our project. This was necessary, as the data used in map
combining only included data that was averaged over total time.
7. Population data
Source
Socio-economic Data and Applications Centre (sedac) website:
http://sedac.ciesin.columbia.edu/data/set/gpw-v3-population-density.
Again, you must create an account to be able to download data, but once you have done this,
select the country and data attributes for your data, selecting asci file type- and then click
‘Download’. You will receive your data as a series of asci files. We had data files for population
densities for the years 1990, 1995 and 2000.
Program/ software
ArcMap GIS software was used for the displaying and processing of this data.
Uploading and pre-processing
These files can be straight away opened and viewed in ArcMap. The steps as to how to do this are
outlined in the methodology for the DEM and derivatives chapter.
8. Exposed Economic Stock data
Source
Values and map exports taken from the PREVIEW application, available on the PreventionWeb
website: http://www.preventionweb.net/english/maps/index.php?cid=116.
The data was not downloaded from here but was used as a visual aid to provide estimations
of economic costs in each of our sub-catchments. The data layer can be found under the drop-down
legend: Contextual layers → Exposed economic stock.
Program/ software
ArcGIS software.
Uploading and analysis
Through observing the distribution of values for the economic stock, an estimation was derived for
the mean value in each of the sub-catchment areas. As the data was not available in its own data set,
the values were identified and applied to the catchments we had defined in ArcGIS from our original
DEM (see section on DEM and Derivatives).
In ArcMap all of the sub-catchment shape files were merged together to form one file. Then,
a table was created to define the values for the mean economic stock. This table was then joined to
the attribute table of the merged sub-catchment shape file in ArcMap, and then the file was run
through the ‘Lookup’ tool (Spatial Analyst → Re-class → lookup) where the sub-catchments were
reclassified according to the added values of economic stock.
Results/ Outputs
A file showing the 5 mean economic stock values for each of the 5 sub-catchments was produced,
which will be used in the overall combining of the maps.
9. Map combining:
Input maps
The input consists of gridded data of all variables that we want to combine in order to find the best
locations for the initiation projects. A list is provided below:
Precipitation
Soil type
Soil depth
Erodibility
Potential SOC restoration
Population density
Exposed economic stock
Program/ software
ArcMap GIS software was used for the displaying and processing of the data.
Excel was used to scale the variables linearly between defined values and combine all the
scaled variables per grid cell to determine which grid cells had average values that exceeded
a certain threshold.
Uploading and pre-processing
Map combining was done in an Excel spread sheet. As different maps have different spatial
resolutions, we first had to make sure we could import the raster data from ArcGIS into Excel in the
same grid size and number. This was ensured by importing information from the raster file with the
highest spatial resolution into Excel, where the row and column were set to each grid cell according
to the number of grid cells, the coordinates, and the grid cell size. This grid cell framework was then
imported back into ArcGIS, where it was applied to all relevant raster files. This resulted in all raster
files being projected onto a map with a certain spatial resolution, where the average value of the
coarser grid cells were applied to the corresponding smaller grid cells with which they would overlap.
Afterwards, all raster data were imported into Excel spread sheets in which the different values of
the maps were scaled and combined.
We made two Excel spread sheets for the scaling process, both of which we will provide to
the commissioner. The reason why we would like the commissioner to access these spread sheets is
two fold. One, we want to show transparency in our methodology and maximise the understanding
of the partners in JustDiggit of how we approached our task. Two, we have indicated in our report
that the way the variables were scaled, and thus the results on the choice of locations, is in some
ways subjective, even though they are based on educated decisions and thorough literature
research. By providing the Excel spread sheets to the commissioner, we allow them to vary the
scaling according to their own interpretation of the weight of the variables in their project decisions.
Analysis and manipulation
Set Up – Combining the Variable Data in Excel
This section provides a brief explanation of what different cells in the Excel sheet refer to. This set up
is identical for the two Excel spread sheets we created.
“Row” and “Col” (blue) refer to the specific grid cell in the raster file.
“P”, “ST”, “SD, etc., refer to specific variables that have a value in each grid cell.
“Min” refers to the value that corresponds to the minimum value of a particular variable.
“Max” refers to the value that corresponds to the maximum value of a particular variable.
“S0” is the scale value that corresponds to the minimum value.
“S100” is the scale value that corresponds to the maximum value.
“S_X” (green) is the corresponding scale of each variable per grid cell. It is the result of a
variable X per grid cell using the following formula: S_X = S0 + (S100-S0)*(X-Min)/(Max-Min).
If any values exceed 100, a value of 100 is given.
“Avg(S)” (red) indicates a column with the average value of all scaled variables per grid cell.
In essence, this is the part where all scaled variables are combined per grid cell (map
combining process).
“Threshold” (yellow) refers to a number on the suitability scale. The threshold values are
used to indicate whether a particular grid cell has a combined suitability value (Avg(S)) that
exceeds the threshold value or not.
