The Impact of Urbanisation on the Hydrological Responses of the Bouskoura Catchment : Analysis of Runoff Coefficient and Flood Flows from 2017 to 2030

The Impact of Urbanisation on the Hydrological Responses of the Bouskoura Catchment : Analysis of Runoff Coefficient and Flood Flows from 2017 to 2030 | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The Impact of Urbanisation on the Hydrological Responses of the Bouskoura Catchment : Analysis of Runoff Coefficient and Flood Flows from 2017 to 2030 sara Salih, mohamed aghad, said chakiri, hajar chekkouch, lhoussaine el mezouary, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7103720/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Urbanisation has become a key driver of hydrological change, particularly in peri-urban catchments of semi-arid regions. This study investigates the impact of land use transformation on runoff dynamics in the Bouskoura watershed, a rapidly urbanizing area on the outskirts of Casablanca, Morocco. Using supervised classification of Sentinel imagery, land use changes between 2017 and 2021 were quantified, revealing a 30.08% increase in urban surfaces and a concurrent decline in vegetated and agricultural areas. To evaluate the hydrological consequences of this transformation, runoff was estimated using the SCS-CN method and simulated through the HEC-HMS model under uniform rainfall conditions for 2017, 2021, and a 2030 projection. The runoff coefficient increased from 69.11 in 2017 to 72.06 in 2021, while peak discharge rose from 28 m³/s to 42 m³/s. Projections for 2030 suggest a peak discharge of 92.9 m³/s, reflecting the expected rise in imperviousness due to ongoing urban expansion. Despite the absence of flow calibration data, the results provide a comparative analysis that demonstrates the sensitivity of runoff to land use change. This research offers a replicable framework for assessing urbanization-induced hydrological shifts in data-scarce environments and highlights the urgent need to integrate stormwater management into urban planning policies in North African cities. Urbanisation Runoff Coefficient Land Use Change HEC-HMS SCS-CN Bouskoura Watershed Hydrological Modelling Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction Urbanization has become one of the most significant drivers of change in hydrological processes across watersheds worldwide. The expansion of impervious surfaces—such as concrete and asphalt—reduces soil infiltration capacity, increases surface runoff, accelerates peak flows, and elevates the risk of urban flooding. In highly developed urban catchments, up to 90% of stormwater is transformed into direct runoff (Sheng, 2009). These changes are particularly critical in semi-arid environments, where both water scarcity and extreme rainfall events amplify vulnerability. In Morocco, the Casablanca region, including the Bouskoura watershed, has experienced intense urban growth over recent decades (Bahi et al, 2016). The development pressure has led to rapid land-use transformations, significantly affecting the hydrological response of peri-urban catchments. However, limited research has focused on quantifying the short- to medium-term impacts of such urbanization on hydrological behavior in this region. Although the 2017–2021 time frame may not represent a "long-term" study in the strictest sense, it corresponds to a phase of accelerated urban expansion that can already produce substantial hydrological consequences. Previous research has acknowledged the general relationship between increased imperviousness and flood risk ( (Zhang & al, 2016); Shuster, 2005), but few studies have provided detailed, model-based assessments of recent land-use dynamics in this area. This study aims to address key questions regarding the impact of recent urbanization on the hydrological functioning of the Bouskoura watershed. Specifically, it investigates how changes in land cover between 2017 and 2021 have influenced runoff volumes and peak discharges, and what trends can be expected by 2030 if current development continues. By answering these questions, the study contributes to a better understanding of how urban growth shapes flood hazards in peri-urban basins under semi-arid climatic conditions. To achieve this, we used the Soil Conservation Service Curve Number (SCS-CN) method embedded in the HEC-HMS hydrological model, based on land-use data from 2017 and 2021. In the absence of detailed hydro-meteorological data, event-based modeling provides an effective approach for estimating surface runoff and assessing urbanization impacts. The SCS-CN method is well-established for runoff estimation in both gauged and ungauged catchments ( (Dawod & al, 2011)), and is particularly suited for studying land-use change effects (Banasik et al, 2014). This work forms part of a broader effort to support sustainable urban water management and flood mitigation strategies in fast-developing regions such as Bouskoura. Study area The Bouskoura watershed is located along Morocco’s Atlantic coast, south of Casablanca, and covers approximately 255 km² (Fig. 1 ). The region is characterized by a temperate oceanic climate, influenced by the Mediterranean Sea, with an average annual rainfall of 317 mm (Climate-Data.org). The topography consists of gentle hills and alluvial plains, combining agricultural land, urban areas, and natural vegetation. Notable landmarks within the watershed include the suburbs of Casablanca, the Bouskoura forest, the Mohammed V International Airport, and surrounding farmland. From a geological standpoint, the basin lies at the edge of the Berrechid plain, dominated by Silurian and Devonian shales with interbedded quartzites. These formations generate a rugged relief known locally as "sokhrate." The downstream areas, including Anfa and Oasis, are composed of Miocene sandy marls. The substrate is largely impermeable, overlain by a shallow soil layer ranging from 20 cm on slopes to approximately 1 meter in valley bottoms, suggesting limited infiltration and groundwater storage capacity (Hasnaoui et al, 2015). Materials and methods Data used Land use : Land use maps for the years 2017 and 2021 were produced utilizing the Google Earth Engine platform, employing a supervised classification method based on the Support Vector Machine (SVM) algorithm. The SVM algorithm is a machine learning technique that identifies the optimal decision boundaries between distinct land cover classes by maximizing the margin between them. This approach is particularly effective in handling complex and high-dimensional datasets, such as satellite imagery. The accuracy of the classification was evaluated using a confusion matrix, which resulted in a Kappa index of 99%, indicating an exceptionally high level of agreement. The final maps classified various land cover types, including built-up areas, trees, crops, rangeland, bare ground, and water. Figures 2 and 3 show how land use in the Bouskoura watershed has shifted from 2017 to 2021. When comparing the two maps, it’s easy to spot a clear rise in urban areas, particularly in the southern and central parts of the watershed. In fact, urban areas increased by 30,08%. This expansion seems to have come at the cost of agricultural land and natural vegetation, reflecting the rapid urban growth in the area. Vegetation areas experienced a 19,03% decrease, while bar soil slightly declined by 3,70%. The most significant reduction was observed in water bodies, which expanded by 380,56%, although this high percentage is mainly due to the small initial water surface in 2017. Meanwhile, dense vegetation increased by 13,91% indicating some localized reforestation or natural regrowth Back in 2017, the landscape was mostly covered by green spaces like cultivated fields and pastures. By 2021, a significant portion of these areas had been replaced with urban infrastructure, suggesting a noticeable increase in impervious surfaces such as roads and buildings This change is probably influenced by factors like population growth and the pressure of urban expansion. From a hydrological point of view, reducing vegetated areas means less water gets absorbed into the ground, which leads to more runoff. This could increase the risk of flooding and affect the watershed’s hydrological response. From a hydrological point of view, reducing vegetated areas means less water gets absorbed into the ground, which leads to more runoff. This could increase the risk of flooding, and affect the watershed’s hydrological response, which fits with the slight rise in runoff coefficients and discharge levels between 2017 and 2021. These changes point to the need for land management strategies that support urban growth while also protecting the watershed’s natural hydrological functions. Hydrological data Hourly rainfall data over a 10-year period (2014–2024) were collected from the Casablanca Mohamed V Airport station (GMMN), located approximately 11 km southeast of the Bouskoura watershed. This station, situated at an