Estimating flood inundation potential using the curve number method and satellite image processing (case study of the Qizil Uzan River in Iran)

Floods are the primary cause of loss of life, destruction of infrastructure, and massive damage to a country's economy. Floods, as natural hazards, are unavoidable. To minimize risks and provide emergency response during natural disasters, various measures must be taken by disaster management officials before a flood occurs. This includes using the latest advanced technologies that can predict the occurrence of a disaster in a timely manner, allowing for the adoption of appropriate response strategies before the disaster strikes (Chabokpour & Azhdan 2020; Rostami et al. 2023).

While the flood risk is largely neglected by the growing population and industrial activities of developed and developing countries, some research studies indicate an increase in flood occurrences, and the frequency of floods has been on the rise in recent years (Aronica et al. 2012). Currently, the risk of flooding is mapped globally using technologies such as satellite imagery and remote sensing. However, such implementation has not yet been successful at the local level, as flooding is a highly dynamic and rapid process, and the speed of image acquisition through satellites is very slow. For instance, satellite revisit times (5–6 days) often exceed the rapid evolution of flash floods (6–12 h), while coarse spatial resolution (>250 m) fails to capture small-scale inundation critical for local infrastructure. Synethic Aperture Radar (SAR)-based flood detection accuracy further drops to <60% in vegetated or mountainous regions (Li et al. 2020), and data processing delays of 8–24 h hinder real-time decision-making (Shah et al. 2019). Additionally, there are limitations in analyzing, receiving, and comprehending the full scale of flood hazards using remote sensing images (Ahmad & Afzal 2019). To increase disaster preparedness, flood hazard information needs to be collected at the local level. With recent advancements in technology, it is now possible to integrate computer models with remote sensing, enabling the continuous simulation of dynamic events such as floods in space and time (Wagenaar et al. 2020). In recent years, computational methodologies grounded in image processing and machine learning have emerged as pivotal tools in disaster management research. Building upon this trend, the present review systematically synthesizes contemporary advancements in disaster response and mitigation strategies that leverage these technologies, with a particular focus on their application in dynamic, large-scale flood hazard assessment. Analyzing existing techniques for identifying flooded areas, along with the gaps in the currently used methods, can aid in developing and proposing a model to improve flood analysis. An important point that needs attention is the lack of focus on post-disaster management systems in the literature, which is related to existing methods for flood detection (Transon et al. 2018).