Columns next to the “Avg(S)” with yellow threshold headers above them are filled with “1”
or “0” depending on whether the “Avg(S)” value of each grid cell exceeded the
corresponding threshold value or not (1 when it exceeds it and 0 when it does not).
The area gives the percentage of the total area of our study area that exceeds the
corresponding threshold value.
“SLOPE_CHECK” indicates whether the slope of a particular grid cell exceeds 35 degrees or
not.
Note that the set up of this Excel sheet linearly interpolates the values between the minimum and
maximum values.
Zero values of certain variables on land were increased by a very small number in order to be
able to distinguish these locations from the ocean. This was done for, for example, slope and
potential SOC restoration.
Differences in Scaling
The reason that we first created two different spread sheets with calculations for map combination is
that we realised there are different ways to scale the variables. We considered two particular ways.
First, we scaled variables on the suitability scale based on their relation to plant growth on a general
basis (spread sheet: Applied_General_Moroc). For example, when annual rainfall exceeds 2000 mm,
above-ground net primary production does not increase or decrease significantly anymore (see
section on precipitation in Appendix). On a general basis, 100 on the suitability scale would thus
correspond to 2000 mm/year. Anything above 2000 mm/year would also correspond to 100 on the
scale and anything below would correspond to suitability based on a type of relation (linear or
nonlinear). However, none of the grid cells within our study area in Morocco ever receives 2000
mm/year. Instead, the maximum value for annual rainfall is around 800 mm/year. In the
Applied_General_Moroc spread sheet, all variables are scaled based on the general relation between
these variables and suitability, regardless of what the maximum and minimum values are in
Morocco.
Second, the other way we used to scale the variables was by using the maximum and
minimum values found in our study area for each variable and corresponded them to 100 and 0 on
the suitability scale, respectively (spread sheet: Applied_Specific_Moroc).
Once we had run the calculations for both sheets, we plotted the average scale of all
variables per grid cell of both scaling methods against each other in order to detect differences. As a
clear linear relation was found in this correlation, it appears that there is a certain proportionality in
the scaling, and therefore the order of best locations is maintained. This is why we decided to choose
one of the two scaling methods for further analysis of the locations. We chose
Applied_Specific_Moroc for two main reasons. One, some of our variables have a nonlinear relation
with corresponding suitability for plant growth. Decreasing the scope to only values found in
Morocco results in more accuracy in the way the variables are scaled (smaller ranges of values can
better be approximated in a linear fashion than larger ranges), and it allows for more freedom in
weighting the variables. Two, this method of scaling is easier to apply, as no general background
knowledge and literature research is necessary to scale the values appropriately.
The entire scaling and map combining process was repeated four times according to four
different hypothetical perspectives that we defined in our report: the sustainability perspective of
Justdiggit (SUSTAIN), the social perspective (SOCIAL), the environmental perspective (ENVIRON), and
a compromise between the environmental and the sustainability perspective, which we refer to as
the combined perspective (COMB). The scaling was adjusted for each of these perspectives by
decreasing or increasing the range of the scale (S0 and S100) for all variables. Decreasing this range
reduces the weighting of a particular variable when creating the average of all scaled variables per
grid cell. Increasing the range increases the weighting of a particular variable when all scaled
variables are averages per grid cell.
Results/Outputs
The output of scaling the variables and combining their maps was an average suitability value per
grid cell, an indication of which grid cells exceeded certain threshold values (from 0 to 95), and the
percentage area that exceeded these threshold values. Twenty columns with the highest exceeded
thresholds were copied and further processed in a separate Excel book to make sure that there was
no overlap in grid cells with different thresholds (this was done using the IF function in Excel). The
data was then added to the grid cell framework with coordinates that was originally made to import
the data in the right format into Excel (see above). These were finally imported into ArcGIS, where
the data points were edited and formatted. Layers with land and province borders, and roads were
added to contextualise the locations. Specific areas were later identified by comparing the maps
created in ArcGIS to Google Earth images and editing the maps in PowerPoint.
10.
Mesoscale meteorology models
Input
Mean planetary boundary height of the months May, June, September, and October
Monthly averages of relative humidity, air temperature, wind velocity, specific humdity at both 1000
mbar and 850 mbar, vertical wind velocity at 1000 mbar and 850 mbar, albedo, evapotranspiration
and sensible heat flux. Wind direction for the month May, June, September, and October at 12:00 am
Data sources
Scientific literature, NASA MODIS, ERA Interim, Wageningen University course MAQ-21806,
Meteorology and Climate, Website Windity, Website AccuWeather, and Expert Ronald Hutjes.
Program/ software
Microsoft Excel, Erdas Imagine, Grid Analysis and Display System (GrADS).
Uploading and pre-processing
As explained in the section Climatic Variables and Administrative boundaries, the Era interim
data is obtained and pre-processed with Matlab. From this data, an average is calculated for whole
our research area (-34 to 30,5 N and 10 to 6,5 W).