elevation of 59 meters, is considered the most representative for the study area, contributing over 55% to the temperature and dew point data used in hydrological estimations (ABHBC, 2024). The rainfall data were processed and adjusted statistically, using IFRAN PLUS software. To compute return periods and synthetic design storms, for hydrological simulation (Fig. 4 ). These precipitation inputs were later used in HEC-HMS to simulate runoff response under different land use scenarios (2017, 2021, and projected 2030). For consistency, the same rainfall event was used in all scenarios, with land use and CN values being the only variables. Demographic data Demographic projections play a key role in estimating the future evolution of urbanization and its effects on runoff, making it possible to anticipate the challenges associated with water resource management. (Grimm, 2008). In 2017, the population of Bouskoura was 102,036. In 2021, it will reach 152397 inhabitants, an increase of 21% in 4 years. In 2030, the projected population is 211888, almost double the 2017 (hcp, 2017) figure (+ 68% over 13 years). Average annual growth between 2014 and 2030 can be estimated at around 4–5%, reflecting rapid urbanization. Population growth often leads to increased demand for new residential and commercial buildings, as well as infrastructure such as roads and public services. This growth can increase the amount of sealed surface area, which in turn increases run-off. Methodology Estimation of Runoff Using the Curve Number (CN) Method The estimation of surface runoff was carried out using the Curve Number (CN) method, developed by the USDA (Table 1 ) Soil Conservation Service (SCS) and detailed in Technical Release 55 (USDA, 1986). This empirical method is widely used in hydrology, to relate land cover and soil characteristics to runoff potential, particularly in the context of urban development. Land use maps for the years 2017 and 2021 were obtained through supervised classification of satellite imagery using Google Earth Engine. These maps were then exported to ArcGIS, where the surface area of each land use class was calculated. Standard CN values were assigned to each class according to SCS guidelines, with adjustments made based on local context and literature. Due to the lack of detailed hydrological soil group (HSG) data in the study area, CN values were assigned based on general land use descriptions under average antecedent moisture conditions (AMC II). Table 1 presents the standard CN values used in this study. Tables 2 and 3 summarize the areal extent of each land use class and the resulting equivalent CN values for 2017 and 2021, respectively. The CN increased from 69.11 in 2017 to 72.06 in 2021. This increase reflects the expansion of impervious urban surfaces, as built-up areas grew from 32.48 ha to 42.25 ha, while vegetation and agricultural land decreased significantly. Table 1 CN Values for Different Land Use and Soil Types (Source: USDA Soil Conservation Service, 1986) Land Use/Soil Type Description CN Value (0-100) Forest Land Dense forest with natural soil, undisturbed 55–70 Grassland Natural grasslands, tall grasses 60–80 Cultivated Land (Agricultural) Annual crops, tilled land 70–85 Bare Soil Bare, dry soil without vegetation 80–90 Rangeland (Grazing Land) Pastures or lightly cultivated land 70–80 Urban Areas Highly impervious urban areas (buildings, roads, etc.) 85–98 Industrial Areas Industrial zones or infrastructure 90–98 Water (Water Bodies) Bodies of water such as rivers, lakes 100 In Tables 1 and 2 , we have listed each soil type along with its corresponding CN value and the equivalent CN for the area shaded by this type. When applying models like the SCS-CN, the choice of CN values is often influenced by prior tests or statistical adjustments to ensure the most accurate results. Generally, higher CN values are selected to represent areas with more runoff, such as those with more impervious surfaces or less vegetation conditions typically found in urbanized areas. Table 2 Calculation of equivalent runoff coefficient in 2017 In 2017 CN CNeq Type of soil Area (ha) Water 0.72 100 72.45 vegetation 67.17 55 3694.88 Vegetation dense 10.21 45 459.85 Built up 32.48 90 2923.35 BARE SOIL 31.12 85 2645.84 CNeq 69.11 Table 3 Calculation of equivalent runoff coefficient in 2021 In 2021 CN CNeq Type of soil Areas (ha) Water 3.46 100 346.71 vegetation 54.39 55 2991.96 Vegetation dense 11.63 45 523.41 Built up 42.25 90 3803.11 Bare Soil 29.97 85 2548.09 CNeq 72.06 Applying the above formula, we find that the CN has evolved from 69.11 in 2017 to 72.06 in 2021. This change is due to an increase in built-up areas from 32.48 ha in 2017 to 42.25 ha in 2021, reflecting increasing urbanization. Arable land has decreased from 67.17km² to 54.39 ha, indicating a likely conversion of agricultural land to built-up areas. A slight decrease in bare land could also be attributed to urban expansion. Hydrological simulation with HEC-HMS To simulate the hydrological behavior of the Bouskoura catchment, the HEC-HMS (Hydrologic Engineering Center – Hydrologic Modeling System) software was used. HEC-HMS is widely recognized for its ability to model rainfall-runoff processes in urban and semi-urban catchments, particularly in the absence of observed streamflow data. This model was selected for its compatibility with the SCS-CN method and its suitability for ungauged basins. The simulation framework included the following elements: Precipitation data from the Casablanca Mohamed V meteorological station (GMMN) at an hourly resolution. Use of the SCS-CN method to estimate infiltration losses. Initial abstraction and antecedent moisture conditions set to average (AMC II). A constant rainfall event (from 2017) applied to all years (2017, 2021, and a 2030 projection) to isolate land use effects. Only the CN value was modified for each scenario, based on the corresponding land use data. Due to the absence of observed discharge data at the outlet of the Bouskoura watershed, no calibration or validation of the HEC-HMS model could be performed. This is a significant limitation of the study. Consequently, the results are intended for comparative analysis rather than absolute prediction. Nevertheless, the model remains a valuable tool for understanding how urbanization affects hydrological responses. Comparative Analysis of Simulation Results The hydrographs simulated for 2017 and 2021 were compared to assess the impact of urban growth on runoff behavior. The increase in CN due to urban expansion resulted in higher peak discharges and total runoff volumes, confirming that urbanization intensifies the hydrological response of the basin. These findings highlight the critical need for integrated urban planning and stormwater management strategies to mitigate the adverse effects of land cover change on hydrology Results and discussion Trends in runoff coefficients Trends in Runoff Coefficients Between 2017 and 2021, the Curve Number (CN) increased from 69.11 to 72.06, reflecting a moderate but significant rise in surface runoff potential. This trend is consistent with the observed expansion of impervious surfaces, particularly built-up areas, which increased by nearly 10 ha during this period. Although the increase in CN may seem limited, it indicates a reduction in infiltration capacity and a corresponding increase in surface runoff, potentially leading to higher peak flows and reduced groundwater recharge during precipitation events (Fletcher, 2013). Such evolution is typical of peri-urban areas undergoing unregulated urban expansion, as highlighted in similar studies across North African basins (e.g., Hasnaoui et al., 2020), where urban sprawl has been shown to degrade the natural hydrological response. Analysis of CN's development based on demographic trends In 2017, CN was 69, 11. In 2021, it has risen to 72.06. An increase of 2,95 in 4 years. This increase, although moderate, reflects a reduction in infiltration and an increase in runoff. Moreover, it can be used as an indicator of future developments. Urbanization projections can be modeled using different approaches (linear, exponential, sigmoid, etc.). This study chose a linear projection to simplify the analysis and avoid introducing uncertainties associated with more complex models requiring additional data. The development of catchment areas because of urbanization generally follows a linear or exponential trend, depending on the dynamics of urban development and land-use planning policies. (Fletcher, 2013) Between 2021 and 2030, the population is expected to increase by 39%. Extrapolation the impact of This increase on CN : - A 21% increase in the population between 2017 and 2021 has increased in CN of 2,95 ; - With a 39% increase in the population between 2021 and 2030, the increase in CN could be proportional. $$\:\varDelta\:CN=\text{2,95}\frac{39}{21}=\text{5,48}$$ Projection: CN 2030 = CN2021 + \(\:\varDelta\:CN\) = 77,9 + 0,15= 77,54 Thus, by 2030, CN is projected