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Article

Spatio-Temporal Evolution of Water-Regulating Ecosystem Services Values in Morocco’s Protected Areas: A Case Study of Ifrane National Park

by
Oumayma Sadgui
1,*,
Abdellatif Khattabi
2 and
Zouhir Dichane
3
1
Applied Economics and Social Sciences in Agriculture, Hassan II Institute of Agronomy and Veterinary Medicine, Rabat 10 101, BP, Morocco
2
National School of Forestry Engineering, Salé 11 000, BP, Morocco
3
Geoengineering and Environment Laboratory, Faculty of Sciences, Moulay Ismail University, Meknes 11 201, BP, Morocco
*
Author to whom correspondence should be addressed.
Land 2025, 14(4), 831; https://doi.org/10.3390/land14040831
Submission received: 31 December 2024 / Revised: 24 February 2025 / Accepted: 1 March 2025 / Published: 11 April 2025
(This article belongs to the Special Issue Soil Ecological Risk Assessment Based on LULC)
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Abstract

:
Water-Regulating Ecosystem Services (WRES) play a crucial role in maintaining water quality and preventing soil erosion, particularly in watershed areas that are vulnerable to Land Use Land Cover Changes (LULCC) and climate change. This study focuses on the Upper Beht Watershed, the most ecologically significant basin of the Ifrane National Park (INP). The main objective is to understand how WRES values respond to the challenges posed by grasslands degradation, agricultural intensification, and urban expansion before and after the park’s creation. In this research, we first analyzed historical Land Use Land Cover (LULC) data from 1992 to 2022 using Google Earth Engine platform. We then employed the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST 3.10.2) models to quantify and map the impacts of ongoing LULCC on the watershed’s capacity to retain sediments and nutrients. Finally, we used the damage costs avoided method for economic assessment of WRES. Our findings demonstrate a notable improvement in the economic value of WRES following the establishment of the park, reaching USD 10,000 per year. In contrast, prior to its creation, this service experienced a decline of USD −7000 per year. This positive trend can be attributed to the expansion of forest cover in areas prioritized for reforestation and conservation interventions. The study highlights the critical importance of continuous WRES monitoring, providing park managers with robust data to advocate for sustained conservation efforts and increased investment in restoration initiatives within protected areas. Moreover, the findings can be used to raise awareness among local communities and encourage their active engagement in sustainable development initiatives.

1. Introduction

Ecosystem services are the benefits humans derive from natural ecosystems, ranging from the provision of resources to the regulation of environmental processes. These services are indispensable for human well-being [1]. The recognition of their importance gained prominence with Costanza’s 1997 study, which introduced economic valuation as a method to integrate ecosystem services into decision-making processes [2]. Among them, water regulation ecosystem services (WRES) play a crucial role in maintaining hydrological balance by ensuring water quality, mitigating soil erosion, and reducing flooding [3,4]. Ecosystems such as forests, grasslands, and wetlands are key providers of these services [5,6]. Given their critical function, the monetization of WRES has become increasingly necessary to support their inclusion in policy-making and environmental planning [7].
In Morocco, WRES are particularly significant in regions such as the Middle Atlas, where forested watersheds contribute to hydrological stability, agricultural productivity, and urban water supply [8,9,10]. One of the most ecologically and hydrologically significant basins in this region is the Upper Beht Watershed, which encompasses extensive forests dominated by Atlas Cedar (Cedrus Atlantica). This watershed plays a key role in securing the country’s mobilizable water resources and sustaining downstream communities [11,12]. However, the capacity of this region to provide WRES has been threatened by LULCC, including grassland degradation, agricultural intensification, and urban expansion [11,13]. In response to these challenges, the Ifrane National Park (INP) was established in 2004 to conserve biodiversity and maintain essential ecosystem services. The park integrates various land-use categories (state-owned, collective, and private lands) and promotes conservation through participatory approaches. Efforts have focused on reforestation and restoration initiatives, wildlife conservation, ecotourism development, and eco-development programs [14].
While conservation actions within protected areas have been widely studied worldwide, research on their impact on WRES remains limited [15,16,17,18,19]. In particular, there is a lack of quantitative assessments of WRES within Morocco’s national parks, leaving a significant knowledge gap regarding the biophysical and economic implications of conservation efforts amid increasing pressures from LULCC and climate change [11,20,21,22,23,24,25]. This study aims to bridge these gaps by providing a localized and detailed assessment of WRES in the Upper Beht Watershed. Specifically, it seeks to achieve the following: (1) Evaluate the impact of Ifrane National Park’s establishment on the hydrological regulation of the Upper Beht Watershed by analyzing WRES trends before and after its creation. (2) Quantify and map changes in WRES over three decades (1992–2022) using remote sensing and spatial modeling techniques. (3) Estimate the economic value of WRES using the “damage costs avoided” approach to highlight their monetary significance.
To achieve these objectives, the study employs Google Earth Engine for remote sensing analysis and integrates Sediment Delivery Ratio (SDR) and Nutrient Delivery Ratio (NDR) models within the InVEST models to quantify sediment and nutrient retention changes over time. Field verification is incorporated to enhance data accuracy. The economic valuation component provides insights into the monetary importance of WRES, reinforcing the need for their integration into conservation policies.
By assessing the evolution of WRES values in relation to LULCC and conservation efforts, this study underscores the role of restoration activities in enhancing ecosystem resilience. Furthermore, it highlights how economic valuation can inform sustainable conservation planning and policymaking, paving the way for innovative financing mechanisms that align ecological preservation with socio-economic objectives. The findings of this research will provide valuable data for park managers, policymakers, and conservation stakeholders to support ongoing WRES monitoring and strengthen water resource management strategies within protected areas.

