Inland Flood Analysis in Irrigated Agricultural Fields Including Drainage Systems and Pump Stations

Abstract

Effective flood management in agricultural fields has become increasingly important due to the rising frequency and intensity of rainfall events driven by climate change. This study investigates the applicability of urban flood analysis models—SWMM (1D) and K-Flood (2D)—to irrigated agricultural fields with artificial drainage systems. A case study was conducted in a rural area near the Sindae drainage station in Cheongju, South Korea, using rainfall data from an extreme weather event in 2017. The models simulated inland flooding and were validated against flood trace maps provided by the Ministry of the Interior and Safety (MOIS). Receiver Operating Characteristic (ROC) analysis showed a true positive rate of 0.565, a false positive rate of 0.21, and an overall accuracy of 0.731, indicating reasonable agreement with observed inundation. Scenario analyses were also conducted to assess the effectiveness of three improvement strategies: reducing the Manning coefficient, increasing pump station capacity, and widening drainage channels. Among them, increasing pump capacity most effectively reduced flood volume, while channel widening had the greatest impact on reducing flood extent. These findings demonstrate the potential of urban flood models for application in agricultural contexts and support data-driven planning for rural flood mitigation.

1. Introduction

Due to climate change, future rainfalls are predicted to become more intense and frequent [1]. Recent extreme rainfall events in Asia, Europe, and North America demonstrate these changes. It was reported that there will be an increase in flood damage, resulting in a negative effect on agricultural productivity, which might instigate major global food scarcity [2]. Agricultural field flooding leads to soil over-saturation and inundation, severely impacting crop productivity and food security. Numerous studies have proposed methods on soil and water management, as well as strategies on integrated land management to manage flood risks in agricultural field [3,4,5,6]. As rainfall intensity increases, several countries have developed guidelines for irrigated agricultural fields, emphasizing the inclusion of irrigation systems in the design of agricultural fields [7,8,9].

Research on the quantitative assessment of flood damage in agricultural fields has been extensively conducted using various hydrodynamic modeling approaches. For instance, HEC-RAS 2D has been widely used for two-dimensional hydraulic simulations that account for rainfall–runoff processes and the influence of hydraulic structures [10], while MIKE FLOOD has been effectively applied to simulate inundation extents based on terrain characteristics [11]. FLO-2D, another widely used two-dimensional flood model, has also been employed to evaluate flood hazards and calibrate roughness coefficients using field surveys or remote sensing data. For example, a recent study demonstrated the utility of FLO-2D in estimating Manning’s roughness through terrestrial remote sensing techniques and simulating flood extents in Samsun, Turkey [12]. Additionally, LISFLOOD has been adopted in large-scale flood forecasting and risk assessment applications due to its ability to couple rainfall–runoff modeling with hydrodynamic routing.

These models are primarily grounded in physically based simulations of surface runoff and flow distribution. In some cases, they have been extended to indirectly estimate crop damage by incorporating crop growth stages and inundation duration [6,11,13]. However, most existing applications do not fully account for the operational characteristics of actual drainage systems in agricultural fields, such as the conditions of channel cross-sections, the operational status of pumping stations, and delays in drainage. This introduces limitations in accurately representing real-world flood dynamics.

During extreme rainfall events, agricultural fields often experience inland flooding due to insufficient drainage capacity, sediment accumulation, vegetation overgrowth within channels, or the limited capacity of pumps. These complex conditions cannot be sufficiently explained by simple depth-based analysis. Therefore, a comprehensive simulation framework that reflects both the physical structure and the operational functionality of the drainage system is essential. However, modeling approaches that explicitly incorporate such multifaceted drainage characteristics remain scarce, which limits the ability to predict the actual spatial extent and duration of agricultural flooding [11].

The need to improve flood management and mitigation strategies is emphasized not only for agricultural fields but also for urban areas with high population density and critical infrastructure. These urban areas are particularly vulnerable to significant casualties and property damage during flood events, which has led to extensive research on urban flood mitigation [10,14,15,16]. In urban areas, flood analysis is typically conducted by applying physical models to drainage channels or by combining one-dimensional stormwater analysis models with two-dimensional finite difference hydraulic models or two-dimensional hydrodynamic models to simulate surface flooding [17]. These urban flood analysis methodologies and models are also considered applicable to inland flood issues in agricultural fields and offer new perspectives for assessing agricultural flood damage.

