From Hazard Maps to Action Plans: Comprehensive Flood Risk Mitigation in the Susurluk Basin

Abstract

Floods pose significant risks worldwide, impacting lives, infrastructure, and economies. The Susurluk basin, covering 24,319 km² in Türkiye, is highly vulnerable to flooding. This study updates the flood management plan for the basin, integrating hydrological modeling, GIS-based flood mapping, and early warning system evaluations in alignment with the EU Flood Directive. A total of 503 hydrodynamic models (226 one-dimensional and 277 two-dimensional) were developed, analyzing 2116 km of stream length. As a result of the evaluation, the capacities of only 33 streams were found to be sufficient. Flood hazard and risk maps for the Q₅₀, Q₁₀₀, Q₅₀₀, and Q₁₀₀₀ return periods identified the remaining 470 high-risk locations as requiring urgent intervention. Economic risk assessments revealed significant exposure of critical infrastructure, especially in urban areas with populations over 100,000. Furthermore, the study introduces a prioritization framework for intervention that balances socioeconomic costs and environmental impacts. Economic damage assessments estimate potential losses in critical infrastructure, including residential areas, industrial zones, and transportation networks. The findings highlight the importance of proactive flood risk mitigation strategies, particularly in high-risk urban centers. Overall, this study provides a data-driven, replicable model for flood risk management, emphasizing early warning systems, spatial analysis, and structural/non-structural mitigation measures. The insights gained from this research can guide policymakers and urban planners in developing adaptive, long-term flood management strategies for flood-prone regions.

1. Introduction

Floods are among the most destructive natural disasters, causing significant loss of life, property damage, and economic disruptions worldwide. As climate change intensifies extreme weather events, the frequency and severity of floods are expected to increase, necessitating a comprehensive approach to flood risk management [1,2]. The European Union’s Flood Directive (2007/60/EC) has set out a framework for flood risk assessment and management, emphasizing the need for a structured approach to mitigate the adverse effects of floods on human health, infrastructure, and the environment [3].

The Susurluk basin, which covers approximately 24,319 km² and represents 3.1% of Türkiye’s surface area, is highly susceptible to flooding due to its hydrological and climatic characteristics. With key urban centers, such as Balıkesir, Bursa, and Canakkale, within its boundaries, floods in this basin have the potential to cause extensive socioeconomic and environmental damage. Historical records indicate that major flood events in the region have led to considerable financial losses and disruptions to local communities [4,5].

Recent advancements in hydrological modeling, geographic information systems (GIS), and remote sensing technologies have significantly improved flood risk assessment capabilities [1,5]. Two-dimensional (2D) hydrodynamic models, such as HEC-RAS and HEC-HMS, have become essential tools in flood mapping and forecasting, allowing for better prediction and management of flood-prone areas [6,7]. These models incorporate hydrological and hydraulic parameters to simulate flood behavior under various scenarios, providing crucial data for risk mitigation strategies.

The integration of GIS with hydrodynamic models has enabled more precise spatial analyses of flood hazards, facilitating the development of flood susceptibility and vulnerability maps [8,9]. GIS-based models assist in identifying high-risk zones by analyzing topography, land use, and historical flood data. Additionally, remote sensing technologies, including satellite imagery and light detection and ranging (LiDAR), have enhanced the accuracy of flood extent and inundation mapping by offering real-time and high-resolution data [9,10,11]. Furthermore, the assimilation of multisource Earth observation data has contributed to more reliable flood forecasting. Satellite missions, such as Sentinel-1 and Surface Water and Ocean Topography (SWOT), provide valuable hydrological insights, improving the calibration of hydrodynamic models [10]. The integration of these datasets helps refine flood hazard assessments, especially in data-scarce regions.

As climate change continues to alter precipitation patterns and increase the frequency of extreme weather events, adaptive and real-time flood risk management approaches are becoming essential [1,11]. The combination of advanced hydrodynamic modeling, GIS applications, remote sensing, and machine learning represents a promising direction for enhancing flood resilience and protecting vulnerable communities.

In Türkiye, the integration of these methodologies within the national flood risk management framework has become a priority. The General Directorate of Water Management under the Ministry of Agriculture and Forestry has been actively updating flood risk management plans for major river basins, including the Susurluk basin [12]. This study builds on these efforts by incorporating multifaceted risk assessment methodologies, including hazard mapping, socioeconomic impact analysis, and early warning system evaluations, to develop a robust and adaptive flood management strategy.