2. Materials and Methods

2.1. Study Area

The study area encompasses the Upper Beht Watershed, the most ecologically and hydrologically significant basin within Ifrane National Park, situated in the western part of the central Middle Atlas region in Morocco (Figure 1).
The Upper Beht Watershed is a major hydrological unit within the Sebou Watershed, receiving inflows from the R’dom river before merging with the Sebou river in the Gharb plain. It plays a critical role in water regulation and supply, acting as a primary left-bank tributary of the Sebou river. The watershed contributes significantly to water availability for downstream reservoirs and irrigated agriculture, with a total irrigated area of 29,000 ha. Its hydrological function includes precipitation capture, seasonal flow regulation, and sediment transport mitigation, which are essential for maintaining regional water resources [26].
The climate is Mediterranean with strong Atlantic influences, characterized by an annual precipitation average of 700 mm, varying from 1000 mm in the highlands to 300 mm in the Saïs plain. Winters are cold, with snowfall in the Middle Atlas, while summers are hot and dry. Annual temperatures range between 15 and 20 °C, with winter lows of 3–7 °C and summer highs of 34–36 °C [26].
The geology of the watershed is diverse, with three dominant soil types: volcanic, calcareous, and dolomitic. These substrates influence soil fertility, water retention capacity, and erosion dynamics [26].
The vegetation cover varies with altitude and land use. The Ifrane national park, established to conserve the region’s biodiversity and ecosystems, hosts extensive forests, particularly in the upstream areas, while lower elevations are dominated by matorral vegetation and seasonal agriculture. The park supports 22% of Morocco’s vascular plant species, with an endemism rate of 25%, notably represented by the Atlas Cedar (Cedrus Atlantica). It is also a key habitat for 33% of the country’s mammalian species, including the endangered barbary macaque (Macaca Sylvanus). Beyond conservation, the INP promotes sustainable resource management and supports local development through eco-tourism and agroecology [27].