This study aims to bridge this gap by adapting urban flood analysis methodologies to agricultural fields with drainage systems. By employing both one-dimensional and two-dimensional flood analysis models, this research evaluates the performance of agricultural drainage systems and compares the simulation results with flood trace maps provided by the Ministry of the Interior and Safety (MOIS). In addition, scenario analyses involving variables such as reduced channel roughness, channel widening, and increased pumping capacity are conducted to evaluate the effectiveness of different flood mitigation strategies. The findings of this study are expected to provide actionable insights for optimizing flood management in agricultural fields.

2. Materials and Methods

2.1. Inland Flood Analysis in Agricultural Fields

This study analyzes inland flooding in agricultural fields with drainage systems using urban flood analysis methodologies, structured into three stages (Figure 1). The first stage involves preprocessing rainfall and flood data, ensuring consistency through the quality control of rainfall data from Automated Synoptic Observing System (ASOS) stations and the temporal alignment of flood data. In the second stage, the EPA Storm Water Management Model (SWMM) is applied as a one-dimensional stormwater analysis tool to identify flood occurrence points, scales, and drainage delays within the agricultural system. The third stage utilizes the K-Flood model, a two-dimensional inundation analysis tool [18], to simulate overall flooding patterns. Results from K-Flood were validated against flood trace maps from the Ministry of the Interior and Safety (MOIS). This methodology also assessed the effectiveness of structural measures, such as drainage channel maintenance and facility capacity improvements, for reducing inland flood damage in the study area.

2.2. SWMM

The EPA SWMM is a model developed by the U.S. Environmental Protection Agency. It is an urban runoff model capable of simulating surface and subsurface flows, as well as tracking runoff in drainage networks generated by rainfall events in urban catchments or watersheds with artificial drainage systems [19]. The SWMM was applied to this agricultural area, given that the study area of this study includes a concrete drainage system.

The model consists of five execution blocks—RUNOFF, TRANSPORT, EXTRAN, STORAGE, and EXECUTIVE—and five auxiliary blocks, further divided into 126 sub-programs for calculations [19]. The RUNOFF block is where initial computations are performed, simulating runoff conditions and the change in water surface in the drainage basin during rainfall events. For stable computation of runoff, the RUNOFF block uses the Newton–Raphson method for the nonlinear storage equation. The continuity equation and Manning’s equation used in conduit flow are represented as Equations (1) and (2), respectively.

$${\displaystyle \frac{\Delta V}{\Delta t}}=Q_I+Q_w+Q_{GW}-Q$$

(1)

$$Q={\displaystyle \frac{1}{n}}{R}^{\raisebox{1ex}{$2$}\!\left/ \!\raisebox{-1ex}{$3$}\right.}{S}^{\raisebox{1ex}{$1$}\!\left/ \!\raisebox{-1ex}{$2$}\right.}A$$

(2)

where $\Delta V$ is represented as the storage volume, $\Delta t$ is the time interval used in differentiation, $Q$ is the outflow, $Q_I$ is the inflow, $Q_w$ is the lateral inflow, and $Q_{GW}$ is the groundwater inflow. Additionally, $n$ pertains to the Manning coefficient, $R$ the hydraulic radius, $S$ the slope of the channel, and $A$ the cross-sectional area of the drainage channel.

In SWMM, the drainage system is composed of nodes and links. A node represents a storage element within the system, such as a manhole or a junction in a channel. Variables related to a node include flow rate, depth, and surface area, with the key dependent variable being the water level. The volume of a node is assumed to be equal to the volume of water stored up to the midpoint of the connecting conduits between nodes. A link connects nodes and serves as a conduit that transmits flow. Key hydraulic characteristics in a link include the Manning coefficient, length, cross-sectional area, hydraulic radius, and surface width, with the important independent variable in the link being the flow rate.

