# ScenaLand: a simple methodology for developing land use and management scenarios

Amandine Valérie Pastor, Joao Pedro Nunes, Rossano Ciampalini, Haithem Bahri, Mohamed Annabi, Mohamed Chikhaoui, Armand Crabit, Stéphane Follain, Jan Jacob Keizer, Jérôme Latron, et al.

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## To cite this version:

Amandine Valérie Pastor, Joao Pedro Nunes, Rossano Ciampalini, Haithem Bahri, Mohamed Annabi, et al.. ScenaLand: a simple methodology for developing land use and management scenarios. Mitigation and Adaptation Strategies for Global Change, 2022, 27 (52), pp.1-29. 10.1007/s11027-022-10024-7. hal-03786435

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Submitted on 12 Jun 2023

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# ScenaLand: a simple methodology for developing land use and management scenarios

Amandine Valérie Pastor¹,²,³,⁴ ID · Joao Pedro Nunes⁴,⁵ · Rossano Ciampalini¹,⁶ · Haithem Bahri⁷ · Mohamed Annabi⁸ · Mohamed Chikhaoui⁹, et al. [full author details at the end of the article]

Received: 1 October 2021 / Accepted: 15 August 2022 / Published online: 9 September 2022
© The Author(s) 2022

## Abstract

Scenarios serve science by testing the sensitivity of a system and/or society to adapt to the future. In this study, we present a new land use scenario methodology called ScenaLand. This methodology aims to develop plausible and contrasting land use and management (LUM) scenarios, useful to explore how LUM (e.g. soil and water conservation techniques) may affect ecosystem services under global change in a wide range of environments. ScenaLand is a method for constructing narrative and spatially explicit land use scenarios that are useful for end-users and impact modellers. This method is innovative because it merges literature and expert knowledge, and its low data requirement makes it easy to be implemented in the context of inter-site comparison, including global change projections. ScenaLand was developed and tested on six different Mediterranean agroecological and socio-economic contexts during the MASCC research project (Mediterranean agricultural soil conservation under global change). The method first highlights the socioeconomic trends of each study site including emerging trends such as new government laws, LUM techniques through a qualitative survey addressed to local experts. Then, the method includes a ranking of driving factors, a matrix about land use evolution, and soil and water conservation techniques. ScenaLand also includes a framework to develop narratives along with two priority axes (contextualized to environmental protection vs. land productivity in this study). In the context of this research project, four contrasting scenarios are proposed: S1 (business-as-usual), S2 (market-oriented), S3 (environmental protection), and S4 (sustainable). Land use maps are then built with the creation of LUM allocation rules based on agroecological zoning. ScenaLand resulted in a robust and easy method to apply with the creation of 24 contrasted scenarios. These scenarios come not only with narratives but also with spatially explicit maps that are potentially used by impact modellers and other end-users. The last part of our study discusses the way the method can be implemented including a comparison between sites and the possibilities to implement ScenaLand in other contexts.

**Keywords** Land degradation · Land use and management scenarios · Narrative · Experts · Soil and water conservation techniques
# 1 Introduction

Land degradation (LD) is recognized as a global environmental problem (Lal 1995, 2003). It is defined as a natural or human-induced process that negatively affects the land to function effectively within an environmental system and can be defined as a process of degrading land from a former state (Zorn and Komac 2013). Recent studies also show that earth-tipping points are closer to their limits and LD has accelerated due to anthropogenic pressures on natural resources including soil and water (Rockström et al. 2009; Steffen et al. 2015; Olsson et al. 2019; Gerten et al. 2020). Recently, LD was made a priority by the United Nations (UN) with the Sustainable Development Goal 15 “Life on land”, the Land Degradation Neutrality (LDN) program (Gilbey et al. 2019), and the UN decade on ecosystem restoration program (2021–2030) (Dubey et al. 2021). In the Mediterranean region, severe LD can impede productive agriculture development (Borrelli et al. 2016; Panagos et al. 2015; Raclot et al. 2018) which is likely to be exacerbated by climate change (Lagacherie et al. 2018; Borrelli et al. 2020). Traditionally, farmers used soil and water conservation (SWC) techniques for land and soil preservation (Bocco 1991; Gessesse et al. 2009; Sartori et al. 2019) commonly known as “indig’enous knowledge” of soil management (Critchley et al. 1994; Makondo and Thomas 2018). Besides, SWC techniques were proven to help mitigate and adapt ecosystems and related ecosystem services to climate change (Delgado et al. 2011; Lal 2014). As shown by Rattan Lal (2014), maintaining fertile soils and/or restoring eroded soils through soil conservation measures have multiple benefits on ecosystem services such as increas-ing water, carbon, and nutrient storage in the soil, increasing soil biodiversity, and con-tributing to climate change mitigation. For instance, Panagos et al. (2015) show that SWC techniques such as cover crops, crop residue restitution, and reduced-tillage prac-tices can decrease soil erosion by 19% in the Mediterranean context. However, with the intensification of agriculture, these techniques have been often neglected despite their proven efficiency in soil conservation (Prosdocimi et al. 2016).

Sustainable soil management requires understanding the biophysical and socio-economic drivers of LD, and better forecasting approaches for land use/management changes (Kok et al. 2006; Terranova et al. 2009; Cerdan et al. 2010; Raclot et al. 2018). Environmental impact studies were developed to assess the impact of anthropogenic practices on the Earth system and the resilience of a system and/or restore this system to “acceptable” and/or better conditions (Jay et al. 2007). For that, scenarios are a useful way to evaluate potential future developments and/or assess potential risks and catastrophic events at different spatial and temporal scales (Kriegler et al. 2012). Additionally, they form an innovative basis to help scientists and policymakers to prepare long-term planning strategies under a wide range of plausible futures. Scenarios were initially used in military planning and gaming and were then taken over by business and environmental planners (Moss et al. 2010). In the 1960s, businesses and organizations used scenarios to explore windows of opportunity and plan long-term investments. Then, scientists started to use scenarios for impact studies. For instance, “A socioeconomic scenario is a combination of quantitative projections and qualitative information such as narratives that jointly characterize a plausible future” (Kriegler et al. 2012). Kok et al. (2006) showed that the involvement of stakeholders improves the quality of developed scenarios, especially in the design of narrative scenarios and in the tasks of identifying past and future socioeconomic trends. Besides, land use and management techniques are often site-specific and depend on pedological and climatic conditions;
<figure><img src="image_3.png" /><figcaption>**Fig. 1** Characterization of the level of comprehensiveness of implausible and plausible futures (Adapted from Parry et al., 2007)</figcaption></figure>

thus, it is important to involve stakeholders to achieve a satisfactory scenario design and implementation (Keizer and Hessel 2019).

