Potential Locations for a Hydrological Corridor, North ... · (semi-)arid areas, such as Morocco....

Project Report Hydrological Corridor, Morocco

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Potential Locations for a Hydrological Corridor, North

West of the Atlas Mountains in Morocco

Project Report Design of Climate Change Mitigation and Adaptation Strategies


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Potential Locations for a Hydrological Corridor, North West of the Atlas Mountains in Morocco This report (product) is produced by students of Wageningen University as part of their MSc-programme. It is not an official publication of Wageningen University or Wageningen UR and the content herein does not represent any formal position or representation by Wageningen University. Copyright © 2016 All rights reserved. No part of this publication may be reproduced or distributed in any form of by any means, without the prior consent of the commissioner and authors.


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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 : Sander de Haas Company relation : Justdiggit Address : Rokin 69 Postal code : 1012 KL Phone : 06 44988559 E-mail : [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 : Ronald Hutjes- Associate professor land atmosphere interactions, WUR Experts : 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


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Foreword

This project report regarding the development of the Hydrological Corridor in Morocco has been produced on behalf of Justdiggit, as a part of the Wageningen University course: Design of Climate Change Mitigation and Adaptation Strategies (ESS-60812). It contains a study on the possibilities and placement of a hydrological corridor in Morocco. Various aspects, including environmental, social, and economic aspects, have been incorporated into the analysis. Our findings are supported by literature studies, acquired data, models, and expert consultation. For the past twelve weeks (March-May 2016), the members of our team have been working together on this project. Our project team consists of five MSc students with different backgrounds from Wageningen University. The project leader, Ilona van der Kroef, studied a BSc in Environmental Sciences at Wageningen University, with a specialisation in system analysis. She holds quite some experience with computer modelling, and has knowledge on ecology, hydrology, and climate studies. Elaine Sellwood, the official contact person, completed a BSc in Geology at the University of Leicester. She has knowledge regarding the earth system and biogeochemical cycles, with some experience in geographic information systems. The secretary, Roos Ottink, completed a BSc in Soil Water and Atmosphere at Wageningen University. She contributes broad knowledge on earth systems sciences, and a focus on soil geography and landscape processes. Elise Droste holds a BSc background in Liberal Arts and Science from Maastricht, with broad knowledge on earth system sciences and biogeochemical cycles, with some modelling experience. Jordy van ‘t Hull completed a BSc in Earth Sciences from the University of Amsterdam. He has a broad background with knowledge on earth system sciences with a current focus on climate studies. We would like to thank our commissioner Sander de Haas from Justdiggit and our supervisor Ronald Hutjes for their guidance throughout this project. We are grateful for the opportunity of working on a real-life consultancy project for Justdiggit. Additionally, we want to thank several experts that have helped us by sharing their knowledge: dr. ir. Jetse Stoorvogel1, dr. Jeroen Shoorl2, dr. ir. Luuk Fleskens3, dr. Judith Klostermann4, Mohammed Messouli5 and Christopher Taylor6.

1 Associate professor Soil-land use interactions at Wageningen UR 2 Associate professor Soil geography and landscape at Wageningen UR 3 Associate professor Soil physics and land management at Wageningen UR 4 Researcher of Climate change and adaptive land and water management at Alterra 5 PhD of Hydrology, oceanography and remote sensing at Cadi Ayyad University, Marrakech 6 Meteorologist at the Centre for Ecology and Hydrology (Natural Environment Research Council in Wallingford


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Summary

Climate change, overgrazing, and unsustainable land-use have caused the loss of vegetation in many (semi-)arid areas, such as Morocco. Reduced levels of vegetation decreases infiltration of water into the soil, which enhances surface runoff and erosion. It is even stated that decreased levels of vegetation alter the small hydrological cycle (Garcia-Carreras et al., 2009), where decreasing moisture from the plants and soil lead to a reduction of cloud formation and thus rainfall. To avoid future water and food stresses, the concept of a hydrological corridor has been created by Justdiggit to re-green (semi-)bare soils and in the long run create large areas of vegetation, to increase the rainfall in that area. By combining literature and conducting model studies, we have looked at the possibility, sustainability, and placement of a hydrological corridor in Morocco. We included environmental, social, and economic aspects and weighed them based on the priorities and perspectives of Justdiggit, the government of Morocco, and us as a team of scientists. The area that is considered is situated between 34N to 30.5N and -10W to -6.5W. The area includes the capital Marrakech and two large rivers: the Oued Tensift and the Oum Er-Rbia. Below follows a short summary of the results for each of our three research questions:

1. From a climatic, environmental, and socio-economic perspective, where are the most suitable locations to initialise the projects within the defined area?

Among different perspectives, we found some variation in the locations that are suitable for re-greening projects by Justdiggit. However, the locations with the highest suitability are found in similar regions for all perspectives. The best locations are situated in the northern part of the Rehamna province in the region of Marrakesh-Safi. The choice of these locations is based on their score on the suitability scale, accessibility, position relative to farmland or other green regions, and proximity to urban areas. We have identified many more locations and ranked them in order of suitability. The commissioner is provided with semi-continuous maps to increase the opportunities for Justdiggit to make informed decisions on other locations as well. The results on accuracy and sensitivity of the variables and map combination system lend additional support for our conclusions.

2. What is the minimum size and distributions of the re-greening projects in order to increase the likelihood of closing the small hydrological cycle?

While our main aim is to determine the most suitable locations, an important additional aim of Justdiggit is to induce more rainfall in Morocco. The microscale (<2km) and mesoscale (2-200km) meteorology are subdivided to provide results for this aim. At local scale, the re-greening projects will result in a milder climate and a more regulated hydrology, reducing flood and erosion risks. This milder climate also includes a higher humidity content in the atmosphere, which can result in more rainfall. However, the used databases and models consist of high uncertainties. Nevertheless, an indication is made on the necessary spatial length of re-greening projects, parallel to the wind direction. The minimum project length should be 65 km in a flat area and 6 km in an area with a topographic rise of 600 meters to have potentially more rainfall in May. The month May is applied in the model, as this month is in the transition between rain season and dry season. The minimum distribution between the projects should be 2.8 km in May, in order to emphasize the ‘rain making’ potential. However, an optimal projects distribution will be between 25 and 50 km.

3. Will the hydrological corridor still have the desired impact on vegetation in 2050?

We argue that, if re-greening and soil organic carbon restoration is initiated immediately, the hydrological corridor will be sustainable for the future, even when climate change continues. Multiple assumptions were made in order to obtain meaningful results.


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The use of world databases and remote sensing data, which is mainly calibrated on the US, may have affected the quality of our data. The most limiting assumptions and data sources include using data from regions besides Morocco and generalising the data to our study area; combining gridded data with different spatial resolutions; correlating fertility to soil type; and using evapotranspiration data of MODIS (moderate-resolution imaging spectroradiometer used for remote imaging) instead of location specific meteorological data. However, the results of the optimal locations seem to be relatively insensitive to these data issues. Unfortunately, the meteorological models are both uncertain and very sensitive, which makes the outcome unreliable and should therefore only be used as an indication. Our results will be available for use by Justdiggit to place a hydrological corridor on a suitable area in Morocco, using site-specific restoration techniques. They will continue this project in cooperation with local citizens and the government of Morocco, starting in the summer of 2016.


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Table of Contents

Foreword ................................................................................................................................................. 4

Summary ................................................................................................................................................. 5

1. Project Scheme ................................................................................................................................ 9

1.1 Introduction ............................................................................................................................. 9

1.2 Significance of this project .......................................................................................................... 10

1.3 Project aim and research questions ............................................................................................ 10

2. Methodology ................................................................................................................................. 11

2.1 Area description .......................................................................................................................... 11

2.2 Methods ...................................................................................................................................... 11

2.2.1 Sub Research Question 1: Most suitable locations for initiation projects ........................... 12

2.2.2 Sub Research Question 2: Minimum total area and distribution of initiation projects ....... 15

2.2.3 Sub Research Question 3: Future climate trends (until 2050) and viability of initiation projects .......................................................................................................................................... 15

3. Results ........................................................................................................................................... 17

3.1 Sub Research Question 1: Most suitable locations for the re-greening projects ....................... 17

3.1.1 Combined perspective locations .......................................................................................... 17

3.1.2 Sustainability perspective locations ..................................................................................... 18

3.1.3 Explanatory considerations for positions of final locations ................................................. 18

3.2 Sub Research Question 2: Minimum area and distribution of the re-greening projects ............ 23

3.2.1 Microscale meteorology ....................................................................................................... 23

3.2.2 Mesoscale meteorology ....................................................................................................... 24

3.3 Sub Research Question 3: Future Viability of the Re-Greening Projects .................................... 25

3.4 Demography and Adaptive Capacity Wheel assessment ............................................................ 28

3.4.1. Demography ........................................................................................................................ 28

3.4.2. Adaptive Capacity Wheel .................................................................................................... 29

4. Discussion ...................................................................................................................................... 31

4.1. Quality assessment of data ........................................................................................................ 31

4.1.1 Climate data ......................................................................................................................... 31

4.1.2 Remote sensing data ............................................................................................................ 31

4.1.3 Global databases & expert data ........................................................................................... 32

4.1.4 Literature data ...................................................................................................................... 33

4.2. Quality assessment of methods ................................................................................................. 33

4.2.1 Sub Research Question 1: Determination of location suitability and map combining process ....................................................................................................................................................... 33

4.2.2 Sub Research Question 2: Modelling practices of mesoscale meteorology ........................ 34

4.2.3 Sub Research Question 3: Literature study on future resilience projects ........................... 34

4.3 Sensitivity analysis of results ....................................................................................................... 35


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4.3.1 Sensitivity analysis of variables included in the map combining process ............................ 35

4.3.2 Sensitivity analysis of variables included in the microscale and mesoscale meteorology ... 35

5. Recommendations and conclusions .............................................................................................. 37

5.1 From an environmental, climatic, and socio-economic perspective, what are the most suitable locations to initialise the projects within the defined area? ............................................................. 37

5.2 What are the minimum size and distribution of the projects in order to increase the likelihood of closing the small hydrological cycle? ............................................................................................ 37

5.3 Will the hydrological corridor still have the desired impact on vegetation in 2050? ................. 38

6. Concluding remarks ....................................................................................................................... 39

7. References ..................................................................................................................................... 40

Appendix ................................................................................................................................................ 42

Appendix A: Map combination .......................................................................................................... 42

A.1 Variables considered for map combination ............................................................................ 42

A.2 Excluded variables ................................................................................................................... 61

References ..................................................................................................................................... 63

Appendix B: Sensitivity analysis of scaled variables .......................................................................... 65

Appendix C: Stakeholder perspectives .............................................................................................. 67

Environmental Perspective ........................................................................................................... 67

Social perspective .......................................................................................................................... 67

Appendix D: Maps ............................................................................................................................. 70

D.1 Maps of Climatic Variables (MatLab) ...................................................................................... 70

D.2 Maps of Precipitation (total average) ..................................................................................... 70

D.3 Maps of Environmental Variables ........................................................................................... 74

D.4 Map of Population Density ..................................................................................................... 77

D.5 Map of Exposed Economic Stock Estimates ............................................................................ 78

D.6 Maps of 20 highest suitability values ...................................................................................... 79

Appendix E: Additional information on suitable locations ................................................................ 83

E.1 Key Features in the Landscape ................................................................................................ 83

E.2 Locations and Coordinates ...................................................................................................... 85

Table E4: Ranked locations, with coordinates and comments on these locations for the social perspective (SOCIAL). ......................................................................................................................... 90

Appendix F: Group work evaluation .................................................................................................. 91


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1. Project Scheme 1.1 Introduction

The general trend of increasing temperatures and decreasing precipitation due to climate change exacerbates the effects of vegetation loss, drought, and land degradation. These effects especially play an increasingly crucial role in (semi-) arid regions, such as regions in Morocco, where soils are being laid bare by overgrazing and unsustainable land use. A very dry, bare soil combined with infrequent extreme precipitation events will reduce water infiltration into the soil. Without sufficient soil moisture, vegetation will remain absent and evapotranspiration (moisture loss from soils and plants) will continue to significantly decrease, supplying less moisture to the small-scale hydrological cycle. This inhibits cloud formation leading to less precipitation occurring on land. This feedback loop has several problematic consequences: it increases water stress, soil drought, erosion, floods, food scarcity, loss of biodiversity, mitigation options, soil conflict, and further developments of climate change (Zucca et al., 2014; Klik et al., 2002; Justdiggit, 2016). These effects will be disastrous for ecosystems and, in the long term, for the living conditions of people in these regions. (Messouli et al., 2011; Zucca et al., 2014; Dahan et al., 2012; Klik et al., 2002). This is especially important to consider with the population of Morocco expected to reach 41.2 million by 2050, with consequential increases in economic and social stresses (Schilling et al, 2012). Therefore, it is essential to intervene in these problems before the damage is irreversible (Messouli et al., 2011; Zucca et al., 2014; Dahan et al., 2012; Klik et al., 2002; Justdiggit, 2016). There have been several initiatives to escape the feedback loop of soil degradation and reduction of rainfall, especially in the past few years (Zucca et al., 2014). Many are referred to as ‘re-greening’ projects, as they stand to promote vegetation establishment and growth. The principles of re-greening projects are extremely attractive for a number of reasons. For one, they fall under the concept of “Building with Nature”, which is a concept that encourages solutions that promote positive, sustainable interactions between people, the planet, and social and environmental profit. This is in contrast with more traditional engineering approaches, such as building hard dikes and pumping up deep groundwater, which are not sustainable. Secondly, re-greening projects are cost-effective compared to traditional approaches and promote climate education and management on a local scale. The Justdiggit organization, formerly known as the Naga Foundation, founded a recent initiative to use trenching techniques to increase water infiltration into the soil and thereby enhance vegetation growth. Their larger aim is to restore the small-scale hydrological cycle and promote the formation of a larger hydrological corridor over time. A hydrological corridor refers to a large area which is vegetated, created by the growth and combining of several smaller re-greening projects and their hydrological cycles. A part of the process is to cooperate with local organisations and stakeholders to ensure a locally integrated approach. The success of their first project in Kenya-Tanzania led them to start a re-greening project in Morocco. The government of Marrakesh encourages the cooperation with Justdiggit, especially since the upcoming UNFCCC Conference of the Parties will be held in this city. In order to introduce Justdiggit’s projects to important delegates of the COP-22, it has been requested to speed up the initiation process of the re-greening projects. These projects are still in their initial phase during which re-greening locations still need to be chosen. Sander de Haas, who is the Chief Technology Officer of the organization, has requested us to provide Justdiggit with necessary information to make informed decisions on this topic. The structure of this report will take you through the aims and significance of our work, followed by methodology in section 2, results in section 3, discussion in section 4, with recommendations and


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conclusions in section 5, and a final small section on concluding remarks. The appendix follows this, at the end of the report.

1.2 Significance of this project

In the projects it carries out, Justdiggit uses a rather simple solution to one of the problems that arise with climate change. Especially on a local scale, they have the potential to make a significant difference. To ensure the success of a re-greening project, it is important that the location harbours the necessary elements to sustain plant growth and project maintenance. However, the implementation of re-greening projects might clash with the current land use and interests of stakeholders. Thus, it is also crucial to take important stakeholders such as the local government, into account when designing and implementing a project plan (Lemenih and Kassa, 2014). Our analyses will include the most important climatic, environmental, and socio-economic variables that affect re-greening plans and, where appropriate, investigate different stakeholder perspectives. Our results will provide Justdiggit with the most informed recommendations to increase the success of their re-greening projects, which provides crucial benefits to the local people and ecosystems of Morocco. On a larger scale, this research can be used as a background for potential hydrological corridors in other regions and countries.

1.3 Project aim and research questions

The aim of the present work is to help Justdiggit in exploring the most suitable locations for the implementation of a hydrological corridor in Morocco. We produced a visual presentation and report on the optimal locations and areas for the initiation of re-greening projects. Our main research question is:

● What are the most suitable locations to position a sustainable* hydrological corridor within the defined area in Morocco?

* Feasibility until 2050/ success in future

From this main research question we have raised several sub research questions: ● From a climatic, environmental, and socio-economic perspective, where are the most suitable locations to initialise the projects within the defined area? ● What is the minimum size and distributions of the projects in order to increase the likelihood of closing the small hydrological cycle? ● Will the hydrological corridor still have the desired impact on vegetation in 2050?

Our final recommendations to Justdiggit will be based on scientific (climate, soil, hydrology, meteorology), social, and economic data, models, and literature. Specifications and technical details of the re-greening techniques themselves are not taken into account in our project or results. Upon receiving our results and conclusions, the foundation itself will decide on the locations they will use and the appropriate re-greening techniques.


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2. Methodology 2.1 Area description

Our study area is located between Casablanca and Agadir, partly covering the regions of Doukkala-Abda and Douar Lambaka (Figure 2.1). The geographical coordinates that mark the boundaries of our study area are 34N to 30.5N and -10W to -6.5W. Within the study area, there are boundaries of several provinces, regions, and water boards. Provinces that are included are Marrakech-Safi and Cassablanca-Settat. These provinces contain two sub-catchments: the Oum Er-Rbia and the Oued Tensift Basins. The river Oued Tensift flows North of Marrakesh towards the ocean in the West. It crosses both regions of Marrakesh-Asfi and Doukala-Abda and is fed by several smaller branches, which originate from the Atlas Mountains. The region between the ocean and Atlas Mountains is seen as an ideal area for rain harvesting techniques due to the moisture content of the air. Water vapour originating from the ocean is blown overland and rains out over the Atlas Mountains, which makes the area south east of the Atlas Mountains very dry. This area also provides potential for vegetation enhancement due to the large amount of different land uses (De Haas, 2016) and restoration techniques that can be applied.