2.2. Mapping Historical LULCC

Satellite data for the years 1992, 2002, 2012, and 2022 were analyzed to assess LULCC within the INP over three decades. These datasets, obtained from the Google Earth Engine (GEE) platform, were derived from the Landsat 5, Landsat7, Landsat 8, and Landsat 9 satellite series. The analysis incorporated all available spectral bands, as well as the Normalized Difference Vegetation Index (NDVI), to enable a detailed examination of spatial and temporal LULCC.
Preprocessing steps, including radiometric and atmospheric corrections, were applied to ensure consistency and comparability across the different time periods [28,29]. LULC classification was performed using the Random Forest (RF) machine learning algorithm, selected for its reliability and high accuracy in remote sensing studies [30,31]. RF leverages ensemble learning by aggregating predictions from multiple decision trees, reducing overfitting and increasing model stability. Its inherent feature selection capabilities are particularly useful for identifying significant variables, such as spectral bands or vegetation indices, critical for accurate classification. RF has been shown to perform well in various LU/LC studies, including integration with advanced techniques like Markov chain models and multi-layer perceptrons for change prediction, achieving high accuracy and interpretability [32]. Combining RF with multi-sensor data, such as optical and SAR images, has further enhanced classification accuracy, achieving over 96% accuracy in some studies [33]. While RF generally excels, challenges remain in distinguishing spectrally similar classes and achieving consistent precision and recall measures, especially in heterogeneous landscapes [34]. Despite these limitations, RF’s balance of simplicity, accuracy, and computational efficiency makes it a preferred choice for large-scale LULC classification in GEE.
In this study, supervised classification categorized LULC into six classes: forests, shrubs, crops, built-up areas, water, and bare soil. The selection of study years, encompassing both pre- and post-establishment periods of the park (created in 2004), provided insights into LULCC before and after the park’s creation. Validation of classification results was conducted using confusion matrices and the Kappa index, demonstrating satisfactory accuracy levels for all time periods analyzed.

2.3. Quantifying Water-Regulating Ecosystem Services Using InVEST

After analyzing historical LULCC, we quantify WRES using the InVEST models, in particular the Sediment Delivery Ratio (SDR) and Nutrient Delivery Ratio (NDR) models [17,18,19,20]. Annual soil loss and the sediment delivery ratio, defined as the proportion of soil loss reaching the stream, are computed by the SDR model, and it is assumed that sediment is transported from the source to the stream and then to the watershed outlet. The Revised Universal Soil Loss Equation (RUSLE) is employed by the model to estimate annual soil loss. The spatial movement of nutrient masses is explained by the NDR model through a simple mass balance methodology, in which LULC and loading rates are considered to determine nutrient loads [35]. The input rasters for the InVEST models are presented in Table 1.

2.4. Economic Assessment of Water-Regulating Ecosystem Services

In this study, a revealed preference economic valuation method is employed, specifically the damage costs avoided method, which quantifies expenses that would have been incurred in the absence of a specific environmental function [41,42,43,44,45]. Damages related to the absence of Water-Regulating Ecosystem Services are primarily associated with potential degradation affecting agricultural lands and rangelands.
Loss in agricultural yields is obtained using the relationship of Den Biggelaar et al. (2004) [46] (Equation (1)).
r = EwP1.224 × 0.0114
where
r: Relative decrease in yield due to erosion (%).
EwP: Erosion rate (t/ha/year).
Then, the relative decrease in yield due to erosion was multiplied by the average crop yield to determine the decline in yield in t/ha (2).
∆R = r ∗ Average crop yield
where
∆R: Relative decrease in yield due to erosion (t/ha);
r: Relative decrease in yield due to erosion (%).
Forage yield losses are determined using Table 2, which provides the percentage of forage productivity losses for each erosion class [47].
After quantifying the damage, its economic cost was estimated using market price. For agricultural losses, the market price of cereals was applied, given that more than 58% of the cultivated land in the study area is allocated to cereal production [48]. For rangeland damages, the valuation was based on the price of fodder barley, as one forage unit is conventionally equated to one kilogram of barley. This assessment was conducted for multiple reference years (1992, 2002, 2012, and 2022), with the corresponding market prices of cereals and barley for each year applied to ensure accurate and temporally consistent cost estimation [49].