Scenarios are described as plausible futures with a high level of comprehensiveness and without an ascribed likelihood (Fig. 1). Comprehensiveness represents the level of understanding of the biophysical and socioeconomic system, while likelihood represents the probability of occurrence (Parry 2007). Figure 1 shows that scenarios are set with a high level of comprehensiveness and within the scope of plausible futures without ascribed likelihood. Scenarios are different from probabilistic futures, which are based on ascribed likelihood. Narratives are useful for designing scenarios because they allow stakeholders to contextualize a plausible future with a high level of comprehensiveness of the future environmental and socioeconomic characteristics. According to Fauré et al. (2017), three types of scenarios exist: predictive (“what will happen?”, “how specific drivers develop, extrapolation of future, business-as-usual (BAU)), explorative<fnref n="1" /> (“what can happen?”, identify qualitative drivers, strategic scenarios, forecasting), and normative<fnref n="2" /> (“what should happen?”, how a specific end-point and/or desirable future can be reached, backcasting). Many environmental impact studies use a mix of these types of scenarios to explore desirable and undesirable futures under global change (Kok et al. 2007, 2018; Kok and van Delden 2009).

<footnote n="1">¹ Normative scenario = organized attempts at evaluating the feasibility and consequences of trying to achieve certain desires or risks to avoid.</footnote>

<footnote n="2">² Exploratory scenario = “what if” theory is explored.</footnote>
On a global scale, climatic and socioeconomic scenarios are usually designed with the Representative Concentration Pathways (RCPs) for plausible greenhouse gases emissions and the Socioeconomic Shared Pathways (SSPs) for exploring socioeconomic global trends such as population rise, gross domestic product (GD) evolution, and consumer preferences (van Vuuren et al. 2011; O'Neill et al. 2015). Each of the SSPs contains a storyline or a narrative (Van Vuuren et al. 2011) and their combination has been set with the recent accomplishment of CMIP6 climate and SSP datasets (O'Neill et al. 2016; Hurtt et al. 2020). At the regional scale, SSPs can be downscaled and contextualized according to the local socioeconomic context, and climate change scenarios can be downscaled with various statistical and numerical methods from global climate models (GCMs) to regional climate models (RCMs) and validated with local climate measurements (Maraun 2016). For example, the European scale project IMPRESSIONS (Impacts and Risks from high-end scenarios: Strategies for innovative solutions) has been successful to design regional-scale scenarios and to involve stakeholders from more than four European countries (Kok et al. 2019).

While climate scenarios are often used in a wide range of environmental projects, SSPs require some regional hypothesis to address a specific environmental problem at a local scale. For example, exploring different agricultural management and soil conservation techniques might be out of the scope of the SSPs. If SSPs were used to test land management techniques, it would require organizing stakeholder workshops to build a hypothesis of land management behind each SSPs, which could be time- and resource-consuming at an early stage of exploration. Moreover, in regions where stakeholders have been overstretched, there might be a "stakeholder fatigue" that leads potentially to the failure of the project (Gramberger et al. 2015). Therefore, impact studies can create their own land use scenarios to explore in a short time the possible landscape evolution (Bracken et al. 2015). Scenarios are often used to feed impact assessment models, integrated assessment models (IAMs), and climate change impacts, adaptation, and vulnerabilities (IAVs) frameworks (Swart et al. 2004). Thus, while scenarios of climate change and socioeconomic are harmonized on the global scale (Hurtt et al. 2020), regional scenarios are difficult to downscale and often need bottom-up readjustments with statistical downscaling techniques (e.g. climate downscaling) and/or by organizing stakeholder workshops to gain knowledge and expertise on a specific site. However, there is a lack of methods and studies allowing the creation of robust local and regional scenarios with low data and input resources (Good-speed and DeBoskey 2020). Therefore, there is a need for a simple and robust approach that can help scientists design plausible land use scenarios, including improved land use management in a systematic way.

This study aims at developing “plausible” scenarios based on expert knowledge within realistic socioeconomic contexts within the frame of the Mediterranean agricultural soil conservation under global change (MASCC)<fnref n="3" /> project. The narrative scenarios and related quantitative inputs created with the method developed in this study named “ScenaLand” provide input for subsequent modelling works such as in the study of Pastor et al. (2019) with the use of an impact erosion model “LandSoil” (Ciampalini et al. 2012, 2017). The specific research question was: “How to create plausible scenarios within a multi-site context to explore future impacts of global change on ecosystem services including soil and water conservation within constrained time and resources?” A comparison between

<footnote n="3">³ Mediterranean agricultural soil conservation under global change: https://masc-project.org/</footnote>
# Scenaland

<figure><img src="image_4.png" /><figcaption>Fig. 2 Methodological flowchart of ScenaLand</figcaption></figure>

different Mediterranean agroecosystems with contrasted socioeconomic contexts was carried out, and future impacts of climate change, LUM change, and soil conservation techniques were explored. The aim was to produce four land use narratives and scenarios in six study sites using the ScenaLand methodology. Finally, with the outcome of the ScenaLand method, an inter-site comparison of the evolution of LUM has been conducted. The novelty of the ScenaLand method is that (i) it provides qualitative information (narratives) based on expert knowledge and literature review, (ii) it comes up with explicit quantitative inputs for LUM for impact modellers (end-users), and (iii) it provides a common framework to take into account site-specific characteristics to facilitate the inter-site comparison of similar SSPs (or trajectories).

## 2 Materials and methods

### 2.1 Description of ScenaLand methodology

ScenaLand is a methodology that provides contrasted scenarios to evaluate the impact of global change. ScenaLand is applied using the following steps: the first step allowed identifying the socioeconomic and biophysical drivers of change; the second step consists on developing a narrative per scenario and per site with the experts; and the last step comprises the construction of LUM maps for each study site based on spatial
<table><caption>**Table 1** Description and ranking variables of socioeconomic trends (survey 1)</caption><thead><tr><td>Driving factors</td><td>Description</td></tr></thead><tbody><tr><td>Economy</td><td>Influence of international market on local production</td></tr><tr><td>Access to natural resources</td><td>Soil, water for irrigation, energy constraints, topography</td></tr><tr><td>Governance</td><td>National and local policies, subsidies, etc</td></tr><tr><td>Recent movement</td><td>Any recent economic or social activity that changed the dynamic of the region</td></tr><tr><td>Climate</td><td>Temperature, rainfall constraints</td></tr><tr><td>Migration/distance to the city</td><td>Is the distance to the city a limitation to local economic development?</td></tr><tr><td>Research and development</td><td>University and NGOs promoting, transferring and collecting knowledge on-site</td></tr></tbody></table>

allocation rules (Fig. 2). Land use and management practices have been incorporated in the scenarios in steps 2 and 3. The common framework is based on two (or more) axes such as environmental protection and/or socioeconomic dimensions to explore a specific research question. To achieve that, each step is addressed by a questionnaire that should be answered by a minimum of three local experts either individually or in groups. Experts are represented by local scientists and/or representatives of local public and/or private institutions. Additionally, a facilitator is designated to moderate the debate and take notes. At the beginning of the exercise, the group agrees on the facilitator who will complete the questionnaire. This method is illustrated in Fig. 2 and in the 3 section and Appendix A-B-C.