2.2 Methods

We have defined our methods within three themes, each corresponding to one of the sub research questions. More detailed information on the technical steps we have taken are provided in both the Appendix and also in a Technical Methodology report, which is a separate document. Figure 2.2 provides a general overview of our methods. The results found for each sub research question have been combined at the end of this report in the form of our recommendations for Justdiggit. Below follows more information on the methods per sub research question.

Figure 2.1: Google Earth image of the study area


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2.2.1 Sub Research Question 1: Most suitable locations for initiation projects

The methodology for the first sub research question is illustrated in green figure 2.2 and addresses the question on which locations in our study area would be the suitable to start re-greening projects. Data on environmental and climatological factors were acquired from various databases, such as ERA Interim for climate data, and the Harmonized World Soil Database (HWSD) for soil data. Raster data (maps) on administrative and physical boundaries were obtained from Natural Earth Data (Natural Earth website). The climatological data was processed in MatLab software to create ASCII files that could be imported and displayed as maps in ArcGIS software. All maps were then layered in ArcGIS, and projected onto the correct geographic coordinates. The types of data and variables that we eventually used in our analysis were based on their relevance to our project. To represent a part of the social aspect in our analysis, we performed an Adaptive Capacity Wheel (ACW) assessment (Gupta et al., 2010) based on literature and website information to display whether social institutions (all the formal and informal rules) would enable the adaptive capacity of society. Due to a lack of sources on local differences in social attitude and adaptive capacity, this assessment is applied on the national level instead. Even though this assessment gives invaluable information on potential strategies that Justdiggit can adopt, the fact that we were unable to incorporate any local differences made it necessary to perform the UCW assessment rather independently from the first sub research question. This is why the ACW assessment will not further

Figure 2.2: Schematic diagram of methodology for all sub-research questions.


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be discussed in this section, but will thoroughly be described in section 3.4 of this report. In order to still represent a spatial variability of a social component in our analysis of the first sub research question, we used data on the population density to gain an approximation of accessibility and availability of local citizens who can participate in the project. Economic aspects were addressed in the form of an exposed economic stock image from the PREVIEW application available on the PreventionWeb website to make an estimation on flood costs as a proxy for economic stability in times of natural hazards occurring To process our data, we created a scaling and weighting system in Excel for the variables: precipitation (P), soil type (ST), erodibility (E), potential SOC restoration (PSOC), and exposed economic stock (FLOOD). This was done to make sure the values were in the same units for the final map combining process. The values of each variable were scaled on a scale, which we call the suitability scale. The full range of the suitability scale is between values of 0 and 100. The suitability is defined (here) as how well a certain value of a variable supports a re-greening project in terms of the aims of Justdiggit, where 100 indicates high suitability. When scaling a certain variable, it is not strictly necessary to use the full range of the suitability scale. In other words, the values of a certain variable can also correspond to a much smaller range on the scale. For example, the values for precipitation may correspond to suitability scale values between 0 and 100 (full scale), while erodibility values may correspond to suitability scale values between 80 and 100. The smaller the range on the suitability scale, the lower the weight of the variable in the map combining process. The decision on how a particular variable is scaled depends generally on two things. First, it depends on the uncertainties in the data of the variable and of its relationship to the success of re-greening projects. Second, the corresponding range on the suitability scale depends on the stakeholder, as the different stakeholders have different aims for different results from the re-greening projects. We were able to manipulate these scales and produce results for four different hypothetical stakeholder perspectives, which we will use as a type of sensitivity analysis. These perspectives are based on a particular emphasis on general interests. We have named these perspectives accordingly: sustainability perspective (SUSTAIN), social perspective (SOCIAL), environmental perspective (ENVIRON), and also a combined perspective between the environmental and the sustainable perspective (COMB), which has the data scaled according to the different accuracies of the data sets. Each of the hypothetical perspectives has been assigned different weights for the variables. For example, we saw a high importance for the population density for the sustainability perspective, as the involvement of local people is important for the maintenance of the projects. For the social perspective, we assigned even more weight to the population data and exposed economic stock, as the focus is more on the wellbeing of people on a larger scale. For the environmental perspective, we put more focus on environmental variables and data error and have given the population data less weight. The combined perspective is a good balance between the sustainability and environmental perspectives. Note that these hypothetical perspectives are simply used for illustration of the effects different weighting can have on the result. The way the variables have been scaled for each of these hypothetical perspectives has not been confirmed by any institution or by any survey. However, it could be interesting for Justdiggit or any other institution to perform such a survey and interviews in order to confirm the weighting that groups of people with different interests might give to each of the variables.


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Table 2.1 shows the ranges and the weighting that each variable has been given for each of the stakeholder perspectives. Figure 2.3 shows the different percentage area of grid cells that adhere to the suitability scale values we put in place. You can see that the social perspective (SOCIAL) has the lowest amount of area that satisfies the higher suitability scale values. This indicates that higher weighting to population density and economic stock can restrict the amount of area that is suitable for re-greening projects. The largest area which satisfies the higher thresholds corresponds to the scaling created for the environmental perspective (ENVIRON). We believe that the most relevant perspectives for Justdiggit are the sustainability and the combined perspectives. These will be the only two perspectives that will be discussed in the results to enable a clearer attainable answer to the first sub-research question. The results of the social and environmental perspectives can be found in the appendix for further consideration. Note that slope is not included in the map combining system as a variable, but any grid cell that has a slope value higher than 16.7 degrees has been excluded from the analysis, as these slopes are likely too steep to support vegetation growth. A clear relation between slope and vegetation growth has not been scientifically reported (see discussion on slope in appendix for more information). However, Dr. Luuk Fleskens has also excluded areas with a slope steeper than 16.7 in his own research for the same reasoning as we did. Additionally, population density values higher than the mean value (109 people per km2) have been removed in order to maintain a linear scaling system that is reasonable. For example, a certain range of population density is desirable for the re-greening projects, but high density populations are linked to a lack of space and urban areas. More detail on the way the variables are scaled can be found in the appendix and more information on the map combining methodology is given in the Technical Methodology Report.

Perspective P ST E PSOC POPU FLOOD SUSTAIN 0 0 40 0 70 50 100 100 90 100 100 100 SOCIAL 0 0 50 0 60 40 100 100 90 100 100 100 ENVIRON 0 0 70 0 80 60 100 100 90 100 100 100 COMB 0 0 70 0 70 50

Table 2.1: ranges and weighting of each variable (precipitation, soil type, erodibility, potential soil organic carbon, population density and flood risk) for each of the four stakeholder perspectives (sustainability, social, environmental and combined).

Figure 2.3: Percentage area that has suitability values that exceed the threshold value for different stakeholder perspectives


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The step after scaling the variables is combining the maps of the individual variables. This is done by calculating the average per grid cell of the suitability scale values for all variables. These are exported back into ArcGIS for further analysis.

2.2.2 Sub Research Question 2: Minimum total area and distribution of initiation projects

The methodology for the second sub research question is represented in blue in the overall schematic methodology overview (figure 2.2). This sub research question addresses the size of the minimum total area that should be re-greened in order to potentially affect local rainfall. The potential effect of vegetation on local rainfall is complex, which is why we took several steps to eventually obtain an estimate on the size of the area. First, mathematical equations were obtained to estimate the length transect that should be re-greened to create an impact on rainfall patterns on land. These equations are combined in a simplified, two-dimensional (2D) model to calculate the lifting condensation level, which is a height at which air becomes saturated and cloud formation occurs. The lifting condensation level is determined for a certain length of an area to be re-greened. For the validity of the calculations, this stretch of re-greened area needs to be aligned parallel to the wind direction and increasing topographic elevation. The lifting condensation level is then compared to the planetary boundary layer height (PBL), which is the height of the top of the well-mixed layer where clouds occur in the atmosphere. The PBL expands vertically during the day due to heat, and shrinks at night. Rainfall can potentially occur when the lifting condensation level is lower than the PBL. Thus, if the re-greening projects can cause a significant decrease in the height of the lifting condensation level, then they can be said to likely increase rainfall. The output of the 2D model is the height of the lifting condensation level, which is calculated according to input variables, such as topographic elevation and relative humidity. Note that the output varies per season, so the lifting condensation level is calculated per month. The value of the lifting condensation level can then be compared to the PBL to determine the potential of effect on cloud formation and, thus, rainfall. However, remember that these values are calculated for a transect only, assuming a constant wind direction. In reality, the wind direction varies throughout the year. The variability in wind direction is then combined with the findings for the required length of the transect to obtain an area. Variables used in this simple 2D model include: monthly values of wind speed, wind direction, air temperature at two meter height, relative humidity monthly values, evaporation, and albedo values. Most of these data were obtained from ERA Interim datasets, except for evaporation and albedo values, which were obtained from MODIS. The climatological variables were processed in MatLab. Furthermore, the variable maximum PBL height is obtained from literature. Another 2D model was used to determine the minimum distribution of individual re-greening areas in order to enhance air turbulence, which contributes to increasing the PBL height, thus influencing cloud formation. This model consists of several variables, including vertical wind velocity at the surface, vertical wind velocity at the top of the PBL, and sensible heat flux.

2.2.3 Sub Research Question 3: Future climate trends (until 2050) and viability of initiation projects

The third sub research question addresses the sustainability of the re-greening projects from a climate change perspective. Its methodology is represented in yellow in figure 2.2. Methods mainly consisted of a literature review, which was specifically conducted on research that explored the anticipated effects of climatic change on the ecosystems in Morocco. New scientific literature regarding Morocco offers readily available, detailed results from climate prediction models, exploring climate as the main driver for vegetation establishment. Model simulations on the effects of vegetation and climate offer a viewpoint into the relationships between the two parameters, which


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in real-life would take years to develop and observe. Additionally, we kindly received datasets on potential soil organic carbon (SOC) restoration that was specifically obtained for our area from Dr. Luuk Fleskens, who created a model for potential SOC restoration predictions. This data is part of his yet-unpublished research. These data are analysed to give additional insight into the future viability of re-greening project in Morocco and to compare for consistency with the results of the literature review.


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3. Results This section presents the results for each sub research question, which were obtained according to the corresponding methodology described above. 3.1 Sub Research Question 1: Most suitable locations for the re-greening projects

The outcome of our research on our sub research question on suitable locations for re-greening projects is a set of maps, one map per hypothetical stakeholder perspective. As mentioned before, we will only discuss the results for the combined (COMB) and the sustainability (SUSTAIN) perspectives in this report (figures 3.1 and 3.2, respectively). The results on the remaining two hypothetical perspectives – environmental and social – can be found in the appendix. The maps show two elements. First, it displays the suitability values that have been calculated per grid cell. Each colour represents a particular suitability scale value. These suitability values include information on six variables – precipitation, soil type, erodibility, potential SOC restoration, population density, and exposed economic stock (representing flood risk) – and their weights with respect to each of the hypothetical perspectives that we created. The maps show areas with suitability values between a range of 65 and 85. A higher value means a higher suitability. Values below 65 were not plotted, because we wanted to focus on the most suitable areas. Values above 85 were not found. More detailed maps can be found in the appendix. Second, certain areas on the maps have been marked with numbered circles. These circles represent general areas that we consider some of the best or most suitable areas to start re-greening projects, based on our research. They are additional to the plotted suitability values, because the best locations do not only depend on the six climatic, environmental, social, and economic variables that were taken into account in the calculation of the suitability values. Instead, they also depend on other additional factors, such as accessibility, proximity to urban areas, the location of other already-green regions, and the dominant wind direction (see Appendix E.1 for an overview). These factors could not be included in the scaling process to obtain the suitability values, so we estimated their relative importance by comparing the areas with reasonable suitability values to Google Earth Images. Note that the numbering of the circles has the sole purpose of easy referencing to each individual location and does not indicate any type of ranking system. All of these circled locations have further been analysed and given an indication of preference, represented by a roman numeral between I and IX, where I indicates the highest preference. However, in the remainder of this section we will only further discuss the locations with the highest preference. As an additional note, each location has been translated to general coordinates. The purpose of these coordinates is only to give a rough indication of the identified locations on other maps and for further investigation of the area by Justdiggit. Table 3.1 gives a general outline on the locations, coordinates, and a brief description of the locations that have been ranked as I for the combined and sustainability perspectives. Similar information on locations with lower preference rankings and for the other hypothetical perspectives (environmental and social) has all been summarised in tables that can be found in the appendix (Appendix E.2). 3.1.1 Combined perspective locations

According to the combined stakeholder perspective in figure 3.1, the best locations lie north of Marrakech, around Ben Guerir. There are clear ideal locations in the North, closer to the coastline, and also small areas running parallel to the North side of the Atlas Mountains. There are also a significantly large number of suitable locations to the south and directly surrounding Marrakech.


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The locations numbered 1, 2, 3, and 4 in figure 3.1 have been given the highest preference, indicated by a roman numeral I (see also table 3.1). These are located near sufficient resources, labour, and accessibility, and have the future potential to grow and influence the climate over a larger area. Locations on the map that are numbered between 5 and 20 have been given a rank in preference between II and IX (see Appendix E.2).

3.1.2 Sustainability perspective locations

According to the sustainability perspective scaling set, the most suitable locations are north of Marrakesh, and slightly west of Settat, as shown by the bright pink areas on figure 3.2. There are also suitable locations further west towards the coast, as well as some smaller locations adjacent to the north side of the Atlas Mountains. Around Marrakesh itself, there are a few locations that surpass suitability values higher than 70, but, due to the excellent accessibility and labour availability, there are areas around the city which could serve as potential re-greening locations, and therefore should be considered. The locations numbered 1, 2, 3 and 4 have been given the highest rank (I) and are considered the best locations to initiate the re-greening projects. As they are all located within reasonably close proximity to one another, following a NE-SW spatial trend similar to the prevailing wind direction, we feel that these locations could succeed over time at conjoining and influencing the climate over a large area.

3.1.3 Explanatory considerations for positions of final locations An explanation is in order as to why the area with the most suitable locations (those with rank I) has such high suitability values. As the most suitable locations are similar for both the combined and the sustainability perspective, we will discuss them together here. The most suitable locations are found in the northern part of the Rehamna province in the region of Marrakesh-Safi. This area receives annual precipitation within a range of 291 to 347 mm. For comparison, the maximum precipitation in the whole of our study area is 436 mm/year. Thus, the amount of annual precipitation is not very high in absolute terms, but becomes more significant in relative terms, considering the requirements of vegetation. The most dominant soil type in the highest ranked area is regosol. Even though the regosol is not a very fertile soil, it has a relatively good available water capacity, texture, bulk density, and topsoil pH. Regosols are also known to develop relatively deep soils. The erodibility is not particularly high or low (0.18 to 0.42). We think that this is a good feature, as there is potential to reduce erodibility by growing vegetation, but at the same time erodibility will not pose huge problems for the implementation of the re-greening activities. The potential soil organic carbon (SOC) values in the area of highest suitability values range from 7.27 to 45 pro mille. The high potential SOC values also follow the spatial pattern visible in our final maps. Therefore, it is possible to conclude that this variable has a significant impact on our final product. Furthermore, the area of the most suitable locations (ranked with I) has a relatively high exposed economic stock, which may indicate that any flood risk that is reduced by re-greened areas can have a large beneficial effect by reducing costs associated with flooding. Lastly, the population density is around 76 people/km2, which is relatively high. This means that there are enough towns and villages in the surrounding area where there may be people interested in supporting and maintaining the re-greening projects. The population density is not considered to be detrimental to the re-greening project. This has been avoided by the way population density was scaled (see methodology on the


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first sub research question). Additionally, our comparison studies with Google Earth Images show ample space and open areas where re-greening projects can take place unhampered. Even though the population density and exposed economic stock scaled values were relatively high in the area with the most suitable locations, their contribution to the overall suitability value is not the most significant, because the data was low in accuracy and thus the weight of these variables was kept relatively small. Therefore, we concluded that precipitation, soil type, and especially potential SOC might have been the most determining factors. The maps discussed in this section and more information on each of the variables are all available to view in the appendix (Appendix D and Appendix A, respectively). Note that we were unable to exclude agricultural regions or regions that were already relatively green in the scaling and map combining system. This means that some regions that showed high suitability values might already be used for agricultural purposes and therefore may perhaps be less relevant to Justdiggit. However, Justdiggit has also shown intention to take agricultural regions into consideration. We have checked all regions with high suitability values whether they were farmland or not (see, for example, the descriptions given in table 3.1). We have also suggested areas near or around farmland regions, as this may help Justdiggit to increase the total size of vegetated area (see the discussion on the second sub research question, section 3.2, for more information). Nevertheless, the fact that some regions with high suitability values in our maps correspond to agricultural or otherwise vegetated areas indicates high reliability of our results.