The MODIS data (Albedo (code MCD43A3, 500m resolution) and Evapotranspiration (code
MOD16A2_m, 1km resolution)) is uploaded in the Erdas Imagine software. As we needed data on
natural vegetation and bare soil, Google Earth is used to find these locations. The used coordinates
are 32.4 N 6.2 W for the area with vegetation and 32.3 N 7.4 W for the area with bare soil. A larger
areas is selected around these coordinates, as one pixel value would be inaccurate.
Evapotranspiration is further modified, as the model resulted in unrealistic number. The values of
Evapotranspiration in the May and October are increased relatively to the months June and
September, so seasonality is included in the data set. This modification is justified, as the infiltration
rates will be increased in the re-greening projects by landscape engineering.
The specific humidity, vertical wind velocity and sensible heat flux data are achieved from
the available data of the course Meteorology and Climate and visualized with the GrADS software.
However, this software produces only images. Therefore, the data is read from the maps, which
resulted in an error the size of the steps in the legend.
The AccuWeather website is used for obtaining data on minimum monthly temperature.
Furthermore, the Windity web is used to research the variation in wind direction. This data is
achieved for the months May, June, September, and October in 2015, as older data was unavailable
at this data source.
The equation and some of the constants are achieved from scientific literature. A model is
produced to calculate the growth of the boundary layer, moisture fluxes, minimum patch size of regreening projects in Excel.
Finally, we received a simplistic model for estimating the relation between re-greening
length, topographic elevation, maximum planetary boundary layer height, evapotranspiration at regreening side and bare soil, current relative humidity, and horizontal wind speed at the surface.
Model structure
To determine the minimum length of re-greening projects in the mean wind direction, we applied a
model combined by our expert Ronald Hutjes. The main idea is that cloud forming occurs when there
is 100% relative humidity in the atmosphere. 100% relative humidity means that the air is totally
saturated with water vapour. Relative humidity is temperature dependent and temperature
decreases with height in a Lapse rate dependent on the actual humidity (concentration water
vapour). Therefore, a height can be calculated that these 100% relative humidity will occur, which is
the lifted condensation level. This variable should be lower than the maximum planetary boundary
height (the height of the well mixed atmosphere (+/- 1500m)).
The evapotranspiration is higher at a vegetated area than a bare soil, so vegetation increases
the water vapour concentration in the atmosphere. furthermore, it reduces heating of the
atmosphere. A lower air temperature can consist less water vapour. Therefore, vegetation lowers the
lifted condensation level. On the other hand, the planetary boundary can be elevated due to
topographic rise, which also increases the change on cloud forming. These are the main processes
included in the model for determining length of re-greening projects. For the actual equation, an
Excel sheet is available with this technological methodology.
Furthermore, we have determined the minimum patch size to enhance stronger turbulence,
which can results in a higher planetary boundary height. The equation is derived from Raupach and
Finnigan (1995) (eq 1.)
LRau = CRau * Uziw*
(1)
LRau is patch length (m) that enhances tabulation can be observed through the whole planetary
boundary layer. CRau is empirical coefficient, which should be around 0.8 (Mahrt, 2000). U is the
mean horizontal wind velocity (m/s). zi is the planetary boundary layer height. w*is the Deardorff
convective velocity scale.
Furthermore, the Windity website is used to achieve data on wind direction variation; these
data is sampled for every day at 12 am of the months May and October 2015. To visualize the
variation, a spider web diagram is produced. Furthermore, this data is used to make estimation on
the necessary size of the re-greening project. For example, the wind direction is for 50% between
South West and South East the area should be 491 km2 with a topographic elevation of 400 meters
in May (eq 2.).
*re-greening length2*degrees wind360
(2)
Results/Outputs
An indication of necessary re-greening length in the wind direction dependent on the topographic
elevation for the months May, June, September, and October; minimum size of patches for the
Months May and October; the variation in wind direction for the Months May and October; and an
example of total size necessary of re-greening projects.
References
FAO (1989). Arid zone forestry: A guide for field technicians. FAO Forestry department. Retrieved from
http://www.fao.org/docrep/t0122e/t0122e00.htm#Contents at 19-04-2016.
Plessis, W.P. (1999). Linear regression relationships between NDVI, vegetation and rainfall in Etosha National
Park, Namibia. Journal of Arid Environments, vol. 42, 235-260.
Mahrt, L. (2000). Surface heterogeneity and vertical structure of the boundary layer. Boundary Layer
Meteorology, vol. 96(1–2), 33 – 62. doi:10.1023/A:1002482332477.
Raupach, M.R., and Finnigan, J.J. (1995). Scale issues in boundary-layer meteorology - surface-energy balances in
heterogeneous terrain. Hydrological Processes, vol. 9(5 – 6), 589 – 612. doi:10.1002/hyp.3360090509.