3. Results

3.1. Historical LULCC Within the INP Watershed

The analysis of LULCC revealed a significant increase in forest cover between 2012 and 2022, the decade following the park’s establishment. Prior to the park’s creation, the region experienced a gradual decline in forested areas due to various anthropogenic activities, including deforestation and land conversion for agricultural purposes. However, the implementation of conservation measures and sustainable management practices within the park has led to a remarkable recovery and expansion of the forest ecosystem.
In contrast, shrubs have continued to decline even after the park’s establishment, primarily due to persistent overgrazing in the area and the conversion of grasslands to agriculture and built-up areas. Similarly, water bodies have exhibited a continuous decrease despite conservation efforts. While climate change is a major contributing factor, the expansion of agricultural activities has further exacerbated the issue by increasing pressure on water resources.
Figure 2 presents the historical LULCC within the INP watershed.
Table 3 illustrates the evolution of each LULC class within the INP watershed.
For the different years analyzed, we achieved a Kappa coefficient value equal to or greater than 90% (Table 4), indicating a near-perfect agreement between the reference classification and the automated classification performed using Google Earth Engine. This high level of concordance signifies that the model accurately replicated the reference data.
The classification accuracy results for the years 1992, 2002, 2012, and 2022 show a clear trend of improvement in the model’s performance over time, as indicated by the increasing overall accuracy and the decreasing disagreement metrics. The overall accuracy increased progressively, starting from 87.5% in 1992 and reaching an impressive 98.96% in 2022. This steady improvement suggests significant advancements in the classification model’s ability to align with reference data. The high accuracy achieved in 2022 highlights the impact of improved input data quality and enhanced feature extraction or modeling techniques over the study period.
The quantity disagreement, which reflects errors related to mismatches in class proportions, exhibited an initial increase from 2.78% in 1992 to 6.45% in 2002. This could be attributed to limitations in the availability or representativeness of the training data during that period. However, this metric showed a consistent decline thereafter, reducing to 4.26% in 2012 and further to 1.04% in 2022. The observed reduction in quantity disagreement over time indicates that the classification model has progressively become better at predicting the correct number of pixels for each class, aligning more closely with the reference data distributions.
Similarly, allocation disagreement, which measures spatial misallocations of classes, was notably high in 1992 (9.72%) and 2002 (11.29%), indicating significant challenges in correctly assigning class locations during the earlier years. However, this metric saw a dramatic reduction to 2.13% in 2012 and was completely eliminated in 2022 (0.00%). The absence of allocation disagreement in 2022 demonstrates a perfect spatial alignment between the predicted and reference data, suggesting substantial advancements in the classification model’s spatial accuracy. These improvements may be attributed to better-distributed training data, higher-resolution satellite imagery, or the adoption of more robust classification algorithms.
These results demonstrate a significant improvement in the accuracy of the LULC classification over the study period. The refinement of classification techniques, coupled with advancements in data quality, has led to a more precise delineation of LULCC. Notably, the reduction in classification errors and inconsistencies highlights the effectiveness of the methodological approach employed. These improvements are particularly relevant for assessing LULCC and their implications for WRES within the study area.

3.2. Water-Regulating Ecosystem Services Evolution

Our results indicate a notable reduction in soil erosion within the INP watershed following the establishment of the park (2012/2022) (Figure 3). This decline can be attributed to the significant expansion of forest cover, which has enhanced soil stability and reduced erosion vulnerability. These findings underscore the critical role of forest restoration and protection within protected areas in mitigating soil degradation.
However, the observed reduction in erosion could have been more substantial if not for the simultaneous expansion of agricultural activities. The increase in agricultural lands has heightened soil sensitivity to erosion, partially offsetting the benefits provided by forest cover recovery.
Figure 4 illustrates the spatial evolution of the biophysical quantity of sediment loss within the INP watershed, highlighting variations across the study period.
Our results also indicate a significant reduction in nutrient export, specifically nitrogen and phosphorus, within the INP watershed following the establishment of the park (2012/2022) (Figure 5). This decline can similarly be attributed to the expansion of forest cover, which demonstrates a higher efficiency in nutrient retention compared to other LULC types. These findings further highlight the pivotal role of forest restoration and protection in enhancing ecosystem services.
However, this reduction could have been significantly greater if not for the concurrent expansion of intensive agricultural activities and the substantial decrease in grasslands. These grasslands, which play a vital role in nutrient retention and erosion control, have either been degraded into bare soil due to overgrazing or converted into agricultural fields.
Figure 6 and Figure 7 presents the spatial evolution of the biophysical quantity of nutrients exported within the INP watershed, highlighting variations from 1992 to 2022.

3.3. Economic Assessment of Water-Regulating Ecosystem Services

The results show an increase in agricultural yield losses cost (Table 5), driven by the expansion of agricultural areas, even after the park’s establishment, leading to greater erosion related damages. Conversely, forage yield losses cost has declined (Table 6), primarily due to a significant reduction in grazing land, which has been converted to agriculture or built-up areas. These findings highlight that, despite efforts to restore forest ecosystems and enhance WRES, the expansion of agriculture continues to undermine these gains, resulting in a low economic value, which would have been much higher.
The economic value of the WRES is determined based on the potential on-site losses in agricultural and forage yields that would occur in the absence of this ecosystem service. The results show that the WRES value was negative before the park’s establishment. However, after the park’s creation (in 2004), the WRES value became positive, estimated at USD 10,000 /Year (Figure 8).