Perspective Location No’

Coordinates Description Rank

COMB 1 32°17’15.26”N 8°00’58.94” W

Close to Marrakesh and some villages inc: Centre Commune Labrikyine; some farms, some empty areas

I

COMB 2 32°17’11.09”N 8°09’42.00” W

Proximity to roads and urban areas; well within province boundaries; some farmland, some uncultivated areas

I

COMB 3 32°31’55.81”N 7°57’13.99” W

Relatively high score; close to provincial border; surrounded by farms, but plentiful uncultivated land

I

COMB 4 32°20’07.46”N 7°55’51.01” W

Relatively high score; Good accessibility; proximity to major city (Ben Guerir) and smaller villages; most of the area is farmland

I

COMB 6 32°07’23.94”N 8°20’05.65” W

Roads nearby, but true accessibility is unclear; some irrigated farmland and nearby villages

I

SUSTAIN 1 32°17’15.26”N 8°00’58.94” W

Close to Marrakesh and some villages, inc Centre Commune Labrikyine; some farms, some uncultivated areas

I


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SUSTAIN 2 32°17’11.09”N 8°09’42.00” W

Proximity to roads and urban areas; well within province boundaries; some farmland, some uncultivated

I

SUSTAIN 3 32°31’55.81”N 7°57’13.99” W

Relatively high score; close to provincial border; surrounded by farms, some uncultivated areas; topography is relatively flat

I

SUSTAIN 4 32°20’07.46”N 7°55’51.01” W

Good accessibility roads; proximity to major city (Ben Guerir) and smaller villages; most of the area is farmland

I

SUSTAIN 5 32°05’23.94”N 8°07’54.68” W

Proximity to roads and some small villages; topography is relatively flat; there is some farmland around

I

Table 3.1: Summary of location numbers, coordinates and a brief descriptions of the locations ranked as I.


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Figure 3.1: Best locations as determined by the combined perspective. Colour Code values refer to numbers on the sensitivity scale, between which grid cell values lie.


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Figure 3.2: Best locations as determined by the sustainability perspective. Colour Code values refer to numbers on the sensitivity scale, between which grid cell values lie.


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3.2 Sub Research Question 2: Minimum area and distribution of the re-greening projects This section discusses the results of the second sub research question on the minimum area required to re-green in order to potentially have an impact on local rainfall. The results presented in this section are merely an estimation of the sizes and distributions of the re-greening projects that will induce rainfall, because the impact of re-vegetated areas on the mesoscale meteorology is hard to prove (Raupach and Finnigan, 1995; Garcia-Carreras et al., 2010; Anthes, 1983). However, the impact of re-greening projects on local climate and hydrology is generally recognized (Anthes, 1983). As the local benefits and the interactions between different meteorology scales should not be neglected (Raupach and Finnigan, 1995), two scales of meteorology will be discussed here: microscale and mesoscale. The purpose of the section on microscale meteorology is to contribute to the overall understanding of the relation between vegetation and meteorology. The section on mesoscale meteorology includes an indication of necessary size, distribution, and geological structure to potentially cause cloud formation and rainfall. 3.2.1 Microscale meteorology

The microscale meteorology phenomena happen on short time-scales and occur on a spatial scale smaller than 2 km (Markowski and Richardson, 2011). These phenomena are driven by solar radiation and hydrology. As shown in figure 3.3, solar energy is reflected back into the atmosphere, but also evaporates water and heats the surface and air. The deviation of these processes depends on surface characteristics, which among others include the amount and type of vegetation (Stoner and Baumgardner, 1981). For example, a higher vegetation content increases the infiltration rate and surface roughness, which causes a higher average soil moisture content and a reduction in peak runoff (Table 3.2) (Anthes, 1983). Higher soil moisture content causes more water to evaporate, which absorbs solar energy. As the total solar energy does not change, the heat of the surface and air will decrease as compensation. However, a humid surface enables more heat storage than a dry surface. This means that, overall, the minimum temperature is higher over a vegetated area than a non-vegetated area. Therefore, an area with natural vegetation consist of a milder climate than a bare soil. Surface characteristic Bare soil Natural vegetation Reference Infiltration rates 194.0 mm/h 438.9 mm/h Berglund et al. (1981) Soil moisture content Low High Anthes (1983) Relative humidity 16.4-35.9 (dry-

wet) 44.5-84.1 (dry-wet)

Unland et al. (1996)

Evapotranspiration 10.23 mm/month 38.44 mm/month MODIS evapotranspiration May

Maximum temperature 34.9 (dry) 33.0 (wet) Unland et al. (1996) Minimum temperature 19.5 (wet) 24.9 (dry) Unland et al. (1996)

Figure 3.3: Visualization of the surface heat fluxes. The size of the arrow represents the size of the energy flux. An area with dense vegetation consists mainly of evapotranspiration fluxes (evaporation and transpiration). An area without vegetation consists of fluxes mainly influencing air and soil temperature (Aleaf website, retrieved at 15-5-2016).


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Roughness 0.01 m 2.0 m Eder et al. (2015) Wind speed near surface

High Low Jonathan (2003)

Erodibility 1.3g/m2 0.5g/m2 Lopez-Bermudez et al (1998) Table 3.2: Surface characteristics differences between bare soil and natural vegetation in a semi-arid climate. Note: the values are examples and not measured in the study area.

3.2.2 Mesoscale meteorology As explained in the section on microscale meteorology, the re-greening of areas have multiple (positive) effects on local climate and hydrology. These small-scale effects are correlated to larger scale impacts (mesoscale). For example, evapotranspiration is positively correlated to water vapour concentration in the atmosphere, which can result in Justdiggit’s aim of inducing more rainfall. However, besides vegetation and bare soil, other factors should be included to potentially achieve this aim. These factors will be discussed in this section, combined with the results obtained from our model. One of the factors that affect mesoscale meteorology is the length (in km) of the re-greening areas in the mean wind direction (hereafter called patch length). As a reminder of our approach to the question on required re-greened area, we first estimated the length of this re-greening transect before translating it into the size of the required area. The required length of the transect depends on seasonality and elevation differences along this transect. With regard to seasonality, it is interesting to consider which months are the most important to take into consideration when addressing our research question. On the one hand, one could specifically look at months in the wet season in order to increase the rainfall in this time period. On the other hand, one could focus on the transition months between seasons in order to prolong the rainy season or to expedite its onset. We opted for the latter option, as the seasonal transition periods are generally most affected by the amount of vegetation (Dirmeyer, 2008). However, it is generally difficult to determine which type of reasoning is most relevant for Morocco, because it does not receive a lot of rainfall in the rainy season. Consider, for example, a region with abundant rainfall during the rainy season. Here it would definitely be more relevant to look at the transition months. However, neither of the approaches has been recommended above the other in the case of Morocco and it would be interesting for Justdiggit to look into the approach of focusing on affecting rainfall in the rainy months.

Figure 3.4.Length of re-greening area vs. topographic rise. Figure 3.5. Height above sea level for our study area

Height Profile above Sea Level


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The other aspect on which the required length of the transect depends on is topographic rise, which is the difference between the lowest and highest elevation point along the transect. Figure 3.4 illustrates how the required length of the transect varies over topographic rise. These calculations have been made for each transition month (May, June, September, and October). A larger topographic rise is associated with a smaller required length of the transect. This is based on the effect whereby, as a parcel of air rises, the water vapour that it contains condenses due to the temperatures that decrease with height in the troposphere. Thus, a higher topographic rise in the landscape can cause a higher rise of a parcel of air and thereby increase condensation, which increases the chances of reaching 100% relative humidity levels. Consequently, a vegetated transect with a higher topographic rise will not need to be as long in length as a vegetated transect with a smaller topographic rise to reach 100%. These relations are shown clearly in figure 3.4. However, the results of the month June and September have become rather unrealistic. This is attributed to the necessary simplifications in the model, which is why we have excluded these months in the analysis to determine the size of the re-greening projects. In our study area, elevation differences range up to 600 meters, excluding the Atlas Mountain range (figure 3.5). These elevation differences result in a minimum length of, for example, 6 km in May for a transect of re-greening projects to potentially induce more rainfall. The relation between the lengths of a re-greening transect and elevation in May assumes a mean wind direction from the South East. However, the wind direction is variable throughout the month in reality (Figure 3.6). Therefore, only re-greening a transect would be insufficient to have a potential impact on rainfall. For May, if the re-greened area should be facing towards the mountains and half of the days are required to have a potential effect on rainfall patterns, the area needs to be 491 km2 (25^2* pi 90/360) with a topographic elevation of 400 meters. Furthermore, the effect of surface heterogeneity is not included in the model used for determining the re-greening area size. Hong et al. (1995) argue that a mosaic structure of bare soil and vegetation can cause instability in the atmosphere, which is favourable for increasing rainfall. Garcia-Carreras et al. (2009) derived an equation for minimum patch length to impact the mesoscale circulation, which results in a minimum patch length of 1.8 km for May and 1.2 km for October. Nevertheless, these values only indicate a minimum level of heterogeneity. Anthes (1983) argued that the optimal patch size is between 25-50 km in an arid climate. Overall, the size of project highly depends on the location of the projects, as topographic elevation is an important factor in the necessary size of the re-greening projects. Furthermore, a mosaic structure can increase the likelihood of closing the small hydrological cycle. 3.3 Sub Research Question 3: Future Viability of the Re-Greening Projects The results of the first sub research question gave suggestions on a number of locations within our study area that are considered to be the most suitable, based on our research. The second sub research question gave an estimation on the size of the total area that would have to be re-greened in order to potentially affect the local rainfall (mesoscale meteorology), which was based on a model. The third and final sub research question aims to give insight into the viability of the re-greening

Figure 3.6. Prominent wind directions in October and May.


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projects up to 2050. A prediction on the effects of a changing climate on vegetation establishment is offered based on a literature study and data received from Dr. Luuk Fleskens. Average annual rainfall in central Morocco is currently around 300-350 mm per year (Tanouti et al., 2014 & Chaponniere et al, 2006). This is expected to decrease by 10-20% by 2050 (Schilling et al., 2012), with a decrease in the number of wet days over the whole country. The length of dry periods is expected to increase, especially in the region west of the Atlas Mountains (Driouech et al., 2010). Such negative predictions in climate are suggestive towards a decrease in soil moisture levels and soil available water for plants and vegetation establishment. This, in turn, will put pressure on ground water and surface water reservoirs by an induced increase in irrigation needs. It will also increases the stress under which plants are required to grow, most likely decreasing the ease of vegetation establishment and growth. The average annual temperature in Morocco is 20°C (Holiday-Weather, 2016). A temperature change for Morocco is estimated to be an increase between 1.2 ◦C and 1 ◦C, depending on which climate scenario is explored, but most scenarios yield a slightly more pronounced increase in temperatures in the mountain region (Schilling et al., 2012). Between 1960 and 2004, a general warming climate trend was observed in climate records, leading to a predicted future migration of arid regions towards the north of Morocco (Aoubouazza et al., 2013). With increased evaporation from higher temperatures and less precipitation, increased stresses will be felt by vegetation as soil moisture decreases and plant growth is limited. At a local scale (and as can be seen in the results of sub research question 2), vegetation cover leads to changes in temperatures due to directly affecting local radiation budgets and surface heat fluxes (Brovkin, 2002). A decrease in vegetation (due to increased climatic stresses) would lead to a positive feedback loop where less vegetation will lead to increased surface temperatures, increasing evaporation, reducing soil moisture, increasing stresses, and further reducing vegetation growth. Based on the above predicted precipitation and temperature changes and associated feedback loops, establishing vegetation in semi-arid and arid regions, such as Morocco, will be difficult in the future. This presents the possibility that Justdiggit’s goals to increase vegetation and stimulating a wetter and cooler climate may be very challenging on longer time scales. However, the area in Morocco under consideration has not always been in the current state that it is in now, and there are many other variables that influence climatic shifts. Simulations have been conducted to recreate a ‘Green Sahara’, where increased levels of vegetation were simulated under increased CO2-induced climate change. The results of such simulations show that a forest and highly vegetated ecosystems in this environment could actually be just as stable as the current desert environment; in both a climate which is similar to present day conditions, and in a predicted elevated CO2 environment (Brovkin, 2002). The implications of such modelling promotes the idea that established vegetation would eventually be able to initiate and drive a wetter climate. Establishing vegetation now in the current climate would promote an alteration of surface energy budgets, and increase local air humidity and precipitation through a coupled land-atmosphere feedback loop. Through the increased use of land management, water harvesting, and monitored irrigation, it can be argued that this vegetation development can be sustainable in the future, alongside the predicted climate changes in atmospheric composition, temperatures, and precipitation.


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Another study that supports the claim that soil organic carbon (and thus vegetation) can still be increased in the future was conducted by Dr. Luuk Fleskens. He created digital maps of SOC (soil organic carbon) (pro mille) for Morocco for current conditions, natural conditions, conditions in 2050

when SOC restoration management strategies are implemented, and conditions in 2050 when no change in SOC management takes place: business as usual (BAU) (figure 3.7). The natural SOC values represent the potential SOC. Looking at the maps of current and natural SOC and comparing these to values in Table 3.3, it is noticeable that there are a lot of areas where there is potential to increase SOC to a higher level; the mean as well as the minimum and maximum values for natural SOC are relatively high. The 2050 SOC restoration map shows both increased values and decreased values. However, table 4 states that the mean and maximum SOC levels have in fact increased. This shows that, by taking measures to restore SOC, it is possible to increase the SOC levels to natural levels, even in the future (2050). The 2050 BAU map shows no significant differences from the current SOC level, but the statistics show a slight decrease.

These findings, which address multiple environmental parameters, generally support Justdiggit’s intentions of inducing a self-driving climate system through local scale vegetation development. Although the future climate predictions indicate increased stresses for the parameters which are most influential for vegetation growth, we feel that implementing vegetation and land changes now in the current climate will, by 2050, be established enough to be self-sustaining, stable, and influential towards creating a wetter and cooler climate for the future.

Current SOC

Natural SOC

2050 restored SOC

2050 BAU SOC

Min 1.214 2.466 1.356 1.214

Max 62.15 72.99 72.99 62.15

Mean 12.19 14.13 14.42 12.06

Std dev 12.66 14.36 14.97 12.53 Table 3.3. Statistics (minimum, maximum, mean and standard deviation) of the four maps in Figure 3.7.

Figure 3.7 Top 30cm Soil Organic Carbon (pro mille) in the study area for current, natural, 2050 restoration and Business As Usual situations


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Figure 3.8. Distribution of major ethnicities across Morocco.

3.4 Demography and Adaptive Capacity Wheel assessment For the implementation of the hydrological corridor in Morroco, the social dimension is pivotal as Justdiggit plans to implement the hydrological corridor, and have it sustained by the local people and government. Therefore, it is essential to assess whether such a project will be supported by the local people and government and whether they have the means to maintain such projects. 3.4.1. Demography The major ethnic groups in Morocco are Arab and Berber, which together count for 99,1% of the Maroc Population (Figure 3.8) (IndexMundi, 2014). The attitude of the ethnicity groups towards adaptation projects is essential to address, as it gives an indication as to the support towards re-greening projects. The essential difference in attitudes between Moroccan Arabs and Berbers lies in linguistics only, but obtaining specific data on this type of information is challenging. Yet, current or pre-existing environmental projects serve as a proxy of attitudes towards climate change adaptation. For example, the Amsing Association, created by a Berber community in the Atlas Mountains, addressed economic isolation and harsh climatic conditions in their region (United Nations Development Programme, 2013). The association has successfully regenerated degraded lands by implementing various strategies. Furthermore, The SEARCH (social, ecological & agricultural resilience in the face of climate change) project was a three year (2011-2013) regional project in the northern part of Morocco (where mostly Arabic-Maroc live) to increase ecological resilience in watershed ecosystems. It was led by the International Union for the Conservation of Nature (IUCN) and adopted a participatory approach throughout the entire process (IUCN, 2013). With such considerations we argue that it is unlikely that either the Arabic or Berber community would impede any adaptation projects towards climate change. Some population statistics were deemed relevant regarding the implementation of the hydrological corridor, especially for the sustainability of the project. Indexmundi (2014) reports that approximately 59% of the population lives in urban areas with a recent urbanization rate of 1,62%. Especially young people leave the traditional rural life and move to the bigger city to find a job. This leaves relatively more older people in rural areas. This trend will continue in the future. Interestingly, the Maroc population is relatively young; 33% is under 15 year of age. This may be an


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advantage for the implementation of Justdiggit’s projects, as young people are likely to be more open for change and adaptations (Furlong & Cartmel, 2007). Climate change awareness is key when it comes to adaptation projects. Concerning the implementation of the hydrological corridor, especially farmers and local inhabitants should be fully aware of the increased likelihood of future natural disasters. Several projects by the Ministry of Education and King Mohammed VI were launched to raise climate change awareness. Furthermore, there is growing interest of the public on this subject (OECD, 2011). The fact that climate change awareness is top priority in Morocco and that there is a growing interest on this issue, it is likely that adaptation projects, such as re-greening bare regions, are welcomed by the government as well as local inhabitants. 3.4.2. Adaptive Capacity Wheel The Adaptive Capacity Wheel (ACW) is a method to assess the characteristics of social institutions that enable adaptive capacity of society in the context of climate change. The term social institution is broadly defined as ‘rule-based social patterns that structure social interactions and governs society’ (Van Koppen & Spaargaren, 2015). In other words, instead of a government, it is an established set of formal and informal rules, and norms that governs society. This section discusses the ACW assessment that was performed on a national level. It was first intended to perform this assessment on different local or regional levels and include this information in the analysis of the first sub research question on the most suitable locations for re-greening projects. However, due to a lack of sources on local information, the ACW was excluded from the approach taken in the methodology of the first sub research question. Instead, it will be presented here as an additional assessment with the aim to give Justdiggit additional tools to develop strategies on ensuring the success of their projects in Morocco. However, this means that the validity of the results of the ACW must always be checked locally, as there may be differences between regions that we cannot access at present. The ACW assessment consists of six dimensions: variety, learning capacity, room for autonomous change, leadership, availability of resources, and fair governance. These dimensions are subdivided into 22 criteria in total, which together form the ACW. The ACW shows to what extent social institutions encourage the adaptive capacity of society to respond to climate change (Gupta et al., 2010). The effect of the social institution on the adaptive capacity of society was expressed in a score between +2 (positive effect) and -2 (negative effect) for each criterion. Thereafter, the mean was calculated to aggregate the overall score. Table 3.4 shows the score of the ACW assessment. Variety scored quite well due to a variety in adaptation strategies and the involvement of all relevant actors in an adaptation project. Furthermore, learning capacity seems to be hampered at some criteria, as there is a lack of monitoring mechanisms and trust among government, business, and citizens. On the other hand, some community-based projects showed that villagers challenged their own cultural norms, such as empowerment of women. In addition, Morocco seems to have serious problems acting according to their own plan, which is a conclusion based on a case study on the Organization for Economic Co-Operation and Development (OECD, 2011) regarding climate funding. The OECD reports that certain criteria must be met in order to receive funding for individual climate projects, and Morocco is not currently meeting these criteria. Therefore, funds’ and programmes’ own procedures tend to dominate national systems. Regarding leadership, Morocco has proved to be one of the global forerunners in addressing climate change, which is therefore very positively scored. Another striking point is the lack of financial resources, even though their climate funding is quite diverse.


