The research introduces a method of flood hazard measurement using satellite imaging coupled with the Soil Conservation Service (SCS) curve number (CN) method. The research area was selected to be the region around the Qizil Uzan River in northwest Iran, which suffers from intensive rainfall and increased susceptibility to flood. Satellite-derived groundcover data are combined with soil type and slope to calculate spatially distributed CN values that describe the stream production potential throughout the landscape. The calculated CN maps exhibited pronounced spatial variation in flood risk over the study area. Especially, Section 15 has been identified as a high-risk zone with its high CN value, indicating a high flood risk that is aggravated by the closeness of residential developments. Furthermore, locations along the riverbed (Sections 3 and 4) also present high flood risks, highlighting the necessity for an integrated river management plan. The study area overall exhibited a high to moderate risk of flooding, with some areas being very susceptible. The integration of the SCS-CN method and satellite data was found beneficial in evaluating the risk of flood, determining the priority areas for targeted intervention, and providing suggestions on sustainable land-use planning interventions.

  • The paper studied flood hazards using available data and image processing.

  • The results of the study can be very useful for decision-makers.

  • The results of the study cover the spatial hazard distribution of the second-longest 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.

Study area

Originating from the Chehel Cheshme Mountains in Kurdistan Province, the Qizil Uzan River becomes Iran's second-largest river which flows across northwest Iran until it reaches the Caspian Sea. The river flows throughout Zanjan, East Azerbaijan, and Gilan provinces while joining three main tributaries consisting of Shahrud and Zanjan and Heyrow rivers. The basin area of the river contains the Ghafelankuh mountainous gorge and the Tarom delta land that supplies irrigation for olive cultivation and rice cultivation in agricultural zones. Approximately 500 million m3 of water flow from the Khalkhāl region through the Qizil Uzan every year thus making it an important sector for the area. At Manjil County, the Khalkhāl River encounters the Shahrud River to create the Sepid Rud River before entering the Sepid Rud Reservoir. The active drainage network in Iran's northwestern areas depends on the Qizil Uzan as a vital component for ecosystem and human activity stewardship (Figure 1). Three major factors create severe flood dangers throughout the Qizil Uzan basin: (1) Urban growth inside previous flood zones together with (2) the fast water runoff from steep land and tight soils along with (3) insufficient water monitoring equipment. The basin requires advanced assessment methods using satellite data to overcome its specific natural and human-made risk conditions.
Figure 1

Schematic depiction of the Qizil Uzan River and its branches.

Figure 1

Schematic depiction of the Qizil Uzan River and its branches.

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Remote sensing data acquisition and processing

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.

Runoff CN

The study addresses the basin's data scarcity by leveraging Sentinel-2's 10-m resolution imagery to classify land use and soil hydrologic groups, overcoming reliance on outdated or low-resolution datasets. The SCS-CN and satellite imagery approach offers several advantages for flood inundation mapping. The first advantage is data availability such that satellite imagery is widely available and frequently updated, providing timely information for flood risk assessment. Second, spatial coverage such that satellite imagery can cover large areas efficiently, enabling comprehensive flood inundation mapping at regional or national scales, and the final advantage is relative simplicity so that the SCS-CN method is relatively simple to implement and requires minimal input data, making it accessible to a wide range of users. The runoff curve number (also referred to as simply the curve number or CN) is an empirical parameter used in hydrology for predicting direct runoff or infiltration from excess rainfall. The CN method was developed by the USDA Natural Resources Conservation Service (NRCS), formerly known as the SCS. The number is still referred to as the ‘SCS-CN’. The runoff CN was derived from empirical analysis of runoff from small catchments and hillslope plots monitored by the USDA. It is widely used and provides an efficient means of estimating the approximate amount of direct runoff from a rainfall event in a particular area. The runoff CN is based on the soil's hydrologic group, land use, treatment, and hydrologic condition of the area. Resources, such as the USDA, provide runoff CN for land cover descriptions and an HSG. Runoff discharge can be computed using Equation (1):
(1)
where Q represents runoff, P represents rainfall, S is the maximum potential soil moisture retention after runoff begins, is the initial abstraction, or the amount of water before runoff, such as infiltration or rainfall interception by vegetation cover. Historically, was assumed. However, more recent research suggests that may be a more appropriate relationship in urban catchments where CN is updated to reflect developed conditions. The runoff CN can then be computed as Equation (2):
(2)

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 (Ia) 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.