Currently, the focus of flood risk management systems is primarily on flood forecasting and creating maps to identify disaster-prone areas (Tranfield et al. 2003). The adoption of satellite image analysis and remote sensing techniques is steadily transforming flood monitoring and assessment. These approaches address many of the shortcomings of traditional flood management methods by offering more dynamic and comprehensive solutions. Werneck et al. (2018) demonstrated the value of computer vision in processing high-resolution satellite images for real-time flood detection. In parallel, Tanoue et al. (2016) explored how multispectral remote sensing data has significantly advanced flood forecasting systems. Supratid et al. (2017) further examined how platforms like Landsat and Sentinel contribute to flood assessments through geospatial data integration. Despite these advancements, studies such as those by Sulaiman & Wahab (2018) often fail to fully harness automated image-processing workflows for refining key hydrological parameters like curve number (CN) and soil moisture retention capacity. Addressing this gap, recent research emphasizes the combined use of satellite-derived land cover classification, slope-adjusted hydrologic soil group (HSG) mapping, and spectral indices. These elements work together to enhance SCS-CN-based flood risk assessments, offering practical solutions for data-scarce regions. This integrated approach not only improves predictive accuracy but also strengthens flood management efforts where conventional datasets are lacking. One of the most important technologies relied upon for flood assessment is remote sensing. Using this technology, information can be extracted from satellite images produced in different bands and electromagnetic frequencies by employing various image-processing methods. By calculating different indices such as soil moisture or greenness indices and classifying each of these indices, one can infer the flood susceptibility of an area. The integration of satellite remote sensing and geospatial technologies has brought significant improvements to flood hazard mapping. By allowing automated, high-resolution analysis of changing hydrological processes, these advancements make flood assessments more precise and efficient. Makker et al. (2019) demonstrated how combining a global positioning system (GPS) with satellite data enhances the spatial accuracy of flood risk assessments, helping pinpoint vulnerable areas more precisely. Building on this work, recent studies use Sentinel-2 satellite imagery alongside GIS-based workflows to create spatially distributed CN maps. These maps are essential for identifying flood-prone areas, particularly in regions where data are limited. This approach represents an important step forward in improving flood management strategies and supporting more informed decision-making in vulnerable communities. Utilizing such technologies for disaster prediction and management by issuing timely warnings and devising appropriate strategies for emergency response actions can help reduce the risk of destruction. Early planning and effective communication can help to save human lives from dangerous situations and natural disasters. It can contribute to supporting the country's economic and social development. Remote sensing technologies, using various recording tools, can obtain data related to objects and infrastructure on the Earth's surface without direct contact (Shah et al. 2019). Recent studies, such as Li et al. (2020), have demonstrated the utility of Sentinel-2 imagery for flood modeling, while Shahabi et al. (2020) highlighted the effectiveness of machine learning for flood susceptibility mapping in Iran. However, these approaches often overlook the integration of slope-adjusted HSG classifications, a critical factor in mountainous regions like the Qizil Uzan basin. Therefore, it is useful in areas where physical contact is not possible (Saravi et al. 2019). Data cannot be collected as efficiently and accurately using ground observations, while remote sensing technology collects the same data over larger areas in the shortest time possible, providing a comprehensive view of the target objects (Riaz & Atif 2020). It can also take pictures of distant objects despite adverse weather conditions, and aerial photographs and satellite images obtained through remote sensing aid in visualizing topography and other Earth features. Such features assist in locating natural disasters and assessing their extent. A wide range of relief operations can be carried out by timely visualization of data obtained from remote sensing sources (Nelson et al. 2020).

