GIS-Based Flood Assessment Using Hydraulic Modeling and Open Source Data: An Example of Application

Abstract

The study explores the impact of floods, phenomena amplified by climate change and human activities, on the natural and anthropogenic environment, focusing on the analysis of a section of the Cigher River in the Crișul Alb basin in western Romania. The research aims to identify areas vulnerable to flooding under different discharge scenarios, assess the impact on agricultural lands, and propose a reproducible methodology based on the integration of GIS technologies, hydraulic modeling in HEC-RAS, and the use of LiDAR data. The methodology includes hydrological analysis, processing of the Digital Elevation Model (DEM), delineation of geometries, hydraulic simulation for four discharge scenarios (S1–S4), and evaluation of the flood impact on agricultural and non-agricultural lands. Evaluated parameters, such as water velocity and flow section areas, highlighted an increased flood risk under maximum discharge conditions. The results show that scenario S4, with a discharge of 60 m³/s, causes extensive flooding, affecting 871 hectares of land with various uses. The conclusions emphasize the importance of using modern technologies for risk management, protecting vulnerable areas, and reducing economic and ecological losses. The proposed methodology is also applicable to other river basins, representing a useful model for developing sustainable strategies for flood prevention and management.

1. Introduction

Floods, phenomena that occur rapidly over time, are considered some of the most damaging hydrogeomorphological events, severely destroying or impacting the areas they affect [1,2,3,4]. These events result from a complex combination of natural and anthropogenic factors [2,5,6], influencing the hydrological regime of a watershed. Scientific research supports the hypothesis that they occur more frequently due to global warming and climate change [7,8,9,10,11].

Floods generate complex effects on natural and anthropogenic environments, including soil erosion, destruction of natural habitats, water pollution through the dispersion of waste and toxic substances, destruction of agricultural crops, and damage to infrastructure, housing, and economic activities. These impacts result in significant biodiversity losses, depletion of resources, and human casualties [12,13]. According to the Global Assessment Report on Disaster Risk Reduction [14], floods affect millions of people worldwide each year, causing severe damage to infrastructure and the economy. For example, in 2023, natural disasters resulted in 86,473 deaths and affected 93.1 million people, with their impact exceeding the 20-year average in terms of human losses, highlighting the increased vulnerability of exposed communities [14].

Flooding of agricultural lands, in addition to the direct precipitation effect caused by raindrops, creates complex abiotic stress [15], including reduced light availability [16], anaerobic conditions that affect microbial metabolism and the biogeochemical cycle of nutrients [17,18], and changes in the physical and chemical properties of soil [19], leading to reduced fertility [20]. All these factors negatively impact the growth and development of crops [21,22], reducing their productivity.

In the context of increasing hydrological risk phenomena caused by climate change and socio-economic factors, increasingly precise methods and models have been developed for hydrological risk modeling and identifying flood-prone areas [23,24,25]. These approaches rely on Geographic Information Systems (GIS), remote sensing, and hydrological and hydraulic modeling techniques [11,26,27,28,29,30,31,32]. GIS plays an important role in flood analysis by mapping flood-prone areas [33,34,35], integrating hydraulic and hydrological applications [36,37], and assessing risk through multi-criteria analysis [38]. Passive and active remote sensing data allow for monitoring flood extent [39], conducting temporal flood analysis [40], and planning land use to prevent risks [41,42]. Additionally, hydraulic modeling enables the analysis of water flow, flow assessment, and forecasting of river behavior [43,44,45].

Currently, numerous specialized computer models and software programs simulate flow characteristics (depth, speed, etc.) and flood-affected areas, including TELEMAC-2D, River-2D, HEC-HMS, HEC-RAS (are open-source and free), MIKE11, and MIKE (are commercial software, requiring a paid license) [25,46]. Among them, HEC-RAS (Hydrologic Engineering Center’s River Analysis System), version 6.5 [47], used in this study, is a program developed by the US Army Corps of Engineers for modeling water flows in rivers and channels. It supports both subcritical and supercritical flows and includes sediment analysis and pollutant transport. Its applications include one-dimensional and two-dimensional river flow modeling, flood modeling, and erosion analysis.

Due to its versatility, the capability for both 1D and 2D modeling, geospatial data integration, and extensive functionalities for flood and hydraulic structure analysis, HEC-RAS, an Open Source solution, is utilized in other studies for flood simulation [48], flood prediction [49], and mapping [50], assessing territorial susceptibility to floods [51], or evaluating the feasibility of hydraulic constructions [52,53].