to reach 77.54, indicating a further reduction in infiltration and an increase in runoff, which could elevate flood risks in the watershed. Variation in simulated flows The hydrological simulations performed using HEC-HMS revealed a marked increase in peak discharge over the study period: 28.4 m³/s in 2017 42.8 m³/s in 2021 92.9 m³/s projected for 2030 This tripling of peak flows over 13 years highlights the sensitivity of runoff response to land-use changes, even in the absence of major changes in rainfall input. Such findings align with the literature, where increases in CN of just 5–10 points have been linked to exponential increases in flow peaks and flood volumes (USDA, 1986; Fletcher et al., 2013). From a water resources management perspective, this trend underlines the need for urban drainage planning, especially in rapidly urbanizing basins like Bouskoura. Without adequate mitigation strategies (e.g., green infrastructure, infiltration basins), the increased runoff may overwhelm existing drainage systems, elevate flood risks, and exacerbate soil erosion and water quality degradation. Figures 7 and 8 illustrate the evolution of peak discharge and runoff volume in the Bouskoura watershed between 2017, 2021, and the 2030 projection. The results indicate that urbanization not only increases peak flows but also significantly modifies the temporal distribution of runoff. The intensification of impervious surfaces accelerates the runoff process, leading to earlier peak times and an increase in total discharge volume. These changes are reflected in the steepening of hydrographs, which are indicative of more intense and rapid flood responses—factors that raise the likelihood of flash floods. Additionally, the prolonged runoff duration observed under more urbanized conditions suggests reduced infiltration capacity, emphasizing the growing need for sustainable stormwater management practices in future urban development strategies. Limitations and Uncertainties Several limitations must be acknowledged in this study: Firstly, the absence of observed flow data prevented the calibration and validation of the hydrological model. Consequently, the simulations presented are scenario-based and are intended to highlight relative variations in hydrological response, rather than provide exact flow estimates. Secondly, the projection of future CN values was based on a linear relationship with population growth, assuming a proportional increase in impervious surfaces. While this approach offers a simplified representation of urban dynamics, it may not capture the complexities of land-use evolution, which can be influenced by regulatory policies, socioeconomic factors, or land-use planning strategies. Moreover, the model does not explicitly consider several parameters that can significantly affect runoff, such as soil compaction, slope variability, vegetation density, or potential changes in precipitation patterns due to climate variability. Despite these limitations, the methodology applied enables a preliminary assessment of urbanization impacts on runoff and supports the identification of potential future hydrological risks in the Bouskoura catchment. These findings may serve as a basis for further studies involving more detailed data and calibrated models. Discussion The results of this study clearly indicate a progressive increase in runoff flows in the Bouskoura watershed from 2017 to 2030, highlighting the significant hydrological impact of urbanization. Simulated peak flows increased from 28.4 m³/s in 2017 to 42.8 m³/s in 2021 (a 50% rise), and are projected to reach 92.9 m³/s by 2030, representing a dramatic increase of 119% compared to 2021. This increase correlates with a rise in the runoff coefficient (CN), which reflects the expansion of impervious surfaces such as roads and buildings and the consequent reduction in infiltration capacity. The transformation of agricultural and vegetated land into built-up areas is the main driver of this trend, consistent with findings reported in other rapidly urbanizing watersheds (Brabec et al, 2002; Walsh et al, 2005). Several studies have documented similar impacts of urbanization on hydrological responses. For example, (Brabec et al, 2002) showed that urbanization increases impervious cover, leading to higher runoff volumes and peak flows, thereby intensifying flood risks. (Walsh et al, 2005) emphasized that urban land-use changes often result in increased runoff, reduced groundwater recharge, and altered stream flow regimes. Similarly, Jiao et al. (2021) found significant increases in peak discharge linked to urban sprawl in Chinese watersheds, highlighting the importance of land-use planning to mitigate hydrological impacts. The variation in flow increase rates between the periods 2017–2021 (+ 50%) and 2021–2030 (+ 119%) may be explained by several factors: Urban development patterns : Some newly urbanized areas still include permeable surfaces (e.g., parks, green spaces) that help mitigate runoff increases by promoting infiltration (Fletcher et al, 2013). Assumption of constant rainfall : The simulations assume steady precipitation over time, isolating land-use changes as the sole driver of flow variations. In reality, climate variability can further influence hydrological responses (Blöschl et al, 2013). While urbanization clearly drives increased runoff and peak flows, other hydrological processes such as infiltration, soil moisture dynamics, and temporary water storage significantly influence basin responses. Future work should incorporate these factors and utilize observed flow data for model calibration to improve prediction accuracy. Conclusion Urbanization alters hydrological function, significantly increasing runoff volumes and the risk of flooding (Andrieu, 2013). Urbanization changes the hydrological dynamics of catchments by increasing impervious surfaces and decreasing infiltration. This study used HEC-HMS hydrological models to examine changes in the runoff coefficient and flows in the Bouskoura basin from 2017 to 2030. Between 2017 and 2030, the runoff coefficient experienced a noticeable rise, moving from 69.11 to 77.56. This change points to significant land use changes, with urban development and expansion having a major impact. Alongside this, peak flows have increased sharply, from 28.4 m³/s in 2017 to 92.9 m³/s by 2030, signaling that urbanization is exerting a stronger influence on the hydrological system. This sharp rise in flows stands in contrast to the more gradual changes seen earlier, pointing to a more direct relationship between urbanization and runoff. As more areas become built up, with fewer permeable surfaces available for water to infiltrate, runoff volumes are on the rise. However, we also need to consider other elements, like the evolution of drainage systems, how stormwater is managed, and how rainfall patterns may have changed. Declarations Author Contribution Sara Salih contributed to the research idea and manuscript writing;Mohamed Aghad and Lhoussaine El Mezouary participated in the supervision and manuscript revision;Sliman contributed to the preparation of the maps;Said Chakiri and Mohamed Sadiki participated in the review and critical revision of the manuscript. References ABHBC. (2024). Agence du Bassin Hydraulique du Bouregreg et de la Chaouia. Andrieu, H. B. (2013). Impact of urbanization on urban hydrology and stormwater management. HAL-INRAE . Bahi et al. (2016). Effects of Urbanization and Seasonal Cycle on the Surface Urban Heat Island Patterns in the Coastal Growing Cities: A Case Study of Casablanca, Morocco. Bakhtiar. (2022). Runoff estimation using SCS-CN and GIS techniques in the Sulaymaniyah sub-basin of the Kurdistan region of Iraq. Banasik et al. (2014). Curve number estimation for a small urban catchment from recorded rainfall-runoff events. Archives of Environmental Protection. Blöschl et al. (2013). Changing climate shifts timing of European floods. Science, 357(6351), 588–590. Booth, D. B. (1997). Urbanization of aquatic systems: Degradation thresholds, stormwater detection, and the limits of mitigation. Journal of the American Water Resources Association . Brabec et al. (2002). Impervious surfaces and water quality: A review of current literature and its implications for watershed planning. Journal of Planning Literature, 16(4), 499–514. Casa. (2025). 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Modeling Low Impact Development Alternatives with SWMM. Sheng. (2009). Watershed urbanization and changing flood behavior across the Los Angeles metropolitan region. Shuster, W. D. (2005). Impacts of impervious surface on watershed hydrology: A review. Urban Water Journal . USDA. (1986). USDA Soil Conservation Service (SCS), 1986. Urban Hydrology for Small Watersheds, Technical Release 55 (TR-55). United States Department of Agriculture, Washington, D.C. Walsh et al. (2005). The urban stream syndrome: current knowledge and the search for a cure. Journal of the North American Benthological Society, 24(3), 706–723 . Zhang & al. (2016). Urbanization and its impact on hydrology and water quality in cities: a global perspective. Urban Water Journal . Additional Declarations No competing interests reported. 