By aligning with international best practices and leveraging state-of-the-art hydrological modeling techniques, this study aims to enhance flood resilience in the Susurluk basin. The boundary conditions of the study are determined by the Susurluk Basin, which is the study area. In addition, among the curve number (CN) values used in hydrology studies are Manning’s roughness coefficients, which are the boundary conditions of hydrodynamic modeling studies, digital elevation model (DEM) resolution (0.2 m × 0.2 m in settlements, 1 m × 1 m in other areas), and art structures and obstructions. The advantages of the study are that the basin is considered as a whole, upstream–downstream measures are evaluated holistically, risk assessment is carried out on a basin basis, responsible and related organizations are evaluated, and the flood management plan is studied as a whole.

The results will not only contribute to local flood mitigation efforts, but also serve as a replicable model for other flood-prone regions, reinforcing the importance of data-driven, evidence-based flood risk management in the face of evolving climatic and hydrological challenges.

2. Study Area

The Susurluk basin is located in the north of Türkiye. The total precipitation area of the basin is approximately 24,319 km² and the mean annual precipitation is 693.20 mm. The Sakarya basin lies to the east, the Gediz basin to the south, the North Aegean basin to the west, and the Marmara basin to the north. Balıkesir, Bursa, Kutuhya, İzmir, Manisa, Bilecik, and Canakkale provinces are located in the basin [12]. The maximum height, minimum height, maximum height at the boundary, and minimum height at the boundary of the basin were calculated using the DEM. In addition, the DEM was used to create exposure and slope maps of the basin.

The two most important factors affecting the CN, another important parameter of the basin, are soil type and land use [13]. Land use and land cover are not homogenously distributed within the borders of the Susurluk basin. While calculating the CN of the Susurluk basin, 2018 CORINE data were used to obtain information on these factors, and the CN was calculated by spatial weighting. The weighted average CN number in the basin was found to vary between 77 and 78.

The Susurluk basin is located in the transition zone from the Black Sea climate to the Mediterranean climate. The effects of this transition are also seen in precipitation. The months with the highest precipitation are the winter months. When the temperature changes of the basin are examined, little temperature difference is observed between day and night. According to the data obtained from the website of the Turkish State Meteorological Service (TSMS), the annual sum of monthly mean precipitation is 675.6 mm in Balıkesir, 707.40 mm in Bursa, 624.40 mm in Canakkale, and 562.20 mm in Kutahya. When the highest and lowest temperatures during the measurement period were evaluated, it was determined that the highest temperature occurred in Bursa province at 43.80 °C, while the lowest temperature occurred in Kutuhya province at −28.10 °C [12].

In the basin-wide rainfall assessment made within the scope of the Susurluk Basin Flood Management Plan Preliminary Report, the number of stations evaluated within the scope of the Thiessen polygon was 89. Approximately one-third of the weather stations (WSs) entering the Thiessen polygon are mostly outside the basin. The TSMS operates most of the stations. Previously closed stations have been activated as automatic weather stations (AWSs). Most of the manual stations opened and operated by the state waterworks (SWWs), mostly for project purposes, were closed in 2005.

In the hydrology studies carried out in the Susurluk basin, meteorological observation stations with an observation period between 1980 and 2020 were identified, and 12 stations (8 within the basin and 4 outside the basin) were selected to use meteorological observation data. Some characteristic information about these selected stations is given in

Table 1. Some characteristics of meteorological gauging stations used in hydrologic calculations.

Station No.	Station Name	Latitude	Longitude
17704	Tavsanlı	39.550	29.500
17116	Bursa	40.233	29.017
17114	Bandırma	40.317	29.983
17695	Keles	39.917	29.067
17676	Uludag	40.117	29.017
17700	Dursunbey	39.583	28.617
17152	Balıkesir	39.650	27.867
17748	Simav	39.083	28.983
17184	Akhisar	38.917	27.817
17750	Gediz	39.050	29.417
17145	Edremit	39.583	27.017
17674	Balıkesir Gonen	40.100	27.650

.

There are 162 stream gauging stations (SGSs) in the basin. SGSs with drainage areas of 1000 km² or larger were selected for use in the HEC-HMS models established for the Susurluk basin. In addition, the selected SGSs were required to have long-term data. In the Susurluk basin, 11 SGSs were determined to meet the required criteria. Some characteristic information about these stations is given in

Table 2. Some characteristics of the stream gauging stations used in calculations.