Although GIS methods, LiDAR data, and hydraulic modeling are widely used tools in flood analysis, most studies focus on large river basins or urban areas, with a lack of detailed research applied to small and medium-sized basins, such as the Cigher River. Furthermore, the impact of floods on agricultural land is often underestimated, and the methodologies used are not always reproducible, which limits their applicability to other regions. Based on these general premises, this study starts from the following working hypothesis: the study area, characterized by complex physical-geographical conditions, has been affected by major hydrological events (flash floods and severe flooding) with significant environmental and societal impacts in the years 1970, 1981, 1995, 2000, 2005, 2006, 2008, 2014, 2020, and 2022 [54]. In this context, the region’s vulnerability to these hydrogeomorphological events is emphasized, and through GIS technologies and hydraulic modeling in HEC-RAS, flood-prone areas can be accurately identified, enabling the development of viable solutions for proactive and careful risk management and disaster prevention.

The aim of this study is to conduct a detailed flood risk analysis on a section of the Cigher River, a tributary of the Crișul Alb River, using hydraulic modeling in HEC-RAS and open-source data. Specifically, the study has the following objectives: (1) to identify flood-prone areas based on different hydrological scenarios, (2) to assess the potential impact of floods on agricultural land, and (3) to propose a reproducible methodology that integrates geospatial data and hydraulic modeling techniques, making it applicable to future studies or territorial planning. This study aims to contribute to a better understanding of hydraulic and hydrogeomorphological processes in the Cigher River basin and, based on the obtained results, to the development of sustainable solutions for flood risk management.

The study stands out for its novelty and originality, integrating modern hydraulic modeling techniques in HEC-RAS and GIS with open-source data, which was applied for the first time in the flood-prone Cigher basin. Additionally, by analyzing diverse hydrological scenarios, evaluating flood impact on agricultural land, and adopting a systematic approach, the study proposes a reproducible, accessible, and applicable methodology for other similar river basins. It also offers valuable insights for flood risk management, territorial planning, and the promotion of sustainable flood prevention strategies.

2. Materials and Methods

2.1. Study Area

As a case study in this article, a section of the Cigher River was selected, a left tributary of the Crișul Alb River, with a length of 18.3 km, located between 46°17′41.425″ N, 21°55′9.597″ E (upstream) and 46°20′55.980″ N, 21°48′52.661″ E (downstream). The Crișul Alb River Basin (Figure 1), which includes the Cigher River sub-basin, belongs to the Crișuri hydrographic space, situated in the western part of Romania, largely overlapping with Arad County [54]. The basin extends approximately along the SE–NW direction.

In the Cigher River basin, according to the Digital Elevation Model [56], the elevation varies between 95 and 792 m. Low-altitude areas are predominant in the northern half, while the southern half is dominated by hills. This elevation variation indicates a diverse topography, with steep slopes in the south, influencing the distribution and “behavior” of the rivers. In high-altitude areas, water flows at a higher speed and can accumulate in low-lying areas where the slope and, consequently, the flow velocity decreases. These areas become susceptible to flooding, particularly during periods of intense rainfall coupled with rapid snowmelt. The high-altitude areas are prone to slope processes (erosion, landslides), which affect slope stability, soil structure, and sediment regimes and, consequently, may impact the hydrological regime and the “behavior” of rivers.

From a climatic perspective, in the high-altitude areas of the Cigher basin, the annual average temperatures range between 4 and 6 °C, and precipitation exceeds 1000 mm. In the hilly areas, temperatures range from 8 to 10 °C with precipitation between 600 and 800 mm, while in the lowland areas, temperatures are 10–11 °C with precipitation between 500 and 600 mm [54,57].

Generally, there is a tendency for the multi-annual average precipitation to increase with altitude; however, the annual variation of precipitation is particularly significant. In most cases, abundant spring precipitation combined with the sudden melting of snow causes flash floods and overflows, resulting in severe environmental impacts.

2.2. Materials and Software Used

For the research, the following datasets were utilized:

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hydrological data, specifically flow rate and water levels, measured at hydrometric stations along the analyzed river segment, obtained from the Crișuri Water Basin Administration [54]. Daily, monthly, and annual average values for five consecutive years were considered;

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Digital Elevation Model (DEM) with a spatial resolution of 1 m (1 point/m²) generated from LiDAR data, a product obtained through the LAKI I and LAKI II projects of the National Agency for Cadastre and Real Estate Publicity (ANCPI) [58];

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field data, specifically detailed topographic surveys, including the measurement of elevation points on the thalweg of the Cigher River and its minor riverbed;

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aerial photographs from the Cigher River basin, captured using a UAV DJI Phantom 4 device available in the GIS and Remote Sensing Laboratory of ULS Timișoara;

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Corine Land Cover (CLC) database, 2018 edition, vector data, used for analyzing the impact of floods on land use classes, available on the Copernicus Land Monitoring Service platform [59];

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auxiliary geospatial data (basin boundaries, hydrographic network, component sub-basins, administrative-territorial maps), available from ABA Crișuri [54] and specialized platforms [55], for locating and characterizing the area of interest.