### 2.1.1 Step 1: Socioeconomic context and biophysical changes

**Step 1.1. Identification of socioeconomic and biophysical key drivers** This step draws on socioeconomic analysis for each study site in the last 50 years to identify socioeconomic trends and biophysical changes (Kriegler et al. 2012). Then, a description and ranking of seven driving factors is conducted with the local experts (Fig. 2, Table 1). This classification allows for an in-depth discussion of the socioeconomic context and an inventory of the emergent facts and plausible futures.

Concerning the reference to the ranking, each stakeholder has to classify the different categories to answer the question: “What are the dominant/slowing factors driving your CS?” This ranking is not necessarily important in the quantitative answer but rather to start a discussion on the socioeconomic drivers of the region with the stakeholders.

**Step 1.2. Land use and management evolution** In this step, an analysis of the land use evolution is carried out including abandoned and emergent LUM from past and actual decades and their possible impacts on economic profit, food self-sufficiency, environmental impact and erosion, rate of abandonment, and a possible shift to another land use based on its biophysical tolerance (slope, soil type, etc.). Then, an analysis of the current and potential water and soil conservation practices is carried out for the rating of environmental benefits, the impact on erosion, and social acceptance. In this step, a weighing sign
<table><caption>**Fig. 3** Likely trends of development of indicators for each scenario; TERI 2009 (Adapted from Kriegler et al. 2012)</caption><thead><tr><th>Scenarios</th><th>Demographics</th><th>Economy</th><th>Social and cultural</th><th>Conflict</th><th>Technological</th><th>Environment</th><th>Governance</th></tr></thead><tbody><tr><td>S1- Business as usual</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>S2 - Market-oriented</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>S3 - Conservative approach focused on environment</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>S4 - Sustainable growth</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr></tbody></table>

(–,–,=,+,++) was used to describe the evolution of one category over time (past (1990–2010)–present (2010–2020)–future (2020–2050)).

## 2.1.2 Step 2: Definition of four scenarios and narratives

This step consists of developing narratives and land use scenarios based on step 1.

**Step 2.1. Construction of trends** The driving factors of the current landscape are defined in this step to develop the business-as-usual (BAU) scenario based on historical and current trends and to create three other revolving contrasted narratives. The shift in socioeconomic and biophysical variables (demography, economy, social and cultural, technological, environment, and governance) is also described for each scenario (Fig. 3). Note that when the study is developed in multiple sites, the four scenarios must be replicated for each site.

**Step 2.2. Construction of narratives** This step consists of developing the four scenarios based on Kriegler et al.'s approach (2012; Fig. 3). Based on the trends developed in step 2.1, a concise narrative (within 300 words) is created for each of the four scenarios: S1 (business-as-usual), S2 (market-oriented), S3 (environmental protection), and S4 (sustainable). According to each local context, trends can be adapted after agreements between experts. In this step, the results of steps 2.1 and 2.2 are sent to local experts, so that the synthesis of the local trends and narrative scenarios can be validated.
### 2.1.3 Step 3. Land use maps creation

The last step consists of translating the narrative scenarios into spatially explicit land use maps including land management practices (tillage, SWC, etc.).

**Step 3.1. Quantification of land use and management changes for each scenario and site** In this step, based on the baseline land use map (S0), a trend evolution is defined for each land use (expressed as a percentage of surface area) corresponding to the narrative as well as to the degree of implementation of each SWC technique chosen by scenario.

**Step 3.2. Creation of topographic zones** Land use and management allocation rules and biophysical constraints based on soil type, slope, and field size are defined with a GIS-based system. Based on these constraints, allocation rules are created to optimize the allocation of land use and SWC techniques.

**Step 3.3. Spatialization of land use maps for each scenario in each study site**" In this last step, a consensus on the final land use surface and the implemented SWC is discussed and agreed upon with the local experts.

## 2.2 Implementation of the ScenaLand method to six case studies

The ScenaLand methodology was developed and applied in the framework of the MASCC (Mediterranean Agricultural Soils Conservation under global Change) project. This research project was developed to address soil erosion and degradation mitigation and adaptation strategies to global change in the Mediterranean region. This project is based on the study of six sites across six Mediterranean countries with different agroecosystems and socioeconomic contexts (Table 2 and Appendix A-B). Some sites can show similarities, for example, half of these sites are characterized by abandoned rural areas (Can Vila, Macieira, and Cannata), while the other half of the sites is having agricultural activity and urbanization pressure on agricultural land (Roujan, Kamech, and Tleta). More details on the MASCC project and related site descriptions can be found at https://mascc-project.org/study-sites and in Appendix A-B-C.

The aim of the MASCC project was to determine how LUM can be adapted to mitigate soil erosion in six study sites of the Mediterranean region, given the local constraints and the different levels of land profitability and environmental protection. The ScenaLand methodology was implemented in all MASCC sites except for the Tleta where step 3 was developed with a different methodology (Hérivaux et al. 2021). The scenarios were elaborated based on two axes: “socioeconomic development” and “environmental protection” to answer the research question of this study: “How to create plausible scenarios within a multi-site context to explore future impacts of global change on ecosystem services including soil and water conservation within constrained time and resources?” (Fig. 4).