4. Discussion

The spatiotemporal and economic assessment of Water Regulation Ecosystem Services (WRES) reveals significant trends at both spatial and economic scales. Before the establishment of Ifrane National Park in 2004, the WRES value in the Upper Beht Watershed was negative, estimated at USD −7000 per year. This deficit was primarily due to agricultural expansion and the decline of grasslands, which led to increased sediment and nutrient losses. Following the park’s establishment, the WRES value turned positive, reaching USD 10,000 per year. This improvement is largely attributed to the expansion of forest cover, which enhanced sediment and nutrient retention, effectively reducing erosion-related losses and contributing to ecosystem stability. Notably, improvements in WRES were most evident in areas where forest cover had been restored.
An analysis of conservation initiatives implemented within the park reveals significant achievements. During the period 2012–2022, more than 2000 hectares were successfully reforested, and over 4000 hectares were placed under protection or designated for restoration and reforestation. These areas were managed through a participatory approach involving local communities. The number of local associations engaged in park management increased from 5 at the park’s inception to 12 at present, reflecting a growing community commitment. Furthermore, there was a nearly 50% reduction in forest-related offenses during this time. These positive indicators align with the findings from remote sensing analysis, which demonstrated an increase in forest cover, thereby confirming the tangible impact of these conservation efforts [50].
However, despite this positive trend, the overall enhancement of WRES was constrained by ongoing agricultural expansion and the regression in grasslands. This shift in grasslands has not only compromised the ecological benefits provided by this areas but has also amplified sediment and nutrient runoff. Intensive agricultural practices often involve excessive use of fertilizers and irrigation, which, combined with the loss of natural vegetation, exacerbate nutrient and sediment leaching into downstream systems. As a result, while reforestation efforts have improved some ecosystem services, the negative impacts of agricultural expansion have partially offset these gains. Several studies have demonstrated that intensive agriculture and the transformation of rangelands into croplands and urban areas significantly reduce sediment and nutrient retention capacity. Agricultural expansion in grazing areas disrupts soil structure, increases surface runoff, and accelerates erosion, leading to higher sediment and nutrient losses. While cropland conversion may enhance agricultural productivity, it often comes at the expense of critical ecosystem functions and services, including WRES. The decline in grazing land, largely replaced by more economically lucrative agricultural activities, further diminishes the landscape’s ability to retain sediments and nutrients, ultimately undermining conservation efforts within protected areas [51,52,53,54,55].
The findings of this study underscore the importance of integrated land management strategies that balance agricultural development with ecosystem conservation to maximize the benefits of soil erosion control and nutrients retention. Despite efforts to restore forest ecosystems, the continued expansion of intensive agriculture and urbanization in the study area continues to counteract these environmental gains. Implementing sustainable agricultural practices and the preservation of grazing lands are critical to optimize ecosystem services such as WRES and limit negative environmental impacts while supporting local economic activities [56,57,58,59,60,61,62,63,64,65,66,67]. To this end, we recommend the following actions:
▪
Enhancing ecosystem service monitoring: Investing in advanced tools for continuous mapping and assessment of ecosystem service dynamics within protected areas will enable data-driven decision-making and adaptive management strategies to address ecosystem changes effectively.
▪
Integrating WRES valuation into policy and decision-making: Incorporating WRES into conservation policies can strengthen environmental initiatives. For example, developing and implementing Payment for Ecosystem Services (PES) mechanisms can provide financial incentives for land users, encouraging sustainable land management practices.
▪
Promoting sustainable practices among local communities: Encouraging agroforestry, agroecology, soil and water conservation techniques, water-efficient crops, and the restoration of degraded grasslands (through seeding and temporary protection) will help mitigate the environmental impacts of agriculture and overgrazing. Awareness campaigns should highlight the value of WRES to foster community engagement.
▪
Integrating WRES dynamics into park management planning: Future revisions of the Park Management and Development Plan (PAG) should consider the spatio-temporal evolution of WRES. This integration will inform more effective conservation strategies and contribute to the sustainable management of park resources.
Notwithstanding the promising outcomes of this research, several limitations should be acknowledged. The spatial resolution of the remote sensing data could limit the accuracy of detecting subtle changes in forest cover and grassland dynamics. Future research should incorporate higher-resolution datasets and more refined modeling assumptions to enhance the reliability of the findings. In addition, while the positive impact of reforestation is evident, the persistent expansion of intensive agriculture continues to exert negative pressures on ecosystem services. More detailed, long-term case studies are needed to fully understand these trade-offs and to evaluate the cumulative impacts of different land management practices.
Finally, this study demonstrates that integrated land management strategies, which balance ecosystem conservation with sustainable agricultural practices, can lead to substantial improvements in WRES. While reforestation and community-driven conservation efforts have yielded significant benefits, the ongoing challenges posed by agricultural expansion must be addressed to secure long-term ecosystem resilience. By leveraging advanced monitoring technologies, enhancing community participation, and embedding ecosystem service valuations into policy frameworks, it is possible to achieve a sustainable balance between economic development and environmental conservation in the Upper Beht Watershed.