The data and scientific information used in the research were processed through various methods and software tools as follows: for processing topographic data collected in the field, AutoCAD Map 3D 2016 software (version 19.0) [60] was used; for geospatial data processing and cartographic representation, ArcGIS 10.4 for Desktop software [61] was employed; for hydraulic modeling, HEC-RAS software, version 6.5 [47], was utilized.

2.3. Research Methodology

In the case study presented in this article, a segment of the Cigher River was analyzed using one-dimensional hydraulic modeling based on steady flow analysis, where it is assumed that the discharge and flow conditions of a river remain constant over time at any point in the system. In other words, in steady flow analysis, water discharge, depth, and velocity remain unchanged at each cross-section of the river during the analyzed time period.

The following steps and phases (Figure 2) were undertaken to obtain the results:

1. Flow and level analysis for the study area focused on:

a. Seasonal and annual variation analysis;

b. Extreme value analysis (EVA); the Weibull method was applied to calculate the non-probability and probability of occurrence, as well as the return period of maximum values (recurrence period). The maximum recorded discharges (Q) and water levels (H) at the Chier hydrometric station over a 5-year period were taken into account.

Using the Weibull method, the probability of occurrence (P) was calculated using Equation (1), while the recurrence period (T) was calculated using Equation (2).

$$\mathrm{P}={\displaystyle \frac{\mathrm{m}}{\mathrm{N}+1}}$$

(1)

where P is the probability that the maximum discharge/level will be reached or exceeded in a given year; m is the rank of the event (the discharge/level values are arranged in descending order, with the highest discharge receiving rank 1); N is the total number of observed years:

$$\mathrm{T}={\displaystyle \frac{1}{\mathrm{P}}}={\displaystyle \frac{\mathrm{N}+1}{\mathrm{m}}}$$

(2)

where T is the recurrence period (in years); P is the previously calculated exceedance probability.

The estimation of maximum flow and water level for different recurrence periods (1 year, 5 years, 10 years, and 15 years) was carried out based on the linear equation (relation 3) and the logarithmic equation (relation 4). This method allows for describing the relationship between the observed maximum flow/level and the return period.

$$\mathrm{Y}=3.9676\mathrm{x}+4.1007$$

(3)

where y is the estimated maximum flow rate (m³/s); xis the recurrence period (years); 3.9676 and 4.1007 are coefficients obtained by adjusting the regression line to the available dataset

$$\mathrm{Y}=0.3733\mathrm{l}\mathrm{n} \left(\mathrm{x}\right)+2.8766$$

(4)

where y is the estimated maximum level (m); x is the recurrence period (years); 0.3733 and 2.8766 are coefficients obtained by fitting the regression line to the available dataset.

c. Establishing scenarios for modeling: four scenarios/simulations (S1, S2, S3, and S4) were defined under different discharge rates and water levels for 1, 5, 10, and 15 years, respectively: S1—Q = 8 m³/s, H = 3 m; S2—Q = 23 m³/s, H = 3.2 m; S3—Q = 41 m³/s, H = 3.35 m; S4—Q = 60 m³/s, and H = 3.5 m.

2. DEM Processing. For the hydraulic analysis, a DEM with a 1-m resolution was used, generated from LiDAR data and processed using ArcGIS 10.8 software. The DEM processing included extracting the area of interest, applying a buffer to optimize the data, and validating it with control points measured in the field. The results of the DEM processing are detailed in the Section 3.1.

3. Geometry creation in HEC-RAS involved building the hydraulic model geometry essential for simulating water flow:

a. tracing the river thalweg (centerline);

b. marking riverbanks to define channel boundaries;

c. delineating the floodplain, identifying areas at risk of flooding during high water levels;

d. drawing cross-sections to detail variations in the riverbed profile; 32 cross-sections were established (numbered upstream to downstream, from ST 32 to ST 1), spaced approximately 500 m apart;

e. correcting geometry errors to ensure model accuracy.

For each cross-section, the Manning coefficient, which describes resistance to flow caused by the riverbed surface, was applied. A value of 0.035 was used for the riverbed and 0.06 for the floodplain.