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Overall, Morocco scored slightly positive (+0.45) on the ACW assessment. Morocco performs very well at some criteria, while there is (a lot of) room for improvement for others. Nevertheless, from a social perspective, there seems to be sufficient support from social institutions for the implementation of the hydrological corridor by Justdiggit. Further detail of the assessment of the ACW is given in Appendix A:1.8.

Dimension Criteria Score Variety Variety of problem frames & solutions 1

Multi-actor, level and actor 2

Room for diversity 1

Redundancy 1

Total 1.25

Learning Capacity Trust -1

Double loop learning 1

Discuss doubts 0

Single loop learning 1

Institutional memory -1

Total 0

Room for autonomous change Continuous access to information -1

Act according to plan -2

Capacity to improvise 2

Total -0.33

Leadership Visionary leadership 2

Entrepreneurial leadership 1

Collaborative leadership 1

Total 1.33

Resources Authority -1

Human resources 1

Financial resources -1

Total -0.33

Fair governance Legitimacy 1

Equity 1

Responsiveness 1

Accountability 0

Total 0.75

Overall 0.45

Table 13.4. Results of the Adaptive Capacity Wheel for Morocco.


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4. Discussion This report has discussed the results on the most suitable locations to start re-greening projects within our study area in Morocco, an estimation on the minimum size of the re-greening area in order to potentially impact rainfall, and the viability of these re-greening projects in the future with regards to climate change. Additionally, the Adaptive Capacity Wheel (ACW) assessment provides insight into the characteristics of social institutions that enable adaptive capacity of society in the context of climate change on a national level. This section separately addresses the quality of the data and methodology presented in previous sections, followed by a sensitivity analysis. This information is important to gain a transparent overview of the quality of our results and analyses. 4.1. Quality assessment of data 4.1.1 Climate data Climate data was obtained to incorporate them in the analysis of the first and second sub research questions, which were on the locations and area size of re-greening projects, respectively. The climate data obtained from ERA Interim are daily averages between 1979 and 2012, which were converted to total or monthly averages in MatLab. Averaging rainfall data over longer periods of time reduces its spatial variation (Hatfield et al., 1997). High-resolution rainfall data is only necessary when using daily input into hydrological models or for urban catchments (Chaubey et al., 1999; Schilling, 1991). As this is not the case for our project, the coarse-resolution rainfall data we used is sufficient for our modelling purposes. This is similar for the results from the Gradsoftware, which are also long term averages. 4.1.2 Remote sensing data Remote sensing data was used for both the first and second sub research questions in order to incorporate spatial variability of a number of variables into the analyses. The remote sensing data obtained during this study consists of different spatial and temporal resolutions. A lower spatial resolution reduces the quality of the end product, as the data consist of less extreme values and spatial variation. More importantly, the accuracy of the estimation of the variable in modified remote sensing data products must be approached with caution due to underlying pre-processing algorithms and assumptions. For example, some components of the evapotranspiration product of MODIS consist of quite some errors and deviations, which inherently lower the accuracy of our results. Similar issues are present in leaf area index (LAI) data, which is often disturbed by the different orientation of leaves and atmospheric disturbances. These disturbances were also evidently present in the data set in the form of data gaps and unrealistic extreme high and low values. Fortunately, all MODIS data is used as data averages for specific locations, which reduces the data variability. Nevertheless, the evapotranspiration data seems to be lower than expected for the months May and October. Therefore, the data is modified relatively to the other two months (July and September). The DEM from the ASTER satellite presents a vertical accuracy of 0.7 meter above bare soil and 7.4 meter above mature Forest (Tachikawa et al., 2011). Furthermore, some stripe features (oddly low values

Figure 4.1. Histograms of DEM and Slope data from ArcGIS.


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in the DEM map in the form of a stripe) are present in the DEM. However, these faults were cleared by standard pre-processing measures, like void- and sink filling. Unfortunately, this resulted in difficulties for modelling flooding and erosion risks. Nevertheless, the data histograms are rather smooth (see Figure 4.1), which implies that this data error is not a large issue in our data. Overall, the remote sensing data seems to have some quality issues. The level of quality is difficult to determine without data for validation. Therefore, the reduction in quality of the end product is also unknown. 4.1.3 Global databases & expert data A number of global databases were used to obtain relevant variable that needed to be incorporated into the analysis of the best locations for re-greening projects (sub research question 1). We attained the soil type map, exposed economic stock, and population density from global databases. Global databases most often consist of a lower quality than site specific data, mainly due to a low numbers of samples or no local samples at all, and the results are based on assumptions. Therefore, the spatial resolution and accuracy is often low. The soil type map from the Harmonised World Soil Database is used as a proxy to determine soil fertility. The data we used only included the soil type, so the variation of fertility within and between fields is not taken into account in our data. Therefore, we have produced a map with Leaf Area Index for Morocco, which we used to check the rainfall and fertility data quality. The most naturally fertile soils (related to soil type) are not necessarily related to high LAI values. This indicates that the natural fertility does not say a lot about the actual fertility and ability of the soil to grow plants. This might be due to fertilisation of naturally poor soils or unsustainable land management of rich soils. Since we used the soil type as a measure of fertility and suitability for plant growth in our final maps, the most suitable locations might turn out not to be that fertile after all. This is why it is necessary to verify the suitable locations in the field. Higher rainfall values approximately correlate with higher LAI values. However, the soil fertility map that we used did not show any correlation. This is due to the fact that we used natural soil fertility of the soils instead of measurements in the field. These compared maps are not actually included in this report. The exposed economic stock values are derived from an application initiated by UNEP (United Nations Environment Programme)/GRID-Geneva (Global Resource information database). For the creation of this data set, local measurements are taken by the UNEP scientists, which increases the accuracy and quality. Additionally, the methodologies on the hazards modelling used were reviewed by a team of 24 independent experts selected by the World Meteorological Organization (WMO) and the United Nations Education and Scientific Cultural Organization (UNESCO). Unfortunately, it was not possible to import this data into another software for processing. Therefore, we had to visually estimate averages of the exposed economic stock per sub catchment within our study area. This significantly decreased the quality of the data we used in our analyses. It should also be kept in mind that exposed economic stock is only a proxy to flood risk, but there may be inconsistencies in this assumption. The map for population density (see appendix D.4) includes small scale variations, which seem to improve the results of this map. Clear distinction between regions are also visible. Therefore, this data set seems to be a combination of local data and regional data. Thus, we cannot ensure that the actual population density at a location is similar to the results provided by the map. Finally, data on soil organic carbon was provided by one of our experts, Dr. Luuk Fleskens. As this data is part of his - as of yet - unpublished work, we do not possess details on its data sources or methods.


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As a summary, our data consist of quite some quality issues. Nevertheless, this reduction in data quality did not cause problems in determining the best locations, mainly because the size of the re-greened areas will exceed the spatial resolution. 4.1.4 Literature data Most of the Adaptive Capacity Wheel assessment, meteorological modelling, and the third sub research question on future viability of the re-greening projects is based on literature research. The main issue was the unavailability of local data, which led us to generalise analysed data on other regions of the world. For example, most of the erodibility factors were not measured in Morocco, but we do use them in our map combining system. Although acquiring values from literature is a common approach to determine relevant information, it still affects the quality of our final outcome. Furthermore, the unavailability of local data was the reason why the Adaptive Capacity Wheel Assessment could not incorporate local differences. The equations for the meteorology model are also mostly obtained from literature. The model would have improved from more site-specific constants and variables. For example, the planetary boundary height (PBL) used in our model is measured in Algeria (31◦55’N 5◦24’E), which is not exactly representative for our study area. The unrepresentativeness of such values render the data input of the meteorology model to be of insufficient quality. Summarizing, the limiting factor within the quality of our literature research is the low abundancy of site specific data. By upscaling some of our data, we managed to significantly reduce the main quality issues. 4.2. Quality assessment of methods 4.2.1 Sub Research Question 1: Determination of location suitability and map combining process The processing of the data before the map combining was done in Excel and also required some assumptions and generalisations to be made. For example, defining the minimum and maximum values on the sensitivity scale to determine the weighting of the variables was not based on scientific research or on calculated statistical errors of the individual variables. They were defined by our own interpretation as to what each stakeholder would value most. The way in which the variables were scaled was based on information found in literature and on logical reasoning. For example, defining the appropriate minimum value for soil type (i.e. the least appropriate soil type) was based on research in to the present soil types and also reasoning that even if a soil has low cultivation potential, anthropogenic fertilisation, and management would be able to rectify this. Without field work there is no way that we could have addressed such an influence on soil type and distribution of fertility. Therefore, our judgements for the scaled values will contain some un-quantified errors and bias due to being based on our own educated judgements. These errors should not be overly influential for the final results as, through addressing the four different stakeholder perspectives and viewing the outcomes, it is clear from our maps that the most suitable areas are not overly influenced by the manipulations of our scaling systems. Sensitivity analysis was also conducted on each of the variables to determine how much impact changing the weight would have on the final results. More detail on this is outlined below in section


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4.3. Overall, the processing of the data in Excel was thorough and, despite relying on literature research and some general assumptions and not being able to fully quantify the errors, produced some clear visual results with identifiable ideal locations within our study area. 4.2.2 Sub Research Question 2: Modelling practices of mesoscale meteorology As explained in the methods, a simplistic 2D model is used to determine the length of re-greening projects in the mean wind direction. The fact that this model is 2D already implies some quality issues. For example, the wind direction is spatially variable (Windity, 2016). This variability is also present in the vertical direction, as wind direction and velocity are different at the top of the atmospheric boundary layer than at the surface. This variability is not included in the 2D model. Nevertheless, the simplicity of the model has resulted in some strange results. Some important assumptions that were made include: the growth of the PBL is linear between the mean and the maximum PBL height; the height of the PBL is the same for re-greened areas and areas with bare soil; the vegetation and bare soil are homogeneous; and cloud formation only happens at 100% relative humidity. Additionally the total received solar radiation is interpolated and does not include the impact of clouds. Furthermore, compared to the model of Ek and Mahrt (1994), the variable entrainment of dry air from the layer above the PBL is missing in the used 2D model. However, sometimes it is better to have simple model to make rough estimates, as quoted by Ernst F. Schumacher “Any intelligent fool can make things bigger, more complex, and more violent. It takes a touch of genius — and a lot of courage to move in the opposite direction.” Additionally, as explained in the Quality assessment of data, some of the data consist of high uncertainty. It might not make sense to increase complexity as this would require better data quality. Nevertheless, this simplification should be taken in to account when using the results, especially because the impact of these simplification is visible in the results. For example, the re-greening length in June is zero meters with a topographic rise of 500 meters, which is probably due to the assumption on planetary boundary growth and the assumption that topographic rise causes a 1:1 increase in planetary boundary height. Therefore, the results should only be used as an indication for re-greening length and not as proof that the re-greening projects increase precipitation. The model for the distributions of the re-greening projects also includes the general issues of being a 2D model. Furthermore, it is only a minimum patch size and not an optimum, which might be more interesting. Nevertheless, it consist of less quality issues than the model for calculating the required length of areas to be re-greened. Therefore, this data can be used as an argument for enhancement of turbulence and thereby cloud formation. 4.2.3 Sub Research Question 3: Literature study on future resilience projects As outlined in section 4.1.5, the use of literature to gain scientific results can reduce the quality of the final result. Using literature to determine whether the re-greening projects would be viable up to 2050 introduces uncertainty as the models and study areas used in the literature may not be fully representative of our study area. Despite some papers directly addressing climatology over Morocco, there is still a problem of scale- the data in the literature is generally referring to a much larger scale than what we are dealing with, and so the results have been generalised to our area, and may not be fully representative of the climate cycle that actually exists at this location. There is also a problem here as the literature used addressed a lot of climate model data that also holds its own uncertainties, which, is, again, not quantified.


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The use of the digital SOC maps also introduces unquantifiable errors into the results of this sub-research question. We are not fully aware of the extent of the errors that are in this data set as they were created by Dr. Luuk Fleskens and donated to us, and so we do not know the full pre-processing steps that were taken to create the map. 4.3 Sensitivity analysis of results 4.3.1 Sensitivity analysis of variables included in the map combining process We performed a sensitivity analysis for each variable that is included in the map combination process performed for sub research question 1 by varying the range of the suitability scale values that correspond to the variable values. Most variables were not very sensitive to differences in ranges of the suitability scale (see Appendix B), except for population density and the exposed economic stock (proxy for flood risk; see Figures 4.2 and 4.3, respectively). This conclusion is based on visual analysis of the shapes of the different curves in the graphs. This means that different perceptions of the importance of these two variables will have the largest effect on the results for the best locations for the re-greening projects. The fact that the sensitivity is relatively minor overall suggests robust results for the identification of the best re-greening locations.

4.3.2 Sensitivity analysis of variables included in the microscale and mesoscale meteorology As explained in the section 3.2.2., the data consist of some uncertainty issues. The most uncertain variables are analysed in this section. The most sensitive variable in the model is the planetary boundary height (PBL). For example, increasing this variable by 100 meters decreases the required length for a re-greening area by 15 km in May, by 9 km in September (from 9km to 0 km), and by 23 km in October. In June, the change in PBL even causes a shift from conditions of no potential rainfall at all to conditions with potential rainfall occurring with a required length of 23 km. The results for the months June and September seem to be unrealistic, as it suggests that a smaller re-greening area is necessary for these drier months than the wetter months. The high sensitivity of the PBL and the fact that its value is not derived from local data, reduces the quality of the product significantly. Furthermore, when the albedo variable for the re-greened area is increased by 0.01 the required length decreases by 1 km in May and increases by 2 km in October. When increasing the albedo with

Figure 4.2. Area that exceeds a certain threshold on the suitability scale for population density. Initial values are: S0=70 and S100=100. As the range of the suitability scale becomes larger, the number of locations that have a suitability scale that exceed the threshold level becomes disproportional to the initial range

Sensitivity Analysis Population Density

Sensitivity Analysis Exposed Economic Stock

Figure 4.3. Area that exceeds a certain threshold on the suitability scale for the proxy for flood risk cost reduction. Initial values are: S0=50 and S100=100. As the range of the suitability scale becomes larger, the number of locations that have a suitability scale that exceed the threshold level becomes disproportional to the initial range


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0.01 for the background area, the required re-greening length decreases with 1 km in both May and October. Another uncertain variable is evapotranspiration. When evapotranspiration increases by 0.1 mm/day at the re-greening project area and at the background for the month May, the required re-greening length reduces by 2 and 3 km, respectively. Similar results are found for the month October; changes in both evapotranspiration are causing a change of 3 km after increasing with 0.1 mm/day. Even though the sensitivity of the albedo and evapotranspiration is relatively low, the uncertainty is relatively high. Therefore, the error in required re-greening length can be over 35 km solely due to the error in albedo and evapotranspiration.


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5. Recommendations and conclusions We provide recommendations for Justdiggit in the section below, provided within the framework of our sub research questions. Combined, the answers to the sub research questions provide an estimation for the answer to our main research question: What are the most suitable locations to position a sustainable* hydrological corridor within the defined area in Morocco?