In the frame of the MASCC project, Scenaland was applied with a participatory approach known as 'consultation approach' (De Vente et al. 2016). We have adapted the way the questionnaires was performed for each location, depending on the number of people available, and on the available time and resources. Each survey was performed in one of the official languages of the country in consultation with local experts and inhabitants. The surveys were sent in advance so that the local team had enough time to gather the
<table><caption>**Table 2** Study sites description</caption><thead><tr><th></th><th>Roujan, France</th><th>Can Vila, Spain</th><th>Cannata, Italy</th><th>Macieira, Portugal</th><th>Kamech, Tunisia</th><th>Tleta, Morocco</th></tr></thead><tbody><tr><td>Area (km²)</td><td>0.92</td><td>4.17</td><td>1.3</td><td>0.94</td><td>2.63</td><td>178</td></tr><tr><td>Outlet coordinates (lat; lon)</td><td>43.5; 3.3</td><td>42.2; 1.8</td><td>37.7; 15.1</td><td>41.3; -8.3</td><td>36.8; 10.9</td><td>35.9; -5.4</td></tr><tr><td>Altitude (m)</td><td>300-400</td><td>1100-1650</td><td>903-1270</td><td>441-620</td><td>80-190</td><td>80-650</td></tr><tr><td>Climate (Koppen classification)</td><td>Mediterranean semi-arid (Csa - Mediterranean)</td><td>Mediterranean subhumid (Cfb - Oceanic)</td><td>Mediterranean semi-arid (Csa - Mediterranean)</td><td>Mediterranean humid (Cfb - Oceanic)</td><td>Between Mediterranean semi-arid and sub-humid (Csa - Mediterranean)</td><td>Between Mediterranean semi-arid and sub-humid (Csa - Mediterranean)</td></tr><tr><td>Mean annual precipitation (mm)</td><td>628</td><td>850</td><td>715</td><td>1300</td><td>645</td><td>690</td></tr><tr><td>Aridity index (FAO classification)<sup>a</sup></td><td>Dry subhumid (0.5-0.65)</td><td>Humid (&gt;= 0.65)</td><td>Semi-arid (0.2-0.5)</td><td>Humid (&gt;= 0.65)</td><td>Semi-arid (0.2-0.5)</td><td>Dry subhumid (0.5-0.65)</td></tr><tr><td>Land use activity</td><td>Traditional vineyard and wine production</td><td>Mountainous abandoned terraces in state of natural afforestation</td><td>90% permanent pasture</td><td>Land cover dominated by commercial forest, pasture and croplands</td><td>Land cover dominated by cereal cropland (75%), in rotation with leguminous crops</td><td>Land cover dominated by annual crops and olive trees (50%), matorral and forest (35%)</td></tr><tr><td>Actual socioeconomic trend</td><td>Pressure on land due to urbanization but strong identity and competitiveness of local regional wines</td><td>Low population and agriculture activity but increase in agriculture activities</td><td>Abandoned pasture with an incentive from the government and PAG subsidies to increase conservation agriculture</td><td>Low agriculture production. High production of short-rotation pines but with a new tendency to change commercial (planted) short-rotation forest to conservation forest as part of the forest burned</td><td>Intense agricultural activity due to the policy incentive to make Tunisia a self-sufficient country</td><td>Intense agricultural activities for subsistence</td></tr></tbody></table>
<table><caption>Table 2 (continued)</caption><thead><tr><th></th><th>Roujan, France</th><th>Can Vila, Spain</th><th>Cannata, Italy</th><th>Macieira, Portugal</th><th>Kamech, Tunisia</th><th>Tleta, Morocco</th></tr></thead><tbody><tr><td>Mean interannual sediment yield (t ha<sup>-1</sup> yr<sup>-1</sup>)</td><td>1</td><td>88.10–6</td><td>1</td><td>~0.1 t ha<sup>-1</sup> yr<sup>-1</sup> without fire, 0.2 t ha<sup>-1</sup> year<sup>-1</sup> with fire. (erosion in burnt area represents 8 and 14 t ha<sup>-1</sup> year<sup>-1</sup>)</td><td>15</td><td>29</td></tr><tr><td>Reference</td><td>Molenat et al. 2018</td><td>Llorens et al. 2018</td><td>Licciardello et al. 2019</td><td>Nunes et al., 2020; Nunes et al. 2018a, b</td><td>Molenat et al. 2018</td><td>Hammouda 2010</td></tr></tbody></table>

<figure><img src="image_5.png" /></figure>

<footnote>ªAridity index, http://www.appsolutelydigital.com/DataPrimer/map72.html? referringer=Baker</footnote>
<figure><img src="image_6.png" /><figcaption>Fig. 4 Research axes of MASCC project surrounding the four narratives with the description of storylines</figcaption></figure>
<figure><img src="image_7.png" /><figcaption>**Fig. 5** Spider diagram of landscape evolution per study site with the highest ranking given to the factor that influences most the landscape according to local experts</figcaption></figure>

necessary information to complete the survey on the day of the meeting between the coor-
dinators of the project and the local team.

To illustrate our methodology, we include in the supplementary material:

- The synthesis of the participatory approaches used in each CS

- The synthesis of all the sites for survey 1

- An example of survey 1 to illustrate Macieira CS

For each CS, once survey 1 was completed, we asked the local partners to continue
with the second survey to develop the narrative and land use maps scenarios for 2050.
The synthesis of the elaboration of narrative and land use trends can be found in the sup-
plementary material “survey 2 Document scenario Macieira.docx” as an example of our
methodology. The detailed MASCC meetings and programs are also presented in the sup-
plementary material.

**3 Results**

**3.1 Step 1: Socioeconomic and biophysical contexts**

A summary of the main driving factors that influence the landscape of each study site is presented below and in Appendix C. The description and the choice of each socioeco- nomic factor were presented in section 2 section and in Table 1. The overall results from the socioeconomic trends (Fig. 5) show that the dominant driver is “economy”, especially in Roujan and Macieira sites where the value of agricultural lands or forestland is high for wine and wood production, respectively. Strong governance policy and incentives have been applied in study sites where land is abandoned; for example, in Cannata, govern- ment incentives have enhanced economic development based on agriculture and/or agro- tourism. Recent movements are also present in study sites with high land abandonment (Macieira and Can Vila), while in study sites with important agriculture production such
<table><caption>**Table 3** Emergent facts and plausible futures in each study site (aggregated results from survey 1)</caption><thead><tr><th></th><th>Emergent facts</th><th>Plausible futures</th></tr></thead><tbody><tr><td>Roujan (FR)</td><td><ul><li>Improved wine quality</li><li>Decreased number of wine-growers, increase surface per farmer</li><li>Urbanization</li></ul></td><td><ul><li>Development of high-quality wines</li><li>Agro-tourism and ecotourism</li><li>Crop diversification (organic market gardening)</li><li>Desertification of winegrowers</li><li>Ecotourism</li></ul></td></tr><tr><td>Can Vila (SP)</td><td><ul><li>Out-migration to cities</li><li>Incentives for ecotourism (e.g. create internet access, EU incentive)</li></ul></td><td></td></tr><tr><td>Cannata (IT)</td><td><ul><li>Ongoing migration to cities</li><li>Lack of transparency in authorization and funding distribution</li><li>Infrastructural delay</li></ul></td><td><ul><li>Integrated farming systems (agriculture + for-stry + pastoralism + services + ecotourism)</li></ul></td></tr><tr><td>Kamech (TU)</td><td><ul><li>Outmigration to cities</li><li>Intention to develop ecotourism (Wi-Fi access, EU funds)</li></ul></td><td><ul><li>Orchard production</li><li>Agro-tourism and ecotourism potentials</li></ul></td></tr><tr><td>Tleta (MO)</td><td><ul><li>Outmigration to cities</li><li>Development of free zones and car industry</li></ul></td><td><ul><li>Excessive urbanization close to roads</li><li>Plantation of fruit trees on sloping lands</li><li>Conservation of forest upstream the watershed</li></ul></td></tr><tr><td>Macieira (PT)</td><td><ul><li>Creation of forest associations to manage the land sustainably and bring technical support to landowners</li></ul></td><td><ul><li>Increasing agro-tourism</li><li>Production of local fruits and market gardening to supply nearby Agueda town</li></ul></td></tr></tbody></table>
as Kamech and Roujan, recent movements have a lower influence because the agriculture production has a high cultural heritage (Roujan) or strong economic government measures have been implemented (e.g. fixed price of cereals in Kamech). The distance to the city/ migration scores high in three out of six sites (Can Vila, Macieira, and Cannata), where most of the rural population migrated out of the rural area to find a job in nearby cities due to historical local agriculture and/or economic collapses. On the other side, in sites such as Roujan, Kamech, and Tleta, there is a clear benefit from being close to the cities, for example, with an increase in agrotourism in local agriculture demand by neo-rural inhabitants. However, crop diversification is limited in the Roujan and Kamech sites where climate and access to natural resources are the most limiting factors to grow new crops due to erratic rainfalls and shallow soils. Research and development (R&D) has an indirect role in most of the study sites as the impact of research on landscape and society is a long-term process. For instance, in France, a prospective study LACCAVE (long-term adaptation to climate change for viticulture and oenology) has been tested for the 2015–2018 period to study climate adaptation of vineyards in terms of future water management and choice of vineyard varieties; it starts now its implementation after approval by the government. In the Tleta, research has led to innovative irrigation techniques supported by public subsidies. While in Portugal, research is mainly centred actions to prevent fires and to develop post-fire conservation techniques (see Appendix C for detailed results from survey 1 on socioeconomic context).