4. Incorporating flow parameters involved entering water flow data into the model, calculated based on discharge and level data from previous stages. For each scenario:

a. discharge rate (Q): S1 = 8 m³/s; S2 = 23 m³/s; S3 = 41 m³/s; S4 = 60 m³/s;

b. flow level (H): S1 = 3 m; S2 = 3.2 m; S3 = 3.35 m; S4 = 3.5 m;

c. The completion of the modeling plan was carried out based on the geometries and flow parameters in a subcritical regime. This regime is characterized by a water flow velocity that is lower than the gravity wave velocity. The subcritical regime occurs when the Froude number (Fr) is less than 1. The estimation of Fr < 1 was performed using the Froude number, calculated according to the standard equation (Equation (5)):

$$\mathrm{F}\mathrm{r}={\displaystyle \frac{\mathrm{V}}{\sqrt{\mathrm{g}\times \mathrm{h}}}}$$

(5)

where V is the average water velocity, determined from the hydraulic modeling results, g is gravitational acceleration (9.81 m/s²), h is the hydraulic depth, calculated based on the cross-sectional geometry and the water levels resulting from the simulation.

In our study, all the values obtained in the analyzed scenarios indicated a subcritical regime (Fr < 1), which is typical for rivers with a low slope and predominantly slow flow.

5. Analysis initialization involved running the designed model under the four different scenarios.

6. Flood impact assessment at maximum discharges on agricultural and non-agricultural lands involved identifying flooded areas and overlaying them with the land use map [59].

7. Result interpretation was carried out in various formats: tabular, graphical, and cartographic (Figure 2).

3. Results

This section will present the results obtained from the research, structured to illustrate the proposed analysis model for identifying flood-vulnerable areas (processing and validation of the DEM, mapping of geometric elements, hydraulic modeling under different scenarios) and estimating the impact of floods on land use in the Cigher basin.

3.1. Processing and Validation of the Digital Elevation Model

In this study, for hydraulic analysis, a DEM with a spatial resolution of 1 m, generated from LiDAR data through the programs named LAKI I and LAKI II, developed by ANCPI, was used according to the research methodology (Figure 2). For modeling, a section of the Cigher River, a tributary of the Crișul Alb River, was selected, located between the confluence with the Timercea River upstream and Valea Mare downstream. The area of interest was extracted from the DEM for the entire watershed using ArcGIS, with a 200-m buffer from the river course (Figure 3).

To validate the accuracy and precision of the DEM data, control points were collected in the field within test zones using GPS equipment. The collected data were imported into AutoCAD 19.0 software using the Stereographic 1970 projection system, and cross-sections of the river valley were created, which were later used for validating the DEM data. It was found that the accuracy of the DEM data is compliant, and thus it can be used for hydraulic modeling.

For each GPS point, we extracted the corresponding altitude value from the DEM and calculated the differences between the DEM values obtained from LiDAR data and the GPS measurements. We analyzed the error distribution and computed statistical indicators such as the mean error (ME), root mean square error (RMSE), and standard deviation to assess the accuracy of the DEM in relation to the field data.

The results showed that the DEM data exhibit high accuracy, and the differences between the measured and modeled elevations were within acceptable limits for hydraulic modeling applications.

The preprocessed Digital Elevation Model was imported into HEC-RAS 6.5 software and converted into the specific format (Figure 3).

3.2. Tracing the Geometric Elements of the Analyzed River Sector

After processing and validating the DEM, the geometric elements required for modeling in the HEC-RAS 6.5 software were delineated on the river sector considered as a case study (Figure 4), specifically:

- the river centerline (thalweg) represents the line along the deepest part of the riverbed and is used to define the geometric configuration of the riverbed within the hydraulic model and to correctly position the cross-sections;

- the left bank and the right bank define the edges of the river and separate the water flow in the minor riverbed under normal conditions from adjacent areas, such as the floodplain;

- floodplain; this allows the identification of regions vulnerable to flooding under various flow scenarios. In this case, analyzing a leveed watercourse, the floodplain is considered as the area between the two levees (Figure 4).

In the river segment considered as a case study, 32 cross-sections were outlined perpendicular to the river’s course. These sections describe the shape and size of the river at various points along its course and are used to define the river’s geometry, including its width, depth, and bank configuration.

The cross-sections play a particularly important role in the simulation and analysis of water flow, providing information about the river’s geometry and allowing for the calculation of hydraulic parameters such as the area through which flow occurs, wetted perimeter, hydraulic radius, and channel bed slope. These parameters are used in flow equations to determine the water velocity and discharge.

The analysis of the cross-section characteristics for the river segment considered as a case study highlights the following aspects: there are significant variations between the defined sections in terms of the height of the left and right banks, indicating a variable relief along the river; the lengths of the left and right banks vary considerably, showing differences in the geometry of each section; the Cutline to XS ratio is consistently one, indicating that the sections are consistently aligned with the river’s course without significant deviations.

3.3. Setting Parameters for Permanent Flow Analysis

In the case study, one-dimensional analysis was applied for hydraulic modeling under conditions of steady, constant flow (steady flow analysis). This type of analysis was used to assess the possible extent of flooding, identifying areas that could be inundated under different flow conditions (varying discharge and water levels).