2.3. K-Flood

K-Flood is a two-dimensional flood inundation analysis model developed to simulate precise flood patterns in various scenarios, including urban flood analysis, river flooding, and dam collapse simulations. The model incorporates Quadtree-based Adaptive Mesh Refinement (AMR) and the Cut Cell Method, which enable efficient automatic grid generation and accurate analysis, resulting in a more efficient and lighter simulation compared to traditional methods [18]. The authors chose this model for its capability to handle complex terrains and provide high-resolution simulations, making it well-suited for detailed flood pattern analysis in the studied agricultural area.

The K-Flood model has been validated in previous studies that demonstrated its capacity to simulate urban and riverine flooding with high spatial resolution and computational efficiency. For example, An et al. (2018) [18] successfully applied K-Flood to simulate inundation caused by dam breaks and urban surface flooding, confirming its reliability and computational performance compared to conventional 2D hydraulic models. The model’s ability to apply adaptive grid refinement and cut-cell techniques allows for the accurate representation of complex geometries without excessive computational cost.

The two-dimensional shallow water equations applied in the model are represented as Equations (3) and (4) [19].

$${\displaystyle \frac{\partial U}{\partial t}}+{\displaystyle \frac{\partial F}{\partial x}}+{\displaystyle \frac{\partial G}{\partial y}}=S+S_f$$

(3)

$$U=\left[\begin{array}{c}h\\ hu\\ hv\end{array}\right],F=\left[\begin{array}{c}hu\\ h{u}^2+g{h}^2/2\\ huv\end{array}\right],G=\left[\begin{array}{c}hv\\ huv\\ h{v}^2+g{h}^2/2\end{array}\right]$$

(4)

$$S=\left[\begin{array}{c}0\\ gh{S}_{0x}\\ gh{S}_{0y}\end{array}\right],S_f=\left[\begin{array}{c}0\\ -gh{S}_{fx}\\ -gh{S}_{fy}\end{array}\right]$$

Here, $U$ represents the conservative flow variables, which include the water depth and the depth-averaged velocities. $F$ and $G$ represent the flow rates in the $x$ and $y$ directions, respectively, and $S$ denotes the source term, which includes effects such as bed slope and external forces. $t$ denotes time, $x$ and $y$ denote the directions in the orthogonal coordinate system, $h$ is the water depth, $u$ and $v$ are depth-averaged velocities in the $x$ and $y$ directions, $g$ is the acceleration due to gravity, $S_0$ represents the bed slope, which is the change in bed elevation ($Z$) in the $x$ and $y$ directions ($S_{0x}=-\partial z{\displaystyle /}\partial x$, $S_{0y}=-\partial z/\partial y$), and $S_f$ is the friction slope. The friction slope, based on the Manning formula, is as follows:

$$S_{fx}={\displaystyle \frac{g{n}^{2}u\sqrt{u^2+v^2}}{h^{1/3}}},S_{fy}={\displaystyle \frac{g{n}^{2}v\sqrt{u^2+v^2}}{h^{1/3}}}$$

(5)

Here, $n$ is Manning’s roughness coefficient. When spatially integrating Equation (3) over a finite volume $\Omega$, the following equation is obtained:

$${\displaystyle \frac{\partial }{\partial t}}{\displaystyle \int Ud\Omega }+{\displaystyle \int \left(\frac{\partial F}{\partial x}+\frac{\partial G}{\partial y}\right)d\Omega }={\displaystyle \int \left(S+S_t\right)}d\Omega$$

(6)

This equation represents the method of discretizing the two-dimensional shallow water equations, which are the governing equations of the model, using finite volume discretization. For more detailed information on K-Flood, please refer to [18].

Flood inundation models traditionally use orthogonal grids for two-dimensional analysis due to their ability to apply high-accuracy numerical techniques [14]. However, representing complex terrains with these grids can be challenging, requiring significant time for grid generation. Recent advancements, such as curvilinear and unstructured grids in models like HEC-RAS [10], address these limitations but remain time-intensive. An alternative is the Adaptive Mesh Refinement (AMR) method, which simplifies grid generation for complex terrains and allows for dynamic grid reconfiguration during simulations for efficient computation. The K-Flood model in this study employs Quadtree grids, a type of AMR with a simple, hierarchical structure where each grid cell can be divided into four sub-cells based on conditions like boundary shape, terrain slope, and elevation, enabling high-resolution dynamic grid refinement over time (Figure 2).