* feasibility until 2050/ success in future 5.1 From an environmental, climatic, and socio-economic perspective, what are the most suitable locations to initialise the projects within the defined area? The most suitable locations for the initiation of re-greening projects have been identified in the northern part of the Rehamna province in the region of Marrakesh-Safi, as shown in Figures 3.1 and 3.2. These results are based on climate data, environmental data, population density, exposed economic stock, infrastructure, and the different stakeholder perspectives. These results are derived from systematic analysis of the data and a robust map combining system, with potential SOC data and precipitation being the most influential variables. However, the uncertainty of our maps should be taken into account, as some of the variables are obtained from world databases, which are often not calibrated on the African continent. This “spatial” error may cause a small shift in the coordinates of the most suitable locations. Nevertheless, the locations we identified are a reliable starting point for Justdiggit to make informed decisions. Moreover, we would strongly argue that further field work is required to determine precise locations within the general areas we have identified. For example, it may be necessary to measure soil depth, as this data was unavailable to us, and to negotiate with local farmers and citizens to specifically address the willingness of the local people to get involved in the projects- something which we were not able to directly address. From a social perspective, it is expected that the willingness to cooperate of local inhabitants in the re-greening projects is high. Therefore, it is very likely that local inhabitants will support adaptation projects, such as the hydrological corridor. Nevertheless, patience and flexibility is preferred, as the Adaptive Capacity Wheel assessment demonstrated that issues are present due to low trust, not acting according plan, and low financial resources. Addressing soil fertility in these locations would also be a recommendation, as from our data, regosols are the dominant soil type within the area with the most suitable locations. However, these soils generally do not have very high fertility. The data used here cannot address anthropogenic fertilisers or local-scale methods for dealing with such a problem, and thus this would be another variable to address in the field. We suggest interacting with willing, local farmers, who are likely to have experience with handling the fertility of such soils. 5.2 What are the minimum size and distribution of the projects in order to increase the likelihood of closing the small hydrological cycle? If a potential impact on rainfall is to be achieved, we advise to plot the re-greening projects in a transect that widens, parallel to the wind direction. The minimum size of the re-greened area is site and season specific. Focus should be on the months in the transition period between seasons, as these are most likely to be affected by changing precipitation patterns. For the month May, for a topographic elevation of 400 m, for a transect facing towards the Atlas Mountains, and for at least


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half of the days to have a potential effect on local rainfall patterns, the minimum area that should be re-greened is estimated to be 491 km2. An estimation of the minimum patch length in May is 1.8 km. Another approach suggests that an optimal patch length between 25 and 50 km in arid climates. However, the results presented in this report on the size and distribution of the re-greening projects should not be used as verification for “rain making” potential to the stakeholders. These results should only be used as an indication for the size and distribution of the re-greening projects. To reduce the uncertainty in these results, site specific measurements should be taken of evapotranspiration and maximum planetary boundary layer height. Using these locally measured values in the model provides more representative results. Nevertheless, it is clear that suitable locations and the structure of re-greening projects must be developed under consideration of one another, in order to have an impact on the climate. Furthermore, our general advice is to locate the re-greening projects at the border of existing vegetated regions, as the likelihood for the vegetation to be established and persist will increase. The most appropriate restoration techniques and implementation methods should be selected based on site specifics, because of the relatively high spatial variation in the selected areas, which we may have missed in our data analysis. Due to the high uncertainty of the meteorological model, we advise Justdiggit not to focus on the potential for increasing precipitation. Instead, we advise to focus on providing the most benefits to the local people and ecosystem by choosing the most suitable locations, such as those suggested in the answer to the first sub research question. We also advice to put more emphasis on other general local benefits of re-greening, such as increased soil water infiltration and increased biodiversity, instead of on the larger scale climate. 5.3 Will the hydrological corridor still have the desired impact on vegetation in 2050? Based on conclusions of studies on the future climate of Morocco, we believe that, even though the climate will become drier and warmer, the stability of forests and highly vegetated ecosystems in the future can be maintained equal to the stability that they have now. Additionally, model simulations on soil organic carbon (SOC) levels indicate that SOC content still has the potential to increase in the future (2050) by re-greening activities. We think the quality of our final sub research question product is sufficient, and we support the initiation of the project and recommend to start as soon as possible.


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6. Concluding remarks Future projects that would still like to address the impact of vegetation on rainfall require research on the impact of re-greening on the mesoscale meteorology, with high quality data directly addressing the particular study area. In terms of finding the most suitable locations for re-greening projects, more specific data on social variability is required, since failure of projects is often due to social implications. For the other variables, a higher spatial resolution would be preferable, with an appropriate computation time and resource availability- which was an issue faced in this research. Finally, the type of rainfall and soil depth should be taken in account in future projects. Throughout this project, many data sets, models and literature studies have been addressed in order to identify and recommend suitable locations for the establishment of a hydrological corridor in Morocco. We feel the contributions that we as a team have made to this project can be taken with a reasonably high level of scientific certainty and successfully contribute to the establishment and success of the Justdiggit project in Morocco.


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Appendix Appendix A: Map combination A.1 Variables considered for map combination The research on the variables that is presented here is used in the scaling and map combination process. It is accompanied with an explanation of how these variables can be scaled. There are different ways in which variables can be scaled. We have investigated two particular ways. One is based on a general relation between a given variable and the suitability/applicability of re-greening projects. This means that the scaling method is not dependent on the values found in Morocco for a given variable, but instead looks at the full range of values that can occur globally. This way of scaling also takes nonlinear relations into account. This section will henceforth refer to this method as the “general scaling method”.

The other way disregards values that are not recorded in our study area in Morocco and instead only applies a linear relation between the real minimum and maximum values of a certain variable to a certain range on the suitability scale. This way of scaling will from now on be referred to as the “specific scaling method”, because it only takes values specifically for our study area in Morocco into account. Please note that the results in the report have been obtained only using the specific scaling method for reasons of simplicity and straightforwardness. Further justification for this decision can be found in the Technical Methodology Report.

A.1.1 Precipitation For arid regions, water is often the most limiting factor to plant growth (Whittaker, 1970). Multiple publications show a linear relationship between vegetation and rainfall for regions will low annual rainfall, although the specifics of this relationship tends to vary between regions and studies: ranging from a nearly 1:1 relationship in Whittaker (1970) to a relationship with a slope of 0.294 in O’Connor et al. (2001). For our study area in Morocco, we assumed a linear relationship between 20mm and 800mm of rainfall. We need to be aware that plants require a minimum amount of water to establish themselves and to survive, so that the intercept is probably negative in reality. General scaling method In order to scale the precipitation values found for our study area in Morocco, we have found values for the slope of the relationship between precipitation and primary production, and the annual precipitation at which vegetation growth is very unlikely.

Due to a lack of time to perform a regression analysis between Normalized Difference Vegetation Index (NDVI) and rainfall (Mahyou et al., 2010) of our own, we opt for taking the average relation between the results of O’Connor et al. (2011) and that of Knapp & Smith’s (2001). This would give us a slope of 0.332. With regards to the second point, the lack of information has led to a somewhat arbitrary decision to set virtually no vegetation growth to 20 mm of annual precipitation. This decision was partially based on the results of Mahyou et al. (2010), who found that the NDVI values under 0.2 indicated drought occurrences in the region. Incidentally, this value corresponds to annual precipitation of 20 mm. This led us to the linear relationship of equation 1.

(1)


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Figure A1 gives a visual representation for using

this relation in scaling annual precipitation of Morocco on the overall scale on which all the variables will be scaled for our objective to find the most optimal locations for Justdiggit’s projects. As Whittaker (1970) indicates a ceiling effect at which the increase in annual precipitation becomes redundant at 1200 mm for annual net primary productivity, this value is set at 100 on the suitability scale. The relationship roughly remains linear between 0 and 800 mm of annual precipitation. Therefore, 800 mm is set at 67 on the scale (800 mm/1200 mm=0.67). 20 mm annual precipitation is set to 0 on the scale.

The scaled precipitation includes the complete range of the annual precipitation that is found in Morocco over the period between 1979 and 2012.

Specific scaling method A different, simpler way to scale precipitation is to find the minimum and maximum values for precipitation in our study area in Morocco and have any values in between be scaled in a linear fashion over the entire range of the suitability scale (between 0 and 100). This linear relationship between precipitation and suitability scale is justified, as Morocco’s precipitation values are at the lower end of the spectrum where precipitation is linearly related to above-ground net primary production. A.1.2 Soil Type A digital soil map was obtained from the Harmonized World Soil Database (FAO/IIASA/ISRIC/ISS-CAS/JRC, 2009), together with maps on available water capacity (mm), drainage classes, soil depth (cm), texture classes, topsoil bulk density (kg/dm3), topsoil organic carbon (% weight), and topsoil pH-H2O (which is the acidity of the soil solution, after adding of water). From these maps, the values or classes of these variables were manually determined for the soil types. Based on a literature study (Arshad et al., 1996; Gregory and Nortcliff, 2013; Perry, L., 2003; Kosmas et al., 2000) and own knowledge, these variables were ranked in classes (poor to good, see table A1) for each soil type (see table A2). Note: here, we only consider soil types found in our study area in Morocco and leave any other soil types aside in our analysis.

Classes (based on the current area)

Available Water Capacity (mm)

Drainage class

Soil depth (cm)

Texture Topsoil bulk density (kg/dm3)

Topsoil Organic Carbon (%)

Topsoil pH

Poor 0 - 15 Poor 10 loamy sand 1.43 – 1.54

0.39 – 0.91 8 to 9

Medium 50 - 100 Imperfect - 30 sandy loam 1.32 – 0.91 – 1.44 7 to 8

Figure A1: Visual representation of scaling precipitation values for Morocco.


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Moderately well

1.43

Good 125 - 150 Well 100 light clay and loam

1.21-1.32 1.44 – 1.96 5.5 - 7

Table A1: Soil variables divided into classes based on the values in the study area.

Soil Type Available Water Capacity (mm)

Drainage class

Soil depth (cm)

Texture Topsoil bulk density (kg/dm3)

Topsoil Organic Carbon (%)

Topsoil pH

Calcisol Poor to good

Medium Good Medium Medium to poor

Poor Medium to poor

Kastanozem Good Good Good Medium Medium to poor

Medium to poor

Poor

Leptosol Poor Medium Poor to medium

Good Good Medium to Good

Medium

Luvisol Poor to good

Medium Good Medium Medium to poor

Poor Good

Phaeozem Poor to good

Medium Good Good Good Good Good

Planosol Good Poor Good Good Poor Medium Good Regosol Good Medium Good Good Good Medium Good Solonchak Good Poor Good Good Good Poor Poor Vertisol Poor Poor Good Good Medium Poor Medium Fluvisol Good Poor Medium

to good Medium Good to

medium Poor Good to

medium

Table A2: Soil types classed from poor to good for soil variables.

From a literature study (ISRIC-WDC Soils, 2016), information on soil fertility and current/suitable land use for each soil type was gathered (see table A3).

Soil Type Soil fertility Land use Calcisol Relatively fertile Animal grazing (limited for

agriculture) Kastanozem Potentially fertile Extensive grazing Leptosol Poor (slopes are more fertile

than flat areas) Forest or extensive grazing

Luvisol Relatively fertile Agriculture Phaeozem Fertile Agriculture Planosol Poor soils (chemically

degraded) Grassland

Regosol Weak soil development, so low fertility

Low volume grazing (or capital-intensive agriculture)

Solonchak Poor soils (too salty for plant Extensive grazing


45

growth) Vertisol Naturally fertile soils Agriculture (when properly

managed) Fluvisol Fertile but young Agriculture and grazing Table A3: Fertility and most suitable land uses for soil types. General scaling method The ranking of the soil types has a range from 0 (no vegetation possible at all) to 100 (extremely production soil). Because we expect that we have no soils consisting of bare rock nor extremely productive soils, the scores will be between 20 and 80. This range of 60 is then divided into scores for each variable. We expect a larger influence of soil fertility on vegetation growth, therefore the scores are higher. Table A4 shows the scores given to the different variables. Classes Score for AWC, dc, sd, txt, bd,

OC, pH Score for soil fertility

Poor 0.5 0 Medium to poor 2 2.5 Medium 4 5 Medium to good 5.5 7.5 Good 7.5 10 Table A4: Scores given to the classes for various soil variables (abbreviations used).

These scores were then added to the minimum score of 20, which gives the final ranking of the soil types (see table A5).

Soil Type Ranked on scores

Comments

Phaeozem 75.5 Very fertile farmland, but may need irrigation. Regosol 65.5 Fertilisation is needed. Best left forested on steep slopes. Kastanozem 61 Potentially rich soils when irrigated. Drought and (wind/water)

erosion are serious threats. Luvisol 59.5 Suitable for wide range of agricultural uses. Erosion control on

steep slopes is needed. Fluvisol 59 Genetically young, but fertile soils. Water management is often

needed (flood risk or irrigation). Planosol 55 Chemically strongly degraded soils, grass vegetation with

scattered shrubs and trees. Vertisols 54.5 Chemically fertile, but drying out and cracking of clay requires

proper management. Solonchak 51.5 Very high salt levels limit plant growth (due to drought stress and

limited nutrient uptake). Only when salts have been flushed away, can good yields occur.

Leptosol 51 Often too shallow for plant growth. When slopes are transformed into terraces, better yield is expected.


46

Calcisol 49 High calcium content (fertile), but unless either drained or irrigated and fertilised, not very suitable for agriculture.

Table A5: Soil types ranked on their scores for how well they support vegetation growth. A higher score means better/faster growth of plants.

Of course, these scores are not completely realistic, because they are based on assumption, estimates and literature examples. The differentiation in influence of soil variables on vegetation growth is now only taken into account for soil fertility, while other variables should also have different weights.

In order to work with the scaling of the soil type as is outlined in table A5, we need to give the soil types themselves a particular number, because the process of combining the mapped variables requires numbers instead of qualitative concepts. The number that is given to each soil type corresponds to its order on the scale relative to the other soil types and the difference between the given values of two different soil types will correspond to the difference in scale between these same two soil types. This is done by dividing the scale value for each soil type by the scale value of the soil type with the highest value (in our case this is Phaeozem with a scale value is 75.5), which is then multiplied by 10 (see table A6).

Soil type Assigned Number Phaeozem 10 Regosol 8,68 Kastanozem 8,08 Luvisol 7,88 Fluvisol 7,81 Planosol 7,28 Vertisol 7,22 Solonchak 6,82 Leptosol 6,75 Calcisol 6,49 Table A6: Overview of the assigned numbers to soil types necessary for map combining. Specific scaling method In the specific scaling method of soil type, the assigned numbers for the different soil types in our study area in Morocco (table 2) were scaled between 0 and 100, in a linear fashion. This way of scaling covers the full range of the suitability scale instead of only a part of it (as in the general scaling method for soil type). Due to the assigned numbers to the soil types, the order of the suitability of each soil type was maintained.

Note: Soil type is scaled on the full suitability scale (from 0 to 100). This decision may be questionable upon the basis that soil fertility can be influenced anthropogenically, such as fertilisation application. This would mean that soil type should have a lower weight in the map combining process. However, we have been specifically asked by our commissioner to not include types of re-greening techniques within our analyses, because the type of technique will be chosen after the location has been decided upon, instead of the other way around. Therefore, we have scaled soil type upon the entire suitability scale.


47

A.1.3 Soil Depth General scaling method Schenk and Jackson (2002) have displayed several regression analyses (see figure A2) on the relationship between rooting depth (Di in m) and above ground plant volume (Vi in m3) for forbs, grasses, and woody plants in the form: log10 (Di) = a + b log10 (Vi).

Ranking soil depth in terms of its influences on plant growth is not straightforward, as many other factors come into play, such as soil fertility. Due to a lack of explicit relationships discussed in literature, we will use the rooting depth discussed in Scheck and Jackson (2002) and treat it as soil depth.

Figure A2: Relationship between rooting depth and above-ground plant volume (Shenk and Jackon, 2002).

We have modified the relationship by writing the above-ground plant volume in function of rooting depth: log10 (Vi) = -b/a + (1/a) log10 (Di). This means that Vi = 10^(-b/a + (1/a) log10 (Di)) (see figure A3).

Since we assumed that our dataset would contain a maximum soil depth of 1.20, we have scaled this at the value 100 and 0cm at 0. Even though we have found nonlinear relationships in literature, we have chosen a linear relationship for the soil depth scaling, due to simplicity. We realise that this is not the most accurate to assume, but we think that this would still be valuable enough in the map combining process.

Figure A3: Above-ground plant volume in function of (rooting) depth (derived from Scheck and Jackson, 2002). Trend line equation is displayed for the average of the plant types.

Specific scaling method The specific scaling method of soil depth entails using the real minimum and maximum values of soil depth found in our study area and corresponding any values in between on the full length of the suitability scale in a linear fashion. Even though figure A3 illustrates a nonlinear relationship between soil depth and plant volume (based on a number of assumptions that have been explained above),


48

assuming a linear relationship can be used as an approximation of the suitability of certain soil depths, because the range of values that actually occur in our study area in Morocco will be relatively small. A.1.4 Erodibility The erodibility factor is a quantitative method for predicting soil erosion, calculated for use of the Universal Soil Loss Equation, developed primarily by Wischmeier and Smith (1978) (eq. 2). 𝐴 = 𝑅 ∗ 𝐾 ∗ 𝐿 ∗ 𝑆 ∗ 𝐶 ∗ 𝑃 (2) Where A is the total soil loss (tonne/acre/year), R is rainfall run-off (SI unit/year), K is the soil erodibility factor (tonne/unit area/year), L is slope length factor, S is slope steepness factor, C is the land cover factor and P is the factor for land management practices. A high K factor indicates a high erodibility. This equation gives a relative idea on the erodibility of soils, which is vital for use with land management planning and practices.