Four scenarios (S1, S2, S3, and S4) were defined, each with a different flow rate and water level, ranging from low-flow conditions to flood potential. For the four scenarios, the flow rates and water levels, forecasted based on historical data, were as follows: S1: Q—8 m³/s; H—3 m; S2: Q—23 m³/s; H—3.2 m; S3: Q—41 m³/s; H—3.35 m; S4: Q—60 m³/s; H—3.5 m;

The determination of the hydrological characteristics for the four scenarios was based on recurrence intervals of 1 year, 5 years, 10 years, and 15 years.

3.4. Hydraulic Modeling

The following section details the hydraulic modeling procedure used in this study.

Figure 5 presents the longitudinal profile of the analyzed section of the Cigher River, illustrating the variation in elevation as a function of distance for different flow scenarios (S1–S4) and associated critical levels. The “EG” lines (energy line) and “WS” lines (water surface) show how energy and water levels vary in each scenario, highlighting critical points. The dotted lines indicate critical levels, which are useful for assessing flood risks or hydraulic issues. As flow increases, the “WS” and “EG” levels rise, indicating greater energy but also increased risks of erosion and flooding. The “ground” line marks the terrain elevation for comparison with water levels.

Figure 6 illustrates in 3D the 32 cross-sections (ST) of the Cigher River section, represented in the four scenarios (S1—S4) and grouped into segments (ST 1—8, ST 9—16, ST 17—24, and ST 25—32).

From Figure 6, a progressive increase in water level and the area covered by water can be observed from S1 to S4, with high-flow scenarios (S3 and S4) indicating complete filling of the riverbed.

The upstream segments (ST 25—32) have a smaller cross-sectional area and transport capacity compared to the downstream segments (ST 1—8), where the river is deeper and wider. This representation underscores the need for monitoring to prevent exceeding maximum capacity, particularly in narrower or shallower areas, and highlights how flow rates influence the river’s capacity under various scenarios.

Figure 7 provides visual representations of some cross-sections along the analyzed river segment (representative examples). These illustrations serve as useful tools for understanding the spatial distribution of key parameters and improving the assessment of river behavior.

The red points in the images (Figure 7) indicate locations on both the left and right banks of the river, offering insights into the river’s geometry and morphology. Additionally, the blue color represents the water level in S1, providing a clear visualization of the hydraulic profile of the river at various points along the analyzed segment. The solid lines represent the water level according to the four generated scenarios.

In Figure 7, the images on the right depict water velocity. It is observed that the highest values are located in the central part of the profile sections, specifically in the Thalweg area.

The comparative analysis of parameters across the four scenarios reveals the following:

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water velocity (Vel Chnl) increases consistently from S1 to S4 as the discharge increases. For example, at ST32, velocity rises from 0.56 m s⁻¹ in S1 to 1.11 m s⁻¹ in S4, while at ST1, the increase is even more pronounced, from 1.96 m s⁻¹ in S1 to 2.53 m s⁻¹ in S4;

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water level (W.S. Elev) increases with each scenario due to the rising discharge. For instance, at ST32, water elevation increases from 138.9 m in S1 to 140.19 m in S4, while at ST1, the increase is from 112.27 m in S1 to 113.31 m in S4;

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flow area increases significantly with discharge as well. At ST32, the flow area expands from 14.21 m² in S1 to 67.43 m² in S4, while at ST1, it grows from 4.08 m² in S1 to 37.12 m² in S4;

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Froude number (Froude # Chl) varies but not linearly with discharge. Overall, the values remain relatively constant across most scenarios, with a slight upward trend at higher discharges.

The water depth varies depending on the analyzed flow rate, increasing progressively from scenario S1 (low flow) to scenario S4 (maximum flow). For example, in ST32, the water depth increases from 1.23 m (S1) to 2.52 m (S4), indicating a doubling of the initial value. The same trend is observed in section 23, where the water depth increases from 1.36 m (S1) to 2.56 m (S4).

In certain sections, such as section 15, the water depth shows negative values for S1 (−0.47 m), indicating an area with a higher bed level where the minimum flow is insufficient to cover the entire section. On the other hand, in sections such as 10 and 11, the depth varies between 1.55 m (S1) and 2.49 m (S4), showing a steady increase in water level as the flow increases. Additionally, in sections with greater variations, such as 30 and 29, the increase in water depth is more pronounced, reaching 3.02 m (S4) in section 30. Conversely, in the upper areas of the profile, such as section 1, the maximum depth reaches only 1.51 m (S4).

This analysis clearly shows that sectors with variable geometry are more susceptible to significant variations in water depth, which may indicate a higher risk of flooding under extreme flow conditions.