3. Study Event and Model Setting

3.1. Study Event

This study involved an analysis of a heavy rainfall event that occurred in Cheongju City on the 16th of July 2017, where a record rainfall of 91.8 mm per hour was recorded [20]. This extreme rainfall was calculated to have a return period of 200 years and caused significant damage, casualties, and the collapse of hydraulic structures, bridges, and roads, as well as flooding in agricultural fields. Due to the significant impact of this event on the agricultural field, this study focuses on the agricultural area near the Shindae Pumping Station, located at the junction of the Miho and Seoknam Rivers in Cheongju City, as identified in the flood trace maps provided by the Ministry of the Interior and Safety [21]. This study simulated the inland flooding in agricultural fields using the heavy rain event data recorded on 16 July 2017, between 00:00 and 23:00.

Figure 3 shows the drainage channels, the downstream pumping station, and the surrounding Miho and Seoknam Rivers in the study area. The study site has a total area of 6.19 km², and is primarily composed of rice paddies, with some fields and greenhouses, as confirmed in the 2017 aerial images taken by the Ministry of Agriculture, Food, and Rural Affairs’ Farm Map [22]. According to the 2023 field survey and relevant infrastructure design guidelines [23], the drainage channels in the study area were designed to accommodate a 20-year rainfall event, in accordance with national rural drainage standards. The drainage system mostly consists of open concrete channels following the natural drainage based on the difference in elevation. These channels had significant blockages due to soil deposition, vegetation growth, and debris accumulation.

The drainage system in the study area follows a network flow towards a downstream reservoir, where the flow is discharged into the Seoknam River through the pumping station. The pumping station, located at the downstream end of the study area, collects drainage from the agricultural fields and conveys it through six compression pumps with a total capacity of 21 m³/s; its location is indicated in Figure 3. Field surveys indicated that the study area is fully surrounded by elevated roads. As a result, no external overflow from the nearby Miho and Seoknam Rivers was observed during the flood event, effectively isolating the area from riverine flooding. However, in the original DEM, these elevated structures can act as artificial barriers that obstruct surface flow, making it necessary to adjust the DEM to reflect actual drainage conditions. To address this, the DEM was corrected by referencing the elevation of surrounding plots and the depth of the drainage channels beneath the roads, thereby restoring the continuity of surface flow through the highway underpasses. These elevated roads are highlighted with green rectangles in Figure 3 to indicate their influence on surface flow and the necessity of DEM correction. In addition, there are no significant upstream watersheds that may contribute to the external flood flow to the area other than rainfall. Using drone-acquired digital elevation models (DEMs) with a 1-m grid resolution, the topography and drainage network of the study area were established using GIS (Geographic Information System) software. The rainfall data used was obtained from the nearest Automated Synoptic Observing System (ASOS) station in Cheongju, provided by the Korea Meteorological Administration data portal [24] (Figure 4). The station is located approximately 5 km from the study area.

3.2. Model Setting

In SWMM, the spatial analysis unit known as the subcatchment typically represents the catchment area of a single building. However, for this study, each subcatchment was set up to correspond to a single paddy field, maintaining the focus on the agricultural irrigation land with an artificial drainage system. The catchment area was divided into 266 subcatchments, with 199 junctions. Links were defined as conduits, and the nodes at the intersections of these links were designated as junctions.

Infiltration was simulated using the Horton method available in SWMM, with parameters defined based on the regional hydrological literature and soil characteristics typical of paddy fields. Although evapotranspiration was not explicitly modeled due to the absence of reliable observation data, this omission is acknowledged as a limitation in the Discussion section. For surface flow routing, each subcatchment was assigned impervious and pervious surface Manning’s roughness coefficients of 0.015 and 0.17, respectively, based on field observations.

In this study, the impervious area does not indicate paved or built-up surfaces, but rather the saturated surface of rice paddies, which rapidly generate surface runoff during extreme rainfall due to limited infiltration capacity. Conversely, the pervious area corresponds to marginal zones within each subcatchment—such as vegetated ridges, field edges, or furrow strips—that allow for partial infiltration. These classifications reflect the hydrological behavior of paddy fields under heavy rainfall, where the majority of the surface behaves similarly to impervious cover due to ponding and quick saturation.