ID	Name	Drainage Area (km2)	Elevation (m)	Min (m3/s)	Max (m3/s)	Mean (m3/s)
D03A004	Deveci Konagı	4888	62	0	950	311.26
D03A034	Osmanlar	1253.9	271	0	430	162.91
D03A052	Sinderler	975,2	294	0	470	149.77
D03A089	Caltılıbuk	4631.37	65	0	530	141.88
E03A002	Dolluk	9629.2	40	0	3374	666.07
E03A014	Kayaca	2278	20	0	1693	521.11
E03A016	Yahyakoy	6454	32	0	2350	666.94
E03A017	Akcasusurluk	21,611.2	2	0	963	454.12
E03A021	Gecitkoy	1290.8	63	0	359	126.41
E03A024	Balıklı	1384	94	0	550	244.85
E03A028	Dereli	1125.6	557	0	312	91.19

.

The Susurluk basin, which covers an area of more than 2 million hectares, discharges its rainfall into the Marmara Sea, and the Uluabat and Manyas lakes through many small and large rivers. The basin contains many large and small rivers, flowing continuously or for short periods of time. The rivers and lakes in the Susurluk basin are shown in Figure 1.

There are many storage facilities (ponds or dams) in the basin, either existing or at the plan–project stage. It was decided that the dams in the basin would be included in the flood discharge calculations by performing a reservoir routing. Accordingly, the dams in the basin were identified, and the necessary data (elevation–area–volume, spillway information, spillway radial cover information (if any), spillway operating instructions, etc.) were obtained to be used in the hydrology calculations of these dams.

According to land-use capability classification in the provincial land asset inventory reports published by the General Directorate of Rural Services, lands are divided into eight classes. The first four classes are mainly suitable for tillage farming. Classes 5, 6, and 7 identify lands that can be used as meadow, pasture, and woodland and are therefore not suitable for arable agriculture. Class 8 lands have no chance of any crop production. The land capability utilization classes of the Susurluk basin and the data belonging to these classes are given in

Table 3. Susurluk basin land-use capability classes.

Land-Use Capability Class	Symbol	Area (ha)	Distribution (%)
Suitable for Tillage Farming	I	90,843	3.74
Suitable for Tillage Farming	II	172,533	7.09
Suitable for Tillage Farming	III	121,203	4.98
Suitable for Tillage Farming	IV	136,532	5.61
Unsuitable for Tillage Farming	V	2398	0.10
Unsuitable for Tillage Farming	VI	454,206	18.68
Unsuitable for Tillage Farming	VII	1,346,667	55.38
Land Unsuitable for Agriculture	VIII	107,546	4.42
Total		2.431.927	100.00

.

Large soil groups are classified by examining them under headings such as climate, precipitation, temperature, vegetation cover, main material of the soil, and events that provide soil formation. The most extensive soil group in the Susurluk basin is calcareous brown forest soils without lime, covering an area of 1,036,001 ha. This soil group constitutes 43% of the basin. The second largest soil group in the basin is brown forest soils, with an area of 537.699 ha [12]. Data on major soil groups in the basin are given in

Table 4. Distribution of Large Soil Groups in Susurluk Basin.

Large Soil Group	Symbol	Area (ha)	Distribution (%)
Alluvial Soils	A	155,400	6.39
Brown Soils	B	10,214	0.42
Chestnut Soils	CE	3405	0.14
Reddish Chestnut Soils	D	2918	0.12
Red Brown Mediterranean Soils	E	76,849	3.16
Reddish Brown Soils	F	243	0.01
Hydromorphic Alluvial Soils	H	3162	0.13
Colluvial Soils	K	51,314	2.11
Brown Forest Soils	M	537,699	22.11
Calcareous Brown Forest Soils	N	1,036,001	42.6
Organic Soils	O	973	0.04
Sierozems	S	1702	0.07
Rendzinas	R	131,810	5.42
Calcareous Brown Soils	U	241,490	9.93
Vertisols	V	70,283	2.89
High Mountain Prairie Soils	Y	1216	0.05
Areas Outside the Large Soil Group	-	107,248	4.42
Total		2.431.927	100.00

.

3. Methodology

3.1. Hydrological Modeling

Within the scope of this study, flood hydrology studies were carried out using the HEC-HMS (Hydrological Modeling System) model developed by the United States Army Corps of Engineers [3]. The HEC-HMS model is a simulation program that can perform hydrological calculations, such as precipitation and runoff, in basin areas of different sizes and characteristics. HEC-HMS can simulate hydrological elements, such as sub-basins, streams, reservoirs, etc., in the catchment area. The HMS model calculates infiltration losses and converts rainfall into runoff by different transform methods. It can use different methods, such as the soil conservation service curve number (SCS-CN) and Green-Ampt methods. It can run them together with Muskingum, Pulse, or Lag translational methods using Clark, Snyder, or SCS unit hydrographs [13,14,15].