Emergent facts and plausible futures described by the local experts as a basis for designing scenarios are summarized in Table 3. Three sites (Macieira, Can Vila, and Kamech) have been identified with a good potential development for ecotourism as a compromise between “economic profit” and “environmental protection” exists. In Roujan, a trend in increased wine quality and crop diversification is observed to improve the system’s resilience towards international wine competition and climate hazards. In Cannata, due to a lack of implementation of CAP subsidies and lack of transparency, agriculture production decreased in the last decades and thus, recent local incentives were taken from the Sicilian government to introduce organic and conservation agriculture. However, on the Tleta and Roujan sites, the recent urban and industrial development has induced pressure on arable lands. Urbanization was also boosting local agriculture productivity with the cultivation of the most productive lands but also on degrading lands. For example, fruit tree plantations have been developed on sloping fields of Tleta watershed to meet local food demand while the government implemented some soil conservation measures for the preservation of the forested lands upstream of the watershed.

## 3.2 Step 2: Definition of potential scenarios and narratives

The second step of the method consisted of developing narratives for the four scenarios described in section 2 based on Kriegler et al.’s approach (2012; Fig. 5). In the first scenario (S1) so-called business as usual (BAU), the actual trend of present land use and agricultural practices are used. In the second scenario (S2) so-called market-oriented, agricultural production capacities are optimized without consideration of environmental sustainability. Economic and technological development are the driving forces in the study case. In the scenario (S3) so-called nature-oriented, the impact of a protective agricultural strategy regardless of agricultural production capacities or socioeconomic acceptability is explored. Environmental measures and governance are powerful drivers of this scenario. In the last scenario (S4) “sustainable growth”, both optimization of land use productivity
Land Use Scenario: Roujan catchment

<figure><img src="image_8.png" /></figure>

Land Use Scenario: CanVila catchment

<figure><img src="image_9.png" /></figure>

Land Use Scenario: Camata catchment

<figure><img src="image_10.png" /></figure>

Land Use Scenario: Macieira catchment

<figure><img src="image_11.png" /></figure>

Land Use Scenario: Kamech catchment

<figure><img src="image_12.png" /></figure>

Land Use Scenario: Tieta catchment

<figure><img src="image_13.png" /><figcaption>Fig. 6 Key points of the narratives for scenarios S1, S2, S3, and S4 for each study site</figcaption></figure>
<table><caption>**Table 4** Soil and water conservation practices per site (X: conservation practices already on-site, C: conservation practices assessed as “feasible and effective” to be implemented on-site by MASCC local experts)</caption><thead><tr><th></th><th>Roujan (FR)</th><th>Can Vila (SP)</th><th>Macieira (PT)</th><th>Cannata (IT)</th><th>Kamech (Tu)</th><th>Tleta (Mo)</th></tr></thead><tbody><tr><td colspan="7">Structural measures ☐ landscape level</td></tr><tr><td>Terraces</td><td>X</td><td>X</td><td>X</td><td></td><td></td><td>X</td></tr><tr><td>Vegetated strips</td><td>C</td><td></td><td>C</td><td></td><td>C</td><td></td></tr><tr><td>Alley-cropping, Hedgerow, horizontal structures</td><td></td><td></td><td></td><td></td><td></td><td>C</td></tr><tr><td colspan="7">Agriculture measures ☐ field level</td></tr><tr><td>Organic agriculture</td><td>X</td><td></td><td></td><td>C</td><td>C</td><td>C</td></tr><tr><td>No-tillage</td><td>C</td><td></td><td></td><td>C</td><td>C</td><td></td></tr><tr><td>Controlled grazing</td><td></td><td></td><td></td><td>C</td><td></td><td></td></tr><tr><td>Agroforestry</td><td></td><td>C</td><td>C</td><td></td><td>C</td><td></td></tr><tr><td>Mulching</td><td></td><td></td><td>C</td><td></td><td></td><td></td></tr></tbody></table>

and implementation of soil conservation measures have been integrated and explored. In this scenario, an equilibrium between economic, social, technological, and environmental development has been sought. The timeframe of each narrative is 30 years (2020–2050) combining four contrasted plausible LUM evolution.

While S1 and S2 scenarios are close to the SSP2 “Middle of the Road” scenario with two levels of environmental protection (low for S2 and medium for S1), social and cultural aspects are more significant in S1 than in S2 for which economic and technological development are dominant. S3 and S4 scenarios are similar to SSP1 known as “sustainable” with different levels of environmental protection (medium for S4, high for S3). Besides, while S4 scenario is a good compromise between environmental protection and economic profit, S3 is mainly oriented towards environmental protection with a strong governance drive. Narrative scenarios have been developed based on qualitative and quantitative results based on step 2. The baseline of each narrative is described in Fig. 4 and is then re-contextualized for each local context (Fig. 6).