For the main soil types in our study area, a literature study was conducted to find K values, which will be used to define our ranking system for the soil types (table A7).

Soil type Sub-catchment K-Factor Calcisols Middle and SE Oum, NW and SE

Tensift 0.37 (Alías et al,1997)

Fluvisols SE Tensift 0.42* Kastanozems NW Oum 0.28* Leptosols SE Oum 0.25 (Boardman and Poesen

2006) Phaeozems Middle Oum 0.18* Planosols 0.42* Regosols Middle Oum and SE Tensift 0.42* Solonchaks 0.60* Vertosols 0.42* Luvisols 0.18* Table A7: K values and presence in sub-catchment of the different soil types. (http://www.fao.org/docrep/009/t0733e/t0733e05.htm. Oum refers to the Oum Er-Rbia Basin, and Tensift to the Oued Tensift. General scaling method The scaling system runs from 0 to 100, with 0 being stable soils, suitable for vegetation establishment and growth, and 100 being highly erodible and detrimental for soil stability. However, similarly to the ranking system for the soil fertility factors, which were included in ranking the soil types, we do not expect any soils to be attributed to 100 on the scale (as this would mean that all the soil has been eroded away), nor to 0, as all soil is dynamic and not perfectly stable. Therefore, the scaled score is assumed to be between 20 and 80.

Range From - To Rank Score Class 0 – 0 0 0 Excellent 0.1 – 0.05 1 5 Excellent 0.06 – 0.1 2 10 Excellent


49

0.11 – 0.15 3 15 Excellent 0.16 – 0.2 4 20 Excellent 0.21 – 0.25 5 25 Excellent 0.26 – 0.3 6 30 Excellent 0.31 – 0.35 7 35 Moderate 0.36 – 0.4 8 40 Moderate 0.41 – 0.45 9 45 Moderate 0.46 – 0.5 10 50 Moderate 0.51 – 0.55 11 55 Poor 0.56 – 0.6 12 60 Poor 0.61 – 0.65 13 65 Poor 0.66 – 0.7 14 70 Poor 0.71 – 0.75 15 75 Extremely Poor 0.76 – 0.8 16 80 Extremely Poor 0.81 – 0.85 17 85 Extremely Poor 0.86 – 0.9 18 90 Extremely Poor 0.91 – 0.95 19 95 Extremely Poor 0.96 – 1 20 100 Extremely Poor Table A8: Ranges and attributed scores for erodibility classes.

The FAO website provided some ranges for the K-factors and assigned a class from ‘Poor’ to ‘Excellent’. However, we increased the number of ranges in order to assign a unique scaling factor per K factor (table A8). The final scaling ranges from 0, corresponding to a K value of 0, to 100, corresponding to a K value of 1. The reason that higher K factors – and thus more erodible soils – are ranked higher than lower K factors is related to an objective of the Justdiggit project to have the highest positive impact possible on social and economic aspects. Re-vegetation increases soil stability and have larger positive impacts on soils that are less stable. We have assumed a linear relationship between the scale (the suitability of the soil for plant growth) and the K factor (with an R2 of 0.99174). Table A9 provides an overview of the end-score of the different K factors for each soil type, which will be used in the final map combining.

Soil type K factor Score Solonchaks 0.6 60 Fluvisols 0.42 45 Planosols 0.42 45 Regosols 0.42 45 Vertosols 0.42 45 Calcisols 0.37 40 Kastanozems 0.28 30 Leptosols 0.25 25 Phaeozems 0.18 20 Luvisols 0.18 20 Table A9: Final scores for the soil erodibility ranking.


50

These scores are based on educated assumptions that the trenching techniques used by Justdiggit Foundation are capable of reducing erodibility by increasing infiltration and, thus, vegetation growth. The K factor values may also be misrepresentative of the soils in this specific area due to the values being taken from various other field areas, and we are aware that these assumptions may be lacking full validity, but we do feel they serve our purposes for this study.

Specific scaling method The K factors found per soil type were scaled on the suitability scale in a linear fashion. The range on the suitability scale that corresponded to the real minimum, maximum, and any real K factor values in between did not cover the full scale. Instead, only a range of 20 to 60 of the suitability scale corresponded to the full range of the K factors. The range on the suitability scale was decreased, because the K factors per soil type were not derived from literature studies in Morocco, but rather in other parts of the world. The possibility that these values are not fully representative for erodibility in our study area in Morocco makes it necessary to decrease the weighting of this variable in the eventual map combining process. This is done by decreasing the range on the suitability scale for this variable. A.1.5 Geology In Morocco, especially in the High Atlas mountains, Cambrian, Jurassic, and Cretaceous karstic limestone landscapes exist (Africa Groundwater Literature Archive, 2016; Akdim, B., 2015). Due to the high porosity and existing fractures of the karst, water in such areas is drained quickly to the groundwater reservoirs. These reservoirs supplement the rivers of Morocco (De Jong et al, 2008; Ait Lemkademe et al., 2011). Due to the high drainage of karst, the groundwater table is deep and less water is available in the soil for plant growth. Therefore, it would be less suitable to place the hydrological corridor on karst areas. However, we have studied the geological map and concluded that no significant karst areas appear within our study area. Therefore, we do not need to take the potential issues related to karst into account in our analysis. A.1.6 Hill Slope The way hill slope was incorporated into the map combining system was identical for both the general and the specific scaling method.

The relationship between hillslope and infiltration rate is still unclear (Fox et al., 1997; Djorovic, 1980; Huat et al., 2005), which is mainly due to the many interdependencies between a soil’s infiltration capacity, and other soil factors, such as hydraulic conductivity (Ribolzi et al., 2011). Additional contributions to a complex relationship are spatial characteristics, such as varying (micro-)topography, soil crusting, and vegetation cover (Huat et al., 2005), as well as rainfall characteristics, such as intensity and raindrop kinetic energy per unit area (Assoiline et al., 2006). These uncertainties and high dependency on the individual catchment (Harden et al., 2011) render the inclusion of slope in the optimisation of the locations for the initialisation projects for Justdiggit unattractive. However, we will exclude very steep slopes from the area that we will consider in our project for several reasons:

- These areas are inappropriate for the Justdiggit project in terms of accessibility. - These areas are most likely in rocky terrain where the techniques used by Justdiggit

Foundation are inapplicable and which likely limit plant growth.


51

- Plant growth is likely hindered on slopes steeper than 16.7 degrees, this value is based on dr. ir. Luuk Fleskens’ unpublished study.

Following Luuk Fleskens’ expertise, we decided to exclude any grid cells within our study area that have slopes that exceed a threshold of 16.7 degrees. We have done this by attributing a value of 1 to slopes below this threshold and a value of 0 to slopes that are equal or exceed this threshold in the Excel spread sheet that is used for map combining. These values are used to exclude grid cells with a value of 0, after the maps have been combined.

A.1.7 Potential SOC Restoration The data we have on potential soil organic carbon (SOC) restoration covers the area 30 to 34N and -7 to -10W, which is a smaller area than our other data sets due to a minor miscommunication between our team and the data donator. The data was kindly provided by dr. Ir. Luuk Fleskens, who produced these maps based on a modelling project he is working on. The results of his model and his methodology, including estimations on investment and maintenance costs, are yet to be published.

SOC is an important determinant of soil quality, as it is the basis for soil fertility, affects soil structure, promotes soil microbial life, and has an important relationship with water holding capacity (Kong et al., 2005; Rawls et al., 2003).

The potential SOC restoration values are based on the difference between actual SOC content of the top 30 cm of the soil and the potential values for SOC content of the top 30 cm of the soil. The actual SOC content is derived from Normalised Difference Vegetation Index (NDVI) values plotted on a spatial map, a distinction between forest and agriculture was taken into account. Additionally, each specific soil type has a range of corresponding vegetation cover, which has also been related back to environmental factors and climate. The potential SOC values are based on soil properties (soil type and elevation) and climate conditions (mainly temperature and precipitation). The difference between actual and potential SOC content says something about by how much SOC would have to increase to get to naturally stable SOC levels. This approach contains an implicit assumption that the natural SOC levels of a soil are always higher than when the land is being managed. This assumption only holds when the land is not subject to irrigated agriculture.

In our analysis, we have used four maps: - Current: current level of organic C content in top 30cm of soil (pro mille) - Natural: potential level of organic C content in top 30cm of soil under natural conditions (pro

mille) - 2050 SOC Restoration: organic C content in 2050 in top 30cm of soil in cells where

restoration is possible (pro mille) - 2050 BAU: Organic C content in 2050 in top 30cm of soil if no restoration is undertaken

(Business As Usual) (pro mille) The SOC restoration map has several blank spaces (NoData), because restoration was not possible at those sites. These blank spaces are filled up with the 2050 BAU map.

We have decided to look at potential SOC restoration under natural conditions rather than under optimal agriculture, as we understand that the techniques that Justdiggit will be implementing are minimal compared to agricultural practices and because Justdiggit promotes its practices as methods that slightly alter natural processes in a way that the system can maintain itself afterwards.

We have visually analysed and compared these maps to find differences in SOC levels. To do this, we used the same colour-scheme and chose a min-max range of 1 to 80 pro mille, which


52

includes all values presented on the four maps. Then we looked at the statistics of the four maps, namely the minimum, maximum, mean, and standard deviation and compared these values.

General scaling method We have decided to scale potential SOC restoration linearly between its minimum (2.45 pro mille) and its maximum (72.99 pro mille) values for Morocco. 0 and 100 pro mille will correspond to 0 and 100 on our suitability scale, respectively, because these are the minimum and maximum levels of SOC restoration on a theoretical level. Specific scaling method We will also look at scaling potential SOC restoration using the minimum and maximum values found in Morocco as 0 and 100 on the suitability scale, respectively.

A.1.8 The Adaptive Capacity Wheel assessment The Adaptive Capacity Wheel (ACW) is a method to assess the characteristics of social institutions that enables adaptive capacity of society in the context of climate change. The term social institution is used in the broad definition and is defined as: ‘institutions are rule-based social patterns that structure social interactions and governs society’ (Van Koppen & Spaargaren, 2015). Social institutions are established sets of norms and rules that govern society. Therefore, with social institutions we mean all the formal and informal rules that govern society, rather than just the government.

The ACW assessment consists of six dimensions: variety, learning capacity, room for autonomous change, leadership, availability of resources and fair governance (see table A10). These dimensions are subdivided into 22 criteria in total, together forming the ACW. The ACW shows whether social institutions encourage the adaptive capacity of society to respond to climate change (Gupta et al., 2010). Furthermore, for each criterion, the effect of the social institution on the adaptive capacity of society was expressed in a score of +2 (positive effect) to -2 (negative effect). Thereafter, the mean was calculated to aggregate the overall score.

The first aim was to collect data for the identified regions (from El Jadida towards Marrakech and Ouarzazate), but it proved a lot of difficulties to obtain the data as it requires detailed data for specific regions. For that reason, it is decided to apply the ACW on a national level. That implies that the ACW does not differentiate between the identified regions, but rather gives a general impression of how social institutions in Morocco enables the adaptive capacity of society in the context of climate change. Therefore, validity of the results of the ACW must always be checked on the local level as there may be differences between regions.

The data used for the ACW were obtained from literature; governmental reports of the Kingdom of Morroco, case studies and NGO reports.

Dimension Criteria Explanation Variety Variety of problem frames and

solutions Room for multiple frames and references, opinions and problem definition

Multi-actor, level and actor Involvement of different actors,

levels and sectors in the governance process

Room for diversity Availability of a wide range of different policy options to


53

tackle a problem Redundancy Presence of overlapping

measures and back-up systems Learning capacity Trust Presence of institutional

patterns that promote mutual respect and trust

Double loop learning Evidence of changes in

assumptions underlying institutional patterns. Double loop learning is present when social actors challenge norms and basic assumptions

Discuss doubts Institutional openness towards uncertainties

Single loop learning Ability of institutional patterns to learn from past experiences and improve their routines

Institutional memory Institutional provision of monitoring and evaluation processes of policy experiences

Room for autonomous change Continuous access to information

Accessibility of data within institutional memory and early warnings systems to individuals

Act according to plan Increasing the ability of individuals to act by providing plans and scripts for action

Capacity to improvise Increasing the capacity of individuals to self-organize and innovate

Leadership Visionary leadership Room for long-term visions and reformist leaders

Entrepreneurial leadership Room for leaders that stimulate actions and undertakings

Collaborative leadership Room for leaders who encourage collaboration between different actors

Resources Authority Provision of accepted or legitimate forms of power; whether or not institutional rules are embedded in constitutional laws

Human resources Availability of expertise, knowledge and human labour


54

Financial resources Availability of financial resources to support policy measures and financial incentives

Fair governance Legitimacy Whether there is public support for a specific institution

Equity Whether or not institutional rules are fair

Table A10: Adaptive Capacity Wheel assessment: criteria and explanation for all six dimensions. In table A11 the score for all the criteria are given, with an explanation for that score. The overall score for Morocco is +0.45


55 Dim

ension Criteria

score Explanation

Variety Variety

of problem

fram

es and solutions +1

- The comm

unity-based association AMSIN

G developed several solutions to combat clim

ate change in the Atlas m

ountain, such as reforestation, construction of dams, greenhouse agriculture w

ith various crops and the developm

ent of early warning system

s (United Nations Developm

ent Programm

e, 2013).

Multi-actor, level and

actor +2

- Implem

entation of Agenda 21; a programm

e of action that defines the objectives and means of

implem

entation of sustainable development. Its principles are solidarity w

ith future generations and with all

the populations of the planet and involvement of all actors of civil society in the decision m

aking progress (Tarradell, 2004; Kingdom

of Morocco, 2012).

- The CGEM label for social responsibility is a solem

n recognition of the respect of Moroccan com

panies of their com

mitm

ent to uphold, defend and promote universal principles of social responsibilities and

sustainable developm

ent: protect

the environm

ent, prevent

corruption, respect

the rules

of fair

competition and increase transparency of corporate governance (Kingdom

of Morocco, 2012; Kingdom

of M

orocco, 2014). - There are a range of civil society organizations in M

orocco. Local NGO

s, including ENDA, M

aghreb and Ribat al Fath, are active and often focused on com

munity-level and local developm

ent issues. Other

manifestations of civil society are also im

portant, including professional associations, universities and local institutions a the com

mune level. Generally, the presence of international N

GOs is lim

ited (OECD, 2011).

- The SEARCH project, led by the International Union for the Conservation of N

ature (IUCN

) adopted a participatory approach at all steps of the process (IU

CN, 2013).

Room

for diversity +1

- SEARCH (social, ecological & agricultural resilience in the face of clim

ate change) is a three year (2011-2013) regional project led by the International Union for the Conservation of N

ature (IUCN

). Several m

ethodologies were developed to increase ecological resilience in w

atershed ecosystems in the O

ued el Kebir w

atershed case (IUCN

, 2013).

Redundancy +1

- The SEARCH project developed several alternative strategies to increase ecological resilience in watershed

ecosystems in the O

ued el Kebir watershed case (IU

CN, 2013).

Total

+1,25

Learning capacity

Trust -1

- Transparency International is an organization of which its m

ain objective is to stop abuse of power, bribery

and secret deals. They work together w

ith governments, business and citizens. The organization created the

Corruption Perception Index, and Morocco is ranked 88 of 168 and has a score of 36 of 100 ( w

ith 0 = highly


56

corrupt, 100 = very clean). The perceived corruption is a proxy for trust (transparency.org).

Double loop learning

+1

- The AMSIN

G association elected wom

en and youth unprecedented leadership and the participation of w

omen has been strongly enhanced by the Association’s endeavours. U

nder traditional, cultural norms and

practices wom

en and men rarely w

ork together and men routinely take on leadership and decision m

aking roles (U

nited Nations Developm

ent Programm

e, 2013).

Discuss doubts 0

- No sufficient data found.

Single loop learning

+1 - Given the success of the project activities executed by the AM

SING association, this developm

ent strategy has been elaborated to m

ore villages within the sam

e region, allowing the entire region to benefit from

the experiences gained in the first project. As such, em

phasis is placed on comm

unications of lessons learned, replication and inform

ing wider policy and practice (U

nited Nations Developm

ent Programm

e, 2013).

Total 0

Room

for

autonomous

change

Continuous access to inform

ation -1

- The ‘new constitution’ executed by King M

ohamm

ed VI enshrines the right to access information (Kingdom

of M

orocco, 2012). - M

orocco scores very poorly on the Open Budget Index (28 points out of 100), as developed by

Transparency International. This index assesses the availability in each country of eight key budget docum

ents, as well as the com

prehensiveness of the data contained in them (transparency.org).

- The OECD (2015) reports that transparency and access to inform

ation somew

hat improved in the last few

years (O

ECD, 2015).

Act according to plan -2

- Although Morocco has dem

onstrated political leadership on climate change, the country needs a lot m

ore capacity to develop and m

anage adaptation programm

es (OECD, 2011).

- Some of the M

oroccan public sectors institutions argue that each of the climate funds has its ow

n priorities, and that M

orocco is required to meet these in order to qualify rather than getting support for its

own plan. Fund and program

mes’ ow

n procedures tend to dominate national system

s (OECD, 2011).