With the HEC-HMS model, hydrological calculations are defined within an interrelated model network data structure. The calculations are made from upstream to downstream. The HEC-HMS model components are as follows:

–

The basin model includes physical properties, such as basin area, stream connection points, and reservoir data.

–

The meteorological model is the part that defines the basin meteorology (such as precipitation, temperature, and flow values).

–

The control specifications contain information about the timing of the model, such as the time of the flood and the time interval to be used in the model.

–

The time series data belong to the part in which the meteorological time series to be used in the model are defined.

The sub-basin elements defined in the catchment area are used to convert precipitation into runoff, so information on loss calculation methods, hydrograph transformations, and base flow must be entered for each sub-basin. The loss method is used to select the method of calculating the rainfall lost by absorption from the ground. In this study, the deficit and constant method, which is also preferred in many applications, was selected as the loss method. This method is based on continuous changes in soil moisture.

The transform method defines how to convert excess rainfall into direct runoff. The Clark unit hydrograph method, which is widely used in modeling, was chosen. Clark (1945) stated that two important parameters—the collection time (transition time) and storage coefficient—in the calculation of the basin unit hydrograph are needed to determine the area–time relationship of the basin [15]. When a baseflow is defined with the baseflow method, the total hydrograph is obtained by adding it to the calculated surface flow hydrograph. The linear reservoir is defined as the baseflow method in the model. This base flow shows the movement of water along the underground layer. The canopy represents the rainfall retained by the vegetation.

There are three different canopy methods in the HEC-HMS, namely dynamic, gridded simple, and simple [16]. The simple canopy method was used in this modeling. This method is a simple representation of the canopy. The canopy blocks all precipitation until the storage capacity is filled. After the storage is filled, if no representation of the surface is included, all other precipitation falls on the surface or directly on the soil. All potential evapotranspiration is used to drain the canopy storage until the water in storage is used up. The potential evapotranspiration is multiplied by the crop coefficient to determine the amount of evapotranspiration from canopy storage and then from the surface and soil components. Only the potential evaporation that is not used after the canopy storage is emptied will be utilized by the surface and soil components [17,18,19]. The methodologies and parameters used in the model are given in Figure 2, using the Orhaneli sub-basin, a sub-basin of the Susurluk basin, as an example.

3.2. Hydraulic Modeling

The coupled model analysis can be performed by recognizing 1&2-dimensional (1D&2D), and lateral weirs with the usage of the HEC-RAS program. Two model analyses were conducted because it is not possible to define bridge or similar lateral factors in the 2D model in the old version of the HEC-RAS program. The 1D analysis is made through the stream bed, the bridges are determined on a 1D model, and the 1D model is integrated into a 2D model through lateral links. On the other hand, it has become possible to define lateral factors, such as bridges, in a 2D model [20].

Both 1D and 2D models provide numerical solutions that consider the continuity and conservation of momentum equations [1,2,4,5,6,7,21,22,23]. Two-dimensional continuity and momentum equations can be written as follows:

$${\displaystyle \frac{\partial H}{\partial t}}+{\displaystyle \frac{\partial \left(hu\right)}{\partial x}}+{\displaystyle \frac{\partial \left(hv\right)}{\partial y}}+q=0$$

(1)

The continuity equation is solved with the finite volume method. As shown in the flood modeling, the flow width is relatively greater than its depth, particularly in streams. As a result, the velocity toward depth (the vertical component of velocity) is assumed to be lower. Thus, the integral of the momentum equation in the direction of depth is appropriate for the modeling of floods. The integral of the momentum equation with respect to depth can be written in the following form:

$${\displaystyle \frac{\partial u}{\partial t}}+u{\displaystyle \frac{\partial u}{\partial x}}+v{\displaystyle \frac{\partial u}{\partial y}}=-g{\displaystyle \frac{\partial H}{\partial x}}+v_t\left({\displaystyle \frac{{\partial }^{2}u}{\partial x^2}}+{\displaystyle \frac{{\partial }^{2}u}{\partial y^2}}\right)-C_{f}u+fv$$

(2)

$${\displaystyle \frac{\partial v}{\partial t}}+u{\displaystyle \frac{\partial v}{\partial x}}+v{\displaystyle \frac{\partial v}{\partial y}}=-g{\displaystyle \frac{\partial H}{\partial y}}+v_t\left({\displaystyle \frac{{\partial }^{2}v}{\partial x^2}}+{\displaystyle \frac{{\partial }^{2}v}{\partial y^2}}\right)-C_{f}v+fu$$