A list of potential soil and water conservation techniques was established for scenarios S3 and S4 (Table 4). Two out of the five sites decided to restore terraces (Can Vila) and/or maintain them (Macieira). Vegetated strips have been introduced in half of the sites to control soil water erosion and runoff (mainly in sites with current intense agriculture production such as Roujan and Kamech). Alley cropping has been chosen for Tleta, which is a specific and well-adapted measure in this region. Concerning agricultural measures, while organic agriculture was already present in Roujan, it was introduced in two other sites: Cannata and Can Vila. No-tillage has been adopted in Roujan, Cannata, and Kamech, while Cannata opted for controlled grazing. Finally, agroforestry has been adopted in two sites (Can Vila and Kamech) where traditional forests and olive plantations have been respectively chosen as the perennial vegetation. In the case of Macieira, post-fire soil erosion practices such as mulching with pine needles have been selected.
<table><caption>**Table 5** Allocation constraints in each study site (land use ending with till means tillage, land use ending with cons means conservation site-specific agriculture as defined by the local experts)</caption><thead><tr><th>LUM</th><th>Roujan</th><th>Can Vila</th><th>Macieira</th><th>Cannata</th><th>Kamech</th><th>Tleta</th></tr></thead><tbody><tr><td>Conservation-oriented crops (and local varieties)</td><td>Slope &gt; = 10%, Low-Land, size &gt; 2 Ha</td><td>Slope &lt; 10%; size &lt; 100</td><td>Slope &lt; 15%; size &lt; 100m<sup>2</sup></td><td>Slope &lt; 10%, size &lt; 0.25 ha</td><td>Slope &gt; 15% and vertic soils</td><td>Slope &gt; 15%</td></tr><tr><td>Conventional crops</td><td>Slope &lt; 10%, LowLand, size &gt; 2 Ha</td><td>Slope &lt; 8%; size &lt; 100</td><td>Slope &lt; 15%; size &lt; 100m<sup>2</sup></td><td>Slope &lt; 10%, size &lt; 0.25 ha</td><td>Slope &lt; 15% and non vertic soils</td><td>Slope &lt; 15%</td></tr><tr><td>Shrubs/Natural vegetation</td><td>Slope &gt; = 10%, High-Land, size &lt; 2 Ha</td><td></td><td>Slope &gt; 10%; size &gt; 100m<sup>2</sup></td><td>Slope &gt; 10%</td><td>Slope &gt; 15% and vertic soils</td><td>Slope &gt; 15%</td></tr><tr><td>Conservation-oriented orchards</td><td>Slope &gt; = 10%, High-Land, size &lt; 2 Ha</td><td>Slope &lt; 10%; size &lt; 1000m<sup>2</sup></td><td>Slope &lt; 15%; size &lt; 100m<sup>2</sup></td><td>Slope &lt; 15%, LowLand</td><td>Slope &gt; 15% and vertic soils</td><td>Slope &gt; 15%</td></tr><tr><td>Conventional orchards</td><td>Slope &lt; 10%, LowLand, size &gt; 2 Ha</td><td></td><td>Slope &lt; 15%; size &lt; 100m<sup>2</sup></td><td>Slope &lt; 10%, LowLand</td><td>Slope &lt; 15% and non vertic soils</td><td>Slope &lt; 15%</td></tr><tr><td>Conservation-oriented vineyard</td><td>Slope &gt; = 10%, High-Land, size &lt; 2 Ha</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Conventional vineyard</td><td>Slope &lt; 10%, LowLand</td><td></td><td>Slope &gt; 10%; size &gt; 100m<sup>2</sup></td><td>Slope &lt; 10%</td><td>Slope: 8–20%</td><td>Slope: 8–20%</td></tr><tr><td>Managed grassland</td><td>Slope &gt; = 10%, High-Land, size &lt; 2 Ha</td><td></td><td>Slope &gt; 10%; size &gt; 100m<sup>2</sup></td><td>Slope &gt; 10%</td><td>Slope &gt; 10%; size &gt; 100 m<sup>2</sup></td><td>Slope &gt; 10%; size &gt; 100 m<sup>2</sup></td></tr><tr><td>Semi-natural and natural grassland</td><td>Slope &gt; = 10%, High-Land, size &lt; 2 Ha</td><td></td><td>Slope &gt; 10%; size &gt; 100m<sup>2</sup></td><td></td><td></td><td></td></tr><tr><td>Forest cons and till* (primary forest and forest plantations)</td><td></td><td>Slope &gt; 8%</td><td>Slope &gt; 10%; size &gt; 100m<sup>2</sup></td><td></td><td></td><td></td></tr><tr><td>Agro-forestry</td><td></td><td>Slope &lt; 10%; size &lt; 1000</td><td></td><td></td><td></td><td></td></tr><tr><td>Urban</td><td>Slope &lt; 10%</td><td></td><td></td><td></td><td>Slope &lt; 15% and no cracks</td><td>Slope &lt; 15%</td></tr><tr><td>Road</td><td>Slope &lt; 10%</td><td></td><td></td><td></td><td></td><td></td></tr></tbody></table>

<footnote>*Only land management differs</footnote>
<figure><img src="image_14.png" /><figcaption>**Fig. 7** Land use distribution in Roujan (A), Can Vila (B), Cannata (C), Macieira (D), and Kamech (E) sites. The scenarios presented are S0 (baseline), S1 (business-as-usual), S2 (market-oriented), S3 (environmental protection), and S4 (sustainable)</figcaption></figure>
<figure><img src="image_15.png" /><figcaption>**Fig. 8** Land use maps produced with the ScenaLand method for five sites. The scenarios presented are S0 (baseline) S1 (business-as-usual), S2 (market-oriented), S3 (environmental protection), and S4 (sustainable)</figcaption></figure>
<figure><img src="image_16.png" /><figcaption>Fig. 8 (continued)</figcaption></figure>
## 3.3 Step 3: land use maps creation

### 3.3.1 Spatial allocation from scenario narratives

In step 3, LUM allocation rules based on landscape characteristics (elevation, soil type, etc.) have been created for each scenario (Table 5). In general, grassland and forests were allocated to slopes and large plots while crops are allocated to flat areas with variable field sizes. When mechanized, the choice of large field size is preferable so that the use of machinery could be optimized. Experts also made sure that the field was accessible to ensure crop cultivation. Other rules have been developed by experts on crop allocation such as in Roujan, where conservation agriculture could be allocated to slopes as the use of machinery is very limited. In other sites such as in Cannata, the main biophysical constraints to agriculture production are defined by the slope and the distance to roads but also the access to machinery.

The following section presents the results of local experts' quantification of land use change and/or the introduction of new land use in the landscape (Fig. 7). In Roujan, the vineyard area has been managed following conservation agriculture practices in S1, while herbicides are partly replaced by chemical weeding in S2. Up to 30% of shrublands have been replaced by crop production in S1 and S2, the same applies to grass strips and ways (totally removed in S2 and partially removed in S1). In S3, the land use has been dominated by organic market-gardening and vineyards under conservation agriculture while shrubs and grass strips have been expanded. In S4, the area of conservation orchards, vineyards, and crops has more than doubled. In Can Vila, experts increased forest patches in S1 and S3 by +44% and +68%, respectively; while in S2 and S4, forested areas decreased by 68% at the expense of crops and grasslands in S2 and extensive grasslands, organic agriculture and agroforestry in S4. Terraces were maintained in S3 and S4 while agroforestry is being considered only in S4. In Macieira, traditional agriculture has been largely replaced by the plantation of eucalyptus and partly replaced by shrubs in S1 and S2. Pine plantation decreased in all scenarios due to its low profitability and sensitivity to pests and diseases, while oak trees have been re-introduced in S3 and S4 as part of biodiversity protection incentives from locals or the government. In Cannata, the traditional conventional wheat production decreased in all scenarios, except in S2 where it increased by 10%. Old varieties of Sicilian wheat have been introduced in S1 and S2 as they are highly profitable and trendy. These varieties have been also introduced in S3 and S4 with low-tillage practices and low input. The same trend applies to orchard production which was introduced as conventional in S1 and S2 and as organic and conservation orchard production in S3 and S4. Intensive pasture has been doubled in S2, while all pasture land has decreased in the remaining scenarios. In S2, shrubland has been replaced by crops and pasture. In Kamech, conventional cereals were increased in S1 and S2 up to 30% and conventional orchard production up to 57%, while conservation agriculture (cereals, legumes, and arboriculture) was introduced in S3 and S4. Part of natural vegetation and most of the fallow was replaced in S1 and S2 by agriculture production.