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Bouskoura\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7103720/v1/55e9cc1a8017ad6a40ee3407.jpg"},{"id":87593408,"identity":"ee815eed-fd43-4c4e-ad9b-f5ce430dcb7d","added_by":"auto","created_at":"2025-07-25 15:18:50","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":99166,"visible":true,"origin":"","legend":"\u003cp\u003eland use map 2017\u003c/p\u003e","description":"","filename":"Picture2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7103720/v1/628cf593e03e4acf8d215d0f.jpg"},{"id":87593410,"identity":"2b1febf4-7869-4e2f-b128-70a9861864bb","added_by":"auto","created_at":"2025-07-25 15:18:50","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":93818,"visible":true,"origin":"","legend":"\u003cp\u003eland use map in 2021\u003c/p\u003e","description":"","filename":"Picture3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7103720/v1/16b7309b94ba65ade5f9b1c0.jpg"},{"id":87594287,"identity":"79023993-ae91-4ee7-8ce5-ef8f7eb699ca","added_by":"auto","created_at":"2025-07-25 15:26:50","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":77129,"visible":true,"origin":"","legend":"\u003cp\u003eStatistical adjustment of rainfall data\u003c/p\u003e","description":"","filename":"Picture4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7103720/v1/08cd67359ba5370d8b905754.jpg"},{"id":87594288,"identity":"65d3c139-b1b8-475f-a12b-b1d7c5bfa784","added_by":"auto","created_at":"2025-07-25 15:26:50","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":51505,"visible":true,"origin":"","legend":"\u003cp\u003eDemographic trends in Bouskoura between 2014 and 2030\u003c/p\u003e","description":"","filename":"Picture5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7103720/v1/5555e901843bc57ea6167c1f.jpg"},{"id":87593413,"identity":"82e2a95b-6a7a-49a3-aafe-0ef8e44054ea","added_by":"auto","created_at":"2025-07-25 15:18:50","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":76925,"visible":true,"origin":"","legend":"\u003cp\u003eType of land use in 2017 and 2021\u003c/p\u003e","description":"","filename":"Picture6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7103720/v1/3d9f1225c695ada7d201bc2c.jpg"},{"id":87594291,"identity":"7388f798-afc1-4b8b-9595-53bfb83c5922","added_by":"auto","created_at":"2025-07-25 15:26:50","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":90727,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of peak discharge and Runoff Volume for Different Scenarios\u003c/p\u003e","description":"","filename":"Picture7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7103720/v1/83ea9c8de694b19423d4d6eb.jpg"},{"id":87593421,"identity":"d235bd80-c2a2-47cd-8229-58ddb927d5b0","added_by":"auto","created_at":"2025-07-25 15:18:50","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":44371,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of Flow in 2017, 2021, and 2030\u003c/p\u003e","description":"","filename":"Picture8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7103720/v1/c9b6d7f8cb7c61b96b73cee1.jpg"},{"id":90733077,"identity":"a728b3ef-3cf5-4a65-8773-1a3e74463acb","added_by":"auto","created_at":"2025-09-06 18:16:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1404156,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7103720/v1/1d508e9e-1c41-48e0-a5b9-03ff10c17ec3.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Impact of Urbanisation on the Hydrological Responses of the Bouskoura Catchment : Analysis of Runoff Coefficient and Flood Flows from 2017 to 2030","fulltext":[{"header":"Introduction","content":"\u003cp\u003eUrbanization has become one of the most significant drivers of change in hydrological processes across watersheds worldwide. The expansion of impervious surfaces\u0026mdash;such as concrete and asphalt\u0026mdash;reduces soil infiltration capacity, increases surface runoff, accelerates peak flows, and elevates the risk of urban flooding. In highly developed urban catchments, up to 90% of stormwater is transformed into direct runoff (Sheng, 2009). These changes are particularly critical in semi-arid environments, where both water scarcity and extreme rainfall events amplify vulnerability.\u003c/p\u003e\u003cp\u003eIn Morocco, the Casablanca region, including the Bouskoura watershed, has experienced intense urban growth over recent decades (Bahi et al, 2016). The development pressure has led to rapid land-use transformations, significantly affecting the hydrological response of peri-urban catchments. However, limited research has focused on quantifying the short- to medium-term impacts of such urbanization on hydrological behavior in this region. Although the 2017\u0026ndash;2021 time frame may not represent a \"long-term\" study in the strictest sense, it corresponds to a phase of accelerated urban expansion that can already produce substantial hydrological consequences. Previous research has acknowledged the general relationship between increased imperviousness and flood risk ( (Zhang \u0026amp; al, 2016); Shuster, 2005), but few studies have provided detailed, model-based assessments of recent land-use dynamics in this area.\u003c/p\u003e\u003cp\u003eThis study aims to address key questions regarding the impact of recent urbanization on the hydrological functioning of the Bouskoura watershed. Specifically, it investigates how changes in land cover between 2017 and 2021 have influenced runoff volumes and peak discharges, and what trends can be expected by 2030 if current development continues. By answering these questions, the study contributes to a better understanding of how urban growth shapes flood hazards in peri-urban basins under semi-arid climatic conditions.\u003c/p\u003e\u003cp\u003eTo achieve this, we used the Soil Conservation Service Curve Number (SCS-CN) method embedded in the HEC-HMS hydrological model, based on land-use data from 2017 and 2021. In the absence of detailed hydro-meteorological data, event-based modeling provides an effective approach for estimating surface runoff and assessing urbanization impacts. The SCS-CN method is well-established for runoff estimation in both gauged and ungauged catchments ( (Dawod \u0026amp; al, 2011)), and is particularly suited for studying land-use change effects (Banasik et al, 2014). This work forms part of a broader effort to support sustainable urban water management and flood mitigation strategies in fast-developing regions such as Bouskoura.\u003c/p\u003e\u003cp\u003e\u003cb\u003eStudy area\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe Bouskoura watershed is located along Morocco\u0026rsquo;s Atlantic coast, south of Casablanca, and covers approximately 255 km\u0026sup2; (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The region is characterized by a temperate oceanic climate, influenced by the Mediterranean Sea, with an average annual rainfall of 317 mm (Climate-Data.org). The topography consists of gentle hills and alluvial plains, combining agricultural land, urban areas, and natural vegetation. Notable landmarks within the watershed include the suburbs of Casablanca, the Bouskoura forest, the Mohammed V International Airport, and surrounding farmland.\u003c/p\u003e\u003cp\u003eFrom a geological standpoint, the basin lies at the edge of the Berrechid plain, dominated by Silurian and Devonian shales with interbedded quartzites. These formations generate a rugged relief known locally as \"sokhrate.\" The downstream areas, including Anfa and Oasis, are composed of Miocene sandy marls. The substrate is largely impermeable, overlain by a shallow soil layer ranging from 20 cm on slopes to approximately 1 meter in valley bottoms, suggesting limited infiltration and groundwater storage capacity (Hasnaoui et al, 2015).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003e\u003cb\u003eData used\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eLand use\u003c/b\u003e :\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eLand use maps for the years 2017 and 2021 were produced utilizing the Google Earth Engine platform, employing a supervised classification method based on the Support Vector Machine (SVM) algorithm. The SVM algorithm is a machine learning technique that identifies the optimal decision boundaries between distinct land cover classes by maximizing the margin between them. This approach is particularly effective in handling complex and high-dimensional datasets, such as satellite imagery. The accuracy of the classification was evaluated using a confusion matrix, which resulted in a Kappa index of 99%, indicating an exceptionally high level of agreement. The final maps classified various land cover types, including built-up areas, trees, crops, rangeland, bare ground, and water.