(3)

• $u,v=\mathit{Velocity}\mathit{components}\mathit{in}\mathit{the}\mathit{Cartesian}\mathit{direction}$
• $g=\mathit{Gravitational}\mathit{acceleration}$
• $v_t=\mathit{Horizontal}\mathit{eddy}\mathit{viscosity}$
• $c_f=\mathit{Coefficient}\mathit{of}\mathit{friction}$
• $R=\mathit{Hydraulic}\mathit{radius}$
• $f=\mathit{Coriolis}\mathit{parameter}$

The floodplain area should be divided into small polygons to solve the above equations [19]. In HEC-RAS 2D modeling, the grid geometry supports up to octagons.

The use of different geometrical solution elements provides flexibility, and field conditions are represented in the most appropriate way [4]. The DEM is an important base for hydrodynamic modeling. A well-prepared sample DEM is suitable for hydrodynamic modeling.

4. Modeling Stages and Results

In order to determine the floods that may occur in the basin, it is necessary to work with appropriate data [1,2,4,21,22]. Within the scope of the studies, the physical characteristics of the basin and meteorological–hydrological data in time series were collected in appropriate formats and in sufficient lengths.

The length and reliability of the dataset used in modeling and statistical studies directly affect the accuracy of the results. For this reason, meteorological and hydrological data, the physical characteristics of the basin, the size, length, slope, soil structure, soil use, and accumulation structures in the basin, the characteristics of these structures, the operating rules, and the past operating results were obtained from the relevant institutions and organizations in the most precise manner [1,2,7,23].

4.1. Preliminary Flood Risk Assessment (PFRA)

In the Flood Risk Preliminary Assessment Report, locations with flood risk were identified within the Susurluk Basin with the criteria specified by anthropogenic effects and disaster risk factors. Accordingly, 1543 settlements were examined throughout the Susurluk basin. Of these, 298 settlements were determined to be at preliminary risk of flooding, and 2116 km of hydrodynamic modeling studies were regarded as necessary in 466 creeks [12].

According to the results of the analyses performed for the Susurluk basin Flood Management Plan Preliminary Flood Risk Assessment Report, seven river classes were determined using the Horton–Strahler method. River lengths for each class are given in

Table 5. Horton–Strahler classes in the Susurluk basin.

Horton–Strahler Class	1	2	3	4	5	6	7
Length (km)	6928.10	3283.30	1503.60	596.40	647.35	179.90	29.80

. Also, the settlements derived from the preliminary flood risk assessment are shown in Figure 3.

4.2. Hydrology Studies

Within the scope of hydrology studies, flood hydrographs were calculated using stochastic and statistical methods and hydrological models (HEC-HMS) for all necessary outlet points on the streams under flood risk. A lumped hydrological model solution method within the HEC-HMS model was used for hydrological modeling. Hydrological analyses of creek tributaries with sufficient basin size were determined by establishing a hydrological model. The Thornthwaite method was selected to calculate potential evapotranspiration in this study. For the creeks in all the settlements at risk (466 creeks), hydrological studies were conducted by flood frequency at a gauging station, regional flood frequency, and synthetic methods accepted in the literature, such as SCS, Mockus, and Snyder unit hydrographs. In flood frequency analyses, Gumbel and Log-Pearson Type-3 distributions are generally found to be appropriate. Chi-square (X²) and Kolmogorov–Smirnov goodness-of-fit-tests were performed to determine the best distribution. Peak flows and hydrographs of floods with return periods of 2, 5, 10, 50, 100, 500, and 1000 years were calculated by the selected classical method that best fit the observed stream hydrographs. The results of the synthetic methods applied for rainfall runoff analysis were used more than the results of the flood frequency analyses. To put it more explicitly, over 700 flood hydrograph calculations were performed for approximately 503 hydrodynamic models. There is a limited number of flow observation stations with long-term data on the main river branches. However, there are several meteorological observation stations with records covering 60 years and more throughout the Susurluk basin.

The obtained flood hydrographs were used as upstream boundary conditions in the hydrodynamic models. The HEC-HMS model of the Orhaneli sub-basin is given in Figure 4 as an example of hydrologic studies.