During the twentieth century, researchers began using early forms of remote sensing by studying aerial photographs to investigate river morphology and involved driving processes. The launch of the Landsat program in 1972 led to the rapid uptake of remote sensing for river research. For example, to identify former river channels or investigate water quality and suspended sediments, map flood hazards, and understand the interactions between rivers and vegetation cover (Ghose et al. 1979). The recent advancements in flood modeling highlight how sediment transport and river dynamics play a key role in shaping flood hazards. Hamidifar et al. (2024) emphasized that sediment dynamics, along with channel morphology, greatly influence flood patterns. They point out that the movement of bed-load sediments can affect peak water surface levels and how flows synchronize. In a similar vein, Maranzoni et al. (2024) stress the importance of incorporating sediment load and hydro-morphodynamic processes when assessing flood risks to create more accurate flood hazard maps. These findings underscore the growing recognition of sediment transport as a critical factor in flood modeling. This is particularly evident in riverine environments, where changes to channel structure may either intensify or reduce flood risks. Future research should explore integrating sediment dynamics into models like the SCS-CN framework, which could significantly improve flood prediction and support better flood management efforts. Multispectral remote sensing records the energy emitted or reflected from objects on the Earth's surface through sensors that can detect specific spectral bands (Fan et al. 2021). Spectral bands constitute a narrow portion of the electromagnetic spectrum, defined by the lowest and highest wavelengths detectable by the sensor. As a result, a raster image is recorded for each of the spectral bands (Hall et al. 2012). Examples of current satellites that employ such sensors include Sentinel-2, Landsat 7, Landsat 8, and MODIS. Martins and Wylland proposed a framework for performing flood prediction on multispectral data obtained from Landsat TM and Sentinel-2 images (Qiu et al. 2021). Li et al. (2020) retrieved soil moisture readings using the Advanced Scatter meter to create a rainfall-runoff model that forecasts floods. The direct relationship between the satellite, soil moisture, and precipitation in the model is used for decision-making on future flood occurrences. Shahabi et al. (2020) used satellite remote sensing to analyze the flood susceptibility of the Haraz watershed in Iran using Sentinel-1's multispectral data and noted the potential of spectral indices in flood zone delineation. While their research utilized more contemporary processing methods, the existing literature speaks to the glaring gap which is the application of satellite land cover datasets over the SCS-CN hydrological models for runoff estimation. As an example, the dynamic effect of satellite-derived soil moisture on slope-adjusted hydrologic HSGs stays unexplained with a multitude of traditional approaches. Hydrological and hydrodynamic models are widely used to simulate floods in terms of magnitude, frequency, and extent in the subcatchment (Garg & Garg 2016). However, the use of hydrodynamic models under limited data conditions and at the administrative level is challenging. Previous studies on flood hazard assessment have adopted an index-based approach, applying various parameters derived from geomorphological and hydrological features extracted from the digital elevation model (DEM) (Samela et al. 2016), land use and land cover information, and urbanization data (Xiao et al. 2016). Another approach that has been employed is the assessment of flood exposure for the provision of early warnings and disaster risk reduction. In the past two decades, many studies have used multi-criteria decision analysis to estimate index-based flood hazard and risk by understanding the role of controlling flood parameters (Kazakis et al. 2015). Geographic information system (GIS)-based multi-criteria decision-making, by organizing criteria hierarchically, addresses complex decision-making problems. Advanced technologies relying on satellite remote sensing and numerical hydrological and weather predictions can identify and monitor severe flood events globally. Ali et al. (2020) examined the emerging role of the Global Flood Partnership, a global network of scientists, users, and private and governmental organizations active in global flood risk management. Integrated flood hazard assessment in the Ebro Delta using GIS and remote sensing data was presented by Rabinovich (2019). This study utilized GIS and remote sensing data to assess flood hazards in the Ebro Delta region of Spain. Flood hazard maps were developed and evaluated the potential impacts of sea level in the region. Flood hazard and hazard mapping using GIS and multi-criteria decision analysis, with a case study in the Bago River basin, Philippines (Lobanova et al. 2018), is another example. This study employs GIS and multi-criteria decision analysis to map flood hazards in the Bago River basin in the Philippines. The research by Ma et al. (2018), also utilized GIS and remote sensing data to assess flood hazards in the Kelantan River basin in Malaysia. The authors produced a flood hazard map and evaluated the potential impacts of climate change on flood hazards in the region.

Regarding the literature review, it can be found that forecasting the flood risk of an area is one of the important issues that are very important for saving human lives and examining the land use and planning for the exploitation of a part of the land. In this regard, the primary objective of the current study is to propose a method for flood risk analysis using satellite imagery and the Soil Conservation Service (SCS) CN method. This involves understanding land cover, soil types, and assigning appropriate CN values to calculate the overall CN for a region, which represents the potential for runoff generation and flooding. Additionally, the study aims to assess the flood potential of the Qizil Uzan River region in northwestern Iran by analyzing satellite imagery, precipitation data, land use/cover (LULC) maps, soil types, and topographic factors such as slope and elevation. Furthermore, the study aims to demonstrate the applicability of integrating remote sensing techniques with the CN method for flood inundation mapping and risk assessment, highlighting the advantages of data availability, spatial coverage, and simplicity of this approach.

Even with progress in evaluating flood risks, adequate measurements are still sorely needed for areas with little data and complex topography like the Qizil Uzan River basin. Available studies tend to use coarse-resolution datasets or fixed land-use maps that should account for human changes as well as physiographic hydrological alteration through the slopes but do not. Also, not many studies incorporate slope-corrected HSG classifications into calculations of CN by using high-resolution satellite images, especially in mountainous areas. This study combines and expands upon previous literature by analyzing the unique and multi-faceted flood risk in the Qizil Uzan basin using slope-adjusted HSG maps, SCS-CN analysis, and Sentinel-2 imagery.

Satellite imagery with high resolution (10–20 m), multispectral capabilities (13 bands), and free-access availability, such as Sentinel-2 (Multispectral Instrument, MSI), was utilized for its superior resolution and comprehensive spectral features. Imagery acquired between 2018 and 2022 with minimal cloud cover (<10%) was prioritized to maintain temporal consistency and data integrity.