Finally, Fig. 8 shows the outcome of the ScenaLand method for five sites. Figure 8 shows contrasted spatially explicit scenarios coming from specific narratives compared to the baseline (S0). For example, for the site of Macieira, the baseline scenario is represented with a mix of extensive grassland and natural forest; the business-as-usual scenario (S1) presents an increase in natural forest to follow the historical and current trends; the
market-oriented scenario (S2) is mainly composed of intensive grassland mixed with con-
ventional crops; the land use composition of the environmental protection scenario (S3) is
a mix of S1 and S2 while the sustainable scenario (S4) is a scenario where the landscape is
composed mainly by natural forest, extensive grassland, and conventional and conservation
crops and orchards. Specific numerical repartition is presented in Fig. 7.

## 4 Discussion

This study presents a new methodology, named ScenaLand, to create narratives and cor-
responding spatially explicit scenarios that are easily applicable, site-generic, and imple-
mentable with limited time and resources. The results of this study using ScenaLand are
satisfactory regarding the creation of contrasting narratives and land use scenarios to
explore different LUM strategies on various soil ecosystem services such as water yield and
soil erosion mitigation. Narratives have been constructed around two axes: environmental
protection (mostly erosion mitigation) and economic profit coming from natural resources
exploitation. ScenaLand has been applied successfully in Pastor et al. (2019) study to show
high potential for erosion mitigation in S3 and S4 scenarios including climate change mit-
igation with the implementation of post-fire mulching and increased soil cover on steep
slopes in the study site of Macieira. During the construction of scenarios, it has been
observed that the socioeconomic context and the political decisions highly influenced agri-
cultural production in each site. For example, similarities were found among the Northern
Mediterranean sites of Macieira, Cannata, and Can Vila, where agricultural production is
low due to rural out-migration following the decrease in agricultural products prices and
the greater job opportunities in nearby cities. In Macieira, the production of eucalyptus,
not very demanding in labour and providing high return value for pulp and timber, has
replaced traditional agriculture (mainly corn-grassland systems). Roujan remains an excep-
tion in the Northern Mediterranean sites, as agriculture and mostly vineyard production
is intensive. This is due to the increased wine quality with time maintaining the region in
a leading position on the European wine market. In contrast, the Southern Mediterranean
sites of Kamech and Tleta are characterized by similar agroecosystems with intense agri-
cultural production systems; however, the political drivers differ between sites. For exam-
ple, in Morocco, agricultural product imports provide 53% of food needs and agricultural
exports have increased from 14 to 43% between 2003 and 2013 (National statistics). While
in Tunisia, the focus on increasing agriculture production and price fixation is driven by
the national policy on self-food sufficiency.

Compared to other land use models that are either focused on stakeholder engagements
(Pedde et al. 2019) or fixed on developing strong land use allocation algorithms (Ver-
burg et al. 2002, 2004; Malek and Verburg 2018), ScenaLand is a mixed bottom-up (for
biophysical parameters) and top-down approach (for socioeconomic information) that is
driven by both socioeconomic and biophysical contexts. Literature shows that the research
objectives and the available resources to design scenarios strongly drive the type of meth-
ods to use to develop scenarios. For example, in the European project "IMPRESSIONS"
(Pedde et al. 2019), the available time and resources allowed for the development of strong
and downscaled SSP narratives following several stakeholders' workshops. These events
allowed stakeholders to work together with game facilitation and other resources that ease
communication and negotiation. Other studies such as in Stigter et al. (2017) have also
used narratives with the development of scenarios for future agricultural development
and irrigation water requirements in Southern Portugal, although there was no creation of explicit land use maps to drive the model. In Choukri et al. (2020), a method developed by Hérivaux et al. (2021) based on strong socioeconomic algorithms and several stakeholder workshops for building scenarios for agricultural development and reservoir siltation in Morocco has been used. Nevertheless, such a method would require a large amount of time and resources to be implemented in a multi-site context such as in the MASCC research project when organizing several stakeholder workshops. Other simple approaches, which relied mainly on land use change analyses to assess present-day socioeconomic trends, or European-scale land use change scenarios to derive future global trends, have also been developed (Nunes et al. 2017; Serpa et al. 2015). While these approaches are arguably simpler than ScenaLand, they lack the validation knowledge provided by multiple experts (and multiple viewpoints) in the scenario design.

Finally, there are also land use allocation models that are based on strong econometric algorithms applied at a global scale such as GLOBIOM (Global Biosphere Management Model) and MAGPIE (Model of Agricultural Production and its Impact on the Environment) (Popp et al. 2014; Havlík et al. 2015). These are valuable tools to study large-scale Earth system dynamics on water use and food production. However, the spatial scale of these models is not adapted to local contexts such as erosion mitigation at the watershed level. Indeed, there is no unique LUM rule for each site (this study). At the local scale, information on socioeconomic context is often downscaled from the region such as in the Macieira case where it was difficult for experts to provide information for the village. Finally, it has been shown that methods mainly based on econometric algorithms can fail such as in a study carried out in a Swiss abandoned and naturally afforested mountainous region, where the BAU scenario has been chosen above modern and optimized scenarios due to a lack of communication with local experts concerning the other scenarios (Soliva and Hunziker 2009).

Despite the satisfactory results of ScenaLand, some improvements might be needed for the validation of LUM techniques. For example, in the MASCC project, the SWC practices have only been biophysically studied and mostly based on expert knowledge, literature review, and existing databases such as the WOCAT (World Overview of Conservation Approaches and Technologies). In some other cases, such as Macieira and Tleta, previous stakeholder workshops also provided stronger validations by local experts than in the other sites. However, it would be interesting to carry out a cost-benefit analysis using for instance the PESERA-DESMICE (Desertification Mitigation Cost-Effectiveness) approach (Fernández and Vega 2016) or evaluate the cost-benefits of soil conservation techniques (Prats et al. 2012, 2016). Furthermore, the validation of such practices can also be achieved through the implementation of scenarios via field experiments and modelling (Pastor et al. 2019; Choukri et al. 2020). It is also uncertain if Scenaland could be used for regions larger than 100 km² where methods based on stronger econometric algorithms might be more easily used for the map creation. However, the use of the first two steps of Scenaland could be used before the creation of the maps with another method. A recent study from Dias et al. (2020) has also used scenarios and impact models in loops so that stakeholders help to decide on the best final adaptation pathways (i.e. sets of adaptation options). Although the implementation of ScenaLand has been highly oriented to maintain and/or restore soil conservation, ScenaLand can benefit other kinds of research such as agronomic land feasibility, climate adaptation strategies, and adaptive laws. In further research, it would be interesting to test ScenaLand in larger-scale regions and contexts. Besides, and in order to implement sustainable scenarios, incentives to help farmers adopt sustainable land management practices can be investigated based on the “Polluter Pays Principle” (Valera et al. 2017) or with the introduction of payments for ecosystem services (Friess et al. 2015; Li and Zipp 2019).
# 5 Conclusion