4.3. Hydraulic Studies

In the generation of flood hazard maps and flood risk maps for the Susurluk basin, maps were obtained according to three different scenarios. Since the effects of maximum flood flows that may occur in the study area are evaluated, this value is determined as Q₅₀₀, Q₁₀₀ for settlements in Türkiye, Q₁₀₀₀ for settlements with a population over 100,000 and Q₅₀ or Q₁₀ + hf for non-settlement areas. During the study period, the occurrence and effects of Q₅₀₀ flood flows in at least five areas were observed and used for validation. In these scenarios, hydrographs calculated according to 50-, 100-, and 500-year recurrence intervals were entered into the model as limit values, and flood simulations were performed. According to the water levels of the floods obtained as a result of these studies, the hazard and risk situations of the regions in the basin were determined. In the flood hazard maps and flood risk maps produced in this way, results were obtained that identified the areas under threat, and response capacity analysis and flood risk analysis were then carried out by evaluating these results.

In all, 503 hydrodynamic models (226 1-dimensional and 277 2-dimensional) were performed. It was determined that the capacities of 33 of the 503 models studied were sufficient, and in the other models, the capacities of the creek beds were determined.

The data produced for the workspace in 2D models are projected with 2-dimensioned surface grid and mesh elements. The grid elements may be defined as rectangular. Modeling of the flows is achieved through the numerical solution of 2-dimensional Saint-Venant equations for any grid element. For the modeling of floods within residential areas and structures, it is known that 1D and 2D combined modeling is the most appropriate.

For the 1D and 2D combined models, the main channel and the hydraulic structures (bridges, dikes, weirs, floodgates, etc.) on the main channel are defined within the 1D model, and flood modeling is made with the establishment of a dynamic link with the 2D model. This integrated method targets the comprehensive, efficient, and accurate presentation of the hydraulic system to benefit from both the 1D and 2D models. The flows are modeled using the HEC-RAS coupled model in such a way that the flows within the river will be modeled in 1D and the flows within the floodplain area will be modeled in 2D.

4.4. Flood Inundation Maps

4.4.1. Map Studies

Cross-sectional readings of hydraulic structures were surveyed for the creeks passing through streams in the study area that were in the last three branches according to the Horton–Strahler method and within the scope of the study.

A 406.78 km long strip map was produced at a scale of 1/1000 for residential areas with a population of less than 500 and the streams within the scope of the Türkiye flood protection law (Law no. 4373) that were determined as risky within the scope of the study. 1800 hydraulic structure surveys were measured on 250 creeks. Sample images of the field studies are given in Figure 5.

Current 1:1000 scale map studies were carried out in provincial and district centers and settlements with a population of over 500. Map studies with a resolution of at least 1:5000 in settlement centers with a population of less than 500, economic activity areas, agricultural areas, etc., were also conducted. Cross-sections were taken to represent the stream bed, at most every 50 m in the provincial and district centers and settlements with a population over 500, and at most every 1000 m in settlements with a population of less than 500, in economic activity areas, and in agricultural areas. In settlements with a population of 100,000 and above, instead of cross-sections that should be taken every 10 m at most, 1:1000 scale strip maps were prepared with the approval of the administration. Cross-sectional readings were made on these maps. At least two cross-sections were taken upstream and downstream of force majeure points, such as section constrictions, section obstructions, hydraulic structures, water accumulation structures, water diverting structures, slope change, etc. For streams passing through settlements with a population of less than 500, measurements were made using global positioning system (GPS) measuring devices. Readings were made considering the specified survey and cross-section locations for each stream, and photographs and/or videos were taken for each cross-section and for hydraulic structures, such as bridges, culverts, and drops [24]. For streams passing through settlements with a population of 500 or more, flights were made using a manned aircraft and a 150 MP camera to take aerial photographs of 8–10 GSD (ground sample distance). Approximately 535 ground control point (GCP) reconnaissances were made for aerial photography. Orthophoto, DEM, and triangular models were produced using the data obtained from these flights. Along the 298 km long risky settlements (463 creeks approximately 2115.61 km in length), 3080 hydraulic structure surveys, 3713 cross-sections, and 535 surface control points were taken. In addition, orthophoto, DEMs, and triangle models for 268 creeks were taken within map studies.

4.4.2. Hydrodynamic Model Studies

Within the scope of the study, hydrodynamic modeling studies were carried out using the best computer conditions in today’s conditions in order to determine the flood distribution areas, the height and velocity of the water at the time of flood, and the capacity of the river bed to prepare flood water depth and flood hazard maps in each stream at risk.