LULC classification was conducted using a supervised maximum-likelihood algorithm in ArcGIS 10.8, with training samples derived from field observations and high-resolution Google Earth imagery. Seven LULC categories were identified: urban, agricultural land, forest, grassland, shrubland, bare soil, and water bodies. The classification achieved a 92% overall accuracy, validated by a confusion matrix (Kappa coefficient = 0.89).

To derive HSG classifications, the FAO Digital Soil Map of the World (DSMW) and ASTER Global DEM (30 m resolution) were integrated. Slope thresholds were applied to adjust HSG classifications, such as upgrading HSG Groups B to C for slopes exceeding 15%, in alignment with U.S. Department of Agriculture (USDA) NRCS guidelines.

Finally, LULC maps derived from Sentinel-2, slope-adjusted HSG layers, and rainfall data were combined to compute spatially distributed CN values for further analysis.

The evaluation of runoff (Q) utilized Equation (1) where rainfall intensity (P) came from Iran Meteorological Organization records for a 20-year return period storm (120 mm/24 h). The initial abstraction (I_a) operation stood at 0.2S based on SCS recommendations.

The Iran Meteorological Organization provided precipitation data which led to selecting 120 mm/24 h rainfall intensity as the design storm event for a return period reaching 20 years. A value has been chosen following existing standards in regional flood risk assessments for historical extreme rainfall events observed in the basin.

The range of CN is from 30 to 100. Lower numbers indicate low runoff potential, while larger numbers are for increasing runoff potential. The lower the CN, the more permeable the soil. As observed in the CN equation, runoff cannot commence until the initial abstraction is satisfied. It is important to note that the CN method is an event-based computation and should not be used for an annual rainfall amount, as this would incorrectly lose the effects of antecedent moisture and the requirement for an initial abstraction threshold.

To calculate the CN for a region, the following can be followed:

• Understanding land cover and soil types: Obtain information about the types of land cover and soil present in the area of interest. Land cover data can be derived from satellite imagery or land cover maps, while soil data can be obtained from soil surveys or soil databases.

• Assigning CN values: Appropriate CN values will be assigned to different combinations of land cover and soil in an area based on lookup tables. CN values are typically available in lookup tables provided by organizations such as the NRCS in the United States. These tables provide CN values based on land cover (e.g., forest, grassland, impervious surfaces) and the HSG (A, B, C, or D).

• Determining area weighting: Calculate the area coverage or percentage of each land cover and soil type in the region. This can be done with land cover and soil maps or by using GIS techniques to calculate the spatial distribution of land cover and soil types.

• Applying weighted average: Use the weighted average method to calculate the overall CN for the region. The CN value for each land cover and soil combination is multiplied by the percentage area coverage. These data are summarized for all land covers and soil types in the region (Equation (3)):

  

  (3)

  where CN1 to CNn are the CN values for each land cover and soil combination, and A1 to An are the corresponding percentage area coverages.

It is important to note that the CN method is a simplified approach, and the accuracy of the results depends on the availability and quality of land cover and soil data, as well as the representativeness of the CN values for the specific region. Local expertise and field observations can aid in refining the CN values to better reflect the hydrologic characteristics of the region. Consulting hydrologic guidelines, literature, or local hydrology experts can provide region or country-specific guidance and appropriate CN values.

The study implements new developments that set it apart from earlier SCS-CN applications together with satellite-based flood risk evaluation work. The research utilizes high-resolution multi-temporal Sentinel-2 imagery (of 10–20 m resolution) to monitor land-use changes alongside their effects on CN values in the Qizil Uzan River region with limited data availability. Spatially precise information about flood risks caused by human-caused rapid development near riverbeds becomes available through this concept because existing regional studies identified this information gap. Slope-adjusted classifications of HSGs appear for the first time along with this method to improve mountain runoff estimation which conventional SCS-CN applications usually fail to consider properly. The third advance of theoretical mapping boundaries happens through identifying functional high-risk flood areas such as Section 15 where residential neighborhoods converge with flood-prone riverbanks. Targeted approaches for risk assessment at the community scale help developers create specific plans for buffering certain areas and establishing warning systems that directly aid emergency preparation efforts in exposed locations. Advanced flood risk assessments become more effective in complex data-poor environments as a result of these innovative techniques.