\u003c/p\u003e\u003cp\u003eFigures \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e show how land use in the Bouskoura watershed has shifted from 2017 to 2021. When comparing the two maps, it\u0026rsquo;s easy to spot a clear rise in urban areas, particularly in the southern and central parts of the watershed. In fact, urban areas increased by 30,08%. This expansion seems to have come at the cost of agricultural land and natural vegetation, reflecting the rapid urban growth in the area. Vegetation areas experienced a 19,03% decrease, while bar soil slightly declined by 3,70%. The most significant reduction was observed in water bodies, which expanded by 380,56%, although this high percentage is mainly due to the small initial water surface in 2017. Meanwhile, dense vegetation increased by 13,91% indicating some localized reforestation or natural regrowth\u003c/p\u003e\u003cp\u003eBack in 2017, the landscape was mostly covered by green spaces like cultivated fields and pastures. By 2021, a significant portion of these areas had been replaced with urban infrastructure, suggesting a noticeable increase in impervious surfaces such as roads and buildings This change is probably influenced by factors like population growth and the pressure of urban expansion.\u003c/p\u003e\u003cp\u003eFrom a hydrological point of view, reducing vegetated areas means less water gets absorbed into the ground, which leads to more runoff. This could increase the risk of flooding and affect the watershed\u0026rsquo;s hydrological response.\u003c/p\u003e\u003cp\u003eFrom a hydrological point of view, reducing vegetated areas means less water gets absorbed into the ground, which leads to more runoff. This could increase the risk of flooding, and affect the watershed\u0026rsquo;s hydrological response, which fits with the slight rise in runoff coefficients and discharge levels between 2017 and 2021. These changes point to the need for land management strategies that support urban growth while also protecting the watershed\u0026rsquo;s natural hydrological functions.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eHydrological data\u003c/b\u003e\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eHourly rainfall data over a 10-year period (2014\u0026ndash;2024) were collected from the Casablanca Mohamed V Airport station (GMMN), located approximately 11 km southeast of the Bouskoura watershed. This station, situated at an elevation of 59 meters, is considered the most representative for the study area, contributing over 55% to the temperature and dew point data used in hydrological estimations (ABHBC, 2024). The rainfall data were processed and adjusted statistically, using IFRAN PLUS software. To compute return periods and synthetic design storms, for hydrological simulation (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThese precipitation inputs were later used in HEC-HMS to simulate runoff response under different land use scenarios (2017, 2021, and projected 2030). For consistency, the same rainfall event was used in all scenarios, with land use and CN values being the only variables.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eDemographic data\u003c/b\u003e\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eDemographic projections play a key role in estimating the future evolution of urbanization and its effects on runoff, making it possible to anticipate the challenges associated with water resource management. (Grimm, 2008). In 2017, the population of Bouskoura was 102,036. In 2021, it will reach 152397 inhabitants, an increase of 21% in 4 years. In 2030, the projected population is 211888, almost double the 2017 (hcp, 2017) figure (+\u0026thinsp;68% over 13 years). Average annual growth between 2014 and 2030 can be estimated at around 4\u0026ndash;5%, reflecting rapid urbanization. Population growth often leads to increased demand for new residential and commercial buildings, as well as infrastructure such as roads and public services. This growth can increase the amount of sealed surface area, which in turn increases run-off.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethodology\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eEstimation of Runoff Using the Curve Number (CN) Method\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe estimation of surface runoff was carried out using the Curve Number (CN) method, developed by the USDA (Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) Soil Conservation Service (SCS) and detailed in Technical Release 55 (USDA, 1986). This empirical method is widely used in hydrology, to relate land cover and soil characteristics to runoff potential, particularly in the context of urban development.\u003c/p\u003e\u003cp\u003eLand use maps for the years 2017 and 2021 were obtained through supervised classification of satellite imagery using Google Earth Engine. These maps were then exported to ArcGIS, where the surface area of each land use class was calculated. Standard CN values were assigned to each class according to SCS guidelines, with adjustments made based on local context and literature.\u003c/p\u003e\u003cp\u003eDue to the lack of detailed hydrological soil group (HSG) data in the study area, CN values were assigned based on general land use descriptions under average antecedent moisture conditions (AMC II). Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the standard CN values used in this study. Tables\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e summarize the areal extent of each land use class and the resulting equivalent CN values for 2017 and 2021, respectively.\u003c/p\u003e\u003cp\u003eThe CN increased from 69.11 in 2017 to 72.06 in 2021. This increase reflects the expansion of impervious urban surfaces, as built-up areas grew from 32.48 ha to 42.25 ha, while vegetation and agricultural land decreased significantly.\u003c/p\u003e\u003cp\u003e\u003cimg src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAU4AAABSCAYAAAA7MD09AAAAAXNSR0IArs4c6QAAAARnQU1BAACxjwv8YQUAAAAJcEhZcwAAFiUAABYlAUlSJPAAAAuwSURBVHhe7d0/aBvnGwfwrzKmumSQhsieLJOKBvrHEjaUNh3spQSHlELAhlTO0DTQFGwiZ+gU3CwN1aUZujjxUJdCBDJdfCGlIKe0pYuxrngJiYLbRTpD8KR3yHa/4WcJ6bVk6053uj/+fkBD7j35ff3kyZP3dO+9ipimaYKIiHp2Qj5ARESHY+EkIrKIhZOIyCIWTiIii1g4iYgsihx2Vz0SiciHiIiOlU4l8sjCeUgzEVGodauBvFQnIrKIhZOIyCIWTiIii1g4iYgsYuEkIrKIhZOIyKJjVTiFEPj+++/xwQcfIBKJIBKJIB6PI5fLQQgBXdexsrIiv81RQgjcuXMHQgi5yTf8EKdWQYgZtfMyhwaRL8eicAohkMvloCgKfvvtN/zwww8wTROmaeK///4DACiKgnQ6jTNnzshvd9SNGzfw8uVLRKNRuclzfopTKz/HjNr5IYcGki/mIY5oDoQXL16Y4+PjZiwWMx89eiQ3N2WzWTMWi5nlcllucsz6+roJwMxms3KT5/wUp1Z+jhm180MOOZ0v3Wpg56P7ur0pKGq1mplMJk0A5vr6utzcZn193ZyamjLr9brc5IjWseTzebm5L7VazRwfH7ediH6KUys3Y9bQb+zCpJ9Y+CGH3MiXbjUwtJfqQgh89tln2NnZQTabxfT0tHxKm+HhYbz77ruuTO+FEFhYWMDVq1flJs/5KU6t/BwzaueHHBp0voS2cP7+++8olUpIJpP49ttv5eaO3nrrLfmQI5aXlzExMYGTJ08CAFKplHyKZ/wUp1Z+jhm180MODTpfQlk4hRC4f/8+AODDDz9EIpGQTzlgbGwMn3/+uXy4b7quo1arIZfLyU2e81OcWvk5ZtTODznkRb6EsnBWKhX8888/AIDLly/LzQNjGAbu3r2LxcVFAMD29jZisRiGh4flUz3hlzi18nvMqJ3XOeRVvoSycG5sbGBvbw/JZBKZTEZuHph8Po8rV64gkUhACIFqtYrTp0+7tgzDKr/EqZXfY0btvM4hr/IllIXTDzRNw9DQ0JEflFulqmpzQXHjNTQ0hM3NTaTT6QNtc3Nz8o/wLbdi1hDm2FkVhli4nS+Hkm+ztzqi2bfy+bwJwJUlD71oXRYhv9wYk91lJF7HqdWgY9ZgN3ZhZCcWXuXQoPIFXWpgKGecjTtq//77L+r1utzsqsayiLW1teYTE6ZpolwuIxaLyad7yss4tQpSzKidFznkh3wJZeHMZDJIJpPY2dnB1taW3HzAysoKdF1v/lnXdaRSqbZjvWosixgbG5ObgP01bK3r1/rpq1/9xgn7l0v9Xs5ZjZkTfZIznMghq6zmixtCWTgTiQS+/PJLAMD8/DwqlYp8StPKygrOnDnT/EvQNA3pdBp7e3vyqUcqFApdl0VUq1Xs7e3hnXfeaR7rpy8n9BMn7Bf9p0+fNv+3f/z4MVRVbXvfUazGzIk+yTn95pBVVvPFNfK1e6sjmn3v5s2bJoDms7Otn3uUy2Xz2rVrHZ+pPeyznnK5bMZisbZnbev1erOvTj+vXq+bU1NTHT9/OayvXvX7M+zG6Y8//mg7N5/PH/j9TIdj1mufvbITu06/T5COd2MnFg12c6hXdvOlX91qYChnnA2qquLFixf49NNP8dVXX0FRFEQiEaRSKfz888/45ptvMDMzI7/NEk3ToCgK7t27BwCYnZ1tmwGpqgpFUVAqlQAApVIJiqL4apZkN07nz59vuyTq9WmNfmJmt09yl90cMgwDo6OjGB0dhWEYKBQKiMfjbfnQT76gxz6scuzrgYUQePjwIdbW1vD3338DAGKxGObm5rC0tIRKpYKtrS1Hnxhwi2EYuHTpEpaXl/u6rOjFIPtyWyMRO11GucWLPslZhmHg9u3bmJycBABEo1EUi0Wsrq7Kp9pmt49uNbDvGafwwf575D3DMPD8+XNcv35dbnKNF32S87a2tnDq1CmcOHECMzMzKBaLjn9O6XQffRXOSqWCyclJrK6u4tGjR3jy5EnbrCkajUJVVWSz2YE8BkXeefDgAZaWlly/m9nKiz7JecViEa9evcKFCxcghMDr16+bM0OnON2H7cJpGAY+/vhjbG5u4scff+z4+UXD5cuX8d577+Hs2bNyE4WAqqrIZDI9bfDgFC/6JOcZhoFnz55hYWEB0Wi0eVfeyVrhRh+2Cqcf9t8jf9A0DQCaOaBpWvOYW7zok9yxu7uLkZGRZhHb2NjAuXPnsLu7K59qmyt9yLfZW3VrbmxPn0wmzVqtJjcfUC6XzYcPH8qHfamxhAP7j28dtZt1PwbZlxsaj9u1vnrNCbu86JPck8/n23Zrz2az5rVr1xz9++ynj2410PJddSEEPvnkE5RKJWSz2SPvShERBVWnGgg7l+pu7L+ndtip5bBXP+uviIj6ZXnGqaoqFhcXkUwm8ddff/HDeSIKrU41EHZmnEREx53twjkyMgJFUeTDREShZ/lSXdM0XLx40ZeX6pFIRD5ERNQzud51qoGwM+N0Y/89p24ONR715Isvvviy8+qV5RknpBtEv/76a9cV+I39945aIE9E5EfdaqDlGSf2d6K5efMmdnZ28P7776NQKEAI0WzXdR1ffPEFotEoiyYRhY6tGWdDpVLBd999h19++aW5i/mbb76J6elp3Lp1i7shEVGgdauBfRVOIqIw61YDbV2qExEdZyycREQWsXCGkBACd+7cabthFxRBHnsQMd72sHCG0I0bN/Dy5ctA7n8a5LEHEeNtDwtnyGiahp9++kk+HAhBHnsQMd72sXCGiGEYmJ+fB4C+voiqG8MwMDExceSTYHa4PXa4PP6gGUS8w4yFMySEEFhYWMDVq1flJt8L8tiDiPHuHwtnSCwvL2NiYgInT54EAKRSKfkU3wry2IOI8e4fC2cI6LqOWq2GXC4nN/lekMceRIy3M1g4A84wDNy9exeLi4sAgO3t7cB8h32Qxx5EjLdzWDgDLp/P48qVK0gkEhBCoFqt4vTp04HYJyDIYw8ixts5LJwBpmkahoaGXNmBqtMeqUNDQ9jc3EQ6nT7QNjc3J/+IQ7k5dgxg/EHjdryPnbYvC5Yc0UweqtVqZjKZNOXvGAdgTk1NmfV6XX5L32q1mjk+Pm6Wy2W5yRIvxm46OP6g8SreYdCtBnLGGUCN5SRra2ttu1eXy2XEYjH5dF8J8tiDiPF2BwtnADWWk4yNjclNAIDh4eG2R+h0XUcqlfLFwm+rY9c07dhcTrvBarypNyycAVMoFLouJ6lWq9jb22t7EkTTNKTT6eZG016yOnZd1/H06dPmDOnx48ddv2+KDrIab+odC2dACCGQy+UwOzuL8fFxuRlCCNy/fx8A8OTJk+ZuN9PT06jVakgmk9I7Bsfu2IUQWFpaAgCMjY3h66+/bmsfBF3XEY/HEY/H22bsfj7+559/2oo39Y6FMwA0TYOiKLh37x4AYHZ2tm3mpaoqFEVBqVQCAJRKJSiK4ovZWT9jP3/+fNtlJJ9wOZoQAh999JGteGN/refo6ChGR0dhGAYKhQLi8bgvcslP+NUZx4BhGLh06RKWl5e7ftYVBI1/vJ0uPck5hmHg9u3bmJycBABEo1EUi0Wsrq7Kp4ZetxrIGScFgmEYeP78Oa5fvy43kcO2trZw6tQpnDhxAjMzMygWi/wsVMLCSYHw4MEDLC0t8Q7wABSLRbx69QoXLlyAEAKvX79uzj7p/1g4yfdUVUUmk0EikZCbyGGGYeDZs2dYWFhANBpFpVIBAJw9e1Y+9Vhj4SRf0zQN2F8d0Phz4xg5b3d3FyMjI81CubGxgXPnzmF3d1c+9Vhj4Qw5Xdfx9ttvN5/RDlLRUVUVFy9exOLiYnMR/Pz8PDKZjHwqOWRjYwMTExPNj0S2t7dRrVbxxhtvyKcea7yrTkTURbcayBknEZFFLJxERBaxcBIRWcTCSURk0ZE3h4iIjrNOJfLQwklERAfxUp2IyCIWTiIii1g4iYgsYuEkIrLof6qT1kl7N08CAAAAAElFTkSuQmCC\"\u003e\u003c/p\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003c/span\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCN Values for Different Land Use and Soil Types\u003c/p\u003e \u003cdiv class=\"Credit\"\u003e\u003cp\u003e(Source: USDA Soil Conservation Service, 1986)\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLand Use/Soil Type\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDescription\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCN Value (0-100)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eForest Land\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDense forest with natural soil, undisturbed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e55\u0026ndash;70\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGrassland\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNatural grasslands, tall grasses\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e60\u0026ndash;80\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCultivated Land (Agricultural)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAnnual crops, tilled land\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e70\u0026ndash;85\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBare Soil\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBare, dry soil without vegetation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e80\u0026ndash;90\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRangeland (Grazing Land)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePastures or lightly cultivated land\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e70\u0026ndash;80\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eUrban Areas\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHighly impervious urban areas (buildings, roads, etc.)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e85\u0026ndash;98\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eIndustrial Areas\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIndustrial zones or infrastructure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e90\u0026ndash;98\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eWater (Water Bodies)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBodies of water such as rivers, lakes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e100\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eIn Tables\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, we have listed each soil type along with its corresponding CN value and the equivalent CN for the area shaded by this type. When applying models like the SCS-CN, the choice of CN values is often influenced by prior tests or statistical adjustments to ensure the most accurate results. Generally, higher CN values are selected to represent areas with more runoff, such as those with more impervious surfaces or less vegetation conditions typically found in urbanized areas.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCalculation of equivalent runoff coefficient in 2017\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eIn 2017\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c4\" namest=\"c3\" rowspan=\"2\"\u003e\u003cp\u003eCN\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eCNeq\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eType of soil\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eArea (ha)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWater\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e72.45\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003evegetation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e67.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e3694.88\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVegetation dense\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e459.85\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBuilt up\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e32.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e2923.35\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBARE SOIL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e31.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e2645.84\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eCNeq\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e69.11\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCalculation of equivalent runoff coefficient in 2021\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eIn 2021\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eCN\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eCNeq\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eType of soil\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAreas (ha)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWater\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e346.71\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003evegetation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e54.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2991.96\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVegetation dense\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e523.41\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBuilt up\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e42.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3803.11\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBare Soil\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e29.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2548.09\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eCNeq\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e72.06\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eApplying the above formula, we find that the CN has evolved from 69.11 in 2017 to 72.06 in 2021. This change is due to an increase in built-up areas from 32.48 ha in 2017 to 42.25 ha in 2021, reflecting increasing urbanization. Arable land has decreased from 67.17km\u0026sup2; to 54.39 ha, indicating a likely conversion of agricultural land to built-up areas. A slight decrease in bare land could also be attributed to urban expansion.\u003c/p\u003e\u003cp\u003e\u003cb\u003eHydrological simulation with HEC-HMS\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo simulate the hydrological behavior of the Bouskoura catchment, the HEC-HMS (Hydrologic Engineering Center \u0026ndash; Hydrologic Modeling System) software was used. HEC-HMS is widely recognized for its ability to model rainfall-runoff processes in urban and semi-urban catchments, particularly in the absence of observed streamflow data.\u003c/p\u003e\u003cp\u003eThis model was selected for its compatibility with the SCS-CN method and its suitability for ungauged basins. The simulation framework included the following elements:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003ePrecipitation data from the Casablanca Mohamed V meteorological station (GMMN) at an hourly resolution.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eUse of the SCS-CN method to estimate infiltration losses.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eInitial abstraction and antecedent moisture conditions set to average (AMC II).\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eA constant rainfall event (from 2017) applied to all years (2017, 2021, and a 2030 projection) to isolate land use effects.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eOnly the CN value was modified for each scenario, based on the corresponding land use data.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eDue to the absence of observed discharge data at the outlet of the Bouskoura watershed, no calibration or validation of the HEC-HMS model could be performed. This is a significant limitation of the study. Consequently, the results are intended for comparative analysis rather than absolute prediction. Nevertheless, the model remains a valuable tool for understanding how urbanization affects hydrological responses.