In this context, cross-sections were taken in the field in order to best represent the creek bed. Potential changes in the creek bed were tried to be represented as much as possible, and cross-section intervals were reduced and cross-sections densified where necessary. In addition, for the best representation of the hydraulic structures to be used in the model, at least two sections—one immediately before and one immediately after the surveys—were taken to ensure that the modeling studies were realistic. Surveys of all hydraulic structures on the creek bed were taken. Within the scope of hydrodynamic modeling studies, 1D, 2D, and integrated 1D/2D models were created with HEC-RAS (Version 6.1.0).

In addition to the cross-sectional surveys, very detailed DEMs were prepared as a basis for 2D hydrodynamic modeling studies (Figure 5).

Hydrodynamic modeling studies were integrated with data from the field, and government agencies and modeling studies were carried out. The results of these modeling studies were validated with the floods experienced, with the model results in line with the information obtained from news sources, official sources, field observations, and local people living in the region. In addition, calibration and validation of the studies were completed with the help of real-time observation data by evaluating the observations made in large rivers from the key curves of SGSs.

Flood inundation and flood hazard maps were created using the flood water depth, velocity, and spreading areas obtained as a result of calibrated modeling studies [25]. Q₅₀, Q₁₀₀, and Q₅₀₀ flood discharges were used in settlements and economic activity areas where the population is less than 100,000 people, Q₁₀₀₀ flood discharge for settlements with a central population of 100,000 people or more, and Q₁₀, Q₅₀, and Q₁₀₀ flood discharges in agricultural areas.

The flood hazard maps were prepared using the results of the hydrodynamic modeling studies. Some examples of flood inundation and flood hazard maps obtained as a result of the modeling studies are presented in Figure 6, Figure 7 and Figure 8.

Using the flood hazard maps report, 503 hydrodynamic models were established (226 1D and 277 2D hydrodynamic modeling), and the capacities of 33 streams were found sufficient. A total of 470 separate model results, excluding 33 hydrodynamic models, were evaluated, flood inundation and hazard maps were produced, and the creek bed capacities that can pass without causing flooding were found for each stream section related to settlements.

4.5. Flood Risk Maps

Using the results obtained from 2D hydrodynamic models (flood inundation maps), flood risk maps (FRMs) were prepared for all relevant settlements. All structures under flood were classified according to their intended use [8,26,27,28,29,30]. Although there are different methodologies in the literature on flood risk maps, the risk values were calculated with the help of the values determined for the European continent from the depth–damage curves given in the report published by the Joint Research Center (JRC) in 2017 [31].

While determining the economic damage values, building unit costs were taken from the document entitled Communiqué on 2022 Building Approximate Unit Costs to be Used in the Calculation of Architecture and Engineering Service Fees, published by the Ministry of Environment, Urbanization, and Climate Change. Economic damage maps (EDMs) were created using these values [32].

In contrast to economic risk maps, since human life cannot be valued, population risk maps are created assuming that all people who may be located in areas where floodwaters spread are affected, regardless of water depth. The population affected by the flood was calculated on the basis of neighborhoods and distributed according to the volumetric weights obtained from the floor heights and areas of all buildings in the relevant neighborhood. The number of people affected for each settlement was determined by using the flood extent areas found for different recurrence periods and the address-based population registration system (ADKNS) data announced by the Türkiye Statistical İnstitute, and affected population maps (APMs) were created.

In the risk calculations, it was considered that critical places, such as educational institutions, health institutions, bus stations, places of worship, parks, industrial facilities, shopping malls, stations, airports, terminals, fuel stations, and veterinarians, all “social hot spots” from which people cannot leave very quickly during flooding, may be more affected by flooding, so the total risk value of these places was multiplied by 1.5.

It is thought that the damage and negative consequences that will occur due to the exposure to flood disasters of structures such as educational institutions, health institutions, and places of worship would be higher than for other structures. Therefore, strategic facility (SF) maps were created to show how many strategic facilities would be affected at different flood flow rates.

It is considered that environmental damage may occur if parks, forests, treatment plants, storage facilities, and similar facilities are exposed to flood disasters. Therefore, environmental damage magnitude (EDM) maps were created to show how many of these facilities would be affected at different flood return periods in flood areas.

As in the maps showing the strategic facilities and the magnitude of environmental damage, commercial facilities, industrial facilities, terminals, highways, railways, and similar facilities that may adversely affect the economy in flood-prone areas were identified, and economic activity (EA) maps were created.

Sample maps of Balıkesir Province Erdek District Center (Western Part), which is only one of the settlements for which flood risk maps were produced, are shared in Figure 9.

For each settlement center, detailed information on the affected building polygons (building type, activity area, number of stores, etc.) was determined and digitized, and their impact status was evaluated. For Balıkesir Province Erdek District Center (Western Part), the affected building types, numbers, and expected damage values are given in

Table 6. Balıkesir Province Erdek District Center (Western Part) affected building types, numbers, and expected damage values.