Proposed method for flood risk analysis

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.

This study combines satellite-derived processing techniques with SCS-CN evaluation approaches and flood hazard mapping methods to determine flood conditions within the Qizil Uzan River basin area. The research used Sentinel-2 imagery and FAO soil maps as well as ASTER DEM data for high-resolution acquisition of land cover and soil type details and slope measurements. The study utilized Sentinel-2 imagery at a resolution between 10 and 20 m to analyze detailed LULC conditions and the ASTER DEM delivered necessary topographic information for slope measurements. The derived LULC maps from the supervised classification of Sentinel-2 imagery received validation by comparing them against ground truth data and existing land-use maps. The process involved merging FAO soil data with slope adjustments derived from topographic analysis to map the HSGs in the basin. The HSG classification procedure aimed to establish mappings that authentically exhibited the hydrologic characteristics of the region. USDA NRCS provided standard lookup tables for assigning CN values according to LULC and HSG combinations. The analysis implemented Equations (1) and (2) to calculate runoff using P values which were derived from data provided by the Iran Meteorological Organization from the 20-year return period. The initial abstraction (Ia) was established at 0.2S according to SCS guidelines to determine the runoff values across different sections of the research area. The combined usage of satellite remote sensing with the SCS-CN method produces an all-inclusive and precise method for flood inundation assessment as illustrated in Figure 2. Spatially distributed CN maps were generated using GIS raster overlay analysis. LULC and slope-adjusted HSG raster layers (10-m resolution) were integrated via cell-by-cell computation, where each pixel's CN value was categorically assigned using NRCS lookup tables. This approach ensured precise spatial alignment of CN values with land cover and soil properties, eliminating the need for statistical interpolation.
Figure 2

Workflow diagram of the integrated SCS-CN and satellite-based methodology.

Figure 2

Workflow diagram of the integrated SCS-CN and satellite-based methodology.

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The Zanjan-Mianeh section of the Qizil Uzan River basin receives the highest amount of precipitation than other parts because the annual rainfall averages 450–500 mm (Iran Meteorological Organization) measures at about 50% above Gilan Province's rainfall totals of 300–350 mm. The intense rainfall patterns within this location surpass 120 mm in 24 h (a 20-year return period) therefore enhancing runoff development and supporting study assessment. The spatial quantitative data reveals that the basin has distinct features regarding flood triggers so researchers need to develop localized hydrological modeling specifically for high-precipitation areas. The image in Figure 3 displays the study area using different bands of satellite imagery. Also, Figure 4 shows the study area in the ultraviolet spectrum. In this regard, the LULC map is crucial for decision-making regarding the curvature of water flow and subsequent classification based on soil type and slope factors. First, the study area boundary will be defined. Then, LULC map data will be collected. Afterward, the amount of precipitation in different years will be examined. Based on the collected information, the CN can be determined. Then, according to the lookup tables, the soil type classification and the risk of runoff generation were identified.
Figure 3

Satellite images of the study area.

Figure 3

Satellite images of the study area.

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Figure 4

The study area with infrared images showing residential areas and elevations of the region.

Figure 4

The study area with infrared images showing residential areas and elevations of the region.

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The flood extent within the study reach was mapped using satellite imagery by applying spectral index analysis (e.g., Normalized Difference Water Index, NDWI) to multispectral bands, enabling the identification and quantification of inundated areas through threshold-based classification of water pixels. It was found that contrary to the water scarcity trend in the country, this study's river reach has gradually become more water-abundant, and there is a risk of runoff generation in this area. Furthermore, this area has residential areas, which further emphasizes the importance of studying this part of the river. The land cover map of the study area is observable in Figure 5. The image is collected from the living atlas database and uses different colors to represent various vegetation covers and specific land uses. Each land-use type, combined with the soil type and certain other hydrological properties, enables the calculation of CN and the runoff volume.
Figure 5

Land-use map of the study area.

Figure 5

Land-use map of the study area.