Author Contributions

Conceptualization, O.S. and A.K.; methodology, O.S.; software, O.S. and Z.D.; validation, A.K.; formal analysis, O.S.; investigation, O.S.; resources, O.S.; data curation, O.S.; writing—original draft preparation, O.S.; writing—review and editing, O.S., Z.D. and A.K.; visualization, O.S.; supervision, A.K. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Data Availability Statement

All data generated or analysed during this study are included in this manuscript.

Conflicts of Interest

The authors declare no conflicts of interest.

Appendix A. Biophysical Tables of SDR and NDR Models

Table A1. Biophysical tables of SDR model.
Table A1. Biophysical tables of SDR model.
LucodeLULC_DescUsle_cUsle_p
1Crops0.191
2Bare soil11
3Water0.041
4Built up area0.11
5Forest0.0031
6Shrubs0.51
Table A2. Biophysical tables of NDR model.
Table A2. Biophysical tables of NDR model.
lucodeLULC_descload_neff_nload_peff_pcrit_len_pcrit_len_n
1Crops12.420.252.210.25150150
2Bare soil10.050.10.05150150
3Water0000150150
4Built up area12.780.084.170.05150150
5Forest2.20.830.2750.8150150
6Shrubs6.280.11.350.25150150

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Figure 1. Map of the geographical location of the study area.
Figure 1. Map of the geographical location of the study area.
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Figure 2. LULCC within the INP watershed: (a,b) represent the period before park establishment, while (c,d) illustrate the period after park establishment.
Figure 2. LULCC within the INP watershed: (a,b) represent the period before park establishment, while (c,d) illustrate the period after park establishment.
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Figure 3. Sediment losses within the INP watershed in tone per year.
Figure 3. Sediment losses within the INP watershed in tone per year.
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Figure 4. Sediment losses maps within the INP watershed: (a,b) represent the period before park establishment, while (c,d) illustrate the period after park establishment.
Figure 4. Sediment losses maps within the INP watershed: (a,b) represent the period before park establishment, while (c,d) illustrate the period after park establishment.
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Figure 5. Nitrogen and phosphorus export in the INP watershed in kg per year.
Figure 5. Nitrogen and phosphorus export in the INP watershed in kg per year.
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Figure 6. Phosphorus export maps within the INP watershed: (a,b) represent the period before park establishment, while (c,d) illustrate the period after park establishment.
Figure 6. Phosphorus export maps within the INP watershed: (a,b) represent the period before park establishment, while (c,d) illustrate the period after park establishment.
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Figure 7. Nitrogen export maps within the INP watershed: (a,b) represent the period before park establishment, while (c,d) illustrate the period after park establishment.
Figure 7. Nitrogen export maps within the INP watershed: (a,b) represent the period before park establishment, while (c,d) illustrate the period after park establishment.
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Figure 8. Economic value of Water-Regulating Ecosystem Services in USD per year.
Figure 8. Economic value of Water-Regulating Ecosystem Services in USD per year.
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Table 1. InVEST models data source.
Table 1. InVEST models data source.
Models InputsInVEST ModelsSource of Inputs
LULCAll InVEST modelsObtained using Google Earth engine platform