The proposed approach integrating the SCS-CN method and satellite imagery demonstrates its effectiveness in assessing flood inundation potential for the Qizil Uzan River region. The spatially distributed CN maps generated through this method provide valuable findings into the spatial variability of infiltration capacity across the landscape, enabling the identification of areas with higher runoff and flooding potential. While the overall study area exhibits a moderate to high flood risk, certain regions stand out as being particularly vulnerable. Section 15, with the highest calculated CN value, emerges as a critical hotspot requiring urgent attention due to its high flooding potential and the presence of residential developments in close proximity. Prioritizing flood mitigation measures and implementing appropriate land-use policies in this area could significantly reduce the risk to human life and property. Moreover, the areas adjacent to the riverbed, Sections 3 and 4, also exhibit elevated flood risks, emphasizing the need for comprehensive flood management strategies along the riparian zones. Implementing sustainable river management practices, such as riparian vegetation restoration and buffer zone establishment, could help mitigate the impacts of flooding in these vulnerable areas. It is crucial to note that the flood potential is highly dependent on precipitation intensity and duration. While the calculated CN values provide an indication of the overall runoff potential, the actual flood risk may vary based on the specific rainfall characteristics of a given event. Integrating the CN-based approach with real-time precipitation data and forecasting models could further enhance the accuracy and timeliness of flood risk assessments. Furthermore, the study highlights the importance of considering the dynamic nature of land-use patterns and their impacts on flood risks. By analyzing historical and projected land-use scenarios, the proposed method can be used to evaluate the effectiveness of land-use planning policies in reducing flood vulnerabilities. This information can inform decision-makers and stakeholders in developing sustainable land-use strategies that balance economic development and flood risk mitigation.

Table 1 presents a compilation of hydrological and geospatial indicators from all 15 sections in the Qizil Uzan River basin. Parts 3, 4, 12, and 15 demonstrate maximum flood exposure based on CN values >85 and runoff (Q) reaching 90 mm levels because the areas contain HSG D impermeable soils and >20% slope angles and urban/residential zones. The maximum annual precipitation of 450–500 mm occurs in these areas that face increased risk from flash floods. Low-risk flood areas exist in forested and grassland regions (Sections 6, 9, and 13) because these areas contain permeable soils (HSG A/B) together with slopes that do not exceed 10%. The table demonstrates how land use interacts with soil type and topography to assess flood hazards which enables targeted efforts for flood mitigation particularly in Section 15.

The high CN values in Sections 3, 4, and 15 (CN > 85) directly correlate with unregulated construction on steep, impermeable soils which is a problem unique to this basin due to its recent urban expansion trends.

Table 2 contains the Qizil Uzan River basin runoff (Q) computations through Equation (1) for the 15 sections. The investigation of runoff data relied on rainfall intensity P at 120 mm representing a 20-year return period according to data from the Iran Meteorological Organization. The calculation of retention parameter (S) involved Equation (2) (S = 1,000/CN − 10) while the initial abstraction (I_a) stood at 0.2S following standard SCS-CN method protocols.

Spatial differences in runoff quantities across the basin appear through varying CN values because these values show actual differences between land-use practices and soil properties and slope characteristics. Sections 3, 4, and 15 demonstrate maximum runoff levels (Q > 90 mm) in both their CN measurements (CN > 85). The areas exhibit high runoff as they contain impermeable soils with urban/residential land use (HSG Group D). Sections 1, 2, and 13 exhibit lower runoff numbers (Q < 45 mm) because these sections possess CN values (CN < 75) and HSG Group A/B permeable soils together with vegetated land coverage that results in enhanced infiltration and reduced surface runoff.