Mapping the results of hydraulic modeling is not just a visualization tool but also a critical element for sustainable water resource planning and management, disaster prevention, and the protection of infrastructure and communities. The use of this tool contributes to better preparedness and adaptation to increasing hydrological risks.

Figure 8 compares two specific areas (Zone 1 and Zone 2) along the Cigher River, analyzed under the four different discharge scenarios (S1–S4). This image is used to visualize the effects of varying discharges on these zones and to assess flood extent and the river’s hydraulic behavior.

In scenario S1, the flow rate is reduced, and the water is largely confined to the riverbed without causing extensive flooding. The surrounding land has higher altitudes and is not affected by floods.

In scenario S2, an increase in flow rate leads to a slight expansion of the area covered by water, which begins to exceed the minor riverbed; however, the flooding remains controlled.

In scenario S3, the higher flow rate results in more extensive flooding, with water inundating areas adjacent to the river, showing a larger coverage of the land by water.

In scenario S4, the maximum flow rate causes severe flooding, with water covering a large part of the floodplain, indicating that scenario S4 represents the maximum risk conditions for this area.

3.5. Estimating the Impact of Floods on Land

The total area of the Cigher Basin is 84,540 hectares. Of this area, 37% is allocated to arable land, primarily in the northern half of the basin along the middle and lower courses of the rivers. Forests account for 32% of the area, mainly located in the southern half of the basin along the upper courses of the rivers, while grasslands occupy 14% of the total area, found at the forest edges or interspersed with arable land (Figure 9). Other land use categories are present in smaller proportions.

From its origin point to the confluence with the Crișul Alb River, the Cigher River, with a total length of 64.2 km, flows through forested areas in its upper section and agricultural land in its middle and lower sections (Figure 9). The river also passes through the localities of Seleuș, Moroda, Șilindia, and Tauț.

In this study, various hydraulic simulation scenarios were developed. For the scenario assuming a maximum flow rate of 60 m³/s, the floodable area was estimated to have an approximate width of 100 m along both banks of the river. Hydraulic modeling was applied to a defined segment of the river, for which the floodable area was estimated. Based on the initial modeling hypothesis, it was assumed that the estimated floodable area for the analyzed segment remains consistent along the entire course of the river. Consequently, land use categories affected by flooding were identified and analyzed to assess the potential impact on these areas.

It was found that, at a flow rate of 60 m³/s, the Cigher River floods a total of 871 hectares, distributed as follows: 392 hectares of arable land, 231 hectares of forested areas, 202 hectares of grasslands, and 46 hectares of built-up land.

The results of the flood extent analysis were obtained for a river section equipped with protective dikes. In the absence of these defense structures, the area of zones affected by flooding could be significantly larger due to the unrestricted flow of water into adjacent lands.

4. Discussion

4.1. The European, National, and Local Context of Floods: Incidence, Trends, and Impact

Although progress has been made in flood forecasting, warning, and risk management, as well as in the implementation of protection measures [62], catastrophic floods continue to pose a major threat at both global and European levels [63], as highlighted by the devastating events of recent decades [64,65,66].

Economic and environmental losses caused by floods have significantly increased over recent decades [67,68] for two main reasons: the rising frequency and intensity of precipitation due to global warming [7,69,70] and the growing population and the value and “density” of anthropogenic assets exposed to floods, as socio-economic development progresses [71].

From a non-economic perspective, research in the field suggests that in developed countries, floods with multiple fatalities are becoming increasingly rare [72,73], while in developing countries, the average number of fatalities remains high [72,74]. Economic development can reduce vulnerability to natural hazards, although the relationship between these factors varies [75,76].

Over the past two decades, Europe has been affected by more than 400 major floods [69], with some of the most devastating being the floods of 1993 and 1995 in Germany, the Netherlands, and France [77], the 2002 floods in the Elbe River basin [78], the 2010 floods in central and eastern Europe [79], and the 2014 floods in the United Kingdom [80]. In Romania, many river basins continue to lack adequate flood protection measures. Generally, the existing defense infrastructure and hydraulic structures, most of which were built in the 1970s, have undergone very few improvements in the subsequent decades [81]. In this context, catastrophic floods with severe environmental and population impacts were recorded in 2005, 2008, 2010, 2014, and 2020 [69], as well as in 2024, particularly in the northeastern part of the country (Siret and Prut river basins).

In the study area, specifically the Cigher River sub-basin, part of the Crișul Alb River basin, numerous hydrological events of varying intensity have been recorded. Among the major ones (severe floods and flash floods) with significant environmental and population impact were those of 1970, 1981, 1995, 2000, 2005, 2006, 2008, 2014, 2020, and 2022 [54]. Even minor floods, occurring annually over small areas, particularly on agricultural land, affect local communities given the rural nature of the area, where agriculture represents an important source of subsistence.