The temporary storage function of paddy fields was implicitly represented through the depression storage parameters, set at 0.05 mm for impervious and 0.2 mm for pervious areas, along with a high imperviousness ratio (95%) to reflect the rapid saturation and runoff characteristics of rice paddies. This combination of infiltration and depression storage settings was used to approximate the dynamic retention behavior of agricultural surfaces under extreme rainfall conditions. Water exchange between paddy fields and adjacent drainage channels was modeled as unregulated flow, assuming an ungated condition across the entire study area.

The elevation, width, and depth of the connecting channels were set up for each junction. The drainage network comprises a 3300-m main drainage channel, running from the upstream to the downstream end, and connecting to secondary drainage channels that reach the periphery of the study area. While most secondary drainage channels were made of concrete, some soil-based channels were inaccessible or intentionally blocked by farmers, limiting data collection. Consequently, narrow soil channels with a width of ≤0.5 m were not included in the simulations due to the lack of comprehensive data.

In addition to the soil channels, the concrete channels were thoroughly surveyed to obtain data such as the width and depth of the main and secondary drainage channels. This survey data was used to configure the conduits in SWMM, based on the physical characteristics of the drainage channels. The flow of water was determined by comparing the elevations of each junction to account for the gradient-induced flow. Figure 5 shows the DEM of the study area and the catchment and drainage system constructed in the SWMM.

The linkage between SWMM and K-Flood was implemented in a one-way coupling structure: time-series discharge data at overflowed junctions in SWMM were exported and used as inflow boundary conditions in K-Flood. This approach enables high-resolution surface flow simulation using the shallow water equations while preserving computational efficiency. Although K-Flood is capable of simulating overland flow independently, SWMM was used to represent the engineered field-scale drainage system more realistically. In particular, simulating narrow drainage channels with centimeter-scale cross-sections using K-Flood alone would require extremely fine grid resolutions and significantly increase computation time. Therefore, SWMM was employed to efficiently model the internal conduit system, while K-Flood was applied to simulate surface and overland flow. To prevent artificial overflow from channels back into fields, drainage zones and cultivated zones were discretized separately in the K-Flood mesh.

To consider the frictional surface flow within the irrigation and drainage channels, this study referred to Manning’s n values for open channels based on Chow’s research [25]. Considering the composition material of the drainage channels and the presence of sediment and vegetation, Manning’s roughness coefficient of 0.03 s/m^1/3 was used. The full set of Manning’s n values applied in the SWMM is summarized in

Table 1. A summary of the Manning’s n values applied in the SWMM for different surface types.

Surface Type	Manning’s n Value	Source
Impervious area (saturated surface of paddy fields)	0.015 s/m1/3	Chow (1959) [25]
Pervious area (berms, grass-covered margins, vegetated zones near drains)	0.17 s/m1/3	Chow (1959) [25]
Open channels (concrete-lined drainage channels)	0.03 s/m1/3	Chow (1959) [25]

.

Additionally, it was assumed that the Shindae Pumping Station, located at the downstream and capable of a maximum flow rate of 21 m³/s, was operating at full capacity during the flood event. Due to the absence of observation data from 2017, challenges were encountered in determining the irrigation depth at that time; therefore, the initial irrigation depth was set to 0 mm.

The flooding volumes at each point from SWMM were calculated and used as input data for K-Flood. Additionally, to prevent outflows from the drainage channels from entering the fields, the drainage channels and cultivated zones were separated. The threshold used to consider an area to be flooded was a flood depth of more than 0.3 m, which is the categorized flood depth for paddy fields according to the drainage improvement design standards of the Ministry of Agriculture, Food, and Rural Affairs of South Korea.