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Additionally, the slope of an area plays a crucial role in the generation of runoff and is an important factor in assessing flood risk. The steeper the slope of an area, the higher the risk of flooding. This data, along with the soil type, will provide the hydrological properties of the soil. Figure 5 also displays the slope and elevations of the study area using Sentinel-2 satellite imagery. Figure 6 combines elevation (hypsometric tint, 0–3,200 m) and slope (grayscale overlay, 0–45%) using a semi-transparent layer (30% opacity) to visualize topographic variations. Elevation data were derived from ASTER DEM, while slope gradients were calculated via ArcGIS's Spatial Analyst tool, with both parameters integrated to assess runoff dynamics. Based on this image, it is evident that the overall elevation of the study area is high, and the surface slope is steep, which could potentially lead to flooding by the river in this region. A detailed analysis of the CN and runoff value in each area will yield more accurate results. These slope and elevation data play a crucial role in determining the CN. Furthermore, this approach is used to assess the impact of land-use changes on flood inundation potential. By analyzing historical and future land-use scenarios, it is possible to evaluate the effectiveness of land-use planning policies in reducing flood risks.
Figure 6

The elevations and slopes of the study area using satellite imagery.

Figure 6

The elevations and slopes of the study area using satellite imagery.

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To extract the soil map of the study area, the global soil map from the FAO database was first obtained. In the next stage, the soil map of Iran was classified by soil type and moisture level (Figure 7). To delimit and differentiate the soil type and moisture level of the study area, map clipping was performed and shapefiles were used to obtain the desired area. The classification of the study area based on the soil type and soil moisture is then obtained. Based on the soil type, there are seven categories, which are represented by different colors according to their moisture levels.
Figure 7

Classification of soil types in the map of Iran and different regions of the country.

Figure 7

Classification of soil types in the map of Iran and different regions of the country.

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By integrating the SCS-CN method with satellite imagery, it was possible to generate spatially distributed CN maps. These maps represent the variation in infiltration capacity across the landscape, providing valuable inputs for flood inundation modeling. The calculation of the CN is then done in each area covered by each soil type. Accordingly, the results obtained from the calculations for each section (Figure 8). Considering that a higher CN indicates a higher potential for flooding, the study area is, on average, a flood-prone region. Section 15, which has the highest CN, is the area with the highest potential for flooding. Additionally, according to the land-use map, it is evident that this area has residential developments, which could be highly dangerous. Furthermore, the areas near the riverbed, numbered 3 and 4, also have a high potential for flooding. As previously mentioned, flood potential is highly dependent on the volume of precipitation, and the CN becomes significant when the rainfall intensity is sufficient to cause runoff through these curves. An example of calculation tables for region 1 is presented in Table 1.
Table 1

Summary of hydrological and topographic parameters for studied sections

SectionLand use/land cover (LULC)Hydrologic soil group (HSG)Slope (%)Curve number (CN)Runoff (Q, mm)Precipitation (mm)
Agricultural land 72 45.2 450–500 
Grassland 68 38.1 450–500 
Urban/residential 22 88 95.6 450–500 
Bare soil 18 86 92.4 450–500 
Mixed vegetation 12 74 49.8 450–500 
Forest 70 41.3 300–350 
Shrubland 78 58.7 450–500 
Agricultural fallow 14 82 69.5 450–500 
Wetland 76 54.1 300–350 
10 Pasture 80 63.2 450–500 
11 Urban/residential 25 84 77.8 450–500 
12 Bare soil 28 90 97.1 450–500 
13 Forest 64 30.5 300–350 
14 Grassland 66 33.8 450–500 
SectionLand use/land cover (LULC)Hydrologic soil group (HSG)Slope (%)Curve number (CN)Runoff (Q, mm)Precipitation (mm)
Agricultural land 72 45.2 450–500 
Grassland 68 38.1 450–500 
Urban/residential 22 88 95.6 450–500 
Bare soil 18 86 92.4 450–500 
Mixed vegetation 12 74 49.8 450–500 
Forest 70 41.3 300–350 
Shrubland 78 58.7 450–500 
Agricultural fallow 14 82 69.5 450–500 
Wetland 76 54.1 300–350 
10 Pasture 80 63.2 450–500 
11 Urban/residential 25 84 77.8 450–500 
12 Bare soil 28 90 97.1 450–500 
13 Forest 64 30.5 300–350 
14 Grassland 66 33.8 450–500 
Figure 8

Flood-prone areas based on numbering for analysis of these areas.