4.2. The Relevance and Applicability of the Proposed Working Model for Flood Risk Analysis

Hydraulic and hydrological models are essential tools for flood risk analysis due to their ability to simulate the complex processes associated with water flow and accumulation phenomena. These models enable the analysis and forecasting of extreme events, such as flash floods or inundations, providing critical information for risk management and spatial planning [82]. For instance, the use of HEC-RAS in this study demonstrated that simulations with varying flow rates can identify vulnerable areas, enabling proactive measures to mitigate risks. The results obtained in HEC-RAS, integrated with geospatial data within the GIS environment, facilitate hydraulic modeling and offer precise and easily interpretable results, which are highly valued in the scientific community [83,84]. As highlighted by Swain (2020) [26] and Cai (2021) [27], the integration of geospatial data and remote sensing technologies in hydro-geomorphological analysis enhances the accuracy of models, significantly contributing to increased territorial resilience. Moreover, hydraulic models facilitate the assessment of the impact of floods on critical infrastructure and agricultural lands, an important aspect of the socio-economic sustainability of communities. According to Bessar (2020) [44] and Tanaka (2018) [43], these tools provide a robust methodological framework for investigating hydrological phenomena while allowing integration with other disciplines, such as ecology or hydraulic engineering.

The workflow proposed in this study combines hydraulic modeling in HEC-RAS, geospatial data, and GIS technologies, offering a reproducible and adaptable methodology. This approach is notable for utilizing open-source data, such as high-resolution spatial DEM generated from LiDAR data and the Corine Land Cover (CLC) database, making it accessible for future applications in other similar river basins. A major feature of the workflow is the application of the steady flow analysis method, considering local parameters that reflect the specific conditions of the analyzed river segment. This detailed approach aligns with the standards recommended in the specialized literature, such as Ameera (2016) [46], which emphasizes the importance of calibrating the model to the specific conditions of each region.

The approach used in this study offers the advantage of integrating a wide range of data and techniques while modeling in HEC-RAS, which provides an optimal balance between versatility and accessibility, being an open-source solution available to all interested parties [48,50].

Furthermore, the applicability of the proposed workflow in this study is reinforced by its impact analysis on agricultural lands for risk evaluation. This holistic approach is more comprehensive compared to studies focusing solely on specific aspects, such as temporal analysis of flood extent [40] or rapid and precise detection of flooded areas [39]. The results obtained in this study confirm the findings of other research that emphasize the importance of GIS and remote sensing technologies in flood risk analysis [33,37].

4.3. The Role of Hydraulic Modeling in Flood Risk Assessment and Management

Hydraulic modeling using specialized software enables the simulation of hydraulic conditions for various scenarios. These simulations are useful for water resource management, flood risk assessment, hydraulic structure design, and environmental protection [85,86].

Studies in the specialized literature and the current study have demonstrated that the HEC-RAS program is a valuable tool for hydraulic analysis, hydrology simulation, flood forecasting, and management [87,88].

Forecasting the magnitude of hydrological and hydrogeological risk phenomena is essential for addressing economic, environmental, and social challenges and remains a fundamental challenge for hydrologists and specialists in related fields [89].

In this study, processed and field-validated DEM data demonstrated high accuracy, confirming their suitability for hydraulic modeling in HEC-RAS. The integration of LiDAR data enabled a precise representation of river geometry, which is essential for reliable hydraulic simulations.

The analysis of cross-sections and hydraulic modeling results highlighted significant variability between river segments, influencing transport capacity and the potential expansion of floodable areas, with an increased risk of severe flooding in narrower and shallower zones, particularly in scenario S4 (60 m³/s).

Parameters such as water velocity or flow cross-sectional area provide valuable insights into river behavior under different conditions. For example, an increase in water velocity along the thalweg and the flow area underscores the risks associated with erosion and transport capacity.

An additional factor that could significantly influence these results is sediment transport, which plays a crucial role in modifying cross-sections and the natural evolution of riverbeds [90]. Studies show that excessive sediment accumulation can contribute to riverbed clogging, reducing water transport capacity and amplifying flood risk, while excessive erosion can affect bank stability and local infrastructure. Although this study did not include a detailed analysis of sediment transport, integrating such a component could increase the complexity of hydraulic modeling and provide a more detailed understanding of river dynamics in high-flow scenarios. In future research, evaluating the interaction between water flow, sediment deposition, and erosion could contribute to a better understanding of hydrological processes and the optimization of flood risk management strategies [90].

The 3D visualizations and longitudinal profiles offered an in-depth understanding of the river’s hydraulic behavior. The representation of floodable areas for each scenario allows for better risk assessment and can guide decision-making in water resource management and infrastructure protection.