With the recent actions required from the United Nations with the LDN and SDG15 on land restoration, it is necessary to design resilient landscapes including the maintenance and/or restoration of fertile and carbon-sink soils. This study presents a new methodology, named ScenaLand, to create narratives and corresponding spatially-explicit scenarios that are easily applicable, site-generic, and implementable with limited time and resources. Besides creating narratives based on the current and historical socioeconomic and bio-physical conditions, ScenaLand provides guidelines to design spatially explicit land use maps to include LUM strategies and contributes to the design of resilient landscapes. The methodology is particularly suitable for studying multi-site case studies and deriving similarities and differences between sites, as illustrated in this study through its implementation within the MASCC project to mitigate soil erosion in six Mediterranean sites. ScenaLand has been designed to be site-generic, and low-time and resource consuming. Its unique way of integrating top-down regional socioeconomics information and bottom-up field experiments from various research projects resulted in a robust and simple methodology. During the first two steps, socioeconomics trends and drivers helped to develop the narratives, while spatially explicit maps were based on LUM allocation rules derived from the topographic zoning and related agricultural activities. Compared to other studies using scenarios based on solely biophysical trends, the results of this study showed that understanding the socioeconomic context of each site is a fundamental step for the development of plausible and socially accepted scenarios. Some contrasted scenarios in terms of environmental and economic performances have also been designed to test the sensitivity of the different landscape and related measures to soil erosion. However, ScenaLand is flexible enough to be used for other purposes, such as in climate adaptation strategies and adaptive laws.

**Supplementary Information** The online version contains supplementary material available at https://doi.org/10.1007/s11027-022-10024-7.

**Acknowledgements** This work and the postdoctoral grant of AV Pastor benefits from the financial support of the MASCC project through ARIMNET2, an ERA-NET funded by the European Union's Seventh Framework Program for research, technological development and demonstration under grant agreement no. 618127. In addition, MMH is beneficiary of a Serra Hunter fellowship (UB-LE-9055) funded by the Generalitat de Catalunya. JJK received the financial support through CESAM (UIDP/50017/2020 + UIDB/50017/2020 + LA/P/0094/2020), from FCT/MCTES through national funds (OE). The authors would like to thank Marie Castellazi for her valuable contribution and shared experience in building land use scenarios, Dominique Rollain for his valuable contribution to the Roujan scenarios and Francesc Gallart for his valuable contribution to the Can Vila study case. We would like to thank all the other persons that contributed to the related surveys. Finally, we would like to send our homage to Dr Yves Le-Bissonnais, a brilliant researcher in the erosion discipline and coauthor of this research article who passed away in 2021.

**Author contribution** The authors of this research paper have directly participated in the planning, execution, or analysis of this study. The authors read and approved the final edition of the manuscript.

**Data availability** All data generated or analysed during this study are included in this published article [and its supplementary information files].

# Declarations

**Conflict of interest** The authors declare no competing interests.
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## Authors and Affiliations

Amandine Valérie Pastor¹,²,³,⁴ ID · Joao Pedro Nunes⁴,⁵ · Rossano Ciampalini¹,⁶ ·

Haithem Bahri⁷ · Mohamed Annabi⁸ · Mohamed Chikhaoui⁹ · Armand Crabit¹⁰ ·

Stéphane Follain¹¹ · Jan Jacob Keizer¹² · Jérôme Latron¹³ · Feliciana Licciardello¹⁴ ·

Laurène Marien¹⁰ · Insaf Mekki⁷ · Mariano Moreno de las Heras¹³,¹⁵,¹⁶ ·

Antonio J. Molina¹³,¹⁶,¹⁷ · Mustapha Naimi⁹ · Mohamed Sabir¹⁸ · Sandra Valente¹² ·

Damien Raclot¹ ID

<figure><img src="image_17.png" /></figure>

Amandine Valérie Pastor
amandine.pastor@inrae.fr

1 LISAH, University Montpellier, INRAE, IRD, Institut Agro, 2 place Pierre Viala, 34070 Montpellier, France

2 ITAP, Univ Montpellier, INRAE, Institut Agro, Montpellier, France

3 Research Group for Environmental Lifecycle and Sustainability Assessment, Montpellier, France

4 cE3c - Centre for Ecology, Evolution and Environmental Changes & CHANGE - Global Change and Sustainability Institute, Faculty of Sciences, University of Lisbon, Lisbon, Portugal

5 Soil Physics and Land Management Group, Wageningen University and Research, Wageningen, the Netherlands

6 Department of Earth Sciences, University of Florence, Via Giorgio La Pira 4, 50121 Florence, Italy

7 Laboratoire Sciences Et Techniques Agronomiques (LR16INRAT05), University of Carthage, INRGREF, 1004 Tunis, Tunisia

8 Laboratoire Sciences Et Techniques Agronomiques (LR16INRAT05), University of Carthage, INRAT, 1004 Tunis, Tunisia

9 Département Des Ressources Naturelles Et Environnement, Institut Agronomique Et Vétérinaire Hassan II, Rabat, Morocco

10 G-EAU, Univ Montpellier, CIRAD, INRAE, Institut Agro, IRD, Montpellier, France

11 Agroécologie - Institut Agro, INRAE, Univ. Bourgogne, Univ. Bourgogne Franche-Comté, Dijon, France

12 Department of Environment and Planning, Centre for Environmental and Marine Studies (CESAM), University of Aveiro, 3810-193 Aveiro, Portugal

13 Surface Hydrology and Erosion Group, Institute of Environmental Assessment and Water Research (IDAEA), Spanish Research Council (CSIC), 08034 Barcelona, Spain

14 Department of Agriculture, Food and Environment, University of Catania, Via Santa Sofia 100, 95123 Catania, Italy

15 Department of Biology and Geology, Physics and Inorganic Chemistry, Area of Ecology, King Juan Carlos University, 28933 Móstoles (Madrid), Spain

16 Department of Geography, University of Barcelona, 08001 Barcelona, Spain

17 Department of Hydraulic Engineering and Environment, Polytechnical University of Valencia, Valencia, Spain

18 Ecole Nationale Forestière d'Ingénieurs (ENFI), Bd Moulay Youssef, Tabriquet, Salé, Morocco