\u003c/p\u003e\u003cp\u003e\u003cb\u003eComparative Analysis of Simulation Results\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe hydrographs simulated for 2017 and 2021 were compared to assess the impact of urban growth on runoff behavior. The increase in CN due to urban expansion resulted in higher peak discharges and total runoff volumes, confirming that urbanization intensifies the hydrological response of the basin.\u003c/p\u003e\u003cp\u003eThese findings highlight the critical need for integrated urban planning and stormwater management strategies to mitigate the adverse effects of land cover change on hydrology\u003c/p\u003e"},{"header":"Results and discussion","content":"\u003cp\u003e\u003cb\u003eTrends in runoff coefficients\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eTrends in Runoff Coefficients\u003c/b\u003e\u003c/p\u003e\u003cp\u003eBetween 2017 and 2021, the Curve Number (CN) increased from 69.11 to 72.06, reflecting a moderate but significant rise in surface runoff potential. This trend is consistent with the observed expansion of impervious surfaces, particularly built-up areas, which increased by nearly 10 ha during this period. Although the increase in CN may seem limited, it indicates a reduction in infiltration capacity and a corresponding increase in surface runoff, potentially leading to higher peak flows and reduced groundwater recharge during precipitation events (Fletcher, 2013).\u003c/p\u003e\u003cp\u003eSuch evolution is typical of peri-urban areas undergoing unregulated urban expansion, as highlighted in similar studies across North African basins (e.g., Hasnaoui et al., 2020), where urban sprawl has been shown to degrade the natural hydrological response.\u003c/p\u003e\u003cp\u003e\u003cb\u003eAnalysis of CN's development based on demographic trends\u003c/b\u003e\u003c/p\u003e\u003cp\u003eIn 2017, CN was 69, 11. In 2021, it has risen to 72.06. An increase of 2,95 in 4 years. This increase, although moderate, reflects a reduction in infiltration and an increase in runoff. Moreover, it can be used as an indicator of future developments.\u003c/p\u003e\u003cp\u003eUrbanization projections can be modeled using different approaches (linear, exponential, sigmoid, etc.). This study chose a linear projection to simplify the analysis and avoid introducing uncertainties associated with more complex models requiring additional data. The development of catchment areas because of urbanization generally follows a linear or exponential trend, depending on the dynamics of urban development and land-use planning policies. (Fletcher, 2013)\u003c/p\u003e\u003cp\u003eBetween 2021 and 2030, the population is expected to increase by 39%. Extrapolation the impact of This increase on CN :\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e- A 21% increase in the population between 2017 and 2021 has increased in CN of 2,95 ;\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e- With a 39% increase in the population between 2021 and 2030, the increase in CN could be proportional.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:\\varDelta\\:CN=\\text{2,95}\\frac{39}{21}=\\text{5,48}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eProjection: CN\u003csub\u003e2030\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;CN2021 + \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varDelta\\:CN\\)\u003c/span\u003e\u003c/span\u003e= 77,9\u0026thinsp;+\u0026thinsp;0,15= 77,54\u003c/p\u003e\u003cp\u003eThus, by 2030, CN is projected to reach 77.54, indicating a further reduction in infiltration and an increase in runoff, which could elevate flood risks in the watershed.\u003c/p\u003e\u003cp\u003e\u003cb\u003eVariation in simulated flows\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe hydrological simulations performed using HEC-HMS revealed a marked increase in peak discharge over the study period:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e28.4 m\u0026sup3;/s in 2017\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e42.8 m\u0026sup3;/s in 2021\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e92.9 m\u0026sup3;/s projected for 2030\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eThis tripling of peak flows over 13 years highlights the sensitivity of runoff response to land-use changes, even in the absence of major changes in rainfall input. Such findings align with the literature, where increases in CN of just 5\u0026ndash;10 points have been linked to exponential increases in flow peaks and flood volumes (USDA, 1986; Fletcher et al., 2013).\u003c/p\u003e\u003cp\u003eFrom a water resources management perspective, this trend underlines the need for urban drainage planning, especially in rapidly urbanizing basins like Bouskoura. Without adequate mitigation strategies (e.g., green infrastructure, infiltration basins), the increased runoff may overwhelm existing drainage systems, elevate flood risks, and exacerbate soil erosion and water quality degradation.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFigures \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e and \u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e illustrate the evolution of peak discharge and runoff volume in the Bouskoura watershed between 2017, 2021, and the 2030 projection. The results indicate that urbanization not only increases peak flows but also significantly modifies the temporal distribution of runoff.\u003c/p\u003e\u003cp\u003eThe intensification of impervious surfaces accelerates the runoff process, leading to earlier peak times and an increase in total discharge volume. These changes are reflected in the steepening of hydrographs, which are indicative of more intense and rapid flood responses\u0026mdash;factors that raise the likelihood of flash floods.\u003c/p\u003e\u003cp\u003eAdditionally, the prolonged runoff duration observed under more urbanized conditions suggests reduced infiltration capacity, emphasizing the growing need for sustainable stormwater management practices in future urban development strategies.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eLimitations and Uncertainties\u003c/b\u003e\u003c/p\u003e\u003cp\u003eSeveral limitations must be acknowledged in this study:\u003c/p\u003e\u003cp\u003eFirstly, the absence of observed flow data prevented the calibration and validation of the hydrological model. Consequently, the simulations presented are scenario-based and are intended to highlight relative variations in hydrological response, rather than provide exact flow estimates.\u003c/p\u003e\u003cp\u003eSecondly, the projection of future CN values was based on a linear relationship with population growth, assuming a proportional increase in impervious surfaces. While this approach offers a simplified representation of urban dynamics, it may not capture the complexities of land-use evolution, which can be influenced by regulatory policies, socioeconomic factors, or land-use planning strategies.\u003c/p\u003e\u003cp\u003eMoreover, the model does not explicitly consider several parameters that can significantly affect runoff, such as soil compaction, slope variability, vegetation density, or potential changes in precipitation patterns due to climate variability.\u003c/p\u003e\u003cp\u003eDespite these limitations, the methodology applied enables a preliminary assessment of urbanization impacts on runoff and supports the identification of potential future hydrological risks in the Bouskoura catchment. These findings may serve as a basis for further studies involving more detailed data and calibrated models.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe results of this study clearly indicate a progressive increase in runoff flows in the Bouskoura watershed from 2017 to 2030, highlighting the significant hydrological impact of urbanization. Simulated peak flows increased from 28.4 m\u0026sup3;/s in 2017 to 42.8 m\u0026sup3;/s in 2021 (a 50% rise), and are projected to reach 92.9 m\u0026sup3;/s by 2030, representing a dramatic increase of 119% compared to 2021.\u003c/p\u003e\u003cp\u003eThis increase correlates with a rise in the runoff coefficient (CN), which reflects the expansion of impervious surfaces such as roads and buildings and the consequent reduction in infiltration capacity. The transformation of agricultural and vegetated land into built-up areas is the main driver of this trend, consistent with findings reported in other rapidly urbanizing watersheds (Brabec et al, 2002; Walsh et al, 2005).\u003c/p\u003e\u003cp\u003eSeveral studies have documented similar impacts of urbanization on hydrological responses. For example, (Brabec et al, 2002) showed that urbanization increases impervious cover, leading to higher runoff volumes and peak flows, thereby intensifying flood risks. (Walsh et al, 2005) emphasized that urban land-use changes often result in increased runoff, reduced groundwater recharge, and altered stream flow regimes. Similarly, Jiao et al. (2021) found significant increases in peak discharge linked to urban sprawl in Chinese watersheds, highlighting the importance of land-use planning to mitigate hydrological impacts.