The four defined scenarios (S1–S4) illustrate a clear progression of flood risks. While the low flow in S1 presents minimal risks, scenario S4 highlights an urgent need for preventive measures such as levee reinforcement or the expansion of buffer zones.

The results obtained are directly applicable to developing land-use plans in flood-prone areas. The study underscores the need for integrated approaches that include both infrastructure measures and ecological strategies for risk mitigation.

Our study highlights the complexity of the interaction between hydraulic parameters and land use, offering valuable perspectives for water resource management and flood prevention planning.

4.4. Floods: Challenges for Agriculture, Grasslands, and Anthropogenic Spaces

Floods impact the physical, chemical, and biological properties of soil, influencing its structure, porosity, texture, nutrient dynamics, microbial communities, and biogeochemical cycles, with significant implications for soil health, agricultural fertility, and ecosystem functioning [22,91,92,93].

The impact of floods on arable land is critical, as these areas are essential for agricultural production and the subsistence of local communities. On flooded lands, chemical and biological changes in the soil can lead to a significant decline in fertility and increased difficulties associated with agricultural management [22]. For instance, nutrient losses or the accumulation of toxic metabolites (ethanol, acetaldehyde) due to water stagnation directly affect crop yields. This issue is particularly relevant for the 392 hectares of arable land in the analyzed area, which require the implementation of remediation strategies such as controlled drainage or chemical soil improvement.

Flooded forested areas (231 hectares) and grasslands (202 hectares) are also affected by flood-induced changes. Water stagnation can cause physiological stress in plants, including disruptions in respiration, photosynthesis, and nutrient absorption [94,95]. These processes can contribute to a decline in plant biodiversity and reduce ecosystems’ capacity to maintain critical functions such as carbon storage or soil erosion prevention.

Flood-prone intravillan lands (46 hectares) are less extensive, but the impact on these areas involves significant social and economic challenges. Flooding in these zones can lead to infrastructural damage, disruption of local economic activities, and increased restoration costs. These effects are exacerbated by the duration and frequency of floods, underscoring the need for integrated risk management plans.

Flooding significantly affects plant growth, crop yields, and quality, with variable losses depending on the crop type, growth stage, and duration of exposure [22]. Consequently, this study highlights the complexity of flood impacts on different land-use types and emphasizes the importance of implementing adaptive strategies to mitigate economic and ecological losses.

4.5. Study Limitations and Future Research Directions

The research presents several limitations, such as the exclusive focus on steady flow scenarios, which do not capture the complex dynamics of flash floods and unsteady flows; the modeling assumes the presence of protective dikes without analyzing the impact of their potential failure due to the lack of hydrological data, the study does not comprehensively integrate the long-term impacts of climate change or the effects of anthropogenic activities, such as urban expansion or land use changes, which may influence the hydrological regime.

When applying the findings to other similar studies, the accuracy and precision of the DEM data and hydrological data used are critical, as they can generate potential processing errors and affect hydro-geomorphological analysis.

Future research perspectives include expanding the applicability of the methodology to other river basins to validate its reproducibility and adaptability. The integration of unsteady flow scenarios and the use of 2D or 3D modeling can provide a more detailed understanding of flood dynamics. Additionally, long-term projections that include the effects of climate change on the hydrological regime could contribute to a deeper understanding of future risks. Technological advancements enable the use of high-resolution satellite data and artificial intelligence to improve the accuracy of floodplain analysis and mapping. Furthermore, assessing existing protective infrastructure and simulating alternative solutions, such as expanding buffer zones or restoring wetlands, is essential for effective risk management. A multidisciplinary approach that integrates ecological, economic, and social perspectives can support the development of sustainable strategies to reduce the impact of floods on communities and the environment.

5. Conclusions

This study presents an integrated approach to flood analysis, combining GIS technologies, hydraulic modeling in HEC-RAS, and LiDAR data to assess flood risk in the Cigher River basin (Romania). The main objectives of the research were to identify flood-prone areas under different flow scenarios, evaluate the impact on agricultural and non-agricultural land, and develop a reproducible methodology applicable to other river basins.

The results of hydraulic simulations revealed that as discharge increases, flood extent expands significantly, with the most severe scenario (S4, 60 m³/s) leading to the inundation of 871 hectares of land. The analysis demonstrated that agricultural land (arable land and grasslands) is the most affected, followed by forested areas, highlighting the need for sustainable flood risk management strategies. By integrating high-resolution geospatial data and computational modeling, the study provided valuable data and cartographic materials that can support decision-makers in reducing risks and efficiently planning land use.

The proposed methodology demonstrates the effectiveness of open-source tools in hydraulic modeling and geospatial analysis, making it accessible for further research and practical applications in flood risk management. Future studies should focus on dynamic flood simulations, integrating the impact of climate change and exploring adaptive mitigation strategies for sustainable water resource management.