Capacity to im

provise +2

- The AMSIN

G association was created by the villagers of Elm

oudaa, a Berber comm

unity located in the Atlas M

ountains, to address economic isolation and harsh clim

atic conditions. The association, which is led

by the youth, has successfully regenerated degraded lands through a traditional land managem

ent practice called ‘azzayn’, a practice in w

hich herders are banned from grazing in protected lands. The association also

executed reforestation projects to reduce soil erosion and to prevent flooding.

Total -0.33

Leadership

Visionary leadership +2

- Launch of the Green Morocco Plan in 2008; a long-term

strategy to drive and reform the agricultural sector


57

to sustainable growth (Kingdom

of Morocco, 2012).

- Morocco w

as among the first countries to sign and ratify the agreem

ents were m

ade in the Rio Summ

it and developed a national clim

ate plan and was presented in Copenhagen 2009. It ratified U

NFCC in 1995,

ratified Kyoto in 2002 (OECD, 2011) and acted as one of the leaders at CO

P Paris. In addition, Morocco is

considered to be one of the global forerunners in addressing climate change and is lauded as a leader in

renewable energy and clim

ate change policy (Schinke, 2015).

Entrepreneurial leadership

+1 -

In the

period of 1992-2012, M

orocco has

undergone considerable reform

s towards

sustainable developm

ent regarding good governance, society, economy and environm

ent. These reforms w

ere spurred by King M

ohamm

ed VI and endorsed by the Moroccan governm

ent, civil society, private sector and the people (Kingdom

of Morocco, 2012).

- In 2011, King Moham

med VI executed a m

ajor constitutional reform w

hich is called ‘the new constitution’.

The new constitutional regim

e of the kingdom is based on balance, collaboration of pow

ers and participatory dem

ocracy (Kingdom of M

orocco, 2012). - The M

ohamm

ed VI Foundation for environmental protection, established in 2001, is a foundation w

ith its m

ain mission is to raise aw

areness and educate citizens, especially among youth, in preserving their

environment

Collaborative leadership

+1 - Associations have becom

e an essential component of the country’s econom

ic and social life. Synergies are developed together w

ith the private sector and the government through partnerships and agreem

ents. A survey, conducted by the high com

mission for planning, show

s that 2000 associations operate in the field of environm

ent (Kingdom of M

orocco, 2012). - Follow

ed by the Earth Summ

it in Rio 1992, Morocco created a m

inisterial department for the

environment. In the period of 1992-2012, this departm

ent underwent several m

ajor changes and its capacity w

ere reinforced. This has resulted in gradual coordination between all stakeholders (Kingdom

of M

orocco, 2012).

Total +1.33

Resources

Authority -1

- The National Com

mittee for Clim

ate Change was set up in 2011 and is chaired by the Departm

ent for the Environm

ent, who are the national focal point for U

NFCC. However, the Com

mittee has no legal basis w

hich raises questions about its authority, ow

nership and power. Each M

inistry tends to guard its own

independence and co-ordination through the Comm

ittee is weak (O

ECD, 2011).

Human resources

+1 - Since the Earth Sum

mit in Rio in 1992, M

orocco emphasized the prom

otion of human developm

ent (e.g.


58

water security, literacy) and environm

ental awareness and environm

ent-friendly development (Kingdom

of M

orocco, 2012). - M

orocco has seen positive achievements in the period 1992-2012. The population grow

th decreased from

2.2% to 1.32%

, GDP per capita tripled and the illiteracy rate decreased with 19%

. Morocco is determ

ined to keep this up (Kingdom

of Morocco, 2012).

- The main objective of the local Agenda 21 is to im

prove the living conditions of populations while ensuring

better managem

ent and conservation of the environment (Kingdom

of Morocco, 2012).

- At the social level of the National Charter for the Environm

ent and Sustainable Development, efforts in the

fight against poverty, illiteracy and gender inequality is kept up to achieve the Millennium

Development

Goals. Young people are offered training, especially for new green jobs (Kingdom

of Morocco, 2012).

- Unem

ployment rem

ains above 9%, population grow

th has fallen, but remains at 1,2%

. Poverty is focused in rural areas w

ith an estimated 2%

of the population living (OECD, 2011).

Financial resources

-1 - A survey, conducted by the high com

mission for planning, reports that m

any associations (2000) working

in the environmental field suffer from

a lack of technical and financial means (Kingdom

of Morocco, 2012).

- Morocco benefits from

external climate change finance from

bilateral donors (meaning that a donor

government provides assistance to a recipient country) and global clim

ate funds. The biggest donors are the European Com

mission and France w

ith substantial loan sources from the African Developm

ent Bank, World

Bank and European Investment bank. How

ever, the OECD concludes that funding is still relatively sm

all and that the country needs to scale it up m

assively if it is to fully address the challenge of adaptation and develop and m

anage programm

es (OECD, 2011).

- Morocco is ranked 84

th out of 143 countries in the 2014 global innovation index. Funding for scientific and technical research did not exceed 0,8%

of GDP in 2010, against 2-5% in the industrialized countries (U

nited N

ations, 2015).

Total -0.33

Fair governance

Legitimacy

+1 - The O

ECD (2011) reports that major initiatives are executed to prom

ote recycling, energy efficiency and conservation by private citizens. There is evidence of strong public support for environm

ental initiatives such as Earth Day.

Equity

+1 - In January 2010, the project of advanced regionalization w

as executed and led by the Advisory Comm

ittee on Regionalization (CCR), in w

hich a major pillar is to prom

ote larger participation of wom

en managing


59

regional and local affairs and to promote equal access of w

omen and m

en to elected offices (Kingdom of

Morocco, 2012).

- The AMSIN

G association is democratically elected. In addition, the com

munity elected to give w

omen and

youth unprecedented leadership positions ( which is a significant step in this traditional com

munity of

Berbers where m

en traditionally dominate in the governance of the affairs of the village. The participation

of wom

en has been enchanced by the Association’s endeavours (United N

ations Development Program

me,

2013).

Responsiveness +1

- The Equity and Reconciliation (IER) report in 2006, which shed light on the hum

an rights violation com

mitted betw

een 1956 and 1999, reports that Morocco has m

ade an important step tow

ards anchoring dem

ocracy in Morocco.

- The ‘new constitution’ executed by King M

ohamm

ed enshrines citizenship and participatory democracy

(Kingdom of M

orocco, 2012). - King M

ohamm

ed VI called in his Throne speech in 2009 for the elaboration of a ‘comprehensive national

charter’ as a part of a sustainable development policy. As part of an exem

plary participatory approach, the charter as subm

itted to a consultation process involving players and citizens building on: Regional consultations (over 8500 participants) and consultations via the internet (127.000 visits and 9000 filled questionnaires).

Accountability

0 - M

orocco participated in the 2008 Paris Declaration Survey ( it establishes and monitors system

to assess progress and ensure that donors and recipients hold each other accountable for their com

mitm

ents). M

orocco scored well above average regarding selecting donors for program

mes, but scores poorly on donor

harmonization (O

ECD, 2011). - The Paris Declaration Survey of 2008 confirm

ed that Morocco does not have a form

al review of

accountability or assessment of progress m

ade. The lack of a single forum for engagem

ent between

government and external partners is a key issue (O

ECD, 2011). - The O

ECD (2011) concludes that, against the five Paris Declaration criteria, progress is made on alignm

ent and accountability. There is how

ever still a lot of room for im

provement on harm

onization and results.

Total 0.75

Overall score

0.45

Table A11: Adaptive Capacity Wheel assessm

ent: scores for all criteria, with an explanation for that score.


60

A.1.9 Population Density Many social aspects that could affect decisions on the geographical location of the re-greening projects were insignificant in so far as could be derived from our available resources. Additionally, Justdiggit plans to provide all required financing and equipment for the projects. This is why we have decided to use population density as an indicator for availability of labour and people who are willing to cooperate with the project. Sander de Haas, commissioner of our project, also conveyed to us that in areas where the population density is high due to urbanisation, the land around the city tends to get abandoned, which opens up opportunities for projects like the one under discussion. General and specific scaling method We thought of different ways of how to scale population density. When looking at a large range of population density, the scaling would likely be strongly nonlinear, as both too low or too high population density could decrease the suitability of these areas for the projects. Additionally, these extremes may have low suitability in different ways. In order to be able to still be able to represent population density on a suitability scale, we opted for deciding on a cut-off point. This cut-off point is a value of population density. Any value of population density that is higher than this cut-off point is converted to 0. Next, the remaining population density values (from the minimum values until the cut-off point) are scaled in a linear fashion on the suitability scale. It was decided that the cut-off point should be equal to the mean population density found in our study area (109 people/km2). As these scaling decisions and the theoretical basis on the relationship between population density and benefits to re-greening projects can be questioned (an optimum population density is very difficult to define for the purposes of re-greening), the corresponding range for the population density values in our area will vary between 70 and 100.

Due to the difficulties in determining a relationship between population density and benefits to re-greening projects, both on a local and global scale, the specific scaling method for population density is the same as for the general scaling method for population density.

A.1.10 Flood Risk Cost Reduction As we were not able to create a flood risk map for the catchments within our study area using the LAPSUS model, we decided to use values for exposed economic stock to approximate the potential damage that floods can cause. These values were averaged per sub catchment, which are random parts of the two catchments, which are not related to any real sub catchments. The exposed economic stock values were obtained from the UNEP/UNISDR (2013) website.

Unfortunately, we have experienced two issues with this map. First, the map does not indicate any units. We would expect it to be in US dollars, but we cannot be certain. This led us to use these values in relative terms. Second, the map on the website was for visual purposes only and could not be exported into ArcGIS. This led us to estimate the average exposed economic stock per sub catchment based on visually comparing the map on the website and the map with sub catchments in ArcMap. The values per grid cell ranged from less than 10 to 31000. The estimated sub catchment averages are shown below.

As mentioned before, we will treat these values in relative terms when it comes to scaling them.


61

Sub catchment Range Estimated Average Middle Oum About half of the sub catchment mainly has values

below 10 and the other half mainly has values between 10 and 200.

100

North-West Oum Between 0 and 200, but about two-thirds of the sub catchment has values between 10 and 200.

140

South-East Oum Mostly below 10, with some grid cells between 10 and 200.

10

South-East Tensift About three-quarters of the sub catchment has values below 10, while one-quarter has values between 10 and 200.

45

North-West Tensift

About half of the catchment has values between 10 and 200, while the other half mainly has values below 10 .

100

Table A12: Range and estimated average exposed economic stock values per sub catchment. General and specific scaling method Due to difficulties encountered with the exposed economic stock map, which hindered studying exposed economic stock on a larger scale, the scaling method is the same for both the general and the specific scaling method for exposed economic stock.

The exposed economic stock values shows in table A12 are scaled linearly on the suitability scale. Higher economic stock values are scaled higher than lower economic stock values (similarly to erodibility), because this means that areas with high exposed economic stock values can benefit the most from re-greening projects, as increased vegetation can reduce flood risks and, therefore, damage to infrastructure. The range of the suitability scale that corresponds to the range of exposed economic stock values lies between 30 and 60. We did not allow the range to cover the full suitability scale, due to uncertainties related to the units and to the representativeness of exposed economic stock for flood risks.

A.2 Excluded variables The following mapped variables have been combined: precipitation, soil type, erodibility, potential SOC restoration, population density, flood risk costs, and hill slope. The way all of these variables are combined is described in the Technical Methodology Report. Below we offer a brief summary for the variables that were considered, but eventually excluded, from the map combining system.

Soil depth was excluded, because we were unable to obtain a soil depth map on time that had a resolution high enough for the purposes of our analysis. The map that we did have, only showed a soil depth of 100cm for our entire study area.

Geological information on the area was not taken into account, because the only possible complication we could find in terms of the relationship between geological rock layers and the re-greening projects -- i.e. the high porosity of karst layers -- was not applicable to our area.

The results from the Adaptive Capacity Wheel assessment indicated marginal spatial differences, especially within our study area. Therefore, we used this assessment as additional


62

information on the viability of the re-greening projects and used population density as a proxy for available participants for the projects.


63

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Appendix B: Sensitivity analysis of scaled variables In this section, we show all the maps created in the sensitivity analysis of the scaled variables.

Figure B1: Area (%) of our study area that exceeds a certain threshold on the suitability scale for precipitation. Initial values are: S0=0 and S100=100.

Figure B2: Area (%) of our study area that exceeds a certain threshold on the suitability scale for soil type. Initial values are: S0=0 and S100=100.

Figure B3: Area (%) of our study area that exceeds a certain threshold on the suitability scale for erodibility. Initial values are: S0=70 and S100=90.


66

Figure B4: Area (%) of our study area that exceeds a certain threshold on the suitability scale for potential SOC restoriation. Initial values are: S0=0 and S100=100.

Figure B5: Area (%) of our study area that exceeds a certain threshold on the suitability scale for population density. Initial values are: S0=70 and S100=100

Figure B6: Area (%) of our study area that exceeds a certain threshold on the suitability scale for exposed economic stock. Initial values are: S0=50 and S100=100


67

Appendix C: Stakeholder perspectives Environmental Perspective The map produced for the environmental perspective (figure C1) shows more locations which have now passed our minimum threshold of 64, and also more locations which pass the threshold of 80. The most suitable locations lie north of Marrakesh, around Ben Guerir, and north from here, towards the coast. The area directly south of Marrakech also has a higher score than the locations presented from the other perspectives, with a large number of locations passing the minimum threshold of 64.

For our final ranking, we have ranked locations 1,2,3 and 4 as the best places to start the projects. They all have good accessibility and sufficient population to act as an implementation and maintenance team. Together they also cover an area in a south west to north east, following the dominant wind direction which will influence the entrainment of moisture from the vegetation in to the air, influencing cloud formation.

Social perspective Adjusting the weighting for the social perspective produced a map showing fewer suitable locations which surpass the threshold of 64, south of Marrakech (figure C2). However, many of the locations are the same as for the other perspectives, with the majority of high scoring locations being to the North of Marrakech around Ben Guerir, and some further north towards the coast.

Locations 1,2,3,4 and 5 have been identified as the best locations to start the project, and these correspond to the same/ very similar locations as found in the other perspectives. Again, these locations over time have the potential to build up together and influence each other, thus being able to influence the climate over a larger area.


68

Figure C1: Most suitable locations for a hydrological corridor in Morocco from the environmental perspective.


69

Figure C2: Most suitable locations for a hydrological corridor in Morocco from the social perspective.


70

Appendix D: Maps D.1 Maps of Climatic Variables (MatLab) Precipitation Within our study area, the precipitation values remain the same or very similar for all grid cells. The largest variation can be seen in the month May, but this variation is still small. However, the spatial resolution is relatively coarse, which may mask spatial variation in precipitation. Nevertheless, solely based on the precipitation data that we have, there is no preference for a particular region within our study area for re-greening projects. Relative humidity More variation in relative humidity is found in the summer months. The relative humidity is highest near the coast. Wind The average wind direction within our study area in winter is from the west towards the east. In summer, the wind direction is from the east to the west. The transition between these seasons is relatively rapid and takes about 2-3 months. The transition periods are the most crucial periods with respect to influencing rainfall events, as described in the report. Therefore, the month May and October therefore receive special attention. In May, the wind comes from the east/north-east. In October, the wind direction in our study area comes from the north/north-east. The wind speed is relatively low for both May and October. Maps The maps of precipitation in mm/day (represented by the colours), relative humidity in % (contours), and wind speed in m/s and wind direction (represented by quivers) are shown in figures D1 and D2. D.2 Maps of Precipitation (total average)


71

Figure D1: M

aps of precipitation (in mm

/day, colours), relative humidity (in %

, contours), and wind speed (in m

/s) and wind direction (in quivers) for m

onth 1 – 6.


72

Figure

D2: M

aps of

precipitation (in

mm

/day, colours),

relative hum

idity (in

%,

contours), and

wind

speed (in

m/s)

and w

ind direction

(in quivers)

for m

onth 6

– 12

Figure D3: Precipitation averages (mm/y) of Morocco, including urban areas and province boundaries.

Project Report Hydrological Corridor Morocco

74

D.3 Maps of Environmental Variables

Figure D4: Soil types (Calcisols, Fluvisols, Kastanozems, Leptosols, Luvisols, Phaeozems, Planosols, Regosols, Solonchaks and Vertisols) of our study area, including urban areas and province boundaries.


75

Figure D5: Erodibility factors (K values) of soil types of our study area, including urban areas and province boundaries.


76

Figure D6: Natural Potential Soil Organic Carbon (pro mille) of our study area, including urban areas and province boundaries.


77

D.4 Map of Population Density

Figure D7: Population density (people/km2) of our study area, including urban areas and province boundaries.


78

D.5 Map of Exposed Economic Stock Estimates

Figure D8: Exposed Economic Stock Estimates (per sub catchment) of our study area, including urban areas and province boundaries.


79

D.6 Maps of 20 highest suitability values D.6.1 Highest suitability values for the combined perspective

Figure D9: 20 Best suitable location for the combined perspective, including urban areas and province boundaries.


80

D.6.2 Highest suitability values for the sustainability perspective

Figure D10: 20 Best suitable locations for the sustainability perspective, including urban areas and province boundaries.


81

D. 6.3 Highest suitability values for the environmental perspective

Figure D11: 20 Best suitable locations for the environmental perspective, including urban areas and province boundaries.


82

D. 6.4 Highest suitability values for the social perspective

Figure D12: 20 Best suitable locations for the social perspective, including urban areas and province boundaries.