Flood Recurrence Period	Structure Type	Economic Loss ($)	Rate (%)	Structure Num. Expected to be Affected
Q500	Other	27,348.00	0.6	8
	Education	92,554.00	1.9	6
	Industrial	3742.00	0.1	3
	Worship Places	27,383.00	0.6	2
	Administrative	129,052.00	2.6	10
	Building	3,267,155.00	66.0	605
	Health	197,485.00	4.0	5
	Sport	7165.00	0.1	2
	Commercial	765,019.00	15.4	125
	Touristic	435,497.00	8.8	19

as an example.

Response Capacity Analysis

The main purpose of the Susurluk basin flood management plan in the long term is to prevent flood hazards in flood-prone areas and to eliminate the need for the construction of flood control facilities in advance, which will be very expensive to build in the future. In the short and medium term, the plan aims to reduce the potential loss of life and property in flood-prone areas and to significantly reduce the need for emergency response by the public during floods. Although it is possible to reduce this need through pre-flood protection and prevention works, it is not possible to eliminate it completely. Therefore, while flood risk is reduced and prevented, the response capacity for potential floods needs to be continuously improved, as summarized in

Table 7. Classification and Criteria Used in the Preparation and Assessment of Flood Response Capacity Maps.

Classification	Sub-Classification Maps	Mapping and Assessment Parameters
CAPACITY (response and ability to cope with flooding)	Flood Control Structures and Early Warning	Existing and under construction Flood Protection Structures, Hydro-meteorological Gauging Network, Siren, Communication and Local Media Tools
	Evacuation	Evacuation Zones, Open and Closed Gathering Areas for People and Animals, Emergency Transportation Routes
	Emergency Facilities and Services	Hospitals, School Build., Fire Sta. Police Sta., Bakeries, Dry Stores, Cold Storage, Some Public Build. Facilities such as Stadiums, Main Transp. Routes, Stations where Transp. Types intersect, Bridges, Tunnels, Energy Transfer Sta., Water Reser.
	Debris and Recycling Sites	Abandoned mines and quarries, etc. suitable for debris and waste storage and recycling

.

After the flood risk maps were completed, the preparation of the flood evacuation maps was started. With the flood evacuation plan maps, for all settlements with a population over 500 (for which 2D hydrodynamic modeling was performed) that are expected to be affected by floods, in case of early warning before the flood, the places to be evacuated in a coordinated manner before or during the flood, the roads to be used (by vehicle and on foot), the duration, etc., were determined and mapped. In this way, an infrastructure was created that decision makers in charge of disaster coordination could directly use before and during floods. Within the scope of this study, 458 flood evacuation points were identified. The evacuation map created for Balıkesir Province Erdek District Center is given in Figure 10.

As a result of all the studies carried out, measures were determined in order to eliminate the flood risk in the areas in the basin determined to have flood risk. The measures identified are given in the table that follows.

Structural measures (1159 in total) that should be implemented before flooding in settlements where flooding is expected to occur were identified. Summary information about the determined structural measures and distribution of risk classes is given in Figure 11 and Figure 12.

5. Conclusions

This study presents a comprehensive approach to flood risk management in the Susurluk basin, integrating hydrological modeling, GIS-based flood mapping, and evaluation of early warning systems. The analysis covered 8 cities and 1153 settlements within these cities. Taking into account the assessments, 503 hydrodynamic models were created. Hydrological and hydraulic analyses of the Susurluk basin and all related maps necessary for flood risk assessment were prepared. According to the study criteria, 226 of these analyses were 1D and 277 were 2D, identifying 470 high-risk locations requiring immediate intervention. Flood hazard and risk maps for multiple return periods (Q50, Q100, Q500, and Q1000) highlighted the significant exposure of critical infrastructure, particularly in densely populated urban areas.

The results emphasize the need for proactive flood risk reduction strategies, including both structural and non-structural measures. The proposed prioritization framework ensures the optimal allocation of resources by balancing socioeconomic impacts and environmental concerns. Furthermore, the study highlights the importance of integrating remote sensing, machine learning techniques, and real-time monitoring systems into flood risk management to improve forecasting accuracy and preparedness.

By aligning with the European Union’s Flood Directive (2007/60/EC) and national water management policies, this research provides a replicable model for other flood-prone regions. Future studies should focus on refining early warning systems, incorporating climate change projections, and improving community engagement to build long-term flood resilience.