\u003c/p\u003e\u003cp\u003eThe variation in flow increase rates between the periods 2017\u0026ndash;2021 (+\u0026thinsp;50%) and 2021\u0026ndash;2030 (+\u0026thinsp;119%) may be explained by several factors:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eUrban development patterns\u003c/b\u003e: Some newly urbanized areas still include permeable surfaces (e.g., parks, green spaces) that help mitigate runoff increases by promoting infiltration (Fletcher et al, 2013).\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eAssumption of constant rainfall\u003c/b\u003e: The simulations assume steady precipitation over time, isolating land-use changes as the sole driver of flow variations. In reality, climate variability can further influence hydrological responses (Bl\u0026ouml;schl et al, 2013).\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eWhile urbanization clearly drives increased runoff and peak flows, other hydrological processes such as infiltration, soil moisture dynamics, and temporary water storage significantly influence basin responses. Future work should incorporate these factors and utilize observed flow data for model calibration to improve prediction accuracy.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eUrbanization alters hydrological function, significantly increasing runoff volumes and the risk of flooding (Andrieu, 2013). Urbanization changes the hydrological dynamics of catchments by increasing impervious surfaces and decreasing infiltration. This study used HEC-HMS hydrological models to examine changes in the runoff coefficient and flows in the Bouskoura basin from 2017 to 2030.\u003c/p\u003e\u003cp\u003eBetween 2017 and 2030, the runoff coefficient experienced a noticeable rise, moving from 69.11 to 77.56. This change points to significant land use changes, with urban development and expansion having a major impact. Alongside this, peak flows have increased sharply, from 28.4 m\u0026sup3;/s in 2017 to 92.9 m\u0026sup3;/s by 2030, signaling that urbanization is exerting a stronger influence on the hydrological system.\u003c/p\u003e\u003cp\u003eThis sharp rise in flows stands in contrast to the more gradual changes seen earlier, pointing to a more direct relationship between urbanization and runoff. As more areas become built up, with fewer permeable surfaces available for water to infiltrate, runoff volumes are on the rise. However, we also need to consider other elements, like the evolution of drainage systems, how stormwater is managed, and how rainfall patterns may have changed.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eSara Salih contributed to the research idea and manuscript writing;Mohamed Aghad and Lhoussaine El Mezouary participated in the supervision and manuscript revision;Sliman contributed to the preparation of the maps;Said Chakiri and Mohamed Sadiki participated in the review and critical revision of the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eABHBC. (2024). \u003cem\u003eAgence du Bassin Hydraulique du Bouregreg et de la Chaouia.\u003c/em\u003e\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eAndrieu, H. B. (2013). Impact of urbanization on urban hydrology and stormwater management. \u003cem\u003eHAL-INRAE\u003c/em\u003e.\u003c/li\u003e\n \u003cli\u003eBahi et al. (2016). Effects of Urbanization and Seasonal Cycle on the Surface Urban Heat Island Patterns in the Coastal Growing Cities: A Case Study of Casablanca, Morocco.\u003c/li\u003e\n \u003cli\u003eBakhtiar. (2022). Runoff estimation using SCS-CN and GIS techniques in the Sulaymaniyah sub-basin of the Kurdistan region of Iraq.\u003c/li\u003e\n \u003cli\u003eBanasik et al. (2014). Curve number estimation for a small urban catchment from recorded rainfall-runoff events. Archives of Environmental Protection.\u003c/li\u003e\n \u003cli\u003eBl\u0026ouml;schl et al. (2013). Changing climate shifts timing of European floods. Science, 357(6351), 588\u0026ndash;590.\u003c/li\u003e\n \u003cli\u003eBooth, D. B. (1997). Urbanization of aquatic systems: Degradation thresholds, stormwater detection, and the limits of mitigation. \u003cem\u003eJournal of the American Water Resources Association\u003c/em\u003e.\u003c/li\u003e\n \u003cli\u003eBrabec et al. (2002). Impervious surfaces and water quality: A review of current literature and its implications for watershed planning. \u003cem\u003eJournal of Planning Literature, 16(4), 499\u0026ndash;514.\u003c/em\u003e\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eCasa. (2025). \u003cem\u003eProtection of the city of Casablanca from the floods of Oued Bouskoura.\u003c/em\u003e CASA AMENAGEMENT.\u003c/li\u003e\n \u003cli\u003eCounty, S. D. (2003). \u003cem\u003eSan Diego County Hydrology manual.\u003c/em\u003e\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eDawod \u0026amp; al. (2011). GIS-based spatial mapping of flash flood hazards in Makkah city, Saudi Arabia.\u003c/li\u003e\n \u003cli\u003eFletcher. (2013). Sustainable urban water management in the face of climate change. Nature Climate Change, 3(1), 2-7. \u003cem\u003eNature Climate Change\u003c/em\u003e.\u003c/li\u003e\n \u003cli\u003eFletcher et al. (2013). Understanding, management and modelling of urban hydrology and its consequences for receiving waters: A state of the art. \u003cem\u003eAdvances in Water Resources\u003c/em\u003e.\u003c/li\u003e\n \u003cli\u003eFoody. (2004). Predicting locations sensitive to flash flooding in an arid environment. J. Hydrol.\u003c/li\u003e\n \u003cli\u003eGheith. (2002). Construction of a hydrologic model for estimating Wadi runoff and groundwater recharge in the Eastern Desert, Egypt.\u003c/li\u003e\n \u003cli\u003eGrimm. (2008). Global Change and the Ecology of Cities.\u003c/li\u003e\n \u003cli\u003eHasnaoui et al. (2015). Mod\u0026eacute;lisation de l\u0026apos;impact de la collecte des eaux pluviales sur l\u0026apos;att\u0026eacute;nuation des crues dans le bassin du Bouskoura et perspectives d\u0026apos;adaptation au changement climatique.\u003c/li\u003e\n \u003cli\u003ehcp. 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The urban stream syndrome: current knowledge and the search for a cure. \u003cem\u003eJournal of the North American Benthological Society, 24(3), 706\u0026ndash;723\u003c/em\u003e.\u003c/li\u003e\n \u003cli\u003eZhang \u0026amp; al. (2016). Urbanization and its impact on hydrology and water quality in cities: a global perspective. \u003cem\u003eUrban Water Journal\u003c/em\u003e.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Urbanisation, Runoff Coefficient, Land Use Change, HEC-HMS, SCS-CN, Bouskoura Watershed, Hydrological Modelling","lastPublishedDoi":"10.21203/rs.3.rs-7103720/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7103720/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eUrbanisation has become a key driver of hydrological change, particularly in peri-urban catchments of semi-arid regions. This study investigates the impact of land use transformation on runoff dynamics in the Bouskoura watershed, a rapidly urbanizing area on the outskirts of Casablanca, Morocco. Using supervised classification of Sentinel imagery, land use changes between 2017 and 2021 were quantified, revealing a 30.08% increase in urban surfaces and a concurrent decline in vegetated and agricultural areas.\u003c/p\u003e\u003cp\u003eTo evaluate the hydrological consequences of this transformation, runoff was estimated using the SCS-CN method and simulated through the HEC-HMS model under uniform rainfall conditions for 2017, 2021, and a 2030 projection. The runoff coefficient increased from 69.11 in 2017 to 72.06 in 2021, while peak discharge rose from 28 m\u0026sup3;/s to 42 m\u0026sup3;/s. Projections for 2030 suggest a peak discharge of 92.9 m\u0026sup3;/s, reflecting the expected rise in imperviousness due to ongoing urban expansion.\u003c/p\u003e\u003cp\u003eDespite the absence of flow calibration data, the results provide a comparative analysis that demonstrates the sensitivity of runoff to land use change. This research offers a replicable framework for assessing urbanization-induced hydrological shifts in data-scarce environments and highlights the urgent need to integrate stormwater management into urban planning policies in North African cities.\u003c/p\u003e","manuscriptTitle":"The Impact of Urbanisation on the Hydrological Responses of the Bouskoura Catchment : Analysis of Runoff Coefficient and Flood Flows from 2017 to 2030","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-25 15:18:45","doi":"10.21203/rs.3.rs-7103720/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0edb4e55-636b-4100-b995-cd0b5de2691d","owner":[],"postedDate":"July 25th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-09-06T18:08:17+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-25 15:18:45","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7103720","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7103720","identity":"rs-7103720","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}