83

Appendix E: Additional information on suitable locations E.1 Key Features in the Landscape A number of key features in the Moroccan landscape become important to take into consideration when deciding on the best locations for re-greening projects, especially because an additional aim of the projects is to increase local rainfall. The features that have been taken into account in our analyses are numbered in the figure (E1) below and have been chosen based on their proximity to areas we have identified with high values on the suitability scale. 1, 3, and 7: Already existing green areas are important to take into consideration, as a large vegetated area is necessary to affect local rainfall. Expanding these existing vegetated areas by re-greening the area around it can therefore help to achieve this aim. 2: Open water, such as lakes, already have relatively high levels of evaporation. The water body encircled in the figure is actually referred to as a river: the Oum Er-Rbia River. Re-greening areas around the Oum Er-Rhia can help increase humidity levels such that they might attain those levels that increase the chance of precipitation. 4, 5, and 6: Changes in elevation can cause an orographic effect. Mountains, for example, force air to rise, causing condensation of humid air. 5 and 6 indicate the beginning of the (High) Atlas Mountains. The significant rise in elevation up to about 4000 meters shows its orographic effect in its relatively green regions. A possibility to increase rainfall, would be to increase the green areas towards the north-west of the Atlas mountains. 4 indicates an elevated area, which might also mean that there are interesting options in re-greening regions to the north from this elevation range. The relative dryness of the area indicates that either the orographic effect is not significant or that the air is too dry. The latter could be influenced by vegetated areas. E.1.1 Wind Direction Although it is not a landscape feature, wind direction is an important aspect to take into account when deciding on a location for re-greening projects if the aim is to also affect rainfall on land. Figures E2 and E3 show the contrasting wind directions in the winter and summer.

Figure E1: Key features in the Moroccan landscape encircled.


84

Figure E2: Precipitation, relative humidity, and wind direction and speed for Morocco in January.

Figure E3: Precipitation, relative humidity, and wind direction and speed for Morocco in July.


85

E.2 Locations and Coordinates E.2.1 Combined Perspective Location number

Rank Coordinates Comments

1 I 32°17’15.26” N 8°00’58.94” W

Relatively high suitability scale values; north of the ridge close to Marrakesh; surrounded by some farms; topography is relatively flat; some areas have not yet been converted to farmland; relatively close to some villages, such as Centre Commune Labrikyine;

2 I 32°17’11.09” N 8°09’42.00” W

Relatively high suitability scale values; proximity to roads and urban areas; well within province boundaries; surrounded by some farmland, but there are also areas that have not been cultivated; topography is relatively flat;

3 I 32°31’55.81” N 7°57’13.99” W

Relatively high suitability scale values; close to border between provinces; surrounded by farms; topography is relatively flat; a lot of area has not been converted to farmland;

4 I 32°20’07.46” N 7°55’51.01” W

Good accessibility roads; proximity to major city (Ben Guerir) and smaller villages; most of the area is farmland;

5 I 32°05’23.94” N 8°07’54.68” W

Relatively high suitability scale values; proximity to roads and some small villages; topography is relatively flat; there is some farmland around;

6 I 32°07’23.94” N 8°20’05.65” W

Relatively high suitability scale values; there are roads nearby, but it is unclear how accessible the location is; some irrigated farmland is located to the south; topography is relatively flat; there are some villages nearby; the location is close to the border between two provinces;

7 III 32°15’53.35” N 7°28’03.36” W

Relatively close to villages, such as Ouled Cherki; abundant farmland is located to the south; topography is relatively flat, but can be more irregular in some areas;

8 III 32°13.30.93”N 7°40’10.74” W

Relatively high suitability scale values; north of the ridge close to Marrakesh; farmland in the south; topography is relatively flat; it seems like it should be accessible; relatively closely situated to Ben Guerir;

9 V 32°20’24.08” N 8°30’59.77” W

Proximity to roads and small villages; extensive farmland region to the north-west; the location itself is mainly farmland;

10 VI 33°09’24.04” N 8°11’48.19” W

Relatively high suitability scale values; close proximity to El Jadida, as well as other smaller villages; it consists of farmland, while surrounded by abundant farmland; the location is easily accessible by road; and is located near the Oum Er-Rbia river; topography is relatively flat;

11 VI 12 VI 33°03’57.84”

N 8°14’07.74” W


13 IV 32°43’52.73” N

Closely situated to the Oum Er-Rbia river; abundant farmland located to the north; slightly irregular topography; closely


86

7°54’07.62” W located to Douar Kachacha. 14 IV 31°39’40.33”

N 7°25’42.98” W

Proximity to Atlas Mountains and to Marrakesh; extended farmland located to the north and green regions on the flanks of the mountains to the south; good accessibility by roads and villages are located nearby; topography is relatively flat and some areas within the location area are farmland;

15 V 32°56’17.56” N 7°20’56.53” W

Proximity to main urban areas; good accessibility; much of the area is farmland;

16 V 32°52’59.96” N 6°57’58.11” W

Proximity to large urban areas: Khouribga; good accessibility roads; most of the area is farmland;

17 VII 32°31’41.58” N 8°06’56.84” W or 32°29’52.26” N 8°30’59.77” W

Proximity to roads; topography is slightly irregular, but there are areas that are relatively flat; a large area of farmland is located to the north-west; the location is surrounded by a small amount of farmland and small villages;

18 IIX 32°37’46.17” N 7°17’23.39” W

Surrounded by some farmland; close proximity to roads and small villages; upwind from Oum Er-Rbia River; topography is relatively flat, but can be slightly irregular in some of the surroundings;

19 IIX 31°45’30.78” N 7°44’51.17” W

Located between two ridges; proximity to ridge near Marrakesh and Marrakesh itself; accessibility is good and there are villages nearby; some of the area is farmland; green farmland is found on the other side of the southern ridge;

20 IX 31°18’17.36” N 8°11’05.06” W

Proximity to Atlas Mountains; proximity to some villages with some surrounding farmland; river to the east (Barrage Lalla Takerkoust); slightly irregular topography; green regions on the flanks of the Atlas Mountains to the south, which can be extended

Table E1: Ranked locations, with coordinates and comments on these locations for the combined perspective (COMB). E.2.2 Sustainability Perspective Location Rank Coordinates Comments 1 I 32°17’15.26”

N 8°00’58.94” W

Relatively high suitability scale values; north of the ridge close to Marrakesh; surrounded by some farms; topography is relatively flat; some areas have not yet been converted to farmland; relatively close to some villages, such as Centre Commune Labrikyine;

2 I 32°17’11.09” N 8°09’42.00” W

Relatively high suitability scale values; proximity to roads and urban areas; well within province boundaries; surrounded by some farmland, but there are also areas that have not been cultivated; topography is relatively flat

3 I 32°31’55.81” N 7°57’13.99” W

Relatively high suitability scale values; close to border between provinces; surrounded by farms; topography is relatively flat; a lot of area has not been converted to farmland;

4 I 32°20’07.46” N

Good accessibility roads; proximity to major city (Ben Guerir) and smaller villages; most of the area is farmland;


87

7°55’51.01” W 5 I 32°05’23.94”

N 8°07’54.68” W

Relatively high suitability scale values; proximity to roads and some small villages; topography is relatively flat; there is some farmland around;

6 II 32°07’23.94” N 8°20’05.65” W

Relatively high suitability scale values; there are roads nearby, but it is unclear how accessible the location is; some irrigated farmland is located to the south; topography is relatively flat; there are some villages nearby; the location is close to the border between two provinces;

7 III 32°15’53.35” N 7°28’03.36” W

Relatively close to villages, such as Ouled Cherki; abundant farmland is located to the south; topography is relatively flat, but can be more irregular in some areas;

8 III 32°13.30.93”N 7°40’10.74” W

Relatively high suitability scale values; north of the ridge close to Marrakesh; farmland in the south; topography is relatively flat; it seems like it should be accessible; relatively closely situated to Ben Guerir;

9 V 32°20’24.08” N 8°30’59.77” W

Proximity to roads and small villages; extensive farmland region to the north-west; the location itself is mainly farmland;

10 VI 33°09’24.04” N 8°11’48.19” W


11 VI 33°19'29.8"N 8°06'24.6"W

SW of the Forêt Chiadma, located in a semi-urban/ farming area, good access and will expand the climate effects of the forest already in place

12 VI 33°03’57.84” N 8°14’07.74” W


13 IV 32°43’52.73” N 7°54’07.62” W

Closely situated to the Oum Er-Rbia river and Douar Kachacha; abundant farmland located to the north; slightly irregular topography;

14 IV 32°38’45.79” N 7°52’28.60” W

Oum Er-Rbia River runs through the northern part of this area; it is surrounded by some farmland and has a large agricultural region to the north; this region also has areas that have not been cultivated yet;

15 IV 32°56’17.56” N 7°20’56.53” W

Proximity to main urban areas; good accessibility; much of the area is farmland;

16 IV 32°52’59.96” N 6°57’58.11” W

Proximity to large urban areas: Khouribga; good accessibility roads; most of the area is farmland;

17 IV 32°35’02.85”N 7°08’21.70” W

Proximity to roads and villages; green regions located to south-east on the flanks of the Atlas Mountains; surrounded by farmland;

18 VII 32°31’41.58”N 8°06’56.84” W or

Proximity to roads; topography is slightly irregular, but there are areas that are relatively flat; a large area of farmland is located to the north-west; the location is surrounded by a small amount


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32°29’52.26”N 8°30’59.77”W

of farmland and small villages;

19 III 32°01’12.54” N 7°52’31.19” W

Irrigated farmland located to the north and south; good accessibility; topography is relatively flat;

20 IIX 31°32’24.88” N 8°42’58.41” W

Closely located to Chichaoua and farmland (to the west); larger farmlands are located more to the south; accessibility is good; proximity to river(bed)

Table E2: Ranked locations, with coordinates and comments on these locations for the sustainability perspective (SUSTAIN). E. 2.3 Environmental Perspective Location Rank Coordinates Description 1 I 32°18'39.8"

N 8°03'33.6" W

Area around Labrikiyne. Urban area North West of Ben Guerir, good road access, some farmland but can develop around this;

2 I 32°21'20.3" N 8°21'55.5" W

Area around Laghenanema, small town clusters, good road access, and empty land to utilise in this high scoring area;

3 I 32°38'17.1" N 7°46'54.4" W

Farming is abundant here, it is adjacent to the Oum Er-Rbia river and topography is reasonably flat;

4 I 32°29'11.0" N 7°55'11.2" W

North West of Skhour des Rehamna, adjacent to road, some vegetation and agriculture;

5 II 32°09'49.9" N 8°24'03.0" W

High scoring Remote area around the small town of Hadj Brahim, with road access;

6 II 32°08'37.4" N 8°12'50.5" W

More remote area. Slightly irregular topography, small towns nearby for labour supply, with road access;

7 V 32°13'51.5" N 7°26'59.9" W

Area around Ouled Cherki, just north of highly agricultural area, with sufficient labour and road accessibility;

8 IX 32°46'54.6" N 8°00'59.4" W

Follow on from locations 9 and 10, but there is not much around this area so may be difficult to initiate/ maintain the project;

9 III 32°51'47.2" N 8°01'56.9" W

Adjacent to the meandering Oum Er-Rbia River, and west of an already vegetated area, this location lies in farmland with easy access and labour;

10 III 32°55'48.7" N 7°57'54.7" W

Slightly East of location 9, closer to Douar Zaouia Cherkaouia, again amongst farmland;

11 IV 33°19'29.8" N 8°06'24.6" W

SW of the Forêt Chiadma, located in a semi-urban/ farming area, good access and will expand the climate effects of the forest already in place;

12 IV 33°15'29.4" N 8°11'22.7" W

SW of location 11, still in farmland with road access. Will follow on nicely from location 11 in the prevailing wind direction;

13 IV 33°12'30.8" N 8°14'07.1"

SW of the Oum Er-Rbia River, following the direction of locations 11 and 12;


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W 14 VI 32°53'20.3"

N 6°56'46.5" W

High scoring area west of Khouribga. Lots of resources and accessibility;

15 VII 32°48'20.8" N 7°03'35.6" W

Area between 14 and 16 amongst farmland with different irrigation techniques (can see circular shape irrigation fields on Google Maps). Good labour availability and access;

16 VII 32°35'54.2" N 7°02'34.1" W

Area around Ouled Bouali, abundant farmland and a town in reasonably high scoring area, will be influenced by locations 14 and 15 due to the prevailing wind direction;

17 X 31°31'39.0" N 7°41'31.9" W

North of the Atlas Mountains. Here is a lot of agriculture, infrastructure and also a reasonable amount of already vegetated areas, but this could be expanded as our results show that this area has reasonably high potential for successful vegetation establishment;

18 IIX 31°52'11.9" N 7°27'59.1" W

Around the area of Ouled Zaida, some agricultural land, good access and labour availability, can follow on from location 7, even though it has a lower re-greening potential

19 IIX 31°47'53.1" N 7°34'51.8" W

Just south of an already green agricultural area, near Ras El Ain. A location here has a reasonably high score for potential re-greening success and will help to enhance the climatic influences which the agricultural lands are most likely already having

Table 2: Ranked locations, with coordinates and comments on these locations for the environmental perspective (ENVIRON). E. 2.4 Social Pespective Location Rank Coordinates Description 1 I 32°16'42.4"

N 8°09'15.5" W

Area West of Ouled Si Bouyahya, lots of agricultural land and very high score from our data.

2 I 32°22'50.8" N 8°20'09.5" W

High scoring area around Boukoudia, which is a small town providing labour and land access.

3 I 32°29'11.0" N 7°55'11.2" W

Around Skhour des Rehamna, with a green area to the SW, to which the re-greening project can be adjoined over time.

4 I 32°15'02.3" N 7°57'35.5" W

Same as Location 1 in the environmental perspective map, but received a lower score. Does however link with locations 1,2 and 3.

5 I 32°32'05.2" N 7°49'53.8" W

To the NE of location 3, also a high scoring area. Some agricultural land and infrastructure

6 II 32°08'49.8" N 8°12'51.3" W

Near the provincial border which may be problematic in terms of gaining permissions, but this area had a very high score. A few small towns and farms around which would be able to manage the project.

7 II 32°09'49.9" N 8°24'03.0" W

Same as location 5 for the environmental perspective, small town area but according to our results has high potential.

8 III 32°55'48.7" Same location and score as location 10 in the environmental


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N 7°57'54.7" W

perspective

9 III 32°51'47.2" N 8°01'56.9" W

Same as location 9 from the environmental perspective

10 IV 33°19'29.8" N 8°06'24.6" W


11 IV 33°15'12.9" N 8°06'37.2" W

South from location 10, lots of agricultural land and road access.

12 V 33°06'16.9" N 8°14'44.6" W

Around Zaouiet Sidi Mohamed Chaoui, with abundant road and labour, south of the Oum Er-Rbia River

13 VII 32°46'54.6" N 8°00'59.4" W

Same as location 7 for the environmental perspective.

14 VI 32°10'34.4" N 7°39'43.1" W

To the west of location 13, near Ararcha, lots of agricultural fields and labour available.

15 VI 32°35'54.2" N 7°02'34.1" W


16 IIX 32°35'54.2" N 7°02'34.1" W

Same as location 16 in the environmental perspective. Amongst farmland, good road access.

17 IX 32°56'06.2" N 7°32'09.6" W

South East from Settat, amongst agricultural lands, with small urban areas and good road access.

Table E4: Ranked locations, with coordinates and comments on these locations for the social perspective (SOCIAL).


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Appendix F: Group work evaluation In this section we evaluate the group work process during the ACT course as we find it very important to reflect on our own work and the problems we encountered.

In the beginning of the project, we were very determined to deliver a product that would match our ambition and dedication. We achieved our ambitious goals through the full dedication of all group members. Nearly all individual tasks were completed according to schedule and a significant amount of hours were spent outside the assigned time for this course. However, we underestimated some of the work, especially for the data acquisition. This was partly due to unforeseen errors, nevertheless we still managed to obtain the necessary data and encountered no big time issues. There were some structural and planning issues due to changing of plans or difficulties. Problems were often solved during group discussion, but we should have kept each other updated on individual tasks and progress more frequently. Everyone participated during discussions and decision making moments and we were always willing to help each other out with individual tasks and questions. During our first meeting our personal learning goals were discussed and each group member was given tasks he/she found challenging. Also, there were no issues nor tension within the group and there was a good balance between professional attitude and social interaction. Finally, we made crucial decisions at the time and we quickly got over disappointments.

Throughout this course, we have reached several learning outcomes. Firstly, we have learned how to interact with the commissioner in a professional way and how to reduce the commissioner’s unrealistic expectations. Secondly, we had to search our own solutions, but also accept help from experts when needed. Thirdly, we had to accept that we had to make assumptions and compromises to our original scientific approach and how to make difficult decisions regarding priorities and planning. Fourthly, we had to adapt our scientific results to make them readable for various audiences. Finally, we believe this course gave us a good taste of how real assignments are conducted and gave us a good preparation for when we have our first real job. We conclude that this course was very valuable and that lots of lessons were learned. We want to emphasize on one particular lesson: keep it simple and realistic. You should ask yourself whether your goals and aims are realistic and necessary.

Potential Locations for a Hydrological Corridor, North ... · (semi-)arid areas, such as Morocco....

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