Biophysical tables (*)All InVEST modelsFrom literature [36,37]
Rasters of precipitation“Nutrient Delivery Ratio”Obtained from the website of CHIRPS [38]
Digital elevation model“Sediment Delivery Ratio”
“Nutrient Delivery Ratio”		From the website of Earth Science Data Systems (ESDS) [39]
Erosivity raster
(R Factor)		“Sediment Delivery Ratio”Calculated from annual and monthly precipitation averages over a 30-year period (1992–2022) obtained from the website of CHIRPS, using the formula of Rango and Arnoldus (1987) [38,40]
Soil erodibility raster
(K factor)		Obtained by attributing the corresponding k factor values [36] to the lithologic facies of the study area
(*) Appendix A.
Table 2. Correspondence between forage productivity loss and level of soil degradation.
Table 2. Correspondence between forage productivity loss and level of soil degradation.
Erosion Classes (t/ha/Year)Loss of Forage Productivity (%)
0–52.5
5–2525
>2545
Table 3. Evolution of LULC classes within the INP watershed (1992/2022).
Table 3. Evolution of LULC classes within the INP watershed (1992/2022).
LU ClassSurface in 1992 (Ha)Surface in 2002 (Ha)Surface in 2012 (Ha)Surface in 2022 (Ha)
Crops699911,76212,45618,502
Bare soil47,15046,53554,07458,242
Water89979649
Built-up1479106976
Forest19,56417,59216,05820,476
Shrubs95,36593,11686,39170,936
Total169,181169,181169,181169,181
Table 4. Kappa coefficient for the classifications.
Table 4. Kappa coefficient for the classifications.
1992200220122022
Kappa coefficient89%91.3%93.8%98.14%
Table 5. Losses in agricultural yields as a function of soil losses.
Table 5. Losses in agricultural yields as a function of soil losses.
Soil Loss Class (t/ha/Year)Erosion Rate Ewp
(t/ha/Year)		Decrease in Yield: r
(%)		Decline in Yield
∆R = rxAverage Yield (2 t/Ha)
(t/ha)		Annual Cost 1992/2002
(USD/Year)		Annual Cost
2002/2012
(USD/Year)		Annual Cost
2012/2022
(USD/Year)
0–52.50.040.079255.914272219.8
5–25150.310.06−81.6177501.6
>25250.590.12−6.88.646.9
Total9167.61612.722,741.4
Table 6. Losses in forage yield as a function of soil losses.
Table 6. Losses in forage yield as a function of soil losses.
Soil Losses
(t/ha)		Loss of Forage Productivity
(%)		Loss of Forage Productivity (UF/ha)Annual Cost
(USD/Year)		Annual Cost
(USD/Year)		Annual Cost
(USD/Year)
0–52.53.3−902.1−6284.8−20,784.5
5–252532.9−1307.4−3618.6−10,803.3
+254559.3−104.7−179.8−1122.9
Total−2314.2−10,083.1−32,710.8
Note: Negative values in the tables indicate a reduction in the cost of lost productivity, reflecting an improvement in WRES, while positive values indicate the opposite.
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Sadgui, O.; Khattabi, A.; Dichane, Z. Spatio-Temporal Evolution of Water-Regulating Ecosystem Services Values in Morocco’s Protected Areas: A Case Study of Ifrane National Park. Land 2025, 14, 831. https://doi.org/10.3390/land14040831

AMA Style

Sadgui O, Khattabi A, Dichane Z. Spatio-Temporal Evolution of Water-Regulating Ecosystem Services Values in Morocco’s Protected Areas: A Case Study of Ifrane National Park. Land. 2025; 14(4):831. https://doi.org/10.3390/land14040831

Chicago/Turabian Style

Sadgui, Oumayma, Abdellatif Khattabi, and Zouhir Dichane. 2025. "Spatio-Temporal Evolution of Water-Regulating Ecosystem Services Values in Morocco’s Protected Areas: A Case Study of Ifrane National Park" Land 14, no. 4: 831. https://doi.org/10.3390/land14040831

APA Style

Sadgui, O., Khattabi, A., & Dichane, Z. (2025). Spatio-Temporal Evolution of Water-Regulating Ecosystem Services Values in Morocco’s Protected Areas: A Case Study of Ifrane National Park. Land, 14(4), 831. https://doi.org/10.3390/land14040831

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