4. Result and Discussion

4.1. Result for Previous Event

The results of runoff simulation at a specific location, using simple rainfall data with the SWMM, are shown in Figure 6. The overflow volumes recorded at selected junctions in the SWMM were used as indicators of flooding and transferred as inputs to K-Flood. After the simulation, flooding was observed at 112 out of a total of 199 junctions. Most of the flood points matched well with the MOIS flood trace maps, which are indicated in red. The major flood nodes, identified as those with a maximum flooding volume exceeding 4 m³/s, were named sequentially from upstream to downstream as a, b, c, d, and e. Figure 7 compares the hourly rainfall and runoff at the major flood nodes (A, B, C, D, E) to illustrate the temporal variation in flooding patterns. The upstream nodes (A, B, and C) showed an immediate response to peak rainfall, with sharp flooding peaks. In contrast, the downstream nodes (D and E) exhibited delayed peak discharges and sustained flooding, indicating slower drainage and higher flood retention in the lower areas of the system. Figure 8 visualizes the predicted flood zones and flood depths at two-hour intervals starting from 8 AM to confirm the inland flooding resulting from drainage delays.

This study evaluated the applicability of urban flood analysis methodologies to irrigated agricultural field with artificial drainage systems. This was performed by comparing the maximum flood depth from the rainfall event on 16 July 2017, with the flood trace maps provided by MOIS. Figure 9 shows the simulated maximum flood depth during the event in comparison with the MOIS flood trace map, including an area-based analysis performed using ROC (Receiver Operating Characteristic) analysis. The ROC analysis evaluates the model’s performance by calculating the true positive rate (TPR), false positive rate (FPR), and accuracy. A TPR closer to 1 indicates a higher rate of correctly identified flood areas, while an FPR closer to 0 indicates fewer false alarms in non-flooded areas. Accuracy measures the overall correctness of the model, with values closer to 1 indicating better performance. Based on the simulations, the ROC analysis can be summarized as shown in

Table 2. ROC analysis results between inundation map and simulation result using K-Flood.

		Prediction
		Positive	Negative
Actual	True	0.91 km2	0.96 km2
	False	0.71 km2	3.61 km2

:

• TPR (true positive rate) was calculated to be 0.565, indicating that out of the total area predicted as ‘Positive’ for flooding in the model (1.62 km²), 0.91 km² was correctly identified as flooded.
• FPR (false positive rate) was calculated to be 0.21, indicating that out of the total area predicted as ‘Negative’ for flooding in the model (4.56 km²), 0.96 km² was incorrectly identified as flooded.
• Accuracy represents the proportion of correctly predicted ‘True’ and ‘False’ flooded areas compared to the total flooded area, which was calculated to be 0.731.

These results confirmed the successful replication of flooding extents for the event and demonstrated the applicability of the developed urban flood analysis methodologies to irrigated agricultural fields with artificial drainage systems.

4.2. Scenario Based Analysis

Lastly, this study also evaluated the effectiveness of drainage management and improvement strategies to reduce inland flooding damage in agricultural fields using the proposed methodology. The analysis was conducted using four different cases, as summarized in

Table 3. Configuration of drainage improvement scenarios for flood damage reduction analysis.

	Manning Coefficient	Pump Capacity	Width of Channels
Original	0.03 s/m1/3	21 m3/s	-
Case. 1	0.015 s/m1/3	21 m3/s	-
Case. 2	0.03 s/m1/3	50 m3/s	-
Case. 3	0.03 s/m1/3	21 m3/s	Doubled
Case. 4	0.015 s/m1/3	50 m3/s	Doubled

. The specific configurations of these cases, including Manning’s coefficient, pump capacity, and the width of channels, are detailed in Table 3. In Case 1, the internal surface of the drainage channels is presumed to be made of smooth concrete, reducing the surface friction, to analyze the effect of reduced flow friction in the drainage channels. Case 2 involves modifying the capacity of the downstream pumping station from 21 m³/s to 50 m³/s to assess the impact of enhanced water handling capacity on reducing the flood damage. In Case 3, the width of the existing channels was doubled to evaluate the effect of increased channel diameter. Lastly, Case 4 combined the improvement strategies of the previous three cases to determine the combined effect of drainage channels’ modified surface friction, increased diameter, and increased capacity of the pumping station. The flood damage reduction in each case was compared to the original analysis results shown in Figure 9.

The maximum flood depth predicted for each case during the flood event using the developed research methodology is presented in Figure 10. The summarized results of the flood area and volume reduction for each case are presented in

Table 4. Simulation results for flood area and volume reduction under different drainage improvement scenarios.