The flood-prone status of Sections 3, 4, and 15 in the study corresponds to their elevated runoff values. Section 15 houses the highest flood risk area with Q = 98.3 mm and CN = 92 because it contains impermeable soils together with steep slopes and residential developments within its vicinity. The study results strongly highlight why proper flood prevention steps need to be implemented strategically in flood-prone regions through strategic measures like protective riparian buffer areas and stormwater management programs.

Table 2 establishes the spatial distribution pattern of flood risk by presenting quantitative data regarding runoff for each measured section. This data-driven approach enhances the accuracy of flood risk assessments and supports evidence-based decision-making for sustainable land-use planning and disaster preparedness.

Table 3 provides a complete summary of CN values, HSG classifications, LULC types, and estimated runoff (Q) for all 15 regions in the Qizil Uzan River basin. The CN values were computed via a systematic combination of LULC data, slope-adjusted HSG classifications, and standard NRCS reference tables. The findings demonstrate pronounced spatial heterogeneity in CN and runoff throughout the study region, which is motivated by variations in land cover, soil characteristics, and topography. The high-risk regions are Sections 3, 4, 11, 12, and 15, with the highest CN values (CN > 85) and large runoff depths (Q > 90 mm). These sections are characterized by the dominance of urban/residential land use (Sections 3, 11, and 15) and bare soil (Sections 4 and 12), HSG Group D soils, and steep slopes (>20%). Section 15, for example, with CN = 92 and Q = 98.3 mm, is the most flood-risky section because it has impermeable surfaces, steep topography, and proximity to residential areas. These results stress the urgent necessity of some flood control measures in areas identified as high-risk.

Moderate-risk zones: The CN values of 72–84 and runoff volumes of 45–80 mm for Sections 1, 5, 7, 8, and 10 are moderate. These are predominantly occupied by agricultural fields, shrubland, and pasture, and have HSG Groups B and C soils. The moderate runoff potential in these zones is a reflection of a balance between permeable surfaces and moderate slopes (10–15%), which allow partial infiltration.

Low-risk areas: Sections 2, 6, 9, 13, and 14 have the lowest values of CN (CN < 70) and runoff volumes (Q < 45 mm). These areas are covered with forest, grassland, and wetland LULC classes, HSG Group A soils, and gentle slopes (<10%). Due to the high infiltration capacity of these areas, surface runoff is greatly decreased, and hence, they are less prone to flooding.

The HSG classes corrected for slope helped to enhance the CN values. For instance, in Section 5, the original HSG Group B was upgraded to Group C due to 10–15% slopes, resulting in a higher CN (74) and runoff (49.8 mm). The correction highlights the importance of incorporating topographic factors in flood hazard assessment, particularly in mountainous terrain like the Qizil Uzan basin.

Overall, Table 3 indicates the overwhelming influence of LULC, soil, and slope on flood potential. By presenting a detailed CN and runoff breakdown for each area, this table enhances the study's reproducibility and transparency, in addition to offering practical suggestions for flood risk management and land-use planning in the Qizil Uzan River basin.

The results of this study are in line with and supplement existing studies on flood risk mapping with the SCS-CN approach and satellite-based techniques. For example, Shahabi et al. (2020) employed Sentinel-1 data coupled with machine learning models to create flood susceptibility maps for the Haraz watershed in Iran, demonstrating the efficiency of remote sensing in mapping areas with high risk. Therefore, our research proves the efficacy of Sentinel-2 imagery for accurate LULC classification and CN mapping, especially in data-poor areas such as the Qizil Uzan River basin.

Section 15 has been rated a high flood risk zone (CN = 92, Q = 98.3 mm) as supported by the study of Aronica et al. (2012), who highlighted the rising influence of urbanization on flood risk. The proximity of houses to the riverbank in Section 15 supports the observations of Ali et al. (2020), who reported corresponding vulnerabilities in the Topľa basin, Slovakia. This necessitates prompt flood-resilient urban planning for high-risk areas.

Our use of slope-adjusted HSG classifications in the present work mitigates a limitation that Samela et al. (2016) identified with the reductionist character of HSG assignments used in conventional SCS-CN applications. By adjusting HSG classifications by slope breakpoints (e.g., from B to C for slopes greater than 15%), our approach provides a more detailed characterization of mountainous runoff response, as seen in Sections 5 and 11.