Figure 8

Flood-prone areas based on numbering for analysis of these areas.

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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 (Ia) stood at 0.2S following standard SCS-CN method protocols.

Table 2

Runoff calculations for each section using Equation (1), based on a 20-year return period with constant rainfall of P = 120 mm

SectionCurve number (CN)Retention parameter (S) (mm)Initial abstraction (Ia) (mm)Rainfall (P) (mm)Runoff (Q) (mm)
72 38.9 7.8 120 45.2 
68 47.1 9.4 120 38.1 
88 13.6 2.7 120 95.6 
86 16.3 3.3 120 92.4 
74 35.1 7.0 120 49.8 
70 42.9 8.6 120 41.3 
78 28.2 5.6 120 58.7 
82 22.0 4.4 120 69.5 
76 31.6 6.3 120 54.1 
10 80 25.0 5.0 120 63.2 
11 84 19.0 3.8 120 77.8 
12 90 11.1 2.2 120 97.1 
13 64 56.3 11.3 120 30.5 
14 66 51.5 10.3 120 33.8 
15 92 8.7 1.7 120 98.3 
SectionCurve number (CN)Retention parameter (S) (mm)Initial abstraction (Ia) (mm)Rainfall (P) (mm)Runoff (Q) (mm)
72 38.9 7.8 120 45.2 
68 47.1 9.4 120 38.1 
88 13.6 2.7 120 95.6 
86 16.3 3.3 120 92.4 
74 35.1 7.0 120 49.8 
70 42.9 8.6 120 41.3 
78 28.2 5.6 120 58.7 
82 22.0 4.4 120 69.5 
76 31.6 6.3 120 54.1 
10 80 25.0 5.0 120 63.2 
11 84 19.0 3.8 120 77.8 
12 90 11.1 2.2 120 97.1 
13 64 56.3 11.3 120 30.5 
14 66 51.5 10.3 120 33.8 
15 92 8.7 1.7 120 98.3 

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.

Table 3

Comprehensive CN values and hydrologic parameters for all study regions

SectionLULC classOriginal HSGSlope-adjusted HSGAssigned CNRunoff (Q) (mm)
Agricultural land B (slope <10%) 72 45.2 
Grassland A (slope <5%) 68 38.1 
Urban/residential D (slope >20%) 88 95.6 
Bare soil C (slope 15–20%) 86 92.4 
Mixed vegetation C (slope 10–15%) 74 49.8 
Forest A (slope <5%) 70 41.3 
Shrubland B (slope <10%) 78 58.7 
Agricultural fallow C (slope 10–15%) 82 69.5 
Wetland A (slope <5%) 76 54.1 
10 Pasture B (slope <10%) 80 63.2 
11 Urban/residential D (slope >20%) 84 77.8 
12 Bare soil D (slope >20%) 90 97.1 
13 Forest A (slope <5%) 64 30.5 
14 Grassland B (slope <10%) 66 33.8 
15 Urban/residential D (slope >20%) 92 98.3 
SectionLULC classOriginal HSGSlope-adjusted HSGAssigned CNRunoff (Q) (mm)
Agricultural land B (slope <10%) 72 45.2 
Grassland A (slope <5%) 68 38.1 
Urban/residential D (slope >20%) 88 95.6 
Bare soil C (slope 15–20%) 86 92.4 
Mixed vegetation C (slope 10–15%) 74 49.8 
Forest A (slope <5%) 70 41.3 
Shrubland B (slope <10%) 78 58.7 
Agricultural fallow C (slope 10–15%) 82 69.5 
Wetland A (slope <5%) 76 54.1 
10 Pasture B (slope <10%) 80 63.2 
11 Urban/residential D (slope >20%) 84 77.8 
12 Bare soil D (slope >20%) 90 97.1 
13 Forest A (slope <5%) 64 30.5 
14 Grassland B (slope <10%) 66 33.8 
15 Urban/residential D (slope >20%) 92 98.3 

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 (Ia = 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.

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