	Flooding Area	Flooding Volume
Original	1.62 km2	1181.579 m3
Case 1	1.46 km2	1171.796 m3
Case 2	1.51 km2	1106.084 m3
Case 3	1.35 km2	1133.474 m3
Case 4	0.99 km2	719.741 m3

. The analysis results for each case are as follows: For Case 1, the simulated flood area decreased from 1.62 km² to 1.46 km², indicating a reduction percentage of about 9.9%, with the flood volume decreasing from 1,181,579 m³ to 1,171,796 m³, a reduction of approximately 0.8%. This suggests that improving the channel surface can enhance water flow and reduce flood risk. In Case 2, increasing the pumping station capacity reduced the flood area by 7% to 1.51 km², and the flood volume decreased to 1,106,084 m³, a reduction of about 6.4%. This highlights the effectiveness of improving drainage capacity in reducing flood volume. Case 3 showed a 16.7% reduction in flood area due to channel widening, with the flood volume decreasing to 1,133,474 m³, a reduction of approximately 4.1%. This indicates that channel widening has the most significant impact on reducing the flood area. Lastly, in Case 4, where all the improvements are applied, there is an oberved 38.5% reduction in flood area and a 39.1% reduction in flood volume, decreasing the total flood volume to 719,741 m³. The results of the simulation emphasize that comprehensive drainage system improvements can yield the most significant effects. However, it was noted that the drainage system in the study area, designed based on a 20-year frequency rainfall, was insufficient to fully address the 200-year frequency rainfall used in this study.

These results demonstrate that both individual and combined drainage improvement measures can effectively reduce flood damage. The analysis using this study’s methodology confirms that drainage management and improvements can significantly contribute to reducing flood damage in agricultural fields. This provides valuable data for future water resource management and disaster prevention planning in similar areas.

5. Conclusions

This study applied and validated urban flood analysis methodologies in irrigated agricultural fields with artificial drainage systems, focusing on a case study near the Shindae Pumping Station in Cheongju, South Korea. By simulating inland flooding under actual intense rainfall events, this study successfully demonstrated the applicability of one-dimensional (SWMM) and two-dimensional (K-Flood) models for analyzing flood patterns in agricultural settings. To validate the simulation results, area-based analysis and ROC analysis were performed using the flood trace maps provided by the Ministry of the Interior and Safety (MOIS) of South Korea. The results of the flood simulation using the study’s methodology showed a true positive rate (TPR) of 0.565, a false positive rate (FPR) of 0.21, and an accuracy of 0.731. These results verified the applicability of urban flood analysis methodologies to irrigated agricultural field with artificial drainage systems.

This study also used methodology to analyze various scenarios for reducing flood damage in agricultural fields, confirming the effectiveness of drainage management and improvements. Scenario analyses revealed that increasing pumping station capacity and widening drainage channels were the most effective individual measures for reducing flood volume and flood area, respectively. Notably, when these measures were combined with channel maintenance (reducing the Manning coefficient), the cumulative impact exceeded the sum of individual improvements, underscoring the importance of an integrated approach. However, this study also highlighted that the current drainage system, designed for a 20-year rainfall event, is inadequate for handling extreme 200-year events, emphasizing the urgent need to update design standards to account for increasing rainfall intensity due to climate change.

Structural measures, such as enhancing drainage systems and pumping stations, require substantial resources and time. Therefore, this study advocates for thorough hydrological and hydraulic analyses to ensure the effective allocation of resources for flood management projects. By adapting urban flood analysis methodologies to agricultural fields, this research provides a framework for more accurate flood damage assessments and offers actionable insights for optimizing flood management strategies. These findings contribute to the broader goal of developing resilient agricultural systems capable of withstanding the challenges posed by climate change.

Despite these contributions, this study acknowledges several limitations. The models relied on limited monitoring data and initial conditions, which may affect their generalizability. In particular, although the temporary storage effect of paddy fields was qualitatively considered using depression storage settings in the SWMM, the volume of retained runoff was not quantitatively assessed. This omission was due to limitations in output resolution and the primary focus on flood propagation. Future work will incorporate explicit volume tracking to evaluate the storage performance and flood mitigation capacity of agricultural fields under varying rainfall scenarios. Further research incorporating diverse environmental conditions and additional data sources is essential for refining the methodology and validating its applicability across various agricultural settings.