Moderate to high flood risks mapped within the Qizil Uzan basin also agree with those of Lobanova et al. (2018) in the Bago River basin in the Philippines, where the same multi-criteria mapping revealed vulnerable riparian areas. But this work adds to previous efforts with quantitative measures of runoff volumes (Q) per area, thereby enabling the prioritization of mitigation action strategically. For example, the high values of runoff in Sections 3 and 4 (Q > 90 mm) are in agreement with research by Ma et al. (2018) in the Kelantan River basin of Malaysia, where steep slopes and impermeable soils significantly increased flood risks.

The use of Sentinel-2 imagery for both LULC mapping and CN mapping builds on the work of Li et al. (2020), in which Sentinel-2 imagery data were applied to river delineation and flood simulation. Our study, however, integrates slope-corrected HSG mappings and antecedent precipitation for enhanced precision in flood hazard analysis, bridging gaps that Wagenaar et al. (2020) identified in their review of remote sensing applications in flood modeling.

In conclusion, this study not only supports the effectiveness of the SCS-CN approach and satellite-based methods for flood risk assessment but also introduces new improvements, such as slope-modified HSG delineations and high-resolution LULC mapping, for addressing region-specific challenges. These innovations complement the current body of literature relating to flood risk management and provide a replicable framework for similar studies in regions of poor data availability and intricate topography.

The SCS-CN method, combined with satellite imagery, provides a valuable tool for estimating flood inundation potential at regional or national scales. The approach offers a practical and relatively straightforward method for flood risk assessment and land-use planning, given the research conducted in the present study for assessing flood risk. For the study of the Qizil Uzan River, considering the land cover and precipitation level in the country, a reach passing through from Zanjan toward Mianeh was selected. The study area comprises 15 different land-use regions combined with various soil types. Each part of the study area contains all four HSGs, which play a crucial role in calculating runoff. Precipitation data has been extracted to determine the flood risk and will be used in more precise flood risk calculations involving precipitation levels. The soil type for each section has been extracted based on the moisture information for that specific area of the study region and the HSG has been determined based on this information and lookup tables. The CN has been calculated based on a combination of soil and land use. According to the calculations, two out of the 15 studied regions have a very high risk of flooding, as they are located near the riverbed. Residential areas exist around the flood-prone regions, which means that high precipitation levels in these areas could pose life-threatening risks. Overall, the integration of remote sensing techniques and the SCS-CN method offers a powerful tool for flood risk assessment and management. By providing spatially explicit information on runoff potential and identifying critical areas of concern, this approach can guide targeted interventions, inform land-use planning decisions, and ultimately contribute to enhancing community resilience against the impacts of flooding. Finally, it was found that in the case of the Qizil Ouzan River, implementing the SCS-CN and satellite imagery approach can significantly enhance flood risk management efforts. By identifying flood-prone areas, authorities can prioritize infrastructure development, implement early warning systems, and promote flood-resilient land-use practices.

While this study demonstrates the promise of Sentinel-2 imagery and the SCS-CN method for flood risk mapping, certain limitations should be noted. First, Sentinel-2's spatial resolution (10–20 m) can miss the detection of fine-scale hydrologic features, i.e., small-scale urban drainage networks or narrow riverine corridors, and consequently underestimate local flood risk. Moreover, the consideration of a constant initial abstraction ratio (I_a = 0.2S) might bring uncertainties to the runoff calculation, especially for urbanized watersheds or watersheds with steep gradient values. These drawbacks could be surmounted in future research efforts by integrating high-resolution data, local calibrations of CN, and dynamic modeling of soil moisture for achieving more accurate flood hazard mapping.

Jafar Chabokpour conceptualized the study, supervised the research, and drafted the manuscript; Mohammad Hosein Jahanpeyma contributed to data analysis and methodology development; Roya Etemadi assisted with data collection and reviewed the manuscript.

All relevant data are included in the paper or its Supplementary Information.

The authors declare there is no conflict.